def build_dataset(params):
if params['REBUILD_DATASET']: # We build a new dataset instance
if(params['VERBOSE'] > 0):
silence=False
logging.info('Building ' + params['DATASET_NAME'] + ' dataset')
else:
silence=True
base_path = params['DATA_ROOT_PATH']
name = params['DATASET_NAME']
ds = Dataset(name, base_path, silence=silence)
##### INPUT DATA
# Let's load the associated images (inputs)
num_cap = 1 # We only extract one feature vector per image
list_train = base_path + '/' + params['IMG_FILES']['train'][0]
list_val = base_path + '/' + params['IMG_FILES']['val'][0]
list_test = base_path + '/' + params['IMG_FILES']['test'][0]
ds.setInput(list_train, 'train',
type='raw-image', id=params['INPUTS_IDS_DATASET'][0],
img_size=params['IMG_SIZE'], img_size_crop=params['IMG_CROP_SIZE'],
repeat_set=num_cap)
ds.setInput(list_val, 'val',
type='raw-image', id=params['INPUTS_IDS_DATASET'][0],
img_size=params['IMG_SIZE'], img_size_crop=params['IMG_CROP_SIZE'],
repeat_set=num_cap)
ds.setInput(list_test, 'test',
type='raw-image', id=params['INPUTS_IDS_DATASET'][0],
img_size=params['IMG_SIZE'], img_size_crop=params['IMG_CROP_SIZE'],
repeat_set=num_cap)
### IMAGES' associated IDs
ds.setInput(base_path + '/' + params['IMG_FILES']['train'][1], 'train',
type='id', id=params['INPUTS_IDS_DATASET'][0] + '_ids',
repeat_set=num_cap)
ds.setInput(base_path + '/' + params['IMG_FILES']['val'][1], 'val',
type='id', id=params['INPUTS_IDS_DATASET'][0] + '_ids',
repeat_set=num_cap)
ds.setInput(base_path + '/' + params['IMG_FILES']['test'][1], 'test',
type='id', id=params['INPUTS_IDS_DATASET'][0] + '_ids',
repeat_set=num_cap)
# Train mean
ds.setTrainMean(params['MEAN_IMAGE'], params['INPUTS_IDS_DATASET'][0])
###### OUTPUT DATA: None
# Process dataset for keeping only one caption per image and storing the rest in a dict() with the following format:
# ds.extra_variables[set_name][id_output][img_position] = [cap1, cap2, cap3, ..., capN]
#keep_n_captions(ds, repeat=[1, 1], n=1, set_names=['val','test'])
# We have finished loading the dataset, now we can store it for using it in the future
saveDataset(ds, params['DATASET_STORE_PATH'])
else:
# We can easily recover it with a single line
ds = loadDataset(params['DATASET_STORE_PATH']+'/Dataset_'+params['DATASET_NAME']+'.pkl')
return ds
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.')
def build_dataset(params):
if params['REBUILD_DATASET']: # We build a new dataset instance
if (params['VERBOSE'] > 0):
silence = False
logging.info('Building ' + params['DATASET_NAME'] + ' dataset')
else:
silence = True
base_path = params['DATA_ROOT_PATH']
name = params['DATASET_NAME']
ds = Dataset(name, base_path, silence=silence)
##### INPUT DATA
# Let's load the images (inputs)
### IMAGES
list_train = base_path + '/' + params['IMG_FILES']['train'][0]
ds.setInput(list_train,
'train',
type='raw-image',
id=params['INPUTS_IDS_DATASET'][0],
img_size=params['IMG_SIZE'],
img_size_crop=params['IMG_CROP_SIZE'],
use_RGB=params['RGB'])
if 'val' in params['IMG_FILES'] and params['IMG_FILES']['val']:
list_val = base_path + '/' + params['IMG_FILES']['val'][0]
ds.setInput(list_val,
'val',
type='raw-image',
id=params['INPUTS_IDS_DATASET'][0],
img_size=params['IMG_SIZE'],
img_size_crop=params['IMG_CROP_SIZE'],
use_RGB=params['RGB'])
if 'test' in params['IMG_FILES'] and params['IMG_FILES']['test']:
list_test = base_path + '/' + params['IMG_FILES']['test'][0]
ds.setInput(list_test,
'test',
type='raw-image',
id=params['INPUTS_IDS_DATASET'][0],
img_size=params['IMG_SIZE'],
img_size_crop=params['IMG_CROP_SIZE'],
use_RGB=params['RGB'])
# Train mean
if params['MEAN_IMAGE']:
# if params['NORMALIZE']:
# params['MEAN_IMAGE'] = [m / 255. for m in params['MEAN_IMAGE']]
ds.setTrainMean(params['MEAN_IMAGE'],
params['INPUTS_IDS_DATASET'][0])
else:
ds.calculateTrainMean(params['INPUTS_IDS_DATASET'][0])
##### OUTPUT DATA
if params['TYPE_OUT'] == '3DLabel':
# Set list of classes (strings)
ds.setClasses(base_path + '/' + params['CLASSES_PATH'],
params['OUTPUTS_IDS_DATASET'][0])
elif params['TYPE_OUT'] == '3DSemanticLabel':
# Set list of classes (strings)
classes_names = []
with open(base_path + '/' + params['CLASSES_PATH'], 'r') as file:
for line in file:
line = line.rstrip('\n').split(',')[0]
classes_names.append(line)
ds.setClasses(classes_names, params['OUTPUTS_IDS_DATASET'][0])
ds.setSemanticClasses(base_path + '/' + params['CLASSES_PATH'],
params['OUTPUTS_IDS_DATASET'][0])
### 3DLabels or 3DSemanticLabels
ds.setOutput(base_path + '/' + params['IMG_FILES']['train'][1],
'train',
type=params['TYPE_OUT'],
id=params['OUTPUTS_IDS_DATASET'][0],
associated_id_in=params['INPUTS_IDS_DATASET'][0],
num_poolings=params['NUM_MODEL_POOLINGS'])
if 'val' in params['IMG_FILES'] and params['IMG_FILES']['val']:
ds.setOutput(base_path + '/' + params['IMG_FILES']['val'][1],
'val',
type=params['TYPE_OUT'],
id=params['OUTPUTS_IDS_DATASET'][0],
associated_id_in=params['INPUTS_IDS_DATASET'][0],
num_poolings=params['NUM_MODEL_POOLINGS'])
if 'test' in params['IMG_FILES'] and params['IMG_FILES']['test']:
ds.setOutput(base_path + '/' + params['IMG_FILES']['test'][1],
'test',
type=params['TYPE_OUT'],
id=params['OUTPUTS_IDS_DATASET'][0],
associated_id_in=params['INPUTS_IDS_DATASET'][0],
num_poolings=params['NUM_MODEL_POOLINGS'])
if params['DISCARD_CLASSES']:
weights = np.ones((params['NUM_CLASSES'], ))
for c in params['DISCARD_CLASSES']:
weights[c] = 0.0
ds.extra_variables['class_weights_' +
params['OUTPUTS_IDS_DATASET'][0]] = weights
if params['WEIGHT_CLASSES']:
weights = params['WEIGHT_CLASSES']
ds.extra_variables['class_weights_' +
params['OUTPUTS_IDS_DATASET'][0]] = weights
### Single multi-label
if params['APPLY_MULTILABEL_CLASSIFICATION']:
n_classes = len(ds.classes[params['OUTPUTS_IDS_DATASET'][0]])
multilabel = convert3DLabels2multilabel(
base_path + '/' + params['IMG_FILES']['train'][1], n_classes)
ds.setOutput(multilabel,
'train',
type='binary',
id=params['OUTPUTS_IDS_DATASET'][1])
if 'val' in params['IMG_FILES'] and params['IMG_FILES']['val']:
multilabel = convert3DLabels2multilabel(
base_path + '/' + params['IMG_FILES']['val'][1], n_classes)
ds.setOutput(multilabel,
'val',
type='binary',
id=params['OUTPUTS_IDS_DATASET'][1])
if 'test' in params['IMG_FILES'] and params['IMG_FILES']['test']:
multilabel = convert3DLabels2multilabel(
base_path + '/' + params['IMG_FILES']['test'][1],
n_classes)
ds.setOutput(multilabel,
'test',
type='binary',
id=params['OUTPUTS_IDS_DATASET'][1])
# We have finished loading the dataset, now we can store it for using it in the future
saveDataset(ds, params['DATASET_STORE_PATH'])
else:
# We can easily recover it with a single line
ds = loadDataset(params['DATASET_STORE_PATH'] + '/Dataset_' +
params['DATASET_NAME'] + '.pkl')
return ds
def build_dataset(params):
if params['REBUILD_DATASET']: # We build a new dataset instance
if(params['VERBOSE'] > 0):
silence=False
logging.info('Building ' + params['DATASET_NAME'] + ' dataset')
else:
silence=True
base_path = params['DATA_ROOT_PATH']
ds = Dataset(params['DATASET_NAME'], base_path+params.get('SUFFIX_DATASET', '/images'), silence=silence)
##### INPUT DATA
### IMAGES
ds.setInput(base_path+'/'+params['IMG_FILES']['train'], 'train',
type='raw-image', id=params['INPUTS_IDS_DATASET'][0],
img_size=params['IMG_SIZE'], img_size_crop=params['IMG_SIZE_CROP'])
ds.setInput(base_path+'/'+params['IMG_FILES']['val'], 'val',
type='raw-image', id=params['INPUTS_IDS_DATASET'][0],
img_size=params['IMG_SIZE'], img_size_crop=params['IMG_SIZE_CROP'])
ds.setInput(base_path+'/'+params['IMG_FILES']['test'], 'test',
type='raw-image', id=params['INPUTS_IDS_DATASET'][0],
img_size=params['IMG_SIZE'], img_size_crop=params['IMG_SIZE_CROP'])
# Set train mean
ds.setTrainMean(mean_image=params['MEAN_IMAGE'], id=params['INPUTS_IDS_DATASET'][0])
##### OUTPUT DATA
if params['CLASSIFICATION_TYPE'] == 'single-label':
# train split
ds.setOutput(base_path + '/' + params['LABELS_FILES']['train'], 'train',
type='categorical', id=params['OUTPUTS_IDS_DATASET'][0])
# val split
ds.setOutput(base_path + '/' + params['LABELS_FILES']['val'], 'val',
type='categorical', id=params['OUTPUTS_IDS_DATASET'][0])
# test split
ds.setOutput(base_path + '/' + params['LABELS_FILES']['test'], 'test',
type='categorical', id=params['OUTPUTS_IDS_DATASET'][0])
elif params['CLASSIFICATION_TYPE'] == 'multi-label':
# Convert list of ingredients into classes
logging.info('Preprocessing list of ingredients for assigning vocabulary as image classes.')
[classes, word2idx, idx2word] = convertIngredientsList2BinaryClasses(base_path,
params['LABELS_FILES'],
params['CLASSES_PATH'],
type_list=params.get('LABELS_TYPE_LIST', 'identifiers'))
# Insert them as outputs
ds.setOutput(classes['train'], 'train', type='binary', id=params['OUTPUTS_IDS_DATASET'][0])
ds.setOutput(classes['val'], 'val', type='binary', id=params['OUTPUTS_IDS_DATASET'][0])
ds.setOutput(classes['test'], 'test', type='binary', id=params['OUTPUTS_IDS_DATASET'][0])
# Insert vocabularies
ds.extra_variables['word2idx_binary'] = word2idx
ds.extra_variables['idx2word_binary'] = idx2word
if 'Food_and_Ingredients' in params['DATASET_NAME']:
# train split
ds.setOutput(base_path + '/' + params['LABELS_FILES_FOOD']['train'], 'train',
type='categorical', id=params['OUTPUTS_IDS_DATASET'][1])
# val split
ds.setOutput(base_path + '/' + params['LABELS_FILES_FOOD']['val'], 'val',
type='categorical', id=params['OUTPUTS_IDS_DATASET'][1])
# test split
ds.setOutput(base_path + '/' + params['LABELS_FILES_FOOD']['test'], 'test',
type='categorical', id=params['OUTPUTS_IDS_DATASET'][1])
# We have finished loading the dataset, now we can store it for using it in the future
saveDataset(ds, params['STORE_PATH'])
else:
# We can easily recover it with a single line
ds = loadDataset(params['STORE_PATH']+'/Dataset_'+params['DATASET_NAME']+'.pkl')
return ds
