| import math
|
| from typing import Iterable, Optional
|
| import torch
|
| from timm.data import Mixup
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| from timm.utils import accuracy, ModelEma
|
|
|
| from ..models.losses.criterion import DomainIndependentLoss, DomainDiscriminativeLoss
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| from ..evaluation.metrics import _eval, compute_preds_sum_out, compute_preds_conditional, compute_preds_sum_prob_w_prior_shift, get_metrics
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| from ..utils import logging_utils
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|
|
| def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
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| data_loader: Iterable, optimizer: torch.optim.Optimizer,
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| device: torch.device, epoch: int, loss_scaler, max_norm: float = 0,
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| model_ema: Optional[ModelEma] = None, mixup_fn: Optional[Mixup] = None, log_writer=None,
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| wandb_logger=None, start_steps=None, lr_schedule_values=None, wd_schedule_values=None,
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| num_training_steps_per_epoch=None, update_freq=None, use_amp=False):
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| model.train(True)
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| metric_logger = logging_utils.MetricLogger(delimiter=" ")
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| metric_logger.add_meter('lr', logging_utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
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| metric_logger.add_meter('min_lr', logging_utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
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| header = 'Epoch: [{}]'.format(epoch)
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| print_freq = 10
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|
|
| optimizer.zero_grad()
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|
|
| for data_iter_step, (samples, targets, groups) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
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| step = data_iter_step // update_freq
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| if step >= num_training_steps_per_epoch:
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| continue
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| it = start_steps + step
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|
|
| if lr_schedule_values is not None or wd_schedule_values is not None and data_iter_step % update_freq == 0:
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| for i, param_group in enumerate(optimizer.param_groups):
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| if lr_schedule_values is not None:
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| param_group["lr"] = lr_schedule_values[it] * param_group["lr_scale"]
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| 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)
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| targets = targets.to(device, non_blocking=True)
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| groups = groups.to(device, non_blocking=True)
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|
|
| if mixup_fn is not None:
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| samples, targets = mixup_fn(samples, targets)
|
|
|
| if use_amp:
|
| with torch.amp.autocast("cuda"):
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| output = model(samples)
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| if isinstance(criterion, DomainIndependentLoss):
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| loss = criterion(output, targets, groups)
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| else:
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| loss = criterion(output, targets)
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| else:
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| output = model(samples)
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| if isinstance(criterion, DomainIndependentLoss):
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| loss = criterion(output, targets, groups)
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| else:
|
| loss = criterion(output, targets)
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| import pdb
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| pdb.set_trace()
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|
|
| loss_value = loss.item()
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|
|
| if not math.isfinite(loss_value):
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| print("Loss is {}, stopping training".format(loss_value))
|
| assert math.isfinite(loss_value)
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|
|
| if use_amp:
|
|
|
| is_second_order = hasattr(optimizer, 'is_second_order') and optimizer.is_second_order
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| loss /= update_freq
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| grad_norm = loss_scaler(loss, optimizer, clip_grad=max_norm,
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| parameters=model.parameters(), create_graph=is_second_order,
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| update_grad=(data_iter_step + 1) % update_freq == 0)
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| if (data_iter_step + 1) % update_freq == 0:
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| optimizer.zero_grad()
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| if model_ema is not None:
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| model_ema.update(model)
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| else:
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| loss /= update_freq
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| loss.backward()
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| if (data_iter_step + 1) % update_freq == 0:
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| optimizer.step()
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| optimizer.zero_grad()
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| if model_ema is not None:
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| model_ema.update(model)
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|
|
| if torch.cuda.is_available():
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| torch.cuda.synchronize()
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|
|
| if mixup_fn is None:
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| if isinstance(criterion, DomainIndependentLoss):
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| 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):
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| preds = compute_preds_sum_prob_w_prior_shift(output, criterion.num_classes, criterion.num_domains)
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| class_acc = (preds == targets).float().sum() / targets.shape[0]
|
| else:
|
| class_acc = (output.max(-1)[-1] == targets).float().mean()
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| else:
|
| class_acc = None
|
|
|
| metric_logger.update(loss=loss_value)
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| 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")
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| log_writer.update(min_lr=min_lr, head="opt")
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| log_writer.update(weight_decay=weight_decay_value, head="opt")
|
| if use_amp:
|
| log_writer.update(grad_norm=grad_norm, head="opt")
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| log_writer.set_step()
|
|
|
| if wandb_logger:
|
| wandb_logger._wandb.log({
|
| 'Rank-0 Batch Wise/train_loss': loss_value,
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| '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})
|
|
|
|
|
| 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 = []
|
|
|
|
|
| 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)
|
|
|
|
|
| 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)
|
|
|
|
|
| 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()}
|
|
|
