File size: 10,712 Bytes
866ee56 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 | import argparse
import itertools
import json
import os
import random
import time
from functools import partial
import torch
from internvl.model.internvl_chat import InternVLChatModel
from internvl.train.dataset import build_transform, dynamic_preprocess
from PIL import Image
from pycocoevalcap.eval import COCOEvalCap
from pycocotools.coco import COCO
from tqdm import tqdm
from transformers import AutoTokenizer
ds_collections = {
'flickr30k': {
'root': 'data/flickr30k/',
'annotation': 'data/flickr30k/flickr30k_test_karpathy.json',
'max_new_tokens': 30,
'min_new_tokens': 8,
},
'coco': {
'root': 'data/coco/',
'annotation': ['data/coco/annotations/coco_karpathy_test.json',
'data/coco/annotations/coco_karpathy_test_gt.json'],
'max_new_tokens': 30,
'min_new_tokens': 8,
},
'nocaps': {
'root': 'data/nocaps/images',
'annotation': 'data/nocaps/nocaps_val_4500_captions.json',
'max_new_tokens': 30,
'min_new_tokens': 8,
},
}
class CaptionDataset(torch.utils.data.Dataset):
def __init__(self, name, root, annotation, prompt, input_size=224, dynamic_image_size=False,
use_thumbnail=False, max_num=6):
if name == 'coco':
self.images = json.load(open(annotation))
else:
self.images = json.load(open(annotation))['images']
self.name = name
self.prompt = prompt
self.root = root
self.input_size = input_size
self.dynamic_image_size = dynamic_image_size
self.use_thumbnail = use_thumbnail
self.max_num = max_num
self.transform = build_transform(is_train=False, input_size=input_size)
def __len__(self):
return len(self.images)
def __getitem__(self, idx):
if self.name == 'coco':
filename = self.images[idx]['image']
image_id = int(filename.split('_')[-1].replace('.jpg', ''))
image_path = os.path.join(self.root, filename)
else:
image_id = self.images[idx]['id']
if 'file_name' in self.images[idx]:
image_path = os.path.join(self.root, self.images[idx]['file_name'])
else:
image_path = os.path.join(self.root, self.images[idx]['image'])
image = Image.open(image_path)
if self.dynamic_image_size:
images = dynamic_preprocess(image, image_size=self.input_size,
use_thumbnail=self.use_thumbnail,
max_num=self.max_num)
else:
images = [image]
pixel_values = [self.transform(image) for image in images]
pixel_values = torch.stack(pixel_values)
return {
'image_id': image_id,
'input_text': self.prompt,
'pixel_values': pixel_values
}
def collate_fn(inputs, tokenizer):
pixel_values = torch.cat([_['pixel_values'] for _ in inputs], dim=0)
image_ids = [_['image_id'] for _ in inputs]
input_texts = [_['input_text'] for _ in inputs]
input_tokens = tokenizer(input_texts, return_tensors='pt')
return pixel_values, image_ids, input_tokens.input_ids, input_tokens.attention_mask
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)
def evaluate_chat_model():
prompt = 'Provide a one-sentence caption for the provided image.'
print('prompt:', prompt)
random.seed(args.seed)
summaries = []
for ds_name in args.datasets:
annotation = ds_collections[ds_name]['annotation']
if type(annotation) == list:
annotation = annotation[0]
dataset = CaptionDataset(
name=ds_name,
root=ds_collections[ds_name]['root'],
annotation=annotation,
prompt=prompt,
input_size=image_size,
dynamic_image_size=args.dynamic,
use_thumbnail=use_thumbnail,
max_num=args.max_num
)
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=False,
collate_fn=partial(collate_fn, tokenizer=tokenizer),
)
image_ids, captions = [], []
for _, (pixel_values, ids, _, _) in tqdm(enumerate(dataloader)):
pixel_values = pixel_values.to(torch.bfloat16).cuda()
generation_config = dict(
num_beams=args.num_beams,
max_new_tokens=ds_collections[ds_name]['max_new_tokens'],
min_new_tokens=ds_collections[ds_name]['min_new_tokens'],
do_sample=True if args.temperature > 0 else False,
temperature=args.temperature,
)
pred = model.chat(
tokenizer=tokenizer,
pixel_values=pixel_values,
question=prompt,
generation_config=generation_config,
verbose=True
)
image_ids.extend(ids)
captions.extend([pred])
torch.distributed.barrier()
world_size = torch.distributed.get_world_size()
merged_ids = [None for _ in range(world_size)]
merged_captions = [None for _ in range(world_size)]
torch.distributed.all_gather_object(merged_ids, image_ids)
torch.distributed.all_gather_object(merged_captions, captions)
merged_ids = [_ for _ in itertools.chain.from_iterable(merged_ids)]
merged_captions = [_ for _ in itertools.chain.from_iterable(merged_captions)]
average_length = sum(len(x.split()) for x in merged_captions) / len(merged_captions)
print(f'Average caption length: {average_length}')
if torch.distributed.get_rank() == 0:
print(f'Evaluating {ds_name} ...')
results = []
for image_id, caption in zip(merged_ids, merged_captions):
results.append({
'image_id': int(image_id),
'caption': caption,
})
time_prefix = time.strftime('%y%m%d%H%M%S', time.localtime())
results_file = f'{ds_name}_{time_prefix}.json'
results_file = os.path.join(args.out_dir, results_file)
json.dump(results, open(results_file, 'w'))
annotation = ds_collections[ds_name]['annotation']
if type(annotation) == list:
annotation = annotation[-1]
coco = COCO(annotation)
coco_result = coco.loadRes(results_file)
coco_eval = COCOEvalCap(coco, coco_result)
coco_eval.evaluate()
summary = coco_eval.eval.items()
print(summary)
summaries.append([args.checkpoint, ds_name, average_length, summary])
torch.distributed.barrier()
out_path = '_'.join(args.checkpoint.split('/')[-2:])
writer = open(os.path.join(args.out_dir, f'{out_path}.txt'), 'a')
print(f"write results to file {os.path.join(args.out_dir, f'{out_path}.txt')}")
for summary in summaries:
print(summary)
writer.write(f'{summary}\n')
writer.close()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--checkpoint', type=str, default='')
parser.add_argument('--datasets', type=str, default='coco,flickr30k,nocaps')
parser.add_argument('--batch-size', type=int, default=1)
parser.add_argument('--num-workers', type=int, default=1)
parser.add_argument('--num-beams', type=int, default=5)
parser.add_argument('--temperature', type=float, default=0.0)
parser.add_argument('--out-dir', type=str, default='results')
parser.add_argument('--seed', type=int, default=0)
parser.add_argument('--dynamic', action='store_true')
parser.add_argument('--max-num', type=int, default=6)
parser.add_argument('--load-in-8bit', action='store_true')
parser.add_argument('--load-in-4bit', action='store_true')
parser.add_argument('--auto', action='store_true')
args = parser.parse_args()
if not os.path.exists(args.out_dir):
os.makedirs(args.out_dir)
args.datasets = args.datasets.split(',')
print('datasets:', args.datasets)
assert args.batch_size == 1, 'Only batch size 1 is supported'
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(int(os.getenv('LOCAL_RANK', 0)))
if args.auto:
os.environ['CUDA_LAUNCH_BLOCKING'] = '1'
kwargs = {'device_map': 'auto'} if args.auto else {}
tokenizer = AutoTokenizer.from_pretrained(args.checkpoint, trust_remote_code=True, use_fast=False)
model = InternVLChatModel.from_pretrained(
args.checkpoint, low_cpu_mem_usage=True, torch_dtype=torch.bfloat16,
load_in_8bit=args.load_in_8bit, load_in_4bit=args.load_in_4bit, **kwargs).eval()
if not args.load_in_8bit and not args.load_in_4bit and not args.auto:
model = model.cuda()
image_size = model.config.force_image_size or model.config.vision_config.image_size
use_thumbnail = model.config.use_thumbnail
total_params = sum(p.numel() for p in model.parameters()) / 1e9
if total_params > 20 or args.dynamic:
args.num_beams = 1
print(f'[test] total_params: {total_params}B, use num_beams: {args.num_beams}')
else:
print(f'[test] total_params: {total_params}B')
print(f'[test] image_size: {image_size}')
print(f'[test] template: {model.config.template}')
print(f'[test] dynamic_image_size: {args.dynamic}')
print(f'[test] use_thumbnail: {use_thumbnail}')
print(f'[test] max_num: {args.max_num}')
evaluate_chat_model()
|