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@@ -22,10 +22,10 @@ transformer_Arcface = transforms.Compose([
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
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])
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])
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-# detransformer = transforms.Compose([
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-# transforms.Normalize([0, 0, 0], [1/0.229, 1/0.224, 1/0.225]),
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-# transforms.Normalize([-0.485, -0.456, -0.406], [1, 1, 1])
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-# ])
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+detransformer = transforms.Compose([
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+ transforms.Normalize([0, 0, 0], [1/0.229, 1/0.224, 1/0.225]),
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+ transforms.Normalize([-0.485, -0.456, -0.406], [1, 1, 1])
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+ ])
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if __name__ == '__main__':
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if __name__ == '__main__':
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opt = TestOptions().parse()
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opt = TestOptions().parse()
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@@ -35,51 +35,52 @@ if __name__ == '__main__':
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model = create_model(opt)
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model = create_model(opt)
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model.eval()
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model.eval()
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+ with torch.no_grad():
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+
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+ pic_a = opt.pic_a_path
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+ img_a = Image.open(pic_a).convert('RGB')
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+ img_a = transformer_Arcface(img_a)
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+ img_id = img_a.view(-1, img_a.shape[0], img_a.shape[1], img_a.shape[2])
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- pic_a = opt.pic_a_path
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- img_a = Image.open(pic_a).convert('RGB')
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- img_a = transformer_Arcface(img_a)
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- img_id = img_a.view(-1, img_a.shape[0], img_a.shape[1], img_a.shape[2])
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-
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- pic_b = opt.pic_b_path
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+ pic_b = opt.pic_b_path
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- img_b = Image.open(pic_b).convert('RGB')
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- img_b = transformer(img_b)
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- img_att = img_b.view(-1, img_b.shape[0], img_b.shape[1], img_b.shape[2])
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+ img_b = Image.open(pic_b).convert('RGB')
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+ img_b = transformer(img_b)
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+ img_att = img_b.view(-1, img_b.shape[0], img_b.shape[1], img_b.shape[2])
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- # convert numpy to tensor
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- img_id = img_id.cuda()
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- img_att = img_att.cuda()
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+ # convert numpy to tensor
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+ img_id = img_id.cuda()
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+ img_att = img_att.cuda()
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- #create latent id
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- img_id_downsample = F.interpolate(img_id, scale_factor=0.5)
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- latend_id = model.netArc(img_id_downsample)
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- latend_id = latend_id.detach().to('cpu')
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- latend_id = latend_id/np.linalg.norm(latend_id,axis=1,keepdims=True)
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- latend_id = latend_id.to('cuda')
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+ #create latent id
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+ img_id_downsample = F.interpolate(img_id, scale_factor=0.5)
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+ latend_id = model.netArc(img_id_downsample)
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+ latend_id = latend_id.detach().to('cpu')
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+ latend_id = latend_id/np.linalg.norm(latend_id,axis=1,keepdims=True)
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+ latend_id = latend_id.to('cuda')
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- ############## Forward Pass ######################
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- img_fake = model(img_id, img_att, latend_id, latend_id, True)
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+ ############## Forward Pass ######################
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+ img_fake = model(img_id, img_att, latend_id, latend_id, True)
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- for i in range(img_id.shape[0]):
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- if i == 0:
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- row1 = img_id[i]
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- row2 = img_att[i]
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- row3 = img_fake[i]
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- else:
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- row1 = torch.cat([row1, img_id[i]], dim=2)
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- row2 = torch.cat([row2, img_att[i]], dim=2)
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- row3 = torch.cat([row3, img_fake[i]], dim=2)
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+ for i in range(img_id.shape[0]):
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+ if i == 0:
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+ row1 = img_id[i]
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+ row2 = img_att[i]
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+ row3 = img_fake[i]
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+ else:
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+ row1 = torch.cat([row1, img_id[i]], dim=2)
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+ row2 = torch.cat([row2, img_att[i]], dim=2)
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+ row3 = torch.cat([row3, img_fake[i]], dim=2)
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- #full = torch.cat([row1, row2, row3], dim=1).detach()
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- full = row3.detach()
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- full = full.permute(1, 2, 0)
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- output = full.to('cpu')
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- output = np.array(output)
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- output = output[..., ::-1]
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+ #full = torch.cat([row1, row2, row3], dim=1).detach()
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+ full = row3.detach()
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+ full = full.permute(1, 2, 0)
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+ output = full.to('cpu')
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+ output = np.array(output)
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+ output = output[..., ::-1]
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- output = output*255
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+ output = output*255
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- cv2.imwrite(opt.output_path + 'result.jpg',output)
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+ cv2.imwrite(opt.output_path + 'result.jpg',output)
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