| | import torch.nn as nn |
| | 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"], |
| | ), |
| | ] |
| | ) |
| | 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): |
| | |
| | """ |
| | 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) |
| | 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 |
| |
|
