ImportError: cannot import name 'draw_bounding_boxes' from 'torchvision.utils'

[abcsunshine]

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Requirements

Python>=3.7.0 with all requirements.txt installed including PyTorch>=1.7. To get started:

git clone https://github.com/ultralytics/yolov5  # clone
cd yolov5
pip install -r requirements.txt  # install

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[AyushExel]

@thepycoder can you take a look please?
@abcsunshine is this error related to clearml logger? Does it work fine without clearml?

[thepycoder]

Can you do a pip freeze and make sure you have torchvision installed? Is it the same version as in the requirements.txt?

[abcsunshine]

I think this is the right version.
However, when I just ignore the import code，it can work well.

[thepycoder]

It seems like the draw_bounding_boxes functionality was only added to torchvision starting from v0.9.0.
In contrast, the requirements of YOLOv5 specify torchvision>=0.8.1, which means if you have 0.8.1 or 0.8.2 installed, it will error because of this import statement.

So the solution for you @abcsunshine is to upgrade torchvision, or (if you don't use ClearML), commenting out the import statement is indeed also fine.

@AyushExel How do you want to solve this? I assume you won't like it to change the whole repo requirements file from torchvision>=0.8.1 to torchvision>=0.9.0. So in that case, we should re-implement the draw_bounding_boxes function without using torchvision. Any other ideas?

[glenn-jocher]

@thepycoder ah got it. Simplest quick fix is to scope the import to place it within the try: import clearml statement. This will reduce the affected userbase.

A more permanent fix might be to use the YOLOv5 Annotator class below. We can't bump the torchvision requirements right now because there are some Jetson Nano tutorials that require torch 1.7.0 torchvision 0.8.1

Annotator class:

yolov5/utils/plots.py

Lines 68 to 126 in b551098

	class Annotator:
	# YOLOv5 Annotator for train/val mosaics and jpgs and detect/hub inference annotations
	def __init__(self, im, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc'):
	assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to Annotator() input images.'
	non_ascii = not is_ascii(example) # non-latin labels, i.e. asian, arabic, cyrillic
	self.pil = pil or non_ascii
	if self.pil: # use PIL
	self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)
	self.draw = ImageDraw.Draw(self.im)
	self.font = check_pil_font(font='Arial.Unicode.ttf' if non_ascii else font,
	size=font_size or max(round(sum(self.im.size) / 2 * 0.035), 12))
	else: # use cv2
	self.im = im
	self.lw = line_width or max(round(sum(im.shape) / 2 * 0.003), 2) # line width
	def box_label(self, box, label='', color=(128, 128, 128), txt_color=(255, 255, 255)):
	# Add one xyxy box to image with label
	if self.pil or not is_ascii(label):
	self.draw.rectangle(box, width=self.lw, outline=color) # box
	if label:
	w, h = self.font.getsize(label) # text width, height
	outside = box[1] - h >= 0 # label fits outside box
	self.draw.rectangle(
	(box[0], box[1] - h if outside else box[1], box[0] + w + 1,
	box[1] + 1 if outside else box[1] + h + 1),
	fill=color,
	)
	# self.draw.text((box[0], box[1]), label, fill=txt_color, font=self.font, anchor='ls') # for PIL>8.0
	self.draw.text((box[0], box[1] - h if outside else box[1]), label, fill=txt_color, font=self.font)
	else: # cv2
	p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
	cv2.rectangle(self.im, p1, p2, color, thickness=self.lw, lineType=cv2.LINE_AA)
	if label:
	tf = max(self.lw - 1, 1) # font thickness
	w, h = cv2.getTextSize(label, 0, fontScale=self.lw / 3, thickness=tf)[0] # text width, height
	outside = p1[1] - h >= 3
	p2 = p1[0] + w, p1[1] - h - 3 if outside else p1[1] + h + 3
	cv2.rectangle(self.im, p1, p2, color, -1, cv2.LINE_AA) # filled
	cv2.putText(self.im,
	label, (p1[0], p1[1] - 2 if outside else p1[1] + h + 2),
	0,
	self.lw / 3,
	txt_color,
	thickness=tf,
	lineType=cv2.LINE_AA)
	def rectangle(self, xy, fill=None, outline=None, width=1):
	# Add rectangle to image (PIL-only)
	self.draw.rectangle(xy, fill, outline, width)
	def text(self, xy, text, txt_color=(255, 255, 255)):
	# Add text to image (PIL-only)
	w, h = self.font.getsize(text) # text width, height
	self.draw.text((xy[0], xy[1] - h + 1), text, fill=txt_color, font=self.font)
	def result(self):
	# Return annotated image as array
	return np.asarray(self.im)

detect.py usage example for box annotation:

yolov5/detect.py

Line 170 in b551098

annotator.box_label(xyxy, label, color=colors(c, True))

[glenn-jocher]

@abcsunshine @thepycoder good news 😃! Your original issue may now be fixed ✅ in PR #8915. This is not a permanent solution but rather a scoped import quickfix.

To receive this update:

• Git – git pull from within your yolov5/ directory or git clone https://github.com/ultralytics/yolov5 again
• PyTorch Hub – Force-reload model = torch.hub.load('ultralytics/yolov5', 'yolov5s', force_reload=True)
• Notebooks – View updated notebooks
• Docker – sudo docker pull ultralytics/yolov5:latest to update your image

Thank you for spotting this issue and informing us of the problem. Please let us know if this update resolves the issue for you, and feel free to inform us of any other issues you discover or feature requests that come to mind. Happy trainings with YOLOv5 🚀!

[glenn-jocher]

@abcsunshine @thepycoder I think this shows the need for another CI check at the torch minimum requirement. I'll add this to the YOLOv5 CI to catch these problems earlier in the future. Trying to add in #8916