def __init__(self,
valsetdir=None,
gtvalsetdir=None,
gray_mode=False,
num_input_frames=NUMFRXSEQ_VAL):
self.gray_mode = gray_mode
# Look for subdirs with individual sequences
seqs_dirs = sorted(glob.glob(os.path.join(valsetdir, VALSEQPATT)))
# open individual sequences and append them to the sequence list
sequences = []
for seq_dir in seqs_dirs:
seq, _, _ = open_sequence(seq_dir, gray_mode, expand_if_needed=False, \
max_num_fr=num_input_frames)
# seq is [num_frames, C, H, W]
sequences.append(seq)
self.sequences = sequences
gts_dirs = sorted(glob.glob(os.path.join(gtvalsetdir, VALSEQPATT)))
gts = []
for gt_dir in gts_dirs:
gt, _, _ = open_sequence(gt_dir, gray_mode, expand_if_needed=False, \
max_num_fr=num_input_frames)
# gt is [num_frames, C, H, W]
gts.append(gt)
self.gts = gts
def test_fastdvdnet(**args):
"""Denoises all sequences present in a given folder. Sequences must be stored as numbered
image sequences. The different sequences must be stored in subfolders under the "test_path" folder.
Inputs:
args (dict) fields:
"model_file": path to model
"test_path": path to sequence to denoise
"suffix": suffix to add to output name
"max_num_fr_per_seq": max number of frames to load per sequence
"noise_sigma": noise level used on test set
"dont_save_results: if True, don't save output images
"no_gpu": if True, run model on CPU
"save_path": where to save outputs as png
"gray": if True, perform denoising of grayscale images instead of RGB
"""
# Start time
start_time = time.time()
# If save_path does not exist, create it
logger = init_logger_test(os.path.dirname(args['save_path']))
# Sets data type according to CPU or GPU modes
if args['cuda']:
device = torch.device('cuda')
else:
device = torch.device('cpu')
# Create models
print('Loading models ...')
model_temp = FastDVDnet(num_input_frames=NUM_IN_FR_EXT)
# Load saved weights
state_temp_dict = torch.load(args['model_file'])
if args['cuda']:
device_ids = [0]
model_temp = nn.DataParallel(model_temp, device_ids=device_ids).cuda()
else:
# CPU mode: remove the DataParallel wrapper
state_temp_dict = remove_dataparallel_wrapper(state_temp_dict)
model_temp.load_state_dict(state_temp_dict)
# Sets the model in evaluation mode (e.g. it removes BN)
model_temp.eval()
with torch.no_grad():
# process data
seq = open_sequence(args['test_path'], args['first'], args['last'],
args['already_norm'])
seq = torch.from_numpy(seq).to(device)
seq_time = time.time()
# Add noise
if not args['already_noisy']:
noise = torch.empty_like(seq).normal_(
mean=0, std=args['noise_sigma']).to(device)
seqn = seq + noise
else:
seqn = seq
noisestd = torch.FloatTensor([args['noise_sigma']]).to(device)
denframes = denoise_seq_fastdvdnet(seq=seqn,
noise_std=noisestd,
temp_psz=NUM_IN_FR_EXT,
model_temporal=model_temp)
# Compute PSNR and log it
stop_time = time.time()
psnr = compute_psnr(denframes.cpu().numpy(), seq.cpu().numpy(), 1.)
psnr_noisy = compute_psnr(seqn.cpu().numpy().squeeze(),
seq.cpu().numpy(), 1.)
loadtime = (seq_time - start_time)
runtime = (stop_time - seq_time)
seq_length = seq.size()[0]
logger.info("Finished denoising {}".format(args['test_path']))
logger.info(
"\tDenoised {} frames in {:.3f}s, loaded seq in {:.3f}s".format(
seq_length, runtime, loadtime))
logger.info("\tPSNR noisy {:.4f}dB, PSNR result {:.4f}dB".format(
psnr_noisy, psnr))
# Save outputs
if not args['dont_save_results']:
# Save sequence
save_out_seq(denframes.cpu(), args['save_path'], args['first'])
# close logger
close_logger(logger)
def test_dvdnet(**args):
"""Denoises all sequences present in a given folder. Sequences must be stored as numbered
image sequences. The different sequences must be stored in subfolders under the "test_path" folder.
Inputs:
args (dict) fields:
"model_spatial_file": path to model of the pretrained spatial denoiser
"model_temp_file": path to model of the pretrained temporal denoiser
"test_path": path to sequence to denoise
"suffix": suffix to add to output name
"max_num_fr_per_seq": max number of frames to load per sequence
"noise_sigma": noise level used on test set
"dont_save_results: if True, don't save output images
"no_gpu": if True, run model on CPU
"save_path": where to save outputs as png
"""
start_time = time.time()
# If save_path does not exist, create it
if not os.path.exists(args['save_path']):
os.makedirs(args['save_path'])
logger = init_logger_test(args['save_path'])
# Sets data type according to CPU or GPU modes
if args['cuda']:
device = torch.device('cuda')
else:
device = torch.device('cpu')
# Create models
model_spa = DVDnet_spatial()
model_temp = DVDnet_temporal(num_input_frames=NUM_IN_FRAMES)
# Load saved weights
state_spatial_dict = torch.load(args['model_spatial_file'])
state_temp_dict = torch.load(args['model_temp_file'])
if args['cuda']:
device_ids = [0]
model_spa = nn.DataParallel(model_spa, device_ids=device_ids).cuda()
model_temp = nn.DataParallel(model_temp, device_ids=device_ids).cuda()
else:
# CPU mode: remove the DataParallel wrapper
state_spatial_dict = remove_dataparallel_wrapper(state_spatial_dict)
state_temp_dict = remove_dataparallel_wrapper(state_temp_dict)
model_spa.load_state_dict(state_spatial_dict)
model_temp.load_state_dict(state_temp_dict)
# Sets the model in evaluation mode (e.g. it removes BN)
model_spa.eval()
model_temp.eval()
with torch.no_grad():
# process data
seq, _, _ = open_sequence(args['test_path'],\
False,\
expand_if_needed=False,\
max_num_fr=args['max_num_fr_per_seq'])
seq = torch.from_numpy(seq[:, np.newaxis, :, :, :]).to(device)
seqload_time = time.time()
# Add noise
noise = torch.empty_like(seq).normal_(mean=0, std=args['noise_sigma']).to(device)
seqn = seq + noise
noisestd = torch.FloatTensor([args['noise_sigma']]).to(device)
denframes = denoise_seq_dvdnet(seq=seqn,\
noise_std=noisestd,\
temp_psz=NUM_IN_FRAMES,\
model_temporal=model_temp,\
model_spatial=model_spa,\
mc_algo=MC_ALGO)
den_time = time.time()
# Compute PSNR and log it
psnr = batch_psnr(denframes, seq.squeeze(), 1.)
psnr_noisy = batch_psnr(seqn.squeeze(), seq.squeeze(), 1.)
print("\tPSNR on {} : {}\n".format(os.path.split(args['test_path'])[-1], psnr))
print("\tDenoising time: {:.2f}s".format(den_time - seqload_time))
print("\tSequence loaded in : {:.2f}s".format(seqload_time - start_time))
print("\tTotal time: {:.2f}s\n".format(den_time - start_time))
logger.info("%s, %s, PSNR noisy %fdB, PSNR %f dB" % \
(args['test_path'], args['suffix'], psnr_noisy, psnr))
# Save outputs
if not args['dont_save_results']:
# Save sequence
save_out_seq(seqn, denframes, args['save_path'], int(args['noise_sigma']*255), \
args['suffix'], args['save_noisy'])
# close logger
close_logger(logger)
def test_fastdvdnet(**args):
"""Denoises all sequences present in a given folder. Sequences must be stored as numbered
image sequences. The different sequences must be stored in subfolders under the "test_path" folder.
Inputs:
args (dict) fields:
"model_file": path to model
"test_path": path to sequence to denoise
"suffix": suffix to add to output name
"max_num_fr_per_seq": max number of frames to load per sequence
"dont_save_results: if True, don't save output images
"no_gpu": if True, run model on CPU
"save_path": where to save outputs as png
"gray": if True, perform denoising of grayscale images instead of RGB
"""
# Start time
start_time = time.time()
# If save_path does not exist, create it
if not os.path.exists(args['save_path']):
os.makedirs(args['save_path'])
logger = init_logger_test(args['save_path'])
# Sets data type according to CPU or GPU modes
if args['cuda']:
device = args['device_id'][0]
else:
device = torch.device('cpu')
# Create models
print('Loading models ...')
model_temp = FastDVDnet(num_input_frames=NUM_IN_FR_EXT)
# Load saved weights
state_temp_dict = torch.load(args['model_file'])
if args['cuda']:
device_ids = args['device_id']
model_temp = nn.DataParallel(model_temp,
device_ids=device_ids).cuda(device)
else:
# CPU mode: remove the DataParallel wrapper
state_temp_dict = remove_dataparallel_wrapper(state_temp_dict)
model_temp.load_state_dict(state_temp_dict)
# Sets the model in evaluation mode (e.g. it removes BN)
model_temp.eval()
gt = None
with torch.no_grad():
# process data
seq, _, _ = open_sequence(args['test_path'],
args['gray'],
expand_if_needed=False,
max_num_fr=args['max_num_fr_per_seq'])
seq = torch.from_numpy(seq).to(device)
seq_time = time.time()
denframes = denoise_seq_fastdvdnet(seq=seq,
temp_psz=NUM_IN_FR_EXT,
model_temporal=model_temp)
if args['gt_path'] is not None:
gt, _, _ = open_sequence(args['gt_path'],
args['gray'],
expand_if_needed=False,
max_num_fr=args['max_num_fr_per_seq'])
gt = torch.from_numpy(gt).to(device)
# Compute PSNR and log it
stop_time = time.time()
if gt is None:
psnr = 0
psnr_noisy = 0
else:
psnr = batch_psnr(denframes, gt, 1.)
psnr_noisy = batch_psnr(seq.squeeze(), gt, 1.)
loadtime = (seq_time - start_time)
runtime = (stop_time - seq_time)
seq_length = seq.size()[0]
logger.info("Finished denoising {}".format(args['test_path']))
logger.info(
"\tDenoised {} frames in {:.3f}s, loaded seq in {:.3f}s".format(
seq_length, runtime, loadtime))
logger.info("\tPSNR noisy {:.4f}dB, PSNR result {:.4f}dB".format(
psnr_noisy, psnr))
# Save outputs
if not args['dont_save_results']:
# Save sequence
save_out_seq(seq, denframes, args['save_path'], 0, args['suffix'],
args['save_noisy'])
# close logger
close_logger(logger)
def test_fastdvdnet(**args):
"""Denoises all sequences present in a given folder. Sequences must be stored as numbered
image sequences. The different sequences must be stored in subfolders under the "test_path" folder.
Inputs:
args (dict) fields:
"model_file": path to model
"test_path": path to sequence to denoise
"suffix": suffix to add to output name
"max_num_fr_per_seq": max number of frames to load per sequence
"noise_sigma": noise level used on test set
"dont_save_results: if True, don't save output images
"no_gpu": if True, run model on CPU
"save_path": where to save outputs as png
"gray": if True, perform denoising of grayscale images instead of RGB
"""
# If save_path does not exist, create it
if not os.path.exists(args['save_path']):
os.makedirs(args['save_path'])
logger = init_logger_test(args['save_path'])
# Sets data type according to CPU or GPU modes
if args['cuda']:
device = torch.device('cuda')
else:
device = torch.device('cpu')
# Create models
print('Loading models ...')
model_temp = FastDVDnet(num_input_frames=NUM_IN_FR_EXT)
# Load saved weights
state_temp_dict = torch.load(args['model_file'])
if args['cuda']:
device_ids = [0]
model_temp = nn.DataParallel(model_temp, device_ids=device_ids).cuda()
else:
# CPU mode: remove the DataParallel wrapper
state_temp_dict = remove_dataparallel_wrapper(state_temp_dict)
model_temp.load_state_dict(state_temp_dict)
# Sets the model in evaluation mode (e.g. it removes BN)
model_temp.eval()
processed_count = 0
# To avoid out of memory issues, we only process one folder at a time.
for tmp_folder in get_next_folder(args['test_path'], args['max_num_fr_per_seq']):
folder = tmp_folder.name
# Start time
print("Processing {}".format(os.listdir(tmp_folder.name)))
logger.info("Processing {}".format(os.listdir(folder)))
start_time = time.time()
with torch.no_grad():
# process data
seq, _, _ = open_sequence(folder,
args['gray'],
expand_if_needed=False,
max_num_fr=args['max_num_fr_per_seq'])
seq = torch.from_numpy(seq).to(device)
seq_time = time.time()
# Add noise
noise = torch.empty_like(seq).normal_(
mean=0, std=args['noise_sigma']).to(device)
seqn = seq + noise
noisestd = torch.FloatTensor([args['noise_sigma']]).to(device)
denframes = denoise_seq_fastdvdnet(seq=seqn,
noise_std=noisestd,
temp_psz=NUM_IN_FR_EXT,
model_temporal=model_temp)
# Compute PSNR and log it
stop_time = time.time()
psnr = batch_psnr(denframes, seq, 1.)
psnr_noisy = batch_psnr(seqn.squeeze(), seq, 1.)
loadtime = (seq_time - start_time)
runtime = (stop_time - seq_time)
seq_length = seq.size()[0]
logger.info("Finished denoising {}".format(args['test_path']))
logger.info("\tDenoised {} frames in {:.3f}s, loaded seq in {:.3f}s".
format(seq_length, runtime, loadtime))
logger.info(
"\tPSNR noisy {:.4f}dB, PSNR result {:.4f}dB".format(psnr_noisy, psnr))
# Save outputs
if not args['dont_save_results']:
# Save sequence
save_out_seq(seqn, denframes, args['save_path'],
int(args['noise_sigma']*255), args['suffix'], args['save_noisy'], processed_count)
# subtract half stride because of the half-steps get_next_folder takes.
processed_count+=seqn.size()[0]
# close logger
close_logger(logger)