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self_retrieval.py
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self_retrieval.py
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"""
Self-retrieval Experiment
"""
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 models
from dataloader_test_random import *
#from dataloaderraw import *
import evalpro_recall_utils
import argparse
import torch
import linecache
# Input arguments and options
parser = argparse.ArgumentParser()
# Input paths
parser.add_argument('--caption_model', type=str, default="TDA",
help='ST, TDA')
parser.add_argument('--model', type=str, default='TDA',
help='path to model to evaluate')
parser.add_argument('--CNN', type=str, default='Resnet101',
help='Resnet152, Resnet101')
parser.add_argument('--cnn_model', type=str, default='Resnet101',
help='Resnet152, Resnet101')
parser.add_argument('--infos_path', type=str, default='',
help='path to infos to evaluate')
parser.add_argument('--datasets', type=str, default='mscoco',
help='mscoco, flickr30k')
# Basic options
parser.add_argument('--batch_size', type=int, default=250,
help='if > 0 then overrule, otherwise load from checkpoint.')
parser.add_argument('--num_images', type=int, default=5000,
help='how many images to use when periodically evaluating the loss? (-1 = all)')
parser.add_argument('--language_eval', type=int, default=0,
help='Evaluate language as well (1 = yes, 0 = no)? BLEU/CIDEr/METEOR/ROUGE_L? requires coco-caption code from Github.')
parser.add_argument('--dump_images', type=int, default=0,
help='Dump images into vis/imgs folder for vis? (1=yes,0=no)')
parser.add_argument('--dump_json', type=int, default=1,
help='Dump json with predictions into vis folder? (1=yes,0=no)')
parser.add_argument('--dump_path', type=int, default=1,
help='Write image paths along with predictions into vis json? (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('--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('--temperature', type=float, default=1.0,
help='temperature when sampling from distributions (i.e. when sample_max = 0). Lower = "safer" predictions.')
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('--max_ppl', type=int, default=0,
help='beam search by max perplexity or max probability.')
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('--decoding_constraint', type=int, default=1,
help='If 1, not allowing same word in a row')
# For evaluation on a folder of images:
parser.add_argument('--image_folder', type=str, default='',
help='If this is nonempty then will predict on the images in this folder path')
parser.add_argument('--image_root', type=str, default='',
help='In case the image paths have to be preprended with a root path to an image folder')
# For evaluation on MSCOCO images from some split:
parser.add_argument('--input_fc_dir', type=str, default='',
help='path to the h5file containing the preprocessed dataset')
parser.add_argument('--input_att_dir', type=str, default='',
help='path to the h5file containing the preprocessed dataset')
parser.add_argument('--input_label_h5', type=str, default='',
help='path to the h5file containing the preprocessed dataset')
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='test2014',
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.')
parser.add_argument('--checkpoint_path', type=str, default='save',
help='directory to store checkpointed models')# 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('--sample_method', type=int, default=0,
help='score is 1 or recall is 0')
opt = parser.parse_args()
path = os.path.join(opt.checkpoint_path,'CSGD') #checkpoint_path = RL_
opt.infos_path = os.path.join(path,'infos_'+str(opt.caption_model)+'.pkl')
with open(opt.infos_path) as f:
infos = cPickle.load(f)
opt.save = infos['opt'].save_checkpoint_every
opt.input_fc_dir = os.path.join('data','fc')
opt.input_att_dir = os.path.join('data','att')
opt.input_label_h5 = infos['opt'].input_label_h5
if len(opt.id) == 0:
opt.id = infos['opt'].id
ignore = ["id", "batch_size", "input_json", "batch_size", "beam_size", "start_from", "language_eval", "checkpoint_path", "num_images"]
for k in vars(infos['opt']).keys():
if k not in ignore:
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.input_json = 'data/random_test.json'
vocab = infos['vocab'] # ix -> word mapping
caption_path = os.path.join(path, 'Sentence_random_5000.json')
caption_data=json.load(open(caption_path,'r'))
# Setup the model
opt.model = os.path.join(path,'model-best.pth')
model = models.setup_pro(opt)
model.load_state_dict(torch.load(opt.model))
model.cuda()
model.eval()
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']
# Set sample options
recallpath = os.path.join(path, 'recall5000.json')
pos_data = []
for ind in xrange(opt.num_images):
recall = evalpro_recall_utils.eval_split(model, caption_data[ind], loader, vars(opt))
rerank = np.argsort(-recall, axis=0).tolist()
pos = rerank.index(ind)
pos_data.append(pos)
if ind % 100 == 0:
# Compute metrics
r1 = 100.0 * len(np.where(pos_data < 1)[0]) / len(pos_data)
r5 = 100.0 * len(np.where(pos_data < 5)[0]) / len(pos_data)
r10 = 100.0 * len(np.where(pos_data < 10)[0]) / len(pos_data)
print(ind, r1, r5, r10)
json.dump(pos_data, open(recallpath, 'w'))
r1 = 100.0 * len(np.where(pos_data < 1)[0]) / len(pos_data)
r5 = 100.0 * len(np.where(pos_data < 5)[0]) / len(pos_data)
r10 = 100.0 * len(np.where(pos_data < 10)[0]) / len(pos_data)
print(r1, r5, r10)