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models_resnet.py
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models_resnet.py
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from __future__ import absolute_import, division, print_function
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import importlib
class conv(nn.Module):
def __init__(self, num_in_layers, num_out_layers, kernel_size, stride):
super(conv, self).__init__()
self.kernel_size = kernel_size
self.conv_base = nn.Conv2d(num_in_layers, num_out_layers, kernel_size=kernel_size, stride=stride)
self.normalize = nn.BatchNorm2d(num_out_layers)
def forward(self, x):
p = int(np.floor((self.kernel_size-1)/2))
p2d = (p, p, p, p)
x = self.conv_base(F.pad(x, p2d))
x = self.normalize(x)
return F.elu(x, inplace=True)
class convblock(nn.Module):
def __init__(self, num_in_layers, num_out_layers, kernel_size):
super(convblock, self).__init__()
self.conv1 = conv(num_in_layers, num_out_layers, kernel_size, 1)
self.conv2 = conv(num_out_layers, num_out_layers, kernel_size, 2)
def forward(self, x):
x = self.conv1(x)
return self.conv2(x)
class maxpool(nn.Module):
def __init__(self, kernel_size):
super(maxpool, self).__init__()
self.kernel_size = kernel_size
def forward(self, x):
p = int(np.floor((self.kernel_size-1) / 2))
p2d = (p, p, p, p)
return F.max_pool2d(F.pad(x, p2d), self.kernel_size, stride=2)
class resconv(nn.Module):
def __init__(self, num_in_layers, num_out_layers, stride):
super(resconv, self).__init__()
self.num_out_layers = num_out_layers
self.stride = stride
self.conv1 = conv(num_in_layers, num_out_layers, 1, 1)
self.conv2 = conv(num_out_layers, num_out_layers, 3, stride)
self.conv3 = nn.Conv2d(num_out_layers, 4*num_out_layers, kernel_size=1, stride=1)
self.conv4 = nn.Conv2d(num_in_layers, 4*num_out_layers, kernel_size=1, stride=stride)
self.normalize = nn.BatchNorm2d(4*num_out_layers)
def forward(self, x):
# do_proj = x.size()[1] != self.num_out_layers or self.stride == 2
do_proj = True
shortcut = []
x_out = self.conv1(x)
x_out = self.conv2(x_out)
x_out = self.conv3(x_out)
if do_proj:
shortcut = self.conv4(x)
else:
shortcut = x
return F.elu(self.normalize(x_out + shortcut), inplace=True)
class resconv_basic(nn.Module):
# for resnet18
def __init__(self, num_in_layers, num_out_layers, stride):
super(resconv_basic, self).__init__()
self.num_out_layers = num_out_layers
self.stride = stride
self.conv1 = conv(num_in_layers, num_out_layers, 3, stride)
self.conv2 = conv(num_out_layers, num_out_layers, 3, 1)
self.conv3 = nn.Conv2d(num_in_layers, num_out_layers, kernel_size=1, stride=stride)
self.normalize = nn.BatchNorm2d(num_out_layers)
def forward(self, x):
# do_proj = x.size()[1] != self.num_out_layers or self.stride == 2
do_proj = True
shortcut = []
x_out = self.conv1(x)
x_out = self.conv2(x_out)
if do_proj:
shortcut = self.conv3(x)
else:
shortcut = x
return F.elu(self.normalize(x_out + shortcut), inplace=True)
def resblock(num_in_layers, num_out_layers, num_blocks, stride):
layers = []
layers.append(resconv(num_in_layers, num_out_layers, stride))
for i in range(1, num_blocks - 1):
layers.append(resconv(4 * num_out_layers, num_out_layers, 1))
layers.append(resconv(4 * num_out_layers, num_out_layers, 1))
return nn.Sequential(*layers)
def resblock_basic(num_in_layers, num_out_layers, num_blocks, stride):
layers = []
layers.append(resconv_basic(num_in_layers, num_out_layers, stride))
for i in range(1, num_blocks):
layers.append(resconv_basic(num_out_layers, num_out_layers, 1))
return nn.Sequential(*layers)
class upconv(nn.Module):
def __init__(self, num_in_layers, num_out_layers, kernel_size, scale):
super(upconv, self).__init__()
self.scale = scale
self.conv1 = conv(num_in_layers, num_out_layers, kernel_size, 1)
def forward(self, x):
x = nn.functional.interpolate(x, scale_factor=self.scale, mode='bilinear', align_corners=True)
return self.conv1(x)
class get_disp(nn.Module):
def __init__(self, num_in_layers):
super(get_disp, self).__init__()
self.conv1 = nn.Conv2d(num_in_layers, 2, kernel_size=3, stride=1)
self.normalize = nn.BatchNorm2d(2)
self.sigmoid = torch.nn.Sigmoid()
def forward(self, x):
p = 1
p2d = (p, p, p, p)
x = self.conv1(F.pad(x, p2d))
x = self.normalize(x)
return 0.3 * self.sigmoid(x)
class Resnet50_md(nn.Module):
def __init__(self, num_in_layers):
super(Resnet50_md, self).__init__()
# encoder
self.conv1 = conv(num_in_layers, 64, 7, 2) # H/2 - 64D
self.pool1 = maxpool(3) # H/4 - 64D
self.conv2 = resblock(64, 64, 3, 2) # H/8 - 256D
self.conv3 = resblock(256, 128, 4, 2) # H/16 - 512D
self.conv4 = resblock(512, 256, 6, 2) # H/32 - 1024D
self.conv5 = resblock(1024, 512, 3, 2) # H/64 - 2048D
# decoder
self.upconv6 = upconv(2048, 512, 3, 2)
self.iconv6 = conv(1024 + 512, 512, 3, 1)
self.upconv5 = upconv(512, 256, 3, 2)
self.iconv5 = conv(512+256, 256, 3, 1)
self.upconv4 = upconv(256, 128, 3, 2)
self.iconv4 = conv(256+128, 128, 3, 1)
self.disp4_layer = get_disp(128)
self.upconv3 = upconv(128, 64, 3, 2)
self.iconv3 = conv(64+64+2, 64, 3, 1)
self.disp3_layer = get_disp(64)
self.upconv2 = upconv(64, 32, 3, 2)
self.iconv2 = conv(32+64+2, 32, 3, 1)
self.disp2_layer = get_disp(32)
self.upconv1 = upconv(32, 16, 3, 2)
self.iconv1 = conv(16+2, 16, 3, 1)
self.disp1_layer = get_disp(16)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.xavier_uniform_(m.weight)
def forward(self, x):
# encoder
x1 = self.conv1(x)
x_pool1 = self.pool1(x1)
x2 = self.conv2(x_pool1)
x3 = self.conv3(x2)
x4 = self.conv4(x3)
x5 = self.conv5(x4)
# skips
skip1 = x1
skip2 = x_pool1
skip3 = x2
skip4 = x3
skip5 = x4
# decoder
upconv6 = self.upconv6(x5)
concat6 = torch.cat((upconv6, skip5), 1)
iconv6 = self.iconv6(concat6)
upconv5 = self.upconv5(iconv6)
concat5 = torch.cat((upconv5, skip4), 1)
iconv5 = self.iconv5(concat5)
upconv4 = self.upconv4(iconv5)
concat4 = torch.cat((upconv4, skip3), 1)
iconv4 = self.iconv4(concat4)
self.disp4 = self.disp4_layer(iconv4)
self.udisp4 = nn.functional.interpolate(self.disp4, scale_factor=2, mode='bilinear', align_corners=True)
upconv3 = self.upconv3(iconv4)
concat3 = torch.cat((upconv3, skip2, self.udisp4), 1)
iconv3 = self.iconv3(concat3)
self.disp3 = self.disp3_layer(iconv3)
self.udisp3 = nn.functional.interpolate(self.disp3, scale_factor=2, mode='bilinear', align_corners=True)
upconv2 = self.upconv2(iconv3)
concat2 = torch.cat((upconv2, skip1, self.udisp3), 1)
iconv2 = self.iconv2(concat2)
self.disp2 = self.disp2_layer(iconv2)
self.udisp2 = nn.functional.interpolate(self.disp2, scale_factor=2, mode='bilinear', align_corners=True)
upconv1 = self.upconv1(iconv2)
concat1 = torch.cat((upconv1, self.udisp2), 1)
iconv1 = self.iconv1(concat1)
self.disp1 = self.disp1_layer(iconv1)
return self.disp1, self.disp2, self.disp3, self.disp4
class Resnet18_md(nn.Module):
def __init__(self, num_in_layers):
super(Resnet18_md, self).__init__()
# encoder
self.conv1 = conv(num_in_layers, 64, 7, 2) # H/2 - 64D
self.pool1 = maxpool(3) # H/4 - 64D
self.conv2 = resblock_basic(64, 64, 2, 2) # H/8 - 64D
self.conv3 = resblock_basic(64, 128, 2, 2) # H/16 - 128D
self.conv4 = resblock_basic(128, 256, 2, 2) # H/32 - 256D
self.conv5 = resblock_basic(256, 512, 2, 2) # H/64 - 512D
# decoder
self.upconv6 = upconv(512, 512, 3, 2)
self.iconv6 = conv(256+512, 512, 3, 1)
self.upconv5 = upconv(512, 256, 3, 2)
self.iconv5 = conv(128+256, 256, 3, 1)
self.upconv4 = upconv(256, 128, 3, 2)
self.iconv4 = conv(64+128, 128, 3, 1)
self.disp4_layer = get_disp(128)
self.upconv3 = upconv(128, 64, 3, 2)
self.iconv3 = conv(64+64 + 2, 64, 3, 1)
self.disp3_layer = get_disp(64)
self.upconv2 = upconv(64, 32, 3, 2)
self.iconv2 = conv(64+32 + 2, 32, 3, 1)
self.disp2_layer = get_disp(32)
self.upconv1 = upconv(32, 16, 3, 2)
self.iconv1 = conv(16+2, 16, 3, 1)
self.disp1_layer = get_disp(16)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.xavier_uniform_(m.weight)
def forward(self, x):
# encoder
x1 = self.conv1(x)
x_pool1 = self.pool1(x1)
x2 = self.conv2(x_pool1)
x3 = self.conv3(x2)
x4 = self.conv4(x3)
x5 = self.conv5(x4)
# skips
skip1 = x1
skip2 = x_pool1
skip3 = x2
skip4 = x3
skip5 = x4
# decoder
upconv6 = self.upconv6(x5)
concat6 = torch.cat((upconv6, skip5), 1)
iconv6 = self.iconv6(concat6)
upconv5 = self.upconv5(iconv6)
concat5 = torch.cat((upconv5, skip4), 1)
iconv5 = self.iconv5(concat5)
upconv4 = self.upconv4(iconv5)
concat4 = torch.cat((upconv4, skip3), 1)
iconv4 = self.iconv4(concat4)
self.disp4 = self.disp4_layer(iconv4)
self.udisp4 = nn.functional.interpolate(self.disp4, scale_factor=2, mode='bilinear', align_corners=True)
upconv3 = self.upconv3(iconv4)
concat3 = torch.cat((upconv3, skip2, self.udisp4), 1)
iconv3 = self.iconv3(concat3)
self.disp3 = self.disp3_layer(iconv3)
self.udisp3 = nn.functional.interpolate(self.disp3, scale_factor=2, mode='bilinear', align_corners=True)
upconv2 = self.upconv2(iconv3)
concat2 = torch.cat((upconv2, skip1, self.udisp3), 1)
iconv2 = self.iconv2(concat2)
self.disp2 = self.disp2_layer(iconv2)
self.udisp2 = nn.functional.interpolate(self.disp2, scale_factor=2, mode='bilinear', align_corners=True)
upconv1 = self.upconv1(iconv2)
concat1 = torch.cat((upconv1, self.udisp2), 1)
iconv1 = self.iconv1(concat1)
self.disp1 = self.disp1_layer(iconv1)
return self.disp1, self.disp2, self.disp3, self.disp4
def class_for_name(module_name, class_name):
# load the module, will raise ImportError if module cannot be loaded
m = importlib.import_module(module_name)
# get the class, will raise AttributeError if class cannot be found
return getattr(m, class_name)
class ResnetModel(nn.Module):
def __init__(self, num_in_layers, encoder='resnet18', pretrained=False):
super(ResnetModel, self).__init__()
assert encoder in ['resnet18', 'resnet34', 'resnet50',\
'resnet101', 'resnet152'],\
"Incorrect encoder type"
if encoder in ['resnet18', 'resnet34']:
filters = [64, 128, 256, 512]
else:
filters = [256, 512, 1024, 2048]
resnet = class_for_name("torchvision.models", encoder)\
(pretrained=pretrained)
if num_in_layers != 3: # Number of input channels
self.firstconv = nn.Conv2d(num_in_layers, 64,
kernel_size=(7, 7), stride=(2, 2),
padding=(3, 3), bias=False)
else:
self.firstconv = resnet.conv1 # H/2
self.firstbn = resnet.bn1
self.firstrelu = resnet.relu
self.firstmaxpool = resnet.maxpool # H/4
# encoder
self.encoder1 = resnet.layer1 # H/4
self.encoder2 = resnet.layer2 # H/8
self.encoder3 = resnet.layer3 # H/16
self.encoder4 = resnet.layer4 # H/32
# decoder
self.upconv6 = upconv(filters[3], 512, 3, 2)
self.iconv6 = conv(filters[2] + 512, 512, 3, 1)
self.upconv5 = upconv(512, 256, 3, 2)
self.iconv5 = conv(filters[1] + 256, 256, 3, 1)
self.upconv4 = upconv(256, 128, 3, 2)
self.iconv4 = conv(filters[0] + 128, 128, 3, 1)
self.disp4_layer = get_disp(128)
self.upconv3 = upconv(128, 64, 3, 1) #
self.iconv3 = conv(64 + 64 + 2, 64, 3, 1)
self.disp3_layer = get_disp(64)
self.upconv2 = upconv(64, 32, 3, 2)
self.iconv2 = conv(64 + 32 + 2, 32, 3, 1)
self.disp2_layer = get_disp(32)
self.upconv1 = upconv(32, 16, 3, 2)
self.iconv1 = conv(16 + 2, 16, 3, 1)
self.disp1_layer = get_disp(16)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.xavier_uniform_(m.weight)
def forward(self, x):
# encoder
x_first_conv = self.firstconv(x)
x = self.firstbn(x_first_conv)
x = self.firstrelu(x)
x_pool1 = self.firstmaxpool(x)
x1 = self.encoder1(x_pool1)
x2 = self.encoder2(x1)
x3 = self.encoder3(x2)
x4 = self.encoder4(x3)
# skips
skip1 = x_first_conv
skip2 = x_pool1
skip3 = x1
skip4 = x2
skip5 = x3
# decoder
upconv6 = self.upconv6(x4)
concat6 = torch.cat((upconv6, skip5), 1)
iconv6 = self.iconv6(concat6)
upconv5 = self.upconv5(iconv6)
concat5 = torch.cat((upconv5, skip4), 1)
iconv5 = self.iconv5(concat5)
upconv4 = self.upconv4(iconv5)
concat4 = torch.cat((upconv4, skip3), 1)
iconv4 = self.iconv4(concat4)
self.disp4 = self.disp4_layer(iconv4)
self.udisp4 = nn.functional.interpolate(self.disp4, scale_factor=1, mode='bilinear', align_corners=True)
self.disp4 = nn.functional.interpolate(self.disp4, scale_factor=0.5, mode='bilinear', align_corners=True)
upconv3 = self.upconv3(iconv4)
concat3 = torch.cat((upconv3, skip2, self.udisp4), 1)
iconv3 = self.iconv3(concat3)
self.disp3 = self.disp3_layer(iconv3)
self.udisp3 = nn.functional.interpolate(self.disp3, scale_factor=2, mode='bilinear', align_corners=True)
upconv2 = self.upconv2(iconv3)
concat2 = torch.cat((upconv2, skip1, self.udisp3), 1)
iconv2 = self.iconv2(concat2)
self.disp2 = self.disp2_layer(iconv2)
self.udisp2 = nn.functional.interpolate(self.disp2, scale_factor=2, mode='bilinear', align_corners=True)
upconv1 = self.upconv1(iconv2)
concat1 = torch.cat((upconv1, self.udisp2), 1)
iconv1 = self.iconv1(concat1)
self.disp1 = self.disp1_layer(iconv1)
return self.disp1, self.disp2, self.disp3, self.disp4