File size: 3,561 Bytes
63fba53 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 | import torch
import os
import json
import gc
import csv
from PIL import Image
from datasets import load_dataset
from collections import defaultdict
from tqdm import tqdm
import numpy as np
from ..utils import save_json,extract
from ..base_dataset import BaseDataset
from ..question_formats import get_report_generation_prompt
class CheXpert_Plus(BaseDataset):
def __init__(self,model,dataset_path,output_path):
self.model = model
self.output_path = output_path
self.dataset_path = dataset_path
self.samples = []
self.chunk_idx = int(os.environ.get("chunk_idx",0))
self.num_chunks = int(os.environ.get("num_chunks",1))
def load_data(self):
self.maybe_download_dataset()
dataset_path = self.dataset_path
json_path = os.path.join(dataset_path,"test.json")
with open(json_path,"r") as f:
dataset = json.load(f)
for idx,sample in tqdm(enumerate(dataset)):
if idx % self.num_chunks == self.chunk_idx:
if sample["findings"].strip() == "" and sample["impression"].strip() == "":
continue
sample = self.construct_messages(sample)
self.samples.append(sample)
print("total samples number:", len(self.samples))
return self.samples
def construct_messages(self,sample):
image_root = os.path.join(self.dataset_path,"images")
image = sample["image"]
image = Image.open(os.path.join(image_root,image))
findings = sample["findings"]
impression = sample["impression"]
findings = "None" if findings.strip() == "" else findings
impression = "None" if impression.strip() == "" else impression
prompt = get_report_generation_prompt()
messages = {"prompt":prompt,"image":image}
sample["messages"] = messages
return sample
def cal_metrics(self,out_samples):
import pandas as pd
predictions_data = []
ground_truth_data = []
for i,sample in enumerate(out_samples):
response = sample["response"]
findings = sample["findings"]
impression = sample["impression"]
golden = f"Findings: {findings} Impression: {impression}."
# 生成唯一的study_id
study_id = f"study_{i+1}"
# 添加预测数据
predictions_data.append({
'study_id': study_id,
'report': response
})
# 添加真实标签数据
ground_truth_data.append({
'study_id': study_id,
'report': golden
})
# 创建DataFrame
predictions_df = pd.DataFrame(predictions_data)
ground_truth_df = pd.DataFrame(ground_truth_data)
prediction_path = os.path.join(self.output_path,'predictions.csv')
ground_truth_path = os.path.join(self.output_path,'ground_truth.csv')
# 保存为CSV文件
predictions_df.to_csv(prediction_path, index=False)
ground_truth_df.to_csv(ground_truth_path, index=False)
return {"total metrics":"please use cal_report_metrics.py to generate metrics"},out_samples
def maybe_download_dataset(self):
if not os.path.exists(self.dataset_path):
raise ValueError(f"Dataset path {self.dataset_path} does not exist. Please download from https://aimi.stanford.edu/datasets/chexpert-plus and put it under {self.dataset_path}.")
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