clould

model_zoo/official/cv/alexnet/README.md

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

model_zoo/official/cv/alexnet/README_CN.md

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

model_zoo/official/cv/alexnet/eval.py

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

model_zoo/official/cv/alexnet/src/config.py

@@ -1,54 +0,0 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""
-network config setting, will be used in train.py
-"""
-
-from easydict import EasyDict as edict
-
-alexnet_cifar10_cfg = edict({
-    'num_classes': 10,
-    'learning_rate': 0.002,
-    'momentum': 0.9,
-    'epoch_size': 30,
-    'batch_size': 32,
-    'buffer_size': 1000,
-    'image_height': 227,
-    'image_width': 227,
-    'save_checkpoint_steps': 1562,
-    'keep_checkpoint_max': 10,
-    'air_name': "alexnet.air",
-})
-
-alexnet_imagenet_cfg = edict({
-    'num_classes': 1000,
-    'learning_rate': 0.13,
-    'momentum': 0.9,
-    'epoch_size': 150,
-    'batch_size': 256,
-    'buffer_size': None, # invalid parameter
-    'image_height': 224,
-    'image_width': 224,
-    'save_checkpoint_steps': 625,
-    'keep_checkpoint_max': 10,
-    'air_name': "alexnet.air",
-
-    # opt
-    'weight_decay': 0.0001,
-    'loss_scale': 1024,
-
-    # lr
-    'is_dynamic_loss_scale': 0,
-})

model_zoo/official/cv/alexnet/src/dataset.py

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

model_zoo/official/cv/alexnet/train.py

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

model_zoo/official/cv/lenet/README.md

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

model_zoo/official/cv/lenet/README_CN.md

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

model_zoo/official/cv/lenet/default_config.yaml

@@ -18,7 +18,7 @@ num_classes: 10
 lr: 0.01
 momentum: 0.9
 epoch_size: 10
-batch_size: 15 # 32
+batch_size: 32
 buffer_size: 1000
 image_height: 32
 image_width: 32
@@ -27,8 +27,8 @@ keep_checkpoint_max: 10
 air_name: "lenet"
 device_id: 0
 file_name: "lenet"
-file_format: "AIR"
+file_format: "MINDIR"
-
+  
 model_name: lenet
 learning_rate: 0.002
 dataset_name: 'mnist'
@@ -37,6 +37,10 @@ dataset_sink_mode: True
 save_checkpoint: True
 save_checkpoint_epochs: 2
 
+# lenet acc calculation
+result_path: '' # "result files path."
+img_path: ''    # "image file path."
+
 ---
 # Config description for each option
 enable_modelarts: 'Whether training on modelarts, default: False'

model_zoo/official/cv/lenet/export.py

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

model_zoo/official/cv/lenet/postprocess.py

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

model_zoo/official/cv/lenet/src/config.py

@@ -1,33 +0,0 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""
-network config setting, will be used in train.py
-"""
-
-from easydict import EasyDict as edict
-
-mnist_cfg = edict({
-    'num_classes': 10,
-    'lr': 0.01,
-    'momentum': 0.9,
-    'epoch_size': 10,
-    'batch_size': 32,
-    'buffer_size': 1000,
-    'image_height': 32,
-    'image_width': 32,
-    'save_checkpoint_steps': 1875,
-    'keep_checkpoint_max': 10,
-    'air_name': "lenet",
-})