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models.py
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# Definition of PyTorch models
from torch import nn
import torch.nn.functional as F
from dpipe.layers.resblock import ResBlock2d
from dpipe.layers.resblock import ResBlock
from dpipe.layers.conv import PreActivation2d
from dpipe.layers.conv import PreActivationND
from IPython import embed
from LoRA.loralib import layers as lora
class FeaturesSegmenter(nn.Module):
def __init__(self, in_channels=16, out_channels=1):
super().__init__()
self.conv1 = nn.Conv2d(in_channels, 12, kernel_size=3, padding=1)
self.bn1 = nn.BatchNorm2d(12)
self.conv2 = nn.Conv2d(12, 8, kernel_size=3, padding=1)
self.bn2 = nn.BatchNorm2d(8)
self.conv3 = nn.Conv2d(8, out_channels, kernel_size=3, padding=1)
def forward(self, x_):
# x = F.relu(self.conv1(x_))
x = F.relu(self.bn1(self.conv1(x_)))
# x = F.relu(self.conv2(x))
x = F.relu(self.bn2(self.conv2(x)))
out = self.conv3(x)
return out
# Shirokikh, Boris, et al. "First U-Net layers contain more domain specific information than the last ones."
# Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning.
# Springer, Cham, 2020. 117-126.
# https://arxiv.org/abs/2008.07357
# https://github.com/kechua/DART20/blob/master/damri/model/unet.py
class UNet2D(nn.Module):
def __init__(self, n_chans_in, n_chans_out, kernel_size=3, padding=1, pooling_size=2, n_filters_init=8,
dropout=False, p=0.1, return_all_activations=False):
super().__init__()
self.kernel_size = kernel_size
self.padding = padding
self.pooling_size = pooling_size
n = n_filters_init
if dropout:
dropout_layer = nn.Dropout(p)
else:
dropout_layer = nn.Identity()
self.init_path = nn.Sequential(
nn.Conv2d(n_chans_in, n, self.kernel_size, padding=self.padding, bias=False),
nn.ReLU(),
ResBlock2d(n, n, kernel_size=self.kernel_size, padding=self.padding),
ResBlock2d(n, n, kernel_size=self.kernel_size, padding=self.padding),
ResBlock2d(n, n, kernel_size=self.kernel_size, padding=self.padding)
)
self.shortcut0 = nn.Conv2d(n, n, 1)
self.down1 = nn.Sequential(
nn.BatchNorm2d(n),
nn.Conv2d(n, n * 2, kernel_size=pooling_size, stride=pooling_size, bias=False),
nn.ReLU(),
dropout_layer,
ResBlock2d(n * 2, n * 2, kernel_size=self.kernel_size, padding=self.padding),
ResBlock2d(n * 2, n * 2, kernel_size=self.kernel_size, padding=self.padding),
ResBlock2d(n * 2, n * 2, kernel_size=self.kernel_size, padding=self.padding)
)
self.shortcut1 = nn.Conv2d(n * 2, n * 2, 1)
self.down2 = nn.Sequential(
nn.BatchNorm2d(n * 2),
nn.Conv2d(n * 2, n * 4, kernel_size=pooling_size, stride=pooling_size, bias=False),
nn.ReLU(),
dropout_layer,
ResBlock2d(n * 4, n * 4, kernel_size=self.kernel_size, padding=self.padding),
ResBlock2d(n * 4, n * 4, kernel_size=self.kernel_size, padding=self.padding),
ResBlock2d(n * 4, n * 4, kernel_size=self.kernel_size, padding=self.padding)
)
self.shortcut2 = nn.Conv2d(n * 4, n * 4, 1)
self.down3 = nn.Sequential(
nn.BatchNorm2d(n * 4),
nn.Conv2d(n * 4, n * 8, kernel_size=pooling_size, stride=pooling_size, bias=False),
nn.ReLU(),
dropout_layer,
ResBlock2d(n * 8, n * 8, kernel_size=self.kernel_size, padding=self.padding),
ResBlock2d(n * 8, n * 8, kernel_size=self.kernel_size, padding=self.padding),
ResBlock2d(n * 8, n * 8, kernel_size=self.kernel_size, padding=self.padding),
dropout_layer
)
self.up3 = nn.Sequential(
ResBlock2d(n * 8, n * 8, kernel_size=self.kernel_size, padding=self.padding),
ResBlock2d(n * 8, n * 8, kernel_size=self.kernel_size, padding=self.padding),
ResBlock2d(n * 8, n * 8, kernel_size=self.kernel_size, padding=self.padding),
nn.BatchNorm2d(n * 8),
nn.ConvTranspose2d(n * 8, n * 4, kernel_size=self.pooling_size, stride=self.pooling_size, bias=False),
nn.ReLU(),
dropout_layer
)
self.up2 = nn.Sequential(
ResBlock2d(n * 4, n * 4, kernel_size=self.kernel_size, padding=self.padding),
ResBlock2d(n * 4, n * 4, kernel_size=self.kernel_size, padding=self.padding),
ResBlock2d(n * 4, n * 4, kernel_size=self.kernel_size, padding=self.padding),
nn.BatchNorm2d(n * 4),
nn.ConvTranspose2d(n * 4, n * 2, kernel_size=self.pooling_size, stride=self.pooling_size, bias=False),
nn.ReLU(),
dropout_layer
)
self.up1 = nn.Sequential(
ResBlock2d(n * 2, n * 2, kernel_size=self.kernel_size, padding=self.padding),
ResBlock2d(n * 2, n * 2, kernel_size=self.kernel_size, padding=self.padding),
ResBlock2d(n * 2, n * 2, kernel_size=self.kernel_size, padding=self.padding),
nn.BatchNorm2d(n * 2),
nn.ConvTranspose2d(n * 2, n, kernel_size=self.pooling_size, stride=self.pooling_size, bias=False),
nn.ReLU(),
dropout_layer
)
self.out_path = nn.Sequential(
ResBlock2d(n, n, kernel_size=1),
PreActivation2d(n, n_chans_out, kernel_size=1),
nn.BatchNorm2d(n_chans_out)
)
self.return_all_activations = return_all_activations
def forward(self, x):
x0_0 = self.init_path[0](x)
x0_1 = self.init_path[1](x0_0)
x0_2 = self.init_path[2](x0_1)
x0_3 = self.init_path[3](x0_2)
x0 = self.init_path[4](x0_3)
x1 = self.down1(x0)
x2 = self.down2(x1)
x3 = self.down3(x2)
skip0 = self.shortcut0(x0)
skip1 = self.shortcut1(x1)
skip2 = self.shortcut2(x2)
x2_up = self.up3(x3)
x1_up = self.up2(x2_up + skip2)
x0_up = self.up1(x1_up + skip1)
x_out = self.out_path(x0_up + skip0)
if not self.return_all_activations:
return x_out
else:
# return [x0, x1, x2, x3, x2_up, skip2, x1_up, skip1, x0_up, skip0, x_out]
# return [x0_2, x0, skip0, x1, skip1, x2, skip2, x3, x2_up, x1_up, x0_up, x_out]
return [x0_2, skip0, x0, x1, skip1, x2, skip2, x3, x2_up, x1_up, x0_up, x_out]
def replace_layers(model, desired_submodules):
# inject LoRA matrices
for name, sub_module in model.named_children():
if name in desired_submodules:
for name, layer in list(sub_module.named_children()):
#Conv2d
if isinstance(layer, nn.Conv2d):
setattr(sub_module, name, lora.Conv2d(
layer.in_channels,
layer.out_channels,
kernel_size=layer.kernel_size[0],
stride = layer.stride,
padding = layer.padding,
bias = layer.bias ,
r=2,
lora_alpha=2))
# ResBlock
elif isinstance(sub_module, nn.Sequential):
for name, layer in list(sub_module.named_children()):
if isinstance(layer, ResBlock):
for i, preactivation_module in enumerate(layer.conv_path):
if isinstance(preactivation_module, PreActivationND) and isinstance(preactivation_module.layer, nn.Conv2d):
setattr(preactivation_module, 'layer', lora.Conv2d(
preactivation_module.layer.in_channels,
preactivation_module.layer.out_channels,
kernel_size=preactivation_module.layer.kernel_size[0],
padding = preactivation_module.layer.padding,
bias = preactivation_module.layer.bias,
r=2,
lora_alpha=2))
elif isinstance(layer,PreActivationND) and isinstance(layer.layer, nn.Conv2d):
new_lora_layer = lora.Conv2d(
in_channels=layer.layer.in_channels,
out_channels=layer.layer.out_channels,
kernel_size=layer.layer.kernel_size[0],
padding=layer.layer.padding[0],
bias=layer.layer.bias is not None,
r=2,
lora_alpha=2)
layer.layer = new_lora_layer
return model