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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 captioning.utils.opts as opts
import captioning.models as models
from captioning.data.dataloader import *
# from captioning.data.dataloaderraw import *
import captioning.utils.eval_utils as eval_utils
import argparse
import captioning.utils.misc as utils
import captioning.modules.losses as losses
import torch
# Input arguments and options
parser = argparse.ArgumentParser()
# Input paths
parser.add_argument('--model', type=str, default='',
help='path to model to evaluate')
parser.add_argument('--cnn_model', type=str, default='resnet101',
help='resnet101, resnet152')
parser.add_argument('--infos_path', type=str, default='',
help='path to infos to evaluate')
parser.add_argument('--only_lang_eval', type=int, default=0,
help='lang eval on saved results')
parser.add_argument('--force', type=int, default=0,
help='force to evaluate no matter if there are results available')
parser.add_argument('--device', type=str, default='cuda',
help='cpu or cuda')
opts.add_eval_options(parser)
opts.add_diversity_opts(parser)
opt = parser.parse_args()
# Load infos
with open(opt.infos_path, 'rb') as f:
infos = utils.pickle_load(f)
# override and collect parameters
replace = ['input_fc_dir', 'input_att_dir', 'input_box_dir', 'input_label_h5', 'input_json', 'batch_size', 'id']
ignore = ['start_from']
for k in vars(infos['opt']).keys():
if k in replace:
setattr(opt, k, getattr(opt, k) or getattr(infos['opt'], k, ''))
elif k not in ignore:
if not k in vars(opt):
vars(opt).update({k: vars(infos['opt'])[k]}) # copy over options from model
vocab = infos['vocab'] # ix -> word mapping
pred_fn = os.path.join('eval_results/', '.saved_pred_'+ opt.id + '_' + opt.split + '.pth')
result_fn = os.path.join('eval_results/', opt.id + '_' + opt.split + '.json')
if opt.only_lang_eval == 1 or (not opt.force and os.path.isfile(pred_fn)):
# if results existed, then skip, unless force is on
if not opt.force:
try:
if os.path.isfile(result_fn):
print(result_fn)
json.load(open(result_fn, 'r'))
print('already evaluated')
os._exit(0)
except:
pass
predictions, n_predictions = torch.load(pred_fn)
lang_stats = eval_utils.language_eval(opt.input_json, predictions, n_predictions, vars(opt), opt.split)
print(lang_stats)
os._exit(0)
# At this point only_lang_eval if 0
if not opt.force:
# Check out if
try:
# if no pred exists, then continue
tmp = torch.load(pred_fn)
# if language_eval == 1, and no pred exists, then continue
if opt.language_eval == 1:
json.load(open(result_fn, 'r'))
print('Result is already there')
os._exit(0)
except:
pass
# Setup the model
opt.vocab = vocab
model = models.setup(opt)
del opt.vocab
model.load_state_dict(torch.load(opt.model, map_location='cpu'))
model.to(opt.device)
model.eval()
crit = losses.LanguageModelCriterion()
# Create the Data Loader instance
if len(opt.image_folder) == 0:
loader = DataLoader(opt)
else:
loader = DataLoaderRaw({'folder_path': opt.image_folder,
'coco_json': opt.coco_json,
'batch_size': opt.batch_size,
'cnn_model': opt.cnn_model})
# 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.dataset.ix_to_word = infos['vocab']
# Set sample options
opt.dataset = opt.input_json
loss, split_predictions, lang_stats = eval_utils.eval_split(model, crit, loader,
vars(opt))
print('loss: ', loss)
if lang_stats:
print(lang_stats)
if opt.dump_json == 1:
# dump the json
json.dump(split_predictions, open('vis/vis.json', 'w'))
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