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import torch.nn as nn |
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import torch.nn.functional as F |
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import torchvision.models as models |
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import torch |
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from einops import rearrange |
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class ModelRes_ft(nn.Module): |
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def __init__( |
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self, |
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res_base_model, |
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out_size, |
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imagenet_pretrain=False, |
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linear_probe=False, |
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use_base=True, |
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): |
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super(ModelRes_ft, self).__init__() |
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self.resnet_dict = { |
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"resnet18": models.resnet18(pretrained=imagenet_pretrain), |
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"resnet50": models.resnet50(pretrained=imagenet_pretrain), |
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} |
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resnet = self._get_res_basemodel(res_base_model) |
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self.use_base = use_base |
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if not self.use_base: |
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num_ftrs = int(resnet.fc.in_features / 2) |
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self.res_features = nn.Sequential(*list(resnet.children())[:-3]) |
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self.res_l1_anatomy = nn.Linear(num_ftrs, num_ftrs) |
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self.res_l2_anatomy = nn.Linear(num_ftrs, 256) |
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self.res_l1_pathology = nn.Linear(num_ftrs, num_ftrs) |
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self.res_l2_pathology = nn.Linear(num_ftrs, 256) |
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self.mask_generator = nn.Linear(num_ftrs, num_ftrs) |
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self.back = nn.Linear(256, num_ftrs) |
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self.last_res = nn.Sequential(*list(resnet.children())[-3:-1]) |
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else: |
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self.res_features = nn.Sequential(*list(resnet.children())[:-1]) |
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self.res_out = nn.Linear(int(resnet.fc.in_features), out_size) |
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def _get_res_basemodel(self, res_model_name): |
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try: |
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res_model = self.resnet_dict[res_model_name] |
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print("Image feature extractor:", res_model_name) |
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return res_model |
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except: |
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raise ( |
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"Invalid model name. Check the config file and pass one of: resnet18 or resnet50" |
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) |
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def image_encoder(self, xis): |
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""" |
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16 torch.Size([16, 1024, 14, 14]) |
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torch.Size([16, 196, 1024]) |
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torch.Size([3136, 1024]) |
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torch.Size([16, 196, 256]) |
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""" |
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batch_size = xis.shape[0] |
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res_fea = self.res_features(xis) |
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res_fea = rearrange(res_fea, "b d n1 n2 -> b (n1 n2) d") |
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x = rearrange(res_fea, "b n d -> (b n) d") |
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mask = self.mask_generator(x) |
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x_pathology = mask * x |
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x_pathology = self.res_l1_pathology(x_pathology) |
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x_pathology = F.relu(x_pathology) |
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x_pathology = self.res_l2_pathology(x_pathology) |
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out_emb_pathology = rearrange(x_pathology, "(b n) d -> b n d", b=batch_size) |
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out_emb_pathology = self.back(out_emb_pathology) |
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out_emb_pathology = rearrange(out_emb_pathology, "b (n1 n2) d -> b d n1 n2", n1=14, n2=14) |
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out_emb_pathology = self.last_res(out_emb_pathology) |
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out_emb_pathology = out_emb_pathology.squeeze() |
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return out_emb_pathology |
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def forward(self, img, linear_probe=False): |
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if self.use_base: |
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x = self.res_features(img) |
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else: |
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x = self.image_encoder(img) |
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x = x.squeeze() |
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if linear_probe: |
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return x |
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else: |
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x = self.res_out(x) |
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return x |
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