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import torch.nn.functional as F
import torchvision.models as models
import torch
from einops import rearrange
class ConvBlock(nn.Module):
"""
Based on https://github.com/kevinlu1211/pytorch-unet-resnet-50-encoder/blob/master/u_net_resnet_50_encoder.py
Helper module that consists of a Conv -> BN -> ReLU
"""
def __init__(
self,
in_channels,
out_channels,
padding=1,
kernel_size=3,
stride=1,
with_nonlinearity=True,
):
super().__init__()
self.conv = nn.Conv2d(
in_channels,
out_channels,
padding=padding,
kernel_size=kernel_size,
stride=stride,
)
self.bn = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU(inplace=True) if with_nonlinearity else None
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
if self.relu is not None:
x = self.relu(x)
return x
class Bridge(nn.Module):
"""
Based on https://github.com/kevinlu1211/pytorch-unet-resnet-50-encoder/blob/master/u_net_resnet_50_encoder.py
"""
def __init__(self, in_channels, out_channels):
super().__init__()
self.bridge = nn.Sequential(
ConvBlock(in_channels, out_channels), ConvBlock(out_channels, out_channels)
)
def forward(self, x):
return self.bridge(x)
class UpBlockForUNetWithResNet50(nn.Module):
"""
Based on https://github.com/kevinlu1211/pytorch-unet-resnet-50-encoder/blob/master/u_net_resnet_50_encoder.py
Up block that encapsulates one up-sampling step which consists of Upsample -> ConvBlock -> ConvBlock
"""
def __init__(
self,
in_channels,
out_channels,
up_conv_in_channels=None,
up_conv_out_channels=None,
upsampling_method="conv_transpose",
):
super().__init__()
if up_conv_in_channels == None:
up_conv_in_channels = in_channels
if up_conv_out_channels == None:
up_conv_out_channels = out_channels
if upsampling_method == "conv_transpose":
self.upsample = nn.ConvTranspose2d(
up_conv_in_channels, up_conv_out_channels, kernel_size=2, stride=2
)
elif upsampling_method == "bilinear":
self.upsample = nn.Sequential(
nn.Upsample(mode="bilinear", scale_factor=2),
nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1),
)
self.conv_block_1 = ConvBlock(in_channels, out_channels)
self.conv_block_2 = ConvBlock(out_channels, out_channels)
def forward(self, up_x, down_x):
"""
:param up_x: this is the output from the previous up block
:param down_x: this is the output from the down block
:return: upsampled feature map
"""
x = self.upsample(up_x)
x = torch.cat([x, down_x], 1)
x = self.conv_block_1(x)
x = self.conv_block_2(x)
return x
class ModelResUNet_ft(nn.Module):
def __init__(
self,
res_base_model,
out_size,
imagenet_pretrain,
linear_probe=False,
use_base=True,
):
super(ModelResUNet_ft, self).__init__()
self.resnet_dict = {
"resnet50": models.resnet50(weights=imagenet_pretrain),
}
resnet = self._get_res_basemodel(res_base_model)
self.use_base = use_base
if not self.use_base:
num_ftrs = int(resnet.fc.in_features / 2)
self.res_features = nn.Sequential(*list(resnet.children())[:-3])
self.res_l1_anatomy = nn.Linear(num_ftrs, num_ftrs)
self.res_l2_anatomy = nn.Linear(num_ftrs, 256)
self.res_l1_pathology = nn.Linear(num_ftrs, num_ftrs)
self.res_l2_pathology = nn.Linear(num_ftrs, 256)
self.mask_generator = nn.Linear(num_ftrs, num_ftrs)
self.back = nn.Linear(256, num_ftrs)
self.last_res = nn.Sequential(*list(resnet.children())[-3:-1])
else:
self.res_features = nn.Sequential(*list(resnet.children())[:-3])
self.d = {
"input": 3,
"conv1": 64,
"conv2": 256,
"conv3": 512,
"conv4": 1024,
"bridge": 1024,
"up1": 512,
"up2": 256,
"up3": 128,
"up4": 64,
}
self.downscale_factors = {
"input": 1,
"conv1": 2,
"conv2": 4,
"conv3": 8,
"conv4": 16,
"bridge": 16,
"up1": 8,
"up2": 4,
"up3": 2,
"up4": 1,
}
self.bridge = Bridge(self.d["conv4"], self.d["bridge"])
self.up_blocks = nn.ModuleList(
[
UpBlockForUNetWithResNet50(
in_channels=self.d["up1"] + self.d["conv3"],
out_channels=self.d["up1"],
up_conv_in_channels=self.d["bridge"],
up_conv_out_channels=self.d["up1"],
),
UpBlockForUNetWithResNet50(
in_channels=self.d["up2"] + self.d["conv2"],
out_channels=self.d["up2"],
up_conv_in_channels=self.d["up1"],
up_conv_out_channels=self.d["up2"],
),
UpBlockForUNetWithResNet50(
in_channels=self.d["up3"] + self.d["conv1"],
out_channels=self.d["up3"],
up_conv_in_channels=self.d["up2"],
up_conv_out_channels=self.d["up3"],
),
UpBlockForUNetWithResNet50(
in_channels=self.d["up4"] + self.d["input"],
out_channels=self.d["up4"],
up_conv_in_channels=self.d["up3"],
up_conv_out_channels=self.d["up4"],
), # concatenated with input
]
)
self.out_size = out_size
self.dropout = nn.Dropout(p=0.2)
self.seg_classifier = nn.Conv1d(
self.d["up4"], out_size, kernel_size=1, bias=True
)
def _get_res_basemodel(self, res_model_name):
try:
res_model = self.resnet_dict[res_model_name]
print("Image feature extractor:", res_model_name)
return res_model
except:
raise (
"Invalid model name. Check the config file and pass one of: resnet18 or resnet50"
)
def image_encoder(self, xis):
# patch features
"""
16 torch.Size([16, 1024, 14, 14])
torch.Size([16, 196, 1024])
torch.Size([3136, 1024])
torch.Size([16, 196, 256])
"""
batch_size = xis.shape[0]
res_fea = self.res_features(xis) # batch_size,feature_size,patch_num,patch_num
res_fea = rearrange(res_fea, "b d n1 n2 -> b (n1 n2) d")
x = rearrange(res_fea, "b n d -> (b n) d")
mask = self.mask_generator(x)
x_pathology = mask * x
x_pathology = self.res_l1_pathology(x_pathology)
x_pathology = F.relu(x_pathology)
x_pathology = self.res_l2_pathology(x_pathology)
out_emb_pathology = rearrange(x_pathology, "(b n) d -> b n d", b=batch_size)
out_emb_pathology = self.back(out_emb_pathology)
out_emb_pathology = rearrange(out_emb_pathology, "b (n1 n2) d -> b d n1 n2", n1=14, n2=14)
out_emb_pathology = out_emb_pathology.squeeze()
return out_emb_pathology
def forward(self, img):
x = img
down_embdding = [x]
for i in range(len(self.res_features)):
x = self.res_features[i](x)
if i == 2 or i == 4 or i == 5:
down_embdding.append(x)
o = self.bridge(x)
for i in range(len(self.up_blocks)):
o = self.up_blocks[i](o, down_embdding[len(down_embdding) - i - 1])
o = self.dropout(o)
batch_size = o.shape[0]
h = o.shape[-2]
w = o.shape[-1]
class_number = o.shape[-3]
o = o.reshape(batch_size, class_number, h * w)
o = self.seg_classifier(o)
o = o.reshape(batch_size, self.out_size, h, w)
return o
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