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'''
Author: Chris Xiao yl.xiao@mail.utoronto.ca
Date: 2023-09-16 19:47:31
LastEditors: Chris Xiao yl.xiao@mail.utoronto.ca
LastEditTime: 2023-09-30 16:56:23
FilePath: /EndoSAM/endoSAM/model.py
Description: EndoSAM model adapter
I Love IU
Copyright (c) 2023 by Chris Xiao yl.xiao@mail.utoronto.ca, All Rights Reserved.
'''
import torch
import torch.nn as nn
from einops import rearrange
from utils import postprocess_masks
class EndoSAMAdapter(nn.Module):
def __init__(self, device,
num_classes,
sam_mask_encoder,
sam_prompt_encoder,
sam_mask_decoder,
num_token=8,
):
super(EndoSAMAdapter, self).__init__()
self.device = device
self.num_classes = num_classes - 1
self.num_token = num_token
self.sam_mask_encoder = sam_mask_encoder.to(self.device)
self.sam_prompt_encoder = sam_prompt_encoder.to(self.device)
self.sam_mask_decoder = sam_mask_decoder.to(self.device)
self.prototype_prompt_encoder = Prototype_Prompt_Encoder(feat_dim=256,
hidden_dim_dense=128,
hidden_dim_sparse=128,
size=64,
num_tokens=self.num_token).to(self.device)
self.learnable_prototypes_model = Learnable_Prototypes(num_classes=self.num_classes, feat_dim = 256).to(self.device)
self.prototypes = self.learnable_prototypes_model()
self.sam_mask_encoder.to(self.device)
self.sam_prompt_encoder.to(self.device)
self.sam_mask_decoder.to(self.device)
for _, param in self.prototype_prompt_encoder.named_parameters():
param.requires_grad = True
for _, param in self.learnable_prototypes_model.named_parameters():
param.requires_grad = True
for _, param in self.sam_mask_decoder.named_parameters():
param.requires_grad = True
for _, param in self.sam_mask_encoder.named_parameters():
param.requires_grad = False
for _, param in self.sam_prompt_encoder.named_parameters():
param.requires_grad = False
def forward(self, x):
sam_features = self.sam_mask_encoder(x)
sam_features = rearrange(sam_features, 'b c h w -> b (h w) c')
cls_ids = torch.tensor(1).repeat(sam_features.shape[0]).to(self.device)
dense_embeddings, sparse_embeddings = self.prototype_prompt_encoder(sam_features, self.prototypes, cls_ids, self.num_classes)
pred = []
pred_quality = []
sam_features = rearrange(sam_features,'b (h w) c -> b c h w', h=64, w=64)
for dense_embedding, sparse_embedding, features_per_image in zip(dense_embeddings.unsqueeze(1), sparse_embeddings.unsqueeze(1), sam_features):
low_res_masks_per_image, mask_quality_per_image = self.sam_mask_decoder(
image_embeddings=features_per_image.unsqueeze(0),
image_pe=self.sam_prompt_encoder.get_dense_pe(),
sparse_prompt_embeddings=sparse_embedding,
dense_prompt_embeddings=dense_embedding,
multimask_output=True,
)
pred_per_image = postprocess_masks(
low_res_masks_per_image,
input_size=(819, 1024),
original_size=(1024, 1280),
)
pred.append(pred_per_image)
pred_quality.append(mask_quality_per_image)
pred = torch.cat(pred, dim=0)
pred_quality = torch.cat(pred_quality,dim=0)
return pred, pred_quality
class Prototype_Prompt_Encoder(nn.Module):
def __init__(self, feat_dim=256,
hidden_dim_dense=128,
hidden_dim_sparse=128,
size=64,
num_tokens=8):
super(Prototype_Prompt_Encoder, self).__init__()
self.dense_fc_1 = nn.Conv2d(feat_dim, hidden_dim_dense, 1)
self.dense_fc_2 = nn.Conv2d(hidden_dim_dense, feat_dim, 1)
self.relu = nn.ReLU()
self.sparse_fc_1 = nn.Conv1d(size*size, hidden_dim_sparse, 1)
self.sparse_fc_2 = nn.Conv1d(hidden_dim_sparse, num_tokens, 1)
pn_cls_embeddings = [nn.Embedding(num_tokens, feat_dim) for _ in range(2)] # one for positive and one for negative
self.pn_cls_embeddings = nn.ModuleList(pn_cls_embeddings)
def forward(self, feat, prototypes, cls_ids, num_classes):
cls_prompts = prototypes.unsqueeze(-1)
cls_prompts = torch.stack([cls_prompts for _ in range(feat.size(0))], dim=0)
feat = torch.stack([feat for _ in range(cls_prompts.size(1))], dim=1)
# compute similarity matrix
sim = torch.matmul(feat, cls_prompts)
# compute class-activated feature
feat = feat + feat*sim
feat_sparse = feat.clone()
# compute dense embeddings
one_hot = torch.nn.functional.one_hot(cls_ids-1,num_classes)
feat = feat[one_hot == 1]
feat = rearrange(feat.squeeze(1),'b (h w) c -> b c h w', h=64, w=64)
dense_embeddings = self.dense_fc_2(self.relu(self.dense_fc_1(feat)))
# compute sparse embeddings
feat_sparse = rearrange(feat_sparse,'b num_cls hw c -> (b num_cls) hw c')
sparse_embeddings = self.sparse_fc_2(self.relu(self.sparse_fc_1(feat_sparse)))
sparse_embeddings = rearrange(sparse_embeddings,'(b num_cls) n c -> b num_cls n c', num_cls=1)
pos_embed = self.pn_cls_embeddings[1].weight.unsqueeze(0).unsqueeze(0) * one_hot.unsqueeze(-1).unsqueeze(-1)
neg_embed = self.pn_cls_embeddings[0].weight.unsqueeze(0).unsqueeze(0) * (1-one_hot).unsqueeze(-1).unsqueeze(-1)
sparse_embeddings = sparse_embeddings + pos_embed.detach() + neg_embed.detach()
sparse_embeddings = rearrange(sparse_embeddings,'b num_cls n c -> b (num_cls n) c')
return dense_embeddings, sparse_embeddings
class Learnable_Prototypes(nn.Module):
def __init__(self, num_classes=7 , feat_dim=256):
super(Learnable_Prototypes, self).__init__()
self.class_embeddings = nn.Embedding(num_classes, feat_dim)
def forward(self):
return self.class_embeddings.weight |