def loadMSVD():
logging.info('Loading MSVD dataset')
# Build basic dataset structure
# we assign it a name and the path were the images are stored
base_path = '/media/HDD_2TB/DATASETS/MSVD/'
name = 'MSVD_VideoDescription'
ds = Dataset(name, base_path)
max_text_len = 35
# Let's load the train, val and test splits of the descriptions (outputs)
# the files include a description per line. In this dataset a variable number
# of descriptions per video are provided.
ds.setOutput(base_path + 'train_descriptions.txt', 'train',
type='text', id='descriptions',
tokenization='tokenize_basic', build_vocabulary=True, max_text_len=max_text_len)
ds.setOutput(base_path + 'val_descriptions.txt', 'val',
type='text', id='descriptions',
tokenization='tokenize_basic', max_text_len=max_text_len)
ds.setOutput(base_path + 'test_descriptions.txt', 'test',
type='text', id='descriptions',
tokenization='tokenize_basic', max_text_len=max_text_len)
# Let's load the associated videos (inputs)
# we must take into account that in this dataset we have a different number of sentences per video,
# for this reason we introduce the parameter 'repeat_set'=num_captions, where num_captions is a list
# containing the number of captions in each video.
num_captions_train = np.load(base_path + 'train_descriptions_counts.npy')
num_captions_val = np.load(base_path + 'val_descriptions_counts.npy')
num_captions_test = np.load(base_path + 'test_descriptions_counts.npy')
ds.setInput([base_path + 'train_imgs_list.txt', base_path + 'train_imgs_counts.txt'],
'train', type='video', id='videos',
repeat_set=num_captions_train)
ds.setInput([base_path + 'val_imgs_list.txt', base_path + 'val_imgs_counts.txt'],
'val', type='video', id='videos',
repeat_set=num_captions_val)
ds.setInput([base_path + 'test_imgs_list.txt', base_path + 'test_imgs_counts.txt'],
'test', type='video', id='videos',
repeat_set=num_captions_test)
# Now let's set the dataset mean image for preprocessing the data
ds.setTrainMean(mean_image=[122.6795, 116.6690, 104.0067], id='videos')
# We have finished loading the dataset, now we can store it for using it in the future
saveDataset(ds, 'Datasets')
# We can easily recover it with a single line
ds = loadDataset('Datasets/Dataset_' + name + '.pkl')
# Lets recover the first batch of data
[X, Y] = ds.getXY('train', 10)
logging.info('Sample data loaded correctly.')
def loadMSVD():
logging.info('Loading MSVD dataset')
# Build basic dataset structure
# we assign it a name and the path were the images are stored
base_path = '/media/HDD_2TB/DATASETS/MSVD/'
name = 'MSVD_VideoDescription'
ds = Dataset(name, base_path)
max_text_len = 35
# Let's load the train, val and test splits of the descriptions (outputs)
# the files include a description per line. In this dataset a variable number
# of descriptions per video are provided.
ds.setOutput(base_path + 'train_descriptions.txt', 'train',
type='text', id='descriptions',
tokenization='tokenize_basic', build_vocabulary=True, max_text_len=max_text_len)
ds.setOutput(base_path + 'val_descriptions.txt', 'val',
type='text', id='descriptions',
tokenization='tokenize_basic', max_text_len=max_text_len)
ds.setOutput(base_path + 'test_descriptions.txt', 'test',
type='text', id='descriptions',
tokenization='tokenize_basic', max_text_len=max_text_len)
# Let's load the associated videos (inputs)
# we must take into account that in this dataset we have a different number of sentences per video,
# for this reason we introduce the parameter 'repeat_set'=num_captions, where num_captions is a list
# containing the number of captions in each video.
num_captions_train = np.load(base_path + 'train_descriptions_counts.npy')
num_captions_val = np.load(base_path + 'val_descriptions_counts.npy')
num_captions_test = np.load(base_path + 'test_descriptions_counts.npy')
ds.setInput([base_path + 'train_imgs_list.txt', base_path + 'train_imgs_counts.txt'],
'train', type='video', id='videos',
repeat_set=num_captions_train)
ds.setInput([base_path + 'val_imgs_list.txt', base_path + 'val_imgs_counts.txt'],
'val', type='video', id='videos',
repeat_set=num_captions_val)
ds.setInput([base_path + 'test_imgs_list.txt', base_path + 'test_imgs_counts.txt'],
'test', type='video', id='videos',
repeat_set=num_captions_test)
# Now let's set the dataset mean image for preprocessing the data
ds.setTrainMean(mean_image=[122.6795, 116.6690, 104.0067], data_id='videos')
# We have finished loading the dataset, now we can store it for using it in the future
saveDataset(ds, 'Datasets')
# We can easily recover it with a single line
ds = loadDataset('Datasets/Dataset_' + name + '.pkl')
# Lets recover the first batch of data
[X, Y] = ds.getXY('train', 10)
logging.info('Sample data loaded correctly.')
def loadFood101():
logging.info('Loading Food101 dataset')
logging.info(
'INFO: in order to load this dataset it must be placed in ../data/Food101/images/ after downloading it form https://www.vision.ee.ethz.ch/datasets_extra/food-101/'
)
base_path = '../data/Food101/'
name = 'Food101'
ds = Dataset(name, base_path + 'images')
# Insert inputs (images)
ds.setInput(base_path + 'meta/train_split.txt',
'train',
type='image',
id='images',
img_size_crop=[227, 227, 3])
ds.setInput(base_path + 'meta/val_split.txt',
'val',
type='image',
id='images')
ds.setInput(base_path + 'meta/test.txt', 'test', type='image', id='images')
# Insert outputs (labels)
ds.setOutput(base_path + 'meta/train_labels.txt',
'train',
type='categorical',
id='labels')
ds.setOutput(base_path + 'meta/val_labels.txt',
'val',
type='categorical',
id='labels')
ds.setOutput(base_path + 'meta/test_labels.txt',
'test',
type='categorical',
id='labels')
# Set list of classes (strings)
ds.setClasses(base_path + 'meta/classes.txt', 'labels')
# Now let's set the dataset mean image for preprocessing the data
ds.setTrainMean(mean_image=[122.6795, 116.6690, 104.0067],
data_id='images')
# We have finished loading the dataset, now we can store it for using it in the future
saveDataset(ds, 'Datasets')
# We can easily recover it with a single line
ds = loadDataset('Datasets/Dataset_' + name + '.pkl')
# Lets recover the first batch of data
[X, Y] = ds.getXY('train', 10)
logging.info('Sample data loaded correctly.')
def loadFlickr8k():
logging.info('Loading Flickr8k dataset')
# Build basic dataset structure
# we assign it a name and the path were the images are stored
base_path = '/media/HDD_2TB/DATASETS/Flickr8k/'
name = 'Flickr8k_ImageDescription'
ds = Dataset(name, base_path + 'Flicker8k_Dataset')
max_text_len = 35
# Let's load the train, val and test splits of the descriptions (outputs)
# the files include a description per line
# and a set of 5 consecutive descriptions correspond to a single input image
ds.setOutput(base_path + 'text/train_descriptions.txt', 'train',
type='text', id='descriptions',
tokenization='tokenize_basic', build_vocabulary=True, max_text_len=max_text_len)
ds.setOutput(base_path + 'text/val_descriptions.txt', 'val',
type='text', id='descriptions',
tokenization='tokenize_basic', max_text_len=max_text_len)
ds.setOutput(base_path + 'text/test_descriptions.txt', 'test',
type='text', id='descriptions',
tokenization='tokenize_basic', max_text_len=max_text_len)
# Let's load the associated images (inputs)
# we must take into account that in this dataset we have 5 sentences per image,
# for this reason we introduce the parameter 'repeat_set'=5
ds.setInput(base_path + 'text/Flickr_8k.trainImages.txt', 'train',
type='image', id='images', repeat_set=5)
ds.setInput(base_path + 'text/Flickr_8k.devImages.txt', 'val',
type='image', id='images', repeat_set=5)
ds.setInput(base_path + 'text/Flickr_8k.testImages.txt', 'test',
type='image', id='images', repeat_set=5)
# Now let's set the dataset mean image for preprocessing the data
ds.setTrainMean(mean_image=[122.6795, 116.6690, 104.0067], id='images')
# We have finished loading the dataset, now we can store it for using it in the future
saveDataset(ds, 'Datasets')
# We can easily recover it with a single line
ds = loadDataset('Datasets/Dataset_' + name + '.pkl')
# Lets recover the first batch of data
[X, Y] = ds.getXY('train', 10)
logging.info('Sample data loaded correctly.')
def loadFlickr8k():
logging.info('Loading Flickr8k dataset')
# Build basic dataset structure
# we assign it a name and the path were the images are stored
base_path = '/media/HDD_2TB/DATASETS/Flickr8k/'
name = 'Flickr8k_ImageDescription'
ds = Dataset(name, base_path + 'Flicker8k_Dataset')
max_text_len = 35
# Let's load the train, val and test splits of the descriptions (outputs)
# the files include a description per line
# and a set of 5 consecutive descriptions correspond to a single input image
ds.setOutput(base_path + 'text/train_descriptions.txt', 'train',
type='text', id='descriptions',
tokenization='tokenize_basic', build_vocabulary=True, max_text_len=max_text_len)
ds.setOutput(base_path + 'text/val_descriptions.txt', 'val',
type='text', id='descriptions',
tokenization='tokenize_basic', max_text_len=max_text_len)
ds.setOutput(base_path + 'text/test_descriptions.txt', 'test',
type='text', id='descriptions',
tokenization='tokenize_basic', max_text_len=max_text_len)
# Let's load the associated images (inputs)
# we must take into account that in this dataset we have 5 sentences per image,
# for this reason we introduce the parameter 'repeat_set'=5
ds.setInput(base_path + 'text/Flickr_8k.trainImages.txt', 'train', type='image', id='images', repeat_set=5)
ds.setInput(base_path + 'text/Flickr_8k.devImages.txt', 'val', type='image', id='images', repeat_set=5)
ds.setInput(base_path + 'text/Flickr_8k.testImages.txt', 'test', type='image', id='images', repeat_set=5)
# Now let's set the dataset mean image for preprocessing the data
ds.setTrainMean(mean_image=[122.6795, 116.6690, 104.0067], data_id='images')
# We have finished loading the dataset, now we can store it for using it in the future
saveDataset(ds, 'Datasets')
# We can easily recover it with a single line
ds = loadDataset('Datasets/Dataset_' + name + '.pkl')
# Lets recover the first batch of data
[X, Y] = ds.getXY('train', 10)
logging.info('Sample data loaded correctly. %d input samples. %d output samples' % (len(X), len(Y)))
def loadFood101():
logging.info('Loading Food101 dataset')
logging.info(
'INFO: in order to load this dataset it must be placed in ../data/Food101/images/ after downloading it form https://www.vision.ee.ethz.ch/datasets_extra/food-101/')
base_path = '../data/Food101/'
name = 'Food101'
ds = Dataset(name, base_path + 'images')
# Insert inputs (images)
ds.setInput(base_path + 'meta/train_split.txt', 'train',
type='image', id='images', img_size_crop=[227, 227, 3])
ds.setInput(base_path + 'meta/val_split.txt', 'val',
type='image', id='images')
ds.setInput(base_path + 'meta/test.txt', 'test',
type='image', id='images')
# Insert outputs (labels)
ds.setOutput(base_path + 'meta/train_labels.txt', 'train',
type='categorical', id='labels')
ds.setOutput(base_path + 'meta/val_labels.txt', 'val',
type='categorical', id='labels')
ds.setOutput(base_path + 'meta/test_labels.txt', 'test',
type='categorical', id='labels')
# Set list of classes (strings)
ds.setClasses(base_path + 'meta/classes.txt', 'labels')
# Now let's set the dataset mean image for preprocessing the data
ds.setTrainMean(mean_image=[122.6795, 116.6690, 104.0067], data_id='images')
# We have finished loading the dataset, now we can store it for using it in the future
saveDataset(ds, 'Datasets')
# We can easily recover it with a single line
ds = loadDataset('Datasets/Dataset_' + name + '.pkl')
# Lets recover the first batch of data
[X, Y] = ds.getXY('train', 10)
logging.info('Sample data loaded correctly.')