multitask_fix_attribute_fc_2ndlstm_sum_s2vt.py

import tensorflow as tf
import pandas as pd
import numpy as np
import os
import sys
import time
import cv2
import argparse
import matplotlib.pyplot as plt
import random
import math
from beam_search import *
from inception_resnet_v2 import *
import glob
from evaluation import *
import multiprocessing
from collections import defaultdict

os.environ["CUDA_VISIBLE_DEVICES"] = "0"
slim = tf.contrib.slim

def parse_args():
    """
    Parse input arguments
    """
    parser = argparse.ArgumentParser(description='Extract a CNN features')
    parser.add_argument('--gpu', dest='gpu_id', help='GPU id to use',
                        default=3, type=int)
    parser.add_argument('--net', dest='model',
                        help='model to test',
                        default=None, type=str)
    parser.add_argument('--dataset', dest='dataset',
                        help='dataset to extract',
                        default='train_val', type=str)
    parser.add_argument('--task', dest='task',
                        help='train or test',
                        default='train', type=str)
    parser.add_argument('--tg', dest='tg',
                        help='target to be extract lstm feature',
                        default='/home/Hao/tik/jukin/data/h5py', type=str)
    parser.add_argument('--ft', dest='ft',
                        help='choose which feature type would be extract',
                        default='lstm1', type=str)

    if len(sys.argv) == 1:
        parser.print_help()
        sys.exit(1)

    args = parser.parse_args()
    return args

def optimistic_restore(session, save_file):
    reader = tf.train.NewCheckpointReader(save_file)
    saved_shapes = reader.get_variable_to_shape_map()
    var_names = sorted([(var.name, var.name.split(':')[0]) for var in tf.global_variables()
			if var.name.split(':')[0] in saved_shapes])
    restore_vars = []
    name2var = dict(zip(map(lambda x:x.name.split(':')[0], tf.global_variables()), tf.global_variables()))
    with tf.variable_scope('', reuse=True):
        for var_name, saved_var_name in var_names:
            curr_var = name2var[saved_var_name]
            var_shape = curr_var.get_shape().as_list()
            if var_shape == saved_shapes[saved_var_name]:
                restore_vars.append(curr_var)
    saver = tf.train.Saver(restore_vars)
    saver.restore(session, save_file)

class Video_Caption_Generator():
    def __init__(self, dim_image, n_words, word_dim, lstm_dim, batch_size, n_lstm_steps, n_video_lstm_step,
                 n_caption_lstm_step, bias_init_vector=None, loss_weight = 1, decay_value = 0.00005, dropout_rate = 0.9,
                 width = 299, height = 299, channels= 3, feature_dim = 1536, label_dim=400, alpha = 0.2):
        self.dim_image = dim_image
        self.n_words = n_words
        self.word_dim = word_dim
        self.lstm_dim = lstm_dim
        self.batch_size = batch_size
        self.n_lstm_steps = n_lstm_steps  #### number of lstm cell
        self.n_video_lstm_step = n_video_lstm_step  ### frame number
        self.n_caption_lstm_step = n_caption_lstm_step  #### caption number
        self.loss_weight = loss_weight
        self.decay_value = decay_value
        self.dropout_rate = dropout_rate
        self.width = width
        self.height = height
        self.channels = channels
        self.label_dim = label_dim
        self.feature_dim = feature_dim
        self.alpha = alpha

        with tf.device("/cpu:0"):
            self.Wemb = self.Wemb = tf.Variable(tf.random_uniform([n_words, word_dim], -0.1, 0.1), dtype=tf.float32,
                                                name='Wemb',trainable=True)  ##without cpu

        #forwardlstm1 = tf.contrib.rnn.BasicLSTMCell(lstm_dim, state_is_tuple=False)
        #backwardlstm11 = tf.contrib.rnn.BasicLSTMCell(lstm_dim, state_is_tuple=False)
        self.lstm1 = tf.contrib.rnn.BasicLSTMCell(lstm_dim, state_is_tuple=False)
        self.lstm1_dropout = tf.contrib.rnn.DropoutWrapper(self.lstm1, output_keep_prob=self.dropout_rate)
        self.lstm2 = tf.contrib.rnn.BasicLSTMCell(lstm_dim, state_is_tuple=False)
        self.lstm2_dropout = tf.contrib.rnn.DropoutWrapper(self.lstm2, output_keep_prob=self.dropout_rate)

        self.encode_image_W = tf.Variable(tf.random_uniform([dim_image, word_dim], -0.1, 0.1), dtype=tf.float32,
                                          name='encode_image_W', trainable=True)
        self.encode_image_b = tf.Variable(tf.zeros([word_dim], tf.float32), name='encode_image_b')

        self.embed_word_W = tf.Variable(tf.random_uniform([lstm_dim, n_words], -0.1, 0.1), dtype=tf.float32,
                                        name='embed_word_W', trainable=True)
        if bias_init_vector is not None:
            self.embed_word_b = tf.Variable(bias_init_vector.astype(np.float32), name='embed_word_b')
        else:
            self.embed_word_b = tf.Variable(tf.zeros([n_words]), name='embed_word_b')
        #### multilabel fc layer weights and bias
        self.attr_W = tf.Variable(tf.random_uniform([conv_dim, self.label_dim], -0.1, 0.1), dtype=tf.float32,
                                  name='attr_W', trainable=True)
        self.attr_b = tf.Variable(tf.zeros([self.label_dim], tf.float32), dtype=tf.float32, name='attr_b')

    def build_model(self):
        ########## inception resnet v2####
        ###preprocessing###
        video_frames = tf.placeholder(tf.float32,[self.batch_size, self.n_video_lstm_step, self.dim_image])
        all_frames = tf.reshape(video_frames,[-1, self.dim_image])

        with slim.arg_scope(inception_resnet_v2_arg_scope()):
          with tf.variable_scope('InceptionResnetV2', 'InceptionResnetV2',
                               reuse=None) as scope:
            with slim.arg_scope([slim.batch_norm, slim.dropout],
                                is_training=False):


                ########### attribute ################
                net = slim.fully_connected(all_frames, conv_dim, activation_fn=None,scope = 'last_fc')####################################################  add  #####
                net = tf.stop_gradient(net)
                #net = slim.flatten(net)
                #net = slim.dropout(net, self.dropout_rate, is_training=True, scope='Dropout')

        video = tf.reshape(video_frames, [self.batch_size, self.n_video_lstm_step, self.dim_image])
        ########### attibute  ###############3
        attribute_video = tf.reshape(net, [self.batch_size, self.n_video_lstm_step, conv_dim])####################################################  add  #####
        #####################################################


        #video = tf.placeholder(tf.float32, [self.batch_size, self.n_video_lstm_step, self.dim_image])  ###llj

        caption = tf.placeholder(tf.int32, [self.batch_size,
                                            self.n_caption_lstm_step])  ####llj    make caption start at n_video_lstm_step
        caption_mask = tf.placeholder(tf.float32, [self.batch_size, self.n_caption_lstm_step])  ##llj

        video_flat = tf.reshape(video, [-1, self.dim_image])
        image_emb = tf.nn.xw_plus_b(video_flat, self.encode_image_W,
                                    self.encode_image_b)  # (batch_size*n_lstm_steps, dim_hidden)
        image_emb = tf.reshape(image_emb, [self.batch_size, self.n_video_lstm_step,
                                           self.word_dim])  ########potential problem in reshape
        #####################  attribute ############
        fusion_feature = image_emb #+ attribute_video####################################################  add / multiply / concatenate #####

        ### add dropout####
        image_emb = fusion_feature

        state1 = tf.zeros([self.batch_size, self.lstm1.state_size], tf.float32)
        state2 = tf.zeros([self.batch_size, self.lstm2.state_size], tf.float32)
        padding = tf.zeros([self.batch_size, self.word_dim], tf.float32)
        concat_padding = tf.zeros([self.batch_size, self.word_dim], tf.float32)

        probs = []
        loss = 0.0

        ##############################  Encoding Stage ##################################
        with tf.variable_scope("s2vt") as scope:
            for i in range(0, self.n_video_lstm_step):
                if i > 0:
                    tf.get_variable_scope().reuse_variables()

                with tf.variable_scope("LSTM1"):
                    output1, state1 = self.lstm1_dropout(image_emb[:, i, :], state1)


                    ####2nd lstm sum ####
                output1 = output1 + attribute_video[:,i,:]

                with tf.variable_scope("LSTM2"):
                    output2, state2 = self.lstm2_dropout(tf.concat([output1, padding], 1), state2)

                    ############################# Decoding Stage ######################################

            for i in range(0, self.n_caption_lstm_step):  ## Phase 2 => only generate captions
                if i == 0:
                    with tf.device("/cpu:0"):
                        current_embed = tf.nn.embedding_lookup(self.Wemb, tf.ones([self.batch_size],
                                                                                  dtype=tf.int64))  ######## embedding begin of sentence <bos>
                else:

                    with tf.device("/cpu:0"):
                        current_embed = tf.nn.embedding_lookup(self.Wemb, caption[:,i - 1])  ##without cpu    ### i-1 correspond to the previous word
                #### add dropout##
                current_embed = tf.layers.dropout(inputs=current_embed,rate = self.dropout_rate)

                tf.get_variable_scope().reuse_variables()
                with tf.variable_scope("LSTM1"):
                    output1, state1 = self.lstm1_dropout(concat_padding, state1)

                with tf.variable_scope("LSTM2"):
                    output2, state2 = self.lstm2_dropout(tf.concat([output1, current_embed], 1), state2)

                    # labels = tf.expand_dims(caption[:, i], 1)#### batch_size x 1 ####### i correspond to current word
                    # indices = tf.expand_dims(tf.range(0, self.batch_size, 1), 1) ###### batch_size x 1
                    # concated = tf.concat([indices, labels],1)  #### make indices and labels pair batchsize x 2
                # onehot_labels = tf.sparse_to_dense(concated, tf.stack([self.batch_size, self.n_words],axis=0), 1.0, 0.0) ##### batch_size number of one hot word vector### batch_size x n_words

                labels = tf.convert_to_tensor(caption[:, i])  #### batch_size x 1 ####### i correspond to current word
                onehot_labels = tf.one_hot(labels, self.n_words, 1.0, 0.0)

                logit_words = tf.nn.xw_plus_b(output2, self.embed_word_W, self.embed_word_b)
                #cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=onehot_labels, logits=logit_words) ### normal losss
                cross_entropy = tf.losses.softmax_cross_entropy(onehot_labels=onehot_labels, logits=logit_words, label_smoothing=0.05) ##### label smoothing
                cross_entropy = cross_entropy * caption_mask[:,i]  ######### need to move caption_mask (cont_sent)one column left ##########################llj
                probs.append(logit_words)

                current_loss = tf.reduce_sum(cross_entropy)
                loss += self.loss_weight * current_loss #+ weight_decay_loss
                #for v in tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES):
                    #print(v)

        ######## multilabel loss########

        ########## divide the batch_size * label_num ###

        weight_decay_loss = tf.add_n([tf.nn.l2_loss(v) for v in tf.trainable_variables() if 'bias' or 'BatchNorm' not in v.name]) \
                            * self.decay_value
        #loss = loss/tf.reduce_sum(caption_mask) + weight_decay_loss  ###normal loss
        loss =  loss / tf.reduce_sum(caption_mask) + weight_decay_loss  #### label smoothing
        return loss, video_frames, caption, caption_mask, probs

    def build_generator(self, beam_size=1, length_normalization_factor=0.5):
        video_frames = tf.placeholder(tf.float32,
                                      [1, self.n_video_lstm_step, self.height, self.width, self.channels])
        all_frames = tf.reshape(video_frames, [-1, self.height, self.width, self.channels])

        with slim.arg_scope(inception_resnet_v2_arg_scope()):
            with tf.variable_scope('InceptionResnetV2', 'InceptionResnetV2',
                                   reuse=None) as scope:
                with slim.arg_scope([slim.batch_norm, slim.dropout],
                                    is_training=False):
                    tf.get_variable_scope().reuse_variables()
                    net, endpoints = inception_resnet_v2_base(all_frames, scope=scope)
                    net = slim.avg_pool2d(net, net.get_shape()[1:3], padding='VALID', scope='AvgPool_1a_8x8')
                    net = slim.flatten(net)
                    net = slim.dropout(net, self.dropout_rate, is_training=False, scope='Dropout')
        video = tf.reshape(net, [1, self.n_video_lstm_step, self.dim_image])

        #video = tf.placeholder(tf.float32, [1, self.n_video_lstm_step, self.dim_image])
        video_flat = tf.reshape(video, [-1, self.dim_image])
        image_emb = tf.nn.xw_plus_b(video_flat, self.encode_image_W, self.encode_image_b)
        image_emb = tf.reshape(image_emb, [1, self.n_video_lstm_step, self.word_dim])
        state1 = tf.zeros([1, self.lstm1.state_size], tf.float32)
        state2 = tf.zeros([1, self.lstm2.state_size], tf.float32)
        padding = tf.zeros([1, self.word_dim], tf.float32)

        sentence = []
        probs = []
        with tf.variable_scope("s2vt") as scope:
            for i in range(0, self.n_video_lstm_step):
                if i > 0:
                    tf.get_variable_scope().reuse_variables()

                with tf.variable_scope("LSTM1"):
                    output1, state1 = self.lstm1(image_emb[:, i, :], state1)

                with tf.variable_scope("LSTM2"):
                    output2, state2 = self.lstm2(tf.concat([output1, padding], 1), state2)

                    ############### decoding ##########
                    # with tf.variable_scope("s2vt") as scope:
            for i in range(0, self.n_caption_lstm_step):
                tf.get_variable_scope().reuse_variables()
                if i ==0:
                    with tf.device('/cpu:0'):
                        current_embed = tf.nn.embedding_lookup(self.Wemb, tf.ones([1],dtype=tf.int64))
                else:
                    with tf.device('/cpu:0'):
                        current_embed = tf.nn.embedding_lookup(self.Wemb, max_prob_index)
                        current_embed = tf.expand_dims(current_embed, 0)

                with tf.variable_scope("LSTM1"):
                    output1, state1 = self.lstm1(padding, state1)
                with tf.variable_scope("LSTM2"):
                    output2, state2 = self.lstm2(tf.concat([output1, current_embed], 1), state2)
                logit_words = tf.nn.xw_plus_b(output2, self.embed_word_W, self.embed_word_b)
                words_probabilities = tf.exp(logit_words) / tf.reduce_sum(tf.exp(logit_words), -1)
                max_prob_index = tf.argmax(words_probabilities, 1)[0]
                sentence.append(max_prob_index)
               # probs.append(words_probabilities[max_prob_index])
                if max_prob_index == 0:
                    break
        return video_frames, sentence, probs

        # return video, tf_sentence
    def build_sampler(self):
        video_frames = tf.placeholder(tf.float32,
                                      [None, self.n_video_lstm_step, self.dim_image])
        all_frames = tf.reshape(video_frames, [-1, self.dim_image])

        with slim.arg_scope(inception_resnet_v2_arg_scope()):
            with tf.variable_scope('InceptionResnetV2', 'InceptionResnetV2',
                                   reuse=None) as scope:
                with slim.arg_scope([slim.batch_norm, slim.dropout],
                                    is_training=False):
                    #tf.get_variable_scope().reuse_variables()

                    attribute_net = slim.fully_connected(all_frames, conv_dim, activation_fn=None,scope = 'last_fc')####################################################  add  #####

        video = tf.reshape(video_frames, [-1, self.n_video_lstm_step, self.dim_image])
        attribute_video = tf.reshape(attribute_net,[-1, self.n_video_lstm_step, conv_dim])####################################################  add  #####
        video_flat = tf.reshape(video, [-1, self.dim_image])
        image_emb = tf.nn.xw_plus_b(video_flat, self.encode_image_W, self.encode_image_b)
        #image_emb = tf.reshape(image_emb, [self.batch_size, self.n_video_lstm_step, self.word_dim])
        #state1 = tf.zeros([self.batch_size, self.lstm1.state_size], tf.float32)
        #state2 = tf.zeros([self.batch_size, self.lstm2.state_size], tf.float32)
        #padding = tf.zeros([self.batch_size, self.word_dim], tf.float32)
        image_emb = tf.reshape(image_emb, [-1, self.n_video_lstm_step, self.word_dim])

        fusion_feature = image_emb #+ attribute_video####################################################  add /mul/concat #####
        image_emb = fusion_feature

        state1 = tf.zeros(tf.stack([tf.shape(video)[0], self.lstm1.state_size]), tf.float32)
        state2 = tf.zeros(tf.stack([tf.shape(video)[0], self.lstm2.state_size]), tf.float32)
        padding = tf.zeros(tf.stack([tf.shape(video)[0], self.word_dim]), tf.float32)
        concat_padding = tf.zeros(tf.stack([tf.shape(video)[0], self.word_dim]), tf.float32)

        sampled_words = []
        probs = []
        with tf.variable_scope("s2vt") as scope:
            for i in range(0, self.n_video_lstm_step):
                if i > 0:
                    tf.get_variable_scope().reuse_variables()

                with tf.variable_scope("LSTM1"):
                    output1, state1 = self.lstm1(image_emb[:, i, :], state1)

                ####2nd lstm sum   ####
                output1 = output1 + attribute_video[:,i,:]

                with tf.variable_scope("LSTM2"):
                    output2, state2 = self.lstm2(tf.concat([output1, padding], 1), state2)

                    ############### decoding ##########
                    # with tf.variable_scope("s2vt") as scope:
            for i in range(0, self.n_caption_lstm_step):
                tf.get_variable_scope().reuse_variables()
                if i ==0:
                    with tf.device('/cpu:0'):
                        #current_embed = tf.nn.embedding_lookup(self.Wemb, tf.ones([self.batch_size],dtype=tf.int64))
                        current_embed = tf.nn.embedding_lookup(self.Wemb, tf.ones([tf.shape(video)[0]], dtype=tf.int64))
                else:
                    with tf.device('/cpu:0'):
                        current_embed = tf.nn.embedding_lookup(self.Wemb, sampled_word)
                        #current_embed = tf.expand_dims(current_embed, 0)

                with tf.variable_scope("LSTM1"):
                    output1, state1 = self.lstm1(concat_padding, state1)
                with tf.variable_scope("LSTM2"):
                    output2, state2 = self.lstm2(tf.concat([output1, current_embed], 1), state2)
                logit_words = tf.nn.xw_plus_b(output2, self.embed_word_W, self.embed_word_b)
                sampled_word = tf.argmax(logit_words,1)
                sampled_words.append(sampled_word)

            sampled_captions = tf.transpose(tf.stack(sampled_words),[1,0])
        return sampled_captions,video_frames


# =====================================================================================
# Global Parameters
# =====================================================================================

# video_train_caption_file = './data/video_corpus.csv'
# video_test_caption_file = './data/video_corpus.csv'

#model_path = './vocab1_models'
model_path = './fix_models'

video_path = '/media/llj/storage/microsoft-corpus/youtube_frame_flow'
#video_path = '/media/llj/storage/microsoft-corpus/select_frames'

#video_path = '/media/llj/storage/microsoft-corpus/select_10_frames'


video_train_feature_file = '/home/llj/tensorflow_s2vt/5frame_train.txt'

video_test_feature_file = '/home/llj/tensorflow_s2vt/5frame_test.txt'

video_train_sent_file = '/media/llj/storage/all_sentences/msvd_sents_train_noval_lc_nopunc.txt'

video_test_sent_file = '/media/llj/storage/all_sentences/msvd_sents_test_lc_nopunc.txt'

#video_train_sent_best_captions = '/home/llj/tensorflow_s2vt/msvd_best_captions'

#vocabulary_file = '/media/llj/storage/all_sentences/coco_msvd_allvocab.txt'
vocabulary_file = '/media/llj/storage/all_sentences/msvd_vocabulary1.txt'

vocab_file = '/home/llj/tensorflow_s2vt/train_most_freq_vocab_400_truncated.txt' ##### attribute labels

feature_model = '/home/llj/tensorflow_s2vt/multilabel_models/batch_size8multilabel_fc3e-1_stopgradient_dim1000-34'
#lstm_model = '/home/llj/tensorflow_s2vt/res_models/best_5frames/batch_size16_5uniform_third_model-11'
#lstm_model = '/home/llj/tensorflow_s2vt/reinforcement_models/mix_loss/reinforce_loss_mix_alpha0.05-23'
#lstm_model = '/home/llj/tensorflow_s2vt/res_models/batch_size16res_model_10frame_dpp-7'

#e2e_model = '/home/llj/tensorflow_s2vt/e2e_models/10batch_size2cnn_model_false-24000'

#multitask_model ='/home/llj/tensorflow_s2vt/multitask_models/10batch_size2alpha_0.01_multitask_model-72000'

#multitask_model ='/home/llj/tensorflow_s2vt/multitask_models/10batch_size2alpha_0.01_bestcaptions_model-54000'

#reinforce_model = '/home/llj/tensorflow_s2vt/reinforcement_models/best_multisampling_sentence_model/reinforce_multisample_model-8'

#out_file = 'multitask_models/2batch_scores_10_noval_alpha0.01_e2e_continue.txt'
#out_file = 'multitask_models/multitask_scores_ontest_bestcaptions.txt'
out_file = 'fix_models/fix_models_attribute_fc_1stlstm_1e-4_dim1000_sum.txt'

model_name = 'fix_models_attribute_fc_2ndlstm_1e-4_dim1000_sum'

cider_img = 'attribute_fc_2ndlstm_1e-4_sum_dim1000_cider.png'

loss_img = 'attribute_fc_2ndlstm_1e-4_sum_dim1000_loss.png'


highest_cider = 0

# =======================================================================================
# Train Parameters
# =======================================================================================
dim_image = 1536
lstm_dim = 1000
word_dim = 500
conv_dim = 1000

n_lstm_step = 40
n_caption_lstm_step = 35
n_video_lstm_step = 5

n_epochs = 30
batch_size = 16
start_learning_rate = 0.0001
#start_learning_rate = 0.0001
width = 299
height = 299
channels = 3

feature_dim = dim_image
nums_label = 400
threshold = 0.5
num_videos = n_video_lstm_step
alpha = 0.05 ###### loss weight of multilabel , 1- alpha: loss weight of captioning

#caption_mask_out = open('caption_masks.txt', 'w')

def get_metrics(scores, labels, num_videos):
    true_positive = 0
    true_negative = 0
    false_positive = 0
    false_negative = 0
    count_pos = 0
    count_neg = 0
    for i in xrange(num_videos):
        for j in xrange(nums_label):
            if labels[i][j] >= threshold:
                true_positive += (scores[i][j] >= threshold)
                false_negative += (scores[i][j] < threshold)
                count_pos += 1
            if labels[i][j] < threshold:
                true_negative += (scores[i][j] < threshold)
                false_positive += (scores[i][j] >= threshold)
                count_neg += 1
    return true_positive, true_negative, false_positive, false_negative, count_pos, count_neg


def get_video_feature_caption_pair(sent_file=video_train_sent_file, feature_file=video_train_feature_file):
    sents = []
    vid = []
    features = {}
    with open(sent_file, 'r') as video_sent_file:
        for line in video_sent_file:
            line = line.strip()
            id_sent = line.split('\t')
            sents.append((id_sent[0], id_sent[1]))
    ######### uniformly sample num_frame_per_video frames
    with open(feature_file, 'r') as video_feature_file:
        for line in video_feature_file:
            splits = line.split(',')
            id_framenum = splits[0]
            video_id = id_framenum.split('_')[0]
            if video_id not in features:
                features[video_id] = []
            features[video_id].append(splits[1:])
    feature_length = [len(v) for v in features.values()]
    print 'length: ', set(feature_length)
    assert len(set(feature_length)) == 1  ######## make sure the feature lengths are all the same
    sents = np.array(sents)
    return sents, features

def get_video_feature_caption_pair_best(sent_file=video_train_sent_file, frame_path=video_path, num_frame_per_video = n_video_lstm_step,prefix='frame_'):
    sents = []
    vid = []
    video_frames = {}
    with open(sent_file, 'r') as video_sent_file:
        for line in video_sent_file:
            line = line.strip()
            id_sent = line.split('\t')
            sents.append((id_sent[0], id_sent[1]))
            if id_sent[0] not in vid:
                vid.append(id_sent[0])
    ########## uniformly sample num_frame_per_video frames
    for vid_name in vid:
        video_frames[vid_name] = []
        video_path = frame_path + '/' + vid_name
        frame_cnt = len(glob.glob(video_path+'/'+prefix+'*'))
        frame_ticks = range(1, 11)
        for tick in frame_ticks:
            name = '{}{:06d}.jpg'.format(prefix, tick)
            frame = os.path.join(video_path,name)
            video_frames[vid_name].append(frame)

    feature_length = [len(v) for v in video_frames.values()]
    print 'length: ', set(feature_length)
    assert len(set(feature_length)) == 1  ######## make sure the feature lengths are all the same
    sents = np.array(sents)
    return sents, video_frames, vid

def preProBuildWordVocab(vocabulary, word_count_threshold=0):
    # borrowed this function from NeuralTalk
    print 'preprocessing word counts and creating vocab based on word count threshold %d' % (word_count_threshold)
    word_counts = {}
    nsents = 0
    vocab = vocabulary

    ixtoword = {}
    # ixtoword[0] = '<pad>'
    ixtoword[1] = '<bos>'
    ixtoword[0] = '<eos>'

    wordtoix = {}
    # wordtoix['<pad>'] = 0
    wordtoix['<bos>'] = 1
    wordtoix['<eos>'] = 0

    for idx, w in enumerate(vocab):
        wordtoix[w] = idx + 2
        ixtoword[idx + 2] = w

    return wordtoix, ixtoword

def image_reading_processing(path):
    video_batch = [[] for x in xrange(len(path))]
    for i in xrange(len(path)):
        for j in xrange(n_video_lstm_step):
            image = cv2.imread(path[i][j], cv2.IMREAD_COLOR)### height,width,channels
            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
            image = cv2.resize(image, (width, height), interpolation=cv2.INTER_CUBIC)
            image = image.astype(np.float32)
            image = 2 * (image/255.0) - 1

            video_batch[i].append(image)
    return video_batch

def sentence_padding_toix(captions_batch, wordtoix):  ###########return dimension is n_caption_lstm_step
    captions_mask = []
    for idx, each_cap in enumerate(captions_batch):
        one_caption_mask = np.ones(n_caption_lstm_step)
        word = each_cap.lower().split(' ')
        if len(word) < n_caption_lstm_step:
            for i in range(len(word), n_caption_lstm_step):
                captions_batch[idx] = captions_batch[idx] + ' <eos>'
                if i != len(word):
                    one_caption_mask[i] = 0
        else:
            new_word = ''
            for i in range(n_caption_lstm_step - 1):
                new_word = new_word + word[i] + ' '
            captions_batch[idx] = new_word + '<eos>'
        # one_caption_mask=np.reshape(one_caption_mask,(-1,n_caption_lstm_step))
        captions_mask.append(one_caption_mask)
    captions_mask = np.reshape(captions_mask, (-1, n_caption_lstm_step))
    caption_batch_ind = []
    for cap in captions_batch:
        current_word_ind = []
        for word in cap.lower().split(' '):
            if word in wordtoix:
                current_word_ind.append(wordtoix[word])
            else:
                current_word_ind.append(wordtoix['<en_unk>'])
        # current_word_ind.append(0)###make one more dimension
        caption_batch_ind.append(current_word_ind)
    i = 0
    #caption_mask_out.write('captions: ' + str(caption_batch_ind) + '\n' + 'masks: ' + str(captions_mask) + '\n')
    return caption_batch_ind, captions_mask

def read_sent_vocab_file(sent_file, vocab_file):
    label_num = 0
    vid_sent = dict()
    vocab = list()
    with open(sent_file, 'r') as f:
        for line in f:
            line = line.strip()
            id_sent = line.split('\t')
            if id_sent[0] not in vid_sent:
                vid_sent[id_sent[0]] = []
            vid_sent[id_sent[0]].append(id_sent[1])

    with open(vocab_file, 'r') as f:
        for line in f:
            line = line.strip()
            vocab.append(line)
            label_num += 1
    return vid_sent, vocab, label_num

def get_multilabel(vid_sentence, vocabulary):
    vid_label = defaultdict()
    for vid, sents in vid_sentence.iteritems():
        label = list()
        for x in sents:
            label1 = list()
            for index, v in enumerate(vocabulary):
                count = 0
                for a in x.split():
                    if v == a:
                        count = 1
                label1.extend([count])
            label.append(label1)
            # print 'label1: ',label1
        label = np.sum(np.array(label), axis=0)
        np.putmask(label, label > 0, 1)

        # print 'label: ',label
        vid_label[vid] = label
    return vid_label

def train():  ###### move caption (input_sentence) one column left and also need to move caption_mask (cont_sent)one column left ########################################################llj
    ######### read caption, video frame path, video id######
    #### best captions
    #train_captions, video_frames, train_vid = get_video_feature_caption_pair(video_train_sent_best_captions, video_path, num_frame_per_video=n_video_lstm_step)
    ########### all captions
    train_captions, train_features = get_video_feature_caption_pair(video_train_sent_file, video_train_feature_file)
    vocabulary = []
    test_captions, test_features = get_video_feature_caption_pair(video_test_sent_file, video_test_feature_file)
    ##### read attribute

    with open(vocabulary_file, 'r') as vocab:
        for line in vocab:
            vocabulary.append(line.rstrip())

    wordtoix, ixtoword = preProBuildWordVocab(vocabulary, word_count_threshold=0)

    if not os.path.exists('./vocab1_data/wordtoix') or not os.path.exists('./vocab1_data/ixtoword'):
        np.save("./vocab1_data/wordtoix", wordtoix)
        np.save('./vocab1_data/ixtoword', ixtoword)

    model = Video_Caption_Generator(
        dim_image=dim_image,
        n_words=len(wordtoix),
        word_dim=word_dim,
        lstm_dim=lstm_dim,
        batch_size=batch_size,
        n_lstm_steps=n_lstm_step,
        n_video_lstm_step=n_video_lstm_step,
        n_caption_lstm_step=n_caption_lstm_step,
        bias_init_vector=None, feature_dim=feature_dim, label_dim=nums_label, alpha=alpha)

    tf_loss, tf_video, tf_caption, tf_caption_mask, tf_probs = model.build_model()
    # config = tf.ConfigProto(allow_soft_placement=True)

    config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=True)
    # config.gpu_options.allocator_type = 'BFC'
    sess = tf.InteractiveSession(config=config)

    # my tensorflow version is 0.12.1, I write the saver with version 1.0
    saver = tf.train.Saver(max_to_keep=100)
    global_step = tf.Variable(0, trainable=False)
    learning_rate = tf.train.exponential_decay(start_learning_rate, global_step,
                                               60000, 0.5, staircase=True)
    #optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)
    optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
    gradients, variables = zip(*optimizer.compute_gradients(tf_loss))
    gradients, _ = tf.clip_by_global_norm(gradients, 10)
    train_op = optimizer.apply_gradients(zip(gradients, variables),global_step=global_step)

    ###################clipping every gradient ################
    #gvs = optimizer.compute_gradients(tf_loss, aggregation_method=tf.AggregationMethod.EXPERIMENTAL_ACCUMULATE_N)
    #capped_gvs = [(tf.clip_by_norm(grad, 10), var) for grad, var in gvs]
    #train_op = optimizer.apply_gradients(capped_gvs,global_step=global_step)
    ############## clipping every gradient####
    #train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(tf_loss, global_step=global_step)
    tf.global_variables_initializer().run()
    #tf.summary.scalar('lr',learning_rate)
    optimistic_restore(sess, feature_model)
    #optimistic_restore(sess,multitask_model)
    #optimistic_restore(sess, '/home/llj/tensorflow_s2vt/e2e_models/10batch_size2cnn_model_false_withval-15000')
    # new_saver = tf.train.Saver()
    # new_saver = tf.train.import_meta_graph('./rgb_models/model-1000.meta')
    # new_saver.restore(sess, tf.train.latest_checkpoint('./models/'))

    loss_fd = open('vocab_loss.txt', 'w')
    loss_to_draw = []
    cider_to_draw = []
   # summary_op = tf.summary.merge_all()
    ############# test before training########

    greedy_captions, greedy_video_features = model.build_sampler()
    ######

    ######################################### caption evaluation ##########################
    with open(out_file, 'a') as f:
        all_decoded_for_eval = {}
        test_index = list(range(len(test_captions)))
        random.shuffle(test_index)
        ref_decoded = {}
        for aa in xrange(0,len(set(test_captions[:,0])),batch_size):

            id = list(set(test_captions[:,0]))[aa:aa+batch_size]
            test_video_batch = [test_features[x] for x in id]

            feed_dict = {greedy_video_features: test_video_batch}
            greedy_words = sess.run(greedy_captions, feed_dict) #### batch_size x num of each words
            greedy_decoded = decode_captions(np.array(greedy_words), ixtoword)
            for videoid in id:
                if videoid not in all_decoded_for_eval:
                    all_decoded_for_eval[videoid] = []

            [all_decoded_for_eval[x].append(y) for x,y in zip(id,greedy_decoded)]

        for num in xrange(0, len(test_captions),batch_size):

            videoid = test_captions[num:num+batch_size,0]
            for id in videoid:
                if id not in ref_decoded:
                    ref_decoded[id] = []
            [ref_decoded[x].append(y) for x,y in zip(videoid,test_captions[num:num+batch_size,1])]

        scores = evaluate_for_particular_captions(all_decoded_for_eval, ref_decoded)

        f.write('before train: ')
        f.write('\n')
        f.write("Bleu_1:" + str(scores['Bleu_1']))
        f.write('\n')
        f.write("Bleu_2:" + str(scores['Bleu_2']))
        f.write('\n')
        f.write("Bleu_3:" + str(scores['Bleu_3']))
        f.write('\n')
        f.write("Bleu_4:" + str(scores['Bleu_4']))
        f.write('\n')
        f.write("ROUGE_L:" + str(scores['ROUGE_L']))
        f.write('\n')
        f.write("CIDEr:" + str(scores['CIDEr']))
        f.write('\n')
        f.write("METEOR:" + str(scores['METEOR']))
        f.write('\n')
        f.write("metric:" + str(
            1 * scores['CIDEr'] ))
        f.write('\n')
######################################### caption evaluation ##########################

    iter_count = 0
    early_stopping = 0 #### stop sign
    highest_cider = 0 ####
    for epoch in range(0, n_epochs):
        ########## sample different frames in every epoch
       # train_captions, video_frames, train_vid = get_video_feature_caption_pair(video_train_sent_file, video_path,num_frame_per_video=n_video_lstm_step)
        loss_to_draw_epoch = []

        ## randomize the video id order
        index = list(range(len(train_captions)))
        random.shuffle(index)

        ### iterate over the video id
        for start, end in zip(range(0, len(index) - batch_size, batch_size), range(batch_size, len(index), batch_size)):
            iter_count += 1
            start_time = time.time()
            vid, sentence = train_captions[index[start:end], 0], train_captions[index[start:end], 1]
            captions_batch = sentence.tolist()
            features_batch = [train_features[x] for x in vid]

            # captions_batch = map(lambda x: '<bos> ' + x, captions_batch)
            captions_ind, captions_mask = sentence_padding_toix(captions_batch, wordtoix)


            _, loss_val = sess.run(
                [train_op, tf_loss],
                feed_dict={
                    tf_video: features_batch,
                    tf_caption: captions_ind,
                    tf_caption_mask: captions_mask
                })
            loss_to_draw_epoch.append(loss_val)

            print 'idx: ', start, ' rate: ', sess.run(learning_rate)," Epoch: ", epoch, " loss: ", loss_val,' Elapsed time: ', str((time.time() - start_time))

            loss_fd.write('epoch ' + str(epoch) + ' loss ' + str(loss_val) + '\n')

        with open(out_file, 'a') as f:
            all_decoded_for_eval = {}
            test_index = list(range(len(test_captions)))
            random.shuffle(test_index)
            ref_decoded = {}
            for aa in xrange(0, len(set(test_captions[:, 0])), batch_size):

                id = list(set(test_captions[:, 0]))[aa:aa + batch_size]
                test_video_batch = [test_features[x] for x in id]

                feed_dict = {greedy_video_features: test_video_batch}
                greedy_words = sess.run(greedy_captions, feed_dict)  #### batch_size x num of each words
                greedy_decoded = decode_captions(np.array(greedy_words), ixtoword)
                for videoid in id:
                    if videoid not in all_decoded_for_eval:
                        all_decoded_for_eval[videoid] = []

                [all_decoded_for_eval[x].append(y) for x, y in zip(id, greedy_decoded)]

            for num in xrange(0, len(test_captions), batch_size):

                videoid = test_captions[num:num + batch_size, 0]
                for id in videoid:
                    if id not in ref_decoded:
                        ref_decoded[id] = []
                [ref_decoded[x].append(y) for x, y in zip(videoid, test_captions[num:num + batch_size, 1])]

            scores = evaluate_for_particular_captions(all_decoded_for_eval, ref_decoded)

            f.write('Epoch %d\n' % epoch)
            f.write('\n')
            f.write("Bleu_1:" + str(scores['Bleu_1']))
            f.write('\n')
            f.write("Bleu_2:" + str(scores['Bleu_2']))
            f.write('\n')
            f.write("Bleu_3:" + str(scores['Bleu_3']))
            f.write('\n')
            f.write("Bleu_4:" + str(scores['Bleu_4']))
            f.write('\n')
            f.write("ROUGE_L:" + str(scores['ROUGE_L']))
            f.write('\n')
            f.write("CIDEr:" + str(scores['CIDEr']))
            f.write('\n')
            f.write("METEOR:" + str(scores['METEOR']))
            f.write('\n')
            f.write("metric:" + str(
                1 * scores['METEOR']))
            f.write('\n')
        print 'CIDEr: ', scores['CIDEr']
        if highest_cider < scores['CIDEr']:
            highest_cider = scores['CIDEr']
            early_stopping = 0

        cider_to_draw.append([scores['CIDEr']])
        plt_save_dir = "./fix_loss_imgs"
        plt_save_img_name = str(epoch) + cider_img
        plt.figure(1)
        plt.plot(range(len(cider_to_draw)), cider_to_draw, color='g')
        plt.grid(True)
        plt.savefig(os.path.join(plt_save_dir, plt_save_img_name))


        loss_to_draw.append(np.mean(loss_to_draw_epoch))
        plt_save_dir = "./fix_loss_imgs"
        plt_save_img_name = str(epoch) + loss_img
        plt.figure(2)
        plt.plot(range(len(loss_to_draw)), loss_to_draw, color='r')
        plt.grid(True)
        plt.savefig(os.path.join(plt_save_dir, plt_save_img_name))

        if np.mod(epoch, 1) == 0:
            print "Epoch ", epoch, " is done. Saving the model ..."
            saver.save(sess, os.path.join(model_path, 'batch_size' + str(batch_size)+ '_'+ model_name), global_step=epoch)
            if early_stopping > 8:
                return
            early_stopping += 1
            print 'highest cider: ',highest_cider


        # draw loss curve every epoch


    loss_fd.close()


def test(model_path='/home/llj/tensorflow_s2vt/vocab1_models/'):
    test_captions, video_frames = get_video_feature_caption_pair(video_test_sent_file, video_path, num_frame_per_video=n_video_lstm_step)

    ixtoword = pd.Series(np.load('./vocab1_data/ixtoword.npy').tolist())

    model = Video_Caption_Generator(
        dim_image=dim_image,
        n_words=len(ixtoword),
        word_dim=word_dim,
        lstm_dim=lstm_dim,
        batch_size=batch_size,
        n_lstm_steps=n_lstm_step,
        n_video_lstm_step=n_video_lstm_step,
        n_caption_lstm_step=n_caption_lstm_step,
        bias_init_vector=None)

    # video_tf, caption_tf, probs_tf, last_embed_tf = model.build_generator()
    video_tf, captions_tf, logprob_tf = model.build_generator()

    config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=True)
    sess = tf.InteractiveSession(config=config)

    for i in xrange(20):
        model_path_last = model_path + str(n_video_lstm_step)+'batch_size'+ str(batch_size)+'cnn_model' + str(i)
        out_file = 'vocab9960_batchsize_'+str(batch_size)+'_ep'+ str(i) + '.txt'
        saver = tf.train.Saver()
        saver.restore(sess, model_path_last)

        test_output_txt_fd = open(out_file, 'w')
        for key, values in video_frames.iteritems():
            video_frames_batch = [video_frames[key]]
            video_batch = image_reading_processing(video_frames_batch)
            generated_word_index = sess.run(captions_tf, feed_dict={video_tf: video_batch})
            generated_words = ixtoword[generated_word_index]

            punctuation = np.argmax(np.array(generated_words) == '<eos>') + 1
            generated_words = generated_words[:punctuation]

            generated_sentence = ' '.join(generated_words)
            generated_sentence = generated_sentence.replace('<bos> ', '')
            generated_sentence = generated_sentence.replace(' <eos>', '')
            print generated_sentence, '\n'
            test_output_txt_fd.write(key + '\t')
            test_output_txt_fd.write(generated_sentence + '\n')

def evaluation(model_path='/home/llj/tensorflow_s2vt/fix_models/'):
    test_captions, test_features = get_video_feature_caption_pair(video_test_sent_file, video_test_feature_file)

    ixtoword = pd.Series(np.load('./vocab1_data/ixtoword.npy').tolist())
    config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=True)
    sess = tf.InteractiveSession(config=config)

    #model_path_last = model_path + '10batch_size2alpha_0.01based_dpp25frame_model-69000' ####39000


    model = Video_Caption_Generator(
        dim_image=dim_image,
        n_words=len(ixtoword),
        word_dim=word_dim,
        lstm_dim=lstm_dim,
        batch_size=batch_size,
        n_lstm_steps=n_lstm_step,
        n_video_lstm_step=n_video_lstm_step,
        n_caption_lstm_step=n_caption_lstm_step,
        bias_init_vector=None)
    greedy_captions, greedy_video_features = model.build_sampler()

    saver = tf.train.Saver()

    #saver.restore(sess, model_path_last)

    with open('fix_models/batch_size16_fix_models_attribute_fc_2ndlstm_1e-4_dim1000_sum_test.txt', 'a') as f:
      for i in xrange(9,10,1):
        model_path_last = model_path + 'batch_size16_fix_models_attribute_fc_2ndlstm_1e-4_dim1000_sum-' +str(i)
        saver.restore(sess,model_path_last)
        all_decoded_for_eval = {}
        test_index = list(range(len(test_captions)))
        random.shuffle(test_index)
        ref_decoded = {}
        for aa in xrange(0,len(set(test_captions[:,0])),batch_size):

            id = list(set(test_captions[:,0]))[aa:aa+batch_size]
            test_video_batch = [test_features[x] for x in id]

            feed_dict = {greedy_video_features: test_video_batch}
            greedy_words = sess.run(greedy_captions, feed_dict) #### batch_size x num of each words
            greedy_decoded = decode_captions(np.array(greedy_words), ixtoword)
            for videoid in id:
                if videoid not in all_decoded_for_eval:
                    all_decoded_for_eval[videoid] = []

            [all_decoded_for_eval[x].append(y) for x,y in zip(id,greedy_decoded)]

        for num in xrange(0, len(test_captions),batch_size):

            videoid = test_captions[num:num+batch_size,0]
            for id in videoid:
                if id not in ref_decoded:
                    ref_decoded[id] = []
            [ref_decoded[x].append(y) for x,y in zip(videoid,test_captions[num:num+batch_size,1])]

        scores = evaluate_for_particular_captions(all_decoded_for_eval, ref_decoded)

        f.write('iteration ' + str(i))
        f.write('\n')
        f.write("Bleu_1:" + str(scores['Bleu_1']))
        f.write('\n')
        f.write("Bleu_2:" + str(scores['Bleu_2']))
        f.write('\n')
        f.write("Bleu_3:" + str(scores['Bleu_3']))
        f.write('\n')
        f.write("Bleu_4:" + str(scores['Bleu_4']))
        f.write('\n')
        f.write("ROUGE_L:" + str(scores['ROUGE_L']))
        f.write('\n')
        f.write("CIDEr:" + str(scores['CIDEr']))
        f.write('\n')
        f.write("METEOR:" + str(scores['METEOR']))
        f.write('\n')
        f.write("metric:" + str(
            1 * scores['METEOR'] ))
        f.write('\n')
        f.write('\n')
    print 'CIDEr: ', scores['CIDEr']

if __name__ == '__main__':
    args = parse_args()
    if args.task == 'train':
        with tf.device('/gpu:' + str(args.gpu_id)):
            train()
    elif args.task == 'test':
        with tf.device('/gpu:' + str(args.gpu_id)):
            test()
    elif args.task == 'evaluate':
        with tf.device('/gpu:' + str(args.gpu_id)):
            evaluation()