text stringlengths 1 93.6k |
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self.res = 1024
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self.network_pkl = "https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/transfer-learning-source-nets/ffhq-res1024-mirror-stylegan2-noaug.pkl"
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self.id_list = sorted(os.listdir(latent_path))
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self.id_list = [e for e in self.id_list]
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self.latent_dict = self.set_latent_dict(latent_path)
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self.normal_dict = self.set_normal_dict(normal_path)
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self.weight_dict = self.set_weight_dict(weight_path)
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self.fpyrs_dict = self.set_fpyrs_dict(self.latent_dict, self.weight_dict)
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self.mask_dict = self.load_mask_dict(mask_path)
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self.normal_idx_dict = self.get_valid_normal_index(self.normal_dict, self.mask_dict)
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self.num_id = len(self.id_list)
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print(f'length: {self.num_id}')
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gc.collect()
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torch.cuda.empty_cache()
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def load_mask_dict(self, mask_path):
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mask_dict = {}
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for id in self.id_list:
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id_base = id_replace(id)
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mask_dict[id] = torch.load(f'{mask_path}/{id_base}_mask.pt').detach().cpu()
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return mask_dict
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def set_latent_dict(self, latent_path):
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latent_dict = {}
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for id in self.id_list:
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latent_dict[id] = os.path.join(os.path.join(latent_path, id), '0.pt')
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return latent_dict
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def set_normal_dict(self, normal_path):
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normal_dict = {}
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for id in self.id_list:
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id_base = id_replace(id)
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id_split = id_base.split('_')
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id_base = id_split[0] + '_' + id_split[1]
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if id.endswith('f'):
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path = os.path.join(normal_path, f'{id_base}_normal_f.png')
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else:
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path = os.path.join(normal_path, f'{id_base}_normal.png')
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normal_dict[id] = img_np2pt(Image.open(path).convert('RGB'))
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return normal_dict
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def set_weight_dict(self, weight_path):
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weight_dict = {}
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for id in self.id_list:
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weight_dict[id] = os.path.join(weight_path, f'{self.weight_name}_{id}.pt')
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return weight_dict
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def set_fpyrs_dict(self, latent_dict, weight_dict):
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fpyrs = {}
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with dnnlib.util.open_url(self.network_pkl) as f:
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data = legacy.load_network_pkl(f)
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G = data['G_ema']
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G_feat = Generator_feat(G.z_dim, G.c_dim, G.w_dim, G.img_resolution, G.img_channels).to(self.device)
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for id in self.id_list:
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G_feat.load_state_dict(torch.load(weight_dict[id]).state_dict())
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latent = torch.load(latent_dict[id]).to(self.device)
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fpyrs[id] = sample_fpyr(G_feat, latent)
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print(id)
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return fpyrs
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def get_valid_normal_index(self, normal_dict, mask_dict):
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normal_idx_dict = {}
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for id in self.id_list:
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bool_norm = normal_dict[id] != 0
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bool_norm = bool_norm[:, :, 0] | bool_norm[:, :, 1] | bool_norm[:, :, 2]
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valid_norm = bool_norm & (mask_dict[id] != 0)
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normal_idx_dict[id] = valid_norm.nonzero()
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return normal_idx_dict
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def __len__(self):
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return len(self.id_list) * self.res * self.res
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def __getitem__(self, index):
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index = index % self.num_id
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id = self.id_list[index]
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normal = self.normal_dict[id]
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bool_norm = self.normal_idx_dict[id]
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pos_idx = np.random.randint(bool_norm.shape[0])
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h, w = bool_norm[pos_idx]
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h = h.item()
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w = w.item()
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