Spaces:
Running
Running
| from torch import nn | |
| import torch | |
| import torch.nn.functional as F | |
| from modules.util import AntiAliasInterpolation2d, make_coordinate_grid | |
| from torchvision import models | |
| import numpy as np | |
| from torch.autograd import grad | |
| import pdb | |
| import depth | |
| 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 | |
| class Transform: | |
| """ | |
| Random tps transformation for equivariance constraints. See Sec 3.3 | |
| """ | |
| def __init__(self, bs, **kwargs): | |
| noise = torch.normal(mean=0, std=kwargs['sigma_affine'] * torch.ones([bs, 2, 3])) | |
| self.theta = noise + torch.eye(2, 3).view(1, 2, 3) | |
| self.bs = bs | |
| if ('sigma_tps' in kwargs) and ('points_tps' in kwargs): | |
| self.tps = True | |
| self.control_points = make_coordinate_grid((kwargs['points_tps'], kwargs['points_tps']), type=noise.type()) | |
| self.control_points = self.control_points.unsqueeze(0) | |
| self.control_params = torch.normal(mean=0, | |
| std=kwargs['sigma_tps'] * torch.ones([bs, 1, kwargs['points_tps'] ** 2])) | |
| else: | |
| self.tps = False | |
| def transform_frame(self, frame): | |
| grid = make_coordinate_grid(frame.shape[2:], type=frame.type()).unsqueeze(0) | |
| grid = grid.view(1, frame.shape[2] * frame.shape[3], 2) | |
| grid = self.warp_coordinates(grid).view(self.bs, frame.shape[2], frame.shape[3], 2) | |
| return F.grid_sample(frame, grid, padding_mode="reflection") | |
| def warp_coordinates(self, coordinates): | |
| theta = self.theta.type(coordinates.type()) | |
| theta = theta.unsqueeze(1) | |
| transformed = torch.matmul(theta[:, :, :, :2], coordinates.unsqueeze(-1)) + theta[:, :, :, 2:] | |
| transformed = transformed.squeeze(-1) | |
| if self.tps: | |
| control_points = self.control_points.type(coordinates.type()) | |
| control_params = self.control_params.type(coordinates.type()) | |
| distances = coordinates.view(coordinates.shape[0], -1, 1, 2) - control_points.view(1, 1, -1, 2) | |
| distances = torch.abs(distances).sum(-1) | |
| result = distances ** 2 | |
| result = result * torch.log(distances + 1e-6) | |
| result = result * control_params | |
| result = result.sum(dim=2).view(self.bs, coordinates.shape[1], 1) | |
| transformed = transformed + result | |
| return transformed | |
| def jacobian(self, coordinates): | |
| new_coordinates = self.warp_coordinates(coordinates) | |
| grad_x = grad(new_coordinates[..., 0].sum(), coordinates, create_graph=True) | |
| grad_y = grad(new_coordinates[..., 1].sum(), coordinates, create_graph=True) | |
| jacobian = torch.cat([grad_x[0].unsqueeze(-2), grad_y[0].unsqueeze(-2)], dim=-2) | |
| return jacobian | |
| 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, generator, discriminator, train_params,opt): | |
| super(GeneratorFullModel, self).__init__() | |
| self.kp_extractor = kp_extractor | |
| self.generator = generator | |
| self.discriminator = discriminator | |
| self.train_params = train_params | |
| self.scales = train_params['scales'] | |
| self.disc_scales = self.discriminator.module.scales | |
| self.pyramid = ImagePyramide(self.scales, generator.module.num_channels) | |
| if torch.cuda.is_available(): | |
| self.pyramid = self.pyramid.cuda() | |
| self.opt = opt | |
| self.loss_weights = train_params['loss_weights'] | |
| if sum(self.loss_weights['perceptual']) != 0: | |
| self.vgg = Vgg19() | |
| if torch.cuda.is_available(): | |
| self.vgg = self.vgg.cuda() | |
| self.depth_encoder = depth.ResnetEncoder(18, False).cuda() | |
| self.depth_decoder = depth.DepthDecoder(num_ch_enc=self.depth_encoder.num_ch_enc, scales=range(4)).cuda() | |
| loaded_dict_enc = torch.load('depth/models/weights_19/encoder.pth',map_location='cpu') | |
| loaded_dict_dec = torch.load('depth/models/weights_19/depth.pth',map_location='cpu') | |
| filtered_dict_enc = {k: v for k, v in loaded_dict_enc.items() if k in self.depth_encoder.state_dict()} | |
| self.depth_encoder.load_state_dict(filtered_dict_enc) | |
| self.depth_decoder.load_state_dict(loaded_dict_dec) | |
| self.set_requires_grad(self.depth_encoder, False) | |
| self.set_requires_grad(self.depth_decoder, False) | |
| self.depth_decoder.eval() | |
| self.depth_encoder.eval() | |
| def set_requires_grad(self, nets, requires_grad=False): | |
| """Set requies_grad=Fasle for all the networks to avoid unnecessary computations | |
| Parameters: | |
| nets (network list) -- a list of networks | |
| requires_grad (bool) -- whether the networks require gradients or not | |
| """ | |
| if not isinstance(nets, list): | |
| nets = [nets] | |
| for net in nets: | |
| if net is not None: | |
| for param in net.parameters(): | |
| param.requires_grad = requires_grad | |
| def forward(self, x): | |
| depth_source = None | |
| depth_driving = None | |
| outputs = self.depth_decoder(self.depth_encoder(x['source'])) | |
| depth_source = outputs[("disp", 0)] | |
| outputs = self.depth_decoder(self.depth_encoder(x['driving'])) | |
| depth_driving = outputs[("disp", 0)] | |
| if self.opt.use_depth: | |
| kp_source = self.kp_extractor(depth_source) | |
| kp_driving = self.kp_extractor(depth_driving) | |
| elif self.opt.rgbd: | |
| source = torch.cat((x['source'],depth_source),1) | |
| driving = torch.cat((x['driving'],depth_driving),1) | |
| kp_source = self.kp_extractor(source) | |
| kp_driving = self.kp_extractor(driving) | |
| else: | |
| kp_source = self.kp_extractor(x['source']) | |
| kp_driving = self.kp_extractor(x['driving']) | |
| generated = self.generator(x['source'], kp_source=kp_source, kp_driving=kp_driving, source_depth = depth_source, driving_depth = depth_driving) | |
| 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['generator_gan'] != 0: | |
| discriminator_maps_generated = self.discriminator(pyramide_generated, kp=detach_kp(kp_driving)) | |
| discriminator_maps_real = self.discriminator(pyramide_real, kp=detach_kp(kp_driving)) | |
| value_total = 0 | |
| for scale in self.disc_scales: | |
| key = 'prediction_map_%s' % scale | |
| value = ((1 - discriminator_maps_generated[key]) ** 2).mean() | |
| value_total += self.loss_weights['generator_gan'] * value | |
| loss_values['gen_gan'] = value_total | |
| if sum(self.loss_weights['feature_matching']) != 0: | |
| value_total = 0 | |
| for scale in self.disc_scales: | |
| key = 'feature_maps_%s' % scale | |
| for i, (a, b) in enumerate(zip(discriminator_maps_real[key], discriminator_maps_generated[key])): | |
| if self.loss_weights['feature_matching'][i] == 0: | |
| continue | |
| value = torch.abs(a - b).mean() | |
| value_total += self.loss_weights['feature_matching'][i] * value | |
| loss_values['feature_matching'] = value_total | |
| if (self.loss_weights['equivariance_value'] + self.loss_weights['equivariance_jacobian']) != 0: | |
| transform = Transform(x['driving'].shape[0], **self.train_params['transform_params']) | |
| transformed_frame = transform.transform_frame(x['driving']) | |
| if self.opt.use_depth: | |
| outputs = self.depth_decoder(self.depth_encoder(transformed_frame)) | |
| depth_transform = outputs[("disp", 0)] | |
| transformed_kp = self.kp_extractor(depth_transform) | |
| elif self.opt.rgbd: | |
| outputs = self.depth_decoder(self.depth_encoder(transformed_frame)) | |
| depth_transform = outputs[("disp", 0)] | |
| transform_img = torch.cat((transformed_frame,depth_transform),1) | |
| transformed_kp = self.kp_extractor(transform_img) | |
| else: | |
| transformed_kp = self.kp_extractor(transformed_frame) | |
| generated['transformed_frame'] = transformed_frame | |
| generated['transformed_kp'] = transformed_kp | |
| ## Value loss part | |
| if self.loss_weights['equivariance_value'] != 0: | |
| value = torch.abs(kp_driving['value'] - transform.warp_coordinates(transformed_kp['value'])).mean() | |
| loss_values['equivariance_value'] = self.loss_weights['equivariance_value'] * value | |
| ## jacobian loss part | |
| if self.loss_weights['equivariance_jacobian'] != 0: | |
| jacobian_transformed = torch.matmul(transform.jacobian(transformed_kp['value']), | |
| transformed_kp['jacobian']) | |
| normed_driving = torch.inverse(kp_driving['jacobian']) | |
| normed_transformed = jacobian_transformed | |
| value = torch.matmul(normed_driving, normed_transformed) | |
| eye = torch.eye(2).view(1, 1, 2, 2).type(value.type()) | |
| value = torch.abs(eye - value).mean() | |
| loss_values['equivariance_jacobian'] = self.loss_weights['equivariance_jacobian'] * value | |
| if self.loss_weights['kp_distance']: | |
| bz,num_kp,kp_dim = kp_source['value'].shape | |
| sk = kp_source['value'].unsqueeze(2)-kp_source['value'].unsqueeze(1) | |
| dk = kp_driving['value'].unsqueeze(2)-kp_driving['value'].unsqueeze(1) | |
| source_dist_loss = (-torch.sign((torch.sqrt((sk*sk).sum(-1)+1e-8)+torch.eye(num_kp).cuda()*0.2)-0.2)+1).mean() | |
| driving_dist_loss = (-torch.sign((torch.sqrt((dk*dk).sum(-1)+1e-8)+torch.eye(num_kp).cuda()*0.2)-0.2)+1).mean() | |
| # driving_dist_loss = (torch.sign(1-(torch.sqrt((dk*dk).sum(-1)+1e-8)+torch.eye(num_kp).cuda()))+1).mean() | |
| value_total = self.loss_weights['kp_distance']*(source_dist_loss+driving_dist_loss) | |
| loss_values['kp_distance'] = value_total | |
| if self.loss_weights['kp_prior']: | |
| bz,num_kp,kp_dim = kp_source['value'].shape | |
| sk = kp_source['value'].unsqueeze(2)-kp_source['value'].unsqueeze(1) | |
| dk = kp_driving['value'].unsqueeze(2)-kp_driving['value'].unsqueeze(1) | |
| dis_loss = torch.relu(0.1-torch.sqrt((sk*sk).sum(-1)+1e-8))+torch.relu(0.1-torch.sqrt((dk*dk).sum(-1)+1e-8)) | |
| bs,nk,_=kp_source['value'].shape | |
| scoor_depth = F.grid_sample(depth_source,kp_source['value'].view(bs,1,nk,-1)) | |
| dcoor_depth = F.grid_sample(depth_driving,kp_driving['value'].view(bs,1,nk,-1)) | |
| sd_loss = torch.abs(scoor_depth.mean(-1,keepdim=True) - kp_source['value'].view(bs,1,nk,-1)).mean() | |
| dd_loss = torch.abs(dcoor_depth.mean(-1,keepdim=True) - kp_driving['value'].view(bs,1,nk,-1)).mean() | |
| value_total = self.loss_weights['kp_distance']*(dis_loss+sd_loss+dd_loss) | |
| loss_values['kp_distance'] = value_total | |
| if self.loss_weights['kp_scale']: | |
| bz,num_kp,kp_dim = kp_source['value'].shape | |
| if self.opt.rgbd: | |
| outputs = self.depth_decoder(self.depth_encoder(generated['prediction'])) | |
| depth_pred = outputs[("disp", 0)] | |
| pred = torch.cat((generated['prediction'],depth_pred),1) | |
| kp_pred = self.kp_extractor(pred) | |
| elif self.opt.use_depth: | |
| outputs = self.depth_decoder(self.depth_encoder(generated['prediction'])) | |
| depth_pred = outputs[("disp", 0)] | |
| kp_pred = self.kp_extractor(depth_pred) | |
| else: | |
| kp_pred = self.kp_extractor(generated['prediction']) | |
| pred_mean = kp_pred['value'].mean(1,keepdim=True) | |
| driving_mean = kp_driving['value'].mean(1,keepdim=True) | |
| pk = kp_source['value']-pred_mean | |
| dk = kp_driving['value']- driving_mean | |
| pred_dist_loss = torch.sqrt((pk*pk).sum(-1)+1e-8) | |
| driving_dist_loss = torch.sqrt((dk*dk).sum(-1)+1e-8) | |
| scale_vec = driving_dist_loss/pred_dist_loss | |
| bz,n = scale_vec.shape | |
| value = torch.abs(scale_vec[:,:n-1]-scale_vec[:,1:]).mean() | |
| value_total = self.loss_weights['kp_scale']*value | |
| loss_values['kp_scale'] = value_total | |
| if self.loss_weights['depth_constraint']: | |
| bz,num_kp,kp_dim = kp_source['value'].shape | |
| outputs = self.depth_decoder(self.depth_encoder(generated['prediction'])) | |
| depth_pred = outputs[("disp", 0)] | |
| value_total = self.loss_weights['depth_constraint']*torch.abs(depth_driving-depth_pred).mean() | |
| loss_values['depth_constraint'] = value_total | |
| return loss_values, generated | |
| class DiscriminatorFullModel(torch.nn.Module): | |
| """ | |
| Merge all discriminator related updates into single model for better multi-gpu usage | |
| """ | |
| def __init__(self, kp_extractor, generator, discriminator, train_params): | |
| super(DiscriminatorFullModel, self).__init__() | |
| self.kp_extractor = kp_extractor | |
| self.generator = generator | |
| self.discriminator = discriminator | |
| self.train_params = train_params | |
| self.scales = self.discriminator.module.scales | |
| self.pyramid = ImagePyramide(self.scales, generator.module.num_channels) | |
| if torch.cuda.is_available(): | |
| self.pyramid = self.pyramid.cuda() | |
| self.loss_weights = train_params['loss_weights'] | |
| def forward(self, x, generated): | |
| pyramide_real = self.pyramid(x['driving']) | |
| pyramide_generated = self.pyramid(generated['prediction'].detach()) | |
| kp_driving = generated['kp_driving'] | |
| discriminator_maps_generated = self.discriminator(pyramide_generated, kp=detach_kp(kp_driving)) | |
| discriminator_maps_real = self.discriminator(pyramide_real, kp=detach_kp(kp_driving)) | |
| loss_values = {} | |
| value_total = 0 | |
| for scale in self.scales: | |
| key = 'prediction_map_%s' % scale | |
| value = (1 - discriminator_maps_real[key]) ** 2 + discriminator_maps_generated[key] ** 2 | |
| value_total += self.loss_weights['discriminator_gan'] * value.mean() | |
| loss_values['disc_gan'] = value_total | |
| return loss_values | |