Update augmented_train.py

augmented_train.py

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+
+NUM_EPOCHS = 50
+AUG_PROB = 0.5
+GRAD_CLIP = 1.0
+SAVE_EVERY = 5
+CHECKPOINT_DIR = "./checkpoints"
+BEST_MODEL_DIR = "./nula-best-model"
+
+MEAN = pt.tensor([0.5,0.5,0.5], device=DEVICE).view(1,3,1,1)
+STD = pt.tensor([0.5,0.5,0.5], device=DEVICE).view(1,3,1,1) 
+
+GLOBAL_POOL_BLUR = BlurPool2d(channels=cfg.in_channels, stride=2).to(DEVICE)
+
+def train_one_epoch(model, loader, optimizer, device, grad_clip=1.0):
+    model.train()
+    
+    total_loss = 0.0
+    total_correct = 0
+    total_examples = 0
+
+    pbar = tqdm(loader, desc="training...", leave=False)
+
+    for batch in pbar:
+        x = batch["pixel_values"].to(device, non_blocking=True)
+        y = batch["labels"].to(device, non_blocking=True)
+        optimizer.zero_grad(set_to_none=True)
+ 
+        B = x.size(0) 
+        mask_aug = pt.rand(B, device=x.device) < AUG_PROB
+
+        if mask_aug.any():
+            with pt.no_grad():
+                x_image = x * STD + MEAN
+                choices = pt.randint(0, 3, (B,), device=x.device)
+                mask_resize = mask_aug & (choices == 0)
+                if mask_resize.any():
+                    scales = pt.empty(mask_resize.sum(), device=x.device).uniform_(0.2, 0.6)
+                    x_subset = x_image[mask_resize]
+
+                    resize_out = []
+                    for i in range(x_subset.size(0)):
+                        resize_out.append(resize_down_up(x_subset[i:i+1], scale=scales[i].item()))
+                    x_image[mask_resize] = pt.cat(resize_out, dim=0)
+                mask_decimate = mask_aug & (choices == 1)
+                if mask_decimate.any():
+                    factors = pt.randint(2, 5, (mask_decimate.sum(),), device=x.device)
+                    x_subset = x_image[mask_decimate]
+
+                    decimate_out = []
+                    for i in range(x_subset.size(0)):
+                        decimate_out.append(decimate(x_subset[i:i+1], factor=int(factors[i].item())))
+                    x_image[mask_decimate] = pt.cat(decimate_out, dim=0)
+                
+                mask_blur = mask_aug & (choices == 2)
+                if mask_blur.any():
+                    x_subset = x_image[mask_blur]
+                    x_down = GLOBAL_POOL_BLUR(x_subset)
+                    x_up = F.interpolate(x_down, size=x_subset.shape[-2:], mode="bilinear", align_corners=False)
+                    x_image[mask_blur] = x_up
+
+                x = (x_image - MEAN) / STD
+
+        out = model(pixel_values=x, labels=y)
+        loss = out.loss
+        logits = out.logits
+        preds = logits.argmax(dim=1)
+        loss.backward()
+        pt.nn.utils.clip_grad_norm_(model.parameters(), max_norm=GRAD_CLIP)
+        optimizer.step()
+
+        total_loss += loss.item() * y.size(0)
+        total_correct += (preds == y).sum().item()
+        total_examples += y.size(0)
+
+        pbar.set_postfix(loss=f"{loss.item():.4f}", acc=f"{100 * total_correct / total_examples:.2f}%")
+
+    return total_loss / total_examples, total_correct / total_examples
+
+@pt.no_grad()
+def evaluate(model, loader, device):
+    model.eval()
+    total_loss = 0.0
+    total_correct = 0
+    total_examples = 0
+    for batch in loader:
+        x = batch["pixel_values"].to(device, non_blocking=True)
+        y = batch["labels"].to(device, non_blocking=True)
+        out = model(pixel_values=x, labels=y)
+        loss = out.loss
+        logits = out.logits 
+
+        total_loss += loss.item() * y.size(0)
+        total_correct += (logits.argmax(dim=1) == y).sum().item()
+        total_examples += y.size(0)
+    return total_loss / total_examples, total_correct / total_examples
+
+best_val_acc = 0.0
+
+for epoch in range(1, NUM_EPOCHS + 1):
+    train_loss, train_acc = train_one_epoch(model, train_loader, optimizer, DEVICE)
+    val_loss, val_acc = evaluate(model, test_loader, DEVICE)
+    scheduler.step()
+
+    if epoch % SAVE_EVERY == 0:
+        model.save_pretrained(f"{CHECKPOINT_DIR}/epoch{epoch}")
+
+    if val_acc > best_val_acc:
+        best_val_acc = val_acc
+        model.save_pretrained(BEST_MODEL_DIR)
+        print(f"new best: {100 * best_val_acc:.2f}%")
+
+    current_lr = optimizer.param_groups[0]["lr"]
+    print("=", "-"*60, "=")
+    print(f"Epoch [{epoch}/{NUM_EPOCHS}]")
+    print(f"lr : {current_lr:.6f}")
+    print(f"train_loss : {train_loss:.4f}")
+    print(f"train_acc : {train_acc * 100:.2f}%")
+    print(f"val_loss : {val_loss:4f}")
+    print(f"val_acc : {val_acc * 100:.2f}%")
+    print(f"best_val_acc : {100 * best_val_acc:.2f}%")
+    print()