DaGAN / modules /model.py
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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