refractored the code

classify/train.py

@@ -27,9 +27,16 @@ import torch.distributed as dist
 import torch.hub as hub
 import torch.optim.lr_scheduler as lr_scheduler
 import torchvision
-from torch.cuda import amp
 from tqdm import tqdm
 
+# version check
+if torch.__version__.startswith("1.8"):
+    Autocast = torch.cuda.amp.autocast
+    GradScaler = torch.cuda.amp.GradScaler
+else:
+    Autocast = torch.amp.autocast
+    GradScaler = torch.amp.GradScaler
+
 FILE = Path(__file__).resolve()
 ROOT = FILE.parents[1]  # YOLOv5 root directory
 if str(ROOT) not in sys.path:
@@ -198,7 +205,7 @@ def train(opt, device):
     t0 = time.time()
     criterion = smartCrossEntropyLoss(label_smoothing=opt.label_smoothing)  # loss function
     best_fitness = 0.0
-    scaler = amp.GradScaler(enabled=cuda)
+    scaler = GradScaler(enabled=cuda)
     val = test_dir.stem  # 'val' or 'test'
     LOGGER.info(
         f"Image sizes {imgsz} train, {imgsz} test\n"
@@ -219,7 +226,7 @@ def train(opt, device):
             images, labels = images.to(device, non_blocking=True), labels.to(device)
 
             # Forward
-            with amp.autocast(enabled=cuda):  # stability issues when enabled
+            with Autocast(enabled=device.type != "cpu"):  # stability issues when enabled
                 loss = criterion(model(images), labels)
 
             # Backward

classify/val.py

@@ -48,6 +48,11 @@ from utils.general import (
 )
 from utils.torch_utils import select_device, smart_inference_mode
 
+#version check 
+if torch.__version__.startswith("1.8"):
+    Autocast = torch.cuda.amp.autocast 
+else:
+    Autocast = torch.amp.autocast
 
 @smart_inference_mode()
 def run(
@@ -108,7 +113,7 @@ def run(
     action = "validating" if dataloader.dataset.root.stem == "val" else "testing"
     desc = f"{pbar.desc[:-36]}{action:>36}" if pbar else f"{action}"
     bar = tqdm(dataloader, desc, n, not training, bar_format=TQDM_BAR_FORMAT, position=0)
-    with torch.cuda.amp.autocast(enabled=device.type != "cpu"):
+    with Autocast(enabled=device.type != "cpu"):
         for images, labels in bar:
             with dt[0]:
                 images, labels = images.to(device, non_blocking=True), labels.to(device)

models/common.py

@@ -20,7 +20,6 @@ import requests
 import torch
 import torch.nn as nn
 from PIL import Image
-from torch.cuda import amp
 
 # Import 'ultralytics' package or install if missing
 try:
@@ -56,6 +55,11 @@ from utils.general import (
 )
 from utils.torch_utils import copy_attr, smart_inference_mode
 
+# version check 
+if torch.__version__.startswith("1.8"):
+    Autocast = torch.cuda.amp.autocast
+else:
+    Autocast = torch.amp.autocast
 
 def autopad(k, p=None, d=1):
     """
@@ -864,7 +868,7 @@ class AutoShape(nn.Module):
             p = next(self.model.parameters()) if self.pt else torch.empty(1, device=self.model.device)  # param
             autocast = self.amp and (p.device.type != "cpu")  # Automatic Mixed Precision (AMP) inference
             if isinstance(ims, torch.Tensor):  # torch
-                with amp.autocast(autocast):
+                with Autocast(enabled=autocast):
                     return self.model(ims.to(p.device).type_as(p), augment=augment)  # inference
 
             # Pre-process
@@ -891,7 +895,7 @@ class AutoShape(nn.Module):
             x = np.ascontiguousarray(np.array(x).transpose((0, 3, 1, 2)))  # stack and BHWC to BCHW
             x = torch.from_numpy(x).to(p.device).type_as(p) / 255  # uint8 to fp16/32
 
-        with amp.autocast(autocast):
+        with Autocast(enabled=autocast):
             # Inference
             with dt[1]:
                 y = self.model(x, augment=augment)  # forward

segment/train.py

@@ -89,6 +89,14 @@ from utils.torch_utils import (
     torch_distributed_zero_first,
 )
 
+# version check
+if torch.__version__.startswith("1.8"):
+    Autocast = torch.cuda.amp.autocast
+    GradScaler = torch.cuda.amp.GradScaler
+else:
+    Autocast = torch.amp.autocast
+    GradScaler = torch.amp.GradScaler
+
 LOCAL_RANK = int(os.getenv("LOCAL_RANK", -1))  # https://pytorch.org/docs/stable/elastic/run.html
 RANK = int(os.getenv("RANK", -1))
 WORLD_SIZE = int(os.getenv("WORLD_SIZE", 1))
@@ -320,7 +328,7 @@ def train(hyp, opt, device, callbacks):
     maps = np.zeros(nc)  # mAP per class
     results = (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)  # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls)
     scheduler.last_epoch = start_epoch - 1  # do not move
-    scaler = torch.cuda.amp.GradScaler(enabled=amp)
+    scaler = GradScaler(enabled=amp)
     stopper, stop = EarlyStopping(patience=opt.patience), False
     compute_loss = ComputeLoss(model, overlap=overlap)  # init loss class
     # callbacks.run('on_train_start')
@@ -380,7 +388,7 @@ def train(hyp, opt, device, callbacks):
                     imgs = nn.functional.interpolate(imgs, size=ns, mode="bilinear", align_corners=False)
 
             # Forward
-            with torch.cuda.amp.autocast(amp):
+            with Autocast(enabled=amp):
                 pred = model(imgs)  # forward
                 loss, loss_items = compute_loss(pred, targets.to(device), masks=masks.to(device).float())
                 if RANK != -1:

train.py

@@ -94,6 +94,14 @@ from utils.torch_utils import (
     torch_distributed_zero_first,
 )
 
+# version check
+if torch.__version__.startswith("1.8"):
+    Autocast = torch.cuda.amp.autocast
+    GradScaler = torch.cuda.amp.GradScaler
+else:
+    Autocast = torch.amp.autocast
+    GradScaler = torch.amp.GradScaler
+
 LOCAL_RANK = int(os.getenv("LOCAL_RANK", -1))  # https://pytorch.org/docs/stable/elastic/run.html
 RANK = int(os.getenv("RANK", -1))
 WORLD_SIZE = int(os.getenv("WORLD_SIZE", 1))
@@ -352,7 +360,7 @@ def train(hyp, opt, device, callbacks):
     maps = np.zeros(nc)  # mAP per class
     results = (0, 0, 0, 0, 0, 0, 0)  # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls)
     scheduler.last_epoch = start_epoch - 1  # do not move
-    scaler = torch.cuda.amp.GradScaler(enabled=amp)
+    scaler = GradScaler(enabled=amp)
     stopper, stop = EarlyStopping(patience=opt.patience), False
     compute_loss = ComputeLoss(model)  # init loss class
     callbacks.run("on_train_start")
@@ -409,7 +417,7 @@ def train(hyp, opt, device, callbacks):
                     imgs = nn.functional.interpolate(imgs, size=ns, mode="bilinear", align_corners=False)
 
             # Forward
-            with torch.cuda.amp.autocast(amp):
+            with Autocast(enabled=amp):
                 pred = model(imgs)  # forward
                 loss, loss_items = compute_loss(pred, targets.to(device))  # loss scaled by batch_size
                 if RANK != -1:

utils/autobatch.py

@@ -12,7 +12,7 @@ from utils.torch_utils import profile
 
 def check_train_batch_size(model, imgsz=640, amp=True):
     """Checks and computes optimal training batch size for YOLOv5 model, given image size and AMP setting."""
-    with torch.cuda.amp.autocast(amp):
+    with torch.amp.autocast("cuda", enabled=amp):
         return autobatch(deepcopy(model).train(), imgsz)  # compute optimal batch size