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model3D.py
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import torch
import torch.nn as nn
def conv_block_3d(in_dim, out_dim, activation):
return nn.Sequential(
nn.Conv3d(in_dim, out_dim, kernel_size=3, stride=1, padding=1),
nn.BatchNorm3d(out_dim),
activation,)
def conv_trans_block_3d(in_dim, out_dim, activation, kernel_size, stride, padding, output_padding):
return nn.Sequential(
# 64, 3, kernel_size=4, stride=(2, 4, 4), bias=False, padding=(1, 8, 8)
nn.ConvTranspose3d(in_dim, out_dim, kernel_size=kernel_size, stride=stride, padding=padding, output_padding=output_padding),
nn.BatchNorm3d(out_dim),
activation,)
def max_pooling_3d():
return nn.MaxPool3d(kernel_size=2, stride=2, padding=0)
def conv_block_2_3d(in_dim, out_dim, activation):
return nn.Sequential(
conv_block_3d(in_dim, out_dim, activation),
nn.Conv3d(out_dim, out_dim, kernel_size=3, stride=1, padding=1),
nn.BatchNorm3d(out_dim),)
class UNet(nn.Module):
def __init__(self, in_dim, out_dim, num_filters):
super(UNet, self).__init__()
self.in_dim = in_dim
self.out_dim = out_dim
self.num_filters = num_filters
activation = nn.LeakyReLU(0.2, inplace=True)
# Down sampling
self.down_1 = conv_block_2_3d(self.in_dim, self.num_filters, activation)
self.pool_1 = max_pooling_3d()
self.down_2 = conv_block_2_3d(self.num_filters, self.num_filters * 2, activation)
self.pool_2 = max_pooling_3d()
self.down_3 = conv_block_2_3d(self.num_filters * 2, self.num_filters * 4, activation)
self.pool_3 = max_pooling_3d()
self.down_4 = conv_block_2_3d(self.num_filters * 4, self.num_filters * 8, activation)
self.pool_4 = max_pooling_3d()
self.down_5 = conv_block_2_3d(self.num_filters * 8, self.num_filters * 16, activation)
self.pool_5 = max_pooling_3d()
# Bridge
self.bridge = conv_block_2_3d(self.num_filters * 16, self.num_filters * 32, activation)
# Up sampling
self.trans_1 = conv_trans_block_3d(self.num_filters * 32, self.num_filters * 32, activation, kernel_size=(1,3,3), stride=(1,2,2), padding=(0,1,1), output_padding=(0,1,1))
self.up_1 = conv_block_2_3d(self.num_filters * 48, self.num_filters * 16, activation,)
self.trans_2 = conv_trans_block_3d(self.num_filters * 16, self.num_filters * 16, activation, kernel_size=(1,3,3), stride=(1,2,2), padding=(0,1,1), output_padding=(0,1,1))
self.up_2 = conv_block_2_3d(self.num_filters * 24, self.num_filters * 8, activation)
self.trans_3 = conv_trans_block_3d(self.num_filters * 8, self.num_filters * 8, activation, kernel_size=(1,3,3), stride=(1,2,2), padding=(0,1,1), output_padding=(0,1,1))
self.up_3 = conv_block_2_3d(self.num_filters * 12, self.num_filters * 4, activation)
self.trans_4 = conv_trans_block_3d(self.num_filters * 4, self.num_filters * 4, activation, kernel_size=(2,3,3), stride=(1,2,2), padding=(0,1,1), output_padding=(0,1,1))
self.up_4 = conv_block_2_3d(self.num_filters * 6, self.num_filters * 2, activation)
self.trans_5 = conv_trans_block_3d(self.num_filters * 2, self.num_filters * 2, activation, kernel_size=(4,3,3), stride=(1,2,2), padding=(0,1,1), output_padding=(0,1,1))
self.up_5 = conv_block_2_3d(self.num_filters * 3, self.num_filters * 1, activation)
# Output
self.out = conv_block_3d(self.num_filters, out_dim, activation)
def forward(self, x):
# Down sampling
down_1 = self.down_1(x) # -> [1, 32, 3, 1250, 1250]
# print('down_1', down_1.shape)
pool_1 = self.pool_1(down_1) # -> [1, 32, 1, 625, 625]
# print('pool_1', pool_1.shape)
down_2 = self.down_2(pool_1) # -> [1, 64, 1, 625, 625]
# print('down_2', down_2.shape)
pool_2 = self.pool_2(down_2) # -> [1, 64, 1, 312, 312]
# print('pool_2', pool_2.shape)
down_3 = self.down_3(pool_2) # -> [1, 128, 1, 312, 312]
# print('down_3', down_3.shape)
pool_3 = self.pool_3(down_3) # -> [1, 128, 1, 156, 156]
# print('pool_3', pool_3.shape)
# down_4 = self.down_4(pool_3) # -> [1, 256, 1, 156, 156]
# print('down_4', down_4.shape)
# pool_4 = self.pool_4(down_4) # -> [1, 256, 1, 78, 78]
# print('pool_4', pool_4.shape)
# down_5 = self.down_5(pool_4) # -> [1, 64, 8, 8, 8]
# pool_5 = self.pool_5(down_5) # -> [1, 64, 4, 4, 4]
# Bridge
bridge = self.down_4(pool_3) # -> [1, 512, 1, 78, 78]
# print('bridge', bridge.shape)
# Up sampling
# trans_1 = self.trans_1(bridge) # -> [1, 128, 8, 8, 8]
# concat_1 = torch.cat([trans_1, down_5], dim=1) # -> [1, 192, 8, 8, 8]
# up_1 = self.up_1(concat_1) # -> [1, 64, 8, 8, 8]
# trans_2 = self.trans_2(bridge) # -> [1, 512, 1, 156, 156]
# print('trans_2', trans_2.shape)
# concat_2 = torch.cat([trans_2, down_4], dim=1) # -> [1, 96, 16, 16, 16]
# up_2 = self.up_2(concat_2) # -> [1, 256, 1, 156, 156]
# print('up_2',up_2.shape)
#
trans_3 = self.trans_3(bridge) # -> [1, 256, 1, 312, 312]
# print('trans_3', trans_3.shape)
concat_3 = torch.cat([trans_3, down_3], dim=1) # -> [1, 48, 32, 32, 32]
up_3 = self.up_3(concat_3) # -> [1, 128, 1, 312, 312]
# print('up_3', up_3.shape)
trans_4 = self.trans_4(up_3) # -> [1, 128, 1, 625, 625]
# print('trans_4', trans_4.shape)
concat_4 = torch.cat([trans_4, down_2], dim=1) # -> [1, 24, 64, 64, 64]
up_4 = self.up_4(concat_4) # -> [1, 64, 1, 625, 625]
# print('up_4', up_4.shape)
trans_5 = self.trans_5(up_4) # -> [1, 64, 3, 1250, 1250]
# print('trans_5', trans_5.shape)
concat_5 = torch.cat([trans_5, down_1], dim=1) # -> [1, 12, 128, 128, 128]
up_5 = self.up_5(concat_5) # -> [1, 32, 3, 1250, 1250]
# print('up_5', up_5.shape)
# Output
out = self.out(up_5) # -> [1, 2, 3, 1250, 1250]
# print('out', out.shape)
return out
if __name__ == "__main__":
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
image_size = 256
x = torch.Tensor(1, 1, 5, image_size, image_size)
x.to(device)
print("x size: {}".format(x.size()))
model = UNet(in_dim=1, out_dim=2, num_filters=32)
out = model(x)
print("out size: {}".format(out.size()))
# print(out.shape,out)