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w2vv_trainer.py
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import os
os.environ["PATH"] += os.pathsep + 'E:\\code\\w2vv-master\\graphviz-2.38\\release\\bin'
from six.moves import xrange
import sys
import time
import json
import random
import numpy as np
from basic.constant import *
from basic.metric import getScorer
from basic.common import makedirsforfile, checkToSkip, writePkl
import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.3
set_session(tf.Session(config=config))
from keras.utils import generic_utils
import tensorboard_logger as tb_logger
from w2vv import get_model
from w2vv_pred import pred_mutual_error_ms
from simpleknn.bigfile import BigFile
from util.dataset import PairDataSet_MS
from util.losser import get_losser
from util.text import get_we_parameter
from util.text2vec import get_text_encoder
from util.evaluation import i2t_map
from util.util import readImgSents
INFO = __file__
def cal_val_perf(all_errors, opt=None):
# validation metric: MAP
i2t_map_score = 0
i2t_map_score = i2t_map(all_errors, n_caption=opt.n_caption)
currscore = i2t_map_score
return currscore
def process(opt, trainCollection, valCollection, testCollection):
rootpath = opt.rootpath
overwrite = opt.overwrite
opt.n_text_layers = list(map(int, opt.n_text_layers.strip().split('-')))
if opt.init_model_from != '':
assert opt.img_feature in opt.init_model_from
init_model_name = opt.init_model_from.strip().split("/")[-1]
train_style = opt.model_name + "_" + INFO + "_ft_" + init_model_name
else:
train_style = opt.model_name + "_" + INFO
# text embedding style
if '@' in opt.text_style and opt.model_name.endswith('_ms'):
rnn_style, bow_style, w2v_style = opt.text_style.strip().split('@')
opt.rnn_style = rnn_style
text_data_path = os.path.join(rootpath, trainCollection, "TextData", "vocabulary", "bow", opt.rnn_vocab)
bow_data_path = os.path.join(rootpath, trainCollection, "TextData", "vocabulary", bow_style, opt.bow_vocab)
w2v_data_path = os.path.join(rootpath, "word2vec", opt.corpus, opt.word2vec)
text_name = opt.bow_vocab + "_rnn_%d_%s_sent_%d" % (opt.rnn_size, opt.rnn_vocab, opt.sent_maxlen)
else:
print(opt.text_style + " is not supported, please check the 'text_style' parameter")
sys.exit(0)
optm_style = opt.optimizer + '_clipnorm_%.1f_lr_%.5f_dp_%.2f_l2_%.5f_%s_bs_%d' % \
(opt.clipnorm, opt.lr, opt.dropout, opt.l2_p, opt.loss_fun, opt.batch_size)
model_style = "-".join(map(str, opt.n_text_layers)) + '_' + opt.hidden_act + '_' + opt.simi_fun
checkpoint_dir = os.path.join(rootpath, trainCollection, opt.checkpoint + '_w2vv_' + valCollection \
+ "_" + opt.text_style + '_' + text_name + "_" + opt.img_feature + "_" + model_style + "_" + opt.postfix)
# output visualization script
#runfile_vis = 'do_visual.sh'
#open( runfile_vis, 'w' ).write('port=$1\ntensorboard --logdir %s --port $port' % checkpoint_dir)
#os.system('chmod +x %s' % runfile_vis)
val_per_hist_file = os.path.join(checkpoint_dir, 'val_per_hist.txt')
'''
if checkToSkip(val_per_hist_file, overwrite):
sys.exit(0)
'''
# else:
# if os.path.exists(checkpoint_dir):
# os.system("rm -r " + checkpoint_dir)
makedirsforfile(val_per_hist_file)
model_file_name = os.path.join(checkpoint_dir, 'model.json')
model_img_name = os.path.join(checkpoint_dir, 'model.png')
tb_logger.configure(checkpoint_dir, flush_secs=5)
# text embedding (text representation)
if '@' in opt.text_style and opt.model_name.endswith('_ms'):
text2vec = get_text_encoder(rnn_style)(text_data_path)
bow2vec = get_text_encoder(bow_style)(bow_data_path)
#print(w2v_data_path)
#print(len(text2vec.vocab))
w2v2vec = get_text_encoder(w2v_style)(w2v_data_path)
if opt.n_text_layers[0] == 0:
opt.n_text_layers[0] = bow2vec.ndims + w2v2vec.ndims
else:
assert opt.n_text_layers[0] == bow2vec.ndims + w2v2vec.ndims
opt.vocab_size = text2vec.n_vocab
opt.embed_size = w2v2vec.ndims
else:
text2vec = get_text_encoder(opt.text_style)(text_data_path, ndims=opt.n_text_layers[0])
if opt.n_text_layers[0] == 0:
opt.n_text_layers[0] = text2vec.ndims
# img2vec
img_feat_path = os.path.join(rootpath, trainCollection, 'FeatureData', opt.img_feature)
img_feats = BigFile(img_feat_path)
val_img_feat_path = os.path.join(rootpath, valCollection, 'FeatureData', opt.img_feature)
val_img_feats = BigFile(val_img_feat_path)
# write out options for evaluation
pkl_file = os.path.join(checkpoint_dir, 'option.pkl')
writePkl(opt, pkl_file)
# define word2visualvec model
if opt.model_name.endswith('_ms'):
we_weights = get_we_parameter(text2vec.vocab, w2v_data_path)
#print("word embedding shape ", we_weights.shape)
#sys.exit()
#print(we_weights.shape)
model = get_model(opt.model_name)(opt, we_weights=we_weights)
else:
model = get_model(opt.model_name)(opt)
model.save_json_model(model_file_name)
model.plot(model_img_name)
model.compile_model(opt.loss_fun, opt=opt)
if opt.init_model_from != '':
print('*'*20)
print('initialize the model form ' + opt.init_model_from)
print('*'*20)
model.init_model(opt.init_model_from)
# training set
caption_file = os.path.join(rootpath, trainCollection, 'TextData', '%s.caption.txt' % trainCollection)
trainData = PairDataSet_MS(caption_file, opt.batch_size, text2vec, bow2vec, w2v2vec, img_feats, flag_maxlen=True, maxlen=opt.sent_maxlen)
val_sent_file = os.path.join(rootpath, valCollection, 'TextData', '%s.caption.txt' % valCollection)
val_img_list, val_sents_id, val_sents = readImgSents(val_sent_file)
losser = get_losser(opt.simi_fun)()
best_validation_perf = 0
n_step = 0
count = 0
lr_count = 0
best_epoch = -1
val_per_hist = []
for epoch in range(opt.max_epochs):
#for epoch in range(1):
print('\nEpoch', epoch)
print("Training...")
print("learning rate: ", model.get_lr())
tb_logger.log_value('lr', model.get_lr(), step=n_step)
train_progbar = generic_utils.Progbar(trainData.datasize)
trainBatchIter = trainData.getBatchData()
for minibatch_index in xrange(trainData.max_batch_size):
#for minibatch_index in xrange(10):
n_step += 1
img_X_batch, text_X_batch = trainBatchIter.__next__()
loss_batch = model.model.train_on_batch(text_X_batch, img_X_batch)
train_progbar.add(img_X_batch.shape[0], values=[("loss", loss_batch)])
tb_logger.log_value('loss', loss_batch, step=n_step)
tb_logger.log_value('n_step', n_step, step=n_step)
#break
print("\nValidating...")
all_errors = pred_mutual_error_ms(val_img_list, val_sents, model, text2vec, bow2vec, w2v2vec, val_img_feats, losser, opt=opt)
this_validation_perf = cal_val_perf(all_errors, opt=opt)
tb_logger.log_value('val_accuracy', this_validation_perf, step=n_step)
val_per_hist.append(this_validation_perf)
print('previous_best_performance: %.3f' % best_validation_perf)
print('current_performance: %.3f' % this_validation_perf)
fout_file = os.path.join(checkpoint_dir, 'epoch_%d.h5' % ( epoch))
lr_count += 1
if this_validation_perf > best_validation_perf:
best_validation_perf = this_validation_perf
count = 0
# save model
model.model.save_weights(fout_file)
if best_epoch != -1:
os.system('rm '+ os.path.join(checkpoint_dir, 'epoch_%d.h5' % ( best_epoch)))
best_epoch = epoch
else:
# when the validation performance has decreased after an epoch,
# we divide the learning rate by 2 and continue training;
# but we use each learning rate for at least 3 epochs.
if lr_count > 2:
model.decay_lr(0.5)
lr_count = 0
count += 1
if count > 10:
print("Early stopping happened")
break
sorted_epoch_perf = sorted(zip(range(len(val_per_hist)), val_per_hist), key = lambda x: x[1], reverse=True)
with open(val_per_hist_file, 'w') as fout:
for i, perf in sorted_epoch_perf:
fout.write("epoch_" + str(i) + " " + str(perf) + "\n")
# generate the shell script for test
templete = ''.join(open( 'TEMPLATE_do_test.sh' ).readlines())
striptStr = templete.replace('@@@rootpath@@@', rootpath)
striptStr = striptStr.replace('@@@trainCollection@@@', trainCollection)
striptStr = striptStr.replace('@@@valCollection@@@', valCollection)
striptStr = striptStr.replace('@@@testCollection@@@', testCollection)
striptStr = striptStr.replace('@@@model_path@@@', checkpoint_dir)
striptStr = striptStr.replace('@@@weight_name@@@', 'epoch_%d.h5' % sorted_epoch_perf[0][0])
striptStr = striptStr.replace('@@@n_caption@@@', str(opt.n_caption))
print(os.path.join(checkpoint_dir, 'epoch_%d.h5' % sorted_epoch_perf[0][0]))
runfile = 'do_test_%s.sh' % (testCollection)
open(runfile, 'w').write(striptStr+'\n')
#os.system('chmod +x %s' % runfile)
# os.system('./'+runfile)
os.system('cp %s/epoch_%d.h5 %s/best_model.h5' % (checkpoint_dir, sorted_epoch_perf[0][0], checkpoint_dir))
def main(argv=None):
if argv is None:
argv = sys.argv[1:]
from optparse import OptionParser
parser = OptionParser(usage="""usage: %prog [opt] trainCollection valCollection testCollection""")
parser.add_option("--overwrite", default=0, type="int", help="overwrite existing file (default: 0)")
parser.add_option("--rootpath", default=ROOT_PATH, type="string", help="rootpath (default: %s)" % ROOT_PATH)
parser.add_option('--checkpoint', type="string", default='cv_w2vv_new', help='output directory to write checkpoints to (default: cv_w2vv_new)')
parser.add_option('--init_model_from', type="string", default='', help='initialize the model parameters from some specific checkpoint?')
parser.add_option("--model_name", default='w2vv_ms', type="string", help="model name (default: w2vv_ms)")
# image feature
parser.add_option("--img_feature", default=DEFAULT_IMG_FEATURE, type="string", help="image feature (default: %s)"%DEFAULT_IMG_FEATURE)
# text embedding
parser.add_option("--text_style", default=DEFAULT_TEXT_STYLE, type="string", help="text embedding style (default: %s)"%DEFAULT_TEXT_STYLE)
# word2vector parameters
parser.add_option("--corpus", default=DEFAULT_CORPUS, type="string", help="corpus using which word2vec was trained (default: %s)" % DEFAULT_CORPUS)
parser.add_option("--word2vec", default=DEFAULT_WORD2VEC, type="string", help="word2vec model (default: %s)" % DEFAULT_WORD2VEC)
# bag-of-words parameters
parser.add_option("--bow_vocab", default=DEFAULT_BOW_VOCAB, type="string", help="bag-of-words vocabulary file name (default: %s)"%DEFAULT_BOW_VOCAB)
parser.add_option("--rnn_vocab", default=DEFAULT_RNN_VOCAB, type="string", help="rnn vocabulary file name(default: %s)"%DEFAULT_RNN_VOCAB)
parser.add_option('--rnn_size', type="int", default=1024, help='size of the rnn (default: 1024)')
parser.add_option("--sent_maxlen", default=32, type="int", help="maximun length of sentece")
# model parameters
parser.add_option('--n_text_layers', type="str", default='0-2048-2048', help='number of neurons in each layers (default: 0-2048-2048')
parser.add_option('--hidden_act', type="str", default='relu', help='activation function on hidden layer (default: relu)')
parser.add_option('--dropout', type="float", default=0.2, help='dropout (default: 0.2)')
# optimization parameters
parser.add_option('--max_epochs', type="int", default=100, help='number of epochs to train (default: 100)')
parser.add_option('--batch_size', type="int", default=100, help='batch size (default: 100)')
parser.add_option('--optimizer', type="string", default='rmsprop', help='optimization algorithm (default: rmsprop)')
parser.add_option('--clipnorm', type="float", default='5.', help='l2 W_regularizer (default: 0)')
parser.add_option('--loss_fun', type="string", default='mse', help='loss function (default: mse)')
parser.add_option("--simi_fun", default='cosine', type="string", help="similarity function: dot cosine")
parser.add_option('--lr', type="float", default=0.0001, help='learning rate (default: 0.001)')
parser.add_option('--l2_p', type="float", default='0.0', help='l2 W_regularizer (default: 0)')
parser.add_option('--n_caption', type="int", default='5', help='number of captions of each image/video (default: 5)')
parser.add_option('--postfix', type="str", default='run_0', help='')
(opt, args) = parser.parse_args(argv)
if len(args) < 3:
parser.print_help()
return 1
print(json.dumps(vars(opt), indent = 2))
return process(opt, args[0], args[1], args[2])
if __name__ == "__main__":
sys.exit(main())