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MindSpore Book
Copyright 2019-2020 Huawei Technologies Co., Ltd
README.en.md 已删除 100644 → 0
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# book
#### Description
The code repository stores the complete practice code in 'Introduction DeepLearning with MindSpore'.
#### Software Architecture
Software architecture description
#### Installation
1. xxxx
2. xxxx
3. xxxx
#### Instructions
1. xxxx
2. xxxx
3. xxxx
#### Contribution
1. Fork the repository
2. Create Feat_xxx branch
3. Commit your code
4. Create Pull Request
#### Gitee Feature
1. You can use Readme\_XXX.md to support different languages, such as Readme\_en.md, Readme\_zh.md
2. Gitee blog [blog.gitee.com](https://blog.gitee.com)
3. Explore open source project [https://gitee.com/explore](https://gitee.com/explore)
4. The most valuable open source project [GVP](https://gitee.com/gvp)
5. The manual of Gitee [https://gitee.com/help](https://gitee.com/help)
6. The most popular members [https://gitee.com/gitee-stars/](https://gitee.com/gitee-stars/)
README.md
浏览文件 @ c929b056
# book
# MindSpore Book
#### 介绍
The code repository stores the complete practice code in 'Introduction DeepLearning with MindSpore'.
#### 软件架构
软件架构说明
## License
#### 安装教程
1. xxxx
2. xxxx
3. xxxx
#### 使用说明
1. xxxx
2. xxxx
3. xxxx
#### 参与贡献
1. Fork 本仓库
2. 新建 Feat_xxx 分支
3. 提交代码
4. 新建 Pull Request
#### 码云特技
1. 使用 Readme\_XXX.md 来支持不同的语言，例如 Readme\_en.md, Readme\_zh.md
2. 码云官方博客 [blog.gitee.com](https://blog.gitee.com)
3. 你可以 [https://gitee.com/explore](https://gitee.com/explore) 这个地址来了解码云上的优秀开源项目
4. [GVP](https://gitee.com/gvp) 全称是码云最有价值开源项目，是码云综合评定出的优秀开源项目
5. 码云官方提供的使用手册 [https://gitee.com/help](https://gitee.com/help)
6. 码云封面人物是一档用来展示码云会员风采的栏目 [https://gitee.com/gitee-stars/](https://gitee.com/gitee-stars/)
- [Apache License 2.0](LICENSE)
- [Creative Commons License version 4.0](LICENSE-CC-BY-4.0)
chapter03/lenet/config.py 0 → 100644
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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.
# ============================================================================
"""
network config setting, will be used in main.py
"""
from easydict import EasyDict as edict
mnist_cfg = edict({
'num_classes': 10,
'lr': 0.01,
'momentum': 0.9,
'epoch_size': 1,
'batch_size': 32,
'buffer_size': 1000,
'image_height': 32,
'image_width': 32,
'save_checkpoint_steps': 1875,
'keep_checkpoint_max': 10,
})
chapter03/lenet/lenet.py 0 → 100644
浏览文件 @ c929b056
# 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.
# ============================================================================
"""LeNet."""
import mindspore.ops.operations as P
import mindspore.nn as nn
from mindspore.common.initializer import TruncatedNormal
def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):
"""weight initial for conv layer"""
weight = weight_variable()
return nn.Conv2d(in_channels, out_channels,
kernel_size=kernel_size, stride=stride, padding=padding,
weight_init=weight, has_bias=False, pad_mode="valid")
def fc_with_initialize(input_channels, out_channels):
"""weight initial for fc layer"""
weight = weight_variable()
bias = weight_variable()
return nn.Dense(input_channels, out_channels, weight, bias)
def weight_variable():
"""weight initial"""
return TruncatedNormal(0.02)
class LeNet5(nn.Cell):
"""
Lenet network
Args:
num_class (int): Num classes. Default: 10.
Returns:
Tensor, output tensor
Examples:
>>> LeNet(num_class=10)
"""
def __init__(self, num_class=10):
super(LeNet5, self).__init__()
self.num_class = num_class
self.batch_size = 32
self.conv1 = conv(1, 6, 5)
self.conv2 = conv(6, 16, 5)
self.fc1 = fc_with_initialize(16 * 5 * 5, 120)
self.fc2 = fc_with_initialize(120, 84)
self.fc3 = fc_with_initialize(84, self.num_class)
self.relu = nn.ReLU()
self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
self.flatten = nn.Flatten()
def construct(self, x):
x = self.conv1(x)
x = self.relu(x)
x = self.max_pool2d(x)
x = self.conv2(x)
x = self.relu(x)
x = self.max_pool2d(x)
x = self.flatten(x)
x = self.fc1(x)
x = self.relu(x)
x = self.fc2(x)
x = self.relu(x)
x = self.fc3(x)
return x
chapter03/lenet/main.py 0 → 100644
浏览文件 @ c929b056
# 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.
# ============================================================================
"""
######################## train and test lenet example ########################
1. train lenet and get network model files(.ckpt) :
python main.py --data_path /home/workspace/mindspore_dataset/Tutorial_Network/Lenet/MNIST_Data
2. test lenet according to model file:
python main.py --data_path /home/workspace/mindspore_dataset/Tutorial_Network/Lenet/MNIST_Data
--mode test --ckpt_path checkpoint_lenet_1-1_1875.ckpt
"""
import os
import argparse
from config import mnist_cfg as cfg
from lenet import LeNet5
import mindspore.dataset as ds
import mindspore.nn as nn
from mindspore import context, Tensor
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor
from mindspore.train import Model
import mindspore.dataset.transforms.vision.c_transforms as CV
import mindspore.dataset.transforms.c_transforms as C
from mindspore.dataset.transforms.vision import Inter
from mindspore.nn.metrics import Accuracy
from mindspore.common import dtype as mstype
def create_dataset(data_path, batch_size=32, repeat_size=1,
num_parallel_workers=1):
"""
create dataset for train or test
"""
# define dataset
mnist_ds = ds.MnistDataset(data_path)
resize_height, resize_width = 32, 32
rescale = 1.0 / 255.0
shift = 0.0
rescale_nml = 1 / 0.3081
shift_nml = -1 * 0.1307 / 0.3081
# define map operations
resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR) # Bilinear mode
rescale_nml_op = CV.Rescale(rescale_nml, shift_nml)
rescale_op = CV.Rescale(rescale, shift)
hwc2chw_op = CV.HWC2CHW()
type_cast_op = C.TypeCast(mstype.int32)
# apply map operations on images
mnist_ds = mnist_ds.map(input_columns="label", operations=type_cast_op, num_parallel_workers=num_parallel_workers)
mnist_ds = mnist_ds.map(input_columns="image", operations=resize_op, num_parallel_workers=num_parallel_workers)
mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_op, num_parallel_workers=num_parallel_workers)
mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_nml_op, num_parallel_workers=num_parallel_workers)
mnist_ds = mnist_ds.map(input_columns="image", operations=hwc2chw_op, num_parallel_workers=num_parallel_workers)
# apply DatasetOps
buffer_size = 10000
mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size) # 10000 as in LeNet train script
mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True)
mnist_ds = mnist_ds.repeat(repeat_size)
return mnist_ds
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='MindSpore MNIST Example')
parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend', 'GPU', 'CPU'],
help='device where the code will be implemented (default: Ascend)')
parser.add_argument('--mode', type=str, default="train", choices=['train', 'test'],
help='implement phase, set to train or test')
parser.add_argument('--data_path', type=str, default="./MNIST_Data",
help='path where the dataset is saved')
parser.add_argument('--ckpt_path', type=str, default="", help='if mode is test, must provide\
path where the trained ckpt file')
parser.add_argument('--dataset_sink_mode', type=bool, default=False, help='dataset_sink_mode is False or True')
args = parser.parse_args()
context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, enable_mem_reuse=False)
network = LeNet5(cfg.num_classes)
net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
repeat_size = cfg.epoch_size
net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum)
config_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_checkpoint_steps,
keep_checkpoint_max=cfg.keep_checkpoint_max)
ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet", config=config_ck)
model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
if args.mode == 'train': # train
ds_train = create_dataset(os.path.join(args.data_path, args.mode), batch_size=cfg.batch_size,
repeat_size=repeat_size)
print("============== Starting Training ==============")
model.train(cfg['epoch_size'], ds_train, callbacks=[ckpoint_cb, LossMonitor()],
dataset_sink_mode=args.dataset_sink_mode)
elif args.mode == 'test': # test
print("============== Starting Testing ==============")
param_dict = load_checkpoint(args.ckpt_path)
load_param_into_net(network, param_dict)
ds_eval = create_dataset(os.path.join(args.data_path, "test"), 32, 1)
acc = model.eval(ds_eval, dataset_sink_mode=args.dataset_sink_mode)
print("============== Accuracy:{} ==============".format(acc))
else:
raise RuntimeError('mode should be train or test, rather than {}'.format(args.mode))
chapter04/alexnet/alexnet.py 0 → 100644
浏览文件 @ c929b056
# 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.
# ============================================================================
"""Alexnet."""
from config import alexnet_cfg as cfg
import mindspore.ops.operations as P
import mindspore.nn as nn
from mindspore.common.initializer import TruncatedNormal
def conv(in_channels, out_channels, kernel_size, stride=1, padding=0, pad_mode="valid"):
weight = weight_variable()
return nn.Conv2d(in_channels, out_channels,
kernel_size=kernel_size, stride=stride, padding=padding,
weight_init=weight, has_bias=False, pad_mode=pad_mode)
def fc_with_initialize(input_channels, out_channels):
weight = weight_variable()
bias = weight_variable()
return nn.Dense(input_channels, out_channels, weight, bias)
def weight_variable():
return TruncatedNormal(0.02) # 0.02
class AlexNet(nn.Cell):
"""
Alexnet
"""
def __init__(self, num_classes=10):
super(AlexNet, self).__init__()
self.batch_size = cfg.batch_size
self.conv1 = conv(3, 96, 11, stride=4)
self.conv2 = conv(96, 256, 5, pad_mode="same")
self.conv3 = conv(256, 384, 3, pad_mode="same")
self.conv4 = conv(384, 384, 3, pad_mode="same")
self.conv5 = conv(384, 256, 3, pad_mode="same")
self.relu = nn.ReLU()
self.max_pool2d = nn.MaxPool2d(kernel_size=3, stride=2)
self.flatten = nn.Flatten()
self.fc1 = fc_with_initialize(6*6*256, 4096)
self.fc2 = fc_with_initialize(4096, 4096)
self.fc3 = fc_with_initialize(4096, num_classes)
def construct(self, x):
x = self.conv1(x)
x = self.relu(x)
x = self.max_pool2d(x)
x = self.conv2(x)
x = self.relu(x)
x = self.max_pool2d(x)
x = self.conv3(x)
x = self.relu(x)
x = self.conv4(x)
x = self.relu(x)
x = self.conv5(x)
x = self.relu(x)
x = self.max_pool2d(x)
x = self.flatten(x)
x = self.fc1(x)
x = self.relu(x)
x = self.fc2(x)
x = self.relu(x)
x = self.fc3(x)
return x
chapter04/alexnet/config.py 0 → 100644
浏览文件 @ c929b056
# 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.
# ============================================================================
"""
network config setting, will be used in main.py
"""
from easydict import EasyDict as edict
alexnet_cfg = edict({
'num_classes': 10,
'learning_rate': 0.002,
'momentum': 0.9,
'epoch_size': 1,
'batch_size': 32,
'buffer_size': 1000,
'image_height': 227,
'image_width': 227,
'save_checkpoint_steps': 1562,
'keep_checkpoint_max': 10,
})
chapter04/alexnet/main.py 0 → 100644
浏览文件 @ c929b056
# 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.
# ============================================================================
"""
AlexNet example tutorial
Usage:
python alexnet.py
with --device_target=GPU: After 20 epoch training, the accuracy is up to 80%
"""
import os
import argparse
from config import alexnet_cfg as cfg
from alexnet import AlexNet
import mindspore.dataset as ds
import mindspore.nn as nn
from mindspore import context
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor
from mindspore.train import Model
import mindspore.dataset.transforms.c_transforms as C
import mindspore.dataset.transforms.vision.c_transforms as CV
from mindspore.nn.metrics import Accuracy
from mindspore.common import dtype as mstype
def create_dataset(data_path, batch_size=32, repeat_size=1):
"""
create dataset for train or test
"""
cifar_ds = ds.Cifar10Dataset(data_path)
rescale = 1.0 / 255.0
shift = 0.0
resize_op = CV.Resize((cfg.image_height, cfg.image_width))
rescale_op = CV.Rescale(rescale, shift)
normalize_op = CV.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
random_crop_op = CV.RandomCrop([32, 32], [4, 4, 4, 4])
random_horizontal_op = CV.RandomHorizontalFlip()
channel_swap_op = CV.HWC2CHW()
typecast_op = C.TypeCast(mstype.int32)
cifar_ds = cifar_ds.map(input_columns="label", operations=typecast_op)
cifar_ds = cifar_ds.map(input_columns="image", operations=random_crop_op)
cifar_ds = cifar_ds.map(input_columns="image", operations=random_horizontal_op)
cifar_ds = cifar_ds.map(input_columns="image", operations=resize_op)
cifar_ds = cifar_ds.map(input_columns="image", operations=rescale_op)
cifar_ds = cifar_ds.map(input_columns="image", operations=normalize_op)
cifar_ds = cifar_ds.map(input_columns="image", operations=channel_swap_op)
cifar_ds = cifar_ds.shuffle(buffer_size=cfg.buffer_size)
cifar_ds = cifar_ds.repeat(repeat_size)
cifar_ds = cifar_ds.batch(batch_size, drop_remainder=True)
return cifar_ds
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='MindSpore AlexNet Example')
parser.add_argument('--mode', type=str, default="train", choices=['train', 'test'],
help='implement phase, set to train or test')
parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend', 'GPU'],
help='device where the code will be implemented (default: Ascend)')
parser.add_argument('--data_path', type=str, default="./", help='path where the dataset is saved')
parser.add_argument('--ckpt_path', type=str, default="./ckpt", help='if mode is test, must provide\
path where the trained ckpt file')
parser.add_argument('--dataset_sink_mode', type=bool, default=False, help='dataset_sink_mode is False or True')
args = parser.parse_args()
context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, enable_mem_reuse=False)
network = AlexNet(cfg.num_classes)
loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
repeat_size = cfg.epoch_size
opt = nn.Momentum(network.trainable_params(), cfg.learning_rate, cfg.momentum)
model = Model(network, loss, opt, metrics={"Accuracy": Accuracy()}) # test
if args.mode == 'train':
print("============== Starting Training ==============")
ds_train = create_dataset(args.data_path,
cfg.batch_size,
repeat_size)
config_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_checkpoint_steps,
keep_checkpoint_max=cfg.keep_checkpoint_max)
ckpoint_cb = ModelCheckpoint(prefix="checkpoint_alexnet", directory=args.ckpt_path, config=config_ck)
model.train(cfg.epoch_size, ds_train, callbacks=[ckpoint_cb, LossMonitor()],
dataset_sink_mode=args.dataset_sink_mode)
elif args.mode == 'test':
print("============== Starting Testing ==============")
param_dict = load_checkpoint(args.ckpt_path)
load_param_into_net(network, param_dict)
ds_eval = create_dataset(args.data_path)
acc = model.eval(ds_eval, dataset_sink_mode=args.dataset_sink_mode)
print("============== Accuracy:{} ==============".format(acc))
else:
raise RuntimeError('mode should be train or test, rather than {}'.format(args.mode))
chapter05/resnet/resnet.py 0 → 100644
浏览文件 @ c929b056
# 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.
# ============================================================================
"""ResNet."""
import mindspore.nn as nn
from mindspore.ops import operations as P
from mindspore.common.initializer import TruncatedNormal, Normal
def weight_variable(fan_in):
"""Init weight."""
stddev = (1.0/fan_in)**0.5
return TruncatedNormal(stddev)
def dense_weight_variable():
"""The weight for dense."""
return Normal(0.01)
def _conv3x3(in_channels, out_channels, stride=1, padding=0, pad_mode='same'):
"""Get a conv2d layer with 3x3 kernel size."""
init_value = weight_variable(in_channels)
return nn.Conv2d(in_channels, out_channels,
kernel_size=3, stride=stride, padding=padding, pad_mode=pad_mode, weight_init=init_value)
def _conv1x1(in_channels, out_channels, stride=1, padding=0, pad_mode='same'):
"""Get a conv2d layer with 1x1 kernel size."""
init_value = weight_variable(in_channels)
return nn.Conv2d(in_channels, out_channels,
kernel_size=1, stride=stride, padding=padding, pad_mode=pad_mode, weight_init=init_value)
def _conv7x7(in_channels, out_channels, stride=1, padding=0, pad_mode='same'):
"""Get a conv2d layer with 7x7 kernel size."""
init_value = weight_variable(in_channels)
return nn.Conv2d(in_channels, out_channels,
kernel_size=7, stride=stride, padding=padding, pad_mode=pad_mode, weight_init=init_value)
def _fused_bn(channels, momentum=0.9):
"""Get a fused batchnorm"""
return nn.BatchNorm2d(channels, eps=1e-4, momentum=momentum, gamma_init=1, beta_init=0)
def _fused_bn_last(channels, momentum=0.9):
"""Get a fused batchnorm"""
return nn.BatchNorm2d(channels, eps=1e-4, momentum=momentum, gamma_init=0, beta_init=0)
class BasicBlock(nn.Cell):
"""
ResNet V1 basic block definition.
Args:
in_channels: Integer. Input channel.
out_channels: Integer. Output channel.
stride: Integer. Stride size for the initial convolutional layer. Default:1.
momentum: Float. Momentum for batchnorm layer. Default:0.1.
Returns:
Tensor, output tensor.
Examples:
BasicBlock(3,256,stride=2,down_sample=True)
"""
expansion = 1
def __init__(self,
in_channels,
out_channels,
stride=1,
momentum=0.9):
super(BasicBlock, self).__init__()
self.conv1 = _conv3x3(in_channels, out_channels, stride=stride)
self.bn1 = _fused_bn(out_channels, momentum=momentum)
self.conv2 = _conv3x3(out_channels, out_channels)
self.bn2 = _fused_bn(out_channels, momentum=momentum)
self.relu = P.ReLU()
self.down_sample_layer = None
self.downsample = (in_channels != out_channels)
if self.downsample:
self.down_sample_layer = nn.SequentialCell([_conv1x1(in_channels,
out_channels,
stride=stride,
padding=0),
_fused_bn(out_channels,
momentum=momentum)])
self.add = P.TensorAdd()
def construct(self, x):
identity = x
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.conv2(x)
x = self.bn2(x)
if self.downsample:
identity = self.down_sample_layer(identity)
out = self.add(x, identity)
out = self.relu(out)
return out
class ResidualBlock(nn.Cell):
"""
ResNet V1 residual block definition.
Args:
in_channels: Integer. Input channel.
out_channels: Integer. Output channel.
stride: Integer. Stride size for the initial convolutional layer. Default:1.
momentum: Float. Momentum for batchnorm layer. Default:0.1.
Returns:
Tensor, output tensor.
Examples:
ResidualBlock(3,256,stride=2,down_sample=True)
"""
expansion = 4
def __init__(self,
in_channels,
out_channels,
stride=1,
momentum=0.9):
super(ResidualBlock, self).__init__()
out_chls = out_channels // self.expansion
self.conv1 = _conv1x1(in_channels, out_chls, stride=1)
self.bn1 = _fused_bn(out_chls, momentum=momentum)
self.conv2 = _conv3x3(out_chls, out_chls, stride=stride)
self.bn2 = _fused_bn(out_chls, momentum=momentum)
self.conv3 = _conv1x1(out_chls, out_channels, stride=1)
self.bn3 = _fused_bn_last(out_channels, momentum=momentum)
self.relu = P.ReLU()
self.downsample = (in_channels != out_channels)
self.stride = stride
if self.downsample:
self.conv_down_sample = _conv1x1(in_channels, out_channels,
stride=stride)
self.bn_down_sample = _fused_bn(out_channels, momentum=momentum)
elif self.stride != 1:
self.maxpool_down = nn.MaxPool2d(kernel_size=1, stride=2, pad_mode='same')
self.add = P.TensorAdd()
def construct(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample:
identity = self.conv_down_sample(identity)
identity = self.bn_down_sample(identity)
elif self.stride != 1:
identity = self.maxpool_down(identity)
out = self.add(out, identity)
out = self.relu(out)
return out
class ResNet(nn.Cell):
"""
ResNet V1 network.
Args:
block: Cell. Block for network.
layer_nums: List. Numbers of different layers.
in_channels: Integer. Input channel.
out_channels: Integer. Output channel.
num_classes: Integer. Class number. Default:100.
Returns:
Tensor, output tensor.
Examples:
ResNet(ResidualBlock,
[3, 4, 6, 3],
[64, 256, 512, 1024],
[256, 512, 1024, 2048],
100)
"""
def __init__(self,
block,
layer_nums,
in_channels,
out_channels,
strides=(1, 2, 2, 2),
num_classes=100):
super(ResNet, self).__init__()
if not len(layer_nums) == len(in_channels) == len(out_channels) == 4:
raise ValueError("the length of "
"layer_num, inchannel, outchannel list must be 4!")
self.conv1 = _conv7x7(3, 64, stride=2)
self.bn1 = _fused_bn(64)
self.relu = P.ReLU()
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='same')
self.layer1 = self._make_layer(block,
layer_nums[0],
in_channel=in_channels[0],
out_channel=out_channels[0],
stride=strides[0])
self.layer2 = self._make_layer(block,
layer_nums[1],
in_channel=in_channels[1],
out_channel=out_channels[1],
stride=strides[1])
self.layer3 = self._make_layer(block,
layer_nums[2],
in_channel=in_channels[2],
out_channel=out_channels[2],
stride=strides[2])
self.layer4 = self._make_layer(block,
layer_nums[3],
in_channel=in_channels[3],
out_channel=out_channels[3],
stride=strides[3])
self.mean = P.ReduceMean(keep_dims=True)
self.end_point = nn.Dense(out_channels[3], num_classes, has_bias=True,
weight_init=dense_weight_variable())
self.squeeze = P.Squeeze()
self.cast = P.Cast()
def _make_layer(self, block, layer_num, in_channel, out_channel, stride):
"""
Make Layer for ResNet.
Args:
block: Cell. Resnet block.
layer_num: Integer. Layer number.
in_channel: Integer. Input channel.
out_channel: Integer. Output channel.
stride:Integer. Stride size for the initial convolutional layer.
Returns:
SequentialCell, the output layer.
Examples:
_make_layer(BasicBlock, 3, 128, 256, 2)
"""
layers = []
resblk = block(in_channel, out_channel, stride=1)
layers.append(resblk)
for _ in range(1, layer_num - 1):
resblk = block(out_channel, out_channel, stride=1)
layers.append(resblk)
resblk = block(out_channel, out_channel, stride=stride)
layers.append(resblk)
return nn.SequentialCell(layers)
def construct(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
c1 = self.maxpool(x)
c2 = self.layer1(c1)
c3 = self.layer2(c2)
c4 = self.layer3(c3)
c5 = self.layer4(c4)
out = self.mean(c5, (2, 3))
out = self.squeeze(out)
out = self.end_point(out)
return out
def resnet50(class_num=10):
"""
Get ResNet50 neural network.
Args:
class_num: Integer. Class number.
Returns:
Cell, cell instance of ResNet50 neural network.
Examples:
resnet50(100)
"""
return ResNet(ResidualBlock,
[3, 4, 6, 3],
[64, 256, 512, 1024],
[256, 512, 1024, 2048],
[2, 2, 2, 1],
class_num)
def resnet101(class_num=10):
"""
Get ResNet101 neural network.
Args:
class_num: Integer. Class number.
Returns:
Cell, cell instance of ResNet101 neural network.
Examples:
resnet101(100)
"""
return ResNet(ResidualBlock,
[3, 4, 23, 3],
[64, 256, 512, 1024],
[256, 512, 1024, 2048],
class_num)
def resnet34(class_num=10):
"""
Get ResNet34 neural network.
Args:
class_num: Integer. Class number.
Returns:
Cell, cell instance of ResNet34 neural network.
Examples:
resnet34(100)
"""
return ResNet(BasicBlock,
[3, 4, 6, 3],
[64, 64, 128, 256],
[64, 128, 256, 512],
class_num)
def resnet18(class_num=10):
"""
Get ResNet18 neural network.
Args:
class_num: Integer. Class number.
Returns:
Cell, cell instance of ResNet18 neural network.
Examples:
resnet18(100)
"""
return ResNet(BasicBlock,
[2, 2, 2, 2],
[64, 64, 128, 256],
[64, 128, 256, 512],
class_num)
chapter05/resnet/resnet_cifar.py 0 → 100644
浏览文件 @ c929b056
# 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.
# ============================================================================
"""
train and evaluate resnet example for cifar10 dataset
1.For environment used for deploying this script please choose Ascend.
2.Aroud 30s per epoch and about 90% accuracy when the number of epoch reaches 34.
"""
import os
import random
import argparse
import numpy as np
import mindspore.nn as nn
import mindspore.common.dtype as mstype
import mindspore.ops.functional as F
import mindspore.dataset as de
import mindspore.dataset.transforms.vision.c_transforms as C
import mindspore.dataset.transforms.c_transforms as C2
from mindspore import Tensor
from mindspore.ops import operations as P
from mindspore.nn.optim.momentum import Momentum
from mindspore.train.model import Model, ParallelMode
from mindspore import context
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from mindspore.communication.management import init
from mindspore.parallel._auto_parallel_context import auto_parallel_context
from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
from resnet import resnet50
random.seed(1)
np.random.seed(1)
de.config.set_seed(1)
parser = argparse.ArgumentParser(description='Image classification.')
parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend', 'GPU'],
help='device where the code will be implemented (default: Ascend)')
parser.add_argument('--run_distribute', type=bool, default=False, help='Run distributei.')
parser.add_argument('--device_num', type=int, default=1, help='Device num.')
parser.add_argument('--do_train', type=bool, default=True, help='Do train or not.')
parser.add_argument('--do_eval', type=bool, default=False, help='Do eval or not.')
parser.add_argument('--epoch_size', type=int, default=1, help='Epoch size.')
parser.add_argument('--batch_size', type=int, default=32, help='Batch size.')
parser.add_argument('--num_classes', type=int, default=10, help='Num classes.')
parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path.')
parser.add_argument('--dataset_path', type=str, default="./datasets/cifar/cifar-10-batches-bin",
help='Dataset path.')
args_opt = parser.parse_args()
#The path of the data.
data_home = args_opt.dataset_path
#Choose the graph_mode as mode, the env is Ascend and save graphs like ir
context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target, save_graphs=True)
if args_opt.device_target == "Ascend":
#Choose one availabe Device to use on users' env.
device_id = int(os.getenv('DEVICE_ID'))
context.set_context(enable_task_sink=True, device_id=device_id)
context.set_context(enable_loop_sink=True)
context.set_context(enable_mem_reuse=False)
def create_dataset(repeat_num=1, training=True):
"""create the dataset of cifar10"""
ds = de.Cifar10Dataset(data_home)
if args_opt.run_distribute:
rank_id = int(os.getenv('RANK_ID'))
rank_size = int(os.getenv('RANK_SIZE'))
ds = de.Cifar10Dataset(data_home, num_shards=rank_size, shard_id=rank_id)
resize_height = 224
resize_width = 224
rescale = 1.0 / 255.0
shift = 0.0
# define map operations
random_crop_op = C.RandomCrop((32, 32), (4, 4, 4, 4)) # padding_mode default CONSTANT
random_horizontal_op = C.RandomHorizontalFlip()
resize_op = C.Resize((resize_height, resize_width)) # interpolation default BILINEAR
rescale_op = C.Rescale(rescale, shift)
normalize_op = C.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
changeswap_op = C.HWC2CHW()
type_cast_op = C2.TypeCast(mstype.int32)
c_trans = []
if training:
c_trans = [random_crop_op, random_horizontal_op]
c_trans += [resize_op, rescale_op, normalize_op,
changeswap_op]
# apply map operations on images
ds = ds.map(input_columns="label", operations=type_cast_op)
ds = ds.map(input_columns="image", operations=c_trans)
# apply repeat operations
ds = ds.repeat(repeat_num)
# apply shuffle operations
ds = ds.shuffle(buffer_size=10)
# apply batch operations
ds = ds.batch(batch_size=args_opt.batch_size, drop_remainder=True)
return ds
if __name__ == '__main__':
if args_opt.do_eval:
context.set_context(enable_hccl=False)
else:
if args_opt.run_distribute:
context.set_context(enable_hccl=True)
context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL, mirror_mean=True)
auto_parallel_context().set_all_reduce_fusion_split_indices([140])
init()
else:
context.set_context(enable_hccl=False)
epoch_size = args_opt.epoch_size
net = resnet50(args_opt.num_classes)
ls = SoftmaxCrossEntropyWithLogits(sparse=True, is_grad=False, reduction="mean")
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, 0.9)
model = Model(net, loss_fn=ls, optimizer=opt, metrics={'acc'})
if args_opt.do_train:
dataset = create_dataset(epoch_size)
batch_num = dataset.get_dataset_size()
config_ck = CheckpointConfig(save_checkpoint_steps=batch_num, keep_checkpoint_max=10)
ckpoint_cb = ModelCheckpoint(prefix="train_resnet_cifar10", directory="./", config=config_ck)
loss_cb = LossMonitor()
model.train(epoch_size, dataset, callbacks=[ckpoint_cb, loss_cb])
if args_opt.do_eval:
if args_opt.checkpoint_path:
param_dict = load_checkpoint(args_opt.checkpoint_path)
load_param_into_net(net, param_dict)
net.set_train(False)
eval_dataset = create_dataset(1, training=False)
res = model.eval(eval_dataset)
print("result: ", res)
chapter06/lstm/config.py 0 → 100644
浏览文件 @ c929b056
# 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.
# ============================================================================
"""
network config
"""
from easydict import EasyDict as edict
# LSTM CONFIG
lstm_cfg = edict({
'num_classes': 2,
'learning_rate': 0.1,
'momentum': 0.9,
'num_epochs': 1,
'batch_size': 64,
'embed_size': 300,
'num_hiddens': 100,
'num_layers': 2,
'bidirectional': True,
'save_checkpoint_steps': 390,
'keep_checkpoint_max': 10
})
chapter06/lstm/main.py 0 → 100644
浏览文件 @ c929b056
# 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.
# ============================================================================
"""
LSTM Tutorial
"""
import os
import shutil
import math
import argparse
import json
from itertools import chain
import numpy as np
from config import lstm_cfg as cfg
import mindspore.nn as nn
import mindspore.context as context
import mindspore.dataset as ds
from mindspore.ops import operations as P
from mindspore import Tensor
from mindspore.common.initializer import initializer
from mindspore.common.parameter import Parameter
from mindspore.mindrecord import FileWriter
from mindspore.train import Model
from mindspore.nn.metrics import Accuracy
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor
# Install gensim with 'pip install gensim'
import gensim
def encode_samples(tokenized_samples, word_to_idx):
""" encode word to index """
features = []
for sample in tokenized_samples:
feature = []
for token in sample:
if token in word_to_idx:
feature.append(word_to_idx[token])
else:
feature.append(0)
features.append(feature)
return features
def pad_samples(features, maxlen=500, pad=0):
""" pad all features to the same length """
padded_features = []
for feature in features:
if len(feature) >= maxlen:
padded_feature = feature[:maxlen]
else:
padded_feature = feature
while len(padded_feature) < maxlen:
padded_feature.append(pad)
padded_features.append(padded_feature)
return padded_features
def read_imdb(path, seg='train'):
""" read imdb dataset """
pos_or_neg = ['pos', 'neg']
data = []
for label in pos_or_neg:
files = os.listdir(os.path.join(path, seg, label))
for file in files:
with open(os.path.join(path, seg, label, file), 'r', encoding='utf8') as rf:
review = rf.read().replace('\n', '')
if label == 'pos':
data.append([review, 1])
elif label == 'neg':
data.append([review, 0])
return data
def tokenizer(text):
return [tok.lower() for tok in text.split(' ')]
def collect_weight(glove_path, vocab, word_to_idx, embed_size):
""" collect weight """
vocab_size = len(vocab)
wvmodel = gensim.models.KeyedVectors.load_word2vec_format(os.path.join(glove_path, 'glove.6B.300d.txt'),
binary=False, encoding='utf-8')
weight_np = np.zeros((vocab_size+1, embed_size)).astype(np.float32)
idx_to_word = {i+1: word for i, word in enumerate(vocab)}
idx_to_word[0] = '<unk>'
for i in range(len(wvmodel.index2word)):
try:
index = word_to_idx[wvmodel.index2word[i]]
except KeyError:
continue
weight_np[index, :] = wvmodel.get_vector(
idx_to_word[word_to_idx[wvmodel.index2word[i]]])
return weight_np
def preprocess(aclimdb_path, glove_path, embed_size):
""" preprocess the train and test data """
train_data = read_imdb(aclimdb_path, 'train')
test_data = read_imdb(aclimdb_path, 'test')
train_tokenized = []
test_tokenized = []
for review, _ in train_data:
train_tokenized.append(tokenizer(review))
for review, _ in test_data:
test_tokenized.append(tokenizer(review))
vocab = set(chain(*train_tokenized))
vocab_size = len(vocab)
print("vocab_size: ", vocab_size)
word_to_idx = {word: i+1 for i, word in enumerate(vocab)}
word_to_idx['<unk>'] = 0
train_features = np.array(pad_samples(encode_samples(train_tokenized, word_to_idx))).astype(np.int32)
train_labels = np.array([score for _, score in train_data]).astype(np.int32)
test_features = np.array(pad_samples(encode_samples(test_tokenized, word_to_idx))).astype(np.int32)
test_labels = np.array([score for _, score in test_data]).astype(np.int32)
weight_np = collect_weight(glove_path, vocab, word_to_idx, embed_size)
return train_features, train_labels, test_features, test_labels, weight_np, vocab_size
def get_imdb_data(labels_data, features_data):
data_list = []
for i, (label, feature) in enumerate(zip(labels_data, features_data)):
data_json = {"id": i,
"label": int(label),
"feature": feature.reshape(-1)}
data_list.append(data_json)
return data_list
def convert_to_mindrecord(embed_size, aclimdb_path, proprocess_path, glove_path):
""" convert imdb dataset to mindrecord """
num_shard = 4
train_features, train_labels, test_features, test_labels, weight_np, _ = \
preprocess(aclimdb_path, glove_path, embed_size)
np.savetxt(os.path.join(proprocess_path, 'weight.txt'), weight_np)
# write mindrecord
schema_json = {"id": {"type": "int32"},
"label": {"type": "int32"},
"feature": {"type": "int32", "shape":[-1]}}
writer = FileWriter(os.path.join(proprocess_path, 'aclImdb_train.mindrecord'), num_shard)
data = get_imdb_data(train_labels, train_features)
writer.add_schema(schema_json, "nlp_schema")
writer.add_index(["id", "label"])
writer.write_raw_data(data)
writer.commit()
writer = FileWriter(os.path.join(proprocess_path, 'aclImdb_test.mindrecord'), num_shard)
data = get_imdb_data(test_labels, test_features)
writer.add_schema(schema_json, "nlp_schema")
writer.add_index(["id", "label"])
writer.write_raw_data(data)
writer.commit()
def init_lstm_weight(
input_size,
hidden_size,
num_layers,
bidirectional,
has_bias=True):
"""Initialize lstm weight."""
num_directions = 1
if bidirectional:
num_directions = 2
weight_size = 0
gate_size = 4 * hidden_size
for layer in range(num_layers):
for _ in range(num_directions):
input_layer_size = input_size if layer == 0 else hidden_size * num_directions
weight_size += gate_size * input_layer_size
weight_size += gate_size * hidden_size
if has_bias:
weight_size += 2 * gate_size
stdv = 1 / math.sqrt(hidden_size)
w_np = np.random.uniform(-stdv, stdv, (weight_size,
1, 1)).astype(np.float32)
w = Parameter(
initializer(
Tensor(w_np), [
weight_size, 1, 1]), name='weight')
return w
def lstm_default_state(batch_size, hidden_size, num_layers, bidirectional):
"""init default input."""
num_directions = 1
if bidirectional:
num_directions = 2
h = Tensor(
np.zeros((num_layers * num_directions, batch_size, hidden_size)).astype(np.float32))
c = Tensor(
np.zeros((num_layers * num_directions, batch_size, hidden_size)).astype(np.float32))
return h, c
class SentimentNet(nn.Cell):
"""Sentiment network structure."""
def __init__(self,
vocab_size,
embed_size,
num_hiddens,
num_layers,
bidirectional,
num_classes,
weight,
batch_size):
super(SentimentNet, self).__init__()
self.embedding = nn.Embedding(vocab_size,
embed_size,
embedding_table=weight)
self.embedding.embedding_table.requires_grad = False
self.trans = P.Transpose()
self.perm = (1, 0, 2)
self.encoder = nn.LSTM(input_size=embed_size,
hidden_size=num_hiddens,
num_layers=num_layers,
has_bias=True,
bidirectional=bidirectional,
dropout=0.0)
w_init = init_lstm_weight(
embed_size,
num_hiddens,
num_layers,
bidirectional)
self.encoder.weight = w_init
self.h, self.c = lstm_default_state(batch_size, num_hiddens, num_layers, bidirectional)
self.concat = P.Concat(1)
if bidirectional:
self.decoder = nn.Dense(num_hiddens * 4, num_classes)
else:
self.decoder = nn.Dense(num_hiddens * 2, num_classes)
def construct(self, inputs):
# (64,500,300)
embeddings = self.embedding(inputs)
embeddings = self.trans(embeddings, self.perm)
output, _ = self.encoder(embeddings, (self.h, self.c))
# states[i] size(64,200) -> encoding.size(64,400)
encoding = self.concat((output[0], output[1]))
outputs = self.decoder(encoding)
return outputs
def create_dataset(base_path, batch_size, num_epochs, is_train):
"""Create dataset for training."""
columns_list = ["feature", "label"]
num_consumer = 4
if is_train:
path = os.path.join(base_path, 'aclImdb_train.mindrecord0')
else:
path = os.path.join(base_path, 'aclImdb_test.mindrecord0')
dtrain = ds.MindDataset(path, columns_list, num_consumer)
dtrain = dtrain.shuffle(buffer_size=dtrain.get_dataset_size())
dtrain = dtrain.batch(batch_size, drop_remainder=True)
dtrain = dtrain.repeat(count=num_epochs)
return dtrain
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='MindSpore LSTM Example')
parser.add_argument('--preprocess', type=str, default='false', choices=['true', 'false'],
help='Whether to perform data preprocessing')
parser.add_argument('--mode', type=str, default="train", choices=['train', 'test'],
help='implement phase, set to train or test')
# Download dataset from 'http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz' and extract to 'aclimdb_path'
parser.add_argument('--aclimdb_path', type=str, default="./aclImdb",
help='path where the dataset is store')
# Download glove from 'http://nlp.stanford.edu/data/glove.6B.zip' and extract to 'glove_path'
# Add a new line '400000 300' at the beginning of 'glove.6B.300d.txt' with '40000' for total words and '300' for vector length
parser.add_argument('--glove_path', type=str, default="./glove",
help='path where the glove is store')
parser.add_argument('--preprocess_path', type=str, default="./preprocess",
help='path where the pre-process data is store')
parser.add_argument('--ckpt_path', type=str, default="./ckpt", help='if mode is test, must provide\
path where the trained ckpt file')
args = parser.parse_args()
context.set_context(
mode=context.GRAPH_MODE,
save_graphs=False,
device_target="GPU")
if args.preprocess == 'true':
print("============== Starting Data Pre-processing ==============")
shutil.rmtree(args.preprocess_path)
os.mkdir(args.preprocess_path)
convert_to_mindrecord(cfg.embed_size, args.aclimdb_path, args.preprocess_path, args.glove_path)
embedding_table = np.loadtxt(os.path.join(args.preprocess_path, "weight.txt")).astype(np.float32)
network = SentimentNet(vocab_size=embedding_table.shape[0],
embed_size=cfg.embed_size,
num_hiddens=cfg.num_hiddens,
num_layers=cfg.num_layers,
bidirectional=cfg.bidirectional,
num_classes=cfg.num_classes,
weight=Tensor(embedding_table),
batch_size=cfg.batch_size)
loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
opt = nn.Momentum(network.trainable_params(), cfg.learning_rate, cfg.momentum)
loss_cb = LossMonitor()
model = Model(network, loss, opt, {'acc': Accuracy()})
if args.mode == 'train':
print("============== Starting Training ==============")
ds_train = create_dataset(args.preprocess_path, cfg.batch_size, cfg.num_epochs, True)
config_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_checkpoint_steps,
keep_checkpoint_max=cfg.keep_checkpoint_max)
ckpoint_cb = ModelCheckpoint(prefix="lstm", directory=args.ckpt_path, config=config_ck)
model.train(cfg.num_epochs, ds_train, callbacks=[ckpoint_cb, loss_cb])
elif args.mode == 'test':
print("============== Starting Testing ==============")
ds_eval = create_dataset(args.preprocess_path, cfg.batch_size, 1, False)
param_dict = load_checkpoint(args.ckpt_path)
load_param_into_net(network, param_dict)
acc = model.eval(ds_eval)
print("============== Accuracy:{} ==============".format(acc))
else:
raise RuntimeError('mode should be train or test, rather than {}'.format(args.mode))
chapter08/me_gcn.py 0 → 100644
浏览文件 @ c929b056
from mindspore.nn.layer import Cell
from mindspore.ops import operations
from mindspore.nn.layer.core import Dense
from mindspore.nn.layer.activation import ReLU
from mindspore.application.gnn import initialize_embedded_graph
from mindspore.application.gnn.base import get_feature, get_neighbor, get_label
from mindspore.ops.nn_ops import Momentum
from mindspore.core.parameter import Parameter
from mindspore.application.gnn.base import NetWithLossClass, GradWrap
class GCNAggregator(Cell):
def __init__(self, in_dim, out_dim):
super(GCNAggregator, self).__init__()
self.add = operations.TensorAdd()
self.div = operations.TensorDiv()
self.spmm = operations.SparseDenseMatmul()
self.fc = Dense(in_dim, out_dim)
self.relu = ReLU()
def construct(self, adj, node_emb, neighbor_emb):
agg_emb = self.spmm(adj[0], adj[1], adj[2], neighbor_emb)
agg_emb = self.add(node_emb, agg_emb)
agg_emb = self.div(agg_emb, adj[3])
agg_emb = self.fc(agg_emb)
agg_emb = self.relu(agg_emb)
return agg_emb
class SingleLayerGCN(Cell):
def __init__(self, in_dim, out_dim, num_classes):
super(SingleLayerGCN, self).__init__()
self.aggregator = GCNAggregator(in_dim, out_dim)
self.output_layer = Dense(out_dim, num_classes)
def construct(self, adj, node_feature, neighbor_feature ):
embeddings = self.aggregator(adj, node_feature, neighbor_feature)
output = self.output_layer(embeddings)
return output
def GCNTrainer(in_dim, out_dim, num_classes,num_epoch, graph_data):
input_node, neighbor_node, node_feature, neighbor_feature, labels = graph_data
network = SingleLayerGCN(in_dim, out_dim, num_classes)
loss_network = NetWithLossClass(network)
train_net = GradWrap(loss_network)
train_net.train(True)
parameters = train_net.weights
momentum = Momentum()
lr_v = Parameter(0.01, name="learning_rate")
momen_v = Parameter(0.01, name="momentum")
for _ in range(num_epoch):
grads = train_net.construct(adj_list[0], node_feature, neighbor_feature, labels)
accumulations = parameters.clone(prefix='moments')
for i, element in enumerate(grads):
updated = momentum(element, accumulations[i], parameters[i], lr_v, momen_v)
parameters[i].set_parameter_data(updated)
initilize_embedded_graph(GRAPH_DIR)
neighbor_node, adj_list = get_neighbor(input_node, k_hop)
node_feature = get_feature(input_node)
neighbor_feature = get_feature(neighbor_node)
labels = get_label(input_node)
graph_data = [input_node, neighbor_node, node_feature, neighbor_feature, labels]
in_dim = IN_DIM
out_dim = OUT_DIM
num_classes = CLASS_NUM
num_epoch = EPOCH_NUM
GCNTrainer(in_dim, out_dim, num_classes,num_epoch, graph_data)
chapter13/lenet_with_summary.py 0 → 100644
浏览文件 @ c929b056
# 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.
# ============================================================================
import os
import argparse
from mindspore import dataset
from mindspore import nn
from mindspore import context, Tensor
from mindspore.train import Model
from mindspore.ops import operations as P
from mindspore.common.initializer import TruncatedNormal
from mindspore.dataset.transforms.vision import c_transforms as transforms
from mindspore.dataset.transforms.vision import Inter
from mindspore.dataset.transforms import c_transforms as C
from mindspore.ops import functional as F
from mindspore.common import dtype as mstype
from mindspore.train.callback import SummaryStep
from mindspore.train.summary.summary_record import SummaryRecord
class CrossEntropyLoss(nn.Cell):
"""
Define loss for network
"""
def __init__(self):
super(CrossEntropyLoss, self).__init__()
self.sm_scalar = P.ScalarSummary()
self.cross_entropy = P.SoftmaxCrossEntropyWithLogits()
self.mean = P.ReduceMean()
self.one_hot = P.OneHot()
self.on_value = Tensor(1.0, mstype.float32)
self.off_value = Tensor(0.0, mstype.float32)
def construct(self, logits, label):
label = self.one_hot(label, F.shape(logits)[1], self.on_value, self.off_value)
loss = self.cross_entropy(logits, label)[0]
loss = self.mean(loss, (-1,))
self.sm_scalar("loss", loss)
return loss
def create_dataset(data_path, batch_size=32, repeat_size=1,
num_parallel_workers=1):
"""
create dataset for train or test
"""
# define dataset
mnist_ds = dataset.MnistDataset(data_path)
resize_height, resize_width = 32, 32
rescale = 1.0 / 255.0
shift = 0.0
# define map operations
resize_op = transforms.Resize((resize_height, resize_width), interpolation=Inter.LINEAR) # Bilinear mode
rescale_op = transforms.Rescale(rescale, shift)
hwc2chw_op = transforms.HWC2CHW()
type_cast_op = C.TypeCast(mstype.int32)
# apply map operations on images
mnist_ds = mnist_ds.map(input_columns="label", operations=type_cast_op, num_parallel_workers=num_parallel_workers)
mnist_ds = mnist_ds.map(input_columns="image", operations=resize_op, num_parallel_workers=num_parallel_workers)
mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_op, num_parallel_workers=num_parallel_workers)
mnist_ds = mnist_ds.map(input_columns="image", operations=hwc2chw_op, num_parallel_workers=num_parallel_workers)
# apply DatasetOps
buffer_size = 10000
mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size)
mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True)
mnist_ds = mnist_ds.repeat(repeat_size)
return mnist_ds
def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):
weight = weight_variable()
return nn.Conv2d(in_channels, out_channels,
kernel_size=kernel_size, stride=stride, padding=padding,
weight_init=weight, has_bias=False, pad_mode="valid")
def fc_with_initialize(input_channels, out_channels):
weight = weight_variable()
bias = weight_variable()
return nn.Dense(input_channels, out_channels, weight, bias)
def weight_variable():
return TruncatedNormal(0.02)
class LeNet5(nn.Cell):
"""
Lenet network
"""
def __init__(self):
super(LeNet5, self).__init__()
self.sm_image = P.ImageSummary()
self.batch_size = 32
self.conv1 = conv(1, 6, 5)
self.conv2 = conv(6, 16, 5)
self.fc1 = fc_with_initialize(16 * 5 * 5, 120)
self.fc2 = fc_with_initialize(120, 84)
self.fc3 = fc_with_initialize(84, 10)
self.relu = nn.ReLU()
self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
self.reshape = P.Reshape()
def construct(self, x):
self.sm_image("image", x)
x = self.conv1(x)
x = self.relu(x)
x = self.max_pool2d(x)
x = self.conv2(x)
x = self.relu(x)
x = self.max_pool2d(x)
x = self.reshape(x, (self.batch_size, -1))
x = self.fc1(x)
x = self.relu(x)
x = self.fc2(x)
x = self.relu(x)
x = self.fc3(x)
return x
def main(data_path, device_target='Ascend', summary_dir='./summary_dir', learning_rate=0.01):
context.set_context(mode=context.GRAPH_MODE, device_target=device_target)
momentum = 0.9
epoch_size = 1
batch_size = 32
network = LeNet5()
network.set_train()
net_loss = CrossEntropyLoss()
net_opt = nn.Momentum(network.trainable_params(), learning_rate, momentum)
model = Model(network, net_loss, net_opt)
# add summary writer
summary_writer = SummaryRecord(log_dir=summary_dir, network=network)
summary_callback = SummaryStep(summary_writer, flush_step=10)
ds = create_dataset(os.path.join(data_path, "train"), batch_size=batch_size)
print("============== Starting Training ==============")
model.train(epoch_size, ds, callbacks=[summary_callback], dataset_sink_mode=False)
summary_writer.close()
print("============== Train End =====================")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='MindSpore LeNet5 with summary Example')
parser.add_argument('--device_target', type=str, default="Ascend",
choices=['Ascend', 'GPU', 'CPU'],
help='Device where the code will be implemented (default: Ascend)')
parser.add_argument('--data_path', type=str, default="./MNIST_Data",
help='Path where the dataset is saved')
parser.add_argument('--summary_dir', type=str, default='./summary_dir',
help='Summaries log directory.')
parser.add_argument('--learning_rate', type=float, default=0.01,
help='Initial learning rate')
args = parser.parse_args()
main(data_path=args.data_path,
device_target=args.device_target,
summary_dir=args.summary_dir,
learning_rate=args.learning_rate)
chapter14/ImageNetDataSimulation/labels_map.txt 0 → 100644
浏览文件 @ c929b056
n00000005 0 data_line
n00000006 1 small_iron_box
n00000007 2 plastic_toothpicks
n00000002 3 orange
chapter14/create_dataset_using_cifar10.py 0 → 100644
浏览文件 @ c929b056
# 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.
# ==============================================================================
import os
from easydict import EasyDict as edict
import mindspore.dataset as de
from mindspore.dataset.transforms import c_transforms as C
from mindspore.dataset.transforms.vision import c_transforms as vision
from mindspore.common import dtype as mstype
import utils
CIFAR_URL = "http://www.cs.toronto.edu/~kriz/"
def download_cifar(target_directory, files, directory_from_tar):
if target_directory == None:
target_directory = utils.create_data_cache_dir()
utils.download_and_uncompress([files], CIFAR_URL, target_directory, is_tar=True)
##if target dir was specify move data from directory created by tar
##and put data into target dir
if target_directory != None:
tar_dir_full_path = os.path.join(target_directory, directory_from_tar)
all_files = os.path.join(tar_dir_full_path, "*")
cmd = "mv " + all_files + " " + target_directory
if os.path.exists(tar_dir_full_path):
print("copy files back to target_directory")
print("Executing: ", cmd)
rc1 = os.system(cmd)
rc2 = os.system("rm -r " + tar_dir_full_path)
if rc1 != 0 or rc2 != 0:
print("error when running command: ", cmd)
download_file = os.path.join(target_directory, files)
print("removing " + download_file)
os.system("rm " + download_file)
##exit with error so that build script will fail
raise SystemError
##change target directory to directory after tar
return target_directory, os.path.join(target_directory, directory_from_tar)
def create_cifar10_dataset(cifar_dir, num_parallel_workers=1):
"""
Creat the cifar10 dataset.
"""
ds = de.Cifar10Dataset(cifar_dir)
training = True
resize_height = 224
resize_width = 224
rescale = 1.0 / 255.0
shift = 0.0
repeat_num = 10
batch_size = 32
# define map operations
random_crop_op = vision.RandomCrop((32, 32), (4, 4, 4, 4)) # padding_mode default CONSTANT
random_horizontal_op = vision.RandomHorizontalFlip()
resize_op = vision.Resize((resize_height, resize_width)) # interpolation default BILINEAR
rescale_op = vision.Rescale(rescale, shift)
normalize_op = vision.Normalize((0.4465, 0.4822, 0.4914), (0.2010, 0.1994, 0.2023))
changeswap_op = vision.HWC2CHW()
type_cast_op = C.TypeCast(mstype.int32)
if training:
c_trans = [random_crop_op, random_horizontal_op]
c_trans += [resize_op, rescale_op, normalize_op,
changeswap_op]
# apply map operations on images
ds = ds.map(input_columns="label", operations=type_cast_op)
ds = ds.map(input_columns="image", operations=c_trans)
# apply repeat operations
ds = ds.repeat(repeat_num)
# apply shuffle operations
ds = ds.shuffle(buffer_size=10)
# apply batch operations
ds = ds.batch(batch_size=batch_size, drop_remainder=True)
return ds
def download_cifar10(target_directory=None):
return download_cifar(target_directory, "cifar-10-binary.tar.gz", "cifar-10-batches-bin")
if __name__ == "__main__":
dataset_dir, _ = download_cifar10()
data_set = create_cifar10_dataset(dataset_dir)
for data in data_set.create_dict_iterator():
print(data['image'].shape)
print(data['label'])
print('------------')
chapter14/create_dataset_using_imagenet.py 0 → 100644
浏览文件 @ c929b056
# 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.
# ==============================================================================
import mindspore.dataset as de
import mindspore.dataset.transforms.vision.py_transforms as F
def create_imagenet_dataset(imagenet_dir):
ds = de.ImageFolderDatasetV2(imagenet_dir)
transform = F.ComposeOp([F.Decode(),
F.RandomHorizontalFlip(0.5),
F.ToTensor(),
F.Normalize((0.491, 0.482, 0.447), (0.247, 0.243, 0.262)),
F.RandomErasing()])
ds = ds.map(input_columns="image", operations=transform())
ds = ds.shuffle(buffer_size=5)
ds = ds.repeat(3)
return ds
if __name__ == "__main__":
data_set = create_imagenet_dataset('ImageNetDataSimulation/images')
count = 0
for data in data_set.create_dict_iterator():
print(data['image'].shape)
print('------------')
count += 1
print(count)
chapter14/create_dataset_using_mindrecord.py 0 → 100644
浏览文件 @ c929b056
# 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.
# ==============================================================================
import collections
import numpy as np
import os
import re
import string
import mindspore._c_dataengine as de_map
import mindspore.dataset as ds
from mindspore._c_dataengine import InterpolationMode
from write_mindrecord import write_mindrecord_tutorial
MINDRECORD_FILE_NAME = "./imagenet.mindrecord"
def create_dataset_using_mindrecord_tutorial():
columns_list = ["data", "file_name", "label"]
num_readers = 4
data_set = ds.MindDataset(MINDRECORD_FILE_NAME, columns_list, num_readers)
# add your data enhance code here
assert data_set.get_dataset_size() == 20
data_set = data_set.repeat(2)
num_iter = 0
for item in data_set.create_dict_iterator():
print("-------------- index {} -----------------".format(num_iter))
# print("-------------- item[label]: {} ---------------------".format(item["label"]))
# print("-------------- item[data]: {} ----------------------".format(item["data"]))
num_iter += 1
assert num_iter == 40
if __name__ == '__main__':
write_mindrecord_tutorial()
create_dataset_using_mindrecord_tutorial()
os.remove(MINDRECORD_FILE_NAME)
os.remove(MINDRECORD_FILE_NAME + ".db")
chapter14/create_dataset_using_mnist.py 0 → 100644
浏览文件 @ c929b056
# 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.
# ==============================================================================
import os
from easydict import EasyDict as edict
import mindspore.dataset as de
import mindspore.dataset.transforms.c_transforms as C
import mindspore.dataset.transforms.vision.c_transforms as VC
from mindspore.dataset.transforms.vision.utils import Inter
from mindspore.common import dtype as mstype
import utils
MNIST_URL = "http://yann.lecun.com/exdb/mnist/"
MNIST_CONFIG = edict({
'num_classes': 10,
'lr': 0.01,
'momentum': 0.9,
'epoch_size': 1,
'batch_size': 32,
'repeat_size': 1,
'buffer_size': 1000,
'image_height': 32,
'image_width': 32,
'save_checkpoint_steps': 1875,
'keep_checkpoint_max': 10,
})
def download_mnist(target_directory=None):
if target_directory is None:
target_directory = utils.create_data_cache_dir()
##create mnst directory
target_directory = os.path.join(target_directory, "mnist")
try:
if not os.path.exists(target_directory):
os.mkdir(target_directory)
except OSError:
print("Creation of the directory %s failed" % target_directory)
files = ['train-images-idx3-ubyte.gz',
'train-labels-idx1-ubyte.gz',
't10k-images-idx3-ubyte.gz',
't10k-labels-idx1-ubyte.gz']
utils.download_and_uncompress(files, MNIST_URL, target_directory, is_tar=False)
return target_directory, os.path.join(target_directory, "datasetSchema.json")
def create_mnist_dataset(mnist_dir, num_parallel_workers=1):
ds = de.MnistDataset(mnist_dir)
# apply map operations on images
ds = ds.map(input_columns="label", operations=C.TypeCast(mstype.int32))
ds = ds.map(input_columns="image",
operations=VC.Resize((MNIST_CONFIG.image_height, MNIST_CONFIG.image_width),
interpolation=Inter.LINEAR),
num_parallel_workers=num_parallel_workers)
ds = ds.map(input_columns="image",
operations=VC.Rescale(1 / 0.3081, -1 * 0.1307 / 0.3081),
num_parallel_workers=num_parallel_workers)
ds = ds.map(input_columns="image",
operations=VC.Rescale(1.0 / 255.0, 0.0),
num_parallel_workers=num_parallel_workers)
ds = ds.map(input_columns="image",
operations=VC.HWC2CHW(),
num_parallel_workers=num_parallel_workers)
# apply DatasetOps
ds = ds.shuffle(buffer_size=MNIST_CONFIG.buffer_size) # 10000 as in LeNet train script
ds = ds.batch(MNIST_CONFIG.batch_size, drop_remainder=True)
ds = ds.repeat(MNIST_CONFIG.repeat_size)
return ds
if __name__ == "__main__":
mnistDir, _ = download_mnist()
data_set = create_mnist_dataset(mnistDir, 2)
for data in data_set.create_dict_iterator():
print(data['image'].shape)
print(data['label'])
print('------------')
chapter14/search_mindrecord.py 0 → 100644
浏览文件 @ c929b056
# 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.
# ============================================================================
import os
import uuid
from mindspore.mindrecord import MindPage, SUCCESS
from write_mindrecord import write_mindrecord_tutorial
MINDRECORD_FILE_NAME = "./imagenet.mindrecord"
def search_mindrecord_tutorial():
reader = MindPage(MINDRECORD_FILE_NAME)
fields = reader.get_category_fields()
assert fields == ['file_name', 'label'], \
'failed on getting candidate category fields.'
ret = reader.set_category_field("label")
assert ret == SUCCESS, 'failed on setting category field.'
info = reader.read_category_info()
# print("category info: {}".format(info))
row = reader.read_at_page_by_id(0, 0, 1)
assert len(row) == 1
assert len(row[0]) == 3
# print("row[0]: {}".format(row[0]))
row1 = reader.read_at_page_by_name("2", 0, 2)
assert len(row1) == 2
assert len(row1[0]) == 3
# print("row1[0]: {}".format(row1[0]))
# print("row1[1]: {}".format(row1[1]))
if __name__ == '__main__':
write_mindrecord_tutorial()
search_mindrecord_tutorial()
os.remove(MINDRECORD_FILE_NAME)
os.remove(MINDRECORD_FILE_NAME + ".db")
chapter14/utils.py 0 → 100644
浏览文件 @ c929b056
# 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.
# ============================================================================
import collections
import json
import os
import re
import string
import numpy as np
import urllib
import urllib.request
def get_data(dir_name):
"""
Get data from imagenet as dict.
Yields:
data (dict of list): imagenet data list which contains dict.
"""
map_file = os.path.join(dir_name, "labels_map.txt")
if not os.path.exists(map_file):
raise Exception("map file {} not exists".format(map_file))
label_dict = {}
with open(map_file) as fp:
line = fp.readline()
while line:
labels = line.split(" ")
label_dict[labels[1]] = labels[0]
line = fp.readline()
# get all the dir which are n02087046, n02094114, n02109525, ...
dir_paths = {}
image_dir = os.path.join(dir_name, "images")
for item in label_dict:
real_path = os.path.join(image_dir, label_dict[item])
if not os.path.isdir(real_path):
print("warning: {} dir is not exist".format(real_path))
continue
dir_paths[item] = real_path
if not dir_paths:
raise Exception("not valid image dir in {}".format(image_dir))
# get the filename, label and image binary as a dict
data_list = []
for label in dir_paths:
for item in os.listdir(dir_paths[label]):
file_name = os.path.join(dir_paths[label], item)
if not item.endswith("JPEG") and not item.endswith("jpg"):
print("warning: {} file is not suffix with JPEG/jpg, skip it.".format(file_name))
continue
data = {}
data["file_name"] = str(file_name)
data["label"] = int(label)
# get the image data
image_file = open(file_name, "rb")
image_bytes = image_file.read()
image_file.close()
data["data"] = image_bytes
data_list.append(data)
return data_list
def create_data_cache_dir():
cwd = os.getcwd()
target_directory = os.path.join(cwd, "data_cache")
try:
if not os.path.exists(target_directory):
os.mkdir(target_directory)
except OSError:
print("Creation of the directory %s failed" % target_directory)
return target_directory
def download_and_uncompress(files, source_url, target_directory, is_tar=False):
for f in files:
url = source_url + f
target_file = os.path.join(target_directory, f)
##check if file already downloaded
if not (os.path.exists(target_file) or os.path.exists(target_file[:-3])):
urllib.request.urlretrieve(url, target_file)
if is_tar:
print("extracting from local tar file " + target_file)
rc = os.system("tar -C " + target_directory + " -xvf " + target_file)
else:
print("unzipping " + target_file)
rc = os.system("gunzip -f " + target_file)
if rc != 0:
print("Failed to uncompress ", target_file, " removing")
os.system("rm " + target_file)
##exit with error so that build script will fail
raise SystemError
else:
print("Using cached dataset at ", target_file)
chapter14/write_mindrecord.py 0 → 100644
浏览文件 @ c929b056
# 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.
# ============================================================================
import os
import uuid
from mindspore.mindrecord import FileWriter, FileReader
from utils import get_data
MINDRECORD_FILE_NAME = "./imagenet.mindrecord"
def write_mindrecord_tutorial():
writer = FileWriter(MINDRECORD_FILE_NAME)
data = get_data("./ImageNetDataSimulation")
schema_json = {"file_name": {"type": "string"},
"label": {"type": "int64"},
"data": {"type": "bytes"}}
writer.add_schema(schema_json, "img_schema")
writer.add_index(["file_name", "label"])
writer.write_raw_data(data)
writer.commit()
reader = FileReader(MINDRECORD_FILE_NAME)
count = 0
for index, x in enumerate(reader.get_next()):
assert len(x) == 3
count = count + 1
# print("#item {}: {}".format(index, x))
assert count == 20
reader.close()
if __name__ == '__main__':
write_mindrecord_tutorial()
os.remove(MINDRECORD_FILE_NAME)
os.remove(MINDRECORD_FILE_NAME + ".db")
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