class CIDErEvalCap:
def __init__(self, df):
# if 'idxs' in df:
# _gts = gts
# _res = res
# else:
# print 'tokenization...'
# tokenizer = PTBTokenizer('gts')
# _gts = tokenizer.tokenize(gts)
# print 'tokenized refs'
# tokenizer = PTBTokenizer('res')
# _res = tokenizer.tokenize(res)
# print 'tokenized cands'
#
# self.gts = _gts
# self.res = _res
self.df = df
self.scorer = CiderD(df=self.df)
def evaluate(self, gts, res):
score, scores = self.scorer.compute_score(gts, res)
return score, scores
def method(self):
return "Cider"
def __init__(self, df):
# if 'idxs' in df:
# _gts = gts
# _res = res
# else:
# print 'tokenization...'
# tokenizer = PTBTokenizer('gts')
# _gts = tokenizer.tokenize(gts)
# print 'tokenized refs'
# tokenizer = PTBTokenizer('res')
# _res = tokenizer.tokenize(res)
# print 'tokenized cands'
#
# self.gts = _gts
# self.res = _res
self.df = df
self.scorer = CiderD(df=self.df)
def evaluate(self):
# =================================================
# Set up scorers
# =================================================
print 'setting up scorers...'
scorers = [(CiderD(df=self.df), "CIDErD")]
# =================================================
# Compute scores
# =================================================
metric_scores = {}
for scorer, method in scorers:
print 'computing %s score...' % (scorer.method())
score, scores = scorer.compute_score(self.gts, self.res)
print "Mean %s score: %0.3f" % (method, score)
metric_scores[method] = list(scores)
return metric_scores
from torch.utils.data import DataLoader
import torch.optim as optim
from tqdm import tqdm
import time
import os
from Model_enc_no_attr.Model import TransformerCap
from coco_loader_with_val import CocoDataset, collate_fn
from ciderD.ciderD import CiderD
import opts
from train_utils import *
from misc.reward import get_self_critical_reward_with_bleu
import json
cider_D = CiderD(df='coco-train-words')
def search_pos(att_feat, model, max_len):
model.eval()
batch_size = att_feat.size(0)
seq = torch.Tensor([[9488]] * batch_size).long().cuda()
pos = get_seq_position(seq)
with torch.no_grad():
for _ in range(max_len + 1):
preds = model(seq, pos, att_feat, return_attn=False)
preds = preds.view(batch_size, seq.size(1), -1)
preds = preds[:, -1, :].max(1)[1].unsqueeze(1)
seq = torch.cat([seq, preds], dim=1)
pos = get_seq_position(seq)
preds = seq[:, 1:]
assert preds.size(1) == max_len + 1