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train_voxel_vp_mp_gan.py
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import tensorflow as tf
import os, sys
import cv2
import numpy as np
from framework.arg_parser.parser_base import parser
from framework.cfg_parser.cfg_mp_gan import MPGANRunnerConfigurator
from framework.cfg_parser.cfg_mp_classifier import MPClassifierRunnerConfigurator
from framework.graph_runner import mp_gan as _runner_gan
from framework.graph_runner import mp_classifier as _runner_classifier
from framework.utils.io import make_dir, write_vox
from cluster import run_cluster
#adding parameters
parser.add_argument('--output_dir', help='root dir for output', default = 'results', type = str)
parser.add_argument('--data_dir', help='data dir for output', default = 'data', type = str)
parser.add_argument('--n_iter', help='number of total iterations', default = 5, type = int)
parser.add_argument('--n_cluster', help='number of cluster', default = 8, type = int)
parser.add_argument('--gpu_id', help='gpu list', default = '0', type = str)
parser.add_argument('--cfg_file_gan', help='yaml for gan', default = '', type = str)
parser.add_argument('--cfg_file_classifier', help='yaml for classifier', default = '', type = str)
def _bytes_feature(value):
"""Returns a bytes_list from a string / byte."""
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
def _int64_feature(value):
return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
def write_tf_record(writer, _mask, _view_label, _cluster_label):
_mask[_mask > 0.5] = 1
_mask[_mask <= 0.5] = 0
_, raw_byte = cv2.imencode(".png", _mask * 255)
raw_byte = raw_byte.tostring()
d_feature = {
'raw_byte': _bytes_feature(raw_byte),
'view_label': _int64_feature(_view_label),
'cluster_label': _int64_feature(_cluster_label)
}
features = tf.train.Features(feature = d_feature)
example = tf.train.Example(features = features)
serialized = example.SerializeToString()
writer.write(serialized)
if __name__ == '__main__':
os.environ["CUDA_VISIBLE_DEVICES"] = 'PCI_BUS_ID'
#parse arguments
args_dict = vars(parser.parse_args())
cfg_file_gan = args_dict['cfg_file_gan']
cfg_file_classifier = args_dict['cfg_file_classifier']
print('Initialzing...')
make_dir(args_dict['output_dir'])
print('Parsing YAML...')
#parse yaml for GAN
#overrde gpu setting
gpu_list = list(map(int, args_dict['gpu_id'].split(',')))
cfg_instance_gan = MPGANRunnerConfigurator()
cfg_instance_gan.load_from_yaml(cfg_file_gan, shared_scope = 'shared', additional_shared_dict = {'gpu_list': gpu_list})
cfg_instance_gan.auto_restart = args_dict['auto_restart']
cfg_instance_gan.max_num_checkpoint = args_dict['max_num_checkpoint']
cfg_instance_gan.debug_tag = args_dict['debug_tag']
cfg_instance_gan.discriminator.network.n_head = args_dict['n_cluster']
#parse yaml for classifier
cfg_instance_classifier = MPClassifierRunnerConfigurator()
cfg_instance_classifier.load_from_yaml(cfg_file_classifier, shared_scope = 'shared', additional_shared_dict = {'gpu_list': gpu_list})
cfg_instance_classifier.auto_restart = args_dict['auto_restart']
cfg_instance_classifier.max_num_checkpoint = args_dict['max_num_checkpoint']
cfg_instance_classifier.debug_tag = args_dict['debug_tag']
#override gpu setting
cfg_instance_classifier.gpu_list = list(map(int, args_dict['gpu_id'].split(',')))
runner_gan = getattr(_runner_gan, cfg_instance_gan.type)()
runner_classifier = getattr(_runner_classifier, cfg_instance_classifier.type)()
for i in range(args_dict['n_iter']):
print('GAN ITERATION {} / {}'.format(i, args_dict['n_iter']))
#making dirs
make_dir(args_dict['output_dir'] + r'/gan_iter_{}'.format(i))
make_dir(args_dict['output_dir'] + r'/view_iter_{}'.format(i))
make_dir(args_dict['output_dir'] + r'/intermediate_data/gen_{}'.format(i))
make_dir(args_dict['output_dir'] + r'/intermediate_data/pred_{}'.format(i))
#STEP 1: Training MP-GAN
#manually set up input and output dirs
cfg_instance_gan.output_dir = args_dict['output_dir'] + r'/gan_iter_{}'.format(i)
cfg_instance_gan.log_dir = cfg_instance_gan.output_dir
if(i == 0):
#in beginning: (1) n_dis = 1; (2) data input as origin data input
cfg_instance_gan.discriminator.network.n_head = 1
cfg_instance_gan.data_loader_train.data_folder = args_dict['data_dir']
else:
#(2) set n_cluster; (2) data input as latest (with predicted view labels)
cfg_instance_gan.discriminator.network.n_head = args_dict['n_cluster']
cfg_instance_gan.data_loader_train.data_folder = args_dict['output_dir'] + r'/intermediate_data/pred_{}'.format(i-1)
runner_gan.load_cfg(cfg_instance_gan)
#build graph
runner_gan.build_graph(add_inference_part = True)
#init tf session
runner_gan.init_session()
#after 2nd mp gan training, fintune from previous model
if(i > 1):
runner_gan.load_previous_model(model = args_dict['output_dir'] + r'/gan_iter_{}/model-{}'.format(i-1, cfg_instance_gan.max_iter - 1))
else:
#still accepts auto-restarting
runner_gan.load_previous_model()
#run training
runner_gan.run_training(model_loaded = True)
#output intermediate data name
batch_image, batch_label = runner_gan.run_generation_image(model_loaded = True, n_gen = 64000)
out_filename = args_dict['output_dir'] + r'/intermediate_data/gen_{}/{}'.format(i, cfg_instance_classifier.data_loader_train.data_name)
writer = tf.python_io.TFRecordWriter(out_filename)
for _k in range(0, 60000):
write_tf_record(writer, batch_image[_k], batch_label[_k], 0)
writer.close()
out_filename = args_dict['output_dir'] + r'/intermediate_data/gen_{}/{}'.format(i, cfg_instance_classifier.data_loader_val.data_name)
writer = tf.python_io.TFRecordWriter(out_filename)
for _k in range(60000, batch_image.shape[0]):
write_tf_record(writer, batch_image[_k], batch_label[_k], 0)
writer.close()
runner_gan.close_session()
n_view_bin = runner_gan.global_data_dict['n_view_bin']
#STEP 2: Training Classifier
#training classifier
#manually set up input and output dirs
print('CLASSIFICATION ITERATION {} / {}'.format(i, args_dict['n_iter']))
cfg_instance_classifier.output_dir = args_dict['output_dir'] + r'/view_iter_{}'.format(i)
cfg_instance_classifier.log_dir = cfg_instance_classifier.output_dir
cfg_instance_classifier.data_loader_train.data_folder = args_dict['output_dir'] + r'/intermediate_data/gen_{}'.format(i)
cfg_instance_classifier.data_loader_val.data_folder = args_dict['output_dir'] + r'/intermediate_data/gen_{}'.format(i)
cfg_instance_classifier.data_loader_test.data_folder = args_dict['data_dir']
cfg_instance_classifier.classifier.network.n_head = n_view_bin
runner_classifier.load_cfg(cfg_instance_classifier)
#build graph
runner_classifier.build_graph()
#init tf session
runner_classifier.init_session()
#run training
runner_classifier.run_training()
#run inference, cluster, write results
_image_array, _prob_array, _view_label_array = runner_classifier.run_inference(model_loaded = True, return_whole_dataset = True)
runner_classifier.close_session()
#cluster (optional)
_cluster_label_array = run_cluster(_prob_array, args_dict['n_cluster'])
#write results
out_filename = args_dict['output_dir'] + r'/intermediate_data/pred_{}/{}'.format(i, cfg_instance_gan.data_loader_train.data_name)
writer = tf.python_io.TFRecordWriter(out_filename)
for i in range(_view_label_array.shape[0]):
write_tf_record(writer, _image_array[i], _view_label_array[i], _cluster_label_array[i])
writer.close()