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models.py
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137 lines (116 loc) · 5.83 KB
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### IMPORTS
import torch
import torch.nn as nn
###MODELS
class U_NET(nn.Module):
"""
This is my simple UNET for understanding how it works
"""
def __init__(self):
super().__init__()
self.encoder = nn.Sequential(
nn.Conv2d(in_channels = 3, # C1
out_channels = 10,
kernel_size = 3,
padding = 1,
stride = 2),
nn.BatchNorm2d(num_features=10), #same as out_channels
nn.ReLU(True),
nn.Conv2d(in_channels = 10, # C2
out_channels = 20,
kernel_size = 3,
padding = 1,
stride = 2),
nn.BatchNorm2d(num_features=20), #same as out_channels
nn.ReLU(True),
nn.Conv2d(in_channels = 20, # C3
out_channels = 30,
kernel_size = 3,
padding = 1,
stride = 2),
nn.BatchNorm2d(num_features=30), #same as out_channels
nn.ReLU(True),
)
self.decoder = nn.Sequential(
nn.Conv2d(in_channels = 30, # C1
out_channels = 20,
kernel_size = 3,
padding = 1,
stride = 1),
nn.Upsample(scale_factor=2, mode='bilinear'),
nn.ReLU(True),
nn.Conv2d(in_channels = 20, # C2
out_channels = 10,
kernel_size = 3,
padding = 1,
stride = 1),
nn.Upsample(scale_factor=2, mode='bilinear'),
nn.ReLU(True),
nn.Conv2d(in_channels = 10, # C2
out_channels = 1,
kernel_size = 3,
padding = 1,
stride = 1),
nn.Upsample(size=(300,300), mode='bilinear'), # I force the image to have the same size as the maps
nn.ReLU(True),
)
def forward(self, x):
y = self.encoder(x)
y = self.decoder(y)
return y
class Map_CNN(nn.Module):
"""
Attemp of UNET with pretrained backbone
"""
def __init__(self, Pre_Trained_Encoder):
super().__init__()
self.encoder = Pre_Trained_Encoder
### Encoder
self.decoder = nn.Sequential(
nn.Conv2d(in_channels = 512, # C1
out_channels = 256,
kernel_size = 3,
padding = 1,
stride = 1),
nn.ReLU(True),
nn.Conv2d(in_channels = 256, # C2
out_channels = 128,
kernel_size = 3,
padding = 1,
stride = 1),
nn.ReLU(True),
nn.Upsample(scale_factor=(2,2), mode='bilinear'),
nn.Conv2d(in_channels = 128, #C3
out_channels = 64,
kernel_size = 3,
padding = 1,
stride = 1),
nn.ReLU(True),
nn.Upsample(scale_factor=(2,2), mode='bilinear'),
nn.Conv2d(in_channels = 64, #C4
out_channels = 1,
kernel_size = 3,
padding = 1,
stride = 1),
nn.ReLU(True),
nn.Upsample(size=(300,300), mode='bilinear'), # I force the image to have the same size as the maps
nn.Conv2d(in_channels = 1, #C5
out_channels = 1,
kernel_size = 1,
padding = 0,
stride = 1)
)
def forward(self, x,mode=None):
y = self.encoder(x)
y = self.decoder(y)
return y
class Pre_Trained_UNET(nn.Module):
def __init__(self, Pre_Trained_Encoder,Pre_Trained_Decoder):
super().__init__()
self.encoder = Pre_Trained_Encoder
### Encoder
self.decoder = Pre_Trained_Decoder
def forward(self, x,mode=None):
y = self.encoder(x)
y = self.decoder(y)
return y