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from torch import nn
import torch
import torch.nn.functional as F
from face_vid2vid.modules.util import AntiAliasInterpolation2d, make_coordinate_grid_2d
from torchvision import models
import numpy as np
from torch.autograd import grad
import face_vid2vid.modules.hopenet as hopenet
from torchvision import transforms
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'] # (bs, k, 3)
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) # (bs, 3, 3)
# keypoint rotation
kp_rotated = torch.einsum('bmp,bkp->bkm', rot_mat, kp)
# keypoint translation
t = t.unsqueeze_(1).repeat(1, kp.shape[1], 1)
kp_t = kp_rotated + t
# add expression deviation
exp = exp.view(exp.shape[0], -1, 3)
kp_transformed = kp_t + exp
if estimate_jacobian:
jacobian = kp_canonical['jacobian'] # (bs, k ,3, 3)
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()
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()
def forward(self, x):
kp_canonical = self.kp_extractor(x['source']) # {'value': value, 'jacobian': jacobian}
he_source = self.he_estimator(x['source']) # {'yaw': yaw, 'pitch': pitch, 'roll': roll, 't': t, 'exp': exp}
he_driving = self.he_estimator(x['driving']) # {'yaw': yaw, 'pitch': pitch, 'roll': roll, 't': t, 'exp': exp}
# {'value': value, 'jacobian': jacobian}
kp_source = keypoint_transformation(kp_canonical, he_source, self.estimate_jacobian)
kp_driving = keypoint_transformation(kp_canonical, he_driving, self.estimate_jacobian)
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
## Value loss part
if self.loss_weights['equivariance_value'] != 0:
# project 3d -> 2d
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
## jacobian loss part
if self.loss_weights['equivariance_jacobian'] != 0:
# project 3d -> 2d
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:
# print(kp_driving['value'].shape) # (bs, k, 3)
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 # set Dt = 0.1
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() # set Zt = 0.33
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
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
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