def main_worker(ngpus_per_node, args):
mean, std = model_utils.get_preprocessing_function(args.colour_space,
args.vision_type)
if args.gpus is not None:
print("Use GPU: {} for training".format(args.gpus))
if args.distributed:
if args.dist_url == "env://" and args.rank == -1:
args.rank = int(os.environ["RANK"])
if args.multiprocessing_distributed:
# For multiprocessing distributed training, rank needs to be the
# global rank among all the processes
args.rank = args.rank * ngpus_per_node + args.gpus
dist.init_process_group(backend=args.dist_backend,
init_method=args.dist_url,
world_size=args.world_size,
rank=args.rank)
# create model
if args.transfer_weights is not None:
print('Transferred model!')
(model, _) = model_utils.which_network(args.transfer_weights[0],
args.task_type,
num_classes=args.old_classes)
which_layer = -1
if len(args.transfer_weights) == 2:
which_layer = args.transfer_weights[1]
model = model_utils.NewClassificationModel(model, which_layer,
args.num_classes)
elif args.custom_arch:
print('Custom model!')
supported_customs = ['resnet_basic_custom', 'resnet_bottleneck_custom']
if os.path.isfile(args.network_name):
checkpoint = torch.load(args.network_name, map_location='cpu')
customs = None
if 'customs' in checkpoint:
customs = checkpoint['customs']
# TODO: num_classes is just for backward compatibility
if 'num_classes' not in customs:
customs['num_classes'] = 1000
model = which_architecture(checkpoint['arch'], customs,
args.contrast_head)
args.network_name = checkpoint['arch']
model.load_state_dict(checkpoint['state_dict'], strict=False)
elif args.network_name in supported_customs:
model = custom_models.__dict__[args.network_name](
args.blocks,
contrast_head=args.contrast_head,
pooling_type=args.pooling_type,
in_chns=len(mean),
num_classes=args.num_classes,
inplanes=args.num_kernels,
kernel_size=args.kernel_size)
elif args.pretrained:
print("=> using pre-trained model '{}'".format(args.network_name))
model = models.__dict__[args.network_name](pretrained=True)
else:
print("=> creating model '{}'".format(args.network_name))
model = models.__dict__[args.network_name]()
# TODO: why load weights is False?
args.out_dir = prepare_training.prepare_output_directories(
dataset_name='contrast',
network_name=args.network_name,
optimiser='sgd',
load_weights=False,
experiment_name=args.experiment_name,
framework='pytorch')
# preparing the output folder
create_dir(args.out_dir)
json_file_name = os.path.join(args.out_dir, 'args.json')
with open(json_file_name, 'w') as fp:
json.dump(dict(args._get_kwargs()), fp, sort_keys=True, indent=4)
if args.distributed:
# For multiprocessing distributed, DistributedDataParallel constructor
# should always set the single device scope, otherwise,
# DistributedDataParallel will use all available devices.
if args.gpus is not None:
torch.cuda.set_device(args.gpus)
model.cuda(args.gpus)
# When using a single GPU per process and per
# DistributedDataParallel, we need to divide the batch size
# ourselves based on the total number of GPUs we have
args.batch_size = int(args.batch_size / ngpus_per_node)
args.workers = int(args.workers / ngpus_per_node)
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[args.gpus])
else:
model.cuda()
# DistributedDataParallel will divide and allocate batch_size to all
# available GPUs if device_ids are not set
model = torch.nn.parallel.DistributedDataParallel(model)
elif args.gpus is not None:
torch.cuda.set_device(args.gpus)
model = model.cuda(args.gpus)
else:
# DataParallel will divide and allocate batch_size to all available GPUs
if (args.network_name.startswith('alexnet')
or args.network_name.startswith('vgg')):
model.features = torch.nn.DataParallel(model.features)
model.cuda()
else:
model = torch.nn.DataParallel(model).cuda()
# define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss().cuda(args.gpus)
# optimiser
if args.transfer_weights is None:
optimizer = torch.optim.SGD(model.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
else:
# for p in model.features.parameters():
# p.requires_grad = False
params_to_optimize = [
{
'params': [p for p in model.features.parameters()],
'lr': 1e-6
},
{
'params': [p for p in model.fc.parameters()]
},
]
optimizer = torch.optim.SGD(params_to_optimize,
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
model_progress = []
model_progress_path = os.path.join(args.out_dir, 'model_progress.csv')
# optionally resume from a checkpoint
# TODO: it would be best if resume load the architecture from this file
# TODO: merge with which_architecture
best_acc1 = 0
if args.resume is not None:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume, map_location='cpu')
args.initial_epoch = checkpoint['epoch']
best_acc1 = checkpoint['best_acc1']
model.load_state_dict(checkpoint['state_dict'])
if args.gpus is not None:
# best_acc1 may be from a checkpoint from a different GPU
best_acc1 = best_acc1.to(args.gpus)
model = model.cuda(args.gpus)
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
if os.path.exists(model_progress_path):
model_progress = np.loadtxt(model_progress_path, delimiter=',')
model_progress = model_progress.tolist()
else:
print("=> no checkpoint found at '{}'".format(args.resume))
cudnn.benchmark = True
train_trans = []
valid_trans = []
both_trans = []
if args.mosaic_pattern is not None:
mosaic_trans = preprocessing.mosaic_transformation(args.mosaic_pattern)
both_trans.append(mosaic_trans)
if args.num_augmentations != 0:
augmentations = preprocessing.random_augmentation(
args.augmentation_settings, args.num_augmentations)
train_trans.append(augmentations)
target_size = default_configs.get_default_target_size(
args.dataset, args.target_size)
# loading the training set
train_trans = [*both_trans, *train_trans]
db_params = {
'colour_space': args.colour_space,
'vision_type': args.vision_type,
'mask_image': args.mask_image
}
if args.dataset in ['imagenet', 'celeba', 'natural']:
path_or_sample = args.data_dir
else:
path_or_sample = args.train_samples
train_dataset = dataloader.train_set(args.dataset,
target_size,
mean,
std,
extra_transformation=train_trans,
data_dir=path_or_sample,
**db_params)
if args.dataset == 'natural':
train_dataset.num_crops = args.batch_size
args.batch_size = 1
if args.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset)
else:
train_sampler = None
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=(train_sampler is None),
num_workers=args.workers,
pin_memory=True,
sampler=train_sampler)
# loading validation set
valid_trans = [*both_trans, *valid_trans]
validation_dataset = dataloader.validation_set(
args.dataset,
target_size,
mean,
std,
extra_transformation=valid_trans,
data_dir=path_or_sample,
**db_params)
if args.dataset == 'natural':
validation_dataset.num_crops = train_dataset.num_crops
args.batch_size = 1
val_loader = torch.utils.data.DataLoader(validation_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
# training on epoch
for epoch in range(args.initial_epoch, args.epochs):
if args.distributed:
train_sampler.set_epoch(epoch)
misc_utils.adjust_learning_rate(optimizer, epoch, args)
# train for one epoch
train_log = train_on_data(train_loader, model, criterion, optimizer,
epoch, args)
# evaluate on validation set
validation_log = validate_on_data(val_loader, model, criterion, args)
model_progress.append([*train_log, *validation_log])
# remember best acc@1 and save checkpoint
acc1 = validation_log[2]
is_best = acc1 > best_acc1
best_acc1 = max(acc1, best_acc1)
if misc_utils.is_saving_node(args.multiprocessing_distributed,
args.rank, ngpus_per_node):
misc_utils.save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.network_name,
'customs': {
'pooling_type': args.pooling_type,
'in_chns': len(mean),
'num_classes': args.num_classes,
'blocks': args.blocks,
'num_kernels': args.num_kernels,
'kernel_size': args.kernel_size
},
'preprocessing': {
'mean': mean,
'std': std
},
'state_dict': model.state_dict(),
'best_acc1': best_acc1,
'optimizer': optimizer.state_dict(),
'target_size': target_size,
},
is_best,
out_folder=args.out_dir)
# TODO: get this header directly as a dictionary keys
header = 'epoch,t_time,t_loss,t_top1,t_top5,v_time,v_loss,v_top1,v_top5'
np.savetxt(model_progress_path,
np.array(model_progress),
delimiter=',',
header=header)
def epochs(model, train_loader, validation_loader, optimizer, args):
model_progress = []
best_euc = np.inf
# optionally resume from a checkpoint
if args.resume is not None:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume, map_location='cpu')
args.initial_epoch = checkpoint['epoch']
best_euc = checkpoint['best_euc']
model.load_state_dict(checkpoint['state_dict'])
model = model.to(args.device)
optimizer.load_state_dict(checkpoint['optimizer'])
print(
"=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']
)
)
model_progress_path = args.resume.replace(
'checkpoint.pth.tar', 'model_progress.csv'
)
if os.path.exists(model_progress_path):
model_progress = np.loadtxt(model_progress_path, delimiter=',')
model_progress = model_progress.tolist()
scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer,
milestones=[int(args.epochs * 0.33), int(args.epochs * 0.66)],
last_epoch=args.initial_epoch - 1
)
file_path = os.path.join(args.out_dir, 'model_progress.csv')
criterion = args.criterion
for epoch in range(args.initial_epoch, args.epochs):
scheduler.step(epoch=epoch)
train_log = train(
train_loader, model, optimizer, criterion, epoch, args
)
validation_log = validate(
validation_loader, model, criterion, args
)
model_progress.append([*train_log, *validation_log])
# remember best euclidean distance and save checkpoint
euc_distance = validation_log[2]
is_best = euc_distance < best_euc
best_euc = max(euc_distance, best_euc)
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.architecture,
'kwargs': args.architecture_kwargs,
'state_dict': model.state_dict(),
'best_euc': best_euc,
'optimizer': optimizer.state_dict(),
'target_size': args.target_size,
},
is_best, out_folder=args.out_dir
)
# TODO: get this header directly as a dictionary keys
header = 'epoch,t_time,t_loss,t_euc,v_time,v_loss,v_euc'
np.savetxt(
file_path, np.array(model_progress), delimiter=',', header=header
)
def main_worker(ngpus_per_node, args):
mean, std = model_utils.get_preprocessing_function(args.colour_space,
args.vision_type)
# preparing the output folder
create_dir(args.out_dir)
if args.gpus is not None:
print("Use GPU: {} for training".format(args.gpus))
if args.distributed:
if args.dist_url == "env://" and args.rank == -1:
args.rank = int(os.environ["RANK"])
if args.multiprocessing_distributed:
# For multiprocessing distributed training, rank needs to be the
# global rank among all the processes
args.rank = args.rank * ngpus_per_node + args.gpus
dist.init_process_group(backend=args.dist_backend,
init_method=args.dist_url,
world_size=args.world_size,
rank=args.rank)
# create model
if args.transfer_weights is not None:
print('Transferred model!')
model = contrast_utils.AFCModel(args.network_name,
args.transfer_weights)
elif args.custom_arch:
print('Custom model!')
supported_customs = ['resnet_basic_custom', 'resnet_bottleneck_custom']
if args.network_name in supported_customs:
model = custom_models.__dict__[args.network_name](
args.blocks,
pooling_type=args.pooling_type,
in_chns=len(mean),
num_classes=args.num_classes,
inplanes=args.num_kernels,
kernel_size=args.kernel_size)
elif args.pretrained:
print("=> using pre-trained model '{}'".format(args.network_name))
model = models.__dict__[args.network_name](pretrained=True)
else:
print("=> creating model '{}'".format(args.network_name))
model = models.__dict__[args.network_name]()
if args.distributed:
# For multiprocessing distributed, DistributedDataParallel constructor
# should always set the single device scope, otherwise,
# DistributedDataParallel will use all available devices.
if args.gpus is not None:
torch.cuda.set_device(args.gpus)
model.cuda(args.gpus)
# When using a single GPU per process and per
# DistributedDataParallel, we need to divide the batch size
# ourselves based on the total number of GPUs we have
args.batch_size = int(args.batch_size / ngpus_per_node)
args.workers = int(args.workers / ngpus_per_node)
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[args.gpus])
else:
model.cuda()
# DistributedDataParallel will divide and allocate batch_size to all
# available GPUs if device_ids are not set
model = torch.nn.parallel.DistributedDataParallel(model)
elif args.gpus is not None:
torch.cuda.set_device(args.gpus)
model = model.cuda(args.gpus)
else:
# DataParallel will divide and allocate batch_size to all available GPUs
if (args.network_name.startswith('alexnet')
or args.network_name.startswith('vgg')):
model.features = torch.nn.DataParallel(model.features)
model.cuda()
else:
model = torch.nn.DataParallel(model).cuda()
# define loss function (criterion) and optimizer
criterion = soft_cross_entropy
# optimiser
if args.transfer_weights is None:
optimizer = torch.optim.SGD(model.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
else:
params_to_optimize = [
{
'params': [p for p in model.parameters() if p.requires_grad]
},
]
optimizer = torch.optim.SGD(params_to_optimize,
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
model_progress = []
model_progress_path = os.path.join(args.out_dir, 'model_progress.csv')
# optionally resume from a checkpoint
# TODO: it would be best if resume load the architecture from this file
# TODO: merge with which_architecture
best_acc1 = 0
if args.resume is not None:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume, map_location='cpu')
args.initial_epoch = checkpoint['epoch']
best_acc1 = checkpoint['best_acc1']
model.load_state_dict(checkpoint['state_dict'])
if args.gpus is not None:
# best_acc1 may be from a checkpoint from a different GPU
best_acc1 = best_acc1.to(args.gpus)
model = model.cuda(args.gpus)
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
if os.path.exists(model_progress_path):
model_progress = np.loadtxt(model_progress_path, delimiter=',')
model_progress = model_progress.tolist()
else:
print("=> no checkpoint found at '{}'".format(args.resume))
cudnn.benchmark = True
train_trans = []
valid_trans = []
both_trans = []
if args.mosaic_pattern is not None:
mosaic_trans = preprocessing.mosaic_transformation(args.mosaic_pattern)
both_trans.append(mosaic_trans)
if args.num_augmentations != 0:
augmentations = preprocessing.random_augmentation(
args.augmentation_settings, args.num_augmentations)
train_trans.append(augmentations)
target_size = default_configs.get_default_target_size(
args.dataset, args.target_size)
final_trans = [
cv2_transforms.ToTensor(),
cv2_transforms.Normalize(mean, std),
]
train_trans.append(
cv2_transforms.RandomResizedCrop(target_size, scale=(0.08, 1.0)))
# loading the training set
train_trans = torch_transforms.Compose(
[*both_trans, *train_trans, *final_trans])
train_dataset = image_quality.BAPPS2afc(root=args.data_dir,
split='train',
transform=train_trans,
concat=0.5)
if args.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset)
else:
train_sampler = None
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=(train_sampler is None),
num_workers=args.workers,
pin_memory=True,
sampler=train_sampler)
valid_trans.extend([
cv2_transforms.Resize(target_size),
cv2_transforms.CenterCrop(target_size),
])
# loading validation set
valid_trans = torch_transforms.Compose(
[*both_trans, *valid_trans, *final_trans])
validation_dataset = image_quality.BAPPS2afc(root=args.data_dir,
split='val',
transform=valid_trans,
concat=0)
val_loader = torch.utils.data.DataLoader(validation_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
# training on epoch
for epoch in range(args.initial_epoch, args.epochs):
if args.distributed:
train_sampler.set_epoch(epoch)
misc_utils.adjust_learning_rate(optimizer, epoch, args)
# train for one epoch
train_log = train_on_data(train_loader, model, criterion, optimizer,
epoch, args)
# evaluate on validation set
validation_log = validate_on_data(val_loader, model, criterion, args)
model_progress.append([*train_log, *validation_log])
# remember best acc@1 and save checkpoint
acc1 = validation_log[2]
is_best = acc1 > best_acc1
best_acc1 = max(acc1, best_acc1)
if misc_utils.is_saving_node(args.multiprocessing_distributed,
args.rank, ngpus_per_node):
misc_utils.save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.network_name,
'customs': {
'pooling_type': args.pooling_type,
'in_chns': len(mean),
'num_classes': args.num_classes,
'blocks': args.blocks,
'num_kernels': args.num_kernels,
'kernel_size': args.kernel_size
},
'transfer_weights': args.transfer_weights,
'preprocessing': {
'mean': mean,
'std': std
},
'state_dict': model.state_dict(),
'best_acc1': best_acc1,
'optimizer': optimizer.state_dict(),
'target_size': target_size,
},
is_best,
out_folder=args.out_dir)
# TODO: get this header directly as a dictionary keys
header = 'epoch,t_time,t_loss,t_top5,v_time,v_loss,v_top1'
np.savetxt(model_progress_path,
np.array(model_progress),
delimiter=',',
header=header)
def main_worker(ngpus_per_node, args):
global best_acc1
is_pill_img = 'wcs_xyz_png_1600' in args.data_dir
mean, std = model_utils.get_preprocessing_function(args.colour_space,
args.vision_type)
# preparing the output folder
create_dir(args.out_dir)
if args.gpus is not None:
print("Use GPU: {} for training".format(args.gpus))
if args.distributed:
if args.dist_url == "env://" and args.rank == -1:
args.rank = int(os.environ["RANK"])
if args.multiprocessing_distributed:
# For multiprocessing distributed training, rank needs to be the
# global rank among all the processes
args.rank = args.rank * ngpus_per_node + args.gpus
dist.init_process_group(backend=args.dist_backend,
init_method=args.dist_url,
world_size=args.world_size,
rank=args.rank)
# create model
if args.prediction:
checkpoint = torch.load(args.network_weights, map_location='cpu')
blocks = checkpoint['customs']['blocks']
pooling_type = checkpoint['customs']['pooling_type']
num_kernels = checkpoint['customs']['num_kernels']
outputs = checkpoint['customs']['outputs']
for key, val in outputs.items():
if 'area' not in val:
outputs[key] = None
model = resnet.__dict__[args.network_name](blocks,
pooling_type=pooling_type,
in_chns=len(mean),
inplanes=num_kernels,
outputs=outputs)
model.load_state_dict(checkpoint['state_dict'])
elif args.transfer_weights is not None:
print('Transferred model!')
(model, _) = model_utils.which_network(args.transfer_weights,
args.task_type,
num_classes=args.old_classes)
model = model_utils.NewClassificationModel(model, args.num_classes)
elif args.custom_arch:
print('Custom model!')
if (args.network_name == 'resnet_basic_custom'
or args.network_name == 'resnet_bottleneck_custom'):
outputs = {'objects': None, 'munsells': None, 'illuminants': None}
imagenet_weights = args.imagenet_weights
if args.object_area is not None:
outputs['objects'] = {
'num_classes': 2100,
'area': args.object_area
}
if args.munsell_area is not None:
outputs['munsells'] = {
'num_classes': 1600,
'area': args.munsell_area
}
if args.illuminant_area is not None:
outputs['illuminants'] = {
'num_classes': 280,
'area': args.illuminant_area
}
model = resnet.__dict__[args.network_name](
args.blocks,
pooling_type=args.pooling_type,
in_chns=len(mean),
inplanes=args.num_kernels,
outputs=outputs,
imagenet_weights=imagenet_weights)
elif args.pretrained:
print("=> using pre-trained model '{}'".format(args.network_name))
model = models.__dict__[args.network_name](pretrained=True)
else:
print("=> creating model '{}'".format(args.network_name))
model = models.__dict__[args.network_name]()
if args.distributed:
# For multiprocessing distributed, DistributedDataParallel constructor
# should always set the single device scope, otherwise,
# DistributedDataParallel will use all available devices.
if args.gpus is not None:
torch.cuda.set_device(args.gpus)
model.cuda(args.gpus)
# When using a single GPU per process and per
# DistributedDataParallel, we need to divide the batch size
# ourselves based on the total number of GPUs we have
args.batch_size = int(args.batch_size / ngpus_per_node)
args.workers = int(args.workers / ngpus_per_node)
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[args.gpus])
else:
model.cuda()
# DistributedDataParallel will divide and allocate batch_size to all
# available GPUs if device_ids are not set
model = torch.nn.parallel.DistributedDataParallel(model)
elif args.gpus is not None:
torch.cuda.set_device(args.gpus)
model = model.cuda(args.gpus)
else:
# DataParallel will divide and allocate batch_size to all available GPUs
if (args.network_name.startswith('alexnet')
or args.network_name.startswith('vgg')):
model.features = torch.nn.DataParallel(model.features)
model.cuda()
else:
model = torch.nn.DataParallel(model).cuda()
# define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss().cuda(args.gpus)
# optimiser
optimizer = torch.optim.SGD(model.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
model_progress = []
model_progress_path = os.path.join(args.out_dir, 'model_progress.csv')
# optionally resume from a checkpoint
# TODO: it would be best if resume load the architecture from this file
# TODO: merge with which_architecture
if args.resume is not None:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume, map_location='cpu')
args.initial_epoch = checkpoint['epoch']
best_acc1 = checkpoint['best_acc1']
model.load_state_dict(checkpoint['state_dict'])
if args.gpus is not None:
# best_acc1 may be from a checkpoint from a different GPU
best_acc1 = best_acc1.to(args.gpus)
model = model.cuda(args.gpus)
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
if os.path.exists(model_progress_path):
model_progress = np.loadtxt(model_progress_path, delimiter=',')
model_progress = model_progress.tolist()
else:
print("=> no checkpoint found at '{}'".format(args.resume))
cudnn.benchmark = True
normalize = transforms.Normalize(mean=mean, std=std)
other_transformations = []
if args.num_augmentations != 0:
augmentations = preprocessing.random_augmentation(
args.augmentation_settings, args.num_augmentations)
other_transformations.append(augmentations)
target_size = get_default_target_size(args.dataset, args.target_size)
train_dataset, validation_dataset = get_train_val_dataset(
args.data_dir, other_transformations, [], normalize,
args.imagenet_weights)
if args.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset)
else:
train_sampler = None
if args.prediction:
manipulation_values = args.parameters['kwargs'][args.manipulation]
manipulation_name = args.parameters['f_name']
for j, manipulation_value in enumerate(manipulation_values):
args.parameters['kwargs'][args.manipulation] = manipulation_value
prediction_transformation = preprocessing.prediction_transformation(
args.parameters, args.colour_space,
tmp_c_space(manipulation_name))
other_transformations = [prediction_transformation]
_, validation_dataset = get_train_val_dataset(
args.data_dir, other_transformations, other_transformations,
normalize, args.imagenet_weights)
val_loader = torch.utils.data.DataLoader(
validation_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
pred_log = predict(val_loader, model, criterion,
torch.device(args.gpus))
from kernelphysiology.dl.utils import prepapre_testing
prepapre_testing.save_predictions(pred_log, args.experiment_name,
args.pred_name, args.dataset,
manipulation_name,
manipulation_value)
return
val_loader = torch.utils.data.DataLoader(validation_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=(train_sampler is None),
num_workers=args.workers,
pin_memory=True,
sampler=train_sampler)
if args.ill_colour is not None:
print('Performing with illuminant correction')
args.ill_colour = np.loadtxt(args.ill_colour, delimiter=',')
# training on epoch
for epoch in range(args.initial_epoch, args.epochs):
if args.distributed:
train_sampler.set_epoch(epoch)
if args.imagenet_weights is None:
adjust_learning_rate(optimizer, epoch, args)
# train for one epoch
train_log = train_on_data(train_loader, model, criterion, optimizer,
epoch, args)
# evaluate on validation set
validation_log = validate_on_data(val_loader, model, criterion, args)
model_progress.append([*train_log, *validation_log])
# remember best acc@1 and save checkpoint
acc1 = validation_log[2]
is_best = acc1 > best_acc1
best_acc1 = max(acc1, best_acc1)
if not args.multiprocessing_distributed or (
args.multiprocessing_distributed
and args.rank % ngpus_per_node == 0):
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.network_name,
'customs': {
'pooling_type': args.pooling_type,
'in_chns': len(mean),
'num_classes': args.num_classes,
'blocks': args.blocks,
'num_kernels': args.num_kernels,
'outputs': outputs
},
'state_dict': model.state_dict(),
'best_acc1': best_acc1,
'optimizer': optimizer.state_dict(),
'target_size': target_size,
},
is_best,
out_folder=args.out_dir)
# TODO: get this header directly as a dictionary keys
header = 'epoch,t_time,t_loss,t_lo,t_lm,t_li,t_ao,t_am,t_ai,' \
'v_time,v_loss,v_lo,v_lm,v_li,v_ao,v_am,v_ai'
np.savetxt(model_progress_path,
np.array(model_progress),
delimiter=',',
header=header)
