help="preload dataset to RAM")
parser.set_defaults(verbose=False)
opt = parser.parse_args()
with open(opt.config) as f:
config = yaml.load(f)
generator = OcclusionAwareGenerator(
**config['model_params']['generator_params'],
**config['model_params']['common_params'])
discriminator = MultiScaleDiscriminator(
**config['model_params']['discriminator_params'],
**config['model_params']['common_params'])
kp_detector = KPDetector(**config['model_params']['kp_detector_params'],
**config['model_params']['common_params'])
dataset = FramesDataset(is_train=(opt.mode == 'train'),
**config['dataset_params'])
if opt.preload:
logging.info('PreLoad Dataset: Start')
pre_list = list(range(len(dataset)))
import multiprocessing.pool as pool
with pool.Pool(4) as pl:
buf = pl.map(dataset.preload, pre_list)
for idx, (i, v) in enumerate(zip(pre_list, buf)):
dataset.buffed[i] = v.copy()
buf[idx] = None
logging.info('PreLoad Dataset: End')
if opt.mode == 'train':
save_dir = opt.save_dir
logging.info("Start training...")
dataset = DatasetRepeater(dataset,
# Declare a key point detector
kp_detector = KPDetector(**config['model_params']['kp_detector_params'],
**config['model_params']['common_params'])
if torch.cuda.is_available():
kp_detector.to(opt.device_ids[0])
# Print network details if using --verbose flag
if opt.verbose:
print(kp_detector)
# Read in dataset details, defined in *.yaml config file, "dataset_params" section
# Refer to ./config/vox-256.yaml for details
# 数据预处理在此步骤完成，并读取进 dataset 变量中
dataset = FramesDataset(is_train=(opt.mode == 'train'),
**config['dataset_params'])
print("Dataset size: {}, repeat number: {}".format(
len(dataset), config['train_params']['num_repeats']))
# Create the logging direction
if not os.path.exists(log_dir):
os.makedirs(log_dir)
# Copy the config file (*.yaml) into the logging path
if not os.path.exists(os.path.join(log_dir, os.path.basename(opt.config))):
copy(opt.config, log_dir)
if opt.mode == 'train':
# Start training
# Look into this part further
print("Training...")
train(config, generator, discriminator, kp_detector, opt.checkpoint,
bg_predictor = BGMotionPredictor(
num_channels=config['model_params']['num_channels'],
**config['model_params']['bg_predictor_params'])
if torch.cuda.is_available():
bg_predictor.to(opt.device_ids[0])
if opt.verbose:
print(bg_predictor)
avd_network = AVDNetwork(num_regions=config['model_params']['num_regions'],
**config['model_params']['avd_network_params'])
if torch.cuda.is_available():
avd_network.to(opt.device_ids[0])
if opt.verbose:
print(avd_network)
dataset = FramesDataset(is_train=(opt.mode.startswith('train')),
**config['dataset_params'])
if not os.path.exists(log_dir):
os.makedirs(log_dir)
if not os.path.exists(os.path.join(log_dir, os.path.basename(opt.config))):
copy(opt.config, log_dir)
if opt.mode == 'train':
print("Training...")
train(config, generator, region_predictor, bg_predictor,
opt.checkpoint, log_dir, dataset, opt.device_ids)
elif opt.mode == 'train_avd':
print("Training Animation via Disentaglement...")
train_avd(config, generator, region_predictor, bg_predictor,
avd_network, opt.checkpoint, log_dir, dataset)
elif opt.mode == 'reconstruction':
print("Reconstruction...")
def prediction(config, generator, kp_detector, checkpoint, log_dir):
dataset = FramesDataset(is_train=True, transform=VideoToTensor(), **config['dataset_params'])
log_dir = os.path.join(log_dir, 'prediction')
png_dir = os.path.join(log_dir, 'png')
if checkpoint is not None:
Logger.load_cpk(checkpoint, generator=generator, kp_detector=kp_detector)
else:
raise AttributeError("Checkpoint should be specified for mode='prediction'.")
dataloader = DataLoader(dataset, batch_size=1, shuffle=False, num_workers=1)
generator = DataParallelWithCallback(generator)
kp_detector = DataParallelWithCallback(kp_detector)
if not os.path.exists(log_dir):
os.makedirs(log_dir)
if not os.path.exists(png_dir):
os.makedirs(png_dir)
print("Extracting keypoints...")
kp_detector.eval()
generator.eval()
keypoints_array = []
prediction_params = config['prediction_params']
for it, x in tqdm(enumerate(dataloader)):
if prediction_params['train_size'] is not None:
if it > prediction_params['train_size']:
break
with torch.no_grad():
keypoints = []
for i in range(x['video'].shape[2]):
kp = kp_detector(x['video'][:, :, i:(i + 1)])
kp = {k: v.data.cpu().numpy() for k, v in kp.items()}
keypoints.append(kp)
keypoints_array.append(keypoints)
predictor = PredictionModule(num_kp=config['model_params']['common_params']['num_kp'],
kp_variance=config['model_params']['common_params']['kp_variance'],
**prediction_params['rnn_params']).cuda()
num_epochs = prediction_params['num_epochs']
lr = prediction_params['lr']
bs = prediction_params['batch_size']
num_frames = prediction_params['num_frames']
init_frames = prediction_params['init_frames']
optimizer = torch.optim.Adam(predictor.parameters(), lr=lr)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, verbose=True, patience=50)
kp_dataset = KPDataset(keypoints_array, num_frames=num_frames)
kp_dataloader = DataLoader(kp_dataset, batch_size=bs)
print("Training prediction...")
for _ in trange(num_epochs):
loss_list = []
for x in kp_dataloader:
x = {k: v.cuda() for k, v in x.items()}
gt = {k: v.clone() for k, v in x.items()}
for k in x:
x[k][:, init_frames:] = 0
prediction = predictor(x)
loss = sum([torch.abs(gt[k][:, init_frames:] - prediction[k][:, init_frames:]).mean() for k in x])
loss.backward()
optimizer.step()
optimizer.zero_grad()
loss_list.append(loss.detach().data.cpu().numpy())
loss = np.mean(loss_list)
scheduler.step(loss)
dataset = FramesDataset(is_train=False, transform=VideoToTensor(), **config['dataset_params'])
dataloader = DataLoader(dataset, batch_size=1, shuffle=False, num_workers=1)
print("Make predictions...")
for it, x in tqdm(enumerate(dataloader)):
with torch.no_grad():
x['video'] = x['video'][:, :, :num_frames]
kp_init = kp_detector(x['video'])
for k in kp_init:
kp_init[k][:, init_frames:] = 0
kp_source = kp_detector(x['video'][:, :, :1])
kp_video = predictor(kp_init)
for k in kp_video:
kp_video[k][:, :init_frames] = kp_init[k][:, :init_frames]
if 'var' in kp_video and prediction_params['predict_variance']:
kp_video['var'] = kp_init['var'][:, (init_frames - 1):init_frames].repeat(1, kp_video['var'].shape[1],
1, 1, 1)
out = generate(generator, appearance_image=x['video'][:, :, :1], kp_appearance=kp_source,
kp_video=kp_video)
x['source'] = x['video'][:, :, :1]
out_video_batch = out['video_prediction'].data.cpu().numpy()
out_video_batch = np.concatenate(np.transpose(out_video_batch, [0, 2, 3, 4, 1])[0], axis=1)
imageio.imsave(os.path.join(png_dir, x['name'][0] + '.png'), (255 * out_video_batch).astype(np.uint8))
image = Visualizer(**config['visualizer_params']).visualize_reconstruction(x, out)
image_name = x['name'][0] + prediction_params['format']
imageio.mimsave(os.path.join(log_dir, image_name), image)
del x, kp_video, kp_source, out
print(motion_generator)
discriminator = Discriminator(
**config['model_params']['discriminator_params'],
**config['model_params']['common_params'])
discriminator.to(opt.device_ids[0])
if opt.verbose:
print(discriminator)
kp_detector = KPDetector(**config['model_params']['kp_detector_params'],
**config['model_params']['common_params'])
kp_detector.to(opt.device_ids[0])
if opt.verbose:
print(kp_detector)
dataset = FramesDataset(is_train=('train' in opt.mode),
**config['dataset_params'])
if opt.mode == 'train':
print("Training...")
train(config, generator, discriminator, kp_detector, opt.checkpoint,
log_dir, dataset, opt.device_ids)
elif opt.mode == 'reconstruction':
print("Reconstruction...")
reconstruction(config, generator, kp_detector, opt.checkpoint, log_dir,
dataset)
elif opt.mode == 'transfer':
print("Transfer...")
transfer(config, generator, kp_detector, opt.checkpoint, log_dir,
dataset)
elif opt.mode == "prediction":
print("Prediction...")
config = yaml.load(f)
log_dir = os.path.join(opt.log_dir,
os.path.basename(opt.config).split('.')[0])
log_dir += ' ' + strftime("%d-%m-%y %H:%M:%S", gmtime())
reconstruction_module = ReconstructionModule(
**config['model_params']['reconstruction_module_params'],
**config['model_params']['common_params'])
reconstruction_module.to(opt.device_ids[0])
if opt.verbose:
print(reconstruction_module)
segmentation_module = SegmentationModule(
**config['model_params']['segmentation_module_params'],
**config['model_params']['common_params'])
segmentation_module.to(opt.device_ids[0])
if opt.verbose:
print(segmentation_module)
dataset = FramesDataset(is_train=True, **config['dataset_params'])
if not os.path.exists(log_dir):
os.makedirs(log_dir)
if not os.path.exists(os.path.join(log_dir, os.path.basename(opt.config))):
copy(opt.config, log_dir)
print("Training...")
train(config, reconstruction_module, segmentation_module, opt.checkpoint,
log_dir, dataset, opt.device_ids)
plt.axes([0.3, 0.3, 0.5, 0.5])
plt.title('Average Error')
plt.plot(train, 'k:', label='train')
plt.plot(validation, 'r', label='validation')
plt.xlabel('Epoch')
plt.ylabel('Average Error')
plt.legend()
results_dir = basePath + folder
sample_file_name = file_name + '.png'
plt.savefig(results_dir + sample_file_name)
if __name__ == "__main__":
#here i load the video dataset like a group of a pictures
face_dataset = FramesDataset(
'file:///media/aleksandr/Files/@Machine/Github/Boiler/train/annotations.csv',
'file:///media/aleksandr/Files/@Machine/Github/Boiler/train')
# here i calculate statistics of bubble boundaries appeariance at every coordinate of image with multiplication by 1000
SummResult = boundaries_summ_conv(face_dataset, 63 * 12000, 64 * 12000,
1000)
sample = face_dataset[1]
fig = plt.figure()
print(1, sample['frame'].shape, sample['heat_transfer'].shape)
ax = plt.subplot(11 // 3 + 1, 3, 1 + 1) #coordinates
plt.tight_layout()
ax.set_title('Sample #{}'.format(1))
ax.axis('off')
print(SummResult)
