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deleted bottleneck in .cuda() #64

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36 changes: 19 additions & 17 deletions FAS_challenge_CVPRW2020/Track2 Single-modal/model1_pytorch/train_CDCNpp_model1.py
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Original file line number Diff line number Diff line change
Expand Up @@ -45,45 +45,47 @@
#val_list = '/wrk/yuzitong/DONOTREMOVE/CVPRW2020/4@3_dev_res.txt'


def contrast_depth_conv(input):
def contrast_depth_conv(input, kernel_filter):
''' compute contrast depth in both of (out, label) '''
'''
input 32x32
output 8x32x32
'''


kernel_filter_list =[
[[1,0,0],[0,-1,0],[0,0,0]], [[0,1,0],[0,-1,0],[0,0,0]], [[0,0,1],[0,-1,0],[0,0,0]],
[[0,0,0],[1,-1,0],[0,0,0]], [[0,0,0],[0,-1,1],[0,0,0]],
[[0,0,0],[0,-1,0],[1,0,0]], [[0,0,0],[0,-1,0],[0,1,0]], [[0,0,0],[0,-1,0],[0,0,1]]
]

kernel_filter = np.array(kernel_filter_list, np.float32)

kernel_filter = torch.from_numpy(kernel_filter.astype(np.float)).float().cuda()
# weights (in_channel, out_channel, kernel, kernel)
kernel_filter = kernel_filter.unsqueeze(dim=1)

input = input.unsqueeze(dim=1).expand(input.shape[0], 8, input.shape[1],input.shape[2])

contrast_depth = F.conv2d(input, weight=kernel_filter, groups=8) # depthwise conv

return contrast_depth


class Contrast_depth_loss(nn.Module): # Pearson range [-1, 1] so if < 0, abs|loss| ; if >0, 1- loss
def __init__(self):
super(Contrast_depth_loss,self).__init__()

kernel_filter_list =[
[[1,0,0],[0,-1,0],[0,0,0]], [[0,1,0],[0,-1,0],[0,0,0]], [[0,0,1],[0,-1,0],[0,0,0]],
[[0,0,0],[1,-1,0],[0,0,0]], [[0,0,0],[0,-1,1],[0,0,0]],
[[0,0,0],[0,-1,0],[1,0,0]], [[0,0,0],[0,-1,0],[0,1,0]], [[0,0,0],[0,-1,0],[0,0,1]]
]

kernel_filter = np.array(kernel_filter_list, np.float32)
kernel_filter = torch.from_numpy(kernel_filter.astype(np.float)).float().cuda()
kernel_filter = kernel_filter.unsqueeze(dim=1)

self.kernel_filter = kernel_filter

return


def forward(self, out, label):
'''
compute contrast depth in both of (out, label),
then get the loss of them
tf.atrous_convd match tf-versions: 1.4
'''
contrast_out = contrast_depth_conv(out)
contrast_label = contrast_depth_conv(label)
contrast_out = contrast_depth_conv(out, self.kernel_filter)
contrast_label = contrast_depth_conv(label, self.kernel_filter)


criterion_MSE = nn.MSELoss().cuda()
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