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在Python和机器学习方面，我是个菜鸟。我正在尝试运行两个与“深度图像抠像”有关的项目：

https://github.com/Joker316701882/Deep-Image-Matting with Tensorflow
https://github.com/huochaitiantang/pytorch-deep-image-matting with Pytorch

我只是试图在这些项目中运行测试，但遇到了各种问题。我可以在没有GPU的机器上运行它们吗？我以为GPU仅用于加快处理速度，但是我只想看到这些在运行配备GPU的计算机之前运行。我先向您道歉，因为我知道自己对此非常感兴趣

当我尝试Tensorflow项目时：

I get an error with this line gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction = args.gpu_fraction) probably because I was tf2 and this requires tf1
After I downgraded to tf1 when I try to run the test I get W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations. and InvalidArgumentError (see above for traceback): No OpKernel was registered to support Op 'MaxPoolWithArgmax' with these attrs. Registered devices: [CPU], Registered kernels: <no registered kernels> and now I'm stuck because I have no clue what this means

当我尝试Pytorch项目时：

First I get this error: RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.load with map_location=torch.device('cpu') to map your storages to the CPU.
So I added map_location=torch.device('cpu') when the model is loaded, but now I get RuntimeError: Error(s) in loading state_dict for VGG16: size mismatch for conv6_1.weight: copying a param with shape torch.Size([512, 512, 1, 1]) from checkpoint, the shape in current model is torch.Size([512, 512, 3, 3]). And I'm stuck again

有人可以帮忙吗？

先感谢您！

曾经的你

2020-05-23 23:32:49

For the PyTorch one, there were two problems and it looks like you've solved the first one on your own with map_location. The second problem is that the weights in your checkpoint and the weights in your model don't have the same shape! A quick detour to the github repo; let's visit net.py in core. Take a look at lines 26 to 28:

# model released before 2019.09.09 should use kernel_size=1 & padding=0
# self.conv6_1 = nn.Conv2d(512, 512, kernel_size=1, padding=0,bias=True)
self.conv6_1 = nn.Conv2d(512, 512, kernel_size=3, padding=1,bias=True)

我猜想检查点正在加载权重，其中conv6_1的内核大小为1而不是3，就像注释掉的代码行一样。因此，尝试取消注释kernel_size = 1的行，并注释掉kernel_size = 3的行。

我可以在没有GPU的机器上使用PyTorch或Tensorflow项目吗？