Can feed tensor the model

[mfoglio]

Why does model(img) works but model(torch.Tensor(img)) does not, despite having the same shape resulting in the following error?

RuntimeError: Sizes of tensors must match except in dimension 2. Got 1 and 2 (The offending index is 0)

img is of shape (720, 1280, 3) and of type int.
Thanks

[github-actions]

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[glenn-jocher]

@mfoglio I'm assuming you're referring to the YOLOv5 PyTorch Hub models? These use our autoShape() wrapper, which is tensor capable but mainly intended for inference directly on images, thus sidestepping any need to have your image as a torch tensor. The full list of supported formats is shown in the autoShape() forward method comments, including the required HWC or BCHW format:

yolov5/models/common.py

Lines 224 to 247 in c8c8da6

	class autoShape(nn.Module):
	# 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
	def __init__(self, model):
	super(autoShape, self).__init__()
	self.model = model.eval()
	def autoshape(self):
	print('autoShape already enabled, skipping... ') # model already converted to model.autoshape()
	return self
	def forward(self, imgs, size=640, augment=False, profile=False):
	# Inference from various sources. For height=720, width=1280, RGB images example inputs are:
	# filename: imgs = 'data/samples/zidane.jpg'
	# URI: = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/zidane.jpg'
	# OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(720,1280,3)
	# PIL: = Image.open('image.jpg') # HWC x(720,1280,3)
	# numpy: = np.zeros((720,1280,3)) # HWC
	# torch: = torch.zeros(16,3,720,1280) # BCHW
	# multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images

[namirinz]

@glenn-jocher

Using torch.hub yolov5s model
running this code

det_results = yolo_model(torch.ones(4,3,720,1280))
Get the error

RuntimeError                              Traceback (most recent call last)
<ipython-input-10-407ec9b2d209> in <module>
      1 image = torch.ones(4,3,720,1280)
      2 print(image.shape)
----> 3 det_results = yolo_model(image)
      4 
      5 #det_results_xyxy, det_results_xywh = process_yolo(det_results, score_thr=0.3)

/home/namriinz/miniconda3/envs/torch_env/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
    887             result = self._slow_forward(*input, **kwargs)
    888         else:
--> 889             result = self.forward(*input, **kwargs)
    890         for hook in itertools.chain(
    891                 _global_forward_hooks.values(),

~/.cache/torch/hub/ultralytics_yolov5_master/models/common.py in forward(self, imgs, size, augment, profile)
    195         p = next(self.model.parameters())  # for device and type
    196         if isinstance(imgs, torch.Tensor):  # torch
--> 197             return self.model(imgs.to(p.device).type_as(p), augment, profile)  # inference
    198 
    199         # Pre-process

/home/namriinz/miniconda3/envs/torch_env/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
    887             result = self._slow_forward(*input, **kwargs)
    888         else:
--> 889             result = self.forward(*input, **kwargs)
    890         for hook in itertools.chain(
    891                 _global_forward_hooks.values(),

~/.cache/torch/hub/ultralytics_yolov5_master/models/yolo.py in forward(self, x, augment, profile)
    121             return torch.cat(y, 1), None  # augmented inference, train
    122         else:
--> 123             return self.forward_once(x, profile)  # single-scale inference, train
    124 
    125     def forward_once(self, x, profile=False):

~/.cache/torch/hub/ultralytics_yolov5_master/models/yolo.py in forward_once(self, x, profile)
    137                 print('%10.1f%10.0f%10.1fms %-40s' % (o, m.np, dt[-1], m.type))
    138 
--> 139             x = m(x)  # run
    140             y.append(x if m.i in self.save else None)  # save output
    141 

/home/namriinz/miniconda3/envs/torch_env/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
    887             result = self._slow_forward(*input, **kwargs)
    888         else:
--> 889             result = self.forward(*input, **kwargs)
    890         for hook in itertools.chain(
    891                 _global_forward_hooks.values(),

~/.cache/torch/hub/ultralytics_yolov5_master/models/common.py in forward(self, x)
    152 
    153     def forward(self, x):
--> 154         return torch.cat(x, self.d)
    155 
    156 

RuntimeError: Sizes of tensors must match except in dimension 2. Got 45 and 46 (The offending index is 0)

[glenn-jocher]

@namirinz this error is caused because you are passing a torch tensor with an image size 720, which does not meet the YOLOv5 stride-multiple constraints. Each image dimension must be an even multiple of 32 for P5 models, or 64 for P6 models. The nearest compatible image shape would be (4,3,736,1280).

PyTorch inference uses a pass-through channel in the autoShape() wrapper which is much simpler than all of the other sources, because the use case is that the model is embedded in a more fully managed solution like detect.py which is supplying checks, pre and post processing:

yolov5/models/common.py

Lines 250 to 252 in c8c8da6

	if isinstance(imgs, torch.Tensor): # torch
	return self.model(imgs.to(p.device).type_as(p), augment, profile) # inference

[glenn-jocher]

@namirinz I will update the comment in the forward method though to avoid confusion in the future.

[glenn-jocher]

@namirinz I've updated the comments in ccbe7de which is part of an existing Hub model PR #2725.

[glenn-jocher]

@namirinz PR #2725 is merged now with updated comments, hopefully this will help future users.