for i in range(3)]
target_h5data = [(os.path.join(data_root, f'barrier_int16_{i}.h5'), 'lab')
for i in range(3)]
aniso_factor = 2 # Anisotropy in z dimension. E.g. 2 means half resolution in z dimension.
common_data_kwargs = { # Common options for training and valid sets.
'aniso_factor': aniso_factor,
'patch_shape': (44, 88, 88),
# 'offset': (8, 20, 20),
'out_channels': 2,
# 'in_memory': True # Uncomment to avoid disk I/O (if you have enough host memory for the data)
}
norm_mean = (155.291411, )
norm_std = (42.599973, )
valid_transform = transforms.Normalize(mean=norm_mean,
std=norm_std,
inplace=True)
print('Loading dataset...')
valid_dataset = PatchCreator(
input_sources=input_h5data,
target_sources=target_h5data,
train=False,
epoch_size=40, # How many samples to use for each validation run
warp_prob=0,
warp_kwargs={'sample_aniso': aniso_factor != 1},
transform=valid_transform,
**common_data_kwargs)
valid_loader = DataLoader(valid_dataset, num_workers=4, pin_memory=True)
# Validation metrics
optimizer_state_dict = state.get('optimizer_state_dict')
lr_sched_state_dict = state.get('lr_sched_state_dict')
if optimizer_state_dict is None:
logger.warning('optimizer_state_dict not found.')
if lr_sched_state_dict is None:
logger.warning('lr_sched_state_dict not found.')
elif isinstance(state, nn.Module):
logger.warning(_warning_str)
model = state
else:
raise ValueError(f'Can\'t load {pretrained}.')
# Transformations to be applied to samples before feeding them to the network
common_transforms = [
transforms.SqueezeTarget(dim=0), # Workaround for neuro_data_cdhw
transforms.Normalize(mean=dataset_mean, std=dataset_std)
]
train_transform = transforms.Compose(common_transforms + [
# transforms.RandomRotate2d(prob=0.9),
# transforms.RandomGrayAugment(channels=[0], prob=0.3),
# transforms.RandomGammaCorrection(gamma_std=0.25, gamma_min=0.25, prob=0.3),
# transforms.AdditiveGaussianNoise(sigma=0.1, channels=[0], prob=0.3),
])
valid_transform = transforms.Compose(common_transforms + [])
# Specify data set
aniso_factor = 2 # Anisotropy in z dimension. E.g. 2 means half resolution in z dimension.
common_data_kwargs = { # Common options for training and valid sets.
'aniso_factor': aniso_factor,
'patch_shape': (44, 88, 88),
# 'offset': (8, 20, 20),
optimizer_state_dict = state.get('optimizer_state_dict')
lr_sched_state_dict = state.get('lr_sched_state_dict')
if optimizer_state_dict is None:
logger.warning('optimizer_state_dict not found.')
if lr_sched_state_dict is None:
logger.warning('lr_sched_state_dict not found.')
elif isinstance(state, nn.Module):
logger.warning(_warning_str)
model = state
else:
raise ValueError(f'Can\'t load {pretrained}.')
# Transformations to be applied to samples before feeding them to the network
common_transforms = [
transforms.SqueezeTarget(dim=0), # Workaround for neuro_data_cdhw
transforms.Normalize(mean=dataset_mean, std=dataset_std)
]
train_transform = transforms.Compose(common_transforms + [
# transforms.RandomGrayAugment(channels=[0], prob=0.3),
# transforms.RandomGammaCorrection(gamma_std=0.25, gamma_min=0.25, prob=0.3),
# transforms.AdditiveGaussianNoise(sigma=0.1, channels=[0], prob=0.3),
])
valid_transform = transforms.Compose(common_transforms + [])
# Specify data set
aniso_factor = 2 # Anisotropy in z dimension. E.g. 2 means half resolution in z dimension.
common_data_kwargs = { # Common options for training and valid sets.
'aniso_factor': aniso_factor,
'patch_shape': (48, 96, 96),
# 'offset': (8, 20, 20),
'num_classes': 2,
max_steps = args.max_steps
lr = 0.0004
lr_stepsize = 1000
lr_dec = 0.995
batch_size = 1
if args.resume is not None: # Load pretrained network params
model.load_state_dict(torch.load(os.path.expanduser(args.resume)))
dataset_mean = (143.97594, )
dataset_std = (44.264744, )
# Transformations to be applied to samples before feeding them to the network
common_transforms = [
transforms.Normalize(mean=dataset_mean, std=dataset_std, inplace=True)
]
train_transform = transforms.Compose(common_transforms + [
transforms.RandomCrop((128, 128)), # Use smaller patches for training
transforms.RandomFlip(),
transforms.AdditiveGaussianNoise(prob=0.5, sigma=0.1)
])
valid_transform = transforms.Compose(common_transforms +
[transforms.RandomCrop((144, 144))])
# Specify data set
train_dataset = SimpleNeuroData2d(train=True,
transform=train_transform,
out_channels=out_channels)
valid_dataset = SimpleNeuroData2d(train=False,
transform=valid_transform,
out_channels=out_channels)
logger.warning('optimizer_state_dict not found.')
if lr_sched_state_dict is None:
logger.warning('lr_sched_state_dict not found.')
elif isinstance(state, nn.Module):
logger.warning(_warning_str)
model = state
else:
raise ValueError(f'Can\'t load {pretrained}.')
dataset_mean = (0.0, )
dataset_std = (1.0, )
# Transformations to be applied to samples before feeding them to the network
common_transforms = [
transforms.SqueezeTarget(dim=0), # Workaround for neuro_data_cdhw
transforms.Normalize(mean=dataset_mean, std=dataset_std)
]
train_transform = transforms.Compose(common_transforms + [
# transforms.RandomRotate2d(prob=0.9),
# transforms.RandomGrayAugment(channels=[0], prob=0.3),
# transforms.RandomGammaCorrection(gamma_std=0.25, gamma_min=0.25, prob=0.3),
# transforms.AdditiveGaussianNoise(sigma=0.1, channels=[0], prob=0.3),
])
valid_transform = transforms.Compose(common_transforms + [])
# Specify data set
aniso_factor = 2 # Anisotropy in z dimension. E.g. 2 means half resolution in z dimension.
common_data_kwargs = { # Common options for training and valid sets.
'aniso_factor': aniso_factor,
'patch_shape': (44, 88, 88),
# 'offset': (8, 20, 20),