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