From ccca86fef9dcdb20f488761cf968b08906444ab3 Mon Sep 17 00:00:00 2001
From: chaoyi-wu <18706207256@163.com>
Date: Sun, 13 Aug 2023 11:19:53 +0800
Subject: [PATCH] upload

---
 Quick_demo/MedKEBERT/config.json             |    27 +
 Quick_demo/MedKEBERT/special_tokens_map.json |     7 +
 Quick_demo/MedKEBERT/tokenizer.json          | 29045 +++++++++++++++++
 Quick_demo/MedKEBERT/tokenizer_config.json   |    15 +
 Quick_demo/MedKEBERT/vocab.txt               | 28895 ++++++++++++++++
 Quick_demo/Model/RadFM/my_embedding_layer.py |    80 +-
 src/Model/RadFM/my_embedding_layer.py        |    10 +-
 7 files changed, 58039 insertions(+), 40 deletions(-)
 create mode 100644 Quick_demo/MedKEBERT/config.json
 create mode 100644 Quick_demo/MedKEBERT/special_tokens_map.json
 create mode 100644 Quick_demo/MedKEBERT/tokenizer.json
 create mode 100644 Quick_demo/MedKEBERT/tokenizer_config.json
 create mode 100644 Quick_demo/MedKEBERT/vocab.txt

diff --git a/Quick_demo/MedKEBERT/config.json b/Quick_demo/MedKEBERT/config.json
new file mode 100644
index 0000000..82962cc
--- /dev/null
+++ b/Quick_demo/MedKEBERT/config.json
@@ -0,0 +1,27 @@
+{
+  "_name_or_path": "xmcmic/Med-KEBERT",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_hidden_states": true,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.24.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}
diff --git a/Quick_demo/MedKEBERT/special_tokens_map.json b/Quick_demo/MedKEBERT/special_tokens_map.json
new file mode 100644
index 0000000..a8b3208
--- /dev/null
+++ b/Quick_demo/MedKEBERT/special_tokens_map.json
@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}
diff --git a/Quick_demo/MedKEBERT/tokenizer.json b/Quick_demo/MedKEBERT/tokenizer.json
new file mode 100644
index 0000000..1fee4cd
--- /dev/null
+++ b/Quick_demo/MedKEBERT/tokenizer.json
@@ -0,0 +1,29045 @@
+{
+  "version": "1.0",
+  "truncation": null,
+  "padding": null,
+  "added_tokens": [
+    {
+      "id": 0,
+      "content": "[PAD]",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 1,
+      "content": "[UNK]",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 2,
+      "content": "[CLS]",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 3,
+      "content": "[SEP]",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 4,
+      "content": "[MASK]",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    }
+  ],
+  "normalizer": {
+    "type": "BertNormalizer",
+    "clean_text": true,
+    "handle_chinese_chars": true,
+    "strip_accents": null,
+    "lowercase": true
+  },
+  "pre_tokenizer": {
+    "type": "BertPreTokenizer"
+  },
+  "post_processor": {
+    "type": "TemplateProcessing",
+    "single": [
+      {
+        "SpecialToken": {
+          "id": "[CLS]",
+          "type_id": 0
+        }
+      },
+      {
+        "Sequence": {
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+          "type_id": 0
+        }
+      },
+      {
+        "SpecialToken": {
+          "id": "[SEP]",
+          "type_id": 0
+        }
+      }
+    ],
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+      {
+        "SpecialToken": {
+          "id": "[CLS]",
+          "type_id": 0
+        }
+      },
+      {
+        "Sequence": {
+          "id": "A",
+          "type_id": 0
+        }
+      },
+      {
+        "SpecialToken": {
+          "id": "[SEP]",
+          "type_id": 0
+        }
+      },
+      {
+        "Sequence": {
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+          "type_id": 1
+        }
+      },
+      {
+        "SpecialToken": {
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+        }
+      }
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+    "special_tokens": {
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+}
\ No newline at end of file
diff --git a/Quick_demo/MedKEBERT/tokenizer_config.json b/Quick_demo/MedKEBERT/tokenizer_config.json
new file mode 100644
index 0000000..f4db0bf
--- /dev/null
+++ b/Quick_demo/MedKEBERT/tokenizer_config.json
@@ -0,0 +1,15 @@
+{
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "name_or_path": "xmcmic/Med-KEBERT",
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+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "special_tokens_map_file": null,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}
diff --git a/Quick_demo/MedKEBERT/vocab.txt b/Quick_demo/MedKEBERT/vocab.txt
new file mode 100644
index 0000000..9d65c84
--- /dev/null
+++ b/Quick_demo/MedKEBERT/vocab.txt
@@ -0,0 +1,28895 @@
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diff --git a/Quick_demo/Model/RadFM/my_embedding_layer.py b/Quick_demo/Model/RadFM/my_embedding_layer.py
index 0c1b9b2..47a3667 100644
--- a/Quick_demo/Model/RadFM/my_embedding_layer.py
+++ b/Quick_demo/Model/RadFM/my_embedding_layer.py
@@ -12,7 +12,7 @@
 from torch.utils.checkpoint import checkpoint
 from torch.autograd import Variable
 import random
-from transformers import AutoTokenizer, AutoModel
+from transformers import AutoTokenizer, AutoModel, AutoConfig, BertModel
 
 class MyEmbedding(nn.Module):
     def __init__(self, num_embeddings=32000, embedding_dim=5120, perceiver_num=32,vis_dim = 768, patch_size=32, frame_patch_size = 4 ,seg_channel = 256):
@@ -26,10 +26,16 @@ def __init__(self, num_embeddings=32000, embedding_dim=5120, perceiver_num=32,vi
         self.frame_patch_size = frame_patch_size
         self.seg_channel = seg_channel
 
-        self.bert_tokenizer = AutoTokenizer.from_pretrained("/gpfs/home/cs/leijiayu/wuchaoyi/multi_modal/src/MedKEBERT")
-        self.bert_model = AutoModel.from_pretrained("/gpfs/home/cs/leijiayu/wuchaoyi/multi_modal/src/MedKEBERT")
+        ## The bert model is useless for generation. Load it just for keeping model the same with the pre-train checkpoint.
+        self.bert_tokenizer = AutoTokenizer.from_pretrained("./MedKEBERT")
+        self.bert_model = BertModel(AutoConfig.from_pretrained("./MedKEBERT/"))
         self.bert_projection_fc = nn.Linear(768,vis_dim)
         
+        ## the MedKEBERT can be downloaded from https://huggingface.co/xmcmic/Med-KEBERT/tree/main ##
+        # self.bert_tokenizer = AutoTokenizer.from_pretrained("xmcmic/Med-KEBERT")
+        # self.bert_model = AutoModel.from_pretrained("xmcmic/Med-KEBERT")
+        # self.bert_projection_fc = nn.Linear(768,vis_dim)
+        
         self.vision_encoder = ViT(
             image_size = 512,          # image size
             frames = 512,               # max number of frames
@@ -80,44 +86,44 @@ def forward(self, text_input, vision_x, key_words_query = None):
             vision_x = rearrange(vision_x, "(b s F) v d -> b s F v d", b=B, s=S,F=1) 
             
             loss_matching = None
-            if key_words_query != None:
-                # key_words_query list[list[str]] B, words, each word matches corresponding vision_x embedding
-                query_words = [item for sublist in key_words_query for item in sublist]
-                query_words = list(set(query_words))
-                if len(query_words)>16:
-                    random.shuffle(query_words)
-                    query_words = query_words[0:16]
-                if query_words != []:
-                    contrastive_labels = torch.zeros(B,len(query_words)) #B Q
-                    for i,sublist in enumerate(key_words_query):
-                        for j,item in enumerate(query_words):
-                            if item in sublist:
-                                contrastive_labels[i,j] = 1 
-                    contrastive_labels = contrastive_labels.to(vision_x.dtype).to(vision_x.device)        
+            # if key_words_query != None:
+            #     # key_words_query list[list[str]] B, words, each word matches corresponding vision_x embedding
+            #     query_words = [item for sublist in key_words_query for item in sublist]
+            #     query_words = list(set(query_words))
+            #     if len(query_words)>16:
+            #         random.shuffle(query_words)
+            #         query_words = query_words[0:16]
+            #     if query_words != []:
+            #         contrastive_labels = torch.zeros(B,len(query_words)) #B Q
+            #         for i,sublist in enumerate(key_words_query):
+            #             for j,item in enumerate(query_words):
+            #                 if item in sublist:
+            #                     contrastive_labels[i,j] = 1 
+            #         contrastive_labels = contrastive_labels.to(vision_x.dtype).to(vision_x.device)        
                     
-                    with torch.no_grad():
-                        query_words_embedding = self.bert_tokenizer(query_words, padding='max_length', truncation=True, max_length=256,return_tensors="pt")
-                        query_words_embedding = self.bert_model(input_ids = query_words_embedding['input_ids'].to(vision_x.device),attention_mask = query_words_embedding['attention_mask'].to(vision_x.device))['last_hidden_state'][:,0,:].to(vision_x.dtype).to(vision_x.device) # Q,D
-                    query_words_embedding = self.bert_projection_fc(query_words_embedding)
-                    query_words_embedding = query_words_embedding.unsqueeze(0).repeat(B,1,1) # B,Q,D
-                    _,N,_ = query_words_embedding.shape
+            #         with torch.no_grad():
+            #             query_words_embedding = self.bert_tokenizer(query_words, padding='max_length', truncation=True, max_length=256,return_tensors="pt")
+            #             query_words_embedding = self.bert_model(input_ids = query_words_embedding['input_ids'].to(vision_x.device),attention_mask = query_words_embedding['attention_mask'].to(vision_x.device))['last_hidden_state'][:,0,:].to(vision_x.dtype).to(vision_x.device) # Q,D
+            #         query_words_embedding = self.bert_projection_fc(query_words_embedding)
+            #         query_words_embedding = query_words_embedding.unsqueeze(0).repeat(B,1,1) # B,Q,D
+            #         _,N,_ = query_words_embedding.shape
                     
-                    image_embedding = vision_x.mean(dim=1) # B V D average pooling 去除掉多模态。
-                    image_embedding = rearrange(image_embedding, "b F v d -> b (F v) d")
-                    pos_embedding = rearrange(pos_embedding, "(b s) v d -> b s v d", b=B, s=S)[:,0,:,:]
+            #         image_embedding = vision_x.mean(dim=1) # B V D average pooling 去除掉多模态。
+            #         image_embedding = rearrange(image_embedding, "b F v d -> b (F v) d")
+            #         pos_embedding = rearrange(pos_embedding, "(b s) v d -> b s v d", b=B, s=S)[:,0,:,:]
                     
-                    image_embedding = image_embedding.transpose(0,1) # (H/P W/P D/P) B  D
-                    pos_embedding = pos_embedding.transpose(0,1) # (H/P W/P D/P) B  D
-                    query_words_embedding = query_words_embedding.transpose(0,1) # N B D
+            #         image_embedding = image_embedding.transpose(0,1) # (H/P W/P D/P) B  D
+            #         pos_embedding = pos_embedding.transpose(0,1) # (H/P W/P D/P) B  D
+            #         query_words_embedding = query_words_embedding.transpose(0,1) # N B D
                     
-                    oo_embedding,_ = self.transformer_decoder(query_words_embedding, image_embedding, pos = pos_embedding) 
-                    oo_embedding = oo_embedding.transpose(0,1) # B Q D
-                    oo_embedding = rearrange(oo_embedding, 'b n d -> (b n) d')
-                    oo_embedding = self.transformer_decoder_mlp(oo_embedding)
-                    oo_embedding = self.cls_head(oo_embedding).mean(dim = -1)
-                    oo_embedding = rearrange(oo_embedding, '(b n) -> b n', b=B, n=N) # B Q 
-                    # oo_embedding = rearrange(oo_embedding, 'b n d -> b (n d)') # B Q 
-                    loss_matching = F.binary_cross_entropy_with_logits(oo_embedding, contrastive_labels) 
+            #         oo_embedding,_ = self.transformer_decoder(query_words_embedding, image_embedding, pos = pos_embedding) 
+            #         oo_embedding = oo_embedding.transpose(0,1) # B Q D
+            #         oo_embedding = rearrange(oo_embedding, 'b n d -> (b n) d')
+            #         oo_embedding = self.transformer_decoder_mlp(oo_embedding)
+            #         oo_embedding = self.cls_head(oo_embedding).mean(dim = -1)
+            #         oo_embedding = rearrange(oo_embedding, '(b n) -> b n', b=B, n=N) # B Q 
+            #         # oo_embedding = rearrange(oo_embedding, 'b n d -> b (n d)') # B Q 
+            #         loss_matching = F.binary_cross_entropy_with_logits(oo_embedding, contrastive_labels) 
                 
             vision_x = self.perceiver(vision_x)  # reshapes to (b, S, n, d)
             #vision_x = checkpoint(self.perceiver,vision_x)
diff --git a/src/Model/RadFM/my_embedding_layer.py b/src/Model/RadFM/my_embedding_layer.py
index 0c1b9b2..ab39a78 100644
--- a/src/Model/RadFM/my_embedding_layer.py
+++ b/src/Model/RadFM/my_embedding_layer.py
@@ -25,9 +25,10 @@ def __init__(self, num_embeddings=32000, embedding_dim=5120, perceiver_num=32,vi
         self.patch_size = patch_size 
         self.frame_patch_size = frame_patch_size
         self.seg_channel = seg_channel
-
-        self.bert_tokenizer = AutoTokenizer.from_pretrained("/gpfs/home/cs/leijiayu/wuchaoyi/multi_modal/src/MedKEBERT")
-        self.bert_model = AutoModel.from_pretrained("/gpfs/home/cs/leijiayu/wuchaoyi/multi_modal/src/MedKEBERT")
+        
+        ## the MedKEBERT can be downloaded from https://huggingface.co/xmcmic/Med-KEBERT/tree/main ##
+        self.bert_tokenizer = AutoTokenizer.from_pretrained("xmcmic/Med-KEBERT")
+        self.bert_model = AutoModel.from_pretrained("xmcmic/Med-KEBERT")
         self.bert_projection_fc = nn.Linear(768,vis_dim)
         
         self.vision_encoder = ViT(
@@ -80,7 +81,10 @@ def forward(self, text_input, vision_x, key_words_query = None):
             vision_x = rearrange(vision_x, "(b s F) v d -> b s F v d", b=B, s=S,F=1) 
             
             loss_matching = None
+             
             if key_words_query != None:
+                ## we do not use the following parts in final version. 
+                ## You can quota the following codes and if so the bert models will be useless.
                 # key_words_query list[list[str]] B, words, each word matches corresponding vision_x embedding
                 query_words = [item for sublist in key_words_query for item in sublist]
                 query_words = list(set(query_words))