Open DwanAI opened 8 months ago
i got the same problem
Have you solved this problem, I think it's a problem with the model channel, but haven't found a way
self.encoder_kv = FeatureGenerator(opt.semantic_nc, opt.ngf, opt.max_multi, norm='instance') this line ;
semantic_nc change 3 channel or pix2pix_model.py preprocess_input change Cut off 3 channels ex : [:,:3, :, :],
of course, self.encoder_q = FeatureGenerator(opt.semantic_nc, opt.ngf, opt.max_multi, norm='instance') self.encoder_kv = FeatureGenerator(opt.semantic_nc+3, opt.ngf, opt.max_multi, norm='instance')
ref_input = torch.cat((ref_img, ref_seg_map), dim=1) out['warp_out'] = [] adaptive_feature_seg = self.encoder_q(seg_map) adaptive_feature_img = self.encoder_kv(ref_input)
for i in range(len(adaptive_feature_seg)):
adaptive_feature_seg[i] = util.feature_normalize(adaptive_feature_seg[i])
adaptive_feature_img[i] = util.feature_normalize(adaptive_feature_img[i])
if self.opt.isTrain and self.opt.weight_novgg_featpair > 0:
real_input = torch.cat((real_img, seg_map), dim=1)
adaptive_feature_img_pair = self.encoder_kv(real_input)
loss_novgg_featpair = 0
weights = [1.0, 1.0, 1.0, 1.0]
for i in range(len(adaptive_feature_img_pair)):
adaptive_feature_img_pair[i] = util.feature_normalize(adaptive_feature_img_pair[i])
loss_novgg_featpair += F.l1_loss(adaptive_feature_seg[i], adaptive_feature_img_pair[i]) * weights[i]
out['loss_novgg_featpair'] = loss_novgg_featpair * self.opt.weight_novgg_featpair
i fond that the train code is runable, but the test code has this problem
if you use this code train labelnc 3. use pretrain model or train labelnc 15 use celeba generator.py ---- Replied Message ---- | From | @.> | | Date | 01/30/2024 14:45 | | To | AiArt-HDU/MATEBIT @.> | | Cc | aiartjc666 @.>, Comment @.> | | Subject | Re: [AiArt-HDU/MATEBIT] Has anyone tried to reproduce for the celeba dataset? (Issue #7) |
i fond that the train code is runable, but the test code has this problem
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thanks, i tried use generator_celebahqedge.py and i can train now, but the test code got some new promblem
by the way, can i train on my own face dataset like vox which does not have segment labels?
you use hed algorithms to get edge like metfaces
---- Replied Message ---- | From | @.> | | Date | 01/30/2024 18:07 | | To | AiArt-HDU/MATEBIT @.> | | Cc | aiartjc666 @.>, Comment @.> | | Subject | Re: [AiArt-HDU/MATEBIT] Has anyone tried to reproduce for the celeba dataset? (Issue #7) |
by the way, can i train on my own face dataset like vox which does not have segment labels?
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how should i set the parameters if i have 1 channel label and 3channel image, if i set label_nc=1, it doesn't work: adaptive_feature_img = self.encoder_kv(ref_img) Given groups=1, weight of size [64, 1, 3, 3], expected input[4, 3, 258, 258] to have 1 channels, but got 3 channels instead
this is channel error . you can label read 3 channels, such as image.open(path).convert("RGB") # this label_nc 3 or init modules encoder_kv (self.semantic_nc + input_channels) # this label_nc 1 forward : .... self.encoder_kv(torch.cat((ref_img, seg_label),dim=1),
generator_celebahqedge
how to use generator_celebahqedge.py
When I change some file paths, I get RuntimeError: Given groups=1, weight of size [64, 15, 3, 3], expected input[2, 3, 258, 258] to have 15 channels, but got 3 channels instead problems