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import torch
from torch import nn
import torch.nn.functional as F
from modules.util import ResBlock2d, SameBlock2d, UpBlock2d, DownBlock2d,SPADEResnetBlock
from modules.dense_motion import *
import pdb
from modules.AdaIN import calc_mean_std,adaptive_instance_normalization
from modules.dynamic_conv import Dynamic_conv2d
class SPADEGenerator(nn.Module):
def __init__(self):
super().__init__()
ic = 256
cc = 4
oc = 64
norm_G = 'spadespectralinstance'
label_nc = 3 + cc
self.compress = nn.Conv2d(ic, cc, 3, padding=1)
self.fc = nn.Conv2d(ic, 2 * ic, 3, padding=1)
self.G_middle_0 = SPADEResnetBlock(2 * ic, 2 * ic, norm_G, label_nc)
self.G_middle_1 = SPADEResnetBlock(2 * ic, 2 * ic, norm_G, label_nc)
self.G_middle_2 = SPADEResnetBlock(2 * ic, 2 * ic, norm_G, label_nc)
# self.G_middle_3 = SPADEResnetBlock(2 * ic, 2 * ic, norm_G, label_nc)
# self.G_middle_4 = SPADEResnetBlock(2 * ic, 2 * ic, norm_G, label_nc)
# self.G_middle_5 = SPADEResnetBlock(2 * ic, 2 * ic, norm_G, label_nc)
self.up_0 = SPADEResnetBlock(2 * ic, ic, norm_G, label_nc)
self.up_1 = SPADEResnetBlock(ic, oc, norm_G, label_nc)
self.conv_img = nn.Conv2d(oc, 3, 3, padding=1)
self.up = nn.Upsample(scale_factor=2)
def forward(self, feature, image):
cp = self.compress(feature)
seg = torch.cat((F.interpolate(cp, size=(image.shape[2], image.shape[3])), image), dim=1) # 7, 256, 256
x = feature # 256, 64, 64
x = self.fc(x) # 512, 64, 64
x = self.G_middle_0(x, seg)
x = self.G_middle_1(x, seg)
x = self.G_middle_2(x, seg)
# x = self.G_middle_3(x, seg)
# x = self.G_middle_4(x, seg)
# x = self.G_middle_5(x, seg)
x = self.up(x) # 256, 128, 128
x = self.up_0(x, seg)
x = self.up(x) # 64, 256, 256
x = self.up_1(x, seg)
x = self.conv_img(F.leaky_relu(x, 2e-1))
# x = torch.tanh(x)
x = F.sigmoid(x)
return x
class DepthAwareAttention(nn.Module):
""" depth-aware attention Layer"""
def __init__(self,in_dim,activation):
super(DepthAwareAttention,self).__init__()
self.chanel_in = in_dim
self.activation = activation
self.query_conv = nn.Conv2d(in_channels = in_dim , out_channels = in_dim//8 , kernel_size= 1)
self.key_conv = nn.Conv2d(in_channels = in_dim , out_channels = in_dim//8 , kernel_size= 1)
self.value_conv = nn.Conv2d(in_channels = in_dim , out_channels = in_dim , kernel_size= 1)
self.gamma = nn.Parameter(torch.zeros(1))
self.softmax = nn.Softmax(dim=-1) #
def forward(self,source,feat):
"""
inputs :
source : input feature maps( B X C X W X H) 256,64,64
driving : input feature maps( B X C X W X H) 256,64,64
returns :
out : self attention value + input feature
attention: B X N X N (N is Width*Height)
"""
m_batchsize,C,width ,height = source.size()
proj_query = self.activation(self.query_conv(source)).view(m_batchsize,-1,width*height).permute(0,2,1) # B X CX(N) [bz,32,64,64]
proj_key = self.activation(self.key_conv(feat)).view(m_batchsize,-1,width*height) # B X C x (*W*H)
energy = torch.bmm(proj_query,proj_key) # transpose check
attention = self.softmax(energy) # BX (N) X (N)
proj_value = self.activation(self.value_conv(feat)).view(m_batchsize,-1,width*height) # B X C X N
out = torch.bmm(proj_value,attention.permute(0,2,1) )
out = out.view(m_batchsize,C,width,height)
out = self.gamma*out + feat
return out,attention
#### main ####
class DepthAwareGenerator(nn.Module):
"""
Generator that given source image and and keypoints try to transform image according to movement trajectories
induced by keypoints. Generator follows Johnson architecture.
"""
def __init__(self, num_channels, num_kp, block_expansion, max_features, num_down_blocks,
num_bottleneck_blocks, estimate_occlusion_map=False, dense_motion_params=None, estimate_jacobian=False):
super(DepthAwareGenerator, self).__init__()
if dense_motion_params is not None:
self.dense_motion_network = DenseMotionNetwork(num_kp=num_kp, num_channels=num_channels,
estimate_occlusion_map=estimate_occlusion_map,
**dense_motion_params)
else:
self.dense_motion_network = None
self.first = SameBlock2d(num_channels, block_expansion, kernel_size=(7, 7), padding=(3, 3))
down_blocks = []
for i in range(num_down_blocks):
in_features = min(max_features, block_expansion * (2 ** i))
out_features = min(max_features, block_expansion * (2 ** (i + 1)))
down_blocks.append(DownBlock2d(in_features, out_features, kernel_size=(3, 3), padding=(1, 1)))
self.down_blocks = nn.ModuleList(down_blocks)
#source depth
self.src_first = SameBlock2d(1, block_expansion, kernel_size=(7, 7), padding=(3, 3))
src_down_blocks = []
for i in range(num_down_blocks):
in_features = min(max_features, block_expansion * (2 ** i))
out_features = min(max_features, block_expansion * (2 ** (i + 1)))
src_down_blocks.append(DownBlock2d(in_features, out_features, kernel_size=(3, 3), padding=(1, 1)))
self.src_down_blocks = nn.ModuleList(src_down_blocks)
# #driving depth
# self.dst_first = SameBlock2d(1, block_expansion, kernel_size=(7, 7), padding=(3, 3))
# dst_down_blocks = []
# for i in range(num_down_blocks):
# in_features = min(max_features, block_expansion * (2 ** i))
# out_features = min(max_features, block_expansion * (2 ** (i + 1)))
# dst_down_blocks.append(DownBlock2d(in_features, out_features, kernel_size=(3, 3), padding=(1, 1)))
# self.dst_down_blocks = nn.ModuleList(dst_down_blocks)
self.AttnModule = DepthAwareAttention(out_features,nn.ReLU())
up_blocks = []
for i in range(num_down_blocks):
in_features = min(max_features, block_expansion * (2 ** (num_down_blocks - i)))
out_features = min(max_features, block_expansion * (2 ** (num_down_blocks - i - 1)))
up_blocks.append(UpBlock2d(in_features, out_features, kernel_size=(3, 3), padding=(1, 1)))
self.up_blocks = nn.ModuleList(up_blocks)
self.bottleneck = torch.nn.Sequential()
in_features = min(max_features, block_expansion * (2 ** num_down_blocks))
for i in range(num_bottleneck_blocks):
self.bottleneck.add_module('r' + str(i), ResBlock2d(in_features, kernel_size=(3, 3), padding=(1, 1)))
self.final = nn.Conv2d(block_expansion, num_channels, kernel_size=(7, 7), padding=(3, 3))
self.estimate_occlusion_map = estimate_occlusion_map
self.num_channels = num_channels
def deform_input(self, inp, deformation):
_, h_old, w_old, _ = deformation.shape
_, _, h, w = inp.shape
if h_old != h or w_old != w:
deformation = deformation.permute(0, 3, 1, 2)
deformation = F.interpolate(deformation, size=(h, w), mode='bilinear')
deformation = deformation.permute(0, 2, 3, 1)
return F.grid_sample(inp, deformation)
def forward(self, source_image, kp_driving, kp_source, source_depth, driving_depth):
# Encoding (downsampling) part
out = self.first(source_image)
for i in range(len(self.down_blocks)):
out = self.down_blocks[i](out)
src_out = self.src_first(source_depth)
for i in range(len(self.src_down_blocks)):
src_out = self.src_down_blocks[i](src_out)
# dst_out = self.dst_first(driving_depth)
# for i in range(len(self.down_blocks)):
# dst_out = self.dst_down_blocks[i](dst_out)
# Transforming feature representation according to deformation and occlusion
output_dict = {}
if self.dense_motion_network is not None:
dense_motion = self.dense_motion_network(source_image=source_image, kp_driving=kp_driving,
kp_source=kp_source)
output_dict['mask'] = dense_motion['mask']
output_dict['sparse_deformed'] = dense_motion['sparse_deformed']
if 'occlusion_map' in dense_motion:
occlusion_map = dense_motion['occlusion_map']
output_dict['occlusion_map'] = occlusion_map
else:
occlusion_map = None
deformation = dense_motion['deformation']
out = self.deform_input(out, deformation)
if occlusion_map is not None:
if out.shape[2] != occlusion_map.shape[2] or out.shape[3] != occlusion_map.shape[3]:
occlusion_map = F.interpolate(occlusion_map, size=out.shape[2:], mode='bilinear')
out = out * occlusion_map
out,attention = self.AttnModule(src_out,out)
output_dict["deformed"] = self.deform_input(source_image, deformation)
output_dict["attention"] = attention
# Decoding part
out = self.bottleneck(out)
for i in range(len(self.up_blocks)):
out = self.up_blocks[i](out)
out = self.final(out)
out = F.sigmoid(out)
output_dict["prediction"] = out
return output_dict
class SPADEDepthAwareGenerator(nn.Module):
"""
Generator that given source image and and keypoints try to transform image according to movement trajectories
induced by keypoints. Generator follows Johnson architecture.
"""
def __init__(self, num_channels, num_kp, block_expansion, max_features, num_down_blocks,
num_bottleneck_blocks, estimate_occlusion_map=False, dense_motion_params=None, estimate_jacobian=False):
super(SPADEDepthAwareGenerator, self).__init__()
if dense_motion_params is not None:
self.dense_motion_network = DenseMotionNetwork(num_kp=num_kp, num_channels=num_channels,
estimate_occlusion_map=estimate_occlusion_map,
**dense_motion_params)
else:
self.dense_motion_network = None
self.first = SameBlock2d(num_channels, block_expansion, kernel_size=(7, 7), padding=(3, 3))
down_blocks = []
for i in range(num_down_blocks):
in_features = min(max_features, block_expansion * (2 ** i))
out_features = min(max_features, block_expansion * (2 ** (i + 1)))
down_blocks.append(DownBlock2d(in_features, out_features, kernel_size=(3, 3), padding=(1, 1)))
self.down_blocks = nn.ModuleList(down_blocks)
#source depth
self.src_first = SameBlock2d(1, block_expansion, kernel_size=(7, 7), padding=(3, 3))
src_down_blocks = []
for i in range(num_down_blocks):
in_features = min(max_features, block_expansion * (2 ** i))
out_features = min(max_features, block_expansion * (2 ** (i + 1)))
src_down_blocks.append(DownBlock2d(in_features, out_features, kernel_size=(3, 3), padding=(1, 1)))
self.src_down_blocks = nn.ModuleList(src_down_blocks)
# #driving depth
# self.dst_first = SameBlock2d(1, block_expansion, kernel_size=(7, 7), padding=(3, 3))
# dst_down_blocks = []
# for i in range(num_down_blocks):
# in_features = min(max_features, block_expansion * (2 ** i))
# out_features = min(max_features, block_expansion * (2 ** (i + 1)))
# dst_down_blocks.append(DownBlock2d(in_features, out_features, kernel_size=(3, 3), padding=(1, 1)))
# self.dst_down_blocks = nn.ModuleList(dst_down_blocks)
self.AttnModule = DepthAwareAttention(out_features,nn.ReLU())
self.decoder = SPADEGenerator()
self.estimate_occlusion_map = estimate_occlusion_map
self.num_channels = num_channels
def deform_input(self, inp, deformation):
_, h_old, w_old, _ = deformation.shape
_, _, h, w = inp.shape
if h_old != h or w_old != w:
deformation = deformation.permute(0, 3, 1, 2)
deformation = F.interpolate(deformation, size=(h, w), mode='bilinear')
deformation = deformation.permute(0, 2, 3, 1)
return F.grid_sample(inp, deformation)
def forward(self, source_image, kp_driving, kp_source, source_depth, driving_depth):
# Encoding (downsampling) part
out = self.first(source_image)
for i in range(len(self.down_blocks)):
out = self.down_blocks[i](out)
src_out = self.src_first(source_depth)
for i in range(len(self.src_down_blocks)):
src_out = self.src_down_blocks[i](src_out)
# dst_out = self.dst_first(driving_depth)
# for i in range(len(self.down_blocks)):
# dst_out = self.dst_down_blocks[i](dst_out)
# Transforming feature representation according to deformation and occlusion
output_dict = {}
if self.dense_motion_network is not None:
dense_motion = self.dense_motion_network(source_image=source_image, kp_driving=kp_driving,
kp_source=kp_source)
output_dict['mask'] = dense_motion['mask']
output_dict['sparse_deformed'] = dense_motion['sparse_deformed']
if 'occlusion_map' in dense_motion:
occlusion_map = dense_motion['occlusion_map']
output_dict['occlusion_map'] = occlusion_map
else:
occlusion_map = None
deformation = dense_motion['deformation']
out = self.deform_input(out, deformation)
if occlusion_map is not None:
if out.shape[2] != occlusion_map.shape[2] or out.shape[3] != occlusion_map.shape[3]:
occlusion_map = F.interpolate(occlusion_map, size=out.shape[2:], mode='bilinear')
out = out * occlusion_map
out,attention = self.AttnModule(src_out,out)
deformed_image = self.deform_input(source_image, deformation)
output_dict["deformed"] = deformed_image
output_dict["attention"] = attention
if occlusion_map is not None:
if deformed_image.shape[2] != occlusion_map.shape[2] or deformed_image.shape[3] != occlusion_map.shape[3]:
occlusion_map = F.interpolate(occlusion_map, size=deformed_image.shape[2:], mode='bilinear')
deformed_image = deformed_image * occlusion_map
out = self.decoder(out, deformed_image)
# # Decoding part
# out = self.bottleneck(out)
# for i in range(len(self.up_blocks)):
# out = self.up_blocks[i](out)
# out = self.final(out)
# out = F.sigmoid(out)
output_dict["prediction"] = out
return output_dict
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