Upload micbench_xcomposer.py with huggingface_hub

micbench_xcomposer.py

@@ -0,0 +1,71 @@
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+ckpt_path = "internlm/internlm-xcomposer2-vl-7b"
+tokenizer = AutoTokenizer.from_pretrained(ckpt_path, trust_remote_code=True)
+# `torch_dtype=torch.float16` 可以令模型以 float16 精度加载，否则 transformers 会将模型加载为 float32，导致显存不足
+model = AutoModelForCausalLM.from_pretrained(ckpt_path, torch_dtype=torch.float16, trust_remote_code=True, device_map={"":"cuda:1"})
+
+model.max_length = 5120
+
+image_folder = "/home/ps/Downloads/datasets/micbench/micbench_imgs/"
+questions_file = "/home/ps/Downloads/datasets/micbench/micbench_{}.json"
+answers_file = "/home/ps/Downloads/InternLM-XComposer/q-bench2-xcomposer2-micbench-{}.jsonl"
+
+split = "test"
+
+import json
+from ast import literal_eval
+from PIL import Image
+import numpy as np
+from tqdm import tqdm
+
+
+with open(questions_file.format(split)) as f:
+    if questions_file.endswith("json"):
+        llvqa_data = json.load(f)
+    else:
+        llvqa_data = [literal_eval(line) for line in f.readlines()]
+    print(llvqa_data[0])
+    
+    model = model.eval() 
+
+correct_ = np.zeros((3,4))
+all_ = np.zeros((3,4))
+
+correct= 0
+
+pbar = tqdm(total=len(llvqa_data))
+
+for i, llddata in enumerate(llvqa_data):
+    img_path_list = llddata["img_path"]
+    if len(img_path_list) > 3:
+        pbar.update(1)
+        continue
+    images = []
+    for img_path in img_path_list:
+        image = Image.open(image_folder+img_path).convert("RGB")
+        image = model.vis_processor(image)
+        images.append(image)
+    image = torch.stack(images)
+    options_prompt = ''
+    for choice, ans in zip(["A.", "B.", "C.", "D."], llddata["candidates"]):
+        options_prompt += f"{choice} {ans}\n"
+        if ans == llddata["correct_ans"]:
+            llddata["correct_choice"] = choice[0]
+    query =  '<ImageHere> '*len(img_path_list) + 'Please answer this question by choosing the correct choice.' 
+    query +=  "Context: N/A\nQuestion: " + llddata["question"] + '\nOptions: ' + options_prompt
+    print(img_path_list, query)
+    with torch.no_grad():
+        with torch.cuda.amp.autocast():
+            response, history = model.chat(tokenizer, query=query, image=image, history=[], do_sample=False, max_new_tokens=6)
+    outputs = response.replace("The answer is ", "")
+    if llddata["correct_choice"] in outputs: 
+        correct += 1
+        #correct_[llddata["type"]][llddata["concern"]]+= 1
+    #all_[llddata["type"]][llddata["concern"]]+= 1
+    
+    with open(answers_file.format(split), "a") as wf:
+        json.dump(llddata, wf)
+    
+    pbar.update(1)
+    pbar.set_description("[Running Accuracy]: {:.4f},[Response]: {}, [Correct Ans]: {}, , [Prog]: {}".format(correct/(i+1), outputs, llddata.get("correct_choice", -1), i+1))