forked from mindspore-Ecosystem/mindspore
!1354 add mobilenetV3 and change mobilenet to mobilenet V2
Merge pull request !1354 from SanjayChan/new_mobilenetv3
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# MobileNetV2 Example
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## Description
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This is an example of training MobileNetV2 with ImageNet2012 dataset in MindSpore.
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## Requirements
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* Install [MindSpore](https://www.mindspore.cn/install/en).
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* Download the dataset [ImageNet2012].
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> Unzip the ImageNet2012 dataset to any path you want and the folder structure should be as follows:
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> ```
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> .
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> ├── train # train dataset
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> └── val # infer dataset
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> ```
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## Example structure
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``` shell
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.
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├── config.py # parameter configuration
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├── dataset.py # data preprocessing
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├── eval.py # infer script
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├── launch.py # launcher for distributed training
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├── lr_generator.py # generate learning rate for each step
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├── run_infer.sh # launch infering
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├── run_train.sh # launch training
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└── train.py # train script
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```
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## Parameter configuration
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Parameters for both training and inference can be set in 'config.py'.
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```
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"num_classes": 1000, # dataset class num
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"image_height": 224, # image height
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"image_width": 224, # image width
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"batch_size": 256, # training or infering batch size
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"epoch_size": 200, # total training epochs, including warmup_epochs
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"warmup_epochs": 4, # warmup epochs
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"lr": 0.4, # base learning rate
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"momentum": 0.9, # momentum
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"weight_decay": 4e-5, # weight decay
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"loss_scale": 1024, # loss scale
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"save_checkpoint": True, # whether save checkpoint
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"save_checkpoint_epochs": 1, # the epoch interval between two checkpoints
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"keep_checkpoint_max": 200, # only keep the last keep_checkpoint_max checkpoint
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"save_checkpoint_path": "./checkpoint" # path to save checkpoint
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```
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## Running the example
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### Train
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#### Usage
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Usage: sh run_train.sh [DEVICE_NUM] [SERVER_IP(x.x.x.x)] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH]
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#### Launch
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```
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# training example
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sh run_train.sh 8 192.168.0.1 0,1,2,3,4,5,6,7 ~/imagenet
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```
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#### Result
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Training result will be stored in the example path. Checkpoints will be stored at `. /checkpoint` by default, and training log will be redirected to `./train/train.log` like followings.
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```
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epoch: [ 0/200], step:[ 624/ 625], loss:[5.258/5.258], time:[140412.236], lr:[0.100]
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epoch time: 140522.500, per step time: 224.836, avg loss: 5.258
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epoch: [ 1/200], step:[ 624/ 625], loss:[3.917/3.917], time:[138221.250], lr:[0.200]
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epoch time: 138331.250, per step time: 221.330, avg loss: 3.917
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```
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### Infer
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#### Usage
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Usage: sh run_infer.sh [DATASET_PATH] [CHECKPOINT_PATH]
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#### Launch
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```
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# infer example
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sh run_infer.sh ~/imagenet ~/train/mobilenet-200_625.ckpt
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```
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> checkpoint can be produced in training process.
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#### Result
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Inference result will be stored in the example path, you can find result like the followings in `val.log`.
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```
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result: {'acc': 0.71976314102564111} ckpt=/path/to/checkpoint/mobilenet-200_625.ckpt
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```
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#!/usr/bin/env bash
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if [ $# != 2 ]
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then
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echo "Usage: sh run_infer.sh [DATASET_PATH] [CHECKPOINT_PATH]"
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exit 1
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fi
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if [ ! -d $1 ]
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then
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echo "error: DATASET_PATH=$1 is not a directory"
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exit 1
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fi
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if [ ! -f $2 ]
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then
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echo "error: CHECKPOINT_PATH=$2 is not a file"
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exit 1
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fi
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BASEPATH=$(cd "`dirname $0`" || exit; pwd)
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export PYTHONPATH=${BASEPATH}:$PYTHONPATH
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export DEVICE_ID=0
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export RANK_ID=0
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export RANK_SIZE=1
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if [ -d "eval" ];
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then
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rm -rf ./eval
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fi
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mkdir ./eval
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cd ./eval || exit
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python ${BASEPATH}/eval.py \
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--checkpoint_path=$2 \
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--dataset_path=$1 &> infer.log & # dataset val folder path
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#!/usr/bin/env bash
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if [ $# != 4 ]
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then
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echo "Usage: sh run_train.sh [DEVICE_NUM] [SERVER_IP(x.x.x.x)] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH]"
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exit 1
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fi
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if [ $1 -lt 1 ] && [ $1 -gt 8 ]
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then
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echo "error: DEVICE_NUM=$1 is not in (1-8)"
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exit 1
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fi
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if [ ! -d $4 ]
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then
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echo "error: DATASET_PATH=$4 is not a directory"
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exit 1
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fi
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BASEPATH=$(cd "`dirname $0`" || exit; pwd)
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export PYTHONPATH=${BASEPATH}:$PYTHONPATH
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if [ -d "train" ];
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then
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rm -rf ./train
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fi
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mkdir ./train
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cd ./train || exit
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python ${BASEPATH}/launch.py \
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--nproc_per_node=$1 \
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--visible_devices=$3 \
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--server_id=$2 \
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--training_script=${BASEPATH}/train.py \
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--dataset_path=$4 &> train.log & # dataset train folder
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""train_imagenet."""
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import os
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import time
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import argparse
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import random
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import numpy as np
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from dataset import create_dataset
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from lr_generator import get_lr
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from config import config
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from mindspore import context
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from mindspore import Tensor
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from mindspore import nn
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from mindspore.model_zoo.mobilenet import mobilenet_v2
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from mindspore.parallel._auto_parallel_context import auto_parallel_context
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from mindspore.nn.optim.momentum import Momentum
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from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
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from mindspore.nn.loss.loss import _Loss
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from mindspore.ops import operations as P
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from mindspore.ops import functional as F
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from mindspore.common import dtype as mstype
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from mindspore.train.model import Model, ParallelMode
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from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, Callback
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from mindspore.train.loss_scale_manager import FixedLossScaleManager
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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import mindspore.dataset.engine as de
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from mindspore.communication.management import init
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random.seed(1)
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np.random.seed(1)
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de.config.set_seed(1)
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parser = argparse.ArgumentParser(description='Image classification')
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parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
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parser.add_argument('--pre_trained', type=str, default=None, help='Pretrained checkpoint path')
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args_opt = parser.parse_args()
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device_id = int(os.getenv('DEVICE_ID'))
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rank_id = int(os.getenv('RANK_ID'))
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rank_size = int(os.getenv('RANK_SIZE'))
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run_distribute = rank_size > 1
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=device_id, save_graphs=False)
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class CrossEntropyWithLabelSmooth(_Loss):
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"""
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CrossEntropyWith LabelSmooth.
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Args:
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smooth_factor (float): smooth factor, default=0.
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num_classes (int): num classes
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Returns:
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None.
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Examples:
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>>> CrossEntropyWithLabelSmooth(smooth_factor=0., num_classes=1000)
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"""
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def __init__(self, smooth_factor=0., num_classes=1000):
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super(CrossEntropyWithLabelSmooth, self).__init__()
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self.onehot = P.OneHot()
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self.on_value = Tensor(1.0 - smooth_factor, mstype.float32)
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self.off_value = Tensor(1.0 * smooth_factor / (num_classes - 1), mstype.float32)
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self.ce = nn.SoftmaxCrossEntropyWithLogits()
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self.mean = P.ReduceMean(False)
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self.cast = P.Cast()
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def construct(self, logit, label):
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one_hot_label = self.onehot(self.cast(label, mstype.int32), F.shape(logit)[1], self.on_value, self.off_value)
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out_loss = self.ce(logit, one_hot_label)
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out_loss = self.mean(out_loss, 0)
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return out_loss
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class Monitor(Callback):
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"""
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Monitor loss and time.
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Args:
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lr_init (numpy array): train lr
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Returns:
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None
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Examples:
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>>> Monitor(100,lr_init=Tensor([0.05]*100).asnumpy())
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"""
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def __init__(self, lr_init=None):
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super(Monitor, self).__init__()
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self.lr_init = lr_init
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self.lr_init_len = len(lr_init)
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def epoch_begin(self, run_context):
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self.losses = []
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self.epoch_time = time.time()
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def epoch_end(self, run_context):
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cb_params = run_context.original_args()
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epoch_mseconds = (time.time() - self.epoch_time) * 1000
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per_step_mseconds = epoch_mseconds / cb_params.batch_num
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print("epoch time: {:5.3f}, per step time: {:5.3f}, avg loss: {:5.3f}".format(epoch_mseconds,
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per_step_mseconds,
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np.mean(self.losses)
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))
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def step_begin(self, run_context):
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self.step_time = time.time()
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def step_end(self, run_context):
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cb_params = run_context.original_args()
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step_mseconds = (time.time() - self.step_time) * 1000
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step_loss = cb_params.net_outputs
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if isinstance(step_loss, (tuple, list)) and isinstance(step_loss[0], Tensor):
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step_loss = step_loss[0]
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if isinstance(step_loss, Tensor):
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step_loss = np.mean(step_loss.asnumpy())
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self.losses.append(step_loss)
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cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num
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print("epoch: [{:3d}/{:3d}], step:[{:5d}/{:5d}], loss:[{:5.3f}/{:5.3f}], time:[{:5.3f}], lr:[{:5.3f}]".format(
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cb_params.cur_epoch_num - 1, cb_params.epoch_num, cur_step_in_epoch, cb_params.batch_num, step_loss,
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np.mean(self.losses), step_mseconds, self.lr_init[cb_params.cur_step_num - 1]))
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if __name__ == '__main__':
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if run_distribute:
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context.set_auto_parallel_context(device_num=rank_size, parallel_mode=ParallelMode.DATA_PARALLEL,
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parameter_broadcast=True, mirror_mean=True)
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auto_parallel_context().set_all_reduce_fusion_split_indices([140])
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init()
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epoch_size = config.epoch_size
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net = mobilenet_v2(num_classes=config.num_classes)
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net.to_float(mstype.float16)
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for _, cell in net.cells_and_names():
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if isinstance(cell, nn.Dense):
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cell.add_flags_recursive(fp32=True)
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if config.label_smooth > 0:
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loss = CrossEntropyWithLabelSmooth(smooth_factor=config.label_smooth, num_classes=config.num_classes)
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else:
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loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean')
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print("train args: ", args_opt, "\ncfg: ", config,
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"\nparallel args: rank_id {}, device_id {}, rank_size {}".format(rank_id, device_id, rank_size))
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dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=True,
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repeat_num=epoch_size, batch_size=config.batch_size)
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step_size = dataset.get_dataset_size()
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if args_opt.pre_trained:
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param_dict = load_checkpoint(args_opt.pre_trained)
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load_param_into_net(net, param_dict)
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loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
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lr = Tensor(get_lr(global_step=0, lr_init=0, lr_end=0, lr_max=config.lr,
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warmup_epochs=config.warmup_epochs, total_epochs=epoch_size, steps_per_epoch=step_size))
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opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum,
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config.weight_decay, config.loss_scale)
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model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale)
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cb = None
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if rank_id == 0:
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cb = [Monitor(lr_init=lr.asnumpy())]
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if config.save_checkpoint:
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config_ck = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_epochs * step_size,
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keep_checkpoint_max=config.keep_checkpoint_max)
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ckpt_cb = ModelCheckpoint(prefix="mobilenet", directory=config.save_checkpoint_path, config=config_ck)
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cb += [ckpt_cb]
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model.train(epoch_size, dataset, callbacks=cb)
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@ -0,0 +1,151 @@
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# MobileNetV2 Description
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MobileNetV2 is tuned to mobile phone CPUs through a combination of hardware- aware network architecture search (NAS) complemented by the NetAdapt algorithm and then subsequently improved through novel architecture advances.Nov 20, 2019.
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[Paper](https://arxiv.org/pdf/1905.02244) Howard, Andrew, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang et al. "Searching for MobileNetV2." In Proceedings of the IEEE International Conference on Computer Vision, pp. 1314-1324. 2019.
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# Model architecture
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The overall network architecture of MobileNetV2 is show below:
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[Link](https://arxiv.org/pdf/1905.02244)
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# Dataset
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Dataset used: [imagenet](http://www.image-net.org/)
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- Dataset size: ~125G, 1.2W colorful images in 1000 classes
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- Train: 120G, 1.2W images
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- Test: 5G, 50000 images
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- Data format: RGB images.
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- Note: Data will be processed in src/dataset.py
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# Features
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# Environment Requirements
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- Hardware（Ascend/GPU）
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- Prepare hardware environment with Ascend or GPU processor. If you want to try Ascend , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
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- Framework
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- [MindSpore](http://10.90.67.50/mindspore/archive/20200506/OpenSource/me_vm_x86/)
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- For more information, please check the resources below：
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- [MindSpore tutorials](https://www.mindspore.cn/tutorial/zh-CN/master/index.html)
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- [MindSpore API](https://www.mindspore.cn/api/zh-CN/master/index.html)
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# Script description
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## Script and sample code
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```python
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├── MobileNetV2
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├── Readme.md
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├── scripts
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│ ├──run_train.sh
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│ ├──run_eval.sh
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├── src
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│ ├──config.py
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│ ├──dataset.py
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│ ├──luanch.py
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│ ├──lr_generator.py
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│ ├──mobilenetV2.py
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├── train.py
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├── eval.py
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```
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## Training process
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### Usage
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||||
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- Ascend: sh run_train.sh Ascend [DEVICE_NUM] [SERVER_IP(x.x.x.x)] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH]
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- GPU: sh run_trian.sh GPU [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH]
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### Launch
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```
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# training example
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Ascend: sh run_train.sh Ascend 8 192.168.0.1 0,1,2,3,4,5,6,7 ~/imagenet/train/
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GPU: sh run_train.sh GPU 8 0,1,2,3,4,5,6,7 ~/imagenet/train/
|
||||
```
|
||||
|
||||
### Result
|
||||
|
||||
Training result will be stored in the example path. Checkpoints will be stored at `. /checkpoint` by default, and training log will be redirected to `./train/train.log` like followings.
|
||||
|
||||
```
|
||||
epoch: [ 0/200], step:[ 624/ 625], loss:[5.258/5.258], time:[140412.236], lr:[0.100]
|
||||
epoch time: 140522.500, per step time: 224.836, avg loss: 5.258
|
||||
epoch: [ 1/200], step:[ 624/ 625], loss:[3.917/3.917], time:[138221.250], lr:[0.200]
|
||||
epoch time: 138331.250, per step time: 221.330, avg loss: 3.917
|
||||
```
|
||||
|
||||
## Eval process
|
||||
|
||||
### Usage
|
||||
|
||||
- Ascend: sh run_infer.sh Ascend [DATASET_PATH] [CHECKPOINT_PATH]
|
||||
- GPU: sh run_infer.sh GPU [DATASET_PATH] [CHECKPOINT_PATH]
|
||||
|
||||
### Launch
|
||||
|
||||
```
|
||||
# infer example
|
||||
Ascend: sh run_infer.sh Ascend ~/imagenet/val/ ~/train/mobilenet-200_625.ckpt
|
||||
GPU: sh run_infer.sh GPU ~/imagenet/val/ ~/train/mobilenet-200_625.ckpt
|
||||
```
|
||||
|
||||
> checkpoint can be produced in training process.
|
||||
|
||||
### Result
|
||||
|
||||
Inference result will be stored in the example path, you can find result like the followings in `val.log`.
|
||||
|
||||
```
|
||||
result: {'acc': 0.71976314102564111} ckpt=/path/to/checkpoint/mobilenet-200_625.ckpt
|
||||
```
|
||||
|
||||
# Model description
|
||||
|
||||
## Performance
|
||||
|
||||
### Training Performance
|
||||
|
||||
| Parameters | MobilenetV2 | |
|
||||
| -------------------------- | ---------------------------------------------------------- | ------------------------- |
|
||||
| Model Version | | large |
|
||||
| Resource | Ascend 910, cpu:2.60GHz 56cores, memory:314G | NV SMX2 V100-32G |
|
||||
| uploaded Date | 05/06/2020 | 05/06/2020 |
|
||||
| MindSpore Version | 0.3.0 | 0.3.0 |
|
||||
| Dataset | ImageNet | ImageNet |
|
||||
| Training Parameters | src/config.py | src/config.py |
|
||||
| Optimizer | Momentum | Momentum |
|
||||
| Loss Function | SoftmaxCrossEntropy | SoftmaxCrossEntropy |
|
||||
| outputs | | |
|
||||
| Loss | | 1.913 |
|
||||
| Accuracy | | ACC1[77.09%] ACC5[92.57%] |
|
||||
| Total time | | |
|
||||
| Params (M) | | |
|
||||
| Checkpoint for Fine tuning | | |
|
||||
| Model for inference | | |
|
||||
|
||||
#### Inference Performance
|
||||
|
||||
| Parameters | GoogLeNet | | |
|
||||
| -------------------------- | ----------------------------- | ------------------------- | -------------------- |
|
||||
| Model Version | V1 | | |
|
||||
| Resource | Huawei 910 | NV SMX2 V100-32G | Huawei 310 |
|
||||
| uploaded Date | 05/06/2020 | 05/22/2020 | |
|
||||
| MindSpore Version | 0.2.0 | 0.2.0 | 0.2.0 |
|
||||
| Dataset | ImageNet, 1.2W | ImageNet, 1.2W | ImageNet, 1.2W |
|
||||
| batch_size | | 130(8P) | |
|
||||
| outputs | | | |
|
||||
| Accuracy | | ACC1[72.07%] ACC5[90.90%] | |
|
||||
| Speed | | | |
|
||||
| Total time | | | |
|
||||
| Model for inference | | | |
|
||||
|
||||
# ModelZoo Homepage
|
||||
[Link](https://gitee.com/mindspore/mindspore/tree/master/mindspore/model_zoo)
|
||||
|
|
@ -0,0 +1,75 @@
|
|||
# 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.
|
||||
# ============================================================================
|
||||
"""
|
||||
eval.
|
||||
"""
|
||||
import os
|
||||
import argparse
|
||||
from mindspore import context
|
||||
from mindspore import nn
|
||||
from mindspore.train.model import Model
|
||||
from mindspore.train.serialization import load_checkpoint, load_param_into_net
|
||||
from mindspore.common import dtype as mstype
|
||||
from src.dataset import create_dataset
|
||||
from src.config import config_ascend, config_gpu
|
||||
from src.mobilenetV2 import mobilenet_v2
|
||||
|
||||
|
||||
parser = argparse.ArgumentParser(description='Image classification')
|
||||
parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
|
||||
parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
|
||||
parser.add_argument('--platform', type=str, default=None, help='run platform')
|
||||
args_opt = parser.parse_args()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
config_platform = None
|
||||
if args_opt.platform == "Ascend":
|
||||
config_platform = config_ascend
|
||||
device_id = int(os.getenv('DEVICE_ID'))
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend",
|
||||
device_id=device_id, save_graphs=False)
|
||||
elif args_opt.platform == "GPU":
|
||||
config_platform = config_gpu
|
||||
context.set_context(mode=context.GRAPH_MODE,
|
||||
device_target="GPU", save_graphs=False)
|
||||
else:
|
||||
raise ValueError("Unsupport platform.")
|
||||
|
||||
loss = nn.SoftmaxCrossEntropyWithLogits(
|
||||
is_grad=False, sparse=True, reduction='mean')
|
||||
net = mobilenet_v2(num_classes=config_platform.num_classes)
|
||||
|
||||
if args_opt.platform == "Ascend":
|
||||
net.to_float(mstype.float16)
|
||||
for _, cell in net.cells_and_names():
|
||||
if isinstance(cell, nn.Dense):
|
||||
cell.to_float(mstype.float32)
|
||||
|
||||
dataset = create_dataset(dataset_path=args_opt.dataset_path,
|
||||
do_train=False,
|
||||
config=config_platform,
|
||||
platform=args_opt.platform,
|
||||
batch_size=config_platform.batch_size)
|
||||
step_size = dataset.get_dataset_size()
|
||||
|
||||
if args_opt.checkpoint_path:
|
||||
param_dict = load_checkpoint(args_opt.checkpoint_path)
|
||||
load_param_into_net(net, param_dict)
|
||||
net.set_train(False)
|
||||
|
||||
model = Model(net, loss_fn=loss, metrics={'acc'})
|
||||
res = model.eval(dataset)
|
||||
print("result:", res, "ckpt=", args_opt.checkpoint_path)
|
||||
|
|
@ -0,0 +1,55 @@
|
|||
#!/usr/bin/env bash
|
||||
# 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.
|
||||
# ============================================================================
|
||||
if [ $# != 3 ]
|
||||
then
|
||||
echo "Ascend: sh run_infer.sh [PLATFORM] [DATASET_PATH] [CHECKPOINT_PATH] \
|
||||
GPU: sh run_infer.sh [PLATFORM] [DATASET_PATH] [CHECKPOINT_PATH]"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# check dataset path
|
||||
if [ ! -d $2 ]
|
||||
then
|
||||
echo "error: DATASET_PATH=$2 is not a directory"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# check checkpoint file
|
||||
if [ ! -f $3 ]
|
||||
then
|
||||
echo "error: CHECKPOINT_PATH=$3 is not a file"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# set environment
|
||||
BASEPATH=$(cd "`dirname $0`" || exit; pwd)
|
||||
export PYTHONPATH=${BASEPATH}:$PYTHONPATH
|
||||
export DEVICE_ID=0
|
||||
export RANK_ID=0
|
||||
export RANK_SIZE=1
|
||||
if [ -d "eval" ];
|
||||
then
|
||||
rm -rf ../eval
|
||||
fi
|
||||
mkdir ../eval
|
||||
cd ../eval || exit
|
||||
|
||||
# luanch
|
||||
python ${BASEPATH}/../eval.py \
|
||||
--platform=$1 \
|
||||
--dataset_path=$2 \
|
||||
--checkpoint_path=$3 \
|
||||
&> infer.log & # dataset val folder path
|
||||
|
|
@ -0,0 +1,95 @@
|
|||
#!/usr/bin/env bash
|
||||
# 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.
|
||||
# ============================================================================
|
||||
|
||||
run_ascend()
|
||||
{
|
||||
if [ $2 -lt 1 ] && [ $2 -gt 8 ]
|
||||
then
|
||||
echo "error: DEVICE_NUM=$2 is not in (1-8)"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
if [ ! -d $5 ]
|
||||
then
|
||||
echo "error: DATASET_PATH=$5 is not a directory"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
BASEPATH=$(cd "`dirname $0`" || exit; pwd)
|
||||
export PYTHONPATH=${BASEPATH}:$PYTHONPATH
|
||||
if [ -d "train" ];
|
||||
then
|
||||
rm -rf ../train
|
||||
fi
|
||||
mkdir ../train
|
||||
cd ../train || exit
|
||||
python ${BASEPATH}/../launch.py \
|
||||
--nproc_per_node=$2 \
|
||||
--visible_devices=$4 \
|
||||
--server_id=$3 \
|
||||
--training_script=${BASEPATH}/train.py \
|
||||
--dataset_path=$5 \
|
||||
--platform=$1 &> train.log & # dataset train folder
|
||||
}
|
||||
|
||||
run_gpu()
|
||||
{
|
||||
if [ $2 -lt 1 ] && [ $2 -gt 8 ]
|
||||
then
|
||||
echo "error: DEVICE_NUM=$2 is not in (1-8)"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
if [ ! -d $4 ]
|
||||
then
|
||||
echo "error: DATASET_PATH=$4 is not a directory"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
BASEPATH=$(cd "`dirname $0`" || exit; pwd)
|
||||
export PYTHONPATH=${BASEPATH}:$PYTHONPATH
|
||||
if [ -d "train" ];
|
||||
then
|
||||
rm -rf ../train
|
||||
fi
|
||||
mkdir ../train
|
||||
cd ../train || exit
|
||||
|
||||
export CUDA_VISIBLE_DEVICES="$3"
|
||||
mpirun -n $2 --allow-run-as-root \
|
||||
python ${BASEPATH}/../train.py \
|
||||
--dataset_path=$4 \
|
||||
--platform=$1 \
|
||||
&> train.log & # dataset train folder
|
||||
}
|
||||
|
||||
if [ $# -gt 5 ] || [ $# -lt 4 ]
|
||||
then
|
||||
echo "Usage:\n \
|
||||
Ascend: sh run_train.sh Ascend [DEVICE_NUM] [SERVER_IP(x.x.x.x)] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH]\n \
|
||||
GPU: sh run_train.sh GPU [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH]\n \
|
||||
"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
if [ $1 = "Ascend" ] ; then
|
||||
run_ascend "$@"
|
||||
elif [ $1 = "GPU" ] ; then
|
||||
run_gpu "$@"
|
||||
else
|
||||
echo "not support platform"
|
||||
fi;
|
||||
|
||||
|
|
@ -17,7 +17,7 @@ network config setting, will be used in train.py and eval.py
|
|||
"""
|
||||
from easydict import EasyDict as ed
|
||||
|
||||
config = ed({
|
||||
config_ascend = ed({
|
||||
"num_classes": 1000,
|
||||
"image_height": 224,
|
||||
"image_width": 224,
|
||||
|
|
@ -34,3 +34,21 @@ config = ed({
|
|||
"keep_checkpoint_max": 200,
|
||||
"save_checkpoint_path": "./checkpoint",
|
||||
})
|
||||
|
||||
config_gpu = ed({
|
||||
"num_classes": 1000,
|
||||
"image_height": 224,
|
||||
"image_width": 224,
|
||||
"batch_size": 64,
|
||||
"epoch_size": 200,
|
||||
"warmup_epochs": 4,
|
||||
"lr": 0.5,
|
||||
"momentum": 0.9,
|
||||
"weight_decay": 4e-5,
|
||||
"label_smooth": 0.1,
|
||||
"loss_scale": 1024,
|
||||
"save_checkpoint": True,
|
||||
"save_checkpoint_epochs": 1,
|
||||
"keep_checkpoint_max": 200,
|
||||
"save_checkpoint_path": "./checkpoint",
|
||||
})
|
||||
|
|
@ -20,10 +20,9 @@ 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
|
||||
from config import config
|
||||
|
||||
|
||||
def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32):
|
||||
def create_dataset(dataset_path, do_train, config, platform, repeat_num=1, batch_size=32):
|
||||
"""
|
||||
create a train or eval dataset
|
||||
|
||||
|
|
@ -36,14 +35,18 @@ def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32):
|
|||
Returns:
|
||||
dataset
|
||||
"""
|
||||
rank_size = int(os.getenv("RANK_SIZE"))
|
||||
rank_id = int(os.getenv("RANK_ID"))
|
||||
|
||||
if rank_size == 1:
|
||||
if platform == "Ascend":
|
||||
rank_size = int(os.getenv("RANK_SIZE"))
|
||||
rank_id = int(os.getenv("RANK_ID"))
|
||||
if rank_size == 1:
|
||||
ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True)
|
||||
else:
|
||||
ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True,
|
||||
num_shards=rank_size, shard_id=rank_id)
|
||||
elif platform == "GPU":
|
||||
ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True)
|
||||
else:
|
||||
ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True,
|
||||
num_shards=rank_size, shard_id=rank_id)
|
||||
raise ValueError("Unsupport platform.")
|
||||
|
||||
resize_height = config.image_height
|
||||
resize_width = config.image_width
|
||||
|
|
@ -20,20 +20,10 @@ from mindspore.ops.operations import TensorAdd
|
|||
from mindspore import Parameter, Tensor
|
||||
from mindspore.common.initializer import initializer
|
||||
|
||||
__all__ = ['MobileNetV2', 'mobilenet_v2']
|
||||
__all__ = ['mobilenet_v2']
|
||||
|
||||
|
||||
def _make_divisible(v, divisor, min_value=None):
|
||||
"""
|
||||
This function is taken from the original tf repo.
|
||||
It ensures that all layers have a channel number that is divisible by 8
|
||||
It can be seen here:
|
||||
https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
|
||||
:param v:
|
||||
:param divisor:
|
||||
:param min_value:
|
||||
:return:
|
||||
"""
|
||||
if min_value is None:
|
||||
min_value = divisor
|
||||
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
|
||||
|
|
@ -55,6 +45,7 @@ class GlobalAvgPooling(nn.Cell):
|
|||
Examples:
|
||||
>>> GlobalAvgPooling()
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
super(GlobalAvgPooling, self).__init__()
|
||||
self.mean = P.ReduceMean(keep_dims=False)
|
||||
|
|
@ -82,6 +73,7 @@ class DepthwiseConv(nn.Cell):
|
|||
Examples:
|
||||
>>> DepthwiseConv(16, 3, 1, 'pad', 1, channel_multiplier=1)
|
||||
"""
|
||||
|
||||
def __init__(self, in_planes, kernel_size, stride, pad_mode, pad, channel_multiplier=1, has_bias=False):
|
||||
super(DepthwiseConv, self).__init__()
|
||||
self.has_bias = has_bias
|
||||
|
|
@ -126,14 +118,19 @@ class ConvBNReLU(nn.Cell):
|
|||
Examples:
|
||||
>>> ConvBNReLU(16, 256, kernel_size=1, stride=1, groups=1)
|
||||
"""
|
||||
def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1):
|
||||
|
||||
def __init__(self, platform, in_planes, out_planes, kernel_size=3, stride=1, groups=1):
|
||||
super(ConvBNReLU, self).__init__()
|
||||
padding = (kernel_size - 1) // 2
|
||||
if groups == 1:
|
||||
conv = nn.Conv2d(in_planes, out_planes, kernel_size, stride, pad_mode='pad',
|
||||
padding=padding)
|
||||
conv = nn.Conv2d(in_planes, out_planes, kernel_size, stride, pad_mode='pad', padding=padding)
|
||||
else:
|
||||
conv = DepthwiseConv(in_planes, kernel_size, stride, pad_mode='pad', pad=padding)
|
||||
if platform == "Ascend":
|
||||
conv = DepthwiseConv(in_planes, kernel_size, stride, pad_mode='pad', pad=padding)
|
||||
elif platform == "GPU":
|
||||
conv = nn.Conv2d(in_planes, out_planes, kernel_size, stride,
|
||||
group=in_planes, pad_mode='pad', padding=padding)
|
||||
|
||||
layers = [conv, nn.BatchNorm2d(out_planes), nn.ReLU6()]
|
||||
self.features = nn.SequentialCell(layers)
|
||||
|
||||
|
|
@ -158,7 +155,8 @@ class InvertedResidual(nn.Cell):
|
|||
Examples:
|
||||
>>> ResidualBlock(3, 256, 1, 1)
|
||||
"""
|
||||
def __init__(self, inp, oup, stride, expand_ratio):
|
||||
|
||||
def __init__(self, platform, inp, oup, stride, expand_ratio):
|
||||
super(InvertedResidual, self).__init__()
|
||||
assert stride in [1, 2]
|
||||
|
||||
|
|
@ -167,12 +165,14 @@ class InvertedResidual(nn.Cell):
|
|||
|
||||
layers = []
|
||||
if expand_ratio != 1:
|
||||
layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1))
|
||||
layers.append(ConvBNReLU(platform, inp, hidden_dim, kernel_size=1))
|
||||
layers.extend([
|
||||
# dw
|
||||
ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim),
|
||||
ConvBNReLU(platform, hidden_dim, hidden_dim,
|
||||
stride=stride, groups=hidden_dim),
|
||||
# pw-linear
|
||||
nn.Conv2d(hidden_dim, oup, kernel_size=1, stride=1, has_bias=False),
|
||||
nn.Conv2d(hidden_dim, oup, kernel_size=1,
|
||||
stride=1, has_bias=False),
|
||||
nn.BatchNorm2d(oup),
|
||||
])
|
||||
self.conv = nn.SequentialCell(layers)
|
||||
|
|
@ -203,7 +203,8 @@ class MobileNetV2(nn.Cell):
|
|||
Examples:
|
||||
>>> MobileNetV2(num_classes=1000)
|
||||
"""
|
||||
def __init__(self, num_classes=1000, width_mult=1.,
|
||||
|
||||
def __init__(self, platform, num_classes=1000, width_mult=1.,
|
||||
has_dropout=False, inverted_residual_setting=None, round_nearest=8):
|
||||
super(MobileNetV2, self).__init__()
|
||||
block = InvertedResidual
|
||||
|
|
@ -226,16 +227,16 @@ class MobileNetV2(nn.Cell):
|
|||
# building first layer
|
||||
input_channel = _make_divisible(input_channel * width_mult, round_nearest)
|
||||
self.out_channels = _make_divisible(last_channel * max(1.0, width_mult), round_nearest)
|
||||
features = [ConvBNReLU(3, input_channel, stride=2)]
|
||||
features = [ConvBNReLU(platform, 3, input_channel, stride=2)]
|
||||
# building inverted residual blocks
|
||||
for t, c, n, s in self.cfgs:
|
||||
output_channel = _make_divisible(c * width_mult, round_nearest)
|
||||
for i in range(n):
|
||||
stride = s if i == 0 else 1
|
||||
features.append(block(input_channel, output_channel, stride, expand_ratio=t))
|
||||
features.append(block(platform, input_channel, output_channel, stride, expand_ratio=t))
|
||||
input_channel = output_channel
|
||||
# building last several layers
|
||||
features.append(ConvBNReLU(input_channel, self.out_channels, kernel_size=1))
|
||||
features.append(ConvBNReLU(platform, input_channel, self.out_channels, kernel_size=1))
|
||||
# make it nn.CellList
|
||||
self.features = nn.SequentialCell(features)
|
||||
# mobilenet head
|
||||
|
|
@ -268,14 +269,19 @@ class MobileNetV2(nn.Cell):
|
|||
m.weight.set_parameter_data(Tensor(np.random.normal(0, np.sqrt(2. / n),
|
||||
m.weight.data.shape()).astype("float32")))
|
||||
if m.bias is not None:
|
||||
m.bias.set_parameter_data(Tensor(np.zeros(m.bias.data.shape(), dtype="float32")))
|
||||
m.bias.set_parameter_data(
|
||||
Tensor(np.zeros(m.bias.data.shape(), dtype="float32")))
|
||||
elif isinstance(m, nn.BatchNorm2d):
|
||||
m.gamma.set_parameter_data(Tensor(np.ones(m.gamma.data.shape(), dtype="float32")))
|
||||
m.beta.set_parameter_data(Tensor(np.zeros(m.beta.data.shape(), dtype="float32")))
|
||||
m.gamma.set_parameter_data(
|
||||
Tensor(np.ones(m.gamma.data.shape(), dtype="float32")))
|
||||
m.beta.set_parameter_data(
|
||||
Tensor(np.zeros(m.beta.data.shape(), dtype="float32")))
|
||||
elif isinstance(m, nn.Dense):
|
||||
m.weight.set_parameter_data(Tensor(np.random.normal(0, 0.01, m.weight.data.shape()).astype("float32")))
|
||||
m.weight.set_parameter_data(Tensor(np.random.normal(
|
||||
0, 0.01, m.weight.data.shape()).astype("float32")))
|
||||
if m.bias is not None:
|
||||
m.bias.set_parameter_data(Tensor(np.zeros(m.bias.data.shape(), dtype="float32")))
|
||||
m.bias.set_parameter_data(
|
||||
Tensor(np.zeros(m.bias.data.shape(), dtype="float32")))
|
||||
|
||||
|
||||
def mobilenet_v2(**kwargs):
|
||||
|
|
@ -0,0 +1,267 @@
|
|||
# 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_imagenet."""
|
||||
import os
|
||||
import time
|
||||
import argparse
|
||||
import random
|
||||
import numpy as np
|
||||
from mindspore import context
|
||||
from mindspore import Tensor
|
||||
from mindspore import nn
|
||||
from mindspore.parallel._auto_parallel_context import auto_parallel_context
|
||||
from mindspore.nn.optim.momentum import Momentum
|
||||
from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
|
||||
from mindspore.nn.loss.loss import _Loss
|
||||
from mindspore.ops import operations as P
|
||||
from mindspore.ops import functional as F
|
||||
from mindspore.common import dtype as mstype
|
||||
from mindspore.train.model import Model, ParallelMode
|
||||
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, Callback
|
||||
from mindspore.train.loss_scale_manager import FixedLossScaleManager
|
||||
from mindspore.train.serialization import load_checkpoint, load_param_into_net
|
||||
from mindspore.communication.management import init
|
||||
import mindspore.dataset.engine as de
|
||||
from src.dataset import create_dataset
|
||||
from src.lr_generator import get_lr
|
||||
from src.config import config_gpu, config_ascend
|
||||
from src.mobilenetV2 import mobilenet_v2
|
||||
|
||||
random.seed(1)
|
||||
np.random.seed(1)
|
||||
de.config.set_seed(1)
|
||||
|
||||
parser = argparse.ArgumentParser(description='Image classification')
|
||||
parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
|
||||
parser.add_argument('--pre_trained', type=str, default=None, help='Pretrained checkpoint path')
|
||||
parser.add_argument('--platform', type=str, default=None, help='run platform')
|
||||
args_opt = parser.parse_args()
|
||||
|
||||
if args_opt.platform == "Ascend":
|
||||
device_id = int(os.getenv('DEVICE_ID'))
|
||||
rank_id = int(os.getenv('RANK_ID'))
|
||||
rank_size = int(os.getenv('RANK_SIZE'))
|
||||
run_distribute = rank_size > 1
|
||||
device_id = int(os.getenv('DEVICE_ID'))
|
||||
context.set_context(mode=context.GRAPH_MODE,
|
||||
device_target="Ascend",
|
||||
device_id=device_id, save_graphs=False)
|
||||
elif args_opt.platform == "GPU":
|
||||
context.set_context(mode=context.GRAPH_MODE,
|
||||
device_target="GPU", save_graphs=False)
|
||||
else:
|
||||
raise ValueError("Unsupport platform.")
|
||||
|
||||
|
||||
class CrossEntropyWithLabelSmooth(_Loss):
|
||||
"""
|
||||
CrossEntropyWith LabelSmooth.
|
||||
|
||||
Args:
|
||||
smooth_factor (float): smooth factor, default=0.
|
||||
num_classes (int): num classes
|
||||
|
||||
Returns:
|
||||
None.
|
||||
|
||||
Examples:
|
||||
>>> CrossEntropyWithLabelSmooth(smooth_factor=0., num_classes=1000)
|
||||
"""
|
||||
|
||||
def __init__(self, smooth_factor=0., num_classes=1000):
|
||||
super(CrossEntropyWithLabelSmooth, self).__init__()
|
||||
self.onehot = P.OneHot()
|
||||
self.on_value = Tensor(1.0 - smooth_factor, mstype.float32)
|
||||
self.off_value = Tensor(1.0 * smooth_factor /
|
||||
(num_classes - 1), mstype.float32)
|
||||
self.ce = nn.SoftmaxCrossEntropyWithLogits()
|
||||
self.mean = P.ReduceMean(False)
|
||||
self.cast = P.Cast()
|
||||
|
||||
def construct(self, logit, label):
|
||||
one_hot_label = self.onehot(self.cast(label, mstype.int32), F.shape(logit)[1],
|
||||
self.on_value, self.off_value)
|
||||
out_loss = self.ce(logit, one_hot_label)
|
||||
out_loss = self.mean(out_loss, 0)
|
||||
return out_loss
|
||||
|
||||
|
||||
class Monitor(Callback):
|
||||
"""
|
||||
Monitor loss and time.
|
||||
|
||||
Args:
|
||||
lr_init (numpy array): train lr
|
||||
|
||||
Returns:
|
||||
None
|
||||
|
||||
Examples:
|
||||
>>> Monitor(100,lr_init=Tensor([0.05]*100).asnumpy())
|
||||
"""
|
||||
|
||||
def __init__(self, lr_init=None):
|
||||
super(Monitor, self).__init__()
|
||||
self.lr_init = lr_init
|
||||
self.lr_init_len = len(lr_init)
|
||||
|
||||
def epoch_begin(self, run_context):
|
||||
self.losses = []
|
||||
self.epoch_time = time.time()
|
||||
|
||||
def epoch_end(self, run_context):
|
||||
cb_params = run_context.original_args()
|
||||
|
||||
epoch_mseconds = (time.time() - self.epoch_time) * 1000
|
||||
per_step_mseconds = epoch_mseconds / cb_params.batch_num
|
||||
print("epoch time: {:5.3f}, per step time: {:5.3f}, avg loss: {:5.3f}".format(epoch_mseconds,
|
||||
per_step_mseconds,
|
||||
np.mean(self.losses)))
|
||||
|
||||
def step_begin(self, run_context):
|
||||
self.step_time = time.time()
|
||||
|
||||
def step_end(self, run_context):
|
||||
cb_params = run_context.original_args()
|
||||
step_mseconds = (time.time() - self.step_time) * 1000
|
||||
step_loss = cb_params.net_outputs
|
||||
|
||||
if isinstance(step_loss, (tuple, list)) and isinstance(step_loss[0], Tensor):
|
||||
step_loss = step_loss[0]
|
||||
if isinstance(step_loss, Tensor):
|
||||
step_loss = np.mean(step_loss.asnumpy())
|
||||
|
||||
self.losses.append(step_loss)
|
||||
cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num
|
||||
|
||||
print("epoch: [{:3d}/{:3d}], step:[{:5d}/{:5d}], loss:[{:5.3f}/{:5.3f}], time:[{:5.3f}], lr:[{:5.3f}]".format(
|
||||
cb_params.cur_epoch_num -
|
||||
1, cb_params.epoch_num, cur_step_in_epoch, cb_params.batch_num, step_loss,
|
||||
np.mean(self.losses), step_mseconds, self.lr_init[cb_params.cur_step_num - 1]))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
if args_opt.platform == "GPU":
|
||||
# train on gpu
|
||||
print("train args: ", args_opt, "\ncfg: ", config_gpu)
|
||||
|
||||
# define net
|
||||
net = mobilenet_v2(num_classes=config_gpu.num_classes, platform="GPU")
|
||||
# define loss
|
||||
if config_gpu.label_smooth > 0:
|
||||
loss = CrossEntropyWithLabelSmooth(
|
||||
smooth_factor=config_gpu.label_smooth, num_classes=config_gpu.num_classes)
|
||||
else:
|
||||
loss = SoftmaxCrossEntropyWithLogits(
|
||||
is_grad=False, sparse=True, reduction='mean')
|
||||
# define dataset
|
||||
epoch_size = config_gpu.epoch_size
|
||||
dataset = create_dataset(dataset_path=args_opt.dataset_path,
|
||||
do_train=True,
|
||||
config=config_gpu,
|
||||
platform=args_opt.platform,
|
||||
repeat_num=epoch_size,
|
||||
batch_size=config_gpu.batch_size)
|
||||
step_size = dataset.get_dataset_size()
|
||||
# resume
|
||||
if args_opt.pre_trained:
|
||||
param_dict = load_checkpoint(args_opt.pre_trained)
|
||||
load_param_into_net(net, param_dict)
|
||||
# define optimizer
|
||||
loss_scale = FixedLossScaleManager(
|
||||
config_gpu.loss_scale, drop_overflow_update=False)
|
||||
lr = Tensor(get_lr(global_step=0,
|
||||
lr_init=0,
|
||||
lr_end=0,
|
||||
lr_max=config_gpu.lr,
|
||||
warmup_epochs=config_gpu.warmup_epochs,
|
||||
total_epochs=epoch_size,
|
||||
steps_per_epoch=step_size))
|
||||
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config_gpu.momentum,
|
||||
config_gpu.weight_decay, config_gpu.loss_scale)
|
||||
# define model
|
||||
model = Model(net, loss_fn=loss, optimizer=opt,
|
||||
loss_scale_manager=loss_scale)
|
||||
|
||||
cb = [Monitor(lr_init=lr.asnumpy())]
|
||||
if config_gpu.save_checkpoint:
|
||||
config_ck = CheckpointConfig(save_checkpoint_steps=config_gpu.save_checkpoint_epochs * step_size,
|
||||
keep_checkpoint_max=config_gpu.keep_checkpoint_max)
|
||||
ckpt_cb = ModelCheckpoint(
|
||||
prefix="mobilenet", directory=config_gpu.save_checkpoint_path, config=config_ck)
|
||||
cb += [ckpt_cb]
|
||||
# begine train
|
||||
model.train(epoch_size, dataset, callbacks=cb)
|
||||
elif args_opt.platform == "Ascend":
|
||||
# train on ascend
|
||||
print("train args: ", args_opt, "\ncfg: ", config_ascend,
|
||||
"\nparallel args: rank_id {}, device_id {}, rank_size {}".format(rank_id, device_id, rank_size))
|
||||
|
||||
if run_distribute:
|
||||
context.set_auto_parallel_context(device_num=rank_size, parallel_mode=ParallelMode.DATA_PARALLEL,
|
||||
parameter_broadcast=True, mirror_mean=True)
|
||||
auto_parallel_context().set_all_reduce_fusion_split_indices([140])
|
||||
init()
|
||||
|
||||
epoch_size = config_ascend.epoch_size
|
||||
net = mobilenet_v2(num_classes=config_ascend.num_classes)
|
||||
net.to_float(mstype.float16)
|
||||
for _, cell in net.cells_and_names():
|
||||
if isinstance(cell, nn.Dense):
|
||||
cell.to_float(mstype.float32)
|
||||
if config_ascend.label_smooth > 0:
|
||||
loss = CrossEntropyWithLabelSmooth(
|
||||
smooth_factor=config_ascend.label_smooth, num_classes=config.num_classes)
|
||||
else:
|
||||
loss = SoftmaxCrossEntropyWithLogits(
|
||||
is_grad=False, sparse=True, reduction='mean')
|
||||
dataset = create_dataset(dataset_path=args_opt.dataset_path,
|
||||
do_train=True,
|
||||
config=config_ascend,
|
||||
platform=args_opt.platform,
|
||||
repeat_num=epoch_size,
|
||||
batch_size=config_ascend.batch_size)
|
||||
step_size = dataset.get_dataset_size()
|
||||
if args_opt.pre_trained:
|
||||
param_dict = load_checkpoint(args_opt.pre_trained)
|
||||
load_param_into_net(net, param_dict)
|
||||
|
||||
loss_scale = FixedLossScaleManager(
|
||||
config_ascend.loss_scale, drop_overflow_update=False)
|
||||
lr = Tensor(get_lr(global_step=0,
|
||||
lr_init=0,
|
||||
lr_end=0,
|
||||
lr_max=config_ascend.lr,
|
||||
warmup_epochs=config_ascend.warmup_epochs,
|
||||
total_epochs=epoch_size,
|
||||
steps_per_epoch=step_size))
|
||||
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config_ascend.momentum,
|
||||
config_ascend.weight_decay, config_ascend.loss_scale)
|
||||
|
||||
model = Model(net, loss_fn=loss, optimizer=opt,
|
||||
loss_scale_manager=loss_scale)
|
||||
|
||||
cb = None
|
||||
if rank_id == 0:
|
||||
cb = [Monitor(lr_init=lr.asnumpy())]
|
||||
if config_ascend.save_checkpoint:
|
||||
config_ck = CheckpointConfig(save_checkpoint_steps=config_ascend.save_checkpoint_epochs * step_size,
|
||||
keep_checkpoint_max=config_ascend.keep_checkpoint_max)
|
||||
ckpt_cb = ModelCheckpoint(
|
||||
prefix="mobilenet", directory=config_ascend.save_checkpoint_path, config=config_ck)
|
||||
cb += [ckpt_cb]
|
||||
model.train(epoch_size, dataset, callbacks=cb)
|
||||
else:
|
||||
raise ValueError("Unsupport platform.")
|
||||
|
|
@ -0,0 +1,152 @@
|
|||
# MobileNetV3 Description
|
||||
|
||||
|
||||
MobileNetV3 is tuned to mobile phone CPUs through a combination of hardware- aware network architecture search (NAS) complemented by the NetAdapt algorithm and then subsequently improved through novel architecture advances.Nov 20, 2019.
|
||||
|
||||
[Paper](https://arxiv.org/pdf/1905.02244) Howard, Andrew, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang et al. "Searching for mobilenetv3." In Proceedings of the IEEE International Conference on Computer Vision, pp. 1314-1324. 2019.
|
||||
|
||||
# Model architecture
|
||||
|
||||
The overall network architecture of MobileNetV3 is show below:
|
||||
|
||||
[Link](https://arxiv.org/pdf/1905.02244)
|
||||
|
||||
# Dataset
|
||||
|
||||
Dataset used: [imagenet](http://www.image-net.org/)
|
||||
|
||||
- Dataset size: ~125G, 1.2W colorful images in 1000 classes
|
||||
- Train: 120G, 1.2W images
|
||||
- Test: 5G, 50000 images
|
||||
- Data format: RGB images.
|
||||
- Note: Data will be processed in src/dataset.py
|
||||
|
||||
|
||||
# Features
|
||||
|
||||
|
||||
# Environment Requirements
|
||||
|
||||
- Hardware（Ascend/GPU）
|
||||
- Prepare hardware environment with Ascend or GPU processor. If you want to try Ascend , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
|
||||
- Framework
|
||||
- [MindSpore](http://10.90.67.50/mindspore/archive/20200506/OpenSource/me_vm_x86/)
|
||||
- For more information, please check the resources below：
|
||||
- [MindSpore tutorials](https://www.mindspore.cn/tutorial/zh-CN/master/index.html)
|
||||
- [MindSpore API](https://www.mindspore.cn/api/zh-CN/master/index.html)
|
||||
|
||||
|
||||
# Script description
|
||||
|
||||
## Script and sample code
|
||||
|
||||
```python
|
||||
├── MobilenetV3
|
||||
├── Readme.md
|
||||
├── scripts
|
||||
│ ├──run_train.sh
|
||||
│ ├──run_eval.sh
|
||||
├── src
|
||||
│ ├──config.py
|
||||
│ ├──dataset.py
|
||||
│ ├──luanch.py
|
||||
│ ├──lr_generator.py
|
||||
│ ├──mobilenetV2.py
|
||||
├── train.py
|
||||
├── eval.py
|
||||
```
|
||||
|
||||
## Training process
|
||||
|
||||
### Usage
|
||||
|
||||
- Ascend: sh run_train.sh Ascend [DEVICE_NUM] [SERVER_IP(x.x.x.x)] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH]
|
||||
- GPU: sh run_trian.sh GPU [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH]
|
||||
|
||||
### Launch
|
||||
|
||||
```
|
||||
# training example
|
||||
Ascend: sh run_train.sh Ascend 8 192.168.0.1 0,1,2,3,4,5,6,7 ~/imagenet/train/
|
||||
GPU: sh run_train.sh GPU 8 0,1,2,3,4,5,6,7 ~/imagenet/train/
|
||||
```
|
||||
|
||||
### Result
|
||||
|
||||
Training result will be stored in the example path. Checkpoints will be stored at `. /checkpoint` by default, and training log will be redirected to `./train/train.log` like followings.
|
||||
|
||||
```
|
||||
epoch: [ 0/200], step:[ 624/ 625], loss:[5.258/5.258], time:[140412.236], lr:[0.100]
|
||||
epoch time: 140522.500, per step time: 224.836, avg loss: 5.258
|
||||
epoch: [ 1/200], step:[ 624/ 625], loss:[3.917/3.917], time:[138221.250], lr:[0.200]
|
||||
epoch time: 138331.250, per step time: 221.330, avg loss: 3.917
|
||||
```
|
||||
|
||||
## Eval process
|
||||
|
||||
### Usage
|
||||
|
||||
- Ascend: sh run_infer.sh Ascend [DATASET_PATH] [CHECKPOINT_PATH]
|
||||
- GPU: sh run_infer.sh GPU [DATASET_PATH] [CHECKPOINT_PATH]
|
||||
|
||||
### Launch
|
||||
|
||||
```
|
||||
# infer example
|
||||
Ascend: sh run_infer.sh Ascend ~/imagenet/val/ ~/train/mobilenet-200_625.ckpt
|
||||
GPU: sh run_infer.sh GPU ~/imagenet/val/ ~/train/mobilenet-200_625.ckpt
|
||||
```
|
||||
|
||||
> checkpoint can be produced in training process.
|
||||
|
||||
### Result
|
||||
|
||||
Inference result will be stored in the example path, you can find result like the followings in `val.log`.
|
||||
|
||||
```
|
||||
result: {'acc': 0.71976314102564111} ckpt=/path/to/checkpoint/mobilenet-200_625.ckpt
|
||||
```
|
||||
|
||||
# Model description
|
||||
|
||||
## Performance
|
||||
|
||||
### Training Performance
|
||||
|
||||
| Parameters | MobilenetV3 | |
|
||||
| -------------------------- | ---------------------------------------------------------- | ------------------------- |
|
||||
| Model Version | | large |
|
||||
| Resource | Ascend 910, cpu:2.60GHz 56cores, memory:314G | NV SMX2 V100-32G |
|
||||
| uploaded Date | 05/06/2020 | 05/06/2020 |
|
||||
| MindSpore Version | 0.3.0 | 0.3.0 |
|
||||
| Dataset | ImageNet | ImageNet |
|
||||
| Training Parameters | src/config.py | src/config.py |
|
||||
| Optimizer | Momentum | Momentum |
|
||||
| Loss Function | SoftmaxCrossEntropy | SoftmaxCrossEntropy |
|
||||
| outputs | | |
|
||||
| Loss | | 1.913 |
|
||||
| Accuracy | | ACC1[77.57%] ACC5[92.51%] |
|
||||
| Total time | | |
|
||||
| Params (M) | | |
|
||||
| Checkpoint for Fine tuning | | |
|
||||
| Model for inference | | |
|
||||
|
||||
#### Inference Performance
|
||||
|
||||
| Parameters | GoogLeNet | | |
|
||||
| -------------------------- | ----------------------------- | ------------------------- | -------------------- |
|
||||
| Model Version | V1 | | |
|
||||
| Resource | Huawei 910 | NV SMX2 V100-32G | Huawei 310 |
|
||||
| uploaded Date | 05/06/2020 | 05/22/2020 | |
|
||||
| MindSpore Version | 0.2.0 | 0.2.0 | 0.2.0 |
|
||||
| Dataset | ImageNet, 1.2W | ImageNet, 1.2W | ImageNet, 1.2W |
|
||||
| batch_size | | 130(8P) | |
|
||||
| outputs | | | |
|
||||
| Accuracy | | ACC1[75.43%] ACC5[92.51%] | |
|
||||
| Speed | | | |
|
||||
| Total time | | | |
|
||||
| Model for inference | | | |
|
||||
|
||||
|
||||
# ModelZoo Homepage
|
||||
[Link](https://gitee.com/mindspore/mindspore/tree/master/mindspore/model_zoo)
|
||||
|
|
@ -17,33 +17,51 @@ eval.
|
|||
"""
|
||||
import os
|
||||
import argparse
|
||||
from dataset import create_dataset
|
||||
from config import config
|
||||
from mindspore import context
|
||||
from mindspore.model_zoo.mobilenet import mobilenet_v2
|
||||
from mindspore import nn
|
||||
from mindspore.train.model import Model
|
||||
from mindspore.train.serialization import load_checkpoint, load_param_into_net
|
||||
from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
|
||||
from mindspore.common import dtype as mstype
|
||||
from src.dataset import create_dataset
|
||||
from src.config import config_ascend, config_gpu
|
||||
from src.mobilenetV2 import mobilenet_v2
|
||||
|
||||
parser = argparse.ArgumentParser(description='Image classification')
|
||||
parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
|
||||
parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
|
||||
parser.add_argument('--platform', type=str, default=None, help='run platform')
|
||||
args_opt = parser.parse_args()
|
||||
|
||||
device_id = int(os.getenv('DEVICE_ID'))
|
||||
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=device_id, save_graphs=False)
|
||||
|
||||
if __name__ == '__main__':
|
||||
loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean')
|
||||
net = mobilenet_v2(num_classes=config.num_classes)
|
||||
net.to_float(mstype.float16)
|
||||
for _, cell in net.cells_and_names():
|
||||
if isinstance(cell, nn.Dense):
|
||||
cell.add_flags_recursive(fp32=True)
|
||||
config_platform = None
|
||||
if args_opt.platform == "Ascend":
|
||||
config_platform = config_ascend
|
||||
device_id = int(os.getenv('DEVICE_ID'))
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend",
|
||||
device_id=device_id, save_graphs=False)
|
||||
elif args_opt.platform == "GPU":
|
||||
config_platform = config_gpu
|
||||
context.set_context(mode=context.GRAPH_MODE,
|
||||
device_target="GPU", save_graphs=False)
|
||||
else:
|
||||
raise ValueError("Unsupport platform.")
|
||||
|
||||
dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=False, batch_size=config.batch_size)
|
||||
loss = nn.SoftmaxCrossEntropyWithLogits(
|
||||
is_grad=False, sparse=True, reduction='mean')
|
||||
net = mobilenet_v2(num_classes=config_platform.num_classes)
|
||||
|
||||
if args_opt.platform == "Ascend":
|
||||
net.to_float(mstype.float16)
|
||||
for _, cell in net.cells_and_names():
|
||||
if isinstance(cell, nn.Dense):
|
||||
cell.to_float(mstype.float32)
|
||||
|
||||
dataset = create_dataset(dataset_path=args_opt.dataset_path,
|
||||
do_train=False,
|
||||
config=config_platform,
|
||||
platform=args_opt.platform,
|
||||
batch_size=config_platform.batch_size)
|
||||
step_size = dataset.get_dataset_size()
|
||||
|
||||
if args_opt.checkpoint_path:
|
||||
|
|
@ -0,0 +1,55 @@
|
|||
#!/usr/bin/env bash
|
||||
# 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.
|
||||
# ============================================================================
|
||||
if [ $# != 3 ]
|
||||
then
|
||||
echo "Ascend: sh run_infer.sh [PLATFORM] [DATASET_PATH] [CHECKPOINT_PATH] \
|
||||
GPU: sh run_infer.sh [PLATFORM] [DATASET_PATH] [CHECKPOINT_PATH]"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# check dataset path
|
||||
if [ ! -d $2 ]
|
||||
then
|
||||
echo "error: DATASET_PATH=$2 is not a directory"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# check checkpoint file
|
||||
if [ ! -f $3 ]
|
||||
then
|
||||
echo "error: CHECKPOINT_PATH=$3 is not a file"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# set environment
|
||||
BASEPATH=$(cd "`dirname $0`" || exit; pwd)
|
||||
export PYTHONPATH=${BASEPATH}:$PYTHONPATH
|
||||
export DEVICE_ID=0
|
||||
export RANK_ID=0
|
||||
export RANK_SIZE=1
|
||||
if [ -d "eval" ];
|
||||
then
|
||||
rm -rf ./eval
|
||||
fi
|
||||
mkdir ./eval
|
||||
cd ./eval || exit
|
||||
|
||||
# luanch
|
||||
python ${BASEPATH}/eval.py \
|
||||
--platform=$1 \
|
||||
--dataset_path=$2 \
|
||||
--checkpoint_path=$3 \
|
||||
&> infer.log & # dataset val folder path
|
||||
|
|
@ -0,0 +1,94 @@
|
|||
#!/usr/bin/env bash
|
||||
# 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.
|
||||
# ============================================================================
|
||||
run_ascend()
|
||||
{
|
||||
if [ $2 -lt 1 ] && [ $2 -gt 8 ]
|
||||
then
|
||||
echo "error: DEVICE_NUM=$2 is not in (1-8)"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
if [ ! -d $5 ]
|
||||
then
|
||||
echo "error: DATASET_PATH=$5 is not a directory"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
BASEPATH=$(cd "`dirname $0`" || exit; pwd)
|
||||
export PYTHONPATH=${BASEPATH}:$PYTHONPATH
|
||||
if [ -d "train" ];
|
||||
then
|
||||
rm -rf ./train
|
||||
fi
|
||||
mkdir ./train
|
||||
cd ./train || exit
|
||||
python ${BASEPATH}/launch.py \
|
||||
--nproc_per_node=$2 \
|
||||
--visible_devices=$4 \
|
||||
--server_id=$3 \
|
||||
--training_script=${BASEPATH}/train.py \
|
||||
--dataset_path=$5 \
|
||||
--platform=$1 &> train.log & # dataset train folder
|
||||
}
|
||||
|
||||
run_gpu()
|
||||
{
|
||||
if [ $2 -lt 1 ] && [ $2 -gt 8 ]
|
||||
then
|
||||
echo "error: DEVICE_NUM=$2 is not in (1-8)"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
if [ ! -d $4 ]
|
||||
then
|
||||
echo "error: DATASET_PATH=$4 is not a directory"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
BASEPATH=$(cd "`dirname $0`" || exit; pwd)
|
||||
export PYTHONPATH=${BASEPATH}:$PYTHONPATH
|
||||
if [ -d "train" ];
|
||||
then
|
||||
rm -rf ./train
|
||||
fi
|
||||
mkdir ./train
|
||||
cd ./train || exit
|
||||
|
||||
export CUDA_VISIBLE_DEVICES="$3"
|
||||
mpirun -n $2 --allow-run-as-root \
|
||||
python ${BASEPATH}/train.py \
|
||||
--dataset_path=$4 \
|
||||
--platform=$1 \
|
||||
&> train.log & # dataset train folder
|
||||
}
|
||||
|
||||
if [ $# -gt 5 ] || [ $# -lt 4 ]
|
||||
then
|
||||
echo "Usage:\n \
|
||||
Ascend: sh run_train.sh Ascend [DEVICE_NUM] [SERVER_IP(x.x.x.x)] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH]\n \
|
||||
GPU: sh run_train.sh GPU [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH]\n \
|
||||
"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
if [ $1 = "Ascend" ] ; then
|
||||
run_ascend "$@"
|
||||
elif [ $1 = "GPU" ] ; then
|
||||
run_gpu "$@"
|
||||
else
|
||||
echo "not support platform"
|
||||
fi;
|
||||
|
||||
|
|
@ -0,0 +1,54 @@
|
|||
# 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 and eval.py
|
||||
"""
|
||||
from easydict import EasyDict as ed
|
||||
|
||||
config_ascend = ed({
|
||||
"num_classes": 1000,
|
||||
"image_height": 224,
|
||||
"image_width": 224,
|
||||
"batch_size": 256,
|
||||
"epoch_size": 200,
|
||||
"warmup_epochs": 4,
|
||||
"lr": 0.4,
|
||||
"momentum": 0.9,
|
||||
"weight_decay": 4e-5,
|
||||
"label_smooth": 0.1,
|
||||
"loss_scale": 1024,
|
||||
"save_checkpoint": True,
|
||||
"save_checkpoint_epochs": 1,
|
||||
"keep_checkpoint_max": 200,
|
||||
"save_checkpoint_path": "./checkpoint",
|
||||
})
|
||||
|
||||
config_gpu = ed({
|
||||
"num_classes": 1000,
|
||||
"image_height": 224,
|
||||
"image_width": 224,
|
||||
"batch_size": 64,
|
||||
"epoch_size": 300,
|
||||
"warmup_epochs": 4,
|
||||
"lr": 0.5,
|
||||
"momentum": 0.9,
|
||||
"weight_decay": 4e-5,
|
||||
"label_smooth": 0.1,
|
||||
"loss_scale": 1024,
|
||||
"save_checkpoint": True,
|
||||
"save_checkpoint_epochs": 1,
|
||||
"keep_checkpoint_max": 500,
|
||||
"save_checkpoint_path": "./checkpoint",
|
||||
})
|
||||
|
|
@ -0,0 +1,85 @@
|
|||
# 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
|
||||
|
||||
|
||||
def create_dataset(dataset_path, do_train, config, platform, repeat_num=1, batch_size=32):
|
||||
"""
|
||||
create a train or eval dataset
|
||||
|
||||
Args:
|
||||
dataset_path(string): the path of dataset.
|
||||
do_train(bool): whether dataset is used for train or eval.
|
||||
repeat_num(int): the repeat times of dataset. Default: 1
|
||||
batch_size(int): the batch size of dataset. Default: 32
|
||||
|
||||
Returns:
|
||||
dataset
|
||||
"""
|
||||
if platform == "Ascend":
|
||||
rank_size = int(os.getenv("RANK_SIZE"))
|
||||
rank_id = int(os.getenv("RANK_ID"))
|
||||
if rank_size == 1:
|
||||
ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True)
|
||||
else:
|
||||
ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True,
|
||||
num_shards=rank_size, shard_id=rank_id)
|
||||
elif platform == "GPU":
|
||||
ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True)
|
||||
else:
|
||||
raise ValueError("Unsupport platform.")
|
||||
|
||||
resize_height = config.image_height
|
||||
resize_width = config.image_width
|
||||
buffer_size = 1000
|
||||
|
||||
# define map operations
|
||||
decode_op = C.Decode()
|
||||
resize_crop_op = C.RandomCropDecodeResize(resize_height, scale=(0.08, 1.0), ratio=(0.75, 1.333))
|
||||
horizontal_flip_op = C.RandomHorizontalFlip(prob=0.5)
|
||||
|
||||
resize_op = C.Resize((256, 256))
|
||||
center_crop = C.CenterCrop(resize_width)
|
||||
rescale_op = C.RandomColorAdjust(brightness=0.4, contrast=0.4, saturation=0.4)
|
||||
normalize_op = C.Normalize(mean=[0.485*255, 0.456*255, 0.406*255], std=[0.229*255, 0.224*255, 0.225*255])
|
||||
change_swap_op = C.HWC2CHW()
|
||||
|
||||
if do_train:
|
||||
trans = [resize_crop_op, horizontal_flip_op, rescale_op, normalize_op, change_swap_op]
|
||||
else:
|
||||
trans = [decode_op, resize_op, center_crop, normalize_op, change_swap_op]
|
||||
|
||||
type_cast_op = C2.TypeCast(mstype.int32)
|
||||
|
||||
ds = ds.map(input_columns="image", operations=trans, num_parallel_workers=8)
|
||||
ds = ds.map(input_columns="label", operations=type_cast_op, num_parallel_workers=8)
|
||||
|
||||
# apply shuffle operations
|
||||
ds = ds.shuffle(buffer_size=buffer_size)
|
||||
|
||||
# apply batch operations
|
||||
ds = ds.batch(batch_size, drop_remainder=True)
|
||||
|
||||
# apply dataset repeat operation
|
||||
ds = ds.repeat(repeat_num)
|
||||
|
||||
return ds
|
||||
|
|
@ -0,0 +1,163 @@
|
|||
# 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.
|
||||
# ============================================================================
|
||||
"""launch train script"""
|
||||
import os
|
||||
import sys
|
||||
import json
|
||||
import subprocess
|
||||
import shutil
|
||||
from argparse import ArgumentParser
|
||||
|
||||
def parse_args():
|
||||
"""
|
||||
parse args .
|
||||
|
||||
Args:
|
||||
|
||||
Returns:
|
||||
args.
|
||||
|
||||
Examples:
|
||||
>>> parse_args()
|
||||
"""
|
||||
parser = ArgumentParser(description="mindspore distributed training launch "
|
||||
"helper utilty that will spawn up "
|
||||
"multiple distributed processes")
|
||||
parser.add_argument("--nproc_per_node", type=int, default=1,
|
||||
help="The number of processes to launch on each node, "
|
||||
"for D training, this is recommended to be set "
|
||||
"to the number of D in your system so that "
|
||||
"each process can be bound to a single D.")
|
||||
parser.add_argument("--visible_devices", type=str, default="0,1,2,3,4,5,6,7",
|
||||
help="will use the visible devices sequentially")
|
||||
parser.add_argument("--server_id", type=str, default="",
|
||||
help="server ip")
|
||||
parser.add_argument("--training_script", type=str,
|
||||
help="The full path to the single D training "
|
||||
"program/script to be launched in parallel, "
|
||||
"followed by all the arguments for the "
|
||||
"training script")
|
||||
# rest from the training program
|
||||
args, unknown = parser.parse_known_args()
|
||||
args.training_script_args = unknown
|
||||
return args
|
||||
|
||||
|
||||
def main():
|
||||
print("start", __file__)
|
||||
args = parse_args()
|
||||
print(args)
|
||||
visible_devices = args.visible_devices.split(',')
|
||||
assert os.path.isfile(args.training_script)
|
||||
assert len(visible_devices) >= args.nproc_per_node
|
||||
print('visible_devices:{}'.format(visible_devices))
|
||||
if not args.server_id:
|
||||
print('pleaser input server ip!!!')
|
||||
exit(0)
|
||||
print('server_id:{}'.format(args.server_id))
|
||||
|
||||
# construct hccn_table
|
||||
hccn_configs = open('/etc/hccn.conf', 'r').readlines()
|
||||
device_ips = {}
|
||||
for hccn_item in hccn_configs:
|
||||
hccn_item = hccn_item.strip()
|
||||
if hccn_item.startswith('address_'):
|
||||
device_id, device_ip = hccn_item.split('=')
|
||||
device_id = device_id.split('_')[1]
|
||||
device_ips[device_id] = device_ip
|
||||
print('device_id:{}, device_ip:{}'.format(device_id, device_ip))
|
||||
hccn_table = {}
|
||||
hccn_table['board_id'] = '0x0000'
|
||||
hccn_table['chip_info'] = '910'
|
||||
hccn_table['deploy_mode'] = 'lab'
|
||||
hccn_table['group_count'] = '1'
|
||||
hccn_table['group_list'] = []
|
||||
instance_list = []
|
||||
usable_dev = ''
|
||||
for instance_id in range(args.nproc_per_node):
|
||||
instance = {}
|
||||
instance['devices'] = []
|
||||
device_id = visible_devices[instance_id]
|
||||
device_ip = device_ips[device_id]
|
||||
usable_dev += str(device_id)
|
||||
instance['devices'].append({
|
||||
'device_id': device_id,
|
||||
'device_ip': device_ip,
|
||||
})
|
||||
instance['rank_id'] = str(instance_id)
|
||||
instance['server_id'] = args.server_id
|
||||
instance_list.append(instance)
|
||||
hccn_table['group_list'].append({
|
||||
'device_num': str(args.nproc_per_node),
|
||||
'server_num': '1',
|
||||
'group_name': '',
|
||||
'instance_count': str(args.nproc_per_node),
|
||||
'instance_list': instance_list,
|
||||
})
|
||||
hccn_table['para_plane_nic_location'] = 'device'
|
||||
hccn_table['para_plane_nic_name'] = []
|
||||
for instance_id in range(args.nproc_per_node):
|
||||
eth_id = visible_devices[instance_id]
|
||||
hccn_table['para_plane_nic_name'].append('eth{}'.format(eth_id))
|
||||
hccn_table['para_plane_nic_num'] = str(args.nproc_per_node)
|
||||
hccn_table['status'] = 'completed'
|
||||
|
||||
# save hccn_table to file
|
||||
table_path = os.getcwd()
|
||||
if not os.path.exists(table_path):
|
||||
os.mkdir(table_path)
|
||||
table_fn = os.path.join(table_path,
|
||||
'rank_table_{}p_{}_{}.json'.format(args.nproc_per_node, usable_dev, args.server_id))
|
||||
with open(table_fn, 'w') as table_fp:
|
||||
json.dump(hccn_table, table_fp, indent=4)
|
||||
sys.stdout.flush()
|
||||
|
||||
# spawn the processes
|
||||
processes = []
|
||||
cmds = []
|
||||
log_files = []
|
||||
env = os.environ.copy()
|
||||
env['RANK_SIZE'] = str(args.nproc_per_node)
|
||||
cur_path = os.getcwd()
|
||||
for rank_id in range(0, args.nproc_per_node):
|
||||
os.chdir(cur_path)
|
||||
device_id = visible_devices[rank_id]
|
||||
device_dir = os.path.join(cur_path, 'device{}'.format(rank_id))
|
||||
env['RANK_ID'] = str(rank_id)
|
||||
env['DEVICE_ID'] = str(device_id)
|
||||
if args.nproc_per_node > 1:
|
||||
env['MINDSPORE_HCCL_CONFIG_PATH'] = table_fn
|
||||
env['RANK_TABLE_FILE'] = table_fn
|
||||
if os.path.exists(device_dir):
|
||||
shutil.rmtree(device_dir)
|
||||
os.mkdir(device_dir)
|
||||
os.chdir(device_dir)
|
||||
cmd = [sys.executable, '-u']
|
||||
cmd.append(args.training_script)
|
||||
cmd.extend(args.training_script_args)
|
||||
log_file = open('{dir}/log{id}.log'.format(dir=device_dir, id=rank_id), 'w')
|
||||
process = subprocess.Popen(cmd, stdout=log_file, stderr=log_file, env=env)
|
||||
processes.append(process)
|
||||
cmds.append(cmd)
|
||||
log_files.append(log_file)
|
||||
for process, cmd, log_file in zip(processes, cmds, log_files):
|
||||
process.wait()
|
||||
if process.returncode != 0:
|
||||
raise subprocess.CalledProcessError(returncode=process, cmd=cmd)
|
||||
log_file.close()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -0,0 +1,54 @@
|
|||
# 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.
|
||||
# ============================================================================
|
||||
"""learning rate generator"""
|
||||
import math
|
||||
import numpy as np
|
||||
|
||||
|
||||
def get_lr(global_step, lr_init, lr_end, lr_max, warmup_epochs, total_epochs, steps_per_epoch):
|
||||
"""
|
||||
generate learning rate array
|
||||
|
||||
Args:
|
||||
global_step(int): total steps of the training
|
||||
lr_init(float): init learning rate
|
||||
lr_end(float): end learning rate
|
||||
lr_max(float): max learning rate
|
||||
warmup_epochs(int): number of warmup epochs
|
||||
total_epochs(int): total epoch of training
|
||||
steps_per_epoch(int): steps of one epoch
|
||||
|
||||
Returns:
|
||||
np.array, learning rate array
|
||||
"""
|
||||
lr_each_step = []
|
||||
total_steps = steps_per_epoch * total_epochs
|
||||
warmup_steps = steps_per_epoch * warmup_epochs
|
||||
for i in range(total_steps):
|
||||
if i < warmup_steps:
|
||||
lr = lr_init + (lr_max - lr_init) * i / warmup_steps
|
||||
else:
|
||||
lr = lr_end + \
|
||||
(lr_max - lr_end) * \
|
||||
(1. + math.cos(math.pi * (i - warmup_steps) / (total_steps - warmup_steps))) / 2.
|
||||
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
|
||||
|
|
@ -0,0 +1,390 @@
|
|||
# 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.
|
||||
# ============================================================================
|
||||
"""MobileNetV3 model define"""
|
||||
from functools import partial
|
||||
import numpy as np
|
||||
import mindspore.nn as nn
|
||||
from mindspore.ops import operations as P
|
||||
from mindspore import Tensor
|
||||
|
||||
|
||||
__all__ = ['mobilenet_v3_large',
|
||||
'mobilenet_v3_small']
|
||||
|
||||
|
||||
def _make_divisible(x, divisor=8):
|
||||
return int(np.ceil(x * 1. / divisor) * divisor)
|
||||
|
||||
|
||||
class Activation(nn.Cell):
|
||||
"""
|
||||
Activation definition.
|
||||
|
||||
Args:
|
||||
act_func(string): activation name.
|
||||
|
||||
Returns:
|
||||
Tensor, output tensor.
|
||||
"""
|
||||
|
||||
def __init__(self, act_func):
|
||||
super(Activation, self).__init__()
|
||||
if act_func == 'relu':
|
||||
self.act = nn.ReLU()
|
||||
elif act_func == 'relu6':
|
||||
self.act = nn.ReLU6()
|
||||
elif act_func in ('hsigmoid', 'hard_sigmoid'):
|
||||
self.act = nn.HSigmoid()
|
||||
elif act_func in ('hswish', 'hard_swish'):
|
||||
self.act = nn.HSwish()
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
def construct(self, x):
|
||||
return self.act(x)
|
||||
|
||||
|
||||
class GlobalAvgPooling(nn.Cell):
|
||||
"""
|
||||
Global avg pooling definition.
|
||||
|
||||
Args:
|
||||
|
||||
Returns:
|
||||
Tensor, output tensor.
|
||||
|
||||
Examples:
|
||||
>>> GlobalAvgPooling()
|
||||
"""
|
||||
|
||||
def __init__(self, keep_dims=False):
|
||||
super(GlobalAvgPooling, self).__init__()
|
||||
self.mean = P.ReduceMean(keep_dims=keep_dims)
|
||||
|
||||
def construct(self, x):
|
||||
x = self.mean(x, (2, 3))
|
||||
return x
|
||||
|
||||
|
||||
class SE(nn.Cell):
|
||||
"""
|
||||
SE warpper definition.
|
||||
|
||||
Args:
|
||||
num_out (int): Output channel.
|
||||
ratio (int): middle output ratio.
|
||||
|
||||
Returns:
|
||||
Tensor, output tensor.
|
||||
|
||||
Examples:
|
||||
>>> SE(4)
|
||||
"""
|
||||
|
||||
def __init__(self, num_out, ratio=4):
|
||||
super(SE, self).__init__()
|
||||
num_mid = _make_divisible(num_out // ratio)
|
||||
self.pool = GlobalAvgPooling(keep_dims=True)
|
||||
self.conv1 = nn.Conv2d(in_channels=num_out, out_channels=num_mid,
|
||||
kernel_size=1, has_bias=True, pad_mode='pad')
|
||||
self.act1 = Activation('relu')
|
||||
self.conv2 = nn.Conv2d(in_channels=num_mid, out_channels=num_out,
|
||||
kernel_size=1, has_bias=True, pad_mode='pad')
|
||||
self.act2 = Activation('hsigmoid')
|
||||
self.mul = P.Mul()
|
||||
|
||||
def construct(self, x):
|
||||
out = self.pool(x)
|
||||
out = self.conv1(out)
|
||||
out = self.act1(out)
|
||||
out = self.conv2(out)
|
||||
out = self.act2(out)
|
||||
out = self.mul(x, out)
|
||||
return out
|
||||
|
||||
|
||||
class Unit(nn.Cell):
|
||||
"""
|
||||
Unit warpper definition.
|
||||
|
||||
Args:
|
||||
num_in (int): Input channel.
|
||||
num_out (int): Output channel.
|
||||
kernel_size (int): Input kernel size.
|
||||
stride (int): Stride size.
|
||||
padding (int): Padding number.
|
||||
num_groups (int): Output num group.
|
||||
use_act (bool): Used activation or not.
|
||||
act_type (string): Activation type.
|
||||
|
||||
Returns:
|
||||
Tensor, output tensor.
|
||||
|
||||
Examples:
|
||||
>>> Unit(3, 3)
|
||||
"""
|
||||
|
||||
def __init__(self, num_in, num_out, kernel_size=1, stride=1, padding=0, num_groups=1,
|
||||
use_act=True, act_type='relu'):
|
||||
super(Unit, self).__init__()
|
||||
self.conv = nn.Conv2d(in_channels=num_in,
|
||||
out_channels=num_out,
|
||||
kernel_size=kernel_size,
|
||||
stride=stride,
|
||||
padding=padding,
|
||||
group=num_groups,
|
||||
has_bias=False,
|
||||
pad_mode='pad')
|
||||
self.bn = nn.BatchNorm2d(num_out)
|
||||
self.use_act = use_act
|
||||
self.act = Activation(act_type) if use_act else None
|
||||
|
||||
def construct(self, x):
|
||||
out = self.conv(x)
|
||||
out = self.bn(out)
|
||||
if self.use_act:
|
||||
out = self.act(out)
|
||||
return out
|
||||
|
||||
|
||||
class ResUnit(nn.Cell):
|
||||
"""
|
||||
ResUnit warpper definition.
|
||||
|
||||
Args:
|
||||
num_in (int): Input channel.
|
||||
num_mid (int): Middle channel.
|
||||
num_out (int): Output channel.
|
||||
kernel_size (int): Input kernel size.
|
||||
stride (int): Stride size.
|
||||
act_type (str): Activation type.
|
||||
use_se (bool): Use SE warpper or not.
|
||||
|
||||
Returns:
|
||||
Tensor, output tensor.
|
||||
|
||||
Examples:
|
||||
>>> ResUnit(16, 3, 1, 1)
|
||||
"""
|
||||
def __init__(self, num_in, num_mid, num_out, kernel_size, stride=1, act_type='relu', use_se=False):
|
||||
super(ResUnit, self).__init__()
|
||||
self.use_se = use_se
|
||||
self.first_conv = (num_out != num_mid)
|
||||
self.use_short_cut_conv = True
|
||||
|
||||
if self.first_conv:
|
||||
self.expand = Unit(num_in, num_mid, kernel_size=1,
|
||||
stride=1, padding=0, act_type=act_type)
|
||||
else:
|
||||
self.expand = None
|
||||
self.conv1 = Unit(num_mid, num_mid, kernel_size=kernel_size, stride=stride,
|
||||
padding=self._get_pad(kernel_size), act_type=act_type, num_groups=num_mid)
|
||||
if use_se:
|
||||
self.se = SE(num_mid)
|
||||
self.conv2 = Unit(num_mid, num_out, kernel_size=1, stride=1,
|
||||
padding=0, act_type=act_type, use_act=False)
|
||||
if num_in != num_out or stride != 1:
|
||||
self.use_short_cut_conv = False
|
||||
self.add = P.TensorAdd() if self.use_short_cut_conv else None
|
||||
|
||||
def construct(self, x):
|
||||
if self.first_conv:
|
||||
out = self.expand(x)
|
||||
else:
|
||||
out = x
|
||||
out = self.conv1(out)
|
||||
if self.use_se:
|
||||
out = self.se(out)
|
||||
out = self.conv2(out)
|
||||
if self.use_short_cut_conv:
|
||||
out = self.add(x, out)
|
||||
return out
|
||||
|
||||
def _get_pad(self, kernel_size):
|
||||
"""set the padding number"""
|
||||
pad = 0
|
||||
if kernel_size == 1:
|
||||
pad = 0
|
||||
elif kernel_size == 3:
|
||||
pad = 1
|
||||
elif kernel_size == 5:
|
||||
pad = 2
|
||||
elif kernel_size == 7:
|
||||
pad = 3
|
||||
else:
|
||||
raise NotImplementedError
|
||||
return pad
|
||||
|
||||
|
||||
class MobileNetV3(nn.Cell):
|
||||
"""
|
||||
MobileNetV3 architecture.
|
||||
|
||||
Args:
|
||||
model_cfgs (Cell): number of classes.
|
||||
num_classes (int): Output number classes.
|
||||
multiplier (int): Channels multiplier for round to 8/16 and others. Default is 1.
|
||||
final_drop (float): Dropout number.
|
||||
round_nearest (list): Channel round to . Default is 8.
|
||||
Returns:
|
||||
Tensor, output tensor.
|
||||
|
||||
Examples:
|
||||
>>> MobileNetV3(num_classes=1000)
|
||||
"""
|
||||
|
||||
def __init__(self, model_cfgs, num_classes=1000, multiplier=1., final_drop=0., round_nearest=8):
|
||||
super(MobileNetV3, self).__init__()
|
||||
self.cfgs = model_cfgs['cfg']
|
||||
self.inplanes = 16
|
||||
self.features = []
|
||||
first_conv_in_channel = 3
|
||||
first_conv_out_channel = _make_divisible(multiplier * self.inplanes)
|
||||
|
||||
self.features.append(nn.Conv2d(in_channels=first_conv_in_channel,
|
||||
out_channels=first_conv_out_channel,
|
||||
kernel_size=3, padding=1, stride=2,
|
||||
has_bias=False, pad_mode='pad'))
|
||||
self.features.append(nn.BatchNorm2d(first_conv_out_channel))
|
||||
self.features.append(Activation('hswish'))
|
||||
for layer_cfg in self.cfgs:
|
||||
self.features.append(self._make_layer(kernel_size=layer_cfg[0],
|
||||
exp_ch=_make_divisible(multiplier * layer_cfg[1]),
|
||||
out_channel=_make_divisible(multiplier * layer_cfg[2]),
|
||||
use_se=layer_cfg[3],
|
||||
act_func=layer_cfg[4],
|
||||
stride=layer_cfg[5]))
|
||||
output_channel = _make_divisible(multiplier * model_cfgs["cls_ch_squeeze"])
|
||||
self.features.append(nn.Conv2d(in_channels=_make_divisible(multiplier * self.cfgs[-1][2]),
|
||||
out_channels=output_channel,
|
||||
kernel_size=1, padding=0, stride=1,
|
||||
has_bias=False, pad_mode='pad'))
|
||||
self.features.append(nn.BatchNorm2d(output_channel))
|
||||
self.features.append(Activation('hswish'))
|
||||
self.features.append(GlobalAvgPooling(keep_dims=True))
|
||||
self.features.append(nn.Conv2d(in_channels=output_channel,
|
||||
out_channels=model_cfgs['cls_ch_expand'],
|
||||
kernel_size=1, padding=0, stride=1,
|
||||
has_bias=False, pad_mode='pad'))
|
||||
self.features.append(Activation('hswish'))
|
||||
if final_drop > 0:
|
||||
self.features.append((nn.Dropout(final_drop)))
|
||||
|
||||
# make it nn.CellList
|
||||
self.features = nn.SequentialCell(self.features)
|
||||
self.output = nn.Conv2d(in_channels=model_cfgs['cls_ch_expand'],
|
||||
out_channels=num_classes,
|
||||
kernel_size=1, has_bias=True, pad_mode='pad')
|
||||
self.squeeze = P.Squeeze(axis=(2, 3))
|
||||
|
||||
self._initialize_weights()
|
||||
|
||||
def construct(self, x):
|
||||
x = self.features(x)
|
||||
x = self.output(x)
|
||||
x = self.squeeze(x)
|
||||
return x
|
||||
|
||||
def _make_layer(self, kernel_size, exp_ch, out_channel, use_se, act_func, stride=1):
|
||||
mid_planes = exp_ch
|
||||
out_planes = out_channel
|
||||
#num_in, num_mid, num_out, kernel_size, stride=1, act_type='relu', use_se=False):
|
||||
layer = ResUnit(self.inplanes, mid_planes, out_planes,
|
||||
kernel_size, stride=stride, act_type=act_func, use_se=use_se)
|
||||
self.inplanes = out_planes
|
||||
return layer
|
||||
|
||||
def _initialize_weights(self):
|
||||
"""
|
||||
Initialize weights.
|
||||
|
||||
Args:
|
||||
|
||||
Returns:
|
||||
None.
|
||||
|
||||
Examples:
|
||||
>>> _initialize_weights()
|
||||
"""
|
||||
for _, m in self.cells_and_names():
|
||||
if isinstance(m, (nn.Conv2d)):
|
||||
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
|
||||
m.weight.set_parameter_data(Tensor(np.random.normal(0, np.sqrt(2. / n),
|
||||
m.weight.data.shape()).astype("float32")))
|
||||
if m.bias is not None:
|
||||
m.bias.set_parameter_data(
|
||||
Tensor(np.zeros(m.bias.data.shape(), dtype="float32")))
|
||||
elif isinstance(m, nn.BatchNorm2d):
|
||||
m.gamma.set_parameter_data(
|
||||
Tensor(np.ones(m.gamma.data.shape(), dtype="float32")))
|
||||
m.beta.set_parameter_data(
|
||||
Tensor(np.zeros(m.beta.data.shape(), dtype="float32")))
|
||||
elif isinstance(m, nn.Dense):
|
||||
m.weight.set_parameter_data(Tensor(np.random.normal(
|
||||
0, 0.01, m.weight.data.shape()).astype("float32")))
|
||||
if m.bias is not None:
|
||||
m.bias.set_parameter_data(
|
||||
Tensor(np.zeros(m.bias.data.shape(), dtype="float32")))
|
||||
|
||||
|
||||
def mobilenet_v3(model_name, **kwargs):
|
||||
"""
|
||||
Constructs a MobileNet V2 model
|
||||
"""
|
||||
model_cfgs = {
|
||||
"large": {
|
||||
"cfg": [
|
||||
# k, exp, c, se, nl, s,
|
||||
[3, 16, 16, False, 'relu', 1],
|
||||
[3, 64, 24, False, 'relu', 2],
|
||||
[3, 72, 24, False, 'relu', 1],
|
||||
[5, 72, 40, True, 'relu', 2],
|
||||
[5, 120, 40, True, 'relu', 1],
|
||||
[5, 120, 40, True, 'relu', 1],
|
||||
[3, 240, 80, False, 'hswish', 2],
|
||||
[3, 200, 80, False, 'hswish', 1],
|
||||
[3, 184, 80, False, 'hswish', 1],
|
||||
[3, 184, 80, False, 'hswish', 1],
|
||||
[3, 480, 112, True, 'hswish', 1],
|
||||
[3, 672, 112, True, 'hswish', 1],
|
||||
[5, 672, 160, True, 'hswish', 2],
|
||||
[5, 960, 160, True, 'hswish', 1],
|
||||
[5, 960, 160, True, 'hswish', 1]],
|
||||
"cls_ch_squeeze": 960,
|
||||
"cls_ch_expand": 1280,
|
||||
},
|
||||
"small": {
|
||||
"cfg": [
|
||||
# k, exp, c, se, nl, s,
|
||||
[3, 16, 16, True, 'relu', 2],
|
||||
[3, 72, 24, False, 'relu', 2],
|
||||
[3, 88, 24, False, 'relu', 1],
|
||||
[5, 96, 40, True, 'hswish', 2],
|
||||
[5, 240, 40, True, 'hswish', 1],
|
||||
[5, 240, 40, True, 'hswish', 1],
|
||||
[5, 120, 48, True, 'hswish', 1],
|
||||
[5, 144, 48, True, 'hswish', 1],
|
||||
[5, 288, 96, True, 'hswish', 2],
|
||||
[5, 576, 96, True, 'hswish', 1],
|
||||
[5, 576, 96, True, 'hswish', 1]],
|
||||
"cls_ch_squeeze": 576,
|
||||
"cls_ch_expand": 1280,
|
||||
}
|
||||
}
|
||||
return MobileNetV3(model_cfgs[model_name], **kwargs)
|
||||
|
||||
|
||||
mobilenet_v3_large = partial(mobilenet_v3, model_name="large")
|
||||
mobilenet_v3_small = partial(mobilenet_v3, model_name="small")
|
||||
|
|
@ -0,0 +1,267 @@
|
|||
# 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_imagenet."""
|
||||
import os
|
||||
import time
|
||||
import argparse
|
||||
import random
|
||||
import numpy as np
|
||||
from mindspore import context
|
||||
from mindspore import Tensor
|
||||
from mindspore import nn
|
||||
from mindspore.parallel._auto_parallel_context import auto_parallel_context
|
||||
from mindspore.nn.optim.momentum import Momentum
|
||||
from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
|
||||
from mindspore.nn.loss.loss import _Loss
|
||||
from mindspore.ops import operations as P
|
||||
from mindspore.ops import functional as F
|
||||
from mindspore.common import dtype as mstype
|
||||
from mindspore.train.model import Model, ParallelMode
|
||||
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, Callback
|
||||
from mindspore.train.loss_scale_manager import FixedLossScaleManager
|
||||
from mindspore.train.serialization import load_checkpoint, load_param_into_net
|
||||
import mindspore.dataset.engine as de
|
||||
from mindspore.communication.management import init
|
||||
from src.dataset import create_dataset
|
||||
from src.lr_generator import get_lr
|
||||
from src.config import config_gpu, config_ascend
|
||||
from src.mobilenetV3 import mobilenet_v3_large
|
||||
|
||||
random.seed(1)
|
||||
np.random.seed(1)
|
||||
de.config.set_seed(1)
|
||||
|
||||
parser = argparse.ArgumentParser(description='Image classification')
|
||||
parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
|
||||
parser.add_argument('--pre_trained', type=str, default=None, help='Pretrained checkpoint path')
|
||||
parser.add_argument('--platform', type=str, default=None, help='run platform')
|
||||
args_opt = parser.parse_args()
|
||||
|
||||
if args_opt.platform == "Ascend":
|
||||
device_id = int(os.getenv('DEVICE_ID'))
|
||||
rank_id = int(os.getenv('RANK_ID'))
|
||||
rank_size = int(os.getenv('RANK_SIZE'))
|
||||
run_distribute = rank_size > 1
|
||||
device_id = int(os.getenv('DEVICE_ID'))
|
||||
context.set_context(mode=context.GRAPH_MODE,
|
||||
device_target="Ascend",
|
||||
device_id=device_id, save_graphs=False)
|
||||
elif args_opt.platform == "GPU":
|
||||
context.set_context(mode=context.GRAPH_MODE,
|
||||
device_target="GPU", save_graphs=False)
|
||||
else:
|
||||
raise ValueError("Unsupport platform.")
|
||||
|
||||
|
||||
class CrossEntropyWithLabelSmooth(_Loss):
|
||||
"""
|
||||
CrossEntropyWith LabelSmooth.
|
||||
|
||||
Args:
|
||||
smooth_factor (float): smooth factor, default=0.
|
||||
num_classes (int): num classes
|
||||
|
||||
Returns:
|
||||
None.
|
||||
|
||||
Examples:
|
||||
>>> CrossEntropyWithLabelSmooth(smooth_factor=0., num_classes=1000)
|
||||
"""
|
||||
|
||||
def __init__(self, smooth_factor=0., num_classes=1000):
|
||||
super(CrossEntropyWithLabelSmooth, self).__init__()
|
||||
self.onehot = P.OneHot()
|
||||
self.on_value = Tensor(1.0 - smooth_factor, mstype.float32)
|
||||
self.off_value = Tensor(1.0 * smooth_factor /
|
||||
(num_classes - 1), mstype.float32)
|
||||
self.ce = nn.SoftmaxCrossEntropyWithLogits()
|
||||
self.mean = P.ReduceMean(False)
|
||||
self.cast = P.Cast()
|
||||
|
||||
def construct(self, logit, label):
|
||||
one_hot_label = self.onehot(self.cast(label, mstype.int32), F.shape(logit)[1],
|
||||
self.on_value, self.off_value)
|
||||
out_loss = self.ce(logit, one_hot_label)
|
||||
out_loss = self.mean(out_loss, 0)
|
||||
return out_loss
|
||||
|
||||
|
||||
class Monitor(Callback):
|
||||
"""
|
||||
Monitor loss and time.
|
||||
|
||||
Args:
|
||||
lr_init (numpy array): train lr
|
||||
|
||||
Returns:
|
||||
None
|
||||
|
||||
Examples:
|
||||
>>> Monitor(100,lr_init=Tensor([0.05]*100).asnumpy())
|
||||
"""
|
||||
|
||||
def __init__(self, lr_init=None):
|
||||
super(Monitor, self).__init__()
|
||||
self.lr_init = lr_init
|
||||
self.lr_init_len = len(lr_init)
|
||||
|
||||
def epoch_begin(self, run_context):
|
||||
self.losses = []
|
||||
self.epoch_time = time.time()
|
||||
|
||||
def epoch_end(self, run_context):
|
||||
cb_params = run_context.original_args()
|
||||
|
||||
epoch_mseconds = (time.time() - self.epoch_time) * 1000
|
||||
per_step_mseconds = epoch_mseconds / cb_params.batch_num
|
||||
print("epoch time: {:5.3f}, per step time: {:5.3f}, avg loss: {:5.3f}".format(epoch_mseconds,
|
||||
per_step_mseconds,
|
||||
np.mean(self.losses)))
|
||||
|
||||
def step_begin(self, run_context):
|
||||
self.step_time = time.time()
|
||||
|
||||
def step_end(self, run_context):
|
||||
cb_params = run_context.original_args()
|
||||
step_mseconds = (time.time() - self.step_time) * 1000
|
||||
step_loss = cb_params.net_outputs
|
||||
|
||||
if isinstance(step_loss, (tuple, list)) and isinstance(step_loss[0], Tensor):
|
||||
step_loss = step_loss[0]
|
||||
if isinstance(step_loss, Tensor):
|
||||
step_loss = np.mean(step_loss.asnumpy())
|
||||
|
||||
self.losses.append(step_loss)
|
||||
cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num
|
||||
|
||||
print("epoch: [{:3d}/{:3d}], step:[{:5d}/{:5d}], loss:[{:5.3f}/{:5.3f}], time:[{:5.3f}], lr:[{:5.3f}]".format(
|
||||
cb_params.cur_epoch_num -
|
||||
1, cb_params.epoch_num, cur_step_in_epoch, cb_params.batch_num, step_loss,
|
||||
np.mean(self.losses), step_mseconds, self.lr_init[cb_params.cur_step_num - 1]))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
if args_opt.platform == "GPU":
|
||||
# train on gpu
|
||||
print("train args: ", args_opt, "\ncfg: ", config_gpu)
|
||||
|
||||
# define net
|
||||
net = mobilenet_v3_large(num_classes=config_gpu.num_classes)
|
||||
# define loss
|
||||
if config_gpu.label_smooth > 0:
|
||||
loss = CrossEntropyWithLabelSmooth(
|
||||
smooth_factor=config_gpu.label_smooth, num_classes=config_gpu.num_classes)
|
||||
else:
|
||||
loss = SoftmaxCrossEntropyWithLogits(
|
||||
is_grad=False, sparse=True, reduction='mean')
|
||||
# define dataset
|
||||
epoch_size = config_gpu.epoch_size
|
||||
dataset = create_dataset(dataset_path=args_opt.dataset_path,
|
||||
do_train=True,
|
||||
config=config_gpu,
|
||||
platform=args_opt.platform,
|
||||
repeat_num=epoch_size,
|
||||
batch_size=config_gpu.batch_size)
|
||||
step_size = dataset.get_dataset_size()
|
||||
# resume
|
||||
if args_opt.pre_trained:
|
||||
param_dict = load_checkpoint(args_opt.pre_trained)
|
||||
load_param_into_net(net, param_dict)
|
||||
# define optimizer
|
||||
loss_scale = FixedLossScaleManager(
|
||||
config_gpu.loss_scale, drop_overflow_update=False)
|
||||
lr = Tensor(get_lr(global_step=0,
|
||||
lr_init=0,
|
||||
lr_end=0,
|
||||
lr_max=config_gpu.lr,
|
||||
warmup_epochs=config_gpu.warmup_epochs,
|
||||
total_epochs=epoch_size,
|
||||
steps_per_epoch=step_size))
|
||||
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config_gpu.momentum,
|
||||
config_gpu.weight_decay, config_gpu.loss_scale)
|
||||
# define model
|
||||
model = Model(net, loss_fn=loss, optimizer=opt,
|
||||
loss_scale_manager=loss_scale)
|
||||
|
||||
cb = [Monitor(lr_init=lr.asnumpy())]
|
||||
if config_gpu.save_checkpoint:
|
||||
config_ck = CheckpointConfig(save_checkpoint_steps=config_gpu.save_checkpoint_epochs * step_size,
|
||||
keep_checkpoint_max=config_gpu.keep_checkpoint_max)
|
||||
ckpt_cb = ModelCheckpoint(
|
||||
prefix="mobilenet", directory=config_gpu.save_checkpoint_path, config=config_ck)
|
||||
cb += [ckpt_cb]
|
||||
# begine train
|
||||
model.train(epoch_size, dataset, callbacks=cb)
|
||||
elif args_opt.platform == "Ascend":
|
||||
# train on ascend
|
||||
print("train args: ", args_opt, "\ncfg: ", config_ascend,
|
||||
"\nparallel args: rank_id {}, device_id {}, rank_size {}".format(rank_id, device_id, rank_size))
|
||||
|
||||
if run_distribute:
|
||||
context.set_auto_parallel_context(device_num=rank_size, parallel_mode=ParallelMode.DATA_PARALLEL,
|
||||
parameter_broadcast=True, mirror_mean=True)
|
||||
auto_parallel_context().set_all_reduce_fusion_split_indices([140])
|
||||
init()
|
||||
|
||||
epoch_size = config_ascend.epoch_size
|
||||
net = mobilenet_v3_large(num_classes=config_ascend.num_classes)
|
||||
net.to_float(mstype.float16)
|
||||
for _, cell in net.cells_and_names():
|
||||
if isinstance(cell, nn.Dense):
|
||||
cell.to_float(mstype.float32)
|
||||
if config_ascend.label_smooth > 0:
|
||||
loss = CrossEntropyWithLabelSmooth(
|
||||
smooth_factor=config_ascend.label_smooth, num_classes=config.num_classes)
|
||||
else:
|
||||
loss = SoftmaxCrossEntropyWithLogits(
|
||||
is_grad=False, sparse=True, reduction='mean')
|
||||
dataset = create_dataset(dataset_path=args_opt.dataset_path,
|
||||
do_train=True,
|
||||
config=config_ascend,
|
||||
platform=args_opt.platform,
|
||||
repeat_num=epoch_size,
|
||||
batch_size=config_ascend.batch_size)
|
||||
step_size = dataset.get_dataset_size()
|
||||
if args_opt.pre_trained:
|
||||
param_dict = load_checkpoint(args_opt.pre_trained)
|
||||
load_param_into_net(net, param_dict)
|
||||
|
||||
loss_scale = FixedLossScaleManager(
|
||||
config_ascend.loss_scale, drop_overflow_update=False)
|
||||
lr = Tensor(get_lr(global_step=0,
|
||||
lr_init=0,
|
||||
lr_end=0,
|
||||
lr_max=config_ascend.lr,
|
||||
warmup_epochs=config_ascend.warmup_epochs,
|
||||
total_epochs=epoch_size,
|
||||
steps_per_epoch=step_size))
|
||||
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config_ascend.momentum,
|
||||
config_ascend.weight_decay, config_ascend.loss_scale)
|
||||
|
||||
model = Model(net, loss_fn=loss, optimizer=opt,
|
||||
loss_scale_manager=loss_scale)
|
||||
|
||||
cb = None
|
||||
if rank_id == 0:
|
||||
cb = [Monitor(lr_init=lr.asnumpy())]
|
||||
if config_ascend.save_checkpoint:
|
||||
config_ck = CheckpointConfig(save_checkpoint_steps=config_ascend.save_checkpoint_epochs * step_size,
|
||||
keep_checkpoint_max=config_ascend.keep_checkpoint_max)
|
||||
ckpt_cb = ModelCheckpoint(
|
||||
prefix="mobilenet", directory=config_ascend.save_checkpoint_path, config=config_ck)
|
||||
cb += [ckpt_cb]
|
||||
model.train(epoch_size, dataset, callbacks=cb)
|
||||
else:
|
||||
raise Exception
|
||||
Loading…
Reference in New Issue