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# coding: utf-8
"""
Appearance extractor(F) defined in paper, which maps the source image s to a 3D appearance feature volume.
"""
import torch
from torch import nn
from .util import SameBlock2d, DownBlock2d, ResBlock3d
class AppearanceFeatureExtractor(nn.Module):
def __init__(self, image_channel, block_expansion, num_down_blocks, max_features, reshape_channel, reshape_depth, num_resblocks):
super(AppearanceFeatureExtractor, self).__init__()
self.image_channel = image_channel
self.block_expansion = block_expansion
self.num_down_blocks = num_down_blocks
self.max_features = max_features
self.reshape_channel = reshape_channel
self.reshape_depth = reshape_depth
self.first = SameBlock2d(image_channel, block_expansion, kernel_size=(3, 3), padding=(1, 1))
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)
self.second = nn.Conv2d(in_channels=out_features, out_channels=max_features, kernel_size=1, stride=1)
self.resblocks_3d = torch.nn.Sequential()
for i in range(num_resblocks):
self.resblocks_3d.add_module('3dr' + str(i), ResBlock3d(reshape_channel, kernel_size=3, padding=1))
def forward(self, source_image):
out = self.first(source_image) # Bx3x256x256 -> Bx64x256x256
for i in range(len(self.down_blocks)):
out = self.down_blocks[i](out)
out = self.second(out)
bs, c, h, w = out.shape # ->Bx512x64x64
f_s = out.view(bs, self.reshape_channel, self.reshape_depth, h, w) # ->Bx32x16x64x64
f_s = self.resblocks_3d(f_s) # ->Bx32x16x64x64
return f_s
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