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-
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+
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+results
+checkpoints/*
+*.npz
+**/*.pyc
+**/__pycache__
+.vscode
diff --git a/License.pdf b/License.pdf
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+# Contact-GraspNet
+
+### Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes
+Martin Sundermeyer, Arsalan Mousavian, Rudolph Triebel, Dieter Fox
+ICRA 2021
+
+[paper](https://arxiv.org/abs/2103.14127), [project page](https://research.nvidia.com/publication/2021-03_Contact-GraspNet%3A--Efficient), [video](http://www.youtube.com/watch?v=qRLKYSLXElM)
+
+
+ 
+
+
+## Installation
+
+This code has been tested with python 3.7, tensorflow 2.2, CUDA 10.1, and CUDNN 7.6.0
+
+Create the conda env
+```
+conda env create -f contact_graspnet_env.yml
+```
+
+### Troubleshooting
+
+- Recompile pointnet2 tf_ops, see [here](pointnet2/tf_ops/HowTO.md)
+
+### Hardware
+Training: 1x Nvidia GPU >= 24GB VRAM, >=64GB RAM
+Inference: 1x Nvidia GPU >= 8GB VRAM (might work with less)
+
+## Download Models and Data
+### Model
+Download trained models from [here](https://drive.google.com/drive/folders/1tBHKf60K8DLM5arm-Chyf7jxkzOr5zGl?usp=sharing) and copy them into the `checkpoints/` folder.
+### Test data
+Download the test data from [here](https://drive.google.com/drive/folders/1v0_QMTUIEOcu09Int5V6N2Nuq7UCtuAA?usp=sharing) and copy them them into the `test_data/` folder.
+
+## Inference
+
+
+Contact-GraspNet can directly predict a 6-DoF grasp distribution from a raw scene point cloud. However, to obtain object-wise grasps, remove background grasps and to achieve denser proposals it is highly recommended to use (unknown) object segmentation [e.g. [1](https://github.com/chrisdxie/uois), [2](https://arxiv.org/abs/2103.06796)] as preprocessing and then use the resulting segmentation map to crop local regions and filter grasp contacts.
+
+Given a .npy/.npz file with a depth map (in meters), camera matrix K and (optionally) a 2D segmentation map, execute:
+
+```shell
+python contact_graspnet/inference.py \
+ --np_path=test_data/*.npy \
+ --local_regions --filter_grasps
+```
+
+
+ 
+
+--> close the window to go to next scene
+
+Given a .npy/.npz file with just a 3D point cloud (in meters), execute [for example](examples/realsense_crop_sigma_001.png):
+```shell
+python contact_graspnet/inference.py --np_path=/path/to/your/pc.npy \
+ --forward_passes=5 \
+ --z_range=[0.2,1.1]
+```
+
+`--np_path`: input .npz/.npy file(s) with 'depth', 'K' and optionally 'segmap', 'rgb' keys. For processing a Nx3 point cloud instead use 'xzy' and optionally 'xyz_color' as keys.
+`--ckpt_dir`: relative path to checkpooint directory. By default `checkpoint/scene_test_2048_bs3_hor_sigma_001` is used. For very clean / noisy depth data consider `scene_2048_bs3_rad2_32` / `scene_test_2048_bs3_hor_sigma_0025` trained with no / strong noise.
+`--local_regions`: Crop 3D local regions around object segments for inference. (only works with segmap)
+`--filter_grasps`: Filter grasp contacts such that they only lie on the surface of object segments. (only works with segmap)
+`--skip_border_objects` Ignore segments touching the depth map boundary.
+`--forward_passes` number of (batched) forward passes. Increase to sample more potential grasp contacts.
+`--z_range` [min, max] z values in meter used to crop the input point cloud, e.g. to avoid grasps in the foreground/background(as above).
+`--arg_configs TEST.second_thres:0.19 TEST.first_thres:0.23` Overwrite config confidence thresholds for successful grasp contacts to get more/less grasp proposals
+
+
+## Training
+
+### Download Data
+
+Download the Acronym dataset, ShapeNet meshes and make them watertight, following these [steps](https://github.com/NVlabs/acronym#using-the-full-acronym-dataset).
+
+Download the training data consisting of 10000 table top training scenes with contact grasp information from [here](https://drive.google.com/drive/folders/1eeEXAISPaStZyjMX8BHR08cdQY4HF4s0?usp=sharing) and extract it to the same folder:
+
+```
+acronym
+├── grasps
+├── meshes
+├── scene_contacts
+└── splits
+```
+
+### Train Contact-GraspNet
+
+When training on a headless server set the environment variable
+```shell
+export PYOPENGL_PLATFORM='egl'
+```
+
+Start training with config `contact_graspnet/config.yaml`
+```
+python contact_graspnet/train.py --ckpt_dir checkpoints/your_model_name \
+ --data_path /path/to/acronym/data
+```
+
+### Generate Contact Grasps and Scenes yourself (optional)
+
+The `scene_contacts` downloaded above are generated from the Acronym dataset. To generate/visualize table-top scenes yourself, also pip install the [acronym_tools]((https://github.com/NVlabs/acronym)) package in your conda environment as described in the acronym repository.
+
+In the first step, object-wise 6-DoF grasps are mapped to their contact points saved in `mesh_contacts`
+
+```
+python tools/create_contact_infos.py /path/to/acronym
+```
+
+From the generated `mesh_contacts` you can create table-top scenes which are saved in `scene_contacts` with
+
+```
+python tools/create_table_top_scenes.py /path/to/acronym
+```
+
+Takes ~3 days in a single thread. Run the command several times to process on multiple cores in parallel.
+
+You can also visualize existing table-top scenes and grasps
+
+```
+python tools/create_table_top_scenes.py /path/to/acronym \
+ --load_existing scene_contacts/000000.npz -vis
+```
+
+## Citation
+
+```
+@article{sundermeyer2021contact,
+ title={Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes},
+ author={Sundermeyer, Martin and Mousavian, Arsalan and Triebel, Rudolph and Fox, Dieter},
+ booktitle={2021 IEEE International Conference on Robotics and Automation (ICRA)},
+ year={2021}
+}
+```
diff --git a/checkpoints/.gitkeep b/checkpoints/.gitkeep
new file mode 100644
index 0000000..e69de29
diff --git a/contact_graspnet/__init__.py b/contact_graspnet/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/contact_graspnet/config.yaml b/contact_graspnet/config.yaml
new file mode 100644
index 0000000..78b736d
--- /dev/null
+++ b/contact_graspnet/config.yaml
@@ -0,0 +1,186 @@
+DATA:
+ scene_contacts_path: scene_contacts
+ gripper_width: 0.08
+ input_normals: false
+ use_uniform_quaternions: False
+ view_sphere:
+ elevation: [30,150]
+ distance_range: [0.9,1.3]
+ train_on_scenes: True
+ labels:
+ to_gpu: False
+ bin_weights:
+ - 0.16652107
+ - 0.21488856
+ - 0.37031708
+ - 0.55618503
+ - 0.75124664
+ - 0.93943357
+ - 1.07824539
+ - 1.19423112
+ - 1.55731375
+ - 3.17161779
+ filter_z: true
+ k: 1
+ max_radius: 0.005
+ num_neg_contacts: 0
+ num_pos_contacts: 8000
+ offset_bins:
+ - 0
+ - 0.00794435329
+ - 0.0158887021
+ - 0.0238330509
+ - 0.0317773996
+ - 0.0397217484
+ - 0.0476660972
+ - 0.055610446
+ - 0.0635547948
+ - 0.0714991435
+ - 0.08
+ z_val: -0.1
+ pc_augm:
+ occlusion_nclusters: 0
+ occlusion_dropout_rate: 0.0
+ sigma: 0.000
+ clip: 0.005
+ depth_augm:
+ sigma: 0.001
+ clip: 0.005
+ gaussian_kernel: 0
+ raw_num_points: 20000
+ ndataset_points: 20000
+ num_point: 2048
+ use_farthest_point: false
+ train_and_test: false
+ num_test_scenes: 1000
+ intrinsics: 'realsense'
+LOSS:
+ min_geom_loss_divisor: 1.0
+ offset_loss_type: sigmoid_cross_entropy
+ too_small_offset_pred_bin_factor: 0
+ topk_confidence: 512
+MODEL:
+ bin_offsets: true
+ dir_vec_length_offset: false
+ grasp_conf_head:
+ conv1d: 1
+ dropout_keep: 0.5
+ grasp_dir_head:
+ conv1d: 3
+ dropout_keep: 0.7
+ joint_head:
+ conv1d: 4
+ dropout_keep: 0.7
+ joint_heads: false
+ asymmetric_model: true
+ model: contact_graspnet
+ pointnet_fp_modules:
+ - mlp:
+ - 256
+ - 256
+ - mlp:
+ - 256
+ - 128
+ - mlp:
+ - 128
+ - 128
+ - 128
+ pointnet_sa_module:
+ group_all: true
+ mlp:
+ - 256
+ - 512
+ - 1024
+ pointnet_sa_modules_msg:
+ - mlp_list:
+ - - 32
+ - 32
+ - 64
+ - - 64
+ - 64
+ - 128
+ - - 64
+ - 96
+ - 128
+ npoint: 2048
+ nsample_list:
+ - 32
+ - 64
+ - 128
+ radius_list:
+ - 0.02
+ - 0.04
+ - 0.08
+ - mlp_list:
+ - - 64
+ - 64
+ - 128
+ - - 128
+ - 128
+ - 256
+ - - 128
+ - 128
+ - 256
+ npoint: 512
+ nsample_list:
+ - 64
+ - 64
+ - 128
+ radius_list:
+ - 0.04
+ - 0.08
+ - 0.16
+ - mlp_list:
+ - - 64
+ - 64
+ - 128
+ - - 128
+ - 128
+ - 256
+ - - 128
+ - 128
+ - 256
+ npoint: 128
+ nsample_list:
+ - 64
+ - 64
+ - 128
+ radius_list:
+ - 0.08
+ - 0.16
+ - 0.32
+ pred_contact_approach: false
+ pred_contact_base: false
+ pred_contact_offset: true
+ pred_contact_success: true
+ pred_grasps_adds: true
+ pred_grasps_adds_gt2pred: false
+OPTIMIZER:
+ max_epoch: 16
+ batch_size: 3
+ learning_rate: 0.001
+ optimizer: adam
+ momentum: 0.9
+ adds_gt2pred_loss_weight: 1
+ adds_loss_weight: 10
+ approach_cosine_loss_weight: 1
+ dir_cosine_loss_weight: 1
+ offset_loss_weight: 1
+ score_ce_loss_weight: 1
+ bn_decay_clip: 0.99
+ bn_decay_decay_rate: 0.5
+ bn_decay_decay_step: 200000
+ bn_init_decay: 0.5
+ decay_rate: 0.7
+ decay_step: 200000
+TEST:
+ first_thres: 0.23
+ second_thres: 0.19
+ allow_zero_margin: 0
+ bin_vals: max
+ center_to_tip: 0.0
+ extra_opening: 0.005
+ max_farthest_points: 150
+ num_samples: 200
+ with_replacement: false
+ filter_thres: 0.0001
diff --git a/contact_graspnet/config_utils.py b/contact_graspnet/config_utils.py
new file mode 100644
index 0000000..1815bb5
--- /dev/null
+++ b/contact_graspnet/config_utils.py
@@ -0,0 +1,66 @@
+import os
+import yaml
+
+def recursive_key_value_assign(d,ks,v):
+ """
+ Recursive value assignment to a nested dict
+
+ Arguments:
+ d {dict} -- dict
+ ks {list} -- list of hierarchical keys
+ v {value} -- value to assign
+ """
+
+ if len(ks) > 1:
+ recursive_key_value_assign(d[ks[0]],ks[1:],v)
+ elif len(ks) == 1:
+ d[ks[0]] = v
+
+def load_config(checkpoint_dir, batch_size=None, max_epoch=None, data_path=None, arg_configs=[], save=False):
+ """
+ Loads yaml config file and overwrites parameters with function arguments and --arg_config parameters
+
+ Arguments:
+ checkpoint_dir {str} -- Checkpoint directory where config file was copied to
+
+ Keyword Arguments:
+ batch_size {int} -- [description] (default: {None})
+ max_epoch {int} -- "epochs" (number of scenes) to train (default: {None})
+ data_path {str} -- path to scenes with contact grasp data (default: {None})
+ arg_configs {list} -- Overwrite config parameters by hierarchical command line arguments (default: {[]})
+ save {bool} -- Save overwritten config file (default: {False})
+
+ Returns:
+ [dict] -- Config
+ """
+
+ config_path = os.path.join(checkpoint_dir, 'config.yaml')
+ config_path = config_path if os.path.exists(config_path) else os.path.join(os.path.dirname(__file__),'config.yaml')
+ with open(config_path,'r') as f:
+ global_config = yaml.load(f)
+
+ for conf in arg_configs:
+ k_str, v = conf.split(':')
+ try:
+ v = eval(v)
+ except:
+ pass
+ ks = [int(k) if k.isdigit() else k for k in k_str.split('.')]
+
+ recursive_key_value_assign(global_config, ks, v)
+
+ if batch_size is not None:
+ global_config['OPTIMIZER']['batch_size'] = int(batch_size)
+ if max_epoch is not None:
+ global_config['OPTIMIZER']['max_epoch'] = int(max_epoch)
+ if data_path is not None:
+ global_config['DATA']['data_path'] = data_path
+
+ global_config['DATA']['classes'] = None
+
+ if save:
+ with open(os.path.join(checkpoint_dir, 'config.yaml'),'w') as f:
+ yaml.dump(global_config, f)
+
+ return global_config
+
diff --git a/contact_graspnet/contact_grasp_estimator.py b/contact_graspnet/contact_grasp_estimator.py
new file mode 100644
index 0000000..23b22bb
--- /dev/null
+++ b/contact_graspnet/contact_grasp_estimator.py
@@ -0,0 +1,388 @@
+import importlib
+import numpy as np
+import sys
+import os
+import time
+
+import tensorflow.compat.v1 as tf
+tf.disable_eager_execution()
+TF2 = True
+
+BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+sys.path.append(os.path.join(BASE_DIR))
+sys.path.append(os.path.join(BASE_DIR, 'pointnet2', 'utils'))
+sys.path.append(os.path.abspath(__file__))
+
+from tf_train_ops import get_bn_decay
+import config_utils
+from data import farthest_points, distance_by_translation_point, preprocess_pc_for_inference, regularize_pc_point_count, depth2pc, reject_median_outliers
+
+class GraspEstimator:
+ """
+ Class for building and inferencing Contact-GraspNet
+
+ :param cfg: config dict
+ """
+ def __init__(self, cfg):
+
+ if 'surface_grasp_logdir_folder' in cfg:
+ # for sim evaluation
+ self._contact_grasp_cfg = config_utils.load_config(cfg['surface_grasp_logdir_folder'], batch_size=1, arg_configs=cfg.arg_configs)
+ self._cfg = cfg
+ self._num_samples = self._cfg.num_samples
+ else:
+ self._contact_grasp_cfg = cfg
+
+ self._model_func = importlib.import_module(self._contact_grasp_cfg['MODEL']['model'])
+ self._num_input_points = self._contact_grasp_cfg['DATA']['raw_num_points'] if 'raw_num_points' in self._contact_grasp_cfg['DATA'] else self._contact_grasp_cfg['DATA']['num_point']
+
+ self.placeholders = self._model_func.placeholder_inputs(self._contact_grasp_cfg['OPTIMIZER']['batch_size'],
+ self._num_input_points,
+ self._contact_grasp_cfg['DATA']['input_normals'])
+ self.model_ops = {}
+
+ def build_network(self):
+ """
+ Build tensorflow graph and grasp representation
+ :returns: tensorflow ops to infer the model predictions
+ """
+ global_config = self._contact_grasp_cfg
+
+ # Note the global_step=step parameter to minimize.
+ # That tells the optimizer to helpfully increment the 'step' parameter for you every time it trains.
+ step = tf.Variable(0)
+ bn_decay = get_bn_decay(step, global_config['OPTIMIZER'])
+ tf.summary.scalar('bn_decay', bn_decay)
+
+ print("--- Get model")
+ # Get model
+ end_points = self._model_func.get_model(self.placeholders['pointclouds_pl'], self.placeholders['is_training_pl'], global_config, bn_decay=bn_decay)
+
+ tf_bin_vals = self._model_func.get_bin_vals(global_config)
+ offset_bin_pred_vals = tf.gather_nd(tf_bin_vals, tf.expand_dims(tf.argmax(end_points['grasp_offset_head'], axis=2), axis=2)) if global_config['MODEL']['bin_offsets'] else end_points['grasp_offset_pred'][:,:,0]
+
+ grasp_preds = self._model_func.build_6d_grasp(end_points['approach_dir_head'], end_points['grasp_dir_head'], end_points['pred_points'], offset_bin_pred_vals, use_tf=True) # b x num_point x 4 x 4
+
+ self.model_ops = {'pointclouds_pl': self.placeholders['pointclouds_pl'],
+ 'cam_poses_pl': self.placeholders['cam_poses_pl'],
+ 'scene_idx_pl': self.placeholders['scene_idx_pl'],
+ 'is_training_pl': self.placeholders['is_training_pl'],
+ 'grasp_dir_pred': end_points['grasp_dir_head'],
+ 'binary_seg_head': end_points['binary_seg_head'],
+ 'binary_seg_pred': end_points['binary_seg_pred'],
+ 'grasp_offset_head': end_points['grasp_offset_head'],
+ 'grasp_offset_pred': end_points['grasp_offset_pred'],
+ 'approach_dir_pred': end_points['approach_dir_head'],
+ 'pred_points': end_points['pred_points'],
+ 'offset_pred_idcs_pc': tf.argmax(end_points['grasp_offset_head'], axis=2) if global_config['MODEL']['bin_offsets'] else None,
+ 'offset_bin_pred_vals': offset_bin_pred_vals,
+ 'grasp_preds': grasp_preds,
+ 'step': step,
+ 'end_points': end_points}
+
+ self.inference_ops = [self.model_ops['grasp_preds'], self.model_ops['binary_seg_pred'], self.model_ops['pred_points']]
+ if self.model_ops['offset_bin_pred_vals'] is None:
+ self.inference_ops.append(self.model_ops['grasp_offset_head'])
+ else:
+ self.inference_ops.append(self.model_ops['offset_bin_pred_vals'])
+
+ return self.model_ops
+
+ def load_weights(self, sess, saver, log_dir, mode='test'):
+ """
+ Load checkpoint weights
+ :param sess: tf.Session
+ :param saver: tf.train.Saver
+ """
+
+ chkpt = tf.train.get_checkpoint_state(log_dir)
+ if chkpt and chkpt.model_checkpoint_path:
+ print(('loading ', chkpt.model_checkpoint_path))
+ saver.restore(sess, chkpt.model_checkpoint_path)
+ else:
+ if mode == 'test':
+ print('no checkpoint in ', log_dir)
+ exit()
+ else:
+ sess.run(tf.global_variables_initializer())
+
+ def filter_segment(self, contact_pts, segment_pc, thres=0.00001):
+ """
+ Filter grasps to obtain contacts on specified point cloud segment
+
+ :param contact_pts: Nx3 contact points of all grasps in the scene
+ :param segment_pc: Mx3 segmented point cloud of the object of interest
+ :param thres: maximum distance in m of filtered contact points from segmented point cloud
+ :returns: Contact/Grasp indices that lie in the point cloud segment
+ """
+ filtered_grasp_idcs = np.array([],dtype=np.int32)
+
+ if contact_pts.shape[0] > 0 and segment_pc.shape[0] > 0:
+ try:
+ dists = contact_pts[:,:3].reshape(-1,1,3) - segment_pc.reshape(1,-1,3)
+ min_dists = np.min(np.linalg.norm(dists,axis=2),axis=1)
+ filtered_grasp_idcs = np.where(min_dists (obj_center - size/2), axis=1) & np.all(full_pc < (obj_center + size/2),axis=1)]
+ if np.any(partial_pc):
+ partial_pc = regularize_pc_point_count(partial_pc, self._contact_grasp_cfg['DATA']['raw_num_points'], use_farthest_point=self._contact_grasp_cfg['DATA']['use_farthest_point'])
+ pc_regions[i] = partial_pc
+ obj_centers[i] = obj_center
+
+ return pc_regions, obj_centers
+
+
+ def predict_grasps(self, sess, pc, constant_offset=False, convert_cam_coords=True, forward_passes=1):
+ """
+ Predict raw grasps on point cloud
+
+ :param sess: tf.Session
+ :param pc: Nx3 point cloud in camera coordinates
+ :param convert_cam_coords: Convert from OpenCV to internal training camera coordinates (x left, y up, z front) and converts grasps back to openCV coordinates
+ :param constant_offset: do not predict offset and place gripper at constant `extra_opening` distance from contact point
+ :param forward_passes: Number of forward passes to run on each point cloud. default: 1
+ :returns: (pred_grasps_cam, pred_scores, pred_points, gripper_openings) Predicted grasps/scores/contact-points/gripper-openings
+ """
+
+ # Convert point cloud coordinates from OpenCV to internal coordinates (x left, y up, z front)
+ pc, pc_mean = preprocess_pc_for_inference(pc.squeeze(), self._num_input_points, return_mean=True, convert_to_internal_coords=convert_cam_coords)
+
+ if len(pc.shape) == 2:
+ pc_batch = pc[np.newaxis,:,:]
+
+ if forward_passes > 1:
+ pc_batch = np.tile(pc_batch, (forward_passes,1,1))
+
+ feed_dict = {self.placeholders['pointclouds_pl']: pc_batch,
+ self.placeholders['is_training_pl']: False}
+
+ # Run model inference
+ pred_grasps_cam, pred_scores, pred_points, offset_pred = sess.run(self.inference_ops, feed_dict=feed_dict)
+
+ pred_grasps_cam = pred_grasps_cam.reshape(-1, *pred_grasps_cam.shape[-2:])
+ pred_points = pred_points.reshape(-1, pred_points.shape[-1])
+ pred_scores = pred_scores.reshape(-1)
+ offset_pred = offset_pred.reshape(-1)
+
+ # uncenter grasps
+ pred_grasps_cam[:,:3, 3] += pc_mean.reshape(-1,3)
+ pred_points[:,:3] += pc_mean.reshape(-1,3)
+
+ if constant_offset:
+ offset_pred = np.array([[self._contact_grasp_cfg['DATA']['gripper_width']-self._contact_grasp_cfg['TEST']['extra_opening']]*self._contact_grasp_cfg['DATA']['num_point']])
+
+ gripper_openings = np.minimum(offset_pred + self._contact_grasp_cfg['TEST']['extra_opening'], self._contact_grasp_cfg['DATA']['gripper_width'])
+
+ with_replacement = self._contact_grasp_cfg['TEST']['with_replacement'] if 'with_replacement' in self._contact_grasp_cfg['TEST'] else False
+
+ selection_idcs = self.select_grasps(pred_points[:,:3], pred_scores,
+ self._contact_grasp_cfg['TEST']['max_farthest_points'],
+ self._contact_grasp_cfg['TEST']['num_samples'],
+ self._contact_grasp_cfg['TEST']['first_thres'],
+ self._contact_grasp_cfg['TEST']['second_thres'] if 'second_thres' in self._contact_grasp_cfg['TEST'] else self._contact_grasp_cfg['TEST']['first_thres'],
+ with_replacement=self._contact_grasp_cfg['TEST']['with_replacement'])
+
+ if not np.any(selection_idcs):
+ selection_idcs=np.array([], dtype=np.int32)
+
+ if 'center_to_tip' in self._contact_grasp_cfg['TEST'] and self._contact_grasp_cfg['TEST']['center_to_tip']:
+ pred_grasps_cam[:,:3, 3] -= pred_grasps_cam[:,:3,2]*(self._contact_grasp_cfg['TEST']['center_to_tip']/2)
+
+ # convert back to opencv coordinates
+ if convert_cam_coords:
+ pred_grasps_cam[:,:2, :] *= -1
+ pred_points[:,:2] *= -1
+
+ return pred_grasps_cam[selection_idcs], pred_scores[selection_idcs], pred_points[selection_idcs].squeeze(), gripper_openings[selection_idcs].squeeze()
+
+ def predict_scene_grasps(self, sess, pc_full, pc_segments={}, local_regions=False, filter_grasps=False, forward_passes=1):
+ """
+ Predict num_point grasps on a full point cloud or in local box regions around point cloud segments.
+
+ Arguments:
+ sess {tf.Session} -- Tensorflow Session
+ pc_full {np.ndarray} -- Nx3 full scene point cloud
+
+ Keyword Arguments:
+ pc_segments {dict[int, np.ndarray]} -- Dict of Mx3 segmented point clouds of objects of interest (default: {{}})
+ local_regions {bool} -- crop 3D local regions around object segments for prediction (default: {False})
+ filter_grasps {bool} -- filter grasp contacts such that they only lie within object segments (default: {False})
+ forward_passes {int} -- Number of forward passes to run on each point cloud. (default: {1})
+
+ Returns:
+ [np.ndarray, np.ndarray, np.ndarray, np.ndarray] -- pred_grasps_cam, scores, contact_pts, gripper_openings
+ """
+
+ pred_grasps_cam, scores, contact_pts, gripper_openings = {}, {}, {}, {}
+
+ # Predict grasps in local regions or full pc
+ if local_regions:
+ pc_regions, _ = self.extract_3d_cam_boxes(pc_full, pc_segments)
+ for k, pc_region in pc_regions.items():
+ pred_grasps_cam[k], scores[k], contact_pts[k], gripper_openings[k] = self.predict_grasps(sess, pc_region, convert_cam_coords=True, forward_passes=forward_passes)
+ else:
+ pc_full = regularize_pc_point_count(pc_full, self._contact_grasp_cfg['DATA']['raw_num_points'])
+ pred_grasps_cam[-1], scores[-1], contact_pts[-1], gripper_openings[-1] = self.predict_grasps(sess, pc_full, convert_cam_coords=True, forward_passes=forward_passes)
+ print('Generated {} grasps'.format(len(pred_grasps_cam[-1])))
+
+ # Filter grasp contacts to lie within object segment
+ if filter_grasps:
+ segment_keys = contact_pts.keys() if local_regions else pc_segments.keys()
+ for k in segment_keys:
+ j = k if local_regions else -1
+ if np.any(pc_segments[k]) and np.any(contact_pts[j]):
+ segment_idcs = self.filter_segment(contact_pts[j], pc_segments[k], thres=self._contact_grasp_cfg['TEST']['filter_thres'])
+
+ pred_grasps_cam[k] = pred_grasps_cam[j][segment_idcs]
+ scores[k] = scores[j][segment_idcs]
+ contact_pts[k] = contact_pts[j][segment_idcs]
+ try:
+ gripper_openings[k] = gripper_openings[j][segment_idcs]
+ except:
+ print('skipped gripper openings {}'.format(gripper_openings[j]))
+
+ if local_regions and np.any(pred_grasps_cam[k]):
+ print('Generated {} grasps for object {}'.format(len(pred_grasps_cam[k]), k))
+ else:
+ print('skipping obj {} since np.any(pc_segments[k]) {} and np.any(contact_pts[j]) is {}'.format(k, np.any(pc_segments[k]), np.any(contact_pts[j])))
+
+ if not local_regions:
+ del pred_grasps_cam[-1], scores[-1], contact_pts[-1], gripper_openings[-1]
+
+ return pred_grasps_cam, scores, contact_pts, gripper_openings
+
+ def select_grasps(self, contact_pts, contact_conf, max_farthest_points = 150, num_grasps = 200, first_thres = 0.25, second_thres = 0.2, with_replacement=False):
+ """
+ Select subset of num_grasps by contact confidence thresholds and farthest contact point sampling.
+
+ 1.) Samples max_farthest_points among grasp contacts with conf > first_thres
+ 2.) Fills up remaining grasp contacts to a maximum of num_grasps with highest confidence contacts with conf > second_thres
+
+ Arguments:
+ contact_pts {np.ndarray} -- num_point x 3 subset of input point cloud for which we have predictions
+ contact_conf {[type]} -- num_point x 1 confidence of the points being a stable grasp contact
+
+ Keyword Arguments:
+ max_farthest_points {int} -- Maximum amount from num_grasps sampled with farthest point sampling (default: {150})
+ num_grasps {int} -- Maximum number of grasp proposals to select (default: {200})
+ first_thres {float} -- first confidence threshold for farthest point sampling (default: {0.6})
+ second_thres {float} -- second confidence threshold for filling up grasp proposals (default: {0.6})
+ with_replacement {bool} -- Return fixed number of num_grasps with conf > first_thres and repeat if there are not enough (default: {False})
+
+ Returns:
+ [np.ndarray] -- Indices of selected contact_pts
+ """
+
+ grasp_conf = contact_conf.squeeze()
+ contact_pts = contact_pts.squeeze()
+
+ conf_idcs_greater_than = np.nonzero(grasp_conf > first_thres)[0]
+ _, center_indexes = farthest_points(contact_pts[conf_idcs_greater_than,:3], np.minimum(max_farthest_points, len(conf_idcs_greater_than)), distance_by_translation_point, return_center_indexes = True)
+
+ remaining_confidences = np.setdiff1d(np.arange(len(grasp_conf)), conf_idcs_greater_than[center_indexes])
+ sorted_confidences = np.argsort(grasp_conf)[::-1]
+ mask = np.in1d(sorted_confidences, remaining_confidences)
+ sorted_remaining_confidence_idcs = sorted_confidences[mask]
+
+ if with_replacement:
+ selection_idcs = list(conf_idcs_greater_than[center_indexes])
+ j=len(selection_idcs)
+ while j < num_grasps and conf_idcs_greater_than.shape[0] > 0:
+ selection_idcs.append(conf_idcs_greater_than[j%len(conf_idcs_greater_than)])
+ j+=1
+ selection_idcs = np.array(selection_idcs)
+
+ else:
+ remaining_idcs = sorted_remaining_confidence_idcs[:num_grasps-len(conf_idcs_greater_than[center_indexes])]
+ remaining_conf_idcs_greater_than = np.nonzero(grasp_conf[remaining_idcs] > second_thres)[0]
+ selection_idcs = np.union1d(conf_idcs_greater_than[center_indexes], remaining_idcs[remaining_conf_idcs_greater_than])
+ return selection_idcs
+
+ def extract_point_clouds(self, depth, K, segmap=None, rgb=None, z_range=[0.2,1.8], segmap_id=0, skip_border_objects=False, margin_px=5):
+ """
+ Converts depth map + intrinsics to point cloud.
+ If segmap is given, also returns segmented point clouds. If rgb is given, also returns pc_colors.
+
+ Arguments:
+ depth {np.ndarray} -- HxW depth map in m
+ K {np.ndarray} -- 3x3 camera Matrix
+
+ Keyword Arguments:
+ segmap {np.ndarray} -- HxW integer array that describes segeents (default: {None})
+ rgb {np.ndarray} -- HxW rgb image (default: {None})
+ z_range {list} -- Clip point cloud at minimum/maximum z distance (default: {[0.2,1.8]})
+ segmap_id {int} -- Only return point cloud segment for the defined id (default: {0})
+ skip_border_objects {bool} -- Skip segments that are at the border of the depth map to avoid artificial edges (default: {False})
+ margin_px {int} -- Pixel margin of skip_border_objects (default: {5})
+
+ Returns:
+ [np.ndarray, dict[int:np.ndarray], np.ndarray] -- Full point cloud, point cloud segments, point cloud colors
+ """
+
+ if K is None:
+ raise ValueError('K is required either as argument --K or from the input numpy file')
+
+ # Convert to pc
+ pc_full, pc_colors = depth2pc(depth, K, rgb)
+
+ # Threshold distance
+ if pc_colors is not None:
+ pc_colors = pc_colors[(pc_full[:,2] < z_range[1]) & (pc_full[:,2] > z_range[0])]
+ pc_full = pc_full[(pc_full[:,2] < z_range[1]) & (pc_full[:,2] > z_range[0])]
+
+ # Extract instance point clouds from segmap and depth map
+ pc_segments = {}
+ if segmap is not None:
+ pc_segments = {}
+ obj_instances = [segmap_id] if segmap_id else np.unique(segmap[segmap>0])
+ for i in obj_instances:
+ if skip_border_objects and not i==segmap_id:
+ obj_i_y, obj_i_x = np.where(segmap==i)
+ if np.any(obj_i_x < margin_px) or np.any(obj_i_x > segmap.shape[1]-margin_px) or np.any(obj_i_y < margin_px) or np.any(obj_i_y > segmap.shape[0]-margin_px):
+ print('object {} not entirely in image bounds, skipping'.format(i))
+ continue
+ inst_mask = segmap==i
+ pc_segment,_ = depth2pc(depth*inst_mask, K)
+ pc_segments[i] = pc_segment[(pc_segment[:,2] < z_range[1]) & (pc_segment[:,2] > z_range[0])] #regularize_pc_point_count(pc_segment, grasp_estimator._contact_grasp_cfg['DATA']['num_point'])
+
+ return pc_full, pc_segments, pc_colors
+
+ def predict_scene_grasps_from_depth_K_and_2d_seg(self, sess, depth, segmap, K, z_range=[0.2,1.8], local_regions=False, filter_grasps=False, segmap_id=0, skip_border_objects=False, margin_px=5):
+ """ Combines converting to point cloud(s) and predicting scene grasps into one function """
+
+ pc_full, pc_segments = self.extract_point_clouds(depth, K, segmap=segmap, local_regions=local_regions, filter_grasps=filter_grasps,
+ segmap_id=segmap_id, skip_border_objects=skip_border_objects, margin_px=margin_px, z_range=z_range)
+
+ return self.predict_scene_grasps(sess, pc_full, pc_segments, local_regions=local_regions, filter_grasps=filter_grasps)
diff --git a/contact_graspnet/contact_graspnet.py b/contact_graspnet/contact_graspnet.py
new file mode 100644
index 0000000..805adca
--- /dev/null
+++ b/contact_graspnet/contact_graspnet.py
@@ -0,0 +1,438 @@
+import os
+import sys
+import numpy as np
+import tensorflow.compat.v1 as tf
+tf.disable_eager_execution()
+TF2 = True
+
+BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+ROOT_DIR = os.path.dirname(os.path.dirname(BASE_DIR))
+
+sys.path.append(os.path.join(BASE_DIR))
+sys.path.append(os.path.join(BASE_DIR, 'pointnet2', 'utils'))
+sys.path.append(os.path.join(BASE_DIR, 'pointnet2'))
+
+import tf_util
+from pointnet_util import pointnet_sa_module, pointnet_fp_module, pointnet_sa_module_msg
+from tf_sampling import farthest_point_sample, gather_point
+from tf_grouping import query_ball_point, group_point, knn_point
+
+import mesh_utils
+
+def placeholder_inputs(batch_size, num_input_points=20000, input_normals=False):
+ """
+ Creates placeholders for input pointclouds, scene indices, camera poses and training/eval mode
+
+ Arguments:
+ batch_size {int} -- batch size
+ num_input_points {int} -- number of input points to the network (default: 20000)
+
+ Keyword Arguments:
+ input_normals {bool} -- whether to use normals as input (default: {False})
+
+ Returns:
+ dict[str:tf.placeholder] -- dict of placeholders
+ """
+
+ pl_dict = {}
+ dim = 6 if input_normals else 3
+ pl_dict['pointclouds_pl'] = tf.placeholder(tf.float32, shape=(batch_size, num_input_points, dim))
+ pl_dict['scene_idx_pl'] = tf.placeholder(tf.int32, ())
+ pl_dict['cam_poses_pl'] = tf.placeholder(tf.float32, shape=(batch_size, 4, 4))
+ pl_dict['is_training_pl'] = tf.placeholder(tf.bool, shape=())
+
+ return pl_dict
+
+def get_bin_vals(global_config):
+ """
+ Creates bin values for grasping widths according to bounds defined in config
+
+ Arguments:
+ global_config {dict} -- config
+
+ Returns:
+ tf.constant -- bin value tensor
+ """
+ bins_bounds = np.array(global_config['DATA']['labels']['offset_bins'])
+ if global_config['TEST']['bin_vals'] == 'max':
+ bin_vals = (bins_bounds[1:] + bins_bounds[:-1])/2
+ bin_vals[-1] = bins_bounds[-1]
+ elif global_config['TEST']['bin_vals'] == 'mean':
+ bin_vals = bins_bounds[1:]
+ else:
+ raise NotImplementedError
+
+ if not global_config['TEST']['allow_zero_margin']:
+ bin_vals = np.minimum(bin_vals, global_config['DATA']['gripper_width']-global_config['TEST']['extra_opening'])
+
+ tf_bin_vals = tf.constant(bin_vals, tf.float32)
+ return tf_bin_vals
+
+def get_model(point_cloud, is_training, global_config, bn_decay=None):
+ """
+ Contact-GraspNet model consisting of a PointNet++ backbone and multiple output heads
+
+ Arguments:
+ point_cloud {tf.placeholder} -- batch of point clouds
+ is_training {bool} -- train or eval mode
+ global_config {dict} -- config
+
+ Keyword Arguments:
+ bn_decay {tf.variable} -- batch norm decay (default: {None})
+
+ Returns:
+ [dict] -- endpoints of the network
+ """
+
+ model_config = global_config['MODEL']
+ data_config = global_config['DATA']
+
+ radius_list_0 = model_config['pointnet_sa_modules_msg'][0]['radius_list']
+ radius_list_1 = model_config['pointnet_sa_modules_msg'][1]['radius_list']
+ radius_list_2 = model_config['pointnet_sa_modules_msg'][2]['radius_list']
+
+ nsample_list_0 = model_config['pointnet_sa_modules_msg'][0]['nsample_list']
+ nsample_list_1 = model_config['pointnet_sa_modules_msg'][1]['nsample_list']
+ nsample_list_2 = model_config['pointnet_sa_modules_msg'][2]['nsample_list']
+
+ mlp_list_0 = model_config['pointnet_sa_modules_msg'][0]['mlp_list']
+ mlp_list_1 = model_config['pointnet_sa_modules_msg'][1]['mlp_list']
+ mlp_list_2 = model_config['pointnet_sa_modules_msg'][2]['mlp_list']
+
+ npoint_0 = model_config['pointnet_sa_modules_msg'][0]['npoint']
+ npoint_1 = model_config['pointnet_sa_modules_msg'][1]['npoint']
+ npoint_2 = model_config['pointnet_sa_modules_msg'][2]['npoint']
+
+ fp_mlp_0 = model_config['pointnet_fp_modules'][0]['mlp']
+ fp_mlp_1 = model_config['pointnet_fp_modules'][1]['mlp']
+ fp_mlp_2 = model_config['pointnet_fp_modules'][2]['mlp']
+
+ input_normals = data_config['input_normals']
+ offset_bins = data_config['labels']['offset_bins']
+ joint_heads = model_config['joint_heads']
+
+ # expensive, rather use random only
+ if 'raw_num_points' in data_config and data_config['raw_num_points'] != data_config['ndataset_points']:
+ point_cloud = gather_point(point_cloud, farthest_point_sample(data_config['ndataset_points'], point_cloud))
+
+ end_points = {}
+ l0_xyz = tf.slice(point_cloud, [0,0,0], [-1,-1,3])
+ l0_points = tf.slice(point_cloud, [0,0,3], [-1,-1,3]) if input_normals else None
+
+ # Set abstraction layers
+ l1_xyz, l1_points = pointnet_sa_module_msg(l0_xyz, l0_points, npoint_0, radius_list_0, nsample_list_0, mlp_list_0, is_training, bn_decay, scope='layer1')
+ l2_xyz, l2_points = pointnet_sa_module_msg(l1_xyz, l1_points, npoint_1, radius_list_1, nsample_list_1,mlp_list_1, is_training, bn_decay, scope='layer2')
+
+ if 'asymmetric_model' in model_config and model_config['asymmetric_model']:
+ l3_xyz, l3_points = pointnet_sa_module_msg(l2_xyz, l2_points, npoint_2, radius_list_2, nsample_list_2,mlp_list_2, is_training, bn_decay, scope='layer3')
+ l4_xyz, l4_points, _ = pointnet_sa_module(l3_xyz, l3_points, npoint=None, radius=None, nsample=None, mlp=model_config['pointnet_sa_module']['mlp'], mlp2=None, group_all=model_config['pointnet_sa_module']['group_all'], is_training=is_training, bn_decay=bn_decay, scope='layer4')
+
+ # Feature Propagation layers
+ l3_points = pointnet_fp_module(l3_xyz, l4_xyz, l3_points, l4_points, fp_mlp_0, is_training, bn_decay, scope='fa_layer1')
+ l2_points = pointnet_fp_module(l2_xyz, l3_xyz, l2_points, l3_points, fp_mlp_1, is_training, bn_decay, scope='fa_layer2')
+ l1_points = pointnet_fp_module(l1_xyz, l2_xyz, l1_points, l2_points, fp_mlp_2, is_training, bn_decay, scope='fa_layer3')
+
+ l0_points = l1_points
+ pred_points = l1_xyz
+ else:
+ l3_xyz, l3_points, _ = pointnet_sa_module(l2_xyz, l2_points, npoint=None, radius=None, nsample=None, mlp=model_config['pointnet_sa_module']['mlp'], mlp2=None, group_all=model_config['pointnet_sa_module']['group_all'], is_training=is_training, bn_decay=bn_decay, scope='layer3')
+
+ # Feature Propagation layers
+ l2_points = pointnet_fp_module(l2_xyz, l3_xyz, l2_points, l3_points, fp_mlp_0, is_training, bn_decay, scope='fa_layer1')
+ l1_points = pointnet_fp_module(l1_xyz, l2_xyz, l1_points, l2_points, fp_mlp_1, is_training, bn_decay, scope='fa_layer2')
+ l0_points = tf.concat([l0_xyz, l0_points],axis=-1) if input_normals else l0_xyz
+ l0_points = pointnet_fp_module(l0_xyz, l1_xyz, l0_points, l1_points, fp_mlp_2, is_training, bn_decay, scope='fa_layer3')
+ pred_points = l0_xyz
+
+ if joint_heads:
+ head = tf_util.conv1d(l0_points, 128, 1, padding='VALID', bn=True, is_training=is_training, scope='fc1', bn_decay=bn_decay)
+ head = tf_util.dropout(head, keep_prob=0.7, is_training=is_training, scope='dp1')
+ head = tf_util.conv1d(head, 4, 1, padding='VALID', activation_fn=None, scope='fc2')
+ grasp_dir_head = tf.slice(head, [0,0,0], [-1,-1,3])
+ grasp_dir_head = tf.math.l2_normalize(grasp_dir_head, axis=2)
+ binary_seg_head = tf.slice(head, [0,0,3], [-1,-1,1])
+ else:
+ # Head for grasp direction
+ grasp_dir_head = tf_util.conv1d(l0_points, 128, 1, padding='VALID', bn=True, is_training=is_training, scope='fc1', bn_decay=bn_decay)
+ grasp_dir_head = tf_util.dropout(grasp_dir_head, keep_prob=0.7, is_training=is_training, scope='dp1')
+ grasp_dir_head = tf_util.conv1d(grasp_dir_head, 3, 1, padding='VALID', activation_fn=None, scope='fc3')
+ grasp_dir_head_normed = tf.math.l2_normalize(grasp_dir_head, axis=2)
+
+ # Head for grasp approach
+ approach_dir_head = tf_util.conv1d(l0_points, 128, 1, padding='VALID', bn=True, is_training=is_training, scope='fc1_app', bn_decay=bn_decay)
+ approach_dir_head = tf_util.dropout(approach_dir_head, keep_prob=0.7, is_training=is_training, scope='dp1_app')
+ approach_dir_head = tf_util.conv1d(approach_dir_head, 3, 1, padding='VALID', activation_fn=None, scope='fc3_app')
+ approach_dir_head_orthog = tf.math.l2_normalize(approach_dir_head - tf.reduce_sum(tf.multiply(grasp_dir_head_normed, approach_dir_head), axis=2, keepdims=True)*grasp_dir_head_normed, axis=2)
+
+ # Head for grasp width
+ if model_config['dir_vec_length_offset']:
+ grasp_offset_head = tf.norm(grasp_dir_head, axis=2, keepdims=True)
+ elif model_config['bin_offsets']:
+ grasp_offset_head = tf_util.conv1d(l0_points, 128, 1, padding='VALID', bn=True, is_training=is_training, scope='fc1_off', bn_decay=bn_decay)
+ grasp_offset_head = tf_util.conv1d(grasp_offset_head, len(offset_bins)-1, 1, padding='VALID', activation_fn=None, scope='fc2_off')
+ else:
+ grasp_offset_head = tf_util.conv1d(l0_points, 128, 1, padding='VALID', bn=True, is_training=is_training, scope='fc1_off', bn_decay=bn_decay)
+ grasp_offset_head = tf_util.dropout(grasp_offset_head, keep_prob=0.7, is_training=is_training, scope='dp1_off')
+ grasp_offset_head = tf_util.conv1d(grasp_offset_head, 1, 1, padding='VALID', activation_fn=None, scope='fc2_off')
+
+ # Head for contact points
+ binary_seg_head = tf_util.conv1d(l0_points, 128, 1, padding='VALID', bn=True, is_training=is_training, scope='fc1_seg', bn_decay=bn_decay)
+ binary_seg_head = tf_util.dropout(binary_seg_head, keep_prob=0.5, is_training=is_training, scope='dp1_seg')
+ binary_seg_head = tf_util.conv1d(binary_seg_head, 1, 1, padding='VALID', activation_fn=None, scope='fc2_seg')
+
+ end_points['grasp_dir_head'] = grasp_dir_head_normed
+ end_points['binary_seg_head'] = binary_seg_head
+ end_points['binary_seg_pred'] = tf.math.sigmoid(binary_seg_head)
+ end_points['grasp_offset_head'] = grasp_offset_head
+ end_points['grasp_offset_pred'] = tf.math.sigmoid(grasp_offset_head) if model_config['bin_offsets'] else grasp_offset_head
+ end_points['approach_dir_head'] = approach_dir_head_orthog
+ end_points['pred_points'] = pred_points
+
+ return end_points
+
+def build_6d_grasp(approach_dirs, base_dirs, contact_pts, thickness, use_tf=False, gripper_depth = 0.1034):
+ """
+ Build 6-DoF grasps + width from point-wise network predictions
+
+ Arguments:
+ approach_dirs {np.ndarray/tf.tensor} -- Nx3 approach direction vectors
+ base_dirs {np.ndarray/tf.tensor} -- Nx3 base direction vectors
+ contact_pts {np.ndarray/tf.tensor} -- Nx3 contact points
+ thickness {np.ndarray/tf.tensor} -- Nx1 grasp width
+
+ Keyword Arguments:
+ use_tf {bool} -- whether inputs and outputs are tf tensors (default: {False})
+ gripper_depth {float} -- distance from gripper coordinate frame to gripper baseline in m (default: {0.1034})
+
+ Returns:
+ np.ndarray -- Nx4x4 grasp poses in camera coordinates
+ """
+ if use_tf:
+ grasps_R = tf.stack([base_dirs, tf.linalg.cross(approach_dirs,base_dirs),approach_dirs], axis=3)
+ grasps_t = contact_pts + tf.expand_dims(thickness,2)/2 * base_dirs - gripper_depth * approach_dirs
+ ones = tf.ones((contact_pts.shape[0], contact_pts.shape[1], 1, 1), dtype=tf.float32)
+ zeros = tf.zeros((contact_pts.shape[0], contact_pts.shape[1], 1, 3), dtype=tf.float32)
+ homog_vec = tf.concat([zeros, ones], axis=3)
+ grasps = tf.concat([tf.concat([grasps_R, tf.expand_dims(grasps_t, 3)], axis=3), homog_vec], axis=2)
+ else:
+ grasps = []
+ for i in range(len(contact_pts)):
+ grasp = np.eye(4)
+
+ grasp[:3,0] = base_dirs[i] / np.linalg.norm(base_dirs[i])
+ grasp[:3,2] = approach_dirs[i] / np.linalg.norm(approach_dirs[i])
+ grasp_y = np.cross( grasp[:3,2],grasp[:3,0])
+ grasp[:3,1] = grasp_y / np.linalg.norm(grasp_y)
+ # base_gripper xyz = contact + thickness / 2 * baseline_dir - gripper_d * approach_dir
+ grasp[:3,3] = contact_pts[i] + thickness[i] / 2 * grasp[:3,0] - gripper_depth * grasp[:3,2]
+ # grasp[0,3] = finger_width
+ grasps.append(grasp)
+ grasps = np.array(grasps)
+
+ return grasps
+
+def get_losses(pointclouds_pl, end_points, dir_labels_pc_cam, offset_labels_pc, grasp_success_labels_pc, approach_labels_pc_cam, global_config):
+ """
+ Computes loss terms from pointclouds, network predictions and labels
+
+ Arguments:
+ pointclouds_pl {tf.placeholder} -- bxNx3 input point clouds
+ end_points {dict[str:tf.variable]} -- endpoints of the network containing predictions
+ dir_labels_pc_cam {tf.variable} -- base direction labels in camera coordinates (bxNx3)
+ offset_labels_pc {tf.variable} -- grasp width labels (bxNx1)
+ grasp_success_labels_pc {tf.variable} -- contact success labels (bxNx1)
+ approach_labels_pc_cam {tf.variable} -- approach direction labels in camera coordinates (bxNx3)
+ global_config {dict} -- config dict
+
+ Returns:
+ [dir_cosine_loss, bin_ce_loss, offset_loss, approach_cosine_loss, adds_loss,
+ adds_loss_gt2pred, gt_control_points, pred_control_points, pos_grasps_in_view] -- All losses (not all are used for training)
+ """
+
+ grasp_dir_head = end_points['grasp_dir_head']
+ grasp_offset_head = end_points['grasp_offset_head']
+ approach_dir_head = end_points['approach_dir_head']
+
+ bin_weights = global_config['DATA']['labels']['bin_weights']
+ tf_bin_weights = tf.constant(bin_weights)
+
+ min_geom_loss_divisor = tf.constant(float(global_config['LOSS']['min_geom_loss_divisor'])) if 'min_geom_loss_divisor' in global_config['LOSS'] else tf.constant(1.)
+ pos_grasps_in_view = tf.math.maximum(tf.reduce_sum(grasp_success_labels_pc, axis=1), min_geom_loss_divisor)
+
+ ### ADS Gripper PC Loss
+ if global_config['MODEL']['bin_offsets']:
+ thickness_pred = tf.gather_nd(get_bin_vals(global_config), tf.expand_dims(tf.argmax(grasp_offset_head, axis=2), axis=2))
+ thickness_gt = tf.gather_nd(get_bin_vals(global_config), tf.expand_dims(tf.argmax(offset_labels_pc, axis=2), axis=2))
+ else:
+ thickness_pred = grasp_offset_head[:,:,0]
+ thickness_gt = offset_labels_pc[:,:,0]
+ pred_grasps = build_6d_grasp(approach_dir_head, grasp_dir_head, pointclouds_pl, thickness_pred, use_tf=True) # b x num_point x 4 x 4
+ gt_grasps_proj = build_6d_grasp(approach_labels_pc_cam, dir_labels_pc_cam, pointclouds_pl, thickness_gt, use_tf=True) # b x num_point x 4 x 4
+ pos_gt_grasps_proj = tf.where(tf.broadcast_to(tf.expand_dims(tf.expand_dims(tf.cast(grasp_success_labels_pc, tf.bool),2),3), gt_grasps_proj.shape), gt_grasps_proj, tf.ones_like(gt_grasps_proj)*100000)
+ # pos_gt_grasps_proj = tf.reshape(pos_gt_grasps_proj, (global_config['OPTIMIZER']['batch_size'], -1, 4, 4))
+
+ gripper = mesh_utils.create_gripper('panda')
+ gripper_control_points = gripper.get_control_point_tensor(global_config['OPTIMIZER']['batch_size']) # b x 5 x 3
+ sym_gripper_control_points = gripper.get_control_point_tensor(global_config['OPTIMIZER']['batch_size'], symmetric=True)
+
+ gripper_control_points_homog = tf.concat([gripper_control_points, tf.ones((global_config['OPTIMIZER']['batch_size'], gripper_control_points.shape[1], 1))], axis=2) # b x 5 x 4
+ sym_gripper_control_points_homog = tf.concat([sym_gripper_control_points, tf.ones((global_config['OPTIMIZER']['batch_size'], gripper_control_points.shape[1], 1))], axis=2) # b x 5 x 4
+
+ # only use per point pred grasps but not per point gt grasps
+ control_points = tf.keras.backend.repeat_elements(tf.expand_dims(gripper_control_points_homog,1), gt_grasps_proj.shape[1], axis=1) # b x num_point x 5 x 4
+ sym_control_points = tf.keras.backend.repeat_elements(tf.expand_dims(sym_gripper_control_points_homog,1), gt_grasps_proj.shape[1], axis=1) # b x num_point x 5 x 4
+ pred_control_points = tf.matmul(control_points, tf.transpose(pred_grasps, perm=[0, 1, 3, 2]))[:,:,:,:3] # b x num_point x 5 x 3
+
+ ### Pred Grasp to GT Grasp ADD-S Loss
+ gt_control_points = tf.matmul(control_points, tf.transpose(pos_gt_grasps_proj, perm=[0, 1, 3, 2]))[:,:,:,:3] # b x num_pos_grasp_point x 5 x 3
+ sym_gt_control_points = tf.matmul(sym_control_points, tf.transpose(pos_gt_grasps_proj, perm=[0, 1, 3, 2]))[:,:,:,:3] # b x num_pos_grasp_point x 5 x 3
+
+ squared_add = tf.reduce_sum((tf.expand_dims(pred_control_points,2)-tf.expand_dims(gt_control_points,1))**2, axis=(3,4)) # b x num_point x num_pos_grasp_point x ( 5 x 3)
+ sym_squared_add = tf.reduce_sum((tf.expand_dims(pred_control_points,2)-tf.expand_dims(sym_gt_control_points,1))**2, axis=(3,4)) # b x num_point x num_pos_grasp_point x ( 5 x 3)
+
+ # symmetric ADD-S
+ neg_squared_adds = -tf.concat([squared_add,sym_squared_add], axis=2) # b x num_point x 2num_pos_grasp_point
+ neg_squared_adds_k = tf.math.top_k(neg_squared_adds, k=1, sorted=False)[0] # b x num_point
+ # If any pos grasp exists
+ min_adds = tf.minimum(tf.reduce_sum(grasp_success_labels_pc, axis=1, keepdims=True), tf.ones_like(neg_squared_adds_k[:,:,0])) * tf.sqrt(-neg_squared_adds_k[:,:,0])#tf.minimum(tf.sqrt(-neg_squared_adds_k), tf.ones_like(neg_squared_adds_k)) # b x num_point
+ adds_loss = tf.reduce_mean(end_points['binary_seg_pred'][:,:,0] * min_adds)
+
+ ### GT Grasp to pred Grasp ADD-S Loss
+ gt_control_points = tf.matmul(control_points, tf.transpose(gt_grasps_proj, perm=[0, 1, 3, 2]))[:,:,:,:3] # b x num_pos_grasp_point x 5 x 3
+ sym_gt_control_points = tf.matmul(sym_control_points, tf.transpose(gt_grasps_proj, perm=[0, 1, 3, 2]))[:,:,:,:3] # b x num_pos_grasp_point x 5 x 3
+
+ neg_squared_adds = -tf.reduce_sum((tf.expand_dims(pred_control_points,1)-tf.expand_dims(gt_control_points,2))**2, axis=(3,4)) # b x num_point x num_pos_grasp_point x ( 5 x 3)
+ neg_squared_adds_sym = -tf.reduce_sum((tf.expand_dims(pred_control_points,1)-tf.expand_dims(sym_gt_control_points,2))**2, axis=(3,4)) # b x num_point x num_pos_grasp_point x ( 5 x 3)
+
+ neg_squared_adds_k_gt2pred, pred_grasp_idcs = tf.math.top_k(neg_squared_adds, k=1, sorted=False) # b x num_pos_grasp_point
+ neg_squared_adds_k_sym_gt2pred, pred_grasp_sym_idcs = tf.math.top_k(neg_squared_adds_sym, k=1, sorted=False) # b x num_pos_grasp_point
+ pred_grasp_idcs_joined = tf.where(neg_squared_adds_k_gt2pred= data.shape[0]:
+ if return_center_indexes:
+ return np.arange(data.shape[0], dtype=np.int32), np.arange(data.shape[0], dtype=np.int32)
+
+ return np.arange(data.shape[0], dtype=np.int32)
+
+ clusters = np.ones((data.shape[0],), dtype=np.int32) * -1
+ distances = np.ones((data.shape[0],), dtype=np.float32) * 1e7
+ centers = []
+ for iter in range(nclusters):
+ index = np.argmax(distances)
+ centers.append(index)
+ shape = list(data.shape)
+ for i in range(1, len(shape)):
+ shape[i] = 1
+
+ broadcasted_data = np.tile(np.expand_dims(data[index], 0), shape)
+ new_distances = dist_func(broadcasted_data, data)
+ distances = np.minimum(distances, new_distances)
+ clusters[distances == new_distances] = iter
+ if verbose:
+ print('farthest points max distance : {}'.format(np.max(distances)))
+
+ if return_center_indexes:
+ if return_distances:
+ return clusters, np.asarray(centers, dtype=np.int32), distances
+ return clusters, np.asarray(centers, dtype=np.int32)
+
+ return clusters
+
+def reject_median_outliers(data, m=0.4, z_only=False):
+ """
+ Reject outliers with median absolute distance m
+
+ Arguments:
+ data {[np.ndarray]} -- Numpy array such as point cloud
+
+ Keyword Arguments:
+ m {[float]} -- Maximum absolute distance from median in m (default: {0.4})
+ z_only {[bool]} -- filter only via z_component (default: {False})
+
+ Returns:
+ [np.ndarray] -- Filtered data without outliers
+ """
+ if z_only:
+ d = np.abs(data[:,2:3] - np.median(data[:,2:3]))
+ else:
+ d = np.abs(data - np.median(data, axis=0, keepdims=True))
+
+ return data[np.sum(d, axis=1) < m]
+
+def regularize_pc_point_count(pc, npoints, use_farthest_point=False):
+ """
+ If point cloud pc has less points than npoints, it oversamples.
+ Otherwise, it downsample the input pc to have npoint points.
+ use_farthest_point: indicates
+
+ :param pc: Nx3 point cloud
+ :param npoints: number of points the regularized point cloud should have
+ :param use_farthest_point: use farthest point sampling to downsample the points, runs slower.
+ :returns: npointsx3 regularized point cloud
+ """
+
+ if pc.shape[0] > npoints:
+ if use_farthest_point:
+ _, center_indexes = farthest_points(pc, npoints, distance_by_translation_point, return_center_indexes=True)
+ else:
+ center_indexes = np.random.choice(range(pc.shape[0]), size=npoints, replace=False)
+ pc = pc[center_indexes, :]
+ else:
+ required = npoints - pc.shape[0]
+ if required > 0:
+ index = np.random.choice(range(pc.shape[0]), size=required)
+ pc = np.concatenate((pc, pc[index, :]), axis=0)
+ return pc
+
+def depth2pc(depth, K, rgb=None):
+ """
+ Convert depth and intrinsics to point cloud and optionally point cloud color
+ :param depth: hxw depth map in m
+ :param K: 3x3 Camera Matrix with intrinsics
+ :returns: (Nx3 point cloud, point cloud color)
+ """
+
+ mask = np.where(depth > 0)
+ x,y = mask[1], mask[0]
+
+ normalized_x = (x.astype(np.float32) - K[0,2])
+ normalized_y = (y.astype(np.float32) - K[1,2])
+
+ world_x = normalized_x * depth[y, x] / K[0,0]
+ world_y = normalized_y * depth[y, x] / K[1,1]
+ world_z = depth[y, x]
+
+ if rgb is not None:
+ rgb = rgb[y,x,:]
+
+ pc = np.vstack((world_x, world_y, world_z)).T
+ return (pc, rgb)
+
+
+def estimate_normals_cam_from_pc(self, pc_cam, max_radius=0.05, k=12):
+ """
+ Estimates normals in camera coords from given point cloud.
+
+ Arguments:
+ pc_cam {np.ndarray} -- Nx3 point cloud in camera coordinates
+
+ Keyword Arguments:
+ max_radius {float} -- maximum radius for normal computation (default: {0.05})
+ k {int} -- Number of neighbors for normal computation (default: {12})
+
+ Returns:
+ [np.ndarray] -- Nx3 point cloud normals
+ """
+ tree = cKDTree(pc_cam, leafsize=pc_cam.shape[0]+1)
+ _, ndx = tree.query(pc_cam, k=k, distance_upper_bound=max_radius, n_jobs=-1) # num_points x k
+
+ for c,idcs in enumerate(ndx):
+ idcs[idcs==pc_cam.shape[0]] = c
+ ndx[c,:] = idcs
+ neighbors = np.array([pc_cam[ndx[:,n],:] for n in range(k)]).transpose((1,0,2))
+ pc_normals = vectorized_normal_computation(pc_cam, neighbors)
+ return pc_normals
+
+def vectorized_normal_computation(pc, neighbors):
+ """
+ Vectorized normal computation with numpy
+
+ Arguments:
+ pc {np.ndarray} -- Nx3 point cloud
+ neighbors {np.ndarray} -- Nxkx3 neigbours
+
+ Returns:
+ [np.ndarray] -- Nx3 normal directions
+ """
+ diffs = neighbors - np.expand_dims(pc, 1) # num_point x k x 3
+ covs = np.matmul(np.transpose(diffs, (0, 2, 1)), diffs) # num_point x 3 x 3
+ covs /= diffs.shape[1]**2
+ # takes most time: 6-7ms
+ eigen_values, eigen_vectors = np.linalg.eig(covs) # num_point x 3, num_point x 3 x 3
+ orders = np.argsort(-eigen_values, axis=1) # num_point x 3
+ orders_third = orders[:,2] # num_point
+ directions = eigen_vectors[np.arange(pc.shape[0]),:,orders_third] # num_point x 3
+ dots = np.sum(directions * pc, axis=1) # num_point
+ directions[dots >= 0] = -directions[dots >= 0]
+ return directions
+
+def load_available_input_data(p, K=None):
+ """
+ Load available data from input file path.
+
+ Numpy files .npz/.npy should have keys
+ 'depth' + 'K' + (optionally) 'segmap' + (optionally) 'rgb'
+ or for point clouds:
+ 'xyz' + (optionally) 'xyz_color'
+
+ png files with only depth data (in mm) can be also loaded.
+ If the image path is from the GraspNet dataset, corresponding rgb, segmap and intrinic are also loaded.
+
+ :param p: .png/.npz/.npy file path that contain depth/pointcloud and optionally intrinsics/segmentation/rgb
+ :param K: 3x3 Camera Matrix with intrinsics
+ :returns: All available data among segmap, rgb, depth, cam_K, pc_full, pc_colors
+ """
+
+ segmap, rgb, depth, pc_full, pc_colors = None, None, None, None, None
+
+ if K is not None:
+ if isinstance(K,str):
+ cam_K = eval(K)
+ cam_K = np.array(K).reshape(3,3)
+
+ if '.np' in p:
+ data = np.load(p, allow_pickle=True)
+ if '.npz' in p:
+ keys = data.files
+ else:
+ keys = []
+ if len(data.shape) == 0:
+ data = data.item()
+ keys = data.keys()
+ elif data.shape[-1] == 3:
+ pc_full = data
+ else:
+ depth = data
+
+ if 'depth' in keys:
+ depth = data['depth']
+ if K is None and 'K' in keys:
+ cam_K = data['K'].reshape(3,3)
+ if 'segmap' in keys:
+ segmap = data['segmap']
+ if 'seg' in keys:
+ segmap = data['seg']
+ if 'rgb' in keys:
+ rgb = data['rgb']
+ rgb = np.array(cv2.cvtColor(rgb, cv2.COLOR_BGR2RGB))
+ elif 'xyz' in keys:
+ pc_full = np.array(data['xyz']).reshape(-1,3)
+ if 'xyz_color' in keys:
+ pc_colors = data['xyz_color']
+ elif '.png' in p:
+ if os.path.exists(p.replace('depth', 'label')):
+ # graspnet data
+ depth, rgb, segmap, K = load_graspnet_data(p)
+ elif os.path.exists(p.replace('depths', 'images').replace('npy', 'png')):
+ rgb = np.array(Image.open(p.replace('depths', 'images').replace('npy', 'png')))
+ else:
+ depth = np.array(Image.open(p))
+ else:
+ raise ValueError('{} is neither png nor npz/npy file'.format(p))
+
+ return segmap, rgb, depth, cam_K, pc_full, pc_colors
+
+def load_graspnet_data(rgb_image_path):
+
+ """
+ Loads data from the GraspNet-1Billion dataset
+ # https://graspnet.net/
+
+ :param rgb_image_path: .png file path to depth image in graspnet dataset
+ :returns: (depth, rgb, segmap, K)
+ """
+
+ depth = np.array(Image.open(rgb_image_path))/1000. # m to mm
+ segmap = np.array(Image.open(rgb_image_path.replace('depth', 'label')))
+ rgb = np.array(Image.open(rgb_image_path.replace('depth', 'rgb')))
+
+ # graspnet images are upside down, rotate for inference
+ # careful: rotate grasp poses back for evaluation
+ depth = np.rot90(depth,2)
+ segmap = np.rot90(segmap,2)
+ rgb = np.rot90(rgb,2)
+
+ if 'kinect' in rgb_image_path:
+ # Kinect azure:
+ K=np.array([[631.54864502 , 0. , 638.43517329],
+ [ 0. , 631.20751953, 366.49904066],
+ [ 0. , 0. , 1. ]])
+ else:
+ # Realsense:
+ K=np.array([[616.36529541 , 0. , 310.25881958],
+ [ 0. , 616.20294189, 236.59980774],
+ [ 0. , 0. , 1. ]])
+
+ return depth, rgb, segmap, K
+
+def center_pc_convert_cam(cam_poses, batch_data):
+ """
+ Converts from OpenGL to OpenCV coordinates, computes inverse of camera pose and centers point cloud
+
+ :param cam_poses: (bx4x4) Camera poses in OpenGL format
+ :param batch_data: (bxNx3) point clouds
+ :returns: (cam_poses, batch_data) converted
+ """
+ # OpenCV OpenGL conversion
+ for j in range(len(cam_poses)):
+ cam_poses[j,:3,1] = -cam_poses[j,:3,1]
+ cam_poses[j,:3,2] = -cam_poses[j,:3,2]
+ cam_poses[j] = inverse_transform(cam_poses[j])
+
+ pc_mean = np.mean(batch_data, axis=1, keepdims=True)
+ batch_data[:,:,:3] -= pc_mean[:,:,:3]
+ cam_poses[:,:3,3] -= pc_mean[:,0,:3]
+
+ return cam_poses, batch_data
+
+
+class PointCloudReader:
+ """
+ Class to load scenes, render point clouds and augment them during training
+
+ Arguments:
+ root_folder {str} -- acronym root folder
+ batch_size {int} -- number of rendered point clouds per-batch
+
+ Keyword Arguments:
+ raw_num_points {int} -- Number of random/farthest point samples per scene (default: {20000})
+ estimate_normals {bool} -- compute normals from rendered point cloud (default: {False})
+ caching {bool} -- cache scenes in memory (default: {True})
+ use_uniform_quaternions {bool} -- use uniform quaternions for camera sampling (default: {False})
+ scene_obj_scales {list} -- object scales in scene (default: {None})
+ scene_obj_paths {list} -- object paths in scene (default: {None})
+ scene_obj_transforms {np.ndarray} -- object transforms in scene (default: {None})
+ num_train_samples {int} -- training scenes (default: {None})
+ num_test_samples {int} -- test scenes (default: {None})
+ use_farthest_point {bool} -- use farthest point sampling to reduce point cloud dimension (default: {False})
+ intrinsics {str} -- intrinsics to for rendering depth maps (default: {None})
+ distance_range {tuple} -- distance range from camera to center of table (default: {(0.9,1.3)})
+ elevation {tuple} -- elevation range (90 deg is top-down) (default: {(30,150)})
+ pc_augm_config {dict} -- point cloud augmentation config (default: {None})
+ depth_augm_config {dict} -- depth map augmentation config (default: {None})
+ """
+ def __init__(
+ self,
+ root_folder,
+ batch_size=1,
+ raw_num_points = 20000,
+ estimate_normals = False,
+ caching=True,
+ use_uniform_quaternions=False,
+ scene_obj_scales=None,
+ scene_obj_paths=None,
+ scene_obj_transforms=None,
+ num_train_samples=None,
+ num_test_samples=None,
+ use_farthest_point = False,
+ intrinsics = None,
+ distance_range = (0.9,1.3),
+ elevation = (30,150),
+ pc_augm_config = None,
+ depth_augm_config = None
+ ):
+ self._root_folder = root_folder
+ self._batch_size = batch_size
+ self._raw_num_points = raw_num_points
+ self._caching = caching
+ self._num_train_samples = num_train_samples
+ self._num_test_samples = num_test_samples
+ self._estimate_normals = estimate_normals
+ self._use_farthest_point = use_farthest_point
+ self._scene_obj_scales = scene_obj_scales
+ self._scene_obj_paths = scene_obj_paths
+ self._scene_obj_transforms = scene_obj_transforms
+ self._distance_range = distance_range
+ self._pc_augm_config = pc_augm_config
+ self._depth_augm_config = depth_augm_config
+
+ self._current_pc = None
+ self._cache = {}
+
+ self._renderer = SceneRenderer(caching=True, intrinsics=intrinsics)
+
+ if use_uniform_quaternions:
+ quat_path = os.path.join(self._root_folder, 'uniform_quaternions/data2_4608.qua')
+ quaternions = [l[:-1].split('\t') for l in open(quat_path, 'r').readlines()]
+
+ quaternions = [[float(t[0]),
+ float(t[1]),
+ float(t[2]),
+ float(t[3])] for t in quaternions]
+ quaternions = np.asarray(quaternions)
+ quaternions = np.roll(quaternions, 1, axis=1)
+ self._all_poses = [tra.quaternion_matrix(q) for q in quaternions]
+ else:
+ self._cam_orientations = []
+ self._elevation = np.array(elevation)/180.
+ for az in np.linspace(0, np.pi * 2, 30):
+ for el in np.linspace(self._elevation[0], self._elevation[1], 30):
+ self._cam_orientations.append(tra.euler_matrix(0, -el, az))
+ self._coordinate_transform = tra.euler_matrix(np.pi/2, 0, 0).dot(tra.euler_matrix(0, np.pi/2, 0))
+
+ def get_cam_pose(self, cam_orientation):
+ """
+ Samples camera pose on shell around table center
+
+ Arguments:
+ cam_orientation {np.ndarray} -- 3x3 camera orientation matrix
+
+ Returns:
+ [np.ndarray] -- 4x4 homogeneous camera pose
+ """
+
+ distance = self._distance_range[0] + np.random.rand()*(self._distance_range[1]-self._distance_range[0])
+
+ extrinsics = np.eye(4)
+ extrinsics[0, 3] += distance
+ extrinsics = cam_orientation.dot(extrinsics)
+
+ cam_pose = extrinsics.dot(self._coordinate_transform)
+ # table height
+ cam_pose[2,3] += self._renderer._table_dims[2]
+ cam_pose[:3,:2]= -cam_pose[:3,:2]
+ return cam_pose
+
+ def _augment_pc(self, pc):
+ """
+ Augments point cloud with jitter and dropout according to config
+
+ Arguments:
+ pc {np.ndarray} -- Nx3 point cloud
+
+ Returns:
+ np.ndarray -- augmented point cloud
+ """
+
+ # not used because no artificial occlusion
+ if 'occlusion_nclusters' in self._pc_augm_config and self._pc_augm_config['occlusion_nclusters'] > 0:
+ pc = self.apply_dropout(pc,
+ self._pc_augm_config['occlusion_nclusters'],
+ self._pc_augm_config['occlusion_dropout_rate'])
+
+ if 'sigma' in self._pc_augm_config and self._pc_augm_config['sigma'] > 0:
+ pc = provider.jitter_point_cloud(pc[np.newaxis, :, :],
+ sigma=self._pc_augm_config['sigma'],
+ clip=self._pc_augm_config['clip'])[0]
+
+
+ return pc[:,:3]
+
+ def _augment_depth(self, depth):
+ """
+ Augments depth map with z-noise and smoothing according to config
+
+ Arguments:
+ depth {np.ndarray} -- depth map
+
+ Returns:
+ np.ndarray -- augmented depth map
+ """
+
+ if 'sigma' in self._depth_augm_config and self._depth_augm_config['sigma'] > 0:
+ clip = self._depth_augm_config['clip']
+ sigma = self._depth_augm_config['sigma']
+ noise = np.clip(sigma*np.random.randn(*depth.shape), -clip, clip)
+ depth += noise
+ if 'gaussian_kernel' in self._depth_augm_config and self._depth_augm_config['gaussian_kernel'] > 0:
+ kernel = self._depth_augm_config['gaussian_kernel']
+ depth_copy = depth.copy()
+ depth = cv2.GaussianBlur(depth,(kernel,kernel),0)
+ depth[depth_copy==0] = depth_copy[depth_copy==0]
+
+ return depth
+
+ def apply_dropout(self, pc, occlusion_nclusters, occlusion_dropout_rate):
+ """
+ Remove occlusion_nclusters farthest points from point cloud with occlusion_dropout_rate probability
+
+ Arguments:
+ pc {np.ndarray} -- Nx3 point cloud
+ occlusion_nclusters {int} -- noof cluster to remove
+ occlusion_dropout_rate {float} -- prob of removal
+
+ Returns:
+ [np.ndarray] -- N > Mx3 point cloud
+ """
+ if occlusion_nclusters == 0 or occlusion_dropout_rate == 0.:
+ return pc
+
+ labels = farthest_points(pc, occlusion_nclusters, distance_by_translation_point)
+
+ removed_labels = np.unique(labels)
+ removed_labels = removed_labels[np.random.rand(removed_labels.shape[0]) < occlusion_dropout_rate]
+ if removed_labels.shape[0] == 0:
+ return pc
+ mask = np.ones(labels.shape, labels.dtype)
+ for l in removed_labels:
+ mask = np.logical_and(mask, labels != l)
+ return pc[mask]
+
+ def get_scene_batch(self, scene_idx=None, return_segmap=False, save=False):
+ """
+ Render a batch of scene point clouds
+
+ Keyword Arguments:
+ scene_idx {int} -- index of the scene (default: {None})
+ return_segmap {bool} -- whether to render a segmap of objects (default: {False})
+ save {bool} -- Save training/validation data to npz file for later inference (default: {False})
+
+ Returns:
+ [batch_data, cam_poses, scene_idx] -- batch of rendered point clouds, camera poses and the scene_idx
+ """
+ dims = 6 if self._estimate_normals else 3
+ batch_data = np.empty((self._batch_size, self._raw_num_points, dims), dtype=np.float32)
+ cam_poses = np.empty((self._batch_size, 4, 4), dtype=np.float32)
+
+ if scene_idx is None:
+ scene_idx = np.random.randint(0,self._num_train_samples)
+
+ obj_paths = [os.path.join(self._root_folder, p) for p in self._scene_obj_paths[scene_idx]]
+ mesh_scales = self._scene_obj_scales[scene_idx]
+ obj_trafos = self._scene_obj_transforms[scene_idx]
+
+ self.change_scene(obj_paths, mesh_scales, obj_trafos, visualize=False)
+
+ batch_segmap, batch_obj_pcs = [], []
+ for i in range(self._batch_size):
+ # 0.005s
+ pc_cam, pc_normals, camera_pose, depth = self.render_random_scene(estimate_normals = self._estimate_normals)
+
+ if return_segmap:
+ segmap, _, obj_pcs = self._renderer.render_labels(depth, obj_paths, mesh_scales, render_pc=True)
+ batch_obj_pcs.append(obj_pcs)
+ batch_segmap.append(segmap)
+
+ batch_data[i,:,0:3] = pc_cam[:,:3]
+ if self._estimate_normals:
+ batch_data[i,:,3:6] = pc_normals[:,:3]
+ cam_poses[i,:,:] = camera_pose
+
+ if save:
+ K = np.array([[616.36529541,0,310.25881958 ],[0,616.20294189,236.59980774],[0,0,1]])
+ data = {'depth':depth, 'K':K, 'camera_pose':camera_pose, 'scene_idx':scene_idx}
+ if return_segmap:
+ data.update(segmap=segmap)
+ np.savez('results/{}_acronym.npz'.format(scene_idx), data)
+
+ if return_segmap:
+ return batch_data, cam_poses, scene_idx, batch_segmap, batch_obj_pcs
+ else:
+ return batch_data, cam_poses, scene_idx
+
+ def render_random_scene(self, estimate_normals=False, camera_pose=None):
+ """
+ Renders scene depth map, transforms to regularized pointcloud and applies augmentations
+
+ Keyword Arguments:
+ estimate_normals {bool} -- calculate and return normals (default: {False})
+ camera_pose {[type]} -- camera pose to render the scene from. (default: {None})
+
+ Returns:
+ [pc, pc_normals, camera_pose, depth] -- [point cloud, point cloud normals, camera pose, depth]
+ """
+ if camera_pose is None:
+ viewing_index = np.random.randint(0, high=len(self._cam_orientations))
+ camera_orientation = self._cam_orientations[viewing_index]
+ camera_pose = self.get_cam_pose(camera_orientation)
+
+ in_camera_pose = copy.deepcopy(camera_pose)
+
+ # 0.005 s
+ _, depth, _, camera_pose = self._renderer.render(in_camera_pose, render_pc=False)
+ depth = self._augment_depth(depth)
+
+ pc = self._renderer._to_pointcloud(depth)
+ pc = regularize_pc_point_count(pc, self._raw_num_points, use_farthest_point=self._use_farthest_point)
+ pc = self._augment_pc(pc)
+
+ pc_normals = estimate_normals_cam_from_pc(pc[:,:3], raw_num_points=self._raw_num_points) if estimate_normals else []
+
+ return pc, pc_normals, camera_pose, depth
+
+ def change_object(self, cad_path, cad_scale):
+ """
+ Change object in pyrender scene
+
+ Arguments:
+ cad_path {str} -- path to CAD model
+ cad_scale {float} -- scale of CAD model
+ """
+
+ self._renderer.change_object(cad_path, cad_scale)
+
+ def change_scene(self, obj_paths, obj_scales, obj_transforms, visualize=False):
+ """
+ Change pyrender scene
+
+ Arguments:
+ obj_paths {list[str]} -- path to CAD models in scene
+ obj_scales {list[float]} -- scales of CAD models
+ obj_transforms {list[np.ndarray]} -- poses of CAD models
+
+ Keyword Arguments:
+ visualize {bool} -- whether to update the visualizer as well (default: {False})
+ """
+ self._renderer.change_scene(obj_paths, obj_scales, obj_transforms)
+ if visualize:
+ self._visualizer.change_scene(obj_paths, obj_scales, obj_transforms)
+
+
+
+ def __del__(self):
+ print('********** terminating renderer **************')
+
+
diff --git a/contact_graspnet/inference.py b/contact_graspnet/inference.py
new file mode 100644
index 0000000..32158e8
--- /dev/null
+++ b/contact_graspnet/inference.py
@@ -0,0 +1,110 @@
+import os
+import sys
+import argparse
+import numpy as np
+import time
+import glob
+import cv2
+
+import tensorflow.compat.v1 as tf
+tf.disable_eager_execution()
+physical_devices = tf.config.experimental.list_physical_devices('GPU')
+tf.config.experimental.set_memory_growth(physical_devices[0], True)
+
+BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+sys.path.append(os.path.join(BASE_DIR))
+import config_utils
+from data import regularize_pc_point_count, depth2pc, load_available_input_data
+
+from contact_grasp_estimator import GraspEstimator
+from visualization_utils import visualize_grasps, show_image
+
+def inference(global_config, checkpoint_dir, input_paths, K=None, local_regions=True, skip_border_objects=False, filter_grasps=True, segmap_id=None, z_range=[0.2,1.8], forward_passes=1):
+ """
+ Predict 6-DoF grasp distribution for given model and input data
+
+ :param global_config: config.yaml from checkpoint directory
+ :param checkpoint_dir: checkpoint directory
+ :param input_paths: .png/.npz/.npy file paths that contain depth/pointcloud and optionally intrinsics/segmentation/rgb
+ :param K: Camera Matrix with intrinsics to convert depth to point cloud
+ :param local_regions: Crop 3D local regions around given segments.
+ :param skip_border_objects: When extracting local_regions, ignore segments at depth map boundary.
+ :param filter_grasps: Filter and assign grasp contacts according to segmap.
+ :param segmap_id: only return grasps from specified segmap_id.
+ :param z_range: crop point cloud at a minimum/maximum z distance from camera to filter out outlier points. Default: [0.2, 1.8] m
+ :param forward_passes: Number of forward passes to run on each point cloud. Default: 1
+ """
+
+ # Build the model
+ grasp_estimator = GraspEstimator(global_config)
+ grasp_estimator.build_network()
+
+ # Add ops to save and restore all the variables.
+ saver = tf.train.Saver(save_relative_paths=True)
+
+ # Create a session
+ config = tf.ConfigProto()
+ config.gpu_options.allow_growth = True
+ config.allow_soft_placement = True
+ sess = tf.Session(config=config)
+
+ # Load weights
+ grasp_estimator.load_weights(sess, saver, checkpoint_dir, mode='test')
+
+ os.makedirs('results', exist_ok=True)
+
+ # Process example test scenes
+ for p in glob.glob(input_paths):
+ print('Loading ', p)
+
+ pc_segments = {}
+ segmap, rgb, depth, cam_K, pc_full, pc_colors = load_available_input_data(p, K=K)
+
+ if segmap is None and (local_regions or filter_grasps):
+ raise ValueError('Need segmentation map to extract local regions or filter grasps')
+
+ if pc_full is None:
+ print('Converting depth to point cloud(s)...')
+ pc_full, pc_segments, pc_colors = grasp_estimator.extract_point_clouds(depth, cam_K, segmap=segmap, rgb=rgb,
+ skip_border_objects=skip_border_objects, z_range=z_range)
+
+ print('Generating Grasps...')
+ pred_grasps_cam, scores, contact_pts, _ = grasp_estimator.predict_scene_grasps(sess, pc_full, pc_segments=pc_segments,
+ local_regions=local_regions, filter_grasps=filter_grasps, forward_passes=forward_passes)
+
+ # Save results
+ np.savez('results/predictions_{}'.format(os.path.basename(p.replace('png','npz').replace('npy','npz'))),
+ pred_grasps_cam=pred_grasps_cam, scores=scores, contact_pts=contact_pts)
+
+ # Visualize results
+ show_image(rgb, segmap)
+ visualize_grasps(pc_full, pred_grasps_cam, scores, plot_opencv_cam=True, pc_colors=pc_colors)
+
+ if not glob.glob(input_paths):
+ print('No files found: ', input_paths)
+
+if __name__ == "__main__":
+
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--ckpt_dir', default='checkpoints/scene_test_2048_bs3_hor_sigma_001', help='Log dir [default: checkpoints/scene_test_2048_bs3_hor_sigma_001]')
+ parser.add_argument('--np_path', default='test_data/7.npy', help='Input data: npz/npy file with keys either "depth" & camera matrix "K" or just point cloud "pc" in meters. Optionally, a 2D "segmap"')
+ parser.add_argument('--png_path', default='', help='Input data: depth map png in meters')
+ parser.add_argument('--K', default=None, help='Flat Camera Matrix, pass as "[fx, 0, cx, 0, fy, cy, 0, 0 ,1]"')
+ parser.add_argument('--z_range', default=[0.2,1.8], help='Z value threshold to crop the input point cloud')
+ parser.add_argument('--local_regions', action='store_true', default=False, help='Crop 3D local regions around given segments.')
+ parser.add_argument('--filter_grasps', action='store_true', default=False, help='Filter grasp contacts according to segmap.')
+ parser.add_argument('--skip_border_objects', action='store_true', default=False, help='When extracting local_regions, ignore segments at depth map boundary.')
+ parser.add_argument('--forward_passes', type=int, default=1, help='Run multiple parallel forward passes to mesh_utils more potential contact points.')
+ parser.add_argument('--segmap_id', type=int, default=0, help='Only return grasps of the given object id')
+ parser.add_argument('--arg_configs', nargs="*", type=str, default=[], help='overwrite config parameters')
+ FLAGS = parser.parse_args()
+
+ global_config = config_utils.load_config(FLAGS.ckpt_dir, batch_size=FLAGS.forward_passes, arg_configs=FLAGS.arg_configs)
+
+ print(str(global_config))
+ print('pid: %s'%(str(os.getpid())))
+
+ inference(global_config, FLAGS.ckpt_dir, FLAGS.np_path if not FLAGS.png_path else FLAGS.png_path, z_range=eval(str(FLAGS.z_range)),
+ K=FLAGS.K, local_regions=FLAGS.local_regions, filter_grasps=FLAGS.filter_grasps, segmap_id=FLAGS.segmap_id,
+ forward_passes=FLAGS.forward_passes, skip_border_objects=FLAGS.skip_border_objects)
+
diff --git a/contact_graspnet/mesh_utils.py b/contact_graspnet/mesh_utils.py
new file mode 100644
index 0000000..65831f9
--- /dev/null
+++ b/contact_graspnet/mesh_utils.py
@@ -0,0 +1,275 @@
+# -*- coding: utf-8 -*-
+"""Helper classes and functions to sample grasps for a given object mesh."""
+
+from __future__ import print_function
+
+import argparse
+from collections import OrderedDict
+import errno
+import json
+import os
+import numpy as np
+import pickle
+from tqdm import tqdm
+import trimesh
+import trimesh.transformations as tra
+
+import tensorflow.compat.v1 as tf
+
+class Object(object):
+ """Represents a graspable object."""
+
+ def __init__(self, filename):
+ """Constructor.
+
+ :param filename: Mesh to load
+ :param scale: Scaling factor
+ """
+ self.mesh = trimesh.load(filename)
+ self.scale = 1.0
+
+ # print(filename)
+ self.filename = filename
+ if isinstance(self.mesh, list):
+ # this is fixed in a newer trimesh version:
+ # https://github.com/mikedh/trimesh/issues/69
+ print("Warning: Will do a concatenation")
+ self.mesh = trimesh.util.concatenate(self.mesh)
+
+ self.collision_manager = trimesh.collision.CollisionManager()
+ self.collision_manager.add_object('object', self.mesh)
+
+ def rescale(self, scale=1.0):
+ """Set scale of object mesh.
+
+ :param scale
+ """
+ self.scale = scale
+ self.mesh.apply_scale(self.scale)
+
+ def resize(self, size=1.0):
+ """Set longest of all three lengths in Cartesian space.
+
+ :param size
+ """
+ self.scale = size / np.max(self.mesh.extents)
+ self.mesh.apply_scale(self.scale)
+
+ def in_collision_with(self, mesh, transform):
+ """Check whether the object is in collision with the provided mesh.
+
+ :param mesh:
+ :param transform:
+ :return: boolean value
+ """
+ return self.collision_manager.in_collision_single(mesh, transform=transform)
+
+
+class PandaGripper(object):
+ """An object representing a Franka Panda gripper."""
+
+ def __init__(self, q=None, num_contact_points_per_finger=10, root_folder=os.path.dirname(os.path.dirname(os.path.abspath(__file__)))):
+ """Create a Franka Panda parallel-yaw gripper object.
+
+ Keyword Arguments:
+ q {list of int} -- opening configuration (default: {None})
+ num_contact_points_per_finger {int} -- contact points per finger (default: {10})
+ root_folder {str} -- base folder for model files (default: {''})
+ """
+ self.joint_limits = [0.0, 0.04]
+ self.root_folder = root_folder
+
+ self.default_pregrasp_configuration = 0.04
+ if q is None:
+ q = self.default_pregrasp_configuration
+
+ self.q = q
+ fn_base = os.path.join(root_folder, 'gripper_models/panda_gripper/hand.stl')
+ fn_finger = os.path.join(root_folder, 'gripper_models/panda_gripper/finger.stl')
+
+ self.base = trimesh.load(fn_base)
+ self.finger_l = trimesh.load(fn_finger)
+ self.finger_r = self.finger_l.copy()
+
+ # transform fingers relative to the base
+ self.finger_l.apply_transform(tra.euler_matrix(0, 0, np.pi))
+ self.finger_l.apply_translation([+q, 0, 0.0584])
+ self.finger_r.apply_translation([-q, 0, 0.0584])
+
+ self.fingers = trimesh.util.concatenate([self.finger_l, self.finger_r])
+ self.hand = trimesh.util.concatenate([self.fingers, self.base])
+
+
+ self.contact_ray_origins = []
+ self.contact_ray_directions = []
+
+ # coords_path = os.path.join(root_folder, 'gripper_control_points/panda_gripper_coords.npy')
+ with open(os.path.join(root_folder,'gripper_control_points/panda_gripper_coords.pickle'), 'rb') as f:
+ self.finger_coords = pickle.load(f, encoding='latin1')
+ finger_direction = self.finger_coords['gripper_right_center_flat'] - self.finger_coords['gripper_left_center_flat']
+ self.contact_ray_origins.append(np.r_[self.finger_coords['gripper_left_center_flat'], 1])
+ self.contact_ray_origins.append(np.r_[self.finger_coords['gripper_right_center_flat'], 1])
+ self.contact_ray_directions.append(finger_direction / np.linalg.norm(finger_direction))
+ self.contact_ray_directions.append(-finger_direction / np.linalg.norm(finger_direction))
+
+ self.contact_ray_origins = np.array(self.contact_ray_origins)
+ self.contact_ray_directions = np.array(self.contact_ray_directions)
+
+ def get_meshes(self):
+ """Get list of meshes that this gripper consists of.
+
+ Returns:
+ list of trimesh -- visual meshes
+ """
+ return [self.finger_l, self.finger_r, self.base]
+
+ def get_closing_rays_contact(self, transform):
+ """Get an array of rays defining the contact locations and directions on the hand.
+
+ Arguments:
+ transform {[nump.array]} -- a 4x4 homogeneous matrix
+ contact_ray_origin {[nump.array]} -- a 4x1 homogeneous vector
+ contact_ray_direction {[nump.array]} -- a 4x1 homogeneous vector
+
+ Returns:
+ numpy.array -- transformed rays (origin and direction)
+ """
+ return transform[:3, :].dot(
+ self.contact_ray_origins.T).T, transform[:3, :3].dot(self.contact_ray_directions.T).T
+
+ def get_control_point_tensor(self, batch_size, use_tf=True, symmetric = False, convex_hull=True):
+ """
+ Outputs a 5 point gripper representation of shape (batch_size x 5 x 3).
+
+ Arguments:
+ batch_size {int} -- batch size
+
+ Keyword Arguments:
+ use_tf {bool} -- outputing a tf tensor instead of a numpy array (default: {True})
+ symmetric {bool} -- Output the symmetric control point configuration of the gripper (default: {False})
+ convex_hull {bool} -- Return control points according to the convex hull panda gripper model (default: {True})
+
+ Returns:
+ np.ndarray -- control points of the panda gripper
+ """
+
+ control_points = np.load(os.path.join(self.root_folder, 'gripper_control_points/panda.npy'))[:, :3]
+ if symmetric:
+ control_points = [[0, 0, 0], control_points[1, :],control_points[0, :], control_points[-1, :], control_points[-2, :]]
+ else:
+ control_points = [[0, 0, 0], control_points[0, :], control_points[1, :], control_points[-2, :], control_points[-1, :]]
+
+ control_points = np.asarray(control_points, dtype=np.float32)
+ if not convex_hull:
+ # actual depth of the gripper different from convex collision model
+ control_points[1:3, 2] = 0.0584
+ control_points = np.tile(np.expand_dims(control_points, 0), [batch_size, 1, 1])
+
+ if use_tf:
+ return tf.convert_to_tensor(control_points)
+
+ return control_points
+
+
+def create_gripper(name, configuration=None, root_folder=os.path.dirname(os.path.dirname(os.path.abspath(__file__)))):
+ """Create a gripper object.
+
+ Arguments:
+ name {str} -- name of the gripper
+
+ Keyword Arguments:
+ configuration {list of float} -- configuration (default: {None})
+ root_folder {str} -- base folder for model files (default: {''})
+
+ Raises:
+ Exception: If the gripper name is unknown.
+
+ Returns:
+ [type] -- gripper object
+ """
+ if name.lower() == 'panda':
+ return PandaGripper(q=configuration, root_folder=root_folder)
+ else:
+ raise Exception("Unknown gripper: {}".format(name))
+
+
+def in_collision_with_gripper(object_mesh, gripper_transforms, gripper_name, silent=False):
+ """Check collision of object with gripper.
+
+ Arguments:
+ object_mesh {trimesh} -- mesh of object
+ gripper_transforms {list of numpy.array} -- homogeneous matrices of gripper
+ gripper_name {str} -- name of gripper
+
+ Keyword Arguments:
+ silent {bool} -- verbosity (default: {False})
+
+ Returns:
+ [list of bool] -- Which gripper poses are in collision with object mesh
+ """
+ manager = trimesh.collision.CollisionManager()
+ manager.add_object('object', object_mesh)
+ gripper_meshes = [create_gripper(gripper_name).hand]
+ min_distance = []
+ for tf in tqdm(gripper_transforms, disable=silent):
+ min_distance.append(np.min([manager.min_distance_single(
+ gripper_mesh, transform=tf) for gripper_mesh in gripper_meshes]))
+
+ return [d == 0 for d in min_distance], min_distance
+
+def grasp_contact_location(transforms, successfuls, collisions, object_mesh, gripper_name='panda', silent=False):
+ """Computes grasp contacts on objects and normals, offsets, directions
+
+ Arguments:
+ transforms {[type]} -- grasp poses
+ collisions {[type]} -- collision information
+ object_mesh {trimesh} -- object mesh
+
+ Keyword Arguments:
+ gripper_name {str} -- name of gripper (default: {'panda'})
+ silent {bool} -- verbosity (default: {False})
+
+ Returns:
+ list of dicts of contact information per grasp ray
+ """
+ res = []
+ gripper = create_gripper(gripper_name)
+ if trimesh.ray.has_embree:
+ intersector = trimesh.ray.ray_pyembree.RayMeshIntersector(
+ object_mesh, scale_to_box=True)
+ else:
+ intersector = trimesh.ray.ray_triangle.RayMeshIntersector(object_mesh)
+ for p, colliding, outcome in tqdm(zip(transforms, collisions, successfuls), total=len(transforms), disable=silent):
+ contact_dict = {}
+ contact_dict['collisions'] = 0
+ contact_dict['valid_locations'] = 0
+ contact_dict['successful'] = outcome
+ contact_dict['grasp_transform'] = p
+ contact_dict['contact_points'] = []
+ contact_dict['contact_directions'] = []
+ contact_dict['contact_face_normals'] = []
+ contact_dict['contact_offsets'] = []
+
+ if colliding:
+ contact_dict['collisions'] = 1
+ else:
+ ray_origins, ray_directions = gripper.get_closing_rays_contact(p)
+
+ locations, index_ray, index_tri = intersector.intersects_location(
+ ray_origins, ray_directions, multiple_hits=False)
+
+ if len(locations) > 0:
+ # this depends on the width of the gripper
+ valid_locations = np.linalg.norm(ray_origins[index_ray]-locations, axis=1) <= 2.0*gripper.q
+
+ if sum(valid_locations) > 1:
+ contact_dict['valid_locations'] = 1
+ contact_dict['contact_points'] = locations[valid_locations]
+ contact_dict['contact_face_normals'] = object_mesh.face_normals[index_tri[valid_locations]]
+ contact_dict['contact_directions'] = ray_directions[index_ray[valid_locations]]
+ contact_dict['contact_offsets'] = np.linalg.norm(ray_origins[index_ray[valid_locations]] - locations[valid_locations], axis=1)
+ # dot_prods = (contact_dict['contact_face_normals'] * contact_dict['contact_directions']).sum(axis=1)
+ # contact_dict['contact_cosine_angles'] = np.cos(dot_prods)
+ res.append(contact_dict)
+
+ return res
\ No newline at end of file
diff --git a/contact_graspnet/scene_renderer.py b/contact_graspnet/scene_renderer.py
new file mode 100644
index 0000000..3cb6f6e
--- /dev/null
+++ b/contact_graspnet/scene_renderer.py
@@ -0,0 +1,305 @@
+from __future__ import print_function
+from __future__ import absolute_import
+
+import numpy as np
+import copy
+import cv2
+import math
+import os
+from copy import deepcopy
+import multiprocessing as mp
+import pyrender
+import trimesh
+import trimesh.transformations as tra
+
+from mesh_utils import Object
+
+class SceneRenderer:
+ def __init__(self, intrinsics=None, fov=np.pi / 6, caching=True, viewing_mode=False):
+ """Renders depth with given intrinsics during training.
+
+ Keyword Arguments:
+ intrinsics {str} -- camera name from 'kinect_azure', 'realsense' (default: {None})
+ fov {float} -- field of view, ignored if inrinsics is not None (default: {np.pi/6})
+ caching {bool} -- whether to cache object meshes (default: {True})
+ viewing_mode {bool} -- visualize scene (default: {False})
+ """
+
+ self._fov = fov
+
+ self._scene = pyrender.Scene()
+ self._table_dims = [1.0, 1.2, 0.6]
+ self._table_pose = np.eye(4)
+ self._viewer = viewing_mode
+ if viewing_mode:
+ self._viewer = pyrender.Viewer(
+ self._scene,
+ use_raymond_lighting=True,
+ run_in_thread=True)
+
+ self._intrinsics = intrinsics
+ if self._intrinsics == 'realsense':
+ self._fx=616.36529541
+ self._fy=616.20294189
+ self._cx=310.25881958
+ self._cy=236.59980774
+ self._znear=0.04
+ self._zfar=20
+ self._height=480
+ self._width=640
+ elif self._intrinsics == 'kinect_azure':
+ self._fx=631.54864502
+ self._fy=631.20751953
+ self._cx=638.43517329
+ self._cy=366.49904066
+ self._znear=0.04
+ self._zfar=20
+ self._height=720
+ self._width=1280
+
+ self._add_table_node()
+ self._init_camera_renderer()
+
+ self._current_context = None
+ self._cache = {} if caching else None
+ self._caching = caching
+
+ def _init_camera_renderer(self):
+ """
+ If not in visualizing mode, initialize camera with given intrinsics
+ """
+
+ if self._viewer:
+ return
+
+ if self._intrinsics in ['kinect_azure', 'realsense']:
+ camera = pyrender.IntrinsicsCamera(self._fx, self._fy, self._cx, self._cy, self._znear, self._zfar)
+ self._camera_node = self._scene.add(camera, pose=np.eye(4), name='camera')
+ self.renderer = pyrender.OffscreenRenderer(viewport_width=self._width,
+ viewport_height=self._height,
+ point_size=1.0)
+ else:
+ camera = pyrender.PerspectiveCamera(yfov=self._fov, aspectRatio=1.0, znear=0.001) # do not change aspect ratio
+ self._camera_node = self._scene.add(camera, pose=tra.euler_matrix(np.pi, 0, 0), name='camera')
+ self.renderer = pyrender.OffscreenRenderer(400, 400)
+
+
+ def _add_table_node(self):
+ """
+ Adds table mesh and sets pose
+ """
+ if self._viewer:
+ return
+ table_mesh = trimesh.creation.box(self._table_dims)
+ mesh = pyrender.Mesh.from_trimesh(table_mesh)
+
+ table_node = pyrender.Node(mesh=mesh, name='table')
+ self._scene.add_node(table_node)
+ self._scene.set_pose(table_node, self._table_pose)
+
+
+ def _load_object(self, path, scale):
+ """
+ Load a mesh, scale and center it
+
+ Arguments:
+ path {str} -- path to mesh
+ scale {float} -- scale of the mesh
+
+ Returns:
+ dict -- contex with loaded mesh info
+ """
+ if (path, scale) in self._cache:
+ return self._cache[(path, scale)]
+ obj = Object(path)
+ obj.rescale(scale)
+
+ tmesh = obj.mesh
+ tmesh_mean = np.mean(tmesh.vertices, 0)
+ tmesh.vertices -= np.expand_dims(tmesh_mean, 0)
+
+ lbs = np.min(tmesh.vertices, 0)
+ ubs = np.max(tmesh.vertices, 0)
+ object_distance = np.max(ubs - lbs) * 5
+
+ mesh = pyrender.Mesh.from_trimesh(tmesh)
+
+ context = {
+ 'name': path + '_' + str(scale),
+ 'tmesh': copy.deepcopy(tmesh),
+ 'distance': object_distance,
+ 'node': pyrender.Node(mesh=mesh, name=path + '_' + str(scale)),
+ 'mesh_mean': np.expand_dims(tmesh_mean, 0),
+ }
+
+ self._cache[(path, scale)] = context
+
+ return self._cache[(path, scale)]
+
+ def change_scene(self, obj_paths, obj_scales, obj_transforms):
+ """Remove current objects and add new ones to the scene
+
+ Arguments:
+ obj_paths {list} -- list of object mesh paths
+ obj_scales {list} -- list of object scales
+ obj_transforms {list} -- list of object transforms
+ """
+ if self._viewer:
+ self._viewer.render_lock.acquire()
+ for n in self._scene.get_nodes():
+ if n.name not in ['table', 'camera', 'parent']:
+ self._scene.remove_node(n)
+
+ if not self._caching:
+ self._cache = {}
+
+ for p,t,s in zip(obj_paths, obj_transforms, obj_scales):
+
+ object_context = self._load_object(p, s)
+ object_context = deepcopy(object_context)
+
+ self._scene.add_node(object_context['node'])
+ self._scene.set_pose(object_context['node'], t)
+
+ if self._viewer:
+ self._viewer.render_lock.release()
+
+ def _to_pointcloud(self, depth):
+ """Convert depth map to point cloud
+
+ Arguments:
+ depth {np.ndarray} -- HxW depth map
+
+ Returns:
+ np.ndarray -- Nx4 homog. point cloud
+ """
+ if self._intrinsics in ['kinect_azure', 'realsense']:
+ fy = self._fy
+ fx = self._fx
+ height = self._height
+ width = self._width
+ cx = self._cx
+ cy = self._cy
+
+ mask = np.where(depth > 0)
+
+ x = mask[1]
+ y = mask[0]
+
+ normalized_x = (x.astype(np.float32) - cx)
+ normalized_y = (y.astype(np.float32) - cy)
+ else:
+ fy = fx = 0.5 / np.tan(self._fov * 0.5) # aspectRatio is one.
+ height = depth.shape[0]
+ width = depth.shape[1]
+
+ mask = np.where(depth > 0)
+
+ x = mask[1]
+ y = mask[0]
+
+ normalized_x = (x.astype(np.float32) - width * 0.5) / width
+ normalized_y = (y.astype(np.float32) - height * 0.5) / height
+
+ world_x = normalized_x * depth[y, x] / fx
+ world_y = normalized_y * depth[y, x] / fy
+ world_z = depth[y, x]
+ ones = np.ones(world_z.shape[0], dtype=np.float32)
+
+ return np.vstack((world_x, world_y, world_z, ones)).T
+
+
+ def render(self, pose, render_pc=True):
+ """Render object or scene in camera pose
+
+ Arguments:
+ pose {np.ndarray} -- 4x4 camera pose
+
+ Keyword Arguments:
+ render_pc {bool} -- whether to convert depth map to point cloud (default: {True})
+
+ Returns:
+ [np.ndarray, np.ndarray, np.ndarray, np.ndarray] -- HxWx3 color, HxW depth, Nx4 point cloud, 4x4 camera pose
+ """
+
+ transferred_pose = pose.copy()
+ self._scene.set_pose(self._camera_node, transferred_pose)
+
+ color, depth = self.renderer.render(self._scene)
+
+ if render_pc:
+ pc = self._to_pointcloud(depth)
+ else:
+ pc = None
+
+ return color, depth, pc, transferred_pose
+
+ def render_labels(self, full_depth, obj_paths, obj_scales, render_pc=False):
+ """Render instance segmentation map
+
+ Arguments:
+ full_depth {np.ndarray} -- HxW depth map
+ obj_paths {list} -- list of object paths in scene
+ obj_scales {list} -- list of object scales in scene
+
+ Keyword Arguments:
+ render_pc {bool} -- whether to return object-wise point clouds (default: {False})
+
+ Returns:
+ [np.ndarray, list, dict] -- integer segmap with 0=background, list of
+ corresponding object names, dict of corresponding point clouds
+ """
+
+ scene_object_nodes = []
+ for n in self._scene.get_nodes():
+ if n.name not in ['camera', 'parent']:
+ n.mesh.is_visible = False
+ if n.name != 'table':
+ scene_object_nodes.append(n)
+
+ obj_names = [path + '_' + str(scale) for path, scale in zip(obj_paths,obj_scales)]
+
+
+ pcs = {}
+ output = np.zeros(full_depth.shape, np.uint8)
+ for n in scene_object_nodes:
+ n.mesh.is_visible = True
+
+ depth = self.renderer.render(self._scene)[1]
+ mask = np.logical_and(
+ (np.abs(depth - full_depth) < 1e-6), np.abs(full_depth) > 0
+ )
+ if not np.any(mask):
+ continue
+ if np.any(output[mask] != 0):
+ raise ValueError('wrong label')
+
+ indices = [i+1 for i, x in enumerate(obj_names) if x == n.name]
+ for i in indices:
+ if not np.any(output==i):
+ print('')
+ output[mask] = i
+ break
+
+ n.mesh.is_visible = False
+
+ if render_pc:
+ pcs[i] = self._to_pointcloud(depth*mask)
+
+ for n in self._scene.get_nodes():
+ if n.name not in ['camera', 'parent']:
+ n.mesh.is_visible = True
+
+ return output, ['BACKGROUND'] + obj_names, pcs
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/contact_graspnet/summaries.py b/contact_graspnet/summaries.py
new file mode 100644
index 0000000..cb6fadb
--- /dev/null
+++ b/contact_graspnet/summaries.py
@@ -0,0 +1,127 @@
+
+import numpy as np
+import os
+
+try:
+ import tensorflow.compat.v1 as tf
+ tf.disable_eager_execution()
+ TF2 = True
+except:
+ import tensorflow as tf
+ TF2 = False
+
+from tensorboard import summary as summary_lib
+
+
+def top_grasp_acc_summaries(ops, thres=[0.62,0.65,0.68]):
+ """
+ Calculates the accuracy of grasp contact predictions at different thresholds
+
+ Arguments:
+ ops {dict} -- dict of tf tensors
+
+ Keyword Arguments:
+ thres {list} -- thresholds to report (default: {[0.62,0.65,0.68]})
+
+ Returns:
+ [tf.variable] -- summary update ops
+ """
+
+ binary_seg = tf.reshape(ops['binary_seg_pred'],[ops['binary_seg_pred'].shape[0],ops['binary_seg_pred'].shape[1]])
+ max_conf_idcs = tf.argmax(binary_seg, axis=1)
+ max_confs = tf.math.reduce_max(binary_seg, axis=1)
+
+ row_idcs = tf.cast(tf.range(ops['grasp_suc_labels_pc'].shape[0]), tf.int64)
+ nd_gather_idcs = tf.stack((row_idcs, max_conf_idcs), axis=1)
+ top_labels = tf.gather_nd(ops['grasp_suc_labels_pc'], nd_gather_idcs)
+
+ acc_update_ops = [0] * len(thres)
+ accs = [0] * len(thres)
+ for i,thr in enumerate(thres):
+ accs[i], acc_update_ops[i] = tf.metrics.accuracy(top_labels, tf.greater(max_confs, tf.constant(thr)))
+ tf.summary.scalar('Top Grasp Accuracy Thres %s' % thr, accs[i])
+
+ return acc_update_ops
+
+def build_summary_ops(ops, sess, global_config):
+ """
+ Collect tensorboard summaries
+
+ Arguments:
+ ops {dict} -- dict of tf tensors
+ sess {tf.Session} -- tf session
+ global_config {dict} -- global config
+
+ Returns:
+ [dict] -- summary update ops
+ """
+
+ dir_loss = ops['dir_loss']
+ bin_ce_loss = ops['bin_ce_loss']
+ grasp_suc_labels_pc = ops['grasp_suc_labels_pc']
+ binary_seg_pred = ops['binary_seg_pred']
+
+ tf.summary.scalar('total loss', ops['loss'])
+ tf.summary.scalar('dir_loss', ops['dir_loss'])
+ tf.summary.scalar('approach_loss', ops['approach_loss'])
+ tf.summary.scalar('adds_loss', ops['adds_loss'])
+ tf.summary.scalar('adds_gt2pred_loss', ops['adds_gt2pred_loss'])
+ tf.summary.scalar('bin_ce_loss', ops['bin_ce_loss'])
+ tf.summary.scalar('offset_loss_x%s' % global_config['OPTIMIZER']['offset_loss_weight'], ops['offset_loss'])
+
+ tf.summary.histogram('labels_grasp_suc', ops['grasp_suc_labels_pc'])
+ tf.summary.histogram('preds_grasp_suc', ops['binary_seg_pred'])
+
+ tf.summary.histogram('offset_predictions', ops['grasp_offset_pred'])
+ tf.summary.histogram('offset_labels', ops['offset_labels_pc'])
+
+ # classification:
+ if global_config['MODEL']['bin_offsets']:
+
+ tf.summary.histogram('offset_predictions_binned', ops['offset_pred_idcs_pc'])
+ tf.summary.histogram('offset_labels_binned', ops['offset_label_idcs_pc'])
+ tf.summary.scalar('offset_bin_classification_accuracy', tf.reduce_mean(tf.cast(tf.equal(ops['offset_pred_idcs_pc'], ops['offset_label_idcs_pc']),tf.float32)))
+
+ bin_mean_preds = ops['offset_bin_pred_vals']
+ tf.summary.scalar('offset_mean_abs_error', tf.reduce_mean(tf.abs(bin_mean_preds - tf.layers.flatten(tf.abs(ops['offset_orig_labels_vals'])))))
+ tf.summary.scalar('offset_max_abs_error', tf.reduce_max(tf.abs(bin_mean_preds - tf.layers.flatten(tf.abs(ops['offset_orig_labels_vals'])))))
+
+ merged = tf.summary.merge_all()
+
+ acc_update_ops = top_grasp_acc_summaries(ops, thres=[0.62,0.65,0.68])
+
+ auc, auc_update_op = tf.metrics.auc(tf.layers.flatten(tf.cast(ops['grasp_suc_labels_pc'], tf.bool)),
+ tf.layers.flatten(ops['binary_seg_pred']), curve='PR', summation_method='careful_interpolation')
+ tf.summary.scalar('AUCPR', auc)
+
+ pr_curve_streaming_func = summary_lib.v1.pr_curve_streaming_op if TF2 else summary_lib.pr_curve_streaming_op
+ _, pr_update_op = pr_curve_streaming_func(name='pr_contact_success',
+ predictions=tf.layers.flatten(ops['binary_seg_pred']),
+ labels=tf.layers.flatten(tf.cast(ops['grasp_suc_labels_pc'], tf.bool)),
+ num_thresholds = 201)
+
+ pr_reset_op = tf.variables_initializer(tf.get_collection(tf.GraphKeys.METRIC_VARIABLES))
+ merged_eval = tf.summary.merge_all()
+
+ sess.run(tf.local_variables_initializer())
+
+ summary_ops = {'merged': merged, 'merged_eval': merged_eval, 'pr_update_op':pr_update_op, 'auc_update_op':auc_update_op, 'acc_update_ops':acc_update_ops, 'pr_reset_op': pr_reset_op}
+ return summary_ops
+
+def build_file_writers(sess, log_dir):
+ """
+ Create TF FileWriters for train and test
+
+ Arguments:
+ sess {tf.Session} -- tf session
+ log_dir {str} -- Checkpoint directory
+
+ Returns:
+ [dict] -- tf.summary.FileWriter
+ """
+
+ train_writer = tf.summary.FileWriter(os.path.join(log_dir, 'train'))#, sess.graph) creates large event files!
+ test_writer = tf.summary.FileWriter(os.path.join(log_dir, 'test'))#, sess.graph)
+ writers ={'train_writer': train_writer, 'test_writer': test_writer}
+
+ return writers
\ No newline at end of file
diff --git a/contact_graspnet/tf_train_ops.py b/contact_graspnet/tf_train_ops.py
new file mode 100644
index 0000000..2bc52b5
--- /dev/null
+++ b/contact_graspnet/tf_train_ops.py
@@ -0,0 +1,256 @@
+
+import numpy as np
+import tensorflow.compat.v1 as tf
+tf.disable_eager_execution()
+TF2 = True
+
+from data import farthest_points
+
+def get_learning_rate(step, optimizer_config):
+ """
+ Return learning rate at training step
+
+ Arguments:
+ step {tf.variable} -- training step
+ optimizer_config {dict} -- optimizer config_path
+
+ Returns:
+ tf.variable -- learning rate
+ """
+
+ batch_size = optimizer_config['batch_size']
+ base_learning_rate = optimizer_config['learning_rate']
+ decay_step = float(optimizer_config['decay_step'])
+ decay_rate = float(optimizer_config['decay_rate'])
+
+ learning_rate = tf.train.exponential_decay(
+ base_learning_rate, # base learning rate.
+ step * batch_size, # current index into the dataset.
+ decay_step, # decay step.
+ decay_rate, # decay rate.
+ staircase=True)
+
+ learning_rate = tf.maximum(learning_rate, 0.00001) # clip the learning rate!
+ return learning_rate
+
+def get_bn_decay(step, optimizer_config):
+ """
+ Return batch norm decay at training step.
+
+ Arguments:
+ step {tf.variable} -- training step
+ optimizer_config {dict} -- optimizer config
+
+ Returns:
+ tf.variable -- batch norm decay
+ """
+
+ batch_size = optimizer_config['batch_size']
+ bn_init_decay = optimizer_config['bn_init_decay']
+ bn_decay_decay_step = optimizer_config['bn_decay_decay_step']
+ bn_decay_decay_rate = optimizer_config['bn_decay_decay_rate']
+ bn_decay_clip = optimizer_config['bn_decay_clip']
+
+ bn_momentum = tf.train.exponential_decay(
+ bn_init_decay,
+ step*batch_size,
+ bn_decay_decay_step,
+ bn_decay_decay_rate,
+ staircase=True)
+ bn_decay = tf.minimum(bn_decay_clip, 1 - bn_momentum)
+ return bn_decay
+
+
+def load_labels_and_losses(grasp_estimator, contact_infos, global_config, train=True):
+ """
+ Loads labels to memory and builds graph for computing losses
+
+ Arguments:
+ grasp_estimator {class} -- Grasp Estimator Instance
+ contact_infos {list(dicts)} -- Per scene mesh: grasp contact information
+ global_config {dict} -- global config
+
+ Keyword Arguments:
+ train {bool} -- training mode (default: {True})
+
+ Returns:
+ dict[str:tf.variables] -- tf references to labels and losses
+ """
+
+ end_points = grasp_estimator.model_ops['end_points']
+ target_point_cloud = end_points['pred_points']
+ scene_idx_pl = grasp_estimator.placeholders['scene_idx_pl']
+ cam_poses_pl = grasp_estimator.placeholders['cam_poses_pl']
+
+ tf_pos_contact_points, tf_pos_contact_dirs, tf_pos_contact_approaches, \
+ tf_pos_finger_diffs, tf_scene_idcs = load_contact_grasps(contact_infos, global_config['DATA'])
+
+ iterator = None
+ idx = scene_idx_pl
+ if train:
+ grasp_dataset = tf.data.Dataset.from_tensor_slices((tf_pos_contact_points, tf_pos_contact_dirs, \
+ tf_pos_contact_approaches, tf_pos_finger_diffs, tf_scene_idcs))
+
+ grasp_dataset = grasp_dataset.repeat()
+ grasp_dataset = grasp_dataset.batch(1)
+ # grasp_dataset = grasp_dataset.prefetch_to_device(5)
+ grasp_dataset = grasp_dataset.prefetch(3)
+ grasp_dataset = grasp_dataset.apply(tf.data.experimental.copy_to_device("/gpu:0"))
+ iterator = grasp_dataset.make_initializable_iterator()
+ next_element = iterator.get_next()
+
+ tf_pos_contact_points_idx, tf_pos_contact_dirs_idx, \
+ tf_pos_contact_approaches_idx, tf_pos_finger_diffs_idx, tf_scene_idcs = next_element
+ idx = 0
+
+ # Get labels
+ dir_labels_pc_cam, offset_labels_pc, \
+ grasp_suc_labels_pc, approach_labels_pc = grasp_estimator._model_func.compute_labels(tf_pos_contact_points_idx[idx], tf_pos_contact_dirs_idx[idx], tf_pos_contact_approaches_idx[idx],
+ tf_pos_finger_diffs_idx[idx], target_point_cloud, cam_poses_pl, global_config)
+
+
+ if global_config['MODEL']['bin_offsets']:
+ orig_offset_labels = offset_labels_pc
+ offset_labels_pc = tf.abs(offset_labels_pc)
+ offset_labels_pc = grasp_estimator._model_func.multi_bin_labels(offset_labels_pc, global_config['DATA']['labels']['offset_bins'])
+
+ # Get losses
+ dir_loss, bin_ce_loss, offset_loss, approach_loss, adds_loss, adds_loss_gt2pred = grasp_estimator._model_func.get_losses(target_point_cloud, end_points, dir_labels_pc_cam,
+ offset_labels_pc, grasp_suc_labels_pc, approach_labels_pc,
+ global_config)
+
+ total_loss = 0
+ if global_config['MODEL']['pred_contact_base']:
+ total_loss += global_config['OPTIMIZER']['dir_cosine_loss_weight'] * dir_loss
+ if global_config['MODEL']['pred_contact_success']:
+ total_loss += global_config['OPTIMIZER']['score_ce_loss_weight'] * bin_ce_loss
+ if global_config['MODEL']['pred_contact_offset']:
+ total_loss += global_config['OPTIMIZER']['offset_loss_weight'] * offset_loss
+ if global_config['MODEL']['pred_contact_approach']:
+ total_loss += global_config['OPTIMIZER']['approach_cosine_loss_weight'] * approach_loss
+ if global_config['MODEL']['pred_grasps_adds']:
+ total_loss += global_config['OPTIMIZER']['adds_loss_weight'] * adds_loss
+ if global_config['MODEL']['pred_grasps_adds_gt2pred']:
+ total_loss += global_config['OPTIMIZER']['adds_gt2pred_loss_weight'] * adds_loss_gt2pred
+
+ tf_bin_vals = grasp_estimator._model_func.get_bin_vals(global_config)
+
+ loss_label_ops = {'loss': total_loss,
+ 'dir_loss': dir_loss,
+ 'bin_ce_loss': bin_ce_loss,
+ 'offset_loss': offset_loss,
+ 'approach_loss': approach_loss,
+ 'adds_loss': adds_loss,
+ 'adds_gt2pred_loss': adds_loss_gt2pred,
+ 'dir_labels_pc_cam': dir_labels_pc_cam,
+ 'offset_labels_pc': offset_labels_pc,
+ 'offset_label_idcs_pc': tf.argmax(offset_labels_pc, axis=2) if global_config['MODEL']['bin_offsets'] else None,
+ 'offset_orig_labels_vals': orig_offset_labels if global_config['MODEL']['bin_offsets'] else None,
+ 'offset_bin_label_vals': tf.gather_nd(tf_bin_vals, tf.expand_dims(tf.argmax(offset_labels_pc, axis=2), axis=2)) if global_config['MODEL']['bin_offsets'] else None,
+ 'grasp_suc_labels_pc': grasp_suc_labels_pc,
+ 'approach_labels_pc': approach_labels_pc,
+ 'tf_bin_vals': tf_bin_vals,
+ 'scene_idx': tf_scene_idcs,
+ 'iterator': iterator
+ }
+
+ return loss_label_ops
+
+def build_train_op(total_loss, step, global_config):
+ """
+ Initializes optimizer and learning rate scheduler
+
+ Arguments:
+ total_loss {tf.variable} -- Weighted sum of all loss terms
+ step {tf.variable} -- training step
+ global_config {dict} -- global config
+
+ Returns:
+ [train_op] -- Operation to run during training
+ """
+
+ print("--- Get training operator")
+ # Get training operator
+ learning_rate = get_learning_rate(step, global_config['OPTIMIZER'])
+ tf.summary.scalar('learning_rate', learning_rate)
+ if global_config['OPTIMIZER']['optimizer'] == 'momentum':
+ momentum = global_config['OPTIMIZER']['momentum']
+ optimizer = tf.train.MomentumOptimizer(learning_rate, momentum=momentum)
+ elif global_config['OPTIMIZER']['optimizer'] == 'adam':
+ optimizer = tf.train.AdamOptimizer(learning_rate)
+ else:
+ optimizer = tf.train.GradientDescentOptimizer(learning_rate)
+
+ train_op = optimizer.minimize(total_loss, global_step=step, var_list=tf.global_variables())
+ if TF2:
+ update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
+ train_op = tf.group([train_op, update_ops])
+
+ return train_op
+
+def load_contact_grasps(contact_list, data_config):
+ """
+ Loads fixed amount of contact grasp data per scene into tf CPU/GPU memory
+
+ Arguments:
+ contact_infos {list(dicts)} -- Per scene mesh: grasp contact information
+ data_config {dict} -- data config
+
+ Returns:
+ [tf_pos_contact_points, tf_pos_contact_dirs, tf_pos_contact_offsets,
+ tf_pos_contact_approaches, tf_pos_finger_diffs, tf_scene_idcs,
+ all_obj_paths, all_obj_transforms] -- tf.constants with per scene grasp data, object paths/transforms in scene
+ """
+
+ num_pos_contacts = data_config['labels']['num_pos_contacts']
+
+ pos_contact_points = []
+ pos_contact_dirs = []
+ pos_finger_diffs = []
+ pos_approach_dirs = []
+
+ for i,c in enumerate(contact_list):
+ contact_directions_01 = c['scene_contact_points'][:,0,:] - c['scene_contact_points'][:,1,:]
+ all_contact_points = c['scene_contact_points'].reshape(-1,3)
+ all_finger_diffs = np.maximum(np.linalg.norm(contact_directions_01,axis=1), np.finfo(np.float32).eps)
+ all_contact_directions = np.empty((contact_directions_01.shape[0]*2, contact_directions_01.shape[1],))
+ all_contact_directions[0::2] = -contact_directions_01 / all_finger_diffs[:,np.newaxis]
+ all_contact_directions[1::2] = contact_directions_01 / all_finger_diffs[:,np.newaxis]
+ all_contact_suc = np.ones_like(all_contact_points[:,0])
+ all_grasp_transform = c['grasp_transforms'].reshape(-1,4,4)
+ all_approach_directions = all_grasp_transform[:,:3,2]
+
+ pos_idcs = np.where(all_contact_suc>0)[0]
+ if len(pos_idcs) == 0:
+ continue
+ print('total positive contact points ', len(pos_idcs))
+
+ all_pos_contact_points = all_contact_points[pos_idcs]
+ all_pos_finger_diffs = all_finger_diffs[pos_idcs//2]
+ all_pos_contact_dirs = all_contact_directions[pos_idcs]
+ all_pos_approach_dirs = all_approach_directions[pos_idcs//2]
+
+ # Use all positive contacts then mesh_utils with replacement
+ if num_pos_contacts > len(all_pos_contact_points)/2:
+ pos_sampled_contact_idcs = np.arange(len(all_pos_contact_points))
+ pos_sampled_contact_idcs_replacement = np.random.choice(np.arange(len(all_pos_contact_points)), num_pos_contacts*2 - len(all_pos_contact_points) , replace=True)
+ pos_sampled_contact_idcs= np.hstack((pos_sampled_contact_idcs, pos_sampled_contact_idcs_replacement))
+ else:
+ pos_sampled_contact_idcs = np.random.choice(np.arange(len(all_pos_contact_points)), num_pos_contacts*2, replace=False)
+
+ pos_contact_points.append(all_pos_contact_points[pos_sampled_contact_idcs,:])
+ pos_contact_dirs.append(all_pos_contact_dirs[pos_sampled_contact_idcs,:])
+ pos_finger_diffs.append(all_pos_finger_diffs[pos_sampled_contact_idcs])
+ pos_approach_dirs.append(all_pos_approach_dirs[pos_sampled_contact_idcs])
+
+ device = "/cpu:0" if 'to_gpu' in data_config['labels'] and not data_config['labels']['to_gpu'] else "/gpu:0"
+ print("grasp label device: ", device)
+
+ with tf.device(device):
+ tf_scene_idcs = tf.constant(np.arange(0,len(pos_contact_points)), tf.int32)
+ tf_pos_contact_points = tf.constant(np.array(pos_contact_points), tf.float32)
+ tf_pos_contact_dirs = tf.math.l2_normalize(tf.constant(np.array(pos_contact_dirs), tf.float32),axis=2)
+ tf_pos_finger_diffs = tf.constant(np.array(pos_finger_diffs), tf.float32)
+ tf_pos_contact_approaches = tf.math.l2_normalize(tf.constant(np.array(pos_approach_dirs), tf.float32),axis=2)
+
+ return tf_pos_contact_points, tf_pos_contact_dirs, tf_pos_contact_approaches, tf_pos_finger_diffs, tf_scene_idcs
diff --git a/contact_graspnet/train.py b/contact_graspnet/train.py
new file mode 100644
index 0000000..57342df
--- /dev/null
+++ b/contact_graspnet/train.py
@@ -0,0 +1,223 @@
+from genericpath import exists
+import os
+import sys
+import argparse
+from datetime import datetime
+import numpy as np
+import time
+from tqdm import tqdm
+
+CONTACT_DIR = os.path.dirname(os.path.abspath(__file__))
+BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+ROOT_DIR = os.path.dirname(os.path.dirname(BASE_DIR))
+sys.path.append(os.path.join(BASE_DIR))
+sys.path.append(os.path.join(ROOT_DIR))
+sys.path.append(os.path.join(BASE_DIR, 'pointnet2', 'models'))
+sys.path.append(os.path.join(BASE_DIR, 'pointnet2', 'utils'))
+
+import tensorflow.compat.v1 as tf
+tf.disable_eager_execution()
+TF2 = True
+physical_devices = tf.config.experimental.list_physical_devices('GPU')
+tf.config.experimental.set_memory_growth(physical_devices[0], True)
+
+import config_utils
+from data import PointCloudReader, load_scene_contacts, center_pc_convert_cam
+from summaries import build_summary_ops, build_file_writers
+from tf_train_ops import load_labels_and_losses, build_train_op
+from contact_grasp_estimator import GraspEstimator
+
+def train(global_config, log_dir):
+ """
+ Trains Contact-GraspNet
+
+ Arguments:
+ global_config {dict} -- config dict
+ log_dir {str} -- Checkpoint directory
+ """
+
+ contact_infos = load_scene_contacts(global_config['DATA']['data_path'],
+ scene_contacts_path=global_config['DATA']['scene_contacts_path'])
+
+ num_train_samples = len(contact_infos)-global_config['DATA']['num_test_scenes']
+ num_test_samples = global_config['DATA']['num_test_scenes']
+
+ print('using %s meshes' % (num_train_samples + num_test_samples))
+ if 'train_and_test' in global_config['DATA'] and global_config['DATA']['train_and_test']:
+ num_train_samples = num_train_samples + num_test_samples
+ num_test_samples = 0
+ print('using train and test data')
+
+ pcreader = PointCloudReader(
+ root_folder=global_config['DATA']['data_path'],
+ batch_size=global_config['OPTIMIZER']['batch_size'],
+ estimate_normals=global_config['DATA']['input_normals'],
+ raw_num_points=global_config['DATA']['raw_num_points'],
+ use_uniform_quaternions = global_config['DATA']['use_uniform_quaternions'],
+ scene_obj_scales = [c['obj_scales'] for c in contact_infos],
+ scene_obj_paths = [c['obj_paths'] for c in contact_infos],
+ scene_obj_transforms = [c['obj_transforms'] for c in contact_infos],
+ num_train_samples = num_train_samples,
+ num_test_samples = num_test_samples,
+ use_farthest_point = global_config['DATA']['use_farthest_point'],
+ intrinsics=global_config['DATA']['intrinsics'],
+ elevation=global_config['DATA']['view_sphere']['elevation'],
+ distance_range=global_config['DATA']['view_sphere']['distance_range'],
+ pc_augm_config=global_config['DATA']['pc_augm'],
+ depth_augm_config=global_config['DATA']['depth_augm']
+ )
+
+ with tf.Graph().as_default():
+
+ # Build the model
+ grasp_estimator = GraspEstimator(global_config)
+ ops = grasp_estimator.build_network()
+
+ # contact_tensors = load_contact_grasps(contact_infos, global_config['DATA'])
+
+ loss_ops = load_labels_and_losses(grasp_estimator, contact_infos, global_config)
+
+ ops.update(loss_ops)
+ ops['train_op'] = build_train_op(ops['loss'], ops['step'], global_config)
+
+ # Add ops to save and restore all the variables.
+ saver = tf.train.Saver(save_relative_paths=True, keep_checkpoint_every_n_hours=4)
+
+ # Create a session
+ config = tf.ConfigProto()
+ config.gpu_options.allow_growth = True
+ config.allow_soft_placement = True
+ sess = tf.Session(config=config)
+
+ # Log summaries
+ summary_ops = build_summary_ops(ops, sess, global_config)
+
+ # Init/Load weights
+ grasp_estimator.load_weights(sess, saver, log_dir, mode='train')
+
+ # sess = tf_debug.LocalCLIDebugWrapperSession(sess)
+ # sess.add_tensor_filter("has_inf_or_nan", tf_debug.has_inf_or_nan)
+ file_writers = build_file_writers(sess, log_dir)
+
+ ## define: epoch = arbitrary number of views of every training scene
+ cur_epoch = sess.run(ops['step']) // num_train_samples
+ for epoch in range(cur_epoch, global_config['OPTIMIZER']['max_epoch']):
+ log_string('**** EPOCH %03d ****' % (epoch))
+
+ sess.run(ops['iterator'].initializer)
+ epoch_time = time.time()
+ step = train_one_epoch(sess, ops, summary_ops, file_writers, pcreader)
+ log_string('trained %s batches in: ' % steps_per_epoch, time.time()-epoch_time)
+
+ # Save the variables to disk.
+ save_path = saver.save(sess, os.path.join(log_dir, "model.ckpt"), global_step=step, write_meta_graph=False)
+ log_string("Model saved in file: %s" % save_path)
+
+ if num_test_samples > 0:
+ eval_time = time.time()
+ eval_validation_scenes(sess, ops, summary_ops, file_writers, pcreader)
+ log_string('evaluation time: ', time.time()-eval_time)
+
+def train_one_epoch(sess, ops, summary_ops, file_writers, pcreader):
+ """ ops: dict mapping from string to tf ops """
+
+ log_string(str(datetime.now()))
+ loss_log = np.zeros((10,7))
+ get_time = time.time()
+
+ for batch_idx in range(pcreader._num_train_samples):
+
+ batch_data, cam_poses, scene_idx = pcreader.get_scene_batch(scene_idx=batch_idx)
+
+ # OpenCV OpenGL conversion
+ cam_poses, batch_data = center_pc_convert_cam(cam_poses, batch_data)
+
+ feed_dict = {ops['pointclouds_pl']: batch_data, ops['cam_poses_pl']: cam_poses,
+ ops['scene_idx_pl']: scene_idx, ops['is_training_pl']: True}
+
+ step, summary, _, loss_val, dir_loss, bin_ce_loss, \
+ offset_loss, approach_loss, adds_loss, adds_gt2pred_loss, scene_idx = sess.run([ops['step'], summary_ops['merged'], ops['train_op'], ops['loss'], ops['dir_loss'],
+ ops['bin_ce_loss'], ops['offset_loss'], ops['approach_loss'], ops['adds_loss'],
+ ops['adds_gt2pred_loss'], ops['scene_idx']], feed_dict=feed_dict)
+ assert scene_idx[0] == scene_idx
+
+ loss_log[batch_idx%10,:] = loss_val, dir_loss, bin_ce_loss, offset_loss, approach_loss, adds_loss, adds_gt2pred_loss
+
+ if (batch_idx+1)%10 == 0:
+ file_writers['train_writer'].add_summary(summary, step)
+ f = tuple(np.mean(loss_log, axis=0)) + ((time.time() - get_time) / 10., )
+ log_string('total loss: %f \t dir loss: %f \t ce loss: %f \t off loss: %f \t app loss: %f adds loss: %f \t adds_gt2pred loss: %f \t batch time: %f' % f)
+ get_time = time.time()
+
+ return step
+
+def eval_validation_scenes(sess, ops, summary_ops, file_writers, pcreader, max_eval_objects=500):
+ """ ops: dict mapping from string to tf ops """
+ is_training = False
+ log_string(str(datetime.now()))
+ loss_log = np.zeros((min(pcreader._num_test_samples, max_eval_objects),7))
+
+ # resets accumulation of pr and auc data
+ sess.run(summary_ops['pr_reset_op'])
+
+ for batch_idx in np.arange(min(pcreader._num_test_samples, max_eval_objects)):
+
+ batch_data, cam_poses, scene_idx = pcreader.get_scene_batch(scene_idx=pcreader._num_train_samples + batch_idx)
+
+ # OpenCV OpenGL conversion
+ cam_poses, batch_data = center_pc_convert_cam(cam_poses, batch_data)
+
+ feed_dict = {ops['pointclouds_pl']: batch_data, ops['cam_poses_pl']: cam_poses,
+ ops['scene_idx_pl']: scene_idx, ops['is_training_pl']: False}
+
+ scene_idx, step, loss_val, dir_loss, bin_ce_loss, offset_loss, approach_loss, adds_loss, adds_gt2pred_loss, pr_summary,_,_,_ = sess.run([ops['scene_idx'], ops['step'], ops['loss'], ops['dir_loss'], ops['bin_ce_loss'],
+ ops['offset_loss'], ops['approach_loss'], ops['adds_loss'], ops['adds_gt2pred_loss'],
+ summary_ops['merged_eval'], summary_ops['pr_update_op'],
+ summary_ops['auc_update_op']] + [summary_ops['acc_update_ops']], feed_dict=feed_dict)
+ assert scene_idx[0] == (pcreader._num_train_samples + batch_idx)
+
+ loss_log[batch_idx,:] = loss_val, dir_loss, bin_ce_loss, offset_loss, approach_loss, adds_loss, adds_gt2pred_loss
+
+ file_writers['test_writer'].add_summary(pr_summary, step)
+ f = tuple(np.mean(loss_log, axis=0))
+ log_string('mean val loss: %f \t mean val dir loss: %f \t mean val ce loss: %f \t mean off loss: %f \t mean app loss: %f \t mean adds loss: %f \t mean adds_gt2pred loss: %f' % f)
+
+ return step
+
+if __name__ == "__main__":
+
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--gpu', type=int, default=0, help='GPU to use [default: GPU 0]')
+ parser.add_argument('--ckpt_dir', default='checkpoints/contact_graspnet', help='Checkpoint dir')
+ parser.add_argument('--data_path', type=str, default=None, help='Grasp data root dir')
+ parser.add_argument('--max_epoch', type=int, default=None, help='Epochs to run')
+ parser.add_argument('--batch_size', type=int, default=None, help='Batch Size during training')
+ parser.add_argument('--arg_configs', nargs="*", type=str, default=[], help='overwrite config parameters')
+ FLAGS = parser.parse_args()
+
+ ckpt_dir = FLAGS.ckpt_dir
+ if not os.path.exists(ckpt_dir):
+ if not os.path.exists(os.path.dirname(ckpt_dir)):
+ ckpt_dir = os.path.join(BASE_DIR, ckpt_dir)
+ os.makedirs(ckpt_dir, exist_ok=True)
+
+ os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+ os.system('cp {} {}'.format(os.path.join(CONTACT_DIR, 'contact_graspnet.py'), ckpt_dir)) # bkp of model def
+ os.system('cp {} {}'.format(os.path.join(CONTACT_DIR, 'train.py'), ckpt_dir)) # bkp of train procedure
+
+ LOG_FOUT = open(os.path.join(ckpt_dir, 'log_train.txt'), 'w')
+ LOG_FOUT.write(str(FLAGS)+'\n')
+ def log_string(out_str):
+ LOG_FOUT.write(out_str+'\n')
+ LOG_FOUT.flush()
+ print(out_str)
+
+ global_config = config_utils.load_config(ckpt_dir, batch_size=FLAGS.batch_size, max_epoch=FLAGS.max_epoch,
+ data_path= FLAGS.data_path, arg_configs=FLAGS.arg_configs, save=True)
+
+ log_string(str(global_config))
+ log_string('pid: %s'%(str(os.getpid())))
+
+ train(global_config, ckpt_dir)
+
+ LOG_FOUT.close()
diff --git a/contact_graspnet/visualization_utils.py b/contact_graspnet/visualization_utils.py
new file mode 100644
index 0000000..ba70d82
--- /dev/null
+++ b/contact_graspnet/visualization_utils.py
@@ -0,0 +1,197 @@
+import numpy as np
+import mayavi.mlab as mlab
+import matplotlib.pyplot as plt
+from matplotlib import cm
+
+import mesh_utils
+
+def plot_mesh(mesh, cam_trafo=np.eye(4), mesh_pose=np.eye(4)):
+ """
+ Plots mesh in mesh_pose from
+
+ Arguments:
+ mesh {trimesh.base.Trimesh} -- input mesh, e.g. gripper
+
+ Keyword Arguments:
+ cam_trafo {np.ndarray} -- 4x4 transformation from world to camera coords (default: {np.eye(4)})
+ mesh_pose {np.ndarray} -- 4x4 transformation from mesh to world coords (default: {np.eye(4)})
+ """
+
+ homog_mesh_vert = np.pad(mesh.vertices, (0, 1), 'constant', constant_values=(0, 1))
+ mesh_cam = homog_mesh_vert.dot(mesh_pose.T).dot(cam_trafo.T)[:,:3]
+ mlab.triangular_mesh(mesh_cam[:, 0],
+ mesh_cam[:, 1],
+ mesh_cam[:, 2],
+ mesh.faces,
+ colormap='Blues',
+ opacity=0.5)
+
+def plot_coordinates(t,r, tube_radius=0.005):
+ """
+ plots coordinate frame
+
+ Arguments:
+ t {np.ndarray} -- translation vector
+ r {np.ndarray} -- rotation matrix
+
+ Keyword Arguments:
+ tube_radius {float} -- radius of the plotted tubes (default: {0.005})
+ """
+ mlab.plot3d([t[0],t[0]+0.2*r[0,0]], [t[1],t[1]+0.2*r[1,0]], [t[2],t[2]+0.2*r[2,0]], color=(1,0,0), tube_radius=tube_radius, opacity=1)
+ mlab.plot3d([t[0],t[0]+0.2*r[0,1]], [t[1],t[1]+0.2*r[1,1]], [t[2],t[2]+0.2*r[2,1]], color=(0,1,0), tube_radius=tube_radius, opacity=1)
+ mlab.plot3d([t[0],t[0]+0.2*r[0,2]], [t[1],t[1]+0.2*r[1,2]], [t[2],t[2]+0.2*r[2,2]], color=(0,0,1), tube_radius=tube_radius, opacity=1)
+
+def show_image(rgb, segmap):
+ """
+ Overlay rgb image with segmentation and imshow segment
+
+ Arguments:
+ rgb {np.ndarray} -- color image
+ segmap {np.ndarray} -- integer segmap of same size as rgb
+ """
+ plt.figure()
+ figManager = plt.get_current_fig_manager()
+ figManager.window.showMaximized()
+
+ plt.ion()
+ plt.show()
+
+ if rgb is not None:
+ plt.imshow(rgb)
+ if segmap is not None:
+ cmap = plt.get_cmap('rainbow')
+ cmap.set_under(alpha=0.0)
+ plt.imshow(segmap, cmap=cmap, alpha=0.5, vmin=0.0001)
+ plt.draw()
+ plt.pause(0.001)
+
+def visualize_grasps(full_pc, pred_grasps_cam, scores, plot_opencv_cam=False, pc_colors=None, gripper_openings=None, gripper_width=0.08):
+ """Visualizes colored point cloud and predicted grasps. If given, colors grasps by segmap regions.
+ Thick grasp is most confident per segment. For scene point cloud predictions, colors grasps according to confidence.
+
+ Arguments:
+ full_pc {np.ndarray} -- Nx3 point cloud of the scene
+ pred_grasps_cam {dict[int:np.ndarray]} -- Predicted 4x4 grasp trafos per segment or for whole point cloud
+ scores {dict[int:np.ndarray]} -- Confidence scores for grasps
+
+ Keyword Arguments:
+ plot_opencv_cam {bool} -- plot camera coordinate frame (default: {False})
+ pc_colors {np.ndarray} -- Nx3 point cloud colors (default: {None})
+ gripper_openings {dict[int:np.ndarray]} -- Predicted grasp widths (default: {None})
+ gripper_width {float} -- If gripper_openings is None, plot grasp widths (default: {0.008})
+ """
+
+ print('Visualizing...takes time')
+ cm = plt.get_cmap('rainbow')
+ cm2 = plt.get_cmap('gist_rainbow')
+
+ fig = mlab.figure('Pred Grasps')
+ mlab.view(azimuth=180, elevation=180, distance=0.2)
+ draw_pc_with_colors(full_pc, pc_colors)
+ colors = [cm(1. * i/len(pred_grasps_cam))[:3] for i in range(len(pred_grasps_cam))]
+ colors2 = {k:cm2(0.5*np.max(scores[k]))[:3] for k in pred_grasps_cam if np.any(pred_grasps_cam[k])}
+
+ if plot_opencv_cam:
+ plot_coordinates(np.zeros(3,),np.eye(3,3))
+ for i,k in enumerate(pred_grasps_cam):
+ if np.any(pred_grasps_cam[k]):
+ gripper_openings_k = np.ones(len(pred_grasps_cam[k]))*gripper_width if gripper_openings is None else gripper_openings[k]
+ if len(pred_grasps_cam) > 1:
+ draw_grasps(pred_grasps_cam[k], np.eye(4), color=colors[i], gripper_openings=gripper_openings_k)
+ draw_grasps([pred_grasps_cam[k][np.argmax(scores[k])]], np.eye(4), color=colors2[k],
+ gripper_openings=[gripper_openings_k[np.argmax(scores[k])]], tube_radius=0.0025)
+ else:
+ colors3 = [cm2(0.5*score)[:3] for score in scores[k]]
+ draw_grasps(pred_grasps_cam[k], np.eye(4), colors=colors3, gripper_openings=gripper_openings_k)
+ mlab.show()
+
+def draw_pc_with_colors(pc, pc_colors=None, single_color=(0.3,0.3,0.3), mode='2dsquare', scale_factor=0.0018):
+ """
+ Draws colored point clouds
+
+ Arguments:
+ pc {np.ndarray} -- Nx3 point cloud
+ pc_colors {np.ndarray} -- Nx3 point cloud colors
+
+ Keyword Arguments:
+ single_color {tuple} -- single color for point cloud (default: {(0.3,0.3,0.3)})
+ mode {str} -- primitive type to plot (default: {'point'})
+ scale_factor {float} -- Scale of primitives. Does not work for points. (default: {0.002})
+
+ """
+
+ if pc_colors is None:
+ mlab.points3d(pc[:, 0], pc[:, 1], pc[:, 2], color=single_color, scale_factor=scale_factor, mode=mode)
+ else:
+ #create direct grid as 256**3 x 4 array
+ def create_8bit_rgb_lut():
+ xl = np.mgrid[0:256, 0:256, 0:256]
+ lut = np.vstack((xl[0].reshape(1, 256**3),
+ xl[1].reshape(1, 256**3),
+ xl[2].reshape(1, 256**3),
+ 255 * np.ones((1, 256**3)))).T
+ return lut.astype('int32')
+
+ scalars = pc_colors[:,0]*256**2 + pc_colors[:,1]*256 + pc_colors[:,2]
+ rgb_lut = create_8bit_rgb_lut()
+ points_mlab = mlab.points3d(pc[:, 0], pc[:, 1], pc[:, 2], scalars, mode=mode, scale_factor=.0018)
+ points_mlab.glyph.scale_mode = 'scale_by_vector'
+ points_mlab.module_manager.scalar_lut_manager.lut._vtk_obj.SetTableRange(0, rgb_lut.shape[0])
+ points_mlab.module_manager.scalar_lut_manager.lut.number_of_colors = rgb_lut.shape[0]
+ points_mlab.module_manager.scalar_lut_manager.lut.table = rgb_lut
+
+def draw_grasps(grasps, cam_pose, gripper_openings, color=(0,1.,0), colors=None, show_gripper_mesh=False, tube_radius=0.0008):
+ """
+ Draws wireframe grasps from given camera pose and with given gripper openings
+
+ Arguments:
+ grasps {np.ndarray} -- Nx4x4 grasp pose transformations
+ cam_pose {np.ndarray} -- 4x4 camera pose transformation
+ gripper_openings {np.ndarray} -- Nx1 gripper openings
+
+ Keyword Arguments:
+ color {tuple} -- color of all grasps (default: {(0,1.,0)})
+ colors {np.ndarray} -- Nx3 color of each grasp (default: {None})
+ tube_radius {float} -- Radius of the grasp wireframes (default: {0.0008})
+ show_gripper_mesh {bool} -- Renders the gripper mesh for one of the grasp poses (default: {False})
+ """
+
+ gripper = mesh_utils.create_gripper('panda')
+ gripper_control_points = gripper.get_control_point_tensor(1, False, convex_hull=False).squeeze()
+ mid_point = 0.5*(gripper_control_points[1, :] + gripper_control_points[2, :])
+ grasp_line_plot = np.array([np.zeros((3,)), mid_point, gripper_control_points[1], gripper_control_points[3],
+ gripper_control_points[1], gripper_control_points[2], gripper_control_points[4]])
+
+ if show_gripper_mesh and len(grasps) > 0:
+ plot_mesh(gripper.hand, cam_pose, grasps[0])
+
+ all_pts = []
+ connections = []
+ index = 0
+ N = 7
+ for i,(g,g_opening) in enumerate(zip(grasps, gripper_openings)):
+ gripper_control_points_closed = grasp_line_plot.copy()
+ gripper_control_points_closed[2:,0] = np.sign(grasp_line_plot[2:,0]) * g_opening/2
+
+ pts = np.matmul(gripper_control_points_closed, g[:3, :3].T)
+ pts += np.expand_dims(g[:3, 3], 0)
+ pts_homog = np.concatenate((pts, np.ones((7, 1))),axis=1)
+ pts = np.dot(pts_homog, cam_pose.T)[:,:3]
+
+ color = color if colors is None else colors[i]
+
+ all_pts.append(pts)
+ connections.append(np.vstack([np.arange(index, index + N - 1.5),
+ np.arange(index + 1, index + N - .5)]).T)
+ index += N
+ # mlab.plot3d(pts[:, 0], pts[:, 1], pts[:, 2], color=color, tube_radius=tube_radius, opacity=1.0)
+
+ # speeds up plot3d because only one vtk object
+ all_pts = np.vstack(all_pts)
+ connections = np.vstack(connections)
+ src = mlab.pipeline.scalar_scatter(all_pts[:,0], all_pts[:,1], all_pts[:,2])
+ src.mlab_source.dataset.lines = connections
+ src.update()
+ lines =mlab.pipeline.tube(src, tube_radius=tube_radius, tube_sides=12)
+ mlab.pipeline.surface(lines, color=color, opacity=1.0)
+
diff --git a/contact_graspnet_env.yml b/contact_graspnet_env.yml
new file mode 100644
index 0000000..3701e8c
--- /dev/null
+++ b/contact_graspnet_env.yml
@@ -0,0 +1,151 @@
+name: contact_graspnet_env
+channels:
+ - conda-forge
+ - anaconda
+ - defaults
+dependencies:
+ - _libgcc_mutex=0.1=main
+ - apptools=4.5.0=py_0
+ - blas=1.0=mkl
+ - bzip2=1.0.8=h7b6447c_0
+ - ca-certificates=2020.10.14=0
+ - certifi=2020.6.20=py37_0
+ - clang=10.0.1=default_hb85057a_2
+ - clang-tools=10.0.1=default_hb85057a_2
+ - clangdev=10.0.1=default_hb85057a_2
+ - clangxx=10.0.1=default_hb85057a_2
+ - configobj=5.0.6=py37_1
+ - cudatoolkit=10.1.243=h6bb024c_0
+ - cudnn=7.6.0=cuda10.1_0
+ - curl=7.71.1=hbc83047_1
+ - cycler=0.10.0=py37_0
+ - dbus=1.13.18=hb2f20db_0
+ - envisage=4.9.2=py_0
+ - expat=2.2.10=he6710b0_2
+ - fontconfig=2.13.0=h9420a91_0
+ - freetype=2.10.4=h5ab3b9f_0
+ - future=0.18.2=py37_1
+ - glib=2.66.1=h92f7085_0
+ - gst-plugins-base=1.14.0=hbbd80ab_1
+ - gstreamer=1.14.0=hb31296c_0
+ - h5py=2.10.0=py37h7918eee_0
+ - hdf4=4.2.13=h3ca952b_2
+ - hdf5=1.10.4=hb1b8bf9_0
+ - icu=58.2=he6710b0_3
+ - intel-openmp=2020.2=254
+ - jpeg=9b=h024ee3a_2
+ - jsoncpp=1.8.4=hfd86e86_0
+ - kiwisolver=1.3.0=py37h2531618_0
+ - krb5=1.18.2=h173b8e3_0
+ - lcms2=2.11=h396b838_0
+ - ld_impl_linux-64=2.33.1=h53a641e_7
+ - libclang=10.0.1=default_hb85057a_2
+ - libclang-cpp=10.0.1=default_hb85057a_2
+ - libclang-cpp10=10.0.1=default_hb85057a_2
+ - libcurl=7.71.1=h20c2e04_1
+ - libedit=3.1.20191231=h14c3975_1
+ - libffi=3.3=he6710b0_2
+ - libgcc-ng=9.1.0=hdf63c60_0
+ - libgfortran-ng=7.3.0=hdf63c60_0
+ - libllvm10=10.0.1=hbcb73fb_5
+ - libnetcdf=4.6.1=h11d0813_2
+ - libogg=1.3.2=h7b6447c_0
+ - libpng=1.6.37=hbc83047_0
+ - libssh2=1.9.0=h1ba5d50_1
+ - libstdcxx-ng=9.1.0=hdf63c60_0
+ - libtheora=1.1.1=h5ab3b9f_1
+ - libtiff=4.1.0=h2733197_0
+ - libuuid=1.0.3=h1bed415_2
+ - libvorbis=1.3.7=h7b6447c_0
+ - libxcb=1.14=h7b6447c_0
+ - libxml2=2.9.10=hb55368b_3
+ - libxslt=1.1.34=hc22bd24_0
+ - llvm-tools=10.0.1=h4a3c616_5
+ - llvmdev=10.0.1=h4a3c616_5
+ - lz4-c=1.8.1.2=h14c3975_0
+ - matplotlib=3.3.2=0
+ - matplotlib-base=3.3.2=py37h817c723_0
+ - mayavi=4.7.1=py37h94891b3_2
+ - mkl=2020.2=256
+ - mkl-service=2.3.0=py37he904b0f_0
+ - mkl_fft=1.2.0=py37h23d657b_0
+ - mkl_random=1.1.1=py37h0573a6f_0
+ - ncurses=6.2=he6710b0_1
+ - numpy=1.19.2=py37h54aff64_0
+ - numpy-base=1.19.2=py37hfa32c7d_0
+ - olefile=0.46=py37_0
+ - openssl=1.1.1h=h7b6447c_0
+ - pcre=8.44=he6710b0_0
+ - pillow=8.0.1=py37he98fc37_0
+ - pip=20.2.4=py37h06a4308_0
+ - pyface=7.1.0=py37_1
+ - pygments=2.7.2=pyhd3eb1b0_0
+ - pyparsing=2.4.7=py_0
+ - pyqt=5.9.2=py37h05f1152_2
+ - pyside2=5.9.0a1=py37h4dc837a_0
+ - python=3.7.9=h7579374_0
+ - python-dateutil=2.8.1=py_0
+ - qt=5.9.7=h5867ecd_1
+ - readline=8.0=h7b6447c_0
+ - setuptools=50.3.1=py37h06a4308_1
+ - shapely=1.7.1
+ - sip=4.19.8=py37hf484d3e_0
+ - six=1.15.0=py_0
+ - sqlite=3.33.0=h62c20be_0
+ - tbb=2020.3=hfd86e86_0
+ - tk=8.6.10=hbc83047_0
+ - tornado=6.0.4=py37h7b6447c_1
+ - tqdm=4.51.0=pyh9f0ad1d_0
+ - traits=6.1.1=py37h7b6447c_0
+ - traitsui=7.1.0=py_0
+ - trimesh=3.8.12=pyh9f0ad1d_0
+ - vtk=8.2.0=py37haa4764d_200
+ - wheel=0.35.1=py_0
+ - xz=5.2.5=h7b6447c_0
+ - zlib=1.2.11=h7b6447c_3
+ - zstd=1.3.7=h0b5b093_0
+ - pip:
+ - absl-py==0.11.0
+ - astor==0.8.1
+ - astunparse==1.6.3
+ - cachetools==4.1.1
+ - chardet==3.0.4
+ - decorator==4.4.2
+ - freetype-py==2.2.0
+ - gast==0.3.3
+ - google-auth==1.23.0
+ - google-auth-oauthlib==0.4.2
+ - google-pasta==0.2.0
+ - grpcio==1.33.2
+ - idna==2.10
+ - imageio==2.9.0
+ - importlib-metadata==2.0.0
+ - keras-applications==1.0.8
+ - keras-preprocessing==1.1.2
+ - markdown==3.3.3
+ - networkx==2.5
+ - oauthlib==3.1.0
+ - opencv-python==4.4.0.46
+ - opt-einsum==3.3.0
+ - protobuf==3.13.0
+ - pyasn1==0.4.8
+ - pyasn1-modules==0.2.8
+ - pyglet==1.5.9
+ - pyopengl==3.1.0
+ - pyrender==0.1.43
+ - pyyaml==5.3.1
+ - requests==2.24.0
+ - requests-oauthlib==1.3.0
+ - rsa==4.6
+ - rtree
+ - scipy==1.4.1
+ - tensorboard==2.2.2
+ - tensorboard-plugin-wit==1.7.0
+ - tensorflow-estimator==2.2.0
+ - tensorflow-gpu==2.2.0
+ - termcolor==1.1.0
+ - urllib3==1.25.11
+ - werkzeug==1.0.1
+ - wrapt==1.12.1
+ - zipp==3.4.0
+ - python-fcl==0.0.12
diff --git a/examples/10.png b/examples/10.png
new file mode 100644
index 0000000..ef1e3f4
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diff --git a/examples/10_segmap.png b/examples/10_segmap.png
new file mode 100644
index 0000000..558c0b5
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diff --git a/examples/2.gif b/examples/2.gif
new file mode 100644
index 0000000..a060f02
Binary files /dev/null and b/examples/2.gif differ
diff --git a/examples/2.png b/examples/2.png
new file mode 100644
index 0000000..5038959
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diff --git a/examples/2_segmap.png b/examples/2_segmap.png
new file mode 100644
index 0000000..c745f51
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diff --git a/examples/7.gif b/examples/7.gif
new file mode 100644
index 0000000..c95493c
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diff --git a/examples/7.png b/examples/7.png
new file mode 100644
index 0000000..01bf38d
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diff --git a/examples/7_segmap.png b/examples/7_segmap.png
new file mode 100644
index 0000000..de5431f
Binary files /dev/null and b/examples/7_segmap.png differ
diff --git a/examples/graspnet_153_kinectazure_0002.png b/examples/graspnet_153_kinectazure_0002.png
new file mode 100644
index 0000000..8147ebb
Binary files /dev/null and b/examples/graspnet_153_kinectazure_0002.png differ
diff --git a/examples/realsense_crop_sigma_001.png b/examples/realsense_crop_sigma_001.png
new file mode 100644
index 0000000..5c72aaa
Binary files /dev/null and b/examples/realsense_crop_sigma_001.png differ
diff --git a/examples/synthetic_test.png b/examples/synthetic_test.png
new file mode 100644
index 0000000..1b7d949
Binary files /dev/null and b/examples/synthetic_test.png differ
diff --git a/gripper_control_points/panda.npy b/gripper_control_points/panda.npy
new file mode 100644
index 0000000..91712dd
Binary files /dev/null and b/gripper_control_points/panda.npy differ
diff --git a/gripper_control_points/panda_gripper_coords.pickle b/gripper_control_points/panda_gripper_coords.pickle
new file mode 100644
index 0000000..a7e38d7
--- /dev/null
+++ b/gripper_control_points/panda_gripper_coords.pickle
@@ -0,0 +1,164 @@
+(dp0
+S'gripper_left_tip_flat'
+p1
+cnumpy.core.multiarray
+_reconstruct
+p2
+(cnumpy
+ndarray
+p3
+(I0
+tp4
+S'b'
+p5
+tp6
+Rp7
+(I1
+(I3
+tp8
+cnumpy
+dtype
+p9
+(S'f8'
+p10
+I0
+I1
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+Rp12
+(I3
+S'<'
+p13
+NNNI-1
+I-1
+I0
+tp14
+bI00
+S'Y\x868\xd6\xc5m\xa4?\x00\x00\x00\x00\x00\x00\x00\x00r\xfaz\xbef\xb9\xbc?'
+p15
+tp16
+bsS'gripper_right_tip_flat'
+p17
+g2
+(g3
+(I0
+tp18
+g5
+tp19
+Rp20
+(I1
+(I3
+tp21
+g12
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+S'Y\x868\xd6\xc5m\xa4\xbf\x00\x00\x00\x00\x00\x00\x00\x00r\xfaz\xbef\xb9\xbc?'
+p22
+tp23
+bsS'gripper_left_tip_indent'
+p24
+g2
+(g3
+(I0
+tp25
+g5
+tp26
+Rp27
+(I1
+(I3
+tp28
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+I00
+S'\xd0\x98I\xd4\x0b>\xa5?\x00\x00\x00\x00\x00\x00\x00\x00r\xfaz\xbef\xb9\xbc?'
+p29
+tp30
+bsS'gripper_right_center_indent'
+p31
+g2
+(g3
+(I0
+tp32
+g5
+tp33
+Rp34
+(I1
+(I3
+tp35
+g12
+I00
+S'\xd0\x98I\xd4\x0b>\xa5\xbf\x00\x00\x00\x00\x00\x00\x00\x00\xd5\th"lx\xba?'
+p36
+tp37
+bsS'l_y'
+p38
+F0.01754
+sS'gripper_left_center_flat'
+p39
+g2
+(g3
+(I0
+tp40
+g5
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+Rp42
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+(I3
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+I00
+S'Y\x868\xd6\xc5m\xa4?\x00\x00\x00\x00\x00\x00\x00\x00\xd5\th"lx\xba?'
+p44
+tp45
+bsS'l_z'
+p46
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+sS'gripper_left_center_indent'
+p47
+g2
+(g3
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+tp48
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+p52
+tp53
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+p54
+g2
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+tp55
+g5
+tp56
+Rp57
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+p59
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+p61
+g2
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+tp62
+g5
+tp63
+Rp64
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+p66
+tp67
+bs.
\ No newline at end of file
diff --git a/gripper_control_points/panda_gripper_coords.yml b/gripper_control_points/panda_gripper_coords.yml
new file mode 100644
index 0000000..343ab77
--- /dev/null
+++ b/gripper_control_points/panda_gripper_coords.yml
@@ -0,0 +1,10 @@
+gripper_left_center_indent: [0.041489, 0, 0.1034]
+gripper_left_center_flat: [0.0399, 0, 0.1034]
+gripper_right_center_indent: [-0.041489, 0, 0.1034]
+gripper_right_center_flat: [-0.0399, 0, 0.1034]
+gripper_left_tip_indent: [0.041489, 0, 0.112204]
+gripper_left_tip_flat: [0.0399, 0, 0.112204]
+gripper_right_tip_indent: [-0.041489, 0, 0.112204]
+gripper_right_tip_flat: [-0.0399, 0, 0.112204]
+l_z: 0.01754
+l_y: 0.01754
diff --git a/gripper_models/featuretype.STL b/gripper_models/featuretype.STL
new file mode 100644
index 0000000..c884893
Binary files /dev/null and b/gripper_models/featuretype.STL differ
diff --git a/gripper_models/panda_gripper/finger.stl b/gripper_models/panda_gripper/finger.stl
new file mode 100644
index 0000000..311b278
Binary files /dev/null and b/gripper_models/panda_gripper/finger.stl differ
diff --git a/gripper_models/panda_gripper/hand.stl b/gripper_models/panda_gripper/hand.stl
new file mode 100644
index 0000000..de404b5
Binary files /dev/null and b/gripper_models/panda_gripper/hand.stl differ
diff --git a/gripper_models/panda_gripper/panda_gripper.obj b/gripper_models/panda_gripper/panda_gripper.obj
new file mode 100644
index 0000000..b1f1e54
--- /dev/null
+++ b/gripper_models/panda_gripper/panda_gripper.obj
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diff --git a/pointnet2/LICENSE b/pointnet2/LICENSE
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--- /dev/null
+++ b/pointnet2/LICENSE
@@ -0,0 +1,25 @@
+PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space.
+
+Copyright (c) 2017, Geometric Computation Group of Stanford University
+
+The MIT License (MIT)
+
+Copyright (c) 2017 Charles R. Qi
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/pointnet2/README.md b/pointnet2/README.md
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--- /dev/null
+++ b/pointnet2/README.md
@@ -0,0 +1,93 @@
+### PointNet++: *Deep Hierarchical Feature Learning on Point Sets in a Metric Space*
+Created by Charles R. Qi, Li (Eric) Yi, Hao Su, Leonidas J. Guibas from Stanford University.
+
+
+
+### Citation
+If you find our work useful in your research, please consider citing:
+
+ @article{qi2017pointnetplusplus,
+ title={PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space},
+ author={Qi, Charles R and Yi, Li and Su, Hao and Guibas, Leonidas J},
+ journal={arXiv preprint arXiv:1706.02413},
+ year={2017}
+ }
+
+### Introduction
+This work is based on our NIPS'17 paper. You can find arXiv version of the paper here or check project webpage for a quick overview. PointNet++ is a follow-up project that builds on and extends PointNet. It is version 2.0 of the PointNet architecture.
+
+PointNet (the v1 model) either transforms features of *individual points* independently or process global features of the *entire point set*. However, in many cases there are well defined distance metrics such as Euclidean distance for 3D point clouds collected by 3D sensors or geodesic distance for manifolds like isometric shape surfaces. In PointNet++ we want to respect *spatial localities* of those point sets. PointNet++ learns hierarchical features with increasing scales of contexts, just like that in convolutional neural networks. Besides, we also observe one challenge that is not present in convnets (with images) -- non-uniform densities in natural point clouds. To deal with those non-uniform densities, we further propose special layers that are able to intelligently aggregate information from different scales.
+
+In this repository we release code and data for our PointNet++ classification and segmentation networks as well as a few utility scripts for training, testing and data processing and visualization.
+
+### Installation
+
+Install TensorFlow. The code is tested under TF1.2 GPU version and Python 2.7 (version 3 should also work) on Ubuntu 14.04. There are also some dependencies for a few Python libraries for data processing and visualizations like `cv2`, `h5py` etc. It's highly recommended that you have access to GPUs.
+
+#### Compile Customized TF Operators
+The TF operators are included under `tf_ops`, you need to compile them (check `tf_xxx_compile.sh` under each ops subfolder) first. Update `nvcc` and `python` path if necessary. The code is tested under TF1.2.0. If you are using earlier version it's possible that you need to remove the `-D_GLIBCXX_USE_CXX11_ABI=0` flag in g++ command in order to compile correctly.
+
+To compile the operators in TF version >=1.4, you need to modify the compile scripts slightly.
+
+First, find Tensorflow include and library paths.
+
+ TF_INC=$(python -c 'import tensorflow as tf; print(tf.sysconfig.get_include())')
+ TF_LIB=$(python -c 'import tensorflow as tf; print(tf.sysconfig.get_lib())')
+
+Then, add flags of `-I$TF_INC/external/nsync/public -L$TF_LIB -ltensorflow_framework` to the `g++` commands.
+
+### Usage
+
+#### Shape Classification
+
+To train a PointNet++ model to classify ModelNet40 shapes (using point clouds with XYZ coordinates):
+
+ python train.py
+
+To see all optional arguments for training:
+
+ python train.py -h
+
+If you have multiple GPUs on your machine, you can also run the multi-GPU version training (our implementation is similar to the tensorflow cifar10 tutorial):
+
+ CUDA_VISIBLE_DEVICES=0,1 python train_multi_gpu.py --num_gpus 2
+
+After training, to evaluate the classification accuracies (with optional multi-angle voting):
+
+ python evaluate.py --num_votes 12
+
+Side Note: For the XYZ+normal experiment reported in our paper: (1) 5000 points are used and (2) a further random data dropout augmentation is used during training (see commented line after `augment_batch_data` in `train.py` and (3) the model architecture is updated such that the `nsample=128` in the first two set abstraction levels, which is suited for the larger point density in 5000-point samplings.
+
+To use normal features for classification: You can get our sampled point clouds of ModelNet40 (XYZ and normal from mesh, 10k points per shape) here (1.6GB). Move the uncompressed data folder to `data/modelnet40_normal_resampled`
+
+#### Object Part Segmentation
+
+To train a model to segment object parts for ShapeNet models:
+
+ cd part_seg
+ python train.py
+
+Preprocessed ShapeNetPart dataset (XYZ, normal and part labels) can be found here (674MB). Move the uncompressed data folder to `data/shapenetcore_partanno_segmentation_benchmark_v0_normal`
+
+#### Semantic Scene Parsing
+
+See `scannet/README` and `scannet/train.py` for details.
+
+#### Visualization Tools
+We have provided a handy point cloud visualization tool under `utils`. Run `sh compile_render_balls_so.sh` to compile it and then you can try the demo with `python show3d_balls.py` The original code is from here.
+
+#### Prepare Your Own Data
+You can refer to here on how to prepare your own HDF5 files for either classification or segmentation. Or you can refer to `modelnet_dataset.py` on how to read raw data files and prepare mini-batches from them. A more advanced way is to use TensorFlow's dataset APIs, for which you can find more documentations here.
+
+### License
+Our code is released under MIT License (see LICENSE file for details).
+
+### Updates
+* 02/23/2018: Added support for multi-gpu training for the classification task.
+* 02/23/2018: Adopted a new way for data loading. No longer require manual data downloading to train a classification network.
+* 02/06/2018: Added sample training code for ScanNet semantic segmentation.
+
+### Related Projects
+
+* PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation by Qi et al. (CVPR 2017 Oral Presentation). Code and data released in GitHub.
+* Frustum PointNets for 3D Object Detection from RGB-D Data by Qi et al. (CVPR 2018) A novel framework for 3D object detection with RGB-D data. Based on 2D boxes from a 2D object detector on RGB images, we extrude the depth maps in 2D boxes to point clouds in 3D space and then realize instance segmentation and 3D bounding box estimation using PointNet/PointNet++. The method proposed has achieved first place on KITTI 3D object detection benchmark on all categories (last checked on 11/30/2017). Code and data release TBD.
diff --git a/pointnet2/data/README.md b/pointnet2/data/README.md
new file mode 100644
index 0000000..1538b83
--- /dev/null
+++ b/pointnet2/data/README.md
@@ -0,0 +1,4 @@
+#### Point Cloud Data
+You can get our sampled point clouds of ModelNet40 (XYZ and normal from mesh, 10k points per shape) at this link. The ShapeNetPart dataset (XYZ, normal and part labels) can be found here.
+
+Uncompress the downloaded data in this directory.
diff --git a/pointnet2/doc/teaser.jpg b/pointnet2/doc/teaser.jpg
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diff --git a/pointnet2/evaluate.py b/pointnet2/evaluate.py
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+++ b/pointnet2/evaluate.py
@@ -0,0 +1,164 @@
+'''
+ Evaluate classification performance with optional voting.
+ Will use H5 dataset in default. If using normal, will shift to the normal dataset.
+'''
+import tensorflow as tf
+import numpy as np
+import argparse
+import socket
+import importlib
+import time
+import os
+import scipy.misc
+import sys
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+ROOT_DIR = BASE_DIR
+sys.path.append(BASE_DIR)
+sys.path.append(os.path.join(ROOT_DIR, 'models'))
+sys.path.append(os.path.join(ROOT_DIR, 'utils'))
+import provider
+import modelnet_dataset
+import modelnet_h5_dataset
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--gpu', type=int, default=0, help='GPU to use [default: GPU 0]')
+parser.add_argument('--model', default='pointnet2_cls_ssg', help='Model name. [default: pointnet2_cls_ssg]')
+parser.add_argument('--batch_size', type=int, default=16, help='Batch Size during training [default: 16]')
+parser.add_argument('--num_point', type=int, default=1024, help='Point Number [256/512/1024/2048] [default: 1024]')
+parser.add_argument('--model_path', default='log/model.ckpt', help='model checkpoint file path [default: log/model.ckpt]')
+parser.add_argument('--dump_dir', default='dump', help='dump folder path [dump]')
+parser.add_argument('--normal', action='store_true', help='Whether to use normal information')
+parser.add_argument('--num_votes', type=int, default=1, help='Aggregate classification scores from multiple rotations [default: 1]')
+FLAGS = parser.parse_args()
+
+
+BATCH_SIZE = FLAGS.batch_size
+NUM_POINT = FLAGS.num_point
+MODEL_PATH = FLAGS.model_path
+GPU_INDEX = FLAGS.gpu
+MODEL = importlib.import_module(FLAGS.model) # import network module
+DUMP_DIR = FLAGS.dump_dir
+if not os.path.exists(DUMP_DIR): os.mkdir(DUMP_DIR)
+LOG_FOUT = open(os.path.join(DUMP_DIR, 'log_evaluate.txt'), 'w')
+LOG_FOUT.write(str(FLAGS)+'\n')
+
+NUM_CLASSES = 40
+SHAPE_NAMES = [line.rstrip() for line in \
+ open(os.path.join(ROOT_DIR, 'data/modelnet40_ply_hdf5_2048/shape_names.txt'))]
+
+HOSTNAME = socket.gethostname()
+
+# Shapenet official train/test split
+if FLAGS.normal:
+ assert(NUM_POINT<=10000)
+ DATA_PATH = os.path.join(ROOT_DIR, 'data/modelnet40_normal_resampled')
+ TRAIN_DATASET = modelnet_dataset.ModelNetDataset(root=DATA_PATH, npoints=NUM_POINT, split='train', normal_channel=FLAGS.normal, batch_size=BATCH_SIZE)
+ TEST_DATASET = modelnet_dataset.ModelNetDataset(root=DATA_PATH, npoints=NUM_POINT, split='test', normal_channel=FLAGS.normal, batch_size=BATCH_SIZE)
+else:
+ assert(NUM_POINT<=2048)
+ TRAIN_DATASET = modelnet_h5_dataset.ModelNetH5Dataset(os.path.join(BASE_DIR, 'data/modelnet40_ply_hdf5_2048/train_files.txt'), batch_size=BATCH_SIZE, npoints=NUM_POINT, shuffle=True)
+ TEST_DATASET = modelnet_h5_dataset.ModelNetH5Dataset(os.path.join(BASE_DIR, 'data/modelnet40_ply_hdf5_2048/test_files.txt'), batch_size=BATCH_SIZE, npoints=NUM_POINT, shuffle=False)
+
+def log_string(out_str):
+ LOG_FOUT.write(out_str+'\n')
+ LOG_FOUT.flush()
+ print(out_str)
+
+def evaluate(num_votes):
+ is_training = False
+
+ with tf.device('/gpu:'+str(GPU_INDEX)):
+ pointclouds_pl, labels_pl = MODEL.placeholder_inputs(BATCH_SIZE, NUM_POINT)
+ is_training_pl = tf.placeholder(tf.bool, shape=())
+
+ # simple model
+ pred, end_points = MODEL.get_model(pointclouds_pl, is_training_pl)
+ MODEL.get_loss(pred, labels_pl, end_points)
+ losses = tf.get_collection('losses')
+ total_loss = tf.add_n(losses, name='total_loss')
+
+ # Add ops to save and restore all the variables.
+ saver = tf.train.Saver()
+
+ # Create a session
+ config = tf.ConfigProto()
+ config.gpu_options.allow_growth = True
+ config.allow_soft_placement = True
+ config.log_device_placement = False
+ sess = tf.Session(config=config)
+
+ # Restore variables from disk.
+ saver.restore(sess, MODEL_PATH)
+ log_string("Model restored.")
+
+ ops = {'pointclouds_pl': pointclouds_pl,
+ 'labels_pl': labels_pl,
+ 'is_training_pl': is_training_pl,
+ 'pred': pred,
+ 'loss': total_loss}
+
+ eval_one_epoch(sess, ops, num_votes)
+
+def eval_one_epoch(sess, ops, num_votes=1, topk=1):
+ is_training = False
+
+ # Make sure batch data is of same size
+ cur_batch_data = np.zeros((BATCH_SIZE,NUM_POINT,TEST_DATASET.num_channel()))
+ cur_batch_label = np.zeros((BATCH_SIZE), dtype=np.int32)
+
+ total_correct = 0
+ total_seen = 0
+ loss_sum = 0
+ batch_idx = 0
+ shape_ious = []
+ total_seen_class = [0 for _ in range(NUM_CLASSES)]
+ total_correct_class = [0 for _ in range(NUM_CLASSES)]
+
+ while TEST_DATASET.has_next_batch():
+ batch_data, batch_label = TEST_DATASET.next_batch(augment=False)
+ bsize = batch_data.shape[0]
+ print('Batch: %03d, batch size: %d'%(batch_idx, bsize))
+ # for the last batch in the epoch, the bsize:end are from last batch
+ cur_batch_data[0:bsize,...] = batch_data
+ cur_batch_label[0:bsize] = batch_label
+
+ batch_pred_sum = np.zeros((BATCH_SIZE, NUM_CLASSES)) # score for classes
+ for vote_idx in range(num_votes):
+ # Shuffle point order to achieve different farthest samplings
+ shuffled_indices = np.arange(NUM_POINT)
+ np.random.shuffle(shuffled_indices)
+ if FLAGS.normal:
+ rotated_data = provider.rotate_point_cloud_by_angle_with_normal(cur_batch_data[:, shuffled_indices, :],
+ vote_idx/float(num_votes) * np.pi * 2)
+ else:
+ rotated_data = provider.rotate_point_cloud_by_angle(cur_batch_data[:, shuffled_indices, :],
+ vote_idx/float(num_votes) * np.pi * 2)
+ feed_dict = {ops['pointclouds_pl']: rotated_data,
+ ops['labels_pl']: cur_batch_label,
+ ops['is_training_pl']: is_training}
+ loss_val, pred_val = sess.run([ops['loss'], ops['pred']], feed_dict=feed_dict)
+ batch_pred_sum += pred_val
+ pred_val = np.argmax(batch_pred_sum, 1)
+ correct = np.sum(pred_val[0:bsize] == batch_label[0:bsize])
+ total_correct += correct
+ total_seen += bsize
+ loss_sum += loss_val
+ batch_idx += 1
+ for i in range(bsize):
+ l = batch_label[i]
+ total_seen_class[l] += 1
+ total_correct_class[l] += (pred_val[i] == l)
+
+ log_string('eval mean loss: %f' % (loss_sum / float(batch_idx)))
+ log_string('eval accuracy: %f'% (total_correct / float(total_seen)))
+ log_string('eval avg class acc: %f' % (np.mean(np.array(total_correct_class)/np.array(total_seen_class,dtype=np.float))))
+
+ class_accuracies = np.array(total_correct_class)/np.array(total_seen_class,dtype=np.float)
+ for i, name in enumerate(SHAPE_NAMES):
+ log_string('%10s:\t%0.3f' % (name, class_accuracies[i]))
+
+
+if __name__=='__main__':
+ with tf.Graph().as_default():
+ evaluate(num_votes=FLAGS.num_votes)
+ LOG_FOUT.close()
diff --git a/pointnet2/modelnet_dataset.py b/pointnet2/modelnet_dataset.py
new file mode 100644
index 0000000..78f326e
--- /dev/null
+++ b/pointnet2/modelnet_dataset.py
@@ -0,0 +1,144 @@
+'''
+ ModelNet dataset. Support ModelNet40, ModelNet10, XYZ and normal channels. Up to 10000 points.
+'''
+
+import os
+import os.path
+import json
+import numpy as np
+import sys
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+ROOT_DIR = BASE_DIR
+sys.path.append(os.path.join(ROOT_DIR, 'utils'))
+import provider
+
+def pc_normalize(pc):
+ l = pc.shape[0]
+ centroid = np.mean(pc, axis=0)
+ pc = pc - centroid
+ m = np.max(np.sqrt(np.sum(pc**2, axis=1)))
+ pc = pc / m
+ return pc
+
+class ModelNetDataset():
+ def __init__(self, root, batch_size = 32, npoints = 1024, split='train', normalize=True, normal_channel=False, modelnet10=False, cache_size=15000, shuffle=None):
+ self.root = root
+ self.batch_size = batch_size
+ self.npoints = npoints
+ self.normalize = normalize
+ if modelnet10:
+ self.catfile = os.path.join(self.root, 'modelnet10_shape_names.txt')
+ else:
+ self.catfile = os.path.join(self.root, 'shape_names.txt')
+ self.cat = [line.rstrip() for line in open(self.catfile)]
+ self.classes = dict(zip(self.cat, range(len(self.cat))))
+ self.normal_channel = normal_channel
+
+ shape_ids = {}
+ if modelnet10:
+ shape_ids['train'] = [line.rstrip() for line in open(os.path.join(self.root, 'modelnet10_train.txt'))]
+ shape_ids['test']= [line.rstrip() for line in open(os.path.join(self.root, 'modelnet10_test.txt'))]
+ else:
+ shape_ids['train'] = [line.rstrip() for line in open(os.path.join(self.root, 'modelnet40_train.txt'))]
+ shape_ids['test']= [line.rstrip() for line in open(os.path.join(self.root, 'modelnet40_test.txt'))]
+ assert(split=='train' or split=='test')
+ shape_names = ['_'.join(x.split('_')[0:-1]) for x in shape_ids[split]]
+ # list of (shape_name, shape_txt_file_path) tuple
+ self.datapath = [(shape_names[i], os.path.join(self.root, shape_names[i], shape_ids[split][i])+'.txt') for i in range(len(shape_ids[split]))]
+
+ self.cache_size = cache_size # how many data points to cache in memory
+ self.cache = {} # from index to (point_set, cls) tuple
+
+ if shuffle is None:
+ if split == 'train': self.shuffle = True
+ else: self.shuffle = False
+ else:
+ self.shuffle = shuffle
+
+ self.reset()
+
+ def _augment_batch_data(self, batch_data):
+ if self.normal_channel:
+ rotated_data = provider.rotate_point_cloud_with_normal(batch_data)
+ rotated_data = provider.rotate_perturbation_point_cloud_with_normal(rotated_data)
+ else:
+ rotated_data = provider.rotate_point_cloud(batch_data)
+ rotated_data = provider.rotate_perturbation_point_cloud(rotated_data)
+
+ jittered_data = provider.random_scale_point_cloud(rotated_data[:,:,0:3])
+ jittered_data = provider.shift_point_cloud(jittered_data)
+ jittered_data = provider.jitter_point_cloud(jittered_data)
+ rotated_data[:,:,0:3] = jittered_data
+ return provider.shuffle_points(rotated_data)
+
+
+ def _get_item(self, index):
+ if index in self.cache:
+ point_set, cls = self.cache[index]
+ else:
+ fn = self.datapath[index]
+ cls = self.classes[self.datapath[index][0]]
+ cls = np.array([cls]).astype(np.int32)
+ point_set = np.loadtxt(fn[1],delimiter=',').astype(np.float32)
+ # Take the first npoints
+ point_set = point_set[0:self.npoints,:]
+ if self.normalize:
+ point_set[:,0:3] = pc_normalize(point_set[:,0:3])
+ if not self.normal_channel:
+ point_set = point_set[:,0:3]
+ if len(self.cache) < self.cache_size:
+ self.cache[index] = (point_set, cls)
+ return point_set, cls
+
+ def __getitem__(self, index):
+ return self._get_item(index)
+
+ def __len__(self):
+ return len(self.datapath)
+
+ def num_channel(self):
+ if self.normal_channel:
+ return 6
+ else:
+ return 3
+
+ def reset(self):
+ self.idxs = np.arange(0, len(self.datapath))
+ if self.shuffle:
+ np.random.shuffle(self.idxs)
+ self.num_batches = (len(self.datapath)+self.batch_size-1) // self.batch_size
+ self.batch_idx = 0
+
+ def has_next_batch(self):
+ return self.batch_idx < self.num_batches
+
+ def next_batch(self, augment=False):
+ ''' returned dimension may be smaller than self.batch_size '''
+ start_idx = self.batch_idx * self.batch_size
+ end_idx = min((self.batch_idx+1) * self.batch_size, len(self.datapath))
+ bsize = end_idx - start_idx
+ batch_data = np.zeros((bsize, self.npoints, self.num_channel()))
+ batch_label = np.zeros((bsize), dtype=np.int32)
+ for i in range(bsize):
+ ps,cls = self._get_item(self.idxs[i+start_idx])
+ batch_data[i] = ps
+ batch_label[i] = cls
+ self.batch_idx += 1
+ if augment: batch_data = self._augment_batch_data(batch_data)
+ return batch_data, batch_label
+
+if __name__ == '__main__':
+ d = ModelNetDataset(root = '../data/modelnet40_normal_resampled', split='test')
+ print(d.shuffle)
+ print(len(d))
+ import time
+ tic = time.time()
+ for i in range(10):
+ ps, cls = d[i]
+ print(time.time() - tic)
+ print(ps.shape, type(ps), cls)
+
+ print(d.has_next_batch())
+ ps_batch, cls_batch = d.next_batch(True)
+ print(ps_batch.shape)
+ print(cls_batch.shape)
diff --git a/pointnet2/modelnet_h5_dataset.py b/pointnet2/modelnet_h5_dataset.py
new file mode 100644
index 0000000..ecd6e16
--- /dev/null
+++ b/pointnet2/modelnet_h5_dataset.py
@@ -0,0 +1,126 @@
+'''
+ ModelNet dataset. Support ModelNet40, XYZ channels. Up to 2048 points.
+ Faster IO than ModelNetDataset in the first epoch.
+'''
+
+import os
+import sys
+import numpy as np
+import h5py
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(BASE_DIR)
+ROOT_DIR = BASE_DIR
+sys.path.append(os.path.join(ROOT_DIR, 'utils'))
+import provider
+
+
+# Download dataset for point cloud classification
+DATA_DIR = os.path.join(ROOT_DIR, 'data')
+if not os.path.exists(DATA_DIR):
+ os.mkdir(DATA_DIR)
+if not os.path.exists(os.path.join(DATA_DIR, 'modelnet40_ply_hdf5_2048')):
+ www = 'https://shapenet.cs.stanford.edu/media/modelnet40_ply_hdf5_2048.zip'
+ zipfile = os.path.basename(www)
+ os.system('wget %s; unzip %s' % (www, zipfile))
+ os.system('mv %s %s' % (zipfile[:-4], DATA_DIR))
+ os.system('rm %s' % (zipfile))
+
+
+def shuffle_data(data, labels):
+ """ Shuffle data and labels.
+ Input:
+ data: B,N,... numpy array
+ label: B,... numpy array
+ Return:
+ shuffled data, label and shuffle indices
+ """
+ idx = np.arange(len(labels))
+ np.random.shuffle(idx)
+ return data[idx, ...], labels[idx], idx
+
+def getDataFiles(list_filename):
+ return [line.rstrip() for line in open(list_filename)]
+
+def load_h5(h5_filename):
+ f = h5py.File(h5_filename)
+ data = f['data'][:]
+ label = f['label'][:]
+ return (data, label)
+
+def loadDataFile(filename):
+ return load_h5(filename)
+
+
+class ModelNetH5Dataset(object):
+ def __init__(self, list_filename, batch_size = 32, npoints = 1024, shuffle=True):
+ self.list_filename = list_filename
+ self.batch_size = batch_size
+ self.npoints = npoints
+ self.shuffle = shuffle
+ self.h5_files = getDataFiles(self.list_filename)
+ self.reset()
+
+ def reset(self):
+ ''' reset order of h5 files '''
+ self.file_idxs = np.arange(0, len(self.h5_files))
+ if self.shuffle: np.random.shuffle(self.file_idxs)
+ self.current_data = None
+ self.current_label = None
+ self.current_file_idx = 0
+ self.batch_idx = 0
+
+ def _augment_batch_data(self, batch_data):
+ rotated_data = provider.rotate_point_cloud(batch_data)
+ rotated_data = provider.rotate_perturbation_point_cloud(rotated_data)
+ jittered_data = provider.random_scale_point_cloud(rotated_data[:,:,0:3])
+ jittered_data = provider.shift_point_cloud(jittered_data)
+ jittered_data = provider.jitter_point_cloud(jittered_data)
+ rotated_data[:,:,0:3] = jittered_data
+ return provider.shuffle_points(rotated_data)
+
+
+ def _get_data_filename(self):
+ return self.h5_files[self.file_idxs[self.current_file_idx]]
+
+ def _load_data_file(self, filename):
+ self.current_data,self.current_label = load_h5(filename)
+ self.current_label = np.squeeze(self.current_label)
+ self.batch_idx = 0
+ if self.shuffle:
+ self.current_data, self.current_label, _ = shuffle_data(self.current_data,self.current_label)
+
+ def _has_next_batch_in_file(self):
+ return self.batch_idx*self.batch_size < self.current_data.shape[0]
+
+ def num_channel(self):
+ return 3
+
+ def has_next_batch(self):
+ # TODO: add backend thread to load data
+ if (self.current_data is None) or (not self._has_next_batch_in_file()):
+ if self.current_file_idx >= len(self.h5_files):
+ return False
+ self._load_data_file(self._get_data_filename())
+ self.batch_idx = 0
+ self.current_file_idx += 1
+ return self._has_next_batch_in_file()
+
+ def next_batch(self, augment=False):
+ ''' returned dimension may be smaller than self.batch_size '''
+ start_idx = self.batch_idx * self.batch_size
+ end_idx = min((self.batch_idx+1) * self.batch_size, self.current_data.shape[0])
+ bsize = end_idx - start_idx
+ batch_label = np.zeros((bsize), dtype=np.int32)
+ data_batch = self.current_data[start_idx:end_idx, 0:self.npoints, :].copy()
+ label_batch = self.current_label[start_idx:end_idx].copy()
+ self.batch_idx += 1
+ if augment: data_batch = self._augment_batch_data(data_batch)
+ return data_batch, label_batch
+
+if __name__=='__main__':
+ d = ModelNetH5Dataset('data/modelnet40_ply_hdf5_2048/train_files.txt')
+ print(d.shuffle)
+ print(d.has_next_batch())
+ ps_batch, cls_batch = d.next_batch(True)
+ print(ps_batch.shape)
+ print(cls_batch.shape)
diff --git a/pointnet2/models/frustrum_pontnets_v2.py b/pointnet2/models/frustrum_pontnets_v2.py
new file mode 100644
index 0000000..79ebb47
--- /dev/null
+++ b/pointnet2/models/frustrum_pontnets_v2.py
@@ -0,0 +1,182 @@
+''' Frustum PointNets v2 Model.
+'''
+from __future__ import print_function
+
+import sys
+import os
+import tensorflow as tf
+import numpy as np
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+ROOT_DIR = os.path.dirname(BASE_DIR)
+sys.path.append(BASE_DIR)
+sys.path.append(os.path.join(ROOT_DIR, 'utils'))
+import tf_util
+from pointnet_util import pointnet_sa_module, pointnet_sa_module_msg, pointnet_fp_module
+from model_util import NUM_HEADING_BIN, NUM_SIZE_CLUSTER, NUM_OBJECT_POINT
+from model_util import point_cloud_masking, get_center_regression_net
+from model_util import placeholder_inputs, parse_output_to_tensors, get_loss
+
+
+def get_instance_seg_v2_net(point_cloud, one_hot_vec,
+ is_training, bn_decay, end_points):
+ ''' 3D instance segmentation PointNet v2 network.
+ Input:
+ point_cloud: TF tensor in shape (B,N,4)
+ frustum point clouds with XYZ and intensity in point channels
+ XYZs are in frustum coordinate
+ one_hot_vec: TF tensor in shape (B,3)
+ length-3 vectors indicating predicted object type
+ is_training: TF boolean scalar
+ bn_decay: TF float scalar
+ end_points: dict
+ Output:
+ logits: TF tensor in shape (B,N,2), scores for bkg/clutter and object
+ end_points: dict
+ '''
+
+ l0_xyz = tf.slice(point_cloud, [0,0,0], [-1,-1,3])
+ l0_points = tf.slice(point_cloud, [0,0,3], [-1,-1,1])
+
+ # Set abstraction layers
+ l1_xyz, l1_points = pointnet_sa_module_msg(l0_xyz, l0_points,
+ 128, [0.2,0.4,0.8], [32,64,128],
+ [[32,32,64], [64,64,128], [64,96,128]],
+ is_training, bn_decay, scope='layer1')
+ l2_xyz, l2_points = pointnet_sa_module_msg(l1_xyz, l1_points,
+ 32, [0.4,0.8,1.6], [64,64,128],
+ [[64,64,128], [128,128,256], [128,128,256]],
+ is_training, bn_decay, scope='layer2')
+ l3_xyz, l3_points, _ = pointnet_sa_module(l2_xyz, l2_points,
+ npoint=None, radius=None, nsample=None, mlp=[128,256,1024],
+ mlp2=None, group_all=True, is_training=is_training,
+ bn_decay=bn_decay, scope='layer3')
+
+ # Feature Propagation layers
+ l3_points = tf.concat([l3_points, tf.expand_dims(one_hot_vec, 1)], axis=2)
+ l2_points = pointnet_fp_module(l2_xyz, l3_xyz, l2_points, l3_points,
+ [128,128], is_training, bn_decay, scope='fa_layer1')
+ l1_points = pointnet_fp_module(l1_xyz, l2_xyz, l1_points, l2_points,
+ [128,128], is_training, bn_decay, scope='fa_layer2')
+ l0_points = pointnet_fp_module(l0_xyz, l1_xyz,
+ tf.concat([l0_xyz,l0_points],axis=-1), l1_points,
+ [128,128], is_training, bn_decay, scope='fa_layer3')
+
+ # FC layers
+ net = tf_util.conv1d(l0_points, 128, 1, padding='VALID', bn=True,
+ is_training=is_training, scope='conv1d-fc1', bn_decay=bn_decay)
+ end_points['feats'] = net
+ net = tf_util.dropout(net, keep_prob=0.7,
+ is_training=is_training, scope='dp1')
+ logits = tf_util.conv1d(net, 2, 1,
+ padding='VALID', activation_fn=None, scope='conv1d-fc2')
+
+ return logits, end_points
+
+def get_3d_box_estimation_v2_net(object_point_cloud, one_hot_vec,
+ is_training, bn_decay, end_points):
+ ''' 3D Box Estimation PointNet v2 network.
+ Input:
+ object_point_cloud: TF tensor in shape (B,M,C)
+ masked point clouds in object coordinate
+ one_hot_vec: TF tensor in shape (B,3)
+ length-3 vectors indicating predicted object type
+ Output:
+ output: TF tensor in shape (B,3+NUM_HEADING_BIN*2+NUM_SIZE_CLUSTER*4)
+ including box centers, heading bin class scores and residuals,
+ and size cluster scores and residuals
+ '''
+ # Gather object points
+ batch_size = object_point_cloud.get_shape()[0]
+
+ l0_xyz = object_point_cloud
+ l0_points = None
+ # Set abstraction layers
+ l1_xyz, l1_points, l1_indices = pointnet_sa_module(l0_xyz, l0_points,
+ npoint=128, radius=0.2, nsample=64, mlp=[64,64,128],
+ mlp2=None, group_all=False,
+ is_training=is_training, bn_decay=bn_decay, scope='ssg-layer1')
+ l2_xyz, l2_points, l2_indices = pointnet_sa_module(l1_xyz, l1_points,
+ npoint=32, radius=0.4, nsample=64, mlp=[128,128,256],
+ mlp2=None, group_all=False,
+ is_training=is_training, bn_decay=bn_decay, scope='ssg-layer2')
+ l3_xyz, l3_points, l3_indices = pointnet_sa_module(l2_xyz, l2_points,
+ npoint=None, radius=None, nsample=None, mlp=[256,256,512],
+ mlp2=None, group_all=True,
+ is_training=is_training, bn_decay=bn_decay, scope='ssg-layer3')
+
+ # Fully connected layers
+ net = tf.reshape(l3_points, [batch_size, -1])
+ net = tf.concat([net, one_hot_vec], axis=1)
+ net = tf_util.fully_connected(net, 512, bn=True,
+ is_training=is_training, scope='fc1', bn_decay=bn_decay)
+ net = tf_util.fully_connected(net, 256, bn=True,
+ is_training=is_training, scope='fc2', bn_decay=bn_decay)
+
+ # The first 3 numbers: box center coordinates (cx,cy,cz),
+ # the next NUM_HEADING_BIN*2: heading bin class scores and bin residuals
+ # next NUM_SIZE_CLUSTER*4: box cluster scores and residuals
+ output = tf_util.fully_connected(net,
+ 3+NUM_HEADING_BIN*2+NUM_SIZE_CLUSTER*4, activation_fn=None, scope='fc3')
+ return output, end_points
+
+
+def get_model(point_cloud, one_hot_vec, is_training, bn_decay=None):
+ ''' Frustum PointNets model. The model predict 3D object masks and
+ amodel bounding boxes for objects in frustum point clouds.
+
+ Input:
+ point_cloud: TF tensor in shape (B,N,4)
+ frustum point clouds with XYZ and intensity in point channels
+ XYZs are in frustum coordinate
+ one_hot_vec: TF tensor in shape (B,3)
+ length-3 vectors indicating predicted object type
+ is_training: TF boolean scalar
+ bn_decay: TF float scalar
+ Output:
+ end_points: dict (map from name strings to TF tensors)
+ '''
+ end_points = {}
+
+ # 3D Instance Segmentation PointNet
+ logits, end_points = get_instance_seg_v2_net(\
+ point_cloud, one_hot_vec,
+ is_training, bn_decay, end_points)
+ end_points['mask_logits'] = logits
+
+ # Masking
+ # select masked points and translate to masked points' centroid
+ object_point_cloud_xyz, mask_xyz_mean, end_points = \
+ point_cloud_masking(point_cloud, logits, end_points)
+
+ # T-Net and coordinate translation
+ center_delta, end_points = get_center_regression_net(\
+ object_point_cloud_xyz, one_hot_vec,
+ is_training, bn_decay, end_points)
+ stage1_center = center_delta + mask_xyz_mean # Bx3
+ end_points['stage1_center'] = stage1_center
+ # Get object point cloud in object coordinate
+ object_point_cloud_xyz_new = \
+ object_point_cloud_xyz - tf.expand_dims(center_delta, 1)
+
+ # Amodel Box Estimation PointNet
+ output, end_points = get_3d_box_estimation_v2_net(\
+ object_point_cloud_xyz_new, one_hot_vec,
+ is_training, bn_decay, end_points)
+
+ # Parse output to 3D box parameters
+ end_points = parse_output_to_tensors(output, end_points)
+ end_points['center'] = end_points['center_boxnet'] + stage1_center # Bx3
+
+ return end_points
+
+if __name__=='__main__':
+ with tf.Graph().as_default():
+ inputs = tf.zeros((32,1024,4))
+ outputs = get_model(inputs, tf.ones((32,3)), tf.constant(True))
+ for key in outputs:
+ print((key, outputs[key]))
+ loss = get_loss(tf.zeros((32,1024),dtype=tf.int32),
+ tf.zeros((32,3)), tf.zeros((32,),dtype=tf.int32),
+ tf.zeros((32,)), tf.zeros((32,),dtype=tf.int32),
+ tf.zeros((32,3)), outputs)
+ print(loss)
\ No newline at end of file
diff --git a/pointnet2/models/pointnet2_cls_msg.py b/pointnet2/models/pointnet2_cls_msg.py
new file mode 100644
index 0000000..1f11c02
--- /dev/null
+++ b/pointnet2/models/pointnet2_cls_msg.py
@@ -0,0 +1,55 @@
+import os
+import sys
+BASE_DIR = os.path.dirname(__file__)
+sys.path.append(BASE_DIR)
+sys.path.append(os.path.join(BASE_DIR, '../utils'))
+import tensorflow as tf
+import numpy as np
+import tf_util
+from pointnet_util import pointnet_sa_module, pointnet_sa_module_msg
+
+def placeholder_inputs(batch_size, num_point):
+ pointclouds_pl = tf.placeholder(tf.float32, shape=(batch_size, num_point, 3))
+ labels_pl = tf.placeholder(tf.int32, shape=(batch_size))
+ return pointclouds_pl, labels_pl
+
+def get_model(point_cloud, is_training, bn_decay=None):
+ """ Classification PointNet, input is BxNx3, output Bx40 """
+ batch_size = point_cloud.get_shape()[0].value
+ num_point = point_cloud.get_shape()[1].value
+ end_points = {}
+
+ l0_xyz = point_cloud
+ l0_points = None
+
+ # Set abstraction layers
+ l1_xyz, l1_points = pointnet_sa_module_msg(l0_xyz, l0_points, 512, [0.1,0.2,0.4], [16,32,128], [[32,32,64], [64,64,128], [64,96,128]], is_training, bn_decay, scope='layer1', use_nchw=True)
+ l2_xyz, l2_points = pointnet_sa_module_msg(l1_xyz, l1_points, 128, [0.2,0.4,0.8], [32,64,128], [[64,64,128], [128,128,256], [128,128,256]], is_training, bn_decay, scope='layer2')
+ l3_xyz, l3_points, _ = pointnet_sa_module(l2_xyz, l2_points, npoint=None, radius=None, nsample=None, mlp=[256,512,1024], mlp2=None, group_all=True, is_training=is_training, bn_decay=bn_decay, scope='layer3')
+
+ # Fully connected layers
+ net = tf.reshape(l3_points, [batch_size, -1])
+ net = tf_util.fully_connected(net, 512, bn=True, is_training=is_training, scope='fc1', bn_decay=bn_decay)
+ net = tf_util.dropout(net, keep_prob=0.4, is_training=is_training, scope='dp1')
+ net = tf_util.fully_connected(net, 256, bn=True, is_training=is_training, scope='fc2', bn_decay=bn_decay)
+ net = tf_util.dropout(net, keep_prob=0.4, is_training=is_training, scope='dp2')
+ net = tf_util.fully_connected(net, 40, activation_fn=None, scope='fc3')
+
+ return net, end_points
+
+
+def get_loss(pred, label, end_points):
+ """ pred: B*NUM_CLASSES,
+ label: B, """
+ loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=pred, labels=label)
+ classify_loss = tf.reduce_mean(loss)
+ tf.summary.scalar('classify loss', classify_loss)
+ tf.add_to_collection('losses', classify_loss)
+ return classify_loss
+
+
+if __name__=='__main__':
+ with tf.Graph().as_default():
+ inputs = tf.zeros((32,1024,3))
+ net, _ = get_model(inputs, tf.constant(True))
+ print(net)
diff --git a/pointnet2/models/pointnet2_cls_ssg.py b/pointnet2/models/pointnet2_cls_ssg.py
new file mode 100644
index 0000000..c750c42
--- /dev/null
+++ b/pointnet2/models/pointnet2_cls_ssg.py
@@ -0,0 +1,61 @@
+"""
+ PointNet++ Model for point clouds classification
+"""
+
+import os
+import sys
+BASE_DIR = os.path.dirname(__file__)
+sys.path.append(BASE_DIR)
+sys.path.append(os.path.join(BASE_DIR, '../utils'))
+import tensorflow as tf
+import numpy as np
+import tf_util
+from pointnet_util import pointnet_sa_module
+
+def placeholder_inputs(batch_size, num_point):
+ pointclouds_pl = tf.placeholder(tf.float32, shape=(batch_size, num_point, 3))
+ labels_pl = tf.placeholder(tf.int32, shape=(batch_size))
+ return pointclouds_pl, labels_pl
+
+def get_model(point_cloud, is_training, bn_decay=None):
+ """ Classification PointNet, input is BxNx3, output Bx40 """
+ batch_size = point_cloud.get_shape()[0].value
+ num_point = point_cloud.get_shape()[1].value
+ end_points = {}
+ l0_xyz = point_cloud
+ l0_points = None
+ end_points['l0_xyz'] = l0_xyz
+
+ # Set abstraction layers
+ # Note: When using NCHW for layer 2, we see increased GPU memory usage (in TF1.4).
+ # So we only use NCHW for layer 1 until this issue can be resolved.
+ l1_xyz, l1_points, l1_indices = pointnet_sa_module(l0_xyz, l0_points, npoint=512, radius=0.2, nsample=32, mlp=[64,64,128], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer1', use_nchw=True)
+ l2_xyz, l2_points, l2_indices = pointnet_sa_module(l1_xyz, l1_points, npoint=128, radius=0.4, nsample=64, mlp=[128,128,256], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer2')
+ l3_xyz, l3_points, l3_indices = pointnet_sa_module(l2_xyz, l2_points, npoint=None, radius=None, nsample=None, mlp=[256,512,1024], mlp2=None, group_all=True, is_training=is_training, bn_decay=bn_decay, scope='layer3')
+
+ # Fully connected layers
+ net = tf.reshape(l3_points, [batch_size, -1])
+ net = tf_util.fully_connected(net, 512, bn=True, is_training=is_training, scope='fc1', bn_decay=bn_decay)
+ net = tf_util.dropout(net, keep_prob=0.5, is_training=is_training, scope='dp1')
+ net = tf_util.fully_connected(net, 256, bn=True, is_training=is_training, scope='fc2', bn_decay=bn_decay)
+ net = tf_util.dropout(net, keep_prob=0.5, is_training=is_training, scope='dp2')
+ net = tf_util.fully_connected(net, 40, activation_fn=None, scope='fc3')
+
+ return net, end_points
+
+
+def get_loss(pred, label, end_points):
+ """ pred: B*NUM_CLASSES,
+ label: B, """
+ loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=pred, labels=label)
+ classify_loss = tf.reduce_mean(loss)
+ tf.summary.scalar('classify loss', classify_loss)
+ tf.add_to_collection('losses', classify_loss)
+ return classify_loss
+
+
+if __name__=='__main__':
+ with tf.Graph().as_default():
+ inputs = tf.zeros((32,1024,3))
+ output, _ = get_model(inputs, tf.constant(True))
+ print(output)
diff --git a/pointnet2/models/pointnet2_part_seg.py b/pointnet2/models/pointnet2_part_seg.py
new file mode 100644
index 0000000..1f7b233
--- /dev/null
+++ b/pointnet2/models/pointnet2_part_seg.py
@@ -0,0 +1,57 @@
+import os
+import sys
+BASE_DIR = os.path.dirname(__file__)
+sys.path.append(BASE_DIR)
+sys.path.append(os.path.join(BASE_DIR, '../utils'))
+import tensorflow as tf
+import numpy as np
+import tf_util
+from pointnet_util import pointnet_sa_module, pointnet_fp_module
+
+def placeholder_inputs(batch_size, num_point):
+ pointclouds_pl = tf.placeholder(tf.float32, shape=(batch_size, num_point, 6))
+ labels_pl = tf.placeholder(tf.int32, shape=(batch_size, num_point))
+ return pointclouds_pl, labels_pl
+
+
+def get_model(point_cloud, is_training, bn_decay=None):
+ """ Part segmentation PointNet, input is BxNx6 (XYZ NormalX NormalY NormalZ), output Bx50 """
+ batch_size = point_cloud.get_shape()[0].value
+ num_point = point_cloud.get_shape()[1].value
+ end_points = {}
+ l0_xyz = tf.slice(point_cloud, [0,0,0], [-1,-1,3])
+ l0_points = tf.slice(point_cloud, [0,0,3], [-1,-1,3])
+
+ # Set Abstraction layers
+ l1_xyz, l1_points, l1_indices = pointnet_sa_module(l0_xyz, l0_points, npoint=512, radius=0.2, nsample=64, mlp=[64,64,128], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer1')
+ l2_xyz, l2_points, l2_indices = pointnet_sa_module(l1_xyz, l1_points, npoint=128, radius=0.4, nsample=64, mlp=[128,128,256], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer2')
+ l3_xyz, l3_points, l3_indices = pointnet_sa_module(l2_xyz, l2_points, npoint=None, radius=None, nsample=None, mlp=[256,512,1024], mlp2=None, group_all=True, is_training=is_training, bn_decay=bn_decay, scope='layer3')
+
+ # Feature Propagation layers
+ l2_points = pointnet_fp_module(l2_xyz, l3_xyz, l2_points, l3_points, [256,256], is_training, bn_decay, scope='fa_layer1')
+ l1_points = pointnet_fp_module(l1_xyz, l2_xyz, l1_points, l2_points, [256,128], is_training, bn_decay, scope='fa_layer2')
+ l0_points = pointnet_fp_module(l0_xyz, l1_xyz, tf.concat([l0_xyz,l0_points],axis=-1), l1_points, [128,128,128], is_training, bn_decay, scope='fa_layer3')
+
+ # FC layers
+ net = tf_util.conv1d(l0_points, 128, 1, padding='VALID', bn=True, is_training=is_training, scope='fc1', bn_decay=bn_decay)
+ end_points['feats'] = net
+ net = tf_util.dropout(net, keep_prob=0.5, is_training=is_training, scope='dp1')
+ net = tf_util.conv1d(net, 50, 1, padding='VALID', activation_fn=None, scope='fc2')
+
+ return net, end_points
+
+
+def get_loss(pred, label):
+ """ pred: BxNxC,
+ label: BxN, """
+ loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=pred, labels=label)
+ classify_loss = tf.reduce_mean(loss)
+ tf.summary.scalar('classify loss', classify_loss)
+ tf.add_to_collection('losses', classify_loss)
+ return classify_loss
+
+if __name__=='__main__':
+ with tf.Graph().as_default():
+ inputs = tf.zeros((32,2048,6))
+ net, _ = get_model(inputs, tf.constant(True))
+ print(net)
diff --git a/pointnet2/models/pointnet2_part_seg_msg_one_hot.py b/pointnet2/models/pointnet2_part_seg_msg_one_hot.py
new file mode 100644
index 0000000..27027ad
--- /dev/null
+++ b/pointnet2/models/pointnet2_part_seg_msg_one_hot.py
@@ -0,0 +1,66 @@
+import os
+import sys
+BASE_DIR = os.path.dirname(__file__)
+sys.path.append(BASE_DIR)
+sys.path.append(os.path.join(BASE_DIR, '../utils'))
+import tensorflow as tf
+import numpy as np
+import tf_util
+from pointnet_util import pointnet_sa_module, pointnet_sa_module_msg, pointnet_fp_module
+
+def placeholder_inputs(batch_size, num_point):
+ pointclouds_pl = tf.placeholder(tf.float32, shape=(batch_size, num_point, 6))
+ labels_pl = tf.placeholder(tf.int32, shape=(batch_size, num_point))
+ cls_labels_pl = tf.placeholder(tf.int32, shape=(batch_size))
+ return pointclouds_pl, labels_pl, cls_labels_pl
+
+NUM_CATEGORIES = 16
+
+def get_model(point_cloud, cls_label, is_training, bn_decay=None):
+ """ Classification PointNet, input is BxNx3, output Bx40 """
+ batch_size = point_cloud.get_shape()[0].value
+ num_point = point_cloud.get_shape()[1].value
+ end_points = {}
+ l0_xyz = tf.slice(point_cloud, [0,0,0], [-1,-1,3])
+ l0_points = tf.slice(point_cloud, [0,0,3], [-1,-1,3])
+
+ # Set abstraction layers
+ l1_xyz, l1_points = pointnet_sa_module_msg(l0_xyz, l0_points, 512, [0.1,0.2,0.4], [32,64,128], [[32,32,64], [64,64,128], [64,96,128]], is_training, bn_decay, scope='layer1')
+ l2_xyz, l2_points = pointnet_sa_module_msg(l1_xyz, l1_points, 128, [0.4,0.8], [64,128], [[128,128,256],[128,196,256]], is_training, bn_decay, scope='layer2')
+ l3_xyz, l3_points, l3_indices = pointnet_sa_module(l2_xyz, l2_points, npoint=None, radius=None, nsample=None, mlp=[256,512,1024], mlp2=None, group_all=True, is_training=is_training, bn_decay=bn_decay, scope='layer3')
+
+ # Feature propagation layers
+ l2_points = pointnet_fp_module(l2_xyz, l3_xyz, l2_points, l3_points, [256,256], is_training, bn_decay, scope='fa_layer1')
+ l1_points = pointnet_fp_module(l1_xyz, l2_xyz, l1_points, l2_points, [256,128], is_training, bn_decay, scope='fa_layer2')
+
+ cls_label_one_hot = tf.one_hot(cls_label, depth=NUM_CATEGORIES, on_value=1.0, off_value=0.0)
+ cls_label_one_hot = tf.reshape(cls_label_one_hot, [batch_size, 1, NUM_CATEGORIES])
+ cls_label_one_hot = tf.tile(cls_label_one_hot, [1,num_point,1])
+ l0_points = pointnet_fp_module(l0_xyz, l1_xyz, tf.concat([cls_label_one_hot, l0_xyz, l0_points],axis=-1), l1_points, [128,128], is_training, bn_decay, scope='fp_layer3')
+
+ # FC layers
+ net = tf_util.conv1d(l0_points, 128, 1, padding='VALID', bn=True, is_training=is_training, scope='fc1', bn_decay=bn_decay)
+ end_points['feats'] = net
+ net = tf_util.dropout(net, keep_prob=0.5, is_training=is_training, scope='dp1')
+ net = tf_util.conv1d(net, 50, 1, padding='VALID', activation_fn=None, scope='fc2')
+
+ return net, end_points
+
+
+def get_loss(pred, label):
+ """ pred: BxNxC,
+ label: BxN, """
+ loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=pred, labels=label)
+ classify_loss = tf.reduce_mean(loss)
+ tf.summary.scalar('classify loss', classify_loss)
+ tf.add_to_collection('losses', classify_loss)
+ return classify_loss
+
+
+
+if __name__=='__main__':
+ with tf.Graph().as_default():
+ inputs = tf.zeros((32,2048,6))
+ cls_labels = tf.zeros((32),dtype=tf.int32)
+ output, ep = get_model(inputs, cls_labels, tf.constant(True))
+ print(output)
diff --git a/pointnet2/models/pointnet2_sem_seg.py b/pointnet2/models/pointnet2_sem_seg.py
new file mode 100644
index 0000000..de88528
--- /dev/null
+++ b/pointnet2/models/pointnet2_sem_seg.py
@@ -0,0 +1,61 @@
+import os
+import sys
+BASE_DIR = os.path.dirname(__file__)
+sys.path.append(BASE_DIR)
+sys.path.append(os.path.join(BASE_DIR, '../utils'))
+import tensorflow as tf
+import numpy as np
+import tf_util
+from pointnet_util import pointnet_sa_module, pointnet_fp_module
+
+def placeholder_inputs(batch_size, num_point):
+ pointclouds_pl = tf.placeholder(tf.float32, shape=(batch_size, num_point, 3))
+ labels_pl = tf.placeholder(tf.int32, shape=(batch_size, num_point))
+ smpws_pl = tf.placeholder(tf.float32, shape=(batch_size, num_point))
+ return pointclouds_pl, labels_pl, smpws_pl
+
+
+def get_model(point_cloud, is_training, num_class, bn_decay=None):
+ """ Semantic segmentation PointNet, input is BxNx3, output Bxnum_class """
+ batch_size = point_cloud.get_shape()[0].value
+ num_point = point_cloud.get_shape()[1].value
+ end_points = {}
+ l0_xyz = point_cloud
+ l0_points = None
+ end_points['l0_xyz'] = l0_xyz
+
+ # Layer 1
+ l1_xyz, l1_points, l1_indices = pointnet_sa_module(l0_xyz, l0_points, npoint=1024, radius=0.1, nsample=32, mlp=[32,32,64], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer1')
+ l2_xyz, l2_points, l2_indices = pointnet_sa_module(l1_xyz, l1_points, npoint=256, radius=0.2, nsample=32, mlp=[64,64,128], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer2')
+ l3_xyz, l3_points, l3_indices = pointnet_sa_module(l2_xyz, l2_points, npoint=64, radius=0.4, nsample=32, mlp=[128,128,256], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer3')
+ l4_xyz, l4_points, l4_indices = pointnet_sa_module(l3_xyz, l3_points, npoint=16, radius=0.8, nsample=32, mlp=[256,256,512], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer4')
+
+ # Feature Propagation layers
+ l3_points = pointnet_fp_module(l3_xyz, l4_xyz, l3_points, l4_points, [256,256], is_training, bn_decay, scope='fa_layer1')
+ l2_points = pointnet_fp_module(l2_xyz, l3_xyz, l2_points, l3_points, [256,256], is_training, bn_decay, scope='fa_layer2')
+ l1_points = pointnet_fp_module(l1_xyz, l2_xyz, l1_points, l2_points, [256,128], is_training, bn_decay, scope='fa_layer3')
+ l0_points = pointnet_fp_module(l0_xyz, l1_xyz, l0_points, l1_points, [128,128,128], is_training, bn_decay, scope='fa_layer4')
+
+ # FC layers
+ net = tf_util.conv1d(l0_points, 128, 1, padding='VALID', bn=True, is_training=is_training, scope='fc1', bn_decay=bn_decay)
+ end_points['feats'] = net
+ net = tf_util.dropout(net, keep_prob=0.5, is_training=is_training, scope='dp1')
+ net = tf_util.conv1d(net, num_class, 1, padding='VALID', activation_fn=None, scope='fc2')
+
+ return net, end_points
+
+
+def get_loss(pred, label, smpw):
+ """ pred: BxNxC,
+ label: BxN,
+ smpw: BxN """
+ classify_loss = tf.losses.sparse_softmax_cross_entropy(labels=label, logits=pred, weights=smpw)
+ tf.summary.scalar('classify loss', classify_loss)
+ tf.add_to_collection('losses', classify_loss)
+ return classify_loss
+
+if __name__=='__main__':
+ with tf.Graph().as_default():
+ inputs = tf.zeros((32,2048,3))
+ net, _ = get_model(inputs, tf.constant(True), 10)
+ print(net)
diff --git a/pointnet2/models/pointnet_cls_basic.py b/pointnet2/models/pointnet_cls_basic.py
new file mode 100644
index 0000000..a668c37
--- /dev/null
+++ b/pointnet2/models/pointnet_cls_basic.py
@@ -0,0 +1,81 @@
+'''
+ PointNet version 1 Model
+ Reference: https://github.com/charlesq34/pointnet
+'''
+import tensorflow as tf
+import numpy as np
+import math
+import sys
+import os
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(BASE_DIR)
+sys.path.append(os.path.join(BASE_DIR, '../utils'))
+import tf_util
+
+def placeholder_inputs(batch_size, num_point):
+ pointclouds_pl = tf.placeholder(tf.float32, shape=(batch_size, num_point, 3))
+ labels_pl = tf.placeholder(tf.int32, shape=(batch_size))
+ return pointclouds_pl, labels_pl
+
+
+def get_model(point_cloud, is_training, bn_decay=None):
+ """ Classification PointNet, input is BxNx3, output Bx40 """
+ batch_size = point_cloud.get_shape()[0].value
+ num_point = point_cloud.get_shape()[1].value
+ end_points = {}
+ input_image = tf.expand_dims(point_cloud, -1)
+
+ # Point functions (MLP implemented as conv2d)
+ net = tf_util.conv2d(input_image, 64, [1,3],
+ padding='VALID', stride=[1,1],
+ bn=True, is_training=is_training,
+ scope='conv1', bn_decay=bn_decay)
+ net = tf_util.conv2d(net, 64, [1,1],
+ padding='VALID', stride=[1,1],
+ bn=True, is_training=is_training,
+ scope='conv2', bn_decay=bn_decay)
+ net = tf_util.conv2d(net, 64, [1,1],
+ padding='VALID', stride=[1,1],
+ bn=True, is_training=is_training,
+ scope='conv3', bn_decay=bn_decay)
+ net = tf_util.conv2d(net, 128, [1,1],
+ padding='VALID', stride=[1,1],
+ bn=True, is_training=is_training,
+ scope='conv4', bn_decay=bn_decay)
+ net = tf_util.conv2d(net, 1024, [1,1],
+ padding='VALID', stride=[1,1],
+ bn=True, is_training=is_training,
+ scope='conv5', bn_decay=bn_decay)
+
+ # Symmetric function: max pooling
+ net = tf_util.max_pool2d(net, [num_point,1],
+ padding='VALID', scope='maxpool')
+
+ # MLP on global point cloud vector
+ net = tf.reshape(net, [batch_size, -1])
+ net = tf_util.fully_connected(net, 512, bn=True, is_training=is_training,
+ scope='fc1', bn_decay=bn_decay)
+ net = tf_util.fully_connected(net, 256, bn=True, is_training=is_training,
+ scope='fc2', bn_decay=bn_decay)
+ net = tf_util.dropout(net, keep_prob=0.7, is_training=is_training,
+ scope='dp1')
+ net = tf_util.fully_connected(net, 40, activation_fn=None, scope='fc3')
+
+ return net, end_points
+
+
+def get_loss(pred, label, end_points):
+ """ pred: B*NUM_CLASSES,
+ label: B, """
+ loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=pred, labels=label)
+ classify_loss = tf.reduce_mean(loss)
+ tf.summary.scalar('classify loss', classify_loss)
+ tf.add_to_collection('losses', classify_loss)
+ return classify_loss
+
+
+if __name__=='__main__':
+ with tf.Graph().as_default():
+ inputs = tf.zeros((32,1024,3))
+ outputs = get_model(inputs, tf.constant(True))
+ print(outputs)
diff --git a/pointnet2/part_seg/command.sh b/pointnet2/part_seg/command.sh
new file mode 100644
index 0000000..a470274
--- /dev/null
+++ b/pointnet2/part_seg/command.sh
@@ -0,0 +1 @@
+python train.py --model pointnet2_part_seg --log_dir log --gpu 1 --max_epoch 201 > log.txt 2>&1 &
diff --git a/pointnet2/part_seg/command_one_hot.sh b/pointnet2/part_seg/command_one_hot.sh
new file mode 100644
index 0000000..09c3630
--- /dev/null
+++ b/pointnet2/part_seg/command_one_hot.sh
@@ -0,0 +1 @@
+python train_one_hot.py --batch_size 8 --model pointnet2_part_seg_msg_one_hot --log_dir log_msg_one_hot --gpu 0 --max_epoch 201 > log_msg_one_hot.txt 2>&1 &
diff --git a/pointnet2/part_seg/evaluate.py b/pointnet2/part_seg/evaluate.py
new file mode 100644
index 0000000..9aafe73
--- /dev/null
+++ b/pointnet2/part_seg/evaluate.py
@@ -0,0 +1,196 @@
+import argparse
+import math
+from datetime import datetime
+import h5py
+import numpy as np
+import tensorflow as tf
+import socket
+import importlib
+import os
+import sys
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+ROOT_DIR = os.path.dirname(BASE_DIR)
+sys.path.append(BASE_DIR)
+sys.path.append(os.path.join(ROOT_DIR, 'models'))
+sys.path.append(os.path.join(ROOT_DIR, 'utils'))
+import tf_util
+import part_dataset_all_normal
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--gpu', type=int, default=0, help='GPU to use [default: GPU 0]')
+parser.add_argument('--model', default='pointnet2_part_seg', help='Model name [default: pointnet2_part_seg]')
+parser.add_argument('--model_path', default='log/model.ckpt', help='model checkpoint file path [default: log/model.ckpt]')
+parser.add_argument('--log_dir', default='log_eval', help='Log dir [default: log_eval]')
+parser.add_argument('--num_point', type=int, default=2048, help='Point Number [default: 2048]')
+parser.add_argument('--batch_size', type=int, default=32, help='Batch Size during training [default: 32]')
+FLAGS = parser.parse_args()
+
+
+VOTE_NUM = 12
+
+
+EPOCH_CNT = 0
+
+BATCH_SIZE = FLAGS.batch_size
+NUM_POINT = FLAGS.num_point
+GPU_INDEX = FLAGS.gpu
+
+MODEL_PATH = FLAGS.model_path
+MODEL = importlib.import_module(FLAGS.model) # import network module
+MODEL_FILE = os.path.join(ROOT_DIR, 'models', FLAGS.model+'.py')
+LOG_DIR = FLAGS.log_dir
+if not os.path.exists(LOG_DIR): os.mkdir(LOG_DIR)
+os.system('cp %s %s' % (MODEL_FILE, LOG_DIR)) # bkp of model def
+os.system('cp train.py %s' % (LOG_DIR)) # bkp of train procedure
+LOG_FOUT = open(os.path.join(LOG_DIR, 'log_train.txt'), 'w')
+LOG_FOUT.write(str(FLAGS)+'\n')
+NUM_CLASSES = 50
+
+# Shapenet official train/test split
+DATA_PATH = os.path.join(ROOT_DIR, 'data', 'shapenetcore_partanno_segmentation_benchmark_v0_normal')
+TEST_DATASET = part_dataset_all_normal.PartNormalDataset(root=DATA_PATH, npoints=NUM_POINT, classification=False, split='test')
+
+def log_string(out_str):
+ LOG_FOUT.write(out_str+'\n')
+ LOG_FOUT.flush()
+ print(out_str)
+
+def evaluate():
+ with tf.Graph().as_default():
+ with tf.device('/gpu:'+str(GPU_INDEX)):
+ pointclouds_pl, labels_pl = MODEL.placeholder_inputs(BATCH_SIZE, NUM_POINT)
+ is_training_pl = tf.placeholder(tf.bool, shape=())
+ print is_training_pl
+
+ print "--- Get model and loss"
+ pred, end_points = MODEL.get_model(pointclouds_pl, is_training_pl)
+ loss = MODEL.get_loss(pred, labels_pl)
+ saver = tf.train.Saver()
+
+ # Create a session
+ config = tf.ConfigProto()
+ config.gpu_options.allow_growth = True
+ config.allow_soft_placement = True
+ sess = tf.Session(config=config)
+ # Restore variables from disk.
+ saver.restore(sess, MODEL_PATH)
+ ops = {'pointclouds_pl': pointclouds_pl,
+ 'labels_pl': labels_pl,
+ 'is_training_pl': is_training_pl,
+ 'pred': pred,
+ 'loss': loss}
+
+ eval_one_epoch(sess, ops)
+
+def get_batch(dataset, idxs, start_idx, end_idx):
+ bsize = end_idx-start_idx
+ batch_data = np.zeros((bsize, NUM_POINT, 6))
+ batch_label = np.zeros((bsize, NUM_POINT), dtype=np.int32)
+ for i in range(bsize):
+ ps,normal,seg = dataset[idxs[i+start_idx]]
+ batch_data[i,:,0:3] = ps
+ batch_data[i,:,3:6] = normal
+ batch_label[i,:] = seg
+ return batch_data, batch_label
+
+def eval_one_epoch(sess, ops):
+ """ ops: dict mapping from string to tf ops """
+ is_training = False
+ test_idxs = np.arange(0, len(TEST_DATASET))
+ # Test on all data: last batch might be smaller than BATCH_SIZE
+ num_batches = (len(TEST_DATASET)+BATCH_SIZE-1)/BATCH_SIZE
+
+ total_correct = 0
+ total_seen = 0
+ loss_sum = 0
+ total_seen_class = [0 for _ in range(NUM_CLASSES)]
+ total_correct_class = [0 for _ in range(NUM_CLASSES)]
+
+ seg_classes = TEST_DATASET.seg_classes
+ shape_ious = {cat:[] for cat in seg_classes.keys()}
+ seg_label_to_cat = {} # {0:Airplane, 1:Airplane, ...49:Table}
+ for cat in seg_classes.keys():
+ for label in seg_classes[cat]:
+ seg_label_to_cat[label] = cat
+
+ log_string(str(datetime.now()))
+ log_string('---- EPOCH %03d EVALUATION ----'%(EPOCH_CNT))
+
+ batch_data = np.zeros((BATCH_SIZE, NUM_POINT, 6))
+ batch_label = np.zeros((BATCH_SIZE, NUM_POINT)).astype(np.int32)
+ for batch_idx in range(num_batches):
+ if batch_idx %20==0:
+ log_string('%03d/%03d'%(batch_idx, num_batches))
+ start_idx = batch_idx * BATCH_SIZE
+ end_idx = min(len(TEST_DATASET), (batch_idx+1) * BATCH_SIZE)
+ cur_batch_size = end_idx-start_idx
+ cur_batch_data, cur_batch_label = get_batch(TEST_DATASET, test_idxs, start_idx, end_idx)
+ if cur_batch_size == BATCH_SIZE:
+ batch_data = cur_batch_data
+ batch_label = cur_batch_label
+ else:
+ batch_data[0:cur_batch_size] = cur_batch_data
+ batch_label[0:cur_batch_size] = cur_batch_label
+
+ # ---------------------------------------------------------------------
+ loss_val = 0
+ pred_val = np.zeros((BATCH_SIZE, NUM_POINT, NUM_CLASSES))
+ for _ in range(VOTE_NUM):
+ feed_dict = {ops['pointclouds_pl']: batch_data,
+ ops['labels_pl']: batch_label,
+ ops['is_training_pl']: is_training}
+ temp_loss_val, temp_pred_val = sess.run([ops['loss'], ops['pred']], feed_dict=feed_dict)
+ loss_val += temp_loss_val
+ pred_val += temp_pred_val
+ loss_val /= float(VOTE_NUM)
+ # ---------------------------------------------------------------------
+
+ # Select valid data
+ cur_pred_val = pred_val[0:cur_batch_size]
+ # Constrain pred to the groundtruth classes (selected by seg_classes[cat])
+ cur_pred_val_logits = cur_pred_val
+ cur_pred_val = np.zeros((cur_batch_size, NUM_POINT)).astype(np.int32)
+ for i in range(cur_batch_size):
+ cat = seg_label_to_cat[cur_batch_label[i,0]]
+ logits = cur_pred_val_logits[i,:,:]
+ cur_pred_val[i,:] = np.argmax(logits[:,seg_classes[cat]], 1) + seg_classes[cat][0]
+ correct = np.sum(cur_pred_val == cur_batch_label)
+ total_correct += correct
+ total_seen += (cur_batch_size*NUM_POINT)
+ if cur_batch_size==BATCH_SIZE:
+ loss_sum += loss_val
+ for l in range(NUM_CLASSES):
+ total_seen_class[l] += np.sum(cur_batch_label==l)
+ total_correct_class[l] += (np.sum((cur_pred_val==l) & (cur_batch_label==l)))
+
+ for i in range(cur_batch_size):
+ segp = cur_pred_val[i,:]
+ segl = cur_batch_label[i,:]
+ cat = seg_label_to_cat[segl[0]]
+ part_ious = [0.0 for _ in range(len(seg_classes[cat]))]
+ for l in seg_classes[cat]:
+ if (np.sum(segl==l) == 0) and (np.sum(segp==l) == 0): # part is not present, no prediction as well
+ part_ious[l-seg_classes[cat][0]] = 1.0
+ else:
+ part_ious[l-seg_classes[cat][0]] = np.sum((segl==l) & (segp==l)) / float(np.sum((segl==l) | (segp==l)))
+ shape_ious[cat].append(np.mean(part_ious))
+
+ all_shape_ious = []
+ for cat in shape_ious.keys():
+ for iou in shape_ious[cat]:
+ all_shape_ious.append(iou)
+ shape_ious[cat] = np.mean(shape_ious[cat])
+ print len(all_shape_ious)
+ mean_shape_ious = np.mean(shape_ious.values())
+ log_string('eval mean loss: %f' % (loss_sum / float(len(TEST_DATASET)/BATCH_SIZE)))
+ log_string('eval accuracy: %f'% (total_correct / float(total_seen)))
+ log_string('eval avg class acc: %f' % (np.mean(np.array(total_correct_class)/np.array(total_seen_class,dtype=np.float))))
+ for cat in sorted(shape_ious.keys()):
+ log_string('eval mIoU of %s:\t %f'%(cat, shape_ious[cat]))
+ log_string('eval mean mIoU: %f' % (mean_shape_ious))
+ log_string('eval mean mIoU (all shapes): %f' % (np.mean(all_shape_ious)))
+
+if __name__ == "__main__":
+ log_string('pid: %s'%(str(os.getpid())))
+ evaluate()
+ LOG_FOUT.close()
diff --git a/pointnet2/part_seg/part_dataset.py b/pointnet2/part_seg/part_dataset.py
new file mode 100644
index 0000000..707fb5d
--- /dev/null
+++ b/pointnet2/part_seg/part_dataset.py
@@ -0,0 +1,131 @@
+'''
+ Dataset for shapenet part segmentaion.
+'''
+
+import os
+import os.path
+import json
+import numpy as np
+import sys
+
+def pc_normalize(pc):
+ l = pc.shape[0]
+ centroid = np.mean(pc, axis=0)
+ pc = pc - centroid
+ m = np.max(np.sqrt(np.sum(pc**2, axis=1)))
+ pc = pc / m
+ return pc
+
+class PartDataset():
+ def __init__(self, root, npoints = 2500, classification = False, class_choice = None, split='train', normalize=True):
+ self.npoints = npoints
+ self.root = root
+ self.catfile = os.path.join(self.root, 'synsetoffset2category.txt')
+ self.cat = {}
+
+ self.classification = classification
+ self.normalize = normalize
+
+ with open(self.catfile, 'r') as f:
+ for line in f:
+ ls = line.strip().split()
+ self.cat[ls[0]] = ls[1]
+ #print(self.cat)
+ if not class_choice is None:
+ self.cat = {k:v for k,v in self.cat.items() if k in class_choice}
+
+ self.meta = {}
+ with open(os.path.join(self.root, 'train_test_split', 'shuffled_train_file_list.json'), 'r') as f:
+ train_ids = set([str(d.split('/')[2]) for d in json.load(f)])
+ with open(os.path.join(self.root, 'train_test_split', 'shuffled_val_file_list.json'), 'r') as f:
+ val_ids = set([str(d.split('/')[2]) for d in json.load(f)])
+ with open(os.path.join(self.root, 'train_test_split', 'shuffled_test_file_list.json'), 'r') as f:
+ test_ids = set([str(d.split('/')[2]) for d in json.load(f)])
+ for item in self.cat:
+ #print('category', item)
+ self.meta[item] = []
+ dir_point = os.path.join(self.root, self.cat[item], 'points')
+ dir_seg = os.path.join(self.root, self.cat[item], 'points_label')
+ #print(dir_point, dir_seg)
+ fns = sorted(os.listdir(dir_point))
+ #print(fns[0][0:-4])
+ if split=='trainval':
+ fns = [fn for fn in fns if ((fn[0:-4] in train_ids) or (fn[0:-4] in val_ids))]
+ elif split=='train':
+ fns = [fn for fn in fns if fn[0:-4] in train_ids]
+ elif split=='val':
+ fns = [fn for fn in fns if fn[0:-4] in val_ids]
+ elif split=='test':
+ fns = [fn for fn in fns if fn[0:-4] in test_ids]
+ else:
+ print('Unknown split: %s. Exiting..'%(split))
+ exit(-1)
+
+ #print(os.path.basename(fns))
+ for fn in fns:
+ token = (os.path.splitext(os.path.basename(fn))[0])
+ self.meta[item].append((os.path.join(dir_point, token + '.pts'), os.path.join(dir_seg, token + '.seg')))
+
+ self.datapath = []
+ for item in self.cat:
+ for fn in self.meta[item]:
+ self.datapath.append((item, fn[0], fn[1]))
+
+
+ self.classes = dict(zip(self.cat, range(len(self.cat))))
+ self.num_seg_classes = 0
+ if not self.classification:
+ for i in range(len(self.datapath)/50):
+ l = len(np.unique(np.loadtxt(self.datapath[i][-1]).astype(np.uint8)))
+ if l > self.num_seg_classes:
+ self.num_seg_classes = l
+ #print(self.num_seg_classes)
+
+ self.cache = {} # from index to (point_set, cls, seg) tuple
+ self.cache_size = 10000
+
+ def __getitem__(self, index):
+ if index in self.cache:
+ point_set, seg, cls = self.cache[index]
+ else:
+ fn = self.datapath[index]
+ cls = self.classes[self.datapath[index][0]]
+ cls = np.array([cls]).astype(np.int32)
+ point_set = np.loadtxt(fn[1]).astype(np.float32)
+ if self.normalize:
+ point_set = pc_normalize(point_set)
+ seg = np.loadtxt(fn[2]).astype(np.int64) - 1
+ #print(point_set.shape, seg.shape)
+ if len(self.cache) < self.cache_size:
+ self.cache[index] = (point_set, seg, cls)
+
+
+ choice = np.random.choice(len(seg), self.npoints, replace=True)
+ #resample
+ point_set = point_set[choice, :]
+ seg = seg[choice]
+ if self.classification:
+ return point_set, cls
+ else:
+ return point_set, seg
+
+ def __len__(self):
+ return len(self.datapath)
+
+
+if __name__ == '__main__':
+ d = PartDataset(root = '../data/shapenetcore_partanno_segmentation_benchmark_v0', class_choice = ['Airplane'], split='test')
+ print(len(d))
+ import time
+ tic = time.time()
+ for i in range(100):
+ ps, seg = d[i]
+ print np.max(seg), np.min(seg)
+ print(time.time() - tic)
+ print(ps.shape, type(ps), seg.shape,type(seg))
+
+ d = PartDataset(root = '../data/shapenetcore_partanno_segmentation_benchmark_v0', classification = True)
+ print(len(d))
+ ps, cls = d[0]
+ print(ps.shape, type(ps), cls.shape,type(cls))
+
diff --git a/pointnet2/part_seg/part_dataset_all_normal.py b/pointnet2/part_seg/part_dataset_all_normal.py
new file mode 100644
index 0000000..e77fd22
--- /dev/null
+++ b/pointnet2/part_seg/part_dataset_all_normal.py
@@ -0,0 +1,138 @@
+'''
+ Dataset for ShapeNetPart segmentation
+'''
+
+import os
+import os.path
+import json
+import numpy as np
+import sys
+
+def pc_normalize(pc):
+ l = pc.shape[0]
+ centroid = np.mean(pc, axis=0)
+ pc = pc - centroid
+ m = np.max(np.sqrt(np.sum(pc**2, axis=1)))
+ pc = pc / m
+ return pc
+
+class PartNormalDataset():
+ def __init__(self, root, npoints = 2500, classification = False, split='train', normalize=True, return_cls_label = False):
+ self.npoints = npoints
+ self.root = root
+ self.catfile = os.path.join(self.root, 'synsetoffset2category.txt')
+ self.cat = {}
+
+ self.classification = classification
+ self.normalize = normalize
+ self.return_cls_label = return_cls_label
+
+ with open(self.catfile, 'r') as f:
+ for line in f:
+ ls = line.strip().split()
+ self.cat[ls[0]] = ls[1]
+ self.cat = {k:v for k,v in self.cat.items()}
+ #print(self.cat)
+
+ self.meta = {}
+ with open(os.path.join(self.root, 'train_test_split', 'shuffled_train_file_list.json'), 'r') as f:
+ train_ids = set([str(d.split('/')[2]) for d in json.load(f)])
+ with open(os.path.join(self.root, 'train_test_split', 'shuffled_val_file_list.json'), 'r') as f:
+ val_ids = set([str(d.split('/')[2]) for d in json.load(f)])
+ with open(os.path.join(self.root, 'train_test_split', 'shuffled_test_file_list.json'), 'r') as f:
+ test_ids = set([str(d.split('/')[2]) for d in json.load(f)])
+ for item in self.cat:
+ #print('category', item)
+ self.meta[item] = []
+ dir_point = os.path.join(self.root, self.cat[item])
+ fns = sorted(os.listdir(dir_point))
+ #print(fns[0][0:-4])
+ if split=='trainval':
+ fns = [fn for fn in fns if ((fn[0:-4] in train_ids) or (fn[0:-4] in val_ids))]
+ elif split=='train':
+ fns = [fn for fn in fns if fn[0:-4] in train_ids]
+ elif split=='val':
+ fns = [fn for fn in fns if fn[0:-4] in val_ids]
+ elif split=='test':
+ fns = [fn for fn in fns if fn[0:-4] in test_ids]
+ else:
+ print('Unknown split: %s. Exiting..'%(split))
+ exit(-1)
+
+ #print(os.path.basename(fns))
+ for fn in fns:
+ token = (os.path.splitext(os.path.basename(fn))[0])
+ self.meta[item].append(os.path.join(dir_point, token + '.txt'))
+
+ self.datapath = []
+ for item in self.cat:
+ for fn in self.meta[item]:
+ self.datapath.append((item, fn))
+
+
+ self.classes = dict(zip(self.cat, range(len(self.cat))))
+ # Mapping from category ('Chair') to a list of int [10,11,12,13] as segmentation labels
+ self.seg_classes = {'Earphone': [16, 17, 18], 'Motorbike': [30, 31, 32, 33, 34, 35], 'Rocket': [41, 42, 43], 'Car': [8, 9, 10, 11], 'Laptop': [28, 29], 'Cap': [6, 7], 'Skateboard': [44, 45, 46], 'Mug': [36, 37], 'Guitar': [19, 20, 21], 'Bag': [4, 5], 'Lamp': [24, 25, 26, 27], 'Table': [47, 48, 49], 'Airplane': [0, 1, 2, 3], 'Pistol': [38, 39, 40], 'Chair': [12, 13, 14, 15], 'Knife': [22, 23]}
+
+ for cat in sorted(self.seg_classes.keys()):
+ print(cat, self.seg_classes[cat])
+
+ self.cache = {} # from index to (point_set, cls, seg) tuple
+ self.cache_size = 20000
+
+ def __getitem__(self, index):
+ if index in self.cache:
+ point_set, normal, seg, cls = self.cache[index]
+ else:
+ fn = self.datapath[index]
+ cat = self.datapath[index][0]
+ cls = self.classes[cat]
+ cls = np.array([cls]).astype(np.int32)
+ data = np.loadtxt(fn[1]).astype(np.float32)
+ point_set = data[:,0:3]
+ if self.normalize:
+ point_set = pc_normalize(point_set)
+ normal = data[:,3:6]
+ seg = data[:,-1].astype(np.int32)
+ if len(self.cache) < self.cache_size:
+ self.cache[index] = (point_set, normal, seg, cls)
+
+
+ choice = np.random.choice(len(seg), self.npoints, replace=True)
+ #resample
+ point_set = point_set[choice, :]
+ seg = seg[choice]
+ normal = normal[choice,:]
+ if self.classification:
+ return point_set, normal, cls
+ else:
+ if self.return_cls_label:
+ return point_set, normal, seg, cls
+ else:
+ return point_set, normal, seg
+
+ def __len__(self):
+ return len(self.datapath)
+
+
+if __name__ == '__main__':
+ d = PartNormalDataset(root = '../data/shapenetcore_partanno_segmentation_benchmark_v0_normal', split='trainval', npoints=3000)
+ print(len(d))
+
+ i = 500
+ ps, normal, seg = d[i]
+ print d.datapath[i]
+ print np.max(seg), np.min(seg)
+ print(ps.shape, seg.shape, normal.shape)
+ print ps
+ print normal
+
+ sys.path.append('../utils')
+ import show3d_balls
+ show3d_balls.showpoints(ps, normal+1, ballradius=8)
+
+ d = PartNormalDataset(root = '../data/shapenetcore_partanno_segmentation_benchmark_v0_normal', classification = True)
+ print(len(d))
+ ps, normal, cls = d[0]
+ print(ps.shape, type(ps), cls.shape,type(cls))
+
diff --git a/pointnet2/part_seg/test.py b/pointnet2/part_seg/test.py
new file mode 100644
index 0000000..3c09451
--- /dev/null
+++ b/pointnet2/part_seg/test.py
@@ -0,0 +1,85 @@
+import tensorflow as tf
+import numpy as np
+import argparse
+import socket
+import importlib
+import time
+import os
+import scipy.misc
+import sys
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(BASE_DIR)
+sys.path.append(os.path.join(BASE_DIR, 'models'))
+sys.path.append(os.path.join(BASE_DIR, 'utils'))
+import provider
+import show3d_balls
+sys.path.append(os.path.join(ROOT_DIR, 'data_prep'))
+import part_dataset
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--gpu', type=int, default=0, help='GPU to use [default: GPU 0]')
+parser.add_argument('--num_point', type=int, default=2048, help='Point Number [default: 2048]')
+parser.add_argument('--category', default='Airplane', help='Which single class to train on [default: Airplane]')
+parser.add_argument('--model', default='pointnet2_part_seg', help='Model name [default: pointnet2_part_seg]')
+parser.add_argument('--model_path', default='log/model.ckpt', help='model checkpoint file path [default: log/model.ckpt]')
+FLAGS = parser.parse_args()
+
+
+MODEL_PATH = FLAGS.model_path
+GPU_INDEX = FLAGS.gpu
+NUM_POINT = FLAGS.num_point
+MODEL = importlib.import_module(FLAGS.model) # import network module
+NUM_CLASSES = 4
+DATA_PATH = os.path.join(ROOT_DIR, 'data', 'shapenetcore_partanno_segmentation_benchmark_v0_normal')
+TEST_DATASET = part_dataset.PartDataset(root=DATA_PATH, npoints=NUM_POINT, classification=False, class_choice=FLAGS.category, split='test')
+
+def get_model(batch_size, num_point):
+ with tf.Graph().as_default():
+ with tf.device('/gpu:'+str(GPU_INDEX)):
+ pointclouds_pl, labels_pl = MODEL.placeholder_inputs(batch_size, num_point)
+ is_training_pl = tf.placeholder(tf.bool, shape=())
+ pred, end_points = MODEL.get_model(pointclouds_pl, is_training_pl)
+ loss = MODEL.get_loss(pred, labels_pl, end_points)
+ saver = tf.train.Saver()
+ # Create a session
+ config = tf.ConfigProto()
+ config.gpu_options.allow_growth = True
+ config.allow_soft_placement = True
+ sess = tf.Session(config=config)
+ # Restore variables from disk.
+ saver.restore(sess, MODEL_PATH)
+ ops = {'pointclouds_pl': pointclouds_pl,
+ 'labels_pl': labels_pl,
+ 'is_training_pl': is_training_pl,
+ 'pred': pred,
+ 'loss': loss}
+ return sess, ops
+
+def inference(sess, ops, pc, batch_size):
+ ''' pc: BxNx3 array, return BxN pred '''
+ assert pc.shape[0]%batch_size == 0
+ num_batches = pc.shape[0]/batch_size
+ logits = np.zeros((pc.shape[0], pc.shape[1], NUM_CLASSES))
+ for i in range(num_batches):
+ feed_dict = {ops['pointclouds_pl']: pc[i*batch_size:(i+1)*batch_size,...],
+ ops['is_training_pl']: False}
+ batch_logits = sess.run(ops['pred'], feed_dict=feed_dict)
+ logits[i*batch_size:(i+1)*batch_size,...] = batch_logits
+ return np.argmax(logits, 2)
+
+if __name__=='__main__':
+
+ import matplotlib.pyplot as plt
+ cmap = plt.cm.get_cmap("hsv", 4)
+ cmap = np.array([cmap(i) for i in range(10)])[:,:3]
+
+ for i in range(len(TEST_DATASET)):
+ ps, seg = TEST_DATASET[i]
+ sess, ops = get_model(batch_size=1, num_point=ps.shape[0])
+ segp = inference(sess, ops, np.expand_dims(ps,0), batch_size=1)
+ segp = segp.squeeze()
+
+ gt = cmap[seg, :]
+ pred = cmap[segp, :]
+ show3d_balls.showpoints(ps, gt, pred, ballradius=8)
diff --git a/pointnet2/part_seg/train.py b/pointnet2/part_seg/train.py
new file mode 100644
index 0000000..1803458
--- /dev/null
+++ b/pointnet2/part_seg/train.py
@@ -0,0 +1,323 @@
+import argparse
+import math
+from datetime import datetime
+import h5py
+import numpy as np
+import tensorflow as tf
+import socket
+import importlib
+import os
+import sys
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+ROOT_DIR = os.path.dirname(BASE_DIR)
+sys.path.append(BASE_DIR)
+sys.path.append(os.path.join(ROOT_DIR, 'models'))
+sys.path.append(os.path.join(ROOT_DIR, 'utils'))
+import provider
+import tf_util
+import part_dataset_all_normal
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--gpu', type=int, default=0, help='GPU to use [default: GPU 0]')
+parser.add_argument('--model', default='model', help='Model name [default: model]')
+parser.add_argument('--log_dir', default='log', help='Log dir [default: log]')
+parser.add_argument('--num_point', type=int, default=2048, help='Point Number [default: 2048]')
+parser.add_argument('--max_epoch', type=int, default=201, help='Epoch to run [default: 201]')
+parser.add_argument('--batch_size', type=int, default=32, help='Batch Size during training [default: 32]')
+parser.add_argument('--learning_rate', type=float, default=0.001, help='Initial learning rate [default: 0.001]')
+parser.add_argument('--momentum', type=float, default=0.9, help='Initial learning rate [default: 0.9]')
+parser.add_argument('--optimizer', default='adam', help='adam or momentum [default: adam]')
+parser.add_argument('--decay_step', type=int, default=200000, help='Decay step for lr decay [default: 200000]')
+parser.add_argument('--decay_rate', type=float, default=0.7, help='Decay rate for lr decay [default: 0.7]')
+FLAGS = parser.parse_args()
+
+EPOCH_CNT = 0
+
+BATCH_SIZE = FLAGS.batch_size
+NUM_POINT = FLAGS.num_point
+MAX_EPOCH = FLAGS.max_epoch
+BASE_LEARNING_RATE = FLAGS.learning_rate
+GPU_INDEX = FLAGS.gpu
+MOMENTUM = FLAGS.momentum
+OPTIMIZER = FLAGS.optimizer
+DECAY_STEP = FLAGS.decay_step
+DECAY_RATE = FLAGS.decay_rate
+
+MODEL = importlib.import_module(FLAGS.model) # import network module
+MODEL_FILE = os.path.join(ROOT_DIR, 'models', FLAGS.model+'.py')
+LOG_DIR = FLAGS.log_dir
+if not os.path.exists(LOG_DIR): os.mkdir(LOG_DIR)
+os.system('cp %s %s' % (MODEL_FILE, LOG_DIR)) # bkp of model def
+os.system('cp train.py %s' % (LOG_DIR)) # bkp of train procedure
+LOG_FOUT = open(os.path.join(LOG_DIR, 'log_train.txt'), 'w')
+LOG_FOUT.write(str(FLAGS)+'\n')
+
+BN_INIT_DECAY = 0.5
+BN_DECAY_DECAY_RATE = 0.5
+BN_DECAY_DECAY_STEP = float(DECAY_STEP)
+BN_DECAY_CLIP = 0.99
+
+HOSTNAME = socket.gethostname()
+
+NUM_CLASSES = 50
+
+# Shapenet official train/test split
+DATA_PATH = os.path.join(ROOT_DIR, 'data', 'shapenetcore_partanno_segmentation_benchmark_v0_normal')
+TRAIN_DATASET = part_dataset_all_normal.PartNormalDataset(root=DATA_PATH, npoints=NUM_POINT, classification=False, split='trainval')
+TEST_DATASET = part_dataset_all_normal.PartNormalDataset(root=DATA_PATH, npoints=NUM_POINT, classification=False, split='test')
+
+def log_string(out_str):
+ LOG_FOUT.write(out_str+'\n')
+ LOG_FOUT.flush()
+ print(out_str)
+
+def get_learning_rate(batch):
+ learning_rate = tf.train.exponential_decay(
+ BASE_LEARNING_RATE, # Base learning rate.
+ batch * BATCH_SIZE, # Current index into the dataset.
+ DECAY_STEP, # Decay step.
+ DECAY_RATE, # Decay rate.
+ staircase=True)
+ learning_rate = tf.maximum(learning_rate, 0.00001) # CLIP THE LEARNING RATE!
+ return learning_rate
+
+def get_bn_decay(batch):
+ bn_momentum = tf.train.exponential_decay(
+ BN_INIT_DECAY,
+ batch*BATCH_SIZE,
+ BN_DECAY_DECAY_STEP,
+ BN_DECAY_DECAY_RATE,
+ staircase=True)
+ bn_decay = tf.minimum(BN_DECAY_CLIP, 1 - bn_momentum)
+ return bn_decay
+
+def train():
+ with tf.Graph().as_default():
+ with tf.device('/gpu:'+str(GPU_INDEX)):
+ pointclouds_pl, labels_pl = MODEL.placeholder_inputs(BATCH_SIZE, NUM_POINT)
+ is_training_pl = tf.placeholder(tf.bool, shape=())
+
+ # Note the global_step=batch parameter to minimize.
+ # That tells the optimizer to helpfully increment the 'batch' parameter for you every time it trains.
+ batch = tf.Variable(0)
+ bn_decay = get_bn_decay(batch)
+ tf.summary.scalar('bn_decay', bn_decay)
+
+ print "--- Get model and loss"
+ # Get model and loss
+ pred, end_points = MODEL.get_model(pointclouds_pl, is_training_pl, bn_decay=bn_decay)
+ loss = MODEL.get_loss(pred, labels_pl)
+ tf.summary.scalar('loss', loss)
+
+ correct = tf.equal(tf.argmax(pred, 2), tf.to_int64(labels_pl))
+ accuracy = tf.reduce_sum(tf.cast(correct, tf.float32)) / float(BATCH_SIZE*NUM_POINT)
+ tf.summary.scalar('accuracy', accuracy)
+
+ print "--- Get training operator"
+ # Get training operator
+ learning_rate = get_learning_rate(batch)
+ tf.summary.scalar('learning_rate', learning_rate)
+ if OPTIMIZER == 'momentum':
+ optimizer = tf.train.MomentumOptimizer(learning_rate, momentum=MOMENTUM)
+ elif OPTIMIZER == 'adam':
+ optimizer = tf.train.AdamOptimizer(learning_rate)
+ train_op = optimizer.minimize(loss, global_step=batch)
+
+ # Add ops to save and restore all the variables.
+ saver = tf.train.Saver()
+
+ # Create a session
+ config = tf.ConfigProto()
+ config.gpu_options.allow_growth = True
+ config.allow_soft_placement = True
+ config.log_device_placement = False
+ sess = tf.Session(config=config)
+
+ # Add summary writers
+ merged = tf.summary.merge_all()
+ train_writer = tf.summary.FileWriter(os.path.join(LOG_DIR, 'train'), sess.graph)
+ test_writer = tf.summary.FileWriter(os.path.join(LOG_DIR, 'test'), sess.graph)
+
+ # Init variables
+ init = tf.global_variables_initializer()
+ sess.run(init)
+
+ ops = {'pointclouds_pl': pointclouds_pl,
+ 'labels_pl': labels_pl,
+ 'is_training_pl': is_training_pl,
+ 'pred': pred,
+ 'loss': loss,
+ 'train_op': train_op,
+ 'merged': merged,
+ 'step': batch,
+ 'end_points': end_points}
+
+ best_acc = -1
+ for epoch in range(MAX_EPOCH):
+ log_string('**** EPOCH %03d ****' % (epoch))
+ sys.stdout.flush()
+
+ train_one_epoch(sess, ops, train_writer)
+ eval_one_epoch(sess, ops, test_writer)
+
+ # Save the variables to disk.
+ if epoch % 10 == 0:
+ save_path = saver.save(sess, os.path.join(LOG_DIR, "model.ckpt"))
+ log_string("Model saved in file: %s" % save_path)
+
+def get_batch(dataset, idxs, start_idx, end_idx):
+ bsize = end_idx-start_idx
+ batch_data = np.zeros((bsize, NUM_POINT, 6))
+ batch_label = np.zeros((bsize, NUM_POINT), dtype=np.int32)
+ for i in range(bsize):
+ ps,normal,seg = dataset[idxs[i+start_idx]]
+ batch_data[i,:,0:3] = ps
+ batch_data[i,:,3:6] = normal
+ batch_label[i,:] = seg
+ return batch_data, batch_label
+
+def train_one_epoch(sess, ops, train_writer):
+ """ ops: dict mapping from string to tf ops """
+ is_training = True
+
+ # Shuffle train samples
+ train_idxs = np.arange(0, len(TRAIN_DATASET))
+ np.random.shuffle(train_idxs)
+ num_batches = len(TRAIN_DATASET)/BATCH_SIZE
+
+ log_string(str(datetime.now()))
+
+ total_correct = 0
+ total_seen = 0
+ loss_sum = 0
+ for batch_idx in range(num_batches):
+ start_idx = batch_idx * BATCH_SIZE
+ end_idx = (batch_idx+1) * BATCH_SIZE
+ batch_data, batch_label = get_batch(TRAIN_DATASET, train_idxs, start_idx, end_idx)
+ # Augment batched point clouds by rotation and jittering
+ #aug_data = batch_data
+ #aug_data = provider.random_scale_point_cloud(batch_data)
+ batch_data[:,:,0:3] = provider.jitter_point_cloud(batch_data[:,:,0:3])
+ feed_dict = {ops['pointclouds_pl']: batch_data,
+ ops['labels_pl']: batch_label,
+ ops['is_training_pl']: is_training,}
+ summary, step, _, loss_val, pred_val = sess.run([ops['merged'], ops['step'],
+ ops['train_op'], ops['loss'], ops['pred']], feed_dict=feed_dict)
+ train_writer.add_summary(summary, step)
+ pred_val = np.argmax(pred_val, 2)
+ correct = np.sum(pred_val == batch_label)
+ total_correct += correct
+ total_seen += (BATCH_SIZE*NUM_POINT)
+ loss_sum += loss_val
+
+ if (batch_idx+1)%10 == 0:
+ log_string(' -- %03d / %03d --' % (batch_idx+1, num_batches))
+ log_string('mean loss: %f' % (loss_sum / 10))
+ log_string('accuracy: %f' % (total_correct / float(total_seen)))
+ total_correct = 0
+ total_seen = 0
+ loss_sum = 0
+
+
+
+def eval_one_epoch(sess, ops, test_writer):
+ """ ops: dict mapping from string to tf ops """
+ global EPOCH_CNT
+ is_training = False
+ test_idxs = np.arange(0, len(TEST_DATASET))
+ # Test on all data: last batch might be smaller than BATCH_SIZE
+ num_batches = (len(TEST_DATASET)+BATCH_SIZE-1)/BATCH_SIZE
+
+ total_correct = 0
+ total_seen = 0
+ loss_sum = 0
+ total_seen_class = [0 for _ in range(NUM_CLASSES)]
+ total_correct_class = [0 for _ in range(NUM_CLASSES)]
+
+ seg_classes = TEST_DATASET.seg_classes
+ shape_ious = {cat:[] for cat in seg_classes.keys()}
+ seg_label_to_cat = {} # {0:Airplane, 1:Airplane, ...49:Table}
+ for cat in seg_classes.keys():
+ for label in seg_classes[cat]:
+ seg_label_to_cat[label] = cat
+
+ log_string(str(datetime.now()))
+ log_string('---- EPOCH %03d EVALUATION ----'%(EPOCH_CNT))
+
+ batch_data = np.zeros((BATCH_SIZE, NUM_POINT, 3))
+ batch_label = np.zeros((BATCH_SIZE, NUM_POINT)).astype(np.int32)
+ for batch_idx in range(num_batches):
+ if batch_idx %20==0:
+ log_string('%03d/%03d'%(batch_idx, num_batches))
+ start_idx = batch_idx * BATCH_SIZE
+ end_idx = min(len(TEST_DATASET), (batch_idx+1) * BATCH_SIZE)
+ cur_batch_size = end_idx-start_idx
+ cur_batch_data, cur_batch_label = get_batch(TEST_DATASET, test_idxs, start_idx, end_idx)
+ if cur_batch_size == BATCH_SIZE:
+ batch_data = cur_batch_data
+ batch_label = cur_batch_label
+ else:
+ batch_data[0:cur_batch_size] = cur_batch_data
+ batch_label[0:cur_batch_size] = cur_batch_label
+
+ # ---------------------------------------------------------------------
+ feed_dict = {ops['pointclouds_pl']: batch_data,
+ ops['labels_pl']: batch_label,
+ ops['is_training_pl']: is_training}
+ summary, step, loss_val, pred_val = sess.run([ops['merged'], ops['step'],
+ ops['loss'], ops['pred']], feed_dict=feed_dict)
+ test_writer.add_summary(summary, step)
+ # ---------------------------------------------------------------------
+
+ # Select valid data
+ cur_pred_val = pred_val[0:cur_batch_size]
+ # Constrain pred to the groundtruth classes (selected by seg_classes[cat])
+ cur_pred_val_logits = cur_pred_val
+ cur_pred_val = np.zeros((cur_batch_size, NUM_POINT)).astype(np.int32)
+ for i in range(cur_batch_size):
+ cat = seg_label_to_cat[cur_batch_label[i,0]]
+ logits = cur_pred_val_logits[i,:,:]
+ cur_pred_val[i,:] = np.argmax(logits[:,seg_classes[cat]], 1) + seg_classes[cat][0]
+ correct = np.sum(cur_pred_val == cur_batch_label)
+ total_correct += correct
+ total_seen += (cur_batch_size*NUM_POINT)
+ if cur_batch_size==BATCH_SIZE:
+ loss_sum += loss_val
+ for l in range(NUM_CLASSES):
+ total_seen_class[l] += np.sum(cur_batch_label==l)
+ total_correct_class[l] += (np.sum((cur_pred_val==l) & (cur_batch_label==l)))
+
+ for i in range(cur_batch_size):
+ segp = cur_pred_val[i,:]
+ segl = cur_batch_label[i,:]
+ cat = seg_label_to_cat[segl[0]]
+ part_ious = [0.0 for _ in range(len(seg_classes[cat]))]
+ for l in seg_classes[cat]:
+ if (np.sum(segl==l) == 0) and (np.sum(segp==l) == 0): # part is not present, no prediction as well
+ part_ious[l-seg_classes[cat][0]] = 1.0
+ else:
+ part_ious[l-seg_classes[cat][0]] = np.sum((segl==l) & (segp==l)) / float(np.sum((segl==l) | (segp==l)))
+ shape_ious[cat].append(np.mean(part_ious))
+
+ all_shape_ious = []
+ for cat in shape_ious.keys():
+ for iou in shape_ious[cat]:
+ all_shape_ious.append(iou)
+ shape_ious[cat] = np.mean(shape_ious[cat])
+ mean_shape_ious = np.mean(shape_ious.values())
+ log_string('eval mean loss: %f' % (loss_sum / float(len(TEST_DATASET)/BATCH_SIZE)))
+ log_string('eval accuracy: %f'% (total_correct / float(total_seen)))
+ log_string('eval avg class acc: %f' % (np.mean(np.array(total_correct_class)/np.array(total_seen_class,dtype=np.float))))
+ for cat in sorted(shape_ious.keys()):
+ log_string('eval mIoU of %s:\t %f'%(cat, shape_ious[cat]))
+ log_string('eval mean mIoU: %f' % (mean_shape_ious))
+ log_string('eval mean mIoU (all shapes): %f' % (np.mean(all_shape_ious)))
+
+ EPOCH_CNT += 1
+ return total_correct/float(total_seen)
+
+
+if __name__ == "__main__":
+ log_string('pid: %s'%(str(os.getpid())))
+ train()
+ LOG_FOUT.close()
diff --git a/pointnet2/part_seg/train_one_hot.py b/pointnet2/part_seg/train_one_hot.py
new file mode 100644
index 0000000..b2bb5aa
--- /dev/null
+++ b/pointnet2/part_seg/train_one_hot.py
@@ -0,0 +1,333 @@
+import argparse
+import math
+from datetime import datetime
+import h5py
+import numpy as np
+import tensorflow as tf
+import socket
+import importlib
+import os
+import sys
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+ROOT_DIR = os.path.dirname(BASE_DIR)
+sys.path.append(BASE_DIR)
+sys.path.append(os.path.join(ROOT_DIR, 'models'))
+sys.path.append(os.path.join(ROOT_DIR, 'utils'))
+import provider
+import tf_util
+import part_dataset_all_normal
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--gpu', type=int, default=0, help='GPU to use [default: GPU 0]')
+parser.add_argument('--model', default='model', help='Model name [default: model]')
+parser.add_argument('--log_dir', default='log', help='Log dir [default: log]')
+parser.add_argument('--num_point', type=int, default=2048, help='Point Number [default: 2048]')
+parser.add_argument('--max_epoch', type=int, default=201, help='Epoch to run [default: 201]')
+parser.add_argument('--batch_size', type=int, default=32, help='Batch Size during training [default: 32]')
+parser.add_argument('--learning_rate', type=float, default=0.001, help='Initial learning rate [default: 0.001]')
+parser.add_argument('--momentum', type=float, default=0.9, help='Initial learning rate [default: 0.9]')
+parser.add_argument('--optimizer', default='adam', help='adam or momentum [default: adam]')
+parser.add_argument('--decay_step', type=int, default=16881*20, help='Decay step for lr decay [default: 200000]')
+parser.add_argument('--decay_rate', type=float, default=0.5, help='Decay rate for lr decay [default: 0.7]')
+FLAGS = parser.parse_args()
+
+EPOCH_CNT = 0
+
+BATCH_SIZE = FLAGS.batch_size
+NUM_POINT = FLAGS.num_point
+MAX_EPOCH = FLAGS.max_epoch
+BASE_LEARNING_RATE = FLAGS.learning_rate
+GPU_INDEX = FLAGS.gpu
+MOMENTUM = FLAGS.momentum
+OPTIMIZER = FLAGS.optimizer
+DECAY_STEP = FLAGS.decay_step
+DECAY_RATE = FLAGS.decay_rate
+
+MODEL = importlib.import_module(FLAGS.model) # import network module
+MODEL_FILE = os.path.join(ROOT_DIR, 'models', FLAGS.model+'.py')
+LOG_DIR = FLAGS.log_dir
+if not os.path.exists(LOG_DIR): os.mkdir(LOG_DIR)
+os.system('cp %s %s' % (MODEL_FILE, LOG_DIR)) # bkp of model def
+os.system('cp train.py %s' % (LOG_DIR)) # bkp of train procedure
+LOG_FOUT = open(os.path.join(LOG_DIR, 'log_train.txt'), 'w')
+LOG_FOUT.write(str(FLAGS)+'\n')
+
+BN_INIT_DECAY = 0.5
+BN_DECAY_DECAY_RATE = 0.5
+BN_DECAY_DECAY_STEP = float(DECAY_STEP)
+BN_DECAY_CLIP = 0.99
+
+HOSTNAME = socket.gethostname()
+
+NUM_CLASSES = 50
+
+# Shapenet official train/test split
+DATA_PATH = os.path.join(ROOT_DIR, 'data', 'shapenetcore_partanno_segmentation_benchmark_v0_normal')
+TRAIN_DATASET = part_dataset_all_normal.PartNormalDataset(root=DATA_PATH, npoints=NUM_POINT, classification=False, split='trainval', return_cls_label=True)
+TEST_DATASET = part_dataset_all_normal.PartNormalDataset(root=DATA_PATH, npoints=NUM_POINT, classification=False, split='test', return_cls_label=True)
+
+def log_string(out_str):
+ LOG_FOUT.write(out_str+'\n')
+ LOG_FOUT.flush()
+ print(out_str)
+
+def get_learning_rate(batch):
+ learning_rate = tf.train.exponential_decay(
+ BASE_LEARNING_RATE, # Base learning rate.
+ batch * BATCH_SIZE, # Current index into the dataset.
+ DECAY_STEP, # Decay step.
+ DECAY_RATE, # Decay rate.
+ staircase=True)
+ learning_rate = tf.maximum(learning_rate, 0.00001) # CLIP THE LEARNING RATE!
+ return learning_rate
+
+def get_bn_decay(batch):
+ bn_momentum = tf.train.exponential_decay(
+ BN_INIT_DECAY,
+ batch*BATCH_SIZE,
+ BN_DECAY_DECAY_STEP,
+ BN_DECAY_DECAY_RATE,
+ staircase=True)
+ bn_decay = tf.minimum(BN_DECAY_CLIP, 1 - bn_momentum)
+ return bn_decay
+
+def train():
+ with tf.Graph().as_default():
+ with tf.device('/gpu:'+str(GPU_INDEX)):
+ pointclouds_pl, labels_pl, cls_labels_pl = MODEL.placeholder_inputs(BATCH_SIZE, NUM_POINT)
+ is_training_pl = tf.placeholder(tf.bool, shape=())
+ print is_training_pl
+
+ # Note the global_step=batch parameter to minimize.
+ # That tells the optimizer to helpfully increment the 'batch' parameter for you every time it trains.
+ batch = tf.Variable(0)
+ bn_decay = get_bn_decay(batch)
+ tf.summary.scalar('bn_decay', bn_decay)
+
+ print "--- Get model and loss"
+ # Get model and loss
+ pred, end_points = MODEL.get_model(pointclouds_pl, cls_labels_pl, is_training_pl, bn_decay=bn_decay)
+ loss = MODEL.get_loss(pred, labels_pl)
+ tf.summary.scalar('loss', loss)
+
+ correct = tf.equal(tf.argmax(pred, 2), tf.to_int64(labels_pl))
+ accuracy = tf.reduce_sum(tf.cast(correct, tf.float32)) / float(BATCH_SIZE*NUM_POINT)
+ tf.summary.scalar('accuracy', accuracy)
+
+ print "--- Get training operator"
+ # Get training operator
+ learning_rate = get_learning_rate(batch)
+ tf.summary.scalar('learning_rate', learning_rate)
+ if OPTIMIZER == 'momentum':
+ optimizer = tf.train.MomentumOptimizer(learning_rate, momentum=MOMENTUM)
+ elif OPTIMIZER == 'adam':
+ optimizer = tf.train.AdamOptimizer(learning_rate)
+ train_op = optimizer.minimize(loss, global_step=batch)
+
+ # Add ops to save and restore all the variables.
+ saver = tf.train.Saver()
+
+ # Create a session
+ config = tf.ConfigProto()
+ config.gpu_options.allow_growth = True
+ config.allow_soft_placement = True
+ config.log_device_placement = False
+ sess = tf.Session(config=config)
+
+ # Add summary writers
+ merged = tf.summary.merge_all()
+ train_writer = tf.summary.FileWriter(os.path.join(LOG_DIR, 'train'), sess.graph)
+ test_writer = tf.summary.FileWriter(os.path.join(LOG_DIR, 'test'), sess.graph)
+
+ # Init variables
+ init = tf.global_variables_initializer()
+ sess.run(init)
+ #sess.run(init, {is_training_pl: True})
+
+ ops = {'pointclouds_pl': pointclouds_pl,
+ 'labels_pl': labels_pl,
+ 'cls_labels_pl': cls_labels_pl,
+ 'is_training_pl': is_training_pl,
+ 'pred': pred,
+ 'loss': loss,
+ 'train_op': train_op,
+ 'merged': merged,
+ 'step': batch,
+ 'end_points': end_points}
+
+ best_acc = -1
+ for epoch in range(MAX_EPOCH):
+ log_string('**** EPOCH %03d ****' % (epoch))
+ sys.stdout.flush()
+
+ train_one_epoch(sess, ops, train_writer)
+ eval_one_epoch(sess, ops, test_writer)
+
+ # Save the variables to disk.
+ if epoch % 10 == 0:
+ save_path = saver.save(sess, os.path.join(LOG_DIR, "model.ckpt"))
+ log_string("Model saved in file: %s" % save_path)
+
+def get_batch(dataset, idxs, start_idx, end_idx):
+ bsize = end_idx-start_idx
+ batch_data = np.zeros((bsize, NUM_POINT, 6))
+ batch_label = np.zeros((bsize, NUM_POINT), dtype=np.int32)
+ batch_cls_label = np.zeros((bsize,), dtype=np.int32)
+ for i in range(bsize):
+ ps,normal,seg,cls = dataset[idxs[i+start_idx]]
+ batch_data[i,:,0:3] = ps
+ batch_data[i,:,3:6] = normal
+ batch_label[i,:] = seg
+ batch_cls_label[i] = cls
+ return batch_data, batch_label, batch_cls_label
+
+def train_one_epoch(sess, ops, train_writer):
+ """ ops: dict mapping from string to tf ops """
+ is_training = True
+
+ # Shuffle train samples
+ train_idxs = np.arange(0, len(TRAIN_DATASET))
+ np.random.shuffle(train_idxs)
+ num_batches = len(TRAIN_DATASET)/BATCH_SIZE
+
+ log_string(str(datetime.now()))
+
+ total_correct = 0
+ total_seen = 0
+ loss_sum = 0
+ for batch_idx in range(num_batches):
+ start_idx = batch_idx * BATCH_SIZE
+ end_idx = (batch_idx+1) * BATCH_SIZE
+ batch_data, batch_label, batch_cls_label = get_batch(TRAIN_DATASET, train_idxs, start_idx, end_idx)
+ # Augment batched point clouds by rotation and jittering
+ #aug_data = batch_data
+ #aug_data = provider.random_scale_point_cloud(batch_data)
+ batch_data[:,:,0:3] = provider.jitter_point_cloud(batch_data[:,:,0:3])
+ feed_dict = {ops['pointclouds_pl']: batch_data,
+ ops['labels_pl']: batch_label,
+ ops['cls_labels_pl']: batch_cls_label,
+ ops['is_training_pl']: is_training,}
+ summary, step, _, loss_val, pred_val = sess.run([ops['merged'], ops['step'],
+ ops['train_op'], ops['loss'], ops['pred']], feed_dict=feed_dict)
+ train_writer.add_summary(summary, step)
+ pred_val = np.argmax(pred_val, 2)
+ correct = np.sum(pred_val == batch_label)
+ total_correct += correct
+ total_seen += (BATCH_SIZE*NUM_POINT)
+ loss_sum += loss_val
+
+ if (batch_idx+1)%10 == 0:
+ log_string(' -- %03d / %03d --' % (batch_idx+1, num_batches))
+ log_string('mean loss: %f' % (loss_sum / 10))
+ log_string('accuracy: %f' % (total_correct / float(total_seen)))
+ total_correct = 0
+ total_seen = 0
+ loss_sum = 0
+
+
+
+def eval_one_epoch(sess, ops, test_writer):
+ """ ops: dict mapping from string to tf ops """
+ global EPOCH_CNT
+ is_training = False
+ test_idxs = np.arange(0, len(TEST_DATASET))
+ # Test on all data: last batch might be smaller than BATCH_SIZE
+ num_batches = (len(TEST_DATASET)+BATCH_SIZE-1)/BATCH_SIZE
+
+ total_correct = 0
+ total_seen = 0
+ loss_sum = 0
+ total_seen_class = [0 for _ in range(NUM_CLASSES)]
+ total_correct_class = [0 for _ in range(NUM_CLASSES)]
+
+ seg_classes = TEST_DATASET.seg_classes
+ shape_ious = {cat:[] for cat in seg_classes.keys()}
+ seg_label_to_cat = {} # {0:Airplane, 1:Airplane, ...49:Table}
+ for cat in seg_classes.keys():
+ for label in seg_classes[cat]:
+ seg_label_to_cat[label] = cat
+
+ log_string(str(datetime.now()))
+ log_string('---- EPOCH %03d EVALUATION ----'%(EPOCH_CNT))
+
+ batch_data = np.zeros((BATCH_SIZE, NUM_POINT, 3))
+ batch_label = np.zeros((BATCH_SIZE, NUM_POINT)).astype(np.int32)
+ batch_cls_label = np.zeros((BATCH_SIZE,)).astype(np.int32)
+ for batch_idx in range(num_batches):
+ if batch_idx %20==0:
+ log_string('%03d/%03d'%(batch_idx, num_batches))
+ start_idx = batch_idx * BATCH_SIZE
+ end_idx = min(len(TEST_DATASET), (batch_idx+1) * BATCH_SIZE)
+ cur_batch_size = end_idx-start_idx
+ cur_batch_data, cur_batch_label, cur_batch_cls_label = get_batch(TEST_DATASET, test_idxs, start_idx, end_idx)
+ if cur_batch_size == BATCH_SIZE:
+ batch_data = cur_batch_data
+ batch_label = cur_batch_label
+ batch_cls_label = cur_batch_cls_label
+ else:
+ batch_data[0:cur_batch_size] = cur_batch_data
+ batch_label[0:cur_batch_size] = cur_batch_label
+ batch_cls_label[0:cur_batch_size] = cur_batch_cls_label
+
+ # ---------------------------------------------------------------------
+ feed_dict = {ops['pointclouds_pl']: batch_data,
+ ops['labels_pl']: batch_label,
+ ops['cls_labels_pl']: batch_cls_label,
+ ops['is_training_pl']: is_training}
+ summary, step, loss_val, pred_val = sess.run([ops['merged'], ops['step'],
+ ops['loss'], ops['pred']], feed_dict=feed_dict)
+ test_writer.add_summary(summary, step)
+ # ---------------------------------------------------------------------
+
+ # Select valid data
+ cur_pred_val = pred_val[0:cur_batch_size]
+ # Constrain pred to the groundtruth classes (selected by seg_classes[cat])
+ cur_pred_val_logits = cur_pred_val
+ cur_pred_val = np.zeros((cur_batch_size, NUM_POINT)).astype(np.int32)
+ for i in range(cur_batch_size):
+ cat = seg_label_to_cat[cur_batch_label[i,0]]
+ logits = cur_pred_val_logits[i,:,:]
+ cur_pred_val[i,:] = np.argmax(logits[:,seg_classes[cat]], 1) + seg_classes[cat][0]
+ correct = np.sum(cur_pred_val == cur_batch_label)
+ total_correct += correct
+ total_seen += (cur_batch_size*NUM_POINT)
+ if cur_batch_size==BATCH_SIZE:
+ loss_sum += loss_val
+ for l in range(NUM_CLASSES):
+ total_seen_class[l] += np.sum(cur_batch_label==l)
+ total_correct_class[l] += (np.sum((cur_pred_val==l) & (cur_batch_label==l)))
+
+ for i in range(cur_batch_size):
+ segp = cur_pred_val[i,:]
+ segl = cur_batch_label[i,:]
+ cat = seg_label_to_cat[segl[0]]
+ part_ious = [0.0 for _ in range(len(seg_classes[cat]))]
+ for l in seg_classes[cat]:
+ if (np.sum(segl==l) == 0) and (np.sum(segp==l) == 0): # part is not present, no prediction as well
+ part_ious[l-seg_classes[cat][0]] = 1.0
+ else:
+ part_ious[l-seg_classes[cat][0]] = np.sum((segl==l) & (segp==l)) / float(np.sum((segl==l) | (segp==l)))
+ shape_ious[cat].append(np.mean(part_ious))
+
+ all_shape_ious = []
+ for cat in shape_ious.keys():
+ for iou in shape_ious[cat]:
+ all_shape_ious.append(iou)
+ shape_ious[cat] = np.mean(shape_ious[cat])
+ mean_shape_ious = np.mean(shape_ious.values())
+ log_string('eval mean loss: %f' % (loss_sum / float(len(TEST_DATASET)/BATCH_SIZE)))
+ log_string('eval accuracy: %f'% (total_correct / float(total_seen)))
+ log_string('eval avg class acc: %f' % (np.mean(np.array(total_correct_class)/np.array(total_seen_class,dtype=np.float))))
+ for cat in sorted(shape_ious.keys()):
+ log_string('eval mIoU of %s:\t %f'%(cat, shape_ious[cat]))
+ log_string('eval mean mIoU: %f' % (mean_shape_ious))
+ log_string('eval mean mIoU (all shapes): %f' % (np.mean(all_shape_ious)))
+
+ EPOCH_CNT += 1
+ return total_correct/float(total_seen)
+
+
+if __name__ == "__main__":
+ log_string('pid: %s'%(str(os.getpid())))
+ train()
+ LOG_FOUT.close()
diff --git a/pointnet2/scannet/README.md b/pointnet2/scannet/README.md
new file mode 100644
index 0000000..2edfe7d
--- /dev/null
+++ b/pointnet2/scannet/README.md
@@ -0,0 +1,9 @@
+### ScanNet Data
+
+Original dataset website: http://www.scan-net.org/
+
+You can get our preprocessed data at here (1.72GB) and refer to the code in `scannet_util.py` for data loading. Note that the virtual scan data is generated on the fly from our preprocessed data.
+
+Some code we used for scannet preprocessing is also included in `preprocessing` folder. You have to download the original ScanNet data and make small modifications in paths in order to run them.
+
+Note: To use ScanNetV2 data, change the tsv file to `scannetv2-labels.combined.tsv` and also update `scannet_util.py` to read the raw class and NYU40 names in the right columns (shifted by 1 compared to the V1 tsv).
diff --git a/pointnet2/scannet/pc_util.py b/pointnet2/scannet/pc_util.py
new file mode 100644
index 0000000..4594f40
--- /dev/null
+++ b/pointnet2/scannet/pc_util.py
@@ -0,0 +1,379 @@
+""" Utility functions for processing point clouds.
+
+Author: Charles R. Qi, Hao Su
+Date: November 2016
+"""
+
+import os
+import sys
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(BASE_DIR)
+
+# Draw point cloud
+from eulerangles import euler2mat
+
+# Point cloud IO
+import numpy as np
+from plyfile import PlyData, PlyElement
+
+
+# ----------------------------------------
+# Point Cloud/Volume Conversions
+# ----------------------------------------
+def point_cloud_label_to_surface_voxel_label(point_cloud, label, res=0.0484):
+ coordmax = np.max(point_cloud,axis=0)
+ coordmin = np.min(point_cloud,axis=0)
+ nvox = np.ceil((coordmax-coordmin)/res)
+ vidx = np.ceil((point_cloud-coordmin)/res)
+ vidx = vidx[:,0]+vidx[:,1]*nvox[0]+vidx[:,2]*nvox[0]*nvox[1]
+ uvidx = np.unique(vidx)
+ if label.ndim==1:
+ uvlabel = [np.argmax(np.bincount(label[vidx==uv].astype(np.uint32))) for uv in uvidx]
+ else:
+ assert(label.ndim==2)
+ uvlabel = np.zeros(len(uvidx),label.shape[1])
+ for i in range(label.shape[1]):
+ uvlabel[:,i] = np.array([np.argmax(np.bincount(label[vidx==uv,i].astype(np.uint32))) for uv in uvidx])
+ return uvidx, uvlabel, nvox
+
+def point_cloud_label_to_surface_voxel_label_fast(point_cloud, label, res=0.0484):
+ coordmax = np.max(point_cloud,axis=0)
+ coordmin = np.min(point_cloud,axis=0)
+ nvox = np.ceil((coordmax-coordmin)/res)
+ vidx = np.ceil((point_cloud-coordmin)/res)
+ vidx = vidx[:,0]+vidx[:,1]*nvox[0]+vidx[:,2]*nvox[0]*nvox[1]
+ uvidx, vpidx = np.unique(vidx,return_index=True)
+ if label.ndim==1:
+ uvlabel = label[vpidx]
+ else:
+ assert(label.ndim==2)
+ uvlabel = label[vpidx,:]
+ return uvidx, uvlabel, nvox
+
+def point_cloud_to_volume_batch(point_clouds, vsize=12, radius=1.0, flatten=True):
+ """ Input is BxNx3 batch of point cloud
+ Output is Bx(vsize^3)
+ """
+ vol_list = []
+ for b in range(point_clouds.shape[0]):
+ vol = point_cloud_to_volume(np.squeeze(point_clouds[b,:,:]), vsize, radius)
+ if flatten:
+ vol_list.append(vol.flatten())
+ else:
+ vol_list.append(np.expand_dims(np.expand_dims(vol, -1), 0))
+ if flatten:
+ return np.vstack(vol_list)
+ else:
+ return np.concatenate(vol_list, 0)
+
+
+def point_cloud_to_volume(points, vsize, radius=1.0):
+ """ input is Nx3 points.
+ output is vsize*vsize*vsize
+ assumes points are in range [-radius, radius]
+ """
+ vol = np.zeros((vsize,vsize,vsize))
+ voxel = 2*radius/float(vsize)
+ locations = (points + radius)/voxel
+ locations = locations.astype(int)
+ vol[locations[:,0],locations[:,1],locations[:,2]] = 1.0
+ return vol
+
+#a = np.zeros((16,1024,3))
+#print point_cloud_to_volume_batch(a, 12, 1.0, False).shape
+
+def volume_to_point_cloud(vol):
+ """ vol is occupancy grid (value = 0 or 1) of size vsize*vsize*vsize
+ return Nx3 numpy array.
+ """
+ vsize = vol.shape[0]
+ assert(vol.shape[1] == vsize and vol.shape[1] == vsize)
+ points = []
+ for a in range(vsize):
+ for b in range(vsize):
+ for c in range(vsize):
+ if vol[a,b,c] == 1:
+ points.append(np.array([a,b,c]))
+ if len(points) == 0:
+ return np.zeros((0,3))
+ points = np.vstack(points)
+ return points
+
+def point_cloud_to_volume_v2_batch(point_clouds, vsize=12, radius=1.0, num_sample=128):
+ """ Input is BxNx3 a batch of point cloud
+ Output is BxVxVxVxnum_samplex3
+ Added on Feb 19
+ """
+ vol_list = []
+ for b in range(point_clouds.shape[0]):
+ vol = point_cloud_to_volume_v2(point_clouds[b,:,:], vsize, radius, num_sample)
+ vol_list.append(np.expand_dims(vol, 0))
+ return np.concatenate(vol_list, 0)
+
+def point_cloud_to_volume_v2(points, vsize, radius=1.0, num_sample=128):
+ """ input is Nx3 points
+ output is vsize*vsize*vsize*num_sample*3
+ assumes points are in range [-radius, radius]
+ samples num_sample points in each voxel, if there are less than
+ num_sample points, replicate the points
+ Added on Feb 19
+ """
+ vol = np.zeros((vsize,vsize,vsize,num_sample,3))
+ voxel = 2*radius/float(vsize)
+ locations = (points + radius)/voxel
+ locations = locations.astype(int)
+ loc2pc = {}
+ for n in range(points.shape[0]):
+ loc = tuple(locations[n,:])
+ if loc not in loc2pc:
+ loc2pc[loc] = []
+ loc2pc[loc].append(points[n,:])
+ #print loc2pc
+
+ for i in range(vsize):
+ for j in range(vsize):
+ for k in range(vsize):
+ if (i,j,k) not in loc2pc:
+ vol[i,j,k,:,:] = np.zeros((num_sample,3))
+ else:
+ pc = loc2pc[(i,j,k)] # a list of (3,) arrays
+ pc = np.vstack(pc) # kx3
+ # Sample/pad to num_sample points
+ if pc.shape[0]>num_sample:
+ choices = np.random.choice(pc.shape[0], num_sample, replace=False)
+ pc = pc[choices,:]
+ elif pc.shape[0]num_sample:
+ choices = np.random.choice(pc.shape[0], num_sample, replace=False)
+ pc = pc[choices,:]
+ elif pc.shape[0] 0)
+ dx = mask[:, 0]
+ dy = mask[:, 1]
+ dv = disk[disk > 0]
+
+ # Order points by z-buffer
+ zorder = np.argsort(points[:, 2])
+ points = points[zorder, :]
+ points[:, 2] = (points[:, 2] - np.min(points[:, 2])) / (np.max(points[:, 2] - np.min(points[:, 2])))
+ max_depth = np.max(points[:, 2])
+
+ for i in range(points.shape[0]):
+ j = points.shape[0] - i - 1
+ x = points[j, 0]
+ y = points[j, 1]
+ xc = canvasSize/2 + (x*space)
+ yc = canvasSize/2 + (y*space)
+ xc = int(np.round(xc))
+ yc = int(np.round(yc))
+
+ px = dx + xc
+ py = dy + yc
+
+ image[px, py] = image[px, py] * 0.7 + dv * (max_depth - points[j, 2]) * 0.3
+
+ image = image / np.max(image)
+ return image
+
+def point_cloud_three_views(points):
+ """ input points Nx3 numpy array (+y is up direction).
+ return an numpy array gray image of size 500x1500. """
+ # +y is up direction
+ # xrot is azimuth
+ # yrot is in-plane
+ # zrot is elevation
+ img1 = draw_point_cloud(points, zrot=110/180.0*np.pi, xrot=45/180.0*np.pi, yrot=0/180.0*np.pi)
+ img2 = draw_point_cloud(points, zrot=70/180.0*np.pi, xrot=135/180.0*np.pi, yrot=0/180.0*np.pi)
+ img3 = draw_point_cloud(points, zrot=180.0/180.0*np.pi, xrot=90/180.0*np.pi, yrot=0/180.0*np.pi)
+ image_large = np.concatenate([img1, img2, img3], 1)
+ return image_large
+
+
+def point_cloud_three_views_demo():
+ """ Demo for draw_point_cloud function """
+ from PIL import Image
+ points = read_ply('../third_party/mesh_sampling/piano.ply')
+ im_array = point_cloud_three_views(points)
+ img = Image.fromarray(np.uint8(im_array*255.0))
+ img.save('piano.jpg')
+
+if __name__=="__main__":
+ point_cloud_three_views_demo()
+
+
+def pyplot_draw_point_cloud(points, output_filename):
+ """ points is a Nx3 numpy array """
+ import matplotlib.pyplot as plt
+ fig = plt.figure()
+ ax = fig.add_subplot(111, projection='3d')
+ ax.scatter(points[:,0], points[:,1], points[:,2])
+ ax.set_xlabel('x')
+ ax.set_ylabel('y')
+ ax.set_zlabel('z')
+ #savefig(output_filename)
+
+def pyplot_draw_volume(vol, output_filename):
+ """ vol is of size vsize*vsize*vsize
+ output an image to output_filename
+ """
+ points = volume_to_point_cloud(vol)
+ pyplot_draw_point_cloud(points, output_filename)
+
+def write_ply_color(points, labels, out_filename, num_classes=None):
+ """ Color (N,3) points with labels (N) within range 0 ~ num_classes-1 as OBJ file """
+ import matplotlib.pyplot as pyplot
+ labels = labels.astype(int)
+ N = points.shape[0]
+ if num_classes is None:
+ num_classes = np.max(labels)+1
+ else:
+ assert(num_classes>np.max(labels))
+ fout = open(out_filename, 'w')
+ colors = [pyplot.cm.hsv(i/float(num_classes)) for i in range(num_classes)]
+ for i in range(N):
+ c = colors[labels[i]]
+ c = [int(x*255) for x in c]
+ fout.write('v %f %f %f %d %d %d\n' % (points[i,0],points[i,1],points[i,2],c[0],c[1],c[2]))
+ fout.close()
+
+def write_ply_rgb(points, colors, out_filename, num_classes=None):
+ """ Color (N,3) points with RGB colors (N,3) within range [0,255] as OBJ file """
+ colors = colors.astype(int)
+ N = points.shape[0]
+ fout = open(out_filename, 'w')
+ for i in range(N):
+ c = colors[i,:]
+ fout.write('v %f %f %f %d %d %d\n' % (points[i,0],points[i,1],points[i,2],c[0],c[1],c[2]))
+ fout.close()
diff --git a/pointnet2/scannet/preprocessing/collect_scannet_scenes.py b/pointnet2/scannet/preprocessing/collect_scannet_scenes.py
new file mode 100644
index 0000000..d648992
--- /dev/null
+++ b/pointnet2/scannet/preprocessing/collect_scannet_scenes.py
@@ -0,0 +1,102 @@
+import scannet_util
+
+CLASS_NAMES = scannet_util.g_label_names
+RAW2SCANNET = scannet_util.g_raw2scannet
+
+import os
+import json
+import sys
+import numpy as np
+BASE_DIR = os.path.dirname(__file__)
+
+sys.path.append(BASE_DIR)
+sys.path.append('../')
+import pc_util
+
+SCANNET_DIR = 'scannet_clean_2'
+SCENE_NAMES = [line.rstrip() for line in open('scannet_all.txt')]
+
+def collect_one_scene_data_label(scene_name, out_filename):
+ # Over-segmented segments: maps from segment to vertex/point IDs
+ data_folder = os.path.join(SCANNET_DIR, scene_name)
+ mesh_seg_filename = os.path.join(data_folder, '%s_vh_clean_2.0.010000.segs.json'%(scene_name))
+ #print mesh_seg_filename
+ with open(mesh_seg_filename) as jsondata:
+ d = json.load(jsondata)
+ seg = d['segIndices']
+ #print len(seg)
+ segid_to_pointid = {}
+ for i in range(len(seg)):
+ if seg[i] not in segid_to_pointid:
+ segid_to_pointid[seg[i]] = []
+ segid_to_pointid[seg[i]].append(i)
+
+ # Raw points in XYZRGBA
+ ply_filename = os.path.join(data_folder, '%s_vh_clean_2.ply' % (scene_name))
+ points = pc_util.read_ply_xyzrgb(ply_filename)
+ log_string(str(points.shape))
+
+ # Instances over-segmented segment IDs: annotation on segments
+ instance_segids = []
+ labels = []
+ annotation_filename = os.path.join(data_folder, '%s.aggregation.json'%(scene_name))
+ #print annotation_filename
+ with open(annotation_filename) as jsondata:
+ d = json.load(jsondata)
+ for x in d['segGroups']:
+ instance_segids.append(x['segments'])
+ labels.append(x['label'])
+
+ #print len(instance_segids)
+ #print labels
+
+ # Each instance's points
+ instance_points_list = []
+ instance_labels_list = []
+ semantic_labels_list = []
+ for i in range(len(instance_segids)):
+ segids = instance_segids[i]
+ pointids = []
+ for segid in segids:
+ pointids += segid_to_pointid[segid]
+ instance_points = points[np.array(pointids),:]
+ instance_points_list.append(instance_points)
+ instance_labels_list.append(np.ones((instance_points.shape[0], 1))*i)
+ if labels[i] not in RAW2SCANNET:
+ label = 'unannotated'
+ else:
+ label = RAW2SCANNET[labels[i]]
+ label = CLASS_NAMES.index(label)
+ semantic_labels_list.append(np.ones((instance_points.shape[0], 1))*label)
+
+ # Refactor data format
+ scene_points = np.concatenate(instance_points_list, 0)
+ scene_points = scene_points[:,0:6] # XYZRGB, disregarding the A
+ instance_labels = np.concatenate(instance_labels_list, 0)
+ semantic_labels = np.concatenate(semantic_labels_list, 0)
+ data = np.concatenate((scene_points, instance_labels, semantic_labels), 1)
+ np.save(out_filename, data)
+
+
+LOG_FOUT = open('log.txt','w')
+def log_string(out_str):
+ LOG_FOUT.write(out_str+'\n')
+ LOG_FOUT.flush()
+ print(out_str)
+
+
+if __name__=='__main__':
+ output_folder = 'scannet_scenes'
+ if not os.path.exists(output_folder):
+ os.mkdir(output_folder)
+
+ for scene_name in SCENE_NAMES:
+ log_string(scene_name)
+ try:
+ out_filename = scene_name+'.npy' # scene0000_00.npy
+ collect_one_scene_data_label(scene_name, os.path.join(output_folder, out_filename))
+ except Exception, e:
+ log_string(scene_name+'ERROR!!')
+ log_string(str(e))
+
+ LOG_FOUT.close()
diff --git a/pointnet2/scannet/preprocessing/demo.py b/pointnet2/scannet/preprocessing/demo.py
new file mode 100644
index 0000000..479e2da
--- /dev/null
+++ b/pointnet2/scannet/preprocessing/demo.py
@@ -0,0 +1,26 @@
+import sys
+import os
+
+BASE_DIR = os.path.dirname(__file__)
+
+sys.path.append(BASE_DIR)
+sys.path.append(os.path.join(BASE_DIR, '../'))
+
+import numpy as np
+import pc_util
+
+data = np.load('scannet_scenes/scene0001_01.npy')
+scene_points = data[:,0:3]
+colors = data[:,3:6]
+instance_labels = data[:,6]
+semantic_labels = data[:,7]
+
+
+output_folder = 'demo_output'
+if not os.path.exists(output_folder):
+ os.mkdir(output_folder)
+
+# Write scene as OBJ file for visualization
+pc_util.write_ply_rgb(scene_points, colors, os.path.join(output_folder, 'scene.obj'))
+pc_util.write_ply_color(scene_points, instance_labels, os.path.join(output_folder, 'scene_instance.obj'))
+pc_util.write_ply_color(scene_points, semantic_labels, os.path.join(output_folder, 'scene_semantic.obj'))
diff --git a/pointnet2/scannet/preprocessing/fetch_label_names.py b/pointnet2/scannet/preprocessing/fetch_label_names.py
new file mode 100644
index 0000000..852ae2e
--- /dev/null
+++ b/pointnet2/scannet/preprocessing/fetch_label_names.py
@@ -0,0 +1,24 @@
+''' scanning through annotation files for all the scenes to get a complete list of categories '''
+
+import os
+import json
+scannet_dir = './scannet/'
+scene_names = [line.rstrip() for line in open('scannet_all.txt')]
+
+labels = set()
+for scene_name in scene_names:
+ path = os.path.join(scannet_dir, scene_name)
+ agg_filename = os.path.join(path, scene_name+'.aggregation.json')
+ with open(agg_filename) as jsondata:
+ d = json.load(jsondata)
+ for x in d['segGroups']:
+ labels.add(x['label'])
+
+fout = open('class_names.txt', 'w')
+for label in list(labels):
+ print label
+ try:
+ fout.write(label+'\n')
+ except:
+ pass
+fout.close()
diff --git a/pointnet2/scannet/preprocessing/scannet-labels.combined.tsv b/pointnet2/scannet/preprocessing/scannet-labels.combined.tsv
new file mode 100644
index 0000000..0f3cc12
--- /dev/null
+++ b/pointnet2/scannet/preprocessing/scannet-labels.combined.tsv
@@ -0,0 +1,1164 @@
+category count nyuId nyu40id eigen13id nyuClass nyu40class eigen13class ModelNet40 ModelNet10 ShapeNetCore55 synsetoffset wnsynsetid wnsynsetkey
+wall 7274 21 1 12 wall wall Wall n04546855 wall.n.01
+chair 5419 5 5 4 chair chair Chair chair chair chair 03001627 n03001627 chair.n.01
+floor 3910 11 2 5 floor floor Floor n03365592 floor.n.01
+table 2664 19 7 10 table table Table table table table 04379243 n04379243 table.n.02
+door 1400 28 8 12 door door Wall door n03221720 door.n.01
+couch 1222 83 6 9 sofa sofa Sofa sofa sofa sofa 04256520 n04256520 sofa.n.01
+cabinet 1106 3 3 6 cabinet cabinet Furniture cabinet 02933112 n02933112 cabinet.n.01
+shelf 889 42 15 6 shelves shelves Furniture bookshelf bookshelf 02871439 n02871439 bookshelf.n.01
+desk 862 36 14 10 desk desk Table desk desk table 04379243 n03179701 desk.n.01
+office chair 837 5 5 4 chair chair Chair chair chair chair 03001627 n04373704 swivel_chair.n.01
+bed 814 157 4 1 bed bed Bed bed bed bed 02818832 n02818832 bed.n.01
+trashcan 688 12 39 6 garbage bin otherfurniture Furniture trash_bin 02747177 n02747177 ashcan.n.01
+pillow 608 119 18 7 pillow pillow Objects pillow 03938244 n03938244 pillow.n.01
+sink 504 24 34 7 sink sink Objects sink n04223580 sink.n.01
+picture 467 64 11 8 picture picture Picture n03931044 picture.n.01
+window 432 59 9 13 window window Window n04587648 window.n.01
+toilet 402 124 33 7 toilet toilet Objects toilet toilet n04446276 toilet.n.01
+bookshelf 400 88 10 6 bookshelf bookshelf Furniture bookshelf bookshelf 02871439 n02871439 bookshelf.n.01
+monitor 395 49 40 7 monitor otherprop Objects monitor monitor tv or monitor 03211117 n03782190 monitor.n.04
+computer 369 46 40 7 computer otherprop Objects n03082979 computer.n.01
+curtain 356 89 16 13 curtain curtain Window curtain n03151077 curtain.n.01
+book 335 1 23 2 book books Books n02870526 book.n.11
+armchair 318 5 5 4 chair chair Chair chair chair chair 03001627 n02738535 armchair.n.01
+coffee table 303 356 39 6 coffee table otherfurniture Furniture table table table 04379243 n03063968 coffee_table.n.01
+drawer 290 174 39 6 drawer otherfurniture Furniture n03233905 drawer.n.01
+box 283 26 29 7 box box Objects n02883344 box.n.01
+refrigerator 269 17 24 6 refridgerator refridgerator Furniture n04070727 refrigerator.n.01
+lamp 255 144 35 7 lamp lamp Objects lamp lamp 03636649 n03636649 lamp.n.02
+kitchen cabinet 252 3 3 6 cabinet cabinet Furniture n02933112 cabinet.n.01
+dining chair 242 5 5 4 chair chair Chair chair chair chair 03001627 n03001627 chair.n.01
+towel 222 135 27 7 towel towel Objects n04459362 towel.n.01
+clothes 214 141 21 7 clothes clothes Objects n02728440 apparel.n.01
+tv 210 172 25 11 television television TV tv or monitor 03211117 n03211117 display.n.06
+nightstand 206 158 32 6 night stand night stand Furniture night_stand night_stand n03015254 chest_of_drawers.n.01
+counter 196 7 12 6 counter counter Furniture table table table 04379243 n03116530 counter.n.01
+dresser 180 169 17 6 dresser dresser Furniture dresser dresser n03015254 chest_of_drawers.n.01
+countertop 176 7 12 6 counter counter Furniture n03118245 countertop.n.01
+stool 165 150 40 7 stool otherprop Objects stool n04326896 stool.n.01
+cushion 141 119 18 7 pillow pillow Objects n03151500 cushion.n.03
+plant 139 82 40 7 plant otherprop Objects plant n00017222 plant.n.02
+ceiling 134 4 22 3 ceiling ceiling Ceiling n02990373 ceiling.n.01
+bathtub 134 136 36 7 bathtub bathtub Objects bathtub bathtub tub 02808440 n02808440 bathtub.n.01
+bedframe 132 157 4 1 bed bed Bed n02822579 bedstead.n.01
+end table 125 19 7 10 table table Table table table table 04379243 n04379243 table.n.02
+dining table 123 19 7 10 table table Table table table table 04379243 n04379243 table.n.02
+keyboard 118 47 40 7 keyboard otherprop Objects keyboard computer keyboard 03085013 n03085013 computer_keyboard.n.01
+bag 116 55 37 7 bag bag Objects suitcase 02773838 n02773838 bag.n.06
+backpack 114 206 40 7 backpack otherprop Objects n02769748 backpack.n.01
+toilet paper 113 139 40 7 toilet paper otherprop Objects n15075141 toilet_tissue.n.01
+printer 111 66 40 7 printer otherprop Objects printer 04004475 n04004475 printer.n.03
+tv stand 103 291 39 6 tv stand otherfurniture Furniture tv_stand n03290653 entertainment_center.n.01
+whiteboard 102 45 30 7 whiteboard whiteboard Objects n03211616 display_panel.n.01
+carpet 99 130 40 7 rug otherprop Objects n04118021 rug.n.01
+blanket 99 312 40 7 blanket otherprop Objects n02849154 blanket.n.01
+shower curtain 99 123 28 7 shower curtain shower curtain Objects curtain n04209239 shower_curtain.n.01
+trash can 94 12 39 6 garbage bin otherfurniture Furniture trash_bin 02747177 n02747177 ashcan.n.01
+closet 94 772 39 6 wardrobe otherfurniture Furniture wardrobe
+stair 89 215 38 7 stairs otherstructure Objects stairs n04314914 step.n.04
+microwave 88 13 40 7 microwave otherprop Objects microwave 03761084 n03761084 microwave.n.02
+washbasin 86 24 34 7 sink sink Objects sink n04553920 washbasin.n.01
+rug 85 130 40 7 rug otherprop Objects n04118021 rug.n.01
+stove 78 242 38 7 stove otherstructure Objects stove 04330267 n04330267 stove.n.02
+shoe 68 149 40 7 shoe otherprop Objects n04199027 shoe.n.01
+computer tower 68 46 40 7 computer otherprop Objects n03082979 computer.n.01
+bottle 66 2 40 7 bottle otherprop Objects bottle bottle 02876657 n02876657 bottle.n.01
+bin 64 307 40 7 bin otherprop Objects n02839910 bin.n.01
+ottoman 63 359 39 6 ottoman otherfurniture Furniture stool n03380724 footstool.n.01
+bench 63 204 39 6 bench otherfurniture Furniture bench bench 02828884 n02828884 bench.n.01
+board 63 408 38 7 board otherstructure Objects
+washing machine 62 278 39 6 washing machine otherfurniture Furniture washing_machine 04554684 n04554684 washer.n.03
+mirror 62 122 19 7 mirror mirror Objects n03773035 mirror.n.01
+copier 61 40 7 otherprop Objects n03257586 duplicator.n.01
+basket 60 39 40 7 basket otherprop Objects basket 02801938 n02801938 basket.n.01
+sofa chair 59 5 5 4 chair chair Chair chair chair chair 03001627 n03001627 chair.n.01
+file cabinet 54 3 3 6 cabinet cabinet Furniture cabinet 02933112 n02933112 cabinet.n.01
+fan 52 74 40 7 fan otherprop Objects n03320046 fan.n.01
+laptop 52 37 40 7 laptop otherprop Objects laptop laptop 03642806 n03642806 laptop.n.01
+shower 49 38 7 otherstructure Objects n04208936 shower.n.01
+paper 48 15 26 7 paper paper Objects n14974264 paper.n.01
+person 48 331 31 7 person person Objects person n05217688 person.n.02
+headboard 47 161 39 6 headboard otherfurniture Furniture n03502200 headboard.n.01
+paper towel dispenser 47 14 40 7 paper towel dispenser otherprop Objects
+faucet 45 9 40 7 faucet otherprop Objects faucet 03325088 n03325088 faucet.n.01
+oven 43 238 38 7 oven otherstructure Objects n03862676 oven.n.01
+footstool 42 359 39 6 ottoman otherfurniture Furniture stool n03380724 footstool.n.01
+blinds 42 80 13 13 blinds blinds Window n02851099 blind.n.03
+rack 41 50 39 6 stand otherfurniture Furniture n04038440 rack.n.05
+plate 39 233 40 7 plate otherprop Objects n03959485 plate.n.04
+blackboard 38 225 38 7 blackboard otherstructure Objects n02846511 blackboard.n.01
+piano 38 298 39 6 piano otherfurniture Furniture piano piano 03928116 n03928116 piano.n.01
+heater 38 111 39 6 heater otherfurniture Furniture n03508101 heater.n.01
+soap 37 133 40 7 soap otherprop Objects
+luggage 36 783 40 7 luggage otherprop Objects n02774630 baggage.n.01
+computer desk 36 36 14 10 desk desk Table desk desk table 04379243 n03179701 desk.n.01
+rail 36 497 38 7 railing otherstructure Objects
+radiator 36 236 39 6 radiator otherfurniture Furniture n04041069 radiator.n.02
+recycle bin 35 307 40 7 bin otherprop Objects
+container 34 140 40 7 container otherprop Objects n03094503 container.n.01
+wardrobe 34 772 39 6 wardrobe otherfurniture Furniture wardrobe n04550184 wardrobe.n.01
+soap dispenser 32 40 7 otherprop Objects n04254120 soap_dispenser.n.01
+telephone 32 32 40 7 telephone otherprop Objects telephone 04401088 n04401088 telephone.n.01
+bucket 32 427 40 7 bucket otherprop Objects n02909870 bucket.n.01
+clock 31 56 40 7 clock otherprop Objects clock 03046257 n03046257 clock.n.01
+stand 29 50 39 6 stand otherfurniture Furniture table table table 04379243 n04301000 stand.n.04
+light 27 62 38 7 light otherstructure Objects n03665366 light.n.02
+laundry basket 27 164 40 7 laundry basket otherprop Objects basket 02801938 n03050864 clothes_hamper.n.01
+pipe 27 41 40 7 pipe otherprop Objects
+round table 26 19 7 10 table table Table table table table 04379243 n04114554 round_table.n.02
+roof 25 4 22 3 ceiling ceiling Ceiling n04105068 roof.n.01
+clothes dryer 25 39 6 otherfurniture Furniture n03251766 dryer.n.01
+coat 23 324 40 7 jacket otherprop Objects n03057021 coat.n.01
+guitar 23 300 40 7 guitar otherprop Objects guitar guitar 03467517 n03467517 guitar.n.01
+desk chair 23 5 5 4 chair chair Chair chair chair chair 03001627 n03001627 chair.n.01
+sheet 22 559 40 7 sheet otherprop Objects
+toilet paper holder 22 647 40 7 toilet paper holder otherprop Objects
+seat 22 524 39 6 furniture otherfurniture Furniture n04161981 seat.n.03
+step 21 38 7 otherstructure Objects n04314914 step.n.04
+speaker 20 54 40 7 speaker otherprop Objects speaker 03691459 n03691459 loudspeaker.n.01
+vending machine 19 220 40 7 machine otherprop Objects n04525305 vending_machine.n.01
+column 19 94 38 7 column otherstructure Objects n03074380 column.n.06
+bicycle 18 189 40 7 bicycle otherprop Objects bicycle 02834778 n02834778 bicycle.n.01
+ladder 18 48 39 6 ladder otherfurniture Furniture stairs n03632277 ladder.n.01
+cover 18 312 40 7 blanket otherprop Objects
+handle 18 758 40 7 handle otherprop Objects n03485997 handle.n.01
+bathroom stall 18 38 7 otherstructure Objects n02873839 booth.n.02
+foosball table 17 510 39 6 foosball table otherfurniture Furniture table table table 04379243 n04379243 table.n.02
+table lamp 17 144 35 7 lamp lamp Objects lamp lamp 03636649 n04380533 table_lamp.n.01
+shower wall 17 21 1 12 wall wall Wall
+chest 17 344 39 6 chest otherfurniture Furniture dresser dresser
+cup 17 35 40 7 cup otherprop Objects cup cup or mug 03797390 n03797390 mug.n.04
+jacket 16 324 40 7 jacket otherprop Objects n03589791 jacket.n.01
+storage bin 16 812 40 7 storage bin otherprop Objects
+screen 16 40 7 otherprop Objects
+coffee maker 16 40 7 otherprop Objects n03063338 coffee_maker.n.01
+hamper 15 39 40 7 basket otherprop Objects basket 02801938 n03482405 hamper.n.02
+dishwasher 15 8 38 7 dishwasher otherstructure Objects dishwasher 03207941 n03207941 dishwasher.n.01
+window frame 15 477 38 7 window frame otherstructure Objects n04589593 window_frame.n.01
+paper towel 15 113 40 7 paper towel otherprop Objects n03887697 paper_towel.n.01
+machine 15 220 40 7 machine otherprop Objects n03699975 machine.n.01
+mat 15 143 20 5 floor mat floor mat Floor n03727837 mat.n.01
+windowsill 14 38 7 otherstructure Objects n04590263 windowsill.n.01
+tap 14 9 40 7 faucet otherprop Objects faucet 03325088 n04559451 water_faucet.n.01
+pool table 14 515 39 6 pool table otherfurniture Furniture table table table 04379243 n03982430 pool_table.n.01
+hand dryer 14 40 7 otherprop Objects
+bar 14 51 38 7 bar otherstructure Objects n02788689 bar.n.03
+frame 14 38 7 otherstructure Objects
+rolling chair 14 5 5 4 chair chair Chair chair chair chair 03001627 n03001627 chair.n.01
+toaster 14 251 40 7 toaster otherprop Objects n04442312 toaster.n.02
+wall frame 14 38 7 otherstructure Objects
+hanger 13 211 40 7 hanger otherprop Objects n03490884 hanger.n.02
+conference table 13 19 7 10 table table Table table table table 04379243 n03090000 conference_table.n.01
+handrail 13 453 38 7 banister otherstructure Objects n02788148 bannister.n.02
+treadmill 13 458 39 6 treadmill otherfurniture Furniture n04477387 treadmill.n.01
+bulletin board 13 408 38 7 board otherstructure Objects
+ironing board 13 313 39 6 ironing board otherfurniture Furniture n03586090 ironing_board.n.01
+fireplace 12 372 38 7 fireplace otherstructure Objects n03346455 fireplace.n.01
+soap dish 12 638 40 7 soap dish otherprop Objects n04254009 soap_dish.n.01
+fabric 12 40 7 otherprop Objects n03309808 fabric.n.01
+kitchen counter 12 7 12 6 counter counter Furniture table table table 04379243 n03116530 counter.n.01
+glass 12 612 38 7 glass otherstructure Objects n03438257 glass.n.02
+doorframe 11 615 38 7 door frame otherstructure Objects n03222722 doorframe.n.01
+table cushion 11 40 7 otherprop Objects
+toilet paper dispenser 11 40 7 otherprop Objects
+slab 11 38 7 otherstructure Objects n04233405 slab.n.01
+mini fridge 11 17 24 6 refridgerator refridgerator Furniture n03273913 electric_refrigerator.n.01
+fire extinguisher 11 10 40 7 fire extinguisher otherprop Objects n03345837 fire_extinguisher.n.01
+shampoo 11 40 7 otherprop Objects
+ball 11 60 40 7 ball otherprop Objects
+hat 11 193 40 7 hat otherprop Objects n03497657 hat.n.01
+shower curtain rod 11 40 7 otherprop Objects
+junk 11 40 7 otherprop Objects n14857897 debris.n.01
+soap holder 10 506 40 7 soap holder otherprop Objects
+staircase 10 215 38 7 stairs otherstructure Objects n04298308 stairway.n.01
+toiletry 10 40 7 otherprop Objects n04447443 toiletry.n.01
+stall door 10 28 8 12 door door Wall door
+framed picture 10 64 11 8 picture picture Picture
+water cooler 10 509 39 6 water cooler otherfurniture Furniture n04559166 water_cooler.n.01
+bags 10 40 7 otherprop Objects suitcase 02773838 n02773838 bag.n.06
+desk lamp 10 144 35 7 lamp lamp Objects lamp lamp 03636649 n03636649 lamp.n.02
+paper cutter 10 108 40 7 paper cutter otherprop Objects n03886940 paper_cutter.n.01
+led tv 9 40 7 otherprop Objects
+switch 9 40 7 otherprop Objects n04372370 switch.n.01
+bed sheet 9 559 40 7 sheet otherprop Objects n04188179 sheet.n.03
+roof frame 9 38 7 otherstructure Objects
+tray 9 179 40 7 tray otherprop Objects n04476259 tray.n.01
+comforter 9 484 40 7 comforter otherprop Objects n04033995 quilt.n.01
+air conditioner 9 79 38 7 air conditioner otherstructure Objects n02686379 air_conditioner.n.01
+shower door 9 28 8 12 door door Wall door
+shirt 9 40 7 otherprop Objects n04197391 shirt.n.01
+swivel chair 9 5 5 4 chair chair Chair chair chair chair 03001627 n04373704 swivel_chair.n.01
+pillar 9 94 38 7 column otherstructure Objects n03073977 column.n.07
+detergent 9 40 7 otherprop Objects
+ledge 9 38 7 otherstructure Objects n09337253 ledge.n.01
+vase 8 78 40 7 vase otherprop Objects vase jar 03593526 n04522168 vase.n.01
+toaster oven 8 275 40 7 toaster oven otherprop Objects n04442441 toaster_oven.n.01
+bedpost 8 40 7 otherprop Objects n02821415 bedpost.n.01
+food 8 40 7 otherprop Objects n00021265 food.n.01
+picture frame 8 40 7 otherprop Objects n03931765 picture_frame.n.01
+poltrone 8 40 7 otherprop Objects
+study table 8 19 7 10 table table Table table table table 04379243 n04379243 table.n.02
+office table 8 19 7 10 table table Table table table table 04379243 n04379243 table.n.02
+mouse 8 103 40 7 mouse otherprop Objects n03793489 mouse.n.04
+storage 8 n03744276 memory.n.04
+nerf gun 8 40 7 otherprop Objects
+table chair 8 5 5 4 chair chair Chair chair chair chair 03001627 n03001627 chair.n.01
+night table 8 19 7 10 table table Table table table table 04379243 n04379243 table.n.02
+computer chair 8 5 5 4 chair chair Chair chair chair chair 03001627 n03001627 chair.n.01
+toilet seat liner dispenser 8 40 7 otherprop Objects
+backrest 7 5 5 4 chair chair Chair n02767433 back.n.08
+chair seat 7 40 7 otherprop Objects
+sink cabinet 7 3 3 6 cabinet cabinet Furniture cabinet 02933112 n02933112 cabinet.n.01
+can 7 329 40 7 can otherprop Objects can 02946921 n02946921 can.n.01
+furniture 7 524 39 6 furniture otherfurniture Furniture n03405725 furniture.n.01
+cart 7 305 40 7 cart otherprop Objects n03484083 handcart.n.01
+stool chair 7 5 5 4 chair chair Chair chair chair chair 03001627 n03001627 chair.n.01
+step stool 7 276 40 7 step stool otherprop Objects stool n04315713 step_stool.n.01
+robe 7 40 7 otherprop Objects n04097866 robe.n.01
+table stand 7 50 39 6 stand otherfurniture Furniture
+stall 7 38 7 otherstructure Objects n02873839 booth.n.02
+dispenser 7 40 7 otherprop Objects n03210683 dispenser.n.01
+storage container 7 140 40 7 container otherprop Objects
+side table 7 19 7 10 table table Table table table table 04379243 n04379243 table.n.02
+partition 7 21 1 12 wall wall Wall n03894379 partition.n.01
+appliance 7 40 7 otherprop Objects
+lotion 7 40 7 otherprop Objects n03690938 lotion.n.01
+pot 7 16 40 7 pot otherprop Objects
+photo 7 508 40 7 photo otherprop Objects n03925226 photograph.n.01
+toilet brush 7 630 40 7 toilet brush otherprop Objects
+scale 7 639 40 7 scale otherprop Objects n04141975 scale.n.07
+tissue box 7 138 40 7 tissue box otherprop Objects
+remote 7 40 7 otherprop Objects remote_control 04074963 n04074963 remote_control.n.01
+light switch 6 301 38 7 light switch otherstructure Objects
+crate 6 183 39 6 crate otherfurniture Furniture n03127925 crate.n.01
+ping pong table 6 625 39 6 ping pong table otherfurniture Furniture table table table 04379243 n04379243 table.n.02
+platform 6 38 7 otherstructure Objects
+pantry 6 38 7 otherstructure Objects n03885535 pantry.n.01
+bath cabinet 6 3 3 6 cabinet cabinet Furniture cabinet 02933112 n02933112 cabinet.n.01
+slipper 6 40 7 otherprop Objects n04241394 slipper.n.01
+sideboard 6 7 12 6 counter counter Furniture
+holder 6 40 7 otherprop Objects n03525454 holder.n.01
+worktop 6 40 7 otherprop Objects
+outlet 6 40 7 otherprop Objects n04548771 wall_socket.n.01
+gas cooker 6 242 38 7 stove otherstructure Objects n03425595 gas_range.n.01
+doorhandle 6 652 40 7 knob otherprop Objects n03222959 doorknob.n.01
+cutting board 6 247 40 7 cutting board otherprop Objects n03025513 chopping_board.n.01
+bathroom sink 6 24 34 7 sink sink Objects sink n04223580 sink.n.01
+controller 6 40 7 otherprop Objects n03096960 control.n.09
+bedding set 6 40 7 otherprop Objects
+mount 6 40 7 otherprop Objects
+decoration 6 40 7 otherprop Objects n03169390 decoration.n.01
+tablet 6 19 7 10 table table Table table table table 04379243 n04379243 table.n.02
+reading table 6 19 7 10 table table Table table table table 04379243 n04379243 table.n.02
+floor covered 6 40 7 otherprop Objects
+cooker 6 267 40 7 utensil otherprop Objects n03101156 cooker.n.01
+file 6 75 40 7 file otherprop Objects filing_cabinet 03337140 n03337140 file.n.03
+archive 6 40 7 otherprop Objects n02735086 archive.n.01
+trolley 5 504 40 7 trolley otherprop Objects n04335435 streetcar.n.01
+wainscoting 5 40 7 otherprop Objects
+lampshade 5 859 40 7 lamp shade otherprop Objects n03637318 lampshade.n.01
+china 5 267 40 7 utensil otherprop Objects n03018209 china.n.02
+sign 5 208 40 7 sign otherprop Objects n04217882 signboard.n.01
+fax machine 5 68 40 7 fax machine otherprop Objects
+mirror frame 5 40 7 otherprop Objects
+projector 5 90 40 7 projector otherprop Objects n04009552 projector.n.02
+sweater 5 40 7 otherprop Objects n04370048 sweater.n.01
+paint can 5 329 40 7 can otherprop Objects can 02946921 n02946921 can.n.01
+heat register 5 40 7 otherprop Objects
+kitchen table 5 19 7 10 table table Table table table table 04379243 n03620967 kitchen_table.n.01
+globe 5 347 40 7 globe otherprop Objects
+toy 5 389 40 7 toy otherprop Objects n03964744 plaything.n.01
+kitchen worktop 5 40 7 otherprop Objects
+paper roll 5 40 7 otherprop Objects
+meeting table 5 19 7 10 table table Table table table table 04379243 n04379243 table.n.02
+vaze 5 40 7 otherprop Objects
+wall clock 5 56 40 7 clock otherprop Objects clock 03046257 n04548280 wall_clock.n.01
+closet door 5 28 8 12 door door Wall door
+pack 5 40 7 otherprop Objects
+doormat 5 143 20 5 floor mat floor mat Floor n03223299 doormat.n.02
+tissue 5 648 40 7 tissue otherprop Objects
+plastic container 5 140 40 7 container otherprop Objects
+statue 5 294 40 7 sculpture otherprop Objects n04306847 statue.n.01
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+penholder 1 464 40 7 pen holder otherprop Objects
+wall panel 1 21 1 12 wall wall Wall n04548503 wall_panel.n.01
+stair landing 1 40 7 otherprop Objects
+wallpapere 1 40 7 otherprop Objects
+water dispencer 1 507 40 7 water dispenser otherprop Objects
+therostat 1 110 40 7 thermostat otherprop Objects
+frying pan 1 318 40 7 frying pan otherprop Objects n03400231 frying_pan.n.01
+separator 1 40 7 otherprop Objects n02995998 centrifuge.n.01
+divider 1 40 7 otherprop Objects
+ceiling lamp 1 144 35 7 lamp lamp Objects lamp lamp 03636649 n03636649 lamp.n.02
+rod 1 40 7 otherprop Objects pistol 03948459 n03427202 gat.n.01
+;amps 1 40 7 otherprop Objects
+vaccum 1 40 7 otherprop Objects
+chair b' 1 40 7 otherprop Objects
+paint bucket 1 427 40 7 bucket otherprop Objects
+lighting 1 40 7 otherprop Objects n03667235 lighting.n.02
+tablecloth 1 292 40 7 tablecloth otherprop Objects n04380143 tablecloth.n.01
+plank 1 408 38 7 board otherstructure Objects n15101854 board.n.02
+sink pipe 1 41 40 7 pipe otherprop Objects
+concrete slab 1 40 7 otherprop Objects
+mini couch 1 83 6 9 sofa sofa Sofa sofa sofa sofa 04256520 n04256520 sofa.n.01
+post it 1 40 7 otherprop Objects
+stair down 1 40 7 otherprop Objects
+spout 1 41 40 7 pipe otherprop Objects n04287153 spout.n.01
+cutlery 1 40 7 otherprop Objects n03154073 cutter.n.06
+magazine rack 1 50 39 6 stand otherfurniture Furniture n03704549 magazine_rack.n.01
+mini printer 1 66 40 7 printer otherprop Objects printer 04004475 n04004475 printer.n.03
+tray box 1 26 29 7 box box Objects
+level 2 stair case 1 851 40 7 case otherprop Objects
+industrial machine 1 220 40 7 machine otherprop Objects
+giường 1 40 7 otherprop Objects
+induction cooktop 1 40 7 otherprop Objects
+artwork 1 40 7 otherprop Objects
+pillowcase 1 851 40 7 case otherprop Objects n02975412 case.n.19
+window wall 1 21 1 12 wall wall Wall
+minibar 1 7 12 6 counter counter Furniture n03769610 minibar.n.01
+laundry detergent 1 40 7 otherprop Objects
+serving plate 1 233 40 7 plate otherprop Objects
+dirty basket 1 39 40 7 basket otherprop Objects basket 02801938 n02801938 basket.n.01
+reading lamp 1 144 35 7 lamp lamp Objects lamp lamp 03636649 n04057981 reading_lamp.n.01
+open cabinet 1 3 3 6 cabinet cabinet Furniture cabinet 02933112 n02933112 cabinet.n.01
+hand truck 1 305 40 7 cart otherprop Objects n03490119 hand_truck.n.01
+pad 1 40 7 otherprop Objects n03195485 diggings.n.02
+laptop table 1 19 7 10 table table Table table table table 04379243 n04379243 table.n.02
+ventilator window 1 59 9 13 window window Window
+esll 1 40 7 otherprop Objects
+sofa bed 1 157 4 1 bed bed Bed bed bed bed 02818832 n02818832 bed.n.01
+safa 1 40 7 otherprop Objects
+heating system 1 40 7 otherprop Objects n03509025 heating_system.n.01
+floor]floo 1 40 7 otherprop Objects
+facial scrub 1 40 7 otherprop Objects
+vent hood 1 40 7 otherprop Objects
+office cupboard 1 40 7 otherprop Objects
+stand fan 1 74 40 7 fan otherprop Objects
+storage shelf 1 40 7 otherprop Objects
+hanging clothes 1 141 21 7 clothes clothes Objects
+fuse box 1 26 29 7 box box Objects
+pizza 1 40 7 otherprop Objects
+personal effect 1 40 7 otherprop Objects
+drawer organizer 1 40 7 otherprop Objects
+main board 1 408 38 7 board otherstructure Objects
+loofa 1 40 7 otherprop Objects
+shower surround 1 40 7 otherprop Objects
+bycicle 1 40 7 otherprop Objects
+all-in-one computer 1 46 40 7 computer otherprop Objects
+box of tissue 1 648 40 7 tissue otherprop Objects
+doorlock 1 646 40 7 door lock otherprop Objects n03223162 doorlock.n.01
+base unit 1 40 7 otherprop Objects
+tennis ball 1 60 40 7 ball otherprop Objects n04409515 tennis_ball.n.01
+snack machine 1 220 40 7 machine otherprop Objects
+laptop bag 1 55 37 7 bag bag Objects
+hallway wall 1 21 1 12 wall wall Wall
+msic 1 40 7 otherprop Objects
+file organizer 1 40 7 otherprop Objects
+fire hose 1 40 7 otherprop Objects n03346004 fire_hose.n.01
+media center 1 40 7 otherprop Objects
+umbrella 1 203 40 7 umbrella otherprop Objects n04507155 umbrella.n.01
+barrier 1 40 7 otherprop Objects n02796623 barrier.n.01
+dirt 1 40 7 otherprop Objects
+subwoofer 1 54 40 7 speaker otherprop Objects speaker 03691459 n04349401 subwoofer.n.01
+table tennis 1 40 7 otherprop Objects
+printer/scanner 1 40 7 otherprop Objects
+drying rack 1 262 39 6 drying rack otherfurniture Furniture
+wppd [ame;owood paneling 1 40 7 otherprop Objects
+toilet robe 1 40 7 otherprop Objects
+printer stand 1 50 39 6 stand otherfurniture Furniture
+shower screen 1 40 7 otherprop Objects
+water bed 1 157 4 1 bed bed Bed bed bed bed 02818832 n04557522 water_bed.n.01
+display sign 1 208 40 7 sign otherprop Objects
+diaper bin 1 307 40 7 bin otherprop Objects
+router 1 303 40 7 router otherprop Objects
+ashtray 1 377 40 7 ashtray otherprop Objects n02747802 ashtray.n.01
+footrest/table 1 40 7 otherprop Objects
+cleaning brush 1 40 7 otherprop Objects
+bathroom desk 1 36 14 10 desk desk Table desk desk table 04379243 n03179701 desk.n.01
+toilet commode 1 40 7 otherprop Objects
+staicase handrail 1 40 7 otherprop Objects
+clothes shelf 1 40 7 otherprop Objects
+dink 1 40 7 otherprop Objects
+toilet seat protectors 1 40 7 otherprop Objects
+stuffware 1 40 7 otherprop Objects
+low table for storage 1 40 7 otherprop Objects
+covered stuff 1 40 7 otherprop Objects
+hood 1 40 7 otherprop Objects
+floor reflection 1 40 7 otherprop Objects
+can opener 1 279 40 7 can opener otherprop Objects n02951585 can_opener.n.01
+top 1 40 7 otherprop Objects
+wheely bin 1 307 40 7 bin otherprop Objects
+book bag 1 55 37 7 bag bag Objects n02870676 book_bag.n.01
+body wash 1 40 7 otherprop Objects
+study chair 1 5 5 4 chair chair Chair chair chair chair 03001627 n03001627 chair.n.01
+stepladder 1 48 39 6 ladder otherfurniture Furniture stairs n04315599 step_ladder.n.01
+paper holder 1 470 40 7 paper holder otherprop Objects
+white 1 40 7 otherprop Objects
+yoga ball 1 60 40 7 ball otherprop Objects
+consol 1 40 7 otherprop Objects
+cloths container 1 140 40 7 container otherprop Objects
+shorts 1 192 40 7 shorts otherprop Objects n04204755 short_circuit.n.01
+emergency exit windows 1 40 7 otherprop Objects
+gym 1 40 7 otherprop Objects n03472112 gymnasium.n.02
+box of tissues 1 40 7 otherprop Objects
+trolly 1 221 39 6 trolly otherfurniture Furniture
+water purifyer 1 93 40 7 water purifier otherprop Objects
+clutter' 1 40 7 otherprop Objects
+wallend 1 40 7 otherprop Objects
+tissue `paper 1 40 7 otherprop Objects
+mop and bucket 1 427 40 7 bucket otherprop Objects
+grocery 1 40 7 otherprop Objects n03461385 grocery_store.n.01
+worktable 1 19 7 10 table table Table table table table 04379243 n04603729 worktable.n.01
+air outlet 1 40 7 otherprop Objects
+fold 1 97 40 7 pen otherprop Objects n03376159 fold.n.06
+toilet commoed 1 40 7 otherprop Objects
+handfold 1 40 7 otherprop Objects
+business chair 1 5 5 4 chair chair Chair chair chair chair 03001627 n03001627 chair.n.01
+llong table chair 1 5 5 4 chair chair Chair chair chair chair 03001627 n03001627 chair.n.01
+books in a shelf 1 40 7 otherprop Objects
+chaurs 1 40 7 otherprop Objects
+tennis table stand 1 50 39 6 stand otherfurniture Furniture
+kitchen water purifier 1 93 40 7 water purifier otherprop Objects
+lid 1 533 40 7 lid otherprop Objects
+electric hob 1 40 7 otherprop Objects
+baggage 1 783 40 7 luggage otherprop Objects
+cpnter 1 40 7 otherprop Objects
+stairs (exit) 1 40 7 otherprop Objects
+equipment 1 40 7 otherprop Objects n03294048 equipment.n.01
+rocking chair 1 5 5 4 chair chair Chair chair chair chair 03001627 n04099969 rocking_chair.n.01
+bunkbed 1 804 39 6 bunk bed otherfurniture Furniture
+divan 1 83 6 9 sofa sofa Sofa sofa sofa sofa 04256520 n03214966 divan.n.01
+bottle of hand sanitizer 1 40 7 otherprop Objects
+sales stall 1 40 7 otherprop Objects
+dehumidifer 1 40 7 otherprop Objects
+wall cover 1 40 7 otherprop Objects
+plumbing 1 40 7 otherprop Objects n03969041 plumbing.n.01
+elliptical machine 1 220 40 7 machine otherprop Objects
+playball 1 40 7 otherprop Objects
+safety bar 1 51 38 7 bar otherstructure Objects
+stereo 1 84 40 7 stereo otherprop Objects n04315948 stereo.n.01
+havlu 1 40 7 otherprop Objects
+handbag 1 40 7 otherprop Objects n02774152 bag.n.04
+furnance 1 551 39 6 furnace otherfurniture Furniture
+shleves 1 40 7 otherprop Objects
+coat hanger 1 400 40 7 coat hanger otherprop Objects n03057920 coat_hanger.n.01
+erar wall 1 21 1 12 wall wall Wall
+bed bench 1 204 39 6 bench otherfurniture Furniture bench bench 02828884 n02828884 bench.n.01
+tissu 1 648 40 7 tissue otherprop Objects
+plastic tub 1 232 40 7 plastic tub otherprop Objects bathtub bathtub tub 02808440 n02808440 bathtub.n.01
+potholder 1 40 7 otherprop Objects n03992115 potholder.n.01
+coffee mug 1 263 40 7 vessel otherprop Objects cup or mug 03797390 n03063599 coffee_mug.n.01
+tennis rcket bag 1 55 37 7 bag bag Objects
+stand lamp 1 144 35 7 lamp lamp Objects lamp lamp 03636649 n03636649 lamp.n.02
+bed/sofa 1 40 7 otherprop Objects
+laundry supply 1 40 7 otherprop Objects
+document shredder 1 40 7 otherprop Objects
+gas-range stove 1 242 38 7 stove otherstructure Objects stove 04330267 n04330267 stove.n.02
+table soccer 1 40 7 otherprop Objects
+cookingrange 1 40 7 otherprop Objects
+bookbags 1 40 7 otherprop Objects
+downstairs 1 40 7 otherprop Objects
+coffeepot 1 893 40 7 coffee pot otherprop Objects n03063689 coffeepot.n.01
+jar 1 70 40 7 jar otherprop Objects jar 03593526 n03593526 jar.n.01
+rear wall 1 21 1 12 wall wall Wall
+part of chair 1 5 5 4 chair chair Chair chair chair chair 03001627 n03001627 chair.n.01
+tape 1 109 40 7 tape otherprop Objects
+drawer table 1 19 7 10 table table Table table table table 04379243 n04379243 table.n.02
+woodenrack 1 40 7 otherprop Objects
+toilet floor 1 11 2 5 floor floor Floor
+cushioned seating 1 40 7 otherprop Objects
+bulls eye 1 40 7 otherprop Objects
+soft mat 1 40 7 otherprop Objects
+snack box 1 26 29 7 box box Objects
+desk organizer 1 40 7 otherprop Objects
+footboard 1 559 40 7 sheet otherprop Objects n03379461 footboard.n.02
+wall hook 1 40 7 otherprop Objects
+chopping board 1 408 38 7 board otherstructure Objects n03025513 chopping_board.n.01
+round picture 1 64 11 8 picture picture Picture
+chimney 1 702 38 7 chimney otherstructure Objects n03017428 chimney.n.01
+studio screen 1 40 7 otherprop Objects
+personal belonging 1 40 7 otherprop Objects
+roll of paper 1 15 26 7 paper paper Objects
+gaming wheel 1 40 7 otherprop Objects
+landlord 1 331 31 7 person person Objects person n10245236 landlord.n.01
+ebd 1 40 7 otherprop Objects
+heater radiator 1 236 39 6 radiator otherfurniture Furniture
+cabinet above 1 40 7 otherprop Objects
+weighted plate 1 233 40 7 plate otherprop Objects
+travelling bag 1 55 37 7 bag bag Objects suitcase 02773838 n02773838 bag.n.06
+desk material 1 40 7 otherprop Objects
+door wall 1 21 1 12 wall wall Wall
+traffic cone 1 6 40 7 cone otherprop Objects cone
+computer mouse 1 103 40 7 mouse otherprop Objects n03793489 mouse.n.04
+coathanger 1 400 40 7 coat hanger otherprop Objects
+bureau 1 524 39 6 furniture otherfurniture Furniture dresser dresser n03015254 chest_of_drawers.n.01
+tyre 1 40 7 otherprop Objects n04440749 tire.n.01
+armchairchair 1 40 7 otherprop Objects
+oven range 1 40 7 otherprop Objects
+pants 1 40 7 otherprop Objects n04489008 trouser.n.01
+chiropractic chair 1 5 5 4 chair chair Chair chair chair chair 03001627 n03001627 chair.n.01
+keg 1 343 39 6 barrel otherfurniture Furniture n03610418 keg.n.02
+spray 1 40 7 otherprop Objects n02754103 atomizer.n.01
+paper trimmer 1 40 7 otherprop Objects
+standing whiteboard 1 45 30 7 whiteboard whiteboard Objects
+desk drawer 1 475 39 6 desk drawer otherfurniture Furniture
+window/windowed door 1 28 8 12 door door Wall door
+soapbox 1 671 40 7 soap box otherprop Objects
+pillow sofa 1 83 6 9 sofa sofa Sofa sofa sofa sofa 04256520 n04256520 sofa.n.01
+centre table 1 19 7 10 table table Table table table table 04379243 n04379243 table.n.02
+doorway 1 609 38 7 door way otherstructure Objects door n03224032 doorway.n.01
+wall and whiteboard 1 45 30 7 whiteboard whiteboard Objects
+laptop computer 1 46 40 7 computer otherprop Objects laptop laptop 03642806 n03642806 laptop.n.01
+scanner/copier 1 40 7 otherprop Objects
+suitcase w/clothes 1 40 7 otherprop Objects
+power pusher 1 40 7 otherprop Objects
+shower faucet handle 1 758 40 7 handle otherprop Objects
+walk 1 40 7 otherprop Objects n04544979 walk.n.05
+matte 1 40 7 otherprop Objects
+atm machine 1 220 40 7 machine otherprop Objects
+garage door 1 850 38 7 garage door otherstructure Objects door
+wals 1 40 7 otherprop Objects
+cabinet aisle 1 40 7 otherprop Objects
+table light 1 62 38 7 light otherstructure Objects
+guillotine paper trimmer 1 40 7 otherprop Objects
+round 2\ 1 40 7 otherprop Objects
+teddy 1 40 7 otherprop Objects n03013580 chemise.n.01
+white board/divider 1 40 7 otherprop Objects
+white wall 1 21 1 12 wall wall Wall
+mark 1 40 7 otherprop Objects n04681387 crisscross.n.01
+partition wall 1 21 1 12 wall wall Wall
+shag rug 1 130 40 7 rug otherprop Objects n04183217 shag_rug.n.01
+upstair way 1 40 7 otherprop Objects
+music stand' 1 820 39 6 music stand otherfurniture Furniture
+recamier 1 40 7 otherprop Objects
+venthole 1 40 7 otherprop Objects n04526241 vent.n.01
+dining seat 1 40 7 otherprop Objects
+toilet cover 1 40 7 otherprop Objects
+personal item 1 40 7 otherprop Objects
+tallboy 1 524 39 6 furniture otherfurniture Furniture dresser dresser n03518305 highboy.n.01
+drawers unit 1 40 7 otherprop Objects
+teapot 1 678 40 7 tea pot otherprop Objects n04398044 teapot.n.01
+cook cabinet 1 3 3 6 cabinet cabinet Furniture cabinet 02933112 n02933112 cabinet.n.01
+wok pan 1 589 40 7 pan otherprop Objects
+tv tray 1 179 40 7 tray otherprop Objects
+round chair 1 5 5 4 chair chair Chair chair chair chair 03001627 n03001627 chair.n.01
+sawhorse 1 40 7 otherprop Objects n04140631 sawhorse.n.01
+kitchen range 1 242 38 7 stove otherstructure Objects n04330340 stove.n.01
+busdrver 1 40 7 otherprop Objects
+barricade 1 40 7 otherprop Objects
+wall ornament 1 40 7 otherprop Objects
+color printer 1 66 40 7 printer otherprop Objects printer 04004475 n04004475 printer.n.03
+sticker 1 725 40 7 sticker otherprop Objects n07272545 gummed_label.n.01
+exit sign 1 86 40 7 exit sign otherprop Objects
+gas stove 1 242 38 7 stove otherstructure Objects stove 04330267 n04330267 stove.n.02
+venta hood 1 40 7 otherprop Objects
+copier/printer 1 40 7 otherprop Objects
+wall-mounted lamp 1 144 35 7 lamp lamp Objects lamp lamp 03636649 n03636649 lamp.n.02
+item box 1 26 29 7 box box Objects
+water puifyer 1 40 7 otherprop Objects
+wall papper 1 40 7 otherprop Objects
+salt and peper 1 737 40 7 salt and pepper otherprop Objects
+printer four 1 40 7 otherprop Objects
+towel fastener 1 40 7 otherprop Objects
+basth 1 40 7 otherprop Objects
+flipflops 1 40 7 otherprop Objects
+bonus 1 40 7 otherprop Objects
+kitchen box 1 26 29 7 box box Objects n02883344 box.n.01
+central heating unit 1 40 7 otherprop Objects
+hanging tubelight 1 40 7 otherprop Objects
+soccer ball 1 837 40 7 soccer ball otherprop Objects n04254680 soccer_ball.n.01
+almarah 1 40 7 otherprop Objects
+canopy 1 40 7 otherprop Objects
+med box 1 26 29 7 box box Objects
+drain 1 567 38 7 drain otherstructure Objects
+panelling 1 21 1 12 wall wall Wall n03882611 paneling.n.01
+bed stand 1 50 39 6 stand otherfurniture Furniture
+deal 1 408 38 7 board otherstructure Objects n15102622 deal.n.04
+massage 1 40 7 otherprop Objects
+safety rail 1 497 38 7 railing otherstructure Objects n04127395 safety_rail.n.01
+vacuumer 1 40 7 otherprop Objects
+binfl 1 40 7 otherprop Objects
+lightbulb 1 566 40 7 light bulb otherprop Objects lamp n03665924 light_bulb.n.01
+door hydraulic 1 40 7 otherprop Objects
+induction cook top 1 40 7 otherprop Objects
+bedstand 1 40 7 otherprop Objects
+calander 1 40 7 otherprop Objects
+set of seats 1 40 7 otherprop Objects
+chocolate bar dispenser 1 40 7 otherprop Objects
+wall unit tv 1 40 7 otherprop Objects
+broomstick 1 328 40 7 broom otherprop Objects n02907082 broomstick.n.01
+bath faucet 1 9 40 7 faucet otherprop Objects faucet 03325088 n03325088 faucet.n.01
+folded cloth 1 40 7 otherprop Objects
+supply 1 40 7 otherprop Objects
+under oven drawer 1 174 39 6 drawer otherfurniture Furniture
+kinect 1 823 40 7 kinect otherprop Objects
+cash 1 40 7 otherprop Objects n10886222 cash.n.03
+dining side wall 1 21 1 12 wall wall Wall
+log 1 40 7 otherprop Objects n03686658 log.n.05
+garden gnome 1 40 7 otherprop Objects
+coucnb 1 40 7 otherprop Objects
+dart 1 40 7 otherprop Objects n03162818 dart.n.01
+dust pan and brush 1 40 7 otherprop Objects
+smoke alarm 1 525 40 7 alarm otherprop Objects n03343737 fire_alarm.n.02
+kitchen top 1 40 7 otherprop Objects
+toilet flush 1 40 7 otherprop Objects
+cooler 1 17 24 6 refridgerator refridgerator Furniture n03102654 cooler.n.01
+kitchen island 1 456 38 7 kitchen island otherstructure Objects n03620600 kitchen_island.n.01
+balcony 1 40 7 otherprop Objects
+escape door 1 28 8 12 door door Wall door
+hammer 1 883 40 7 hammer otherprop Objects n03481172 hammer.n.02
+wall and paiting 1 40 7 otherprop Objects
+kitch shelf 1 40 7 otherprop Objects
+handwasher 1 40 7 otherprop Objects
+vanity top 1 40 7 otherprop Objects
+bodyboard 1 40 7 otherprop Objects
+messenger bag 1 55 37 7 bag bag Objects
+stationary bike 1 40 7 otherprop Objects
+cabinet countertop 1 40 7 otherprop Objects
+ping pong padle 1 40 7 otherprop Objects
+teapoy 1 40 7 otherprop Objects
+clothes basket 1 39 40 7 basket otherprop Objects basket 02801938 n03050864 clothes_hamper.n.01
+xbox 1 628 40 7 xbox otherprop Objects xbox
+both 1 40 7 otherprop Objects
+foosball 1 40 7 otherprop Objects
+soad stand 1 50 39 6 stand otherfurniture Furniture
+prop 1 40 7 otherprop Objects n02692086 airplane_propeller.n.01
+buddha 1 40 7 otherprop Objects
+reflection in a mirror 1 122 19 7 mirror mirror Objects
+bar stol 1 40 7 otherprop Objects
+oven/stove 1 40 7 otherprop Objects
+patterned rug 1 130 40 7 rug otherprop Objects
+window panel 1 40 7 otherprop Objects
+vault 1 40 7 otherprop Objects
+dust bin cover 1 40 7 otherprop Objects
+throw 1 872 40 7 throw otherprop Objects n04429971 throw.n.04
+painting and frame 1 40 7 otherprop Objects
+covered piano 1 298 39 6 piano otherfurniture Furniture piano piano 03928116 n03928116 piano.n.01
+drawer unit 1 40 7 otherprop Objects
+aircon 1 40 7 otherprop Objects
+package 1 40 7 otherprop Objects n03871083 package.n.02
+gas vent 1 40 7 otherprop Objects
+block 1 40 7 otherprop Objects
+cloth container 1 140 40 7 container otherprop Objects
+additional printer 1 66 40 7 printer otherprop Objects printer 04004475 n04004475 printer.n.03
+danger sign 1 208 40 7 sign otherprop Objects
+game machine 1 220 40 7 machine otherprop Objects
+light fixture 1 40 7 otherprop Objects
+utility 1 40 7 otherprop Objects n04516874 utility.n.06
+base rack 1 40 7 otherprop Objects
+staircase landing 1 40 7 otherprop Objects
+szll 1 40 7 otherprop Objects
+piano note 1 40 7 otherprop Objects
+bboks 1 40 7 otherprop Objects
+cabord 1 40 7 otherprop Objects
+central table 1 19 7 10 table table Table table table table 04379243 n04379243 table.n.02
+splash 1 40 7 otherprop Objects n04682319 splash.n.04
+suit 1 40 7 otherprop Objects n04350905 suit.n.01
+cook top 1 40 7 otherprop Objects
+jug 1 687 40 7 jug otherprop Objects bottle bottle 02876657 n03603722 jug.n.01
+stepstool 1 276 40 7 step stool otherprop Objects
+tripod 1 50 39 6 stand otherfurniture Furniture n04485082 tripod.n.01
+cover box 1 26 29 7 box box Objects
+baby crib 1 485 39 6 crib otherfurniture Furniture
+air condisnor 1 40 7 otherprop Objects
+water softner 1 40 7 otherprop Objects
+chandelier 1 342 38 7 chandelier otherstructure Objects n03005285 chandelier.n.01
+floor patterning 1 40 7 otherprop Objects
+tablet top 1 40 7 otherprop Objects
+smoke detector 1 40 7 otherprop Objects
+baseball cap 1 40 7 otherprop Objects cap 02954340 n02799323 baseball_cap.n.01
+tissue roll holder 1 40 7 otherprop Objects
+case of water 1 40 7 otherprop Objects
+wall-organizer 1 40 7 otherprop Objects
+piece 1 40 7 otherprop Objects n03343853 firearm.n.01
+wheelbarrel 1 40 7 otherprop Objects
+desktop item 1 40 7 otherprop Objects
+tv showcase 1 40 7 otherprop Objects
+chelves 1 40 7 otherprop Objects
+toothbrush 1 127 40 7 toothbrush otherprop Objects n04453156 toothbrush.n.01
+chiffonière 1 40 7 otherprop Objects
+leg towel 1 135 27 7 towel towel Objects
+flowers/decorations 1 40 7 otherprop Objects
+snake toy 1 389 40 7 toy otherprop Objects
+cabinet's side 1 40 7 otherprop Objects
+bedroom chair 1 5 5 4 chair chair Chair chair chair chair 03001627 n03001627 chair.n.01
+drum 1 145 40 7 drum otherprop Objects n03249569 drum.n.01
+liquid soap 1 133 40 7 soap otherprop Objects
+set of bedding 1 40 7 otherprop Objects
+night lamp 1 144 35 7 lamp lamp Objects lamp lamp 03636649 n03636649 lamp.n.02
+post board 1 408 38 7 board otherstructure Objects
+measuring cup 1 730 40 7 measuring cup otherprop Objects cup n03733805 measuring_cup.n.01
+baseboard heater 1 111 39 6 heater otherfurniture Furniture
+paper shelf 1 40 7 otherprop Objects
+alert sheet 1 559 40 7 sheet otherprop Objects
+duster 1 115 40 7 duster otherprop Objects n03258330 dustcloth.n.01
+snooker table 1 19 7 10 table table Table table table table 04379243 n03982430 pool_table.n.01
+leg rest 1 40 7 otherprop Objects
+wall storage 1 40 7 otherprop Objects
+office board 1 408 38 7 board otherstructure Objects
+bathroom counter 1 7 12 6 counter counter Furniture table table table 04379243 n03116530 counter.n.01
+table sofa 1 83 6 9 sofa sofa Sofa sofa sofa sofa 04256520 n04256520 sofa.n.01
+glass-topped table 1 19 7 10 table table Table table table table 04379243 n04379243 table.n.02
+racket bat 1 40 7 otherprop Objects
+fridge handle 1 758 40 7 handle otherprop Objects
+stove top 1 40 7 otherprop Objects
+monitor from pc 1 40 7 otherprop Objects
+stick 1 529 40 7 stick otherprop Objects
diff --git a/pointnet2/scannet/preprocessing/scannet_util.py b/pointnet2/scannet/preprocessing/scannet_util.py
new file mode 100644
index 0000000..c830991
--- /dev/null
+++ b/pointnet2/scannet/preprocessing/scannet_util.py
@@ -0,0 +1,21 @@
+
+
+g_label_names = ['unannotated', 'wall', 'floor', 'chair', 'table', 'desk', 'bed', 'bookshelf', 'sofa', 'sink', 'bathtub', 'toilet', 'curtain', 'counter', 'door', 'window', 'shower curtain', 'refridgerator', 'picture', 'cabinet', 'otherfurniture']
+
+def get_raw2scannet_label_map():
+ lines = [line.rstrip() for line in open('scannet-labels.combined.tsv')]
+ lines = lines[1:]
+ raw2scannet = {}
+ for i in range(len(lines)):
+ label_classes_set = set(g_label_names)
+ elements = lines[i].split('\t')
+ raw_name = elements[0]
+ nyu40_name = elements[6]
+ if nyu40_name not in label_classes_set:
+ raw2scannet[raw_name] = 'unannotated'
+ else:
+ raw2scannet[raw_name] = nyu40_name
+ return raw2scannet
+
+
+g_raw2scannet = get_raw2scannet_label_map()
diff --git a/pointnet2/scannet/scannet_dataset.py b/pointnet2/scannet/scannet_dataset.py
new file mode 100644
index 0000000..4b586cc
--- /dev/null
+++ b/pointnet2/scannet/scannet_dataset.py
@@ -0,0 +1,182 @@
+import pickle
+import os
+import sys
+import numpy as np
+import pc_util
+import scene_util
+
+class ScannetDataset():
+ def __init__(self, root, npoints=8192, split='train'):
+ self.npoints = npoints
+ self.root = root
+ self.split = split
+ self.data_filename = os.path.join(self.root, 'scannet_%s.pickle'%(split))
+ with open(self.data_filename,'rb') as fp:
+ self.scene_points_list = pickle.load(fp)
+ self.semantic_labels_list = pickle.load(fp)
+ if split=='train':
+ labelweights = np.zeros(21)
+ for seg in self.semantic_labels_list:
+ tmp,_ = np.histogram(seg,range(22))
+ labelweights += tmp
+ labelweights = labelweights.astype(np.float32)
+ labelweights = labelweights/np.sum(labelweights)
+ self.labelweights = 1/np.log(1.2+labelweights)
+ elif split=='test':
+ self.labelweights = np.ones(21)
+ def __getitem__(self, index):
+ point_set = self.scene_points_list[index]
+ semantic_seg = self.semantic_labels_list[index].astype(np.int32)
+ coordmax = np.max(point_set,axis=0)
+ coordmin = np.min(point_set,axis=0)
+ smpmin = np.maximum(coordmax-[1.5,1.5,3.0], coordmin)
+ smpmin[2] = coordmin[2]
+ smpsz = np.minimum(coordmax-smpmin,[1.5,1.5,3.0])
+ smpsz[2] = coordmax[2]-coordmin[2]
+ isvalid = False
+ for i in range(10):
+ curcenter = point_set[np.random.choice(len(semantic_seg),1)[0],:]
+ curmin = curcenter-[0.75,0.75,1.5]
+ curmax = curcenter+[0.75,0.75,1.5]
+ curmin[2] = coordmin[2]
+ curmax[2] = coordmax[2]
+ curchoice = np.sum((point_set>=(curmin-0.2))*(point_set<=(curmax+0.2)),axis=1)==3
+ cur_point_set = point_set[curchoice,:]
+ cur_semantic_seg = semantic_seg[curchoice]
+ if len(cur_semantic_seg)==0:
+ continue
+ mask = np.sum((cur_point_set>=(curmin-0.01))*(cur_point_set<=(curmax+0.01)),axis=1)==3
+ vidx = np.ceil((cur_point_set[mask,:]-curmin)/(curmax-curmin)*[31.0,31.0,62.0])
+ vidx = np.unique(vidx[:,0]*31.0*62.0+vidx[:,1]*62.0+vidx[:,2])
+ isvalid = np.sum(cur_semantic_seg>0)/len(cur_semantic_seg)>=0.7 and len(vidx)/31.0/31.0/62.0>=0.02
+ if isvalid:
+ break
+ choice = np.random.choice(len(cur_semantic_seg), self.npoints, replace=True)
+ point_set = cur_point_set[choice,:]
+ semantic_seg = cur_semantic_seg[choice]
+ mask = mask[choice]
+ sample_weight = self.labelweights[semantic_seg]
+ sample_weight *= mask
+ return point_set, semantic_seg, sample_weight
+ def __len__(self):
+ return len(self.scene_points_list)
+
+class ScannetDatasetWholeScene():
+ def __init__(self, root, npoints=8192, split='train'):
+ self.npoints = npoints
+ self.root = root
+ self.split = split
+ self.data_filename = os.path.join(self.root, 'scannet_%s.pickle'%(split))
+ with open(self.data_filename,'rb') as fp:
+ self.scene_points_list = pickle.load(fp)
+ self.semantic_labels_list = pickle.load(fp)
+ if split=='train':
+ labelweights = np.zeros(21)
+ for seg in self.semantic_labels_list:
+ tmp,_ = np.histogram(seg,range(22))
+ labelweights += tmp
+ labelweights = labelweights.astype(np.float32)
+ labelweights = labelweights/np.sum(labelweights)
+ self.labelweights = 1/np.log(1.2+labelweights)
+ elif split=='test':
+ self.labelweights = np.ones(21)
+ def __getitem__(self, index):
+ point_set_ini = self.scene_points_list[index]
+ semantic_seg_ini = self.semantic_labels_list[index].astype(np.int32)
+ coordmax = np.max(point_set_ini,axis=0)
+ coordmin = np.min(point_set_ini,axis=0)
+ nsubvolume_x = np.ceil((coordmax[0]-coordmin[0])/1.5).astype(np.int32)
+ nsubvolume_y = np.ceil((coordmax[1]-coordmin[1])/1.5).astype(np.int32)
+ point_sets = list()
+ semantic_segs = list()
+ sample_weights = list()
+ isvalid = False
+ for i in range(nsubvolume_x):
+ for j in range(nsubvolume_y):
+ curmin = coordmin+[i*1.5,j*1.5,0]
+ curmax = coordmin+[(i+1)*1.5,(j+1)*1.5,coordmax[2]-coordmin[2]]
+ curchoice = np.sum((point_set_ini>=(curmin-0.2))*(point_set_ini<=(curmax+0.2)),axis=1)==3
+ cur_point_set = point_set_ini[curchoice,:]
+ cur_semantic_seg = semantic_seg_ini[curchoice]
+ if len(cur_semantic_seg)==0:
+ continue
+ mask = np.sum((cur_point_set>=(curmin-0.001))*(cur_point_set<=(curmax+0.001)),axis=1)==3
+ choice = np.random.choice(len(cur_semantic_seg), self.npoints, replace=True)
+ point_set = cur_point_set[choice,:] # Nx3
+ semantic_seg = cur_semantic_seg[choice] # N
+ mask = mask[choice]
+ if sum(mask)/float(len(mask))<0.01:
+ continue
+ sample_weight = self.labelweights[semantic_seg]
+ sample_weight *= mask # N
+ point_sets.append(np.expand_dims(point_set,0)) # 1xNx3
+ semantic_segs.append(np.expand_dims(semantic_seg,0)) # 1xN
+ sample_weights.append(np.expand_dims(sample_weight,0)) # 1xN
+ point_sets = np.concatenate(tuple(point_sets),axis=0)
+ semantic_segs = np.concatenate(tuple(semantic_segs),axis=0)
+ sample_weights = np.concatenate(tuple(sample_weights),axis=0)
+ return point_sets, semantic_segs, sample_weights
+ def __len__(self):
+ return len(self.scene_points_list)
+
+class ScannetDatasetVirtualScan():
+ def __init__(self, root, npoints=8192, split='train'):
+ self.npoints = npoints
+ self.root = root
+ self.split = split
+ self.data_filename = os.path.join(self.root, 'scannet_%s.pickle'%(split))
+ with open(self.data_filename,'rb') as fp:
+ self.scene_points_list = pickle.load(fp)
+ self.semantic_labels_list = pickle.load(fp)
+ if split=='train':
+ labelweights = np.zeros(21)
+ for seg in self.semantic_labels_list:
+ tmp,_ = np.histogram(seg,range(22))
+ labelweights += tmp
+ labelweights = labelweights.astype(np.float32)
+ labelweights = labelweights/np.sum(labelweights)
+ self.labelweights = 1/np.log(1.2+labelweights)
+ elif split=='test':
+ self.labelweights = np.ones(21)
+ def __getitem__(self, index):
+ point_set_ini = self.scene_points_list[index]
+ semantic_seg_ini = self.semantic_labels_list[index].astype(np.int32)
+ sample_weight_ini = self.labelweights[semantic_seg_ini]
+ point_sets = list()
+ semantic_segs = list()
+ sample_weights = list()
+ for i in xrange(8):
+ smpidx = scene_util.virtual_scan(point_set_ini,mode=i)
+ if len(smpidx)<300:
+ continue
+ point_set = point_set_ini[smpidx,:]
+ semantic_seg = semantic_seg_ini[smpidx]
+ sample_weight = sample_weight_ini[smpidx]
+ choice = np.random.choice(len(semantic_seg), self.npoints, replace=True)
+ point_set = point_set[choice,:] # Nx3
+ semantic_seg = semantic_seg[choice] # N
+ sample_weight = sample_weight[choice] # N
+ point_sets.append(np.expand_dims(point_set,0)) # 1xNx3
+ semantic_segs.append(np.expand_dims(semantic_seg,0)) # 1xN
+ sample_weights.append(np.expand_dims(sample_weight,0)) # 1xN
+ point_sets = np.concatenate(tuple(point_sets),axis=0)
+ semantic_segs = np.concatenate(tuple(semantic_segs),axis=0)
+ sample_weights = np.concatenate(tuple(sample_weights),axis=0)
+ return point_sets, semantic_segs, sample_weights
+ def __len__(self):
+ return len(self.scene_points_list)
+
+if __name__=='__main__':
+ d = ScannetDatasetWholeScene(root = './data', split='test', npoints=8192)
+ labelweights_vox = np.zeros(21)
+ for ii in xrange(len(d)):
+ print ii
+ ps,seg,smpw = d[ii]
+ for b in xrange(ps.shape[0]):
+ _, uvlabel, _ = pc_util.point_cloud_label_to_surface_voxel_label_fast(ps[b,smpw[b,:]>0,:], seg[b,smpw[b,:]>0], res=0.02)
+ tmp,_ = np.histogram(uvlabel,range(22))
+ labelweights_vox += tmp
+ print labelweights_vox[1:].astype(np.float32)/np.sum(labelweights_vox[1:].astype(np.float32))
+ exit()
+
+
diff --git a/pointnet2/scannet/scene_util.py b/pointnet2/scannet/scene_util.py
new file mode 100644
index 0000000..b50c421
--- /dev/null
+++ b/pointnet2/scannet/scene_util.py
@@ -0,0 +1,73 @@
+import os
+import sys
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(BASE_DIR)
+
+import numpy as np
+from sklearn.neighbors import NearestNeighbors
+from numpy import linalg as la
+import scipy.io as sio
+
+def cart2sph(xyz):
+ xy = xyz[:,0]**2+xyz[:,1]**2
+ aer = np.zeros(xyz.shape)
+ aer[:,2] = np.sqrt(xy+xyz[:,2]**2)
+ aer[:,1] = np.arctan2(xyz[:,2],np.sqrt(xy))
+ aer[:,0] = np.arctan2(xyz[:,1],xyz[:,0])
+ return aer
+
+# generate virtual scan of a scene by subsampling the point cloud
+def virtual_scan(xyz, mode=-1):
+ camloc = np.mean(xyz,axis=0)
+ camloc[2] = 1.5 # human height
+ if mode==-1:
+ view_dr = np.array([2*np.pi*np.random.random(), np.pi/10*(np.random.random()-0.75)])
+ camloc[:2] -= (0.8+0.7*np.random.random())*np.array([np.cos(view_dr[0]),np.sin(view_dr[0])])
+ else:
+ view_dr = np.array([np.pi/4*mode, 0])
+ camloc[:2] -= np.array([np.cos(view_dr[0]),np.sin(view_dr[0])])
+ ct_ray_dr = np.array([np.cos(view_dr[1])*np.cos(view_dr[0]), np.cos(view_dr[1])*np.sin(view_dr[0]), np.sin(view_dr[1])])
+ hr_dr = np.cross(ct_ray_dr, np.array([0,0,1]))
+ hr_dr /= la.norm(hr_dr)
+ vt_dr = np.cross(hr_dr, ct_ray_dr)
+ vt_dr /= la.norm(vt_dr)
+ xx = np.linspace(-0.6,0.6,200) #200
+ yy = np.linspace(-0.45,0.45,150) #150
+ xx, yy = np.meshgrid(xx,yy)
+ xx = xx.reshape(-1,1)
+ yy = yy.reshape(-1,1)
+ rays = xx*hr_dr.reshape(1,-1)+yy*vt_dr.reshape(1,-1)+ct_ray_dr.reshape(1,-1)
+ rays_aer = cart2sph(rays)
+ local_xyz = xyz-camloc.reshape(1,-1)
+ local_aer = cart2sph(local_xyz)
+ nbrs = NearestNeighbors(n_neighbors=1, algorithm='kd_tree').fit(rays_aer[:,:2])
+ mindd, minidx = nbrs.kneighbors(local_aer[:,:2])
+ mindd = mindd.reshape(-1)
+ minidx = minidx.reshape(-1)
+
+ sub_idx = mindd<0.01
+ if sum(sub_idx)<100:
+ return np.ones(0)
+ sub_r = local_aer[sub_idx,2]
+ sub_minidx = minidx[sub_idx]
+ min_r = float('inf')*np.ones(np.max(sub_minidx)+1)
+ for i in xrange(len(sub_r)):
+ if sub_r[i]min_r[sub_minidx[i]]:
+ sub_smpidx[i] = 0
+ smpidx = np.where(sub_idx)[0]
+ smpidx = smpidx[sub_smpidx==1]
+ return smpidx
+
+if __name__=='__main__':
+ pc = np.load('scannet_dataset/scannet_scenes/scene0015_00.npy')
+ print pc.shape
+ xyz = pc[:,:3]
+ seg = pc[:,7]
+ smpidx = virtual_scan(xyz,mode=2)
+ xyz = xyz[smpidx,:]
+ seg = seg[smpidx]
+ sio.savemat('tmp.mat',{'pc':xyz,'seg':seg})
diff --git a/pointnet2/scannet/train.py b/pointnet2/scannet/train.py
new file mode 100644
index 0000000..9841907
--- /dev/null
+++ b/pointnet2/scannet/train.py
@@ -0,0 +1,433 @@
+import argparse
+import math
+from datetime import datetime
+#import h5pyprovider
+import numpy as np
+import tensorflow as tf
+import socket
+import importlib
+import os
+import sys
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+ROOT_DIR = os.path.dirname(BASE_DIR)
+sys.path.append(BASE_DIR) # model
+sys.path.append(ROOT_DIR) # provider
+sys.path.append(os.path.join(ROOT_DIR, 'utils'))
+import provider
+import tf_util
+import pc_util
+sys.path.append(os.path.join(ROOT_DIR, 'data_prep'))
+import scannet_dataset
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--gpu', type=int, default=0, help='GPU to use [default: GPU 0]')
+parser.add_argument('--model', default='model', help='Model name [default: model]')
+parser.add_argument('--log_dir', default='log', help='Log dir [default: log]')
+parser.add_argument('--num_point', type=int, default=8192, help='Point Number [default: 8192]')
+parser.add_argument('--max_epoch', type=int, default=201, help='Epoch to run [default: 201]')
+parser.add_argument('--batch_size', type=int, default=32, help='Batch Size during training [default: 32]')
+parser.add_argument('--learning_rate', type=float, default=0.001, help='Initial learning rate [default: 0.001]')
+parser.add_argument('--momentum', type=float, default=0.9, help='Initial learning rate [default: 0.9]')
+parser.add_argument('--optimizer', default='adam', help='adam or momentum [default: adam]')
+parser.add_argument('--decay_step', type=int, default=200000, help='Decay step for lr decay [default: 200000]')
+parser.add_argument('--decay_rate', type=float, default=0.7, help='Decay rate for lr decay [default: 0.7]')
+FLAGS = parser.parse_args()
+
+EPOCH_CNT = 0
+
+BATCH_SIZE = FLAGS.batch_size
+NUM_POINT = FLAGS.num_point
+MAX_EPOCH = FLAGS.max_epoch
+BASE_LEARNING_RATE = FLAGS.learning_rate
+GPU_INDEX = FLAGS.gpu
+MOMENTUM = FLAGS.momentum
+OPTIMIZER = FLAGS.optimizer
+DECAY_STEP = FLAGS.decay_step
+DECAY_RATE = FLAGS.decay_rate
+
+MODEL = importlib.import_module(FLAGS.model) # import network module
+MODEL_FILE = os.path.join(BASE_DIR, FLAGS.model+'.py')
+LOG_DIR = FLAGS.log_dir
+if not os.path.exists(LOG_DIR): os.mkdir(LOG_DIR)
+os.system('cp %s %s' % (MODEL_FILE, LOG_DIR)) # bkp of model def
+os.system('cp train.py %s' % (LOG_DIR)) # bkp of train procedure
+LOG_FOUT = open(os.path.join(LOG_DIR, 'log_train.txt'), 'w')
+LOG_FOUT.write(str(FLAGS)+'\n')
+
+BN_INIT_DECAY = 0.5
+BN_DECAY_DECAY_RATE = 0.5
+BN_DECAY_DECAY_STEP = float(DECAY_STEP)
+BN_DECAY_CLIP = 0.99
+
+HOSTNAME = socket.gethostname()
+
+NUM_CLASSES = 21
+
+# Shapenet official train/test split
+DATA_PATH = os.path.join(ROOT_DIR,'data','scannet_data_pointnet2')
+TRAIN_DATASET = scannet_dataset.ScannetDataset(root=DATA_PATH, npoints=NUM_POINT, split='train')
+TEST_DATASET = scannet_dataset.ScannetDataset(root=DATA_PATH, npoints=NUM_POINT, split='test')
+TEST_DATASET_WHOLE_SCENE = scannet_dataset.ScannetDatasetWholeScene(root=DATA_PATH, npoints=NUM_POINT, split='test')
+
+
+def log_string(out_str):
+ LOG_FOUT.write(out_str+'\n')
+ LOG_FOUT.flush()
+ print(out_str)
+
+def get_learning_rate(batch):
+ learning_rate = tf.train.exponential_decay(
+ BASE_LEARNING_RATE, # Base learning rate.
+ batch * BATCH_SIZE, # Current index into the dataset.
+ DECAY_STEP, # Decay step.
+ DECAY_RATE, # Decay rate.
+ staircase=True)
+ learing_rate = tf.maximum(learning_rate, 0.00001) # CLIP THE LEARNING RATE!
+ return learning_rate
+
+def get_bn_decay(batch):
+ bn_momentum = tf.train.exponential_decay(
+ BN_INIT_DECAY,
+ batch*BATCH_SIZE,
+ BN_DECAY_DECAY_STEP,
+ BN_DECAY_DECAY_RATE,
+ staircase=True)
+ bn_decay = tf.minimum(BN_DECAY_CLIP, 1 - bn_momentum)
+ return bn_decay
+
+def train():
+ with tf.Graph().as_default():
+ with tf.device('/gpu:'+str(GPU_INDEX)):
+ pointclouds_pl, labels_pl, smpws_pl = MODEL.placeholder_inputs(BATCH_SIZE, NUM_POINT)
+ is_training_pl = tf.placeholder(tf.bool, shape=())
+ print is_training_pl
+
+ # Note the global_step=batch parameter to minimize.
+ # That tells the optimizer to helpfully increment the 'batch' parameter for you every time it trains.
+ batch = tf.Variable(0)
+ bn_decay = get_bn_decay(batch)
+ tf.summary.scalar('bn_decay', bn_decay)
+
+ print "--- Get model and loss"
+ # Get model and loss
+ pred, end_points = MODEL.get_model(pointclouds_pl, is_training_pl, NUM_CLASSES, bn_decay=bn_decay)
+ loss = MODEL.get_loss(pred, labels_pl, smpws_pl)
+ tf.summary.scalar('loss', loss)
+
+ correct = tf.equal(tf.argmax(pred, 2), tf.to_int64(labels_pl))
+ accuracy = tf.reduce_sum(tf.cast(correct, tf.float32)) / float(BATCH_SIZE*NUM_POINT)
+ tf.summary.scalar('accuracy', accuracy)
+
+ print "--- Get training operator"
+ # Get training operator
+ learning_rate = get_learning_rate(batch)
+ tf.summary.scalar('learning_rate', learning_rate)
+ if OPTIMIZER == 'momentum':
+ optimizer = tf.train.MomentumOptimizer(learning_rate, momentum=MOMENTUM)
+ elif OPTIMIZER == 'adam':
+ optimizer = tf.train.AdamOptimizer(learning_rate)
+ train_op = optimizer.minimize(loss, global_step=batch)
+
+ # Add ops to save and restore all the variables.
+ saver = tf.train.Saver()
+
+ # Create a session
+ config = tf.ConfigProto()
+ config.gpu_options.allow_growth = True
+ config.allow_soft_placement = True
+ config.log_device_placement = False
+ sess = tf.Session(config=config)
+
+ # Add summary writers
+ merged = tf.summary.merge_all()
+ train_writer = tf.summary.FileWriter(os.path.join(LOG_DIR, 'train'), sess.graph)
+ test_writer = tf.summary.FileWriter(os.path.join(LOG_DIR, 'test'), sess.graph)
+
+ # Init variables
+ init = tf.global_variables_initializer()
+ sess.run(init)
+ #sess.run(init, {is_training_pl: True})
+
+ ops = {'pointclouds_pl': pointclouds_pl,
+ 'labels_pl': labels_pl,
+ 'smpws_pl': smpws_pl,
+ 'is_training_pl': is_training_pl,
+ 'pred': pred,
+ 'loss': loss,
+ 'train_op': train_op,
+ 'merged': merged,
+ 'step': batch,
+ 'end_points': end_points}
+
+ best_acc = -1
+ for epoch in range(MAX_EPOCH):
+ log_string('**** EPOCH %03d ****' % (epoch))
+ sys.stdout.flush()
+
+ train_one_epoch(sess, ops, train_writer)
+ if epoch%5==0:
+ acc = eval_one_epoch(sess, ops, test_writer)
+ acc = eval_whole_scene_one_epoch(sess, ops, test_writer)
+ if acc > best_acc:
+ best_acc = acc
+ save_path = saver.save(sess, os.path.join(LOG_DIR, "best_model_epoch_%03d.ckpt"%(epoch)))
+ log_string("Model saved in file: %s" % save_path)
+
+ # Save the variables to disk.
+ if epoch % 10 == 0:
+ save_path = saver.save(sess, os.path.join(LOG_DIR, "model.ckpt"))
+ log_string("Model saved in file: %s" % save_path)
+
+def get_batch_wdp(dataset, idxs, start_idx, end_idx):
+ bsize = end_idx-start_idx
+ batch_data = np.zeros((bsize, NUM_POINT, 3))
+ batch_label = np.zeros((bsize, NUM_POINT), dtype=np.int32)
+ batch_smpw = np.zeros((bsize, NUM_POINT), dtype=np.float32)
+ for i in range(bsize):
+ ps,seg,smpw = dataset[idxs[i+start_idx]]
+ batch_data[i,...] = ps
+ batch_label[i,:] = seg
+ batch_smpw[i,:] = smpw
+
+ dropout_ratio = np.random.random()*0.875 # 0-0.875
+ drop_idx = np.where(np.random.random((ps.shape[0]))<=dropout_ratio)[0]
+ batch_data[i,drop_idx,:] = batch_data[i,0,:]
+ batch_label[i,drop_idx] = batch_label[i,0]
+ batch_smpw[i,drop_idx] *= 0
+ return batch_data, batch_label, batch_smpw
+
+def get_batch(dataset, idxs, start_idx, end_idx):
+ bsize = end_idx-start_idx
+ batch_data = np.zeros((bsize, NUM_POINT, 3))
+ batch_label = np.zeros((bsize, NUM_POINT), dtype=np.int32)
+ batch_smpw = np.zeros((bsize, NUM_POINT), dtype=np.float32)
+ for i in range(bsize):
+ ps,seg,smpw = dataset[idxs[i+start_idx]]
+ batch_data[i,...] = ps
+ batch_label[i,:] = seg
+ batch_smpw[i,:] = smpw
+ return batch_data, batch_label, batch_smpw
+
+def train_one_epoch(sess, ops, train_writer):
+ """ ops: dict mapping from string to tf ops """
+ is_training = True
+
+ # Shuffle train samples
+ train_idxs = np.arange(0, len(TRAIN_DATASET))
+ np.random.shuffle(train_idxs)
+ num_batches = len(TRAIN_DATASET)/BATCH_SIZE
+
+ log_string(str(datetime.now()))
+
+ total_correct = 0
+ total_seen = 0
+ loss_sum = 0
+ for batch_idx in range(num_batches):
+ start_idx = batch_idx * BATCH_SIZE
+ end_idx = (batch_idx+1) * BATCH_SIZE
+ batch_data, batch_label, batch_smpw = get_batch_wdp(TRAIN_DATASET, train_idxs, start_idx, end_idx)
+ # Augment batched point clouds by rotation
+ aug_data = provider.rotate_point_cloud_z(batch_data)
+ feed_dict = {ops['pointclouds_pl']: aug_data,
+ ops['labels_pl']: batch_label,
+ ops['smpws_pl']:batch_smpw,
+ ops['is_training_pl']: is_training,}
+ summary, step, _, loss_val, pred_val = sess.run([ops['merged'], ops['step'],
+ ops['train_op'], ops['loss'], ops['pred']], feed_dict=feed_dict)
+ train_writer.add_summary(summary, step)
+ pred_val = np.argmax(pred_val, 2)
+ correct = np.sum(pred_val == batch_label)
+ total_correct += correct
+ total_seen += (BATCH_SIZE*NUM_POINT)
+ loss_sum += loss_val
+ if (batch_idx+1)%10 == 0:
+ log_string(' -- %03d / %03d --' % (batch_idx+1, num_batches))
+ log_string('mean loss: %f' % (loss_sum / 10))
+ log_string('accuracy: %f' % (total_correct / float(total_seen)))
+ total_correct = 0
+ total_seen = 0
+ loss_sum = 0
+
+# evaluate on randomly chopped scenes
+def eval_one_epoch(sess, ops, test_writer):
+ """ ops: dict mapping from string to tf ops """
+ global EPOCH_CNT
+ is_training = False
+ test_idxs = np.arange(0, len(TEST_DATASET))
+ num_batches = len(TEST_DATASET)/BATCH_SIZE
+
+ total_correct = 0
+ total_seen = 0
+ loss_sum = 0
+ total_seen_class = [0 for _ in range(NUM_CLASSES)]
+ total_correct_class = [0 for _ in range(NUM_CLASSES)]
+
+ total_correct_vox = 0
+ total_seen_vox = 0
+ total_seen_class_vox = [0 for _ in range(NUM_CLASSES)]
+ total_correct_class_vox = [0 for _ in range(NUM_CLASSES)]
+
+ log_string(str(datetime.now()))
+ log_string('---- EPOCH %03d EVALUATION ----'%(EPOCH_CNT))
+
+ labelweights = np.zeros(21)
+ labelweights_vox = np.zeros(21)
+ for batch_idx in range(num_batches):
+ start_idx = batch_idx * BATCH_SIZE
+ end_idx = (batch_idx+1) * BATCH_SIZE
+ batch_data, batch_label, batch_smpw = get_batch(TEST_DATASET, test_idxs, start_idx, end_idx)
+
+ aug_data = provider.rotate_point_cloud_z(batch_data)
+
+ feed_dict = {ops['pointclouds_pl']: aug_data,
+ ops['labels_pl']: batch_label,
+ ops['smpws_pl']: batch_smpw,
+ ops['is_training_pl']: is_training}
+ summary, step, loss_val, pred_val = sess.run([ops['merged'], ops['step'],
+ ops['loss'], ops['pred']], feed_dict=feed_dict)
+ test_writer.add_summary(summary, step)
+ pred_val = np.argmax(pred_val, 2) # BxN
+ correct = np.sum((pred_val == batch_label) & (batch_label>0) & (batch_smpw>0)) # evaluate only on 20 categories but not unknown
+ total_correct += correct
+ total_seen += np.sum((batch_label>0) & (batch_smpw>0))
+ loss_sum += loss_val
+ tmp,_ = np.histogram(batch_label,range(22))
+ labelweights += tmp
+ for l in range(NUM_CLASSES):
+ total_seen_class[l] += np.sum((batch_label==l) & (batch_smpw>0))
+ total_correct_class[l] += np.sum((pred_val==l) & (batch_label==l) & (batch_smpw>0))
+
+ for b in xrange(batch_label.shape[0]):
+ _, uvlabel, _ = pc_util.point_cloud_label_to_surface_voxel_label_fast(aug_data[b,batch_smpw[b,:]>0,:], np.concatenate((np.expand_dims(batch_label[b,batch_smpw[b,:]>0],1),np.expand_dims(pred_val[b,batch_smpw[b,:]>0],1)),axis=1), res=0.02)
+ total_correct_vox += np.sum((uvlabel[:,0]==uvlabel[:,1])&(uvlabel[:,0]>0))
+ total_seen_vox += np.sum(uvlabel[:,0]>0)
+ tmp,_ = np.histogram(uvlabel[:,0],range(22))
+ labelweights_vox += tmp
+ for l in range(NUM_CLASSES):
+ total_seen_class_vox[l] += np.sum(uvlabel[:,0]==l)
+ total_correct_class_vox[l] += np.sum((uvlabel[:,0]==l) & (uvlabel[:,1]==l))
+
+ log_string('eval mean loss: %f' % (loss_sum / float(num_batches)))
+ log_string('eval point accuracy vox: %f'% (total_correct_vox / float(total_seen_vox)))
+ log_string('eval point avg class acc vox: %f' % (np.mean(np.array(total_correct_class_vox[1:])/(np.array(total_seen_class_vox[1:],dtype=np.float)+1e-6))))
+ log_string('eval point accuracy: %f'% (total_correct / float(total_seen)))
+ log_string('eval point avg class acc: %f' % (np.mean(np.array(total_correct_class[1:])/(np.array(total_seen_class[1:],dtype=np.float)+1e-6))))
+ labelweights_vox = labelweights_vox[1:].astype(np.float32)/np.sum(labelweights_vox[1:].astype(np.float32))
+ caliweights = np.array([0.388,0.357,0.038,0.033,0.017,0.02,0.016,0.025,0.002,0.002,0.002,0.007,0.006,0.022,0.004,0.0004,0.003,0.002,0.024,0.029])
+ log_string('eval point calibrated average acc: %f' % (np.average(np.array(total_correct_class[1:])/(np.array(total_seen_class[1:],dtype=np.float)+1e-6),weights=caliweights)))
+ per_class_str = 'vox based --------'
+ for l in range(1,NUM_CLASSES):
+ per_class_str += 'class %d weight: %f, acc: %f; ' % (l,labelweights_vox[l-1],total_correct_class[l]/float(total_seen_class[l]))
+ log_string(per_class_str)
+ EPOCH_CNT += 1
+ return total_correct/float(total_seen)
+
+# evaluate on whole scenes to generate numbers provided in the paper
+def eval_whole_scene_one_epoch(sess, ops, test_writer):
+ """ ops: dict mapping from string to tf ops """
+ global EPOCH_CNT
+ is_training = False
+ test_idxs = np.arange(0, len(TEST_DATASET_WHOLE_SCENE))
+ num_batches = len(TEST_DATASET_WHOLE_SCENE)
+
+ total_correct = 0
+ total_seen = 0
+ loss_sum = 0
+ total_seen_class = [0 for _ in range(NUM_CLASSES)]
+ total_correct_class = [0 for _ in range(NUM_CLASSES)]
+
+ total_correct_vox = 0
+ total_seen_vox = 0
+ total_seen_class_vox = [0 for _ in range(NUM_CLASSES)]
+ total_correct_class_vox = [0 for _ in range(NUM_CLASSES)]
+
+ log_string(str(datetime.now()))
+ log_string('---- EPOCH %03d EVALUATION WHOLE SCENE----'%(EPOCH_CNT))
+
+ labelweights = np.zeros(21)
+ labelweights_vox = np.zeros(21)
+ is_continue_batch = False
+
+ extra_batch_data = np.zeros((0,NUM_POINT,3))
+ extra_batch_label = np.zeros((0,NUM_POINT))
+ extra_batch_smpw = np.zeros((0,NUM_POINT))
+ for batch_idx in range(num_batches):
+ if not is_continue_batch:
+ batch_data, batch_label, batch_smpw = TEST_DATASET_WHOLE_SCENE[batch_idx]
+ batch_data = np.concatenate((batch_data,extra_batch_data),axis=0)
+ batch_label = np.concatenate((batch_label,extra_batch_label),axis=0)
+ batch_smpw = np.concatenate((batch_smpw,extra_batch_smpw),axis=0)
+ else:
+ batch_data_tmp, batch_label_tmp, batch_smpw_tmp = TEST_DATASET_WHOLE_SCENE[batch_idx]
+ batch_data = np.concatenate((batch_data,batch_data_tmp),axis=0)
+ batch_label = np.concatenate((batch_label,batch_label_tmp),axis=0)
+ batch_smpw = np.concatenate((batch_smpw,batch_smpw_tmp),axis=0)
+ if batch_data.shape[0]0) & (batch_smpw>0)) # evaluate only on 20 categories but not unknown
+ total_correct += correct
+ total_seen += np.sum((batch_label>0) & (batch_smpw>0))
+ loss_sum += loss_val
+ tmp,_ = np.histogram(batch_label,range(22))
+ labelweights += tmp
+ for l in range(NUM_CLASSES):
+ total_seen_class[l] += np.sum((batch_label==l) & (batch_smpw>0))
+ total_correct_class[l] += np.sum((pred_val==l) & (batch_label==l) & (batch_smpw>0))
+
+ for b in xrange(batch_label.shape[0]):
+ _, uvlabel, _ = pc_util.point_cloud_label_to_surface_voxel_label_fast(aug_data[b,batch_smpw[b,:]>0,:], np.concatenate((np.expand_dims(batch_label[b,batch_smpw[b,:]>0],1),np.expand_dims(pred_val[b,batch_smpw[b,:]>0],1)),axis=1), res=0.02)
+ total_correct_vox += np.sum((uvlabel[:,0]==uvlabel[:,1])&(uvlabel[:,0]>0))
+ total_seen_vox += np.sum(uvlabel[:,0]>0)
+ tmp,_ = np.histogram(uvlabel[:,0],range(22))
+ labelweights_vox += tmp
+ for l in range(NUM_CLASSES):
+ total_seen_class_vox[l] += np.sum(uvlabel[:,0]==l)
+ total_correct_class_vox[l] += np.sum((uvlabel[:,0]==l) & (uvlabel[:,1]==l))
+
+ log_string('eval whole scene mean loss: %f' % (loss_sum / float(num_batches)))
+ log_string('eval whole scene point accuracy vox: %f'% (total_correct_vox / float(total_seen_vox)))
+ log_string('eval whole scene point avg class acc vox: %f' % (np.mean(np.array(total_correct_class_vox[1:])/(np.array(total_seen_class_vox[1:],dtype=np.float)+1e-6))))
+ log_string('eval whole scene point accuracy: %f'% (total_correct / float(total_seen)))
+ log_string('eval whole scene point avg class acc: %f' % (np.mean(np.array(total_correct_class[1:])/(np.array(total_seen_class[1:],dtype=np.float)+1e-6))))
+ labelweights = labelweights[1:].astype(np.float32)/np.sum(labelweights[1:].astype(np.float32))
+ labelweights_vox = labelweights_vox[1:].astype(np.float32)/np.sum(labelweights_vox[1:].astype(np.float32))
+ caliweights = np.array([0.388,0.357,0.038,0.033,0.017,0.02,0.016,0.025,0.002,0.002,0.002,0.007,0.006,0.022,0.004,0.0004,0.003,0.002,0.024,0.029])
+ caliacc = np.average(np.array(total_correct_class_vox[1:])/(np.array(total_seen_class_vox[1:],dtype=np.float)+1e-6),weights=caliweights)
+ log_string('eval whole scene point calibrated average acc vox: %f' % caliacc)
+
+ per_class_str = 'vox based --------'
+ for l in range(1,NUM_CLASSES):
+ per_class_str += 'class %d weight: %f, acc: %f; ' % (l,labelweights_vox[l-1],total_correct_class_vox[l]/float(total_seen_class_vox[l]))
+ log_string(per_class_str)
+ EPOCH_CNT += 1
+ return caliacc
+
+
+if __name__ == "__main__":
+ log_string('pid: %s'%(str(os.getpid())))
+ train()
+ LOG_FOUT.close()
diff --git a/pointnet2/tf_ops/3d_interpolation/compile.sh b/pointnet2/tf_ops/3d_interpolation/compile.sh
new file mode 100644
index 0000000..ee5c18b
--- /dev/null
+++ b/pointnet2/tf_ops/3d_interpolation/compile.sh
@@ -0,0 +1,5 @@
+TF_CFLAGS=( $(python -c 'import tensorflow as tf; print(" ".join(tf.sysconfig.get_compile_flags()))') )
+TF_LFLAGS=( $(python -c 'import tensorflow as tf; print(" ".join(tf.sysconfig.get_link_flags()))') )
+
+g++ -std=c++11 -shared -o tf_interpolate_so.so tf_interpolate.cpp \
+ ${TF_CFLAGS[@]} -fPIC -lcudart ${TF_LFLAGS[@]}
diff --git a/pointnet2/tf_ops/3d_interpolation/interpolate.cpp b/pointnet2/tf_ops/3d_interpolation/interpolate.cpp
new file mode 100644
index 0000000..b7d0dd0
--- /dev/null
+++ b/pointnet2/tf_ops/3d_interpolation/interpolate.cpp
@@ -0,0 +1,169 @@
+#include
+#include
+#include // memset
+#include // rand, RAND_MAX
+#include // sqrtf
+#include
+#include
+using namespace std;
+float randomf(){
+ return (rand()+0.5)/(RAND_MAX+1.0);
+}
+static double get_time(){
+ timespec tp;
+ clock_gettime(CLOCK_MONOTONIC,&tp);
+ return tp.tv_sec+tp.tv_nsec*1e-9;
+}
+
+// Find three nearest neigbors with square distance
+// input: xyz1 (b,n,3), xyz2(b,m,3)
+// output: dist (b,n,3), idx (b,n,3)
+void threenn_cpu(int b, int n, int m, const float *xyz1, const float *xyz2, float *dist, int *idx) {
+ for (int i=0;i
+#include
+#include // memset
+#include // rand, RAND_MAX
+#include // sqrtf
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/shape_inference.h"
+#include "tensorflow/core/framework/common_shape_fns.h"
+using namespace tensorflow;
+
+REGISTER_OP("ThreeNN")
+ .Input("xyz1: float32")
+ .Input("xyz2: float32")
+ .Output("dist: float32")
+ .Output("idx: int32")
+ .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
+ c->set_output(0, c->input(0));
+ c->set_output(1, c->input(0));
+ return Status::OK();
+ });
+REGISTER_OP("ThreeInterpolate")
+ .Input("points: float32")
+ .Input("idx: int32")
+ .Input("weight: float32")
+ .Output("out: float32")
+ .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
+ ::tensorflow::shape_inference::ShapeHandle dims1; // (b,m,c)
+ c->WithRank(c->input(0), 3, &dims1);
+ ::tensorflow::shape_inference::ShapeHandle dims2; // (b,n,3)
+ c->WithRank(c->input(1), 3, &dims2);
+ // (b,n,c)
+ ::tensorflow::shape_inference::ShapeHandle output = c->MakeShape({c->Dim(dims1, 0), c->Dim(dims2, 1), c->Dim(dims1, 2)});
+ c->set_output(0, output);
+ return Status::OK();
+ });
+REGISTER_OP("ThreeInterpolateGrad")
+ .Input("points: float32")
+ .Input("idx: int32")
+ .Input("weight: float32")
+ .Input("grad_out: float32")
+ .Output("grad_points: float32")
+ .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
+ c->set_output(0, c->input(0));
+ return Status::OK();
+ });
+
+float randomf(){
+ return (rand()+0.5)/(RAND_MAX+1.0);
+}
+static double get_time(){
+ timespec tp;
+ clock_gettime(CLOCK_MONOTONIC,&tp);
+ return tp.tv_sec+tp.tv_nsec*1e-9;
+}
+
+// Find three nearest neigbors with square distance
+// input: xyz1 (b,n,3), xyz2(b,m,3)
+// output: dist (b,n,3), idx (b,n,3)
+void threenn_cpu(int b, int n, int m, const float *xyz1, const float *xyz2, float *dist, int *idx) {
+ for (int i=0;iinput(0);
+ OP_REQUIRES(context, xyz1_tensor.dims()==3 && xyz1_tensor.shape().dim_size(2)==3, errors::InvalidArgument("ThreeNN expects (b,n,3) xyz1 shape."));
+ int b = xyz1_tensor.shape().dim_size(0);
+ int n = xyz1_tensor.shape().dim_size(1);
+
+ const Tensor& xyz2_tensor = context->input(1);
+ OP_REQUIRES(context, xyz2_tensor.dims()==3 && xyz2_tensor.shape().dim_size(2)==3, errors::InvalidArgument("ThreeNN expects (b,m,3) xyz2 shape."));
+ int m = xyz2_tensor.shape().dim_size(1);
+
+ Tensor *dist_tensor = nullptr;
+ OP_REQUIRES_OK(context, context->allocate_output(0, TensorShape{b,n,3}, &dist_tensor));
+ Tensor *idx_tensor = nullptr;
+ OP_REQUIRES_OK(context, context->allocate_output(1, TensorShape{b,n,3}, &idx_tensor));
+
+ auto xyz1_flat = xyz1_tensor.flat();
+ const float *xyz1 = &(xyz1_flat(0));
+ auto xyz2_flat = xyz2_tensor.flat();
+ const float *xyz2 = &(xyz2_flat(0));
+ auto dist_flat = dist_tensor->flat();
+ float *dist = &(dist_flat(0));
+ auto idx_flat = idx_tensor->flat();
+ int *idx = &(idx_flat(0));
+ threenn_cpu(b,n,m,xyz1,xyz2,dist,idx);
+ }
+};
+REGISTER_KERNEL_BUILDER(Name("ThreeNN").Device(DEVICE_CPU), ThreeNNOp);
+
+
+
+class ThreeInterpolateOp: public OpKernel{
+ public:
+ explicit ThreeInterpolateOp(OpKernelConstruction * context):OpKernel(context){}
+
+ void Compute(OpKernelContext * context) override {
+ const Tensor& points_tensor=context->input(0);
+ OP_REQUIRES(context, points_tensor.dims()==3, errors::InvalidArgument("ThreeInterpolate expects (b,m,c) points shape"));
+ int b = points_tensor.shape().dim_size(0);
+ int m = points_tensor.shape().dim_size(1);
+ int c = points_tensor.shape().dim_size(2);
+
+ const Tensor& idx_tensor=context->input(1);
+ OP_REQUIRES(context,idx_tensor.dims()==3 && idx_tensor.shape().dim_size(0)==b && idx_tensor.shape().dim_size(2)==3, errors::InvalidArgument("ThreeInterpolate expects (b,n,3) idx shape"));
+ int n = idx_tensor.shape().dim_size(1);
+ const Tensor& weight_tensor=context->input(2);
+ OP_REQUIRES(context,weight_tensor.dims()==3 && weight_tensor.shape().dim_size(0)==b && weight_tensor.shape().dim_size(1)==n && weight_tensor.shape().dim_size(2)==3, errors::InvalidArgument("ThreeInterpolate expects (b,n,3) weight shape"));
+
+ Tensor * out_tensor = nullptr;
+ OP_REQUIRES_OK(context, context->allocate_output(0,TensorShape{b,n,c}, &out_tensor));
+
+ auto points_flat = points_tensor.flat();
+ const float *points = &(points_flat(0));
+ auto idx_flat = idx_tensor.flat();
+ const int *idx = &(idx_flat(0));
+ auto weight_flat = weight_tensor.flat();
+ const float *weight = &(weight_flat(0));
+ auto out_flat = out_tensor->flat();
+ float *out = &(out_flat(0));
+ threeinterpolate_cpu(b,m,c,n,points,idx,weight,out);
+ }
+};
+REGISTER_KERNEL_BUILDER(Name("ThreeInterpolate").Device(DEVICE_CPU),ThreeInterpolateOp);
+
+
+class ThreeInterpolateGradOp: public OpKernel{
+ public:
+ explicit ThreeInterpolateGradOp(OpKernelConstruction * context):OpKernel(context){}
+
+ void Compute(OpKernelContext * context) override {
+ const Tensor& points_tensor=context->input(0);
+ OP_REQUIRES(context, points_tensor.dims()==3, errors::InvalidArgument("ThreeInterpolateGrad expects (b,m,c) points shape"));
+ int b = points_tensor.shape().dim_size(0);
+ int m = points_tensor.shape().dim_size(1);
+ int c = points_tensor.shape().dim_size(2);
+
+ const Tensor& idx_tensor=context->input(1);
+ OP_REQUIRES(context,idx_tensor.dims()==3 && idx_tensor.shape().dim_size(0)==b, errors::InvalidArgument("ThreeInterpolateGrad expects (b,n,3) idx shape"));
+ int n = idx_tensor.shape().dim_size(1);
+ const Tensor& weight_tensor=context->input(2);
+ OP_REQUIRES(context,weight_tensor.dims()==3 && weight_tensor.shape().dim_size(0)==b && weight_tensor.shape().dim_size(1)==n && weight_tensor.shape().dim_size(2)==3, errors::InvalidArgument("ThreeInterpolateGrad expects (b,n,3) weight shape"));
+
+ const Tensor& grad_out_tensor=context->input(3);
+ OP_REQUIRES(context,grad_out_tensor.dims()==3 && grad_out_tensor.shape().dim_size(0)==b && grad_out_tensor.shape().dim_size(1)==n && grad_out_tensor.shape().dim_size(2)==c, errors::InvalidArgument("ThreeInterpolateGrad expects (b,n,c) grad_out shape"));
+
+ Tensor * grad_points_tensor = nullptr;
+ OP_REQUIRES_OK(context, context->allocate_output(0,TensorShape{b,m,c}, &grad_points_tensor));
+
+ auto points_flat = points_tensor.flat();
+ const float *points = &(points_flat(0));
+ auto idx_flat = idx_tensor.flat();
+ const int *idx = &(idx_flat(0));
+ auto weight_flat = weight_tensor.flat();
+ const float *weight = &(weight_flat(0));
+ auto grad_out_flat = grad_out_tensor.flat();
+ const float *grad_out = &(grad_out_flat(0));
+ auto grad_points_flat = grad_points_tensor->flat();
+ float *grad_points = &(grad_points_flat(0));
+ memset(grad_points, 0, sizeof(float)*b*m*c);
+ threeinterpolate_grad_cpu(b,n,c,m,grad_out,idx,weight,grad_points);
+ }
+};
+REGISTER_KERNEL_BUILDER(Name("ThreeInterpolateGrad").Device(DEVICE_CPU),ThreeInterpolateGradOp);
+
+
diff --git a/pointnet2/tf_ops/3d_interpolation/tf_interpolate.py b/pointnet2/tf_ops/3d_interpolation/tf_interpolate.py
new file mode 100644
index 0000000..945ac17
--- /dev/null
+++ b/pointnet2/tf_ops/3d_interpolation/tf_interpolate.py
@@ -0,0 +1,65 @@
+from __future__ import print_function
+try:
+ import tensorflow.compat.v1 as tf
+ tf.disable_eager_execution()
+except:
+ import tensorflow as tf
+
+from tensorflow.python.framework import ops
+import sys
+import os
+BASE_DIR = os.path.dirname(__file__)
+sys.path.append(BASE_DIR)
+interpolate_module=tf.load_op_library(os.path.join(BASE_DIR, 'tf_interpolate_so.so'))
+def three_nn(xyz1, xyz2):
+ '''
+ Input:
+ xyz1: (b,n,3) float32 array, unknown points
+ xyz2: (b,m,3) float32 array, known points
+ Output:
+ dist: (b,n,3) float32 array, distances to known points
+ idx: (b,n,3) int32 array, indices to known points
+ '''
+ return interpolate_module.three_nn(xyz1, xyz2)
+ops.NoGradient('ThreeNN')
+def three_interpolate(points, idx, weight):
+ '''
+ Input:
+ points: (b,m,c) float32 array, known points
+ idx: (b,n,3) int32 array, indices to known points
+ weight: (b,n,3) float32 array, weights on known points
+ Output:
+ out: (b,n,c) float32 array, interpolated point values
+ '''
+ return interpolate_module.three_interpolate(points, idx, weight)
+@tf.RegisterGradient('ThreeInterpolate')
+def _three_interpolate_grad(op, grad_out):
+ points = op.inputs[0]
+ idx = op.inputs[1]
+ weight = op.inputs[2]
+ return [interpolate_module.three_interpolate_grad(points, idx, weight, grad_out), None, None]
+
+if __name__=='__main__':
+ import numpy as np
+ import time
+ np.random.seed(100)
+ pts = np.random.random((32,128,64)).astype('float32')
+ tmp1 = np.random.random((32,512,3)).astype('float32')
+ tmp2 = np.random.random((32,128,3)).astype('float32')
+ with tf.device('/cpu:0'):
+ points = tf.constant(pts)
+ xyz1 = tf.constant(tmp1)
+ xyz2 = tf.constant(tmp2)
+ dist, idx = three_nn(xyz1, xyz2)
+ weight = tf.ones_like(dist)/3.0
+ interpolated_points = three_interpolate(points, idx, weight)
+ with tf.Session('') as sess:
+ now = time.time()
+ for _ in range(100):
+ ret = sess.run(interpolated_points)
+ print(time.time() - now)
+ print(ret.shape, ret.dtype)
+ #print ret
+
+
+
diff --git a/pointnet2/tf_ops/3d_interpolation/tf_interpolate_compile.sh b/pointnet2/tf_ops/3d_interpolation/tf_interpolate_compile.sh
new file mode 100644
index 0000000..34c9fda
--- /dev/null
+++ b/pointnet2/tf_ops/3d_interpolation/tf_interpolate_compile.sh
@@ -0,0 +1,5 @@
+# TF1.2
+g++ -std=c++11 tf_interpolate.cpp -o tf_interpolate_so.so -shared -fPIC -I /usr/local/lib/python2.7/dist-packages/tensorflow/include -I /usr/local/cuda-8.0/include -lcudart -L /usr/local/cuda-8.0/lib64/ -O2 -D_GLIBCXX_USE_CXX11_ABI=0
+
+# TF1.4
+#g++ -std=c++11 tf_interpolate.cpp -o tf_interpolate_so.so -shared -fPIC -I /usr/local/lib/python2.7/dist-packages/tensorflow/include -I /usr/local/cuda-8.0/include -I /usr/local/lib/python2.7/dist-packages/tensorflow/include/external/nsync/public -lcudart -L /usr/local/cuda-8.0/lib64/ -L/usr/local/lib/python2.7/dist-packages/tensorflow -ltensorflow_framework -O2 -D_GLIBCXX_USE_CXX11_ABI=0
diff --git a/pointnet2/tf_ops/3d_interpolation/tf_interpolate_op_test.py b/pointnet2/tf_ops/3d_interpolation/tf_interpolate_op_test.py
new file mode 100644
index 0000000..5c365fa
--- /dev/null
+++ b/pointnet2/tf_ops/3d_interpolation/tf_interpolate_op_test.py
@@ -0,0 +1,29 @@
+from __future__ import print_function
+try:
+ import tensorflow.compat.v1 as tf
+ tf.disable_eager_execution()
+except:
+ import tensorflow as tf
+import numpy as np
+from tf_interpolate import three_nn, three_interpolate
+
+class GroupPointTest(tf.test.TestCase):
+ def test(self):
+ pass
+
+ def test_grad(self):
+ with self.test_session():
+ points = tf.constant(np.random.random((1,8,16)).astype('float32'))
+ print(points)
+ xyz1 = tf.constant(np.random.random((1,128,3)).astype('float32'))
+ xyz2 = tf.constant(np.random.random((1,8,3)).astype('float32'))
+ dist, idx = three_nn(xyz1, xyz2)
+ weight = tf.ones_like(dist)/3.0
+ interpolated_points = three_interpolate(points, idx, weight)
+ print(interpolated_points)
+ err = tf.test.compute_gradient_error(points, (1,8,16), interpolated_points, (1,128,16))
+ print(err)
+ self.assertLess(err, 1e-4)
+
+if __name__=='__main__':
+ tf.test.main()
diff --git a/pointnet2/tf_ops/3d_interpolation/tf_interpolate_so.so b/pointnet2/tf_ops/3d_interpolation/tf_interpolate_so.so
new file mode 100755
index 0000000..cfa756b
Binary files /dev/null and b/pointnet2/tf_ops/3d_interpolation/tf_interpolate_so.so differ
diff --git a/pointnet2/tf_ops/3d_interpolation/visu_interpolation.py b/pointnet2/tf_ops/3d_interpolation/visu_interpolation.py
new file mode 100644
index 0000000..5b5836e
--- /dev/null
+++ b/pointnet2/tf_ops/3d_interpolation/visu_interpolation.py
@@ -0,0 +1,44 @@
+''' Visualize part segmentation '''
+import os
+import sys
+ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+sys.path.append('/home/rqi/Projects/toolkits/visualization')
+from show3d_balls import showpoints
+import numpy as np
+from tf_interpolate import three_nn, three_interpolate
+import tensorflow as tf
+
+
+pts2 = np.array([[0,0,1],[1,0,0],[0,1,0],[1,1,0]]).astype('float32')
+xyz1 = np.random.random((100,3)).astype('float32')
+xyz2 = np.array([[0,0,0],[1,0,0],[0,1,0],[1,1,1]]).astype('float32')
+
+def fun(xyz1,xyz2,pts2):
+ with tf.device('/cpu:0'):
+ points = tf.constant(np.expand_dims(pts2,0))
+ xyz1 = tf.constant(np.expand_dims(xyz1,0))
+ xyz2 = tf.constant(np.expand_dims(xyz2,0))
+ dist, idx = three_nn(xyz1, xyz2)
+ #weight = tf.ones_like(dist)/3.0
+ dist = tf.maximum(dist, 1e-10)
+ norm = tf.reduce_sum((1.0/dist),axis=2,keep_dims=True)
+ norm = tf.tile(norm, [1,1,3])
+ print norm
+ weight = (1.0/dist) / norm
+ interpolated_points = three_interpolate(points, idx, weight)
+ with tf.Session('') as sess:
+ tmp,pts1,d,w = sess.run([xyz1, interpolated_points, dist, weight])
+ #print w
+ pts1 = pts1.squeeze()
+ return pts1
+
+pts1 = fun(xyz1,xyz2,pts2)
+all_pts = np.zeros((104,3))
+all_pts[0:100,:] = pts1
+all_pts[100:,:] = pts2
+all_xyz = np.zeros((104,3))
+all_xyz[0:100,:]=xyz1
+all_xyz[100:,:]=xyz2
+showpoints(xyz2, pts2, ballradius=8)
+showpoints(xyz1, pts1, ballradius=8)
+showpoints(all_xyz, all_pts, ballradius=8)
diff --git a/pointnet2/tf_ops/HowTO.md b/pointnet2/tf_ops/HowTO.md
new file mode 100644
index 0000000..0f621e0
--- /dev/null
+++ b/pointnet2/tf_ops/HowTO.md
@@ -0,0 +1,52 @@
+
+In a conda env with python 3.7:
+
+Install tensorflow 2 with pip:
+```
+pip install tensorflow-gpu==2.2
+```
+Install CUDNN with conda:
+```
+conda install cudnn=7.6
+```
+
+Install CUDA 10.1 from [here](https://developer.nvidia.com/cuda-10.1-download-archive-update2). If you have an unusual install path, also set `$CUDA_HOME` environment variable.
+
+
+Create a symbolic link of tensorflow_framework.so.x to tensorflow_framework.so [(Issue48)](https://github.com/charlesq34/pointnet2/issues/48#issuecomment-608135179):
+```
+cd {YOUR_CONDA_PATH}/envs/6dofgrasp_py3/lib/python3.7/site-packages/tensorflow/
+ln -s libtensorflow_framework.so.2 libtensorflow_framework.so
+```
+also export the LD_LIBRARY_PATH here:
+```
+export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:{YOUR_CONDA_PATH}/envs/6dofgrasp_py3/lib/python3.7/site-packages/tensorflow
+```
+
+### Compile PointNet CUDA kernels
+```
+cd pointnet2/tf_ops/grouping
+sh compile.sh
+```
+```
+cd pointnet2/tf_ops/d_interpolation
+sh compile.sh
+```
+```
+cd pointnet2/tf_ops/sampling
+sh compile.sh
+```
+
+## Tests
+
+Check whether tensorflow_framework.so.x was found in
+```
+ldd tf_grouping_so.so
+```
+
+Run compiled operation:
+```
+python tf_grouping_op_test.py
+```
+
+If there is no segfault you are good to go
\ No newline at end of file
diff --git a/pointnet2/tf_ops/grouping/compile.sh b/pointnet2/tf_ops/grouping/compile.sh
new file mode 100644
index 0000000..e9d2884
--- /dev/null
+++ b/pointnet2/tf_ops/grouping/compile.sh
@@ -0,0 +1,9 @@
+TF_CFLAGS=( $(python -c 'import tensorflow as tf; print(" ".join(tf.sysconfig.get_compile_flags()))') )
+TF_LFLAGS=( $(python -c 'import tensorflow as tf; print(" ".join(tf.sysconfig.get_link_flags()))') )
+
+$CUDA_HOME/bin/nvcc -std=c++11 -c -o tf_grouping_g.cu.o tf_grouping_g.cu \
+ ${TF_CFLAGS[@]} -D GOOGLE_CUDA=1 -x cu -Xcompiler -fPIC
+
+g++ -std=c++11 -shared -o tf_grouping_so.so tf_grouping.cpp \
+ tf_grouping_g.cu.o ${TF_CFLAGS[@]} -fPIC -lcudart ${TF_LFLAGS[@]}
+
diff --git a/pointnet2/tf_ops/grouping/test/compile.sh b/pointnet2/tf_ops/grouping/test/compile.sh
new file mode 100644
index 0000000..e1824dd
--- /dev/null
+++ b/pointnet2/tf_ops/grouping/test/compile.sh
@@ -0,0 +1,6 @@
+g++ query_ball_point.cpp -o query_ball_point
+nvcc query_ball_point.cu -o query_ball_point_cuda
+nvcc query_ball_point_block.cu -o query_ball_point_block
+nvcc query_ball_point_grid.cu -o query_ball_point_grid
+g++ -Wall selection_sort.cpp -o selection_sort
+nvcc selection_sort.cu -o selection_sort_cuda
diff --git a/pointnet2/tf_ops/grouping/test/query_ball_point.cpp b/pointnet2/tf_ops/grouping/test/query_ball_point.cpp
new file mode 100644
index 0000000..4e28051
--- /dev/null
+++ b/pointnet2/tf_ops/grouping/test/query_ball_point.cpp
@@ -0,0 +1,119 @@
+#include
+#include
+#include // memset
+#include // rand, RAND_MAX
+#include // sqrtf
+#include
+#include
+using namespace std;
+float randomf(){
+ return (rand()+0.5)/(RAND_MAX+1.0);
+}
+static double get_time(){
+ timespec tp;
+ clock_gettime(CLOCK_MONOTONIC,&tp);
+ return tp.tv_sec+tp.tv_nsec*1e-9;
+}
+// input: radius (1), nsample (1), xyz1 (b,n,3), xyz2 (b,m,3)
+// output: idx (b,m,nsample)
+void query_ball_point_cpu(int b, int n, int m, float radius, int nsample, const float *xyz1, const float *xyz2, int *idx) {
+ for (int i=0;i
+#include
+#include // memset
+#include // rand, RAND_MAX
+#include // sqrtf
+#include
+#include
+using namespace std;
+float randomf(){
+ return (rand()+0.5)/(RAND_MAX+1.0);
+}
+static double get_time(){
+ timespec tp;
+ clock_gettime(CLOCK_MONOTONIC,&tp);
+ return tp.tv_sec+tp.tv_nsec*1e-9;
+}
+// input: radius (1), nsample (1), xyz1 (b,n,3), xyz2 (b,m,3)
+// output: idx (b,m,nsample)
+__global__ void query_ball_point_gpu(int b, int n, int m, float radius, int nsample, const float *xyz1, const float *xyz2, int *idx) {
+ for (int i=0;i>>(b,n,m,radius,nsample,xyz1,xyz2,idx);
+ cudaDeviceSynchronize();
+ printf("query_ball_point gpu time %f\n",get_time()-t0);
+
+ t0=get_time();
+ group_point_gpu<<<1,1>>>(b,n,c,m,nsample,points,idx,out);
+ cudaDeviceSynchronize();
+ printf("grou_point gpu time %f\n",get_time()-t0);
+
+ t0=get_time();
+ group_point_grad_gpu<<<1,1>>>(b,n,c,m,nsample,grad_out,idx,grad_points);
+ cudaDeviceSynchronize();
+ printf("grou_point_grad gpu time %f\n",get_time()-t0);
+
+ cudaFree(xyz1);
+ cudaFree(xyz2);
+ cudaFree(points);
+ cudaFree(idx);
+ cudaFree(out);
+ cudaFree(grad_out);
+ cudaFree(grad_points);
+ return 0;
+}
diff --git a/pointnet2/tf_ops/grouping/test/query_ball_point_block.cu b/pointnet2/tf_ops/grouping/test/query_ball_point_block.cu
new file mode 100644
index 0000000..477fb3b
--- /dev/null
+++ b/pointnet2/tf_ops/grouping/test/query_ball_point_block.cu
@@ -0,0 +1,134 @@
+#include
+#include
+#include // memset
+#include // rand, RAND_MAX
+#include // sqrtf
+#include
+#include
+using namespace std;
+float randomf(){
+ return (rand()+0.5)/(RAND_MAX+1.0);
+}
+static double get_time(){
+ timespec tp;
+ clock_gettime(CLOCK_MONOTONIC,&tp);
+ return tp.tv_sec+tp.tv_nsec*1e-9;
+}
+// input: radius (1), nsample (1), xyz1 (b,n,3), xyz2 (b,m,3)
+// output: idx (b,m,nsample)
+__global__ void query_ball_point_gpu(int b, int n, int m, float radius, int nsample, const float *xyz1, const float *xyz2, int *idx) {
+ int index = threadIdx.x;
+ xyz1 += n*3*index;
+ xyz2 += m*3*index;
+ idx += m*nsample*index;
+
+ for (int j=0;j>>(b,n,m,radius,nsample,xyz1,xyz2,idx);
+ cudaDeviceSynchronize();
+ printf("query_ball_point gpu time %f\n",get_time()-t0);
+
+ t0=get_time();
+ group_point_gpu<<<1,b>>>(b,n,c,m,nsample,points,idx,out);
+ cudaDeviceSynchronize();
+ printf("grou_point gpu time %f\n",get_time()-t0);
+
+ t0=get_time();
+ group_point_grad_gpu<<<1,b>>>(b,n,c,m,nsample,grad_out,idx,grad_points);
+ cudaDeviceSynchronize();
+ printf("grou_point_grad gpu time %f\n",get_time()-t0);
+
+ cudaFree(xyz1);
+ cudaFree(xyz2);
+ cudaFree(points);
+ cudaFree(idx);
+ cudaFree(out);
+ cudaFree(grad_out);
+ cudaFree(grad_points);
+ return 0;
+}
diff --git a/pointnet2/tf_ops/grouping/test/query_ball_point_grid.cu b/pointnet2/tf_ops/grouping/test/query_ball_point_grid.cu
new file mode 100644
index 0000000..dcfadba
--- /dev/null
+++ b/pointnet2/tf_ops/grouping/test/query_ball_point_grid.cu
@@ -0,0 +1,144 @@
+#include
+#include
+#include // memset
+#include // rand, RAND_MAX
+#include // sqrtf
+#include
+#include
+using namespace std;
+float randomf(){
+ return (rand()+0.5)/(RAND_MAX+1.0);
+}
+static double get_time(){
+ timespec tp;
+ clock_gettime(CLOCK_MONOTONIC,&tp);
+ return tp.tv_sec+tp.tv_nsec*1e-9;
+}
+// input: radius (1), nsample (1), xyz1 (b,n,3), xyz2 (b,m,3)
+// output: idx (b,m,nsample)
+__global__ void query_ball_point_gpu(int b, int n, int m, float radius, int nsample, const float *xyz1, const float *xyz2, int *idx) {
+ int batch_index = blockIdx.x;
+ xyz1 += n*3*batch_index;
+ xyz2 += m*3*batch_index;
+ idx += m*nsample*batch_index;
+
+ int index = threadIdx.x;
+ int stride = blockDim.x;
+
+ for (int j=index;j>>(b,n,m,radius,nsample,xyz1,xyz2,idx);
+ cudaDeviceSynchronize();
+ printf("query_ball_point gpu time %f\n",get_time()-t0);
+
+ t0=get_time();
+ group_point_gpu<<>>(b,n,c,m,nsample,points,idx,out);
+ cudaDeviceSynchronize();
+ printf("grou_point gpu time %f\n",get_time()-t0);
+
+ t0=get_time();
+ group_point_grad_gpu<<>>(b,n,c,m,nsample,grad_out,idx,grad_points);
+ cudaDeviceSynchronize();
+ printf("grou_point_grad gpu time %f\n",get_time()-t0);
+
+ cudaFree(xyz1);
+ cudaFree(xyz2);
+ cudaFree(points);
+ cudaFree(idx);
+ cudaFree(out);
+ cudaFree(grad_out);
+ cudaFree(grad_points);
+ return 0;
+}
diff --git a/pointnet2/tf_ops/grouping/test/selection_sort.cpp b/pointnet2/tf_ops/grouping/test/selection_sort.cpp
new file mode 100644
index 0000000..6f0839e
--- /dev/null
+++ b/pointnet2/tf_ops/grouping/test/selection_sort.cpp
@@ -0,0 +1,94 @@
+#include
+#include
+#include // memset
+#include // rand, RAND_MAX
+#include // sqrtf
+#include
+#include
+using namespace std;
+float randomf(){
+ return (rand()+0.5)/(RAND_MAX+1.0);
+}
+static double get_time(){
+ timespec tp;
+ clock_gettime(CLOCK_MONOTONIC,&tp);
+ return tp.tv_sec+tp.tv_nsec*1e-9;
+}
+
+// input: k (1), distance matrix dist (b,m,n)
+// output: idx (b,m,n), val (b,m,n)
+void selection_sort_cpu(int b, int n, int m, int k, const float *dist, int *idx, float *val) {
+ float *p_dist;
+ float tmp;
+ int tmpi;
+ for (int i=0;i
+#include
+#include // memset
+#include // rand, RAND_MAX
+#include // sqrtf
+#include
+#include
+using namespace std;
+float randomf(){
+ return (rand()+0.5)/(RAND_MAX+1.0);
+}
+static double get_time(){
+ timespec tp;
+ clock_gettime(CLOCK_MONOTONIC,&tp);
+ return tp.tv_sec+tp.tv_nsec*1e-9;
+}
+
+// input: k (1), distance matrix dist (b,m,n)
+// output: idx (b,m,k), val (b,m,k)
+__global__ void selection_sort_gpu(int b, int n, int m, int k, float *dist, int *idx, float *val) {
+ int batch_index = blockIdx.x;
+ dist+=m*n*batch_index;
+ idx+=m*k*batch_index;
+ val+=m*k*batch_index;
+
+ int index = threadIdx.x;
+ int stride = blockDim.x;
+
+ float *p_dist;
+ for (int j=index;j>>(b,n,m,k,dist,idx,val);
+ cudaDeviceSynchronize();
+ printf("selection sort cpu time %f\n",get_time()-t0);
+
+ return 0;
+}
diff --git a/pointnet2/tf_ops/grouping/test/selection_sort_const.cu b/pointnet2/tf_ops/grouping/test/selection_sort_const.cu
new file mode 100644
index 0000000..9666849
--- /dev/null
+++ b/pointnet2/tf_ops/grouping/test/selection_sort_const.cu
@@ -0,0 +1,92 @@
+#include
+#include
+#include // memset
+#include // rand, RAND_MAX
+#include // sqrtf
+#include
+#include
+using namespace std;
+float randomf(){
+ return (rand()+0.5)/(RAND_MAX+1.0);
+}
+static double get_time(){
+ timespec tp;
+ clock_gettime(CLOCK_MONOTONIC,&tp);
+ return tp.tv_sec+tp.tv_nsec*1e-9;
+}
+
+// input: k (1), distance matrix dist (b,m,n)
+// output: idx (b,m,n), dist_out (b,m,n)
+__global__ void selection_sort_gpu(int b, int n, int m, int k, const float *dist, int *outi, float *out) {
+ int batch_index = blockIdx.x;
+ dist+=m*n*batch_index;
+ outi+=m*n*batch_index;
+ out+=m*n*batch_index;
+
+ int index = threadIdx.x;
+ int stride = blockDim.x;
+
+ // copy from dist to dist_out
+ for (int j=index;j>>(b,n,m,k,dist,idx,dist_out);
+ cudaDeviceSynchronize();
+ printf("selection sort cpu time %f\n",get_time()-t0);
+
+ //for (int i=0;i
+#include
+#include // memset
+#include // rand, RAND_MAX
+#include // sqrtf
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/shape_inference.h"
+#include "tensorflow/core/framework/common_shape_fns.h"
+#include
+using namespace tensorflow;
+
+REGISTER_OP("QueryBallPoint")
+ .Attr("radius: float")
+ .Attr("nsample: int")
+ .Input("xyz1: float32")
+ .Input("xyz2: float32")
+ .Output("idx: int32")
+ .Output("pts_cnt: int32")
+ .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
+ ::tensorflow::shape_inference::ShapeHandle dims2; // batch_size * npoint * 3
+ c->WithRank(c->input(1), 3, &dims2);
+ int nsample;
+ TF_RETURN_IF_ERROR(c->GetAttr("nsample", &nsample));
+ ::tensorflow::shape_inference::ShapeHandle output1 = c->MakeShape({c->Dim(dims2, 0), c->Dim(dims2, 1), nsample});
+ c->set_output(0, output1);
+ ::tensorflow::shape_inference::ShapeHandle output2 = c->MakeShape({c->Dim(dims2, 0), c->Dim(dims2, 1)});
+ c->set_output(1, output2);
+ return Status::OK();
+ });
+REGISTER_OP("SelectionSort")
+ .Attr("k: int")
+ .Input("dist: float32")
+ .Output("outi: int32")
+ .Output("out: float32")
+ .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
+ c->set_output(0, c->input(0));
+ c->set_output(1, c->input(0));
+ return Status::OK();
+ });
+REGISTER_OP("GroupPoint")
+ .Input("points: float32")
+ .Input("idx: int32")
+ .Output("out: float32")
+ .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
+ ::tensorflow::shape_inference::ShapeHandle dims1; // batch_size * ndataset * channels
+ c->WithRank(c->input(0), 3, &dims1);
+ ::tensorflow::shape_inference::ShapeHandle dims2; // batch_size * npoints * nsample
+ c->WithRank(c->input(1), 3, &dims2);
+ // batch_size * npoints * nsample * channels
+ ::tensorflow::shape_inference::ShapeHandle output = c->MakeShape({c->Dim(dims2, 0), c->Dim(dims2, 1), c->Dim(dims2, 2), c->Dim(dims1, 2)});
+ c->set_output(0, output);
+ return Status::OK();
+ });
+REGISTER_OP("GroupPointGrad")
+ .Input("points: float32")
+ .Input("idx: int32")
+ .Input("grad_out: float32")
+ .Output("grad_points: float32")
+ .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
+ c->set_output(0, c->input(0));
+ return Status::OK();
+ });
+
+
+void queryBallPointLauncher(int b, int n, int m, float radius, int nsample, const float *xyz1, const float *xyz2, int *idx, int *pts_cnt);
+class QueryBallPointGpuOp : public OpKernel {
+ public:
+ explicit QueryBallPointGpuOp(OpKernelConstruction* context) : OpKernel(context) {
+ OP_REQUIRES_OK(context, context->GetAttr("radius", &radius_));
+ OP_REQUIRES(context, radius_ > 0, errors::InvalidArgument("QueryBallPoint expects positive radius"));
+
+ OP_REQUIRES_OK(context, context->GetAttr("nsample", &nsample_));
+ OP_REQUIRES(context, nsample_ > 0, errors::InvalidArgument("QueryBallPoint expects positive nsample"));
+ }
+
+ void Compute(OpKernelContext* context) override {
+ const Tensor& xyz1_tensor = context->input(0);
+ OP_REQUIRES(context, xyz1_tensor.dims()==3 && xyz1_tensor.shape().dim_size(2)==3, errors::InvalidArgument("QueryBallPoint expects (batch_size, ndataset, 3) xyz1 shape."));
+ int b = xyz1_tensor.shape().dim_size(0);
+ int n = xyz1_tensor.shape().dim_size(1);
+
+ const Tensor& xyz2_tensor = context->input(1);
+ OP_REQUIRES(context, xyz2_tensor.dims()==3 && xyz2_tensor.shape().dim_size(2)==3, errors::InvalidArgument("QueryBallPoint expects (batch_size, npoint, 3) xyz2 shape."));
+ int m = xyz2_tensor.shape().dim_size(1);
+
+ Tensor *idx_tensor = nullptr;
+ OP_REQUIRES_OK(context, context->allocate_output(0, TensorShape{b,m,nsample_}, &idx_tensor));
+ Tensor *pts_cnt_tensor = nullptr;
+ OP_REQUIRES_OK(context, context->allocate_output(1, TensorShape{b,m}, &pts_cnt_tensor));
+
+ auto xyz1_flat = xyz1_tensor.flat();
+ const float *xyz1 = &(xyz1_flat(0));
+ auto xyz2_flat = xyz2_tensor.flat();
+ const float *xyz2 = &(xyz2_flat(0));
+ auto idx_flat = idx_tensor->flat();
+ int *idx = &(idx_flat(0));
+ auto pts_cnt_flat = pts_cnt_tensor->flat();
+ int *pts_cnt = &(pts_cnt_flat(0));
+ queryBallPointLauncher(b,n,m,radius_,nsample_,xyz1,xyz2,idx,pts_cnt);
+ }
+ private:
+ float radius_;
+ int nsample_;
+};
+REGISTER_KERNEL_BUILDER(Name("QueryBallPoint").Device(DEVICE_GPU), QueryBallPointGpuOp);
+
+void selectionSortLauncher(int b, int n, int m, int k, const float *dist, int *outi, float *out);
+class SelectionSortGpuOp : public OpKernel {
+ public:
+ explicit SelectionSortGpuOp(OpKernelConstruction* context) : OpKernel(context) {
+ OP_REQUIRES_OK(context, context->GetAttr("k", &k_));
+ OP_REQUIRES(context, k_ > 0, errors::InvalidArgument("SelectionSort expects positive k"));
+ }
+
+ void Compute(OpKernelContext* context) override {
+ const Tensor& dist_tensor = context->input(0);
+ OP_REQUIRES(context, dist_tensor.dims()==3, errors::InvalidArgument("SelectionSort expects (b,m,n) dist shape."));
+ int b = dist_tensor.shape().dim_size(0);
+ int m = dist_tensor.shape().dim_size(1);
+ int n = dist_tensor.shape().dim_size(2);
+
+ Tensor *outi_tensor = nullptr;
+ OP_REQUIRES_OK(context, context->allocate_output(0, TensorShape{b,m,n}, &outi_tensor));
+ Tensor *out_tensor = nullptr;
+ OP_REQUIRES_OK(context, context->allocate_output(1, TensorShape{b,m,n}, &out_tensor));
+
+ auto dist_flat = dist_tensor.flat();
+ const float *dist = &(dist_flat(0));
+ auto outi_flat = outi_tensor->flat();
+ int *outi = &(outi_flat(0));
+ auto out_flat = out_tensor->flat();
+ float *out = &(out_flat(0));
+ selectionSortLauncher(b,n,m,k_,dist,outi,out);
+ }
+ private:
+ int k_;
+};
+REGISTER_KERNEL_BUILDER(Name("SelectionSort").Device(DEVICE_GPU), SelectionSortGpuOp);
+
+
+void groupPointLauncher(int b, int n, int c, int m, int nsample, const float *points, const int *idx, float *out);
+class GroupPointGpuOp: public OpKernel{
+ public:
+ explicit GroupPointGpuOp(OpKernelConstruction * context):OpKernel(context){}
+
+ void Compute(OpKernelContext * context) override {
+ const Tensor& points_tensor=context->input(0);
+ OP_REQUIRES(context, points_tensor.dims()==3, errors::InvalidArgument("GroupPoint expects (batch_size, num_points, channel) points shape"));
+ int b = points_tensor.shape().dim_size(0);
+ int n = points_tensor.shape().dim_size(1);
+ int c = points_tensor.shape().dim_size(2);
+
+ const Tensor& idx_tensor=context->input(1);
+ OP_REQUIRES(context,idx_tensor.dims()==3 && idx_tensor.shape().dim_size(0)==b, errors::InvalidArgument("GroupPoint expects (batch_size, npoints, nsample) idx shape"));
+ int m = idx_tensor.shape().dim_size(1);
+ int nsample = idx_tensor.shape().dim_size(2);
+
+ Tensor * out_tensor = nullptr;
+ OP_REQUIRES_OK(context, context->allocate_output(0,TensorShape{b,m,nsample,c}, &out_tensor));
+
+ auto points_flat = points_tensor.flat();
+ const float *points = &(points_flat(0));
+ auto idx_flat = idx_tensor.flat();
+ const int *idx = &(idx_flat(0));
+ auto out_flat = out_tensor->flat();
+ float *out = &(out_flat(0));
+ groupPointLauncher(b,n,c,m,nsample,points,idx,out);
+ }
+};
+REGISTER_KERNEL_BUILDER(Name("GroupPoint").Device(DEVICE_GPU),GroupPointGpuOp);
+
+void groupPointGradLauncher(int b, int n, int c, int m, int nsample, const float *grad_out, const int *idx, float *grad_points);
+class GroupPointGradGpuOp: public OpKernel{
+ public:
+ explicit GroupPointGradGpuOp(OpKernelConstruction * context):OpKernel(context){}
+
+ void Compute(OpKernelContext * context) override {
+ const Tensor& points_tensor=context->input(0);
+ OP_REQUIRES(context, points_tensor.dims()==3, errors::InvalidArgument("GroupPointGrad expects (batch_size, num_points, channel) points shape"));
+ int b = points_tensor.shape().dim_size(0);
+ int n = points_tensor.shape().dim_size(1);
+ int c = points_tensor.shape().dim_size(2);
+
+ const Tensor& idx_tensor=context->input(1);
+ OP_REQUIRES(context,idx_tensor.dims()==3 && idx_tensor.shape().dim_size(0)==b, errors::InvalidArgument("GroupPointGrad expects (batch_size, npoints, nsample) idx shape"));
+ int m = idx_tensor.shape().dim_size(1);
+ int nsample = idx_tensor.shape().dim_size(2);
+
+ const Tensor& grad_out_tensor=context->input(2);
+ OP_REQUIRES(context,grad_out_tensor.dims()==4 && grad_out_tensor.shape().dim_size(0)==b && grad_out_tensor.shape().dim_size(1)==m && grad_out_tensor.shape().dim_size(2)==nsample && grad_out_tensor.shape().dim_size(3)==c, errors::InvalidArgument("GroupPointGrad expects (batch_size, npoints, nsample, channel) grad_out shape"));
+
+ Tensor * grad_points_tensor = nullptr;
+ OP_REQUIRES_OK(context, context->allocate_output(0,TensorShape{b,n,c}, &grad_points_tensor));
+
+ auto points_flat = points_tensor.flat();
+ const float *points = &(points_flat(0));
+ auto idx_flat = idx_tensor.flat();
+ const int *idx = &(idx_flat(0));
+ auto grad_out_flat = grad_out_tensor.flat();
+ const float *grad_out = &(grad_out_flat(0));
+ auto grad_points_flat = grad_points_tensor->flat();
+ float *grad_points = &(grad_points_flat(0));
+ cudaMemset(grad_points, 0, sizeof(float)*b*n*c);
+ groupPointGradLauncher(b,n,c,m,nsample,grad_out,idx,grad_points);
+ }
+};
+REGISTER_KERNEL_BUILDER(Name("GroupPointGrad").Device(DEVICE_GPU),GroupPointGradGpuOp);
+
+
diff --git a/pointnet2/tf_ops/grouping/tf_grouping.py b/pointnet2/tf_ops/grouping/tf_grouping.py
new file mode 100644
index 0000000..709348f
--- /dev/null
+++ b/pointnet2/tf_ops/grouping/tf_grouping.py
@@ -0,0 +1,122 @@
+from __future__ import print_function
+try:
+ import tensorflow.compat.v1 as tf
+ tf.disable_eager_execution()
+except:
+ import tensorflow as tf
+from tensorflow.python.framework import ops
+import sys
+import os
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(BASE_DIR)
+grouping_module=tf.load_op_library(os.path.join(BASE_DIR, 'tf_grouping_so.so'))
+def query_ball_point(radius, nsample, xyz1, xyz2):
+ '''
+ Input:
+ radius: float32, ball search radius
+ nsample: int32, number of points selected in each ball region
+ xyz1: (batch_size, ndataset, 3) float32 array, input points
+ xyz2: (batch_size, npoint, 3) float32 array, query points
+ Output:
+ idx: (batch_size, npoint, nsample) int32 array, indices to input points
+ pts_cnt: (batch_size, npoint) int32 array, number of unique points in each local region
+ '''
+ #return grouping_module.query_ball_point(radius, nsample, xyz1, xyz2)
+ return grouping_module.query_ball_point(xyz1, xyz2, radius, nsample)
+ops.NoGradient('QueryBallPoint')
+def select_top_k(k, dist):
+ '''
+ Input:
+ k: int32, number of k SMALLEST elements selected
+ dist: (b,m,n) float32 array, distance matrix, m query points, n dataset points
+ Output:
+ idx: (b,m,n) int32 array, first k in n are indices to the top k
+ dist_out: (b,m,n) float32 array, first k in n are the top k
+ '''
+ return grouping_module.selection_sort(dist, k)
+ops.NoGradient('SelectionSort')
+def group_point(points, idx):
+ '''
+ Input:
+ points: (batch_size, ndataset, channel) float32 array, points to sample from
+ idx: (batch_size, npoint, nsample) int32 array, indices to points
+ Output:
+ out: (batch_size, npoint, nsample, channel) float32 array, values sampled from points
+ '''
+ return grouping_module.group_point(points, idx)
+@tf.RegisterGradient('GroupPoint')
+def _group_point_grad(op, grad_out):
+ points = op.inputs[0]
+ idx = op.inputs[1]
+ return [grouping_module.group_point_grad(points, idx, grad_out), None]
+
+def knn_point(k, xyz1, xyz2):
+ '''
+ Input:
+ k: int32, number of k in k-nn search
+ xyz1: (batch_size, ndataset, c) float32 array, input points
+ xyz2: (batch_size, npoint, c) float32 array, query points
+ Output:
+ val: (batch_size, npoint, k) float32 array, L2 distances
+ idx: (batch_size, npoint, k) int32 array, indices to input points
+ '''
+ b = xyz1.get_shape()[0]
+ n = xyz1.get_shape()[1]
+ c = xyz1.get_shape()[2]
+ m = xyz2.get_shape()[1]
+ print(b, n, c, m)
+ print(xyz1, (b,1,n,c))
+ xyz1 = tf.tile(tf.reshape(xyz1, (b,1,n,c)), [1,m,1,1])
+ xyz2 = tf.tile(tf.reshape(xyz2, (b,m,1,c)), [1,1,n,1])
+ print(xyz1.shape, xyz2.shape)
+ dist = tf.reduce_sum((xyz1-xyz2)**2, -1)
+ print(dist.shape, k)
+
+ outi, out = select_top_k(k, dist)
+ idx = tf.slice(outi, [0,0,0], [-1,-1,k])
+ val = tf.slice(out, [0,0,0], [-1,-1,k])
+ print(idx, val)
+ #val, idx = tf.nn.top_k(-dist, k=k) # ONLY SUPPORT CPU
+ return val, idx
+
+if __name__=='__main__':
+ knn=True
+ import numpy as np
+ import time
+ np.random.seed(100)
+ pts = np.random.random((32,512,64)).astype('float32')
+ tmp1 = np.random.random((32,512,3)).astype('float32')
+ print(tmp1[0,0,:])
+ tmp2 = np.random.random((32,128,3)).astype('float32')
+ with tf.device('/gpu:0'):
+ points = tf.constant(pts)
+ xyz1 = tf.constant(tmp1)
+ xyz2 = tf.constant(tmp2)
+ radius = 0.1
+ nsample = 2
+ if knn:
+ dists, idx = knn_point(nsample, xyz1, xyz2)
+ grouped_points = group_point(points, idx)
+ print('here')
+ else:
+ idx, pts_cnt = query_ball_point(radius, nsample, xyz1, xyz2)
+ index_sum = tf.where(tf.reduce_sum(idx, axis=2)>0)
+ print(index_sum.shape, idx.shape)
+ tf.gather_nd(idx, index_sum)
+ grouped_points = group_point(xyz1, idx)
+ #grouped_points_grad = tf.ones_like(grouped_points)
+ #points_grad = tf.gradients(grouped_points, points, grouped_points_grad)
+ with tf.Session('') as sess:
+ now = time.time()
+ print(now)
+ for _ in range(1):
+ ret, d,idcs = sess.run([grouped_points, dists, idx])
+ print(d[0,5,:2])
+ print(ret[0])
+ print(idcs[0])
+ print(idcs.shape,ret.shape, ret.dtype)
+ print(d)
+ print(time.time() - now)
+ # print(ret)
+
+
diff --git a/pointnet2/tf_ops/grouping/tf_grouping_compile.sh b/pointnet2/tf_ops/grouping/tf_grouping_compile.sh
new file mode 100644
index 0000000..b193367
--- /dev/null
+++ b/pointnet2/tf_ops/grouping/tf_grouping_compile.sh
@@ -0,0 +1,8 @@
+#/bin/bash
+/usr/local/cuda-8.0/bin/nvcc tf_grouping_g.cu -o tf_grouping_g.cu.o -c -O2 -DGOOGLE_CUDA=1 -x cu -Xcompiler -fPIC
+
+# TF1.2
+g++ -std=c++11 tf_grouping.cpp tf_grouping_g.cu.o -o tf_grouping_so.so -shared -fPIC -I /usr/local/lib/python2.7/dist-packages/tensorflow/include -I /usr/local/cuda-8.0/include -lcudart -L /usr/local/cuda-8.0/lib64/ -O2 -D_GLIBCXX_USE_CXX11_ABI=0
+
+# TF1.4
+#g++ -std=c++11 tf_grouping.cpp tf_grouping_g.cu.o -o tf_grouping_so.so -shared -fPIC -I /usr/local/lib/python2.7/dist-packages/tensorflow/include -I /usr/local/cuda-8.0/include -I /usr/local/lib/python2.7/dist-packages/tensorflow/include/external/nsync/public -lcudart -L /usr/local/cuda-8.0/lib64/ -L/usr/local/lib/python2.7/dist-packages/tensorflow -ltensorflow_framework -O2 -D_GLIBCXX_USE_CXX11_ABI=0
diff --git a/pointnet2/tf_ops/grouping/tf_grouping_g.cu b/pointnet2/tf_ops/grouping/tf_grouping_g.cu
new file mode 100644
index 0000000..578330d
--- /dev/null
+++ b/pointnet2/tf_ops/grouping/tf_grouping_g.cu
@@ -0,0 +1,141 @@
+// input: radius (1), nsample (1), xyz1 (b,n,3), xyz2 (b,m,3)
+// output: idx (b,m,nsample), pts_cnt (b,m)
+__global__ void query_ball_point_gpu(int b, int n, int m, float radius, int nsample, const float *xyz1, const float *xyz2, int *idx, int *pts_cnt) {
+ int batch_index = blockIdx.x;
+ xyz1 += n*3*batch_index;
+ xyz2 += m*3*batch_index;
+ idx += m*nsample*batch_index;
+ pts_cnt += m*batch_index; // counting how many unique points selected in local region
+
+ int index = threadIdx.x;
+ int stride = blockDim.x;
+
+ for (int j=index;j>>(b,n,m,radius,nsample,xyz1,xyz2,idx,pts_cnt);
+ //cudaDeviceSynchronize();
+}
+void selectionSortLauncher(int b, int n, int m, int k, const float *dist, int *outi, float *out) {
+ selection_sort_gpu<<>>(b,n,m,k,dist,outi,out);
+ //cudaDeviceSynchronize();
+}
+void groupPointLauncher(int b, int n, int c, int m, int nsample, const float *points, const int *idx, float *out){
+ group_point_gpu<<>>(b,n,c,m,nsample,points,idx,out);
+ //cudaDeviceSynchronize();
+}
+void groupPointGradLauncher(int b, int n, int c, int m, int nsample, const float *grad_out, const int *idx, float *grad_points){
+ group_point_grad_gpu<<>>(b,n,c,m,nsample,grad_out,idx,grad_points);
+ //group_point_grad_gpu<<<1,1>>>(b,n,c,m,nsample,grad_out,idx,grad_points);
+ //cudaDeviceSynchronize();
+}
diff --git a/pointnet2/tf_ops/grouping/tf_grouping_g.cu.o b/pointnet2/tf_ops/grouping/tf_grouping_g.cu.o
new file mode 100644
index 0000000..ece163f
Binary files /dev/null and b/pointnet2/tf_ops/grouping/tf_grouping_g.cu.o differ
diff --git a/pointnet2/tf_ops/grouping/tf_grouping_op_test.py b/pointnet2/tf_ops/grouping/tf_grouping_op_test.py
new file mode 100644
index 0000000..c8603c7
--- /dev/null
+++ b/pointnet2/tf_ops/grouping/tf_grouping_op_test.py
@@ -0,0 +1,33 @@
+from __future__ import print_function
+try:
+ import tensorflow.compat.v1 as tf
+ tf.disable_eager_execution()
+except:
+ import tensorflow as tf
+import numpy as np
+from tf_grouping import query_ball_point, group_point
+
+class GroupPointTest(tf.test.TestCase):
+ def test(self):
+ pass
+
+ def test_grad(self):
+ with tf.device('/gpu:0'):
+ points = tf.constant(np.random.random((1,128,16)).astype('float32'))
+ print(points)
+ xyz1 = tf.constant(np.random.random((1,128,3)).astype('float32'))
+ xyz2 = tf.constant(np.random.random((1,8,3)).astype('float32'))
+ radius = 0.3
+ nsample = 32
+ idx, pts_cnt = query_ball_point(radius, nsample, xyz1, xyz2)
+ grouped_points = group_point(points, idx)
+ print(grouped_points)
+
+ with self.test_session():
+ print("---- Going to compute gradient error")
+ err = tf.test.compute_gradient_error(points, (1,128,16), grouped_points, (1,8,32,16))
+ print(err)
+ self.assertLess(err, 1e-4)
+
+if __name__=='__main__':
+ tf.test.main()
diff --git a/pointnet2/tf_ops/grouping/tf_grouping_so.so b/pointnet2/tf_ops/grouping/tf_grouping_so.so
new file mode 100755
index 0000000..83a99a5
Binary files /dev/null and b/pointnet2/tf_ops/grouping/tf_grouping_so.so differ
diff --git a/pointnet2/tf_ops/sampling/compile.sh b/pointnet2/tf_ops/sampling/compile.sh
new file mode 100644
index 0000000..7d9e77f
--- /dev/null
+++ b/pointnet2/tf_ops/sampling/compile.sh
@@ -0,0 +1,8 @@
+TF_CFLAGS=( $(python -c 'import tensorflow as tf; print(" ".join(tf.sysconfig.get_compile_flags()))') )
+TF_LFLAGS=( $(python -c 'import tensorflow as tf; print(" ".join(tf.sysconfig.get_link_flags()))') )
+
+$CUDA_HOME/bin/nvcc -std=c++11 -c -o tf_sampling_g.cu.o tf_sampling_g.cu \
+ ${TF_CFLAGS[@]} -D GOOGLE_CUDA=1 -x cu -Xcompiler -fPIC
+
+g++ -std=c++11 -shared -o tf_sampling_so.so tf_sampling.cpp \
+ tf_sampling_g.cu.o ${TF_CFLAGS[@]} -fPIC -lcudart ${TF_LFLAGS[@]}
diff --git a/pointnet2/tf_ops/sampling/tf_sampling.cpp b/pointnet2/tf_ops/sampling/tf_sampling.cpp
new file mode 100644
index 0000000..fb3dd28
--- /dev/null
+++ b/pointnet2/tf_ops/sampling/tf_sampling.cpp
@@ -0,0 +1,179 @@
+/* Furthest point sampling
+ * Original author: Haoqiang Fan
+ * Modified by Charles R. Qi
+ * All Rights Reserved. 2017.
+ */
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/shape_inference.h"
+#include "tensorflow/core/framework/common_shape_fns.h"
+#include
+
+using namespace tensorflow;
+
+REGISTER_OP("ProbSample")
+ .Input("inp: float32")
+ .Input("inpr: float32")
+ .Output("out: int32")
+ .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
+ ::tensorflow::shape_inference::ShapeHandle dims1; // batch_size * ncategory
+ c->WithRank(c->input(0), 2, &dims1);
+ ::tensorflow::shape_inference::ShapeHandle dims2; // batch_size * npoints
+ c->WithRank(c->input(1), 2, &dims2);
+ // batch_size * npoints
+ ::tensorflow::shape_inference::ShapeHandle output = c->MakeShape({c->Dim(dims2, 0), c->Dim(dims2, 1)});
+ c->set_output(0, output);
+ return Status::OK();
+ });
+REGISTER_OP("FarthestPointSample")
+ .Attr("npoint: int")
+ .Input("inp: float32")
+ .Output("out: int32")
+ .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
+ ::tensorflow::shape_inference::ShapeHandle dims1; // batch_size * npoint * 3
+ c->WithRank(c->input(0), 3, &dims1);
+ int npoint;
+ TF_RETURN_IF_ERROR(c->GetAttr("npoint", &npoint));
+ ::tensorflow::shape_inference::ShapeHandle output = c->MakeShape({c->Dim(dims1, 0), npoint});
+ c->set_output(0, output);
+ return Status::OK();
+ });
+REGISTER_OP("GatherPoint")
+ .Input("inp: float32")
+ .Input("idx: int32")
+ .Output("out: float32")
+ .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
+ ::tensorflow::shape_inference::ShapeHandle dims1; // batch_size * ndataset * 3
+ c->WithRank(c->input(0), 3, &dims1);
+ ::tensorflow::shape_inference::ShapeHandle dims2; // batch_size * npoints
+ c->WithRank(c->input(1), 2, &dims2);
+ // batch_size * npoints * 3
+ ::tensorflow::shape_inference::ShapeHandle output = c->MakeShape({c->Dim(dims1, 0), c->Dim(dims2, 1), c->Dim(dims1, 2)});
+ c->set_output(0, output);
+ return Status::OK();
+ });
+REGISTER_OP("GatherPointGrad")
+ .Input("inp: float32")
+ .Input("idx: int32")
+ .Input("out_g: float32")
+ .Output("inp_g: float32")
+ .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
+ c->set_output(0, c->input(0));
+ return Status::OK();
+ });
+
+void probsampleLauncher(int b,int n,int m,const float * inp_p,const float * inp_r,float * temp,int * out);
+class ProbSampleGpuOp: public OpKernel{
+ public:
+ explicit ProbSampleGpuOp(OpKernelConstruction* context):OpKernel(context){}
+ void Compute(OpKernelContext * context)override{
+ const Tensor& inp_tensor=context->input(0);
+ const Tensor& inpr_tensor=context->input(1);
+ auto inp_flat=inp_tensor.flat();
+ auto inpr_flat=inpr_tensor.flat();
+ const float * inp=&(inp_flat(0));
+ const float * inpr=&(inpr_flat(0));
+ OP_REQUIRES(context,inp_tensor.dims()==2,errors::InvalidArgument("ProbSample expects (batch_size,num_choices) inp shape"));
+ int b=inp_tensor.shape().dim_size(0);
+ int n=inp_tensor.shape().dim_size(1);
+ OP_REQUIRES(context,inpr_tensor.dims()==2 && inpr_tensor.shape().dim_size(0)==b,errors::InvalidArgument("ProbSample expects (batch_size,num_points) inpr shape"));
+ int m=inpr_tensor.shape().dim_size(1);
+ Tensor * out_tensor=NULL;
+ OP_REQUIRES_OK(context,context->allocate_output(0,TensorShape{b,m},&out_tensor));
+ auto out_flat=out_tensor->flat();
+ int * out=&(out_flat(0));
+ Tensor temp_tensor;
+ OP_REQUIRES_OK(context,context->allocate_temp(DataTypeToEnum::value,TensorShape{b,n},&temp_tensor));
+ auto temp_flat=temp_tensor.flat();
+ float * temp=&(temp_flat(0));
+ probsampleLauncher(b,n,m,inp,inpr,temp,out);
+ }
+};
+REGISTER_KERNEL_BUILDER(Name("ProbSample").Device(DEVICE_GPU), ProbSampleGpuOp);
+
+void farthestpointsamplingLauncher(int b,int n,int m,const float * inp,float * temp,int * out);
+class FarthestPointSampleGpuOp: public OpKernel{
+ public:
+ explicit FarthestPointSampleGpuOp(OpKernelConstruction* context):OpKernel(context) {
+ OP_REQUIRES_OK(context, context->GetAttr("npoint", &npoint_));
+ OP_REQUIRES(context, npoint_ > 0, errors::InvalidArgument("FarthestPointSample expects positive npoint"));
+ }
+ void Compute(OpKernelContext * context)override{
+ int m = npoint_;
+
+ const Tensor& inp_tensor=context->input(0);
+ OP_REQUIRES(context,inp_tensor.dims()==3 && inp_tensor.shape().dim_size(2)==3,errors::InvalidArgument("FarthestPointSample expects (batch_size,num_points,3) inp shape"));
+ int b=inp_tensor.shape().dim_size(0);
+ int n=inp_tensor.shape().dim_size(1);
+ auto inp_flat=inp_tensor.flat();
+ const float * inp=&(inp_flat(0));
+ Tensor * out_tensor;
+ OP_REQUIRES_OK(context,context->allocate_output(0,TensorShape{b,m},&out_tensor));
+ auto out_flat=out_tensor->flat();
+ int * out=&(out_flat(0));
+ Tensor temp_tensor;
+ OP_REQUIRES_OK(context,context->allocate_temp(DataTypeToEnum::value,TensorShape{32,n},&temp_tensor));
+ auto temp_flat=temp_tensor.flat();
+ float * temp=&(temp_flat(0));
+ farthestpointsamplingLauncher(b,n,m,inp,temp,out);
+ }
+ private:
+ int npoint_;
+};
+REGISTER_KERNEL_BUILDER(Name("FarthestPointSample").Device(DEVICE_GPU),FarthestPointSampleGpuOp);
+
+void gatherpointLauncher(int b,int n,int m,const float * inp,const int * idx,float * out);
+class GatherPointGpuOp: public OpKernel{
+ public:
+ explicit GatherPointGpuOp(OpKernelConstruction * context):OpKernel(context){}
+ void Compute(OpKernelContext * context)override{
+ const Tensor& inp_tensor=context->input(0);
+ OP_REQUIRES(context,inp_tensor.dims()==3 && inp_tensor.shape().dim_size(2)==3,errors::InvalidArgument("GatherPoint expects (batch_size,num_points,3) inp shape"));
+ int b=inp_tensor.shape().dim_size(0);
+ int n=inp_tensor.shape().dim_size(1);
+ const Tensor& idx_tensor=context->input(1);
+ OP_REQUIRES(context,idx_tensor.dims()==2 && idx_tensor.shape().dim_size(0)==b,errors::InvalidArgument("GatherPoint expects (batch_size,num_result) idx shape"));
+ int m=idx_tensor.shape().dim_size(1);
+ auto inp_flat=inp_tensor.flat();
+ const float * inp=&(inp_flat(0));
+ auto idx_flat=idx_tensor.flat();
+ const int * idx=&(idx_flat(0));
+ Tensor * out_tensor=NULL;
+ OP_REQUIRES_OK(context,context->allocate_output(0,TensorShape{b,m,3},&out_tensor));
+ auto out_flat=out_tensor->flat();
+ float * out=&(out_flat(0));
+ gatherpointLauncher(b,n,m,inp,idx,out);
+ }
+};
+REGISTER_KERNEL_BUILDER(Name("GatherPoint").Device(DEVICE_GPU),GatherPointGpuOp);
+
+void scatteraddpointLauncher(int b,int n,int m,const float * out_g,const int * idx,float * inp_g);
+class GatherPointGradGpuOp: public OpKernel{
+ public:
+ explicit GatherPointGradGpuOp(OpKernelConstruction * context):OpKernel(context){}
+ void Compute(OpKernelContext * context)override{
+ const Tensor& inp_tensor=context->input(0);
+ OP_REQUIRES(context,inp_tensor.dims()==3 && inp_tensor.shape().dim_size(2)==3,errors::InvalidArgument("GatherPointGradGpuOp expects (batch_size,num_points,3) inp"));
+ int b=inp_tensor.shape().dim_size(0);
+ int n=inp_tensor.shape().dim_size(1);
+ const Tensor& idx_tensor=context->input(1);
+ OP_REQUIRES(context,idx_tensor.dims()==2 && idx_tensor.shape().dim_size(0)==b,errors::InvalidArgument("GatherPointGradGpuOp expects (batch_size,num_result) idx shape"));
+ int m=idx_tensor.shape().dim_size(1);
+ auto inp_flat=inp_tensor.flat();
+ const float * inp=&(inp_flat(0));
+ auto idx_flat=idx_tensor.flat();
+ const int * idx=&(idx_flat(0));
+ const Tensor& out_g_tensor=context->input(2);
+ OP_REQUIRES(context,out_g_tensor.dims()==3 && out_g_tensor.shape().dim_size(0)==b && out_g_tensor.shape().dim_size(1)==m && out_g_tensor.shape().dim_size(2)==3,errors::InvalidArgument("GatherPointGradGpuOp expects (batch_size,num_result,3) out_g shape"));
+ auto out_g_flat=out_g_tensor.flat();
+ const float * out_g=&(out_g_flat(0));
+ Tensor * inp_g_tensor=NULL;
+ OP_REQUIRES_OK(context,context->allocate_output(0,TensorShape{b,n,3},&inp_g_tensor));
+ auto inp_g_flat=inp_g_tensor->flat();
+ float * inp_g=&(inp_g_flat(0));
+ cudaMemset(inp_g,0,b*n*3*4);
+ scatteraddpointLauncher(b,n,m,out_g,idx,inp_g);
+ }
+};
+REGISTER_KERNEL_BUILDER(Name("GatherPointGrad").Device(DEVICE_GPU),GatherPointGradGpuOp);
+
diff --git a/pointnet2/tf_ops/sampling/tf_sampling.py b/pointnet2/tf_ops/sampling/tf_sampling.py
new file mode 100644
index 0000000..e4351cd
--- /dev/null
+++ b/pointnet2/tf_ops/sampling/tf_sampling.py
@@ -0,0 +1,98 @@
+from __future__ import print_function
+''' Furthest point sampling
+Original author: Haoqiang Fan
+Modified by Charles R. Qi
+All Rights Reserved. 2017.
+'''
+try:
+ import tensorflow.compat.v1 as tf
+ tf.disable_eager_execution()
+except:
+ import tensorflow as tf
+from tensorflow.python.framework import ops
+import sys
+import os
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(BASE_DIR)
+print(BASE_DIR)
+sampling_module=tf.load_op_library(os.path.join(BASE_DIR, 'tf_sampling_so.so'))
+def prob_sample(inp,inpr):
+ '''
+input:
+ batch_size * ncategory float32
+ batch_size * npoints float32
+returns:
+ batch_size * npoints int32
+ '''
+ return sampling_module.prob_sample(inp,inpr)
+ops.NoGradient('ProbSample')
+# TF1.0 API requires set shape in C++
+#@tf.RegisterShape('ProbSample')
+#def _prob_sample_shape(op):
+# shape1=op.inputs[0].get_shape().with_rank(2)
+# shape2=op.inputs[1].get_shape().with_rank(2)
+# return [tf.TensorShape([shape2.dims[0],shape2.dims[1]])]
+def gather_point(inp,idx):
+ '''
+input:
+ batch_size * ndataset * 3 float32
+ batch_size * npoints int32
+returns:
+ batch_size * npoints * 3 float32
+ '''
+ return sampling_module.gather_point(inp,idx)
+#@tf.RegisterShape('GatherPoint')
+#def _gather_point_shape(op):
+# shape1=op.inputs[0].get_shape().with_rank(3)
+# shape2=op.inputs[1].get_shape().with_rank(2)
+# return [tf.TensorShape([shape1.dims[0],shape2.dims[1],shape1.dims[2]])]
+@tf.RegisterGradient('GatherPoint')
+def _gather_point_grad(op,out_g):
+ inp=op.inputs[0]
+ idx=op.inputs[1]
+ return [sampling_module.gather_point_grad(inp,idx,out_g),None]
+def farthest_point_sample(npoint,inp):
+ '''
+input:
+ int32
+ batch_size * ndataset * 3 float32
+returns:
+ batch_size * npoint int32
+ '''
+ return sampling_module.farthest_point_sample(inp, npoint)
+ops.NoGradient('FarthestPointSample')
+
+
+if __name__=='__main__':
+ import numpy as np
+ np.random.seed(100)
+ triangles=np.random.rand(1,5,3,3).astype('float32')
+ with tf.device('/gpu:0'):
+ inp=tf.constant(triangles)
+ tria=inp[:,:,0,:]
+ trib=inp[:,:,1,:]
+ tric=inp[:,:,2,:]
+ areas=tf.sqrt(tf.reduce_sum(tf.cross(trib-tria,tric-tria)**2,2)+1e-9)
+ randomnumbers=tf.random_uniform((1,8192))
+ triids=prob_sample(areas,randomnumbers)
+ tria_sample=gather_point(tria,triids)
+ trib_sample=gather_point(trib,triids)
+ tric_sample=gather_point(tric,triids)
+ us=tf.random_uniform((1,8192))
+ vs=tf.random_uniform((1,8192))
+ uplusv=1-tf.abs(us+vs-1)
+ uminusv=us-vs
+ us=(uplusv+uminusv)*0.5
+ vs=(uplusv-uminusv)*0.5
+ pt_sample=tria_sample+(trib_sample-tria_sample)*tf.expand_dims(us,-1)+(tric_sample-tria_sample)*tf.expand_dims(vs,-1)
+ print('pt_sample: ', pt_sample)
+ reduced_sample=gather_point(pt_sample,farthest_point_sample(1024,pt_sample))
+ print(reduced_sample)
+ with tf.Session('') as sess:
+ ret=sess.run(reduced_sample)
+ print(ret.shape,ret.dtype)
+ try:
+ import cPickle as pickle
+ except:
+ import pickle
+ pickle.dump(ret,open('1.pkl','wb'),-1)
diff --git a/pointnet2/tf_ops/sampling/tf_sampling_compile.sh b/pointnet2/tf_ops/sampling/tf_sampling_compile.sh
new file mode 100644
index 0000000..6e4d9c0
--- /dev/null
+++ b/pointnet2/tf_ops/sampling/tf_sampling_compile.sh
@@ -0,0 +1,8 @@
+#/bin/bash
+/home/msundermeyer/.conda/envs/6dofgrasp/bin/nvcc tf_sampling_g.cu -o tf_sampling_g.cu.o -c -O2 -DGOOGLE_CUDA=1 -x cu -Xcompiler -fPIC
+
+# TF1.2
+g++ -std=c++11 tf_sampling.cpp tf_sampling_g.cu.o -o tf_sampling_so.so -shared -fPIC -I /usr/local/lib/python2.7/dist-packages/tensorflow/include -I /usr/local/cuda-8.0/include -lcudart -L /usr/local/cuda-8.0/lib64/ -O2 -D_GLIBCXX_USE_CXX11_ABI=0
+
+# TF1.4
+#g++ -std=c++11 tf_sampling.cpp tf_sampling_g.cu.o -o tf_sampling_so.so -shared -fPIC -I /usr/local/lib/python2.7/dist-packages/tensorflow/include -I /usr/local/cuda-8.0/include -I /usr/local/lib/python2.7/dist-packages/tensorflow/include/external/nsync/public -lcudart -L /usr/local/cuda-8.0/lib64/ -L/usr/local/lib/python2.7/dist-packages/tensorflow -ltensorflow_framework -O2 -D_GLIBCXX_USE_CXX11_ABI=0
diff --git a/pointnet2/tf_ops/sampling/tf_sampling_g.cu b/pointnet2/tf_ops/sampling/tf_sampling_g.cu
new file mode 100644
index 0000000..6e28bc7
--- /dev/null
+++ b/pointnet2/tf_ops/sampling/tf_sampling_g.cu
@@ -0,0 +1,212 @@
+/* Furthest point sampling GPU implementation
+ * Original author: Haoqiang Fan
+ * Modified by Charles R. Qi
+ * All Rights Reserved. 2017.
+ */
+
+__global__ void cumsumKernel(int b,int n,const float * __restrict__ inp,float * __restrict__ out){
+ const int BlockSize=2048;
+ const int paddingLevel=5;
+ __shared__ float buffer4[BlockSize*4];
+ __shared__ float buffer[BlockSize+(BlockSize>>paddingLevel)];
+ for (int i=blockIdx.x;i>2;
+ for (int k=threadIdx.x*4;k>2)+(k>>(2+paddingLevel))]=v4;
+ }else{
+ float v=0;
+ for (int k2=k;k2>2)+(k>>(2+paddingLevel))]=v;
+ }
+ }
+ int u=0;
+ for (;(2<>(u+1));k+=blockDim.x){
+ int i1=(((k<<1)+2)<>paddingLevel;
+ i2+=i2>>paddingLevel;
+ buffer[i1]+=buffer[i2];
+ }
+ }
+ u--;
+ for (;u>=0;u--){
+ __syncthreads();
+ for (int k=threadIdx.x;k>(u+1));k+=blockDim.x){
+ int i1=(((k<<1)+3)<>paddingLevel;
+ i2+=i2>>paddingLevel;
+ buffer[i1]+=buffer[i2];
+ }
+ }
+ __syncthreads();
+ for (int k=threadIdx.x*4;k>2)-1)+(((k>>2)-1)>>paddingLevel);
+ buffer4[k]+=buffer[k2];
+ buffer4[k+1]+=buffer[k2];
+ buffer4[k+2]+=buffer[k2];
+ buffer4[k+3]+=buffer[k2];
+ }
+ }
+ __syncthreads();
+ for (int k=threadIdx.x;k>paddingLevel)]+runningsum2;
+ float r2=runningsum+t;
+ runningsum2=t-(r2-runningsum);
+ runningsum=r2;
+ __syncthreads();
+ }
+ }
+}
+
+__global__ void binarysearchKernel(int b,int n,int m,const float * __restrict__ dataset,const float * __restrict__ query, int * __restrict__ result){
+ int base=1;
+ while (base=1;k>>=1)
+ if (r>=k && dataset[i*n+r-k]>=q)
+ r-=k;
+ result[i*m+j]=r;
+ }
+ }
+}
+__global__ void farthestpointsamplingKernel(int b,int n,int m,const float * __restrict__ dataset,float * __restrict__ temp,int * __restrict__ idxs){
+ if (m<=0)
+ return;
+ const int BlockSize=512;
+ __shared__ float dists[BlockSize];
+ __shared__ int dists_i[BlockSize];
+ const int BufferSize=3072;
+ __shared__ float buf[BufferSize*3];
+ for (int i=blockIdx.x;ibest){
+ best=d2;
+ besti=k;
+ }
+ }
+ dists[threadIdx.x]=best;
+ dists_i[threadIdx.x]=besti;
+ for (int u=0;(1<>(u+1))){
+ int i1=(threadIdx.x*2)<>>(b,n,inp,out);
+}
+//require b*n working space
+void probsampleLauncher(int b,int n,int m,const float * inp_p,const float * inp_r,float * temp,int * out){
+ cumsumKernel<<<32,512>>>(b,n,inp_p,temp);
+ binarysearchKernel<<>>(b,n,m,temp,inp_r,out);
+}
+//require 32*n working space
+void farthestpointsamplingLauncher(int b,int n,int m,const float * inp,float * temp,int * out){
+ farthestpointsamplingKernel<<<32,512>>>(b,n,m,inp,temp,out);
+}
+void gatherpointLauncher(int b,int n,int m,const float * inp,const int * idx,float * out){
+ gatherpointKernel<<>>(b,n,m,inp,idx,out);
+}
+void scatteraddpointLauncher(int b,int n,int m,const float * out_g,const int * idx,float * inp_g){
+ scatteraddpointKernel<<>>(b,n,m,out_g,idx,inp_g);
+}
+
diff --git a/pointnet2/tf_ops/sampling/tf_sampling_g.cu.o b/pointnet2/tf_ops/sampling/tf_sampling_g.cu.o
new file mode 100644
index 0000000..e68deeb
Binary files /dev/null and b/pointnet2/tf_ops/sampling/tf_sampling_g.cu.o differ
diff --git a/pointnet2/tf_ops/sampling/tf_sampling_so.so b/pointnet2/tf_ops/sampling/tf_sampling_so.so
new file mode 100755
index 0000000..fd6f973
Binary files /dev/null and b/pointnet2/tf_ops/sampling/tf_sampling_so.so differ
diff --git a/pointnet2/train.py b/pointnet2/train.py
new file mode 100644
index 0000000..0e36b06
--- /dev/null
+++ b/pointnet2/train.py
@@ -0,0 +1,285 @@
+'''
+ Single-GPU training.
+ Will use H5 dataset in default. If using normal, will shift to the normal dataset.
+'''
+import argparse
+import math
+from datetime import datetime
+import h5py
+import numpy as np
+import tensorflow as tf
+import socket
+import importlib
+import os
+import sys
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+ROOT_DIR = BASE_DIR
+sys.path.append(BASE_DIR)
+sys.path.append(os.path.join(ROOT_DIR, 'models'))
+sys.path.append(os.path.join(ROOT_DIR, 'utils'))
+import provider
+import tf_util
+import modelnet_dataset
+import modelnet_h5_dataset
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--gpu', type=int, default=0, help='GPU to use [default: GPU 0]')
+parser.add_argument('--model', default='pointnet2_cls_ssg', help='Model name [default: pointnet2_cls_ssg]')
+parser.add_argument('--log_dir', default='log', help='Log dir [default: log]')
+parser.add_argument('--num_point', type=int, default=1024, help='Point Number [default: 1024]')
+parser.add_argument('--max_epoch', type=int, default=251, help='Epoch to run [default: 251]')
+parser.add_argument('--batch_size', type=int, default=16, help='Batch Size during training [default: 16]')
+parser.add_argument('--learning_rate', type=float, default=0.001, help='Initial learning rate [default: 0.001]')
+parser.add_argument('--momentum', type=float, default=0.9, help='Initial learning rate [default: 0.9]')
+parser.add_argument('--optimizer', default='adam', help='adam or momentum [default: adam]')
+parser.add_argument('--decay_step', type=int, default=200000, help='Decay step for lr decay [default: 200000]')
+parser.add_argument('--decay_rate', type=float, default=0.7, help='Decay rate for lr decay [default: 0.7]')
+parser.add_argument('--normal', action='store_true', help='Whether to use normal information')
+FLAGS = parser.parse_args()
+
+EPOCH_CNT = 0
+
+BATCH_SIZE = FLAGS.batch_size
+NUM_POINT = FLAGS.num_point
+MAX_EPOCH = FLAGS.max_epoch
+BASE_LEARNING_RATE = FLAGS.learning_rate
+GPU_INDEX = FLAGS.gpu
+MOMENTUM = FLAGS.momentum
+OPTIMIZER = FLAGS.optimizer
+DECAY_STEP = FLAGS.decay_step
+DECAY_RATE = FLAGS.decay_rate
+
+MODEL = importlib.import_module(FLAGS.model) # import network module
+MODEL_FILE = os.path.join(ROOT_DIR, 'models', FLAGS.model+'.py')
+LOG_DIR = FLAGS.log_dir
+if not os.path.exists(LOG_DIR): os.mkdir(LOG_DIR)
+os.system('cp %s %s' % (MODEL_FILE, LOG_DIR)) # bkp of model def
+os.system('cp train.py %s' % (LOG_DIR)) # bkp of train procedure
+LOG_FOUT = open(os.path.join(LOG_DIR, 'log_train.txt'), 'w')
+LOG_FOUT.write(str(FLAGS)+'\n')
+
+BN_INIT_DECAY = 0.5
+BN_DECAY_DECAY_RATE = 0.5
+BN_DECAY_DECAY_STEP = float(DECAY_STEP)
+BN_DECAY_CLIP = 0.99
+
+HOSTNAME = socket.gethostname()
+
+NUM_CLASSES = 40
+
+# Shapenet official train/test split
+if FLAGS.normal:
+ assert(NUM_POINT<=10000)
+ DATA_PATH = os.path.join(ROOT_DIR, 'data/modelnet40_normal_resampled')
+ TRAIN_DATASET = modelnet_dataset.ModelNetDataset(root=DATA_PATH, npoints=NUM_POINT, split='train', normal_channel=FLAGS.normal, batch_size=BATCH_SIZE)
+ TEST_DATASET = modelnet_dataset.ModelNetDataset(root=DATA_PATH, npoints=NUM_POINT, split='test', normal_channel=FLAGS.normal, batch_size=BATCH_SIZE)
+else:
+ assert(NUM_POINT<=2048)
+ TRAIN_DATASET = modelnet_h5_dataset.ModelNetH5Dataset(os.path.join(BASE_DIR, 'data/modelnet40_ply_hdf5_2048/train_files.txt'), batch_size=BATCH_SIZE, npoints=NUM_POINT, shuffle=True)
+ TEST_DATASET = modelnet_h5_dataset.ModelNetH5Dataset(os.path.join(BASE_DIR, 'data/modelnet40_ply_hdf5_2048/test_files.txt'), batch_size=BATCH_SIZE, npoints=NUM_POINT, shuffle=False)
+
+def log_string(out_str):
+ LOG_FOUT.write(out_str+'\n')
+ LOG_FOUT.flush()
+ print(out_str)
+
+def get_learning_rate(batch):
+ learning_rate = tf.train.exponential_decay(
+ BASE_LEARNING_RATE, # Base learning rate.
+ batch * BATCH_SIZE, # Current index into the dataset.
+ DECAY_STEP, # Decay step.
+ DECAY_RATE, # Decay rate.
+ staircase=True)
+ learning_rate = tf.maximum(learning_rate, 0.00001) # CLIP THE LEARNING RATE!
+ return learning_rate
+
+def get_bn_decay(batch):
+ bn_momentum = tf.train.exponential_decay(
+ BN_INIT_DECAY,
+ batch*BATCH_SIZE,
+ BN_DECAY_DECAY_STEP,
+ BN_DECAY_DECAY_RATE,
+ staircase=True)
+ bn_decay = tf.minimum(BN_DECAY_CLIP, 1 - bn_momentum)
+ return bn_decay
+
+def train():
+ with tf.Graph().as_default():
+ with tf.device('/gpu:'+str(GPU_INDEX)):
+ pointclouds_pl, labels_pl = MODEL.placeholder_inputs(BATCH_SIZE, NUM_POINT)
+ is_training_pl = tf.placeholder(tf.bool, shape=())
+
+ # Note the global_step=batch parameter to minimize.
+ # That tells the optimizer to helpfully increment the 'batch' parameter
+ # for you every time it trains.
+ batch = tf.get_variable('batch', [],
+ initializer=tf.constant_initializer(0), trainable=False)
+ bn_decay = get_bn_decay(batch)
+ tf.summary.scalar('bn_decay', bn_decay)
+
+ # Get model and loss
+ pred, end_points = MODEL.get_model(pointclouds_pl, is_training_pl, bn_decay=bn_decay)
+ MODEL.get_loss(pred, labels_pl, end_points)
+ losses = tf.get_collection('losses')
+ total_loss = tf.add_n(losses, name='total_loss')
+ tf.summary.scalar('total_loss', total_loss)
+ for l in losses + [total_loss]:
+ tf.summary.scalar(l.op.name, l)
+
+ correct = tf.equal(tf.argmax(pred, 1), tf.to_int64(labels_pl))
+ accuracy = tf.reduce_sum(tf.cast(correct, tf.float32)) / float(BATCH_SIZE)
+ tf.summary.scalar('accuracy', accuracy)
+
+ print "--- Get training operator"
+ # Get training operator
+ learning_rate = get_learning_rate(batch)
+ tf.summary.scalar('learning_rate', learning_rate)
+ if OPTIMIZER == 'momentum':
+ optimizer = tf.train.MomentumOptimizer(learning_rate, momentum=MOMENTUM)
+ elif OPTIMIZER == 'adam':
+ optimizer = tf.train.AdamOptimizer(learning_rate)
+ train_op = optimizer.minimize(total_loss, global_step=batch)
+
+ # Add ops to save and restore all the variables.
+ saver = tf.train.Saver()
+
+ # Create a session
+ config = tf.ConfigProto()
+ config.gpu_options.allow_growth = True
+ config.allow_soft_placement = True
+ config.log_device_placement = False
+ sess = tf.Session(config=config)
+
+ # Add summary writers
+ merged = tf.summary.merge_all()
+ train_writer = tf.summary.FileWriter(os.path.join(LOG_DIR, 'train'), sess.graph)
+ test_writer = tf.summary.FileWriter(os.path.join(LOG_DIR, 'test'), sess.graph)
+
+ # Init variables
+ init = tf.global_variables_initializer()
+ sess.run(init)
+
+ ops = {'pointclouds_pl': pointclouds_pl,
+ 'labels_pl': labels_pl,
+ 'is_training_pl': is_training_pl,
+ 'pred': pred,
+ 'loss': total_loss,
+ 'train_op': train_op,
+ 'merged': merged,
+ 'step': batch,
+ 'end_points': end_points}
+
+ best_acc = -1
+ for epoch in range(MAX_EPOCH):
+ log_string('**** EPOCH %03d ****' % (epoch))
+ sys.stdout.flush()
+
+ train_one_epoch(sess, ops, train_writer)
+ eval_one_epoch(sess, ops, test_writer)
+
+ # Save the variables to disk.
+ if epoch % 10 == 0:
+ save_path = saver.save(sess, os.path.join(LOG_DIR, "model.ckpt"))
+ log_string("Model saved in file: %s" % save_path)
+
+
+def train_one_epoch(sess, ops, train_writer):
+ """ ops: dict mapping from string to tf ops """
+ is_training = True
+
+ log_string(str(datetime.now()))
+
+ # Make sure batch data is of same size
+ cur_batch_data = np.zeros((BATCH_SIZE,NUM_POINT,TRAIN_DATASET.num_channel()))
+ cur_batch_label = np.zeros((BATCH_SIZE), dtype=np.int32)
+
+ total_correct = 0
+ total_seen = 0
+ loss_sum = 0
+ batch_idx = 0
+ while TRAIN_DATASET.has_next_batch():
+ batch_data, batch_label = TRAIN_DATASET.next_batch(augment=True)
+ #batch_data = provider.random_point_dropout(batch_data)
+ bsize = batch_data.shape[0]
+ cur_batch_data[0:bsize,...] = batch_data
+ cur_batch_label[0:bsize] = batch_label
+
+ feed_dict = {ops['pointclouds_pl']: cur_batch_data,
+ ops['labels_pl']: cur_batch_label,
+ ops['is_training_pl']: is_training,}
+ summary, step, _, loss_val, pred_val = sess.run([ops['merged'], ops['step'],
+ ops['train_op'], ops['loss'], ops['pred']], feed_dict=feed_dict)
+ train_writer.add_summary(summary, step)
+ pred_val = np.argmax(pred_val, 1)
+ correct = np.sum(pred_val[0:bsize] == batch_label[0:bsize])
+ total_correct += correct
+ total_seen += bsize
+ loss_sum += loss_val
+ if (batch_idx+1)%50 == 0:
+ log_string(' ---- batch: %03d ----' % (batch_idx+1))
+ log_string('mean loss: %f' % (loss_sum / 50))
+ log_string('accuracy: %f' % (total_correct / float(total_seen)))
+ total_correct = 0
+ total_seen = 0
+ loss_sum = 0
+ batch_idx += 1
+
+ TRAIN_DATASET.reset()
+
+def eval_one_epoch(sess, ops, test_writer):
+ """ ops: dict mapping from string to tf ops """
+ global EPOCH_CNT
+ is_training = False
+
+ # Make sure batch data is of same size
+ cur_batch_data = np.zeros((BATCH_SIZE,NUM_POINT,TEST_DATASET.num_channel()))
+ cur_batch_label = np.zeros((BATCH_SIZE), dtype=np.int32)
+
+ total_correct = 0
+ total_seen = 0
+ loss_sum = 0
+ batch_idx = 0
+ shape_ious = []
+ total_seen_class = [0 for _ in range(NUM_CLASSES)]
+ total_correct_class = [0 for _ in range(NUM_CLASSES)]
+
+ log_string(str(datetime.now()))
+ log_string('---- EPOCH %03d EVALUATION ----'%(EPOCH_CNT))
+
+ while TEST_DATASET.has_next_batch():
+ batch_data, batch_label = TEST_DATASET.next_batch(augment=False)
+ bsize = batch_data.shape[0]
+ # for the last batch in the epoch, the bsize:end are from last batch
+ cur_batch_data[0:bsize,...] = batch_data
+ cur_batch_label[0:bsize] = batch_label
+
+ feed_dict = {ops['pointclouds_pl']: cur_batch_data,
+ ops['labels_pl']: cur_batch_label,
+ ops['is_training_pl']: is_training}
+ summary, step, loss_val, pred_val = sess.run([ops['merged'], ops['step'],
+ ops['loss'], ops['pred']], feed_dict=feed_dict)
+ test_writer.add_summary(summary, step)
+ pred_val = np.argmax(pred_val, 1)
+ correct = np.sum(pred_val[0:bsize] == batch_label[0:bsize])
+ total_correct += correct
+ total_seen += bsize
+ loss_sum += loss_val
+ batch_idx += 1
+ for i in range(0, bsize):
+ l = batch_label[i]
+ total_seen_class[l] += 1
+ total_correct_class[l] += (pred_val[i] == l)
+
+ log_string('eval mean loss: %f' % (loss_sum / float(batch_idx)))
+ log_string('eval accuracy: %f'% (total_correct / float(total_seen)))
+ log_string('eval avg class acc: %f' % (np.mean(np.array(total_correct_class)/np.array(total_seen_class,dtype=np.float))))
+ EPOCH_CNT += 1
+
+ TEST_DATASET.reset()
+ return total_correct/float(total_seen)
+
+
+if __name__ == "__main__":
+ log_string('pid: %s'%(str(os.getpid())))
+ train()
+ LOG_FOUT.close()
diff --git a/pointnet2/train_multi_gpu.py b/pointnet2/train_multi_gpu.py
new file mode 100644
index 0000000..6087ec0
--- /dev/null
+++ b/pointnet2/train_multi_gpu.py
@@ -0,0 +1,359 @@
+'''
+ Multi-GPU training.
+ Near linear scale acceleration for multi-gpus on a single machine.
+ Will use H5 dataset in default. If using normal, will shift to the normal dataset.
+'''
+
+import argparse
+import math
+from datetime import datetime
+import h5py
+import numpy as np
+import tensorflow as tf
+import socket
+import importlib
+import os
+import sys
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+ROOT_DIR = BASE_DIR
+sys.path.append(BASE_DIR)
+sys.path.append(os.path.join(ROOT_DIR, 'models'))
+sys.path.append(os.path.join(ROOT_DIR, 'utils'))
+import provider
+import tf_util
+import modelnet_dataset
+import modelnet_h5_dataset
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--num_gpus', type=int, default=1, help='How many gpus to use [default: 1]')
+parser.add_argument('--model', default='pointnet2_cls_ssg', help='Model name [default: pointnet2_cls_ssg]')
+parser.add_argument('--log_dir', default='log', help='Log dir [default: log]')
+parser.add_argument('--num_point', type=int, default=1024, help='Point Number [default: 1024]')
+parser.add_argument('--max_epoch', type=int, default=251, help='Epoch to run [default: 251]')
+parser.add_argument('--batch_size', type=int, default=32, help='Batch Size during training [default: 32]')
+parser.add_argument('--learning_rate', type=float, default=0.001, help='Initial learning rate [default: 0.001]')
+parser.add_argument('--momentum', type=float, default=0.9, help='Initial learning rate [default: 0.9]')
+parser.add_argument('--optimizer', default='adam', help='adam or momentum [default: adam]')
+parser.add_argument('--decay_step', type=int, default=200000, help='Decay step for lr decay [default: 200000]')
+parser.add_argument('--decay_rate', type=float, default=0.7, help='Decay rate for lr decay [default: 0.7]')
+parser.add_argument('--normal', action='store_true', help='Whether to use normal information')
+FLAGS = parser.parse_args()
+
+EPOCH_CNT = 0
+
+NUM_GPUS = FLAGS.num_gpus
+BATCH_SIZE = FLAGS.batch_size
+assert(BATCH_SIZE % NUM_GPUS == 0)
+DEVICE_BATCH_SIZE = BATCH_SIZE / NUM_GPUS
+
+NUM_POINT = FLAGS.num_point
+MAX_EPOCH = FLAGS.max_epoch
+BASE_LEARNING_RATE = FLAGS.learning_rate
+MOMENTUM = FLAGS.momentum
+OPTIMIZER = FLAGS.optimizer
+DECAY_STEP = FLAGS.decay_step
+DECAY_RATE = FLAGS.decay_rate
+
+MODEL = importlib.import_module(FLAGS.model) # import network module
+MODEL_FILE = os.path.join(ROOT_DIR, 'models', FLAGS.model+'.py')
+LOG_DIR = FLAGS.log_dir
+if not os.path.exists(LOG_DIR): os.mkdir(LOG_DIR)
+os.system('cp %s %s' % (MODEL_FILE, LOG_DIR)) # bkp of model def
+os.system('cp train.py %s' % (LOG_DIR)) # bkp of train procedure
+LOG_FOUT = open(os.path.join(LOG_DIR, 'log_train.txt'), 'w')
+LOG_FOUT.write(str(FLAGS)+'\n')
+
+BN_INIT_DECAY = 0.5
+BN_DECAY_DECAY_RATE = 0.5
+BN_DECAY_DECAY_STEP = float(DECAY_STEP)
+BN_DECAY_CLIP = 0.99
+
+HOSTNAME = socket.gethostname()
+
+NUM_CLASSES = 40
+
+# Shapenet official train/test split
+if FLAGS.normal:
+ assert(NUM_POINT<=10000)
+ DATA_PATH = os.path.join(ROOT_DIR, 'data/modelnet40_normal_resampled')
+ TRAIN_DATASET = modelnet_dataset.ModelNetDataset(root=DATA_PATH, npoints=NUM_POINT, split='train', normal_channel=FLAGS.normal, batch_size=BATCH_SIZE)
+ TEST_DATASET = modelnet_dataset.ModelNetDataset(root=DATA_PATH, npoints=NUM_POINT, split='test', normal_channel=FLAGS.normal, batch_size=BATCH_SIZE)
+else:
+ assert(NUM_POINT<=2048)
+ TRAIN_DATASET = modelnet_h5_dataset.ModelNetH5Dataset(os.path.join(BASE_DIR, 'data/modelnet40_ply_hdf5_2048/train_files.txt'), batch_size=BATCH_SIZE, npoints=NUM_POINT, shuffle=True)
+ TEST_DATASET = modelnet_h5_dataset.ModelNetH5Dataset(os.path.join(BASE_DIR, 'data/modelnet40_ply_hdf5_2048/test_files.txt'), batch_size=BATCH_SIZE, npoints=NUM_POINT, shuffle=False)
+
+def log_string(out_str):
+ LOG_FOUT.write(out_str+'\n')
+ LOG_FOUT.flush()
+ print(out_str)
+
+def average_gradients(tower_grads):
+ """Calculate the average gradient for each shared variable across all towers.
+ Note that this function provides a synchronization point across all towers.
+ From tensorflow tutorial: cifar10/cifar10_multi_gpu_train.py
+ Args:
+ tower_grads: List of lists of (gradient, variable) tuples. The outer list
+ is over individual gradients. The inner list is over the gradient
+ calculation for each tower.
+ Returns:
+ List of pairs of (gradient, variable) where the gradient has been averaged
+ across all towers.
+ """
+ average_grads = []
+ for grad_and_vars in zip(*tower_grads):
+ # Note that each grad_and_vars looks like the following:
+ # ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN))
+ grads = []
+ #for g, _ in grad_and_vars:
+ for g, v in grad_and_vars:
+ # Add 0 dimension to the gradients to represent the tower.
+ expanded_g = tf.expand_dims(g, 0)
+
+ # Append on a 'tower' dimension which we will average over below.
+ grads.append(expanded_g)
+
+ # Average over the 'tower' dimension.
+ grad = tf.concat(axis=0, values=grads)
+ grad = tf.reduce_mean(grad, 0)
+
+ # Keep in mind that the Variables are redundant because they are shared
+ # across towers. So .. we will just return the first tower's pointer to
+ # the Variable.
+ v = grad_and_vars[0][1]
+ grad_and_var = (grad, v)
+ average_grads.append(grad_and_var)
+ return average_grads
+
+
+def get_learning_rate(batch):
+ learning_rate = tf.train.exponential_decay(
+ BASE_LEARNING_RATE, # Base learning rate.
+ batch * BATCH_SIZE, # Current index into the dataset.
+ DECAY_STEP, # Decay step.
+ DECAY_RATE, # Decay rate.
+ staircase=True)
+ learning_rate = tf.maximum(learning_rate, 0.00001) # CLIP THE LEARNING RATE!
+ return learning_rate
+
+def get_bn_decay(batch):
+ bn_momentum = tf.train.exponential_decay(
+ BN_INIT_DECAY,
+ batch*BATCH_SIZE,
+ BN_DECAY_DECAY_STEP,
+ BN_DECAY_DECAY_RATE,
+ staircase=True)
+ bn_decay = tf.minimum(BN_DECAY_CLIP, 1 - bn_momentum)
+ return bn_decay
+
+def train():
+ with tf.Graph().as_default():
+ with tf.device('/cpu:0'):
+ pointclouds_pl, labels_pl = MODEL.placeholder_inputs(BATCH_SIZE, NUM_POINT)
+ is_training_pl = tf.placeholder(tf.bool, shape=())
+
+ # Note the global_step=batch parameter to minimize.
+ # That tells the optimizer to helpfully increment the 'batch' parameter
+ # for you every time it trains.
+ batch = tf.get_variable('batch', [],
+ initializer=tf.constant_initializer(0), trainable=False)
+ bn_decay = get_bn_decay(batch)
+ tf.summary.scalar('bn_decay', bn_decay)
+
+ # Set learning rate and optimizer
+ learning_rate = get_learning_rate(batch)
+ tf.summary.scalar('learning_rate', learning_rate)
+ if OPTIMIZER == 'momentum':
+ optimizer = tf.train.MomentumOptimizer(learning_rate, momentum=MOMENTUM)
+ elif OPTIMIZER == 'adam':
+ optimizer = tf.train.AdamOptimizer(learning_rate)
+
+ # -------------------------------------------
+ # Get model and loss on multiple GPU devices
+ # -------------------------------------------
+ # Allocating variables on CPU first will greatly accelerate multi-gpu training.
+ # Ref: https://github.com/kuza55/keras-extras/issues/21
+ MODEL.get_model(pointclouds_pl, is_training_pl, bn_decay=bn_decay)
+
+ tower_grads = []
+ pred_gpu = []
+ total_loss_gpu = []
+ for i in range(NUM_GPUS):
+ with tf.variable_scope(tf.get_variable_scope(), reuse=True):
+ with tf.device('/gpu:%d'%(i)), tf.name_scope('gpu_%d'%(i)) as scope:
+ # Evenly split input data to each GPU
+ pc_batch = tf.slice(pointclouds_pl,
+ [i*DEVICE_BATCH_SIZE,0,0], [DEVICE_BATCH_SIZE,-1,-1])
+ label_batch = tf.slice(labels_pl,
+ [i*DEVICE_BATCH_SIZE], [DEVICE_BATCH_SIZE])
+
+ pred, end_points = MODEL.get_model(pc_batch,
+ is_training=is_training_pl, bn_decay=bn_decay)
+
+ MODEL.get_loss(pred, label_batch, end_points)
+ losses = tf.get_collection('losses', scope)
+ total_loss = tf.add_n(losses, name='total_loss')
+ for l in losses + [total_loss]:
+ tf.summary.scalar(l.op.name, l)
+
+ grads = optimizer.compute_gradients(total_loss)
+ tower_grads.append(grads)
+
+ pred_gpu.append(pred)
+ total_loss_gpu.append(total_loss)
+
+ # Merge pred and losses from multiple GPUs
+ pred = tf.concat(pred_gpu, 0)
+ total_loss = tf.reduce_mean(total_loss_gpu)
+
+ # Get training operator
+ grads = average_gradients(tower_grads)
+ train_op = optimizer.apply_gradients(grads, global_step=batch)
+
+ correct = tf.equal(tf.argmax(pred, 1), tf.to_int64(labels_pl))
+ accuracy = tf.reduce_sum(tf.cast(correct, tf.float32)) / float(BATCH_SIZE)
+ tf.summary.scalar('accuracy', accuracy)
+
+ # Add ops to save and restore all the variables.
+ saver = tf.train.Saver()
+
+ # Create a session
+ config = tf.ConfigProto()
+ config.gpu_options.allow_growth = True
+ config.allow_soft_placement = True
+ config.log_device_placement = False
+ sess = tf.Session(config=config)
+
+ # Add summary writers
+ merged = tf.summary.merge_all()
+ train_writer = tf.summary.FileWriter(os.path.join(LOG_DIR, 'train'), sess.graph)
+ test_writer = tf.summary.FileWriter(os.path.join(LOG_DIR, 'test'), sess.graph)
+
+ # Init variables
+ init = tf.global_variables_initializer()
+ sess.run(init)
+
+ ops = {'pointclouds_pl': pointclouds_pl,
+ 'labels_pl': labels_pl,
+ 'is_training_pl': is_training_pl,
+ 'pred': pred,
+ 'loss': total_loss,
+ 'train_op': train_op,
+ 'merged': merged,
+ 'step': batch,
+ 'end_points': end_points}
+
+ best_acc = -1
+ for epoch in range(MAX_EPOCH):
+ log_string('**** EPOCH %03d ****' % (epoch))
+ sys.stdout.flush()
+
+ train_one_epoch(sess, ops, train_writer)
+ eval_one_epoch(sess, ops, test_writer)
+
+ # Save the variables to disk.
+ if epoch % 10 == 0:
+ save_path = saver.save(sess, os.path.join(LOG_DIR, "model.ckpt"))
+ log_string("Model saved in file: %s" % save_path)
+
+
+def train_one_epoch(sess, ops, train_writer):
+ """ ops: dict mapping from string to tf ops """
+ is_training = True
+
+ log_string(str(datetime.now()))
+
+ # Make sure batch data is of same size
+ cur_batch_data = np.zeros((BATCH_SIZE,NUM_POINT,TRAIN_DATASET.num_channel()))
+ cur_batch_label = np.zeros((BATCH_SIZE), dtype=np.int32)
+
+ total_correct = 0
+ total_seen = 0
+ loss_sum = 0
+ batch_idx = 0
+ while TRAIN_DATASET.has_next_batch():
+ batch_data, batch_label = TRAIN_DATASET.next_batch(augment=True)
+ #batch_data = provider.random_point_dropout(batch_data)
+ bsize = batch_data.shape[0]
+ cur_batch_data[0:bsize,...] = batch_data
+ cur_batch_label[0:bsize] = batch_label
+
+ feed_dict = {ops['pointclouds_pl']: cur_batch_data,
+ ops['labels_pl']: cur_batch_label,
+ ops['is_training_pl']: is_training,}
+ summary, step, _, loss_val, pred_val = sess.run([ops['merged'], ops['step'],
+ ops['train_op'], ops['loss'], ops['pred']], feed_dict=feed_dict)
+ train_writer.add_summary(summary, step)
+ pred_val = np.argmax(pred_val, 1)
+ correct = np.sum(pred_val[0:bsize] == batch_label[0:bsize])
+ total_correct += correct
+ total_seen += bsize
+ loss_sum += loss_val
+ if (batch_idx+1)%50 == 0:
+ log_string(' ---- batch: %03d ----' % (batch_idx+1))
+ log_string('mean loss: %f' % (loss_sum / 50))
+ log_string('accuracy: %f' % (total_correct / float(total_seen)))
+ total_correct = 0
+ total_seen = 0
+ loss_sum = 0
+ batch_idx += 1
+
+ TRAIN_DATASET.reset()
+
+def eval_one_epoch(sess, ops, test_writer):
+ """ ops: dict mapping from string to tf ops """
+ global EPOCH_CNT
+ is_training = False
+
+ # Make sure batch data is of same size
+ cur_batch_data = np.zeros((BATCH_SIZE,NUM_POINT,TEST_DATASET.num_channel()))
+ cur_batch_label = np.zeros((BATCH_SIZE), dtype=np.int32)
+
+ total_correct = 0
+ total_seen = 0
+ loss_sum = 0
+ batch_idx = 0
+ shape_ious = []
+ total_seen_class = [0 for _ in range(NUM_CLASSES)]
+ total_correct_class = [0 for _ in range(NUM_CLASSES)]
+
+ log_string(str(datetime.now()))
+ log_string('---- EPOCH %03d EVALUATION ----'%(EPOCH_CNT))
+
+ while TEST_DATASET.has_next_batch():
+ batch_data, batch_label = TEST_DATASET.next_batch(augment=False)
+ bsize = batch_data.shape[0]
+ # for the last batch in the epoch, the bsize:end are from last batch
+ cur_batch_data[0:bsize,...] = batch_data
+ cur_batch_label[0:bsize] = batch_label
+
+ feed_dict = {ops['pointclouds_pl']: cur_batch_data,
+ ops['labels_pl']: cur_batch_label,
+ ops['is_training_pl']: is_training}
+ summary, step, loss_val, pred_val = sess.run([ops['merged'], ops['step'],
+ ops['loss'], ops['pred']], feed_dict=feed_dict)
+ test_writer.add_summary(summary, step)
+ pred_val = np.argmax(pred_val, 1)
+ correct = np.sum(pred_val[0:bsize] == batch_label[0:bsize])
+ total_correct += correct
+ total_seen += bsize
+ loss_sum += loss_val
+ batch_idx += 1
+ for i in range(0, bsize):
+ l = batch_label[i]
+ total_seen_class[l] += 1
+ total_correct_class[l] += (pred_val[i] == l)
+
+ log_string('eval mean loss: %f' % (loss_sum / float(batch_idx)))
+ log_string('eval accuracy: %f'% (total_correct / float(total_seen)))
+ log_string('eval avg class acc: %f' % (np.mean(np.array(total_correct_class)/np.array(total_seen_class,dtype=np.float))))
+ EPOCH_CNT += 1
+
+ TEST_DATASET.reset()
+ return total_correct/float(total_seen)
+
+
+if __name__ == "__main__":
+ log_string('pid: %s'%(str(os.getpid())))
+ train()
+ LOG_FOUT.close()
diff --git a/pointnet2/utils/README.md b/pointnet2/utils/README.md
new file mode 100644
index 0000000..6d2bfad
--- /dev/null
+++ b/pointnet2/utils/README.md
@@ -0,0 +1,6 @@
+## Utilility Functions for 3D Point Cloud Deep Learning
+
+### visualization tool
+
+ sh compile_render_balls_so.sh
+ python show3d_balls.py
diff --git a/pointnet2/utils/compile_render_balls_so.sh b/pointnet2/utils/compile_render_balls_so.sh
new file mode 100644
index 0000000..dc493f6
--- /dev/null
+++ b/pointnet2/utils/compile_render_balls_so.sh
@@ -0,0 +1,2 @@
+g++ -std=c++11 render_balls_so.cpp -o render_balls_so.so -shared -fPIC -O2 -D_GLIBCXX_USE_CXX11_ABI=0
+
diff --git a/pointnet2/utils/pc_util.py b/pointnet2/utils/pc_util.py
new file mode 100644
index 0000000..81f63d8
--- /dev/null
+++ b/pointnet2/utils/pc_util.py
@@ -0,0 +1,315 @@
+""" Utility functions for processing point clouds.
+
+Author: Charles R. Qi, Hao Su
+Date: November 2016
+"""
+
+import os
+import sys
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(BASE_DIR)
+
+# Draw point cloud
+from eulerangles import euler2mat
+
+# Point cloud IO
+import numpy as np
+from plyfile import PlyData, PlyElement
+
+
+# ----------------------------------------
+# Point Cloud/Volume Conversions
+# ----------------------------------------
+
+def point_cloud_to_volume_batch(point_clouds, vsize=12, radius=1.0, flatten=True):
+ """ Input is BxNx3 batch of point cloud
+ Output is Bx(vsize^3)
+ """
+ vol_list = []
+ for b in range(point_clouds.shape[0]):
+ vol = point_cloud_to_volume(np.squeeze(point_clouds[b,:,:]), vsize, radius)
+ if flatten:
+ vol_list.append(vol.flatten())
+ else:
+ vol_list.append(np.expand_dims(np.expand_dims(vol, -1), 0))
+ if flatten:
+ return np.vstack(vol_list)
+ else:
+ return np.concatenate(vol_list, 0)
+
+
+def point_cloud_to_volume(points, vsize, radius=1.0):
+ """ input is Nx3 points.
+ output is vsize*vsize*vsize
+ assumes points are in range [-radius, radius]
+ """
+ vol = np.zeros((vsize,vsize,vsize))
+ voxel = 2*radius/float(vsize)
+ locations = (points + radius)/voxel
+ locations = locations.astype(int)
+ vol[locations[:,0],locations[:,1],locations[:,2]] = 1.0
+ return vol
+
+#a = np.zeros((16,1024,3))
+#print point_cloud_to_volume_batch(a, 12, 1.0, False).shape
+
+def volume_to_point_cloud(vol):
+ """ vol is occupancy grid (value = 0 or 1) of size vsize*vsize*vsize
+ return Nx3 numpy array.
+ """
+ vsize = vol.shape[0]
+ assert(vol.shape[1] == vsize and vol.shape[1] == vsize)
+ points = []
+ for a in range(vsize):
+ for b in range(vsize):
+ for c in range(vsize):
+ if vol[a,b,c] == 1:
+ points.append(np.array([a,b,c]))
+ if len(points) == 0:
+ return np.zeros((0,3))
+ points = np.vstack(points)
+ return points
+
+def point_cloud_to_volume_v2_batch(point_clouds, vsize=12, radius=1.0, num_sample=128):
+ """ Input is BxNx3 a batch of point cloud
+ Output is BxVxVxVxnum_samplex3
+ Added on Feb 19
+ """
+ vol_list = []
+ for b in range(point_clouds.shape[0]):
+ vol = point_cloud_to_volume_v2(point_clouds[b,:,:], vsize, radius, num_sample)
+ vol_list.append(np.expand_dims(vol, 0))
+ return np.concatenate(vol_list, 0)
+
+def point_cloud_to_volume_v2(points, vsize, radius=1.0, num_sample=128):
+ """ input is Nx3 points
+ output is vsize*vsize*vsize*num_sample*3
+ assumes points are in range [-radius, radius]
+ samples num_sample points in each voxel, if there are less than
+ num_sample points, replicate the points
+ Added on Feb 19
+ """
+ vol = np.zeros((vsize,vsize,vsize,num_sample,3))
+ voxel = 2*radius/float(vsize)
+ locations = (points + radius)/voxel
+ locations = locations.astype(int)
+ loc2pc = {}
+ for n in range(points.shape[0]):
+ loc = tuple(locations[n,:])
+ if loc not in loc2pc:
+ loc2pc[loc] = []
+ loc2pc[loc].append(points[n,:])
+ #print loc2pc
+
+ for i in range(vsize):
+ for j in range(vsize):
+ for k in range(vsize):
+ if (i,j,k) not in loc2pc:
+ vol[i,j,k,:,:] = np.zeros((num_sample,3))
+ else:
+ pc = loc2pc[(i,j,k)] # a list of (3,) arrays
+ pc = np.vstack(pc) # kx3
+ # Sample/pad to num_sample points
+ if pc.shape[0]>num_sample:
+ choices = np.random.choice(pc.shape[0], num_sample, replace=False)
+ pc = pc[choices,:]
+ elif pc.shape[0]num_sample:
+ choices = np.random.choice(pc.shape[0], num_sample, replace=False)
+ pc = pc[choices,:]
+ elif pc.shape[0] 0)
+ dx = mask[:, 0]
+ dy = mask[:, 1]
+ dv = disk[disk > 0]
+
+ # Order points by z-buffer
+ zorder = np.argsort(points[:, 2])
+ points = points[zorder, :]
+ points[:, 2] = (points[:, 2] - np.min(points[:, 2])) / (np.max(points[:, 2] - np.min(points[:, 2])))
+ max_depth = np.max(points[:, 2])
+
+ for i in range(points.shape[0]):
+ j = points.shape[0] - i - 1
+ x = points[j, 0]
+ y = points[j, 1]
+ xc = canvasSize/2 + (x*space)
+ yc = canvasSize/2 + (y*space)
+ xc = int(np.round(xc))
+ yc = int(np.round(yc))
+
+ px = dx + xc
+ py = dy + yc
+
+ image[px, py] = image[px, py] * 0.7 + dv * (max_depth - points[j, 2]) * 0.3
+
+ image = image / np.max(image)
+ return image
+
+def point_cloud_three_views(points):
+ """ input points Nx3 numpy array (+y is up direction).
+ return an numpy array gray image of size 500x1500. """
+ # +y is up direction
+ # xrot is azimuth
+ # yrot is in-plane
+ # zrot is elevation
+ img1 = draw_point_cloud(points, zrot=110/180.0*np.pi, xrot=45/180.0*np.pi, yrot=0/180.0*np.pi)
+ img2 = draw_point_cloud(points, zrot=70/180.0*np.pi, xrot=135/180.0*np.pi, yrot=0/180.0*np.pi)
+ img3 = draw_point_cloud(points, zrot=180.0/180.0*np.pi, xrot=90/180.0*np.pi, yrot=0/180.0*np.pi)
+ image_large = np.concatenate([img1, img2, img3], 1)
+ return image_large
+
+
+def point_cloud_three_views_demo():
+ """ Demo for draw_point_cloud function """
+ from PIL import Image
+ points = read_ply('../third_party/mesh_sampling/piano.ply')
+ im_array = point_cloud_three_views(points)
+ img = Image.fromarray(np.uint8(im_array*255.0))
+ img.save('piano.jpg')
+
+if __name__=="__main__":
+ point_cloud_three_views_demo()
+
+
+def pyplot_draw_point_cloud(points, output_filename):
+ """ points is a Nx3 numpy array """
+ import matplotlib.pyplot as plt
+ fig = plt.figure()
+ ax = fig.add_subplot(111, projection='3d')
+ ax.scatter(points[:,0], points[:,1], points[:,2])
+ ax.set_xlabel('x')
+ ax.set_ylabel('y')
+ ax.set_zlabel('z')
+ #savefig(output_filename)
+
+def pyplot_draw_volume(vol, output_filename):
+ """ vol is of size vsize*vsize*vsize
+ output an image to output_filename
+ """
+ points = volume_to_point_cloud(vol)
+ pyplot_draw_point_cloud(points, output_filename)
+
+def write_ply_color(points, labels, out_filename, num_classes=None):
+ """ Color (N,3) points with labels (N) within range 0 ~ num_classes-1 as OBJ file """
+ import matplotlib.pyplot as pyplot
+ labels = labels.astype(int)
+ N = points.shape[0]
+ if num_classes is None:
+ num_classes = np.max(labels)+1
+ else:
+ assert(num_classes>np.max(labels))
+ fout = open(out_filename, 'w')
+ #colors = [pyplot.cm.hsv(i/float(num_classes)) for i in range(num_classes)]
+ colors = [pyplot.cm.jet(i/float(num_classes)) for i in range(num_classes)]
+ for i in range(N):
+ c = colors[labels[i]]
+ c = [int(x*255) for x in c]
+ fout.write('v %f %f %f %d %d %d\n' % (points[i,0],points[i,1],points[i,2],c[0],c[1],c[2]))
+ fout.close()
diff --git a/pointnet2/utils/pointnet_util.py b/pointnet2/utils/pointnet_util.py
new file mode 100644
index 0000000..7634318
--- /dev/null
+++ b/pointnet2/utils/pointnet_util.py
@@ -0,0 +1,243 @@
+""" PointNet++ Layers
+
+Author: Charles R. Qi
+Date: November 2017
+"""
+
+import os
+import sys
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+ROOT_DIR = os.path.dirname(BASE_DIR)
+sys.path.append(os.path.join(ROOT_DIR, 'utils'))
+sys.path.append(os.path.join(ROOT_DIR, 'tf_ops/sampling'))
+sys.path.append(os.path.join(ROOT_DIR, 'tf_ops/grouping'))
+sys.path.append(os.path.join(ROOT_DIR, 'tf_ops/3d_interpolation'))
+from tf_sampling import farthest_point_sample, gather_point
+from tf_grouping import query_ball_point, group_point, knn_point
+from tf_interpolate import three_nn, three_interpolate
+try:
+ import tensorflow.compat.v1 as tf
+ tf.disable_eager_execution()
+ TF2 = True
+except:
+ import tensorflow as tf
+ TF2 = False
+import numpy as np
+import tf_util
+
+def sample_and_group(npoint, radius, nsample, xyz, points, knn=False, use_xyz=True):
+ '''
+ Input:
+ npoint: int32
+ radius: float32
+ nsample: int32
+ xyz: (batch_size, ndataset, 3) TF tensor
+ points: (batch_size, ndataset, channel) TF tensor, if None will just use xyz as points
+ knn: bool, if True use kNN instead of radius search
+ use_xyz: bool, if True concat XYZ with local point features, otherwise just use point features
+ Output:
+ new_xyz: (batch_size, npoint, 3) TF tensor
+ new_points: (batch_size, npoint, nsample, 3+channel) TF tensor
+ idx: (batch_size, npoint, nsample) TF tensor, indices of local points as in ndataset points
+ grouped_xyz: (batch_size, npoint, nsample, 3) TF tensor, normalized point XYZs
+ (subtracted by seed point XYZ) in local regions
+ '''
+
+ new_xyz = gather_point(xyz, farthest_point_sample(npoint, xyz)) # (batch_size, npoint, 3)
+ if knn:
+ _,idx = knn_point(nsample, xyz, new_xyz)
+ else:
+ idx, pts_cnt = query_ball_point(radius, nsample, xyz, new_xyz)
+ grouped_xyz = group_point(xyz, idx) # (batch_size, npoint, nsample, 3)
+ grouped_xyz -= tf.tile(tf.expand_dims(new_xyz, 2), [1,1,nsample,1]) # translation normalization
+ if points is not None:
+ grouped_points = group_point(points, idx) # (batch_size, npoint, nsample, channel)
+ if use_xyz:
+ new_points = tf.concat([grouped_xyz, grouped_points], axis=-1) # (batch_size, npoint, nample, 3+channel)
+ else:
+ new_points = grouped_points
+ else:
+ new_points = grouped_xyz
+
+ return new_xyz, new_points, idx, grouped_xyz
+
+
+def sample_and_group_all(xyz, points, use_xyz=True):
+ '''
+ Inputs:
+ xyz: (batch_size, ndataset, 3) TF tensor
+ points: (batch_size, ndataset, channel) TF tensor, if None will just use xyz as points
+ use_xyz: bool, if True concat XYZ with local point features, otherwise just use point features
+ Outputs:
+ new_xyz: (batch_size, 1, 3) as (0,0,0)
+ new_points: (batch_size, 1, ndataset, 3+channel) TF tensor
+ Note:
+ Equivalent to sample_and_group with npoint=1, radius=inf, use (0,0,0) as the centroid
+ '''
+ if TF2:
+ batch_size = xyz.get_shape()[0]
+ nsample = xyz.get_shape()[1]
+ else:
+ batch_size = xyz.get_shape()[0].value
+ nsample = xyz.get_shape()[1].value
+
+
+ new_xyz = tf.constant(np.tile(np.array([0,0,0]).reshape((1,1,3)), (batch_size,1,1)),dtype=tf.float32) # (batch_size, 1, 3)
+ idx = tf.constant(np.tile(np.array(range(nsample)).reshape((1,1,nsample)), (batch_size,1,1)))
+ grouped_xyz = tf.reshape(xyz, (batch_size, 1, nsample, 3)) # (batch_size, npoint=1, nsample, 3)
+ if points is not None:
+ if use_xyz:
+ new_points = tf.concat([xyz, points], axis=2) # (batch_size, 16, 259)
+ else:
+ new_points = points
+ new_points = tf.expand_dims(new_points, 1) # (batch_size, 1, 16, 259)
+ else:
+ new_points = grouped_xyz
+ return new_xyz, new_points, idx, grouped_xyz
+
+
+def pointnet_sa_module(xyz, points, npoint, radius, nsample, mlp, mlp2, group_all, is_training, bn_decay, scope, bn=True, pooling='max', knn=False, use_xyz=True, use_nchw=False):
+ ''' PointNet Set Abstraction (SA) Module
+ Input:
+ xyz: (batch_size, ndataset, 3) TF tensor
+ points: (batch_size, ndataset, channel) TF tensor
+ npoint: int32 -- #points sampled in farthest point sampling
+ radius: float32 -- search radius in local region
+ nsample: int32 -- how many points in each local region
+ mlp: list of int32 -- output size for MLP on each point
+ mlp2: list of int32 -- output size for MLP on each region
+ group_all: bool -- group all points into one PC if set true, OVERRIDE
+ npoint, radius and nsample settings
+ use_xyz: bool, if True concat XYZ with local point features, otherwise just use point features
+ use_nchw: bool, if True, use NCHW data format for conv2d, which is usually faster than NHWC format
+ Return:
+ new_xyz: (batch_size, npoint, 3) TF tensor
+ new_points: (batch_size, npoint, mlp[-1] or mlp2[-1]) TF tensor
+ idx: (batch_size, npoint, nsample) int32 -- indices for local regions
+ '''
+ data_format = 'NCHW' if use_nchw else 'NHWC'
+ with tf.variable_scope(scope) as sc:
+ # Sample and Grouping
+ if group_all:
+ nsample = xyz.get_shape()[1]
+ if not TF2:
+ nsample = nsample.value
+ new_xyz, new_points, idx, grouped_xyz = sample_and_group_all(xyz, points, use_xyz)
+ else:
+ new_xyz, new_points, idx, grouped_xyz = sample_and_group(npoint, radius, nsample, xyz, points, knn, use_xyz)
+
+ # Point Feature Embedding
+ if use_nchw: new_points = tf.transpose(new_points, [0,3,1,2])
+ for i, num_out_channel in enumerate(mlp):
+ new_points = tf_util.conv2d(new_points, num_out_channel, [1,1],
+ padding='VALID', stride=[1,1],
+ bn=bn, is_training=is_training,
+ scope='conv%d'%(i), bn_decay=bn_decay,
+ data_format=data_format)
+ if use_nchw: new_points = tf.transpose(new_points, [0,2,3,1])
+
+ # Pooling in Local Regions
+ if pooling=='max':
+ new_points = tf.reduce_max(new_points, axis=[2], keepdims=True, name='maxpool')
+ elif pooling=='avg':
+ new_points = tf.reduce_mean(new_points, axis=[2], keepdims=True, name='avgpool')
+ elif pooling=='weighted_avg':
+ with tf.variable_scope('weighted_avg'):
+ dists = tf.norm(grouped_xyz,axis=-1,ord=2,keepdims=True)
+ exp_dists = tf.exp(-dists * 5)
+ weights = exp_dists/tf.reduce_sum(exp_dists,axis=2,keepdims=True) # (batch_size, npoint, nsample, 1)
+ new_points *= weights # (batch_size, npoint, nsample, mlp[-1])
+ new_points = tf.reduce_sum(new_points, axis=2, keepdims=True)
+ elif pooling=='max_and_avg':
+ max_points = tf.reduce_max(new_points, axis=[2], keepdims=True, name='maxpool')
+ avg_points = tf.reduce_mean(new_points, axis=[2], keepdims=True, name='avgpool')
+ new_points = tf.concat([avg_points, max_points], axis=-1)
+
+ # [Optional] Further Processing
+ if mlp2 is not None:
+ if use_nchw: new_points = tf.transpose(new_points, [0,3,1,2])
+ for i, num_out_channel in enumerate(mlp2):
+ new_points = tf_util.conv2d(new_points, num_out_channel, [1,1],
+ padding='VALID', stride=[1,1],
+ bn=bn, is_training=is_training,
+ scope='conv_post_%d'%(i), bn_decay=bn_decay,
+ data_format=data_format)
+ if use_nchw: new_points = tf.transpose(new_points, [0,2,3,1])
+
+ new_points = tf.squeeze(new_points, [2]) # (batch_size, npoints, mlp2[-1])
+ return new_xyz, new_points, idx
+
+def pointnet_sa_module_msg(xyz, points, npoint, radius_list, nsample_list, mlp_list, is_training, bn_decay, scope, bn=True, use_xyz=True, use_nchw=False):
+ ''' PointNet Set Abstraction (SA) module with Multi-Scale Grouping (MSG)
+ Input:
+ xyz: (batch_size, ndataset, 3) TF tensor
+ points: (batch_size, ndataset, channel) TF tensor
+ npoint: int32 -- #points sampled in farthest point sampling
+ radius: list of float32 -- search radius in local region
+ nsample: list of int32 -- how many points in each local region
+ mlp: list of list of int32 -- output size for MLP on each point
+ use_xyz: bool, if True concat XYZ with local point features, otherwise just use point features
+ use_nchw: bool, if True, use NCHW data format for conv2d, which is usually faster than NHWC format
+ Return:
+ new_xyz: (batch_size, npoint, 3) TF tensor
+ new_points: (batch_size, npoint, \sum_k{mlp[k][-1]}) TF tensor
+ '''
+ data_format = 'NCHW' if use_nchw else 'NHWC'
+ with tf.variable_scope(scope) as sc:
+ new_xyz = gather_point(xyz, farthest_point_sample(npoint, xyz))
+ new_points_list = []
+ for i in range(len(radius_list)):
+ radius = radius_list[i]
+ nsample = nsample_list[i]
+ idx, pts_cnt = query_ball_point(radius, nsample, xyz, new_xyz)
+ grouped_xyz = group_point(xyz, idx)
+ grouped_xyz -= tf.tile(tf.expand_dims(new_xyz, 2), [1,1,nsample,1])
+ if points is not None:
+ grouped_points = group_point(points, idx)
+ if use_xyz:
+ grouped_points = tf.concat([grouped_points, grouped_xyz], axis=-1)
+ else:
+ grouped_points = grouped_xyz
+ if use_nchw: grouped_points = tf.transpose(grouped_points, [0,3,1,2])
+ for j,num_out_channel in enumerate(mlp_list[i]):
+ grouped_points = tf_util.conv2d(grouped_points, num_out_channel, [1,1],
+ padding='VALID', stride=[1,1], bn=bn, is_training=is_training,
+ scope='conv%d_%d'%(i,j), bn_decay=bn_decay)
+ if use_nchw: grouped_points = tf.transpose(grouped_points, [0,2,3,1])
+ new_points = tf.reduce_max(grouped_points, axis=[2])
+ new_points_list.append(new_points)
+ new_points_concat = tf.concat(new_points_list, axis=-1)
+ return new_xyz, new_points_concat
+
+
+def pointnet_fp_module(xyz1, xyz2, points1, points2, mlp, is_training, bn_decay, scope, bn=True):
+ ''' PointNet Feature Propogation (FP) Module
+ Input:
+ xyz1: (batch_size, ndataset1, 3) TF tensor
+ xyz2: (batch_size, ndataset2, 3) TF tensor, sparser than xyz1
+ points1: (batch_size, ndataset1, nchannel1) TF tensor
+ points2: (batch_size, ndataset2, nchannel2) TF tensor
+ mlp: list of int32 -- output size for MLP on each point
+ Return:
+ new_points: (batch_size, ndataset1, mlp[-1]) TF tensor
+ '''
+ with tf.variable_scope(scope) as sc:
+ dist, idx = three_nn(xyz1, xyz2)
+ dist = tf.maximum(dist, 1e-10)
+ norm = tf.reduce_sum((1.0/dist),axis=2,keepdims=True)
+ norm = tf.tile(norm,[1,1,3])
+ weight = (1.0/dist) / norm
+ interpolated_points = three_interpolate(points2, idx, weight)
+
+ if points1 is not None:
+ new_points1 = tf.concat(axis=2, values=[interpolated_points, points1]) # B,ndataset1,nchannel1+nchannel2
+ else:
+ new_points1 = interpolated_points
+ new_points1 = tf.expand_dims(new_points1, 2)
+ for i, num_out_channel in enumerate(mlp):
+ new_points1 = tf_util.conv2d(new_points1, num_out_channel, [1,1],
+ padding='VALID', stride=[1,1],
+ bn=bn, is_training=is_training,
+ scope='conv_%d'%(i), bn_decay=bn_decay)
+ new_points1 = tf.squeeze(new_points1, [2]) # B,ndataset1,mlp[-1]
+ return new_points1
diff --git a/pointnet2/utils/provider.py b/pointnet2/utils/provider.py
new file mode 100644
index 0000000..5cae966
--- /dev/null
+++ b/pointnet2/utils/provider.py
@@ -0,0 +1,247 @@
+import os
+import sys
+import numpy as np
+import h5py
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(BASE_DIR)
+
+def shuffle_data(data, labels):
+ """ Shuffle data and labels.
+ Input:
+ data: B,N,... numpy array
+ label: B,... numpy array
+ Return:
+ shuffled data, label and shuffle indices
+ """
+ idx = np.arange(len(labels))
+ np.random.shuffle(idx)
+ return data[idx, ...], labels[idx], idx
+
+def shuffle_points(batch_data):
+ """ Shuffle orders of points in each point cloud -- changes FPS behavior.
+ Use the same shuffling idx for the entire batch.
+ Input:
+ BxNxC array
+ Output:
+ BxNxC array
+ """
+ idx = np.arange(batch_data.shape[1])
+ np.random.shuffle(idx)
+ return batch_data[:,idx,:]
+
+def rotate_point_cloud(batch_data):
+ """ Randomly rotate the point clouds to augument the dataset
+ rotation is per shape based along up direction
+ Input:
+ BxNx3 array, original batch of point clouds
+ Return:
+ BxNx3 array, rotated batch of point clouds
+ """
+ rotated_data = np.zeros(batch_data.shape, dtype=np.float32)
+ for k in xrange(batch_data.shape[0]):
+ rotation_angle = np.random.uniform() * 2 * np.pi
+ cosval = np.cos(rotation_angle)
+ sinval = np.sin(rotation_angle)
+ rotation_matrix = np.array([[cosval, 0, sinval],
+ [0, 1, 0],
+ [-sinval, 0, cosval]])
+ shape_pc = batch_data[k, ...]
+ rotated_data[k, ...] = np.dot(shape_pc.reshape((-1, 3)), rotation_matrix)
+ return rotated_data
+
+def rotate_point_cloud_z(batch_data):
+ """ Randomly rotate the point clouds to augument the dataset
+ rotation is per shape based along up direction
+ Input:
+ BxNx3 array, original batch of point clouds
+ Return:
+ BxNx3 array, rotated batch of point clouds
+ """
+ rotated_data = np.zeros(batch_data.shape, dtype=np.float32)
+ for k in xrange(batch_data.shape[0]):
+ rotation_angle = np.random.uniform() * 2 * np.pi
+ cosval = np.cos(rotation_angle)
+ sinval = np.sin(rotation_angle)
+ rotation_matrix = np.array([[cosval, sinval, 0],
+ [-sinval, cosval, 0],
+ [0, 0, 1]])
+ shape_pc = batch_data[k, ...]
+ rotated_data[k, ...] = np.dot(shape_pc.reshape((-1, 3)), rotation_matrix)
+ return rotated_data
+
+def rotate_point_cloud_with_normal(batch_xyz_normal):
+ ''' Randomly rotate XYZ, normal point cloud.
+ Input:
+ batch_xyz_normal: B,N,6, first three channels are XYZ, last 3 all normal
+ Output:
+ B,N,6, rotated XYZ, normal point cloud
+ '''
+ for k in xrange(batch_xyz_normal.shape[0]):
+ rotation_angle = np.random.uniform() * 2 * np.pi
+ cosval = np.cos(rotation_angle)
+ sinval = np.sin(rotation_angle)
+ rotation_matrix = np.array([[cosval, 0, sinval],
+ [0, 1, 0],
+ [-sinval, 0, cosval]])
+ shape_pc = batch_xyz_normal[k,:,0:3]
+ shape_normal = batch_xyz_normal[k,:,3:6]
+ batch_xyz_normal[k,:,0:3] = np.dot(shape_pc.reshape((-1, 3)), rotation_matrix)
+ batch_xyz_normal[k,:,3:6] = np.dot(shape_normal.reshape((-1, 3)), rotation_matrix)
+ return batch_xyz_normal
+
+def rotate_perturbation_point_cloud_with_normal(batch_data, angle_sigma=0.06, angle_clip=0.18):
+ """ Randomly perturb the point clouds by small rotations
+ Input:
+ BxNx6 array, original batch of point clouds and point normals
+ Return:
+ BxNx3 array, rotated batch of point clouds
+ """
+ rotated_data = np.zeros(batch_data.shape, dtype=np.float32)
+ for k in xrange(batch_data.shape[0]):
+ angles = np.clip(angle_sigma*np.random.randn(3), -angle_clip, angle_clip)
+ Rx = np.array([[1,0,0],
+ [0,np.cos(angles[0]),-np.sin(angles[0])],
+ [0,np.sin(angles[0]),np.cos(angles[0])]])
+ Ry = np.array([[np.cos(angles[1]),0,np.sin(angles[1])],
+ [0,1,0],
+ [-np.sin(angles[1]),0,np.cos(angles[1])]])
+ Rz = np.array([[np.cos(angles[2]),-np.sin(angles[2]),0],
+ [np.sin(angles[2]),np.cos(angles[2]),0],
+ [0,0,1]])
+ R = np.dot(Rz, np.dot(Ry,Rx))
+ shape_pc = batch_data[k,:,0:3]
+ shape_normal = batch_data[k,:,3:6]
+ rotated_data[k,:,0:3] = np.dot(shape_pc.reshape((-1, 3)), R)
+ rotated_data[k,:,3:6] = np.dot(shape_normal.reshape((-1, 3)), R)
+ return rotated_data
+
+
+def rotate_point_cloud_by_angle(batch_data, rotation_angle):
+ """ Rotate the point cloud along up direction with certain angle.
+ Input:
+ BxNx3 array, original batch of point clouds
+ Return:
+ BxNx3 array, rotated batch of point clouds
+ """
+ rotated_data = np.zeros(batch_data.shape, dtype=np.float32)
+ for k in xrange(batch_data.shape[0]):
+ #rotation_angle = np.random.uniform() * 2 * np.pi
+ cosval = np.cos(rotation_angle)
+ sinval = np.sin(rotation_angle)
+ rotation_matrix = np.array([[cosval, 0, sinval],
+ [0, 1, 0],
+ [-sinval, 0, cosval]])
+ shape_pc = batch_data[k,:,0:3]
+ rotated_data[k,:,0:3] = np.dot(shape_pc.reshape((-1, 3)), rotation_matrix)
+ return rotated_data
+
+def rotate_point_cloud_by_angle_with_normal(batch_data, rotation_angle):
+ """ Rotate the point cloud along up direction with certain angle.
+ Input:
+ BxNx6 array, original batch of point clouds with normal
+ scalar, angle of rotation
+ Return:
+ BxNx6 array, rotated batch of point clouds iwth normal
+ """
+ rotated_data = np.zeros(batch_data.shape, dtype=np.float32)
+ for k in xrange(batch_data.shape[0]):
+ #rotation_angle = np.random.uniform() * 2 * np.pi
+ cosval = np.cos(rotation_angle)
+ sinval = np.sin(rotation_angle)
+ rotation_matrix = np.array([[cosval, 0, sinval],
+ [0, 1, 0],
+ [-sinval, 0, cosval]])
+ shape_pc = batch_data[k,:,0:3]
+ shape_normal = batch_data[k,:,3:6]
+ rotated_data[k,:,0:3] = np.dot(shape_pc.reshape((-1, 3)), rotation_matrix)
+ rotated_data[k,:,3:6] = np.dot(shape_normal.reshape((-1,3)), rotation_matrix)
+ return rotated_data
+
+
+
+def rotate_perturbation_point_cloud(batch_data, angle_sigma=0.06, angle_clip=0.18):
+ """ Randomly perturb the point clouds by small rotations
+ Input:
+ BxNx3 array, original batch of point clouds
+ Return:
+ BxNx3 array, rotated batch of point clouds
+ """
+ rotated_data = np.zeros(batch_data.shape, dtype=np.float32)
+ for k in xrange(batch_data.shape[0]):
+ angles = np.clip(angle_sigma*np.random.randn(3), -angle_clip, angle_clip)
+ Rx = np.array([[1,0,0],
+ [0,np.cos(angles[0]),-np.sin(angles[0])],
+ [0,np.sin(angles[0]),np.cos(angles[0])]])
+ Ry = np.array([[np.cos(angles[1]),0,np.sin(angles[1])],
+ [0,1,0],
+ [-np.sin(angles[1]),0,np.cos(angles[1])]])
+ Rz = np.array([[np.cos(angles[2]),-np.sin(angles[2]),0],
+ [np.sin(angles[2]),np.cos(angles[2]),0],
+ [0,0,1]])
+ R = np.dot(Rz, np.dot(Ry,Rx))
+ shape_pc = batch_data[k, ...]
+ rotated_data[k, ...] = np.dot(shape_pc.reshape((-1, 3)), R)
+ return rotated_data
+
+
+def jitter_point_cloud(batch_data, sigma=0.01, clip=0.05):
+ """ Randomly jitter points. jittering is per point.
+ Input:
+ BxNx3 array, original batch of point clouds
+ Return:
+ BxNx3 array, jittered batch of point clouds
+ """
+ B, N, C = batch_data.shape
+ assert(clip > 0)
+ jittered_data = np.clip(sigma * np.random.randn(B, N, C), -1*clip, clip)
+ jittered_data += batch_data
+ return jittered_data
+
+def shift_point_cloud(batch_data, shift_range=0.1):
+ """ Randomly shift point cloud. Shift is per point cloud.
+ Input:
+ BxNx3 array, original batch of point clouds
+ Return:
+ BxNx3 array, shifted batch of point clouds
+ """
+ B, N, C = batch_data.shape
+ shifts = np.random.uniform(-shift_range, shift_range, (B,3))
+ for batch_index in range(B):
+ batch_data[batch_index,:,:] += shifts[batch_index,:]
+ return batch_data
+
+
+def random_scale_point_cloud(batch_data, scale_low=0.8, scale_high=1.25):
+ """ Randomly scale the point cloud. Scale is per point cloud.
+ Input:
+ BxNx3 array, original batch of point clouds
+ Return:
+ BxNx3 array, scaled batch of point clouds
+ """
+ B, N, C = batch_data.shape
+ scales = np.random.uniform(scale_low, scale_high, B)
+ for batch_index in range(B):
+ batch_data[batch_index,:,:] *= scales[batch_index]
+ return batch_data
+
+def random_point_dropout(batch_pc, max_dropout_ratio=0.875):
+ ''' batch_pc: BxNx3 '''
+ for b in range(batch_pc.shape[0]):
+ dropout_ratio = np.random.random()*max_dropout_ratio # 0~0.875
+ drop_idx = np.where(np.random.random((batch_pc.shape[1]))<=dropout_ratio)[0]
+ if len(drop_idx)>0:
+ batch_pc[b,drop_idx,:] = batch_pc[b,0,:] # set to the first point
+ return batch_pc
+
+
+def getDataFiles(list_filename):
+ return [line.rstrip() for line in open(list_filename)]
+
+def load_h5(h5_filename):
+ f = h5py.File(h5_filename)
+ data = f['data'][:]
+ label = f['label'][:]
+ return (data, label)
+
+def loadDataFile(filename):
+ return load_h5(filename)
diff --git a/pointnet2/utils/render_balls_so.cpp b/pointnet2/utils/render_balls_so.cpp
new file mode 100644
index 0000000..e95aeba
--- /dev/null
+++ b/pointnet2/utils/render_balls_so.cpp
@@ -0,0 +1,58 @@
+#include
+#include
+#include
+#include
+using namespace std;
+
+struct PointInfo{
+ int x,y,z;
+ float r,g,b;
+};
+
+extern "C"{
+
+void render_ball(int h,int w,unsigned char * show,int n,int * xyzs,float * c0,float * c1,float * c2,int r){
+ r=max(r,1);
+ vector depth(h*w,-2100000000);
+ vector pattern;
+ for (int dx=-r;dx<=r;dx++)
+ for (int dy=-r;dy<=r;dy++)
+ if (dx*dx+dy*dy=h || y2<0 || y2>=w) && depth[x2*w+y2]0:
+ show[:,:,0]=np.maximum(show[:,:,0],np.roll(show[:,:,0],1,axis=0))
+ if magnifyBlue>=2:
+ show[:,:,0]=np.maximum(show[:,:,0],np.roll(show[:,:,0],-1,axis=0))
+ show[:,:,0]=np.maximum(show[:,:,0],np.roll(show[:,:,0],1,axis=1))
+ if magnifyBlue>=2:
+ show[:,:,0]=np.maximum(show[:,:,0],np.roll(show[:,:,0],-1,axis=1))
+ if showrot:
+ cv2.putText(show,'xangle %d'%(int(xangle/np.pi*180)),(30,showsz-30),0,0.5,cv2.cv.CV_RGB(255,0,0))
+ cv2.putText(show,'yangle %d'%(int(yangle/np.pi*180)),(30,showsz-50),0,0.5,cv2.cv.CV_RGB(255,0,0))
+ cv2.putText(show,'zoom %d%%'%(int(zoom*100)),(30,showsz-70),0,0.5,cv2.cv.CV_RGB(255,0,0))
+ changed=True
+ while True:
+ if changed:
+ render()
+ changed=False
+ cv2.imshow('show3d',show)
+ if waittime==0:
+ cmd=cv2.waitKey(10)%256
+ else:
+ cmd=cv2.waitKey(waittime)%256
+ if cmd==ord('q'):
+ break
+ elif cmd==ord('Q'):
+ sys.exit(0)
+
+ if cmd==ord('t') or cmd == ord('p'):
+ if cmd == ord('t'):
+ if c_gt is None:
+ c0=np.zeros((len(xyz),),dtype='float32')+255
+ c1=np.zeros((len(xyz),),dtype='float32')+255
+ c2=np.zeros((len(xyz),),dtype='float32')+255
+ else:
+ c0=c_gt[:,0]
+ c1=c_gt[:,1]
+ c2=c_gt[:,2]
+ else:
+ if c_pred is None:
+ c0=np.zeros((len(xyz),),dtype='float32')+255
+ c1=np.zeros((len(xyz),),dtype='float32')+255
+ c2=np.zeros((len(xyz),),dtype='float32')+255
+ else:
+ c0=c_pred[:,0]
+ c1=c_pred[:,1]
+ c2=c_pred[:,2]
+ if normalizecolor:
+ c0/=(c0.max()+1e-14)/255.0
+ c1/=(c1.max()+1e-14)/255.0
+ c2/=(c2.max()+1e-14)/255.0
+ c0=np.require(c0,'float32','C')
+ c1=np.require(c1,'float32','C')
+ c2=np.require(c2,'float32','C')
+ changed = True
+
+
+
+ if cmd==ord('n'):
+ zoom*=1.1
+ changed=True
+ elif cmd==ord('m'):
+ zoom/=1.1
+ changed=True
+ elif cmd==ord('r'):
+ zoom=1.0
+ changed=True
+ elif cmd==ord('s'):
+ cv2.imwrite('show3d.png',show)
+ if waittime!=0:
+ break
+ return cmd
+if __name__=='__main__':
+ np.random.seed(100)
+ showpoints(np.random.randn(2500,3))
+
diff --git a/pointnet2/utils/tf_util.py b/pointnet2/utils/tf_util.py
new file mode 100644
index 0000000..f66500f
--- /dev/null
+++ b/pointnet2/utils/tf_util.py
@@ -0,0 +1,661 @@
+""" Wrapper functions for TensorFlow layers.
+
+Author: Charles R. Qi
+Date: November 2017
+"""
+
+import numpy as np
+try:
+ import tensorflow.compat.v1 as tf
+ tf.disable_eager_execution()
+ TF2 = True
+except:
+ import tensorflow as tf
+ TF2 = False
+
+def _variable_on_cpu(name, shape, initializer, use_fp16=False):
+ """Helper to create a Variable stored on CPU memory.
+ Args:
+ name: name of the variable
+ shape: list of ints
+ initializer: initializer for Variable
+ Returns:
+ Variable Tensor
+ """
+ with tf.device("/cpu:0"):
+ dtype = tf.float16 if use_fp16 else tf.float32
+ var = tf.get_variable(name, shape, initializer=initializer, dtype=dtype)
+ return var
+
+def _variable_with_weight_decay(name, shape, stddev, wd, use_xavier=True):
+ """Helper to create an initialized Variable with weight decay.
+
+ Note that the Variable is initialized with a truncated normal distribution.
+ A weight decay is added only if one is specified.
+
+ Args:
+ name: name of the variable
+ shape: list of ints
+ stddev: standard deviation of a truncated Gaussian
+ wd: add L2Loss weight decay multiplied by this float. If None, weight
+ decay is not added for this Variable.
+ use_xavier: bool, whether to use xavier initializer
+
+ Returns:
+ Variable Tensor
+ """
+ if use_xavier:
+ if TF2:
+ initializer = tf.keras.initializers.glorot_normal()
+ else:
+ initializer = tf.contrib.layers.xavier_initializer()
+ else:
+ initializer = tf.truncated_normal_initializer(stddev=stddev)
+ var = _variable_on_cpu(name, shape, initializer)
+ if wd is not None:
+ weight_decay = tf.multiply(tf.nn.l2_loss(var), wd, name='weight_loss')
+ tf.add_to_collection('losses', weight_decay)
+ return var
+
+
+def conv1d(inputs,
+ num_output_channels,
+ kernel_size,
+ scope,
+ stride=1,
+ padding='SAME',
+ data_format='NHWC',
+ use_xavier=True,
+ stddev=1e-3,
+ weight_decay=None,
+ activation_fn=tf.nn.relu,
+ bn=False,
+ bn_decay=None,
+ is_training=None):
+ """ 1D convolution with non-linear operation.
+
+ Args:
+ inputs: 3-D tensor variable BxLxC
+ num_output_channels: int
+ kernel_size: int
+ scope: string
+ stride: int
+ padding: 'SAME' or 'VALID'
+ data_format: 'NHWC' or 'NCHW'
+ use_xavier: bool, use xavier_initializer if true
+ stddev: float, stddev for truncated_normal init
+ weight_decay: float
+ activation_fn: function
+ bn: bool, whether to use batch norm
+ bn_decay: float or float tensor variable in [0,1]
+ is_training: bool Tensor variable
+
+ Returns:
+ Variable tensor
+ """
+ with tf.variable_scope(scope) as sc:
+ assert(data_format=='NHWC' or data_format=='NCHW')
+ if data_format == 'NHWC':
+ num_in_channels = inputs.get_shape()[-1]
+ elif data_format=='NCHW':
+ num_in_channels = inputs.get_shape()[1]
+
+ if not TF2:
+ num_in_channels = num_in_channels.value
+ kernel_shape = [kernel_size,
+ num_in_channels, num_output_channels]
+ kernel = _variable_with_weight_decay('weights',
+ shape=kernel_shape,
+ use_xavier=use_xavier,
+ stddev=stddev,
+ wd=weight_decay)
+ outputs = tf.nn.conv1d(inputs, kernel,
+ stride=stride,
+ padding=padding,
+ data_format=data_format)
+ biases = _variable_on_cpu('biases', [num_output_channels],
+ tf.constant_initializer(0.0))
+ outputs = tf.nn.bias_add(outputs, biases, data_format=data_format)
+
+ if bn:
+ outputs = batch_norm_for_conv1d(outputs, is_training,
+ bn_decay=bn_decay, scope='bn',
+ data_format=data_format)
+
+ if activation_fn is not None:
+ outputs = activation_fn(outputs)
+ return outputs
+
+
+
+
+def conv2d(inputs,
+ num_output_channels,
+ kernel_size,
+ scope,
+ stride=[1, 1],
+ padding='SAME',
+ data_format='NHWC',
+ use_xavier=True,
+ stddev=1e-3,
+ weight_decay=None,
+ activation_fn=tf.nn.relu,
+ bn=False,
+ bn_decay=None,
+ is_training=None):
+ """ 2D convolution with non-linear operation.
+
+ Args:
+ inputs: 4-D tensor variable BxHxWxC
+ num_output_channels: int
+ kernel_size: a list of 2 ints
+ scope: string
+ stride: a list of 2 ints
+ padding: 'SAME' or 'VALID'
+ data_format: 'NHWC' or 'NCHW'
+ use_xavier: bool, use xavier_initializer if true
+ stddev: float, stddev for truncated_normal init
+ weight_decay: float
+ activation_fn: function
+ bn: bool, whether to use batch norm
+ bn_decay: float or float tensor variable in [0,1]
+ is_training: bool Tensor variable
+
+ Returns:
+ Variable tensor
+ """
+ with tf.variable_scope(scope) as sc:
+ kernel_h, kernel_w = kernel_size
+ assert(data_format=='NHWC' or data_format=='NCHW')
+ if data_format == 'NHWC':
+ num_in_channels = inputs.get_shape()[-1]
+ elif data_format=='NCHW':
+ num_in_channels = inputs.get_shape()[1]
+ if not TF2:
+ num_in_channels = num_in_channels.value
+ kernel_shape = [kernel_h, kernel_w,
+ num_in_channels, num_output_channels]
+ kernel = _variable_with_weight_decay('weights',
+ shape=kernel_shape,
+ use_xavier=use_xavier,
+ stddev=stddev,
+ wd=weight_decay)
+ stride_h, stride_w = stride
+ outputs = tf.nn.conv2d(inputs, kernel,
+ [1, stride_h, stride_w, 1],
+ padding=padding,
+ data_format=data_format)
+ biases = _variable_on_cpu('biases', [num_output_channels],
+ tf.constant_initializer(0.0))
+ outputs = tf.nn.bias_add(outputs, biases, data_format=data_format)
+
+ if bn:
+ outputs = batch_norm_for_conv2d(outputs, is_training,
+ bn_decay=bn_decay, scope='bn',
+ data_format=data_format)
+
+ if activation_fn is not None:
+ outputs = activation_fn(outputs)
+ return outputs
+
+
+def conv2d_transpose(inputs,
+ num_output_channels,
+ kernel_size,
+ scope,
+ stride=[1, 1],
+ padding='SAME',
+ use_xavier=True,
+ stddev=1e-3,
+ weight_decay=None,
+ activation_fn=tf.nn.relu,
+ bn=False,
+ bn_decay=None,
+ is_training=None):
+ """ 2D convolution transpose with non-linear operation.
+
+ Args:
+ inputs: 4-D tensor variable BxHxWxC
+ num_output_channels: int
+ kernel_size: a list of 2 ints
+ scope: string
+ stride: a list of 2 ints
+ padding: 'SAME' or 'VALID'
+ use_xavier: bool, use xavier_initializer if true
+ stddev: float, stddev for truncated_normal init
+ weight_decay: float
+ activation_fn: function
+ bn: bool, whether to use batch norm
+ bn_decay: float or float tensor variable in [0,1]
+ is_training: bool Tensor variable
+
+ Returns:
+ Variable tensor
+
+ Note: conv2d(conv2d_transpose(a, num_out, ksize, stride), a.shape[-1], ksize, stride) == a
+ """
+ with tf.variable_scope(scope) as sc:
+ kernel_h, kernel_w = kernel_size
+ num_in_channels = inputs.get_shape()[-1]
+ if not TF2:
+ num_in_channels = num_in_channels.value
+ kernel_shape = [kernel_h, kernel_w,
+ num_output_channels, num_in_channels] # reversed to conv2d
+ kernel = _variable_with_weight_decay('weights',
+ shape=kernel_shape,
+ use_xavier=use_xavier,
+ stddev=stddev,
+ wd=weight_decay)
+ stride_h, stride_w = stride
+
+ # from slim.convolution2d_transpose
+ def get_deconv_dim(dim_size, stride_size, kernel_size, padding):
+ dim_size *= stride_size
+
+ if padding == 'VALID' and dim_size is not None:
+ dim_size += max(kernel_size - stride_size, 0)
+ return dim_size
+
+ # caculate output shape
+ batch_size = inputs.get_shape()[0]
+ height = inputs.get_shape()[1]
+ width = inputs.get_shape()[2]
+ if not TF2:
+ batch_size = batch_size.value
+ height = height.value
+ width = width.value
+
+ out_height = get_deconv_dim(height, stride_h, kernel_h, padding)
+ out_width = get_deconv_dim(width, stride_w, kernel_w, padding)
+ output_shape = [batch_size, out_height, out_width, num_output_channels]
+
+ outputs = tf.nn.conv2d_transpose(inputs, kernel, output_shape,
+ [1, stride_h, stride_w, 1],
+ padding=padding)
+ biases = _variable_on_cpu('biases', [num_output_channels],
+ tf.constant_initializer(0.0))
+ outputs = tf.nn.bias_add(outputs, biases)
+
+ if bn:
+ outputs = batch_norm_for_conv2d(outputs, is_training,
+ bn_decay=bn_decay, scope='bn')
+
+ if activation_fn is not None:
+ outputs = activation_fn(outputs)
+ return outputs
+
+
+
+def conv3d(inputs,
+ num_output_channels,
+ kernel_size,
+ scope,
+ stride=[1, 1, 1],
+ padding='SAME',
+ use_xavier=True,
+ stddev=1e-3,
+ weight_decay=None,
+ activation_fn=tf.nn.relu,
+ bn=False,
+ bn_decay=None,
+ is_training=None):
+ """ 3D convolution with non-linear operation.
+
+ Args:
+ inputs: 5-D tensor variable BxDxHxWxC
+ num_output_channels: int
+ kernel_size: a list of 3 ints
+ scope: string
+ stride: a list of 3 ints
+ padding: 'SAME' or 'VALID'
+ use_xavier: bool, use xavier_initializer if true
+ stddev: float, stddev for truncated_normal init
+ weight_decay: float
+ activation_fn: function
+ bn: bool, whether to use batch norm
+ bn_decay: float or float tensor variable in [0,1]
+ is_training: bool Tensor variable
+
+ Returns:
+ Variable tensor
+ """
+ with tf.variable_scope(scope) as sc:
+ kernel_d, kernel_h, kernel_w = kernel_size
+ num_in_channels = inputs.get_shape()[-1]
+ if not TF2:
+ num_in_channels = num_in_channels.value
+ kernel_shape = [kernel_d, kernel_h, kernel_w,
+ num_in_channels, num_output_channels]
+ kernel = _variable_with_weight_decay('weights',
+ shape=kernel_shape,
+ use_xavier=use_xavier,
+ stddev=stddev,
+ wd=weight_decay)
+ stride_d, stride_h, stride_w = stride
+ outputs = tf.nn.conv3d(inputs, kernel,
+ [1, stride_d, stride_h, stride_w, 1],
+ padding=padding)
+ biases = _variable_on_cpu('biases', [num_output_channels],
+ tf.constant_initializer(0.0))
+ outputs = tf.nn.bias_add(outputs, biases)
+
+ if bn:
+ outputs = batch_norm_for_conv3d(outputs, is_training,
+ bn_decay=bn_decay, scope='bn')
+
+ if activation_fn is not None:
+ outputs = activation_fn(outputs)
+ return outputs
+
+def fully_connected(inputs,
+ num_outputs,
+ scope,
+ use_xavier=True,
+ stddev=1e-3,
+ weight_decay=None,
+ activation_fn=tf.nn.relu,
+ bn=False,
+ bn_decay=None,
+ is_training=None):
+ """ Fully connected layer with non-linear operation.
+
+ Args:
+ inputs: 2-D tensor BxN
+ num_outputs: int
+
+ Returns:
+ Variable tensor of size B x num_outputs.
+ """
+ with tf.variable_scope(scope) as sc:
+ num_input_units = inputs.get_shape()[-1]
+ if not TF2:
+ num_input_units = num_input_units.value
+ weights = _variable_with_weight_decay('weights',
+ shape=[num_input_units, num_outputs],
+ use_xavier=use_xavier,
+ stddev=stddev,
+ wd=weight_decay)
+ outputs = tf.matmul(inputs, weights)
+ biases = _variable_on_cpu('biases', [num_outputs],
+ tf.constant_initializer(0.0))
+ outputs = tf.nn.bias_add(outputs, biases)
+
+ if bn:
+ outputs = batch_norm_for_fc(outputs, is_training, bn_decay, 'bn')
+
+ if activation_fn is not None:
+ outputs = activation_fn(outputs)
+ return outputs
+
+
+def max_pool2d(inputs,
+ kernel_size,
+ scope,
+ stride=[2, 2],
+ padding='VALID'):
+ """ 2D max pooling.
+
+ Args:
+ inputs: 4-D tensor BxHxWxC
+ kernel_size: a list of 2 ints
+ stride: a list of 2 ints
+
+ Returns:
+ Variable tensor
+ """
+ with tf.variable_scope(scope) as sc:
+ kernel_h, kernel_w = kernel_size
+ stride_h, stride_w = stride
+ outputs = tf.nn.max_pool(inputs,
+ ksize=[1, kernel_h, kernel_w, 1],
+ strides=[1, stride_h, stride_w, 1],
+ padding=padding,
+ name=sc.name)
+ return outputs
+
+def avg_pool2d(inputs,
+ kernel_size,
+ scope,
+ stride=[2, 2],
+ padding='VALID'):
+ """ 2D avg pooling.
+
+ Args:
+ inputs: 4-D tensor BxHxWxC
+ kernel_size: a list of 2 ints
+ stride: a list of 2 ints
+
+ Returns:
+ Variable tensor
+ """
+ with tf.variable_scope(scope) as sc:
+ kernel_h, kernel_w = kernel_size
+ stride_h, stride_w = stride
+ outputs = tf.nn.avg_pool(inputs,
+ ksize=[1, kernel_h, kernel_w, 1],
+ strides=[1, stride_h, stride_w, 1],
+ padding=padding,
+ name=sc.name)
+ return outputs
+
+
+def max_pool3d(inputs,
+ kernel_size,
+ scope,
+ stride=[2, 2, 2],
+ padding='VALID'):
+ """ 3D max pooling.
+
+ Args:
+ inputs: 5-D tensor BxDxHxWxC
+ kernel_size: a list of 3 ints
+ stride: a list of 3 ints
+
+ Returns:
+ Variable tensor
+ """
+ with tf.variable_scope(scope) as sc:
+ kernel_d, kernel_h, kernel_w = kernel_size
+ stride_d, stride_h, stride_w = stride
+ outputs = tf.nn.max_pool3d(inputs,
+ ksize=[1, kernel_d, kernel_h, kernel_w, 1],
+ strides=[1, stride_d, stride_h, stride_w, 1],
+ padding=padding,
+ name=sc.name)
+ return outputs
+
+def avg_pool3d(inputs,
+ kernel_size,
+ scope,
+ stride=[2, 2, 2],
+ padding='VALID'):
+ """ 3D avg pooling.
+
+ Args:
+ inputs: 5-D tensor BxDxHxWxC
+ kernel_size: a list of 3 ints
+ stride: a list of 3 ints
+
+ Returns:
+ Variable tensor
+ """
+ with tf.variable_scope(scope) as sc:
+ kernel_d, kernel_h, kernel_w = kernel_size
+ stride_d, stride_h, stride_w = stride
+ outputs = tf.nn.avg_pool3d(inputs,
+ ksize=[1, kernel_d, kernel_h, kernel_w, 1],
+ strides=[1, stride_d, stride_h, stride_w, 1],
+ padding=padding,
+ name=sc.name)
+ return outputs
+
+
+def batch_norm_template_unused(inputs, is_training, scope, moments_dims, bn_decay):
+ """ NOTE: this is older version of the util func. it is deprecated.
+ Batch normalization on convolutional maps and beyond...
+ Ref.: http://stackoverflow.com/questions/33949786/how-could-i-use-batch-normalization-in-tensorflow
+
+ Args:
+ inputs: Tensor, k-D input ... x C could be BC or BHWC or BDHWC
+ is_training: boolean tf.Varialbe, true indicates training phase
+ scope: string, variable scope
+ moments_dims: a list of ints, indicating dimensions for moments calculation
+ bn_decay: float or float tensor variable, controling moving average weight
+ Return:
+ normed: batch-normalized maps
+ """
+ with tf.variable_scope(scope) as sc:
+ num_channels = inputs.get_shape()[-1]
+ if not TF2:
+ num_channels = num_channels.value
+ beta = _variable_on_cpu(name='beta',shape=[num_channels],
+ initializer=tf.constant_initializer(0))
+ gamma = _variable_on_cpu(name='gamma',shape=[num_channels],
+ initializer=tf.constant_initializer(1.0))
+ batch_mean, batch_var = tf.nn.moments(inputs, moments_dims, name='moments')
+ decay = bn_decay if bn_decay is not None else 0.9
+ ema = tf.train.ExponentialMovingAverage(decay=decay)
+ # Operator that maintains moving averages of variables.
+ # Need to set reuse=False, otherwise if reuse, will see moments_1/mean/ExponentialMovingAverage/ does not exist
+ # https://github.com/shekkizh/WassersteinGAN.tensorflow/issues/3
+ with tf.variable_scope(tf.get_variable_scope(), reuse=False):
+ ema_apply_op = tf.cond(is_training,
+ lambda: ema.apply([batch_mean, batch_var]),
+ lambda: tf.no_op())
+
+ # Update moving average and return current batch's avg and var.
+ def mean_var_with_update():
+ with tf.control_dependencies([ema_apply_op]):
+ return tf.identity(batch_mean), tf.identity(batch_var)
+
+ # ema.average returns the Variable holding the average of var.
+ mean, var = tf.cond(is_training,
+ mean_var_with_update,
+ lambda: (ema.average(batch_mean), ema.average(batch_var)))
+ normed = tf.nn.batch_normalization(inputs, mean, var, beta, gamma, 1e-3)
+ return normed
+
+
+def batch_norm_template(inputs, is_training, scope, moments_dims_unused, bn_decay, data_format='NHWC'):
+ """ https://www.tensorflow.org/api_docs/python/tf/compat/v1/layers/batch_normalization
+
+ Args:
+ inputs: Tensor, k-D input ... x C could be BC or BHWC or BDHWC
+ is_training: boolean tf.Varialbe, true indicates training phase
+ scope: string, variable scope
+ moments_dims: a list of ints, indicating dimensions for moments calculation
+ bn_decay: float or float tensor variable, controling moving average weight
+ data_format: 'NHWC' or 'NCHW'
+ Return:
+ normed: batch-normalized maps
+ """
+ bn_decay = bn_decay if bn_decay is not None else 0.9
+ if not TF2:
+ return tf.contrib.layers.batch_norm(inputs,
+ center=True, scale=True,
+ is_training=is_training, decay=bn_decay,updates_collections=None,
+ scope=scope,
+ data_format=data_format)
+
+ if data_format == 'NHWC':
+ axis = -1
+ elif data_format == 'NCHW':
+ axis = 1
+ else:
+ raise NotImplementedError(
+ 'data_format {} is not supported for batch norm.'.format(data_format)
+ )
+
+ return tf.compat.v1.layers.batch_normalization(
+ inputs, center=True, scale=True, training=is_training,
+ momentum=bn_decay, axis=axis, name=scope,
+ )
+ #return tf.contrib.layers.batch_norm(inputs,
+ # center=True, scale=True,
+ # is_training=is_training, decay=bn_decay,updates_collections=None,
+ # scope=scope,
+ # data_format=data_format)
+
+
+def batch_norm_for_fc(inputs, is_training, bn_decay, scope):
+ """ Batch normalization on FC data.
+
+ Args:
+ inputs: Tensor, 2D BxC input
+ is_training: boolean tf.Varialbe, true indicates training phase
+ bn_decay: float or float tensor variable, controling moving average weight
+ scope: string, variable scope
+ Return:
+ normed: batch-normalized maps
+ """
+ return batch_norm_template(inputs, is_training, scope, [0,], bn_decay)
+
+
+def batch_norm_for_conv1d(inputs, is_training, bn_decay, scope, data_format):
+ """ Batch normalization on 1D convolutional maps.
+
+ Args:
+ inputs: Tensor, 3D BLC input maps
+ is_training: boolean tf.Varialbe, true indicates training phase
+ bn_decay: float or float tensor variable, controling moving average weight
+ scope: string, variable scope
+ data_format: 'NHWC' or 'NCHW'
+ Return:
+ normed: batch-normalized maps
+ """
+ return batch_norm_template(inputs, is_training, scope, [0,1], bn_decay, data_format)
+
+
+
+
+def batch_norm_for_conv2d(inputs, is_training, bn_decay, scope, data_format):
+ """ Batch normalization on 2D convolutional maps.
+
+ Args:
+ inputs: Tensor, 4D BHWC input maps
+ is_training: boolean tf.Varialbe, true indicates training phase
+ bn_decay: float or float tensor variable, controling moving average weight
+ scope: string, variable scope
+ data_format: 'NHWC' or 'NCHW'
+ Return:
+ normed: batch-normalized maps
+ """
+ return batch_norm_template(inputs, is_training, scope, [0,1,2], bn_decay, data_format)
+
+
+def batch_norm_for_conv3d(inputs, is_training, bn_decay, scope):
+ """ Batch normalization on 3D convolutional maps.
+
+ Args:
+ inputs: Tensor, 5D BDHWC input maps
+ is_training: boolean tf.Varialbe, true indicates training phase
+ bn_decay: float or float tensor variable, controling moving average weight
+ scope: string, variable scope
+ Return:
+ normed: batch-normalized maps
+ """
+ return batch_norm_template(inputs, is_training, scope, [0,1,2,3], bn_decay)
+
+
+def dropout(inputs,
+ is_training,
+ scope,
+ keep_prob=0.5,
+ noise_shape=None):
+ """ Dropout layer.
+
+ Args:
+ inputs: tensor
+ is_training: boolean tf.Variable
+ scope: string
+ keep_prob: float in [0,1]
+ noise_shape: list of ints
+
+ Returns:
+ tensor variable
+ """
+ with tf.variable_scope(scope) as sc:
+ outputs = tf.cond(is_training,
+ lambda: tf.nn.dropout(inputs, keep_prob, noise_shape),
+ lambda: inputs)
+ return outputs
diff --git a/tools/benchmark_query_kd_np_tf.py b/tools/benchmark_query_kd_np_tf.py
new file mode 100644
index 0000000..90f472f
--- /dev/null
+++ b/tools/benchmark_query_kd_np_tf.py
@@ -0,0 +1,98 @@
+from __future__ import print_function
+import sys
+import numpy as np
+from scipy.spatial import cKDTree
+import time
+import tensorflow as tf
+from tqdm import tqdm
+
+N = 5000
+k = 1
+dim = 3
+radius = 0.1
+
+np_matmul_time = []
+np_sort_time = []
+kd_build_time = []
+kd_query_time = []
+tf_time = []
+tf_pointnet_time = []
+
+@tf.function
+def tf_nn(x):
+ dists = tf.norm(tf.expand_dims(x,0)-tf.expand_dims(x,1),axis=2)
+ _,topk = tf.math.top_k(dists, k=k, sorted=False)
+ return topk
+
+# np.random.randint*
+@tf.function
+def tf_queryball(x):
+ dists = tf.norm(tf.expand_dims(x,0)-tf.expand_dims(x,1),axis=2)
+ queries = tf.math.less(dists, radius)
+ idcs = tf.where(queries)
+ return idcs
+ # _,topk = tf.math.top_k(dists, k=k, sorted=False)
+ # return topk
+
+@tf.function
+def tf_knn_max_dist(x):
+
+ squared_dists_all = tf.norm(tf.expand_dims(x,0)-tf.expand_dims(x,1),axis=2)#tf.reduce_sum((tf.expand_dims(x,0)-tf.expand_dims(x,1))**2,axis=2)
+ neg_squared_dists_k, close_contact_pt_idcs = tf.math.top_k(-squared_dists_all, k=k, sorted=False)
+ squared_dists_k = -neg_squared_dists_k
+ loss_mask_pc = tf.cast(tf.reduce_mean(squared_dists_k, axis=1) < radius**2, tf.float32)
+ return loss_mask_pc
+
+# warmup tf
+a=np.random.rand(N,dim).astype(np.float32)
+tf_queryball(a)
+# tf_pointnet(a)
+tf_knn_max_dist(a)
+
+for i in tqdm(range(10)):
+ a=np.random.rand(N,dim).astype(np.float32)
+
+ # start_time_tf = time.time()
+ # tf_time.append(time.time()-start_time_tf)
+ start_time_tf = time.time()
+ f= tf_nn(a)
+ tf_time.append(time.time()-start_time_tf)
+
+
+ start_time_tf = time.time()
+ tf_queryball(a)
+ tf_pointnet_time.append(time.time()-start_time_tf)
+
+ start_time_np = time.time()
+ d=np.linalg.norm(a[np.newaxis,:,:]-a[:,np.newaxis,:],axis=2)
+ np_matmul_time.append(time.time() - start_time_np)
+ start_time_np = time.time()
+ sorted = np.argpartition(d, k, axis=1)
+ np_sort_time.append(time.time()-start_time_np)
+
+ start_time_kd = time.time()
+ tree = cKDTree(a, leafsize=a.shape[0]+1)
+ kd_build_time.append(time.time() - start_time_kd)
+ start_time_kd = time.time()
+ distances, ndx = tree.query(a, k=k, n_jobs=-1)
+ # tree.query_ball_point(a, 0.1)
+ kd_query_time.append(time.time()-start_time_kd)
+
+print('np_matmul_time: ', np.mean(np_matmul_time))
+print('np_sort_time: ', np.mean(np_sort_time))
+print('kd_build_time: ', np.mean(kd_build_time))
+print('kd_query_time: ', np.mean(kd_query_time))
+print('#'*100)
+print('np_brute: ', np.mean(np_sort_time) + np.mean(np_matmul_time))
+print('tf_brute: ', np.mean(tf_time))
+print('tf_pointnet: ', np.mean(tf_pointnet_time))
+print('kd: ', np.mean(kd_build_time) + np.mean(kd_query_time))
+
+# tf_pointnet_query ball: 0.0019501209259033202 (N=1024, k =20, radius=0.1)
+# tf_brute_force_query_ball: 0.0015201091766357422 (N=1024, k=all, radius=0.1)
+# tf_brute_force_knn_with_dist_thres: 0.0009593963623046875 (N=1024, k =20, radius=0.1)
+# ckdtree_time_total: 0.00626897811889648 (N=1024, k =20)
+# Notes:
+# 1.) queury ball + sampling is different than knn + dist thres, but for our disjoint sets we should use the latter.
+# 2.) If you import the cuda kernels, the tf_brute_force knn becomes 3x slower (0.0024s). That means that the cu kernels also change the tf behavior in different parts of the code.
+# 3.) When the pointnet guys created their repo, tf had no gpu implementation of some functions like tf.math.top_k(). Now they have, and it seems to be faster.
diff --git a/tools/color.py b/tools/color.py
new file mode 100644
index 0000000..01cef77
--- /dev/null
+++ b/tools/color.py
@@ -0,0 +1,12 @@
+class color:
+ PURPLE = '\033[95m'
+ CYAN = '\033[96m'
+ DARKCYAN = '\033[36m'
+ BLUE = '\033[94m'
+ GREEN = '\033[92m'
+ YELLOW = '\033[93m'
+ RED = '\033[91m'
+ BOLD = '\033[1m'
+ UNDERLINE = '\033[4m'
+ END = '\033[0m'
+
diff --git a/tools/create_contact_infos.py b/tools/create_contact_infos.py
new file mode 100644
index 0000000..6741a66
--- /dev/null
+++ b/tools/create_contact_infos.py
@@ -0,0 +1,124 @@
+import os
+import numpy as np
+import h5py
+import glob
+import argparse
+import sys
+
+ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+sys.path.append(ROOT_DIR)
+
+from contact_graspnet.data import PointCloudReader
+from contact_graspnet.mesh_utils import in_collision_with_gripper, grasp_contact_location
+
+def grasps_contact_info(grasp_tfs, successfuls, obj_mesh, check_collisions=True):
+ """
+ Check the collision of the grasps and compute contact points, normals and directions
+
+ Arguments:
+ grasp_tfs {np.ndarray} -- Mx4x4 grasp transformations
+ successfuls {np.ndarray} -- Binary Mx1 successful grasps
+ obj_mesh {trimesh.base.Trimesh} -- Mesh of the object
+
+ Keyword Arguments:
+ check_collisions {bool} -- whether to check for collisions (default: {True})
+
+ Returns:
+ [dict] -- object contact dictionary with all necessary information
+ """
+ print('evaluating {} grasps'.format(len(grasp_tfs)))
+ if check_collisions:
+ collisions, _ = in_collision_with_gripper(
+ obj_mesh,
+ grasp_tfs,
+ gripper_name='panda',
+ silent=True,
+ )
+ contact_dicts = grasp_contact_location(
+ grasp_tfs,
+ successfuls,
+ collisions if check_collisions else [0]*len(successfuls),
+ object_mesh=obj_mesh,
+ gripper_name='panda',
+ silent=True,
+ )
+
+ return contact_dicts
+
+def read_object_grasp_data_acronym(root_folder, h5_path):
+ """
+ Read object grasp data from the acronym dataset and loads mesh
+
+ Arguments:
+ root_folder {str} -- root folder of acronym dataset
+ h5_path {str} -- relative path to grasp h5 file
+
+ Returns:
+ [grasps, success, cad_path, cad_scale] -- grasp trafos, grasp success, absolute mesh path, mesh scale
+ """
+
+ abs_h5_path = os.path.join(root_folder, 'grasps', h5_path)
+ data = h5py.File(abs_h5_path, "r")
+ mesh_fname = os.path.join(root_folder, data["object/file"][()])#.decode('utf-8')
+ mesh_scale = data["object/scale"][()]
+ grasps = np.array(data["grasps/transforms"])
+ success = np.array(data["grasps/qualities/flex/object_in_gripper"])
+
+ positive_grasps = grasps[success==1, :, :]
+ negative_grasps = grasps[success==0, :, :]
+
+ print('positive grasps: {} negative grasps: {}'.format(positive_grasps.shape, negative_grasps.shape))
+
+ return grasps, success, mesh_fname, mesh_scale
+
+def save_contact_data(pcreader, grasp_path, target_path='mesh_contacts'):
+ """
+ Maps acronym grasp data to contact information on meshes and saves them as npz file
+
+ Arguments:
+ grasp_path {str} -- path to grasp json file
+ pcreader {Object} -- PointCloudReader instance from data.py
+
+ """
+
+ target_path = os.path.join(pcreader._root_folder, target_path)
+ if not os.path.exists(target_path):
+ os.makedirs(target_path)
+
+ contact_dir_path = os.path.join(target_path, os.path.basename(grasp_path.replace('.h5','.npz')))
+ if not os.path.exists(os.path.dirname(contact_dir_path)):
+ os.makedirs(os.path.dirname(contact_dir_path))
+ if os.path.exists(contact_dir_path):
+ return
+
+ output_grasps, output_labels, cad_path, cad_scale = read_object_grasp_data_acronym(pcreader._root_folder, grasp_path)
+ pcreader.change_object(cad_path, cad_scale)
+
+ context = pcreader._renderer._cache[(cad_path,cad_scale)]
+ obj_mesh = context['tmesh']
+ obj_mesh_mean = context['mesh_mean']
+
+ output_grasps[:,:3,3] -= obj_mesh_mean
+ contact_dicts = grasps_contact_info(output_grasps, list(output_labels), obj_mesh, check_collisions=False)
+
+ contact_dict_of_arrays = {}
+ for d in contact_dicts:
+ for k in d:
+ contact_dict_of_arrays.setdefault(k,[]).append(d[k])
+
+ np.savez(contact_dir_path, **contact_dict_of_arrays)
+
+if __name__ == '__main__':
+ parser = argparse.ArgumentParser(description="Grasp data reader")
+ parser.add_argument('root_folder', help='Root dir with acronym grasps, meshes and splits', type=str)
+ args = parser.parse_args()
+ print('Root folder', args.root_folder)
+
+ pcreader = PointCloudReader(root_folder=args.root_folder)
+
+ grasp_paths = glob.glob(os.path.join(args.root_folder, 'grasps', '*.h5'))
+
+ print('Computing grasp contacts...')
+ for grasp_path in grasp_paths:
+ print('Reading: ', grasp_path)
+ save_contact_data(pcreader, grasp_path)
\ No newline at end of file
diff --git a/tools/create_table_top_scenes.py b/tools/create_table_top_scenes.py
new file mode 100644
index 0000000..deab73b
--- /dev/null
+++ b/tools/create_table_top_scenes.py
@@ -0,0 +1,511 @@
+import glob
+import os
+import random
+import trimesh
+import numpy as np
+import json
+import argparse
+import signal
+import trimesh.transformations as tra
+
+from acronym_tools import Scene, load_mesh, create_gripper_marker
+
+BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+def load_contacts(root_folder, data_splits, splits=['train'], min_pos_contacts=1):
+ """
+ Load grasps and contacts into memory
+
+ Arguments:
+ root_folder {str} -- path to acronym data
+ data_splits {dict} -- dict of categories of train/test object grasp files
+
+ Keyword Arguments:
+ splits {list} -- object data split(s) to use for scene generation
+ min_pos_contacts {int} -- minimum successful grasp contacts to consider object
+
+ Returns:
+ [dict] -- h5 file names as keys and grasp transforms + contact points as values
+ """
+ contact_infos = {}
+ for category_paths in data_splits.values():
+ for split in splits:
+ for grasp_path in category_paths[split]:
+ contact_path = os.path.join(root_folder, 'mesh_contacts', grasp_path.replace('.h5','.npz'))
+ if os.path.exists(contact_path):
+ npz = np.load(contact_path)
+ if 'contact_points' in npz:
+ all_contact_suc = npz['successful'].reshape(-1)
+ pos_idcs = np.where(all_contact_suc>0)[0]
+ if len(pos_idcs) > min_pos_contacts:
+ contact_infos[grasp_path] = {}
+ contact_infos[grasp_path]['successful'] = npz['successful']
+ contact_infos[grasp_path]['grasp_transform'] = npz['grasp_transform']
+ contact_infos[grasp_path]['contact_points'] = npz['contact_points']
+ if not contact_infos:
+ print('Warning: No mesh_contacts found. Please run tools/create_contact_infos.py first!')
+ return contact_infos
+
+def load_splits(root_folder):
+ """
+ Load splits of training and test objects
+
+ Arguments:
+ root_folder {str} -- path to acronym data
+
+ Returns:
+ [dict] -- dict of category-wise train/test object grasp files
+ """
+ split_dict = {}
+ split_paths = glob.glob(os.path.join(root_folder, 'splits/*.json'))
+ for split_p in split_paths:
+ category = os.path.basename(split_p).split('.json')[0]
+ splits = json.load(open(split_p,'r'))
+ split_dict[category] = {}
+ split_dict[category]['train'] = [obj_p.replace('.json', '.h5') for obj_p in splits['train']]
+ split_dict[category]['test'] = [obj_p.replace('.json', '.h5') for obj_p in splits['test']]
+ return split_dict
+
+class TableScene(Scene):
+ """
+ Holds current table-top scene, samples object poses and checks grasp collisions.
+
+ Arguments:
+ root_folder {str} -- path to acronym data
+ gripper_path {str} -- relative path to gripper collision mesh
+
+ Keyword Arguments:
+ lower_table {float} -- lower table to permit slight grasp collisions between table and object/gripper (default: {0.02})
+ """
+
+ def __init__(self, root_folder, gripper_path, lower_table=0.02, splits=['train']):
+
+ super().__init__()
+ self.root_folder = root_folder
+ self.splits= splits
+ self.gripper_mesh = trimesh.load(os.path.join(BASE_DIR, gripper_path))
+
+ self._table_dims = [1.0, 1.2, 0.6]
+ self._table_support = [0.6, 0.6, 0.6]
+ self._table_pose = np.eye(4)
+ self.table_mesh = trimesh.creation.box(self._table_dims)
+ self.table_support = trimesh.creation.box(self._table_support)
+
+ self.data_splits = load_splits(root_folder)
+ self.category_list = list(self.data_splits.keys())
+ self.contact_infos = load_contacts(root_folder, self.data_splits, splits=self.splits)
+
+ self._lower_table = lower_table
+
+ self._scene_count = 0
+
+ def get_random_object(self):
+
+ """Return random scaled but not yet centered object mesh
+
+ Returns:
+ [trimesh.Trimesh, str] -- ShapeNet mesh from a random category, h5 file path
+ """
+
+ while True:
+ random_category = random.choice(self.category_list)
+ cat_obj_paths = [obj_p for split in self.splits for obj_p in self.data_splits[random_category][split]]
+ if cat_obj_paths:
+ random_grasp_path = random.choice(cat_obj_paths)
+ if random_grasp_path in self.contact_infos:
+ break
+
+ obj_mesh = load_mesh(os.path.join(self.root_folder, 'grasps', random_grasp_path), self.root_folder)
+
+ mesh_mean = np.mean(obj_mesh.vertices, 0, keepdims=True)
+ obj_mesh.vertices -= mesh_mean
+
+ return obj_mesh, random_grasp_path
+
+ def _get_random_stable_pose(self, stable_poses, stable_poses_probs, thres=0.005):
+ """Return a stable pose according to their likelihood.
+
+ Args:
+ stable_poses (list[np.ndarray]): List of stable poses as 4x4 matrices.
+ stable_poses_probs (list[float]): List of probabilities.
+ thres (float): Threshold of pose stability to include for sampling
+
+ Returns:
+ np.ndarray: homogeneous 4x4 matrix
+ """
+
+
+ # Random pose with unique (avoid symmetric poses) stability prob > thres
+ _,unique_idcs = np.unique(stable_poses_probs.round(decimals=3), return_index=True)
+ unique_idcs = unique_idcs[::-1]
+ unique_stable_poses_probs = stable_poses_probs[unique_idcs]
+ n = len(unique_stable_poses_probs[unique_stable_poses_probs>thres])
+ index = unique_idcs[np.random.randint(n)]
+
+ # index = np.random.choice(len(stable_poses), p=stable_poses_probs)
+ inplane_rot = tra.rotation_matrix(
+ angle=np.random.uniform(0, 2.0 * np.pi), direction=[0, 0, 1]
+ )
+ return inplane_rot.dot(stable_poses[index])
+
+ def find_object_placement(self, obj_mesh, max_iter):
+ """Try to find a non-colliding stable pose on top of any support surface.
+
+ Args:
+ obj_mesh (trimesh.Trimesh): Object mesh to be placed.
+ max_iter (int): Maximum number of attempts to place to object randomly.
+
+ Raises:
+ RuntimeError: In case the support object(s) do not provide any support surfaces.
+
+ Returns:
+ bool: Whether a placement pose was found.
+ np.ndarray: Homogenous 4x4 matrix describing the object placement pose. Or None if none was found.
+ """
+ support_polys, support_T = self._get_support_polygons()
+ if len(support_polys) == 0:
+ raise RuntimeError("No support polygons found!")
+
+ # get stable poses for object
+ stable_obj = obj_mesh.copy()
+ stable_obj.vertices -= stable_obj.center_mass
+ stable_poses, stable_poses_probs = stable_obj.compute_stable_poses(
+ threshold=0, sigma=0, n_samples=20
+ )
+ # stable_poses, stable_poses_probs = obj_mesh.compute_stable_poses(threshold=0, sigma=0, n_samples=1)
+
+ # Sample support index
+ support_index = max(enumerate(support_polys), key=lambda x: x[1].area)[0]
+
+ iter = 0
+ colliding = True
+ while iter < max_iter and colliding:
+
+ # Sample position in plane
+ pts = trimesh.path.polygons.sample(
+ support_polys[support_index], count=1
+ )
+
+ # To avoid collisions with the support surface
+ pts3d = np.append(pts, 0)
+
+ # Transform plane coordinates into scene coordinates
+ placement_T = np.dot(
+ support_T[support_index],
+ trimesh.transformations.translation_matrix(pts3d),
+ )
+
+ pose = self._get_random_stable_pose(stable_poses, stable_poses_probs)
+
+ placement_T = np.dot(
+ np.dot(placement_T, pose), tra.translation_matrix(-obj_mesh.center_mass)
+ )
+
+ # Check collisions
+ colliding = self.is_colliding(obj_mesh, placement_T)
+
+ iter += 1
+
+ return not colliding, placement_T if not colliding else None
+
+ def is_colliding(self, mesh, transform, eps=1e-6):
+ """
+ Whether given mesh collides with scene
+
+ Arguments:
+ mesh {trimesh.Trimesh} -- mesh
+ transform {np.ndarray} -- mesh transform
+
+ Keyword Arguments:
+ eps {float} -- minimum distance detected as collision (default: {1e-6})
+
+ Returns:
+ [bool] -- colliding or not
+ """
+ dist = self.collision_manager.min_distance_single(mesh, transform=transform)
+ return dist < eps
+
+ def load_suc_obj_contact_grasps(self, grasp_path):
+ """
+ Loads successful object grasp contacts
+
+ Arguments:
+ grasp_path {str} -- acronym grasp path
+
+ Returns:
+ [np.ndarray, np.ndarray] -- Mx4x4 grasp transforms, Mx3 grasp contacts
+ """
+ contact_info = self.contact_infos[grasp_path]
+
+ suc_grasps = contact_info['successful'].reshape(-1)
+ gt_grasps = contact_info['grasp_transform'].reshape(-1,4,4)
+ gt_contacts = contact_info['contact_points'].reshape(-1,3)
+
+ suc_gt_contacts = gt_contacts[np.repeat(suc_grasps,2)>0]
+ suc_gt_grasps = gt_grasps[suc_grasps>0]
+
+ return suc_gt_grasps, suc_gt_contacts
+
+ def set_mesh_transform(self, name, transform):
+ """
+ Set mesh transform for collision manager
+
+ Arguments:
+ name {str} -- mesh name
+ transform {np.ndarray} -- 4x4 homog mesh pose
+ """
+ self.collision_manager.set_transform(name, transform)
+ self._poses[name] = transform
+
+ def save_scene_grasps(self, output_dir, scene_filtered_grasps, scene_filtered_contacts, obj_paths, obj_transforms, obj_scales, obj_grasp_idcs):
+ """
+ Save scene_contact infos in output_dir
+
+ Arguments:
+ output_dir {str} -- absolute output directory
+ scene_filtered_grasps {np.ndarray} -- Nx4x4 filtered scene grasps
+ scene_filtered_contacts {np.ndarray} -- Nx2x3 filtered scene contacts
+ obj_paths {list} -- list of object paths in scene
+ obj_transforms {list} -- list of object transforms in scene
+ obj_scales {list} -- list of object scales in scene
+ obj_grasp_idcs {list} -- list of starting grasp idcs for each object
+ """
+ if not os.path.exists(output_dir):
+ os.makedirs(output_dir)
+
+ contact_info = {}
+ contact_info['obj_paths'] = obj_paths
+ contact_info['obj_transforms'] = obj_transforms
+ contact_info['obj_scales'] = obj_scales
+ contact_info['grasp_transforms'] = scene_filtered_grasps
+ contact_info['scene_contact_points'] = scene_filtered_contacts
+ contact_info['obj_grasp_idcs'] = np.array(obj_grasp_idcs)
+ output_path = os.path.join(output_dir, '{:06d}.npz'.format(self._scene_count))
+ while os.path.exists(output_path):
+ self._scene_count += 1
+ output_path = os.path.join(output_dir, '{:06d}.npz'.format(self._scene_count))
+ np.savez(output_path, **contact_info)
+ self._scene_count += 1
+
+ def _transform_grasps(self, grasps, contacts, obj_transform):
+ """
+ Transform grasps and contacts into given object transform
+
+ Arguments:
+ grasps {np.ndarray} -- Nx4x4 grasps
+ contacts {np.ndarray} -- 2Nx3 contacts
+ obj_transform {np.ndarray} -- 4x4 mesh pose
+
+ Returns:
+ [np.ndarray, np.ndarray] -- transformed grasps and contacts
+ """
+ transformed_grasps = np.matmul(obj_transform, grasps)
+ contacts_homog = np.concatenate((contacts, np.ones((contacts.shape[0], 1))),axis=1)
+ transformed_contacts = np.dot(contacts_homog, obj_transform.T)[:,:3]
+ return transformed_grasps, transformed_contacts
+
+ def _filter_colliding_grasps(self, transformed_grasps, transformed_contacts):
+ """
+ Filter out colliding grasps
+
+ Arguments:
+ transformed_grasps {np.ndarray} -- Nx4x4 grasps
+ transformed_contacts {np.ndarray} -- 2Nx3 contact points
+
+ Returns:
+ [np.ndarray, np.ndarray] -- Mx4x4 filtered grasps, Mx2x3 filtered contact points
+ """
+ filtered_grasps = []
+ filtered_contacts = []
+ for i,g in enumerate(transformed_grasps):
+ if not self.is_colliding(self.gripper_mesh, g):
+ filtered_grasps.append(g)
+ filtered_contacts.append(transformed_contacts[2*i:2*(i+1)])
+ return np.array(filtered_grasps).reshape(-1,4,4), np.array(filtered_contacts).reshape(-1,2,3)
+
+ def reset(self):
+ """
+ Reset, i.e. remove scene objects
+ """
+ for name in self._objects:
+ self.collision_manager.remove_object(name)
+ self._objects = {}
+ self._poses = {}
+ self._support_objects = []
+
+ def load_existing_scene(self, path):
+ """
+ Load an existing scene_contacts scene for visualization
+
+ Arguments:
+ path {str} -- abs path to scene_contacts npz file
+
+ Returns:
+ [np.ndarray, list, list] -- scene_grasps, list of obj paths, list of object transforms
+ """
+ self.add_object('table', self.table_mesh, self._table_pose)
+ self._support_objects.append(self.table_support)
+
+ inp = np.load(os.path.join(self.root_folder, path))
+ scene_filtered_grasps = inp['grasp_transforms']
+ scene_contacts = inp['scene_contact_points']
+ obj_transforms = inp['obj_transforms']
+ obj_paths = inp['obj_paths']
+ obj_scales = inp['obj_scales']
+
+ for obj_path,obj_transform,obj_scale in zip(obj_paths,obj_transforms,obj_scales):
+ obj_mesh = trimesh.load(os.path.join(self.root_folder, obj_path))
+ obj_mesh = obj_mesh.apply_scale(obj_scale)
+ mesh_mean = np.mean(obj_mesh.vertices, 0, keepdims=True)
+ obj_mesh.vertices -= mesh_mean
+ self.add_object(obj_path, obj_mesh, obj_transform)
+ return scene_filtered_grasps, scene_contacts, obj_paths, obj_transforms
+
+
+ def handler(self, signum, frame):
+ raise Exception("Could not place object ")
+
+ def arrange(self, num_obj, max_iter=100, time_out = 8):
+ """
+ Arrange random table top scene with contact grasp annotations
+
+ Arguments:
+ num_obj {int} -- number of objects
+
+ Keyword Arguments:
+ max_iter {int} -- maximum iterations to try placing an object (default: {100})
+ time_out {int} -- maximum time to try placing an object (default: {8})
+
+ Returns:
+ [np.ndarray, np.ndarray, list, list, list, list] --
+ scene_filtered_grasps, scene_filtered_contacts, obj_paths, obj_transforms, obj_scales, obj_grasp_idcs
+
+ """
+
+ self._table_pose[2,3] -= self._lower_table
+ self.add_object('table', self.table_mesh, self._table_pose)
+
+ self._support_objects.append(self.table_support)
+
+ obj_paths = []
+ obj_transforms = []
+ obj_scales = []
+ grasp_paths = []
+
+ for i in range(num_obj):
+ obj_mesh, random_grasp_path = self.get_random_object()
+ signal.signal(signal.SIGALRM, self.handler)
+ signal.alarm(8)
+ try:
+ success, placement_T = self.find_object_placement(obj_mesh, max_iter)
+ except Exception as exc:
+ print(exc, random_grasp_path, " after {} seconds!".format(time_out))
+ continue
+ signal.alarm(0)
+ if success:
+ self.add_object(random_grasp_path, obj_mesh, placement_T)
+ obj_scales.append(float(random_grasp_path.split('_')[-1].split('.h5')[0]))
+ obj_paths.append(os.path.join('meshes', '/'.join(random_grasp_path.split('_')[:2]) + '.obj'))
+ obj_transforms.append(placement_T)
+ grasp_paths.append(random_grasp_path)
+ else:
+ print("Couldn't place object", random_grasp_path, " after {} iterations!".format(max_iter))
+ print('Placed {} objects'.format(len(obj_paths)))
+
+ # self.set_mesh_transform('table', self._table_pose)
+
+ scene_filtered_grasps = []
+ scene_filtered_contacts = []
+ obj_grasp_idcs = []
+ grasp_count = 0
+
+ for obj_transform, grasp_path in zip(obj_transforms, grasp_paths):
+ grasps, contacts = self.load_suc_obj_contact_grasps(grasp_path)
+ transformed_grasps, transformed_contacts = self._transform_grasps(grasps, contacts, obj_transform)
+ filtered_grasps, filtered_contacts = self._filter_colliding_grasps(transformed_grasps, transformed_contacts)
+
+ scene_filtered_grasps.append(filtered_grasps)
+ scene_filtered_contacts.append(filtered_contacts)
+ grasp_count += len(filtered_contacts)
+ obj_grasp_idcs.append(grasp_count)
+
+ scene_filtered_grasps = np.concatenate(scene_filtered_grasps,0)
+ scene_filtered_contacts = np.concatenate(scene_filtered_contacts,0)
+
+ self._table_pose[2,3] += self._lower_table
+ self.set_mesh_transform('table', self._table_pose)
+
+ return scene_filtered_grasps, scene_filtered_contacts, obj_paths, obj_transforms, obj_scales, obj_grasp_idcs
+
+ def visualize(self, scene_grasps, scene_contacts=None):
+ """
+ Visualizes table top scene with grasps
+
+ Arguments:
+ scene_grasps {np.ndarray} -- Nx4x4 grasp transforms
+ scene_contacts {np.ndarray} -- Nx2x3 grasp contacts
+ """
+ print('Visualizing scene and grasps.. takes time')
+
+ gripper_marker = create_gripper_marker(color=[0, 255, 0])
+ gripper_markers = [gripper_marker.copy().apply_transform(t) for t in scene_grasps]
+
+ colors = np.ones((scene_contacts.shape[0]*2,4))*255
+ colors[:,0:2] = 0
+ scene_contact_scene = trimesh.Scene(trimesh.points.PointCloud(scene_contacts.reshape(-1,3), colors=colors))
+
+ # show scene together with successful and collision-free grasps of all objects
+ trimesh.scene.scene.append_scenes(
+ [self.colorize().as_trimesh_scene(), trimesh.Scene(gripper_markers), scene_contact_scene]
+ ).show()
+
+if __name__ == "__main__":
+
+ parser = argparse.ArgumentParser(description="Grasp data reader")
+ parser.add_argument('root_folder', help='Root dir with grasps, meshes, mesh_contacts and splits', type=str)
+ parser.add_argument('--num_grasp_scenes', type=int, default=10000)
+ parser.add_argument('--splits','--list', nargs='+')
+ parser.add_argument('--max_iterations', type=int, default=100)
+ parser.add_argument('--gripper_path', type=str, default='gripper_models/panda_gripper/panda_gripper.obj')
+ parser.add_argument('--min_num_objects', type=int, default=8)
+ parser.add_argument('--max_num_objects', type=int, default=12)
+ parser.add_argument('--start_index', type=int, default=0)
+ parser.add_argument('--load_existing', type=str, default=None)
+ parser.add_argument('--output_dir', type=str, default='scene_contacts')
+ parser.add_argument('-vis', action='store_true', default=False)
+ args = parser.parse_args()
+
+ root_folder = args.root_folder
+ splits = args.splits if args.splits else ['train']
+ max_iterations = args.max_iterations
+ gripper_path = args.gripper_path
+ number_of_scenes = args.num_grasp_scenes
+ min_num_objects = args.min_num_objects
+ max_num_objects = args.max_num_objects
+ start_index = args.start_index
+ load_existing = args.load_existing
+ output_dir = args.output_dir
+ visualize = args.vis
+
+ table_scene = TableScene(root_folder, gripper_path, splits=splits)
+ table_scene._scene_count = start_index
+
+ print('Root folder', args.root_folder)
+ output_dir = os.path.join(root_folder, output_dir)
+
+ while table_scene._scene_count < number_of_scenes:
+
+ table_scene.reset()
+
+ if load_existing is None:
+ print('generating %s/%s' % (table_scene._scene_count, number_of_scenes))
+ num_objects = np.random.randint(min_num_objects,max_num_objects+1)
+ scene_grasps, scene_contacts, obj_paths, obj_transforms, obj_scales, obj_grasp_idcs = table_scene.arrange(num_objects, max_iterations)
+ if not visualize:
+ table_scene.save_scene_grasps(output_dir, scene_grasps, scene_contacts, obj_paths, obj_transforms, obj_scales, obj_grasp_idcs)
+ else:
+ scene_grasps,scene_contacts, _,_ = table_scene.load_existing_scene(load_existing)
+
+ if visualize:
+ table_scene.visualize(scene_grasps, scene_contacts)
+ table_scene._scene_count +=1
\ No newline at end of file
diff --git a/tools/dict_diff.py b/tools/dict_diff.py
new file mode 100644
index 0000000..2a1a9e1
--- /dev/null
+++ b/tools/dict_diff.py
@@ -0,0 +1,28 @@
+from easydict import EasyDict as edict
+import copy
+
+def findDiff(d1, d2, path="", diff_dict = {}):
+ orig_path = copy.deepcopy(path)
+ for k in d1.keys():
+ if not d2.has_key(k):
+ print path, ":"
+ print "keys not in d2: " + k, "\n"
+ else:
+ if type(d1[k]) in [edict, dict]:
+ if path == "":
+ path = k
+ else:
+ path = path + "->" + k
+ diff_dict = findDiff(d1[k],d2[k], path, diff_dict)
+ path = orig_path
+ else:
+ if d1[k] != d2[k]:
+ print path, ":"
+ print " - ", k," : ", d1[k]
+ print " + ", k," : ", d2[k]
+ diff_dict[k] = d2[k]
+ diff_dict[k + '_dictpath'] = copy.deepcopy(path)
+ # path=""
+
+ return diff_dict
+
\ No newline at end of file
diff --git a/tools/plot_pr.py b/tools/plot_pr.py
new file mode 100644
index 0000000..8e77b69
--- /dev/null
+++ b/tools/plot_pr.py
@@ -0,0 +1,237 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from dict_diff import findDiff
+import sys
+import glob2
+import os
+import yaml
+sys.path.append('/home/msundermeyer/ngc_ws/6dof-graspnet/contact_graspnet')
+import utilities
+import copy
+from color import color
+from scipy import spatial
+
+
+def metric_coverage_success_rate(grasps_list, scores_list, flex_outcomes_list, gt_grasps_list, visualize, num_scenes=100):
+ """
+ Computes the coverage success rate for grasps of multiple objects.
+
+ Args:
+ grasps_list: list of numpy array, each numpy array is the predicted
+ grasps for each object. Each numpy array has shape (n, 4, 4) where
+ n is the number of predicted grasps for each object.
+ scores_list: list of numpy array, each numpy array is the predicted
+ scores for each grasp of the corresponding object.
+ flex_outcomes_list: list of numpy array, each element of the numpy
+ array indicates whether that grasp succeeds in grasping the object
+ or not.
+ gt_grasps_list: list of numpy array. Each numpy array has shape of
+ (m, 4, 4) where m is the number of groundtruth grasps for each
+ object.
+ visualize: bool. If True, it will plot the curve.
+
+ Returns:
+ auc: float, area under the curve for the success-coverage plot.
+ """
+ RADIUS = 0.02
+
+ all_trees = []
+ all_grasps = []
+ all_object_indexes = []
+ all_scores = []
+ all_flex_outcomes = []
+ visited = set()
+ tot_num_gt_grasps = 0
+ for i in range(num_scenes):
+ print('building kd-tree {}/{}'.format(i+1, len(grasps_list[:num_scenes])))
+ gt_grasps = np.asarray(gt_grasps_list[i]).copy()
+ all_trees.append(spatial.KDTree(gt_grasps[:, :3, 3]))
+ tot_num_gt_grasps += gt_grasps.shape[0]
+ print(np.mean(flex_outcomes_list[i]))
+ try:
+ print(len(grasps_list[i]), len(scores_list[i].reshape(-1)), len(flex_outcomes_list[i]))
+ except:
+ import pdb; pdb.set_trace()
+ for g, s, f in zip(grasps_list[i], scores_list[i].reshape(-1), flex_outcomes_list[i]):
+ all_grasps.append(np.asarray(g).copy())
+ all_object_indexes.append(i)
+ all_scores.append(s)
+ all_flex_outcomes.append(f)
+
+ all_grasps = np.asarray(all_grasps)
+
+ all_scores = np.asarray(all_scores)
+ order = np.argsort(-all_scores)
+ num_covered_so_far = 0
+ correct_grasps_so_far = 0
+ num_visited_grasps_so_far = 0
+
+ precisions = []
+ recalls = []
+ prev_score = None
+
+ for oindex, index in enumerate(order):
+ if oindex % 1000 == 0:
+ print(oindex, len(order))
+
+ object_id = all_object_indexes[index]
+ close_indexes = all_trees[object_id].query_ball_point(all_grasps[index, :3, 3], RADIUS)
+
+ num_new_covered_gt_grasps = 0
+
+ for close_index in close_indexes:
+ key = (object_id, close_index)
+ if key in visited:
+ continue
+
+ visited.add(key)
+ num_new_covered_gt_grasps += 1
+
+ correct_grasps_so_far += all_flex_outcomes[index]
+ num_visited_grasps_so_far += 1
+ num_covered_so_far += num_new_covered_gt_grasps
+ if prev_score is not None and abs(prev_score - all_scores[index]) < 1e-3:
+ precisions[-1] = float(correct_grasps_so_far) / num_visited_grasps_so_far
+ recalls[-1] = float(num_covered_so_far) / tot_num_gt_grasps
+ else:
+ precisions.append(float(correct_grasps_so_far) / num_visited_grasps_so_far)
+ recalls.append(float(num_covered_so_far) / tot_num_gt_grasps)
+ prev_score = all_scores[index]
+
+ auc = 0
+ for i in range(1, len(precisions)):
+ auc += (recalls[i] - recalls[i-1]) * (precisions[i] + precisions[i-1]) * 0.5
+
+ if visualize:
+ import matplotlib.pyplot as plt
+ plt.plot(recalls, precisions)
+ plt.title('Simulator Precision-Coverage Curves auc = {0:02f}'.format(auc))
+ plt.xlabel('Coverage')
+ plt.ylabel('Precision')
+ plt.xlim([0, 0.6])
+ plt.ylim([0,1.0])
+ # plt.show()
+
+
+ return auc, {'precisions': precisions, 'recalls': recalls, 'auc': auc, 'cfg': None}
+
+# pr_data = glob2.glob(os.path.join(sys.argv[1],'*','*','outfile.npy')) + glob2.glob(os.path.join(sys.argv[1],'*','outfile.npy')) + glob2.glob(os.path.join(sys.argv[1],'outfile.npy'))
+pr_data = glob2.glob(os.path.join(sys.argv[1],'*','*','all_full_results.npz')) + glob2.glob(os.path.join(sys.argv[1],'*','all_full_results.npz')) + glob2.glob(os.path.join(sys.argv[1],'all_full_results.npz'))
+
+default_compare = True
+if default_compare:
+ default_config = utilities.load_config('/home/msundermeyer/ngc_ws/6dof-graspnet/contact_graspnet')
+else:
+ default_config = np.load(pr_data[0], allow_pickle=True).item()['cfg']
+
+legends = []
+all_diff_dicts = {}
+cfgs = {}
+aucs_01 = {}
+name_dict = {}
+
+gt_grasps = []
+for p in range(100):
+ y=np.load('/home/msundermeyer/datasets/visibility_filtered_gt_grasp/{}_filtered_gt_grasps.npz'.format(p), allow_pickle=True)
+ gt_grasps.append(y['gt_grasp_scene_trafos'])
+
+
+
+for abc in pr_data:
+ try:
+ x=np.load(os.path.join(os.path.dirname(abc), 'flex_temp', 'all_full_results.npz'), allow_pickle=True)
+ except:
+ x=np.load(os.path.join(os.path.dirname(os.path.dirname(abc)), 'flex_temp', 'all_full_results.npz'), allow_pickle=True)
+
+ auc, _ = metric_coverage_success_rate(x['grasps'], x['scores'], x['flex_outcomes'], gt_grasps, True)
+
+ base_dir = os.path.dirname(os.path.dirname(abc))
+ outfile_data = glob2.glob(os.path.join(base_dir,'*','*','outfile.npy')) + glob2.glob(os.path.join(base_dir,'*','outfile.npy')) + glob2.glob(os.path.join(base_dir,'outfile.npy'))
+
+ if outfile_data:
+ d = outfile_data[0]
+ print(d)
+ a=np.load(d, allow_pickle=True).item()
+
+ if d.split('/')[2] == 'outfile.npy':
+ names = os.listdir(os.path.dirname(d))
+ print(names)
+ name = names[0]
+ else:
+ try:
+ name = d.split('/')[5]
+ except:
+ name = d.split('/')[4]
+
+ print(50*'#')
+ print(name)
+ all_diff_dicts[name] = copy.deepcopy(findDiff(default_config, a['cfg'], diff_dict={}))
+ cfgs[name] = a['cfg']
+ print(all_diff_dicts)
+
+ b=[]
+ for g in x['scores']:
+ b += list(g)
+ print(np.histogram(b))
+ i = np.argsort(b)
+ o = []
+ for g in x['flex_outcomes']:
+ o += list(g)
+ # np.histogram(np.array(o)[i[-1000:]])
+ # np.histogram(np.array(o)[i[-4000:-2000]])
+ # np.histogram(np.array(o)[i[-4000:-3000]])
+ print(np.histogram(o))
+ print(a['auc'])
+ aucs_01[name] = a['auc']
+ print(50*'#')
+ recalls = np.array(a['recalls'])
+ precisions = np.array(a['precisions'])
+ recalls_01 = recalls[recalls<0.1]
+ precisions_01 = precisions[recalls<0.1]
+
+ deltas = recalls_01[1:]-recalls_01[:-1]
+ aucs_01[name] = np.dot(deltas,precisions_01[:-1])*10
+
+
+ # plt.plot(a['recalls'], a['precisions'])
+ legends.append(d.split('/')[1] + ' - ' + name + ' (auc: {:.4f})'.format(auc))
+ # legends.append(d.split('/')[1] + ' - ' + name + ' (auc: {:.4f})'.format(a['auc']) + ' (auc01: {:.4f})'.format(aucs_01[name]))
+ else:
+ legends.append(base_dir)
+
+
+all_changed_keys = {k:v for d in all_diff_dicts for k,v in all_diff_dicts[d].items()}
+stri = (1+len(all_diff_dicts))*"{:<10}"
+
+print("{:<30}".format("Parameter") + stri.format('default' ,*[d for d in all_diff_dicts]))
+for k,v in all_changed_keys.items():
+ if '_dictpath' in k:
+ keys = v.split('->')
+ cfg_tmp = copy.deepcopy(default_config)
+ if keys[0] == "":
+ continue
+ for k1 in keys:
+ cfg_tmp = cfg_tmp[k1]
+
+ string = "{:<30} {:<10}".format(k[:-9], cfg_tmp[k[:-9]])
+ for d in all_diff_dicts:
+ if k in all_diff_dicts[d]:
+ value = all_diff_dicts[d][k[:-9]]
+ string += color.GREEN + "{:<10}".format(value) + color.END
+ else:
+ value = cfg_tmp[k[:-9]]
+ string += "{:<10}".format(value)
+
+ print(string)
+
+
+
+plt.title('Simulator Precision-Coverage Curves')
+plt.legend(legends)
+plt.xlabel('Coverage')
+plt.ylabel('Precision')
+plt.xlim([0, 0.6])
+plt.ylim([0,1.0])
+
+plt.show()
+
diff --git a/tools/ply_to_npz.py b/tools/ply_to_npz.py
new file mode 100644
index 0000000..d42cc53
--- /dev/null
+++ b/tools/ply_to_npz.py
@@ -0,0 +1,35 @@
+import numpy as np
+import sys
+
+def read_ply_file(ply_file, load_cols=True):
+ """Naive conversion from ply to numpy arrays
+
+ Arguments:
+ ply_file {str} -- [description]
+
+ Keyword Arguments:
+ load_cols {bool} -- load vertex colors (default: {True})
+
+ Returns:
+ dict -- vertex coordinates and optionally colors
+ """
+ ret_dict = {}
+ assert ply_file.endswith('.ply')
+ with open(ply_file, 'r') as f:
+ lines = f.readlines()
+ verts_num = int(lines[3].split(' ')[-1])
+ verts_lines = lines[11:11 + 2*verts_num:2]
+ ret_dict['xyz'] = np.array([list(map(float, l.strip().split(' '))) for l in verts_lines])
+ if load_cols:
+ cols_lines = lines[12:12 + 2*verts_num:2]
+ ret_dict['xyz_color'] = np.array([list(map(int, l.strip().split(' '))) for l in cols_lines])
+
+ return ret_dict
+
+file_name = sys.argv[1]
+ret_dict = read_ply_file(file_name)
+
+# OpenGL to OpenCV
+ret_dict['xyz'][:,1:] = -ret_dict['xyz'][:,1:]
+
+np.savez(file_name.replace('.ply','.npz'), xyz=ret_dict['xyz'], xyz_color=ret_dict['xyz_color'])
\ No newline at end of file
diff --git a/uniform_quaternions/data2_4608.qua b/uniform_quaternions/data2_4608.qua
new file mode 100644
index 0000000..6a7769a
--- /dev/null
+++ b/uniform_quaternions/data2_4608.qua
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new file mode 100644
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