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# Copyright 2020 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.
# ============================================================================
"""
create train or eval dataset.
"""
import os
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
import mindspore.dataset.vision.c_transforms as C
import mindspore.dataset.transforms.c_transforms as C2
from mindspore.communication.management import init, get_rank, get_group_size
def create_dataset1(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend", distribute=False):
"""
create a train or evaluate cifar10 dataset for resnet50
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
target(str): the device target. Default: Ascend
distribute(bool): data for distribute or not. Default: False
Returns:
dataset
"""
if target == "Ascend":
device_num, rank_id = _get_rank_info()
else:
if distribute:
init()
rank_id = get_rank()
device_num = get_group_size()
else:
device_num = 1
if device_num == 1:
data_set = ds.Cifar10Dataset(dataset_path, num_parallel_workers=8, shuffle=True)
else:
data_set = ds.Cifar10Dataset(dataset_path, num_parallel_workers=8, shuffle=True,
num_shards=device_num, shard_id=rank_id)
# define map operations
trans = []
if do_train:
trans += [
C.RandomCrop((32, 32), (4, 4, 4, 4)),
C.RandomHorizontalFlip(prob=0.5)
]
trans += [
C.Resize((224, 224)),
C.Rescale(1.0 / 255.0, 0.0),
C.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),
C.HWC2CHW()
]
type_cast_op = C2.TypeCast(mstype.int32)
data_set = data_set.map(operations=type_cast_op, input_columns="label", num_parallel_workers=8)
data_set = data_set.map(operations=trans, input_columns="image", num_parallel_workers=8)
# apply batch operations
data_set = data_set.batch(batch_size, drop_remainder=True)
# apply dataset repeat operation
data_set = data_set.repeat(repeat_num)
return data_set
def create_dataset2(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend", distribute=False):
"""
create a train or eval imagenet2012 dataset for resnet50
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
target(str): the device target. Default: Ascend
distribute(bool): data for distribute or not. Default: False
Returns:
dataset
"""
if target == "Ascend":
device_num, rank_id = _get_rank_info()
else:
if distribute:
init()
rank_id = get_rank()
device_num = get_group_size()
else:
device_num = 1
if device_num == 1:
data_set = ds.ImageFolderDataset(dataset_path, num_parallel_workers=8, shuffle=True)
else:
data_set = ds.ImageFolderDataset(dataset_path, num_parallel_workers=8, shuffle=True,
num_shards=device_num, shard_id=rank_id)
image_size = 224
mean = [0.485 * 255, 0.456 * 255, 0.406 * 255]
std = [0.229 * 255, 0.224 * 255, 0.225 * 255]
# define map operations
if do_train:
trans = [
C.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
C.RandomHorizontalFlip(prob=0.5),
C.Normalize(mean=mean, std=std),
C.HWC2CHW()
]
else:
trans = [
C.Decode(),
C.Resize(256),
C.CenterCrop(image_size),
C.Normalize(mean=mean, std=std),
C.HWC2CHW()
]
type_cast_op = C2.TypeCast(mstype.int32)
data_set = data_set.map(operations=trans, input_columns="image", num_parallel_workers=8)
data_set = data_set.map(operations=type_cast_op, input_columns="label", num_parallel_workers=8)
# apply batch operations
data_set = data_set.batch(batch_size, drop_remainder=True)
# apply dataset repeat operation
data_set = data_set.repeat(repeat_num)
return data_set
def create_dataset3(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend", distribute=False):
"""
create a train or eval imagenet2012 dataset for resnet101
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
target(str): the device target. Default: Ascend
distribute(bool): data for distribute or not. Default: False
Returns:
dataset
"""
if target == "Ascend":
device_num, rank_id = _get_rank_info()
else:
if distribute:
init()
rank_id = get_rank()
device_num = get_group_size()
else:
device_num = 1
rank_id = 1
if device_num == 1:
data_set = ds.ImageFolderDataset(dataset_path, num_parallel_workers=8, shuffle=True)
else:
data_set = ds.ImageFolderDataset(dataset_path, num_parallel_workers=8, shuffle=True,
num_shards=device_num, shard_id=rank_id)
image_size = 224
mean = [0.475 * 255, 0.451 * 255, 0.392 * 255]
std = [0.275 * 255, 0.267 * 255, 0.278 * 255]
# define map operations
if do_train:
trans = [
C.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
C.RandomHorizontalFlip(rank_id / (rank_id + 1)),
C.Normalize(mean=mean, std=std),
C.HWC2CHW()
]
else:
trans = [
C.Decode(),
C.Resize(256),
C.CenterCrop(image_size),
C.Normalize(mean=mean, std=std),
C.HWC2CHW()
]
type_cast_op = C2.TypeCast(mstype.int32)
data_set = data_set.map(operations=trans, input_columns="image", num_parallel_workers=8)
data_set = data_set.map(operations=type_cast_op, input_columns="label", num_parallel_workers=8)
# apply batch operations
data_set = data_set.batch(batch_size, drop_remainder=True)
# apply dataset repeat operation
data_set = data_set.repeat(repeat_num)
return data_set
def create_dataset4(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend", distribute=False):
"""
create a train or eval imagenet2012 dataset for se-resnet50
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
target(str): the device target. Default: Ascend
distribute(bool): data for distribute or not. Default: False
Returns:
dataset
"""
if target == "Ascend":
device_num, rank_id = _get_rank_info()
else:
if distribute:
init()
rank_id = get_rank()
device_num = get_group_size()
else:
device_num = 1
if device_num == 1:
data_set = ds.ImageFolderDataset(dataset_path, num_parallel_workers=12, shuffle=True)
else:
data_set = ds.ImageFolderDataset(dataset_path, num_parallel_workers=12, shuffle=True,
num_shards=device_num, shard_id=rank_id)
image_size = 224
mean = [123.68, 116.78, 103.94]
std = [1.0, 1.0, 1.0]
# define map operations
if do_train:
trans = [
C.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
C.RandomHorizontalFlip(prob=0.5),
C.Normalize(mean=mean, std=std),
C.HWC2CHW()
]
else:
trans = [
C.Decode(),
C.Resize(292),
C.CenterCrop(256),
C.Normalize(mean=mean, std=std),
C.HWC2CHW()
]
type_cast_op = C2.TypeCast(mstype.int32)
data_set = data_set.map(operations=trans, input_columns="image", num_parallel_workers=12)
data_set = data_set.map(operations=type_cast_op, input_columns="label", num_parallel_workers=12)
# apply batch operations
data_set = data_set.batch(batch_size, drop_remainder=True)
# apply dataset repeat operation
data_set = data_set.repeat(repeat_num)
return data_set
def _get_rank_info():
"""
get rank size and rank id
"""
rank_size = int(os.environ.get("RANK_SIZE", 1))
if rank_size > 1:
rank_size = get_group_size()
rank_id = get_rank()
else:
rank_size = 1
rank_id = 0
return rank_size, rank_id
Python
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