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model.py
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model.py
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# Copyright 2020 by Andrey Ignatov. All Rights Reserved.
import torch.nn as nn
import torch
class PyNET(nn.Module):
def __init__(self, level, instance_norm=True, instance_norm_level_1=False):
super(PyNET, self).__init__()
self.level = level
self.conv_l1_d1 = ConvMultiBlock(4, 32, 3, instance_norm=False)
self.pool1 = nn.MaxPool2d(2, 2)
self.conv_l2_d1 = ConvMultiBlock(32, 64, 3, instance_norm=instance_norm)
self.pool2 = nn.MaxPool2d(2, 2)
self.conv_l3_d1 = ConvMultiBlock(64, 128, 3, instance_norm=instance_norm)
self.pool3 = nn.MaxPool2d(2, 2)
self.conv_l4_d1 = ConvMultiBlock(128, 256, 3, instance_norm=instance_norm)
self.pool4 = nn.MaxPool2d(2, 2)
# -------------------------------------
self.conv_l5_d1 = ConvMultiBlock(256, 512, 3, instance_norm=instance_norm)
self.conv_l5_d2 = ConvMultiBlock(512, 512, 3, instance_norm=instance_norm)
self.conv_l5_d3 = ConvMultiBlock(512, 512, 3, instance_norm=instance_norm)
self.conv_l5_d4 = ConvMultiBlock(512, 512, 3, instance_norm=instance_norm)
self.conv_t4a = UpsampleConvLayer(512, 256, 3)
self.conv_t4b = UpsampleConvLayer(512, 256, 3)
self.conv_l5_out = ConvLayer(512, 3, kernel_size=3, stride=1, relu=False)
self.output_l5 = nn.Sigmoid()
# -------------------------------------
self.conv_l4_d3 = ConvMultiBlock(512, 256, 3, instance_norm=instance_norm)
self.conv_l4_d4 = ConvMultiBlock(256, 256, 3, instance_norm=instance_norm)
self.conv_l4_d5 = ConvMultiBlock(256, 256, 3, instance_norm=instance_norm)
self.conv_l4_d6 = ConvMultiBlock(256, 256, 3, instance_norm=instance_norm)
self.conv_l4_d8 = ConvMultiBlock(512, 256, 3, instance_norm=instance_norm)
self.conv_t3a = UpsampleConvLayer(256, 128, 3)
self.conv_t3b = UpsampleConvLayer(256, 128, 3)
self.conv_l4_out = ConvLayer(256, 3, kernel_size=3, stride=1, relu=False)
self.output_l4 = nn.Sigmoid()
# -------------------------------------
self.conv_l3_d3 = ConvMultiBlock(256, 128, 5, instance_norm=instance_norm)
self.conv_l3_d4 = ConvMultiBlock(256, 128, 5, instance_norm=instance_norm)
self.conv_l3_d5 = ConvMultiBlock(256, 128, 5, instance_norm=instance_norm)
self.conv_l3_d6 = ConvMultiBlock(256, 128, 5, instance_norm=instance_norm)
self.conv_l3_d8 = ConvMultiBlock(512, 128, 3, instance_norm=instance_norm)
self.conv_t2a = UpsampleConvLayer(128, 64, 3)
self.conv_t2b = UpsampleConvLayer(128, 64, 3)
self.conv_l3_out = ConvLayer(128, 3, kernel_size=3, stride=1, relu=False)
self.output_l3 = nn.Sigmoid()
# -------------------------------------
self.conv_l2_d3 = ConvMultiBlock(128, 64, 5, instance_norm=instance_norm)
self.conv_l2_d5 = ConvMultiBlock(192, 64, 7, instance_norm=instance_norm)
self.conv_l2_d6 = ConvMultiBlock(192, 64, 7, instance_norm=instance_norm)
self.conv_l2_d7 = ConvMultiBlock(192, 64, 7, instance_norm=instance_norm)
self.conv_l2_d8 = ConvMultiBlock(192, 64, 7, instance_norm=instance_norm)
self.conv_l2_d10 = ConvMultiBlock(256, 64, 5, instance_norm=instance_norm)
self.conv_l2_d12 = ConvMultiBlock(192, 64, 3, instance_norm=instance_norm)
self.conv_t1a = UpsampleConvLayer(64, 32, 3)
self.conv_t1b = UpsampleConvLayer(64, 32, 3)
self.conv_l2_out = ConvLayer(64, 3, kernel_size=3, stride=1, relu=False)
self.output_l2 = nn.Sigmoid()
# -------------------------------------
self.conv_l1_d3 = ConvMultiBlock(64, 32, 5, instance_norm=False)
self.conv_l1_d5 = ConvMultiBlock(96, 32, 7, instance_norm=instance_norm_level_1)
self.conv_l1_d6 = ConvMultiBlock(96, 32, 9, instance_norm=instance_norm_level_1)
self.conv_l1_d7 = ConvMultiBlock(128, 32, 9, instance_norm=instance_norm_level_1)
self.conv_l1_d8 = ConvMultiBlock(128, 32, 9, instance_norm=instance_norm_level_1)
self.conv_l1_d9 = ConvMultiBlock(128, 32, 9, instance_norm=instance_norm_level_1)
self.conv_l1_d10 = ConvMultiBlock(128, 32, 7, instance_norm=instance_norm_level_1)
self.conv_l1_d12 = ConvMultiBlock(128, 32, 5, instance_norm=instance_norm_level_1)
self.conv_l1_d14 = ConvMultiBlock(128, 32, 3, instance_norm=False)
self.conv_l1_out = ConvLayer(32, 3, kernel_size=3, stride=1, relu=False)
self.output_l1 = nn.Sigmoid()
self.conv_t0 = UpsampleConvLayer(32, 16, 3)
# -------------------------------------
self.conv_l0_d1 = ConvLayer(16, 3, kernel_size=3, stride=1, relu=False)
self.output_l0 = nn.Sigmoid()
def level_5(self, pool4):
conv_l5_d1 = self.conv_l5_d1(pool4)
conv_l5_d2 = self.conv_l5_d2(conv_l5_d1)
conv_l5_d3 = self.conv_l5_d3(conv_l5_d2)
conv_l5_d4 = self.conv_l5_d4(conv_l5_d3)
conv_t4a = self.conv_t4a(conv_l5_d4)
conv_t4b = self.conv_t4b(conv_l5_d4)
conv_l5_out = self.conv_l5_out(conv_l5_d4)
output_l5 = self.output_l5(conv_l5_out)
return output_l5, conv_t4a, conv_t4b
def level_4(self, conv_l4_d1, conv_t4a, conv_t4b):
conv_l4_d2 = torch.cat([conv_l4_d1, conv_t4a], 1)
conv_l4_d3 = self.conv_l4_d3(conv_l4_d2)
conv_l4_d4 = self.conv_l4_d4(conv_l4_d3) + conv_l4_d3
conv_l4_d5 = self.conv_l4_d5(conv_l4_d4) + conv_l4_d4
conv_l4_d6 = self.conv_l4_d6(conv_l4_d5)
conv_l4_d7 = torch.cat([conv_l4_d6, conv_t4b], 1)
conv_l4_d8 = self.conv_l4_d8(conv_l4_d7)
conv_t3a = self.conv_t3a(conv_l4_d8)
conv_t3b = self.conv_t3b(conv_l4_d8)
conv_l4_out = self.conv_l4_out(conv_l4_d8)
output_l4 = self.output_l4(conv_l4_out)
return output_l4, conv_t3a, conv_t3b
def level_3(self, conv_l3_d1, conv_t3a, conv_t3b):
conv_l3_d2 = torch.cat([conv_l3_d1, conv_t3a], 1)
conv_l3_d3 = self.conv_l3_d3(conv_l3_d2) + conv_l3_d2
conv_l3_d4 = self.conv_l3_d4(conv_l3_d3) + conv_l3_d3
conv_l3_d5 = self.conv_l3_d5(conv_l3_d4) + conv_l3_d4
conv_l3_d6 = self.conv_l3_d6(conv_l3_d5)
conv_l3_d7 = torch.cat([conv_l3_d6, conv_l3_d1, conv_t3b], 1)
conv_l3_d8 = self.conv_l3_d8(conv_l3_d7)
conv_t2a = self.conv_t2a(conv_l3_d8)
conv_t2b = self.conv_t2b(conv_l3_d8)
conv_l3_out = self.conv_l3_out(conv_l3_d8)
output_l3 = self.output_l3(conv_l3_out)
return output_l3, conv_t2a, conv_t2b
def level_2(self, conv_l2_d1, conv_t2a, conv_t2b):
conv_l2_d2 = torch.cat([conv_l2_d1, conv_t2a], 1)
conv_l2_d3 = self.conv_l2_d3(conv_l2_d2)
conv_l2_d4 = torch.cat([conv_l2_d3, conv_l2_d1], 1)
conv_l2_d5 = self.conv_l2_d5(conv_l2_d4) + conv_l2_d4
conv_l2_d6 = self.conv_l2_d6(conv_l2_d5) + conv_l2_d5
conv_l2_d7 = self.conv_l2_d7(conv_l2_d6) + conv_l2_d6
conv_l2_d8 = self.conv_l2_d8(conv_l2_d7)
conv_l2_d9 = torch.cat([conv_l2_d8, conv_l2_d1], 1)
conv_l2_d10 = self.conv_l2_d10(conv_l2_d9)
conv_l2_d11 = torch.cat([conv_l2_d10, conv_t2b], 1)
conv_l2_d12 = self.conv_l2_d12(conv_l2_d11)
conv_t1a = self.conv_t1a(conv_l2_d12)
conv_t1b = self.conv_t1b(conv_l2_d12)
conv_l2_out = self.conv_l2_out(conv_l2_d12)
output_l2 = self.output_l2(conv_l2_out)
return output_l2, conv_t1a, conv_t1b
def level_1(self, conv_l1_d1, conv_t1a, conv_t1b):
conv_l1_d2 = torch.cat([conv_l1_d1, conv_t1a], 1)
conv_l1_d3 = self.conv_l1_d3(conv_l1_d2)
conv_l1_d4 = torch.cat([conv_l1_d3, conv_l1_d1], 1)
conv_l1_d5 = self.conv_l1_d5(conv_l1_d4)
conv_l1_d6 = self.conv_l1_d6(conv_l1_d5)
conv_l1_d7 = self.conv_l1_d7(conv_l1_d6) + conv_l1_d6
conv_l1_d8 = self.conv_l1_d8(conv_l1_d7) + conv_l1_d7
conv_l1_d9 = self.conv_l1_d9(conv_l1_d8) + conv_l1_d8
conv_l1_d10 = self.conv_l1_d10(conv_l1_d9)
conv_l1_d11 = torch.cat([conv_l1_d10, conv_l1_d1], 1)
conv_l1_d12 = self.conv_l1_d12(conv_l1_d11)
conv_l1_d13 = torch.cat([conv_l1_d12, conv_t1b, conv_l1_d1], 1)
conv_l1_d14 = self.conv_l1_d14(conv_l1_d13)
conv_t0 = self.conv_t0(conv_l1_d14)
conv_l1_out = self.conv_l1_out(conv_l1_d14)
output_l1 = self.output_l1(conv_l1_out)
return output_l1, conv_t0
def level_0(self, conv_t0):
conv_l0_d1 = self.conv_l0_d1(conv_t0)
output_l0 = self.output_l0(conv_l0_d1)
return output_l0
def forward(self, x):
conv_l1_d1 = self.conv_l1_d1(x)
pool1 = self.pool1(conv_l1_d1)
conv_l2_d1 = self.conv_l2_d1(pool1)
pool2 = self.pool2(conv_l2_d1)
conv_l3_d1 = self.conv_l3_d1(pool2)
pool3 = self.pool3(conv_l3_d1)
conv_l4_d1 = self.conv_l4_d1(pool3)
pool4 = self.pool4(conv_l4_d1)
output_l5, conv_t4a, conv_t4b = self.level_5(pool4)
if self.level < 5:
output_l4, conv_t3a, conv_t3b = self.level_4(conv_l4_d1, conv_t4a, conv_t4b)
if self.level < 4:
output_l3, conv_t2a, conv_t2b = self.level_3(conv_l3_d1, conv_t3a, conv_t3b)
if self.level < 3:
output_l2, conv_t1a, conv_t1b = self.level_2(conv_l2_d1, conv_t2a, conv_t2b)
if self.level < 2:
output_l1, conv_t0 = self.level_1(conv_l1_d1, conv_t1a, conv_t1b)
if self.level < 1:
output_l0 = self.level_0(conv_t0)
if self.level == 0:
enhanced = output_l0
if self.level == 1:
enhanced = output_l1
if self.level == 2:
enhanced = output_l2
if self.level == 3:
enhanced = output_l3
if self.level == 4:
enhanced = output_l4
if self.level == 5:
enhanced = output_l5
return enhanced
class ConvMultiBlock(nn.Module):
def __init__(self, in_channels, out_channels, max_conv_size, instance_norm):
super(ConvMultiBlock, self).__init__()
self.max_conv_size = max_conv_size
self.conv_3a = ConvLayer(in_channels, out_channels, kernel_size=3, stride=1, instance_norm=instance_norm)
self.conv_3b = ConvLayer(out_channels, out_channels, kernel_size=3, stride=1, instance_norm=instance_norm)
if max_conv_size >= 5:
self.conv_5a = ConvLayer(in_channels, out_channels, kernel_size=5, stride=1, instance_norm=instance_norm)
self.conv_5b = ConvLayer(out_channels, out_channels, kernel_size=5, stride=1, instance_norm=instance_norm)
if max_conv_size >= 7:
self.conv_7a = ConvLayer(in_channels, out_channels, kernel_size=7, stride=1, instance_norm=instance_norm)
self.conv_7b = ConvLayer(out_channels, out_channels, kernel_size=7, stride=1, instance_norm=instance_norm)
if max_conv_size >= 9:
self.conv_9a = ConvLayer(in_channels, out_channels, kernel_size=9, stride=1, instance_norm=instance_norm)
self.conv_9b = ConvLayer(out_channels, out_channels, kernel_size=9, stride=1, instance_norm=instance_norm)
def forward(self, x):
out_3 = self.conv_3a(x)
output_tensor = self.conv_3b(out_3)
if self.max_conv_size >= 5:
out_5 = self.conv_5a(x)
out_5 = self.conv_5b(out_5)
output_tensor = torch.cat([output_tensor, out_5], 1)
if self.max_conv_size >= 7:
out_7 = self.conv_7a(x)
out_7 = self.conv_7b(out_7)
output_tensor = torch.cat([output_tensor, out_7], 1)
if self.max_conv_size >= 9:
out_9 = self.conv_9a(x)
out_9 = self.conv_9b(out_9)
output_tensor = torch.cat([output_tensor, out_9], 1)
return output_tensor
class ConvLayer(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride, relu=True, instance_norm=False):
super(ConvLayer, self).__init__()
reflection_padding = kernel_size // 2
self.reflection_pad = nn.ReflectionPad2d(reflection_padding)
self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size, stride)
self.instance_norm = instance_norm
self.instance = None
self.relu = None
if instance_norm:
self.instance = nn.InstanceNorm2d(out_channels, affine=True)
if relu:
self.relu = nn.LeakyReLU(0.2)
def forward(self, x):
out = self.reflection_pad(x)
out = self.conv2d(out)
if self.instance_norm:
out = self.instance(out)
if self.relu:
out = self.relu(out)
return out
class UpsampleConvLayer(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, upsample=2, stride=1, relu=True):
super(UpsampleConvLayer, self).__init__()
self.upsample = nn.Upsample(scale_factor=upsample, mode='bilinear', align_corners=True)
reflection_padding = kernel_size // 2
self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding)
self.conv2d = torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride)
if relu:
self.relu = nn.LeakyReLU(0.2)
def forward(self, x):
out = self.upsample(x)
out = self.reflection_pad(out)
out = self.conv2d(out)
if self.relu:
out = self.relu(out)
return out