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dataset.py
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dataset.py
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import os
import math
import numpy as np
import pandas as pd
from tqdm import tqdm
from data_generation import creatDataset
from utils.vocabulary import Vocabulary
class DataSet(object):
def __init__(self,
emotion_files,
image_files,
batch_size,
word_idxs=None,
masks=None,
is_train=False,
shuffle=False):
self.emotion_files = np.array(emotion_files)
self.image_files = np.array(image_files)
self.word_idxs = np.array(word_idxs)
self.masks = np.array(masks)
self.batch_size = batch_size
self.is_train = is_train
self.shuffle = shuffle
self.setup()
def setup(self):
""" Setup the dataset. """
self.count = len(self.image_files)
self.num_batches = int(np.ceil(self.count * 1.0 / self.batch_size))
self.fake_count = self.num_batches * self.batch_size - self.count
self.idxs = list(range(self.count))
self.reset()
def reset(self):
""" Reset the dataset. """
self.current_idx = 0
if self.shuffle:
np.random.shuffle(self.idxs)
def next_batch(self):
""" Fetch the next batch. """
assert self.has_next_batch()
if self.has_full_next_batch():
start, end = self.current_idx, \
self.current_idx + self.batch_size
current_idxs = self.idxs[start:end]
else:
start, end = self.current_idx, self.count
current_idxs = self.idxs[start:end] + \
list(np.random.choice(self.count, self.fake_count))
emotion_files = self.emotion_files[current_idxs]
image_files = self.image_files[current_idxs]
if self.is_train:
word_idxs = self.word_idxs[current_idxs]
masks = self.masks[current_idxs]
self.current_idx += self.batch_size
return emotion_files, image_files, word_idxs, masks
else:
self.current_idx += self.batch_size
return emotion_files, image_files
def has_next_batch(self):
""" Determine whether there is a batch left. """
return self.current_idx < self.count
def has_full_next_batch(self):
""" Determine whether there is a full batch left. """
return self.current_idx + self.batch_size <= self.count
def prepare_train_data(config):
ann = pd.read_csv(config.annotation_file_train)
print("Building the vocabulary...")
vocabulary = Vocabulary(config.vocabulary_size)
if not os.path.exists(config.vocabulary_file):
print('No vocabulary found, create new one')
vocabulary.build(ann['caption'])
vocabulary.save(config.vocabulary_file)
else:
vocabulary.load(config.vocabulary_file)
print("Vocabulary built.")
# print("Number of words = %d" %(vocabulary.size))
print("Processing the captions...")
if config.data == 'CK+':
captions = ann['caption'].values
image_files = ann['image_file'].values
data_files = ann['emotion'].values
else:
captions = ann['caption'].values
image_files = ann['image_file'].values
data_files = ann['data'].values
if not os.path.exists(config.temp_data_file):
word_idxs = []
masks = []
for caption in tqdm(captions):
current_word_idxs_ = vocabulary.process_sentence(caption)
current_num_words = len(current_word_idxs_)
current_word_idxs = np.zeros(config.max_caption_length,
dtype = np.int32)
current_masks = np.zeros(config.max_caption_length)
current_word_idxs[:current_num_words] = np.array(current_word_idxs_)
current_masks[:current_num_words] = 1.0
word_idxs.append(current_word_idxs)
masks.append(current_masks)
word_idxs = np.array(word_idxs)
masks = np.array(masks)
data = {'word_idxs': word_idxs, 'masks': masks}
np.save(config.temp_data_file, data)
else:
data = np.load(config.temp_data_file, allow_pickle=True).item()
word_idxs = data['word_idxs']
masks = data['masks']
print("Captions processed.")
print("Number of captions = %d" %(len(captions)))
print("Building the dataset...")
dataset = DataSet(data_files,
image_files,
config.batch_size,
word_idxs,
masks,
True,
True)
print("Dataset built.")
return dataset
def prepare_eval_data(config):
""" Prepare the data for evaluating the model. """
ann = pd.read_csv(config.annotation_file_eval)
if config.data == 'CK+':
image_files = ann['image_file'].values
data_files = ann['emotion'].values
else:
image_files = ann['image_file'].values
data_files = ann['data'].values
print("Building the vocabulary...")
if os.path.exists(config.vocabulary_file):
vocabulary = Vocabulary(config.vocabulary_size,
config.vocabulary_file)
else:
vocabulary = build_vocabulary(config)
print("Vocabulary built.")
# print("Number of words = %d" %(vocabulary.size))
print("Building the dataset...")
dataset = DataSet(data_files, image_files, config.batch_size)
print("Dataset built.")
return dataset, vocabulary
def prepare_test_data(config):
""" Prepare the data for testing the model. """
files = os.listdir(config.test_image_dir)
image_files = [os.path.join(config.test_image_dir, f) for f in files
if f.lower().endswith('.jpg') or f.lower().endswith('.jpeg')]
image_ids = list(range(len(image_files)))
print("Building the vocabulary...")
if os.path.exists(config.vocabulary_file):
vocabulary = Vocabulary(config.vocabulary_size,
config.vocabulary_file)
else:
vocabulary = build_vocabulary(config)
print("Vocabulary built.")
print("Number of words = %d" %(vocabulary.size))
print("Building the dataset...")
dataset = DataSet(image_ids, image_files, config.batch_size)
print("Dataset built.")
return dataset, vocabulary
def build_vocabulary(config):
""" Build the vocabulary from the training data and save it to a file. """
ann = pd.read_csv(config.annotation_file_train)
vocabulary = Vocabulary(config.vocabulary_size)
vocabulary.build(ann['caption'])
vocabulary.save(config.vocabulary_file)
return vocabulary
if __name__ == '__main__':
from config import Config
config = Config()
data = prepare_train_data(config)
print(data.next_batch())
# print(data.image_files)
# print(data.idxs)
# print(data.num_batches)
ann = pd.read_csv(config.annotation_file_eval)
image_files = ann['image_file'].values
# print(image_files)