Update TorchScript suffix to `*.torchscript` (#5856)

detect.py

@@ -81,18 +81,18 @@ def run(weights=ROOT / 'yolov5s.pt',  # model.pt path(s)
     imgsz = check_img_size(imgsz, s=stride)  # check image size
 
     # Half
-    half &= (pt or engine) and device.type != 'cpu'  # half precision only supported by PyTorch on CUDA
-    if pt:
+    half &= (pt or jit or engine) and device.type != 'cpu'  # half precision only supported by PyTorch on CUDA
+    if pt or jit:
         model.model.half() if half else model.model.float()
 
     # Dataloader
     if webcam:
         view_img = check_imshow()
         cudnn.benchmark = True  # set True to speed up constant image size inference
-        dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt and not jit)
+        dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt)
         bs = len(dataset)  # batch_size
     else:
-        dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt and not jit)
+        dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt)
         bs = 1  # batch_size
     vid_path, vid_writer = [None] * bs, [None] * bs
 

export.py

@@ -5,7 +5,7 @@ Export a YOLOv5 PyTorch model to other formats. TensorFlow exports authored by h
 Format                  | Example                   | Export `include=(...)` argument
 ---                     | ---                       | ---
 PyTorch                 | yolov5s.pt                | -
-TorchScript             | yolov5s.torchscript.pt    | 'torchscript'
+TorchScript             | yolov5s.torchscript       | 'torchscript'
 ONNX                    | yolov5s.onnx              | 'onnx'
 CoreML                  | yolov5s.mlmodel           | 'coreml'
 TensorFlow SavedModel   | yolov5s_saved_model/      | 'saved_model'
@@ -19,7 +19,7 @@ Usage:
 
 Inference:
     $ python path/to/detect.py --weights yolov5s.pt
-                                         yolov5s.torchscript.pt
+                                         yolov5s.torchscript
                                          yolov5s.onnx
                                          yolov5s.mlmodel  (under development)
                                          yolov5s_saved_model
@@ -66,7 +66,7 @@ def export_torchscript(model, im, file, optimize, prefix=colorstr('TorchScript:'
     # YOLOv5 TorchScript model export
     try:
         LOGGER.info(f'\n{prefix} starting export with torch {torch.__version__}...')
-        f = file.with_suffix('.torchscript.pt')
+        f = file.with_suffix('.torchscript')
 
         ts = torch.jit.trace(model, im, strict=False)
         d = {"shape": im.shape, "stride": int(max(model.stride)), "names": model.names}

models/common.py

@@ -279,7 +279,7 @@ class DetectMultiBackend(nn.Module):
     def __init__(self, weights='yolov5s.pt', device=None, dnn=True):
         # Usage:
         #   PyTorch:      weights = *.pt
-        #   TorchScript:            *.torchscript.pt
+        #   TorchScript:            *.torchscript
         #   CoreML:                 *.mlmodel
         #   TensorFlow:             *_saved_model
         #   TensorFlow:             *.pb
@@ -289,10 +289,10 @@ class DetectMultiBackend(nn.Module):
         #   TensorRT:               *.engine
         super().__init__()
         w = str(weights[0] if isinstance(weights, list) else weights)
-        suffix, suffixes = Path(w).suffix.lower(), ['.pt', '.onnx', '.engine', '.tflite', '.pb', '', '.mlmodel']
+        suffix = Path(w).suffix.lower()
+        suffixes = ['.pt', '.torchscript', '.onnx', '.engine', '.tflite', '.pb', '', '.mlmodel']
         check_suffix(w, suffixes)  # check weights have acceptable suffix
-        pt, onnx, engine, tflite, pb, saved_model, coreml = (suffix == x for x in suffixes)  # backend booleans
-        jit = pt and 'torchscript' in w.lower()
+        pt, jit, onnx, engine, tflite, pb, saved_model, coreml = (suffix == x for x in suffixes)  # backend booleans
         stride, names = 64, [f'class{i}' for i in range(1000)]  # assign defaults
 
         if jit:  # TorchScript
@@ -304,10 +304,10 @@ class DetectMultiBackend(nn.Module):
                 stride, names = int(d['stride']), d['names']
         elif pt:  # PyTorch
             from models.experimental import attempt_load  # scoped to avoid circular import
-            model = torch.jit.load(w) if 'torchscript' in w else attempt_load(weights, map_location=device)
+            model = attempt_load(weights, map_location=device)
             stride = int(model.stride.max())  # model stride
             names = model.module.names if hasattr(model, 'module') else model.names  # get class names
-        elif coreml:  # CoreML *.mlmodel
+        elif coreml:  # CoreML
             import coremltools as ct
             model = ct.models.MLModel(w)
         elif dnn:  # ONNX OpenCV DNN

utils/activations.py

@@ -18,8 +18,8 @@ class SiLU(nn.Module):  # export-friendly version of nn.SiLU()
 class Hardswish(nn.Module):  # export-friendly version of nn.Hardswish()
     @staticmethod
     def forward(x):
-        # return x * F.hardsigmoid(x)  # for torchscript and CoreML
-        return x * F.hardtanh(x + 3, 0.0, 6.0) / 6.0  # for torchscript, CoreML and ONNX
+        # return x * F.hardsigmoid(x)  # for TorchScript and CoreML
+        return x * F.hardtanh(x + 3, 0.0, 6.0) / 6.0  # for TorchScript, CoreML and ONNX
 
 
 # Mish https://github.com/digantamisra98/Mish --------------------------------------------------------------------------

val.py

@@ -111,7 +111,7 @@ def run(data,
     # Initialize/load model and set device
     training = model is not None
     if training:  # called by train.py
-        device, pt, engine = next(model.parameters()).device, True, False  # get model device, PyTorch model
+        device, pt, jit, engine = next(model.parameters()).device, True, False, False  # get model device, PyTorch model
 
         half &= device.type != 'cpu'  # half precision only supported on CUDA
         model.half() if half else model.float()
@@ -124,10 +124,10 @@ def run(data,
 
         # Load model
         model = DetectMultiBackend(weights, device=device, dnn=dnn)
-        stride, pt, engine = model.stride, model.pt, model.engine
+        stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine
         imgsz = check_img_size(imgsz, s=stride)  # check image size
-        half &= (pt or engine) and device.type != 'cpu'  # half precision only supported by PyTorch on CUDA
-        if pt:
+        half &= (pt or jit or engine) and device.type != 'cpu'  # half precision only supported by PyTorch on CUDA
+        if pt or jit:
             model.model.half() if half else model.model.float()
         elif engine:
             batch_size = model.batch_size
@@ -166,7 +166,7 @@ def run(data,
     pbar = tqdm(dataloader, desc=s, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}')  # progress bar
     for batch_i, (im, targets, paths, shapes) in enumerate(pbar):
         t1 = time_sync()
-        if pt or engine:
+        if pt or jit or engine:
             im = im.to(device, non_blocking=True)
             targets = targets.to(device)
         im = im.half() if half else im.float()  # uint8 to fp16/32