p47068915
/
mindspore
forked from mindspore-Ecosystem/mindspore
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

!22323 gpu for squeezenet 

Merge pull request !22323 from 郑彬/squeezenet_gpu
This commit is contained in:
i-robot 2021-08-31 08:37:39 +00:00 committed by Gitee
parent 033b4706f2 1c44d8500f
commit b021a58caa
9 changed files with 486 additions and 151 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

282
model_zoo/official/cv/squeezenet/README.md
View File

@ -92,6 +92,19 @@ After installing MindSpore via the official website, you can start training and
Usage: bash scripts/run_eval.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DEVICE_ID] [DATA_PATH] [CHECKPOINT_PATH]
```
- running on GPU
```bash
# distributed training
Usage: bash scripts/run_distribute_train_gpu.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)
# standalone training
Usage: bash scripts/run_standalone_train.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DEVICE_ID] [DATA_PATH] [PRETRAINED_CKPT_PATH](optional)
# run evaluation example
Usage: bash scripts/run_eval_gpu.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DEVICE_ID] [DATASET_PATH] [CHECKPOINT_PATH]
```
- running on CPU
```bash
@ -139,46 +152,53 @@ After installing MindSpore via the official website, you can start training and
## [Script and Sample Code](#contents)
```shell
```text
.
└── squeezenet
├── README.md
├── ascend310_infer # application for 310 inference
├── ascend310_infer # application for 310 inference
├── scripts
├── run_distribute_train.sh # launch ascend distributed training(8 pcs)
├── run_standalone_train.sh # launch ascend standalone training(1 pcs)
├── run_eval.sh # launch ascend evaluation
├── run_infer_310.sh # shell script for 310 infer
├── run_distribute_train.sh # launch ascend distributed training(8 pcs)
├── run_distribute_train_gpu.sh # launch GPU distributed training(8 pcs)
├── run_standalone_train.sh # launch ascend standalone training(1 pcs)
├── run_standalone_train_gpu.sh # launch GPU standalone training(1 pcs)
├── run_train_cpu.sh # launch CPU training
├── run_eval.sh # launch ascend evaluation
├── run_eval_gpu.sh # launch GPU evaluation
├── run_eval_cpu.sh # launch CPU evaluation
├── run_infer_310.sh # shell script for 310 infer
├── src
├── dataset.py # data preprocessing
├── CrossEntropySmooth.py # loss definition for ImageNet dataset
├── lr_generator.py # generate learning rate for each step
└── squeezenet.py # squeezenet architecture, including squeezenet and squeezenet_residual
├── dataset.py # data preprocessing
├── CrossEntropySmooth.py # loss definition for ImageNet dataset
├── lr_generator.py # generate learning rate for each step
└── squeezenet.py # squeezenet architecture, including squeezenet and squeezenet_residual
├── model_utils
│ ├── device_adapter.py # device adapter
│ ├── local_adapter.py # local adapter
│ ├── moxing_adapter.py # moxing adapter
│ ├── config.py # parameter analysis
│ ├── device_adapter.py # device adapter
│ ├── local_adapter.py # local adapter
│ ├── moxing_adapter.py # moxing adapter
│ └── config.py # parameter analysis
├── squeezenet_cifar10_config.yaml # parameter configuration
├── squeezenet_imagenet_config.yaml # parameter configuration
├── squeezenet_residual_cifar10_config.yaml # parameter configuration
├── squeezenet_residual_imagenet_config.yaml # parameter configuration
├── train.py # train net
├── eval.py # eval net
└── export.py # export checkpoint files into geir/onnx
├── postprocess.py # postprocess script
├── preprocess.py # preprocess script
├── export.py # export checkpoint files into geir/onnx
├── postprocess.py # postprocess script
├── preprocess.py # preprocess script
├── requirements.txt
└── mindspore_hub_conf.py # mindspore hub interface
```
## [Script Parameters](#contents)
Parameters for both training and evaluation can be set in config.py
Parameters for both training and evaluation can be set in *.yaml
- config for SqueezeNet, CIFAR-10 dataset
```py
"class_num": 10, # dataset class num
"batch_size": 32, # batch size of input tensor
"global_batch_size": 32, # the total batch_size for training and evaluation
"loss_scale": 1024, # loss scale
"momentum": 0.9, # momentum
"weight_decay": 1e-4, # weight decay
@ -199,7 +219,7 @@ Parameters for both training and evaluation can be set in config.py
```py
"class_num": 1000, # dataset class num
"batch_size": 32, # batch size of input tensor
"global_batch_size": 256, # the total batch_size for training and evaluation
"loss_scale": 1024, # loss scale
"momentum": 0.9, # momentum
"weight_decay": 7e-5, # weight decay
@ -222,7 +242,7 @@ Parameters for both training and evaluation can be set in config.py
```py
"class_num": 10, # dataset class num
"batch_size": 32, # batch size of input tensor
"global_batch_size": 32, # the total batch_size for training and evaluation
"loss_scale": 1024, # loss scale
"momentum": 0.9, # momentum
"weight_decay": 1e-4, # weight decay
@ -243,7 +263,7 @@ Parameters for both training and evaluation can be set in config.py
```py
"class_num": 1000, # dataset class num
"batch_size": 32, # batch size of input tensor
"global_batch_size": 256, # The total batch_size for training and evaluation
"loss_scale": 1024, # loss scale
"momentum": 0.9, # momentum
"weight_decay": 7e-5, # weight decay
@ -262,7 +282,7 @@ Parameters for both training and evaluation can be set in config.py
"lr_max": 0.01, # maximum learning rate
```
For more configuration details, please refer the script `config.py`.
For more configuration details, please refer the file `*.yaml`.
## [Training Process](#contents)
@ -469,137 +489,137 @@ Inference result is saved in current path, you can find result like this in acc.
#### SqueezeNet on CIFAR-10
| Parameters | Ascend |
| -------------------------- | ----------------------------------------------------------- |
| Model Version | SqueezeNet |
| Resource | Ascend 910; CPU 2.60GHz, 192cores; Memory 755G; OS Euler2.8 |
| uploaded Date | 11/06/2020 (month/day/year) |
| MindSpore Version | 1.0.0 |
| Dataset | CIFAR-10 |
| Training Parameters | epoch=120, steps=195, batch_size=32, lr=0.01 |
| Optimizer | Momentum |
| Loss Function | Softmax Cross Entropy |
| outputs | probability |
| Loss | 0.0496 |
| Speed | 1pc: 16.7 ms/step; 8pcs: 17.0 ms/step |
| Total time | 1pc: 55.5 mins; 8pcs: 15.0 mins |
| Parameters (M) | 4.8 |
| Checkpoint for Fine tuning | 6.4M (.ckpt file) |
| Scripts | [squeezenet script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/squeezenet) |
| Parameters | Ascend | GPU |
| -------------------------- | ----------------------------------------------------------- | --- |
| Model Version | SqueezeNet | SqueezeNet |
| Resource | Ascend 910; CPU 2.60GHz, 192cores; Memory 755G; OS Euler2.8 | NV SMX2 V100-32G |
| uploaded Date | 11/06/2020 (month/day/year) | 8/26/2021 (month/day/year) |
| MindSpore Version | 1.0.0 | 1.4.0 |
| Dataset | CIFAR-10 | CIFAR-10 |
| Training Parameters | epoch=120, steps=195, batch_size=32, lr=0.01 | 1pc:epoch=120, steps=1562, batch_size=32, lr=0.01; 8pcs:epoch=120, steps=1562, batch_size=4, lr=0.01|
| Optimizer | Momentum | Momentum |
| Loss Function | Softmax Cross Entropy | Softmax Cross Entropy |
| outputs | probability | probability |
| Loss | 0.0496 | 1pc:0.0892, 8pcs:0.0130 |
| Speed | 1pc: 16.7 ms/step; 8pcs: 17.0 ms/step | 1pc: 28.6 ms/step; 8pcs: 10.8 ms/step |
| Total time | 1pc: 55.5 mins; 8pcs: 15.0 mins | 1pc: 90mins; 8pcs: 34mins |
| Parameters (M) | 4.8 | 0.74 |
| Checkpoint for Fine tuning | 6.4M (.ckpt file) | 6.4M (.ckpt file)|
| Scripts | [squeezenet script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/squeezenet) | [squeezenet script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/squeezenet) |
#### SqueezeNet on ImageNet
| Parameters | Ascend |
| -------------------------- | ----------------------------------------------------------- |
| Model Version | SqueezeNet |
| Resource | Ascend 910; CPU 2.60GHz, 192cores; Memory 755G; OS Euler2.8 |
| uploaded Date | 11/06/2020 (month/day/year) |
| MindSpore Version | 1.0.0 |
| Dataset | ImageNet |
| Training Parameters | epoch=200, steps=5004, batch_size=32, lr=0.01 |
| Optimizer | Momentum |
| Loss Function | Softmax Cross Entropy |
| outputs | probability |
| Loss | 2.9150 |
| Speed | 8pcs: 19.9 ms/step |
| Total time | 8pcs: 5.2 hours |
| Parameters (M) | 4.8 |
| Checkpoint for Fine tuning | 13.3M (.ckpt file) |
| Scripts | [squeezenet script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/squeezenet) |
| Parameters | Ascend | GPU |
| -------------------------- | ----------------------------------------------------------- | --- |
| Model Version | SqueezeNet | SqueezeNet |
| Resource | Ascend 910; CPU 2.60GHz, 192cores; Memory 755G; OS Euler2.8 | NV SMX2 V100-32G |
| uploaded Date | 11/06/2020 (month/day/year) | 8/26/2021 (month/day/year) |
| MindSpore Version | 1.0.0 | 1.4.0 |
| Dataset | ImageNet | ImageNet |
| Training Parameters | epoch=200, steps=5004, batch_size=32, lr=0.01 | epoch=200, steps=5004, batch_size=32, lr=0.01 |
| Optimizer | Momentum | Momentum |
| Loss Function | Softmax Cross Entropy | Softmax Cross Entropy |
| outputs | probability | probability |
| Loss | 2.9150 | 3.009 |
| Speed | 8pcs: 19.9 ms/step | 8pcs: 43.5ms/step|
| Total time | 8pcs: 5.2 hours | 8pcs: 12.1 hours |
| Parameters (M) | 4.8 | 1.25 |
| Checkpoint for Fine tuning | 13.3M (.ckpt file) | 13.3M (.ckpt file) |
| Scripts | [squeezenet script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/squeezenet) | [squeezenet script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/squeezenet) |
#### SqueezeNet_Residual on CIFAR-10
| Parameters | Ascend |
| -------------------------- | ----------------------------------------------------------- |
| Model Version | SqueezeNet_Residual |
| Resource | Ascend 910; CPU 2.60GHz, 192cores; Memory 755G; OS Euler2.8 |
| uploaded Date | 11/06/2020 (month/day/year) |
| MindSpore Version | 1.0.0 |
| Dataset | CIFAR-10 |
| Training Parameters | epoch=150, steps=195, batch_size=32, lr=0.01 |
| Optimizer | Momentum |
| Loss Function | Softmax Cross Entropy |
| outputs | probability |
| Loss | 0.0641 |
| Speed | 1pc: 16.9 ms/step; 8pcs: 17.3 ms/step |
| Total time | 1pc: 68.6 mins; 8pcs: 20.9 mins |
| Parameters (M) | 4.8 |
| Checkpoint for Fine tuning | 6.5M (.ckpt file) |
| Scripts | [squeezenet script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/squeezenet) |
| Parameters | Ascend | GPU |
| -------------------------- | ----------------------------------------------------------- | --- |
| Model Version | SqueezeNet_Residual | SqueezeNet_Residual |
| Resource | Ascend 910; CPU 2.60GHz, 192cores; Memory 755G; OS Euler2.8 | NV SMX2 V100-32G |
| uploaded Date | 11/06/2020 (month/day/year) | 8/26/2021 (month/day/year) |
| MindSpore Version | 1.0.0 | 1.4.0 |
| Dataset | CIFAR-10 | CIFAR-10 |
| Training Parameters | epoch=150, steps=195, batch_size=32, lr=0.01 | 1pc:epoch=150, steps=1562, batch_size=32, lr=0.01; 8pcs: epoch=150, steps=1562, batch_size=4|
| Optimizer | Momentum | Momentum
| Loss Function | Softmax Cross Entropy | Softmax Cross Entropy
| outputs | probability | probability
| Loss | 0.0641 | 1pc: 0.0402; 8pcs:0.004 |
| Speed | 1pc: 16.9 ms/step; 8pcs: 17.3 ms/step | 1pc: 29.4 ms/step; 8pcs:11.0 ms/step |
| Total time | 1pc: 68.6 mins; 8pcs: 20.9 mins | 1pc: 115 mins; 8pcs: 43.5 mins |
| Parameters (M) | 4.8 | 0.74 |
| Checkpoint for Fine tuning | 6.5M (.ckpt file) | 6.5M (.ckpt file) |
| Scripts | [squeezenet script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/squeezenet) | [squeezenet script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/squeezenet) |
#### SqueezeNet_Residual on ImageNet
| Parameters | Ascend |
| -------------------------- | ----------------------------------------------------------- |
| Model Version | SqueezeNet_Residual |
| Resource | Ascend 910; CPU 2.60GHz, 192cores; Memory 755G; OS Euler2.8 |
| uploaded Date | 11/06/2020 (month/day/year) |
| MindSpore Version | 1.0.0 |
| Dataset | ImageNet |
| Training Parameters | epoch=300, steps=5004, batch_size=32, lr=0.01 |
| Optimizer | Momentum |
| Loss Function | Softmax Cross Entropy |
| outputs | probability |
| Loss | 2.9040 |
| Speed | 8pcs: 20.2 ms/step |
| Total time | 8pcs: 8.0 hours |
| Parameters (M) | 4.8 |
| Checkpoint for Fine tuning | 15.3M (.ckpt file) |
| Scripts | [squeezenet script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/squeezenet) |
| Parameters | Ascend | GPU |
| -------------------------- | ----------------------------------------------------------- | --- |
| Model Version | SqueezeNet_Residual | SqueezeNet_Residual |
| Resource | Ascend 910; CPU 2.60GHz, 192cores; Memory 755G; OS Euler2.8 | NV SMX2 V100-32G |
| uploaded Date | 11/06/2020 (month/day/year) | 8/26/2021 (month/day/year) |
| MindSpore Version | 1.0.0 | 1.4.0 |
| Dataset | ImageNet | ImageNet |
| Training Parameters | epoch=300, steps=5004, batch_size=32, lr=0.01 | epoch=300, steps=5004, batch_size=32, lr=0.01 |
| Optimizer | Momentum | Momentum |
| Loss Function | Softmax Cross Entropy | Softmax Cross Entropy |
| outputs | probability | probability |
| Loss | 2.9040 | 2.969 |
| Speed | 8pcs: 20.2 ms/step | 8pcs: 44.1 ms/step |
| Total time | 8pcs: 8.0 hours | 8pcs: 18.4 hours |
| Parameters (M) | 4.8 | 1.25 |
| Checkpoint for Fine tuning | 15.3M (.ckpt file) | 15.3M (.ckpt file) |
| Scripts | [squeezenet script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/squeezenet) | [squeezenet script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/squeezenet) |
### Inference Performance
#### SqueezeNet on CIFAR-10
| Parameters | Ascend |
| ------------------- | --------------------------- |
| Model Version | SqueezeNet |
| Resource | Ascend 910; OS Euler2.8 |
| Uploaded Date | 11/06/2020 (month/day/year) |
| MindSpore Version | 1.0.0 |
| Dataset | CIFAR-10 |
| batch_size | 32 |
| outputs | probability |
| Accuracy | 1pc: 89.0%; 8pcs: 84.4% |
| Parameters | Ascend | GPU |
| ------------------- | --------------------------- | --- |
| Model Version | SqueezeNet | SqueezeNet |
| Resource | Ascend 910; OS Euler2.8 | NV SMX2 V100-32G |
| Uploaded Date | 11/06/2020 (month/day/year) | 8/26/2021 (month/day/year) |
| MindSpore Version | 1.0.0 | 1.4.0 |
| Dataset | CIFAR-10 | CIFAR-10 |
| batch_size | 32 | 1pc:32; 8pcs:4 |
| outputs | probability | probability |
| Accuracy | 1pc: 89.0%; 8pcs: 84.4% | 1pc: 89.0%; 8pcs: 88.8%|
#### SqueezeNet on ImageNet
| Parameters | Ascend |
| ------------------- | --------------------------- |
| Model Version | SqueezeNet |
| Resource | Ascend 910; OS Euler2.8 |
| Uploaded Date | 11/06/2020 (month/day/year) |
| MindSpore Version | 1.0.0 |
| Dataset | ImageNet |
| batch_size | 32 |
| outputs | probability |
| Accuracy | 8pcs: 58.5%(TOP1), 81.1%(TOP5) |
| Parameters | Ascend | GPU |
| ------------------- | --------------------------- | --- |
| Model Version | SqueezeNet | SqueezeNet |
| Resource | Ascend 910; OS Euler2.8 | NV SMX2 V100-32G |
| Uploaded Date | 11/06/2020 (month/day/year) | 8/26/2021 (month/day/year) |
| MindSpore Version | 1.0.0 | 1.4.0 |
| Dataset | ImageNet | ImageNet |
| batch_size | 32 | 32 |
| outputs | probability | probability |
| Accuracy | 8pcs: 58.5%(TOP1), 81.1%(TOP5) | 8pcs: 58.5%(TOP1), 80.7%(TOP5) |
#### SqueezeNet_Residual on CIFAR-10
| Parameters | Ascend |
| ------------------- | --------------------------- |
| Model Version | SqueezeNet_Residual |
| Resource | Ascend 910; OS Euler2.8 |
| Uploaded Date | 11/06/2020 (month/day/year) |
| MindSpore Version | 1.0.0 |
| Dataset | CIFAR-10 |
| batch_size | 32 |
| outputs | probability |
| Accuracy | 1pc: 90.8%; 8pcs: 87.4% |
| Parameters | Ascend | GPU |
| ------------------- | --------------------------- | --- |
| Model Version | SqueezeNet_Residual | SqueezeNet_Residual |
| Resource | Ascend 910; OS Euler2.8 | NV SMX2 V100-32G |
| Uploaded Date | 11/06/2020 (month/day/year) | 8/26/2021 (month/day/year) |
| MindSpore Version | 1.0.0 | 1.4.0 |
| Dataset | CIFAR-10 | CIFAR-10 |
| batch_size | 32 | 1pc:32; 8pcs:4 |
| outputs | probability | probability |
| Accuracy | 1pc: 90.8%; 8pcs: 87.4% | 1pc: 90.7%; 8pcs: 90.5% |
#### SqueezeNet_Residual on ImageNet
| Parameters | Ascend |
| ------------------- | --------------------------- |
| Model Version | SqueezeNet_Residual |
| Resource | Ascend 910; OS Euler2.8 |
| Uploaded Date | 11/06/2020 (month/day/year) |
| MindSpore Version | 1.0.0 |
| Dataset | ImageNet |
| batch_size | 32 |
| outputs | probability |
| Accuracy | 8pcs: 60.9%(TOP1), 82.6%(TOP5) |
| Parameters | Ascend | GPU |
| ------------------- | --------------------------- | --- |
| Model Version | SqueezeNet_Residual | SqueezeNet_Residual |
| Resource | Ascend 910; OS Euler2.8 | NV SMX2 V100-32G |
| Uploaded Date | 11/06/2020 (month/day/year) | 8/24/2021 (month/day/year) |
| MindSpore Version | 1.0.0 | 1.4.0 |
| Dataset | ImageNet | ImageNet |
| batch_size | 32 | 32 |
| outputs | probability | probability |
| Accuracy | 8pcs: 60.9%(TOP1), 82.6%(TOP5) | 8pcs: 60.2%(TOP1), 82.3%(TOP5)|
### 310 Inference Performance

107
model_zoo/official/cv/squeezenet/scripts/run_distribute_train_gpu.sh Normal file
View File

@ -0,0 +1,107 @@
#!/bin/bash
# Copyright 2021 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.
# ============================================================================
if [ $# != 3 ] && [ $# != 4 ]
then
echo "Usage: bash scripts/run_distribute_train_gpu.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)"
exit 1
fi
if [ $1 != "squeezenet" ] && [ $1 != "squeezenet_residual" ]
then
echo "error: the selected net is neither squeezenet nor squeezenet_residual"
exit 1
fi
if [ $2 != "cifar10" ] && [ $2 != "imagenet" ]
then
echo "error: the selected dataset is neither cifar10 nor imagenet"
exit 1
fi
get_real_path(){
if [ "${1:0:1}" == "/" ]; then
echo "$1"
else
echo "$(realpath -m $PWD/$1)"
fi
}
PATH1=$(get_real_path $3)
if [ $# == 4 ]
then
PATH2=$(get_real_path $4)
fi
if [ ! -d $PATH1 ]
then
echo "error: DATASET_PATH=$PATH1 is not a directory"
exit 1
fi
if [ $# == 4 ] && [ ! -f $PATH2 ]
then
echo "error: PRETRAINED_CKPT_PATH=$PATH2 is not a file"
exit 1
fi
ulimit -u unlimited
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
export RANK_SIZE=8
BASE_PATH=$(dirname "$(dirname "$(readlink -f $0)")")
CONFIG_FILE="${BASE_PATH}/squeezenet_cifar10_config.yaml"
if [ $1 == "squeezenet" ] && [ $2 == "cifar10" ]; then
CONFIG_FILE="${BASE_PATH}/squeezenet_cifar10_config.yaml"
elif [ $1 == "squeezenet" ] && [ $2 == "imagenet" ]; then
CONFIG_FILE="${BASE_PATH}/squeezenet_imagenet_config.yaml"
elif [ $1 == "squeezenet_residual" ] && [ $2 == "cifar10" ]; then
CONFIG_FILE="${BASE_PATH}/squeezenet_residual_cifar10_config.yaml"
elif [ $1 == "squeezenet_residual" ] && [ $2 == "imagenet" ]; then
CONFIG_FILE="${BASE_PATH}/squeezenet_residual_imagenet_config.yaml"
else
echo "error: the selected dataset is not in supported set{squeezenet, squeezenet_residual, cifar10, imagenet}"
exit 1
fi
TRAIN_OUTPUT=$BASE_PATH/train_parallel_$1_$2
if [ -d $TRAIN_OUTPUT ]; then
rm -rf $TRAIN_OUTPUT
fi
mkdir $TRAIN_OUTPUT
cp ./train.py $TRAIN_OUTPUT
cp -r ./src $TRAIN_OUTPUT
cp -r ./model_utils $TRAIN_OUTPUT
cp $CONFIG_FILE $TRAIN_OUTPUT
cd $TRAIN_OUTPUT || exit
if [ $# == 3 ]
then
mpirun --allow-run-as-root -n $RANK_SIZE --output-filename log_output --merge-stderr-to-stdout \
python train.py --net_name=$1 --dataset=$2 --run_distribute=True --output_path='./output'\
--device_target="GPU" --data_path=$PATH1 \
--config_path=${CONFIG_FILE##*/} &> log &
fi
if [ $# == 4 ]
then
mpirun --allow-run-as-root -n $RANK_SIZE --output-filename log_output --merge-stderr-to-stdout \
python train.py --net_name=$1 --dataset=$2 --run_distribute=True --output_path='./output'\
--device_target="GPU" --data_path=$PATH1 --pre_trained=$PATH2 \
--config_path=${CONFIG_FILE##*/} &> log &
fi
cd ..

99
model_zoo/official/cv/squeezenet/scripts/run_eval_gpu.sh Normal file
View File

@ -0,0 +1,99 @@
#!/bin/bash
# Copyright 2021 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.
# ============================================================================
if [ $# != 5 ]
then
echo "Usage: bash scripts/run_eval_gpu.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DEVICE_ID] [DATASET_PATH] [CHECKPOINT_PATH]"
exit 1
fi
if [ $1 != "squeezenet" ] && [ $1 != "squeezenet_residual" ]
then
echo "error: the selected net is neither squeezenet nor squeezenet_residual"
exit 1
fi
if [ $2 != "cifar10" ] && [ $2 != "imagenet" ]
then
echo "error: the selected dataset is neither cifar10 nor imagenet"
exit 1
fi
get_real_path(){
if [ "${1:0:1}" == "/" ]; then
echo "$1"
else
echo "$(realpath -m $PWD/$1)"
fi
}
PATH1=$(get_real_path $4)
PATH2=$(get_real_path $5)
if [ ! -d $PATH1 ]
then
echo "error: DATASET_PATH=$PATH1 is not a directory"
exit 1
fi
if [ ! -f $PATH2 ]
then
echo "error: CHECKPOINT_PATH=$PATH2 is not a file"
exit 1
fi
expr $3 + 0 &>/dev/null
if [ $? != 0 ]; then
echo "DEVICE_ID=$3 is not an integer!"
exit 1
fi
ulimit -u unlimited
export CUDA_VISIBLE_DEVICES=$3
BASE_PATH=$(dirname "$(dirname "$(readlink -f $0)")")
CONFIG_FILE="${BASE_PATH}/squeezenet_cifar10_config.yaml"
if [ $1 == "squeezenet" ] && [ $2 == "cifar10" ]; then
CONFIG_FILE="${BASE_PATH}/squeezenet_cifar10_config.yaml"
elif [ $1 == "squeezenet" ] && [ $2 == "imagenet" ]; then
CONFIG_FILE="${BASE_PATH}/squeezenet_imagenet_config.yaml"
elif [ $1 == "squeezenet_residual" ] && [ $2 == "cifar10" ]; then
CONFIG_FILE="${BASE_PATH}/squeezenet_residual_cifar10_config.yaml"
elif [ $1 == "squeezenet_residual" ] && [ $2 == "imagenet" ]; then
CONFIG_FILE="${BASE_PATH}/squeezenet_residual_imagenet_config.yaml"
else
echo "error: the selected dataset is not in supported set{squeezenet, squeezenet_residual, cifar10, imagenet}"
exit 1
fi
EVAL_OUTPUT=$BASE_PATH/eval_$3_$1_$2
if [ -d $EVAL_OUTPUT ];
then
rm -rf $EVAL_OUTPUT
fi
mkdir $EVAL_OUTPUT
cp ./eval.py $EVAL_OUTPUT
cp -r ./src $EVAL_OUTPUT
cp -r ./model_utils $EVAL_OUTPUT
cp $CONFIG_FILE $EVAL_OUTPUT
cd $EVAL_OUTPUT || exit
env > env.log
echo "start evaluation for device $3"
python eval.py --net_name=$1 --dataset=$2 --data_path=$PATH1 --checkpoint_file_path=$PATH2 --device_target="GPU" \
--config_path=${CONFIG_FILE##*/} --output_path='./output' &> log &
cd ..

109
model_zoo/official/cv/squeezenet/scripts/run_standalone_train_gpu.sh Normal file
View File

@ -0,0 +1,109 @@
#!/bin/bash
# Copyright 2021 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.
# ============================================================================
if [ $# != 4 ] && [ $# != 5 ]
then
echo "Usage: bash scripts/run_standalone_train.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DEVICE_ID] [DATA_PATH] [PRETRAINED_CKPT_PATH](optional)"
exit 1
fi
if [ $1 != "squeezenet" ] && [ $1 != "squeezenet_residual" ]
then
echo "error: the selected net is neither squeezenet nor squeezenet_residual"
exit 1
fi
if [ $2 != "cifar10" ] && [ $2 != "imagenet" ]
then
echo "error: the selected dataset is neither cifar10 nor imagenet"
exit 1
fi
get_real_path(){
if [ "${1:0:1}" == "/" ]; then
echo "$1"
else
echo "$(realpath -m $PWD/$1)"
fi
}
PATH1=$(get_real_path $4)
if [ $# == 5 ]
then
PATH2=$(get_real_path $5)
fi
if [ ! -d $PATH1 ]
then
echo "error: DATASET_PATH=$PATH1 is not a directory"
exit 1
fi
if [ $# == 5 ] && [ ! -f $PATH2 ]
then
echo "error: PRETRAINED_CKPT_PATH=$PATH2 is not a file"
exit 1
fi
expr $3 + 0 &>/dev/null
if [ $? = 2 ]; then
echo "DEVICE_ID=$3 is not an integer!"
exit 1
fi
ulimit -u unlimited
export CUDA_VISIBLE_DEVICES=$3
BASE_PATH=$(dirname "$(dirname "$(readlink -f $0)")")
CONFIG_FILE="${BASE_PATH}/squeezenet_cifar10_config.yaml"
if [ $1 == "squeezenet" ] && [ $2 == "cifar10" ]; then
CONFIG_FILE="${BASE_PATH}/squeezenet_cifar10_config.yaml"
elif [ $1 == "squeezenet" ] && [ $2 == "imagenet" ]; then
CONFIG_FILE="${BASE_PATH}/squeezenet_imagenet_config.yaml"
elif [ $1 == "squeezenet_residual" ] && [ $2 == "cifar10" ]; then
CONFIG_FILE="${BASE_PATH}/squeezenet_residual_cifar10_config.yaml"
elif [ $1 == "squeezenet_residual" ] && [ $2 == "imagenet" ]; then
CONFIG_FILE="${BASE_PATH}/squeezenet_residual_imagenet_config.yaml"
else
echo "error: the selected dataset is not in supported set{squeezenet, squeezenet_residual, cifar10, imagenet}"
exit 1
fi
TRAIN_OUTPUT=$BASE_PATH/train_standalone$3_$1_$2
if [ -d $TRAIN_OUTPUT ];
then
rm -rf $TRAIN_OUTPUT
fi
mkdir $TRAIN_OUTPUT
cp ./train.py $TRAIN_OUTPUT
cp -r ./src $TRAIN_OUTPUT
cp -r ./model_utils $TRAIN_OUTPUT
cp $CONFIG_FILE $TRAIN_OUTPUT
cd $TRAIN_OUTPUT || exit
echo "start training for device $3"
env > env.log
if [ $# == 4 ]
then
python train.py --net_name=$1 --dataset=$2 --data_path=$PATH1 --config_path=${CONFIG_FILE##*/} \
--output_path='./output' --device_target='GPU' &> log &
fi
if [ $# == 5 ]
then
python train.py --net_name=$1 --dataset=$2 --data_path=$PATH1 --pre_trained=$PATH2 \
--config_path=${CONFIG_FILE##*/} --output_path='./output' --device_target='GPU' &> log &
fi
cd ..

4
model_zoo/official/cv/squeezenet/squeezenet_cifar10_config.yaml
View File

@ -21,7 +21,7 @@ checkpoint_file_path: "suqeezenet_cifar10-120_195.ckpt"
net_name: "suqeezenet"
dataset : "cifar10"
class_num: 10
batch_size: 32
global_batch_size: 32
loss_scale: 1024
momentum: 0.9
weight_decay: 0.0001
@ -55,7 +55,7 @@ load_path: "The location of checkpoint for obs"
device_target: "Target device type, available: [Ascend, GPU, CPU]"
enable_profiling: "Whether enable profiling while training, default: False"
num_classes: "Class for dataset"
batch_size: "Batch size for training and evaluation"
global_batch_size: "The total batch_size for training and evaluation"
epoch_size: "Total training epochs."
keep_checkpoint_max: "keep the last keep_checkpoint_max checkpoint"
checkpoint_path: "The location of the checkpoint file."

4
model_zoo/official/cv/squeezenet/squeezenet_imagenet_config.yaml
View File

@ -21,7 +21,7 @@ checkpoint_file_path: "suqeezenet_imagenet-200_5004.ckpt"
net_name: "suqeezenet"
dataset : "imagenet"
class_num: 1000
batch_size: 32
global_batch_size: 256
loss_scale: 1024
momentum: 0.9
weight_decay: 0.00007
@ -57,7 +57,7 @@ load_path: 'The location of checkpoint for obs'
device_target: 'Target device type, available: [Ascend, GPU, CPU]'
enable_profiling: 'Whether enable profiling while training, default: False'
num_classes: 'Class for dataset'
batch_size: "Batch size for training and evaluation"
global_batch_size: "The total batch_size for training and evaluation"
epoch_size: "Total training epochs."
keep_checkpoint_max: "keep the last keep_checkpoint_max checkpoint"
checkpoint_path: "The location of the checkpoint file."

4
model_zoo/official/cv/squeezenet/squeezenet_residual_cifar10_config.yaml
View File

@ -21,7 +21,7 @@ checkpoint_file_path: "suqeezenet_residual_cifar10-150_195.ckpt"
net_name: "suqeezenet_residual"
dataset : "cifar10"
class_num: 10
batch_size: 32
global_batch_size: 32
loss_scale: 1024
momentum: 0.9
weight_decay: 0.0001
@ -55,7 +55,7 @@ load_path: "The location of checkpoint for obs"
device_target: "Target device type, available: [Ascend, GPU, CPU]"
enable_profiling: "Whether enable profiling while training, default: False"
num_classes: "Class for dataset"
batch_size: "Batch size for training and evaluation"
global_batch_size: "The total batch_size for training and evaluation."
epoch_size: "Total training epochs."
keep_checkpoint_max: "keep the last keep_checkpoint_max checkpoint"
checkpoint_path: "The location of the checkpoint file."

4
model_zoo/official/cv/squeezenet/squeezenet_residual_imagenet_config.yaml
View File

@ -21,7 +21,7 @@ checkpoint_file_path: "suqeezenet_residual_imagenet-300_5004.ckpt"
net_name: "suqeezenet_residual"
dataset : "imagenet"
class_num: 1000
batch_size: 32
global_batch_size: 256
loss_scale: 1024
momentum: 0.9
weight_decay: 0.00007
@ -57,7 +57,7 @@ load_path: "The location of checkpoint for obs"
device_target: "Target device type, available: [Ascend, GPU, CPU]"
enable_profiling: "Whether enable profiling while training, default: False"
num_classes: "Class for dataset"
batch_size: "Batch size for training and evaluation"
global_batch_size: "The total batch_size for training and evaluation"
epoch_size: "Total training epochs."
keep_checkpoint_max: "keep the last keep_checkpoint_max checkpoint"
checkpoint_path: "The location of the checkpoint file."

24
model_zoo/official/cv/squeezenet/train.py
View File

@ -13,7 +13,6 @@
# limitations under the License.
# ============================================================================
"""train squeezenet."""
import os
from mindspore import context
from mindspore import Tensor
from mindspore.nn.optim.momentum import Momentum
@ -27,6 +26,7 @@ from mindspore.communication.management import init, get_rank, get_group_size
from mindspore.common import set_seed
from model_utils.config import config
from model_utils.moxing_adapter import moxing_wrapper
from model_utils.device_adapter import get_device_id
from src.lr_generator import get_lr
from src.CrossEntropySmooth import CrossEntropySmooth
@ -54,33 +54,37 @@ def train_net():
# init context
context.set_context(mode=context.GRAPH_MODE,
device_target=target)
device_num = 1
if config.run_distribute:
if target == "Ascend":
device_id = int(os.getenv('DEVICE_ID'))
device_id = get_device_id()
device_num = config.device_num
context.set_context(device_id=device_id,
enable_auto_mixed_precision=True)
context.set_auto_parallel_context(
device_num=config.device_num,
device_num=device_num,
parallel_mode=ParallelMode.DATA_PARALLEL,
gradients_mean=True)
init()
# GPU target
else:
print("Squeezenet training on GPU performs badly now, and it is still in research..."
"See model_zoo/research/cv/squeezenet to get up-to-date details.")
init()
device_num = get_group_size()
context.set_auto_parallel_context(
device_num=get_group_size(),
device_num=device_num,
parallel_mode=ParallelMode.DATA_PARALLEL,
gradients_mean=True)
ckpt_save_dir = ckpt_save_dir + "/ckpt_" + str(
get_rank()) + "/"
# obtain the actual batch_size
if not hasattr(config, "global_batch_size"):
raise AttributeError("'config' object has no attribute 'global_batch_size', please check the yaml file.")
batch_size = max(config.global_batch_size // device_num, 1)
# create dataset
dataset = create_dataset(dataset_path=config.data_path,
do_train=True,
repeat_num=1,
batch_size=config.batch_size,
batch_size=batch_size,
target=target)
step_size = dataset.get_dataset_size()
@ -132,10 +136,6 @@ def train_net():
amp_level="O2",
keep_batchnorm_fp32=False)
else:
if target == "GPU":
# GPU target
print("Squeezenet training on GPU performs badly now, and it is still in research..."
"See model_zoo/research/cv/squeezenet to get up-to-date details.")
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()),
lr,
config.momentum,