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# --------------------------------------------------------
# Copyright (2025) Bytedance Ltd. and/or its affiliates
# Licensed under the Apache License, Version 2.0 (the "License")
# Grasp Any Region Project
# Written by Haochen Wang
# --------------------------------------------------------
import os
import re
from copy import deepcopy
import numpy as np
import torch
from PIL import Image
from torch.utils.data import Dataset
class SingleRegionCaptionDataset(Dataset):
os.environ["TOKENIZERS_PARALLELISM"] = "true"
def __init__(
self,
image,
mask,
processor,
prompt_token="<Prompt1>",
prompt_number=5,
visual_prompt_tokens=[
"<Prompt0>",
"<Prompt1>",
"<Prompt2>",
"<Prompt3>",
"<Prompt4>",
"<NO_Prompt>",
],
data_dtype=torch.bfloat16,
**kwargs,
):
self.processor = processor
self.prompt_token = prompt_token
self.prompt_number = prompt_number
self.special_tokens = visual_prompt_tokens
self.visual_prompt_ids = {
token: self.processor.tokenizer.convert_tokens_to_ids(token) - 128256
for token in self.special_tokens
}
self.image = image
self.mask = mask
self.data_dtype = data_dtype
def __len__(self):
return len(self.coco.anns)
def _parse_annotations(self):
image = self.image
mask = self.mask # binary mask
np.array(image)
mask_np = mask.astype(np.uint8)
filled_matrix = -1 * np.ones((image.height, image.width), dtype=np.uint8)
prompt_token = self.prompt_token
prompt_id = self.visual_prompt_ids.get(
prompt_token, self.visual_prompt_ids["<NO_Prompt>"]
)
assert prompt_id < 16, f"prompt_id should be less than {16}, got {prompt_id}"
fill_area = (filled_matrix == -1) & mask_np.astype(bool)
filled_matrix[fill_area] = prompt_id
filled_matrix[filled_matrix == -1] = self.visual_prompt_ids["<NO_Prompt>"]
bboxes = {}
prompt_idx = int(re.match(r"<Prompt(\d+)>", prompt_token).group(1))
non_zero_coords = np.argwhere(mask_np)
y_min, x_min = non_zero_coords.min(axis=0)
y_max, x_max = non_zero_coords.max(axis=0)
bbox = (
x_min / image.width,
y_min / image.height,
x_max / image.width,
y_max / image.height,
)
bboxes[
str(
self.processor.tokenizer.convert_tokens_to_ids(
f"<|reserved_special_token_{prompt_idx + 2}|>"
)
)
] = bbox
data_dict = {
"image": image,
"visual_prompt": Image.fromarray(filled_matrix),
"bboxes": bboxes,
}
return data_dict
def __getitem__(self, index):
data_dict = deepcopy(self._parse_annotations())
image = data_dict["image"]
visual_prompt = data_dict["visual_prompt"]
prompt_idx = int(re.match(r"<Prompt(\d+)>", self.prompt_token).group(1))
# <|reserved_special_token_{idx}|> actually starts from 2
qs = f"There are some objects I am curious about: {self.prompt_token};\n{self.prompt_token}: <|reserved_special_token_{prompt_idx + 2}|>Describe this masked region in detail."
qs = qs.replace(
f"<|reserved_special_token_{prompt_idx + 2}|>",
f"<|reserved_special_token_{prompt_idx + 2}|>" * 256,
)
user_content = [{"type": "image", "image": image}, {"type": "text", "text": qs}]
messages = [
{"role": "user", "content": user_content},
]
# Prepare input for model
raw_prompt = self.processor.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=False,
)
model_inputs = self.processor(
text=[raw_prompt],
images=[image],
visual_prompts=[visual_prompt],
return_tensors="pt",
)
pixel_values = model_inputs["pixel_values"]
mask_values = model_inputs["mask_values"]
input_ids = model_inputs["input_ids"].squeeze(0)
attention_mask = model_inputs["attention_mask"].squeeze(0)
aspect_ratio = model_inputs["aspect_ratio"]
ret = dict(
input_ids=input_ids.cuda().unsqueeze(0),
attention_mask=attention_mask.cuda().to(self.data_dtype).unsqueeze(0),
pixel_values=pixel_values.cuda().to(self.data_dtype).flatten(0, 1),
global_mask_values=mask_values.cuda().to(self.data_dtype).squeeze(),
bboxes=[data_dict["bboxes"]],
aspect_ratios=aspect_ratio.unsqueeze(0).cuda(),
)
return ret
class MultiRegionDataset(Dataset):
os.environ["TOKENIZERS_PARALLELISM"] = "true"
def __init__(
self,
image,
masks,
question_str,
processor,
prompt_token="<Prompt1>",
prompt_number=5,
visual_prompt_tokens=[
"<Prompt0>",
"<Prompt1>",
"<Prompt2>",
"<Prompt3>",
"<Prompt4>",
"<NO_Prompt>",
],
data_dtype=torch.bfloat16,
**kwargs,
):
self.processor = processor
self.prompt_token = prompt_token
self.prompt_number = prompt_number
self.special_tokens = visual_prompt_tokens
self.visual_prompt_ids = {
token: self.processor.tokenizer.convert_tokens_to_ids(token) - 128256
for token in self.special_tokens
}
self.image = image
self.masks = masks
self.question_str = question_str
self.data_dtype = data_dtype
def __len__(self):
return len(self.coco.anns)
def _parse_annotations(self):
image = self.image
masks = self.masks # binary mask
width, height = image.size
np.array(image)
masks_np = [np.array(mask).astype(np.uint8) for mask in masks]
for mask_id, mask in enumerate(masks_np):
if image.width != mask.shape[1] or image.height != mask.shape[0]:
mask = mask.resize(image.size, Image.NEAREST)
masks[mask_id] = mask
masks_np[mask_id] = np.array(mask).astype(np.unint8)
prompt_matches = set(re.findall(r"<Prompt\d+>", self.question_str))
assert len(prompt_matches) == len(masks)
objects_desc = "There are some objects I am curious about: "
sub_image_desc = ""
for matched_prompt in prompt_matches:
objects_desc += f"{matched_prompt}; "
prompt_idx = int(re.match(r"<Prompt(\d+)>", matched_prompt).group(1))
sub_image_desc += (
f"{matched_prompt}: <|reserved_special_token_{prompt_idx + 2}|>\n"
)
sub_image_desc = sub_image_desc.replace(
f"<|reserved_special_token_{prompt_idx + 2}|>",
f"<|reserved_special_token_{prompt_idx + 2}|>" * 256,
)
prompt = objects_desc + "\n" + sub_image_desc + "\n" + self.question_str
filled_matrix = -1 * np.ones((image.height, image.width), dtype=np.uint8)
bboxes = {}
for matched_prompt in prompt_matches:
prompt_idx = int(re.match(r"<Prompt(\d+)>", matched_prompt).group(1))
mask = masks[prompt_idx]
prompt_token = matched_prompt
prompt_id = self.visual_prompt_ids.get(
prompt_token, self.visual_prompt_ids["<NO_Prompt>"]
)
assert (
prompt_id < self.prompt_number + 1
), f"prompt_id should be less than {self.prompt_numbers + 1}, got {prompt_id}"
fill_area = (filled_matrix == -1) & mask.astype(bool)
filled_matrix[fill_area] = prompt_id
non_zero_coords = np.argwhere(masks_np[mask_id])
y_min, x_min = non_zero_coords.min(axis=0)
y_max, x_max = non_zero_coords.max(axis=0)
bbox = (
x_min / image.width,
y_min / image.height,
x_max / image.width,
y_max / image.height,
)
bboxes[
str(
self.processor.tokenizer.convert_tokens_to_ids(
f"<|reserved_special_token_{prompt_idx + 2}|>"
)
)
] = bbox
filled_matrix[filled_matrix == -1] = self.visual_prompt_ids["<NO_Prompt>"]
# convert masks to PIL.Image
masks = [
Image.fromarray((masks_np[i] * 255).astype(np.uint8))
for i in range(len(masks))
]
data_dict = {
"image": image,
"visual_prompt": Image.fromarray(filled_matrix),
"bboxes": bboxes,
"prompt": prompt,
}
return data_dict
def __getitem__(self, index):
data_dict = self._parse_annotations()
image = data_dict["image"]
visual_prompt = data_dict["visual_prompt"]
qs = data_dict["prompt"]
user_content = [{"type": "image", "image": image}, {"type": "text", "text": qs}]
messages = [
{"role": "user", "content": user_content},
]
# Prepare input for model
raw_prompt = self.processor.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=False,
)
model_inputs = self.processor(
text=[raw_prompt],
images=[image],
visual_prompts=[visual_prompt],
return_tensors="pt",
)
pixel_values = model_inputs["pixel_values"]
mask_values = model_inputs["mask_values"]
input_ids = model_inputs["input_ids"].squeeze(0)
attention_mask = model_inputs["attention_mask"].squeeze(0)
aspect_ratio = model_inputs["aspect_ratio"]
ret = dict(
input_ids=input_ids.cuda().unsqueeze(0),
attention_mask=attention_mask.cuda().to(self.data_dtype).unsqueeze(0),
pixel_values=pixel_values.cuda().to(self.data_dtype).flatten(0, 1),
global_mask_values=mask_values.cuda().to(self.data_dtype).squeeze(),
bboxes=[data_dict["bboxes"]],
aspect_ratios=aspect_ratio.unsqueeze(0).cuda(),
)
return ret
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