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eval.py
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eval.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
import numpy as np
import time
import os
from six.moves import cPickle
import opts
import models
from dataloader import *
import eval_utils
import argparse
import misc.utils as utils
import torch
import torch.nn as nn
# Input arguments and options
parser = argparse.ArgumentParser()
# Input paths
parser.add_argument('--g_model_path', type=str, default='',
help='path to generator to evaluate')
parser.add_argument('--d_model_path', type=str, default='',
help='path to discrimiator to evaluate')
parser.add_argument('--infos_path', type=str, default='',
help='path to infos to evaluate')
# Basic options
parser.add_argument('--batch_size', type=int, default=0,
help='if > 0 then overrule, otherwise load from checkpoint.')
parser.add_argument('--num_videos', type=int, default=-1,
help='how many images to use when periodically evaluating the loss? (-1 = all)')
parser.add_argument('--language_eval', type=int, default=1,
help='Evaluate language as well (1 = yes, 0 = no)? BLEU/CIDEr/METEOR/ROUGE_L? requires coco-caption code from Github.')
parser.add_argument('--use_context', type=int, default=1,
help='use context during evaluation for dense model (1=yes,0=no)')
# Sampling options
parser.add_argument('--sample_max', type=int, default=1,
help='1 = sample argmax words. 0 = sample from distributions.')
parser.add_argument('--num_samples', type=int, default=100,
help='number to sample for each image/video. Used when sample_max is 0')
parser.add_argument('--num_captions', type=int, default=1,
help='number of captions to consider for each image/video.')
parser.add_argument('--max_ppl', type=int, default=0,
help='beam search by max perplexity or max probability.')
parser.add_argument('--beam_size', type=int, default=1,
help='used when sample_max = 1, indicates number of beams in beam search. Usually 2 or 3 works well. More is not better. Set this to 1 for faster runtime but a bit worse performance.')
parser.add_argument('--group_size', type=int, default=1,
help='used for diverse beam search. if group_size is 1, then it\'s normal beam search')
parser.add_argument('--diversity_lambda', type=float, default=0.5,
help='used for diverse beam search. Usually from 0.2 to 0.8. Higher value of lambda produces a more diverse list')
parser.add_argument('--temperature', type=float, default=1.0,
help='temperature when sampling from distributions (i.e. when sample_max = 0). Lower = "safer" predictions.')
parser.add_argument('--decoding_constraint', type=int, default=0,
help='If 1, not allowing same word in a row')
# Input options
parser.add_argument('--input_fc_dir', type=str, default='',
help='path to the h5file containing the preprocessed dataset')
parser.add_argument('--input_img_dir', type=str, default='',
help='path to the image file')
parser.add_argument('--input_box_dir', type=str, default='',
help='path to the bottomup file')
parser.add_argument('--input_label_h5', type=str, default='',
help='path to the h5file containing the preprocessed dataset')
parser.add_argument('--glove_npy', type=str, default='',
help='path to glove numpy')
parser.add_argument('--input_json', type=str, default='',
help='path to the json file containing additional info and vocab. empty = fetch from model checkpoint.')
parser.add_argument('--split', type=str, default='test',
help='if running on MSCOCO images, which split to use: val|test|train')
parser.add_argument('--coco_json', type=str, default='',
help='if nonempty then use this file in DataLoaderRaw (see docs there). Used only in MSCOCO test evaluation, where we have a specific json file of only test set images.')
# misc
parser.add_argument('--id', type=str, default='',
help='an id identifying this run/job. used only if language_eval = 1 for appending to intermediate files')
parser.add_argument('--verbose', type=int, default=1,
help='verbse print.')
parser.add_argument('--verbose_beam', type=int, default=0,
help='if we need to print out all beam search beams.')
parser.add_argument('--verbose_loss', type=int, default=0,
help='if we need to calculate loss.')
parser.add_argument('--verbose_video', type=int, default=1,
help='if we need to get all the language metrics for video evaluation.')
# weights for hybrid discriminator
parser.add_argument('--vis_weight', type=float, default=0.8,
help='weight for visual discriminator in adversarial inference')
parser.add_argument('--lang_weight', type=float, default=0.2,
help='weight for lang discriminator in adversarial inference')
parser.add_argument('--pair_weight', type=float, default=1.0,
help='weight for pair discriminator in adversarial inference')
opt = parser.parse_args()
# Load infos
with open(opt.infos_path) as f:
infos = cPickle.load(f)
# override and collect parameters
if len(opt.input_fc_dir) == 0:
opt.input_fc_dir = infos['opt'].input_fc_dir
opt.input_img_dir = infos['opt'].input_img_dir
opt.input_box_dir = infos['opt'].input_box_dir
if len(opt.input_label_h5) == 0:
opt.input_label_h5 = infos['opt'].input_label_h5
if len(opt.input_json) == 0:
opt.input_json = infos['opt'].input_json
if opt.batch_size == 0:
opt.batch_size = infos['opt'].batch_size
if len(opt.glove_npy) == 0:
opt.glove_npy = infos['opt'].glove_npy
if len(opt.id) == 0:
opt.id = infos['opt'].id
ignore1 =["input_fc_dir", "input_img_dir", "input_box_dir", "input_label_h5", "glove_npy", "input_json"]
ignore2 = ["id", "batch_size", "beam_size", "start_from", "language_eval", "g_start_from", "d_start_from", "temperature",
"vis_weight", "lang_weight", "pair_weight"]
for k in vars(infos['opt']).keys():
if k not in ignore1 and k not in ignore2:
if k in vars(opt):
assert vars(opt)[k] == vars(infos['opt'])[k], k + ' option not consistent'
else:
vars(opt).update({k: vars(infos['opt'])[k]}) # copy over options from model
opt.vocab = infos['vocab'] # ix -> word mapping
# Setup the model
gen_model,dis_model = models.setup(opt)
gen_model.load_state_dict(torch.load(opt.g_model_path))
gen_model.cuda()
gen_model.eval()
crit = utils.LanguageModelCriterion()
if len(opt.d_model_path) > 0 :
dis_model.load_state_dict(torch.load(opt.d_model_path))
dis_model.cuda()
dis_model.eval()
gan_crit = nn.BCELoss().cuda()
else:
dis_model = None
gan_crit = None
# Create the Data Loader instance
loader = DataLoader(opt)
# When eval using provided pretrained model, the vocab may be different from what you have in your cocotalk.json
# So make sure to use the vocab in infos file.
loader.ix_to_word = infos['vocab']
loss, split_predictions, lang_stats, _, div = eval_utils.eval_split(gen_model, crit, loader, dis_model, gan_crit, eval_kwargs=vars(opt))
print('loss: ', loss)
if lang_stats:
print(lang_stats)