| import json |
| import os |
| from PIL import Image |
| from tqdm import tqdm |
| import matplotlib.pyplot as plt |
| import numpy as np |
|
|
|
|
| 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 |
|
|
|
|
| from multiprocessing import Pool |
| import functools |
|
|
|
|
| def calculate_image_dimension(item, images_folder): |
| image_path = os.path.join(images_folder, item["image"]) |
| try: |
| with Image.open(image_path) as img: |
| width, height = img.size |
| return width, height |
| except Exception as e: |
| print(f"Error opening {image_path}: {e}") |
| return None, None |
|
|
|
|
| def calculate_image_dimensions_multiprocess(filtered_data, images_folder, num_processes=256): |
| with Pool(num_processes) as p: |
| dimensions = list(tqdm(p.imap(functools.partial(calculate_image_dimension, images_folder=images_folder), filtered_data), total=len(filtered_data), 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 |
|
|
|
|
| import argparse |
|
|
|
|
| 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.") |
| 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 = args.json_path.split("/")[-1].replace(".json", "") |
| json_path = args.json_path |
| llava_instruct_name = os.path.basename(json_path).replace(".json", "") |
| images_folder = args.images_folder |
|
|
| data = load_data(json_path) |
| filtered_data = filter_data(data) |
|
|
| if len(filtered_data) != 0: |
| 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(widths) |
| max_height = max(heights) |
| print(f"Max width: {max_width}, Max height: {max_height}") |
|
|
| tokenized_lengths = calculate_tokenized_lengths(data) |
|
|
| fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 12)) |
|
|
| if len(filtered_data) != 0: |
| |
| if min(widths) == max(widths): |
| widths_bins = [min(widths), max(widths) + 1] |
| else: |
| widths_bins = np.arange(min(widths), max(widths) + 100, 100) |
|
|
| if min(heights) == max(heights): |
| heights_bins = [min(heights), max(heights) + 1] |
| else: |
| heights_bins = 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) |
|
|
| |
| hist, bin_edges = np.histogram(tokenized_lengths, bins=np.arange(0, max(tokenized_lengths) + 10, 100)) |
| bins = np.arange(0, max(tokenized_lengths) + 10, 100) |
| ax2.bar(bin_edges[:-1], hist, width=7, edgecolor="black", log=True) |
|
|
| |
| n = 8 |
| 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"/mnt/bn/vl-research/workspace/boli01/projects/LLaVA_Next/notebooks/sft_data/dist_{llava_instruct_name}_combined.png") |
| print(f"Plots saved to /mnt/bn/vl-research/workspace/boli01/projects/LLaVA_Next/notebooks/sft_data/dist_{llava_instruct_name}_combined.png") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
