def _get_tf_dataset(data_dir, train_mode=True, epochs=1, batch_size=1000,
line_per_sample=1000, rank_size=None, rank_id=None):
"""
get_tf_dataset
"""
dataset_files = []
file_prefix_name = 'train' if train_mode else 'test'
shuffle = train_mode
for (dirpath, _, filenames) in os.walk(data_dir):
for filename in filenames:
if file_prefix_name in filename and "tfrecord" in filename:
dataset_files.append(os.path.join(dirpath, filename))
schema = de.Schema()
schema.add_column('feat_ids', de_type=mstype.int32)
schema.add_column('feat_vals', de_type=mstype.float32)
schema.add_column('label', de_type=mstype.float32)
if rank_size is not None and rank_id is not None:
ds = de.TFRecordDataset(dataset_files=dataset_files, shuffle=shuffle, schema=schema, num_parallel_workers=8,
num_shards=rank_size, shard_id=rank_id, shard_equal_rows=True)
else:
ds = de.TFRecordDataset(dataset_files=dataset_files, shuffle=shuffle, schema=schema, num_parallel_workers=8)
ds = ds.batch(int(batch_size / line_per_sample),
drop_remainder=True)
ds = ds.map(operations=(lambda x, y, z: (
np.array(x).flatten().reshape(batch_size, 39),
np.array(y).flatten().reshape(batch_size, 39),
np.array(z).flatten().reshape(batch_size, 1))),
input_columns=['feat_ids', 'feat_vals', 'label'],
columns_order=['feat_ids', 'feat_vals', 'label'], num_parallel_workers=8)
#if train_mode:
ds = ds.repeat(epochs)
return ds
def _get_tf_dataset(directory,
train_mode=True,
epochs=1,
batch_size=1000,
line_per_sample=1000,
rank_size=None,
rank_id=None):
"""
Get dataset with tfrecord format.
Args:
directory (str): Dataset directory.
train_mode (bool): Whether dataset is use for train or eval (default=True).
epochs (int): Dataset epoch size (default=1).
batch_size (int): Dataset batch size (default=1000).
line_per_sample (int): The number of sample per line (default=1000).
rank_size (int): The number of device, not necessary for single device (default=None).
rank_id (int): Id of device, not necessary for single device (default=None).
Returns:
Dataset.
"""
dataset_files = []
file_prefixt_name = 'train' if train_mode else 'test'
shuffle = train_mode
for (dir_path, _, filenames) in os.walk(directory):
for filename in filenames:
if file_prefixt_name in filename and 'tfrecord' in filename:
dataset_files.append(os.path.join(dir_path, filename))
schema = de.Schema()
schema.add_column('feat_ids', de_type=mstype.int32)
schema.add_column('feat_vals', de_type=mstype.float32)
schema.add_column('label', de_type=mstype.float32)
if rank_size is not None and rank_id is not None:
ds = de.TFRecordDataset(dataset_files=dataset_files,
shuffle=shuffle,
schema=schema,
num_parallel_workers=8,
num_shards=rank_size,
shard_id=rank_id,
shard_equal_rows=True,
num_samples=3000)
else:
ds = de.TFRecordDataset(dataset_files=dataset_files,
shuffle=shuffle,
schema=schema,
num_parallel_workers=8,
num_samples=3000)
ds = ds.batch(int(batch_size / line_per_sample), drop_remainder=True)
ds = ds.map(operations=(lambda x, y, z:
(np.array(x).flatten().reshape(batch_size, 39),
np.array(y).flatten().reshape(batch_size, 39),
np.array(z).flatten().reshape(batch_size, 1))),
input_columns=['feat_ids', 'feat_vals', 'label'],
column_order=['feat_ids', 'feat_vals', 'label'],
num_parallel_workers=8)
ds = ds.repeat(epochs)
return ds
def _get_tf_dataset(data_dir,
schema_dict,
input_shape_dict,
train_mode=True,
epochs=1,
batch_size=4096,
line_per_sample=4096,
rank_size=None,
rank_id=None):
"""
_get_tf_dataset
"""
dataset_files = []
file_prefix_name = 'train' if train_mode else 'eval'
shuffle = bool(train_mode)
for (dirpath, _, filenames) in os.walk(data_dir):
for filename in filenames:
if file_prefix_name in filename and "tfrecord" in filename:
dataset_files.append(os.path.join(dirpath, filename))
schema = de.Schema()
float_key_list = ["label", "continue_val"]
columns_list = []
for key, attr_dict in schema_dict.items():
print("key: {}; shape: {}".format(key, attr_dict["tf_shape"]))
columns_list.append(key)
if key in set(float_key_list):
ms_dtype = mstype.float32
else:
ms_dtype = mstype.int32
schema.add_column(key, de_type=ms_dtype)
if rank_size is not None and rank_id is not None:
ds = de.TFRecordDataset(dataset_files=dataset_files,
shuffle=shuffle,
schema=schema,
num_parallel_workers=8,
num_shards=rank_size,
shard_id=rank_id,
shard_equal_rows=True)
else:
ds = de.TFRecordDataset(dataset_files=dataset_files,
shuffle=shuffle,
schema=schema,
num_parallel_workers=8)
ds = ds.batch(int(batch_size / line_per_sample), drop_remainder=True)
operations_list = []
for key in columns_list:
operations_list.append(
lambda x: np.array(x).flatten().reshape(input_shape_dict[key]))
print("ssssssssssssssssssssss---------------------" * 10)
print(input_shape_dict)
print("---------------------" * 10)
print(schema_dict)
def mixup(a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u):
a = np.asarray(a.reshape(batch_size, ))
b = np.array(b).flatten().reshape(batch_size, -1)
c = np.array(c).flatten().reshape(batch_size, -1)
d = np.array(d).flatten().reshape(batch_size, -1)
e = np.array(e).flatten().reshape(batch_size, -1)
f = np.array(f).flatten().reshape(batch_size, -1)
g = np.array(g).flatten().reshape(batch_size, -1)
h = np.array(h).flatten().reshape(batch_size, -1)
i = np.array(i).flatten().reshape(batch_size, -1)
j = np.array(j).flatten().reshape(batch_size, -1)
k = np.array(k).flatten().reshape(batch_size, -1)
l = np.array(l).flatten().reshape(batch_size, -1)
m = np.array(m).flatten().reshape(batch_size, -1)
n = np.array(n).flatten().reshape(batch_size, -1)
o = np.array(o).flatten().reshape(batch_size, -1)
p = np.array(p).flatten().reshape(batch_size, -1)
q = np.array(q).flatten().reshape(batch_size, -1)
r = np.array(r).flatten().reshape(batch_size, -1)
s = np.array(s).flatten().reshape(batch_size, -1)
t = np.array(t).flatten().reshape(batch_size, -1)
u = np.array(u).flatten().reshape(batch_size, -1)
return a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u
ds = ds.map(
operations=mixup,
input_columns=[
'label', 'continue_val', 'indicator_id', 'emb_128_id',
'emb_64_single_id', 'multi_doc_ad_category_id',
'multi_doc_ad_category_id_mask', 'multi_doc_event_entity_id',
'multi_doc_event_entity_id_mask', 'multi_doc_ad_entity_id',
'multi_doc_ad_entity_id_mask', 'multi_doc_event_topic_id',
'multi_doc_event_topic_id_mask', 'multi_doc_event_category_id',
'multi_doc_event_category_id_mask', 'multi_doc_ad_topic_id',
'multi_doc_ad_topic_id_mask', 'ad_id', 'display_ad_and_is_leak',
'display_id', 'is_leak'
],
columns_order=[
'label', 'continue_val', 'indicator_id', 'emb_128_id',
'emb_64_single_id', 'multi_doc_ad_category_id',
'multi_doc_ad_category_id_mask', 'multi_doc_event_entity_id',
'multi_doc_event_entity_id_mask', 'multi_doc_ad_entity_id',
'multi_doc_ad_entity_id_mask', 'multi_doc_event_topic_id',
'multi_doc_event_topic_id_mask', 'multi_doc_event_category_id',
'multi_doc_event_category_id_mask', 'multi_doc_ad_topic_id',
'multi_doc_ad_topic_id_mask', 'display_id', 'ad_id',
'display_ad_and_is_leak', 'is_leak'
],
num_parallel_workers=8)
ds = ds.repeat(epochs)
return ds
def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32):
"""
create a train or eval dataset
Args:
dataset_path(string): the path of dataset.
do_train(bool): whether dataset is used for train or eval.
repeat_num(int): the repeat times of dataset. Default: 1
batch_size(int): the batch size of dataset. Default: 32
Returns:
dataset
"""
device_num = int(os.getenv("RANK_SIZE"))
rank_id = int(os.getenv("DEVICE_ID"))
data_path_list = []
for root, dirs, files in os.walk(dataset_path):
for file in files:
data_path_list.append(os.path.join(dataset_path, file))
schema = dt.Schema()
schema.add_column('image', de_type=mstype.uint8,
shape=[112, 112,
1]) # Binary data usually use uint8 here.
schema.add_column('label', de_type=mstype.int64, shape=[])
if device_num == 1:
ds = dt.TFRecordDataset(data_path_list,
num_parallel_workers=4,
shuffle=True,
schema=schema,
shard_equal_rows=True)
else:
ds = dt.TFRecordDataset(data_path_list,
num_parallel_workers=4,
shuffle=True,
num_shards=device_num,
shard_id=rank_id,
schema=schema,
shard_equal_rows=True)
resize_height = config.image_height
resize_width = config.image_width
rescale = 1.0 / 255.0
shift = 0.0
resize_op = C.Resize((resize_height, resize_width))
rescale_op = C.Rescale(rescale, shift)
change_swap_op = C.HWC2CHW()
trans = []
type_cast_op_train = C2.TypeCast(mstype.float32)
trans += [resize_op, rescale_op, type_cast_op_train, change_swap_op]
#trans += [resize_op, rescale_op, type_cast_op_train]
#trans += [resize_op, type_cast_op_train, change_swap_op]
type_cast_op = C2.TypeCast(mstype.int32)
ds = ds.map(input_columns="label", operations=type_cast_op)
ds = ds.map(input_columns="image", operations=trans)
ds = ds.shuffle(buffer_size=config.buffer_size)
ds = ds.batch(batch_size, drop_remainder=True)
ds = ds.repeat(repeat_num)
return ds
