From dd2c40fed6e8346d53a2412f6b6fc09113977721 Mon Sep 17 00:00:00 2001 From: root Date: Fri, 25 Sep 2020 09:52:13 +0800 Subject: [PATCH] mobilenetv2+ssd gpu --- model_zoo/official/cv/ssd/README.md | 153 +++++++++++++----- model_zoo/official/cv/ssd/eval.py | 4 +- .../ssd/scripts/run_distribute_train_gpu.sh | 77 +++++++++ .../official/cv/ssd/scripts/run_eval_gpu.sh | 66 ++++++++ model_zoo/official/cv/ssd/src/ssd.py | 2 + model_zoo/official/cv/ssd/train.py | 42 +++-- 6 files changed, 290 insertions(+), 54 deletions(-) create mode 100644 model_zoo/official/cv/ssd/scripts/run_distribute_train_gpu.sh create mode 100644 model_zoo/official/cv/ssd/scripts/run_eval_gpu.sh diff --git a/model_zoo/official/cv/ssd/README.md b/model_zoo/official/cv/ssd/README.md index f1e40c4e22..c507805600 100644 --- a/model_zoo/official/cv/ssd/README.md +++ b/model_zoo/official/cv/ssd/README.md @@ -82,7 +82,8 @@ Dataset used: [COCO2017]() # [Quick Start](#contents) -After installing MindSpore via the official website, you can start training and evaluation on Ascend as follows: +After installing MindSpore via the official website, you can start training and evaluation as follows: +- runing on Ascend ``` # distributed training on Ascend @@ -91,6 +92,14 @@ sh run_distribute_train.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] [RANK_TABLE_ # run eval on Ascend sh run_eval.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID] ``` +- runing on GPU +``` +# distributed training on GPU +sh run_distribute_train_gpu.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] + +# run eval on GPU +sh run_eval_gpu.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID] +``` # [Script Description](#contents) @@ -100,21 +109,24 @@ sh run_eval.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID] . └─ cv └─ ssd - ├─ README.md ## descriptions about SSD + ├─ README.md ## descriptions about SSD ├─ scripts - └─ run_distribute_train.sh ## shell script for distributed on ascend - └─ run_eval.sh ## shell script for eval on ascend + ├─ run_distribute_train.sh ## shell script for distributed on ascend + ├─ run_distribute_train_gpu.sh ## shell script for distributed on gpu + ├─ run_eval.sh ## shell script for eval on ascend + └─ run_eval_gpu.sh ## shell script for eval on gpu ├─ src - ├─ __init__.py ## init file - ├─ box_util.py ## bbox utils - ├─ coco_eval.py ## coco metrics utils - ├─ config.py ## total config - ├─ dataset.py ## create dataset and process dataset - ├─ init_params.py ## parameters utils - ├─ lr_schedule.py ## learning ratio generator - └─ ssd.py ## ssd architecture - ├─ eval.py ## eval scripts - └─ train.py ## train scripts + ├─ __init__.py ## init file + ├─ box_util.py ## bbox utils + ├─ coco_eval.py ## coco metrics utils + ├─ config.py ## total config + ├─ dataset.py ## create dataset and process dataset + ├─ init_params.py ## parameters utils + ├─ lr_schedule.py ## learning ratio generator + └─ ssd.py ## ssd architecture + ├─ eval.py ## eval scripts + ├─ train.py ## train scripts + └─ mindspore_hub_conf.py ## mindspore hub interface ``` ## [Script Parameters](#contents) @@ -145,10 +157,9 @@ sh run_eval.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID] ## [Training Process](#contents) -### Training on Ascend - To train the model, run `train.py`. If the `mindrecord_dir` is empty, it will generate [mindrecord](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/convert_dataset.html) files by `coco_root`(coco dataset) or `iamge_dir` and `anno_path`(own dataset). **Note if mindrecord_dir isn't empty, it will use mindrecord_dir instead of raw images.** +### Training on Ascend - Distribute mode @@ -183,6 +194,34 @@ epoch: 500 step: 458, loss is 0.5548882 epoch time: 39064.8467540741, per step time: 85.29442522723602 ``` +### Training on GPU + +- Distribute mode + +``` + sh run_distribute_train_gpu.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] [PRE_TRAINED](optional) [PRE_TRAINED_EPOCH_SIZE](optional) +``` +We need five or seven parameters for this scripts. +- `DEVICE_NUM`: the device number for distributed train. +- `EPOCH_NUM`: epoch num for distributed train. +- `LR`: learning rate init value for distributed train. +- `DATASET`：the dataset mode for distributed train. +- `PRE_TRAINED :` the path of pretrained checkpoint file, it is better to use absolute path. +- `PRE_TRAINED_EPOCH_SIZE :` the epoch num of pretrained. + + Training result will be stored in the current path, whose folder name is "LOG". Under this, you can find checkpoint files together with result like the followings in log + +``` +epoch: 1 step: 1, loss is 420.11783 +epoch: 1 step: 2, loss is 434.11032 +epoch: 1 step: 3, loss is 476.802 +... +epoch: 1 step: 458, loss is 3.1283689 +epoch time: 150753.701, per step time: 329.157 +... + +``` + ## [Evaluation Process](#contents) ### Evaluation on Ascend @@ -218,41 +257,73 @@ Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.697 mAP: 0.23808886505483504 ``` +### Evaluation on GPU + +``` +sh run_eval_gpu.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID] +``` +We need two parameters for this scripts. +- `DATASET`：the dataset mode of evaluation dataset. +- `CHECKPOINT_PATH`: the absolute path for checkpoint file. +- `DEVICE_ID`: the device id for eval. + +> checkpoint can be produced in training process. + +Inference result will be stored in the example path, whose folder name begins with "eval". Under this, you can find result like the followings in log. + +``` +Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.224 +Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.375 +Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.228 +Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.034 +Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.189 +Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.407 +Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.243 +Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.382 +Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.417 +Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.120 +Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.425 +Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.686 + +======================================== + +mAP: 0.2244936111705981 +``` # [Model Description](#contents) ## [Performance](#contents) ### Evaluation Performance -| Parameters | Ascend | -| -------------------------- | -------------------------------------------------------------| -| Model Version | SSD V1 | -| Resource | Ascend 910 ；CPU 2.60GHz，56cores；Memory，314G | -| uploaded Date | 06/01/2020 (month/day/year) | -| MindSpore Version | 0.3.0-alpha | -| Dataset | COCO2017 | -| Training Parameters | epoch = 500, batch_size = 32 | -| Optimizer | Momentum | -| Loss Function | Sigmoid Cross Entropy,SmoothL1Loss | -| Speed | 8pcs: 90ms/step | -| Total time | 8pcs: 4.81hours | -| Parameters (M) | 34 | -| Scripts | https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/ssd | +| Parameters | Ascend | GPU | +| -------------------------- | -------------------------------------------------------------| -------------------------------------------------------------| +| Model Version | SSD V1 | SSD V1 | +| Resource | Ascend 910 ；CPU 2.60GHz，56cores；Memory，314G | NV SMX2 V100-16G | +| uploaded Date | 06/01/2020 (month/day/year) | 09/24/2020 (month/day/year) | +| MindSpore Version | 0.3.0-alpha | 1.0.0 | +| Dataset | COCO2017 | COCO2017 | +| Training Parameters | epoch = 500, batch_size = 32 | epoch = 800, batch_size = 32 | +| Optimizer | Momentum | Momentum | +| Loss Function | Sigmoid Cross Entropy,SmoothL1Loss | Sigmoid Cross Entropy,SmoothL1Loss | +| Speed | 8pcs: 90ms/step | 8pcs: 121ms/step | +| Total time | 8pcs: 4.81hours | 8pcs: 12.31hours | +| Parameters (M) | 34 | 34 | +| Scripts | https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/ssd | https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/ssd | ### Inference Performance -| Parameters | Ascend | -| ------------------- | ----------------------------| -| Model Version | SSD V1 | -| Resource | Ascend 910 | -| Uploaded Date | 06/01/2020 (month/day/year) | -| MindSpore Version | 0.3.0-alpha | -| Dataset | COCO2017 | -| batch_size | 1 | -| outputs | mAP | -| Accuracy | IoU=0.50: 23.8% | -| Model for inference | 34M(.ckpt file) | +| Parameters | Ascend | GPU | +| ------------------- | ----------------------------| ----------------------------| +| Model Version | SSD V1 | SSD V1 | +| Resource | Ascend 910 | GPU | +| Uploaded Date | 06/01/2020 (month/day/year) | 09/24/2020 (month/day/year) | +| MindSpore Version | 0.3.0-alpha | 1.0.0 | +| Dataset | COCO2017 | COCO2017 | +| batch_size | 1 | 1 | +| outputs | mAP | mAP | +| Accuracy | IoU=0.50: 23.8% | IoU=0.50: 22.4% | +| Model for inference | 34M(.ckpt file) | 34M(.ckpt file) | # [Description of Random Situation](#contents) diff --git a/model_zoo/official/cv/ssd/eval.py b/model_zoo/official/cv/ssd/eval.py index 87827cbd7b..b89271f778 100644 --- a/model_zoo/official/cv/ssd/eval.py +++ b/model_zoo/official/cv/ssd/eval.py @@ -71,9 +71,11 @@ if __name__ == '__main__': parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.") parser.add_argument("--dataset", type=str, default="coco", help="Dataset, default is coco.") parser.add_argument("--checkpoint_path", type=str, required=True, help="Checkpoint file path.") + parser.add_argument("--run_platform", type=str, default="Ascend", choices=("Ascend", "GPU"), + help="run platform, only support Ascend and GPU.") args_opt = parser.parse_args() - context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id) + context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.run_platform, device_id=args_opt.device_id) prefix = "ssd_eval.mindrecord" mindrecord_dir = config.mindrecord_dir diff --git a/model_zoo/official/cv/ssd/scripts/run_distribute_train_gpu.sh b/model_zoo/official/cv/ssd/scripts/run_distribute_train_gpu.sh new file mode 100644 index 0000000000..5f27a22d32 --- /dev/null +++ b/model_zoo/official/cv/ssd/scripts/run_distribute_train_gpu.sh @@ -0,0 +1,77 @@ +#!/bin/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. +# ============================================================================ + +echo "==============================================================================================================" +echo "Please run the scipt as: " +echo "sh run_distribute_train_gpu.sh DEVICE_NUM EPOCH_SIZE LR DATASET PRE_TRAINED PRE_TRAINED_EPOCH_SIZE" +echo "for example: sh run_distribute_train_gpu.sh 8 500 0.2 coco /opt/ssd-300.ckpt(optional) 200(optional)" +echo "It is better to use absolute path." +echo "=================================================================================================================" + +if [ $# != 4 ] && [ $# != 6 ] +then + echo "Usage: sh run_distribute_train_gpu.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] \ +[PRE_TRAINED](optional) [PRE_TRAINED_EPOCH_SIZE](optional)" + exit 1 +fi + +# Before start distribute train, first create mindrecord files. +BASE_PATH=$(cd "`dirname $0`" || exit; pwd) +cd $BASE_PATH/../ || exit +python train.py --only_create_dataset=True --run_platform="GPU" + +echo "After running the scipt, the network runs in the background. The log will be generated in LOG/log.txt" + +export RANK_SIZE=$1 +EPOCH_SIZE=$2 +LR=$3 +DATASET=$4 +PRE_TRAINED=$5 +PRE_TRAINED_EPOCH_SIZE=$6 + +rm -rf LOG +mkdir ./LOG +cp ./*.py ./LOG +cp -r ./src ./LOG +cd ./LOG || exit + +if [ $# == 4 ] +then + mpirun -allow-run-as-root -n $RANK_SIZE --output-filename log_output --merge-stderr-to-stdout \ + python train.py \ + --distribute=True \ + --lr=$LR \ + --dataset=$DATASET \ + --device_num=$RANK_SIZE \ + --loss_scale=1 \ + --run_platform="GPU" \ + --epoch_size=$EPOCH_SIZE > log.txt 2>&1 & +fi + +if [ $# == 6 ] +then + mpirun -allow-run-as-root -n $RANK_SIZE --output-filename log_output --merge-stderr-to-stdout \ + python train.py \ + --distribute=True \ + --lr=$LR \ + --dataset=$DATASET \ + --device_num=$RANK_SIZE \ + --pre_trained=$PRE_TRAINED \ + --pre_trained_epoch_size=$PRE_TRAINED_EPOCH_SIZE \ + --loss_scale=1 \ + --run_platform="GPU" \ + --epoch_size=$EPOCH_SIZE > log.txt 2>&1 & +fi diff --git a/model_zoo/official/cv/ssd/scripts/run_eval_gpu.sh b/model_zoo/official/cv/ssd/scripts/run_eval_gpu.sh new file mode 100644 index 0000000000..a46fc8aced --- /dev/null +++ b/model_zoo/official/cv/ssd/scripts/run_eval_gpu.sh @@ -0,0 +1,66 @@ +#!/bin/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 [ $# != 3 ] +then + echo "Usage: sh run_eval_gpu.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID]" +exit 1 +fi + +get_real_path(){ + if [ "${1:0:1}" == "/" ]; then + echo "$1" + else + echo "$(realpath -m $PWD/$1)" + fi +} + +DATASET=$1 +CHECKPOINT_PATH=$(get_real_path $2) +echo $DATASET +echo $CHECKPOINT_PATH + +if [ ! -f $CHECKPOINT_PATH ] +then + echo "error: CHECKPOINT_PATH=$PATH2 is not a file" +exit 1 +fi + +export DEVICE_NUM=1 +export DEVICE_ID=$3 +export RANK_SIZE=$DEVICE_NUM +export RANK_ID=0 + +BASE_PATH=$(cd "`dirname $0`" || exit; pwd) +cd $BASE_PATH/../ || exit + +if [ -d "eval$3" ]; +then + rm -rf ./eval$3 +fi + +mkdir ./eval$3 +cp ./*.py ./eval$3 +cp -r ./src ./eval$3 +cd ./eval$3 || exit +env > env.log +echo "start infering for device $DEVICE_ID" +python eval.py \ + --dataset=$DATASET \ + --checkpoint_path=$CHECKPOINT_PATH \ + --run_platform="GPU" \ + --device_id=$3 > log.txt 2>&1 & +cd .. diff --git a/model_zoo/official/cv/ssd/src/ssd.py b/model_zoo/official/cv/ssd/src/ssd.py index 89d85887d6..3a65a51a8a 100644 --- a/model_zoo/official/cv/ssd/src/ssd.py +++ b/model_zoo/official/cv/ssd/src/ssd.py @@ -250,6 +250,8 @@ class SSD300(nn.Cell): pred_loc, pred_label = self.multi_box(multi_feature) if not self.is_training: pred_label = self.activation(pred_label) + pred_loc = F.cast(pred_loc, mstype.float32) + pred_label = F.cast(pred_label, mstype.float32) return pred_loc, pred_label diff --git a/model_zoo/official/cv/ssd/train.py b/model_zoo/official/cv/ssd/train.py index ca12c686e3..c18dc72f77 100644 --- a/model_zoo/official/cv/ssd/train.py +++ b/model_zoo/official/cv/ssd/train.py @@ -20,12 +20,12 @@ import argparse import ast import mindspore.nn as nn from mindspore import context, Tensor -from mindspore.communication.management import init +from mindspore.communication.management import init, get_rank from mindspore.train.callback import CheckpointConfig, ModelCheckpoint, LossMonitor, TimeMonitor from mindspore.train import Model from mindspore.context import ParallelMode from mindspore.train.serialization import load_checkpoint, load_param_into_net -from mindspore.common import set_seed +from mindspore.common import set_seed, dtype from src.ssd import SSD300, SSDWithLossCell, TrainingWrapper, ssd_mobilenet_v2 from src.config import config from src.dataset import create_ssd_dataset, data_to_mindrecord_byte_image, voc_data_to_mindrecord @@ -53,20 +53,36 @@ def main(): parser.add_argument("--loss_scale", type=int, default=1024, help="Loss scale, default is 1024.") parser.add_argument("--filter_weight", type=ast.literal_eval, default=False, help="Filter weight parameters, default is False.") + parser.add_argument("--run_platform", type=str, default="Ascend", choices=("Ascend", "GPU"), + help="run platform, only support Ascend and GPU.") args_opt = parser.parse_args() - context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id) - - if args_opt.distribute: - device_num = args_opt.device_num - context.reset_auto_parallel_context() - context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True, - device_num=device_num) + if args_opt.run_platform == "Ascend": + context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id) + if args_opt.distribute: + device_num = args_opt.device_num + context.reset_auto_parallel_context() + context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True, + device_num=device_num) + init() + rank = args_opt.device_id % device_num + else: + rank = 0 + device_num = 1 + elif args_opt.run_platform == "GPU": + context.set_context(mode=context.GRAPH_MODE, device_target="GPU", device_id=args_opt.device_id) init() - rank = args_opt.device_id % device_num + if args_opt.distribute: + device_num = args_opt.device_num + context.reset_auto_parallel_context() + context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True, + device_num=device_num) + rank = get_rank() + else: + rank = 0 + device_num = 1 else: - rank = 0 - device_num = 1 + raise ValueError("Unsupported platform.") print("Start create dataset!") @@ -113,6 +129,8 @@ def main(): backbone = ssd_mobilenet_v2() ssd = SSD300(backbone=backbone, config=config) + if args_opt.run_platform == "GPU": + ssd.to_float(dtype.float16) net = SSDWithLossCell(ssd, config) init_net_param(net)