Upload README.md

README.md

@@ -7,13 +7,7 @@ license: apache-2.0
 在运行脚本之前，首先安装如下必要的依赖。
 
 ```shell
-pip install --upgrade pip
-pip install torch transformers==4.40.0
-pip install sentencepiece protobuf
-pip install accelerate pillow
-pip install ninja
-pip install packaging
-pip install flash-attn --no-build-isolation
+pip install torch transformers==4.40.0 accelerate pillow sentencepiece
 ```
 
 ```python
@@ -24,27 +18,6 @@ from PIL import Image
 import warnings
 import numpy as np
 
-def tokenizer_image_token(prompt, tokenizer, image_token_index, return_tensors=None):
-    prompt_chunks = [tokenizer(chunk).input_ids for chunk in prompt.split('<image>')]
-
-    def insert_separator(X, sep):
-        return [ele for sublist in zip(X, [sep] * len(X)) for ele in sublist][:-1]
-
-    input_ids = []
-    offset = 0
-    if len(prompt_chunks) > 0 and len(prompt_chunks[0]) > 0 and prompt_chunks[0][0] == tokenizer.bos_token_id:
-        offset = 1
-        input_ids.append(prompt_chunks[0][0])
-
-    for x in insert_separator(prompt_chunks, [image_token_index] * (offset + 1)):
-        input_ids.extend(x[offset:])
-
-    if return_tensors is not None:
-        if return_tensors == 'pt':
-            return torch.tensor(input_ids, dtype=torch.long)
-        raise ValueError(f'Unsupported tensor type: {return_tensors}')
-    return input_ids
-
 # set device
 device = 'cuda'  # or cpu
 torch.set_default_device(device)
@@ -65,6 +38,28 @@ tokenizer = AutoTokenizer.from_pretrained(
 img_path = 'sample/4927.png'
 prompt = 'Based on the image, first describe what you see in the figure, then predict the construction_cdl and image_cdl and calibrate it.'
 text = f"<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<image>\n{prompt}<|im_end|>\n<|im_start|>assistant\n"
+
+def tokenizer_image_token(prompt, tokenizer, image_token_index, return_tensors=None):
+    prompt_chunks = [tokenizer(chunk).input_ids for chunk in prompt.split('<image>')]
+
+    def insert_separator(X, sep):
+        return [ele for sublist in zip(X, [sep] * len(X)) for ele in sublist][:-1]
+
+    input_ids = []
+    offset = 0
+    if len(prompt_chunks) > 0 and len(prompt_chunks[0]) > 0 and prompt_chunks[0][0] == tokenizer.bos_token_id:
+        offset = 1
+        input_ids.append(prompt_chunks[0][0])
+
+    for x in insert_separator(prompt_chunks, [image_token_index] * (offset + 1)):
+        input_ids.extend(x[offset:])
+
+    if return_tensors is not None:
+        if return_tensors == 'pt':
+            return torch.tensor(input_ids, dtype=torch.long)
+        raise ValueError(f'Unsupported tensor type: {return_tensors}')
+    return input_ids
+    
 input_ids = tokenizer_image_token(text, tokenizer, -200, return_tensors='pt').unsqueeze(0).cuda()
 
 # image, sample images can be found in images folder