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()