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ResFCN256.py
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"""
@author: samuel ko
@date: 2019.07.18
@readme: The implementation of PRNet Network
@notice: PyTorch only support odd convolution to keep half downsample.
"""
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision.models import *
import numpy as np
def conv3x3(in_planes, out_planes, stride=1, dilation=1, padding='same'):
"""3x3 convolution with padding"""
if padding == 'same':
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False, dilation=dilation)
class ResBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1,
kernel_size=3,
norm_layer=None):
super(ResBlock, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
self.shortcut_conv = nn.Conv2d(inplanes, planes, kernel_size=1, stride=stride)
self.conv1 = nn.Conv2d(inplanes, planes // 2, kernel_size=1, stride=1, padding=0)
self.conv2 = nn.Conv2d(planes // 2, planes // 2, kernel_size=kernel_size, stride=stride, padding=kernel_size // 2)
self.conv3 = nn.Conv2d(planes // 2, planes, kernel_size=1, stride=1, padding=0)
self.normalizer_fn = norm_layer(planes)
self.activation_fn = nn.ReLU(inplace=True)
self.stride = stride
self.out_planes = planes
def forward(self, x):
shortcut = x
(_, _, _, x_planes) = x.size()
if self.stride != 1 or x_planes != self.out_planes:
shortcut = self.shortcut_conv(x)
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
x += shortcut
x = self.normalizer_fn(x)
x = self.activation_fn(x)
return x
class ResFCN256(nn.Module):
def __init__(self, resolution_input=256, resolution_output=256, channel=3, size=16):
super().__init__()
self.input_resolution = resolution_input
self.output_resolution = resolution_output
self.channel = channel
self.size = size
# Encoder
self.block0 = conv3x3(in_planes=3, out_planes=self.size, padding='same')
self.block1 = ResBlock(inplanes=self.size, planes=self.size * 2, stride=2)
self.block2 = ResBlock(inplanes=self.size * 2, planes=self.size * 2, stride=1)
self.block3 = ResBlock(inplanes=self.size * 2, planes=self.size * 4, stride=2)
self.block4 = ResBlock(inplanes=self.size * 4, planes=self.size * 4, stride=1)
self.block5 = ResBlock(inplanes=self.size * 4, planes=self.size * 8, stride=2)
self.block6 = ResBlock(inplanes=self.size * 8, planes=self.size * 8, stride=1)
self.block7 = ResBlock(inplanes=self.size * 8, planes=self.size * 16, stride=2)
self.block8 = ResBlock(inplanes=self.size * 16, planes=self.size * 16, stride=1)
self.block9 = ResBlock(inplanes=self.size * 16, planes=self.size * 32, stride=2)
self.block10 = ResBlock(inplanes=self.size * 32, planes=self.size * 32, stride=1)
# Decoder
self.upsample0 = nn.ConvTranspose2d(self.size * 32, self.size * 32, kernel_size=3, stride=1,
padding=1) # keep shape invariant.
self.upsample1 = nn.ConvTranspose2d(self.size * 32, self.size * 16, kernel_size=4, stride=2,
padding=1) # half downsample.
self.upsample2 = nn.ConvTranspose2d(self.size * 16, self.size * 16, kernel_size=3, stride=1,
padding=1) # keep shape invariant.
self.upsample3 = nn.ConvTranspose2d(self.size * 16, self.size * 16, kernel_size=3, stride=1,
padding=1) # keep shape invariant.
self.upsample4 = nn.ConvTranspose2d(self.size * 16, self.size * 8, kernel_size=4, stride=2,
padding=1) # half downsample.
self.upsample5 = nn.ConvTranspose2d(self.size * 8, self.size * 8, kernel_size=3, stride=1,
padding=1) # keep shape invariant.
self.upsample6 = nn.ConvTranspose2d(self.size * 8, self.size * 8, kernel_size=3, stride=1,
padding=1) # keep shape invariant.
self.upsample7 = nn.ConvTranspose2d(self.size * 8, self.size * 4, kernel_size=4, stride=2,
padding=1) # half downsample.
self.upsample8 = nn.ConvTranspose2d(self.size * 4, self.size * 4, kernel_size=3, stride=1,
padding=1) # keep shape invariant.
self.upsample9 = nn.ConvTranspose2d(self.size * 4, self.size * 4, kernel_size=3, stride=1,
padding=1) # keep shape invariant.
self.upsample10 = nn.ConvTranspose2d(self.size * 4, self.size * 2, kernel_size=4, stride=2,
padding=1) # half downsample.
self.upsample11 = nn.ConvTranspose2d(self.size * 2, self.size * 2, kernel_size=3, stride=1,
padding=1) # keep shape invariant.
self.upsample12 = nn.ConvTranspose2d(self.size * 2, self.size, kernel_size=4, stride=2,
padding=1) # half downsample.
self.upsample13 = nn.ConvTranspose2d(self.size, self.size, kernel_size=3, stride=1,
padding=1) # keep shape invariant.
self.upsample14 = nn.ConvTranspose2d(self.size, self.channel, kernel_size=3, stride=1,
padding=1) # keep shape invariant.
self.upsample15 = nn.ConvTranspose2d(self.channel, self.channel, kernel_size=3, stride=1,
padding=1) # keep shape invariant.
self.upsample16 = nn.ConvTranspose2d(self.channel, self.channel, kernel_size=3, stride=1,
padding=1) # keep shape invariant.
# ACT
self.sigmoid = nn.Sigmoid()
def forward(self, x):
se = self.block0(x) # 256 x 256 x 16
se = self.block1(se) # 128 x 128 x 32
se = self.block2(se) # 128 x 128 x 32
se = self.block3(se) # 64 x 64 x 64
se = self.block4(se) # 64 x 64 x 64
se = self.block5(se) # 32 x 32 x 128
se = self.block6(se) # 32 x 32 x 128
se = self.block7(se) # 16 x 16 x 256
se = self.block8(se) # 16 x 16 x 256
se = self.block9(se) # 8 x 8 x 512
se = self.block10(se) # 8 x 8 x 512
pd = self.upsample0(se) # 8 x 8 x 512
pd = self.upsample1(pd) # 16 x 16 x 256
pd = self.upsample2(pd) # 16 x 16 x 256
pd = self.upsample3(pd) # 16 x 16 x 256
pd = self.upsample4(pd) # 32 x 32 x 128
pd = self.upsample5(pd) # 32 x 32 x 128
pd = self.upsample6(pd) # 32 x 32 x 128
pd = self.upsample7(pd) # 64 x 64 x 64
pd = self.upsample8(pd) # 64 x 64 x 64
pd = self.upsample9(pd) # 64 x 64 x 64
pd = self.upsample10(pd) # 128 x 128 x 32
pd = self.upsample11(pd) # 128 x 128 x 32
pd = self.upsample12(pd) # 256 x 256 x 16
pd = self.upsample13(pd) # 256 x 256 x 16
pd = self.upsample14(pd) # 256 x 256 x 3
pd = self.upsample15(pd) # 256 x 256 x 3
pos = self.upsample16(pd) # 256 x 256 x 3
pos = self.sigmoid(pos)
return pos
"""
net = ResFCN256()
print(net)
x = torch.randn(1, 3, 256, 256)
out = net(x)
print(x.shape)
print(out.shape)
"""