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decoder.py
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import torch.nn as nn
from helper import ResidualBlock, NonLocalBlock, UpSampleBlock, GroupNorm, Swish
from encoder import Normalize, nonlinearity,ResnetBlock,AttnBlock,Upsample
import numpy as np
import torch
# class Decoder(nn.Module):
# def __init__(self, args):
# super(Decoder, self).__init__()
# channels = [512, 256, 256, 128, 128]
# attn_resolutions = [16]
# num_res_blocks = 3
# resolution = 16
#
# in_channels = channels[0]
# layers = [nn.Conv2d(args.latent_dim, in_channels, 3, 1, 1),
# ResidualBlock(in_channels, in_channels),
# NonLocalBlock(in_channels),
# ResidualBlock(in_channels, in_channels)]
#
# for i in range(len(channels)):
# out_channels = channels[i]
# for j in range(num_res_blocks):
# layers.append(ResidualBlock(in_channels, out_channels))
# in_channels = out_channels
# if resolution in attn_resolutions:
# layers.append(NonLocalBlock(in_channels))
# if i != 4:
# layers.append(UpSampleBlock(in_channels))
# resolution *= 2
#
# layers.append(GroupNorm(in_channels))
# layers.append(Swish())
# layers.append(nn.Conv2d(in_channels, args.image_channels, 3, 1, 1))
# self.model = nn.Sequential(*layers)
#
# def forward(self, x):
# return self.model(x)
#
class Decoder(nn.Module):
def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
resolution, z_channels, give_pre_end=False, **ignorekwargs):
super().__init__()
self.ch = ch
self.temb_ch = 0
self.num_resolutions = len(ch_mult)
self.num_res_blocks = num_res_blocks
self.resolution = resolution
self.in_channels = in_channels
self.give_pre_end = give_pre_end
# compute in_ch_mult, block_in and curr_res at lowest res
in_ch_mult = (1,)+tuple(ch_mult)
block_in = ch*ch_mult[self.num_resolutions-1]
curr_res = resolution // 2**(self.num_resolutions-1)
self.z_shape = (1,z_channels,curr_res,curr_res)
print("Working with z of shape {} = {} dimensions.".format(
self.z_shape, np.prod(self.z_shape)))
# z to block_in
self.conv_in = torch.nn.Conv2d(z_channels,
block_in,
kernel_size=3,
stride=1,
padding=1)
# middle
self.mid = nn.Module()
self.mid.block_1 = ResnetBlock(in_channels=block_in,
out_channels=block_in,
temb_channels=self.temb_ch,
dropout=dropout)
self.mid.attn_1 = AttnBlock(block_in)
self.mid.block_2 = ResnetBlock(in_channels=block_in,
out_channels=block_in,
temb_channels=self.temb_ch,
dropout=dropout)
# upsampling
self.up = nn.ModuleList()
for i_level in reversed(range(self.num_resolutions)):
block = nn.ModuleList()
attn = nn.ModuleList()
block_out = ch*ch_mult[i_level]
for i_block in range(self.num_res_blocks+1):
block.append(ResnetBlock(in_channels=block_in,
out_channels=block_out,
temb_channels=self.temb_ch,
dropout=dropout))
block_in = block_out
if curr_res in attn_resolutions:
attn.append(AttnBlock(block_in))
up = nn.Module()
up.block = block
up.attn = attn
if i_level != 0:
up.upsample = Upsample(block_in, resamp_with_conv)
curr_res = curr_res * 2
self.up.insert(0, up) # prepend to get consistent order
# end
self.norm_out = Normalize(block_in)
self.conv_out = torch.nn.Conv2d(block_in,
out_ch,
kernel_size=3,
stride=1,
padding=1)
def forward(self, z):
#assert z.shape[1:] == self.z_shape[1:]
self.last_z_shape = z.shape
# timestep embedding
temb = None
# z to block_in
h = self.conv_in(z)
# middle
h = self.mid.block_1(h, temb)
h = self.mid.attn_1(h)
h = self.mid.block_2(h, temb)
# upsampling
for i_level in reversed(range(self.num_resolutions)):
for i_block in range(self.num_res_blocks+1):
h = self.up[i_level].block[i_block](h, temb)
if len(self.up[i_level].attn) > 0:
h = self.up[i_level].attn[i_block](h)
if i_level != 0:
h = self.up[i_level].upsample(h)
# end
if self.give_pre_end:
return h
h = self.norm_out(h)
h = nonlinearity(h)
h = self.conv_out(h)
return h