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fcn3d.py
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fcn3d.py
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import torch
import torch.nn as nn
import os
import numpy as np
class FCN3D(nn.Module):
def __init__(self, in_channels =1,n_class = 3):
super().__init__()
# conv1
self.conv1_1 = nn.Conv3d(in_channels, 8, 3, padding=60)
# torch.nn.Conv3d(in_channels, out_channels, kernel_size,
# stride=1, padding=0, dilation=1, groups=1, bias=True)
self.relu1_1 = nn.ReLU(inplace=True)
self.conv1_2 = nn.Conv3d(8, 8, 3, padding=1)
self.relu1_2 = nn.ReLU(inplace=True)
self.pool1 = nn.MaxPool3d(2, stride=2, ceil_mode=True) # 1/2
# conv2
self.conv2_1 = nn.Conv3d(8, 16, 3, padding=15)
self.relu2_1 = nn.ReLU(inplace=True)
self.conv2_2 = nn.Conv3d(16, 16, 3, padding=1)
self.relu2_2 = nn.ReLU(inplace=True)
self.pool2 = nn.MaxPool3d(2, stride=2, ceil_mode=True) # 1/4
# conv3
self.conv3_1 = nn.Conv3d(16, 32, 3, padding=1)
self.relu3_1 = nn.ReLU(inplace=True)
self.conv3_2 = nn.Conv3d(32, 32, 3, padding=1)
self.relu3_2 = nn.ReLU(inplace=True)
self.conv3_3 = nn.Conv3d(32, 32, 3, padding=1)
self.relu3_3 = nn.ReLU(inplace=True)
self.pool3 = nn.MaxPool3d(2, stride=2, ceil_mode=True) # 1/8
# conv4
self.conv4_1 = nn.Conv3d(32, 64, 3, padding=1)
self.relu4_1 = nn.ReLU(inplace=True)
self.conv4_2 = nn.Conv3d(64, 64, 3, padding=1)
self.relu4_2 = nn.ReLU(inplace=True)
self.conv4_3 = nn.Conv3d(64, 64, 3, padding=1)
self.relu4_3 = nn.ReLU(inplace=True)
self.pool4 = nn.MaxPool3d(2, stride=2, ceil_mode=True) # 1/16
# conv5
self.conv5_1 = nn.Conv3d(64, 64, 3, padding=1)
self.relu5_1 = nn.ReLU(inplace=True)
self.conv5_2 = nn.Conv3d(64, 64, 3, padding=1)
self.relu5_2 = nn.ReLU(inplace=True)
self.conv5_3 = nn.Conv3d(64, 64, 3, padding=1)
self.relu5_3 = nn.ReLU(inplace=True)
self.pool5 = nn.MaxPool3d(2, stride=2, ceil_mode=True) # 1/32
# fc6
self.fc6 = nn.Conv3d(64, 512, 7)
self.relu6 = nn.ReLU(inplace=True)
self.drop6 = nn.Dropout3d()
# fc7
self.fc7 = nn.Conv3d(512, 512, 1)
self.relu7 = nn.ReLU(inplace=True)
self.drop7 = nn.Dropout3d()
self.score_fr = nn.Conv3d(512, n_class, 1)
self.score_pool3 = nn.Conv3d(32, n_class, 1)
self.score_pool4 = nn.Conv3d(64, n_class, 1)
self.upscore2 = nn.ConvTranspose3d(
n_class, n_class, 4, stride=2, bias=False)
self.upscore8 = nn.ConvTranspose3d(
n_class, n_class, 16, stride=8, bias=False)
self.upscore_pool4 = nn.ConvTranspose3d(
n_class, n_class, 4, stride=2, bias=False)
self._initialize_weights()
def get_upsampling_weight(self, in_channels, out_channels, kernel_size):
"""Make a 2D bilinear kernel suitable for upsampling"""
factor = (kernel_size + 1) // 2
if kernel_size % 2 == 1:
center = factor - 1
else:
center = factor - 0.5
og = np.ogrid[:kernel_size, :kernel_size, :kernel_size]
filt = (1 - abs(og[0] - center) / factor) * \
(1 - abs(og[1] - center) / factor)
weight = np.zeros((in_channels, out_channels, kernel_size, kernel_size, kernel_size),
dtype=np.float64)
weight[range(in_channels), range(out_channels), :, :, :] = filt
return torch.from_numpy(weight).float()
def _initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv3d):
m.weight.data.zero_()
m.weight.data.normal_(0.0, 0.1)
if m.bias is not None:
m.bias.data.zero_()
if isinstance(m, nn.ConvTranspose3d):
assert m.kernel_size[0] == m.kernel_size[1]
initial_weight = self.get_upsampling_weight(
m.in_channels, m.out_channels, m.kernel_size[0])
m.weight.data.copy_(initial_weight)
def forward(self, x):
h = x
h = self.relu1_1(self.conv1_1(h))
h = self.relu1_2(self.conv1_2(h))
h = self.pool1(h)
h = self.relu2_1(self.conv2_1(h))
h = self.relu2_2(self.conv2_2(h))
h = self.pool2(h)
h = self.relu3_1(self.conv3_1(h))
h = self.relu3_2(self.conv3_2(h))
h = self.relu3_3(self.conv3_3(h))
h = self.pool3(h)
pool3 = h # 1/8
h = self.relu4_1(self.conv4_1(h))
h = self.relu4_2(self.conv4_2(h))
h = self.relu4_3(self.conv4_3(h))
h = self.pool4(h)
pool4 = h # 1/16
h = self.relu5_1(self.conv5_1(h))
h = self.relu5_2(self.conv5_2(h))
h = self.relu5_3(self.conv5_3(h))
h = self.pool5(h)
h = self.relu6(self.fc6(h))
h = self.drop6(h)
h = self.relu7(self.fc7(h))
h = self.drop7(h)
h = self.score_fr(h)
h = self.upscore2(h)
upscore2 = h # 1/16
h = self.score_pool4(pool4 * 0.01) # XXX: scaling to train at once
h = h[:, :, 5:5 + upscore2.size()[2], 5:5 + upscore2.size()[3], 5:5 + upscore2.size()[4]]
score_pool4c = h # 1/16
h = upscore2 + score_pool4c # 1/16
h = self.upscore_pool4(h)
upscore_pool4 = h # 1/8
h = self.score_pool3(pool3 * 0.0001) # XXX: scaling to train at once
h = h[:, :,
9:9 + upscore_pool4.size()[2],
9:9 + upscore_pool4.size()[3],
9:9 + upscore_pool4.size()[4]]
score_pool3c = h # 1/8
h = upscore_pool4 + score_pool3c # 1/8
h = self.upscore8(h) # dim: 88^3
h = h[:, :, 31:31 + x.size()[2], 31:31 + x.size()[3], 31:31 + x.size()[4]].contiguous()
return h
if __name__ == '__main__':
import torch
from torchsummary import summary
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = FCN3D(in_channels=1, n_class=3).to(device)
# 生成伪3D数据测试模型
input_shape = (1, 1, 64, 64, 64) # (batch_size, channels, height, width, depth)
input= torch.randn(input_shape).to(device)
print("input_shape: ", input.shape)
output = model(input)
print("output_shape: ", output.shape)
summary(model, input_size=(1, 64, 64, 64), device=device.type)
# print(model)
# print(model.state_dict().keys())
# print(model.state_dict().keys())