!570 Add ST & Optimize

Merge pull request !570 from moran/wizard_dev

mindinsight/wizard/conf/templates/network/alexnet/README.md-tpl

@@ -11,7 +11,7 @@ These are examples of training AlexNet with dataset in MindSpore.
 - Download the dataset, the directory structure is as follows:
 
 {% if dataset=='Cifar10' %}
-CIFAR-10
+Cifar10
 
 ```
 └─Data

mindinsight/wizard/conf/templates/network/alexnet/train.py-tpl

@@ -27,7 +27,8 @@ from src.alexnet import AlexNet
 import mindspore.nn as nn
 from mindspore import context
 from mindspore import Tensor
-from mindspore.train import Model, ParallelMode
+from mindspore.train import Model
+from mindspore.context import ParallelMode
 from mindspore.nn.metrics import Accuracy
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
 from mindspore.train.loss_scale_manager import FixedLossScaleManager

mindinsight/wizard/conf/templates/network/lenet/src/config.py-tpl

@@ -21,7 +21,7 @@ cfg = edict({
     'num_classes': 10,
     {% if optimizer=='Momentum' %}
     'lr': 0.01,
-    "momentum": 0.9,
+    'momentum': 0.9,
     {% elif optimizer=='SGD' %}
     'lr': 0.01,
     {% else %}

mindinsight/wizard/conf/templates/network/lenet/train.py-tpl

@@ -21,10 +21,11 @@ import os
 import argparse
 
 import mindspore.nn as nn
-from mindspore import context, ParallelMode
+from mindspore import context
 from mindspore.communication.management import init, get_rank
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, 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.nn.metrics import Accuracy
 

mindinsight/wizard/conf/templates/network/resnet50/README.md-tpl

@@ -11,7 +11,7 @@ These are examples of training ResNet50 with dataset in MindSpore.
 - Download the dataset, the directory structure is as follows:
 
 {% if dataset=='Cifar10' %}
-CIFAR-10
+Cifar10
 
 ```
 └─Data
@@ -50,7 +50,6 @@ ImageNet
     └── run_standalone_train_gpu.sh        # launch gpu standalone training(1 pcs)
   ├── src
     ├── config.py                          # parameter configuration
-    ├── crossentropy.py                    # loss definition for ImageNet2012 dataset
     ├── dataset.py                         # data preprocessing
     ├── lr_generator.py                    # generate learning rate for each step
     └── resnet50.py                        # resNet50 network definition

mindinsight/wizard/conf/templates/network/resnet50/eval.py-tpl

@@ -37,7 +37,7 @@ np.random.seed(1)
 de.config.set_seed(1)
 
 
-from src.resnet50 import resnet50 as resnet
+from src.resnet50 import resnet50
 
 
 from src.config import cfg
@@ -57,7 +57,7 @@ if __name__ == '__main__':
     step_size = dataset.get_dataset_size()
 
     # define net
-    net = resnet(class_num=cfg.num_classes)
+    net = resnet50(class_num=cfg.num_classes)
 
     # load checkpoint
     param_dict = load_checkpoint(args_opt.checkpoint_path)

mindinsight/wizard/conf/templates/network/resnet50/src/config.py-tpl

@@ -23,36 +23,35 @@ cfg = ed({
     {% elif dataset=='ImageNet' %}
     'num_classes': 1001,
     {% endif %}
-    "batch_size": 32,
-    "loss_scale": 1024,
+    'batch_size': 32,
+    'loss_scale': 1024,
     {% if optimizer=='Momentum' %}
-    "lr": 0.01,
-    "momentum": 0.9,
-    "lr": 0.01,
+    'lr': 0.01,
+    'momentum': 0.9,
     {% elif optimizer=='SGD' %}
     'lr': 0.01,
     {% else %}
     'lr': 0.001,
     {% endif %}
-    "image_height": 224,
-    "image_width": 224,
-    "weight_decay": 1e-4,
-    "epoch_size": 1,
-    "pretrain_epoch_size": 1,
-    "save_checkpoint": True,
-    "save_checkpoint_epochs": 5,
-    "keep_checkpoint_max": 10,
-    "save_checkpoint_path": "./",
+    'image_height': 224,
+    'image_width': 224,
+    'weight_decay': 1e-4,
+    'epoch_size': 1,
+    'pretrain_epoch_size': 1,
+    'save_checkpoint': True,
+    'save_checkpoint_epochs': 5,
+    'keep_checkpoint_max': 10,
+    'save_checkpoint_path': './',
     {% if dataset=='ImageNet' %}
-    "warmup_epochs": 0,
-    "lr_decay_mode": "cosine",
+    'warmup_epochs': 0,
+    'lr_decay_mode': 'cosine',
     {% elif dataset=='Cifar10' %}
-    "warmup_epochs": 5,
-    "lr_decay_mode": "poly",
+    'warmup_epochs': 5,
+    'lr_decay_mode': 'poly',
     {% endif %}
-    "use_label_smooth": True,
-    "label_smooth_factor": 0.1,
-    "lr_init": 0.01,
-    "lr_end": 0.00001,
-    "lr_max": 0.1
+    'use_label_smooth': True,
+    'label_smooth_factor': 0.1,
+    'lr_init': 0.01,
+    'lr_end': 0.00001,
+    'lr_max': 0.1
 })

mindinsight/wizard/conf/templates/network/resnet50/src/crossentropy.py-tpl

-# 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.
-# ============================================================================
-"""define loss function for network"""
-from mindspore.nn.loss.loss import _Loss
-from mindspore.ops import operations as P
-from mindspore.ops import functional as F
-from mindspore import Tensor
-from mindspore.common import dtype as mstype
-import mindspore.nn as nn
-
-
-class CrossEntropy(_Loss):
-    """the redefined loss function with SoftmaxCrossEntropyWithLogits"""
-
-    def __init__(self, smooth_factor=0., num_classes=1001):
-        super(CrossEntropy, 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)
-
-    def construct(self, logit, label):
-        one_hot_label = self.onehot(label, F.shape(logit)[1], self.on_value, self.off_value)
-        loss = self.ce(logit, one_hot_label)
-        loss = self.mean(loss, 0)
-        return loss

mindinsight/wizard/conf/templates/network/resnet50/train.py-tpl

@@ -21,7 +21,8 @@ from mindspore import context
 from mindspore import Tensor
 from mindspore import dataset as de
 from mindspore.parallel._auto_parallel_context import auto_parallel_context
-from mindspore.train.model import Model, ParallelMode
+from mindspore.train.model import Model
+from mindspore.context import ParallelMode
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
 from mindspore.train.loss_scale_manager import FixedLossScaleManager
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
@@ -46,7 +47,7 @@ np.random.seed(1)
 de.config.set_seed(1)
 
 
-from src.resnet50 import resnet50 as resnet
+from src.resnet50 import resnet50
 from src.config import cfg
 from src.dataset import create_dataset
 
@@ -80,7 +81,7 @@ if __name__ == '__main__':
     step_size = dataset.get_dataset_size()
 
     # define net
-    net = resnet(class_num=cfg.num_classes)
+    net = resnet50(class_num=cfg.num_classes)
 
     # init weight
     if args_opt.pre_trained:
@@ -156,13 +157,10 @@ if __name__ == '__main__':
         {% elif loss=='SoftmaxCrossEntropyExpand' %}
         loss = nn.SoftmaxCrossEntropyExpand(sparse=True)
         {% endif %}
-        {% if optimizer=='Momentum' %}
-        opt = nn.Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), learning_rate=lr, momentum=cfg.momentum)
-        {% else %}
-        opt = nn.{{optimizer}}(filter(lambda x: x.requires_grad, net.get_parameters()), learning_rate=lr)
-        {% endif %}
         {% endif %}
-        model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'})
+        loss_scale = FixedLossScaleManager(cfg.loss_scale, drop_overflow_update=False)
+        model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'},
+                      amp_level="O2", keep_batchnorm_fp32=True)
 
     # define callbacks
     time_cb = TimeMonitor(data_size=step_size)

tests/st/func/wizard/test_alexnet.py

+# 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.
+# ============================================================================
+
+"""
+Function:
+    Test the various combinations based on AlexNet.
+"""
+import os
+import pytest
+
+from mindinsight.wizard.base.utility import load_network_maker
+
+NETWORK_NAME = 'alexnet'
+
+
+class TestAlexNet:
+    """Test AlexNet Module"""
+
+    @pytest.mark.level0
+    @pytest.mark.env_single
+    @pytest.mark.platform_x86_cpu
+    @pytest.mark.platform_arm_ascend_training
+    @pytest.mark.platform_x86_gpu_training
+    @pytest.mark.platform_x86_ascend_training
+    @pytest.mark.parametrize('params', [{
+        'config': {'loss': 'SoftmaxCrossEntropyWithLogits',
+                   'optimizer': 'Momentum',
+                   'dataset': 'Cifar10'},
+        'dataset_loader_name': 'Cifar10Dataset'
+    }, {
+        'config': {'loss': 'SoftmaxCrossEntropyWithLogits',
+                   'optimizer': 'Adam',
+                   'dataset': 'Cifar10'},
+        'dataset_loader_name': 'Cifar10Dataset'
+    }, {
+        'config': {'loss': 'SoftmaxCrossEntropyWithLogits',
+                   'optimizer': 'SGD',
+                   'dataset': 'Cifar10'},
+        'dataset_loader_name': 'Cifar10Dataset'
+    }, {
+        'config': {'loss': 'SoftmaxCrossEntropyExpand',
+                   'optimizer': 'Momentum',
+                   'dataset': 'Cifar10'},
+        'dataset_loader_name': 'Cifar10Dataset'
+    }, {
+        'config': {'loss': 'SoftmaxCrossEntropyExpand',
+                   'optimizer': 'Adam',
+                   'dataset': 'Cifar10'},
+        'dataset_loader_name': 'Cifar10Dataset'
+    }, {
+        'config': {'loss': 'SoftmaxCrossEntropyExpand',
+                   'optimizer': 'SGD',
+                   'dataset': 'Cifar10'},
+        'dataset_loader_name': 'Cifar10Dataset'
+    }, {
+        'config': {'loss': 'SoftmaxCrossEntropyWithLogits',
+                   'optimizer': 'Momentum',
+                   'dataset': 'ImageNet'},
+        'dataset_loader_name': 'ImageFolderDatasetV2'
+    }, {
+        'config': {'loss': 'SoftmaxCrossEntropyWithLogits',
+                   'optimizer': 'Adam',
+                   'dataset': 'ImageNet'},
+        'dataset_loader_name': 'ImageFolderDatasetV2'
+    }, {
+        'config': {'loss': 'SoftmaxCrossEntropyWithLogits',
+                   'optimizer': 'SGD',
+                   'dataset': 'ImageNet'},
+        'dataset_loader_name': 'ImageFolderDatasetV2'
+    }, {
+        'config': {'loss': 'SoftmaxCrossEntropyExpand',
+                   'optimizer': 'Momentum',
+                   'dataset': 'ImageNet'},
+        'dataset_loader_name': 'ImageFolderDatasetV2'
+    }, {
+        'config': {'loss': 'SoftmaxCrossEntropyExpand',
+                   'optimizer': 'Adam',
+                   'dataset': 'ImageNet'},
+        'dataset_loader_name': 'ImageFolderDatasetV2'
+    }, {
+        'config': {'loss': 'SoftmaxCrossEntropyExpand',
+                   'optimizer': 'SGD',
+                   'dataset': 'ImageNet'},
+        'dataset_loader_name': 'ImageFolderDatasetV2'
+    }])
+    def test_combinations(self, params):
+        """Do testing"""
+
+        network_maker_name = NETWORK_NAME
+        config = params['config']
+        dataset_loader_name = params['dataset_loader_name']
+
+        network_maker = load_network_maker(network_maker_name)
+        network_maker.configure(config)
+
+        self.source_files = network_maker.generate(**config)
+
+        self.check_scripts()
+        self.check_src(dataset_loader_name, config)
+        self.check_train_eval_readme(config['dataset'], config['loss'], config['optimizer'])
+
+    def check_src(self, dataset_name, config):
+        """Check src file"""
+        dataset_is_right = False
+        config_dataset_is_right = False
+        config_optimizer_is_right = False
+        network_is_right = False
+        generator_lr_is_right = False
+        for source_file in self.source_files:
+            if source_file.file_relative_path == 'src/dataset.py':
+                if dataset_name in source_file.content:
+                    dataset_is_right = True
+            if source_file.file_relative_path == os.path.join('src', NETWORK_NAME.lower()+'.py'):
+                network_is_right = True
+            if source_file.file_relative_path == 'src/generator_lr.py':
+                generator_lr_is_right = True
+            if source_file.file_relative_path == 'src/config.py':
+                content = source_file.content
+
+                if config['dataset'] == 'Cifar10':
+                    if "'num_classes': 10" in content:
+                        config_dataset_is_right = True
+                elif config['dataset'] == 'ImageNet':
+                    if "'num_classes': 1001" in content:
+                        config_dataset_is_right = True
+
+                if config['optimizer'] == 'Momentum':
+                    if "'lr': 0.002" in content:
+                        config_optimizer_is_right = True
+                elif config['optimizer'] == 'SGD':
+                    if "'lr': 0.01" in content:
+                        config_optimizer_is_right = True
+                else:
+                    if "'lr': 0.001" in content:
+                        config_optimizer_is_right = True
+        assert dataset_is_right
+        assert config_dataset_is_right
+        assert config_optimizer_is_right
+        assert network_is_right
+        assert generator_lr_is_right
+
+    def check_train_eval_readme(self, dataset_name, loss_name, optimizer_name):
+        """Check train and eval"""
+
+        train_is_right = False
+        eval_is_right = False
+        readme_is_right = False
+        for source_file in self.source_files:
+            if source_file.file_relative_path == 'train.py':
+                content = source_file.content
+                if 'alexnet' in content and loss_name in content and optimizer_name in content:
+                    train_is_right = True
+
+            if source_file.file_relative_path == 'eval.py':
+                content = source_file.content
+                if 'alexnet' in content and loss_name in content:
+                    eval_is_right = True
+
+            if source_file.file_relative_path == 'README.md':
+                content = source_file.content
+                if 'AlexNet' in content and dataset_name in content:
+                    readme_is_right = True
+        assert train_is_right
+        assert eval_is_right
+        assert readme_is_right
+
+    def check_scripts(self):
+        """Check scripts"""
+
+        exist_run_distribute_train = False
+        exist_run_distribute_train_gpu = False
+        exist_run_eval = False
+        exist_run_eval_gpu = False
+        exist_run_standalone_train = False
+        exist_run_standalone_train_gpu = False
+
+        for source_file in self.source_files:
+            if source_file.file_relative_path == 'scripts/run_distribute_train.sh':
+                exist_run_distribute_train = True
+            if source_file.file_relative_path == 'scripts/run_distribute_train_gpu.sh':
+                exist_run_distribute_train_gpu = True
+            if source_file.file_relative_path == 'scripts/run_eval.sh':
+                exist_run_eval = True
+            if source_file.file_relative_path == 'scripts/run_eval_gpu.sh':
+                exist_run_eval_gpu = True
+            if source_file.file_relative_path == 'scripts/run_standalone_train.sh':
+                exist_run_standalone_train = True
+            if source_file.file_relative_path == 'scripts/run_standalone_train_gpu.sh':
+                exist_run_standalone_train_gpu = True
+
+        assert exist_run_distribute_train
+        assert exist_run_distribute_train_gpu
+        assert exist_run_eval
+        assert exist_run_eval_gpu
+        assert exist_run_standalone_train
+        assert exist_run_standalone_train_gpu

tests/st/func/wizard/test_lenet.py

+# 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.
+# ============================================================================
+
+"""
+Function:
+    Test the various combinations based on LeNet.
+"""
+import os
+import pytest
+from mindinsight.wizard.base.utility import load_network_maker
+
+NETWORK_NAME = 'lenet'
+
+
+class TestLeNet:
+    """Test LeNet Module"""
+
+    @pytest.mark.level0
+    @pytest.mark.env_single
+    @pytest.mark.platform_x86_cpu
+    @pytest.mark.platform_arm_ascend_training
+    @pytest.mark.platform_x86_gpu_training
+    @pytest.mark.platform_x86_ascend_training
+    @pytest.mark.parametrize('params', [{
+        'config': {'loss': 'SoftmaxCrossEntropyWithLogits',
+                   'optimizer': 'Momentum',
+                   'dataset': 'MNIST'},
+        'dataset_loader_name': 'MnistDataset'
+    }, {
+        'config': {'loss': 'SoftmaxCrossEntropyWithLogits',
+                   'optimizer': 'Adam',
+                   'dataset': 'MNIST'},
+        'dataset_loader_name': 'MnistDataset'
+    }, {
+        'config': {'loss': 'SoftmaxCrossEntropyWithLogits',
+                   'optimizer': 'SGD',
+                   'dataset': 'MNIST'},
+        'dataset_loader_name': 'MnistDataset'
+    }, {
+        'config': {'loss': 'SoftmaxCrossEntropyExpand',
+                   'optimizer': 'Momentum',
+                   'dataset': 'MNIST'},
+        'dataset_loader_name': 'MnistDataset'
+    }, {
+        'config': {'loss': 'SoftmaxCrossEntropyExpand',
+                   'optimizer': 'Adam',
+                   'dataset': 'MNIST'},
+        'dataset_loader_name': 'MnistDataset'
+    }, {
+        'config': {'loss': 'SoftmaxCrossEntropyWithLogits',
+                   'optimizer': 'SGD',
+                   'dataset': 'MNIST'},
+        'dataset_loader_name': 'MnistDataset'
+    }])
+    def test_combinations(self, params):
+        """Do testing"""
+
+        network_maker_name = NETWORK_NAME
+        config = params['config']
+        dataset_loader_name = params['dataset_loader_name']
+
+        network_maker = load_network_maker(network_maker_name)
+        network_maker.configure(config)
+
+        self.source_files = network_maker.generate(**config)
+
+        self.check_scripts()
+        self.check_src(dataset_loader_name, config)
+        self.check_train_eval_readme(config['loss'], config['optimizer'])
+
+    def check_src(self, dataset_name, config):
+        """Check src file"""
+        dataset_is_right = False
+        config_optimizer_is_right = False
+        network_is_right = False
+        for source_file in self.source_files:
+            if source_file.file_relative_path == 'src/dataset.py':
+                if dataset_name in source_file.content:
+                    dataset_is_right = True
+            if source_file.file_relative_path == os.path.join('src', NETWORK_NAME.lower() + '.py'):
+                network_is_right = True
+            if source_file.file_relative_path == 'src/config.py':
+                content = source_file.content
+
+                if config['optimizer'] == 'Momentum':
+                    if "'lr': 0.01" in content and \
+                            "'momentum': 0.9" in content:
+                        config_optimizer_is_right = True
+                elif config['optimizer'] == 'SGD':
+                    if "'lr': 0.01" in content:
+                        config_optimizer_is_right = True
+                else:
+                    if "'lr': 0.001" in content:
+                        config_optimizer_is_right = True
+        assert dataset_is_right
+        assert config_optimizer_is_right
+        assert network_is_right
+
+    def check_train_eval_readme(self, loss_name, optimizer_name):
+        """Check train and eval"""
+
+        train_is_right = False
+        eval_is_right = False
+        readme_is_right = False
+        for source_file in self.source_files:
+            if source_file.file_relative_path == 'train.py':
+                content = source_file.content
+                if 'LeNet5' in content and loss_name in content and optimizer_name in content:
+                    train_is_right = True
+
+            if source_file.file_relative_path == 'eval.py':
+                content = source_file.content
+                if 'LeNet5' in content and loss_name in content:
+                    eval_is_right = True
+
+            if source_file.file_relative_path == 'README.md':
+                content = source_file.content
+                if 'LeNet' in content:
+                    readme_is_right = True
+        assert train_is_right
+        assert eval_is_right
+        assert readme_is_right
+
+    def check_scripts(self):
+        """Check scripts"""
+
+        exist_run_distribute_train = False
+        exist_run_distribute_train_gpu = False
+        exist_run_eval = False
+        exist_run_eval_gpu = False
+        exist_run_standalone_train = False
+        exist_run_standalone_train_gpu = False
+
+        for source_file in self.source_files:
+            if source_file.file_relative_path == 'scripts/run_distribute_train.sh':
+                exist_run_distribute_train = True
+            if source_file.file_relative_path == 'scripts/run_distribute_train_gpu.sh':
+                exist_run_distribute_train_gpu = True
+            if source_file.file_relative_path == 'scripts/run_eval.sh':
+                exist_run_eval = True
+            if source_file.file_relative_path == 'scripts/run_eval_gpu.sh':
+                exist_run_eval_gpu = True
+            if source_file.file_relative_path == 'scripts/run_standalone_train.sh':
+                exist_run_standalone_train = True
+            if source_file.file_relative_path == 'scripts/run_standalone_train_gpu.sh':
+                exist_run_standalone_train_gpu = True
+
+        assert exist_run_distribute_train
+        assert exist_run_distribute_train_gpu
+        assert exist_run_eval
+        assert exist_run_eval_gpu
+        assert exist_run_standalone_train
+        assert exist_run_standalone_train_gpu

tests/st/func/wizard/test_resnet50.py

+# 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.
+# ============================================================================
+
+"""
+Function:
+    Test the various combinations based on ResNet50.
+"""
+import os
+import pytest
+
+from mindinsight.wizard.base.utility import load_network_maker
+
+NETWORK_NAME = 'resnet50'
+
+
+class TestResNet50:
+    """Test ResNet50 Module"""
+
+    @pytest.mark.level0
+    @pytest.mark.env_single
+    @pytest.mark.platform_x86_cpu
+    @pytest.mark.platform_arm_ascend_training
+    @pytest.mark.platform_x86_gpu_training
+    @pytest.mark.platform_x86_ascend_training
+    @pytest.mark.parametrize('params', [{
+        'config': {'loss': 'SoftmaxCrossEntropyWithLogits',
+                   'optimizer': 'Momentum',
+                   'dataset': 'Cifar10'},
+        'dataset_loader_name': 'Cifar10Dataset'
+    }, {
+        'config': {'loss': 'SoftmaxCrossEntropyWithLogits',
+                   'optimizer': 'Adam',
+                   'dataset': 'Cifar10'},
+        'dataset_loader_name': 'Cifar10Dataset'
+    }, {
+        'config': {'loss': 'SoftmaxCrossEntropyWithLogits',
+                   'optimizer': 'SGD',
+                   'dataset': 'Cifar10'},
+        'dataset_loader_name': 'Cifar10Dataset'
+    }, {
+        'config': {'loss': 'SoftmaxCrossEntropyExpand',
+                   'optimizer': 'Momentum',
+                   'dataset': 'Cifar10'},
+        'dataset_loader_name': 'Cifar10Dataset'
+    }, {
+        'config': {'loss': 'SoftmaxCrossEntropyExpand',
+                   'optimizer': 'Adam',
+                   'dataset': 'Cifar10'},
+        'dataset_loader_name': 'Cifar10Dataset'
+    }, {
+        'config': {'loss': 'SoftmaxCrossEntropyExpand',
+                   'optimizer': 'SGD',
+                   'dataset': 'Cifar10'},
+        'dataset_loader_name': 'Cifar10Dataset'
+    }, {
+        'config': {'loss': 'SoftmaxCrossEntropyWithLogits',
+                   'optimizer': 'Momentum',
+                   'dataset': 'ImageNet'},
+        'dataset_loader_name': 'ImageFolderDatasetV2'
+    }, {
+        'config': {'loss': 'SoftmaxCrossEntropyWithLogits',
+                   'optimizer': 'Adam',
+                   'dataset': 'ImageNet'},
+        'dataset_loader_name': 'ImageFolderDatasetV2'
+    }, {
+        'config': {'loss': 'SoftmaxCrossEntropyWithLogits',
+                   'optimizer': 'SGD',
+                   'dataset': 'ImageNet'},
+        'dataset_loader_name': 'ImageFolderDatasetV2'
+    }, {
+        'config': {'loss': 'SoftmaxCrossEntropyExpand',
+                   'optimizer': 'Momentum',
+                   'dataset': 'ImageNet'},
+        'dataset_loader_name': 'ImageFolderDatasetV2'
+    }, {
+        'config': {'loss': 'SoftmaxCrossEntropyExpand',
+                   'optimizer': 'Adam',
+                   'dataset': 'ImageNet'},
+        'dataset_loader_name': 'ImageFolderDatasetV2'
+    }, {
+        'config': {'loss': 'SoftmaxCrossEntropyExpand',
+                   'optimizer': 'SGD',
+                   'dataset': 'ImageNet'},
+        'dataset_loader_name': 'ImageFolderDatasetV2'
+    }])
+    def test_combinations(self, params):
+        """Do testing"""
+
+        network_maker_name = NETWORK_NAME
+        config = params['config']
+        dataset_loader_name = params['dataset_loader_name']
+
+        network_maker = load_network_maker(network_maker_name)
+        network_maker.configure(config)
+
+        self.source_files = network_maker.generate(**config)
+
+        self.check_scripts()
+        self.check_src(dataset_loader_name, config)
+        self.check_train_eval_readme(config['dataset'], config['loss'], config['optimizer'])
+
+    def check_src(self, dataset_name, config):
+        """Check src file"""
+        dataset_is_right = False
+        config_dataset_is_right = False
+        config_optimizer_is_right = False
+        network_is_right = False
+        generator_lr_is_right = False
+        for source_file in self.source_files:
+            if source_file.file_relative_path == 'src/dataset.py':
+                if dataset_name in source_file.content:
+                    dataset_is_right = True
+            if source_file.file_relative_path == os.path.join('src', NETWORK_NAME.lower() + '.py'):
+                network_is_right = True
+            if source_file.file_relative_path == 'src/lr_generator.py':
+                generator_lr_is_right = True
+            if source_file.file_relative_path == 'src/config.py':
+                content = source_file.content
+
+                if config['dataset'] == 'Cifar10':
+                    if "'num_classes': 10" in content \
+                            and "'warmup_epochs': 5" in content \
+                            and "'lr_decay_mode': 'poly'" in content:
+                        config_dataset_is_right = True
+                elif config['dataset'] == 'ImageNet':
+                    if "'num_classes': 1001" in content \
+                            and "'warmup_epochs': 0" in content \
+                            and "'lr_decay_mode': 'cosine'":
+                        config_dataset_is_right = True
+
+                if config['optimizer'] == 'Momentum':
+                    if "'lr': 0.01" in content and \
+                            "'momentum': 0.9" in content:
+                        config_optimizer_is_right = True
+                elif config['optimizer'] == 'SGD':
+                    if "'lr': 0.01" in content:
+                        config_optimizer_is_right = True
+                else:
+                    if "'lr': 0.001" in content:
+                        config_optimizer_is_right = True
+        assert dataset_is_right
+        assert config_dataset_is_right
+        assert config_optimizer_is_right
+        assert network_is_right
+        assert generator_lr_is_right
+
+    def check_train_eval_readme(self, dataset_name, loss_name, optimizer_name):
+        """Check train and eval"""
+
+        train_is_right = False
+        eval_is_right = False
+        readme_is_right = False
+        for source_file in self.source_files:
+            if source_file.file_relative_path == 'train.py':
+                content = source_file.content
+                if 'resnet50' in content and loss_name in content and optimizer_name in content:
+                    train_is_right = True
+
+            if source_file.file_relative_path == 'eval.py':
+                content = source_file.content
+                if 'resnet50' in content and loss_name in content:
+                    eval_is_right = True
+
+            if source_file.file_relative_path == 'README.md':
+                content = source_file.content
+                if 'ResNet50' in content and dataset_name in content:
+                    readme_is_right = True
+        assert train_is_right
+        assert eval_is_right
+        assert readme_is_right
+
+    def check_scripts(self):
+        """Check scripts"""
+
+        exist_run_distribute_train = False
+        exist_run_distribute_train_gpu = False
+        exist_run_eval = False
+        exist_run_eval_gpu = False
+        exist_run_standalone_train = False
+        exist_run_standalone_train_gpu = False
+
+        for source_file in self.source_files:
+            if source_file.file_relative_path == 'scripts/run_distribute_train.sh':
+                exist_run_distribute_train = True
+            if source_file.file_relative_path == 'scripts/run_distribute_train_gpu.sh':
+                exist_run_distribute_train_gpu = True
+            if source_file.file_relative_path == 'scripts/run_eval.sh':
+                exist_run_eval = True
+            if source_file.file_relative_path == 'scripts/run_eval_gpu.sh':
+                exist_run_eval_gpu = True
+            if source_file.file_relative_path == 'scripts/run_standalone_train.sh':
+                exist_run_standalone_train = True
+            if source_file.file_relative_path == 'scripts/run_standalone_train_gpu.sh':
+                exist_run_standalone_train_gpu = True
+
+        assert exist_run_distribute_train
+        assert exist_run_distribute_train_gpu
+        assert exist_run_eval
+        assert exist_run_eval_gpu
+        assert exist_run_standalone_train
+        assert exist_run_standalone_train_gpu