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models.py
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models.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from config import device, im_size
class conv2DBatchNormRelu(nn.Module):
def __init__(
self,
in_channels,
n_filters,
k_size,
stride,
padding,
bias=True,
dilation=1,
with_bn=True,
with_relu=True
):
super(conv2DBatchNormRelu, self).__init__()
conv_mod = nn.Conv2d(int(in_channels),
int(n_filters),
kernel_size=k_size,
padding=padding,
stride=stride,
bias=bias,
dilation=dilation, )
if with_bn:
if with_relu:
self.cbr_unit = nn.Sequential(conv_mod, nn.BatchNorm2d(int(n_filters)), nn.ReLU(inplace=True))
else:
self.cbr_unit = nn.Sequential(conv_mod, nn.BatchNorm2d(int(n_filters)))
else:
if with_relu:
self.cbr_unit = nn.Sequential(conv_mod, nn.ReLU(inplace=True))
else:
self.cbr_unit = nn.Sequential(conv_mod)
def forward(self, inputs):
outputs = self.cbr_unit(inputs)
return outputs
class segnetDown2_2(nn.Module):
def __init__(self, in_size, mid_size, out_size):
super(segnetDown2_2, self).__init__()
self.conv1 = conv2DBatchNormRelu(in_size, mid_size, k_size=3, stride=1, padding=1)
self.conv2 = conv2DBatchNormRelu(mid_size, out_size, k_size=3, stride=1, padding=1)
self.maxpool_with_argmax = nn.MaxPool2d(2, 2, return_indices=True)
def forward(self, inputs):
outputs = self.conv1(inputs)
outputs = self.conv2(outputs)
unpooled_shape = outputs.size()
outputs, indices = self.maxpool_with_argmax(outputs)
return outputs, indices, unpooled_shape
class segnetDown2(nn.Module):
def __init__(self, in_size, out_size):
super(segnetDown2, self).__init__()
self.conv1 = conv2DBatchNormRelu(in_size, out_size, k_size=3, stride=1, padding=1)
self.conv2 = conv2DBatchNormRelu(out_size, out_size, k_size=3, stride=1, padding=1)
self.maxpool_with_argmax = nn.MaxPool2d(2, 2, return_indices=True)
def forward(self, inputs):
outputs = self.conv1(inputs)
outputs = self.conv2(outputs)
unpooled_shape = outputs.size()
outputs, indices = self.maxpool_with_argmax(outputs)
return outputs, indices, unpooled_shape
class segnetDown3_2(nn.Module):
def __init__(self, in_size, mid_size, mid2_size, out_size):
super(segnetDown3_2, self).__init__()
self.conv1 = conv2DBatchNormRelu(in_size, mid_size, k_size=3, stride=1, padding=1)
self.conv2 = conv2DBatchNormRelu(mid_size, mid2_size, k_size=3, stride=1, padding=1)
self.conv3 = conv2DBatchNormRelu(mid2_size, out_size, k_size=3, stride=1, padding=1)
self.maxpool_with_argmax = nn.MaxPool2d(2, 2, return_indices=True)
def forward(self, inputs):
outputs = self.conv1(inputs)
outputs = self.conv2(outputs)
outputs = self.conv3(outputs)
unpooled_shape = outputs.size()
outputs, indices = self.maxpool_with_argmax(outputs)
return outputs, indices, unpooled_shape
class segnetDown3(nn.Module):
def __init__(self, in_size, out_size):
super(segnetDown3, self).__init__()
self.conv1 = conv2DBatchNormRelu(in_size, out_size, k_size=3, stride=1, padding=1)
self.conv2 = conv2DBatchNormRelu(out_size, out_size, k_size=3, stride=1, padding=1)
self.conv3 = conv2DBatchNormRelu(out_size, out_size, k_size=3, stride=1, padding=1)
self.maxpool_with_argmax = nn.MaxPool2d(2, 2, return_indices=True)
def forward(self, inputs):
outputs = self.conv1(inputs)
outputs = self.conv2(outputs)
outputs = self.conv3(outputs)
unpooled_shape = outputs.size()
outputs, indices = self.maxpool_with_argmax(outputs)
return outputs, indices, unpooled_shape
class segnetUp1(nn.Module):
def __init__(self, in_size, out_size):
super(segnetUp1, self).__init__()
self.unpool = nn.MaxUnpool2d(2, 2)
self.conv = conv2DBatchNormRelu(in_size, out_size, k_size=5, stride=1, padding=2, with_relu=False)
def forward(self, inputs, indices, output_shape):
outputs = self.unpool(input=inputs, indices=indices, output_size=output_shape)
outputs = self.conv(outputs)
return outputs
class DIMModel(nn.Module):
def __init__(self, n_classes=1, in_channels=4, is_unpooling=True, pretrain=True):
super(DIMModel, self).__init__()
self.in_channels = in_channels
self.is_unpooling = is_unpooling
self.pretrain = pretrain
self.down1 = segnetDown2(self.in_channels, 64)
self.down2 = segnetDown2(64, 128)
self.down3 = segnetDown3(128, 256)
self.down4 = segnetDown3(256, 512)
self.down5 = segnetDown3(512, 512)
self.up5 = segnetUp1(512, 512)
self.up4 = segnetUp1(512, 256)
self.up3 = segnetUp1(256, 128)
self.up2 = segnetUp1(128, 64)
self.up1 = segnetUp1(64, n_classes)
self.sigmoid = nn.Sigmoid()
if self.pretrain:
import torchvision.models as models
vgg16 = models.vgg16()
self.init_vgg16_params(vgg16)
def forward(self, inputs):
# inputs: [N, 4, 320, 320]
feature_maps = []
down1, indices_1, unpool_shape1 = self.down1(inputs)
down2, indices_2, unpool_shape2 = self.down2(down1)
down3, indices_3, unpool_shape3 = self.down3(down2)
down4, indices_4, unpool_shape4 = self.down4(down3)
down5, indices_5, unpool_shape5 = self.down5(down4)
up5 = self.up5(down5, indices_5, unpool_shape5)
up4 = self.up4(up5, indices_4, unpool_shape4)
up3 = self.up3(up4, indices_3, unpool_shape3)
up2 = self.up2(up3, indices_2, unpool_shape2)
up1 = self.up1(up2, indices_1, unpool_shape1)
# decoder features
feature_maps.append(down2)
feature_maps.append(down3)
feature_maps.append(down4)
feature_maps.append(down5)
x = torch.squeeze(up1, dim=1) # [N, 1, 320, 320] -> [N, 320, 320]
x = self.sigmoid(x)
return feature_maps, x
def extract_feature(self, inputs):
feature_maps = []
down1, indices_1, unpool_shape1 = self.down1(inputs)
down2 = self.down2.conv1(down1)
down2 = self.down2.conv2.cbr_unit[0](down2) #conv
feature1 = self.down2.conv2.cbr_unit[1](down2) #bn
down2 = self.down2.conv2.cbr_unit[2](feature1) #relu
down2, indices_2 = self.down2.maxpool_with_argmax(down2)
down3 = self.down3.conv1(down2)
down3 = self.down3.conv2(down3)
down3 = self.down3.conv3.cbr_unit[0](down3) #conv
feature2 = self.down3.conv3.cbr_unit[1](down3) #bn
down3 = self.down3.conv3.cbr_unit[2](feature2) #relu
down3, indices_3 = self.down3.maxpool_with_argmax(down3)
down4 = self.down4.conv1(down3)
down4 = self.down4.conv2(down4)
down4 = self.down4.conv3.cbr_unit[0](down4) #conv
feature3 = self.down4.conv3.cbr_unit[1](down4) #bn
down4 = self.down4.conv3.cbr_unit[2](feature3) #relu
down4, indices_4 = self.down4.maxpool_with_argmax(down4)
down5 = self.down5.conv1(down4)
down5 = self.down5.conv2(down5)
down5 = self.down5.conv3.cbr_unit[0](down5) #conv
feature4 = self.down5.conv3.cbr_unit[1](down5) #bn
down5 = self.down5.conv3.cbr_unit[2](feature4) #relu
down5, indices_5 = self.down5.maxpool_with_argmax(down5)
up5 = self.up5(down5, indices_5, feature4.size())
up4 = self.up4(up5, indices_4, feature3.size())
up3 = self.up3(up4, indices_3, feature2.size())
up2 = self.up2(up3, indices_2, feature1.size())
up1 = self.up1(up2, indices_1, unpool_shape1)
feature1, _ = self.down2.maxpool_with_argmax(feature1)
feature2, _ = self.down2.maxpool_with_argmax(feature2)
feature3, _ = self.down2.maxpool_with_argmax(feature3)
feature4, _ = self.down2.maxpool_with_argmax(feature4)
feature_maps.append(feature1)
feature_maps.append(feature2)
feature_maps.append(feature3)
feature_maps.append(feature4)
x = torch.squeeze(up1, dim=1) # [N, 1, 320, 320] -> [N, 320, 320]
x = self.sigmoid(x)
return feature_maps, x
def get_bn_before_relu(self):
bn1 = self.down2.conv2.cbr_unit[1]
bn2 = self.down3.conv3.cbr_unit[1]
bn3 = self.down4.conv3.cbr_unit[1]
bn4 = self.down5.conv3.cbr_unit[1]
return [bn1, bn2, bn3, bn4]
def init_vgg16_params(self, vgg16):
blocks = [self.down1, self.down2, self.down3, self.down4, self.down5]
ranges = [[0, 4], [5, 9], [10, 16], [17, 23], [24, 29]]
features = list(vgg16.features.children())
vgg_layers = []
for _layer in features:
if isinstance(_layer, nn.Conv2d):
vgg_layers.append(_layer)
merged_layers = []
for idx, conv_block in enumerate(blocks):
if idx < 2:
units = [conv_block.conv1.cbr_unit, conv_block.conv2.cbr_unit]
else:
units = [
conv_block.conv1.cbr_unit,
conv_block.conv2.cbr_unit,
conv_block.conv3.cbr_unit,
]
for _unit in units:
for _layer in _unit:
if isinstance(_layer, nn.Conv2d):
merged_layers.append(_layer)
assert len(vgg_layers) == len(merged_layers)
for l1, l2 in zip(vgg_layers, merged_layers):
if isinstance(l1, nn.Conv2d) and isinstance(l2, nn.Conv2d):
if l1.weight.size() == l2.weight.size() and l1.bias.size() == l2.bias.size():
l2.weight.data = l1.weight.data
l2.bias.data = l1.bias.data
class DIMModel_student(nn.Module):
def __init__(self, n_classes=1, in_channels=4, is_unpooling=True, cfg=None):
super(DIMModel_student, self).__init__()
self.in_channels = in_channels
self.is_unpooling = is_unpooling
self.cfg = cfg
self.down1 = segnetDown2_2(self.in_channels, self.cfg[0], self.cfg[1]) # 4, 32
self.down2 = segnetDown2_2(self.cfg[1], self.cfg[2], self.cfg[3]) # 32, 64
self.down3 = segnetDown3_2(self.cfg[3], self.cfg[4], self.cfg[5], self.cfg[6]) # 64, 128
self.down4 = segnetDown3_2(self.cfg[6], self.cfg[7], self.cfg[8], self.cfg[9]) # 128, 256
self.down5 = segnetDown3_2(self.cfg[9], self.cfg[10], self.cfg[11], self.cfg[12]) # 256, 256
self.up5 = segnetUp1(self.cfg[12], self.cfg[13]) # 256, 256
self.up4 = segnetUp1(self.cfg[13], self.cfg[14]) # 256, 128
self.up3 = segnetUp1(self.cfg[14], self.cfg[15]) # 128, 64
self.up2 = segnetUp1(self.cfg[15], self.cfg[16]) # 64, 32
self.up1 = segnetUp1(self.cfg[16], n_classes)
self.sigmoid = nn.Sigmoid()
self.feature2 = nn.Conv2d(self.cfg[3], 128, kernel_size=1, stride=1, padding=0, bias=False)
self.feature3 = nn.Conv2d(self.cfg[6], 256, kernel_size=1, stride=1, padding=0, bias=False)
self.feature4 = nn.Conv2d(self.cfg[9], 512, kernel_size=1, stride=1, padding=0, bias=False)
self.feature5 = nn.Conv2d(self.cfg[12], 512, kernel_size=1, stride=1, padding=0, bias=False)
# OFD
self.feature_trans2 = self.build_feature_connector(self.cfg[3], 128)
self.feature_trans3 = self.build_feature_connector(self.cfg[6], 256)
self.feature_trans4 = self.build_feature_connector(self.cfg[9], 512)
self.feature_trans5 = self.build_feature_connector(self.cfg[12], 512)
def forward(self, inputs):
# inputs: [N, 4, 320, 320]
feature_maps = []
down1, indices_1, unpool_shape1 = self.down1(inputs)
down2, indices_2, unpool_shape2 = self.down2(down1)
down3, indices_3, unpool_shape3 = self.down3(down2)
down4, indices_4, unpool_shape4 = self.down4(down3)
down5, indices_5, unpool_shape5 = self.down5(down4)
up5 = self.up5(down5, indices_5, unpool_shape5)
up4 = self.up4(up5, indices_4, unpool_shape4)
up3 = self.up3(up4, indices_3, unpool_shape3)
up2 = self.up2(up3, indices_2, unpool_shape2)
up1 = self.up1(up2, indices_1, unpool_shape1)
# encoder features
feature_maps.append(down2)
feature_maps.append(down3)
feature_maps.append(down4)
feature_maps.append(down5)
# for channel similarity
down2 = self.feature2(down2)
down3 = self.feature3(down3)
down4 = self.feature4(down4)
down5 = self.feature5(down5)
feature_maps.append(down2)
feature_maps.append(down3)
feature_maps.append(down4)
feature_maps.append(down5)
x = torch.squeeze(up1, dim=1) # [N, 1, 320, 320] -> [N, 320, 320]
x = self.sigmoid(x)
return feature_maps, x
def extract_feature(self, inputs):
# inputs: [N, 4, 320, 320]
feature_maps = []
down1, indices_1, unpool_shape1 = self.down1(inputs)
down2 = self.down2.conv1(down1)
down2 = self.down2.conv2.cbr_unit[0](down2) # conv
feature1 = self.down2.conv2.cbr_unit[1](down2) # bn
down2 = self.down2.conv2.cbr_unit[2](feature1) # relu
down2, indices_2 = self.down2.maxpool_with_argmax(down2)
down3 = self.down3.conv1(down2)
down3 = self.down3.conv2(down3)
down3 = self.down3.conv3.cbr_unit[0](down3) # conv
feature2 = self.down3.conv3.cbr_unit[1](down3) # bn
down3 = self.down3.conv3.cbr_unit[2](feature2) # relu
down3, indices_3 = self.down3.maxpool_with_argmax(down3)
down4 = self.down4.conv1(down3)
down4 = self.down4.conv2(down4)
down4 = self.down4.conv3.cbr_unit[0](down4) # conv
feature3 = self.down4.conv3.cbr_unit[1](down4) # bn
down4 = self.down4.conv3.cbr_unit[2](feature3) # relu
down4, indices_4 = self.down4.maxpool_with_argmax(down4)
down5 = self.down5.conv1(down4)
down5 = self.down5.conv2(down5)
down5 = self.down5.conv3.cbr_unit[0](down5) # conv
feature4 = self.down5.conv3.cbr_unit[1](down5) # bn
down5 = self.down5.conv3.cbr_unit[2](feature4) # relu
down5, indices_5 = self.down5.maxpool_with_argmax(down5)
up5 = self.up5(down5, indices_5, feature4.size())
up4 = self.up4(up5, indices_4, feature3.size())
up3 = self.up3(up4, indices_3, feature2.size())
up2 = self.up2(up3, indices_2, feature1.size())
up1 = self.up1(up2, indices_1, unpool_shape1)
feature1, _ = self.down2.maxpool_with_argmax(feature1)
feature2, _ = self.down2.maxpool_with_argmax(feature2)
feature3, _ = self.down2.maxpool_with_argmax(feature3)
feature4, _ = self.down2.maxpool_with_argmax(feature4)
feature1 = self.feature_trans2(feature1)
feature2 = self.feature_trans3(feature2)
feature3 = self.feature_trans4(feature3)
feature4 = self.feature_trans5(feature4)
feature_maps.append(feature1)
feature_maps.append(feature2)
feature_maps.append(feature3)
feature_maps.append(feature4)
x = torch.squeeze(up1, dim=1) # [N, 1, 320, 320] -> [N, 320, 320]
x = self.sigmoid(x)
return feature_maps, x
def build_feature_connector(self, s_channel, t_channel):
C = [nn.Conv2d(s_channel, t_channel, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(t_channel)]
for m in C:
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
return nn.Sequential(*C)
if __name__ == '__main__':
model = DIMModel_student()
print(model)
param = 0
x = torch.rand(1,4,320,320)
model(x)
for p in model.parameters():
param += p.numel()
print(param)
#summary(model, (4, im_size, im_size))