def switch_backbones(bone_name):
from nets.resnet import resnet18, resnet34, resnet50, resnet101, resnet152, \
resnext50_32x4d, resnext101_32x8d, wide_resnet50_2, wide_resnet101_2
if bone_name == "resnet18":
return resnet18()
elif bone_name == "resnet34":
return resnet34()
elif bone_name == "resnet50":
return resnet50()
elif bone_name == "resnet101":
return resnet101()
elif bone_name == "resnet152":
return resnet152()
elif bone_name == "resnext50_32x4d":
return resnext50_32x4d()
elif bone_name == "resnext101_32x8d":
return resnext101_32x8d()
elif bone_name == "wide_resnet50_2":
return wide_resnet50_2()
elif bone_name == "wide_resnet101_2":
return wide_resnet101_2()
else:
raise NotImplementedError(bone_name)
def _main(args):
#### Preparing Train Dataset ####
train_data_root = './datasets/standford_online_products/train'
train_data_transform = torch_transforms.Resize((225, 225))
train_num_retrieval_per_class = 10
train_pca_n_components = 2
train_pos_neighbor, train_neg_neighbor = (False, False)
train_dataloader = sop.loader( train_data_root, \
data_transform=train_data_transform, \
eval_mode=True, \
eval_num_retrieval=train_num_retrieval_per_class, \
neg_neighbor=train_neg_neighbor, \
pos_neighbor=train_pos_neighbor
)
#### Preparing Test Dataset ####
test_data_root = './datasets/standford_online_products/test'
test_data_transform = torch_transforms.Resize((225, 225))
test_num_retrieval_per_class = 10
test_pca_n_components = 2
test_pos_neighbor, test_neg_neighbor = (False, False)
test_dataloader = sop.loader( test_data_root, \
data_transform=test_data_transform, \
eval_mode=True, \
eval_num_retrieval=test_num_retrieval_per_class, \
neg_neighbor=test_neg_neighbor, \
pos_neighbor=test_pos_neighbor
)
#### Preparing Validation Dataset ####
val_data_root = './datasets/standford_online_products/val'
val_num_retrieval_per_class = test_num_retrieval_per_class
val_data_transform = torch_transforms.Resize((225, 225))
val_pca_n_components = 2
val_pos_neighbor, val_neg_neighbor = (False, False)
val_dataloader = sop.loader(val_data_root, \
data_transform=val_data_transform, \
eval_mode=True, \
eval_num_retrieval=val_num_retrieval_per_class,\
neg_neighbor=val_neg_neighbor, \
pos_neighbor=val_pos_neighbor
)
#### Preparing Pytorch ####
device = args.device
assert (device in [
'cpu', 'multi'
]) or (len(device.split(':')) == 2 and device.split(':')[0] == 'cuda'
and int(device.split(':')[1]) < torch.cuda.device_count()
), 'Uknown device: {}'.format(device)
torch.manual_seed(0)
if args.device != 'multi':
device = torch.device(args.device)
if args.gpu and torch.cuda.is_available():
torch.cuda.manual_seed_all(0)
#### Training Parameters ####
start_epoch, num_epoch = (args.start_epoch, args.epochs)
num_workers = args.num_workers
check_counter = 10
#### Reports Address ####
reports_root = './reports'
analysis_num = args.analysis
reports_path = '{}/{}'.format(reports_root, analysis_num)
loading_model_path = '{}/models'.format(reports_path)
#### Constructing Model ####
pretrained = args.pretrained
num_classes = val_dataloader.num_classes()
#### Constructing Model ####
pretrained = args.pretrained
num_classes = val_dataloader.num_classes()
model = None
if args.resnet_type == 'resnet18':
model = resnet.resnet18(pretrained=pretrained, num_classes=num_classes)
elif args.resnet_type == 'resnet34':
model = resnet.resnet34(pretrained=pretrained, num_classes=num_classes)
elif args.resnet_type == 'resnet50':
model = resnet.resnet50(pretrained=pretrained, num_classes=num_classes)
elif args.resnet_type == 'resnet101':
model = resnet.resnet101(pretrained=pretrained,
num_classes=num_classes)
elif args.resnet_type == 'resnet152':
model = resnet.resnet152(pretrained=pretrained,
num_classes=num_classes)
elif args.resnet_type == 'resnext50_32x4d':
model = resnet.resnext50_32x4d(pretrained=pretrained,
num_classes=num_classes)
# elif args.resnet_type=='resnext101_32x8d':
# model = resnet.resnext101_32x8d(pretrained=pretrained, num_classes=num_classes)
model, optimizer = resnet.load(loading_model_path,
'resnet_epoch_{}'.format(start_epoch),
model)
if args.gpu and torch.cuda.is_available():
if device == 'multi':
model = nn.DataParallel(model)
else:
model = model.cuda(device=device)
plot_representation(model, train_dataloader, device, args.gpu
and torch.cuda.is_available(), num_workers,
train_pca_n_components)