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from typing import Iterable, Optional
import torch
from timm.data import Mixup
from timm.utils import accuracy, ModelEma
from ..models.losses.criterion import DomainIndependentLoss, DomainDiscriminativeLoss
from ..evaluation.metrics import _eval, compute_preds_sum_out, compute_preds_conditional, compute_preds_sum_prob_w_prior_shift, get_metrics
from ..utils import logging_utils
def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
data_loader: Iterable, optimizer: torch.optim.Optimizer,
device: torch.device, epoch: int, loss_scaler, max_norm: float = 0,
model_ema: Optional[ModelEma] = None, mixup_fn: Optional[Mixup] = None, log_writer=None,
wandb_logger=None, start_steps=None, lr_schedule_values=None, wd_schedule_values=None,
num_training_steps_per_epoch=None, update_freq=None, use_amp=False):
model.train(True)
metric_logger = logging_utils.MetricLogger(delimiter=" ")
metric_logger.add_meter('lr', logging_utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
metric_logger.add_meter('min_lr', logging_utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
header = 'Epoch: [{}]'.format(epoch)
print_freq = 10
optimizer.zero_grad()
for data_iter_step, (samples, targets, groups) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
step = data_iter_step // update_freq
if step >= num_training_steps_per_epoch:
continue
it = start_steps + step # global training iteration
# Update LR & WD for the first acc
if lr_schedule_values is not None or wd_schedule_values is not None and data_iter_step % update_freq == 0:
for i, param_group in enumerate(optimizer.param_groups):
if lr_schedule_values is not None:
param_group["lr"] = lr_schedule_values[it] * param_group["lr_scale"]
if wd_schedule_values is not None and param_group["weight_decay"] > 0:
param_group["weight_decay"] = wd_schedule_values[it]
samples = samples.to(device, non_blocking=True)
targets = targets.to(device, non_blocking=True)
groups = groups.to(device, non_blocking=True)
if mixup_fn is not None:
samples, targets = mixup_fn(samples, targets)
if use_amp:
with torch.amp.autocast("cuda"):
output = model(samples)
if isinstance(criterion, DomainIndependentLoss):
loss = criterion(output, targets, groups)
else:
loss = criterion(output, targets)
else: # full precision
output = model(samples)
if isinstance(criterion, DomainIndependentLoss):
loss = criterion(output, targets, groups)
else:
loss = criterion(output, targets)
import pdb
pdb.set_trace()
loss_value = loss.item()
if not math.isfinite(loss_value): # this could trigger if using AMP
print("Loss is {}, stopping training".format(loss_value))
assert math.isfinite(loss_value)
if use_amp:
# this attribute is added by timm on one optimizer (adahessian)
is_second_order = hasattr(optimizer, 'is_second_order') and optimizer.is_second_order
loss /= update_freq
grad_norm = loss_scaler(loss, optimizer, clip_grad=max_norm,
parameters=model.parameters(), create_graph=is_second_order,
update_grad=(data_iter_step + 1) % update_freq == 0)
if (data_iter_step + 1) % update_freq == 0:
optimizer.zero_grad()
if model_ema is not None:
model_ema.update(model)
else: # full precision
loss /= update_freq
loss.backward()
if (data_iter_step + 1) % update_freq == 0:
optimizer.step()
optimizer.zero_grad()
if model_ema is not None:
model_ema.update(model)
if torch.cuda.is_available():
torch.cuda.synchronize()
if mixup_fn is None:
if isinstance(criterion, DomainIndependentLoss):
preds = compute_preds_sum_out(output, criterion.num_classes, criterion.num_domains)
class_acc = (preds == targets).float().sum() / targets.shape[0]
elif isinstance(criterion, DomainDiscriminativeLoss):
preds = compute_preds_sum_prob_w_prior_shift(output, criterion.num_classes, criterion.num_domains)
class_acc = (preds == targets).float().sum() / targets.shape[0]
else:
class_acc = (output.max(-1)[-1] == targets).float().mean()
else:
class_acc = None
metric_logger.update(loss=loss_value)
metric_logger.update(class_acc=class_acc)
min_lr = 10.
max_lr = 0.
for group in optimizer.param_groups:
min_lr = min(min_lr, group["lr"])
max_lr = max(max_lr, group["lr"])
metric_logger.update(lr=max_lr)
metric_logger.update(min_lr=min_lr)
weight_decay_value = None
for group in optimizer.param_groups:
if group["weight_decay"] > 0:
weight_decay_value = group["weight_decay"]
metric_logger.update(weight_decay=weight_decay_value)
if use_amp:
metric_logger.update(grad_norm=grad_norm)
if log_writer is not None:
log_writer.update(loss=loss_value, head="loss")
log_writer.update(class_acc=class_acc, head="loss")
log_writer.update(lr=max_lr, head="opt")
log_writer.update(min_lr=min_lr, head="opt")
log_writer.update(weight_decay=weight_decay_value, head="opt")
if use_amp:
log_writer.update(grad_norm=grad_norm, head="opt")
log_writer.set_step()
if wandb_logger:
wandb_logger._wandb.log({
'Rank-0 Batch Wise/train_loss': loss_value,
'Rank-0 Batch Wise/train_max_lr': max_lr,
'Rank-0 Batch Wise/train_min_lr': min_lr
}, commit=False)
if class_acc:
wandb_logger._wandb.log({'Rank-0 Batch Wise/train_class_acc': class_acc}, commit=False)
if use_amp:
wandb_logger._wandb.log({'Rank-0 Batch Wise/train_grad_norm': grad_norm}, commit=False)
wandb_logger._wandb.log({'Rank-0 Batch Wise/global_train_step': it})
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print("Averaged stats:", metric_logger)
return {k: meter.global_avg for k, meter in metric_logger.meters.items()}
@torch.no_grad()
def evaluate(data_loader, model, device, use_amp=False, criterion=None):
if criterion is None:
criterion = torch.nn.CrossEntropyLoss()
metric_logger = logging_utils.MetricLogger(delimiter=" ")
header = 'Test:'
y_true = []
y_pred = []
groups = []
# switch to evaluation mode
model.eval()
for batch in metric_logger.log_every(data_loader, 10, header):
images = batch[0]
target = batch[1]
group = batch[2]
images = images.to(device, non_blocking=True)
target = target.to(device, non_blocking=True)
group = group.to(device, non_blocking=True)
# compute output
if use_amp:
with torch.amp.autocast("cuda"):
output = model(images)
if isinstance(criterion, DomainIndependentLoss):
loss = criterion(output, target, group)
else:
loss = criterion(output, target)
else:
output = model(images)
if isinstance(criterion, DomainIndependentLoss):
loss = criterion(output, target, group)
else:
loss = criterion(output, target)
import pdb
pdb.set_trace()
if isinstance(criterion, DomainIndependentLoss) and not criterion.conditional_accuracy:
preds = compute_preds_sum_out(output,criterion.num_classes, criterion.num_domains)
acc1 = (preds == target).float().sum() / target.shape[0]
elif isinstance(criterion, DomainIndependentLoss) and criterion.conditional_accuracy:
preds = compute_preds_conditional(output,criterion.num_classes, criterion.num_domains, group)
acc1 = (preds == target).float().sum() / target.shape[0]
elif isinstance(criterion, DomainDiscriminativeLoss):
preds = compute_preds_sum_prob_w_prior_shift(output, criterion.num_classes, criterion.num_domains)
acc1 = (preds == target).float().sum() / target.shape[0]
else:
preds = _eval(output)[0]
acc1 = accuracy(output, target, topk=(1,5))[0]
y_true.extend(target.cpu().tolist())
y_pred.extend(preds.cpu().tolist())
groups.extend(group.cpu().tolist())
batch_size = images.shape[0]
metric_logger.update(loss=loss.item())
metric_logger.meters['acc1'].update(acc1.item(), n=batch_size)
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print('* Global acc@1 {top1.global_avg:.3f} loss {losses.global_avg:.3f}'
.format(top1=metric_logger.acc1, losses=metric_logger.loss))
malignant_recall, malignant_precision, malignant_f1, malignant_dpd = get_metrics(y_true, y_pred, groups)
metric_logger.meters['malignant_recall'].update(malignant_recall)
metric_logger.meters['malignant_precision'].update(malignant_precision)
metric_logger.meters['malignant_f1'].update(malignant_f1)
metric_logger.meters['malignant_dpd'].update(malignant_dpd)
return {k: meter.global_avg for k, meter in metric_logger.meters.items()}
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