diff --git a/research/cv/mobilenetv3_large/README_CN.md b/research/cv/mobilenetv3_large/README_CN.md
new file mode 100644
index 0000000000000000000000000000000000000000..d9705935ea6f7e8ceb62f7f90524cde8d3cac3d2
--- /dev/null
+++ b/research/cv/mobilenetv3_large/README_CN.md
@@ -0,0 +1,185 @@
+# 鐩綍
+
+- [鐩綍](#鐩綍)
+- [MobileNetV3鎻忚堪](#mobilenetv3鎻忚堪)
+- [妯″瀷鏋舵瀯](#妯″瀷鏋舵瀯)
+- [鏁版嵁闆哴(#鏁版嵁闆�)
+- [鐜瑕佹眰](#鐜瑕佹眰)
+- [鑴氭湰璇存槑](#鑴氭湰璇存槑)
+ - [鑴氭湰鍜岀ず渚嬩唬鐮乚(#鑴氭湰鍜岀ず渚嬩唬鐮�)
+ - [鑴氭湰鍙傛暟](#鑴氭湰鍙傛暟)
+ - [璁粌杩囩▼](#璁粌杩囩▼)
+ - [鍚姩](#鍚姩)
+ - [缁撴灉](#缁撴灉)
+ - [璇勪及杩囩▼](#璇勪及杩囩▼)
+ - [鍚姩](#鍚姩-1)
+ - [缁撴灉](#缁撴灉-1)
+- [妯″瀷璇存槑](#妯″瀷璇存槑)
+ - [璁粌鎬ц兘](#璁粌鎬ц兘)
+- [闅忔満鎯呭喌鐨勬弿杩癩(#闅忔満鎯呭喌鐨勬弿杩�)
+- [ModelZoo 涓婚〉](#modelzoo-涓婚〉)
+
+<!-- /TOC -->
+
+# MobileNetV3鎻忚堪
+
+MobileNetV3缁撳悎纭欢鎰熺煡绁炵粡缃戠粶鏋舵瀯鎼滅储锛圢AS锛夊拰NetAdapt绠楁硶锛屽凡缁忓彲浠ョЩ妞嶅埌鎵嬫満CPU涓婅繍琛岋紝鍚庣画闅忔柊鏋舵瀯杩涗竴姝ヤ紭鍖栨敼杩涖€傦紙2019骞�11鏈�20鏃ワ級
+
+[璁烘枃](https://arxiv.org/pdf/1905.02244)锛欻oward, 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.
+
+# 妯″瀷鏋舵瀯
+
+MobileNetV3鎬讳綋缃戠粶鏋舵瀯濡備笅锛�
+
+[閾炬帴](https://arxiv.org/pdf/1905.02244)
+
+# 鏁版嵁闆�
+
+浣跨敤鐨勬暟鎹泦锛歔ImageNet(ILSVRC2012)](http://www.image-net.org/)
+
+- 鏁版嵁闆嗗ぇ灏�: 146G, 1330k 1000绫诲僵鑹插浘鍍�
+ - 璁粌: 140G, 1280k寮犲浘鐗�
+ - 娴嬭瘯: 6G, 50k寮犲浘鐗�
+- 鏁版嵁鏍煎紡锛歊GB
+ - 娉細鏁版嵁鍦╯rc/dataset.py涓鐞嗐€�
+
+# 鐜瑕佹眰
+
+- 纭欢锛圓scend锛�
+ - 浣跨敤Ascend鏉ユ惌寤虹‖浠剁幆澧冦€�
+- 妗嗘灦
+ - [MindSpore](https://www.mindspore.cn/install)
+- 濡傞渶鏌ョ湅璇︽儏锛岃鍙傝濡備笅璧勬簮锛�
+ - [MindSpore 鏁欑▼](https://www.mindspore.cn/tutorials/zh-CN/master/index.html)
+ - [MindSpore Python API](https://www.mindspore.cn/docs/api/zh-CN/master/index.html)
+
+# 鑴氭湰璇存槑
+
+## 鑴氭湰鍜岀ず渚嬩唬鐮�
+
+```bash
+鈹溾攢鈹€ mobileNetv3_large
+ 鈹溾攢鈹€ README_CN.md # MobileNetV3鐩稿叧鎻忚堪
+ 鈹溾攢鈹€ scripts
+ 鈹� 鈹溾攢鈹€run_standalone_train.sh # 鐢ㄤ簬鍗曞崱璁粌鐨剆hell鑴氭湰
+ 鈹� 鈹溾攢鈹€run_distribute_train.sh # 鐢ㄤ簬鍏崱璁粌鐨剆hell鑴氭湰
+ 鈹� 鈹斺攢鈹€run_eval.sh # 鐢ㄤ簬璇勪及鐨剆hell鑴氭湰
+ 鈹溾攢鈹€ src
+ 鈹� 鈹溾攢鈹€config.py # 鍙傛暟閰嶇疆
+ 鈹� 鈹溾攢鈹€dataset.py # 鍒涘缓鏁版嵁闆�
+ 鈹� 鈹溾攢鈹€lr_generator.py # 閰嶇疆瀛︿範鐜�
+ 鈹� 鈹溾攢鈹€mobilenetV3.py # MobileNetV3鏋舵瀯
+ 鈹溾攢鈹€ eval.py # 璇勪及鑴氭湰
+ 鈹溾攢鈹€ export.py # 妯″瀷鏍煎紡杞崲鑴氭湰
+ 鈹斺攢鈹€ train.py # 璁粌鑴氭湰
+```
+
+## 鑴氭湰鍙傛暟
+
+妯″瀷璁粌鍜岃瘎浼拌繃绋嬩腑浣跨敤鐨勫弬鏁板彲浠ュ湪config.py涓缃�:
+
+```python
+ "num_classes": 1000,
+ "image_height": 224,
+ "image_width": 224,
+ "batch_size": 150,
+ "epoch_size": 370,
+ "warmup_epochs": 4,
+ "lr_init": 0.0,
+ "lr_end": 0.00,
+ "lr": 1.54,
+ "momentum": 0.9,
+ "weight_decay": 4e-5,
+ "label_smooth": 0.1,
+ "weight_init": "he_uniform",
+ "loss_scale": 1024,
+ "save_checkpoint": True,
+ "save_checkpoint_epochs": 1,
+ "keep_checkpoint_max": 50,
+ "save_checkpoint_path": "./",
+ "export_format": "MINDIR",
+ "export_file": "mobilenetv3_large",
+```
+
+## 璁粌杩囩▼
+
+### 鍚姩
+
+鎮ㄥ彲浠ヤ娇鐢╬ython鎴杝hell鑴氭湰杩涜璁粌銆�
+
+```shell
+# 璁粌绀轰緥
+ python:
+ Ascend鍗曞崱璁粌绀轰緥: python train.py --train_dataset_path [TRAIN_DATA_DIR] --eval_dataset_path [EVAL_DATA_DIR] --device_id [DEVICE_ID] --run_distribute False
+
+ shell:
+ Ascend鍗曞崱璁粌绀轰緥: bash ./scripts/run_standalone_train.sh [DEVICE_ID] [TRAIN_DATA_DIR] [EVAL_DATA_DIR]
+ Ascend鍏崱骞惰璁粌:
+ bash ./run_distribute_train.sh [RANK_TABLE_FILE] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [TRAIN_DATA_DIR] [EVAL_DATA_DIR]
+```
+
+### 缁撴灉
+
+ckpt鏂囦欢灏嗗瓨鍌ㄥ湪 `./ckpts_rank_0` 璺緞涓嬶紝璁粌鏃ュ織灏嗚璁板綍鍒� `log.txt` 涓€傝缁冩棩蹇楅儴鍒嗙ず渚嬪涓嬶細
+
+```shell
+epoch 1: epoch time: 553262.126, per step time: 518.521, avg loss: 5.270
+epoch 2: epoch time: 151033.049, per step time: 141.549, avg loss: 4.529
+epoch 3: epoch time: 150605.300, per step time: 141.148, avg loss: 4.101
+epoch 4: epoch time: 150638.805, per step time: 141.180, avg loss: 4.014
+epoch 5: epoch time: 150594.088, per step time: 141.138, avg loss: 3.607
+```
+
+## 璇勪及杩囩▼
+
+### 鍚姩
+
+鎮ㄥ彲浠ヤ娇鐢╬ython鎴杝hell鑴氭湰杩涜璇勪及銆�
+
+```shell
+# 璇勪及绀轰緥
+ python:
+ python eval.py --device_id [DEVICE_ID] --checkpoint_path [PATH_CHECKPOINT] --dataset_path [DATA_DIR]
+ shell:
+ bash ./scripts/run_eval.sh [DEVICE_ID] [PATH_CHECKPOINT] [EVAL_DATA_DIR]
+```
+
+> 璁粌杩囩▼涓彲浠ョ敓鎴恈kpt鏂囦欢銆�
+
+### 缁撴灉
+
+鍙互鍦� `evaluation_ascend/eval.log` 鏌ョ湅璇勪及缁撴灉銆�
+
+```shell
+metric: {'Loss': 6.346325377444248, 'Top1-Acc': 0.744024024024024, 'Top5-Acc': 0.916956956956957}
+```
+
+# 妯″瀷璇存槑
+
+## 璁粌鎬ц兘
+
+| 鍙傛暟 | Ascend |
+| -------------------------- | ------------------------------------- |
+| 妯″瀷鍚嶇О | mobilenetv3_large |
+| 妯″瀷鐗堟湰 | v1.0 |
+| 杩愯鐜 | HUAWEI CLOUD Modelarts |
+| Mindspore鐗堟湰 | 1.3.0 |
+| 涓婁紶鏃堕棿 | 2021-9-17 |
+| 鏁版嵁闆� | imagenet |
+| 璁粌鍙傛暟 | src/config.py |
+| 浼樺寲鍣� | Momentum |
+| 鎹熷け鍑芥暟 | CrossEntropyWithLabelSmooth |
+| 鏈€缁堟崯澶� | 2.01 |
+| 绮剧‘搴� (8p) | Top1[74.4%], Top5[91.7%] |
+| 璁粌鎬绘椂闂� (8p) | 40h |
+| 璇勪及鎬绘椂闂� | 2min |
+| 鍙傛暟閲� (M) | 43.4M |
+| 鑴氭湰 | [閾炬帴](https://gitee.com/mindspore/models/tree/master/research/cv/mobilenetv3_large) |
+
+# 闅忔満鎯呭喌鐨勬弿杩�
+
+鎴戜滑鍦� `dataset.py` 鍜� `train.py` 鑴氭湰涓缃簡闅忔満绉嶅瓙銆�
+
+# ModelZoo
+
+璇锋牳瀵瑰畼鏂� [涓婚〉](https://gitee.com/mindspore/models)銆�
\ No newline at end of file
diff --git a/research/cv/mobilenetv3_large/eval.py b/research/cv/mobilenetv3_large/eval.py
new file mode 100644
index 0000000000000000000000000000000000000000..bfee4dcdb603562255dc6b003f8f166d676705cb
--- /dev/null
+++ b/research/cv/mobilenetv3_large/eval.py
@@ -0,0 +1,61 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""evaluate_imagenet"""
+import argparse
+
+import mindspore.nn as nn
+from mindspore import context
+from mindspore.train.model import Model
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
+
+from src.dataset import create_dataset
+from src.mobilenetV3 import mobilenet_v3_large
+from src.config import config_ascend as config
+
+def parse_args():
+ '''parse_args'''
+ parser = argparse.ArgumentParser(description='image classification evaluation')
+ parser.add_argument('--dataset_path', type=str, default='', help='Dataset path')
+ parser.add_argument('--checkpoint_path', type=str, default='', help='checkpoint of mobilenetv3_large')
+ parser.add_argument('--device_id', type=int, default=0, help='device id')
+ args_opt = parser.parse_args()
+ return args_opt
+
+if __name__ == '__main__':
+ args = parse_args()
+
+ # set device id
+ context.set_context(device_id=args.device_id)
+ context.set_context(mode=context.GRAPH_MODE, device_target='Ascend')
+
+ # define network
+ net = mobilenet_v3_large(num_classes=config.num_classes, activation="Softmax")
+ ckpt = load_checkpoint(args.checkpoint_path)
+ load_param_into_net(net, ckpt)
+ net.set_train(False)
+
+ # define dataset
+ dataset = create_dataset(dataset_path=args.dataset_path, do_train=False, config=config, repeat_num=1,
+ batch_size=config.batch_size)
+ loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
+ eval_metrics = {'Loss': nn.Loss(),
+ 'Top1-Acc': nn.Top1CategoricalAccuracy(),
+ 'Top5-Acc': nn.Top5CategoricalAccuracy()}
+
+ model = Model(net, loss, optimizer=None, metrics=eval_metrics)
+ print('='*20, 'Evalute start', '='*20)
+ metrics = model.eval(dataset)
+ print("metric: ", metrics)
diff --git a/research/cv/mobilenetv3_large/export.py b/research/cv/mobilenetv3_large/export.py
new file mode 100644
index 0000000000000000000000000000000000000000..ad96960b8c5e0fb9f0dae00a59ab8bbc3fee420b
--- /dev/null
+++ b/research/cv/mobilenetv3_large/export.py
@@ -0,0 +1,38 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""
+mobilenetv3_large export.
+"""
+import argparse
+import numpy as np
+from mindspore import context, Tensor, load_checkpoint, load_param_into_net, export
+from src.config import config_ascend
+from src.mobilenetV3 import mobilenet_v3_large
+
+
+parser = argparse.ArgumentParser(description='Image classification')
+parser.add_argument('--checkpoint_path', type=str, required=True, help='Checkpoint file path')
+args_opt = parser.parse_args()
+
+if __name__ == '__main__':
+ context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+ net = mobilenet_v3_large(num_classes=config_ascend.num_classes, multiplier=1.)
+
+ param_dict = load_checkpoint(args_opt.checkpoint_path)
+ load_param_into_net(net, param_dict)
+ input_shp = [1, 3, config_ascend.image_height, config_ascend.image_width]
+ input_array = Tensor(np.random.uniform(-1.0, 1.0, size=input_shp).astype(np.float32))
+ export(net, input_array, file_name=config_ascend.export_file, file_format=config_ascend.export_format)
diff --git a/research/cv/mobilenetv3_large/scripts/run_distribute_train.sh b/research/cv/mobilenetv3_large/scripts/run_distribute_train.sh
new file mode 100644
index 0000000000000000000000000000000000000000..6be6c086e9b2583f97c42a8add8c74bc92e14fea
--- /dev/null
+++ b/research/cv/mobilenetv3_large/scripts/run_distribute_train.sh
@@ -0,0 +1,89 @@
+#!/bin/bash
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+if [ $# != 4 ]; then
+ echo "Usage: bash ./run_distribute_train.sh [RANK_TABLE_FILE] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [TRAIN_DATA_DIR] [EVAL_DATA_DIR]"
+ exit 1
+fi
+
+get_real_path() {
+ if [ "${1:0:1}" == "/" ]; then
+ echo "$1"
+ else
+ echo "$(realpath -m $PWD/$1)"
+ fi
+}
+
+RANK_TABLE_FILE=$(get_real_path $1)
+echo $RANK_TABLE_FILE
+
+if [ ! -f $RANK_TABLE_FILE ]
+then
+ echo "error: RANK_TABLE_FILE=$RANK_TABLE_FILE is not a file."
+exit 1
+fi
+
+VISIABLE_DEVICES=$2
+IFS="," read -r -a CANDIDATE_DEVICE <<< "$VISIABLE_DEVICES"
+export RANK_SIZE=${#CANDIDATE_DEVICE[@]}
+
+TRAIN_DATA_DIR=$(get_real_path $3)
+echo $TRAIN_DATA_DIR
+
+if [ ! -d $TRAIN_DATA_DIR ]
+then
+ echo "error: TRAIN_DATA_DIR=$TRAIN_DATA_DIR is not a directory."
+exit 1
+fi
+
+EVAL_DATA_DIR=$(get_real_path $4)
+echo $EVAL_DATA_DIR
+
+if [ ! -d $EVAL_DATA_DIR ]
+then
+ echo "error: EVAL_DATA_DIR=$EVAL_DATA_DIR is not a directory."
+exit 1
+fi
+
+export RANK_TABLE_FILE=$RANK_TABLE_FILE
+
+cores=`cat /proc/cpuinfo|grep "processor" |wc -l`
+echo "the number of logical core" $cores
+avg_core_per_rank=`expr $cores \/ $RANK_SIZE`
+core_gap=`expr $avg_core_per_rank \- 1`
+echo "avg_core_per_rank" $avg_core_per_rank
+echo "core_gap" $core_gap
+for((i=0;i<RANK_SIZE;i++))
+do
+ start=`expr $i \* $avg_core_per_rank`
+ export DEVICE_ID=${CANDIDATE_DEVICE[i]}
+ export RANK_ID=$i
+ export DEPLOY_MODE=0
+ export GE_USE_STATIC_MEMORY=1
+ end=`expr $start \+ $core_gap`
+ cmdopt=$start"-"$end
+
+ rm -rf train_parallel$i
+ mkdir ./train_parallel$i
+ cp *.py ./train_parallel$i
+ cd ./train_parallel$i || exit
+ echo "start training for rank $i, device $DEVICE_ID rank_id $RANK_ID"
+
+ env > env.log
+ taskset -c $cmdopt python -u ../train.py \
+ --train_dataset_path=$TRAIN_DATA_DIR --eval_dataset_path=$EVAL_DATA_DIR --device_id=${CANDIDATE_DEVICE[i]} > log.txt 2>&1 &
+ cd ../
+done
diff --git a/research/cv/mobilenetv3_large/scripts/run_eval.sh b/research/cv/mobilenetv3_large/scripts/run_eval.sh
new file mode 100644
index 0000000000000000000000000000000000000000..51bfcbd92c5f3af3aaba7210749bda9b675e2ecd
--- /dev/null
+++ b/research/cv/mobilenetv3_large/scripts/run_eval.sh
@@ -0,0 +1,58 @@
+#!/bin/bash
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+if [ $# != 3 ]; then
+ echo "Usage: bash ./scripts/run_eval.sh [DEVICE_ID] [PATH_CHECKPOINT] [EVAL_DATA_DIR]"
+ exit 1
+fi
+
+get_real_path() {
+ if [ "${1:0:1}" == "/" ]; then
+ echo "$1"
+ else
+ echo "$(realpath -m $PWD/$1)"
+ fi
+}
+
+DEVICE_ID=$1
+
+CHECKPOINT_PATH=$(get_real_path $2)
+echo $CHECKPOINT_PATH
+
+if [ ! -f $CHECKPOINT_PATH ]
+then
+ echo "error: CHECKPOINT_PATH=$CHECKPOINT_PATH is not a file."
+exit 1
+fi
+
+DATA_DIR=$(get_real_path $3)
+echo $DATA_DIR
+
+if [ ! -d $DATA_DIR ]
+then
+ echo "error: DATA_DIR=$DATA_DIR is not a directory."
+exit 1
+fi
+
+export RANK_SIZE=1
+
+rm -rf evaluation_ascend
+mkdir ./evaluation_ascend
+cd ./evaluation_ascend || exit
+echo "start evaluating for device id $DEVICE_ID"
+env > env.log
+python ../eval.py --dataset_path=$DATA_DIR --checkpoint_path=$CHECKPOINT_PATH --device_id=$DEVICE_ID > eval.log 2>&1 &
+cd ../
diff --git a/research/cv/mobilenetv3_large/scripts/run_standalone_train.sh b/research/cv/mobilenetv3_large/scripts/run_standalone_train.sh
new file mode 100644
index 0000000000000000000000000000000000000000..8d5f58695d505f271c8943c4ef18c4cf44e2d225
--- /dev/null
+++ b/research/cv/mobilenetv3_large/scripts/run_standalone_train.sh
@@ -0,0 +1,54 @@
+#!/bin/bash
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+if [ $# != 3 ]; then
+ echo "Usage: bash ./scripts/run_standalone_train.sh [DEVICE_ID] [TRAIN_DATA_DIR] [EVAL_DATA_DIR]"
+ exit 1
+fi
+
+get_real_path() {
+ if [ "${1:0:1}" == "/" ]; then
+ echo "$1"
+ else
+ echo "$(realpath -m $PWD/$1)"
+ fi
+}
+
+export DEVICE_ID=$1
+export RANK_SIZE=1
+
+TRAIN_DATA_DIR=$(get_real_path $2)
+echo $TRAIN_DATA_DIR
+
+if [ ! -d $TRAIN_DATA_DIR ]
+then
+ echo "error: TRAIN_DATA_DIR=$TRAIN_DATA_DIR is not a directory."
+exit 1
+fi
+
+EVAL_DATA_DIR=$(get_real_path $3)
+echo $EVAL_DATA_DIR
+
+if [ ! -d $EVAL_DATA_DIR ]
+then
+ echo "error: EVAL_DATA_DIR=$EVAL_DATA_DIR is not a directory."
+exit 1
+fi
+
+python ./train.py \
+ --train_dataset_path=$TRAIN_DATA_DIR \
+ --eval_dataset_path=$EVAL_DATA_DIR \
+ --run_distribute=False > log.txt 2>&1 &
\ No newline at end of file
diff --git a/research/cv/mobilenetv3_large/src/config.py b/research/cv/mobilenetv3_large/src/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..d2d63725fe28a5589d644b7fe8f133148e364f96
--- /dev/null
+++ b/research/cv/mobilenetv3_large/src/config.py
@@ -0,0 +1,41 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""
+network config setting, will be used in main.py
+"""
+from easydict import EasyDict as ed
+
+config_ascend = ed({
+ "num_classes": 1000,
+ "image_height": 224,
+ "image_width": 224,
+ "batch_size": 150,
+ "epoch_size": 370,
+ "warmup_epochs": 4,
+ "lr_init": 0.0,
+ "lr_end": 0.00,
+ "lr": 1.54,
+ "momentum": 0.9,
+ "weight_decay": 4e-5,
+ "label_smooth": 0.1,
+ "weight_init": "he_uniform",
+ "loss_scale": 1024,
+ "save_checkpoint": True,
+ "save_checkpoint_epochs": 1,
+ "keep_checkpoint_max": 50,
+ "save_checkpoint_path": "./",
+ "export_format": "MINDIR",
+ "export_file": "mobilenetv3_large",
+})
diff --git a/research/cv/mobilenetv3_large/src/dataset.py b/research/cv/mobilenetv3_large/src/dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..49ebdacc5adce53891d73e564aacfea6254947a1
--- /dev/null
+++ b/research/cv/mobilenetv3_large/src/dataset.py
@@ -0,0 +1,82 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""Create train or eval dataset."""
+import mindspore.common.dtype as mstype
+import mindspore.dataset as de
+import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.transforms.c_transforms as C2
+from mindspore.communication.management import get_rank, get_group_size
+
+def create_dataset(dataset_path, do_train, config, repeat_num=1, batch_size=32, run_distribute=True):
+ """
+ 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 not do_train:
+ data_set = de.ImageFolderDataset(dataset_path, num_parallel_workers=8, shuffle=True)
+ else:
+ if run_distribute:
+ rank_id = get_rank()
+ rank_size = get_group_size()
+ data_set = de.ImageFolderDataset(dataset_path, num_parallel_workers=8, shuffle=True,
+ num_shards=rank_size, shard_id=rank_id)
+ else:
+ data_set = de.ImageFolderDataset(dataset_path, num_parallel_workers=8, shuffle=True)
+
+ 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)
+ 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)
+
+ data_set = data_set.map(operations=trans, input_columns="image", num_parallel_workers=8)
+ data_set = data_set.map(operations=type_cast_op, input_columns="label", num_parallel_workers=8)
+
+ # apply shuffle operations
+ data_set = data_set.shuffle(buffer_size=buffer_size)
+
+ # apply batch operations
+ data_set = data_set.batch(batch_size, drop_remainder=True)
+
+ # apply dataset repeat operation
+ data_set = data_set.repeat(repeat_num)
+
+ return data_set
diff --git a/research/cv/mobilenetv3_large/src/lr_generator.py b/research/cv/mobilenetv3_large/src/lr_generator.py
new file mode 100644
index 0000000000000000000000000000000000000000..f6f9dcda1bdbd6b14153f557f56507492d680b55
--- /dev/null
+++ b/research/cv/mobilenetv3_large/src/lr_generator.py
@@ -0,0 +1,54 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""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
diff --git a/research/cv/mobilenetv3_large/src/mobilenetV3.py b/research/cv/mobilenetv3_large/src/mobilenetV3.py
new file mode 100644
index 0000000000000000000000000000000000000000..2171f28243d1f4b0d5d3fca6e30c99732f64c16c
--- /dev/null
+++ b/research/cv/mobilenetv3_large/src/mobilenetV3.py
@@ -0,0 +1,422 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""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']
+
+class hswish(nn.Cell):
+ """hswish"""
+ def construct(self, x):
+ out = x * nn.ReLU6()(x + 3) / 6
+ return out
+
+
+class hsigmoid(nn.Cell):
+ """hsigmoid"""
+ def construct(self, x):
+ out = nn.ReLU6()(x + 3) / 6
+ return out
+
+def _make_divisible(x, divisor=8):
+ """_make_divisible"""
+ 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 = hsigmoid()
+ elif act_func in ('hswish', 'hard_swish'):
+ self.act = 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): Numbers of output channels.
+ 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.Add() if self.use_short_cut_conv else None
+
+ def construct(self, x):
+ """construct"""
+ 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, include_top=True, activation="None"):
+ super(MobileNetV3, self).__init__()
+ print(num_classes)
+ 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.include_top = include_top
+ self.need_activation = False
+ if self.include_top:
+ 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))
+ if activation != "None":
+ self.need_activation = True
+ if activation == "Sigmoid":
+ self.activation = P.Sigmoid()
+ elif activation == "Softmax":
+ self.activation = P.Softmax()
+ else:
+ raise NotImplementedError(f"The activation {activation} not in [Sigmoid, Softmax].")
+
+ self._initialize_weights()
+
+ def construct(self, x):
+ x = self.features(x)
+ if self.include_top:
+ x = self.output(x)
+ x = self.squeeze(x)
+ if self.need_activation:
+ x = self.activation(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
+
+ 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()
+ """
+ self.init_parameters_data()
+ 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_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_data(
+ Tensor(np.zeros(m.bias.data.shape, dtype="float32")))
+ elif isinstance(m, nn.BatchNorm2d):
+ m.gamma.set_data(
+ Tensor(np.ones(m.gamma.data.shape, dtype="float32")))
+ m.beta.set_data(
+ Tensor(np.zeros(m.beta.data.shape, dtype="float32")))
+ elif isinstance(m, nn.Dense):
+ m.weight.set_data(Tensor(np.random.normal(
+ 0, 0.01, m.weight.data.shape).astype("float32")))
+ if m.bias is not None:
+ m.bias.set_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")
diff --git a/research/cv/mobilenetv3_large/train.py b/research/cv/mobilenetv3_large/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..75fbd94374d79316bd0b03df2c6bdd1130eec8b4
--- /dev/null
+++ b/research/cv/mobilenetv3_large/train.py
@@ -0,0 +1,292 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""train_imagenet."""
+
+import os
+import time
+import argparse
+import ast
+import numpy as np
+
+from mindspore import context
+from mindspore import Tensor
+from mindspore import nn
+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
+from mindspore.context import 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.common import set_seed
+from mindspore.communication.management import init
+from mindspore import save_checkpoint
+from src.dataset import create_dataset
+from src.lr_generator import get_lr
+from src.config import config_ascend
+from src.mobilenetV3 import mobilenet_v3_large
+
+
+set_seed(1)
+
+parser = argparse.ArgumentParser(description='Image classification')
+parser.add_argument('--data_url', type=str)
+parser.add_argument('--train_url', type=str)
+parser.add_argument('--train_dataset_path', type=str, default=None, help='Dataset path')
+parser.add_argument('--eval_dataset_path', type=str, default=None, help='Dataset path')
+parser.add_argument('--device_id', type=int, default=0, help='device id of Ascend. (Default: 0)')
+parser.add_argument('--pre_trained', type=str, default=None, help='Pretrained checkpoint path')
+parser.add_argument('--is_modelarts', type=ast.literal_eval, default=False, help='modelarts')
+parser.add_argument('--run_distribute', type=ast.literal_eval, default=True, help='Run distribute')
+args_opt = parser.parse_args()
+
+class SaveCallback(Callback):
+ """
+ SaveCallback.
+
+ Args:
+ model_save (nn.Cell): the network.
+ eval_dataset_save (dataset): dataset used to evaluation.
+ save_file_path (string): the path to save checkpoint.
+
+ Returns:
+ None.
+
+ Examples:
+ >>> SaveCallback(model, dataset, './save_ckpt')
+ """
+ def __init__(self, model_save, eval_dataset_save, save_file_path):
+ super(SaveCallback, self).__init__()
+ self.model = model_save
+ self.eval_dataset = eval_dataset_save
+ self.acc = 0.75
+ self.save_path = save_file_path
+
+ def step_end(self, run_context):
+ cb_params = run_context.original_args()
+
+ result = self.model.eval(self.eval_dataset)
+ print(result)
+ if result['Top1-Acc'] > self.acc:
+ self.acc = result['Top1-Acc']
+ file_name = self.save_path + str(self.acc) + ".ckpt"
+ save_checkpoint(save_obj=cb_params.train_network, ckpt_file_name=file_name)
+ print("Save the maximum accuracy checkpoint,the accuracy is", self.acc)
+
+
+class CrossEntropyWithLabelSmooth(_Loss):
+ """
+ CrossEntropyWith LabelSmooth.
+
+ Args:
+ smooth_factor (float): smooth factor for label smooth. Default is 0.
+ num_classes (int): number of classes. Default is 1000.
+
+ 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):
+ """step_end"""
+ 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__':
+
+ config = config_ascend
+
+ # print configuration
+ print("train args: ", args_opt)
+ print("cfg: ", config)
+
+ device_id = args_opt.device_id
+
+ # set context and device init
+ if args_opt.run_distribute:
+ context.set_context(mode=context.GRAPH_MODE, device_target='Ascend', device_id=device_id)
+ init()
+ context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True)
+ else:
+ context.set_context(device_id=device_id)
+
+ # define net
+ net = mobilenet_v3_large(num_classes=config.num_classes)
+
+ # define loss
+ if config.label_smooth > 0:
+ loss = CrossEntropyWithLabelSmooth(
+ smooth_factor=config.label_smooth, num_classes=config.num_classes)
+ else:
+ loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
+
+ # define dataset
+ epoch_size = config.epoch_size
+
+ if args_opt.is_modelarts:
+ import moxing as mox
+
+ mox.file.copy_parallel(src_url=args_opt.data_url, dst_url='/cache/dataset/device_' + os.getenv('DEVICE_ID'))
+ train_dataset_path = '/cache/dataset/device_' + str(device_id) + '/train'
+ eval_dataset_path = '/cache/dataset/device_' + str(device_id) + '/val'
+ dataset = create_dataset(dataset_path=train_dataset_path,
+ do_train=True,
+ config=config,
+ repeat_num=1,
+ batch_size=args_opt.batch_size)
+ eval_dataset = create_dataset(dataset_path=eval_dataset_path,
+ do_train=False,
+ config=config,
+ repeat_num=1,
+ batch_size=args_opt.batch_size)
+
+ else:
+ dataset = create_dataset(dataset_path=args_opt.train_dataset_path,
+ do_train=True,
+ config=config,
+ repeat_num=1,
+ batch_size=config.batch_size,
+ run_distribute=args_opt.run_distribute)
+
+ eval_dataset = create_dataset(
+ dataset_path=args_opt.eval_dataset_path,
+ do_train=False,
+ config=config,
+ repeat_num=1,
+ batch_size=config.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.loss_scale, drop_overflow_update=False)
+ lr = Tensor(get_lr(global_step=0,
+ lr_init=config.lr_init,
+ lr_end=config.lr_end,
+ lr_max=config.lr,
+ warmup_epochs=config.warmup_epochs,
+ total_epochs=epoch_size,
+ steps_per_epoch=step_size))
+
+ # define optimizer
+ opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum,
+ config.weight_decay,
+ config.loss_scale)
+
+ # define evaluation metrics
+ eval_metrics = {'Loss': nn.Loss(),
+ 'Top1-Acc': nn.Top1CategoricalAccuracy(),
+ 'Top5-Acc': nn.Top5CategoricalAccuracy()}
+ # define model
+ model = Model(net, loss_fn=loss, optimizer=opt,
+ loss_scale_manager=loss_scale, metrics=eval_metrics)
+
+ cb = [Monitor(lr_init=lr.asnumpy())]
+
+ if args_opt.is_modelarts:
+ save_checkpoint_path = '/cache/train_output/device_' + str(device_id) + '/'
+ else:
+ rank = 0
+ save_checkpoint_path = 'ckpts_rank_' + str(rank)
+ ckp_save_step = config.save_checkpoint_epochs * step_size
+ config_ck = CheckpointConfig(save_checkpoint_steps=ckp_save_step, keep_checkpoint_max=config.keep_checkpoint_max)
+ ckpoint_cb = ModelCheckpoint(prefix=f"mobilenetv3",
+ directory=save_checkpoint_path, config=config_ck)
+ save_cb = SaveCallback(model, eval_dataset, save_checkpoint_path)
+ cb += [ckpoint_cb, save_cb]
+
+ # begin train
+ model.train(epoch_size, dataset, callbacks=cb, dataset_sink_mode=True)
+ if args_opt.is_modelarts:
+ mox.file.copy_parallel(src_url='/cache/train_output', dst_url=args_opt.train_url)