Comments (5)
BrightXiaoHan
commented on July 20, 2024
3
You can call .cuda() or to('gpu:0') to move model to gpu.
For example:
pnet = Pnet().cuda()
output = pnet(input_tensor)
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oyxhust
commented on July 20, 2024
I also meet this issue. Have you solve it?
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lianuo
commented on July 20, 2024
same issue
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fuxuliu
commented on July 20, 2024
Like this, For example:
pnet.cuda() rnet.cuda() onet.cuda() offsets = output[0].cpu().data.numpy() probs = output[1].cpu().data.numpy()
finish the rest like that
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mayuanjason
commented on July 20, 2024
Hi sunguwei,
I just refactor this repo, you can check it in https://github.com/mayuanjason/MTCNN_face_detection_alignment_pytorch
It can run on GPU.
Here are some improvments I made:
• Transfer all numpy operation to torch operation, so that it can benefit from GPU acceleration.
• Automatic run on 'CPU' or 'GPU'.
• Based on the latest version of pytorch (1.3)
• Real-time face tracking
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Related Issues (10)
- Preprocess Image HOT 2
- Does this code give exact same output as the MATLAB's version? Have you tested it?
- Which dataset is this model trained on? HOT 1
- Upload to PyPI
- Why need to switch dim2 a dim3 when convert Caffe weights to PyTorch?
- How can I get your OpenVino version .ipynb?
- Can use pytorch directly train the mtcnn? HOT 4
- Program crashes when there is no face in the image HOT 5
- AttributeError: 'PNet' object has no attribute 'named_parameters' HOT 1
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