def main():
# Retrieve config file
p = create_config(args)
print(colored(p, 'red'))
# Model
print(colored('Retrieve model', 'blue'))
model = get_model(p)
print('Model is {}'.format(model.__class__.__name__))
print(model)
model = torch.nn.DataParallel(model)
model = model.cuda()
# CUDNN
print(colored('Set CuDNN benchmark', 'blue'))
torch.backends.cudnn.benchmark = True
# Dataset
print(colored('Retrieve dataset', 'blue'))
transforms = get_val_transformations(p)
train_dataset = get_train_dataset(p, transforms)
val_dataset = get_val_dataset(p, transforms)
train_dataloader = get_val_dataloader(p, train_dataset)
val_dataloader = get_val_dataloader(p, val_dataset)
print('Dataset contains {}/{} train/val samples'.format(
len(train_dataset), len(val_dataset)))
# Memory Bank
print(colored('Build MemoryBank', 'blue'))
memory_bank_train = MemoryBank(len(train_dataset), 2048, p['num_classes'],
p['temperature'])
memory_bank_train.cuda()
memory_bank_val = MemoryBank(len(val_dataset), 2048, p['num_classes'],
p['temperature'])
memory_bank_val.cuda()
# Load the official MoCoV2 checkpoint
print(colored('Downloading moco v2 checkpoint', 'blue'))
os.system(
'wget -L https://dl.fbaipublicfiles.com/moco/moco_checkpoints/moco_v2_800ep/moco_v2_800ep_pretrain.pth.tar'
)
moco_state = torch.load('moco_v2_800ep_pretrain.pth.tar',
map_location='cpu')
# Transfer moco weights
print(colored('Transfer MoCo weights to model', 'blue'))
new_state_dict = {}
state_dict = moco_state['state_dict']
for k in list(state_dict.keys()):
# Copy backbone weights
if k.startswith('module.encoder_q'
) and not k.startswith('module.encoder_q.fc'):
new_k = 'module.backbone.' + k[len('module.encoder_q.'):]
new_state_dict[new_k] = state_dict[k]
# Copy mlp weights
elif k.startswith('module.encoder_q.fc'):
new_k = 'module.contrastive_head.' + k[len('module.encoder_q.fc.'
):]
new_state_dict[new_k] = state_dict[k]
else:
raise ValueError('Unexpected key {}'.format(k))
model.load_state_dict(new_state_dict)
os.system('rm -rf moco_v2_800ep_pretrain.pth.tar')
# Save final model
print(colored('Save pretext model', 'blue'))
torch.save(model.module.state_dict(), p['pretext_model'])
model.module.contrastive_head = torch.nn.Identity(
) # In this case, we mine the neighbors before the MLP.
# Mine the topk nearest neighbors (Train)
# These will be used for training with the SCAN-Loss.
topk = 50
print(
colored('Mine the nearest neighbors (Train)(Top-%d)' % (topk), 'blue'))
transforms = get_val_transformations(p)
train_dataset = get_train_dataset(p, transforms)
fill_memory_bank(train_dataloader, model, memory_bank_train)
indices, acc = memory_bank_train.mine_nearest_neighbors(topk)
print('Accuracy of top-%d nearest neighbors on train set is %.2f' %
(topk, 100 * acc))
np.save(p['topk_neighbors_train_path'], indices)
# Mine the topk nearest neighbors (Validation)
# These will be used for validation.
topk = 5
print(colored('Mine the nearest neighbors (Val)(Top-%d)' % (topk), 'blue'))
fill_memory_bank(val_dataloader, model, memory_bank_val)
print('Mine the neighbors')
indices, acc = memory_bank_val.mine_nearest_neighbors(topk)
print('Accuracy of top-%d nearest neighbors on val set is %.2f' %
(topk, 100 * acc))
np.save(p['topk_neighbors_val_path'], indices)
def main():
# Retrieve config file
p = create_config(args.config_env, args.config_exp)
print(colored(p, 'red'))
# Model
print(colored('Retrieve model', 'blue'))
model = get_model(p)
print('Model is {}'.format(model.__class__.__name__))
print('Model parameters: {:.2f}M'.format(sum(p.numel() for p in model.parameters()) / 1e6))
print(model)
model = model.cuda()
# CUDNN
print(colored('Set CuDNN benchmark', 'blue'))
torch.backends.cudnn.benchmark = True
# Dataset
print(colored('Retrieve dataset', 'blue'))
train_transforms = get_train_transformations(p)
print('Train transforms:', train_transforms)
val_transforms = get_val_transformations(p)
print('Validation transforms:', val_transforms)
train_dataset = get_train_dataset(p, train_transforms, to_augmented_dataset=True,
split='train+unlabeled') # Split is for stl-10
val_dataset = get_val_dataset(p, val_transforms)
train_dataloader = get_train_dataloader(p, train_dataset)
val_dataloader = get_val_dataloader(p, val_dataset)
print('Dataset contains {}/{} train/val samples'.format(len(train_dataset), len(val_dataset)))
# Memory Bank
print(colored('Build MemoryBank', 'blue'))
base_dataset = get_train_dataset(p, val_transforms, split='train') # Dataset w/o augs for knn eval
base_dataloader = get_val_dataloader(p, base_dataset)
memory_bank_base = MemoryBank(len(base_dataset),
p['model_kwargs']['features_dim'],
p['num_classes'], p['criterion_kwargs']['temperature'])
memory_bank_base.cuda()
memory_bank_val = MemoryBank(len(val_dataset),
p['model_kwargs']['features_dim'],
p['num_classes'], p['criterion_kwargs']['temperature'])
memory_bank_val.cuda()
# Criterion
print(colored('Retrieve criterion', 'blue'))
criterion = get_criterion(p)
print('Criterion is {}'.format(criterion.__class__.__name__))
criterion = criterion.cuda()
# Optimizer and scheduler
print(colored('Retrieve optimizer', 'blue'))
optimizer = get_optimizer(p, model)
print(optimizer)
# Checkpoint
if os.path.exists(p['pretext_checkpoint']):
print(colored('Restart from checkpoint {}'.format(p['pretext_checkpoint']), 'blue'))
checkpoint = torch.load(p['pretext_checkpoint'], map_location='cpu')
optimizer.load_state_dict(checkpoint['optimizer'])
model.load_state_dict(checkpoint['model'])
model.cuda()
start_epoch = checkpoint['epoch']
else:
print(colored('No checkpoint file at {}'.format(p['pretext_checkpoint']), 'blue'))
start_epoch = 0
model = model.cuda()
# Training
print(colored('Starting main loop', 'blue'))
for epoch in range(start_epoch, p['epochs']):
print(colored('Epoch %d/%d' %(epoch, p['epochs']), 'yellow'))
print(colored('-'*15, 'yellow'))
# Adjust lr
lr = adjust_learning_rate(p, optimizer, epoch)
print('Adjusted learning rate to {:.5f}'.format(lr))
# Train
print('Train ...')
simclr_train(train_dataloader, model, criterion, optimizer, epoch)
# Fill memory bank
print('Fill memory bank for kNN...')
fill_memory_bank(base_dataloader, model, memory_bank_base)
# Evaluate (To monitor progress - Not for validation)
print('Evaluate ...')
top1 = contrastive_evaluate(val_dataloader, model, memory_bank_base)
print('Result of kNN evaluation is %.2f' %(top1))
# Checkpoint
print('Checkpoint ...')
torch.save({'optimizer': optimizer.state_dict(), 'model': model.state_dict(),
'epoch': epoch + 1}, p['pretext_checkpoint'])
if epoch in [50, 75]:
# Save final model
# torch.save(model.state_dict(), p['pretext_model'])
# Mine the topk nearest neighbors at the very end (Train)
# These will be served as input to the SCAN loss.
print(colored('Fill memory bank for mining the nearest neighbors (train) ...', 'blue'))
fill_memory_bank(base_dataloader, model, memory_bank_base)
topk = 20
print('Mine the nearest neighbors (Top-%d)' %(topk))
indices, acc = memory_bank_base.mine_nearest_neighbors(topk)
print('Accuracy of top-%d nearest neighbors on train set is %.2f' %(topk, 100*acc))
# np.save(p['topk_neighbors_train_path'], indices)
# Mine the topk nearest neighbors at the very end (Val)
# These will be used for validation.
print(colored('Fill memory bank for mining the nearest neighbors (val) ...', 'blue'))
fill_memory_bank(val_dataloader, model, memory_bank_val)
topk = 5
print('Mine the nearest neighbors (Top-%d)' %(topk))
indices, acc = memory_bank_val.mine_nearest_neighbors(topk)
print('Accuracy of top-%d nearest neighbors on val set is %.2f' %(topk, 100*acc))
# np.save(p['topk_neighbors_val_path'], indices)
# Save final model
torch.save(model.state_dict(), p['pretext_model'])
# Mine the topk nearest neighbors at the very end (Train)
# These will be served as input to the SCAN loss.
print(colored('Fill memory bank for mining the nearest neighbors (train) ...', 'blue'))
fill_memory_bank(base_dataloader, model, memory_bank_base)
topk = 20
print('Mine the nearest neighbors (Top-%d)' %(topk))
indices, acc = memory_bank_base.mine_nearest_neighbors(topk)
print('Accuracy of top-%d nearest neighbors on train set is %.2f' %(topk, 100*acc))
np.save(p['topk_neighbors_train_path'], indices)
# Mine the topk nearest neighbors at the very end (Val)
# These will be used for validation.
print(colored('Fill memory bank for mining the nearest neighbors (val) ...', 'blue'))
fill_memory_bank(val_dataloader, model, memory_bank_val)
topk = 5
print('Mine the nearest neighbors (Top-%d)' %(topk))
indices, acc = memory_bank_val.mine_nearest_neighbors(topk)
print('Accuracy of top-%d nearest neighbors on val set is %.2f' %(topk, 100*acc))
np.save(p['topk_neighbors_val_path'], indices)
def main():
# Retrieve config file
p = create_config(args.config_env, args.config_exp)
print(colored(p, 'red'))
# Model
print(colored('Retrieve model', 'green'))
model = get_model(p)
print('Model is {}'.format(model.__class__.__name__))
print('Model parameters: {:.2f}M'.format(
sum(p.numel() for p in model.parameters()) / 1e6))
print(model)
model = model.to(device)
# CUDNN
print(colored('Set CuDNN benchmark', 'green'))
torch.backends.cudnn.benchmark = True
# Dataset
print(colored('Retrieve dataset', 'green'))
train_transforms = get_train_transformations(p)
print('Train transforms:', train_transforms)
val_transforms = get_val_transformations(p)
print('Validation transforms:', val_transforms)
train_dataset = get_train_dataset(p,
train_transforms,
to_augmented_dataset=True,
split='train') # Split is for stl-10
val_dataset = get_val_dataset(p, val_transforms)
train_dataloader = get_val_dataloader(p, train_dataset)
val_dataloader = get_val_dataloader(p, val_dataset)
print('Dataset contains {}/{} train/val samples'.format(
len(train_dataset), len(val_dataset)))
# Memory Bank
print(colored('Build MemoryBank', 'green'))
base_dataset = get_train_dataset(
p, val_transforms, split='train') # Dataset w/o augs for knn eval
base_dataloader = get_val_dataloader(p, base_dataset)
memory_bank_base = MemoryBank(len(base_dataset),
p['model_kwargs']['features_dim'],
p['num_classes'],
p['criterion_kwargs']['temperature'])
memory_bank_base.to(device)
memory_bank_val = MemoryBank(len(val_dataset),
p['model_kwargs']['features_dim'],
p['num_classes'],
p['criterion_kwargs']['temperature'])
memory_bank_val.to(device)
# Checkpoint
if os.path.exists(p['pretext_checkpoint']):
print(
colored(
'Restart from checkpoint {}'.format(p['pretext_checkpoint']),
'green'))
checkpoint = torch.load(p['pretext_checkpoint'], map_location='cpu')
# optimizer.load_state_dict(checkpoint['optimizer'])
model.load_state_dict(checkpoint['model'])
model.to(device)
# start_epoch = checkpoint['epoch']
else:
print(
colored('No checkpoint file at {}'.format(p['pretext_checkpoint']),
'green'))
start_epoch = 0
model = model.to(device)
# # Training
# print(colored('Starting main loop', 'green'))
# with torch.no_grad():
# model.eval()
# total_top1, total_top5, total_num, feature_bank = 0.0, 0.0, 0, []
#
# # progress_bar = tqdm(train_dataloader)
# for idx, batch in enumerate(train_dataloader):
# images = batch['image'].to(device, non_blocking=True)
# # target = batch['target'].to(device, non_blocking=True)
#
# output = model(images)
# feature = F.normalize(output, dim=1)
# feature_bank.append(feature)
#
# if idx % 25 == 0:
# print("Feature bank buidling : {} / {}".format(idx, len(train_dataset)/p["batch_size"]))
#
# # [D, N]
# feature_bank = torch.cat(feature_bank, dim=0).t().contiguous()
# print(colored("Feature bank created. Similarity index starts now", "green"))
# print(feature_bank.size())
#
# for idx, batch in enumerate(train_dataloader):
#
# images = batch['image'].to(device, non_blocking=True)
# # target = batch['target'].to(device, non_blocking=True)
#
# output = model(images)
# feature = F.normalize(output, dim=1)
#
# sim_indices = knn_predict(feature, feature_bank, "", "", 10, 0.1)
#
# print(sim_indices)
#
# if idx == 10:
# break
# # Mine the topk nearest neighbors at the very end (Train)
# # These will be served as input to the SCAN loss.
# print(colored('Fill memory bank for mining the nearest neighbors (train) ...', 'green'))
# fill_memory_bank(base_dataloader, model, memory_bank_base)
# topk = 20
# print('Mine the nearest neighbors (Top-%d)' %(topk))
# indices, acc = memory_bank_base.mine_nearest_neighbors(topk)
# print('Accuracy of top-%d nearest neighbors on train set is %.2f' %(topk, 100*acc))
# np.save(p['topk_neighbors_train_path'], indices)
# Mine the topk nearest neighbors at the very end (Val)
# These will be used for validation.
print(
colored('Fill memory bank for mining the nearest neighbors (val) ...',
'green'))
fill_memory_bank(val_dataloader, model, memory_bank_val)
topk = 5
print('Mine the nearest neighbors (Top-%d)' % (topk))
indices, acc = memory_bank_val.mine_nearest_neighbors(topk)
print('Accuracy of top-%d nearest neighbors on val set is %.2f' %
(topk, 100 * acc))
np.save(p['topk_neighbors_val_path'], indices)
def main():
# Retrieve config file
p = create_config(args.config_env, args.config_exp)
print(colored(p, 'red'))
# Model
print(colored('Retrieve model', 'blue'))
model = get_model(p)
print('Model is {}'.format(model.__class__.__name__))
print('Model parameters: {:.2f}M'.format(
sum(p.numel() for p in model.parameters()) / 1e6))
print(model)
model = model.cuda()
# CUDNN
print(colored('Set CuDNN benchmark', 'blue'))
torch.backends.cudnn.benchmark = True
# Dataset
val_transforms = get_val_transformations(p)
print('Validation transforms:', val_transforms)
val_dataset = get_val_dataset(p, val_transforms)
val_dataloader = get_val_dataloader(p, val_dataset)
print('Dataset contains {} val samples'.format(len(val_dataset)))
# Memory Bank
print(colored('Build MemoryBank', 'blue'))
base_dataset = get_train_dataset(
p, val_transforms, split='train') # Dataset w/o augs for knn eval
base_dataloader = get_val_dataloader(p, base_dataset)
memory_bank_base = MemoryBank(len(base_dataset),
p['model_kwargs']['features_dim'],
p['num_classes'],
p['criterion_kwargs']['temperature'])
memory_bank_base.cuda()
memory_bank_val = MemoryBank(len(val_dataset),
p['model_kwargs']['features_dim'],
p['num_classes'],
p['criterion_kwargs']['temperature'])
memory_bank_val.cuda()
# Checkpoint
assert os.path.exists(p['pretext_checkpoint'])
print(
colored('Restart from checkpoint {}'.format(p['pretext_checkpoint']),
'blue'))
checkpoint = torch.load(p['pretext_checkpoint'], map_location='cpu')
model.load_state_dict(checkpoint)
model.cuda()
# Save model
torch.save(model.state_dict(), p['pretext_model'])
# Mine the topk nearest neighbors at the very end (Train)
# These will be served as input to the SCAN loss.
print(
colored(
'Fill memory bank for mining the nearest neighbors (train) ...',
'blue'))
fill_memory_bank(base_dataloader, model, memory_bank_base)
topk = 20
print('Mine the nearest neighbors (Top-%d)' % (topk))
indices, acc = memory_bank_base.mine_nearest_neighbors(topk)
print('Accuracy of top-%d nearest neighbors on train set is %.2f' %
(topk, 100 * acc))
np.save(p['topk_neighbors_train_path'], indices)
# Mine the topk nearest neighbors at the very end (Val)
# These will be used for validation.
print(
colored('Fill memory bank for mining the nearest neighbors (val) ...',
'blue'))
fill_memory_bank(val_dataloader, model, memory_bank_val)
topk = 5
print('Mine the nearest neighbors (Top-%d)' % (topk))
indices, acc = memory_bank_val.mine_nearest_neighbors(topk)
print('Accuracy of top-%d nearest neighbors on val set is %.2f' %
(topk, 100 * acc))
np.save(p['topk_neighbors_val_path'], indices)
def main():
# Retrieve config file
p = create_config(args.config_env, args.config_exp)
print(colored(p, 'red'))
# Model
print(colored('Retrieve model', 'green'))
model = get_model(p)
print('Model is {}'.format(model.__class__.__name__))
print(model)
# model = torch.nn.DataParallel(model)
model = model.to(device)
# CUDNN
print(colored('Set CuDNN benchmark', 'green'))
torch.backends.cudnn.benchmark = True
# Dataset
print(colored('Retrieve dataset', 'green'))
transforms = get_val_transformations(p)
train_dataset = get_train_dataset(p, transforms)
val_dataset = get_val_dataset(p, transforms)
train_dataloader = get_val_dataloader(p, train_dataset)
val_dataloader = get_val_dataloader(p, val_dataset)
print('Dataset contains {}/{} train/val samples'.format(len(train_dataset), len(val_dataset)))
# Memory Bank
print(colored('Build MemoryBank', 'green'))
memory_bank_train = MemoryBank(len(train_dataset), 2048, p['num_classes'], p['temperature'])
memory_bank_train.to(device)
memory_bank_val = MemoryBank(len(val_dataset), 2048, p['num_classes'], p['temperature'])
memory_bank_val.to(device)
# Load the official MoCoV2 checkpoint
print(colored('Downloading moco v2 checkpoint', 'green'))
# os.system('wget -L https://dl.fbaipublicfiles.com/moco/moco_checkpoints/moco_v2_800ep/moco_v2_800ep_pretrain.pth.tar')
# Uploaded the model to Mist : Johan
moco_state = torch.load(main_dir + model_dir + 'moco_v2_800ep_pretrain.pth.tar', map_location=device)
# Transfer moco weights
print(colored('Transfer MoCo weights to model', 'green'))
new_state_dict = {}
state_dict = moco_state['state_dict']
# for k in list(state_dict.keys()):
# # Copy backbone weights
# if k.startswith('module.encoder_q') and not k.startswith('module.encoder_q.fc'):
# new_k = 'module.backbone.' + k[len('module.encoder_q.'):]
# new_state_dict[new_k] = state_dict[k]
#
# # Copy mlp weights
# elif k.startswith('module.encoder_q.fc'):
# new_k = 'module.contrastive_head.' + k[len('module.encoder_q.fc.'):]
# new_state_dict[new_k] = state_dict[k]
#
# else:
# raise ValueError('Unexpected key {}'.format(k))
#Changed by Johan
for k, v in state_dict.items():
if "conv" in k or "bn" in k or "layer" in k:
new_k = "backbone." + k.split("module.encoder_q.")[1]
new_state_dict[new_k] = v
else:
new_k = "contrastive_head." + k.split("module.encoder_q.fc.")[1]
new_state_dict[new_k] = v
model.load_state_dict(new_state_dict)
# os.system('rm -rf moco_v2_800ep_pretrain.pth.tar')
# Save final model
print(colored('Save pretext model', 'green'))
torch.save(model.state_dict(), p['pretext_model'])
# model.contrastive_head = torch.nn.Identity() # In this case, we mine the neighbors before the MLP.
model.contrastive_head = Identity()
def main():
# Read config file
print(colored('Read config file {} ...'.format(args.config_exp), 'blue'))
with open(args.config_exp, 'r') as stream:
config = yaml.safe_load(stream)
config[
'batch_size'] = 512 # To make sure we can evaluate on a single 1080ti
print(config)
# Get dataset
print(colored('Get validation dataset ...', 'blue'))
transforms = get_val_transformations(config)
dataset = get_val_dataset(config, transforms)
dataloader = get_val_dataloader(config, dataset)
print('Number of samples: {}'.format(len(dataset)))
# Get model
print(colored('Get model ...', 'blue'))
model = get_model(config)
print(model)
# Read model weights
print(colored('Load model weights ...', 'blue'))
state_dict = torch.load(args.model, map_location='cpu')
if config['setup'] in ['simclr', 'moco', 'selflabel']:
model.load_state_dict(state_dict)
elif config['setup'] == 'scan':
model.load_state_dict(state_dict['model'])
else:
raise NotImplementedError
# CUDA
model.cuda()
# Perform evaluation
if config['setup'] in ['simclr', 'moco']:
print(
colored(
'Perform evaluation of the pretext task (setup={}).'.format(
config['setup']), 'blue'))
print('Create Memory Bank')
if config['setup'] == 'simclr': # Mine neighbors after MLP
memory_bank = MemoryBank(len(dataset),
config['model_kwargs']['features_dim'],
config['num_classes'],
config['criterion_kwargs']['temperature'])
else: # Mine neighbors before MLP
memory_bank = MemoryBank(len(dataset),
config['model_kwargs']['features_dim'],
config['num_classes'],
config['temperature'])
memory_bank.cuda()
print('Fill Memory Bank')
fill_memory_bank(dataloader, model, memory_bank)
print('Mine the nearest neighbors')
for topk in [1, 5, 20]: # Similar to Fig 2 in paper
_, acc = memory_bank.mine_nearest_neighbors(topk)
print(
'Accuracy of top-{} nearest neighbors on validation set is {:.2f}'
.format(topk, 100 * acc))
elif config['setup'] in ['scan', 'selflabel']:
print(
colored(
'Perform evaluation of the clustering model (setup={}).'.
format(config['setup']), 'blue'))
head = state_dict['head'] if config['setup'] == 'scan' else 0
predictions, features = get_predictions(config,
dataloader,
model,
return_features=True)
clustering_stats = hungarian_evaluate(head,
predictions,
dataset.classes,
compute_confusion_matrix=True)
print(clustering_stats)
if args.visualize_prototypes:
prototype_indices = get_prototypes(config, predictions[head],
features, model)
visualize_indices(prototype_indices, dataset,
clustering_stats['hungarian_match'])
else:
raise NotImplementedError