Initial release of SwiftFormer

util/engine.py

@@ -0,0 +1,101 @@
+"""
+Train and eval functions used in main.py
+"""
+import math
+import sys
+from typing import Iterable, Optional
+
+import torch
+
+from timm.data import Mixup
+from timm.utils import accuracy, ModelEma
+
+from .losses import DistillationLoss
+import util.utils as utils
+
+
+def train_one_epoch(model: torch.nn.Module, criterion: DistillationLoss,
+                    data_loader: Iterable, optimizer: torch.optim.Optimizer,
+                    device: torch.device, epoch: int, loss_scaler,
+                    clip_grad: float = 0,
+                    clip_mode: str = 'norm',
+                    model_ema: Optional[ModelEma] = None, mixup_fn: Optional[Mixup] = None,
+                    set_training_mode=True):
+    model.train(set_training_mode)
+    metric_logger = utils.MetricLogger(delimiter="  ")
+    metric_logger.add_meter('lr', utils.SmoothedValue(
+        window_size=1, fmt='{value:.6f}'))
+    header = 'Epoch: [{}]'.format(epoch)
+    print_freq = 100
+
+    for samples, targets in metric_logger.log_every(
+            data_loader, print_freq, header):
+        samples = samples.to(device, non_blocking=True)
+        targets = targets.to(device, non_blocking=True)
+
+        if mixup_fn is not None:
+            samples, targets = mixup_fn(samples, targets)
+
+        if True:  # with torch.cuda.amp.autocast():
+            outputs = model(samples)
+            loss = criterion(samples, outputs, targets)
+
+        loss_value = loss.item()
+
+        if not math.isfinite(loss_value):
+            print("Loss is {}, stopping training".format(loss_value))
+            sys.exit(1)
+
+        optimizer.zero_grad()
+
+        # This attribute is added by timm on one optimizer (adahessian)
+        is_second_order = hasattr(
+            optimizer, 'is_second_order') and optimizer.is_second_order
+        loss_scaler(loss, optimizer, clip_grad=clip_grad, clip_mode=clip_mode,
+                    parameters=model.parameters(), create_graph=is_second_order)
+
+        torch.cuda.synchronize()
+        if model_ema is not None:
+            model_ema.update(model)
+
+        metric_logger.update(loss=loss_value)
+        metric_logger.update(lr=optimizer.param_groups[0]["lr"])
+    # 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):
+    criterion = torch.nn.CrossEntropyLoss()
+
+    metric_logger = utils.MetricLogger(delimiter="  ")
+    header = 'Test:'
+
+    # Switch to evaluation mode
+    model.eval()
+
+    for images, target in metric_logger.log_every(data_loader, 10, header):
+        images = images.to(device, non_blocking=True)
+        target = target.to(device, non_blocking=True)
+
+        # Compute output
+        with torch.cuda.amp.autocast():
+            output = model(images)
+            loss = criterion(output, target)
+
+        acc1, acc5 = accuracy(output, target, topk=(1, 5))
+
+        batch_size = images.shape[0]
+        metric_logger.update(loss=loss.item())
+        metric_logger.meters['acc1'].update(acc1.item(), n=batch_size)
+        metric_logger.meters['acc5'].update(acc5.item(), n=batch_size)
+
+    # Gather the stats from all processes
+    metric_logger.synchronize_between_processes()
+    print('* Acc@1 {top1.global_avg:.3f} Acc@5 {top5.global_avg:.3f} loss {losses.global_avg:.3f}'
+          .format(top1=metric_logger.acc1, top5=metric_logger.acc5, losses=metric_logger.loss))
+    print(output.mean().item(), output.std().item())
+
+    return {k: meter.global_avg for k, meter in metric_logger.meters.items()}