Fix typo in README regarding data path

README.md

@@ -1,6 +1,17 @@
+**Read this in other languages: [English](README.md), [中文](README_zh.md).**
+
 # OpenVLA Lightweight Version
 
-OpenVLA的轻量化版本
+Lightweight version of OpenVLA, based on the llava-next framework
+
+Features:
+
+1.The main model is only 0.5B, using qwen2-0.5B.
+
+2.Does not use RLDS format datasets, instead fine-tuned using mllm format.
+
+3.Does not occupy inherent tokens of the LLM model, instead using an additional 271 tokens to represent actions.
+# How to use
 
 ## Installation
 
@@ -11,7 +22,7 @@ Follow the steps below to install the OpenVLA-Qwen lightweight version.
 Clone the repository to your local machine:
 
 ```bash
-git clone https://github.com/IP127000/openVLA-Qwen.git
+git clone https://github.com/IP127000/openVLA-Qwen2-0.5B.git
 cd openVLA-Qwen
 ```
 ### 2. Create a new conda environment and activate it:
@@ -24,9 +35,32 @@ conda activate vla-qwen
 pip install --upgrade pip
 pip install -e ".[train]"
 ```
+### 4. Download the weights from huggingface
+```bash
+cd openVLA-Qwen2-0.5B
+mkdir models
+cd models
+git clone https://huggingface.co/lmms-lab/llava-onevision-qwen2-0.5b-ov
+```
+### 5. Change the file path in scripts/train/finetune_ov_vla.sh
+```bash
+PREV_STAGE_CHECKPOINT="models/llava-onevision-qwen2-0.5b-ov" 
+--data_path scripts/train/vla.yaml
+--image_folder /images \
+```
+### 6. Change the data path in scripts/train/vla.yaml
+```bash
+json_path: /root/data/vla_llava.json
+```
+
+### 7. start to train
+```bash
+cd /openVLA-Qwen2-0.5B
+./scripts/train/finetune_ov_vla.sh
+```
 ## Acknowledgements
 
-I would like to express our gratitude to the following repositories:
+This project makes use of the following open-source projects, and I would like to express my gratitude to their creators:
 
-- [OpenVLA](https://github.com/openvla/openvla) - for providing the core structure and functionality that inspired OpenVLA-Qwen.
-- [LLaVA_OpenVLA](https://github.com/Darren-greenhand/LLaVA-Next) - for their contribution to data preprocessing techniques.
+- [OpenVLA](https://github.com/openvla/openvla) - For providing the core structure and functionality that inspired OpenVLA-Qwen.
+- [LLaVA_OpenVLA](https://github.com/Darren-greenhand/LLaVA-Next) - For contribution to data preprocessing techniques.

README_zh.md

@@ -0,0 +1,63 @@
+**Read this in other languages: [English](README.md), [中文](README_zh.md).**
+
+# OpenVLA Lightweight Version
+
+基于llava-next框架的OpenVLA轻量版。
+
+特性：
+
+主模型只有0.5B，使用qwen2-0.5B。
+
+不使用RLDS格式的数据集，而是采用mllm格式进行微调。
+
+不占用LLM模型的固有token，而是使用额外的271个token来表示动作。
+# 如何使用
+
+## Installation
+
+按照以下步骤安装OpenVLA-Qwen轻量版。
+
+### 1. Clone the repository
+
+Clone the repository to your local machine:
+
+```bash
+git clone https://github.com/IP127000/openVLA-Qwen.git
+cd openVLA-Qwen
+```
+### 2. Create a new conda environment and activate it:
+```bash
+conda create -n vla-qwen python=3.10 -y
+conda activate vla-qwen
+```
+### 3. Install the required dependencies
+```bash
+pip install --upgrade pip
+pip install -e ".[train]"
+```
+### 4. Download the weights from huggingface
+```bash
+cd openVLA-Qwen/models
+git clone git clone https://huggingface.co/lmms-lab/llava-onevision-qwen2-0.5b-ov
+```
+### 5. Change the file path in scripts/train/finetune_ov_vla.sh
+```bash
+PREV_STAGE_CHECKPOINT="models/llava-onevision-qwen2-0.5b-ov" 
+--data_path scripts/train/vla.yaml
+--image_folder /images \
+```
+### 6. Change the data paht in scripts/train/vla.yaml
+```bash
+json_path: /root/data/vla_llava.json
+```
+### 7. 开始训练
+```bash
+cd /openVLA-Qwen2-0.5B
+./scripts/train/finetune_ov_vla.sh
+```
+## 致谢
+
+本项目使用了以下开源项目，在此向其开发者表示感谢：
+
+- [OpenVLA](https://github.com/openvla/openvla) - 感谢提供核心结构和功能，启发了OpenVLA-Qwen。
+- [LLaVA_OpenVLA](https://github.com/Darren-greenhand/LLaVA-Next) - 感谢他对数据处理技术的贡献。

llava/actiontokenizer.py

@@ -8,72 +8,10 @@ from typing import List, Union
 
 import numpy as np
 from transformers import PreTrainedTokenizerBase
-
-
-# class ActionTokenizer:
-#     def __init__(
-#         self, tokenizer: PreTrainedTokenizerBase, bins: int = 256, min_action: int = -1, max_action: int = 1
-#     ) -> None:
-#         """
-#         Discretizes continuous robot actions into N bins per dimension and maps to the least used tokens.
-
-#         NOTE =>> by default, assumes a BPE-style tokenizer akin to the LlamaTokenizer, where *the least used tokens*
-#                  appear at the end of the vocabulary!
-
-#         :param tokenizer: Base LLM/VLM tokenizer to extend.
-#         :param bins: Number of bins for each continuous value; we'll adopt a uniform binning strategy.
-#         :param min_action: Minimum action value (for clipping, setting lower bound on bin interval).
-#         :param max_action: Maximum action value (for clipping, setting upper bound on bin interval).
-#         """
-#         self.tokenizer, self.n_bins, self.min_action, self.max_action = tokenizer, bins, min_action, max_action
-
-#         # Create Uniform Bins + Compute Bin Centers
-#         self.bins = np.linspace(min_action, max_action, self.n_bins)
-#         self.bin_centers = (self.bins[:-1] + self.bins[1:]) / 2.0
-
-#         # [Contract] Set "action_token_begin_idx" based on `self.tokenizer.vocab_size - (self.n_bins + 1)`
-#         #   =>> Assumes we're always overwriting the final `n_bins` tokens of the vocabulary!
-#         self.action_token_begin_idx: int = int(self.tokenizer.vocab_size - (self.n_bins + 1))
-
-#     def __call__(self, action: np.ndarray) -> Union[str, List[str]]:
-#         """Clip & bin actions to *the last `n_bins` tokens* of the vocabulary (e.g., tokenizer.vocab[-256:])."""
-#         action = np.clip(action, a_min=float(self.min_action), a_max=float(self.max_action))
-#         discretized_action = np.digitize(action, self.bins)
-
-#         # Handle single element vs. batch
-#         if len(discretized_action.shape) == 1:
-#             return self.tokenizer.decode(list(self.tokenizer.vocab_size - discretized_action))
-#         else:
-#             return self.tokenizer.batch_decode((self.tokenizer.vocab_size - discretized_action).tolist())
-
-#     def decode_token_ids_to_actions(self, action_token_ids: np.ndarray) -> np.ndarray:
-#         """
-#         Returns continuous actions for discrete action token IDs.
-
-#         NOTE =>> Because of the way the actions are discretized w.r.t. the bins (and not the bin centers), the
-#                  digitization returns bin indices between [1, # bins], inclusive, when there are actually only
-#                  (# bins - 1) bin intervals.
-
-#                  Therefore, if the digitization returns the last possible index, we map this to the last bin interval.
-
-#         EXAMPLE =>> Let's say self._bins has 256 values. Then self._bin_centers has 255 values. Digitization returns
-#                     indices between [1, 256]. We subtract 1 from all indices so that they are between [0, 255]. There
-#                     is still one index (i==255) that would cause an out-of-bounds error if used to index into
-#                     self._bin_centers. Therefore, if i==255, we subtract 1 from it so that it just becomes the index of
-#                     the last bin center. We implement this simply via clipping between [0, 255 - 1].
-#         """
-#         discretized_actions = self.tokenizer.vocab_size - action_token_ids
-#         discretized_actions = np.clip(discretized_actions - 1, a_min=0, a_max=self.bin_centers.shape[0] - 1)
-
-#         return self.bin_centers[discretized_actions]
-
-#     @property
-#     def vocab_size(self) -> int:
-#         return self.n_bins
     
 class ActionTokenizer:
     def __init__(
-        self, tokenizer: PreTrainedTokenizerBase, bins: int = 285, min_action = -0.0025, max_action = 0.0025
+        self, tokenizer: PreTrainedTokenizerBase, bins: int = 271, min_action = -1.0, max_action = 1.0
         # self, tokenizer: PreTrainedTokenizerBase, bins: int = 256, min_action: int = -1, max_action: int = 1
     ) -> None:
         self.tokenizer, self.n_bins, self.min_action, self.max_action = tokenizer, bins, min_action, max_action
@@ -93,8 +31,6 @@ class ActionTokenizer:
         # print('initializing action tokenizer, the new len(tokenizer) is:', len(self.tokenizer))
 
     def __call__(self, action: np.ndarray) -> Union[str, List[str]]:
-
-
         action = np.clip(action, a_min=float(self.min_action), a_max=float(self.max_action))
         discretized_action = np.digitize(action, self.bins) - 1
         discretized_action = np.clip(discretized_action, a_min=0, a_max=self.n_bins - 1)
@@ -114,7 +50,36 @@ class ActionTokenizer:
         action_indices = vectorized_lookup(action_token_ids)
         return self.bin_centers[action_indices]
 
-    # 返回所有 tokens 的数量，包括动作 tokens
+    def actionValue_to_actionName(self,action):
+        action_token_ids=self.__call__(action)
+        actionName=self.tokenizer.decode(action_token_ids)
+        return actionName
+
+    def actionName_to_actionValue(self,actionName):
+        action_ids=self.tokenizer(actionName)
+        action_ids=action_ids["input_ids"]
+        actionValue=self.decode_token_ids_to_actions(action_ids)
+        return actionValue
+
+    def str_to_mllm(self, result_str):
+        new_result=result_str
+        for token in self.action_tokens:
+            value=self.actionName_to_actionValue(token)
+            new_result=new_result.replace(token, str(value[0]))
+        return new_result
+
+    def value_to_mllm(self, result_str):
+        new_result=result_str
+        data=[]
+        for token in self.action_tokens:
+            if new_result.startswith(token):
+                value=self.actionName_to_actionValue(token)
+                data.append(float(value))
+            elif token in new_result:
+                value=self.actionName_to_actionValue(token)
+                data.append(float(value))
+        return data
+
     @property
     def vocab_size(self) -> int:
         return len(self.tokenizer)
@@ -125,4 +90,4 @@ class ActionTokenizer:
 
     @property
     def action_vocab_size(self) -> int:
-        return len(self.action_tokens)    
+        return len(self.action_tokens)    

playground/2d_hist.py

@@ -1,132 +0,0 @@
-import json
-import os
-from PIL import Image
-from tqdm import tqdm
-import matplotlib.pyplot as plt
-import numpy as np
-from multiprocessing import Pool
-import functools
-import argparse
-
-
-def load_data(json_path):
-    with open(json_path, "r") as f:
-        return json.load(f)
-
-
-def filter_data(data):
-    filtered_data = [item for item in data if "image" in item]
-    return filtered_data
-
-
-def calculate_image_dimension(image_path, images_folder):
-    full_path = os.path.join(images_folder, image_path)
-    try:
-        with Image.open(full_path) as img:
-            width, height = img.size
-            return width, height
-    except Exception as e:
-        print(f"Error opening {full_path}: {e}")
-        return None, None
-
-
-def calculate_image_dimensions_multiprocess(filtered_data, images_folder, num_processes=256):
-    image_paths = []
-    for item in filtered_data:
-        if isinstance(item["image"], list):
-            image_paths.extend(item["image"])
-        else:
-            image_paths.append(item["image"])
-
-    with Pool(num_processes) as p:
-        dimensions = list(
-            tqdm(
-                p.imap(functools.partial(calculate_image_dimension, images_folder=images_folder), image_paths),
-                total=len(image_paths),
-                desc="Calculating image dimensions",
-            )
-        )
-    widths, heights = zip(*[dim for dim in dimensions if dim[0] is not None])
-    return list(widths), list(heights)
-
-
-def tokenize(text):
-    return text.split()
-
-
-def calculate_tokenized_lengths(data):
-    lengths = []
-    for item in tqdm(data, desc="Tokenizing conversations"):
-        for conversation in item["conversations"]:
-            tokenized_value = tokenize(conversation["value"])
-            lengths.append(len(tokenized_value))
-    return lengths
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Process data for LLaVA_Next project.")
-    parser.add_argument(
-        "--json_path",
-        type=str,
-        help="Path to the JSON file containing data.",
-        default="/mnt/bn/vl-research/data/llava_instruct/real_vision_flan/llava_ofa_DEMON-FULL.json",
-    )
-    parser.add_argument(
-        "--images_folder",
-        type=str,
-        default="/mnt/bn/vl-research/data/llava_data",
-        help="Path to the folder containing images.",
-    )
-    args = parser.parse_args()
-
-    llava_instruct_name = os.path.basename(args.json_path).replace(".json", "")
-    images_folder = args.images_folder
-
-    data = load_data(args.json_path)
-    filtered_data = filter_data(data)
-
-    print(f"Total data items: {len(data)}, Filtered data items: {len(filtered_data)}")
-    widths, heights = calculate_image_dimensions_multiprocess(filtered_data, images_folder)
-    max_width, max_height = max(widths), max(heights)
-    print(f"Max width: {max_width}, Max height: {max_height}")
-
-    tokenized_lengths = calculate_tokenized_lengths(filtered_data)
-
-    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 12))
-
-    # Plot 2D histogram
-    widths_bins = [min(widths), max(widths) + 1] if min(widths) == max(widths) else np.arange(min(widths), max(widths) + 100, 100)
-    heights_bins = [min(heights), max(heights) + 1] if min(heights) == max(heights) else np.arange(min(heights), max(heights) + 100, 100)
-
-    h, xedges, yedges, image = ax1.hist2d(widths, heights, bins=[widths_bins, heights_bins], cmap=plt.cm.jet, density=True)
-    fig.colorbar(image, ax=ax1)
-    ax1.set_xlabel("Width")
-    ax1.set_ylabel("Height")
-    ax1.set_title(
-        f"dist_{llava_instruct_name}_2d_w_h\nMax width: {max(widths)}, Max height: {max(heights)}",
-        fontsize=10,
-    )
-
-    # Plot histogram
-    hist, bin_edges = np.histogram(tokenized_lengths, bins=np.arange(0, max(tokenized_lengths) + 10, 10))
-    bins = np.arange(0, max(tokenized_lengths) + 10, 10)
-    ax2.bar(bin_edges[:-1], hist, width=7, edgecolor="black", log=True)
-
-    # Display every nth label on the x-axis
-    n = 8  # Adjust this value to control the number of labels displayed
-    ticks = bins[::n]
-    tick_labels = [int(tick) for tick in ticks]
-    ax2.set_xticks(ticks)
-    ax2.set_xticklabels(tick_labels, rotation=90, fontsize=8)
-
-    ax2.set_xlim(min(bin_edges), max(bin_edges))
-    ax2.set_xlabel("Tokenized Length")
-    ax2.set_ylabel("Count (log scale)")
-    ax2.set_title(f"dist_{llava_instruct_name}_tokenized_length", fontsize=8)
-
-    plt.tight_layout()
-    plt.savefig(f"./dist_{llava_instruct_name}_combined.png")
-
-
-if __name__ == "__main__":
-    main()

playground/data_checker.py

@@ -1,364 +0,0 @@
-import json
-import os
-from tqdm import tqdm
-from multiprocessing import Pool, cpu_count
-import yaml
-
-
-class DataProcessor:
-    def __init__(self, file_path, image_root, video_root):
-        self.file_path = file_path
-        self.image_root = image_root
-        self.data = None
-        self.video_root = video_root
-        self.load_data()
-
-    def load_data(self):
-        if self.file_path.endswith(".json"):
-            with open(self.file_path, "r") as f:
-                self.data = json.load(f)
-        elif self.file_path.endswith(".yaml"):
-            with open(self.file_path, "r") as f:
-                self.data = yaml.safe_load(f)
-        elif self.file_path.endswith(".jsonl"):
-            with open(self.file_path, "r") as f:
-                self.data = [json.loads(line) for line in f.readlines()]
-        else:
-            raise ValueError("Unsupported file format")
-
-    def load_json_data(self, json_path):
-        if json_path.endswith(".jsonl"):
-            cur_data_dict = []
-            with open(json_path, "r") as json_file:
-                for line in json_file:
-                    cur_data_dict.append(json.loads(line.strip()))
-            return cur_data_dict
-        elif json_path.endswith(".json"):
-            with open(json_path, "r") as f:
-                return json.load(f)
-        else:
-            raise ValueError("Unsupported file format")
-
-    def check_image_existence(self, data):
-        if "image" in data:
-            if type(data["image"]) == list:
-                images = data["image"]
-            else:
-                images = [data["image"]]
-
-            for image in images:
-                full_image_path = os.path.join(self.image_root, image)
-                if not os.path.exists(full_image_path):
-                    print(f"WARNING!!! {full_image_path} not exists !!!")
-
-        if "video" in data:
-            full_video_path = os.path.join(self.video_root, data["video"])
-            if not os.path.exists(full_video_path):
-                print(f"WARNING!!! {full_video_path} not exists !!!")
-
-        # if data["conversations"][0]["value"].count("<image>") > 1:
-        #     print(f"WARNING!!! {data['conversations'][0]['value']} has more than one <image> !!!")
-
-    def check_item_structure(self, item):
-        if not all(key in item for key in ["conversations"]):
-            print(f"WARNING!!! Item {item.get('id', 'unknown')} is missing required fields!")
-            return False
-
-        conversations = item["conversations"]
-        if not isinstance(conversations, list) or len(conversations) < 2 or len(conversations) % 2 != 0:
-            print(f"WARNING!!! Item {item['id']} has invalid conversations structure!")
-            return False
-
-        for i, conv in enumerate(conversations):
-            if not all(key in conv for key in ["from", "value"]):
-                print(f"WARNING!!! Item {item['id']} has invalid conversation format!")
-                return False
-
-            expected_from = "human" if i % 2 == 0 else "gpt"
-            if conv["from"] != expected_from:
-                print(f"WARNING!!! Item {item['id']} has incorrect conversation order!")
-                return False
-
-        return True
-
-    def check_image_and_structure(self, item):
-        if not self.check_item_structure(item):
-            return
-
-        # self.check_image_existence(item)
-
-    def process_images(self):
-        if isinstance(self.data, list):
-            args = [d for d in self.data]
-            with Pool(processes=cpu_count()) as pool:
-                list(tqdm(pool.imap(self.check_image_and_structure, args), total=len(self.data)))
-        elif isinstance(self.data, dict):
-            for d in self.data["datasets"]:
-                dd_json_path = d["json_path"]
-                data = self.load_json_data(dd_json_path)
-                args = [d for d in data]
-                with Pool(processes=cpu_count()) as pool:
-                    list(tqdm(pool.imap(self.check_image_and_structure, args), total=len(data), desc=f"Processing {dd_json_path}"))
-
-    def count_items(self):
-        if isinstance(self.data, list):  # Assuming JSON data loaded directly
-            return len(self.data)
-        elif isinstance(self.data, dict):  # Assuming YAML data loaded
-            total_items_count = 0
-            for d in self.data["datasets"]:
-                dd_json_path = d["json_path"]
-                data = self.load_json_data(dd_json_path)
-                current_items_count = len(data)
-
-                sampling_strategy = d["sampling_strategy"]
-                try:
-                    if sampling_strategy != "all":
-                        percentage = float(sampling_strategy.split(":")[-1].replace("%", "")) / 100.0
-                    else:
-                        percentage = 1.0
-                except Exception as e:
-                    print(f"Error: {e}")
-                    percentage = 1.0
-
-                sampling_count = int(current_items_count * percentage)
-                total_items_count += sampling_count
-                print(f"{dd_json_path}: {sampling_count}")
-            return total_items_count
-
-    def stat_data(self):
-        if isinstance(self.data, dict):
-            cur_lens_list = []
-            single_image_count = 0
-            multiple_image_count = 0
-            video_count = 0
-            total_count = 0
-            text_count = 0
-            max_tokens_item = None
-            max_tokens = 0
-
-            for d in self.data["datasets"]:
-                dd_json_path = d["json_path"]
-                data = self.load_json_data(dd_json_path)
-                sampling_strategy = d["sampling_strategy"]
-
-                try:
-                    if sampling_strategy != "all":
-                        percentage = float(sampling_strategy.split(":")[-1].replace("%", "")) / 100.0
-                    else:
-                        percentage = 1.0
-                except Exception as e:
-                    print(f"Error parsing sampling strategy: {e}")
-                    percentage = 1.0
-
-                sampled_count = int(len(data) * percentage)
-                print(f"{dd_json_path}: {sampled_count} (sampled from {len(data)})")
-
-                for item in data[:sampled_count]:
-                    conversations = item["conversations"]
-                    cur_len = sum([len(conv["value"].split()) for conv in conversations])
-                    cur_lens_list.append(cur_len)
-
-                    if cur_len > max_tokens:
-                        max_tokens = cur_len
-                        max_tokens_item = item
-
-                    total_count += 1
-                    if "image" in item:
-                        if isinstance(item["image"], list):
-                            if len(item["image"]) > 1:
-                                multiple_image_count += 1
-                            else:
-                                single_image_count += 1
-                        else:
-                            single_image_count += 1
-                    elif "video" in item:
-                        video_count += 1
-                    else:
-                        text_count += 1
-
-            print(f"Max length: {max(cur_lens_list)}, Min length: {min(cur_lens_list)}, Average length: {sum(cur_lens_list) / len(cur_lens_list)}")
-            print(f"Total items: {total_count}")
-            print(f"Text items: {text_count} ({text_count/total_count*100:.2f}%)")
-            print(f"Single image items: {single_image_count} ({single_image_count/total_count*100:.2f}%)")
-            print(f"Multiple image items: {multiple_image_count} ({multiple_image_count/total_count*100:.2f}%)")
-            print(f"Video items: {video_count} ({video_count/total_count*100:.2f}%)")
-
-            print("\nItem with the largest number of tokens:")
-            print(f"Token count: {max_tokens}")
-            print("Item content:")
-            print(json.dumps(max_tokens_item, indent=2))
-
-    def filter_data(self):
-        if isinstance(self.data, dict):
-            for d in self.data["datasets"]:
-                dd_json_path = d["json_path"]
-                print(f"Processing {dd_json_path}")
-                data = self.load_json_data(dd_json_path)
-
-                filtered_data = []
-                mismatch_data = []
-                mismatch_flag = False
-                for item in data:
-                    try:
-                        if "image" in item:
-                            num_image = len(item["image"]) if isinstance(item["image"], list) else 1
-                        else:
-                            num_image = 0
-
-                        if "video" in item:
-                            num_video = len(item["video"]) if isinstance(item["video"], list) else 1
-                        else:
-                            num_video = 0
-
-                        num_visuals = num_image + num_video
-                        conv_text = ""
-                        for conv in item["conversations"]:
-                            conv_text += conv["value"]
-
-                        num_img_token_appearance = conv_text.count("<image>")
-                        if len(conv_text) == 0:
-                            print(f"Conversation text is empty for {item}")
-
-                        if num_img_token_appearance == num_visuals or num_img_token_appearance < num_visuals and len(conv_text) > 0:
-                            filtered_data.append(item)
-                        elif num_img_token_appearance > num_visuals:
-                            item["num_img_token_appearance"] = num_img_token_appearance
-                            item["num_visuals"] = num_visuals
-                            mismatch_data.append(item)
-
-                            if not mismatch_flag:
-                                print(f"Data mismatch for {item}")
-
-                            mismatch_flag = True
-                    except Exception as e:
-                        print(f"Error: {e}")
-                        print()
-
-                if mismatch_flag:
-                    print(f"Data mismatch for {dd_json_path}")
-
-                if len(filtered_data) < len(data):
-                    saving_dd_json_path = dd_json_path.replace(".jsonl", f"fltd_{len(filtered_data)}.json").replace(".json", f"fltd_{len(filtered_data)}.json")
-                    with open(saving_dd_json_path, "w") as f:
-                        json.dump(filtered_data, f, indent=2)
-                    print(f"Filtered data count: {len(filtered_data)}")
-                else:
-                    pass
-
-    def stat_and_filter_data(self, threshold):
-        if isinstance(self.data, dict):
-            cur_lens_list = []
-            single_image_count = 0
-            multiple_image_count = 0
-            video_count = 0
-            total_count = 0
-            text_count = 0
-
-            for d in self.data["datasets"]:
-                dd_json_path = d["json_path"]
-                data = self.load_json_data(dd_json_path)
-                sampling_strategy = d["sampling_strategy"]
-                filtered_data = []
-
-                try:
-                    if sampling_strategy != "all":
-                        percentage = float(sampling_strategy.split(":")[-1].replace("%", "")) / 100.0
-                    else:
-                        percentage = 1.0
-                except Exception as e:
-                    print(f"Error parsing sampling strategy: {e}")
-                    percentage = 1.0
-
-                sampled_count = int(len(data) * percentage)
-                print(f"{dd_json_path}: {sampled_count} (sampled from {len(data)})")
-
-                save_flag = False
-                for item in data:
-                    total_count += 1
-                    conversations = item["conversations"]
-                    filtered_conversations = []
-                    current_token_count = 0
-
-                    for i in range(0, len(conversations), 2):
-                        if i + 1 < len(conversations):
-                            human_conv = conversations[i]
-                            gpt_conv = conversations[i + 1]
-                            pair_tokens = len(human_conv["value"].split()) + len(gpt_conv["value"].split())
-
-                            if current_token_count + pair_tokens <= threshold:
-                                filtered_conversations.extend([human_conv, gpt_conv])
-                                current_token_count += pair_tokens
-                            else:
-                                save_flag = True
-                                break
-
-                    if filtered_conversations:
-                        item["conversations"] = filtered_conversations
-                        cur_len = sum([len(conv["value"].split()) for conv in filtered_conversations])
-                        cur_lens_list.append(cur_len)
-                        filtered_data.append(item)
-
-                        if "image" in item:
-                            if isinstance(item["image"], list):
-                                if len(item["image"]) > 1:
-                                    multiple_image_count += 1
-                                else:
-                                    single_image_count += 1
-                            else:
-                                single_image_count += 1
-                        elif "video" in item:
-                            video_count += 1
-                        else:
-                            text_count += 1
-
-                # Save filtered data for each dataset
-                if filtered_data and save_flag:
-                    if dd_json_path.endswith(".jsonl"):
-                        output_file = dd_json_path.replace(".jsonl", f"_filtered_{threshold}tokens_{len(filtered_data)}.jsonl")
-                        with open(output_file, "w") as f:
-                            for item in filtered_data:
-                                f.write(json.dumps(item) + "\n")
-                    else:
-                        output_file = dd_json_path.replace(".json", f"_filtered_{threshold}tokens_{len(filtered_data)}.json")
-                        with open(output_file, "w") as f:
-                            json.dump(filtered_data, f, indent=2)
-                    print(f"Filtered data for {dd_json_path} saved to: {output_file}")
-
-            print(f"Max length: {max(cur_lens_list)}, Min length: {min(cur_lens_list)}, Average length: {sum(cur_lens_list) / len(cur_lens_list)}")
-            print(f"Total items: {total_count}")
-            print(f"Text items: {text_count} ({text_count/total_count*100:.2f}%)")
-            print(f"Single image items: {single_image_count} ({single_image_count/total_count*100:.2f}%)")
-            print(f"Multiple image items: {multiple_image_count} ({multiple_image_count/total_count*100:.2f}%)")
-            print(f"Video items: {video_count} ({video_count/total_count*100:.2f}%)")
-
-
-def main(file_path, image_root, operation, video_root, threshold=None):
-    processor = DataProcessor(file_path, image_root, video_root)
-    if operation == "check":
-        processor.process_images()
-    elif operation == "count":
-        total_items = processor.count_items()
-        print(f"Total items: {total_items}")
-    elif operation == "filter":
-        processor.filter_data()
-    elif operation == "stat":
-        processor.stat_data()
-    elif operation == "stat_and_filter":
-        if threshold is None:
-            raise ValueError("Threshold must be provided for stat_and_filter operation")
-        processor.stat_and_filter_data(threshold)
-    else:
-        raise ValueError("Unsupported operation")
-
-
-if __name__ == "__main__":
-    import argparse
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--file_path", type=str, default="/mnt/bn/vl-research/workspace/boli01/projects/LLaVA_Next/scripts/i18n/scale_llms/next_continual.yaml")
-    parser.add_argument("--image_root", type=str, default="/mnt/bn/vl-research/data/llava_data")
-    parser.add_argument("--video_root", type=str, default="/mnt/bn/vl-research/data/llava_video")
-    parser.add_argument("--operation", type=str, default="filter")
-    parser.add_argument("--threshold", type=int, default=None, help="Threshold for stat_and_filter operation")
-    args = parser.parse_args()
-    main(args.file_path, args.image_root, args.operation, args.video_root, args.threshold)

playground/demo/video_demo.py

@@ -1,335 +0,0 @@
-import argparse
-import torch
-
-from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
-from llava.conversation import conv_templates, SeparatorStyle
-from llava.model.builder import load_pretrained_model
-from llava.utils import disable_torch_init
-from llava.mm_utils import process_anyres_image,tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria
-
-import json
-import os
-import math
-from tqdm import tqdm
-from decord import VideoReader, cpu
-
-from transformers import AutoConfig
-
-import cv2
-import base64
-import openai
-
-from PIL import Image
-
-
-
-import numpy as np
-
-def split_list(lst, n):
-    """Split a list into n (roughly) equal-sized chunks"""
-    chunk_size = math.ceil(len(lst) / n)  # integer division
-    return [lst[i : i + chunk_size] for i in range(0, len(lst), chunk_size)]
-
-
-def get_chunk(lst, n, k):
-    chunks = split_list(lst, n)
-    return chunks[k]
-
-
-def parse_args():
-    """
-    Parse command-line arguments.
-    """
-    parser = argparse.ArgumentParser()
-
-    # Define the command-line arguments
-    parser.add_argument("--video_path", help="Path to the video files.", required=True)
-    parser.add_argument("--output_dir", help="Directory to save the model results JSON.", required=True)
-    parser.add_argument("--output_name", help="Name of the file for storing results JSON.", required=True)
-    parser.add_argument("--model-path", type=str, default="facebook/opt-350m")
-    parser.add_argument("--model-base", type=str, default=None)
-    parser.add_argument("--conv-mode", type=str, default=None)
-    parser.add_argument("--chunk-idx", type=int, default=0)
-    parser.add_argument("--mm_resampler_type", type=str, default="spatial_pool")
-    parser.add_argument("--mm_spatial_pool_stride", type=int, default=4)
-    parser.add_argument("--mm_spatial_pool_out_channels", type=int, default=1024)
-    parser.add_argument("--mm_spatial_pool_mode", type=str, default="average")
-    parser.add_argument("--image_aspect_ratio", type=str, default="anyres")
-    parser.add_argument("--image_grid_pinpoints", type=str, default="[(224, 448), (224, 672), (224, 896), (448, 448), (448, 224), (672, 224), (896, 224)]")
-    parser.add_argument("--mm_patch_merge_type", type=str, default="spatial_unpad")
-    parser.add_argument("--overwrite", type=lambda x: (str(x).lower() == 'true'), default=True)
-    parser.add_argument("--for_get_frames_num", type=int, default=4)
-    parser.add_argument("--load_8bit",  type=lambda x: (str(x).lower() == 'true'), default=False)
-    parser.add_argument("--prompt", type=str, default=None) 
-    parser.add_argument("--api_key", type=str, help="OpenAI API key")
-    parser.add_argument("--mm_newline_position", type=str, default="no_token")
-    parser.add_argument("--force_sample", type=lambda x: (str(x).lower() == 'true'), default=False)
-    parser.add_argument("--add_time_instruction", type=str, default=False)
-    return parser.parse_args()
-
-def load_video(video_path,args):
-    if args.for_get_frames_num == 0:
-        return np.zeros((1, 336, 336, 3))
-    vr = VideoReader(video_path, ctx=cpu(0),num_threads=1)
-    total_frame_num = len(vr)
-    video_time = total_frame_num / vr.get_avg_fps()
-    fps = round(vr.get_avg_fps())
-    frame_idx = [i for i in range(0, len(vr), fps)]
-    frame_time = [i/fps for i in frame_idx]
-    if len(frame_idx) > args.for_get_frames_num or args.force_sample:
-        sample_fps = args.for_get_frames_num
-        uniform_sampled_frames = np.linspace(0, total_frame_num - 1, sample_fps, dtype=int)
-        frame_idx = uniform_sampled_frames.tolist()
-        frame_time = [i/vr.get_avg_fps() for i in frame_idx]
-    frame_time = ",".join([f"{i:.2f}s" for i in frame_time])
-    spare_frames = vr.get_batch(frame_idx).asnumpy()
-    # import pdb;pdb.set_trace()
-
-    return spare_frames,frame_time,video_time
-
-
-
-
-def load_video_base64(path):
-    video = cv2.VideoCapture(path)
-
-    base64Frames = []
-    while video.isOpened():
-        success, frame = video.read()
-        if not success:
-            break
-        _, buffer = cv2.imencode(".jpg", frame)
-        base64Frames.append(base64.b64encode(buffer).decode("utf-8"))
-
-    video.release()
-    # print(len(base64Frames), "frames read.")
-    return base64Frames
-
-
-def run_inference(args):
-    """
-    Run inference on ActivityNet QA DataSet using the Video-ChatGPT model.
-
-    Args:
-        args: Command-line arguments.
-    """
-    # Initialize the model
-    if "gpt4v" != args.model_path:
-        model_name = get_model_name_from_path(args.model_path)
-        # Set model configuration parameters if they exist
-        if args.overwrite == True:
-            overwrite_config = {}
-            overwrite_config["mm_spatial_pool_mode"] = args.mm_spatial_pool_mode
-            overwrite_config["mm_spatial_pool_stride"] = args.mm_spatial_pool_stride
-            overwrite_config["mm_newline_position"] = args.mm_newline_position
-
-            cfg_pretrained = AutoConfig.from_pretrained(args.model_path)
-
-            # import pdb;pdb.set_trace()
-            if "qwen" not in args.model_path.lower():
-                if "224" in cfg_pretrained.mm_vision_tower:
-                    # suppose the length of text tokens is around 1000, from bo's report
-                    least_token_number = args.for_get_frames_num*(16//args.mm_spatial_pool_stride)**2 + 1000
-                else:
-                    least_token_number = args.for_get_frames_num*(24//args.mm_spatial_pool_stride)**2 + 1000
-
-                scaling_factor = math.ceil(least_token_number/4096)
-                if scaling_factor >= 2:
-                    if "vicuna" in cfg_pretrained._name_or_path.lower():
-                        print(float(scaling_factor))
-                        overwrite_config["rope_scaling"] = {"factor": float(scaling_factor), "type": "linear"}
-                    overwrite_config["max_sequence_length"] = 4096 * scaling_factor
-                    overwrite_config["tokenizer_model_max_length"] = 4096 * scaling_factor
-
-            tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name, load_8bit=args.load_8bit, overwrite_config=overwrite_config)
-        else:
-            tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name)
-    else:
-        pass
-
-    # import pdb;pdb.set_trace()
-    if getattr(model.config, "force_sample", None) is not None:
-        args.force_sample = model.config.force_sample
-    else:
-        args.force_sample = False
-
-    # import pdb;pdb.set_trace()
-
-    if getattr(model.config, "add_time_instruction", None) is not None:
-        args.add_time_instruction = model.config.add_time_instruction
-    else:
-        args.add_time_instruction = False
-
-    # Create the output directory if it doesn't exist
-    if not os.path.exists(args.output_dir):
-        os.makedirs(args.output_dir)
-
-    output_name = args.output_name
-    answers_file = os.path.join(args.output_dir, f"{output_name}.json")
-    ans_file = open(answers_file, "w")
-
-    video_path = args.video_path
-
-    all_video_pathes = []
-
-    # Check if the video_path is a directory or a file
-    if os.path.isdir(video_path):
-        # If it's a directory, loop over all files in the directory
-        for filename in os.listdir(video_path):
-                    # Load the video file
-            cur_video_path = os.path.join(video_path, f"{filename}")
-            all_video_pathes.append(os.path.join(video_path, cur_video_path))
-    else:
-        # If it's a file, just process the video
-        all_video_pathes.append(video_path) 
-
-    # import pdb;pdb.set_trace()
-    for video_path in all_video_pathes:
-
-        sample_set = {}
-        question = args.prompt
-        sample_set["Q"] = question
-        sample_set["video_name"] = video_path
-        
-
-        # Check if the video exists
-        if os.path.exists(video_path):
-            if "gpt4v" != args.model_path:
-                video,frame_time,video_time = load_video(video_path, args)
-                video = image_processor.preprocess(video, return_tensors="pt")["pixel_values"].half().cuda()
-                video = [video]
-            else:
-                spare_frames,frame_time,video_time = load_video_base64(video_path)
-                interval = int(len(video) / args.for_get_frames_num)
-
-        # try:
-        # Run inference on the video and add the output to the list
-        if "gpt4v" != args.model_path:
-            qs = question
-            if args.add_time_instruction:
-                time_instruciton = f"The video lasts for {video_time:.2f} seconds, and {len(video[0])} frames are uniformly sampled from it. These frames are located at {frame_time}.Please answer the following questions related to this video."
-                qs = f'{time_instruciton}\n{qs}'
-            if model.config.mm_use_im_start_end:
-                qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + "\n" + qs
-            else:
-                qs = DEFAULT_IMAGE_TOKEN + "\n" + qs
-
-            conv = conv_templates[args.conv_mode].copy()
-            conv.append_message(conv.roles[0], qs)
-            conv.append_message(conv.roles[1], None)
-            prompt = conv.get_prompt()
-
-            input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).cuda()
-            if tokenizer.pad_token_id is None:
-                if "qwen" in tokenizer.name_or_path.lower():
-                    print("Setting pad token to bos token for qwen model.")
-                    tokenizer.pad_token_id = 151643
-                    
-            attention_masks = input_ids.ne(tokenizer.pad_token_id).long().cuda()
-
-            stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
-            keywords = [stop_str]
-            stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
-
-            cur_prompt = question
-        else:
-            prompt = question
-
-        system_error = ""
-
-        if "gpt4v" != args.model_path:
-
-
-            with torch.inference_mode():
-                # model.update_prompt([[cur_prompt]])
-                # import pdb;pdb.set_trace()
-                # output_ids = model.generate(inputs=input_ids, images=video, attention_mask=attention_masks, modalities="video", do_sample=True, temperature=0.2, max_new_tokens=1024, use_cache=True, stopping_criteria=[stopping_criteria])
-                if "mistral" not in cfg_pretrained._name_or_path.lower():
-                    output_ids = model.generate(inputs=input_ids, images=video, attention_mask=attention_masks, modalities="video", do_sample=False, temperature=0.0, max_new_tokens=1024, top_p=0.1,num_beams=1,use_cache=True, stopping_criteria=[stopping_criteria])
-                    # output_ids = model.generate(inputs=input_ids, images=video, attention_mask=attention_masks, modalities="video", do_sample=True, temperature=0.2, max_new_tokens=1024, use_cache=True, stopping_criteria=[stopping_criteria])
-                else:
-                    output_ids = model.generate(inputs=input_ids, images=video, attention_mask=attention_masks, modalities="video", do_sample=False, temperature=0.0, max_new_tokens=1024, top_p=0.1, num_beams=1, use_cache=True)
-                    # output_ids = model.generate(inputs=input_ids, images=video, attention_mask=attention_masks, modalities="video", do_sample=True, temperature=0.2, max_new_tokens=1024, use_cache=True)
-        else:
-            openai.api_key = args.api_key  # Your API key here
-
-            max_num_retries = 0
-            retry = 5
-            PROMPT_MESSAGES = [
-                {
-                    "role": "user",
-                    "content": [
-                        f"These are frames from a video that I want to upload. Answer me one question of this video: {prompt}",
-                        *map(lambda x: {"image": x, "resize": 336}, video[0::interval]),
-                    ],
-                },
-            ]
-            params = {
-                "model": "gpt-4-vision-preview", #gpt-4-1106-vision-preview
-                "messages": PROMPT_MESSAGES,
-                "max_tokens": 1024,
-            }
-            sucess_flag=False
-            while max_num_retries < retry:
-                try:
-                    result = openai.ChatCompletion.create(**params)
-                    outputs = result.choices[0].message.content
-                    sucess_flag = True
-                    break
-                except Exception as inst :
-                    if 'error' in dir(inst):
-                        # import pdb;pdb.set_trace()
-                        if  inst.error.code == 'rate_limit_exceeded':
-                            if "TPM" in inst.error.message:
-                                time.sleep(30)
-                                continue
-                            else:
-                                import pdb;pdb.set_trace()
-                        elif inst.error.code == 'insufficient_quota':
-                            print(f'insufficient_quota key')
-                            exit()
-                        elif inst.error.code == 'content_policy_violation':
-                            print(f'content_policy_violation')
-                            system_error = "content_policy_violation"
-
-                            break
-                        print('Find error message in response: ',str(inst.error.message), 'error code: ', str(inst.error.code))
-
-                    continue
-            if not sucess_flag:
-                print(f'Calling OpenAI failed after retrying for {max_num_retries} times. Check the logs for details.')
-                exit()
-
-        if "gpt4v" != args.model_path:
-            outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip()
-        else:
-            print(len(video[0::interval]))
-        
-        print(f"Question: {prompt}\n")
-        print(f"Response: {outputs}\n")
-
-        if "gpt4v" == args.model_path:
-            if system_error == 'content_policy_violation':
-                continue
-            elif system_error == "":
-                continue
-            else:
-                import pdb;pdb.set_trace()
-
-        # import pdb;pdb.set_trace()
-        if "mistral" not in cfg_pretrained._name_or_path.lower():
-            if outputs.endswith(stop_str):
-                outputs = outputs[: -len(stop_str)]
-
-        outputs = outputs.strip()
-
-        sample_set["pred"] = outputs
-        ans_file.write(json.dumps(sample_set, ensure_ascii=False) + "\n")
-        ans_file.flush()
-
-    ans_file.close()
-
-
-if __name__ == "__main__":
-    args = parse_args()
-    run_inference(args)

playground/equal_splitter.py

@@ -1,38 +0,0 @@
-import json
-from math import ceil
-
-
-def split_json_file(input_file, n_splits):
-    # Read the JSON file
-    with open(input_file, "r") as file:
-        data = json.load(file)
-
-    # Calculate the size of each split
-    total_items = len(data)
-    items_per_split = ceil(total_items / n_splits)
-
-    # Split the data and save into separate files
-    for i in range(n_splits):
-        start_index = i * items_per_split
-        end_index = min((i + 1) * items_per_split, total_items)
-        split_data = data[start_index:end_index]
-
-        # Write the split data to a new JSON file
-        with open(f"{input_file.split('.')[0]}_split_{i}.json", "w") as split_file:
-            json.dump(split_data, split_file, indent=4)
-
-
-def main():
-    import argparse
-
-    parser = argparse.ArgumentParser(description="Split a JSON file into multiple parts.")
-    parser.add_argument("--input_file", type=str, help="The JSON file to split")
-    parser.add_argument("--n_splits", type=int, help="The number of splits")
-
-    args = parser.parse_args()
-
-    split_json_file(args.input_file, args.n_splits)
-
-
-if __name__ == "__main__":
-    main()

playground/remove_mid_ckpt.py

@@ -1,35 +0,0 @@
-import os
-import shutil
-import glob
-
-
-def remove_checkpoints(directory, pattern):
-    # Walk through the directory
-    for root, dirs, files in os.walk(directory):
-        # Use glob to find paths matching the pattern
-        for file_path in glob.glob(os.path.join(root, pattern)):
-            # Check if it is a directory
-            if "llava-1.6-mistral-7b" in file_path:
-                continue
-            if os.path.isdir(file_path):
-                # Remove the directory
-                print(f"Removing {file_path}")
-                input("Press Enter to continue...")
-                shutil.rmtree(file_path)
-                print(f"Removed directory: {file_path}")
-            else:
-                print(f"Removing {file_path}")
-                input("Press Enter to continue...")
-                # Remove the file
-                os.remove(file_path)
-                print(f"Removed file: {file_path}")
-
-
-# Directory containing the checkpoints
-directory = "/mnt/bn/vl-research/checkpoints/feng/"
-
-# Pattern to match in the file names
-pattern = "global_step*"
-
-# Call the function
-remove_checkpoints(directory, pattern)

playground/sgl_llava_inference_multinode.py

@@ -1,125 +0,0 @@
-import argparse
-import json
-import time
-import os
-import tqdm
-import sglang as sgl
-from sglang.test.test_utils import select_sglang_backend
-from sglang.utils import dump_state_text
-
-
-@sgl.function
-def image_description(s, image_file):
-    prompt = "Please generate detailed descriptions of the given image."
-    s += sgl.user(sgl.image(image_file) + prompt)
-    s += sgl.assistant(sgl.gen("answer", max_tokens=1024, temperature=0.0))
-
-
-def load_progress(progress_file):
-    print(f"Load progress from {progress_file}")
-    if os.path.exists(progress_file):
-        with open(progress_file, "r") as f:
-            return json.load(f)
-    return {"last_index": -1, "last_chunk": -1, "results": [], "annotations": []}
-
-
-def save_progress(progress_file, progress_data):
-    with open(progress_file, "w") as f:
-        json.dump(progress_data, f, indent=2)
-
-
-def find_images_in_subfolders(folder_path):
-    image_extensions = (".png", ".jpg", ".jpeg", ".gif", ".bmp")
-    image_files = []
-    for root, dirs, files in os.walk(folder_path):
-        for file in files:
-            if file.endswith(image_extensions):
-                image_files.append(os.path.join(root, file))
-    return image_files
-
-
-def main(args):
-    dist_rank = args.dist
-    dist_size = args.total_dist
-
-    base_dir = os.path.dirname(args.result_file)
-    os.makedirs(base_dir, exist_ok=True)  # Ensure the base directory exists
-    progress_file = f"{base_dir}/progress_{dist_rank}_or_{dist_size}.json"
-    progress_data = load_progress(progress_file)
-
-    with open(args.json_path, "r") as fp:
-        data = json.load(fp)
-
-    image_files = [os.path.join(args.images_root, item["image"]) for item in data]
-    image_files = image_files[: args.limit] if args.limit > 0 else image_files
-
-    # Shard the data
-    shard_size = len(image_files) // dist_size
-    start_index = shard_size * dist_rank
-    end_index = start_index + shard_size if dist_rank < dist_size - 1 else len(image_files)
-    shard_files = image_files[start_index:end_index]
-
-    print(f"Querying {len(shard_files)} images from index {start_index} to {end_index - 1}")
-
-    # Select backend
-    backend = select_sglang_backend(args)
-    sgl.set_default_backend(backend)
-
-    tic = time.time()
-    batch_size = args.parallel
-    for batch_start in tqdm.tqdm(range(0, len(shard_files), batch_size)):
-        batch_end = min(batch_start + batch_size, len(shard_files))
-        if batch_start <= progress_data.get("last_index", -1):
-            print(f"Skipping already processed batch starting at {batch_start}")
-            continue
-        batch_arguments = [{"image_file": image_file} for image_file in shard_files[batch_start:batch_end]]
-        try:
-            batch_states = image_description.run_batch(batch_arguments, temperature=0, num_threads=args.parallel, progress_bar=False)
-            for i, ret in enumerate(batch_states):
-                image_file = batch_arguments[i]["image_file"]
-                caption = ret.text().split("ASSISTANT:")[-1].strip()
-                progress_data["annotations"].append({"image_file": image_file, "caption": caption})
-                progress_data["last_index"] = batch_start + i  # Update last_index relative to this rank's shard
-
-            save_progress(progress_file, progress_data)
-        except Exception as e:
-            print(f"Error during batch processing: {e}")
-            save_progress(progress_file, progress_data)
-            break
-
-    latency = time.time() - tic
-    print(f"Latency: {latency:.3f}")
-
-    value = {
-        "task": "image_captioning",
-        "backend": args.backend,
-        "num_gpus": 1,
-        "latency": round(latency, 3),
-        "num_requests": len(shard_files),
-        "parallel": args.parallel,
-        "results": progress_data["annotations"],
-    }
-
-    result_file = args.result_file.replace(".json", f"_shard_{dist_rank}_or_{dist_size}.json")
-    print(f"Write output to {result_file}")
-    with open(result_file, "w") as fout:
-        json.dump(value, fout, indent=2)
-
-    save_progress(progress_file, progress_data)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--images_root", type=str, default="/mnt/bn/vl-research/data/llava_data/cc3m")
-    parser.add_argument("--json_path", type=str, default="/mnt/bn/vl-research/data/llava_instruct/cc3m_recap_requery_363707.json")
-    parser.add_argument("--max_tokens", type=int, default=1024)
-    parser.add_argument("--parallel", type=int, default=32)
-    parser.add_argument("--backend", type=str, default="srt")
-    parser.add_argument("--host", type=str, default="http://127.0.0.1")
-    parser.add_argument("--port", type=int, default=30000)
-    parser.add_argument("--result_file", type=str, default="/mnt/bn/vl-research/workspace/boli01/projects/LLaVA_Next/playground/sgl_llava_inference.json")
-    parser.add_argument("--limit", type=int, default=-1)
-    parser.add_argument("--dist", type=int, default=0, help="The rank of the distributed machine")
-    parser.add_argument("--total_dist", type=int, default=6, help="Total number of distributed machines")
-    args = parser.parse_args()
-    main(args)

playground/upload_data.py

@@ -1,217 +0,0 @@
-from datasets import Dataset, Features, Value, ClassLabel, Sequence, Image
-import json
-import PIL.Image as pil_image
-from io import BytesIO
-from tqdm import tqdm
-
-json_paths = [
-    # "/mnt/bn/vl-research/data/llava_instruct/real_vision_flan/mavis_math_metagen_87358.json",
-    # "/mnt/bn/vl-research/data/llava_instruct/real_vision_flan/mavis_math_rule_geo_100000.json",
-    # "/mnt/bn/vl-research/data/llava_instruct/real_vision_flan/k12_printing_train_256646.json",
-    # "/mnt/bn/vl-research/data/llava_instruct/real_vision_flan/iiit5k_annotations_2000.json",
-    # "/mnt/bn/vl-research/data/llava_instruct/real_vision_flan/hme100k_train_clean_74502.json",
-    # "/mnt/bn/vl-research/data/llava_instruct/real_vision_flan/ai2d_azuregpt_detailed_understanding_4874.json",
-    # "/mnt/bn/vl-research/data/llava_instruct/real_vision_flan/infographic_vqa_4404.json",
-    # "/mnt/bn/vl-research/data/llava_instruct/real_vision_flan/infographic_azuregpt4v_1992.json",
-    # "/mnt/bn/vl-research/data/llava_instruct/real_vision_flan/lrv_chart_1787.json",
-    # "/mnt/bn/vl-research/data/llava_instruct/real_vision_flan/lrv_normal_gpt4v_filtered_10500.json",
-    # "/mnt/bn/vl-research/data/llava_instruct/real_vision_flan/scienceqa_nona_context_19218.json",
-    # "/mnt/bn/vl-research/data/llava_instruct/real_vision_flan/allava_instruct_vflan4v_20000.json",
-    # "/mnt/bn/vl-research/data/llava_instruct/real_vision_flan/allava_instruct_laion4v_50000.json",
-    # "/mnt/bn/vl-research/data/llava_instruct/real_vision_flan/textocr_gpt4v_train_converted_25114.json",
-    # "/mnt/bn/vl-research/data/llava_instruct/real_vision_flan/ai2d_train_internvl_single_12413.json",
-    # "/mnt/bn/vl-research/data/llava_instruct/real_vision_flan/textcaps_train_21952.json",
-    # "/mnt/bn/vl-research/data/llava_instruct/ureader_new/ureader_qa_sft.json",
-    # "/mnt/bn/vl-research/data/llava_instruct/ureader_new/ureader_cap_sft.json",
-    # "/mnt/bn/vl-research/data/llava_instruct/ureader_new/ureader_ie_sft.json",
-    # "/mnt/bn/vl-research/data/llava_instruct/ureader_new/ureader_kg_sft.json",
-    # "/mnt/bn/vl-research/data/llava_instruct/real_vision_flan/vision_flan_filtered_186070.json",
-    # "/mnt/bn/vl-research/data/llava_instruct/real_vision_flan/mathqa_29837.json",
-    # "/mnt/bn/vl-research/data/llava_instruct/real_vision_flan/geo3k_2101.json",
-    # "/mnt/bn/vl-research/data/llava_instruct/real_vision_flan/geo170k_qa_converted_67833.json",
-    # "/mnt/bn/vl-research/data/llava_instruct/real_vision_flan/geo170k_align_converted_60252.json",
-    # "/mnt/bn/vl-research/data/llava_instruct/real_vision_flan/sharegpt4v-coco-50k.json",
-    # "/mnt/bn/vl-research/data/llava_instruct/real_vision_flan/sharegpt4v-knowledge-2k.json",
-    # "/mnt/bn/vl-research/data/llava_instruct/real_vision_flan/sharegpt4v-llava-30k.json",
-    # "/mnt/bn/vl-research/data/llava_instruct/real_vision_flan/sharegpt4v-sam-20k.json",
-    # "/mnt/bn/vl-research/data/llava_instruct/real_vision_flan/MathV360K_CLEVR-Math_5290.json",
-    # "/mnt/bn/vl-research/data/llava_instruct/real_vision_flan/MathV360K_FigureQA_17597.json",
-    # "/mnt/bn/vl-research/data/llava_instruct/real_vision_flan/MathV360K_Geometry3K_9734.json",
-    # "/mnt/bn/vl-research/data/llava_instruct/real_vision_flan/MathV360K_GeoQA+_17172.json",
-    # "/mnt/bn/vl-research/data/llava_instruct/real_vision_flan/MathV360K_GEOS_508.json",
-    # "/mnt/bn/vl-research/data/llava_instruct/real_vision_flan/MathV360K_IconQA_22599.json",
-    # "/mnt/bn/vl-research/data/llava_instruct/real_vision_flan/MathV360K_MapQA_5235.json",
-    # "/mnt/bn/vl-research/data/llava_instruct/real_vision_flan/MathV360K_PMC-VQA_35958.json",
-    # "/mnt/bn/vl-research/data/llava_instruct/real_vision_flan/MathV360K_Super-CLEVR_8652.json",
-    # "/mnt/bn/vl-research/data/llava_instruct/real_vision_flan/MathV360K_TabMWP_22462.json",
-    # "/mnt/bn/vl-research/data/llava_instruct/real_vision_flan/MathV360K_UniGeo_11959.json",
-    # "/mnt/bn/vl-research/data/llava_instruct/real_vision_flan/MathV360K_VizWiz_6614.json",
-    # "/mnt/bn/vl-research/data/llava_instruct/real_vision_flan/magpie_pro_qwen2_72b_st_300000_sp_token_fltd_299992.json",
-    # "/mnt/bn/vl-research/data/llava_instruct/real_vision_flan/magpie_pro_l3_80b_st_300000.json",
-    # "/mnt/bn/vl-research/data/llava_instruct/real_vision_flan/magpie_pro_l3_80b_mt_300000_sp_token_fltd_299998.json",
-    # "/mnt/bn/vl-research/data/llava_instruct/real_vision_flan/image_textualization_dataset_filtered.json",
-    # "/mnt/bn/vl-research/data/llava_instruct/real_vision_flan/cambrian_filtered_gpt4vo_sp_token_fltd_max10k.json",
-    "/mnt/bn/vl-research/data/llava_instruct/real_vision_flan/sharegpt4o_dataset.jsonl",
-    "/mnt/bn/vl-research/data/llava_instruct/cauldron/ai2d_llava_format_2434.json",
-    "/mnt/bn/vl-research/data/llava_instruct/cauldron/aokvqa_16539_llava_format.json",
-    "/mnt/bn/vl-research/data/llava_instruct/cauldron/chart2text_26961.json",
-    "/mnt/bn/vl-research/data/llava_instruct/cauldron/chartqa_18265_llava_format.json",
-    "/mnt/bn/vl-research/data/llava_instruct/cauldron/clevr_70000_llava_format.json",
-    "/mnt/bn/vl-research/data/llava_instruct/cauldron/diagram_image_to_text_300.json",
-    "/mnt/bn/vl-research/data/llava_instruct/cauldron/dvqa_200000_llava_format.json",
-    "/mnt/bn/vl-research/data/llava_instruct/cauldron/figureqa_100000_llava_format.json",
-    "/mnt/bn/vl-research/data/llava_instruct/cauldron/geomverse_9303.json",
-    "/mnt/bn/vl-research/data/llava_instruct/cauldron/hateful_memes_8500_llava_format.json",
-    "/mnt/bn/vl-research/data/llava_instruct/cauldron/hitab_2500_llava_format.json",
-    "/mnt/bn/vl-research/data/llava_instruct/cauldron/iam_5663.json",
-    "/mnt/bn/vl-research/data/llava_instruct/cauldron/raven_42000.json",
-    "/mnt/bn/vl-research/data/llava_instruct/cauldron/iconqa_llava_format_27307.json",
-    "/mnt/bn/vl-research/data/llava_instruct/cauldron/infographic_vqa_2118_llava_format.json",
-    "/mnt/bn/vl-research/data/llava_instruct/cauldron/intergps_1280_llava_format.json",
-    "/mnt/bn/vl-research/data/llava_instruct/cauldron/mapqa_37417_llava_format.json",
-    "/mnt/bn/vl-research/data/llava_instruct/cauldron/multihiertt_7619.json",
-    "/mnt/bn/vl-research/data/llava_instruct/cauldron/rendered_text_10000.json",
-    "/mnt/bn/vl-research/data/llava_instruct/cauldron/robut_sqa_8514.json",
-    "/mnt/bn/vl-research/data/llava_instruct/cauldron/robut_wikisql_74989.json",
-    "/mnt/bn/vl-research/data/llava_instruct/cauldron/robut_wtq_38246_llava_format.json",
-    "/mnt/bn/vl-research/data/llava_instruct/cauldron/screen2words_15730.json",
-    "/mnt/bn/vl-research/data/llava_instruct/cauldron/scienceqa_llava_format_4976.json",
-    "/mnt/bn/vl-research/data/llava_instruct/cauldron/tabmwp_22722.json",
-    "/mnt/bn/vl-research/data/llava_instruct/cauldron/tallyqa_98680_llava_format.json",
-    "/mnt/bn/vl-research/data/llava_instruct/cauldron/st_vqa_17247_llava_format.json",
-    "/mnt/bn/vl-research/data/llava_instruct/cauldron/tqa_llava_format_27307.json",
-    "/mnt/bn/vl-research/data/llava_instruct/cauldron/visual7w_llava_format_14366.json",
-    "/mnt/bn/vl-research/data/llava_instruct/cauldron/visualmrc_3027.json",
-    "/mnt/bn/vl-research/data/llava_instruct/cauldron/vqarad_313_llava_format.json",
-    "/mnt/bn/vl-research/data/llava_instruct/cauldron/vsr_2157_llava_format.json",
-    "/mnt/bn/vl-research/data/llava_instruct/cauldron/vistext_9969.json",
-    "/mnt/bn/vl-research/data/llava_instruct/cauldron/websight_10000.json"
-]
-
-short_names = [
-    # "mavis_math_metagen",
-    # "mavis_math_rule_geo",
-    # "k12_printing",
-    # "iiit5k",
-    # "hme100k",
-    # "ai2d(gpt4v)",
-    # "infographic_vqa",
-    # "infographic(gpt4v)",
-    # "lrv_chart",
-    # "lrv_normal(filtered)",
-    # "scienceqa(nona_context)",
-    # "allava_instruct_vflan4v",
-    # "allava_instruct_laion4v",
-    # "textocr(gpt4v)",
-    # "ai2d(internvl)",
-    # "textcaps",
-    # "ureader_qa", # need to re-upload
-    # "ureader_cap", # need to re-upload
-    # "ureader_ie", # need to re-upload
-    # "ureader_kg", # need to re-upload
-    # "vision_flan(filtered)",
-    # "mathqa",
-    # "geo3k",
-    # "geo170k(qa)",
-    # "geo170k(align)",
-    # "sharegpt4v(coco)",
-    # "sharegpt4v(knowledge)",
-    # "sharegpt4v(llava)",
-    # "sharegpt4v(sam)",
-    # "CLEVR-Math(MathV360K)",
-    # "FigureQA(MathV360K)",
-    # "Geometry3K(MathV360K)",
-    # "GeoQA+(MathV360K)",
-    # "GEOS(MathV360K)",
-    # "IconQA(MathV360K)",
-    # "MapQA(MathV360K)",
-    # "PMC-VQA(MathV360K)",
-    # "Super-CLEVR(MathV360K)",
-    # "TabMWP(MathV360K)",
-    # "UniGeo(MathV360K)",
-    # "VizWiz(MathV360K)",
-    # "magpie_pro(qwen2_72b_st)",
-    # "magpie_pro(l3_80b_st)",
-    # "magpie_pro(l3_80b_mt)",
-    # "image_textualization(filtered)",
-    # "cambrian(filtered_gpt4vo)", # need to re-upload
-    "sharegpt4o",
-    "ai2d(cauldron,llava_format)",
-    "aokvqa(cauldron,llava_format)",
-    "chart2text(cauldron)",
-    "chartqa(cauldron,llava_format)",
-    "clevr(cauldron,llava_format)",
-    "diagram_image_to_text(cauldron)",
-    "dvqa(cauldron,llava_format)",
-    "figureqa(cauldron,llava_format)",
-    "geomverse(cauldron)",
-    "hateful_memes(cauldron,llava_format)",
-    "hitab(cauldron,llava_format)",
-    "iam(cauldron)",
-    "raven(cauldron)",
-    "iconqa(cauldron,llava_format)",
-    "infographic_vqa_llava_format",
-    "intergps(cauldron,llava_format)",
-    "mapqa(cauldron,llava_format)",
-    "multihiertt(cauldron)",
-    "rendered_text(cauldron)",
-    "robut_sqa(cauldron)",
-    "robut_wikisql(cauldron)",
-    "robut_wtq(cauldron,llava_format)",
-    "screen2words(cauldron)",
-    "scienceqa(cauldron,llava_format)",
-    "tabmwp(cauldron)",
-    "tallyqa(cauldron,llava_format)",
-    "st_vqa(cauldron,llava_format)",
-    "tqa(cauldron,llava_format)",
-    "visual7w(cauldron,llava_format)",
-    "visualmrc(cauldron)",
-    "vqarad(cauldron,llava_format)",
-    "vsr(cauldron,llava_format)",
-    "vistext(cauldron)",
-    "websight(cauldron)"
-]
-
-def upload_data(json_path, short_name):
-    def gen():
-        if json_path.endswith(".jsonl"):
-            with open(json_path, "r") as f:
-                data = [json.loads(line) for line in f]
-        else:
-            with open(json_path, "r") as f:
-                data = json.load(f)
-
-        preview_index = 5
-        idx = 0
-        for item in tqdm(data):
-            if preview_index > 0:
-                preview_index -= 1
-                print(item)
-                continue
-
-            try:
-                if "image" in item:
-                    image_path = f"/mnt/bn/vl-research/data/llava_data/{item['image']}"
-                    try:
-                        with open(image_path, "rb") as img_file:
-                            image = pil_image.open(BytesIO(img_file.read()))
-                    except:
-                        print(f"Failed to load image {item['image']}")
-                        continue
-                else:
-                    image = None
-
-                item_id = item["id"] if "id" in item else f"{idx:06d}"
-                yield {"id": item_id, "image": image, "conversations": item["conversations"], "data_source": short_name}
-                idx += 1
-                
-            except Exception as e:
-                print(e)
-                continue
-
-
-    hf_dataset = Dataset.from_generator(generator=gen, num_proc=32)
-    hf_dataset.push_to_hub("lmms-lab/LLaVA-OneVision-Data", config_name=short_name, split="train")
-
-for json_path, short_name in zip(json_paths, short_names):
-    upload_data(json_path, short_name)

scripts/train/finetune_ov_vla.sh

@@ -3,22 +3,11 @@ export NCCL_IB_DISABLE=0
 export NCCL_IB_GID_INDEX=3
 export NCCL_SOCKET_IFNAME=eth0
 export NCCL_DEBUG=INFO
-VISION_MODEL_VERSION="google/siglip-so400m-patch14-384"
-VISION_MODEL_VERSION_CLEAN="${VISION_MODEL_VERSION//\//_}"
-
-############### Pretrain ################
-
-BASE_RUN_NAME="llavanext-google_siglip-so400m-patch14-384-Qwen_Qwen2-7B-Instruct-mlp2x_gelu-pretrain_blip558k_plain"
-echo "BASE_RUN_NAME: ${BASE_RUN_NAME}"
-
 ############### Finetune ################
 MIXTURE="qwen"
-
-# Stage 2
 PROMPT_VERSION="qwen_vla"
-RUN_NAME="llava-openvla-${MIXTURE}" 
-PREV_STAGE_CHECKPOINT="/mnt/d/temp/huggingface/hub/llava_qwen_vla" 
-# PREV_STAGE_CHECKPOINT="/mnt/d/ckpt/llava-openvla-qwen"
+RUN_NAME="openvla-${MIXTURE}" 
+PREV_STAGE_CHECKPOINT="models/llava-onevision-qwen2-0.5b-ov" 
 echo "PREV_STAGE_CHECKPOINT: ${PREV_STAGE_CHECKPOINT}"
 echo "MID_RUN_NAME: ${RUN_NAME}"
 
@@ -30,8 +19,8 @@ ACCELERATE_CPU_AFFINITY=1 torchrun --nproc_per_node="${NUM_GPUS}" --nnodes="${NN
     --deepspeed scripts/zero2.json \
     --model_name_or_path $PREV_STAGE_CHECKPOINT \
     --version $PROMPT_VERSION \
-    --data_path /root/code/openvla-llava/LLaVA-Next/scripts/train/vla.yaml \
-    --image_folder /root/code/openvla-llava/LLaVA-Next/images \
+    --data_path scripts/train/vla.yaml \
+    --image_folder /images \
     --mm_tunable_parts="mm_language_model" \
     --mm_vision_tower_lr=2e-6 \
     --vision_tower google/siglip-so400m-patch14-384 \
@@ -45,7 +34,7 @@ ACCELERATE_CPU_AFFINITY=1 torchrun --nproc_per_node="${NUM_GPUS}" --nnodes="${NN
     --mm_patch_merge_type spatial_unpad \
     --bf16 True \
     --run_name $RUN_NAME \
-    --output_dir /mnt/d/ckpt_3_28/$RUN_NAME \
+    --output_dir /$RUN_NAME \
     --num_train_epochs 5 \
     --per_device_train_batch_size 8 \
     --per_device_eval_batch_size 100 \
@@ -70,5 +59,3 @@ ACCELERATE_CPU_AFFINITY=1 torchrun --nproc_per_node="${NUM_GPUS}" --nnodes="${NN
     --dataloader_drop_last True \
     --frames_upbound 1
 exit 0;
-
-# You can delete the sdpa attn_implementation if you want to use flash attn

scripts/train/vla.yaml

@@ -1,3 +1,3 @@
 datasets:
-  - json_path: /root/code/openvla-llava/LLaVA-Next/vla_llava.json
+  - json_path: /root/data/vla_llava.json
     sampling_strategy: "all"