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)
# Set up optimization
optimizer = optim.Adam(model.parameters(),
weight_decay=0.5e-4,
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),
# 'in_memory': True # Uncomment to avoid disk I/O (if you have enough host memory for the data)
}
train_dataset = PatchCreator(input_sources=[
input_h5data[i] for i in range(len(input_h5data)) if i not in valid_indices
],
batch_size = 1
model = get_model()
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)
]
train_transform = transforms.Compose(common_transforms + [
transforms.RandomCrop((128, 128)) # Use smaller patches for training
])
valid_transform = transforms.Compose(common_transforms + [])# Specify data set
train_dataset = SimpleNeuroData2d(train=True, transform=train_transform,
num_classes=2)
valid_dataset = SimpleNeuroData2d(train=False, transform=valid_transform,
num_classes=2)
# Set up optimization
optimizer = optim.Adam(
model.parameters(),
weight_decay=0.5e-4,
lr=lr,
amsgrad=True
)
lr_sched = optim.lr_scheduler.StepLR(optimizer, lr_stepsize, lr_dec)
n_classes = 9
data_init_kwargs = {"raw_only": False, "nb_views": 2, 'train_fraction': 0.95,
'nb_views_renderinglocations': 4, #'view_key': "4_large_fov",
"reduce_context": 0, "reduce_context_fact": 1, 'ctgt_key': "ctgt_v2", 'random_seed': 0,
"binary_views": False, "n_classes": n_classes, 'class_weights': [1] * n_classes}
if args.resume is not None: # Load pretrained network
print('Resuming model from {}.'.format(s.path.expanduser(args.resume)))
try: # Assume it's a state_dict for the model
model.load_state_dict(torch.load(os.path.expanduser(args.resume)))
except _pickle.UnpicklingError as exc:
# Assume it's a complete saved ScriptModule
model = torch.jit.load(os.path.expanduser(args.resume), map_location=device)
# Specify data set
transform = transforms.Compose([RandomFlip(ndim_spatial=2), ])
train_dataset = CelltypeViewsE3(train=True, transform=transform, **data_init_kwargs)
valid_dataset = CelltypeViewsE3(train=False, transform=transform, **data_init_kwargs)
# Set up optimization
optimizer = optim.SGD(
model.parameters(),
weight_decay=0.5e-4,
lr=lr,
# amsgrad=True
)
# lr_sched = optim.lr_scheduler.StepLR(optimizer, lr_stepsize, lr_dec)
lr_sched = SGDR(optimizer, 20000, 3)
schedulers = {'lr': lr_sched}
# All these metrics assume a binary classification problem. If you have
# non-binary targets, remember to adapt the metrics!
if args.resume is not None: # Load pretrained network
try: # Assume it's a state_dict for the model
model.load_state_dict(torch.load(os.path.expanduser(args.resume)))
except _pickle.UnpicklingError as exc:
# Assume it's a complete saved ScriptModule
model = torch.jit.load(os.path.expanduser(args.resume),
map_location=device)
drop_func = transforms.DropIfTooMuchBG(bg_id=3, threshold=0.9)
# Transformations to be applied to samples before feeding them to the network
common_transforms = [
#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=0.05, channels=[0], prob=0.1),
transforms.RandomBlurring({'probability': 0.1}), drop_func
])
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, 144, 144),
'num_classes': 6,
# 'offset': (20, 46, 46),
'target_discrete_ix': [3, 4, 5]
}
type_args = list(range(len(input_h5data)))
if args.resume is not None: # Load pretrained network params
model.load_state_dict(torch.load(os.path.expanduser(args.resume)))
# These statistics are computed from the training dataset.
# Remember to re-compute and change them when switching the dataset.
dataset_mean = (155.291411,)
dataset_std = (42.599973,)
# 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.AdditiveGaussianNoise(sigma=0.1, channels=[0], prob=0.3),
# transforms.RandomBlurring({'probability': 0.5})
])
valid_transform = transforms.Compose(common_transforms + [])
# Specify data set
common_data_kwargs = { # Common options for training and valid sets.
'aniso_factor': 2,
'patch_shape': (48, 96, 96),
'classes': [0, 1],
}
train_dataset = PatchCreator(
input_h5data=input_h5data[:2],
target_h5data=target_h5data[:2],
train=True,
epoch_size=args.epoch_size,
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),
]
train_transform = transforms.Compose(common_transforms + [
transforms.RandomFlip(ndim_spatial=3),
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,
# 'in_memory': True # Uncomment to avoid disk I/O (if you have enough host memory for the data)
}
train_dataset = PatchCreator(input_h5data=[
input_h5data[i] for i in range(len(input_h5data)) if i not in valid_indices
