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msrvtt_caption_s2s_step.py
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import numpy as np
import os
import h5py
import math
from utils import MsrDataUtil
from model import CaptionModel
os.environ["CUDA_VISIBLE_DEVICES"]="0"
import tensorflow as tf
import cPickle as pickle
import time
import json
def exe_train(sess, data, batch_size, v2i, hf, feature_shape,
train, loss, input_video, input_captions, y, capl=16):
np.random.shuffle(data)
total_data = len(data)
num_batch = int(round(total_data*1.0/batch_size))
total_loss = 0.0
# for batch_idx in xrange(num_batch):
for batch_idx in xrange(500):
# if batch_idx < 100:
batch_caption = data[batch_idx*batch_size:min((batch_idx+1)*batch_size,total_data)]
data_v = MsrDataUtil.getBatchStepVideoFeature(batch_caption,hf,feature_shape)
data_c, data_y = MsrDataUtil.getNewBatchTrainCaption(batch_caption, v2i, capl=capl)
_, l = sess.run([train,loss],feed_dict={input_video:data_v, input_captions:data_c, y:data_y})
total_loss += l
print(' batch_idx:%d/%d, loss:%.5f' %(batch_idx+1,num_batch,l))
total_loss = total_loss/num_batch
return total_loss
def exe_test(sess, data, batch_size, v2i, i2v, hf, feature_shape,
predict_words, input_video, input_captions, y, capl=16):
caption_output = []
total_data = len(data)
num_batch = int(round(total_data*1.0/batch_size))+1
for batch_idx in xrange(num_batch):
batch_caption = data[batch_idx*batch_size:min((batch_idx+1)*batch_size,total_data)]
data_v = MsrDataUtil.getBatchStepVideoFeature(batch_caption,hf,feature_shape)
data_c, data_y = MsrDataUtil.getBatchTestCaption(batch_caption, v2i, capl=capl)
[gw] = sess.run([predict_words],feed_dict={input_video:data_v, input_captions:data_c, y:data_y})
generated_captions = MsrDataUtil.convertCaptionI2V(batch_caption, gw, i2v)
for idx, sen in enumerate(generated_captions):
print('%s : %s' %(batch_caption[idx].keys()[0],sen))
caption_output.append({'image_id':batch_caption[idx].keys()[0],'caption':sen})
js = {}
js['val_predictions'] = caption_output
return js
def evaluate_mode_by_shell(res_path,js):
with open(res_path, 'w') as f:
json.dump(js, f)
command ='/home/xyj/usr/local/tools/caption/caption_eval/msrvtt_eval.sh '+ res_path
os.system(command)
def main(hf,f_type,capl=16, d_w2v=512, output_dim=512,
feature_shape=None,lr=0.01,
batch_size=64,total_epoch=100,
file=None,pretrained_model=None):
'''
capl: the length of caption
'''
# Create vocabulary
v2i, train_data, val_data, test_data = MsrDataUtil.create_vocabulary_word2vec(file, capl=capl, v2i={'': 0, 'UNK':1,'BOS':2, 'EOS':3})
i2v = {i:v for v,i in v2i.items()}
print('building model ...')
voc_size = len(v2i)
input_video = tf.placeholder(tf.float32, shape=(None,)+feature_shape,name='input_video')
input_captions = tf.placeholder(tf.int32, shape=(None,capl), name='input_captions')
y = tf.placeholder(tf.int32,shape=(None, capl,len(v2i)))
captionModel = CaptionModel.CaptionModel(input_video, input_captions, voc_size, d_w2v, output_dim)
predict_score, predict_words = captionModel.build_model()
loss = tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=predict_score)
loss = tf.reduce_mean(loss)+sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES))
optimizer = tf.train.RMSPropOptimizer(lr,decay=0.9, momentum=0.0, epsilon=1e-8)
train = optimizer.minimize(loss)
'''
configure && runtime environment
'''
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.3
# sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
config.log_device_placement=False
sess = tf.Session(config=config)
init = tf.global_variables_initializer()
sess.run(init)
with sess.as_default():
saver = tf.train.Saver(sharded=True,max_to_keep=total_epoch)
if pretrained_model is not None:
saver.restore(sess, pretrained_model)
print('restore pre trained file:' + pretrained_model)
for epoch in xrange(total_epoch):
# # shuffle
print('Epoch: %d/%d, Batch_size: %d' %(epoch+1,total_epoch,batch_size))
# # train phase
tic = time.time()
total_loss = exe_train(sess, train_data, batch_size, v2i, hf, feature_shape, train, loss, input_video, input_captions, y, capl=capl)
print(' --Train--, Loss: %.5f, .......Time:%.3f' %(total_loss,time.time()-tic))
tic = time.time()
js = exe_test(sess, test_data, batch_size, v2i, i2v, hf, feature_shape,
predict_words, input_video, input_captions, y, capl=capl)
print(' --Val--, .......Time:%.3f' %(time.time()-tic))
#save model
export_path = '/home/xyj/usr/local/saved_model/msrvtt2017/s2s'+'_'+f_type+'/'+'lr'+str(lr)+'_f'+str(feature_shape[0])
if not os.path.exists(export_path+'/model'):
os.makedirs(export_path+'/model')
print('mkdir %s' %export_path+'/model')
if not os.path.exists(export_path+'/res'):
os.makedirs(export_path+'/res')
print('mkdir %s' %export_path+'/res')
# eval
res_path = export_path+'/res/'+f_type+'_E'+str(epoch+1)+'.json'
evaluate_mode_by_shell(res_path,js)
save_path = saver.save(sess, export_path+'/model/'+'E'+str(epoch+1)+'_L'+str(total_loss)+'.ckpt')
print("Model saved in file: %s" % save_path)
if __name__ == '__main__':
lr = 0.0002
video_feature_dims=1024
timesteps_v=10 # sequences length for video
# hight = 7
# width = 7
feature_shape = (timesteps_v,video_feature_dims)
f_type = 'step_GoogleNet'
# feature_path = '/home/xyj/usr/local/data/in5b-'+str(timesteps_v)+'fpv.h5'
#feature_path = '/home/xyj/usr/local/data/YouTube/feature/pool5_7x7_s1-'+str(timesteps_v)+'f.h5'
feature_path = '/mnt/data3/yzw/MSRVTT2017/features/googlenet_pl5_f40.h5'
'''
---------------------------------
'''
hf = h5py.File(feature_path,'r')['images']
pretrained_model = None
main(hf,f_type,capl=16, d_w2v=512, output_dim=512,
feature_shape=feature_shape,lr=lr,
batch_size=64,total_epoch=100,
file='/home/xyj/usr/local/data/msrvtt',pretrained_model=None)