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predict_test.py
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import functools
import operator
import os
import time
import numpy as np
import config
import model
class VideoDescriptionInference(object):
"""
Initialize the parameters for the model
"""
def __init__(self, config):
self.latent_dim = config.latent_dim
self.num_encoder_tokens = config.num_encoder_tokens
self.num_decoder_tokens = config.num_decoder_tokens
self.time_steps_encoder = config.time_steps_encoder
self.max_probability = config.max_probability
# models
self.tokenizer, self.inf_encoder_model, self.inf_decoder_model = model.inference_model()
self.save_model_path = config.save_model_path
self.test_path = config.test_path
self.search_type = config.search_type
def greedy_search(self, loaded_array):
"""
:param f: the loaded numpy array after creating videos to frames and extracting features
:return: the final sentence which has been predicted greedily
"""
inv_map = self.index_to_word()
states_value = self.inf_encoder_model.predict(loaded_array.reshape(-1, 80, 4096))
target_seq = np.zeros((1, 1, 1500))
sentence = ''
target_seq[0, 0, self.tokenizer.word_index['bos']] = 1
for i in range(15):
output_tokens, h, c = self.inf_decoder_model.predict([target_seq] + states_value)
states_value = [h, c]
output_tokens = output_tokens.reshape(self.num_decoder_tokens)
y_hat = np.argmax(output_tokens)
if y_hat == 0:
continue
if inv_map[y_hat] is None:
break
else:
sentence = sentence + inv_map[y_hat] + ' '
target_seq = np.zeros((1, 1, 1500))
target_seq[0, 0, y_hat] = 1
return ' '.join(sentence.split()[:-1])
def decode_sequence2bs(self, input_seq):
states_value = self.inf_encoder_model.predict(input_seq)
target_seq = np.zeros((1, 1, self.num_decoder_tokens))
target_seq[0, 0, self.tokenizer.word_index['bos']] = 1
self.beam_search(target_seq, states_value, [], [], 0)
return decode_seq
def beam_search(self, target_seq, states_value, prob, path, lens):
"""
:param target_seq: the array that is fed into the model to predict the next word
:param states_value: previous state that is fed into the lstm cell
:param prob: probability of predicting a word
:param path: list of words from each sentence
:param lens: number of words
:return: final sentence
"""
global decode_seq
node = 2
output_tokens, h, c = self.inf_decoder_model.predict(
[target_seq] + states_value)
output_tokens = output_tokens.reshape(self.num_decoder_tokens)
sampled_token_index = output_tokens.argsort()[-node:][::-1]
states_value = [h, c]
for i in range(node):
if sampled_token_index[i] == 0:
sampled_char = ''
else:
sampled_char = list(self.tokenizer.word_index.keys())[
list(self.tokenizer.word_index.values()).index(sampled_token_index[i])]
MAX_LEN = 12
if sampled_char != 'eos' and lens <= MAX_LEN:
p = output_tokens[sampled_token_index[i]]
if sampled_char == '':
p = 1
prob_new = list(prob)
prob_new.append(p)
path_new = list(path)
path_new.append(sampled_char)
target_seq = np.zeros((1, 1, self.num_decoder_tokens))
target_seq[0, 0, sampled_token_index[i]] = 1.
self.beam_search(target_seq, states_value, prob_new, path_new, lens + 1)
else:
p = output_tokens[sampled_token_index[i]]
prob_new = list(prob)
prob_new.append(p)
p = functools.reduce(operator.mul, prob_new, 1)
if p > self.max_probability:
decode_seq = path
self.max_probability = p
def decoded_sentence_tuning(self, decoded_sentence):
decode_str = []
filter_string = ['bos', 'eos']
uni_gram = {}
last_string = ""
for idx2, c in enumerate(decoded_sentence):
if c in uni_gram:
uni_gram[c] += 1
else:
uni_gram[c] = 1
if last_string == c and idx2 > 0:
continue
if c in filter_string:
continue
if len(c) > 0:
decode_str.append(c)
if idx2 > 0:
last_string = c
return decode_str
def index_to_word(self):
# inverts word tokenizer
index_to_word = {value: key for key, value in self.tokenizer.word_index.items()}
return index_to_word
def get_test_data(self):
"""
loads all the numpy files
:return: two lists containing all the video arrays and the video Id
"""
X_test = []
X_test_filename = []
with open(os.path.join(self.test_path, 'testing_id.txt')) as testing_file:
lines = testing_file.readlines()
for filename in lines:
filename = filename.strip()
f = np.load(os.path.join(self.test_path, 'feat', filename + '.npy'))
X_test.append(f)
X_test_filename.append(filename[:-4])
X_test = np.array(X_test)
return X_test, X_test_filename
def test(self):
"""
writes the captions of all the testing videos in a text file
"""
X_test, X_test_filename = self.get_test_data()
# generate inference test outputs
with open(os.path.join(self.test_path, 'test_%s.txt' % self.search_type), 'w') as file:
for idx, x in enumerate(X_test):
file.write(X_test_filename[idx] + ',')
if self.search_type is 'greedy':
start = time.time()
decoded_sentence = self.greedy_search(x.reshape(-1, 80, 4096))
file.write(decoded_sentence + ',{:.2f}'.format(time.time()-start))
else:
start = time.time()
decoded_sentence = self.decode_sequence2bs(x.reshape(-1, 80, 4096))
decode_str = self.decoded_sentence_tuning(decoded_sentence)
for d in decode_str:
file.write(d + ' ')
file.write(',{:.2f}'.format(time.time() - start))
file.write('\n')
# re-init max prob
self.max_probability = -1
if __name__ == "__main__":
video_to_text = VideoDescriptionInference(config)
video_to_text.test()