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import argparse
import torch
import evaluation
import scipy.stats
from models.clip import clip
from utils import collate_fn
from evaluation import PACScore, RefPACScore
from models import open_clip
from data import Flickr8k
from torch.utils.data import DataLoader
_MODELS = {
"ViT-B/32": "checkpoints/clip_ViT-B-32.pth",
"open_clip_ViT-L/14": "checkpoints/openClip_ViT-L-14.pth"
}
def compute_correlation_scores(dataloader, model, preprocess, args):
gen = {}
gts = {}
human_scores = list()
ims_cs = list()
gen_cs = list()
gts_cs = list()
all_scores = dict()
model.eval()
for it, (images, candidates, references, scores) in enumerate(iter(dataloader)):
for i, (im_i, gts_i, gen_i, score_i) in enumerate(zip(images, references, candidates, scores)):
gen['%d_%d' % (it, i)] = [gen_i, ]
gts['%d_%d' % (it, i)] = gts_i
ims_cs.append(im_i)
gen_cs.append(gen_i)
gts_cs.append(gts_i)
human_scores.append(score_i)
gts = evaluation.PTBTokenizer.tokenize(gts)
gen = evaluation.PTBTokenizer.tokenize(gen)
all_scores_metrics = evaluation.get_all_metrics(gts, gen, return_per_cap=True)
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'] = 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'] = refpac_scores
for k, v in all_scores.items():
kendalltau_b = 100 * scipy.stats.kendalltau(v, human_scores, variant='b')[0]
kendalltau_c = 100 * scipy.stats.kendalltau(v, human_scores, variant='c')[0]
print('%s \t Kendall Tau-b: %.3f \t Kendall Tau-c: %.3f'
% (k, kendalltau_b, kendalltau_c))
def compute_scores(model, preprocess, args):
args.datasets = ['flickr8k_expert', 'flickr8k_cf']
args.batch_size_compute_score = 10
for d in args.datasets:
print("Computing correlation scores on dataset: " + d)
if d == 'flickr8k_expert':
dataset = Flickr8k(json_file='flickr8k.json')
dataloader = DataLoader(dataset, batch_size=args.batch_size_compute_score, shuffle=False, collate_fn=collate_fn)
elif d == 'flickr8k_cf':
dataset = Flickr8k(json_file='crowdflower_flickr8k.json')
dataloader = DataLoader(dataset, batch_size=args.batch_size_compute_score, shuffle=False, collate_fn=collate_fn)
compute_correlation_scores(dataloader, model, preprocess, args)
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('--compute_refpac', action='store_true')
args = parser.parse_args()
device = "cuda" if torch.cuda.is_available() else "cpu"
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()
compute_scores(model, preprocess, args)
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