RuntimeError: Expected object of type torch.cuda.FloatTensor but found type torch.FloatTensor for argument #2 'other' about yolov3 HOT 4 CLOSED

Multi-GPU training is not supported yet. See Issue #21.

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Because the box2 is torch.FloatTensor, the anchor_vec is on cpu. while the box1 is on GPU.
so, just use .cuda() to transform the data into torch.cuda.FloatTensor()
` box2 = anchor_vec.cuda().unsqueeze(1)

    inter_area = torch.min(box1, box2).prod(2)`

but, when you fix this, the below will also come out some bug.
` txy[b, a, gj, gi] = gxy - gxy.floor()

    # Width and height
    twh[b, a, gj, gi] = torch.log(gwh/ anchor_vec[a]) `

you need to transform the data type to GPU or Cuda according to the error info.
However, the main reason for multi-GPU training lies in
for i, (imgs, targets, _, _) in enumerate(dataloader):
where the imgs is a tensor, but the targets are lists. When parallel the imgs.to(device). The imgs are divided into batch_size/GPU_nums. But the targets cannot targets.to(device)(since it is a list), and the targets are the same num as the batch_size, cannot distribute into every GPUs.

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if nM > 0: lxy = k * MSELoss(xy[mask], txy[mask]) lwh = k * MSELoss(wh[mask], twh[mask])
the xy, txy, wh, twh is not the same dims as the batch_size.
the xy, wh is batch_size/GPU_nums.
but the txy, twh is the targets_nums( batch_size). There will occur some error.

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@longxianlei we just PRd our under-development multi_gpu branch into the master branch, so multi-GPU functionality now works. Many of the items you raised above should be resolved. See #135 for more info.

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