| from torch import nn |
| import torch |
| import torch.nn.functional as F |
| from modules.util import AntiAliasInterpolation2d, TPS |
| from torchvision import models |
| import numpy as np |
|
|
|
|
| class Vgg19(torch.nn.Module): |
| """ |
| Vgg19 network for perceptual loss. See Sec 3.3. |
| """ |
| def __init__(self, requires_grad=False): |
| super(Vgg19, self).__init__() |
| vgg_pretrained_features = models.vgg19(pretrained=True).features |
| self.slice1 = torch.nn.Sequential() |
| self.slice2 = torch.nn.Sequential() |
| self.slice3 = torch.nn.Sequential() |
| self.slice4 = torch.nn.Sequential() |
| self.slice5 = torch.nn.Sequential() |
| for x in range(2): |
| self.slice1.add_module(str(x), vgg_pretrained_features[x]) |
| for x in range(2, 7): |
| self.slice2.add_module(str(x), vgg_pretrained_features[x]) |
| for x in range(7, 12): |
| self.slice3.add_module(str(x), vgg_pretrained_features[x]) |
| for x in range(12, 21): |
| self.slice4.add_module(str(x), vgg_pretrained_features[x]) |
| for x in range(21, 30): |
| self.slice5.add_module(str(x), vgg_pretrained_features[x]) |
|
|
| self.mean = torch.nn.Parameter(data=torch.Tensor(np.array([0.485, 0.456, 0.406]).reshape((1, 3, 1, 1))), |
| requires_grad=False) |
| self.std = torch.nn.Parameter(data=torch.Tensor(np.array([0.229, 0.224, 0.225]).reshape((1, 3, 1, 1))), |
| requires_grad=False) |
|
|
| if not requires_grad: |
| for param in self.parameters(): |
| param.requires_grad = False |
|
|
| def forward(self, X): |
| X = (X - self.mean) / self.std |
| h_relu1 = self.slice1(X) |
| h_relu2 = self.slice2(h_relu1) |
| h_relu3 = self.slice3(h_relu2) |
| h_relu4 = self.slice4(h_relu3) |
| h_relu5 = self.slice5(h_relu4) |
| out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5] |
| return out |
|
|
|
|
| class ImagePyramide(torch.nn.Module): |
| """ |
| Create image pyramide for computing pyramide perceptual loss. See Sec 3.3 |
| """ |
| def __init__(self, scales, num_channels): |
| super(ImagePyramide, self).__init__() |
| downs = {} |
| for scale in scales: |
| downs[str(scale).replace('.', '-')] = AntiAliasInterpolation2d(num_channels, scale) |
| self.downs = nn.ModuleDict(downs) |
|
|
| def forward(self, x): |
| out_dict = {} |
| for scale, down_module in self.downs.items(): |
| out_dict['prediction_' + str(scale).replace('-', '.')] = down_module(x) |
| return out_dict |
|
|
|
|
| def detach_kp(kp): |
| return {key: value.detach() for key, value in kp.items()} |
|
|
|
|
| class GeneratorFullModel(torch.nn.Module): |
| """ |
| Merge all generator related updates into single model for better multi-gpu usage |
| """ |
|
|
| def __init__(self, kp_extractor, bg_predictor, dense_motion_network, inpainting_network, train_params, *kwargs): |
| super(GeneratorFullModel, self).__init__() |
| self.kp_extractor = kp_extractor |
| self.inpainting_network = inpainting_network |
| self.dense_motion_network = dense_motion_network |
|
|
| self.bg_predictor = None |
| if bg_predictor: |
| self.bg_predictor = bg_predictor |
| self.bg_start = train_params['bg_start'] |
|
|
| self.train_params = train_params |
| self.scales = train_params['scales'] |
|
|
| self.pyramid = ImagePyramide(self.scales, inpainting_network.num_channels) |
| if torch.cuda.is_available(): |
| self.pyramid = self.pyramid.cuda() |
|
|
| self.loss_weights = train_params['loss_weights'] |
| self.dropout_epoch = train_params['dropout_epoch'] |
| self.dropout_maxp = train_params['dropout_maxp'] |
| self.dropout_inc_epoch = train_params['dropout_inc_epoch'] |
| self.dropout_startp =train_params['dropout_startp'] |
| |
| if sum(self.loss_weights['perceptual']) != 0: |
| self.vgg = Vgg19() |
| if torch.cuda.is_available(): |
| self.vgg = self.vgg.cuda() |
|
|
|
|
| def forward(self, x, epoch): |
| kp_source = self.kp_extractor(x['source']) |
| kp_driving = self.kp_extractor(x['driving']) |
| bg_param = None |
| if self.bg_predictor: |
| if(epoch>=self.bg_start): |
| bg_param = self.bg_predictor(x['source'], x['driving']) |
| |
| if(epoch>=self.dropout_epoch): |
| dropout_flag = False |
| dropout_p = 0 |
| else: |
| |
| dropout_flag = True |
| dropout_p = min(epoch/self.dropout_inc_epoch * self.dropout_maxp + self.dropout_startp, self.dropout_maxp) |
| |
| dense_motion = self.dense_motion_network(source_image=x['source'], kp_driving=kp_driving, |
| kp_source=kp_source, bg_param = bg_param, |
| dropout_flag = dropout_flag, dropout_p = dropout_p) |
| generated = self.inpainting_network(x['source'], dense_motion) |
| generated.update({'kp_source': kp_source, 'kp_driving': kp_driving}) |
|
|
| loss_values = {} |
|
|
| pyramide_real = self.pyramid(x['driving']) |
| pyramide_generated = self.pyramid(generated['prediction']) |
|
|
| |
| if sum(self.loss_weights['perceptual']) != 0: |
| value_total = 0 |
| for scale in self.scales: |
| x_vgg = self.vgg(pyramide_generated['prediction_' + str(scale)]) |
| y_vgg = self.vgg(pyramide_real['prediction_' + str(scale)]) |
|
|
| for i, weight in enumerate(self.loss_weights['perceptual']): |
| value = torch.abs(x_vgg[i] - y_vgg[i].detach()).mean() |
| value_total += self.loss_weights['perceptual'][i] * value |
| loss_values['perceptual'] = value_total |
|
|
| |
| if self.loss_weights['equivariance_value'] != 0: |
| transform_random = TPS(mode = 'random', bs = x['driving'].shape[0], **self.train_params['transform_params']) |
| transform_grid = transform_random.transform_frame(x['driving']) |
| transformed_frame = F.grid_sample(x['driving'], transform_grid, padding_mode="reflection",align_corners=True) |
| transformed_kp = self.kp_extractor(transformed_frame) |
|
|
| generated['transformed_frame'] = transformed_frame |
| generated['transformed_kp'] = transformed_kp |
| |
| warped = transform_random.warp_coordinates(transformed_kp['fg_kp']) |
| kp_d = kp_driving['fg_kp'] |
| value = torch.abs(kp_d - warped).mean() |
| loss_values['equivariance_value'] = self.loss_weights['equivariance_value'] * value |
|
|
| |
| if self.loss_weights['warp_loss'] != 0: |
| occlusion_map = generated['occlusion_map'] |
| encode_map = self.inpainting_network.get_encode(x['driving'], occlusion_map) |
| decode_map = generated['warped_encoder_maps'] |
| value = 0 |
| for i in range(len(encode_map)): |
| value += torch.abs(encode_map[i]-decode_map[-i-1]).mean() |
|
|
| loss_values['warp_loss'] = self.loss_weights['warp_loss'] * value |
| |
| |
| if self.bg_predictor and epoch >= self.bg_start and self.loss_weights['bg'] != 0: |
| bg_param_reverse = self.bg_predictor(x['driving'], x['source']) |
| value = torch.matmul(bg_param, bg_param_reverse) |
| eye = torch.eye(3).view(1, 1, 3, 3).type(value.type()) |
| value = torch.abs(eye - value).mean() |
| loss_values['bg'] = self.loss_weights['bg'] * value |
|
|
| return loss_values, generated |
|
|
