# Copyright 2019 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""
Cifar10 convert tool for MindRecord.
"""
from importlib import import_module
import os
import numpy as np
from mindspore import log as logger
from .cifar10 import Cifar10
from ..common.exceptions import PathNotExistsError
from ..filewriter import FileWriter
from ..shardutils import check_filename, ExceptionThread, SUCCESS, FAILED
try:
cv_import = import_module("cv2")
except ModuleNotFoundError:
cv_import = None
__all__ = ['Cifar10ToMR']
[docs]class Cifar10ToMR:
"""
A class to transform from cifar10 to MindRecord.
Note:
For details about Examples, please refer to `Converting the CIFAR-10 Dataset <https://
www.mindspore.cn/tutorials/zh-CN/r2.0.0-alpha/advanced/dataset/record.html#converting-the-cifar-10-dataset>`_ .
Args:
source (str): The cifar10 directory to be transformed.
destination (str): MindRecord file path to transform into, ensure that no file with the same name
exists in the directory.
Raises:
ValueError: If source or destination is invalid.
"""
def __init__(self, source, destination):
check_filename(source)
self.source = source
files = os.listdir(self.source)
train_data_flag = False
test_data_flag = False
for file in files:
if file.startswith("data_batch_"):
train_data_flag = True
if file.startswith("test_batch"):
test_data_flag = True
if not train_data_flag:
raise PathNotExistsError("data_batch_*")
if not test_data_flag:
raise PathNotExistsError("test_batch")
check_filename(destination)
self.destination = destination
self.writer = None
[docs] def run(self, fields=None):
"""
Execute transformation from cifar10 to MindRecord.
Args:
fields (list[str], optional): A list of index fields. Default: None. For index field settings,
please refer to :func:`mindspore.mindrecord.FileWriter.add_index` .
Returns:
MSRStatus, SUCCESS or FAILED.
"""
if fields and not isinstance(fields, list):
raise ValueError("The parameter fields should be None or list")
cifar10_data = Cifar10(self.source, False)
cifar10_data.load_data()
images = cifar10_data.images
logger.info("train images: {}".format(images.shape))
labels = cifar10_data.labels
logger.info("train images label: {}".format(labels.shape))
test_images = cifar10_data.Test.images
logger.info("test images: {}".format(test_images.shape))
test_labels = cifar10_data.Test.labels
logger.info("test images label: {}".format(test_labels.shape))
data_list = _construct_raw_data(images, labels)
test_data_list = _construct_raw_data(test_images, test_labels)
if _generate_mindrecord(self.destination, data_list, fields, "img_train") != SUCCESS:
return FAILED
if _generate_mindrecord(self.destination + "_test", test_data_list, fields, "img_test") != SUCCESS:
return FAILED
return SUCCESS
def _construct_raw_data(images, labels):
"""
Construct raw data from cifar10 data.
Args:
images (list): image list from cifar10.
labels (list): label list from cifar10.
Returns:
list[dict], data dictionary constructed from cifar10.
"""
if not cv_import:
raise ModuleNotFoundError("opencv-python module not found, please use pip install it.")
raw_data = []
for i, img in enumerate(images):
label = np.int(labels[i][0])
_, img = cv_import.imencode(".jpeg", img[..., [2, 1, 0]])
row_data = {"id": int(i),
"data": img.tobytes(),
"label": int(label)}
raw_data.append(row_data)
return raw_data
def _generate_mindrecord(file_name, raw_data, fields, schema_desc):
"""
Generate MindRecord file from raw data.
Args:
file_name (str): File name of MindRecord File.
fields (list[str]): Fields would be set as index which
could not belong to blob fields and type could not be 'array' or 'bytes'.
raw_data (dict): dict of raw data.
schema_desc (str): String of schema description.
Returns:
MSRStatus, SUCCESS or FAILED.
"""
schema = {"id": {"type": "int64"}, "label": {"type": "int64"},
"data": {"type": "bytes"}}
logger.info("transformed MindRecord schema is: {}".format(schema))
writer = FileWriter(file_name, 1)
writer.add_schema(schema, schema_desc)
if fields and isinstance(fields, list):
writer.add_index(fields)
writer.write_raw_data(raw_data)
return writer.commit()