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models.py
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models.py
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import torch
import torch.nn as nn
from torchsummary import summary
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(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(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]
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)
x = torch.squeeze(up1, dim=1) # [N, 1, 320, 320] -> [N, 320, 320]
x = self.sigmoid(x)
return x
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
if __name__ == '__main__':
model = DIMModel().to(device)
summary(model, (4, im_size, im_size))