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提交 65e9b27b 编写于 作者: M mindspore-ci-bot 提交者： Gitee
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!6 rm scripts in experiment_3, download instead 

Merge pull request !6 from dyonghan/experiment_3
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experiment_3/3-Computer_Vision.md
浏览文件 @ 65e9b27b
...@@ -2,7 +2,7 @@ ...@@ -2,7 +2,7 @@
## 实验介绍 ## 实验介绍
本实验主要介绍使用MindSpore在CIFAR10数据集上训练ResNet50。本实验建议使用MindSpore model_zoo中提供的ResNet50。 本实验主要介绍使用MindSpore在CIFAR-10数据集上训练ResNet50。本实验使用MindSpore model_zoo中提供的ResNet50模型定义，以及MindSpore官网教程[在云上使用MindSpore](https://www.mindspore.cn/tutorial/zh-CN/0.2.0-alpha/advanced_use/use_on_the_cloud.html)里的训练脚本。
## 实验目的 ## 实验目的
...@@ -42,7 +42,7 @@ ...@@ -42,7 +42,7 @@
### 数据集准备 ### 数据集准备
CIFAR-10是一个图片分类数据集，包含60000张32x32的彩色物体图片，训练集50000张，测试集10000张，共10类，每类6000张。CIFAR-10数据集的官网：[THE MNIST DATABASE](http://www.cs.toronto.edu/~kriz/cifar.html)。 CIFAR-10是一个图片分类数据集，包含60000张32x32的彩色物体图片，训练集50000张，测试集10000张，共10类，每类6000张。CIFAR-10数据集的官网：[The CIFAR-10 and CIFAR-100 datasets](http://www.cs.toronto.edu/~kriz/cifar.html)。
从CIFAR-10官网下载“CIFAR-10 binary version (suitable for C programs)”到本地并解压。 从CIFAR-10官网下载“CIFAR-10 binary version (suitable for C programs)”到本地并解压。
...@@ -56,7 +56,8 @@ CIFAR-10是一个图片分类数据集，包含60000张32x32的彩色物体图 ...@@ -56,7 +56,8 @@ CIFAR-10是一个图片分类数据集，包含60000张32x32的彩色物体图
``` ```
experiment_3 experiment_3
├── 脚本等文件 ├── dataset.py
├── resnet50_train.py
└── cifar10 └── cifar10
├── batches.meta.txt ├── batches.meta.txt
├── eval ├── eval
...@@ -71,8 +72,6 @@ experiment_3 ...@@ -71,8 +72,6 @@ experiment_3
## 实验步骤 ## 实验步骤
参考MindSpore官网[在云上使用MindSpore](https://www.mindspore.cn/tutorial/zh-CN/0.2.0-alpha/advanced_use/use_on_the_cloud.html)。
### 代码梳理 ### 代码梳理
- resnet50_train.py：主脚本，包含性能测试`PerformanceCallback`、动态学习率`get_lr`、执行函数`resnet50_train`等函数； - resnet50_train.py：主脚本，包含性能测试`PerformanceCallback`、动态学习率`get_lr`、执行函数`resnet50_train`等函数；
...@@ -154,7 +153,7 @@ def get_lr(global_step, ...@@ -154,7 +153,7 @@ def get_lr(global_step,
return learning_rate return learning_rate
``` ```
MindSpore支持直接读取cifar10数据集： MindSpore支持直接读取CIFAR-10数据集：
```python ```python
if device_num == 1 or not do_train: if device_num == 1 or not do_train:
...@@ -221,7 +220,15 @@ class ResNet(nn.Cell): ...@@ -221,7 +220,15 @@ class ResNet(nn.Cell):
ResNet的不同版本均由5个阶段（stage）组成，其中ResNet50结构为Convx1 -> ResidualBlockx3 -> ResidualBlockx4 -> ResidualBlockx6 -> ResidualBlockx5 -> Pooling+FC。 ResNet的不同版本均由5个阶段（stage）组成，其中ResNet50结构为Convx1 -> ResidualBlockx3 -> ResidualBlockx4 -> ResidualBlockx6 -> ResidualBlockx5 -> Pooling+FC。
`ResidualBlock`为残差模块，相比传统卷积多了一个short-cut支路，用于将浅层的信息直接传递到深层，使得网络可以很深，而不会出现训练时梯度消失/爆炸的问题： ![ResNet Architectures](images/resnet_archs.png)
[1] 图片来源于https://arxiv.org/pdf/1512.03385.pdf
`ResidualBlock`为残差模块，相比传统卷积多了一个short-cut支路，用于将浅层的信息直接传递到深层，使得网络可以很深，而不会出现训练时梯度消失/爆炸的问题。ResNet50采用了下图右侧Bottleneck形式的残差模块：
![ResNet Block](images/resnet_block.png)
[2] 图片来源于https://arxiv.org/pdf/1512.03385.pdf
```python ```python
class ResidualBlock(nn.Cell): class ResidualBlock(nn.Cell):
...@@ -245,7 +252,6 @@ class ResidualBlock(nn.Cell): ...@@ -245,7 +252,6 @@ class ResidualBlock(nn.Cell):
self.relu = nn.ReLU() self.relu = nn.ReLU()
# 如果in
self.down_sample = False self.down_sample = False
if stride != 1 or in_channel != out_channel: if stride != 1 or in_channel != out_channel:
self.down_sample = True self.down_sample = True
...@@ -269,6 +275,7 @@ class ResidualBlock(nn.Cell): ...@@ -269,6 +275,7 @@ class ResidualBlock(nn.Cell):
out = self.conv3(out) out = self.conv3(out)
out = self.bn3(out) out = self.bn3(out)
# ResNet50未使用带有下采样的残差支路
if self.down_sample: if self.down_sample:
identity = self.down_sample_layer(identity) identity = self.down_sample_layer(identity)
...@@ -330,9 +337,9 @@ mox.file.copy_parallel(src_url='output', dst_url='s3://OBS/PATH') ...@@ -330,9 +337,9 @@ mox.file.copy_parallel(src_url='output', dst_url='s3://OBS/PATH')
## 实验结论 ## 实验结论
本实验主要介绍使用MindSpore在CIFAR10数据集上训练ResNet50，了解了以下知识点： 本实验主要介绍使用MindSpore在CIFAR-10数据集上训练ResNet50，了解了以下知识点：
- 性能测试 - 使用自定义Callback实现性能监测；
- 动态学习率 - 使用动态学习率提升训练效果；
- model_zoo：resnet50 - 加载CIFAR-10数据集、数据增强；
- cifar10数据集、数据增强 - ResNet50模型的结构及其MindSpore实现。
experiment_3/dataset.py 已删除 100644 → 0
浏览文件 @ 4c201ec7
# 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.engine as de
import mindspore.dataset.transforms.vision.c_transforms as C
import mindspore.dataset.transforms.c_transforms as C2
device_id = int(os.getenv('DEVICE_ID'))
device_num = int(os.getenv('RANK_SIZE'))
def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32):
"""
Create a train or eval dataset.
Args:
dataset_path (str): 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.
"""
if do_train:
dataset_path = os.path.join(dataset_path, 'train')
do_shuffle = True
else:
dataset_path = os.path.join(dataset_path, 'eval')
do_shuffle = False
if device_num == 1 or not do_train:
ds = de.Cifar10Dataset(dataset_path, num_parallel_workers=8, shuffle=do_shuffle)
else:
ds = de.Cifar10Dataset(dataset_path, num_parallel_workers=8, shuffle=do_shuffle,
num_shards=device_num, shard_id=device_id)
resize_height = 224
resize_width = 224
buffer_size = 100
rescale = 1.0 / 255.0
shift = 0.0
# define map operations
random_crop_op = C.RandomCrop((32, 32), (4, 4, 4, 4))
random_horizontal_flip_op = C.RandomHorizontalFlip(device_id / (device_id + 1))
resize_op = C.Resize((resize_height, resize_width))
rescale_op = C.Rescale(rescale, shift)
normalize_op = C.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010])
change_swap_op = C.HWC2CHW()
trans = []
if do_train:
trans += [random_crop_op, random_horizontal_flip_op]
trans += [resize_op, rescale_op, normalize_op, change_swap_op]
type_cast_op = C2.TypeCast(mstype.int32)
ds = ds.map(input_columns="label", num_parallel_workers=8, operations=type_cast_op)
ds = ds.map(input_columns="image", num_parallel_workers=8, operations=trans)
# apply batch operations
ds = ds.batch(batch_size, drop_remainder=True)
# apply dataset repeat operation
ds = ds.repeat(repeat_num)
return ds
experiment_3/resnet50_train.py 已删除 100644 → 0
浏览文件 @ 4c201ec7
# 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.
# ============================================================================
"""ResNet50 model train with MindSpore"""
import os
import argparse
import random
import time
import numpy as np
import moxing as mox
from mindspore import context
from mindspore import Tensor
from mindspore.nn.optim.momentum import Momentum
from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
from mindspore.train.model import Model, ParallelMode
from mindspore.train.callback import Callback, LossMonitor
from mindspore.train.loss_scale_manager import FixedLossScaleManager
import mindspore.dataset.engine as de
from dataset import create_dataset, device_id, device_num
from mindspore.model_zoo.resnet import resnet50
random.seed(1)
np.random.seed(1)
de.config.set_seed(1)
class PerformanceCallback(Callback):
"""
Training performance callback.
Args:
batch_size (int): Batch number for one step.
"""
def __init__(self, batch_size):
super(PerformanceCallback, self).__init__()
self.batch_size = batch_size
self.last_step = 0
self.epoch_begin_time = 0
def step_begin(self, run_context):
self.epoch_begin_time = time.time()
def step_end(self, run_context):
params = run_context.original_args()
cost_time = time.time() - self.epoch_begin_time
train_steps = params.cur_step_num -self.last_step
print(f'epoch {params.cur_epoch_num} cost time = {cost_time}, train step num: {train_steps}, '
f'one step time: {1000*cost_time/train_steps} ms, '
f'train samples per second of cluster: {device_num*train_steps*self.batch_size/cost_time:.1f}\n')
self.last_step = run_context.original_args().cur_step_num
def get_lr(global_step,
total_epochs,
steps_per_epoch,
lr_init=0.01,
lr_max=0.1,
warmup_epochs=5):
"""
Generate learning rate array.
Args:
global_step (int): Initial step of training.
total_epochs (int): Total epoch of training.
steps_per_epoch (float): Steps of one epoch.
lr_init (float): Initial learning rate. Default: 0.01.
lr_max (float): Maximum learning rate. Default: 0.1.
warmup_epochs (int): The number of warming up epochs. Default: 5.
Returns:
np.array, learning rate array.
"""
lr_each_step = []
total_steps = steps_per_epoch * total_epochs
warmup_steps = steps_per_epoch * warmup_epochs
if warmup_steps != 0:
inc_each_step = (float(lr_max) - float(lr_init)) / float(warmup_steps)
else:
inc_each_step = 0
for i in range(int(total_steps)):
if i < warmup_steps:
lr = float(lr_init) + inc_each_step * float(i)
else:
base = ( 1.0 - (float(i) - float(warmup_steps)) / (float(total_steps) - float(warmup_steps)) )
lr = float(lr_max) * base * base
if lr < 0.0:
lr = 0.0
lr_each_step.append(lr)
current_step = global_step
lr_each_step = np.array(lr_each_step).astype(np.float32)
learning_rate = lr_each_step[current_step:]
return learning_rate
def resnet50_train(args_opt):
epoch_size = args_opt.epoch_size
batch_size = 32
class_num = 10
loss_scale_num = 1024
local_data_path = '/cache/data'
# set graph mode and parallel mode
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False)
context.set_context(enable_task_sink=True, device_id=device_id)
context.set_context(enable_loop_sink=True)
context.set_context(enable_mem_reuse=True)
if device_num > 1:
context.set_auto_parallel_context(device_num=device_num,
parallel_mode=ParallelMode.DATA_PARALLEL,
mirror_mean=True)
local_data_path = os.path.join(local_data_path, str(device_id))
# data download
print('Download data.')
mox.file.copy_parallel(src_url=args_opt.data_url, dst_url=local_data_path)
# create dataset
print('Create train and evaluate dataset.')
train_dataset = create_dataset(dataset_path=local_data_path, do_train=True,
repeat_num=epoch_size, batch_size=batch_size)
eval_dataset = create_dataset(dataset_path=local_data_path, do_train=False,
repeat_num=1, batch_size=batch_size)
train_step_size = train_dataset.get_dataset_size()
print('Create dataset success.')
# create model
net = resnet50(class_num = class_num)
loss = SoftmaxCrossEntropyWithLogits(sparse=True)
lr = Tensor(get_lr(global_step=0, total_epochs=epoch_size, steps_per_epoch=train_step_size))
opt = Momentum(net.trainable_params(), lr, momentum=0.9, weight_decay=1e-4, loss_scale=loss_scale_num)
loss_scale = FixedLossScaleManager(loss_scale_num, False)
model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'})
# define performance callback to show ips and loss callback to show loss for every epoch
performance_cb = PerformanceCallback(batch_size)
loss_cb = LossMonitor()
cb = [performance_cb, loss_cb]
print(f'Start run training, total epoch: {epoch_size}.')
model.train(epoch_size, train_dataset, callbacks=cb)
if device_num == 1 or device_id == 0:
print(f'Start run evaluation.')
output = model.eval(eval_dataset)
print(f'Evaluation result: {output}.')
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='ResNet50 train.')
parser.add_argument('--data_url', required=True, default=None, help='Location of data.')
parser.add_argument('--train_url', required=True, default=None, help='Location of training outputs.')
parser.add_argument('--epoch_size', type=int, default=90, help='Train epoch size.')
args_opt, unknown = parser.parse_known_args()
resnet50_train(args_opt)
print('ResNet50 training success!')
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