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| import os | |
| import argparse | |
| import torch | |
| import json | |
| import evaluation | |
| import numpy as np | |
| from models.clip import clip | |
| from evaluation import PACScore, RefPACScore | |
| from models import open_clip | |
| _MODELS = { | |
| "ViT-B/32": "checkpoints/clip_ViT-B-32.pth", | |
| "open_clip_ViT-L/14": "checkpoints/openClip_ViT-L-14.pth" | |
| } | |
| def compute_scores(model, preprocess, image_ids, candidates, references, args): | |
| gen = {} | |
| gts = {} | |
| ims_cs = list() | |
| gen_cs = list() | |
| gts_cs = list() | |
| all_scores = dict() | |
| model.eval() | |
| for i, (im_i, gts_i, gen_i) in enumerate(zip(image_ids, references, candidates)): | |
| gen['%d' % (i)] = [gen_i, ] | |
| gts['%d' % (i)] = gts_i | |
| ims_cs.append(im_i) | |
| gen_cs.append(gen_i) | |
| gts_cs.append(gts_i) | |
| gts = evaluation.PTBTokenizer.tokenize(gts) | |
| gen = evaluation.PTBTokenizer.tokenize(gen) | |
| all_scores_metrics = evaluation.get_all_metrics(gts, gen) | |
| for k, v in all_scores_metrics.items(): | |
| if k == 'BLEU': | |
| all_scores['BLEU-1'] = v[0] | |
| all_scores['BLEU-4'] = v[-1] | |
| else: | |
| all_scores[k] = v | |
| # PAC-S | |
| _, pac_scores, candidate_feats, len_candidates = PACScore( | |
| model, preprocess, ims_cs, gen_cs, device, w=2.0) | |
| all_scores['PAC-S'] = np.mean(pac_scores) | |
| # RefPAC-S | |
| if args.compute_refpac: | |
| _, per_instance_text_text = RefPACScore( | |
| model, gts_cs, candidate_feats, device, torch.tensor(len_candidates)) | |
| refpac_scores = 2 * pac_scores * per_instance_text_text / \ | |
| (pac_scores + per_instance_text_text) | |
| all_scores['RefPAC-S'] = np.mean(refpac_scores) | |
| return all_scores | |
| if __name__ == '__main__': | |
| # Argument parsing | |
| parser = argparse.ArgumentParser(description='PAC-S evaluation') | |
| parser.add_argument('--clip_model', type=str, default='ViT-B/32', | |
| choices=['ViT-B/32', 'open_clip_ViT-L/14']) | |
| parser.add_argument('--image_dir', type=str, default='example/images') | |
| parser.add_argument('--candidates_json', type=str, | |
| default='example/good_captions.json') | |
| parser.add_argument('--references_json', type=str, default='example/refs.json') | |
| parser.add_argument('--compute_refpac', action='store_true') | |
| args = parser.parse_args() | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| image_ids = [img_id for img_id in os.listdir(args.image_dir)] | |
| with open(args.candidates_json) as f: | |
| candidates = json.load(f) | |
| candidates = [candidates[cid.split('.')[0]] for cid in image_ids] | |
| with open(args.references_json) as f: | |
| references = json.load(f) | |
| references = [references[cid.split('.')[0]] for cid in image_ids] | |
| image_ids = [os.path.join(args.image_dir, img_id) for img_id in image_ids] | |
| if args.clip_model.startswith('open_clip'): | |
| print("Using Open CLIP Model: " + args.clip_model) | |
| model, _, preprocess = open_clip.create_model_and_transforms( | |
| 'ViT-L-14', pretrained='laion2b_s32b_b82k') | |
| else: | |
| print("Using CLIP Model: " + args.clip_model) | |
| model, preprocess = clip.load(args.clip_model, device=device) | |
| model = model.to(device) | |
| model = model.float() | |
| checkpoint = torch.load(_MODELS[args.clip_model]) | |
| model.load_state_dict(checkpoint['state_dict']) | |
| model.eval() | |
| all_scores = compute_scores( | |
| model, preprocess, image_ids, candidates, references, args) | |
| for k, v in all_scores.items(): | |
| print('%s: %.4f' % (k, v)) | |