def run(dataset_dir):
if not tf.gfile.Exists(dataset_dir):
tf.gfile.MakeDirs(dataset_dir)
if _dataset_exists(dataset_dir):
print('Dataset files already exist. Exiting without re-creating them.')
return
dataset_utils.download_and_uncompress_tarball(_DATA_URL, dataset_dir)
photo_filenames, class_names = _get_filenames_and_classes(dataset_dir)
class_names_to_ids = dict(zip(class_names, range(len(class_names))))
# Divide into train and test:
random.seed(_RANDOM_SEED)
random.shuffle(photo_filenames)
training_filenames = photo_filenames[_NUM_VALIDATION:]
validation_filenames = photo_filenames[:_NUM_VALIDATION]
_convert_dataset('train', training_filenames, class_names_to_ids,
dataset_dir)
_convert_dataset('validation', validation_filenames, class_names_to_ids,
dataset_dir)
labels_to_class_names = dict(zip(range(len(class_names)), class_names))
dataset_utils.write_label_file(labels_to_class_names, dataset_dir)
_clean_up_temporary_files(dataset_dir)
print('\nFinished converting the Flowers dataset!')
def run(dataset_dir):
"""Runs the download and conversion operation.
Args:
dataset_dir: The dataset directory where the dataset is stored.
"""
if not tf.gfile.Exists(dataset_dir):
tf.gfile.MakeDirs(dataset_dir)
if _dataset_exists(dataset_dir):
print('Dataset files already exist. Exiting without re-creating them.')
return
dataset_utils.download_and_uncompress_tarball(_DATA_URL, dataset_dir)
photo_filenames, class_names = _get_filenames_and_classes(dataset_dir)
class_names_to_ids = dict(zip(class_names, range(len(class_names))))
# Divide into train and test:
random.seed(_RANDOM_SEED)
random.shuffle(photo_filenames)
training_filenames = photo_filenames[_NUM_VALIDATION:]
validation_filenames = photo_filenames[:_NUM_VALIDATION]
# First, convert the training and validation sets.
_convert_dataset('train', training_filenames, class_names_to_ids,
dataset_dir)
_convert_dataset('validation', validation_filenames, class_names_to_ids,
dataset_dir)
# Finally, write the labels file:
labels_to_class_names = dict(zip(range(len(class_names)), class_names))
write_label_file(labels_to_class_names, dataset_dir)
# _clean_up_temporary_files(dataset_dir)
print('\nFinished converting the Flowers dataset!')
def getvector(imagedir):
slim = tf.contrib.slim
batch_size = 3
image_size = v3.inception_v3.default_image_size
url = "http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz"
checkpoints_dir = os.getcwd()
if not tf.gfile.Exists(checkpoints_dir + '/inception_v3.ckpt'):
dataset_utils.download_and_uncompress_tarball(url, checkpoints_dir)
with tf.Graph().as_default():
# imagedir = '/home/jiexun/Desktop/Siraj/ImageChallenge/Necessary/train/cat.0.jpg'
image_string = tf.read_file(imagedir)
image = tf.image.decode_jpeg(image_string, channels=3)
#Intentando reparar el resize
'''
print(image.shape)
print(image)
image=tf.cast(image, tf.float32)
image=tf.image.resize_images(image, [299, 299])
print(image.shape)
print(image)
image_jpg = tf.image.encode_jpeg(image)
plt.imshow(image_jpg)
'''
#fin de parche
processed_image = inception_preprocessing.preprocess_image(image, image_size, image_size, is_training=False)
processed_images = tf.expand_dims(processed_image, 0)
# Create the model, use the default arg scope to configure the batch norm parameters.
print('Inicializando el modelo InceptionV3...')
with slim.arg_scope(v3.inception_v3_arg_scope()):
vector, _ = v3.inception_v3(processed_images, num_classes=1001, is_training=False)
init_fn = slim.assign_from_checkpoint_fn(os.path.join(checkpoints_dir, 'inception_v3.ckpt'),
slim.get_model_variables('InceptionV3'))
with tf.Session() as sess:
init_fn(sess)
np_image, vector = sess.run([image, vector])
a = np.asarray([x for xs in vector for xss in xs for xsss in xss for x in xsss])
np.reshape(a, (1, 2048))
return a
def main(_):
"""Runs the download and conversion operation.
Args:
dataset_dir: The dataset directory where the dataset is stored.
"""
dataset_dir = FLAGS.dataset_dir
if not tf.gfile.Exists(dataset_dir):
tf.gfile.MakeDirs(dataset_dir)
training_filename = _get_output_filename(dataset_dir, 'train')
testing_filename = _get_output_filename(dataset_dir, 'test')
if tf.gfile.Exists(training_filename) and tf.gfile.Exists(testing_filename):
print('Dataset files already exist. Exiting without re-creating them.')
return
dataset_utils.download_and_uncompress_tarball(_DATA_URL, dataset_dir)
# First, process the training data:
with tf.python_io.TFRecordWriter(training_filename) as tfrecord_writer:
offset = 0
for i in range(_NUM_TRAIN_FILES):
filename = os.path.join(dataset_dir,
'cifar-10-batches-py',
'data_batch_%d' % (i + 1)) # 1-indexed.
offset = _add_to_tfrecord(filename, tfrecord_writer, offset)
# Next, process the testing data:
with tf.python_io.TFRecordWriter(testing_filename) as tfrecord_writer:
filename = os.path.join(dataset_dir,
'cifar-10-batches-py',
'test_batch')
_add_to_tfrecord(filename, tfrecord_writer)
# Finally, write the labels file:
labels_to_class_names = dict(zip(range(len(_CLASS_NAMES)), _CLASS_NAMES))
dataset_utils.write_label_file(labels_to_class_names, dataset_dir)
_clean_up_temporary_files(dataset_dir)
print('\nFinished converting the Cifar10 dataset!')
# Base url
TF_MODELS_URL = "http://download.tensorflow.org/models/"
# Modify this path for a different CNN
INCEPTION_V3_URL = TF_MODELS_URL + "inception_v3_2016_08_28.tar.gz"
INCEPTION_V4_URL = TF_MODELS_URL + "inception_v4_2016_09_09.tar.gz"
# Directory to save model checkpoints
MODELS_DIR = "models/cnn"
INCEPTION_V3_CKPT_PATH = MODELS_DIR + "/inception_v3.ckpt"
INCEPTION_V4_CKPT_PATH = MODELS_DIR + "/inception_v4.ckpt"
# Make the model directory if it does not exist
if not tf.gfile.Exists(MODELS_DIR):
tf.gfile.MakeDirs(MODELS_DIR)
# Download the appropriate model if haven't already done so
if not os.path.exists(INCEPTION_V3_CKPT_PATH):
dataset_utils.download_and_uncompress_tarball(INCEPTION_V3_URL, MODELS_DIR)
if not os.path.exists(INCEPTION_V4_CKPT_PATH):
dataset_utils.download_and_uncompress_tarball(INCEPTION_V4_URL, MODELS_DIR)
#Processing Images
import inception_preprocessing
# This can be modified depending on the model used and the training image dataset
def process_image(image):
root_dir = "images/"
filename = root_dir + image
with open(filename, "rb") as f:
image_str = f.read()
if image.endswith('jpg'):
raw_image = tf.image.decode_jpeg(image_str, channels=3)
print("DEBUG MODE")
BATCH_SIZE = 2
EPOCHS = 1
else:
BATCH_SIZE = 16
EPOCHS = 90
classes = util.pkl_load(workspace.class_pkl)
csv_files = workspace.csvs
if not tf.gfile.Exists(workspace.inception_cpkt):
tf.gfile.MakeDirs(workspace.inception_cpkt)
if not os.path.exists(os.path.join(workspace.inception_cpkt, os.path.basename(workspace.inception_url))):
dataset_utils.download_and_uncompress_tarball(workspace.inception_url,
workspace.inception_cpkt)
def get_init_fn():
"""Returns a function run by the chief worker to warm-start the training."""
checkpoint_exclude_scopes = ["InceptionV1/Logits", "InceptionV1/AuxLogits"]
exclusions = [scope.strip() for scope in checkpoint_exclude_scopes]
variables_to_restore = []
for var in slim.get_model_variables():
excluded = False
for exclusion in exclusions:
if var.op.name.startswith(exclusion):
excluded = True
break
BATCH_SIZE = 2
EPOCHS = 1
else:
BATCH_SIZE = 16
EPOCHS = 90
classes = util.pkl_load(workspace.class_pkl)
csv_files = workspace.csvs
if not tf.gfile.Exists(workspace.inception_cpkt):
tf.gfile.MakeDirs(workspace.inception_cpkt)
if not os.path.exists(
os.path.join(workspace.inception_cpkt,
os.path.basename(workspace.inception_url))):
dataset_utils.download_and_uncompress_tarball(workspace.inception_url,
workspace.inception_cpkt)
def get_init_fn():
"""Returns a function run by the chief worker to warm-start the training."""
checkpoint_exclude_scopes = ["InceptionV1/Logits", "InceptionV1/AuxLogits"]
exclusions = [scope.strip() for scope in checkpoint_exclude_scopes]
variables_to_restore = []
for var in slim.get_model_variables():
excluded = False
for exclusion in exclusions:
if var.op.name.startswith(exclusion):
excluded = True
break
import dataset_utils
import numpy as np
import os
import tensorflow as tf
url = "http://download.tensorflow.org/models/inception_resnet_v2_2016_08_30.tar.gz"
checkpoints_dir = '/tmp/checkpoints'
if not tf.gfile.Exists(checkpoints_dir):
tf.gfile.MakeDirs(checkpoints_dir)
dataset_utils.download_and_uncompress_tarball(url, checkpoints_dir)