# 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 # image_channel: 3 # block_expansion: 64 # num_down_blocks: 2 # max_features: 512 # reshape_channel: 32 # reshape_depth: 16 # num_resblocks: 6 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