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import argparse
import itertools
import json
import os
import re
from functools import partial
import torch
from torchvision.ops.boxes import box_area
from tqdm import tqdm
from transformers import AutoModelForCausalLM, AutoTokenizer
ds_collections = {
'refcoco_val': 'data/refcoco/refcoco_val.jsonl',
'refcoco_testA': 'data/refcoco/refcoco_testA.jsonl',
'refcoco_testB': 'data/refcoco/refcoco_testB.jsonl',
'refcoco+_val': 'data/refcoco+/refcoco+_val.jsonl',
'refcoco+_testA': 'data/refcoco+/refcoco+_testA.jsonl',
'refcoco+_testB': 'data/refcoco+/refcoco+_testB.jsonl',
'refcocog_val': 'data/refcocog/refcocog_val.jsonl',
'refcocog_test': 'data/refcocog/refcocog_test.jsonl',
}
def box_iou(boxes1, boxes2):
area1 = box_area(boxes1)
area2 = box_area(boxes2)
lt = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2]
rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2]
wh = (rb - lt).clamp(min=0) # [N,M,2]
inter = wh[:, :, 0] * wh[:, :, 1] # [N,M]
union = area1[:, None] + area2 - inter
iou = inter / union
return iou, union
def collate_fn(batches, tokenizer):
texts = [_['text'] for _ in batches]
bboxes = [_['bbox'] for _ in batches]
hws = [_['hw'] for _ in batches]
input_ids = tokenizer(texts, return_tensors='pt', padding='longest')
return input_ids.input_ids, input_ids.attention_mask, bboxes, hws
class RefCOCODataset(torch.utils.data.Dataset):
def __init__(self, test, tokenizer, prompt):
self.datas = open(test).readlines()
self.tokenizer = tokenizer
self.prompt = prompt
def __len__(self):
return len(self.datas)
def __getitem__(self, idx):
data = json.loads(self.datas[idx].strip())
image = data['image']
text = data['sent']
bbox = data['bbox']
w, h = data['width'], data['height']
return {
'text': self.prompt.format(image, text),
'bbox': bbox,
'hw': (h, w),
}
class InferenceSampler(torch.utils.data.sampler.Sampler):
def __init__(self, size):
self._size = int(size)
assert size > 0
self._rank = torch.distributed.get_rank()
self._world_size = torch.distributed.get_world_size()
self._local_indices = self._get_local_indices(size, self._world_size,
self._rank)
@staticmethod
def _get_local_indices(total_size, world_size, rank):
shard_size = total_size // world_size
left = total_size % world_size
shard_sizes = [shard_size + int(r < left) for r in range(world_size)]
begin = sum(shard_sizes[:rank])
end = min(sum(shard_sizes[:rank + 1]), total_size)
return range(begin, end)
def __iter__(self):
yield from self._local_indices
def __len__(self):
return len(self._local_indices)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--checkpoint', type=str, default='')
parser.add_argument('--dataset', type=str, default='')
parser.add_argument('--batch-size', type=int, default=1)
parser.add_argument('--num-workers', type=int, default=1)
args = parser.parse_args()
torch.distributed.init_process_group(
backend='nccl',
world_size=int(os.getenv('WORLD_SIZE', '1')),
rank=int(os.getenv('RANK', '0')),
)
torch.cuda.set_device(torch.distributed.get_rank())
model = AutoModelForCausalLM.from_pretrained(
args.checkpoint, device_map='cuda', trust_remote_code=True).eval()
tokenizer = AutoTokenizer.from_pretrained(args.checkpoint,
trust_remote_code=True)
tokenizer.padding_side = 'left'
tokenizer.pad_token_id = tokenizer.eod_id
prompt = '<img>{}</img><ref>{}</ref><box>'
dataset = RefCOCODataset(test=ds_collections[args.dataset],
tokenizer=tokenizer,
prompt=prompt)
dataloader = torch.utils.data.DataLoader(
dataset=dataset,
sampler=InferenceSampler(len(dataset)),
batch_size=args.batch_size,
num_workers=args.num_workers,
pin_memory=True,
drop_last=True,
collate_fn=partial(collate_fn, tokenizer=tokenizer),
)
outputs = []
for _, (input_ids, attention_mask, bboxes,
hws) in tqdm(enumerate(dataloader)):
pred = model.generate(
input_ids=input_ids.cuda(),
attention_mask=attention_mask.cuda(),
do_sample=False,
num_beams=1,
max_new_tokens=28,
min_new_tokens=10,
length_penalty=1,
num_return_sequences=1,
use_cache=True,
pad_token_id=tokenizer.eod_id,
eos_token_id=tokenizer.eod_id,
)
answers = [
tokenizer.decode(_[input_ids.size(1):].cpu(),
skip_special_tokens=True) for _ in pred
]
for bbox, hw, answer in zip(bboxes, hws, answers):
outputs.append({
'answer': answer,
'gt_bbox': bbox,
'hw': hw,
})
torch.distributed.barrier()
world_size = torch.distributed.get_world_size()
merged_outputs = [None for _ in range(world_size)]
torch.distributed.all_gather_object(merged_outputs, outputs)
merged_outputs = [_ for _ in itertools.chain.from_iterable(merged_outputs)]
PATTERN = re.compile(r'\((.*?)\),\((.*?)\)')
if torch.distributed.get_rank() == 0:
correct = total_cnt = 0
for i, output in enumerate(merged_outputs):
predict_bbox = re.findall(PATTERN, output['answer'])
try:
if ',' not in predict_bbox[0][0] or ',' not in predict_bbox[0][
1]:
predict_bbox = (0., 0., 0., 0.)
else:
x1, y1 = [
float(tmp) for tmp in predict_bbox[0][0].split(',')
]
x2, y2 = [
float(tmp) for tmp in predict_bbox[0][1].split(',')
]
predict_bbox = (x1, y1, x2, y2)
except:
predict_bbox = (0., 0., 0., 0.)
target_bbox = torch.tensor(output['gt_bbox'],
dtype=torch.float32).view(-1, 4)
predict_bbox = torch.tensor(predict_bbox,
dtype=torch.float32).view(-1, 4) / 999
predict_bbox[:, 0::2] *= output['hw'][1]
predict_bbox[:, 1::2] *= output['hw'][0]
iou, _ = box_iou(predict_bbox, target_bbox)
iou = iou.item()
total_cnt += 1
if iou >= 0.5:
correct += 1
print(f'Precision @ 1: {correct / total_cnt} \n')
torch.distributed.barrier()
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