| | 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 |
| |
|
| |
|
| | 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): |
| | 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.scales |
| | self.pyramid = ImagePyramide(self.scales, generator.num_channels) |
| | if torch.cuda.is_available(): |
| | self.pyramid = self.pyramid.cuda() |
| |
|
| | 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() |
| |
|
| | def forward(self, x): |
| | 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) |
| | 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']) |
| | transformed_kp = self.kp_extractor(transformed_frame) |
| |
|
| | generated['transformed_frame'] = transformed_frame |
| | generated['transformed_kp'] = transformed_kp |
| |
|
| | |
| | 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 |
| |
|
| | |
| | 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 |
| |
|
| | 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.scales |
| | self.pyramid = ImagePyramide(self.scales, generator.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 |
| |
|
