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import sys
sys.path.append("./pacscore")
import argparse
import torch
import pacscore.evaluation as evaluation
import scipy.stats
from pacscore.models.clip import clip
from pacscore.utils import collate_fn
from pacscore.evaluation import PACScore, RefPACScore
from pacscore.models import open_clip
from data import Flickr8k
from torch.utils.data import DataLoader
from polos.metrics.regression_metrics import RegressionReport
from polos.models import load_checkpoint
import argparse
from polos.models import load_checkpoint
from PIL import Image
from utils import *
def collect_coef(memory, dataset_name, method, coef_tensor):
memory.setdefault(dataset_name, {})
coef = {k : round(float(v.numpy() if not isinstance(v,float) else v),4) for k, v in coef_tensor.items()}
memory[dataset_name].update({method : coef})
gprint(f"[{dataset_name}]",method,coef)
def compute_correlation_scores(memory, dataloader, pacscore, polos, preprocess, args):
gen = {}
gts = {}
human_scores = list()
ims_cs = list()
gen_cs = list()
gts_cs = list()
all_scores = dict()
pacscore.eval()
polos.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(pacscore, preprocess, ims_cs, gen_cs, args.device, w=2.0)
all_scores['PAC-S'] = pac_scores
# RefPAC-S
_, per_instance_text_text = RefPACScore(pacscore, gts_cs, candidate_feats, args.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
# Polos
data = [{
"mt" : gen,
"refs": refs,
"img": Image.open(image).convert("RGB")
} for image, refs, gen in zip(ims_cs, gts_cs, gen_cs)
]
_, sys_score = polos.predict(data,cuda=True,batch_size=32)
all_scores['Polos'] = sys_score
del data
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))
collect_coef(memory,
args.dataset_name,
k,
{"Kendall" : kendalltau_c if args.kendall_type == "c" else kendalltau_b}
)
return memory
def compute_scores(memory, pacscore, polos, 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(root='data_en/flickr8k/',json_file='flickr8k.json')
dataloader = DataLoader(dataset, batch_size=args.batch_size_compute_score, shuffle=False, collate_fn=collate_fn)
args.kendall_type = "c"
elif d == 'flickr8k_cf':
dataset = Flickr8k(root='data_en/flickr8k/',json_file='crowdflower_flickr8k.json')
dataloader = DataLoader(dataset, batch_size=args.batch_size_compute_score, shuffle=False, collate_fn=collate_fn)
args.kendall_type = "b"
args.dataset_name = d
memory = compute_correlation_scores(memory, dataloader, pacscore, polos, preprocess, args)
return memory
def compute_flickr(args,checkpoint,memory,tops):
# Polos
polos = load_checkpoint(checkpoint)
# PAC-S
device = "cuda" if torch.cuda.is_available() else "cpu"
pacscore, preprocess = clip.load("ViT-B/32", device=device)
pacscore = pacscore.to(device)
pacscore = pacscore.float()
checkpoint = torch.load("pacscore/checkpoints/clip_ViT-B-32.pth") # Use checkpoints trained with PACScore
pacscore.load_state_dict(checkpoint['state_dict'])
pacscore.eval()
args.device = device
memory = compute_scores(memory, pacscore, polos, preprocess, args)
return memory, tops |