Feedback

.gitignore

@@ -0,0 +1,4 @@
+*.pyc
+*.ipynb_checkpoints*
+*__pycache__*
+*.off

.gitmodules

@@ -0,0 +1,3 @@
+[submodule "src/src/pykdtree"]
+	path = src/src/pykdtree
+	url = https://github.com/storpipfugl/pykdtree.git

README.md

@@ -0,0 +1,133 @@
+# Occupancy Networks for Single View Reconstruction
+
+__Team__: Noisy Pixels
+
+__Team Members__:<br>
+Shubham Dokania _(2020701016)_<br>
+Shanthika Shankar Naik _(2020701013)_ <br>
+Sai Amrit Patnaik _(2020701026)_ <br>
+Madhvi Panchal _(2019201061)_ <br>
+
+__Assigned TA__: Meher Shashwat Nigam <br><br>
+
+This project is undertaken as a part of the Computer Vision coursework at IIIT Hyderabad in Spring semester 2021. The paper implemented in this project is: [Occupancy Networks: Learning 3D Reconstruction in Function Space](https://openaccess.thecvf.com/content_CVPR_2019/papers/Mescheder_Occupancy_Networks_Learning_3D_Reconstruction_in_Function_Space_CVPR_2019_paper.pdf) by _Mescheder et. al._
+
+The approach focuses on implicit learning of 3D surface as a continuous decision boundary of a non-linear classifier. Occupancy networks implicitly represent the 3D surface as the continuous decision boundary of a deep neural network classifier. The details of the implementation have been outlined in the project report and the proposal document which can be found [here](./resources/proposal.pdf).
+
+The following sections outline how to run the demo and some examples of the expected output from running the mentioned scripts.
+
+Code structure:
+```
+- resources
+  - propossal.pdf
+  - mid_eval.pdf
+- src
+  - dataset
+    - __init__.py
+    - dataloader.py
+  - models
+    - __init__.py
+    - encoder.py
+    - decoder.py
+  - viz
+    - visualization.py
+  - train.py
+  - test.py
+  - run.py
+- demo.py
+- README.md
+- proposal.pdf
+```
+
+In the above structure, the source code for the whole implementation can be found in the `src` directory. The scripts each contain a description of the functions/classes implemented and provide a wrapper to experiment with the flow of the program.
+
+Metrics Functionality uses pykdtree library. pykdtree is a kd-tree implementation for fast nearest neighbour search in Python.The implementation is based on scipy.spatial.cKDTree and libANN by combining the best features from both and focus on implementation efficiency.
+
+
+Dataset
+---
+
+Download the dataset for shapenet from: [here](https://s3.eu-central-1.amazonaws.com/avg-projects/occupancy_networks/data/dataset_small_v1.1.zip)
+
+Then to process the dataset, use the script as: `python3 src/dataset/data_process.py --dataroot <unzipped dataset path> --output <your data output path>`
+
+This script will process the dataset and prepare it in the form of HDF5 files for each object separately. This will also apply the point encoding on the dataset.
+
+Setup
+---
+
+To setup the required libraries for mesh processing, run the following command:
+```
+python3 setup.py build_ext --inplace
+```
+
+The following also need to be installed to run the code properly:
+```
+pip3 install --user pytorch-lightning efficientnet-pytorch pykdtree
+```
+
+Training
+---
+
+To train the model, use the following command:
+```
+$ python3 src/train.py --help
+usage: train.py [-h] [--cdim CDIM] [--hdim HDIM] [--pdim PDIM] [--data_root DATA_ROOT] [--batch_size BATCH_SIZE] [--output_path OUTPUT_PATH] [--exp_name EXP_NAME] [--encoder ENCODER] [--decoder DECODER]
+
+Argument parser for training the model
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --cdim CDIM           feature dimension
+  --hdim HDIM           hidden size for decoder
+  --pdim PDIM           points input size for decoder
+  --data_root DATA_ROOT
+                        location of the parsed and processed dataset
+  --batch_size BATCH_SIZE
+                        Training batch size
+  --output_path OUTPUT_PATH
+                        Model saving and checkpoint paths
+  --exp_name EXP_NAME   Name of the experiment. Artifacts will be created with this name
+  --encoder ENCODER     Name of the Encoder architecture to use
+  --decoder DECODER     Name of the decoder architecture to use
+```
+
+Fill the values accordingly for the configuration and the model shall start training. We can also make use of mixed precision training via pytorch lightning. To do this, edit the `src/trainer.py` script.
+
+To view the training progress, run tensorboard in your experiment directory `tensorboard --logdir=<experiment directory>`
+
+
+Evaluation
+----
+To run evaluation on the test set (selective objects: roughly 500 for now. Change it in the script for more), use the following script:
+
+```
+$ python3 run_evals.py --help
+usage: run_evals.py [-h] [--cdim CDIM] [--hdim HDIM] [--pdim PDIM] [--data_root DATA_ROOT] [--batch_size BATCH_SIZE] [--output_path OUTPUT_PATH] [--exp_name EXP_NAME] [--encoder ENCODER] [--decoder DECODER]
+                    [--checkpoint CHECKPOINT]
+
+Argument parser for training the model
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --cdim CDIM           feature dimension
+  --hdim HDIM           hidden size for decoder
+  --pdim PDIM           points input size for decoder
+  --data_root DATA_ROOT
+                        location of the parsed and processed dataset
+  --batch_size BATCH_SIZE
+                        Training batch size
+  --output_path OUTPUT_PATH
+                        Model saving and checkpoint paths
+  --exp_name EXP_NAME   Name of the experiment. Artifacts will be created with this name
+  --encoder ENCODER     Name of the Encoder architecture to use
+  --decoder DECODER     Name of the decoder architecture to use
+  --checkpoint CHECKPOINT
+                        Checkpoint Path
+```
+
+
+Visualization
+---
+
+To generate 3D models and meshes, use `jupyter` notebook or lab environment, and run `check_model.ipynb`. Keep the config flags same as evaluation and tweak the save path to see results.