update

README.md

@@ -24,6 +24,27 @@ 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)
@@ -45,8 +66,7 @@ img_path = 'sample/4927.png'
 qs = 'As shown in the diagram, AE/AB=1/4, M is the midpoint of segment AC, BE is parallel to CP, EA is parallel to CP. Find the ratio of the length of line BC to the length of line CD.'
 prompt = f'Using the provided geometric image and question, first predict the construction_cdl and image_cdl. Then, give a detailed step-by-step solution.\nThe question is:\n{qs}'
 text = f'<|im_start|>user\n<image>\n{prompt}<|im_end|>\n<|im_start|>assistant\n'
-text_chunks = [tokenizer(chunk).input_ids for chunk in text.split('<image>')]
-input_ids = torch.tensor(text_chunks[0] + [-200] + text_chunks[1][1:], dtype=torch.long).unsqueeze(0).to(device)
+input_ids = tokenizer_image_token(text, tokenizer, -200, return_tensors='pt').unsqueeze(0).cuda()
 
 # image, sample images can be found in images folder
 image = Image.open(img_path).convert('RGB')
@@ -101,6 +121,26 @@ import warnings
 import numpy as np
 import re
 
+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
 
 def parse_cdl(input_string):
     # 使用正则表达式查找各个部分
@@ -166,8 +206,7 @@ image = Image.open(img_path).convert('RGB')
 # formalization
 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"
-text_chunks = [formalization_tokenizer(chunk).input_ids for chunk in text.split('<image>')]
-input_ids = torch.tensor(text_chunks[0] + [-200] + text_chunks[1][1:], dtype=torch.long).unsqueeze(0).to(device)
+input_ids = tokenizer_image_token(text, formalization_tokenizer, -200, return_tensors='pt').unsqueeze(0).cuda()
 
 # generate
 image_tensor = formalization_model.process_images([image], formalization_model.config).to(dtype=formalization_model.dtype, device=device)
@@ -200,8 +239,7 @@ predict_imgCDL = cdl_info['image_cdl']
 qs = 'As shown in the diagram, AE/AB=1/4, M is the midpoint of segment AC, BE is parallel to CP, EA is parallel to CP. Find the ratio of the length of line BC to the length of line CD.'
 prompt = f'Using the provided geometric image and the possibly erroneous construction_cdl and image_cdl, first calibrate the construction_cdl and image_cdl, then give a detailed step-by-step solution to the question.\nThe initial construction_cdl is:\n{predict_consCDL}\nThe initial image_cdl is:\n{predict_imgCDL}\nThe question is:\n{qs}'
 text = f'<|im_start|>user\n<image>\n{prompt}<|im_end|>\n<|im_start|>assistant\n'
-text_chunks = [reason_tokenizer(chunk).input_ids for chunk in text.split('<image>')]
-input_ids = torch.tensor(text_chunks[0] + [-200] + text_chunks[1][1:], dtype=torch.long).unsqueeze(0).to(device)
+input_ids = tokenizer_image_token(text, reason_tokenizer, -200, return_tensors='pt').unsqueeze(0).cuda()