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seg_models_v2.py
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from math import log
import torch
import torch.nn as nn
import torchvision.models as models
import torch.nn.functional as F
""" Parts of the U-Net model
Taken from: https://github.com/milesial/Pytorch-UNet/blob/master/unet/unet_parts.py
"""
class DoubleConv(nn.Module):
"""(convolution => [BN] => ReLU) * 2"""
def __init__(self, in_channels, out_channels, mid_channels=None):
super().__init__()
if not mid_channels:
mid_channels = out_channels
self.double_conv = nn.Sequential(
nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1),
nn.BatchNorm2d(mid_channels),
nn.ReLU(inplace=True),
nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True)
)
def forward(self, x):
return self.double_conv(x)
class Down(nn.Module):
"""Downscaling with maxpool then double conv"""
def __init__(self, in_channels, out_channels):
super().__init__()
self.maxpool_conv = nn.Sequential(
nn.MaxPool2d(2),
DoubleConv(in_channels, out_channels)
)
def forward(self, x):
return self.maxpool_conv(x)
class Up(nn.Module):
"""Upscaling then double conv"""
def __init__(self, in_channels, out_channels, bilinear=True):
super().__init__()
# if bilinear, use the normal convolutions to reduce the number of channels
if bilinear:
self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
self.conv = DoubleConv(in_channels, out_channels, in_channels // 2)
else:
self.up = nn.ConvTranspose2d(in_channels, in_channels // 2, kernel_size=2, stride=2)
self.conv = DoubleConv(in_channels, out_channels)
def forward(self, x1, x2):
x1 = self.up(x1)
# input is CHW
diffY = x2.size()[2] - x1.size()[2]
diffX = x2.size()[3] - x1.size()[3]
x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2,
diffY // 2, diffY - diffY // 2])
# if you have padding issues, see
# https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a
# https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd
x = torch.cat([x2, x1], dim=1)
return self.conv(x)
class OutConv(nn.Module):
def __init__(self, in_channels, out_channels):
super(OutConv, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)
def forward(self, x):
return self.conv(x)
""" Full assembly of the parts to form the complete network
Adapted from: https://github.com/milesial/Pytorch-UNet/blob/master/unet/unet_model.py
"""
class UNet(nn.Module):
def __init__(self, n_channels, init_filters, n_classes, bilinear=True):
super(UNet, self).__init__()
self.n_channels = n_channels
self.init_filters = init_filters
self.n_classes = n_classes
self.bilinear = bilinear
# self.inc = DoubleConv(n_channels, init_filters)
# self.down1 = Down(init_filters, init_filters*2)
# self.down2 = Down(init_filters*2, init_filters*4)
# self.down3 = Down(init_filters*4, init_filters*8)
# factor = 2 if bilinear else 1
# self.down4 = Down(init_filters*8, init_filters*16 // factor)
# self.up1 = Up(init_filters*16, init_filters*8 // factor, bilinear)
# self.up2 = Up(init_filters*8, init_filters*4 // factor, bilinear)
# self.up3 = Up(init_filters*4, init_filters*2 // factor, bilinear)
# self.up4 = Up(init_filters*2, init_filters, bilinear)
# self.outc = OutConv(init_filters, n_classes)
self.inc = DoubleConv(n_channels, init_filters)
self.down1 = Down(init_filters, init_filters*2)
self.down2 = Down(init_filters*2, init_filters*4)
self.down3 = Down(init_filters*4, init_filters*8)
self.down4 = Down(init_filters*8, init_filters*16)
factor = 2 if bilinear else 1
self.down5 = Down(init_filters*16, init_filters*32 // factor)
self.up1 = Up(init_filters*32, init_filters*16 // factor, bilinear)
self.up2 = Up(init_filters*16, init_filters*8 // factor, bilinear)
self.up3 = Up(init_filters*8, init_filters*4 // factor, bilinear)
self.up4 = Up(init_filters*4, init_filters*2 // factor, bilinear)
self.up5 = Up(init_filters*2, init_filters, bilinear)
self.outc = OutConv(init_filters, n_classes)
def forward(self, x):
# x1 = self.inc(x)
# x2 = self.down1(x1)
# x3 = self.down2(x2)
# x4 = self.down3(x3)
# x5 = self.down4(x4)
# x = self.up1(x5, x4)
# x = self.up2(x, x3)
# x = self.up3(x, x2)
# x = self.up4(x, x1)
x1 = self.inc(x)
x2 = self.down1(x1)
x3 = self.down2(x2)
x4 = self.down3(x3)
x5 = self.down4(x4)
x6 = self.down5(x5)
x = self.up1(x6, x5)
x = self.up2(x, x4)
x = self.up3(x, x3)
x = self.up4(x, x2)
x = self.up5(x, x1)
logits = self.outc(x)
out_final = F.softmax(logits, dim=1)
return logits, out_final
# #############################################################################
# ---------------------------- UNet Encoder Model -----------------------------
# #############################################################################
class UNetEncoder(nn.Module):
def __init__(self, n_channels, init_filters, bilinear=True):
super(UNetEncoder, self).__init__()
self.n_channels = n_channels
self.init_filters = init_filters
self.bilinear = bilinear
# self.inc = DoubleConv(n_channels, init_filters)
# self.down1 = Down(init_filters, init_filters*2)
# self.down2 = Down(init_filters*2, init_filters*4)
# self.down3 = Down(init_filters*4, init_filters*8)
# factor = 2 if bilinear else 1
# self.down4 = Down(init_filters*8, init_filters*16 // factor)
self.inc = DoubleConv(n_channels, init_filters)
self.down1 = Down(init_filters, init_filters*2)
self.down2 = Down(init_filters*2, init_filters*4)
self.down3 = Down(init_filters*4, init_filters*8)
self.down4 = Down(init_filters*8, init_filters*16)
factor = 2 if bilinear else 1
self.down5 = Down(init_filters*16, init_filters*32 // factor)
def forward(self, x):
context_features = []
x1 = self.inc(x)
context_features.append(x1)
x2 = self.down1(x1)
context_features.append(x2)
x3 = self.down2(x2)
context_features.append(x3)
x4 = self.down3(x3)
context_features.append(x4)
x5 = self.down4(x4)
# return x5, context_features
context_features.append(x5)
x6 = self.down5(x5)
return x6, context_features
# #############################################################################
# ---------------------------- UNet Decoder Model -----------------------------
# #############################################################################
class UNetDecoder(nn.Module):
def __init__(self, init_filters, n_classes, bilinear=True):
super(UNetDecoder, self).__init__()
self.init_filters = init_filters
self.n_classes = n_classes
self.bilinear = bilinear
# factor = 2 if bilinear else 1
# self.up1 = Up(init_filters*16, init_filters*8 // factor, bilinear)
# self.up2 = Up(init_filters*8, init_filters*4 // factor, bilinear)
# self.up3 = Up(init_filters*4, init_filters*2 // factor, bilinear)
# self.up4 = Up(init_filters*2, init_filters, bilinear)
# self.outc = OutConv(init_filters, n_classes)
factor = 2 if bilinear else 1
self.up1 = Up(init_filters*32, init_filters*16 // factor, bilinear)
self.up2 = Up(init_filters*16, init_filters*8 // factor, bilinear)
self.up3 = Up(init_filters*8, init_filters*4 // factor, bilinear)
self.up4 = Up(init_filters*4, init_filters*2 // factor, bilinear)
self.up5 = Up(init_filters*2, init_filters, bilinear)
self.outc = OutConv(init_filters, n_classes)
def forward(self, enc_out, context_features):
# x1, x2, x3, x4 = context_features # getting these from the encoder
# x = self.up1(enc_out, x4) # enc_out is x5 in the full UNet model
# x = self.up2(x, x3)
# x = self.up3(x, x2)
# x = self.up4(x, x1)
x1, x2, x3, x4, x5 = context_features # getting these from the encoder
x = self.up1(enc_out, x5) # enc_out is x6 in the full UNet model
x = self.up2(x, x4)
x = self.up3(x, x3)
x = self.up4(x, x2)
x = self.up5(x, x1)
logits = self.outc(x)
out_final = F.softmax(logits, dim=1)
return logits, out_final
# #############################################################################
# ---------------------------- G1 Projector Head -----------------------------
# #############################################################################
class ProjectorHead(nn.Module):
def __init__(self, encoder_init_filters, out_dim):
super(ProjectorHead, self).__init__()
self.out_dim = out_dim
# self.projector_g1 = nn.Sequential(
# nn.Linear(((encoder_init_filters*16)//2)*(12*12), 3200), # final enc output is 12x12 (ie downsampled to 12x12), which is then multiplied by 128 filters for flattening
# nn.ReLU(),
# nn.Linear(3200, 1024),
# nn.ReLU(),
# nn.Linear(1024, out_dim)
# )
self.projector_g1 = nn.Sequential(
nn.Linear(((encoder_init_filters*32)//2)*(6*6), 1024), # final enc output is 12x12 (ie downsampled to 12x12), which is then multiplied by 128 filters for flattening
nn.ReLU(),
nn.Linear(1024, out_dim)
)
def forward(self, encoder_out):
out = torch.flatten(encoder_out, 1)
projector_out = self.projector_g1(out)
return projector_out
if __name__ == "__main__":
input = torch.randn(8, 1, 192, 192)
# model = ModifiedResnet(downloaded_net, num_classes=4)
# model = UNet(n_channels=1, init_filters=16, n_classes=4)
# print(model)
# logits, out_final = model(input)
# print(f"logits shape: {logits.shape}")
# print(f"final output shape: {out_final.shape}")
encoder_model = UNetEncoder(n_channels=1, init_filters=16)
enc_out, feats = encoder_model(input)
print(f"enc out shape: {enc_out.shape}")
proj_head = ProjectorHead(encoder_init_filters=16, out_dim=128)
proj_out = proj_head(enc_out)
print(f"projector out shape: {proj_out.shape}")