`AutoShape()` models as `DetectMultiBackend()` instances (#5845)

* Update AutoShape()

* autodownload ONNX

* Cleanup

* Finish updates

* Add Usage

* Update

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* fix device

* Update hubconf.py

* Update common.py

* smart param selection

* autodownload all formats

* autopad only pytorch models

* new_shape edits

* stride tensor fix

* Cleanup

export.py

@@ -411,7 +411,7 @@ def parse_opt():
     parser.add_argument('--int8', action='store_true', help='CoreML/TF INT8 quantization')
     parser.add_argument('--dynamic', action='store_true', help='ONNX/TF: dynamic axes')
     parser.add_argument('--simplify', action='store_true', help='ONNX: simplify model')
-    parser.add_argument('--opset', type=int, default=13, help='ONNX: opset version')
+    parser.add_argument('--opset', type=int, default=14, help='ONNX: opset version')
     parser.add_argument('--verbose', action='store_true', help='TensorRT: verbose log')
     parser.add_argument('--workspace', type=int, default=4, help='TensorRT: workspace size (GB)')
     parser.add_argument('--topk-per-class', type=int, default=100, help='TF.js NMS: topk per class to keep')

hubconf.py

@@ -5,6 +5,7 @@ PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5/
 Usage:
     import torch
     model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
+    model = torch.hub.load('ultralytics/yolov5:master', 'custom', 'path/to/yolov5s.onnx')  # file from branch
 """
 
 import torch
@@ -27,26 +28,25 @@ def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbo
     """
     from pathlib import Path
 
-    from models.common import AutoShape
-    from models.experimental import attempt_load
+    from models.common import AutoShape, DetectMultiBackend
     from models.yolo import Model
     from utils.downloads import attempt_download
     from utils.general import check_requirements, intersect_dicts, set_logging
     from utils.torch_utils import select_device
 
-    file = Path(__file__).resolve()
     check_requirements(exclude=('tensorboard', 'thop', 'opencv-python'))
     set_logging(verbose=verbose)
 
-    save_dir = Path('') if str(name).endswith('.pt') else file.parent
-    path = (save_dir / name).with_suffix('.pt')  # checkpoint path
+    name = Path(name)
+    path = name.with_suffix('.pt') if name.suffix == '' else name  # checkpoint path
     try:
         device = select_device(('0' if torch.cuda.is_available() else 'cpu') if device is None else device)
 
         if pretrained and channels == 3 and classes == 80:
-            model = attempt_load(path, map_location=device)  # download/load FP32 model
+            model = DetectMultiBackend(path, device=device)  # download/load FP32 model
+            # model = models.experimental.attempt_load(path, map_location=device)  # download/load FP32 model
         else:
-            cfg = list((Path(__file__).parent / 'models').rglob(f'{name}.yaml'))[0]  # model.yaml path
+            cfg = list((Path(__file__).parent / 'models').rglob(f'{path.name}.yaml'))[0]  # model.yaml path
             model = Model(cfg, channels, classes)  # create model
             if pretrained:
                 ckpt = torch.load(attempt_download(path), map_location=device)  # load

models/common.py

@@ -276,7 +276,7 @@ class Concat(nn.Module):
 
 class DetectMultiBackend(nn.Module):
     # YOLOv5 MultiBackend class for python inference on various backends
-    def __init__(self, weights='yolov5s.pt', device=None, dnn=True):
+    def __init__(self, weights='yolov5s.pt', device=None, dnn=False):
         # Usage:
         #   PyTorch:      weights = *.pt
         #   TorchScript:            *.torchscript
@@ -287,6 +287,8 @@ class DetectMultiBackend(nn.Module):
         #   ONNX Runtime:           *.onnx
         #   OpenCV DNN:             *.onnx with dnn=True
         #   TensorRT:               *.engine
+        from models.experimental import attempt_download, attempt_load  # scoped to avoid circular import
+
         super().__init__()
         w = str(weights[0] if isinstance(weights, list) else weights)
         suffix = Path(w).suffix.lower()
@@ -294,6 +296,7 @@ class DetectMultiBackend(nn.Module):
         check_suffix(w, suffixes)  # check weights have acceptable suffix
         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
+        attempt_download(w)  # download if not local
 
         if jit:  # TorchScript
             LOGGER.info(f'Loading {w} for TorchScript inference...')
@@ -303,11 +306,12 @@ class DetectMultiBackend(nn.Module):
                 d = json.loads(extra_files['config.txt'])  # extra_files dict
                 stride, names = int(d['stride']), d['names']
         elif pt:  # PyTorch
-            from models.experimental import attempt_load  # scoped to avoid circular import
             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
+            self.model = model  # explicitly assign for to(), cpu(), cuda(), half()
         elif coreml:  # CoreML
+            LOGGER.info(f'Loading {w} for CoreML inference...')
             import coremltools as ct
             model = ct.models.MLModel(w)
         elif dnn:  # ONNX OpenCV DNN
@@ -316,7 +320,7 @@ class DetectMultiBackend(nn.Module):
             net = cv2.dnn.readNetFromONNX(w)
         elif onnx:  # ONNX Runtime
             LOGGER.info(f'Loading {w} for ONNX Runtime inference...')
-            check_requirements(('onnx', 'onnxruntime-gpu' if torch.has_cuda else 'onnxruntime'))
+            check_requirements(('onnx', 'onnxruntime-gpu' if torch.cuda.is_available() else 'onnxruntime'))
             import onnxruntime
             session = onnxruntime.InferenceSession(w, None)
         elif engine:  # TensorRT
@@ -376,7 +380,7 @@ class DetectMultiBackend(nn.Module):
         if self.pt:  # PyTorch
             y = self.model(im) if self.jit else self.model(im, augment=augment, visualize=visualize)
             return y if val else y[0]
-        elif self.coreml:  # CoreML *.mlmodel
+        elif self.coreml:  # CoreML
             im = im.permute(0, 2, 3, 1).cpu().numpy()  # torch BCHW to numpy BHWC shape(1,320,192,3)
             im = Image.fromarray((im[0] * 255).astype('uint8'))
             # im = im.resize((192, 320), Image.ANTIALIAS)
@@ -433,24 +437,28 @@ class AutoShape(nn.Module):
     # YOLOv5 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
     conf = 0.25  # NMS confidence threshold
     iou = 0.45  # NMS IoU threshold
-    classes = None  # (optional list) filter by class, i.e. = [0, 15, 16] for COCO persons, cats and dogs
+    agnostic = False  # NMS class-agnostic
     multi_label = False  # NMS multiple labels per box
+    classes = None  # (optional list) filter by class, i.e. = [0, 15, 16] for COCO persons, cats and dogs
     max_det = 1000  # maximum number of detections per image
 
     def __init__(self, model):
         super().__init__()
         LOGGER.info('Adding AutoShape... ')
         copy_attr(self, model, include=('yaml', 'nc', 'hyp', 'names', 'stride', 'abc'), exclude=())  # copy attributes
+        self.dmb = isinstance(model, DetectMultiBackend)  # DetectMultiBackend() instance
+        self.pt = not self.dmb or model.pt  # PyTorch model
         self.model = model.eval()
 
     def _apply(self, fn):
         # Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers
         self = super()._apply(fn)
-        m = self.model.model[-1]  # Detect()
-        m.stride = fn(m.stride)
-        m.grid = list(map(fn, m.grid))
-        if isinstance(m.anchor_grid, list):
-            m.anchor_grid = list(map(fn, m.anchor_grid))
+        if self.pt:
+            m = self.model.model.model[-1] if self.dmb else self.model.model[-1]  # Detect()
+            m.stride = fn(m.stride)
+            m.grid = list(map(fn, m.grid))
+            if isinstance(m.anchor_grid, list):
+                m.anchor_grid = list(map(fn, m.anchor_grid))
         return self
 
     @torch.no_grad()
@@ -465,7 +473,7 @@ class AutoShape(nn.Module):
         #   multiple:        = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...]  # list of images
 
         t = [time_sync()]
-        p = next(self.model.parameters())  # for device and type
+        p = next(self.model.parameters()) if self.pt else torch.zeros(1)  # for device and type
         if isinstance(imgs, torch.Tensor):  # torch
             with amp.autocast(enabled=p.device.type != 'cpu'):
                 return self.model(imgs.to(p.device).type_as(p), augment, profile)  # inference
@@ -489,8 +497,8 @@ class AutoShape(nn.Module):
             g = (size / max(s))  # gain
             shape1.append([y * g for y in s])
             imgs[i] = im if im.data.contiguous else np.ascontiguousarray(im)  # update
-        shape1 = [make_divisible(x, int(self.stride.max())) for x in np.stack(shape1, 0).max(0)]  # inference shape
-        x = [letterbox(im, new_shape=shape1, auto=False)[0] for im in imgs]  # pad
+        shape1 = [make_divisible(x, self.stride) for x in np.stack(shape1, 0).max(0)]  # inference shape
+        x = [letterbox(im, new_shape=shape1 if self.pt else size, auto=False)[0] for im in imgs]  # pad
         x = np.stack(x, 0) if n > 1 else x[0][None]  # stack
         x = np.ascontiguousarray(x.transpose((0, 3, 1, 2)))  # BHWC to BCHW
         x = torch.from_numpy(x).to(p.device).type_as(p) / 255  # uint8 to fp16/32
@@ -498,12 +506,12 @@ class AutoShape(nn.Module):
 
         with amp.autocast(enabled=p.device.type != 'cpu'):
             # Inference
-            y = self.model(x, augment, profile)[0]  # forward
+            y = self.model(x, augment, profile)  # forward
             t.append(time_sync())
 
             # Post-process
-            y = non_max_suppression(y, self.conf, iou_thres=self.iou, classes=self.classes,
-                                    multi_label=self.multi_label, max_det=self.max_det)  # NMS
+            y = non_max_suppression(y if self.dmb else y[0], self.conf, iou_thres=self.iou, classes=self.classes,
+                                    agnostic=self.agnostic, multi_label=self.multi_label, max_det=self.max_det)  # NMS
             for i in range(n):
                 scale_coords(shape1, y[i][:, :4], shape0[i])
 

utils/general.py

@@ -455,7 +455,9 @@ def download(url, dir='.', unzip=True, delete=True, curl=False, threads=1):
 
 
 def make_divisible(x, divisor):
-    # Returns x evenly divisible by divisor
+    # Returns nearest x divisible by divisor
+    if isinstance(divisor, torch.Tensor):
+        divisor = int(divisor.max())  # to int
     return math.ceil(x / divisor) * divisor