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Hi zhan, I appreciate your work so much! However , I met some bugs when I tried to use your code.
1、models/networks/correspondence.py (line 385)
elif self.opt.warp_mask_losstype == 'cycle': # f_div_C_v = F.softmax(f_WTA.transpose(1, 2), dim=-1) f_WTA_v = f.transpose(1, 2) f_div_C_v = f_WTA_v / f_WTA_v.sum(-1).view(-1, N, 1) seg = F.interpolate(seg_map, scale_factor=1 / self.down, mode='nearest') channel = seg.shape[1] seg = seg.view(batch_size, channel, -1) seg = seg.permute(0, 2, 1) warp_mask_to_ref = torch.matmul(f_div_C_v, seg) # 2*1936*channel warp_mask = torch.matmul(f_div_C, warp_mask_to_ref) # 2*1936*channel warp_mask = warp_mask.permute(0, 2, 1).contiguous() coor_out['warp_mask'] = warp_mask.view(batch_size, channel, feat_height, feat_width) # 2*3*44*44
'f' and 'f_div_C' is not defined
2、models/networks/generator.py (line 47)
self.conv_img1 = nn.Conv2d(1, seg, 3, padding=1) self.up = nn.Upsample(scale_factor=2)
'seg' is not defined
would you please fix these bugs?
The text was updated successfully, but these errors were encountered:
same problem.
Sorry, something went wrong.
Seems you set --warp_mask_losstype cycle in training, you may follow the default setting of --warp_mask_losstype direct
I have corrected the bug in generator.py
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Hi zhan, I appreciate your work so much! However , I met some bugs when I tried to use your code.
'f' and 'f_div_C' is not defined
'seg' is not defined
would you please fix these bugs?
The text was updated successfully, but these errors were encountered: