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import argparse
import re
import math
import os
import torch
import tqdm
from pycocotools import mask as _mask
import numpy as np
from PIL import Image
from pycocotools.coco import COCO
from transformers import (AutoModel, AutoModelForCausalLM, AutoTokenizer,
BitsAndBytesConfig, CLIPImageProcessor,
CLIPVisionModel, GenerationConfig)
import json
from utils import _init_dist_pytorch, get_dist_info, get_rank, collect_results_cpu
def parse_args():
parser = argparse.ArgumentParser(description='RefCocog region caption')
parser.add_argument('model_path', help='hf model path.')
parser.add_argument(
'--output-path',
default='./region_cap_pred.json',
help='save path of the prediction')
parser.add_argument(
'--launcher',
choices=['none', 'pytorch', 'slurm', 'mpi'],
default='none',
help='job launcher')
parser.add_argument('--local_rank', '--local-rank', type=int, default=0)
args = parser.parse_args()
if 'LOCAL_RANK' not in os.environ:
os.environ['LOCAL_RANK'] = str(args.local_rank)
return args
class RegionCapInferenceDataset:
def __init__(self,
image_folder,
annotation_file=None,
):
self.image_folder = image_folder
self.coco = COCO(annotation_file)
self.image_dict = self.coco.imgs
self.ann_dict = self.coco.anns
self.image_dict_keys = list(self.image_dict.keys())
def __len__(self):
return len(self.image_dict_keys)
def decode_mask(self, annotation, image_info):
flag = False
masks = []
for ann_id in range(1):
ann = {"segmentation": annotation}
if len(ann["segmentation"]) == 0:
m = np.zeros((image_info["height"], image_info["width"])).astype(
np.uint8
)
masks.append(m)
continue
if type(ann["segmentation"][0]) == list: # polygon
rle = _mask.frPyObjects(
ann["segmentation"], image_info["height"], image_info["width"]
)
else:
rle = ann["segmentation"]
for i in range(len(rle)):
if not isinstance(rle[i]["counts"], bytes):
rle[i]["counts"] = rle[i]["counts"].encode()
m = _mask.decode(rle)
m = np.sum(m, axis=2) # sometimes there are multiple binary map (corresponding to multiple segs)
m = m.astype(np.uint8) # convert to np.uint8
masks.append(m)
masks = np.stack(masks, axis=0)
return masks
def get_questions(self):
# question = "<image>\nPlease give me a short description of the region in the picture marked by region1. Please response in a word."
question = "<image>\nPlease give me a short description of the region in the picture marked by region1."
return question
def __getitem__(self, index):
data_dict = {}
image_id = self.image_dict_keys[index]
image_file = self.image_dict[image_id]['file_name']
questions = self.get_questions()
data_dict['image_file'] = image_file
image_file = os.path.join(self.image_folder, image_file)
image = Image.open(image_file).convert('RGB')
masks = self.ann_dict[image_id]['segmentation']
image_info = self.image_dict[image_id]
masks = self.decode_mask(masks, image_info)
data_dict['image'] = image
data_dict['text'] = questions
data_dict['img_id'] = image_id
data_dict['mask_prompts'] = [masks]
return data_dict
ANNOTATION_FILE = './data/region_caption/refcocog/finetune_refcocog_val_with_mask.json'
IMAGE_FOLDER = './data/glamm_data/images/coco2014/train2014/'
def main():
args = parse_args()
if args.launcher != 'none':
_init_dist_pytorch('nccl')
rank, world_size = get_dist_info()
torch.cuda.set_device(rank)
else:
rank = 0
world_size = 1
# build model
model = AutoModel.from_pretrained(
args.model_path,
torch_dtype=torch.bfloat16,
low_cpu_mem_usage=True,
use_flash_attn=True,
trust_remote_code=True,
).eval().cuda()
tokenizer = AutoTokenizer.from_pretrained(
args.model_path,
trust_remote_code=True,
)
dataset = RegionCapInferenceDataset(
image_folder=IMAGE_FOLDER,
annotation_file=ANNOTATION_FILE,
)
results = []
n_samples = len(dataset)
per_rank_samples = math.ceil(n_samples / world_size) + 1
per_rank_ids = range(per_rank_samples * rank,
min(n_samples, per_rank_samples * (rank + 1)))
for idx in tqdm.tqdm(per_rank_ids):
data_batch = dataset[idx]
result_dict = {'image_id': data_batch['img_id'], 'image_file': data_batch['image_file']}
del data_batch['img_id'], data_batch['image_file']
prediction = model.predict_forward(**data_batch, tokenizer=tokenizer)['prediction']
text_output = prediction.replace("<s>", "").replace("\n", "") \
.replace("region1", '').replace("Region1", '').replace("The region marked by", "").replace("The region marked as", "").replace("The region marked", "") \
.replace("is", "").replace("shows", "").replace(':', '').replace(" ", " ").replace(" ", " ")
text_output = text_output.split("ASSISTANT: ")[-1]
cleaned_str = re.sub(r'<.*?>', '', text_output)
cleaned_str = cleaned_str.replace('[SEG]', '')
cleaned_str = ' '.join(cleaned_str.split()).strip("'")
cleaned_str = cleaned_str.strip()
result_dict["caption"] = cleaned_str
result_dict["prediction"] = cleaned_str
results.append(result_dict)
tmpdir = './dist_test_temp_regioncap_' + args.model_path.replace('/', '').replace('.', '')
results = collect_results_cpu(results, len(dataset), tmpdir=tmpdir)
if get_rank() == 0:
with open(args.output_path, 'w') as json_file:
json.dump(results, json_file, indent=2)
if __name__ == '__main__':
main()
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