p60975123
/
mindspore
forked from mindspore-Ecosystem/mindspore
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

!16883 alexnet_lenet update 

From: @huchunmei
Reviewed-by: @oacjiewen,@c_34
Signed-off-by: @c_34
This commit is contained in:
mindspore-ci-bot 2021-05-27 11:45:06 +08:00 committed by Gitee
parent 3a0ed182ca fe296e457a
commit e34fa42980
12 changed files with 50 additions and 116 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

10
model_zoo/official/cv/alexnet/README.md
View File

@ -84,9 +84,15 @@ sh run_standalone_eval_ascend.sh [DATA_PATH] [CKPT_NAME]
├── src
│ ├──dataset.py // creating dataset
│ ├──alexnet.py // alexnet architecture
│ ├──config.py // parameter configuration
│ └──model_utils
│ ├──config.py // Processing configuration parameters
│ ├──device_adapter.py // Get cloud ID
│ ├──local_adapter.py // Get local ID
│ └──moxing_adapter.py // Parameter processing
├── default_config.yaml // Training parameter profile(cifar10)
├── config_imagenet.yaml // Training parameter profile(imagenet)
├── train.py // training script
├── eval.py // evaluation script
├── eval.py // evaluation script
```
### [Script Parameters](#contents)

8
model_zoo/official/cv/alexnet/README_CN.md
View File

@ -88,7 +88,13 @@ sh run_standalone_eval_ascend.sh [DATA_PATH] [CKPT_NAME]
├── src
│ ├──dataset.py // 创建数据集
│ ├──alexnet.py // AlexNet架构
│ ├──config.py // 参数配置
| └──model_utils
| ├──config.py // 训练配置
| ├──device_adapter.py // 获取云上id
| ├──local_adapter.py // 获取本地id
| └──moxing_adapter.py // 参数处理
├── default_config.yaml // 训练参数配置文件
├── config_imagenet.yaml // 训练参数配置文件
├── train.py // 训练脚本
├── eval.py // 评估脚本
```

4
model_zoo/official/cv/alexnet/eval.py
View File

@ -53,7 +53,7 @@ def eval_alexnet():
network = AlexNet(config.num_classes, phase='test')
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
opt = nn.Momentum(network.trainable_params(), config.learning_rate, config.momentum)
ds_eval = create_dataset_cifar10(config.data_path, config.batch_size, status="test", \
ds_eval = create_dataset_cifar10(config, config.data_path, config.batch_size, status="test", \
target=config.device_target)
param_dict = load_checkpoint(config.ckpt_path)
print("load checkpoint from [{}].".format(config.ckpt_path))
@ -64,7 +64,7 @@ def eval_alexnet():
elif config.dataset_name == 'imagenet':
network = AlexNet(config.num_classes, phase='test')
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
ds_eval = create_dataset_imagenet(config.data_path, config.batch_size, training=False)
ds_eval = create_dataset_imagenet(config, config.data_path, config.batch_size, training=False)
param_dict = load_checkpoint(config.ckpt_path)
print("load checkpoint from [{}].".format(config.ckpt_path))
load_param_into_net(network, param_dict)

54
model_zoo/official/cv/alexnet/src/config.py
View File

@ -1,54 +0,0 @@
# 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 train.py
"""
from easydict import EasyDict as edict
alexnet_cifar10_cfg = edict({
'num_classes': 10,
'learning_rate': 0.002,
'momentum': 0.9,
'epoch_size': 30,
'batch_size': 32,
'buffer_size': 1000,
'image_height': 227,
'image_width': 227,
'save_checkpoint_steps': 1562,
'keep_checkpoint_max': 10,
'air_name': "alexnet.air",
})
alexnet_imagenet_cfg = edict({
'num_classes': 1000,
'learning_rate': 0.13,
'momentum': 0.9,
'epoch_size': 150,
'batch_size': 256,
'buffer_size': None, # invalid parameter
'image_height': 224,
'image_width': 224,
'save_checkpoint_steps': 625,
'keep_checkpoint_max': 10,
'air_name': "alexnet.air",
# opt
'weight_decay': 0.0001,
'loss_scale': 1024,
# lr
'is_dynamic_loss_scale': 0,
})

9
model_zoo/official/cv/alexnet/src/dataset.py
View File

@ -22,10 +22,9 @@ import mindspore.dataset.transforms.c_transforms as C
import mindspore.dataset.vision.c_transforms as CV
from mindspore.common import dtype as mstype
from mindspore.communication.management import get_rank, get_group_size
from .config import alexnet_cifar10_cfg, alexnet_imagenet_cfg
def create_dataset_cifar10(data_path, batch_size=32, repeat_size=1, status="train", target="Ascend"):
def create_dataset_cifar10(cfg, data_path, batch_size=32, repeat_size=1, status="train", target="Ascend"):
"""
create dataset for train or test
"""
@ -40,7 +39,7 @@ def create_dataset_cifar10(data_path, batch_size=32, repeat_size=1, status="trai
num_shards=device_num, shard_id=rank_id)
rescale = 1.0 / 255.0
shift = 0.0
cfg = alexnet_cifar10_cfg
# cfg = alexnet_cifar10_cfg
resize_op = CV.Resize((cfg.image_height, cfg.image_width))
rescale_op = CV.Rescale(rescale, shift)
@ -65,7 +64,7 @@ def create_dataset_cifar10(data_path, batch_size=32, repeat_size=1, status="trai
return cifar_ds
def create_dataset_imagenet(dataset_path, batch_size=32, repeat_num=1, training=True,
def create_dataset_imagenet(cfg, dataset_path, batch_size=32, repeat_num=1, training=True,
num_parallel_workers=None, shuffle=None, sampler=None, class_indexing=None):
"""
create a train or eval imagenet2012 dataset for resnet50
@ -82,7 +81,7 @@ def create_dataset_imagenet(dataset_path, batch_size=32, repeat_num=1, training=
"""
device_num, rank_id = _get_rank_info()
cfg = alexnet_imagenet_cfg
# cfg = alexnet_imagenet_cfg
num_parallel_workers = 16
if device_num == 1:

4
model_zoo/official/cv/alexnet/train.py
View File

@ -82,9 +82,9 @@ def train_alexnet():
context.set_context(device_id=get_device_id())
if config.dataset_name == "cifar10":
ds_train = create_dataset_cifar10(config.data_path, config.batch_size, target=config.device_target)
ds_train = create_dataset_cifar10(config, config.data_path, config.batch_size, target=config.device_target)
elif config.dataset_name == "imagenet":
ds_train = create_dataset_imagenet(config.data_path, config.batch_size)
ds_train = create_dataset_imagenet(config, config.data_path, config.batch_size)
else:
raise ValueError("Unsupported dataset.")

7
model_zoo/official/cv/lenet/README.md
View File

@ -100,7 +100,12 @@ sh run_standalone_eval_ascend.sh [DATA_PATH] [CKPT_NAME]
│ ├──aipp.cfg // aipp config
│ ├──dataset.py // creating dataset
│ ├──lenet.py // lenet architecture
│ ├──config.py // parameter configuration
│ └──model_utils
│ ├──config.py // Processing configuration parameters
│ ├──device_adapter.py // Get cloud ID
│ ├──local_adapter.py // Get local ID
│ └──moxing_adapter.py // Parameter processing
├── default_config.yaml // Training parameter profile(ascend)
├── train.py // training script
├── eval.py // evaluation script
├── postprocess.py // postprocess script

17
model_zoo/official/cv/lenet/README_CN.md
View File

@ -102,12 +102,17 @@ sh run_standalone_eval_ascend.sh [DATA_PATH] [CKPT_NAME]
│ ├──run_standalone_eval_ascend.sh // Ascend评估
├── src
│ ├──aipp.cfg // aipp配置文件
│ ├──dataset.py // 创建数据集
│ ├──lenet.py // Lenet架构
│ ├──config.py // 参数配置
├── train.py // 训练脚本
├── eval.py // 评估脚本
├── postprocess.py // 310推理后处理脚本
│ ├──dataset.py // 创建数据集
│ ├──lenet.py // Lenet架构
| └──model_utils
| ├──config.py // 训练配置
| ├──device_adapter.py // 获取云上id
| ├──local_adapter.py // 获取本地id
| └──moxing_adapter.py // 参数处理
├── default_config.yaml // 训练参数配置文件
├── train.py // 训练脚本
├── eval.py // 评估脚本
├── postprocess.py // 310推理后处理脚本
```
## 脚本参数

8
model_zoo/official/cv/lenet/default_config.yaml
View File

@ -18,7 +18,7 @@ num_classes: 10
lr: 0.01
momentum: 0.9
epoch_size: 10
batch_size: 15 # 32
batch_size: 32
buffer_size: 1000
image_height: 32
image_width: 32
@ -27,7 +27,7 @@ keep_checkpoint_max: 10
air_name: "lenet"
device_id: 0
file_name: "lenet"
file_format: "AIR"
file_format: "MINDIR"
model_name: lenet
learning_rate: 0.002
@ -37,6 +37,10 @@ dataset_sink_mode: True
save_checkpoint: True
save_checkpoint_epochs: 2
# lenet acc calculation
result_path: '' # "result files path."
img_path: '' # "image file path."
---
# Config description for each option
enable_modelarts: 'Whether training on modelarts, default: False'

2
model_zoo/official/cv/lenet/export.py
View File

@ -12,7 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""export checkpoint file into air, mindir models"""
"""export checkpoint file into air, onnx, mindir models"""
from src.model_utils.config import config
from src.model_utils.device_adapter import get_device_id

8
model_zoo/official/cv/lenet/postprocess.py
View File

@ -14,14 +14,10 @@
# ============================================================================
"""post process for 310 inference"""
import os
import argparse
import numpy as np
from src.model_utils.config import config
batch_size = 1
parser = argparse.ArgumentParser(description="lenet acc calculation")
parser.add_argument("--result_path", type=str, required=True, help="result files path.")
parser.add_argument("--img_path", type=str, required=True, help="image file path.")
args = parser.parse_args()
def calcul_acc(labels, preds):
@ -44,4 +40,4 @@ def get_result(result_path, img_path):
if __name__ == '__main__':
get_result(args.result_path, args.img_path)
get_result(config.result_path, config.img_path)

33
model_zoo/official/cv/lenet/src/config.py
View File

@ -1,33 +0,0 @@
# 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 train.py
"""
from easydict import EasyDict as edict
mnist_cfg = edict({
'num_classes': 10,
'lr': 0.01,
'momentum': 0.9,
'epoch_size': 10,
'batch_size': 32,
'buffer_size': 1000,
'image_height': 32,
'image_width': 32,
'save_checkpoint_steps': 1875,
'keep_checkpoint_max': 10,
'air_name': "lenet",
})