mirror of https://github.com/inclusionAI/AReaL
0731_4
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@ -4,26 +4,50 @@ from typing import Any, Dict, Optional, Union
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from datasets import load_dataset
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from datasets.distributed import split_dataset_by_node
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from PIL import Image
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from PIL.Image import Image as ImageObject
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def convert_image(
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image: Union[Dict[str, Any], ImageObject, str],
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max_pixels: Optional[int],
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) -> ImageObject:
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if max_pixels is not None and (image.width * image.height) > max_pixels:
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resize_factor = math.sqrt(max_pixels / (image.width * image.height))
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width, height = int(image.width * resize_factor), int(
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image.height * resize_factor
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)
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image = image.resize((width, height))
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if image.mode != "RGB":
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image = image.convert("RGB")
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image: Union[Dict[str, Any], Image.Image, str],
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target_width: int,
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target_height: int,
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) -> Image.Image:
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"""
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Convert the image by padding it to the target width and height.
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"""
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# Get the current size of the image
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width, height = image.size
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# Calculate padding for width and height
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pad_width = max(target_width - width, 0)
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pad_height = max(target_height - height, 0)
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# Calculate padding for left, right, top, bottom
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left = pad_width // 2
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top = pad_height // 2
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# Create a new image with target size and a white background
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new_image = Image.new("RGB", (target_width, target_height), (255, 255, 255))
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# Paste the original image into the center of the new image
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new_image.paste(image, (left, top))
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with BytesIO() as output:
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image.save(output, format="JPEG")
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new_image.save(output, format="JPEG")
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return output.getvalue()
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def get_max_image_size(dataset):
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"""
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Traverse the dataset to find the maximum width and height across all images.
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"""
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max_width, max_height = 0, 0
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for example in dataset:
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for image in example["images"]:
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width, height = image.size
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max_width = max(max_width, width)
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max_height = max(max_height, height)
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return max_width, max_height
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def get_geometry3k_sft_dataset(path, split, processor, rank, world_size):
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"""
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@ -36,6 +60,8 @@ def get_geometry3k_sft_dataset(path, split, processor, rank, world_size):
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dataset = load_dataset(path=path, split=split)
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dataset = split_dataset_by_node(dataset, rank=rank, world_size=world_size)
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max_width, max_height = get_max_image_size(dataset)
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tokenizer = processor.tokenizer
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def process_example(example, idx):
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@ -50,7 +76,7 @@ def get_geometry3k_sft_dataset(path, split, processor, rank, world_size):
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)
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processed_images = []
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for image in images:
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processed_images.append(convert_image(image, 336 * 336))
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processed_images.append(convert_image(image, max_width, max_height))
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example["images"] = processed_images
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example["seq"] = example["problem"] + example["answer"] + tokenizer.eos_token
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@ -92,9 +118,12 @@ def get_geometry3k_rl_dataset(path, split, processor, rank, world_size):
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dataset = load_dataset(path=path, split=split)
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dataset = split_dataset_by_node(dataset, rank=rank, world_size=world_size)
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max_width, max_height = get_max_image_size(dataset)
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def process(sample):
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processed_images = [
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convert_image(image, 1024 * 1024) for image in sample["images"]
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convert_image(image, max_width, max_height) for image in sample["images"]
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]
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if "qwen" in processor.image_processor.image_processor_type.lower():
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image_token = "<|vision_start|><|image_pad|><|vision_end|>"
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@ -1,9 +1,9 @@
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import re
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def extract_answer(pred_str, data_name, use_last_number=True):
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match = re.findall(r"\[([0-9\.]+)\]", pred_str)
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if match:
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return match[-1]
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matches = re.findall(r"\[([^\]]+)\]", pred_str)
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if matches:
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return matches[-1]
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return ""
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@ -19,7 +19,7 @@ def geometry3k_reward_fn(
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return 0
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if ans is None:
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return 0
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print(f"sol: {sol}, ans: {ans}")
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# print(f"sol: {sol}, ans: {ans}")
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from realhf.impl.dataset.math_parser import math_equal
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if math_equal(sol, ans):
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print(f"completions: {completions}, answer: {answer}")
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