def test_read_parquet_images_tf_dataset(self):
temp_dir = tempfile.mkdtemp()
try:
ParquetDataset.write("file://" + temp_dir,
images_generator(),
images_schema,
block_size=4)
path = "file://" + temp_dir
output_types = {
"id": tf.string,
"image": tf.string,
"label": tf.float32
}
dataset = read_parquet("tf_dataset",
input_path=path,
output_types=output_types)
for dt in dataset.take(1):
print(dt.keys())
dataloader = read_parquet("dataloader", input_path=path)
cur_dl = iter(dataloader)
while True:
try:
print(next(cur_dl)['label'])
except StopIteration:
break
finally:
shutil.rmtree(temp_dir)
def test_read_parquet_images_tf_dataset(self):
temp_dir = tempfile.mkdtemp()
try:
ParquetDataset.write("file://" + temp_dir,
images_generator(),
images_schema,
block_size=4)
path = "file://" + temp_dir
output_types = {
"id": tf.string,
"image": tf.string,
"label": tf.float32
}
dataset = read_parquet("tf_dataset",
path=path,
output_types=output_types)
for dt in dataset.take(1):
print(dt.keys())
num_shards, rank = 3, 1
dataset_shard = read_parquet("tf_dataset",
path=path,
config={
"num_shards": num_shards,
"rank": rank
},
output_types=output_types)
assert len(list(dataset_shard)) <= len(list(dataset)) // num_shards, \
"len of dataset_shard should be 1/`num_shards` of the whole dataset."
dataloader = read_parquet("dataloader", path=path)
dataloader_shard = read_parquet("dataloader",
path=path,
config={
"num_shards": num_shards,
"rank": rank
})
cur_dl = iter(dataloader_shard)
cur_count = 0
while True:
try:
print(next(cur_dl)['label'])
cur_count += 1
except StopIteration:
break
assert cur_count == len(list(dataset_shard))
finally:
shutil.rmtree(temp_dir)
def data_creator(config, batch_size):
dataset = read_parquet("tf_dataset",
input_path=path,
output_types=output_types,
output_shapes=output_shapes)
dataset = dataset.shuffle(10)
dataset = dataset.map(lambda data_dict:
(data_dict["image"], data_dict["label"]))
dataset = dataset.map(parse_data_train)
dataset = dataset.batch(batch_size)
return dataset
def val_data_creator(config, batch_size):
val_dataset = read_parquet(format="tf_dataset", path=voc_val_path,
output_types=output_types,
output_shapes=output_shapes)
val_dataset = val_dataset.map(
lambda data_dict: (data_dict["image"], data_dict["label"]))
val_dataset = val_dataset.map(parse_data_train)
val_dataset = val_dataset.batch(batch_size)
val_dataset = val_dataset.map(lambda x, y: (
transform_images(x, DEFAULT_IMAGE_SIZE),
transform_targets(y, anchors, anchor_masks, DEFAULT_IMAGE_SIZE)))
return val_dataset
def train_data_creator(config, batch_size):
train_dataset = read_parquet(format="tf_dataset", path=voc_train_path,
output_types=output_types,
output_shapes=output_shapes)
train_dataset = train_dataset.map(
lambda data_dict: (data_dict["image"], data_dict["label"]))
train_dataset = train_dataset.map(parse_data_train)
train_dataset = train_dataset.shuffle(buffer_size=512)
train_dataset = train_dataset.batch(batch_size)
train_dataset = train_dataset.map(lambda x, y: (
transform_images(x, DEFAULT_IMAGE_SIZE),
transform_targets(y, anchors, anchor_masks, DEFAULT_IMAGE_SIZE)))
train_dataset = train_dataset.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)
return train_dataset
def test_read_parquet_images_tf_dataset(self):
temp_dir = tempfile.mkdtemp()
try:
ParquetDataset.write("file://" + temp_dir, images_generator(),
images_schema)
path = "file://" + temp_dir
output_types = {
"id": tf.string,
"image": tf.string,
"label": tf.float32
}
dataset = read_parquet("tf_dataset",
input_path=path,
output_types=output_types)
for dt in dataset.take(1):
print(dt.keys())
finally:
shutil.rmtree(temp_dir)
