generate_words_tf_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 *

os.environ["CUDA_VISIBLE_DEVICES"] = "0"


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 = 20, decay_value = 0.00005, dropout_rate = 0.9, k_value = 5000):
        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.k_value = k_value

        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')

    def build_model(self):
        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
        #caption_mask = tf.ones([self.batch_size, self.n_caption_lstm_step],tf.float32)
        caption_mask = []
        one_mask = [[1] for x in xrange(self.batch_size)]
        one_mask = tf.Variable(one_mask,trainable=False,dtype=tf.float32)
        steps = tf.placeholder(tf.int32, shape=())

        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
        ### add dropout####
        image_emb = tf.layers.dropout(inputs=image_emb,rate=self.dropout_rate)

        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)

        probs = []
        loss = 0.0

##################### compute choose word probability###
        #k = tf.convert_to_tensor([self.k_value],dtype=tf.float64)
        onehundred_percent = tf.convert_to_tensor([1.00001], dtype=tf.float64)

        #step_num = tf.cast(steps,dtype=tf.float64)
        #print 'inner steps: ',self.steps
        #true_word_prob = tf.expand_dims(tf.divide(k,tf.add(k,tf.exp(tf.divide(step_num,k)))),0)

        true_word_prob = tf.expand_dims(tf.convert_to_tensor([0.5],dtype=tf.float64), 0)
        pre_prob = tf.concat([true_word_prob,tf.subtract(onehundred_percent,true_word_prob)],1)
        probabilities = tf.tile(pre_prob,[batch_size,1])
        log_probs = tf.log(probabilities)
        row_indice = tf.cast(tf.expand_dims(tf.range(0,batch_size),1),tf.int64)

        #previous_words = tf.zeros(self.batch_size)

        ##############################  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)

                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
                indice0 = tf.multinomial(log_probs, num_samples=1)
                indice = tf.concat([row_indice, indice0], 1)
                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:
                    generated_word = tf.stop_gradient(generated_word)
                    words = tf.concat([tf.expand_dims(caption[:,i-1],1), tf.expand_dims(generated_word,1)],1)
                    previous_words = tf.gather_nd(words,indice)
                    #for caption_step in xrange(self.n_caption_lstm_step):
                       # for b_size in xrange(batch_size):
                            #if previous_words[b_size, caption_step] == 0:
                                #caption_mask[b_size, caption_step:] = 0
                    with tf.device("/cpu:0"):
                        #current_embed = tf.nn.embedding_lookup(self.Wemb, caption[:,i-1])
                        current_embed = tf.nn.embedding_lookup(self.Wemb, previous_words)  ##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(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
                logit_words = tf.nn.xw_plus_b(output2, self.embed_word_W, self.embed_word_b)
                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)
                ####### generated words and scores ####
                generated_word = tf.cast(tf.argmax(logit_words, 1),tf.int32)
                one_mask = tf.multiply(one_mask, tf.cast(tf.not_equal(generated_word, tf.convert_to_tensor([0],tf.int32)),tf.float32))
                caption_mask.append(one_mask)

                cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=onehot_labels, logits=logit_words)
                cross_entropy = cross_entropy * one_mask  ######### 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)
        caption_mask = tf.stack(caption_mask)
        weight_decay_loss = tf.add_n([tf.nn.l2_loss(v) for v in tf.trainable_variables() if 'bias' not in v.name]) \
                            * self.decay_value
        loss = loss/tf.reduce_sum(caption_mask) + weight_decay_loss
        return loss, video, caption, caption_mask, probs, steps

    def build_generator(self, beam_size=1, length_normalization_factor=0.5):
        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, sentence, probs

        # return video, tf_sentence


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

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

model_path = './generated_words_models'

video_train_feature_file = '/media/llj/storage/all_sentences/msvd_inception_globalpool_train_origin.txt'

video_test_feature_file = '/media/llj/storage/all_sentences/msvd_inception_globalpool_test_origin.txt'

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

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

vocabulary_file = '/media/llj/storage/all_sentences/coco_msvd_allvocab.txt'
#vocabulary_file = '/media/llj/storage/all_sentences/msvd_vocabulary1.txt'
# =======================================================================================
# Train Parameters
# =======================================================================================
dim_image = 1024
lstm_dim = 1000
word_dim = 500

n_lstm_step = 60
n_caption_lstm_step = 35
n_video_lstm_step = 25

n_epochs = 15
batch_size = 16
start_learning_rate = 0.001
#caption_mask_out = open('caption_masks.txt', 'w')


def get_video_feature_caption_pair(sent_file=video_train_sent_file, feature_file=video_train_feature_file):
    sents = []
    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]))
    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 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 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 train():  ###### move caption (input_sentence) one column left and also need to move caption_mask (cont_sent)one column left ########################################################llj
    train_captions, train_features = get_video_feature_caption_pair(video_train_sent_file, video_train_feature_file)
    vocabulary = []
    steps =0

    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('./data/wordtoix') or os.path.exists('./data/ixtoword'):
        np.save("./data/wordtoix", wordtoix)
        np.save('./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)

    tf_loss, tf_video, tf_caption, tf_caption_mask, tf_probs, tf_steps = model.build_model()
    # config = tf.ConfigProto(allow_soft_placement=True)
    saver = tf.train.Saver(max_to_keep=100)
    config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=True)
    # config.gpu_options.allocator_type = 'BFC'
    sess = tf.InteractiveSession(config=config)

    global_step = tf.Variable(0, trainable=False, name='g_step')
    learning_rate = tf.train.exponential_decay(start_learning_rate, global_step,
                                               20000, 0.5, staircase=True)
    optimizer = tf.train.GradientDescentOptimizer(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)
    #train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(tf_loss, global_step=global_step)
    sess.run(tf.global_variables_initializer())
    #tf.summary.scalar('lr',learning_rate)
    #print 'variables: ',sess.run(tf.global_variables())
    optimistic_restore(sess, '/home/llj/tensorflow_s2vt/models/model-15')
    #saver.restore(sess, '/home/llj/tensorflow_s2vt/models/model-15')
    # 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('loss.txt', 'w')
    loss_to_draw = []
   # summary_op = tf.summary.merge_all()

    for epoch in range(0, n_epochs):
        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), range(batch_size, len(index) + batch_size, batch_size)):
            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_steps: steps
                })
            loss_to_draw_epoch.append(loss_val)

            steps += 1
            print 'idx: ', start, ' rate: ', sess.run(learning_rate)," Epoch: ", epoch, " loss: ", loss_val,\
                ' Elapsed time: ', str((time.time() - start_time)) , 'steps: ',steps
            loss_fd.write('epoch ' + str(epoch) + ' loss ' + str(loss_val) + '\n')

        # draw loss curve every epoch
        loss_to_draw.append(np.mean(loss_to_draw_epoch))
        plt_save_dir = "./prob-generated_words_loss_imgs"
        plt_save_img_name = str(epoch) + '_1.png'
        plt.plot(range(len(loss_to_draw)), loss_to_draw, color='g')
        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, 'prob-generated-model'), global_step=epoch)

    loss_fd.close()


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

    ixtoword = pd.Series(np.load('./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(15):
        model_path_last = model_path + 'prob-generated-' + 'model-' + str(i) #+ '.data-00000-of-00001'
        out_file = 'generated_vocab51915_batchsize_16_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 test_features.iteritems():
            generated_word_index = sess.run(captions_tf, feed_dict={video_tf: [test_features[key]]})
            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')


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()