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 main(args):
args = parse_arguments(args)
if args.imagenet_dir is None:
args.imagenet_dir = '/home/arash/Software/imagenet/raw-data/validation/'
colour_space = args.colour_space
target_size = args.target_size
if args.repeat:
target_size = int(args.target_size / 2)
mean, std = model_utils.get_preprocessing_function(colour_space,
'trichromat')
extra_transformations = []
if args.noise is not None:
noise_kwargs = {'amount': float(args.noise[1])}
extra_transformations.append(
cv2_preprocessing.UniqueTransformation(imutils.gaussian_noise,
**noise_kwargs))
if args.mosaic_pattern is not None:
mosaic_trans = cv2_preprocessing.MosaicTransformation(
args.mosaic_pattern)
extra_transformations.append(mosaic_trans)
# testing setting
freqs = args.freqs
if freqs is None:
if args.model_fest:
test_sfs = [
107.558006181068, 60.2277887428434, 42.5666842683747,
30.1176548488805, 21.2841900445655, 15.0589946730047,
10.6611094334144, 7.5293662203794, 5.33055586478039,
4.01568430264343
]
args.gabor = 'model_fest'
else:
if target_size == 256:
t4s = [
target_size / 2,
target_size / 2.5,
target_size / 3,
target_size / 3.5,
target_size / 3.75,
target_size / 4,
]
else:
# assuming 128
t4s = [64]
sf_base = ((target_size / 2) / np.pi)
test_sfs = [
sf_base / e for e in [
*np.arange(1, 21), *np.arange(21, 61, 5),
*np.arange(61, t4s[-1], 25), *t4s
]
]
else:
if len(freqs) == 3:
test_sfs = np.linspace(freqs[0], freqs[1], int(freqs[2]))
else:
test_sfs = freqs
# so the sfs gets sorted
test_sfs = np.unique(test_sfs)
contrasts = args.contrasts
if contrasts is None:
test_contrasts = [0.5]
else:
test_contrasts = contrasts
test_thetas = np.linspace(0, np.pi, 7)
test_rhos = np.linspace(0, np.pi, 4)
test_ps = [0.0, 1.0]
if args.pretrained:
if args.side_by_side:
if args.scale_factor is None:
scale_factor = (args.target_size / 256)**2
else:
scale_factor = args.scale_factor
model = pretrained_models.NewClassificationModel(
args.model_path,
grey_width=args.grey_width == 40,
scale_factor=scale_factor)
else:
if args.scale_factor is None:
scale_factor = (args.target_size / 128)**2
else:
scale_factor = args.scale_factor
model = models_csf.ContrastDiscrimination(
args.model_path,
grey_width=args.grey_width == 40,
scale_factor=scale_factor)
else:
model, _ = model_utils.which_network_classification(args.model_path, 2)
model.eval()
model.cuda()
mean_std = None
if args.visualise:
mean_std = (mean, std)
if args.avg_illuminant < 0:
max_high = 1 + 2 * args.avg_illuminant
elif args.avg_illuminant > 0:
max_high = 1 + -2 * args.avg_illuminant
else:
max_high = 1.0
mid_contrast = (0 + max_high) / 2
all_results = None
csf_flags = [mid_contrast for _ in test_sfs]
if args.db == 'gratings':
for i in range(len(csf_flags)):
low = 0
high = max_high
j = 0
while csf_flags[i] is not None:
print('%.2d %.3d Doing %f - %f %f %f' %
(i, j, test_sfs[i], csf_flags[i], low, high))
test_samples = {
'amp': [csf_flags[i]],
'lambda_wave': [test_sfs[i]],
'theta': test_thetas,
'rho': test_rhos,
'side': test_ps,
'avg_illuminant': args.avg_illuminant
}
db_params = {
'colour_space': colour_space,
'vision_type': args.vision_type,
'repeat': args.repeat,
'mask_image': args.gabor,
'grey_width': args.grey_width,
'side_by_side': args.side_by_side
}
db = dataloader.validation_set(args.db,
target_size,
mean,
std,
extra_transformations,
data_dir=test_samples,
**db_params)
db.contrast_space = args.contrast_space
db_loader = torch.utils.data.DataLoader(
db,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True,
)
if args.side_by_side:
new_results, all_results = run_gratings(
db_loader,
model,
args.out_file,
args.print,
mean_std=mean_std,
old_results=all_results)
else:
new_results, all_results = run_gratings_separate(
db_loader,
model,
args.out_file,
args.print,
mean_std=mean_std,
old_results=all_results)
new_contrast, low, high = sensitivity_sf(new_results,
test_sfs[i],
varname='all',
th=0.75,
low=low,
high=high)
if (abs(csf_flags[i] - max_high) < 1e-3
or new_contrast == csf_flags[i] or j == 20):
print('had to skip', csf_flags[i])
csf_flags[i] = None
else:
csf_flags[i] = new_contrast
j += 1