denseeval3m.py

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 denseloader3m import *

import deneval_utils3m_1 as eval_utils
import argparse
import misc.utils as utils
import torch

# Input arguments and options
parser = argparse.ArgumentParser()
# Input paths
parser.add_argument('--model', type=str, default='data/model-best.pth',
                help='path to model to evaluate')
parser.add_argument('--cnn_model', type=str,  default='resnet152',
                help='resnet101, resnet152')
parser.add_argument('--infos_path', type=str, default='',
                help='path to infos to evaluate')
parser.add_argument('--seq_per_img', type=int, default=5,
                        help='number of caption per image to evaluate')
parser.add_argument('--perss_onehot_h5', type=str, default="data/person_onehot_added1.h5",
                        help='one hot vector of personality')
opts.add_eval_options(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_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
epoch=infos['epoch']

print("we are evaluating ", epoch, " epoch")

# Setup the model
opt.vocab = vocab
model = models.setup(opt)

del opt.vocab
model.load_state_dict(torch.load(opt.model))
#torch.cuda.set_device(1)
model.cuda()
model.eval()

if opt.use_dl>0:
    crit = utils.LanguageModelCriterionDL()
else:
    crit = utils.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.ix_to_word = infos['vocab']

# Set sample options
opt.datset = opt.input_json
split_predictions, lang_stats = eval_utils.eval_split(model, crit, loader, vars(opt))

if lang_stats:
    print(lang_stats)

if opt.dump_json == 1:
    # dump the json
    json.dump(split_predictions, open('eval_results/vis.json', 'w'))