dataset_sizes = {'train': len(train_data), 'test': len(test_data)}
dataloaders = dict()
dataloaders['train'] = data.DataLoader(train_data,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers
)
dataloaders['test'] = data.DataLoader(test_data,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers
)
if args.model in ['VGG11_bn', 'ResNet18', 'DenseNet3_40', 'LeNet', 'VGG11']:
model = Network().construct(args.model, config)
else:
raise Exception('Unknown model: {}'.format())
model = model.to(device)
if args.dp:
model = nn.DataParallel(model, args.dp)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=args.learning_rate, momentum=args.mom, weight_decay=args.weight_decay)
scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=args.milestones, gamma=args.lr_gamma)
acc_list = train(model,
optimizer,
scheduler,
dataloaders,
raise Exception('Unknown dataset: {}'.format(args.dataset))
dataloaders = dict()
dataloaders['train'] = data.DataLoader(train_data,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers)
dataloaders['test'] = data.DataLoader(test_data,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers)
# if args.model in ['VGG11', 'VGG11_bn', 'VGG13', 'VGG13_bn', 'VGG16', 'VGG16_bn', 'VGG19', 'VGG19_bn', 'ResNet18', 'DenseNet3_40', 'LeNet', 'MobileNet', 'FullyConnected']:
if args.model in model_choices:
model = Network().construct(args.model, config)
else:
raise Exception('Unknown model: {}'.format())
model = model.to(device)
if args.loss == 'ce':
criterion = nn.CrossEntropyLoss()
else:
raise Exception('Only cross entropy is allowed: {}'.format(args.loss))
if args.optimizer == 'sgd':
optimizer = optim.SGD(model.parameters(),
lr=args.learning_rate,
weight_decay=args.weight_decay,
momentum=0.9)
dataloaders = dict()
dataloaders['train'] = data.DataLoader(train_data,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers
)
dataloaders['test'] = data.DataLoader(test_data,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers
)
# if args.model in ['VGG11', 'VGG11_bn', 'VGG13', 'VGG13_bn', 'VGG16', 'VGG16_bn', 'VGG19', 'VGG19_bn', 'ResNet18', 'DenseNet3_40', 'LeNet', 'MobileNet', 'FullyConnected']:
if args.model in model_choices:
model = Network().construct(args.model, config)
else:
raise Exception('Unknown model: {}'.format())
model = model.to(device)
if args.loss == 'ce':
criterion = nn.CrossEntropyLoss()
else:
raise Exception('Only cross entropy is allowed: {}'.format(args.loss))
if args.optimizer == 'sgd':
optimizer = optim.SGD(model.parameters(), lr=args.learning_rate, weight_decay=args.weight_decay, momentum=0.9)
elif args.optimizer == 'adam':
optimizer = optim.Adam(model.parameters(), lr=args.learning_rate, weight_decay=args.weight_decay)
else:
else:
raise Exception('Unknown dataset: {}'.format(args.dataset))
dataloaders = dict()
dataloaders['train'] = data.DataLoader(train_data,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers)
dataloaders['test'] = data.DataLoader(test_data,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers)
if args.model in model_choices:
model = Network().construct(args.model, config)
else:
raise Exception('Unknown model: {}'.format())
model = model.to(device)
if args.loss == 'ce':
criterion = nn.CrossEntropyLoss()
else:
raise Exception('Only cross entropy is allowed: {}'.format(
args.loss))
if args.optimizer == 'sgd':
optimizer = optim.SGD(model.parameters(),
lr=args.learning_rate,
weight_decay=args.weight_decay,
padded_im_size = 32
num_classes = 11
im_size = 32
epc_seed = 0
config = Config(input_ch=input_ch,
padded_im_size=padded_im_size,
num_classes=num_classes,
im_size=im_size,
epc_seed=epc_seed
)
dataset_sizes = {'train': 73257, 'test': 26032}
else:
raise Exception('Should not have reached here')
if model_name in model_choices:
model = Network().construct(model_name, config)
else:
raise Exception('Unknown model: {}'.format())
full_eigenspectrums_path = osp.join(ckpt_dir, 'training_eigenspectrum_full.npy')
full_eigenspectrums = np.load(full_eigenspectrums_path)
valid_layers = get_valid_layers(model)
match_sum = {layer_name: 0 for layer_name in valid_layers}
epoch_dists_key = model_name + '+' + dataset
epoch_dists[epoch_dists_key] = np.zeros(50)
distances[epoch_dists_key]= {layer_name: np.zeros(50) for layer_name in valid_layers}
valid_model_layers[epoch_dists_key] = valid_layers
for layer_name in valid_layers:
for epoch in range(50):
layerwise_eigenspectrums_paths = osp.join(ckpt_dir, 'training_eigenspectrums_epoch_{}_layer_{}.npz'.format(epoch, layer_name))
import pdb
pdb.set_trace()
assert osp.exists(args.run), '{} was not found'.format(args.run)
assert osp.isdir(args.run), '{} is not a directory'.format(args.run)
images_dir = osp.join(args.run, 'images')
assert osp.exists(images_dir), '{} was not found'.format(images_dir)
assert osp.isdir(images_dir), '{} is not a directory'.format(images_dir)
collective_dir = osp.join(images_dir, 'collective')
if not osp.exists(collective_dir):
os.makedirs(collective_dir)
model = Network().construct(args.model, Config())
text_size = 30
font = ImageFont.truetype("Roboto-Light.ttf", text_size)
not_found = list()
num_layers = 0
height, width = None, None
for layer_name, _ in model.named_parameters():
if height is None:
dummy_image_path = osp.join(
images_dir, '{}_{}p_tsne.png'.format(layer_name,
args.perplexity))
assert osp.exists(dummy_image_path)
dummy_image = Image.open(dummy_image_path)
height = dummy_image.height
shuffle=True,
num_workers=args.workers)
dataloaders['test'] = data.DataLoader(test_data,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers)
if args.dataset == 'cifar100':
info.num_classes = 100
if args.model == 'efficient':
model = EfficientNetB0()
elif args.model.startswith('vgg'):
model = VGG(args.model)
elif args.model == 'resnet18':
model = Network().construct('ResNet18', Config())
elif args.model == 'resnet34':
model = ResNet34(num_classes=10)
elif args.model == 'resnet50':
model = ResNet50()
elif args.model == 'resnet101':
model = ResNet101()
elif args.model == 'densenet121':
model = DenseNet121()
elif args.model == 'mobilenet':
model = MobileNet()
elif args.model == 'mobilenetv2':
model = MobileNetV2()
elif args.model == 'lenet':
model = LeNet()
elif args.model == 'densenet40':