def evaluate(args, model, eval_dataloader, metrics):
# Eval!
logger.info(" Num examples = %d", len(eval_dataloader))
logger.info(" Batch size = %d", args.eval_batch_size)
eval_loss = AverageMeter()
metrics.reset()
preds = []
targets = []
pbar = ProgressBar(n_total=len(eval_dataloader), desc='Evaluating')
for bid, batch in enumerate(eval_dataloader):
model.eval()
batch = tuple(t.to(args.device) for t in batch)
with torch.no_grad():
inputs = {'input_ids': batch[0],
'attention_mask': batch[1],
'labels': batch[3]}
inputs['token_type_ids'] = batch[2]
outputs = model(**inputs)
loss, logits = outputs[:2]
eval_loss.update(loss.item(), n=1)
preds.append(logits.cpu().detach())
targets.append(inputs['labels'].cpu().detach())
pbar(bid)
preds = torch.cat(preds, dim=0).cpu().detach()
targets = torch.cat(targets, dim=0).cpu().detach()
metrics(preds, targets)
eval_log = {"eval_acc": metrics.value(),
'eval_loss': eval_loss.avg}
return eval_log
def init_losses(self):
# 5个损失:总损失,位置损失,置信度损失,类别损失,各检测头的损失
# 总损失是位置损失,置信度损失,类别损失3个之和,各损失均是一段时间的平均损失
# 各检测头的损失按顺序分别是32,16,8处的
self.losses = {
'loss': AverageMeter(),
'giou_loss': AverageMeter(),
'conf_loss': AverageMeter(),
'class_loss': AverageMeter(),
'loss_per_branch': [AverageMeter() for _ in range(3)],
}
def eval(val_loader, model, criterion, device, out_file):
'''
Run evaluation
'''
losses = AverageMeter()
accs = AverageMeter()
# switch to eval mode
model.eval()
with torch.no_grad():
for i, (x, target) in enumerate(val_loader):
x = x.to(device)
target = target.to(device)
# Forward pass
logits = model(x)
loss = criterion(logits, target)
# measure accuracy and record loss
acc = accuracy_topk(logits.data, target)
accs.update(acc.item(), x.size(0))
losses.update(loss.item(), x.size(0))
text = '\n Test\t Loss ({loss.avg:.4f})\t Accuracy ({prec.avg:.3f})\n'.format(
loss=losses, prec=accs)
print(text)
with open(out_file, 'a') as f:
f.write(text)
def validate(val_loader, backbone, model, acc_prefixes, args):
batch_time = AverageMeter('Time', ':.3f')
# switch to evaluate mode
model.eval()
# TODO: Aniruddha
pred_var_stack, labels_var_stack = [torch.Tensor()]*5, torch.Tensor()
with torch.no_grad():
end = time.time()
for i, (images, target) in enumerate(val_loader):
images = images.cuda(non_blocking=True)
target = target.cuda(non_blocking=True)
# compute output
features = backbone(images)
outputs = model(features)
if not i:
acc_meters = [
NoBatchAverageMeter('', ':11.2f')
for i in range(len(outputs))
]
progress = NoTabProgressMeter(
len(val_loader),
[batch_time, *acc_meters],
prefix='Test: ')
# measure accuracy
for output, acc_meter in zip(outputs, acc_meters):
acc1, _ = accuracy(output, target, topk=(1, 5))
acc_meter.update(acc1[0], images.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0 or i == len(val_loader)-1:
line = progress.display(i)
len_prefixes = len(acc_prefixes) * len(acc_prefixes[0])
prefix_line = ' ' * (len(line) - len_prefixes)
prefix_line += ''.join(acc_prefixes)
logger.info(prefix_line)
logger.info(line)
for layer_id in range(5):
pred_var_stack[layer_id] = torch.cat((pred_var_stack[layer_id], outputs[layer_id].cpu()), dim=0)
labels_var_stack = torch.cat((labels_var_stack, target.cpu()), dim=0)
return acc_meters, pred_var_stack, labels_var_stack
def train(train_loader):
pbar = ProgressBar(n_batch=len(train_loader))
train_loss = AverageMeter()
train_acc = AverageMeter()
count = 0
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output, loss = model(data, y=target, loss_fn=nn.CrossEntropyLoss())
pred = output.argmax(
dim=1, keepdim=True) # get the index of the max log-probability
correct = pred.eq(target.view_as(pred)).sum().item()
loss.backward()
optimizer.step()
count += data.size(0)
train_acc.update(correct, n=1)
pbar.batch_step(batch_idx=batch_idx,
info={
'loss': loss.item(),
'acc': correct / data.size(0)
},
bar_type='Training')
train_loss.update(loss.item(), n=1)
print(' ')
return {'loss': train_loss.avg, 'acc': train_acc.sum / count}
def validation(opt, val_loader, model, epoch):
# average meters to record the training statistics
batch_time = AverageMeter()
data_time = AverageMeter()
train_logger = LogCollector()
model.val_start()
end = time.time()
loss = 0
for i, val_data in enumerate(val_loader):
data_time.update(time.time() - end)
model.logger = train_logger
loss = (model.val_forward(*val_data) + loss * i) / (i + 1)
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
# Print log info
logging.info('Epoch: [{0}]\t'
'{e_log}\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'.format(
epoch,
batch_time=batch_time,
data_time=data_time,
e_log=str(model.logger)))
return loss
def train(train_loader):
pbar = ProgressBar(n_total=len(train_loader), desc='Training')
train_loss = AverageMeter()
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = model(data)
loss = loss_fn(output, target)
loss.backward()
optimizer.step()
pbar(step=batch_idx, info={'loss': loss.item()})
train_loss.update(loss.item(), n=1)
return {'loss': train_loss.avg}
def train(train_loader, lr_scheduler=None):
pbar = ProgressBar(n_batch=len(train_loader))
train_loss = AverageMeter()
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = model(data)
loss = loss_fn(output, target)
loss.backward()
optimizer.step()
if lr_scheduler is not None:
lr_scheduler.step()
pbar.batch_step(batch_idx=batch_idx,
info={'loss': loss.item()},
bar_type='Training')
train_loss.update(loss.item(), n=1)
return {'loss': train_loss.avg}
def train(dataloader):
pbar = ProgressBar(n_total=len(dataloader), desc='Training')
train_loss = AverageMeter()
model.train()
for batch_idx, batch in enumerate(dataloader):
b_features, b_target, b_idx = batch['features'].to(
DEVICE), batch['target'].to(DEVICE), batch['idx'].to(DEVICE)
optimizer.zero_grad()
with autocast():
logits, probs = model(b_features)
loss = F.cross_entropy(logits, b_target)
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
scheduler.step()
pbar(step=batch_idx, info={'loss': loss.item()})
train_loss.update(loss.item(), n=1)
return {'loss': train_loss.avg}
def eval(self, loader):
self.model.eval()
losses = AverageMeter()
correct = 0
with torch.no_grad():
pbar = ImProgressBar(len(loader))
for i, (imgs, targets) in enumerate(loader):
imgs, targets = imgs.cuda(), targets.cuda()
outputs = self.model(imgs)
_, predicted = torch.max(outputs.data, dim=1)
correct += (predicted == targets).sum().item()
loss = self.criterion(outputs, targets)
losses.update(loss.item(), 1)
pbar.update(i)
pbar.finish()
return losses.avg, correct / len(loader.dataset)
def train(dataloader):
pbar = ProgressBar(n_total=len(dataloader), desc='Training')
train_loss = AverageMeter()
for batch_idx, batch in enumerate(dataloader):
# forward
probas = model.forward(batch['features'])
# backward
grad_w, grad_b = model.backward(batch['features'], batch['target'],
probas)
# manual regularization -- account for mini-batches
l2_reg = model.LAMBDA * model.weights / len(dataloader)
# update weights
model.weights -= learning_rate * (grad_w + l2_reg)
model.bias -= learning_rate * grad_b
# record loss
loss = model._logit_cost(batch['target'], probas)
# update meter
train_loss.update(loss.item(), n=1)
# update progress bar
pbar(step=batch_idx, info={'batch_loss': loss.item()})
return {'train_loss': train_loss.avg}
def train(self):
self.model.train()
losses = AverageMeter()
correct = 0
pbar = ImProgressBar(len(self.train_loader))
for i, (imgs, targets) in enumerate(self.train_loader):
imgs, targets = imgs.cuda(), targets.cuda()
outputs = self.model(imgs)
_, predicted = torch.max(outputs.data, dim=1)
correct += (predicted == targets).sum().item()
loss = self.criterion(outputs, targets)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
losses.update(loss.item(), 1)
pbar.update(i)
pbar.finish()
return losses.avg, correct / len(self.train_loader.dataset)
def train(train_loader, backbone, model, optimizer, acc_prefixes, epoch, args):
batch_time = AverageMeter('B', ':.2f')
data_time = AverageMeter('D', ':.2f')
# switch to train mode
model.train()
end = time.time()
for i, (images, target) in enumerate(train_loader):
# measure data loading time
data_time.update(time.time() - end)
images = images.cuda(non_blocking=True)
target = target.cuda(non_blocking=True)
with torch.no_grad():
features = backbone(images)
outputs = model(features)
if not i:
acc_meters = [
NoBatchAverageMeter('', ':>11.2f')
for i in range(len(outputs))
]
progress = NoTabProgressMeter(
len(train_loader),
[batch_time, data_time, *acc_meters],
prefix="Epoch: [{}]".format(epoch))
# measure accuracy
optimizer.zero_grad()
for output, acc_meter in zip(outputs, acc_meters):
loss = F.cross_entropy(output, target)
loss.backward()
acc1, _ = accuracy(output, target, topk=(1, 5))
acc_meter.update(acc1[0], images.size(0))
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
line = progress.display(i)
len_prefixes = len(acc_prefixes) * len(acc_prefixes[0])
prefix_line = ' ' * (len(line) - len_prefixes)
prefix_line += ''.join(acc_prefixes)
logger.info(prefix_line)
logger.info(line)
def train(dataloader):
pbar = ProgressBar(n_total=len(dataloader), desc='Training')
train_loss = AverageMeter()
for batch_idx, batch in enumerate(dataloader):
# forward
y_hat = model.forward(batch['features'].float())
# backward
grad_w, grad_b = model.backward(batch['features'], batch['target'],
y_hat)
# manual regularization\
l2_reg = model.LAMBDA * model.weights
l2_reg = l2_reg.reshape(2, 1)
# update weights
model.weights -= learning_rate * (grad_w + l2_reg).view(-1)
model.bias -= (learning_rate * grad_b).view(-1)
# record loss
loss = model.loss(batch['target'], y_hat)
# update meter
train_loss.update(loss.item(), n=1)
# update progress bar
pbar(step=batch_idx, info={'batch_loss': loss.item()})
return {'train_loss': train_loss.avg}
def train(train_loader,
model,
criterion,
optimizer,
epoch,
device,
print_freq=25):
'''
Run one train epoch
'''
losses = AverageMeter()
# switch to train mode
model.train()
for i, (id, mask, target) in enumerate(train_loader):
id = id.to(device)
mask = mask.to(device)
target = target.to(device)
# Forward pass
logits = model(id, mask)
loss = criterion(logits, target)
# Backward pass and update
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure accuracy and record loss
losses.update(loss.item(), id.size(0))
if i % print_freq == 0:
print('Epoch: [{0}][{1}/{2}]\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})'.format(
epoch, i, len(train_loader), loss=losses))
def train(dataloader):
pbar = ProgressBar(n_total=len(dataloader), desc='Training')
train_loss = AverageMeter()
model.train()
for batch_idx, batch in enumerate(dataloader):
b_features, b_target, b_idx = batch['features'].to(
DEVICE), batch['target'].to(DEVICE), batch['idx'].to(DEVICE)
optimizer.zero_grad()
with autocast():
logits, probs = model(b_features)
loss = F.cross_entropy(logits, b_target)
# regularize loss -- but not the intercept
LAMBDA, L2 = 2, 0.
for name, p in model.named_parameters():
if 'weight' in name:
L2 = L2 + (p**2).sum()
loss = loss + 2. / b_target.size(0) * LAMBDA * L2
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
scheduler.step()
pbar(step=batch_idx, info={'loss': loss.item()})
train_loss.update(loss.item(), n=1)
return {'loss': train_loss.avg}
def eval(val_loader, model, criterion, device):
'''
Run evaluation
'''
losses = AverageMeter()
# switch to eval mode
model.eval()
with torch.no_grad():
for i, (id, mask, target) in enumerate(val_loader):
id = id.to(device)
mask = mask.to(device)
target = target.to(device)
# Forward pass
logits = model(id, mask)
loss = criterion(logits, target)
# measure accuracy and record loss
losses.update(loss.item(), id.size(0))
print('Test\t Loss ({loss.avg:.4f})\n'.format(loss=losses))
def train_epoch(dataset, keep_prob=0.5, batch_size=2048):
_accs = AverageMeter()
_losses = AverageMeter()
sampleNum = dataset.sample_number
for batch in tqdm(range(sampleNum // batch_size)):
X_batch, y_batch = dataset.next_batch(batch_size=batch_size)
feed_dict = {
X_input: X_batch,
y_input: y_batch,
keepProb: keep_prob,
batch_size: batch_size
}
fetches = [acc, loss, train_op]
_acc, _loss, _ = sess.run(fetches, feed_dict)
_accs.update(_acc)
_losses.update(_loss)
return _accs, _losses
def train(opt, train_loader, model, epoch):
# average meters to record the training statistics
batch_time = AverageMeter()
data_time = AverageMeter()
train_logger = LogCollector()
# switch to train mode
model.train_start()
end = time.time()
for i, train_data in enumerate(train_loader):
# measure data loading time
data_time.update(time.time() - end)
# make sure train logger is used
model.logger = train_logger
# Update the model
model.train_forward(*train_data)
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
# Print log info
if model.Eiters % opt.log_step == 0:
logging.info(
'Epoch: [{0}][{1}/{2}]\t'
'{e_log}\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'.format(
epoch,
i,
len(train_loader),
batch_time=batch_time,
data_time=data_time,
e_log=str(model.logger)))
# Record logs in tensorboard
tb_logger.log_value('epoch', epoch, step=model.Eiters)
tb_logger.log_value('step', i, step=model.Eiters)
tb_logger.log_value('batch_time', batch_time.val, step=model.Eiters)
tb_logger.log_value('data_time', data_time.val, step=model.Eiters)
model.logger.tb_log(tb_logger, step=model.Eiters)
def test(test_loader):
pbar = ProgressBar(n_batch=len(test_loader))
valid_loss = AverageMeter()
valid_acc = AverageMeter()
model.eval()
count = 0
with torch.no_grad():
for batch_idx, (data, target) in enumerate(test_loader):
data, target = data.to(device), target.to(device)
output, loss = model(data, y=target, loss_fn=nn.CrossEntropyLoss())
pred = output.argmax(
dim=1,
keepdim=True) # get the index of the max log-probability
correct = pred.eq(target.view_as(pred)).sum().item()
valid_loss.update(loss, n=data.size(0))
valid_acc.update(correct, n=1)
count += data.size(0)
pbar.batch_step(batch_idx=batch_idx, info={}, bar_type='Testing')
return {'valid_loss': valid_loss.avg, 'valid_acc': valid_acc.sum / count}
def test(test_loader):
pbar = ProgressBar(n_total=len(test_loader),desc='Testing')
valid_loss = AverageMeter()
valid_acc = AverageMeter()
model.eval()
count = 0
with torch.no_grad():
for batch_idx,(data, target) in enumerate(test_loader):
data, target = data.to(device), target.to(device)
output = model(data)
loss = loss_fn(output, target).item() # sum up batch loss
pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability
correct = pred.eq(target.view_as(pred)).sum().item()
valid_loss.update(loss,n = data.size(0))
valid_acc.update(correct, n=1)
count += data.size(0)
pbar(step=batch_idx)
return {'valid_loss':valid_loss.avg,
'valid_acc':valid_acc.sum /count}
def train(train_loader,
model,
criterion,
optimizer,
epoch,
device,
out_file,
print_freq=1):
'''
Run one train epoch
'''
losses = AverageMeter()
accs = AverageMeter()
# switch to train mode
model.train()
for i, (x, target) in enumerate(train_loader):
x = x.to(device)
target = target.to(device)
# Forward pass
logits = model(x)
loss = criterion(logits, target)
# Backward pass and update
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure accuracy and record loss
acc = accuracy_topk(logits.data, target)
accs.update(acc.item(), x.size(0))
losses.update(loss.item(), x.size(0))
if i % print_freq == 0:
text = '\n Epoch: [{0}][{1}/{2}]\t Loss {loss.val:.4f} ({loss.avg:.4f})\t Accuracy {prec.val:.3f} ({prec.avg:.3f})'.format(
epoch, i, len(train_loader), loss=losses, prec=accs)
print(text)
with open(out_file, 'a') as f:
f.write(text)
def test(dataloader):
pbar = ProgressBar(n_total=len(dataloader), desc='Testing')
valid_loss = AverageMeter()
valid_acc = AverageMeter()
count = 0
for batch_idx, batch in enumerate(dataloader):
# forward -- skip backward prop
probas = model.forward(batch['features'])
# record loss
loss = model._logit_cost(batch['target'], probas)
# get predictions
prediction = torch.where(probas > 0.5, torch.tensor(1, device=device),
torch.tensor(0, device=device)).view(-1)
# compare
correct = prediction.eq(batch['target']).sum().item()
valid_loss.update(loss.item(), n=batch['features'].size(0))
valid_acc.update(correct, n=1)
count += batch['features'].size(0)
pbar(step=batch_idx)
return {'valid_loss': valid_loss.avg, 'valid_acc': valid_acc.sum / count}
def main(args):
train_info = []
best_epoch = np.zeros(5)
for val_folder_index in range(5):
best_balance_acc = 0
whole_train_list = ['D8E6', '117E', '676F', 'E2D7', 'BE52']
val_WSI_list = whole_train_list[val_folder_index]
train_WSI_list = whole_train_list
train_WSI_list.pop(val_folder_index)
train_directory = '../data/finetune/1percent/'
valid_directory = '../data/finetune/1percent'
dataset = {}
dataset_train0 = datasets.ImageFolder(
root=train_directory + train_WSI_list[0],
transform=get_aug(train=False,
train_classifier=True,
**args.aug_kwargs))
dataset_train1 = datasets.ImageFolder(
root=train_directory + train_WSI_list[1],
transform=get_aug(train=False,
train_classifier=True,
**args.aug_kwargs))
dataset_train2 = datasets.ImageFolder(
root=train_directory + train_WSI_list[2],
transform=get_aug(train=False,
train_classifier=True,
**args.aug_kwargs))
dataset_train3 = datasets.ImageFolder(
root=train_directory + train_WSI_list[3],
transform=get_aug(train=False,
train_classifier=True,
**args.aug_kwargs))
dataset['valid'] = datasets.ImageFolder(
root=valid_directory + val_WSI_list,
transform=get_aug(train=False,
train_classifier=False,
**args.aug_kwargs))
dataset['train'] = data.ConcatDataset(
[dataset_train0, dataset_train1, dataset_train2, dataset_train3])
train_loader = torch.utils.data.DataLoader(
dataset=dataset['train'],
batch_size=args.eval.batch_size,
shuffle=True,
**args.dataloader_kwargs)
test_loader = torch.utils.data.DataLoader(
dataset=dataset['valid'],
batch_size=args.eval.batch_size,
shuffle=False,
**args.dataloader_kwargs)
model = get_backbone(args.model.backbone)
classifier = nn.Linear(in_features=model.output_dim,
out_features=9,
bias=True).to(args.device)
assert args.eval_from is not None
save_dict = torch.load(args.eval_from, map_location='cpu')
msg = model.load_state_dict(
{
k[9:]: v
for k, v in save_dict['state_dict'].items()
if k.startswith('backbone.')
},
strict=True)
# print(msg)
model = model.to(args.device)
model = torch.nn.DataParallel(model)
classifier = torch.nn.DataParallel(classifier)
# define optimizer
optimizer = get_optimizer(
args.eval.optimizer.name,
classifier,
lr=args.eval.base_lr * args.eval.batch_size / 256,
momentum=args.eval.optimizer.momentum,
weight_decay=args.eval.optimizer.weight_decay)
# define lr scheduler
lr_scheduler = LR_Scheduler(
optimizer,
args.eval.warmup_epochs,
args.eval.warmup_lr * args.eval.batch_size / 256,
args.eval.num_epochs,
args.eval.base_lr * args.eval.batch_size / 256,
args.eval.final_lr * args.eval.batch_size / 256,
len(train_loader),
)
loss_meter = AverageMeter(name='Loss')
acc_meter = AverageMeter(name='Accuracy')
# Start training
global_progress = tqdm(range(0, args.eval.num_epochs),
desc=f'Evaluating')
for epoch in global_progress:
loss_meter.reset()
model.eval()
classifier.train()
local_progress = tqdm(train_loader,
desc=f'Epoch {epoch}/{args.eval.num_epochs}',
disable=True)
for idx, (images, labels) in enumerate(local_progress):
classifier.zero_grad()
with torch.no_grad():
feature = model(images.to(args.device))
preds = classifier(feature)
loss = F.cross_entropy(preds, labels.to(args.device))
loss.backward()
optimizer.step()
loss_meter.update(loss.item())
lr = lr_scheduler.step()
local_progress.set_postfix({
'lr': lr,
"loss": loss_meter.val,
'loss_avg': loss_meter.avg
})
writer.add_scalar('Valid/Loss', loss_meter.avg, epoch)
writer.add_scalar('Valid/Lr', lr, epoch)
writer.flush()
PATH = 'checkpoint/exp_0228_triple_1percent/' + val_WSI_list + '/' + val_WSI_list + '_tunelinear_' + str(
epoch) + '.pth'
torch.save(classifier, PATH)
classifier.eval()
correct, total = 0, 0
acc_meter.reset()
pred_label_for_f1 = np.array([])
true_label_for_f1 = np.array([])
for idx, (images, labels) in enumerate(test_loader):
with torch.no_grad():
feature = model(images.to(args.device))
preds = classifier(feature).argmax(dim=1)
correct = (preds == labels.to(args.device)).sum().item()
preds_arr = preds.cpu().detach().numpy()
labels_arr = labels.cpu().detach().numpy()
pred_label_for_f1 = np.concatenate(
[pred_label_for_f1, preds_arr])
true_label_for_f1 = np.concatenate(
[true_label_for_f1, labels_arr])
acc_meter.update(correct / preds.shape[0])
f1 = f1_score(true_label_for_f1,
pred_label_for_f1,
average='macro')
balance_acc = balanced_accuracy_score(true_label_for_f1,
pred_label_for_f1)
print('Epoch: ', str(epoch),
f'Accuracy = {acc_meter.avg * 100:.2f}')
print('F1 score = ', f1, 'balance acc: ', balance_acc)
if balance_acc > best_balance_acc:
best_epoch[val_folder_index] = epoch
best_balance_acc = balance_acc
train_info.append([val_WSI_list, epoch, f1, balance_acc])
with open('checkpoint/exp_0228_triple_1percent/train_info.csv', 'w') as f:
# using csv.writer method from CSV package
write = csv.writer(f)
write.writerows(train_info)
print(best_epoch)
def train_step(self, epoch, trainloader):
"""Training step for each epoch.
Args:
epoch: current epoch
trainloader: dataloader for train set
Return:
None
"""
self.model.train()
epoch_loss = AverageMeter()
batch_time = AverageMeter()
data_time = AverageMeter()
metrics_meter = dict()
for k in self.measures.train.keys():
metrics_meter[k] = AverageMeter()
bar = Bar('Processing', max=len(trainloader))
end = time.time()
for batch_idx, (data, targets) in enumerate(trainloader):
data_time.update(time.time() - end)
if self.config.cuda:
data, targets = data.cuda(), targets.cuda()
preds = self.model(data)
loss = self.model.loss_function(preds, targets)
# backward
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
# calculate measure statistics
batch_measure = dict()
for k, func in self.metrics_func.items():
if k.startswith('top'):
batch_measure[k] = func(preds, targets)[0]
else:
batch_measure[k] = func(preds, targets)
if isinstance(batch_measure[k], t.autograd.Variable):
batch_measure[k] = batch_measure[k].item()
metrics_meter[k].update(batch_measure[k], data.size(0))
# record statistics
self.stats.batch_loss.append(loss.item())
epoch_loss.update(loss.item())
batch_time.update(time.time() - end)
end = time.time()
#plot progress
measure_bar = ' | '.join(
['%s : %.4f' % (k, v.avg) for k, v in metrics_meter.items()])
bar.suffix = '({batch}/{size}) Data: {data:.3f}s | Batch: {bt:.3f}s | Total: {total:} | ETA: {eta:} | Loss: {loss:.4f} | '.format(
batch=batch_idx + 1,
size=len(trainloader),
data=data_time.avg,
bt=batch_time.avg,
total=bar.elapsed_td,
eta=bar.eta_td,
loss=epoch_loss.avg) + measure_bar
bar.next()
bar.finish()
for k in metrics_meter.keys():
self.measures.val[k] = metrics_meter[k].avg
# plot on tensorboard
'''
for k, v in metrics_meter.items():
self.metrics[k].append(v.avg)
log_value('train %s' % k, v.avg, epoch)
'''
self.stats.train_epoch_loss.append(epoch_loss.avg)
# log_value('epoch_loss', epoch_loss.avg, epoch)
logger.info(
('%02i - ' % (epoch + 1)) +
' / '.join(['train loss %.5f' % epoch_loss.avg] +
[k + ' %.5f' % v.avg
for k, v in metrics_meter.items()]))
def validate(self, epoch, valdataloader):
self.model.eval()
epoch_loss = AverageMeter()
batch_time = AverageMeter()
data_time = AverageMeter()
metrics_meter = dict()
for k in self.measures.val.keys():
metrics_meter[k] = AverageMeter()
end = time.time()
bar = Bar('Processing', max=len(valdataloader))
with t.no_grad():
for batch_idx, (data, targets) in enumerate(valdataloader):
data_time.update(time.time() - end)
if self.config.cuda:
data, targets = data.cuda(), targets.cuda()
preds = self.model(data)
loss = self.model.loss_function(preds, targets)
# calculate measure statistics
batch_measure = dict()
for k, func in self.metrics_func.items():
if k.startswith('top'):
batch_measure[k] = func(preds, targets)[0]
else:
batch_measure[k] = func(preds, targets)
if isinstance(batch_measure[k], t.autograd.Variable):
batch_measure[k] = batch_measure[k].item()
metrics_meter[k].update(batch_measure[k], data.size(0))
self.stats.batch_loss.append(loss.item())
epoch_loss.update(loss.item())
batch_time.update(time.time() - end)
end = time.time()
#plot progress
measure_bar = ' | '.join([
'%s : %.4f' % (k, v.avg) for k, v in metrics_meter.items()
])
bar.suffix = '({batch}/{size}) Data: {data:.3f}s | Batch: {bt:.3f}s | Total: {total:} | ETA: {eta:} | Loss: {loss:.4f} | '.format(
batch=batch_idx + 1,
size=len(valdataloader),
data=data_time.avg,
bt=batch_time.avg,
total=bar.elapsed_td,
eta=bar.eta_td,
loss=epoch_loss.avg) + measure_bar
bar.next()
bar.finish()
for k in metrics_meter.keys():
self.measures.val[k] = metrics_meter[k]
self.stats.eval_epoch_loss.append(epoch_loss.avg)
#log_value('val loss', epoch_loss.avg, epoch)
#for k, v in metrics_meter.items():
# self.metrics[k].append(v.avg)
# log_value('val %s' % k, v.avg, epoch)
to_log = dict([('epoch', epoch)] + [(k, v.avg)
for k, v in metrics_meter.items()])
logger.debug("__log__:%s" % json.dumps(to_log))
return to_log
def main(args):
train_set = get_dataset(
args.dataset,
args.data_dir,
transform=get_aug(args.model, args.image_size, train=False, train_classifier=True),
train=True,
download=args.download, # default is False
debug_subset_size=args.batch_size if args.debug else None
)
test_set = get_dataset(
args.dataset,
args.data_dir,
transform=get_aug(args.model, args.image_size, train=False, train_classifier=False),
train=False,
download=args.download, # default is False
debug_subset_size=args.batch_size if args.debug else None
)
train_loader = torch.utils.data.DataLoader(
dataset=train_set,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True,
drop_last=True
)
test_loader = torch.utils.data.DataLoader(
dataset=test_set,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
pin_memory=True,
drop_last=True
)
model = get_backbone(args.backbone)
classifier = nn.Linear(in_features=model.output_dim, out_features=10, bias=True).to(args.device)
assert args.eval_from is not None
save_dict = torch.load(args.eval_from, map_location='cpu')
msg = model.load_state_dict({k[9:]:v for k, v in save_dict['state_dict'].items() if k.startswith('backbone.')}, strict=True)
# print(msg)
model = model.to(args.device)
model = torch.nn.DataParallel(model)
# if torch.cuda.device_count() > 1: classifier = torch.nn.SyncBatchNorm.convert_sync_batchnorm(classifier)
classifier = torch.nn.DataParallel(classifier)
# define optimizer
optimizer = get_optimizer(
args.optimizer, classifier,
lr=args.base_lr*args.batch_size/256,
momentum=args.momentum,
weight_decay=args.weight_decay)
# define lr scheduler
lr_scheduler = LR_Scheduler(
optimizer,
args.warmup_epochs, args.warmup_lr*args.batch_size/256,
args.num_epochs, args.base_lr*args.batch_size/256, args.final_lr*args.batch_size/256,
len(train_loader),
)
loss_meter = AverageMeter(name='Loss')
acc_meter = AverageMeter(name='Accuracy')
# Start training
global_progress = tqdm(range(0, args.num_epochs), desc=f'Evaluating')
for epoch in global_progress:
loss_meter.reset()
model.eval()
classifier.train()
local_progress = tqdm(train_loader, desc=f'Epoch {epoch}/{args.num_epochs}', disable=args.hide_progress)
for idx, (images, labels) in enumerate(local_progress):
classifier.zero_grad()
with torch.no_grad():
feature = model(images.to(args.device))
preds = classifier(feature)
loss = F.cross_entropy(preds, labels.to(args.device))
loss.backward()
optimizer.step()
loss_meter.update(loss.item())
lr = lr_scheduler.step()
local_progress.set_postfix({'lr':lr, "loss":loss_meter.val, 'loss_avg':loss_meter.avg})
if args.head_tail_accuracy and epoch != 0 and (epoch+1) != args.num_epochs: continue
local_progress=tqdm(test_loader, desc=f'Test {epoch}/{args.num_epochs}', disable=args.hide_progress)
classifier.eval()
correct, total = 0, 0
acc_meter.reset()
for idx, (images, labels) in enumerate(local_progress):
with torch.no_grad():
feature = model(images.to(args.device))
preds = classifier(feature).argmax(dim=1)
correct = (preds == labels.to(args.device)).sum().item()
acc_meter.update(correct/preds.shape[0])
local_progress.set_postfix({'accuracy': acc_meter.avg})
global_progress.set_postfix({"epoch":epoch, 'accuracy':acc_meter.avg*100})
def main(args, model=None):
assert args.eval_from is not None or model is not None
train_set = get_dataset(
args.dataset,
args.data_dir,
transform=get_aug(args.model,
args.image_size,
train=False,
train_classifier=True),
train=True,
download=args.download, # default is False
debug_subset_size=args.batch_size
if args.debug else None # Use a subset of dataset for debugging.
)
test_set = get_dataset(
args.dataset,
args.data_dir,
transform=get_aug(args.model,
args.image_size,
train=False,
train_classifier=False),
train=False,
download=args.download, # default is False
debug_subset_size=args.batch_size if args.debug else None)
train_loader = torch.utils.data.DataLoader(dataset=train_set,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True,
drop_last=True)
test_loader = torch.utils.data.DataLoader(dataset=test_set,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
pin_memory=True,
drop_last=True)
model = get_backbone(args.backbone)
classifier = nn.Linear(in_features=model.output_dim,
out_features=len(train_set.classes),
bias=True).to(args.device)
if args.local_rank >= 0 and not torch.distributed.is_initialized():
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(backend="nccl",
init_method="env://")
if model is None:
model = get_backbone(args.backbone).to(args.device)
save_dict = torch.load(args.eval_from, map_location=args.device)
model.load_state_dict(
{
k[9:]: v
for k, v in save_dict['state_dict'].items()
if k.startswith('backbone.')
},
strict=True)
output_dim = model.output_dim
if args.local_rank >= 0:
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[args.local_rank],
output_device=args.local_rank,
find_unused_parameters=True)
classifier = nn.Linear(in_features=output_dim, out_features=10,
bias=True).to(args.device)
if args.local_rank >= 0:
classifier = torch.nn.parallel.DistributedDataParallel(
classifier,
device_ids=[args.local_rank],
output_device=args.local_rank)
# define optimizer
optimizer = get_optimizer(args.optimizer,
classifier,
lr=args.base_lr * args.batch_size / 256,
momentum=args.momentum,
weight_decay=args.weight_decay)
# TODO: linear lr warm up for byol simclr swav
# args.warm_up_epochs
# define lr scheduler
lr_scheduler = LR_Scheduler(optimizer, args.warmup_epochs,
args.warmup_lr * args.batch_size / 256,
args.num_epochs,
args.base_lr * args.batch_size / 256,
args.final_lr * args.batch_size / 256,
len(train_loader))
loss_meter = AverageMeter(name='Loss')
acc_meter = AverageMeter(name='Accuracy')
# Start training
global_progress = tqdm(range(0, args.num_epochs), desc=f'Evaluating')
for epoch in global_progress:
loss_meter.reset()
model.eval()
classifier.train()
local_progress = tqdm(train_loader,
desc=f'Epoch {epoch}/{args.num_epochs}',
disable=args.hide_progress)
for idx, (images, labels) in enumerate(local_progress):
classifier.zero_grad()
with torch.no_grad():
feature = model(images.to(args.device))
preds = classifier(feature)
loss = F.cross_entropy(preds, labels.to(args.device))
loss.backward()
optimizer.step()
loss_meter.update(loss.item())
lr = lr_scheduler.step()
local_progress.set_postfix({
'lr': lr,
"loss": loss_meter.val,
'loss_avg': loss_meter.avg
})
if args.head_tail_accuracy and epoch != 0 and (epoch +
1) != args.num_epochs:
continue
local_progress = tqdm(test_loader,
desc=f'Test {epoch}/{args.num_epochs}',
disable=args.hide_progress)
classifier.eval()
correct, total = 0, 0
acc_meter.reset()
for idx, (images, labels) in enumerate(local_progress):
with torch.no_grad():
feature = model(images.to(args.device))
preds = classifier(feature).argmax(dim=1)
correct = (preds == labels.to(args.device)).sum().item()
acc_meter.update(correct / preds.shape[0])
local_progress.set_postfix({'accuracy': acc_meter.avg})
global_progress.set_postfix({
"epoch": epoch,
'accuracy': acc_meter.avg * 100
})
def main(args):
train_set = get_dataset(
args.dataset,
args.data_dir,
transform=get_aug(args.model,
args.image_size,
train=False,
train_classifier=True),
train=True,
download=args.download # default is False
)
test_set = get_dataset(
args.dataset,
args.data_dir,
transform=get_aug(args.model,
args.image_size,
train=False,
train_classifier=False),
train=False,
download=args.download # default is False
)
if args.debug:
args.batch_size = 20
args.num_epochs = 2
args.num_workers = 0
train_set = torch.utils.data.Subset(train_set, range(
0, args.batch_size)) # take only one batch
test_set = torch.utils.data.Subset(test_set, range(0, args.batch_size))
train_loader = torch.utils.data.DataLoader(dataset=train_set,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True,
drop_last=True)
test_loader = torch.utils.data.DataLoader(dataset=train_set,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
pin_memory=True,
drop_last=True)
# define model
# model = get_model(args.model, args.backbone)
backbone = get_backbone(args.backbone, castrate=False)
in_features = backbone.fc.in_features
backbone.fc = nn.Identity()
model = backbone
assert args.eval_from is not None
save_dict = torch.load(args.eval_from, map_location='cpu')
msg = model.load_state_dict(
{
k[9:]: v
for k, v in save_dict['state_dict'].items()
if k.startswith('backbone.')
},
strict=True)
print(msg)
model = model.to(args.device)
model = torch.nn.DataParallel(model)
# if torch.cuda.device_count() > 1: model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
classifier = nn.Linear(in_features=in_features, out_features=10,
bias=True).to(args.device)
classifier = torch.nn.DataParallel(classifier)
# breakpoint()
# define optimizer
optimizer = get_optimizer(args.optimizer,
classifier,
lr=args.base_lr * args.batch_size / 256,
momentum=args.momentum,
weight_decay=args.weight_decay)
# TODO: linear lr warm up for byol simclr swav
# args.warm_up_epochs
# define lr scheduler
lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer,
args.num_epochs,
eta_min=0)
loss_meter = AverageMeter(name='Loss')
acc_meter = AverageMeter(name='Accuracy')
# Start training
for epoch in tqdm(range(0, args.num_epochs), desc=f'Evaluating'):
loss_meter.reset()
model.eval()
classifier.train()
p_bar = tqdm(train_loader, desc=f'Epoch {epoch}/{args.num_epochs}')
for idx, (images, labels) in enumerate(p_bar):
# breakpoint()
classifier.zero_grad()
with torch.no_grad():
feature = model(images.to(args.device))
# breakpoint()
preds = classifier(feature)
loss = F.cross_entropy(preds, labels.to(args.device))
# loss = model.forward(images1.to(args.device), images2.to(args.device))
loss.backward()
optimizer.step()
loss_meter.update(loss.item())
p_bar.set_postfix({
"loss": loss_meter.val,
'loss_avg': loss_meter.avg
})
lr_scheduler.step()
p_bar = tqdm(test_loader, desc=f'Test {epoch}/{args.num_epochs}')
classifier.eval()
correct, total = 0, 0
acc_meter.reset()
for idx, (images, labels) in enumerate(p_bar):
with torch.no_grad():
feature = model(images.to(args.device))
preds = classifier(feature).argmax(dim=1)
correct = (preds == labels.to(args.device)).sum().item()
acc_meter.update(correct / preds.shape[0])
p_bar.set_postfix({'accuracy': acc_meter.avg})
def main(args):
train_loader = torch.utils.data.DataLoader(dataset=get_dataset(
transform=get_aug(train=False,
train_classifier=True,
**args.aug_kwargs),
train=True,
**args.dataset_kwargs),
batch_size=args.eval.batch_size,
shuffle=True,
**args.dataloader_kwargs)
test_loader = torch.utils.data.DataLoader(dataset=get_dataset(
transform=get_aug(train=False,
train_classifier=False,
**args.aug_kwargs),
train=False,
**args.dataset_kwargs),
batch_size=args.eval.batch_size,
shuffle=False,
**args.dataloader_kwargs)
model = get_backbone(args.model.backbone)
classifier = nn.Linear(in_features=model.output_dim,
out_features=10,
bias=True).to(args.device)
assert args.eval_from is not None
save_dict = torch.load(args.eval_from, map_location='cpu')
msg = model.load_state_dict(
{
k[9:]: v
for k, v in save_dict['state_dict'].items()
if k.startswith('backbone.')
},
strict=True)
# print(msg)
model = model.to(args.device)
model = torch.nn.DataParallel(model)
# if torch.cuda.device_count() > 1: classifier = torch.nn.SyncBatchNorm.convert_sync_batchnorm(classifier)
classifier = torch.nn.DataParallel(classifier)
# define optimizer
optimizer = get_optimizer(args.eval.optimizer.name,
classifier,
lr=args.eval.base_lr * args.eval.batch_size /
256,
momentum=args.eval.optimizer.momentum,
weight_decay=args.eval.optimizer.weight_decay)
# define lr scheduler
lr_scheduler = LR_Scheduler(
optimizer,
args.eval.warmup_epochs,
args.eval.warmup_lr * args.eval.batch_size / 256,
args.eval.num_epochs,
args.eval.base_lr * args.eval.batch_size / 256,
args.eval.final_lr * args.eval.batch_size / 256,
len(train_loader),
)
loss_meter = AverageMeter(name='Loss')
acc_meter = AverageMeter(name='Accuracy')
# Start training
global_progress = tqdm(range(0, args.eval.num_epochs), desc=f'Evaluating')
for epoch in global_progress:
loss_meter.reset()
model.eval()
classifier.train()
local_progress = tqdm(train_loader,
desc=f'Epoch {epoch}/{args.eval.num_epochs}',
disable=True)
for idx, (images, labels) in enumerate(local_progress):
# this will take the images and stick them to one another using the batch dimension
# so it expects [C x H x W] and will turn each into a [1 x C x H x W] and then for N it will
# concatenate them into a big tensor of [N x C x H x W]
if type(images) == list:
print(images[1].shape, len(images))
images = torch.cat(
[image.unsqueeze(dim=0) for image in images], dim=0)
classifier.zero_grad()
with torch.no_grad():
feature = model(images.to(args.device))
preds = classifier(feature)
loss = F.cross_entropy(preds, labels.to(args.device))
loss.backward()
optimizer.step()
loss_meter.update(loss.item())
lr = lr_scheduler.step()
local_progress.set_postfix({
'lr': lr,
"loss": loss_meter.val,
'loss_avg': loss_meter.avg
})
classifier.eval()
correct, total = 0, 0
acc_meter.reset()
for idx, (images, labels) in enumerate(test_loader):
with torch.no_grad():
feature = model(images.to(args.device))
preds = classifier(feature).argmax(dim=1)
correct = (preds == labels.to(args.device)).sum().item()
acc_meter.update(correct / preds.shape[0])
print(f'Accuracy = {acc_meter.avg*100:.2f}')
