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import os
import tensorflow as tf
# @tf.function
def build_data(split, shuffle=False):
"""Build CLEVR dataset."""
file_path_base = './'
# file_path_base = '/scratch/LargeTaskPlatform/daibo/VoE_dataset/'
def _parse_tfr_element(element):
# use the same structure as above; it's kinda an outline of the structure we now want to create
data = {
'raw_image': tf.io.FixedLenFeature([], tf.string),
'mask': tf.io.FixedLenFeature([], tf.string),
'slot': tf.io.VarLenFeature(tf.float32),
}
content = tf.io.parse_single_example(element, data)
raw_image = content['raw_image']
raw_mask = content['mask']
raw_slot = content['slot']
image_ori = tf.io.parse_tensor(raw_image, out_type=tf.uint8)
image = tf.cast(image_ori, tf.float32)
image = ((image / 255.0) - 0.5) * 2.0
# get our 'feature'-- our image -- and reshape it appropriately
raw_mask = tf.io.parse_tensor(raw_mask, out_type=tf.uint8)
mask = tf.cast(raw_mask, tf.float32)
mask = tf.clip_by_value(mask, 0.0, 1.0)
slot = raw_slot.values
return {
"image_ori": image_ori,
"raw_mask": raw_mask,
"image": image,
"mask": mask,
"slot": slot
}
AUTOTUNE = tf.data.AUTOTUNE
if split == "train":
num_file = 100
file_path = os.path.join(file_path_base, 'train')
filename = [
os.path.join(file_path, "train-part-{:0>3}.tfrecord".format(i))
for i in range(num_file)
]
elif split in ["collision", "blocking", "continuity"]:
num_file = 6
file_path = os.path.join(file_path_base, "test")
file_path = os.path.join(file_path, split)
filename = [
os.path.join(file_path, "eval-part-{:0>3}.tfrecord".format(i))
for i in range(num_file)
]
elif split in ["permanence"]:
num_file = 4
file_path = os.path.join(file_path_base, "test")
file_path = os.path.join(file_path, split)
filename = [
os.path.join(file_path, "eval-part-{:0>3}.tfrecord".format(i))
for i in range(4)
]
elif split == "eval":
num_file = 4
file_path = os.path.join(file_path_base, "test")
eval_list = ["collision", "blocking", "permanence", "continuity"]
filename = [
file_path + i + '/' + "eval-part-000.tfrecord" for i in eval_list
]
else:
raise ValueError("Error dataset type")
if shuffle:
filename = tf.data.Dataset.from_tensor_slices(filename)
filename = filename.shuffle(num_file)
ds = filename.interleave(
lambda x: tf.data.TFRecordDataset(x, compression_type="GZIP"),
cycle_length=num_file,
block_length=1)
ds = ds.shuffle(1000)
else:
ds = tf.data.TFRecordDataset(filename, compression_type="GZIP")
ds = ds.map(_parse_tfr_element, num_parallel_calls=AUTOTUNE)
return ds
def load_data(batch_size, split, **kwargs):
ds = build_data(split=split, **kwargs)
ds = ds.batch(batch_size, drop_remainder=True)
return ds
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