| from torch import nn |
| import torch |
| import torch.nn.functional as F |
| from modules.util import AntiAliasInterpolation2d, make_coordinate_grid_2d |
| from torchvision import models |
| import numpy as np |
| from torch.autograd import grad |
| import modules.hopenet as hopenet |
| from torchvision import transforms |
|
|
| import random |
| from basicsr.data.degradations import random_add_gaussian_noise_pt, random_add_poisson_noise_pt |
| from prefetch_generator import BackgroundGenerator |
| from basicsr.utils.img_process_util import filter2D |
| from basicsr.utils import DiffJPEG, USMSharp |
|
|
|
|
| class Vgg19(torch.nn.Module): |
| """ |
| Vgg19 network for perceptual loss. |
| """ |
| 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. |
| """ |
| 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. |
| """ |
| 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_2d((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_2d(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()} |
|
|
|
|
| def headpose_pred_to_degree(pred): |
| device = pred.device |
| idx_tensor = [idx for idx in range(66)] |
| idx_tensor = torch.FloatTensor(idx_tensor).to(device) |
| pred = F.softmax(pred) |
| degree = torch.sum(pred*idx_tensor, axis=1) * 3 - 99 |
|
|
| return degree |
|
|
| ''' |
| # beta version |
| def get_rotation_matrix(yaw, pitch, roll): |
| yaw = yaw / 180 * 3.14 |
| pitch = pitch / 180 * 3.14 |
| roll = roll / 180 * 3.14 |
| |
| roll = roll.unsqueeze(1) |
| pitch = pitch.unsqueeze(1) |
| yaw = yaw.unsqueeze(1) |
| |
| roll_mat = torch.cat([torch.ones_like(roll), torch.zeros_like(roll), torch.zeros_like(roll), |
| torch.zeros_like(roll), torch.cos(roll), -torch.sin(roll), |
| torch.zeros_like(roll), torch.sin(roll), torch.cos(roll)], dim=1) |
| roll_mat = roll_mat.view(roll_mat.shape[0], 3, 3) |
| |
| pitch_mat = torch.cat([torch.cos(pitch), torch.zeros_like(pitch), torch.sin(pitch), |
| torch.zeros_like(pitch), torch.ones_like(pitch), torch.zeros_like(pitch), |
| -torch.sin(pitch), torch.zeros_like(pitch), torch.cos(pitch)], dim=1) |
| pitch_mat = pitch_mat.view(pitch_mat.shape[0], 3, 3) |
| |
| yaw_mat = torch.cat([torch.cos(yaw), -torch.sin(yaw), torch.zeros_like(yaw), |
| torch.sin(yaw), torch.cos(yaw), torch.zeros_like(yaw), |
| torch.zeros_like(yaw), torch.zeros_like(yaw), torch.ones_like(yaw)], dim=1) |
| yaw_mat = yaw_mat.view(yaw_mat.shape[0], 3, 3) |
| |
| rot_mat = torch.einsum('bij,bjk,bkm->bim', roll_mat, pitch_mat, yaw_mat) |
| |
| return rot_mat |
| ''' |
|
|
| def get_rotation_matrix(yaw, pitch, roll): |
| yaw = yaw / 180 * 3.14 |
| pitch = pitch / 180 * 3.14 |
| roll = roll / 180 * 3.14 |
|
|
| roll = roll.unsqueeze(1) |
| pitch = pitch.unsqueeze(1) |
| yaw = yaw.unsqueeze(1) |
|
|
| pitch_mat = torch.cat([torch.ones_like(pitch), torch.zeros_like(pitch), torch.zeros_like(pitch), |
| torch.zeros_like(pitch), torch.cos(pitch), -torch.sin(pitch), |
| torch.zeros_like(pitch), torch.sin(pitch), torch.cos(pitch)], dim=1) |
| pitch_mat = pitch_mat.view(pitch_mat.shape[0], 3, 3) |
|
|
| yaw_mat = torch.cat([torch.cos(yaw), torch.zeros_like(yaw), torch.sin(yaw), |
| torch.zeros_like(yaw), torch.ones_like(yaw), torch.zeros_like(yaw), |
| -torch.sin(yaw), torch.zeros_like(yaw), torch.cos(yaw)], dim=1) |
| yaw_mat = yaw_mat.view(yaw_mat.shape[0], 3, 3) |
|
|
| roll_mat = torch.cat([torch.cos(roll), -torch.sin(roll), torch.zeros_like(roll), |
| torch.sin(roll), torch.cos(roll), torch.zeros_like(roll), |
| torch.zeros_like(roll), torch.zeros_like(roll), torch.ones_like(roll)], dim=1) |
| roll_mat = roll_mat.view(roll_mat.shape[0], 3, 3) |
|
|
| rot_mat = torch.einsum('bij,bjk,bkm->bim', pitch_mat, yaw_mat, roll_mat) |
|
|
| return rot_mat |
|
|
| def keypoint_transformation(kp_canonical, he, estimate_jacobian=True): |
| kp = kp_canonical['value'] |
| yaw, pitch, roll = he['yaw'], he['pitch'], he['roll'] |
| t, exp = he['t'], he['exp'] |
| |
| yaw = headpose_pred_to_degree(yaw) |
| pitch = headpose_pred_to_degree(pitch) |
| roll = headpose_pred_to_degree(roll) |
|
|
| rot_mat = get_rotation_matrix(yaw, pitch, roll) |
| |
| |
| kp_rotated = torch.einsum('bmp,bkp->bkm', rot_mat, kp) |
|
|
| |
| t = t.unsqueeze_(1).repeat(1, kp.shape[1], 1) |
| kp_t = kp_rotated + t |
|
|
| |
| exp = exp.view(exp.shape[0], -1, 3) |
| kp_transformed = kp_t + exp |
|
|
| if estimate_jacobian: |
| jacobian = kp_canonical['jacobian'] |
| jacobian_transformed = torch.einsum('bmp,bkps->bkms', rot_mat, jacobian) |
| else: |
| jacobian_transformed = None |
|
|
| return {'value': kp_transformed, 'jacobian': jacobian_transformed} |
|
|
| class GeneratorFullModel(torch.nn.Module): |
| """ |
| Merge all generator related updates into single model for better multi-gpu usage |
| """ |
|
|
| def __init__(self, kp_extractor, he_estimator, generator, discriminator, train_params, estimate_jacobian=True): |
| super(GeneratorFullModel, self).__init__() |
| self.kp_extractor = kp_extractor |
| self.he_estimator = he_estimator |
| 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.image_channel) |
| if torch.cuda.is_available(): |
| self.pyramid = self.pyramid.cuda() |
|
|
| self.loss_weights = train_params['loss_weights'] |
|
|
| self.estimate_jacobian = estimate_jacobian |
|
|
| if sum(self.loss_weights['perceptual']) != 0: |
| self.vgg = Vgg19() |
| if torch.cuda.is_available(): |
| self.vgg = self.vgg.cuda() |
|
|
| self.L1 = nn.L1Loss().cuda() |
|
|
| if self.loss_weights['headpose'] != 0: |
| self.hopenet = hopenet.Hopenet(models.resnet.Bottleneck, [3, 4, 6, 3], 66) |
| print('Loading hopenet') |
| hopenet_state_dict = torch.load(train_params['hopenet_snapshot']) |
| self.hopenet.load_state_dict(hopenet_state_dict) |
| if torch.cuda.is_available(): |
| self.hopenet = self.hopenet.cuda() |
| self.hopenet.eval() |
| |
| self.jpeger = DiffJPEG(differentiable=False).cuda() |
| self.usm_sharpener = USMSharp().cuda() |
|
|
| self.resize_prob = [0.2, 0.7, 0.1] |
| self.resize_range= [0.5, 1.2] |
| self.noise_range = [1, 10] |
| self.poisson_scale_range =[0.05, 1] |
| self.jpeg_range = [10, 25] |
| |
| self.opt_scale = 1 |
|
|
| self.resize_prob2 = [0.3, 0.4, 0.3] |
| self.resize_range2= [0.5, 1.2] |
| self.noise_range2 = [1, 10] |
| self.poisson_scale_range2 = [0.05, 1.0] |
| self.jpeg_range2 = [10, 25] |
|
|
|
|
| def forward(self, x, config): |
| kp_canonical = self.kp_extractor(x['source']) |
|
|
| he_source = self.he_estimator(x['source']) |
| he_driving = self.he_estimator(x['driving']) |
|
|
| |
| kp_source = keypoint_transformation(kp_canonical, he_source, self.estimate_jacobian) |
| kp_driving = keypoint_transformation(kp_canonical, he_driving, self.estimate_jacobian) |
| if config['train_params']['low_quality_train']: |
| |
| |
| |
| x_source = x['source'] |
| x_source = filter2D(x_source, x['kernel']) |
| |
| |
| updown_type = random.choices(['up', 'down', 'keep'], self.resize_prob)[0] |
| if updown_type == 'up': |
| scale = np.random.uniform(1, self.resize_range[1]) |
| elif updown_type == 'down': |
| scale = np.random.uniform(self.resize_range[0], 1) |
| else: |
| scale = 1 |
| mode = random.choice(['area', 'bilinear', 'bicubic']) |
| x_source = F.interpolate(x_source, scale_factor=scale, mode=mode) |
|
|
| |
| gray_noise_prob = 0.4 |
| if np.random.uniform() < 0.5: |
| x_source = random_add_gaussian_noise_pt( |
| x_source, sigma_range=self.noise_range, clip=True, rounds=False, gray_prob=gray_noise_prob) |
| else: |
| x_source = random_add_poisson_noise_pt( |
| x_source, |
| scale_range=self.poisson_scale_range, |
| gray_prob=gray_noise_prob, |
| clip=True, |
| rounds=False) |
| |
| jpeg_p = x_source.new_zeros(x_source.size(0)).uniform_(*self.jpeg_range) |
| x_source = torch.clamp(x_source, 0, 1) |
| x_source = self.jpeger(x_source, quality=jpeg_p) |
| |
| |
| |
| if np.random.uniform() < 0.8: |
| x_source = filter2D(x_source, x['kernel2'].cuda()) |
| |
| updown_type = random.choices(['up', 'down', 'keep'], self.resize_prob2)[0] |
| if updown_type == 'up': |
| scale = np.random.uniform(1, self.resize_range2[1]) |
| elif updown_type == 'down': |
| scale = np.random.uniform(self.resize_range2[0], 1) |
| else: |
| scale = 1 |
| mode = random.choice(['area', 'bilinear', 'bicubic']) |
| x_source = F.interpolate( |
| x_source, size=(int(config['dataset_params']['frame_shape'][0] / self.opt_scale * scale), int(config['dataset_params']['frame_shape'][1] / self.opt_scale * scale)), mode=mode) |
| |
| gray_noise_prob = 0.4 |
| if np.random.uniform() < 0.5: |
| x_source = random_add_gaussian_noise_pt( |
| x_source, sigma_range=self.noise_range2, clip=True, rounds=False, gray_prob=gray_noise_prob) |
| else: |
| x_source = random_add_poisson_noise_pt( |
| x_source, |
| scale_range=self.poisson_scale_range2, |
| gray_prob=gray_noise_prob, |
| clip=True, |
| rounds=False) |
|
|
| |
| |
| |
| |
| |
| |
| |
| if np.random.uniform() < 0.5: |
| |
| mode = random.choice(['area', 'bilinear', 'bicubic']) |
| x_source = F.interpolate(x_source, size=(config['dataset_params']['frame_shape'][0] // self.opt_scale, config['dataset_params']['frame_shape'][1] // self.opt_scale), mode=mode) |
| x_source = filter2D(x_source, x['sinc_kernel'].cuda()) |
| |
| jpeg_p = x_source.new_zeros(x_source.size(0)).uniform_(*self.jpeg_range2) |
| x_source = torch.clamp(x_source, 0, 1) |
| x_source = self.jpeger(x_source, quality=jpeg_p) |
| else: |
| |
| jpeg_p = x_source.new_zeros(x_source.size(0)).uniform_(*self.jpeg_range2) |
| x_source = torch.clamp(x_source, 0, 1) |
| x_source = self.jpeger(x_source, quality=jpeg_p) |
| |
| mode = random.choice(['area', 'bilinear', 'bicubic']) |
| x_source = F.interpolate(x_source, size=(config['dataset_params']['frame_shape'][0] // self.opt_scale, config['dataset_params']['frame_shape'][1] // self.opt_scale), mode=mode) |
| x_source = filter2D(x_source, x['sinc_kernel'].cuda()) |
|
|
| |
| lq = torch.clamp((x_source * 255.0).round(), 0, 255) / 255. |
| lq_img = lq.contiguous() |
|
|
|
|
| generated = self.generator(lq_img, kp_source=kp_source, kp_driving=kp_driving) |
| else: |
| 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) |
| discriminator_maps_real = self.discriminator(pyramide_real) |
| value_total = 0 |
| for scale in self.disc_scales: |
| key = 'prediction_map_%s' % scale |
| if self.train_params['gan_mode'] == 'hinge': |
| value = -torch.mean(discriminator_maps_generated[key]) |
| elif self.train_params['gan_mode'] == 'ls': |
| value = ((1 - discriminator_maps_generated[key]) ** 2).mean() |
| else: |
| raise ValueError('Unexpected gan_mode {}'.format(self.train_params['gan_mode'])) |
|
|
| 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_he_driving = self.he_estimator(transformed_frame) |
|
|
| transformed_kp = keypoint_transformation(kp_canonical, transformed_he_driving, self.estimate_jacobian) |
|
|
| generated['transformed_frame'] = transformed_frame |
| generated['transformed_kp'] = transformed_kp |
|
|
| |
| if self.loss_weights['equivariance_value'] != 0: |
| |
| kp_driving_2d = kp_driving['value'][:, :, :2] |
| transformed_kp_2d = transformed_kp['value'][:, :, :2] |
| value = torch.abs(kp_driving_2d - transform.warp_coordinates(transformed_kp_2d)).mean() |
| loss_values['equivariance_value'] = self.loss_weights['equivariance_value'] * value |
|
|
| |
| if self.loss_weights['equivariance_jacobian'] != 0: |
| |
| transformed_kp_2d = transformed_kp['value'][:, :, :2] |
| transformed_jacobian_2d = transformed_kp['jacobian'][:, :, :2, :2] |
| jacobian_transformed = torch.matmul(transform.jacobian(transformed_kp_2d), |
| transformed_jacobian_2d) |
| |
| jacobian_2d = kp_driving['jacobian'][:, :, :2, :2] |
| normed_driving = torch.inverse(jacobian_2d) |
| 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['keypoint'] != 0: |
| |
| value_total = 0 |
| for i in range(kp_driving['value'].shape[1]): |
| for j in range(kp_driving['value'].shape[1]): |
| dist = F.pairwise_distance(kp_driving['value'][:, i, :], kp_driving['value'][:, j, :], p=2, keepdim=True) ** 2 |
| dist = 0.1 - dist |
| dd = torch.gt(dist, 0) |
| value = (dist * dd).mean() |
| value_total += value |
|
|
| kp_mean_depth = kp_driving['value'][:, :, -1].mean(-1) |
| value_depth = torch.abs(kp_mean_depth - 0.33).mean() |
|
|
| value_total += value_depth |
| loss_values['keypoint'] = self.loss_weights['keypoint'] * value_total |
|
|
| if self.loss_weights['headpose'] != 0: |
| transform_hopenet = transforms.Compose([transforms.Resize(size=(224, 224)), |
| transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])]) |
| driving_224 = transform_hopenet(x['driving']) |
|
|
| yaw_gt, pitch_gt, roll_gt = self.hopenet(driving_224) |
| yaw_gt = headpose_pred_to_degree(yaw_gt) |
| pitch_gt = headpose_pred_to_degree(pitch_gt) |
| roll_gt = headpose_pred_to_degree(roll_gt) |
|
|
| yaw, pitch, roll = he_driving['yaw'], he_driving['pitch'], he_driving['roll'] |
| yaw = headpose_pred_to_degree(yaw) |
| pitch = headpose_pred_to_degree(pitch) |
| roll = headpose_pred_to_degree(roll) |
|
|
| value = torch.abs(yaw - yaw_gt).mean() + torch.abs(pitch - pitch_gt).mean() + torch.abs(roll - roll_gt).mean() |
| loss_values['headpose'] = self.loss_weights['headpose'] * value |
|
|
| if self.loss_weights['expression'] != 0: |
| value = torch.norm(he_driving['exp'], p=1, dim=-1).mean() |
| loss_values['expression'] = self.loss_weights['expression'] * value |
|
|
| loss_values['reconstruction'] = self.loss_weights['reconstruction'] * self.L1(generated['prediction'], x['driving']) |
| |
| 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.image_channel) |
| if torch.cuda.is_available(): |
| self.pyramid = self.pyramid.cuda() |
|
|
| self.loss_weights = train_params['loss_weights'] |
|
|
| self.zero_tensor = None |
|
|
| def get_zero_tensor(self, input): |
| if self.zero_tensor is None: |
| self.zero_tensor = torch.FloatTensor(1).fill_(0).cuda() |
| self.zero_tensor.requires_grad_(False) |
| return self.zero_tensor.expand_as(input) |
|
|
| def forward(self, x, generated): |
| pyramide_real = self.pyramid(x['driving']) |
| pyramide_generated = self.pyramid(generated['prediction'].detach()) |
|
|
| discriminator_maps_generated = self.discriminator(pyramide_generated) |
| discriminator_maps_real = self.discriminator(pyramide_real) |
|
|
| loss_values = {} |
| value_total = 0 |
| for scale in self.scales: |
| key = 'prediction_map_%s' % scale |
| if self.train_params['gan_mode'] == 'hinge': |
| value = -torch.mean(torch.min(discriminator_maps_real[key]-1, self.get_zero_tensor(discriminator_maps_real[key]))) - torch.mean(torch.min(-discriminator_maps_generated[key]-1, self.get_zero_tensor(discriminator_maps_generated[key]))) |
| elif self.train_params['gan_mode'] == 'ls': |
| value = ((1 - discriminator_maps_real[key]) ** 2 + discriminator_maps_generated[key] ** 2).mean() |
| else: |
| raise ValueError('Unexpected gan_mode {}'.format(self.train_params['gan_mode'])) |
|
|
| value_total += self.loss_weights['discriminator_gan'] * value |
| loss_values['disc_gan'] = value_total |
|
|
| return loss_values |
|
|
