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.gitattributes

@@ -57,3 +57,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+valid_one_points_indices_split.json filter=lfs diff=lfs merge=lfs -text
+valid_one_points_indices.json filter=lfs diff=lfs merge=lfs -text

dataset_statistics_json.py

@@ -0,0 +1,49 @@
+import os
+import hashlib
+import datasets
+import json
+
+
+root_path = './Datasets/'
+data = datasets.load_dataset("allenai/pixmo-points", split="train", cache_dir=root_path)
+len_data = len(data)
+image_folder = os.path.join(root_path,"pixmo_images")
+
+
+hash_image_all = set()
+indices_one_point_all = []
+hash_image_one_point_all = set()
+
+record = {'index':[],'image_hash':set()}
+
+for i,example in enumerate(data):
+    print(i,len_data,f"{(i / len_data) * 100:.3f}%")
+    points = example['points']
+    image_hash = example["image_sha256"]
+    if len(points) == 1:
+        indices_one_point_all.append(i)
+        hash_image_one_point_all.add(image_hash)
+
+    
+    
+    hash_image_all.add(image_hash)
+    image_url = example["image_url"]
+    image_path = os.path.join(image_folder, f"{image_hash}.jpg")
+    
+    if not os.path.exists(image_path):
+        print('image does not exist, skip')
+        continue  # 图片不存在，跳过
+    if os.path.exists(image_path) and len(points)==1:
+        record['index'].append(i)
+        record['image_hash'].add(image_hash)
+
+record['number of indices'] = len(record['index'])
+record['number of images'] = len(record['image_hash'])
+
+print('all images',len(hash_image_all))
+print('all one point',len(indices_one_point_all),len(hash_image_one_point_all))
+print('downloaded one ponit','number of indices',len(record['index']),'images:',len(record['image_hash']))
+
+record['image_hash'] = list(record['image_hash'])
+with open("valid_one_points_indices.json", "w") as f:
+    json.dump(record, f, indent=4)

delete_hash_mismatch.py

@@ -0,0 +1,30 @@
+import os
+import hashlib
+import datasets
+
+
+root_path = './Datasets/'
+data = datasets.load_dataset("allenai/pixmo-points", split="train", cache_dir=root_path)
+len_data = len(data)
+image_folder = os.path.join(root_path,"pixmo_images")
+
+
+for i,example in enumerate(data):
+    print(i,len_data,f"{(i / len_data) * 100:.3f}%")
+    # break
+    image_hash = example["image_sha256"]
+    image_path = os.path.join(image_folder, f"{image_hash}.jpg")
+    
+    if not os.path.exists(image_path):
+        print('image does not exist, skip')
+        continue  # 图片不存在，跳过
+    
+    with open(image_path, "rb") as img_file:
+        image_bytes = img_file.read()
+        file_hash = hashlib.sha256(image_bytes).hexdigest()
+        print('hash matches')
+    
+    if file_hash != image_hash:
+        os.remove(image_path)  # 哈希不匹配，删除图片
+        print('hash mismatch, delete')
+    # break

download_pixmo.py

@@ -0,0 +1,191 @@
+import datasets
+from hashlib import sha256
+import requests
+
+from pathlib import Path
+import json
+import multiprocessing
+from concurrent.futures import ThreadPoolExecutor, as_completed
+import threading
+
+# 创建一个全局锁
+lock = threading.Lock()
+
+root_path = './Datasets/'
+cache_dir = Path(root_path, "pixmo_images")
+cache_dir.mkdir(parents=True, exist_ok=True)
+
+# 加载数据集
+data = datasets.load_dataset("allenai/pixmo-points", split="train", cache_dir=root_path)
+
+print(f"Dataset size: {len(data)}")
+len_data = len(data)
+
+# 线程池参数
+# MAX_WORKERS = 12  # 可调整，建议 5-20 之间
+cpu_cores = multiprocessing.cpu_count()
+MAX_WORKERS = min(64, cpu_cores * 4)
+print(f"CPU 核心数: {cpu_cores}, 线程池大小: {MAX_WORKERS}")
+
+# 记录成功和失败的下载
+success_indices = []
+failed_indices = []
+
+def download_image(i, example, timeout=15):
+    """ 下载图片并验证哈希 """
+    # print(i,len_data,f"{(i / len_data) * 100:.3f} %")
+    points = example['points']
+    if len(points) != 1:
+        return None, None  # 仅处理 `len(points) == 1` 的数据
+
+    image_url = example["image_url"]
+    image_filename = f"{example['image_sha256']}.jpg"
+    image_path = cache_dir / image_filename
+
+    if image_path.exists():
+        print(image_path,'exists, skip')
+        return i, None  # 已下载则跳过
+
+    try:
+        # 下载图片
+        # 下载时设置进程锁，避免多个进行写入一个同名文件
+        image_bytes = requests.get(image_url, timeout=timeout).content
+        with lock:
+            with open(image_path, "wb") as f:
+                f.write(image_bytes)
+
+            # 验证 SHA256
+            byte_hash = sha256(image_bytes).hexdigest()
+            if byte_hash != example["image_sha256"]:
+                # with lock:  # 加锁删除错误文件，防止并发删除问题
+                image_path.unlink()
+            print(image_path,'hash mismatch, delete')
+            return None, i  # 记录失败
+        
+        print(image_path,'save success!')
+        return i, None  # 记录成功
+        
+    except requests.exceptions.Timeout:
+        print(f"Timeout while downloading {image_url}. Skipping...")
+        return None, i
+    except requests.exceptions.RequestException as e:
+        print(f"Error downloading {image_url}: {e}")
+        return None, i  # 记录失败
+
+
+completed_count = 0
+
+# 多线程下载
+with ThreadPoolExecutor(max_workers=MAX_WORKERS) as executor:
+    # future_to_index = {executor.submit(download_image, i, data[i]): i for i in range(1038456,len_data)}
+    
+    # for future in as_completed(future_to_index):
+    #     success, failed = future.result()
+    #     if success is not None:
+    #         success_indices.append(success)
+    #     if failed is not None:
+    #         failed_indices.append(failed)
+
+    start_index = 1541083
+    future_to_index = {}
+
+    # 采用步进分配索引，避免相邻 i 被同一时间下载
+    for offset in range(0,MAX_WORKERS):  # 12 个线程，每个线程处理不同的起点
+        # for i in range(1541083 + offset, len_data, MAX_WORKERS):  # 每次步长为 MAX_WORKERS
+        for i in range(0 + offset, 1541083, MAX_WORKERS):  # 每次步长为 MAX_WORKERS
+            future = executor.submit(download_image, i, data[i])
+            future_to_index[future] = i
+
+    # 处理结果
+    for future in as_completed(future_to_index):
+        success, failed = future.result()
+        if success is not None:
+            success_indices.append(success)
+        if failed is not None:
+            failed_indices.append(failed)
+        
+        # 在主线程更新进度
+        completed_count += 1
+        # print(f"Progress: {completed_count}/{len_data} ({(completed_count / len_data) * 100:.3f}%)")
+        print(f"Progress: {completed_count}/{1541083} ({(completed_count / 1541083) * 100:.3f}%)")
+
+# 保存成功和失败的索引
+with open("one_points_indices.json", "w") as f:
+    json.dump(success_indices, f, indent=4)
+with open("failed_indices.json", "w") as f:
+    json.dump(failed_indices, f, indent=4)
+
+print(f"Total successful downloads: {len(success_indices)}")
+print(f"Total failed downloads: {len(failed_indices)}")
+
+
+# import datasets
+# from hashlib import sha256
+# import requests
+# import os
+# from pathlib import Path
+# from hashlib import sha256
+# import requests
+# from datasets import load_dataset
+# import json
+
+# # os.environ["HF_ENDPOINT"] = "https://hf-mirror.com"
+# # export HF_ENDPOINT=https://hf-mirror.com
+
+# root_path = './Datasets/'
+
+
+# cache_dir = Path(root_path, "pixmo_images")
+
+# cache_dir.mkdir(parents=True, exist_ok=True)
+
+# # 加载数据集
+# data = load_dataset("allenai/pixmo-points", split="train", cache_dir=root_path)
+
+# print(len(data))
+# len_data = len(data)
+# def download_image(example, timeout=10):
+#     # print(example)
+#     points = example['points']
+#     len_points = len(points)
+#     if len_points > 1: 
+#         return None
+#     if len_points ==0:
+#         return None
+#     image_url = example["image_url"]
+#     print(example['image_sha256'],len_points,points)
+#     image_filename = f"{example['image_sha256']}.jpg"
+#     image_path = cache_dir / image_filename
+
+#     # 如果图片未缓存，则下载
+#     if not image_path.exists():
+#         try:
+#             # 设置超时参数
+#             image_bytes = requests.get(image_url, timeout=timeout).content
+#             with open(image_path, "wb") as f:
+#                 f.write(image_bytes)
+
+#             # 验证 SHA256
+#             byte_hash = sha256(image_bytes).hexdigest()
+#             assert byte_hash == example["image_sha256"], "SHA256 mismatch!"
+#         except requests.exceptions.Timeout:
+#             print(f"Timeout while downloading {image_url}. Skipping...")
+#         except Exception as e:
+#             print(f"Error downloading {image_url}: {e}")
+#     return True
+
+# # 预下载所有图片（仅第一次运行需要）
+# summ = 0
+# success_indices = []  # 用于存储成功的 i 值
+
+# for i, example in enumerate(data):
+#     print(i, len_data, f"{(i / len_data) * 100:.2f} %")
+#     s = download_image(example)
+#     if s:  
+#         summ += 1
+#         success_indices.append(i)  # 记录成功的 i
+
+# # 将成功的 i 值保存到 JSON 文件
+# with open("one_points_indices.json", "w") as f:
+#     json.dump(success_indices, f, indent=4)
+# print("Total successful downloads:", summ)

get_all_instructions.py

@@ -0,0 +1,47 @@
+import os
+import json
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from tqdm import tqdm  # 导入 tqdm 库
+import datasets
+
+# 假设 PixmoDataset 类已经定义
+root_path = './Datasets/'
+data = datasets.load_dataset("allenai/pixmo-points", split="train", cache_dir=root_path)
+len_data = len(data)
+image_folder = os.path.join(root_path,"pixmo_images")
+
+valid_one_points_indices = '/home/panwen.hu/workspace/jian.zhang/EAI/EAI2025/pixmo-points/Datasets/valid_one_points_indices.json'
+
+def load_json(file_path):
+    with open(file_path, 'r', encoding='utf-8') as f:
+        data = json.load(f)
+    return data
+
+
+data_json = load_json(valid_one_points_indices)
+index_list = data_json.get("index", [])
+
+ins_all = set()
+
+def process_item(i):
+    print(i,len_data,f"{(i / len_data) * 100:.3f}%")
+    item = data[i]
+    instruction = item['label']
+    return instruction
+
+# 使用 ThreadPoolExecutor 来并行处理
+with ThreadPoolExecutor(max_workers=64) as executor:  # max_workers 可以根据你的CPU核心数调整
+    # 提交任务到线程池
+    futures = [
+        executor.submit(process_item, i)  for i in index_list
+    ]
+    
+    # 使用 tqdm 显示进度
+    for future in tqdm(as_completed(futures), total=len_data, desc="Processing"):
+        instruction = future.result()
+        ins_all.add(instruction)
+
+# 保存结果到 JSON 文件
+json_path = os.path.join('/home/panwen.hu/workspace/jian.zhang/EAI/EAI2025/Afford-RDT/data/encode_language/', "pixmo_all_instructions_one_point.json")
+with open(json_path, "w", encoding="utf-8") as f:
+    json.dump(list(ins_all), f, indent=4)

split_all_data_index.py

@@ -0,0 +1,37 @@
+import os
+import json
+from datasets import load_dataset
+
+def load_json(file_path):
+    with open(file_path, 'r', encoding='utf-8') as f:
+        data = json.load(f)
+    return data
+
+class DatasetSaver:
+    def __init__(self, data_root):
+        self.data_root = data_root
+        # os.makedirs(self.data_root, exist_ok=True)
+        self.split_file = os.path.join(self.data_root, "valid_one_points_indices.json")
+        self.one_point_indices = load_json(self.split_file)["index"]
+
+
+    def save_data(self):
+        dataset = load_dataset("allenai/pixmo-points", split="train", cache_dir=self.data_root)
+        
+        for idx in self.one_point_indices:
+            
+            print(f" {idx + 1} files...",idx/len(dataset)*100,"%")
+        
+            data = dataset[idx] 
+            file_path = os.path.join(self.data_root, f"pixmo_data/{idx}.json")
+            with open(file_path, "w", encoding="utf-8") as f:
+                json.dump(data, f, ensure_ascii=False, indent=4)
+            print('saved')
+            
+
+        
+        print("All data saved successfully.")
+
+# 使用示例
+data_saver = DatasetSaver('/home/rongtao/jianzhang/pixmo-points/Datasets')
+data_saver.save_data()

train_test_split.py

@@ -0,0 +1,40 @@
+import json
+import random
+
+# 读取 JSON 文件
+def load_json(file_path):
+    with open(file_path, 'r', encoding='utf-8') as f:
+        data = json.load(f)
+    return data
+
+# 划分数据集
+def split_data(index_list, train_ratio=0.8):
+    random.shuffle(index_list)  # 打乱数据集
+    split_point = int(len(index_list) * train_ratio)
+    train_set = sorted(index_list[:split_point])  # 训练集排序
+    test_set = sorted(index_list[split_point:])  # 测试集排序
+    return train_set, test_set
+
+# 保存 JSON 文件
+def save_json(data, file_path):
+    with open(file_path, 'w', encoding='utf-8') as f:
+        json.dump(data, f, ensure_ascii=False, indent=4)
+
+# 运行
+if __name__ == "__main__":
+    input_file = "/home/panwen.hu/workspace/jian.zhang/EAI/EAI2025/pixmo-points/Datasets/valid_one_points_indices.json"  # 原始JSON文件
+    output_file = "./Datasets/valid_one_points_indices_split.json"  # 保存新数据的JSON文件
+    
+    data = load_json(input_file)
+    index_list = data.get("index", [])
+    
+    train_set, test_set = split_data(index_list, train_ratio=0.996374199)
+    print(len(train_set),len(test_set))
+    
+    new_data = {
+        "train": train_set,
+        "test": test_set
+    }
+    
+    save_json(new_data, output_file)
+    print("数据集划分完成，已保存到", output_file)

valid_one_points_indices.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b9f3390de255b40a2b0a257f82cbbb507b468da56185d4a55bf3556311d71911
+size 26473234

valid_one_points_indices_split.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9fd130b27de4d8d490392a596a36165a326b38895a609aecf2ac33698d1f0cec
+size 16732470