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import torch
from torch import nn
import torch.nn.functional as F
from modules.util import ResBlock2d, SameBlock2d, UpBlock2d, DownBlock2d
from modules.dense_motion import DenseMotionNetwork
from modules.nerf_verts_util import RenderModel
class SPADE_layer(nn.Module):
def __init__(self, norm_channel, label_channel):
super(SPADE_layer, self).__init__()
self.param_free_norm = nn.InstanceNorm2d(norm_channel, affine=False)
hidden_channel = 128
self.mlp_shared = nn.Sequential(
nn.Conv2d(label_channel, hidden_channel, kernel_size=3, padding=1),
nn.ReLU()
)
self.mlp_gamma = nn.Conv2d(hidden_channel, norm_channel, kernel_size=3, padding=1)
self.mlp_beta = nn.Conv2d(hidden_channel, norm_channel, kernel_size=3, padding=1)
def forward(self, x, modulation_in):
normalized = self.param_free_norm(x)
modulation_in = F.interpolate(modulation_in, size=x.size()[2:], mode='nearest')
actv = self.mlp_shared(modulation_in)
gamma = self.mlp_gamma(actv)
beta = self.mlp_beta(actv)
out = normalized * (1 + gamma) + beta
return out
class SPADE_block(nn.Module):
def __init__(self, norm_channel, label_channel, out_channel):
super(SPADE_block, self).__init__()
self.SPADE_0 = SPADE_layer(norm_channel, label_channel)
self.relu_0 = nn.ReLU()
self.conv_0 = nn.Conv2d(norm_channel, norm_channel, kernel_size=3, padding=1)
self.SPADE_1 = SPADE_layer(norm_channel, label_channel)
self.relu_1 = nn.ReLU()
self.conv_1 = nn.Conv2d(norm_channel, out_channel, kernel_size=3, padding=1)
def forward(self, x, modulation_in):
out = self.SPADE_0(x, modulation_in)
out = self.relu_0(out)
out = self.conv_0(out)
out = self.SPADE_1(out, modulation_in)
out = self.relu_1(out)
out = self.conv_1(out)
return out
class SPADE_decoder(nn.Module):
def __init__(self, in_channel, mid_channel):
super(SPADE_decoder, self).__init__()
self.in_channel = in_channel
self.mid_channel = mid_channel
self.seg_conv = nn.Sequential(
nn.Conv2d(in_channel, mid_channel, kernel_size=3, padding=1),
nn.ReLU()
)
self.SPADE_0 = SPADE_block(in_channel, mid_channel, in_channel // 4)
self.up_0 = nn.UpsamplingBilinear2d(scale_factor=2)
in_channel = in_channel // 4
self.SPADE_1 = SPADE_block(in_channel, mid_channel, in_channel // 4)
self.up_1 = nn.UpsamplingBilinear2d(scale_factor=2)
in_channel = in_channel // 4
self.SPADE_2 = SPADE_block(in_channel, mid_channel, in_channel)
self.SPADE_3 = SPADE_block(in_channel, mid_channel, in_channel)
self.final = nn.Sequential(
nn.Conv2d(in_channel, 3, kernel_size=7, padding=3),
nn.Sigmoid()
)
def forward(self, x):
seg = self.seg_conv(x)
x = self.SPADE_0(x, seg)
x = self.up_0(x)
x = self.SPADE_1(x, seg)
x = self.up_1(x)
x = self.SPADE_2(x, seg)
x = self.SPADE_3(x, seg)
x = self.final(x)
return x
def calc_mean_std(feat, eps=1e-5):
# eps is a small value added to the variance to avoid divide-by-zero.
size = feat.size()
assert (len(size) == 4)
N, C = size[:2]
feat_var = feat.view(N, C, -1).var(dim=2) + eps
feat_std = feat_var.sqrt().view(N, C, 1, 1)
feat_mean = feat.view(N, C, -1).mean(dim=2).view(N, C, 1, 1)
return feat_mean, feat_std
def adaptive_instance_normalization(x, modulation_in):
assert (x.size()[:2] == modulation_in.size()[:2])
size = x.size()
style_mean, style_std = calc_mean_std(modulation_in)
content_mean, content_std = calc_mean_std(x)
normalized_feat = (x - content_mean.expand(
size)) / content_std.expand(size)
return normalized_feat * style_std.expand(size) + style_mean.expand(size)
class AdaIN_layer(nn.Module):
def __init__(self, norm_channel, label_channel):
super(AdaIN_layer, self).__init__()
self.param_free_norm = nn.InstanceNorm2d(norm_channel, affine=False)
self.mlp_shared = nn.Sequential(
nn.Conv2d(label_channel, norm_channel, kernel_size=3, padding=1),
nn.ReLU()
)
def forward(self, x, modulation_in):
normalized = self.param_free_norm(x)
modulation_in = self.mlp_shared(modulation_in)
out = adaptive_instance_normalization(normalized, modulation_in)
return out
class OcclusionAwareGenerator_SPADE(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, render_params=None,
estimate_jacobian=False):
super(OcclusionAwareGenerator_SPADE, 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)
in_features = min(max_features, block_expansion * (2 ** num_down_blocks))
self.Render_model = RenderModel(in_channels=in_features, **render_params)
self.decoder = SPADE_decoder(in_channel=in_features * 2, mid_channel=128)
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):
# Encoding (downsampling) part
out = self.first(source_image)
for i in range(len(self.down_blocks)):
out = self.down_blocks[i](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
output_dict["deformed"] = self.deform_input(source_image, deformation)
# render part
render_result = self.Render_model(feature=out)
output_dict['render'] = render_result['mini_pred']
output_dict['point_pred'] = render_result['point_pred']
out = torch.cat((out, render_result['render']), dim=1)
# out = self.merge_conv(out)
# Decoding part
out = self.decoder(out)
output_dict["prediction"] = out
return output_dict
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