create wizard module.

mindinsight/utils/constant.py

@@ -32,6 +32,7 @@ class MindInsightModules(Enum):
     PROFILERMGR = 6
     SCRIPTCONVERTER = 7
     SYSMETRIC = 8
+    WIZARD = 9
 
 
 class GeneralErrors(Enum):
@@ -84,3 +85,7 @@ class ScriptConverterErrors(Enum):
 class SysmetricErrors(Enum):
     """Enum definition for sysmetric errors."""
     DSMI_QUERYING_NONZERO = 1
+
+
+class WizardErrors(Enum):
+    """Enum definition for mindwizard errors."""

mindinsight/wizard/__init__.py

@@ -0,0 +1,14 @@
+# 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.
+# ============================================================================

mindinsight/wizard/base/__init__.py

@@ -0,0 +1,14 @@
+# 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.
+# ============================================================================

mindinsight/wizard/base/dataset.py

@@ -0,0 +1,32 @@
+# 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.
+# ============================================================================
+"""BaseDataset module."""
+
+
+class BaseDataset:
+    """BaseDataset code generator."""
+    name = 'BaseDataset'
+    settings = {}
+
+    def __init__(self):
+        pass
+
+    def configure(self):
+        """Configure the dataset options."""
+        raise NotImplementedError("Not provide a configure method in the subclass.")
+
+    def generate(self, **options):
+        """Generate dataset scripts."""
+        raise NotImplementedError("Not provide a generate method in the subclass.")

mindinsight/wizard/base/network.py

@@ -0,0 +1,29 @@
+# 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.
+# ============================================================================
+"""BaseNetwork module."""
+
+
+class BaseNetwork:
+    """BaseNetwork code generator."""
+    name = 'BaseNetwork'
+    settings = {}
+
+    def configure(self, settings=None):
+        """Configure the dataset options."""
+        raise NotImplementedError("Not provide a configure method in the subclass.")
+
+    def generate(self, **options):
+        """Generate network definition scripts."""
+        raise NotImplementedError("Not provide a generate method in the subclass.")

mindinsight/wizard/base/source_file.py

@@ -0,0 +1,68 @@
+# 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.
+# ============================================================================
+"""Source file module."""
+import os
+import shutil
+import stat
+from pathlib import Path
+
+from mindinsight.wizard.common.exceptions import OSPermissionError, TemplateFileError
+
+
+class SourceFile:
+    """Network code generator."""
+    file_relative_path = ''
+    template_file_path = ''
+    content = ''
+
+    @staticmethod
+    def _make_dir(directory):
+        permissions = os.R_OK | os.W_OK | os.X_OK
+        mode = permissions << 6
+        os.makedirs(directory, mode=mode, exist_ok=True)
+        return directory
+
+    def write(self, project_directory):
+        """Generate the network files."""
+        template_file_path = Path(self.template_file_path)
+        if not template_file_path.is_file():
+            raise TemplateFileError("Template file %s is not exist." % self.template_file_path)
+        new_file_path = os.path.join(project_directory, self.file_relative_path)
+        self._make_dir(os.path.dirname(new_file_path))
+        with open(new_file_path, 'w', encoding='utf-8') as fp:
+            fp.write(self.content)
+        try:
+            shutil.copymode(self.template_file_path, new_file_path)
+            self.set_writeable(new_file_path)
+            if new_file_path.endswith('.sh'):
+                self.set_executable(new_file_path)
+        except OSError:
+            raise OSPermissionError("Notice: Set permission bits failed on %s." % new_file_path)
+
+    @staticmethod
+    def set_writeable(file_name):
+        """Add write permission."""
+        if not os.access(file_name, os.W_OK):
+            file_stat = os.stat(file_name)
+            permissions = stat.S_IMODE(file_stat.st_mode) | stat.S_IWUSR
+            os.chmod(file_name, permissions)
+
+    @staticmethod
+    def set_executable(file_name):
+        """Add executable permission."""
+        if not os.access(file_name, os.X_OK):
+            file_stat = os.stat(file_name)
+            permissions = stat.S_IMODE(file_stat.st_mode) | stat.S_IXUSR
+            os.chmod(file_name, permissions)

mindinsight/wizard/base/templates.py

@@ -0,0 +1,91 @@
+# 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.
+# ============================================================================
+"""BaseNetwork module."""
+import os
+
+from jinja2 import Template
+
+from mindinsight.wizard.base.source_file import SourceFile
+
+
+def render_template(template_file_path, context):
+    with open(template_file_path, encoding='utf-8') as fp:
+        content = fp.read()
+    template = Template(content, trim_blocks=True, lstrip_blocks=True, keep_trailing_newline=True)
+    return template.render(context)
+
+
+class TemplateManager:
+    """BaseNetwork code generator."""
+    replace_template_suffixes = [('.py-tpl', '.py')]
+
+    def __init__(self, template_base_dir, exclude_dirs=None, exclude_files=None):
+        self.template_base_dir = template_base_dir
+        self.exclude_dirs = self._get_exclude_paths(template_base_dir, exclude_dirs)
+        self.exclude_files = self._get_exclude_paths(template_base_dir, exclude_files)
+
+    @staticmethod
+    def _get_exclude_paths(base_dir, exclude_paths):
+        """Convert exclude path to absolute directory path."""
+        exclude_abs_paths = []
+        if exclude_paths is None:
+            return exclude_abs_paths
+
+        for exclude_path in exclude_paths:
+            if exclude_path.startswith(base_dir):
+                exclude_abs_paths.append(exclude_path)
+            else:
+                exclude_abs_paths.append(os.path.join(base_dir, exclude_path))
+        return exclude_abs_paths
+
+    def get_template_files(self):
+        """Get template files for template directory."""
+        template_files = []
+        for root, sub_dirs, files in os.walk(self.template_base_dir):
+            for sub_dir in sub_dirs[:]:
+                if sub_dir.startswith('.') or \
+                        sub_dir == '__pycache__' or \
+                        os.path.join(root, sub_dir) in self.exclude_dirs:
+                    sub_dirs.remove(sub_dir)
+
+            for filename in files:
+                if os.path.join(root, filename) not in self.exclude_files:
+                    template_file_path = os.path.join(root, filename)
+                    template_files.append(template_file_path)
+        return template_files
+
+    def render(self, **options):
+        """Generate the network files."""
+        source_files = []
+        template_files = self.get_template_files()
+        extensions = tuple(options.get('extensions', '.py'))
+        for template_file in template_files:
+            new_file_path = template_file
+            template_file_path = template_file
+            for template_suffix, new_file_suffix in self.replace_template_suffixes:
+                if new_file_path.endswith(template_suffix):
+                    new_file_path = new_file_path[:-len(template_suffix)] + new_file_suffix
+                    break
+
+            source_file = SourceFile()
+            source_file.file_relative_path = new_file_path[len(self.template_base_dir) + 1:]
+            source_file.template_file_path = template_file_path
+            if new_file_path.endswith(extensions):
+                source_file.content = render_template(template_file_path, options)
+            else:
+                with open(template_file_path, encoding='utf-8') as fp:
+                    source_file.content = fp.read()
+            source_files.append(source_file)
+        return source_files

mindinsight/wizard/base/utility.py

@@ -0,0 +1,41 @@
+# 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.
+# ============================================================================
+"""NetworkUtility module."""
+from importlib import import_module
+
+
+from mindinsight.wizard.common.exceptions import CommandError
+
+
+def find_network_maker_names():
+    return ['lenet', 'alexnet', 'resnet50']
+
+
+def load_network_maker(network_name):
+    module = import_module(f'mindinsight.wizard.network.{network_name.lower()}')
+    return module.Network()
+
+
+def load_dataset_maker(dataset_name, **kwargs):
+    module = import_module(f'mindinsight.wizard.dataset.{dataset_name.lower()}')
+    return module.Dataset(**kwargs)
+
+
+def process_prompt_choice(value, prompt_type):
+    """Convert command value to business value."""
+    if value is not None:
+        idx = prompt_type(value)
+        return idx
+    raise CommandError("The choice is not exist, please choice again.")

mindinsight/wizard/cli.py

@@ -0,0 +1,49 @@
+# 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.
+# ============================================================================
+"""Command module."""
+import os
+import sys
+import argparse
+
+import mindinsight
+from mindinsight.wizard.create_project import CreateProject
+
+
+def cli_entry():
+    """Entry point for mindwizard CLI."""
+
+    permissions = os.R_OK | os.W_OK | os.X_OK
+    os.umask(permissions << 3 | permissions)
+
+    parser = argparse.ArgumentParser(
+        prog='wizard',
+        description='MindWizard CLI entry point (version: {})'.format(mindinsight.__version__))
+
+    parser.add_argument(
+        '--version',
+        action='version',
+        version='%(prog)s ({})'.format(mindinsight.__version__))
+    command = CreateProject()
+    command.add_arguments(parser)
+    argv = sys.argv[1:]
+    if not argv or argv[0] == 'help':
+        argv = ['-h']
+
+    args = parser.parse_args(argv)
+    command.invoke(vars(args))
+
+
+if __name__ == '__main__':
+    cli_entry()

mindinsight/wizard/common/__init__.py

@@ -0,0 +1,14 @@
+# 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.
+# ============================================================================

mindinsight/wizard/common/exceptions.py

@@ -0,0 +1,49 @@
+# 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 custom exception."""
+from enum import unique
+
+from mindinsight.utils.constant import WizardErrors
+from mindinsight.utils.exceptions import MindInsightException
+
+
+@unique
+class WizardErrorCodes(WizardErrors):
+    """Wizard error codes."""
+    CODE_SYNTAX_ERROR = 1
+    OS_PERMISSION_ERROR = 2
+    COMMAND_ERROR = 3
+    TEMPLATE_FILE_ERROR = 4
+
+
+class CodeSyntaxError(MindInsightException):
+    """The CodeSyntaxError class definition."""
+    def __init__(self, msg):
+        super(CodeSyntaxError, self).__init__(WizardErrorCodes.CODE_SYNTAX_ERROR, msg)
+
+
+class OSPermissionError(MindInsightException):
+    def __init__(self, msg):
+        super(OSPermissionError, self).__init__(WizardErrorCodes.OS_PERMISSION_ERROR, msg)
+
+
+class CommandError(MindInsightException):
+    def __init__(self, msg):
+        super(CommandError, self).__init__(WizardErrorCodes.COMMAND_ERROR, msg)
+
+
+class TemplateFileError(MindInsightException):
+    def __init__(self, msg):
+        super(TemplateFileError, self).__init__(WizardErrorCodes.TEMPLATE_FILE_ERROR, msg)

mindinsight/wizard/conf/__init__.py

@@ -0,0 +1,14 @@
+# 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.
+# ============================================================================

mindinsight/wizard/conf/constants.py

@@ -0,0 +1,24 @@
+# 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.
+# ============================================================================
+"""Constants module for wizard."""
+import os
+
+
+TEMPLATES_BASE_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'templates')
+SUPPORT_MINDSPORE_VERSION = '0.7.0'
+SUPPORT_RUN_DRIVER_VERSION = 'C75'
+SUPPORT_CUDA_VERSION = '10.1'
+
+QUESTION_START = '>>> '

mindinsight/wizard/conf/templates/network/alexnet/dataset/cifar10/dataset.py-tpl

@@ -0,0 +1,82 @@
+# 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.
+# ============================================================================
+"""
+Produce the dataset
+"""
+
+import os
+import mindspore.dataset as ds
+import mindspore.dataset.transforms.c_transforms as C
+import mindspore.dataset.transforms.vision.c_transforms as CV
+from mindspore.common import dtype as mstype
+from .config import cfg
+from mindspore.communication.management import init, get_rank, get_group_size
+
+def create_dataset(data_path, batch_size=32, repeat_size=1, do_train=True, target="Ascend"):
+    """
+    create dataset for train or test
+    """
+    if target == "Ascend":
+        device_num, rank_id = _get_rank_info()
+    elif target == "GPU":
+        init("nccl")
+        rank_id = get_rank()
+        device_num = get_group_size()
+    else:
+        device_num = 1
+
+    if device_num == 1:
+        cifar_ds = ds.Cifar10Dataset(data_path, num_parallel_workers=8)
+    else:
+        cifar_ds = ds.Cifar10Dataset(data_path, num_parallel_workers=8, num_shards=device_num, shard_id=rank_id)
+    rescale = 1.0 / 255.0
+    shift = 0.0
+
+    resize_op = CV.Resize((cfg.image_height, cfg.image_width))
+    rescale_op = CV.Rescale(rescale, shift)
+    normalize_op = CV.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
+    if do_train:
+        random_crop_op = CV.RandomCrop([32, 32], [4, 4, 4, 4])
+        random_horizontal_op = CV.RandomHorizontalFlip()
+    channel_swap_op = CV.HWC2CHW()
+    typecast_op = C.TypeCast(mstype.int32)
+    cifar_ds = cifar_ds.map(input_columns="label", operations=typecast_op)
+    if do_train:
+        cifar_ds = cifar_ds.map(input_columns="image", operations=random_crop_op)
+        cifar_ds = cifar_ds.map(input_columns="image", operations=random_horizontal_op)
+    cifar_ds = cifar_ds.map(input_columns="image", operations=resize_op)
+    cifar_ds = cifar_ds.map(input_columns="image", operations=rescale_op)
+    cifar_ds = cifar_ds.map(input_columns="image", operations=normalize_op)
+    cifar_ds = cifar_ds.map(input_columns="image", operations=channel_swap_op)
+
+    cifar_ds = cifar_ds.shuffle(buffer_size=1000)
+    cifar_ds = cifar_ds.batch(batch_size, drop_remainder=True)
+    cifar_ds = cifar_ds.repeat(repeat_size)
+    return cifar_ds
+
+def _get_rank_info():
+    """
+    get rank size and rank id
+    """
+    rank_size = int(os.environ.get("RANK_SIZE", 1))
+
+    if rank_size > 1:
+        rank_size = get_group_size()
+        rank_id = get_rank()
+    else:
+        rank_size = 1
+        rank_id = 0
+
+    return rank_size, rank_id

mindinsight/wizard/conf/templates/network/alexnet/dataset/imagenet/dataset.py-tpl

@@ -0,0 +1,104 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""
+create train or eval dataset.
+"""
+import os
+import mindspore.common.dtype as mstype
+import mindspore.dataset.engine as de
+import mindspore.dataset.transforms.vision.c_transforms as C
+import mindspore.dataset.transforms.c_transforms as C2
+from mindspore.communication.management import init, get_rank, get_group_size
+
+from .config import cfg
+
+def create_dataset(data_path, batch_size=32, repeat_size=1, do_train=True, target="Ascend"):
+    """
+    create a train or eval imagenet 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
+        target(string): the target of device. Default: "Ascend"
+
+    Returns:
+        dataset
+    """
+
+    if target == "Ascend":
+         device_num, rank_id = _get_rank_info()
+    elif target == "GPU":
+        init("nccl")
+        rank_id = get_rank()
+        device_num = get_group_size()
+    else:
+        device_num = 1
+
+    if device_num == 1:
+        ds = de.ImageFolderDatasetV2(data_path, num_parallel_workers=8, shuffle=True)
+    else:
+        ds = de.ImageFolderDatasetV2(data_path, num_parallel_workers=8, shuffle=True,
+                                     num_shards=device_num, shard_id=rank_id)
+
+    image_size = cfg.image_height
+    mean = [0.485 * 255, 0.456 * 255, 0.406 * 255]
+    std = [0.229 * 255, 0.224 * 255, 0.225 * 255]
+
+    # define map operations
+    if do_train:
+        trans = [
+            C.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
+            C.RandomHorizontalFlip(prob=0.5),
+            C.Normalize(mean=mean, std=std),
+            C.HWC2CHW()
+        ]
+    else:
+        trans = [
+            C.Decode(),
+            C.Resize(256),
+            C.CenterCrop(image_size),
+            C.Normalize(mean=mean, std=std),
+            C.HWC2CHW()
+        ]
+
+    type_cast_op = C2.TypeCast(mstype.int32)
+
+    ds = ds.map(input_columns="image", num_parallel_workers=8, operations=trans)
+    ds = ds.map(input_columns="label", num_parallel_workers=8, operations=type_cast_op)
+
+    # apply batch operations
+    ds = ds.batch(batch_size, drop_remainder=True)
+
+    # apply dataset repeat operation
+    ds = ds.repeat(repeat_size)
+
+    return ds
+
+def _get_rank_info():
+    """
+    get rank size and rank id
+    """
+    rank_size = int(os.environ.get("RANK_SIZE", 1))
+
+    if rank_size > 1:
+        rank_size = get_group_size()
+        rank_id = get_rank()
+    else:
+        rank_size = 1
+        rank_id = 0
+
+    return rank_size, rank_id

mindinsight/wizard/conf/templates/network/alexnet/dataset/mnist/dataset.py-tpl

@@ -0,0 +1,98 @@
+# 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.
+# ============================================================================
+"""
+Produce the dataset
+"""
+import os
+
+import mindspore.dataset as ds
+import mindspore.dataset.transforms.vision.c_transforms as CV
+import mindspore.dataset.transforms.c_transforms as C
+from mindspore.dataset.transforms.vision import Inter
+from mindspore.common import dtype as mstype
+from mindspore.communication.management import init, get_rank, get_group_size
+
+from .config import cfg
+from mindspore.communication.management import init, get_rank, get_group_size
+
+def create_dataset(data_path, batch_size=32, repeat_size=1, do_train=True, target="Ascend"):
+    """
+    create dataset for train or test
+    """
+
+    if do_train:
+        data_path = os.path.join(data_path, "train")
+    else:
+        data_path = os.path.join(data_path, "test")
+
+    if target == 'Ascend':
+        device_num, rank_id = _get_rank_info()
+    elif target == 'GPU':
+        init("nccl")
+        rank_id = get_rank()
+        device_num = get_group_size()
+    else:
+        device_num = 1
+
+    # define dataset
+    if device_num == 1:
+        mnist_ds = ds.MnistDataset(data_path)
+    else:
+        mnist_ds = ds.MnistDataset(data_path, num_parallel_workers=8, shuffle=True,
+                                   num_shards=device_num, shard_id=rank_id)
+
+    resize_height, resize_width = cfg.image_height, cfg.image_width
+    rescale = 1.0 / 255.0
+    shift = 0.0
+    rescale_nml = 1 / 0.3081
+    shift_nml = -1 * 0.1307 / 0.3081
+
+    # define map operations
+    resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR)  # Bilinear mode
+    rescale_nml_op = CV.Rescale(rescale_nml, shift_nml)
+    rescale_op = CV.Rescale(rescale, shift)
+    hwc2chw_op = CV.HWC2CHW()
+    type_cast_op = C.TypeCast(mstype.int32)
+
+    # apply map operations on images
+    mnist_ds = mnist_ds.map(input_columns="label", operations=type_cast_op)
+    mnist_ds = mnist_ds.map(input_columns="image", operations=resize_op)
+    mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_op)
+    mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_nml_op)
+    mnist_ds = mnist_ds.map(input_columns="image", operations=hwc2chw_op)
+
+    # apply DatasetOps
+    buffer_size = 10000
+    mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size)
+    mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True)
+    mnist_ds = mnist_ds.repeat(repeat_size)
+
+    return mnist_ds
+
+
+def _get_rank_info():
+    """
+    get rank size and rank id
+    """
+    rank_size = int(os.environ.get("RANK_SIZE", 1))
+
+    if rank_size > 1:
+        rank_size = get_group_size()
+        rank_id = get_rank()
+    else:
+        rank_size = 1
+        rank_id = 0
+
+    return rank_size, rank_id

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

@@ -0,0 +1,67 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""
+######################## eval alexnet example ########################
+eval alexnet according to model file:
+python eval.py --data_path /YourDataPath --ckpt_path Your.ckpt
+"""
+
+import argparse
+from src.config import cfg
+from src.dataset import create_dataset
+from src.alexnet import AlexNet
+import mindspore.nn as nn
+from mindspore import context
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from mindspore.train import Model
+from mindspore.nn.metrics import Accuracy
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='MindSpore AlexNet Example')
+    parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend', 'GPU'],
+                        help='device where the code will be implemented (default: Ascend)')
+    parser.add_argument('--data_path', type=str, default="./", help='path where the dataset is saved')
+    parser.add_argument('--ckpt_path', type=str, default="./ckpt", help='if is test, must provide\
+                        path where the trained ckpt file')
+    parser.add_argument('--dataset_sink_mode', type=str, default='True', choices = ['True', 'False'],
+                        help='DataSet sink mode is True or False')
+    args = parser.parse_args()
+
+    context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
+    data_path = args.data_path
+    dataset_sink_mode = args.dataset_sink_mode=='True'
+
+    network = AlexNet(cfg.num_classes)
+    {% if loss=='SoftmaxCrossEntropyWithLogits' %}
+    net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
+    {% elif loss=='SoftmaxCrossEntropyExpand' %}
+    net_loss = nn.SoftmaxCrossEntropyExpand(sparse=True)
+    {% endif %}
+    {% if optimizer=='Lamb' %}
+    net_opt = nn.Lamb(network.trainable_params(), learning_rate=cfg.lr)
+    {% elif optimizer=='Momentum' %}
+    net_opt = nn.Momentum(network.trainable_params(), learning_rate=cfg.lr, momentum=cfg.momentum)
+    {% endif %}
+    model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
+
+    print("============== Starting Testing ==============")
+    param_dict = load_checkpoint(args.ckpt_path)
+    load_param_into_net(network, param_dict)
+    do_train = False
+    ds_eval = create_dataset(data_path=data_path, batch_size=cfg.batch_size, do_train=do_train,
+                             target=args.device_target)
+    acc = model.eval(ds_eval, dataset_sink_mode=dataset_sink_mode)
+    print("============== {} ==============".format(acc))

mindinsight/wizard/conf/templates/network/alexnet/scripts/run_distribute_train.sh

@@ -0,0 +1,87 @@
+#!/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 [ $# != 2 ] && [ $# != 3 ]
+then
+	echo "Usage: sh run_distribute_train.sh [DATASET_PATH] [MINDSPORE_HCCL_CONFIG_PATH] [PRETRAINED_CKPT_PATH](optional)"
+exit 1
+fi
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+
+PATH1=$(get_real_path $1)
+PATH2=$(get_real_path $2)
+
+if [ ! -d $PATH1 ]
+then
+    echo "error: DATASET_PATH=$PATH1 is not a directory"
+exit 1
+fi
+
+if [ ! -f $PATH2 ]
+then
+    echo "error: MINDSPORE_HCCL_CONFIG_PATH=$PATH2 is not a file"
+exit 1
+fi
+
+if [ $# == 3 ]
+then
+    PATH3=$(get_real_path $3)
+fi
+
+if [ $# == 3 ] && [ ! -f $PATH3 ]
+then
+    echo "error: PRETRAINED_CKPT_PATH=$PATH3 is not a file"
+exit 1
+fi
+
+ulimit -u unlimited
+export DEVICE_NUM=8
+export RANK_SIZE=8
+export MINDSPORE_HCCL_CONFIG_PATH=$PATH2
+export RANK_TABLE_FILE=$PATH2
+rank_start=$((DEVICE_NUM * SERVER_ID))
+
+for((i=0; i<DEVICE_NUM; i++))
+do
+    export DEVICE_ID=$i
+    export RANK_ID=$((rank_start + i))
+    rm -rf ./train_parallel$i
+    mkdir ./train_parallel$i
+    cp ../*.py ./train_parallel$i
+    cp *.sh ./train_parallel$i
+    cp -r ../src ./train_parallel$i
+    cd ./train_parallel$i || exit
+    echo "start training for rank $RANK_ID, device $DEVICE_ID"
+    env > env.log
+    if [ $# == 2 ]
+    then
+        python train.py --run_distribute=True --device_num=$DEVICE_NUM --dataset_path=$PATH1 --dataset_sink_mode=False &> log &
+    fi
+
+    if [ $# == 3 ]
+    then
+        python train.py --run_distribute=True --device_num=$DEVICE_NUM --dataset_path=$PATH1 --pre_trained=$PATH2 --dataset_sink_mode=False &> log &
+    fi
+
+    cd ..
+done

mindinsight/wizard/conf/templates/network/alexnet/scripts/run_distribute_train_gpu.sh

@@ -0,0 +1,53 @@
+#!/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 [ $# != 1 ]
+then
+	echo "Usage: sh run_distribute_train_gpu.sh [DATASET_PATH]"
+exit 1
+fi
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+
+PATH1=$(get_real_path $1)
+
+if [ ! -d $PATH1 ]
+then
+    echo "error: DATASET_PATH=$PATH1 is not a directory"
+exit 1
+fi
+
+
+ulimit -u unlimited
+export DEVICE_NUM=4
+export RANK_SIZE=4
+
+rm -rf ./train_parallel
+mkdir ./train_parallel
+cp ../*.py ./train_parallel
+cp *.sh ./train_parallel
+cp -r ../src ./train_parallel
+cd ./train_parallel || exit
+
+mpirun --allow-run-as-root -n $RANK_SIZE \
+python train.py --run_distribute=True \
+--device_num=$DEVICE_NUM --device_target="GPU" --dataset_path=$PATH1 &> log &

mindinsight/wizard/conf/templates/network/alexnet/scripts/run_eval.sh

@@ -0,0 +1,65 @@
+#!/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 [ $# != 2 ]
+then
+    echo "Usage: sh run_eval.sh [DATASET_PATH] [CHECKPOINT_PATH]"
+exit 1
+fi
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+
+PATH1=$(get_real_path $1)
+PATH2=$(get_real_path $2)
+
+
+if [ ! -d $PATH1 ]
+then
+    echo "error: DATASET_PATH=$PATH1 is not a directory"
+exit 1
+fi
+
+if [ ! -f $PATH2 ]
+then
+    echo "error: CHECKPOINT_PATH=$PATH2 is not a file"
+exit 1
+fi
+
+ulimit -u unlimited
+export DEVICE_NUM=1
+export DEVICE_ID=0
+export RANK_SIZE=$DEVICE_NUM
+export RANK_ID=0
+
+if [ -d "eval" ];
+then
+    rm -rf ./eval
+fi
+mkdir ./eval
+cp ../*.py ./eval
+cp *.sh ./eval
+cp -r ../src ./eval
+cd ./eval || exit
+env > env.log
+echo "start evaluation for device $DEVICE_ID"
+python eval.py --dataset_path=$PATH1 --checkpoint_path=$PATH2 &> log &
+cd ..

mindinsight/wizard/conf/templates/network/alexnet/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 [ $# != 2 ]
+then
+    echo "Usage: sh run_eval_gpu.sh [DATASET_PATH] [CHECKPOINT_PATH]"
+exit 1
+fi
+
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+
+PATH1=$(get_real_path $1)
+PATH2=$(get_real_path $2)
+
+
+if [ ! -d $PATH1 ]
+then
+    echo "error: DATASET_PATH=$PATH1 is not a directory"
+exit 1
+fi
+
+if [ ! -f $PATH2 ]
+then
+    echo "error: CHECKPOINT_PATH=$PATH2 is not a file"
+exit 1
+fi
+
+ulimit -u unlimited
+export DEVICE_NUM=1
+export DEVICE_ID=0
+export RANK_SIZE=$DEVICE_NUM
+export RANK_ID=0
+
+if [ -d "eval" ];
+then
+    rm -rf ./eval
+fi
+mkdir ./eval
+cp ../*.py ./eval
+cp *.sh ./eval
+cp -r ../src ./eval
+cd ./eval || exit
+env > env.log
+echo "start evaluation for device $DEVICE_ID"
+python eval.py --dataset_path=$PATH1 --checkpoint_path=$PATH2 --device_target="GPU" &> log &
+cd ..

mindinsight/wizard/conf/templates/network/alexnet/scripts/run_standalone_train.sh

@@ -0,0 +1,76 @@
+#!/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 [ $# != 1 ] && [ $# != 2 ]
+then
+    echo "Usage: sh run_standalone_train.sh [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)"
+exit 1
+fi
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+
+PATH1=$(get_real_path $1)
+
+if [ $# == 2 ]
+then
+    PATH2=$(get_real_path $2)
+fi
+
+if [ ! -d $PATH1 ]
+then
+    echo "error: DATASET_PATH=$PATH1 is not a directory"
+exit 1
+fi
+
+if [ $# == 2 ] && [ ! -f $PATH2 ]
+then
+    echo "error: PRETRAINED_CKPT_PATH=$PATH2 is not a file"
+exit 1
+fi
+
+ulimit -u unlimited
+export DEVICE_NUM=1
+export DEVICE_ID=0
+export RANK_ID=0
+export RANK_SIZE=1
+
+if [ -d "train" ];
+then
+    rm -rf ./train
+fi
+mkdir ./train
+cp ../*.py ./train
+cp *.sh ./train
+cp -r ../src ./train
+cd ./train || exit
+echo "start training for device $DEVICE_ID"
+env > env.log
+if [ $# == 1 ]
+then
+    python train.py --dataset_path=$PATH1 --dataset_sink_mode=False &> log &
+fi
+
+if [ $# == 2 ]
+then
+    python train.py --dataset_path=$PATH1 --pre_trained=$PATH2 --dataset_sink_mode=False &> log &
+fi
+cd ..

mindinsight/wizard/conf/templates/network/alexnet/scripts/run_standalone_train_gpu.sh

@@ -0,0 +1,59 @@
+#!/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 [ $# != 1 ]
+then
+    echo "Usage: sh run_standalone_train_gpu.sh [DATASET_PATH]"
+exit 1
+fi
+
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+
+PATH1=$(get_real_path $1)
+
+
+if [ ! -d $PATH1 ]
+then
+    echo "error: DATASET_PATH=$PATH1 is not a directory"
+exit 1
+fi
+
+
+ulimit -u unlimited
+export DEVICE_NUM=1
+export DEVICE_ID=0
+export RANK_ID=0
+export RANK_SIZE=1
+
+if [ -d "train" ];
+then
+    rm -rf ./train
+fi
+mkdir ./train
+cp ../*.py ./train
+cp *.sh ./train
+cp -r ../src ./train
+cd ./train || exit
+python train.py --device_target="GPU" --dataset_path=$PATH1 &> log &
+
+cd ..

mindinsight/wizard/conf/templates/network/alexnet/src/__init__.py-tpl

@@ -0,0 +1,14 @@
+# 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.
+# ============================================================================

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

@@ -0,0 +1,73 @@
+# 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.
+# ============================================================================
+"""Alexnet."""
+import mindspore.nn as nn
+from mindspore.common.initializer import TruncatedNormal
+from mindspore.ops import operations as P
+
+def conv(in_channels, out_channels, kernel_size, stride=1, padding=0, pad_mode="valid"):
+    weight = weight_variable()
+    return nn.Conv2d(in_channels, out_channels,
+                     kernel_size=kernel_size, stride=stride, padding=padding,
+                     weight_init=weight, has_bias=False, pad_mode=pad_mode)
+
+def fc_with_initialize(input_channels, out_channels):
+    weight = weight_variable()
+    bias = weight_variable()
+    return nn.Dense(input_channels, out_channels, weight, bias)
+
+def weight_variable():
+    return TruncatedNormal(0.02)
+
+
+class AlexNet(nn.Cell):
+    """
+    Alexnet
+    """
+    def __init__(self, num_classes=10, channel=3):
+        super(AlexNet, self).__init__()
+        self.conv1 = conv(channel, 96, 11, stride=4)
+        self.conv2 = conv(96, 256, 5, pad_mode="same")
+        self.conv3 = conv(256, 384, 3, pad_mode="same")
+        self.conv4 = conv(384, 384, 3, pad_mode="same")
+        self.conv5 = conv(384, 256, 3, pad_mode="same")
+        self.relu = nn.ReLU()
+        self.max_pool2d = P.MaxPool(ksize=3, strides=2)
+        self.flatten = nn.Flatten()
+        self.fc1 = fc_with_initialize(6*6*256, 4096)
+        self.fc2 = fc_with_initialize(4096, 4096)
+        self.fc3 = fc_with_initialize(4096, num_classes)
+
+    def construct(self, x):
+        x = self.conv1(x)
+        x = self.relu(x)
+        x = self.max_pool2d(x)
+        x = self.conv2(x)
+        x = self.relu(x)
+        x = self.max_pool2d(x)
+        x = self.conv3(x)
+        x = self.relu(x)
+        x = self.conv4(x)
+        x = self.relu(x)
+        x = self.conv5(x)
+        x = self.relu(x)
+        x = self.max_pool2d(x)
+        x = self.flatten(x)
+        x = self.fc1(x)
+        x = self.relu(x)
+        x = self.fc2(x)
+        x = self.relu(x)
+        x = self.fc3(x)
+        return x

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

@@ -0,0 +1,42 @@
+# 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
+
+cfg = edict({
+    {% if dataset=='MNIST' %}
+    'num_classes': 10,
+    {% elif dataset=='Cifar10' %}
+    'num_classes': 10,
+    {% elif dataset=='ImageNet' %}
+    'num_classes': 1001,
+    {% endif %}
+    'lr': 0.002,
+    {% if optimizer=='Momentum' %}
+    "momentum": 0.9,
+    {% endif %}
+    'epoch_size': 1,
+    'batch_size': 32,
+    'buffer_size': 1000,
+    'image_height': 227,
+    'image_width': 227,
+    'save_checkpoint': True,
+    'save_checkpoint_epochs': 5,
+    'keep_checkpoint_max': 10,
+    'save_checkpoint_path': './'
+})

mindinsight/wizard/conf/templates/network/alexnet/src/generator_lr.py-tpl

@@ -0,0 +1,44 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""learning rate generator"""
+import numpy as np
+
+
+def get_lr(current_step, lr_max, total_epochs, steps_per_epoch):
+    """
+    generate learning rate array
+
+    Args:
+       current_step(int): current steps of the training
+       lr_max(float): max learning rate
+       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
+    decay_epoch_index = [0.8 * total_steps]
+    for i in range(total_steps):
+        if i < decay_epoch_index[0]:
+            lr = lr_max
+        else:
+            lr = lr_max * 0.1
+        lr_each_step.append(lr)
+    lr_each_step = np.array(lr_each_step).astype(np.float32)
+    learning_rate = lr_each_step[current_step:]
+
+    return learning_rate

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

@@ -0,0 +1,122 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""
+######################## train alexnet example ########################
+train alexnet and get network model files(.ckpt) :
+python train.py --data_path /YourDataPath
+"""
+
+import argparse
+from src.config import cfg
+from src.dataset import create_dataset
+from src.generator_lr import get_lr
+from src.alexnet import AlexNet
+import mindspore.nn as nn
+from mindspore import context
+from mindspore import Tensor
+from mindspore.train import Model
+from mindspore.nn.metrics import Accuracy
+from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
+from mindspore.parallel._auto_parallel_context import auto_parallel_context
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from mindspore.communication.management import init, get_rank, get_group_size
+import mindspore.common.initializer as weight_init
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='MindSpore AlexNet Example')
+    parser.add_argument('--run_distribute', type=bool, default=False, help='Run distribute')
+    parser.add_argument('--device_num', type=int, default=1, help='Device num')
+    parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend', 'GPU', 'CPU'],
+                        help='device where the code will be implemented (default: Ascend)')
+    parser.add_argument('--data_path', type=str, default="./", help='path where the dataset is saved')
+    parser.add_argument('--pre_trained', type=str, default=None, help='Pre-trained checkpoint path')
+    parser.add_argument('--dataset_sink_mode', type=str, default='True', choices = ['True', 'False'],
+                        help='DataSet sink mode is True or False')
+    args = parser.parse_args()
+
+    target = args.device_target
+    ckpt_save_dir = cfg.save_checkpoint_path
+    dataset_sink_mode = args.dataset_sink_mode=='True'
+
+    context.set_context(mode=context.GRAPH_MODE, device_target=target)
+    if args.run_distribute:
+        if target == "Ascend":
+            device_id = int(os.getenv('DEVICE_ID'))
+            context.set_context(device_id=device_id, enable_auto_mixed_precision=True)
+            context.set_auto_parallel_context(device_num=args.device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
+                                              mirror_mean=True)
+            auto_parallel_context().set_all_reduce_fusion_split_indices([107, 160])
+
+            init()
+        # GPU target
+        else:
+            init("nccl")
+            context.set_auto_parallel_context(device_num=get_group_size(), parallel_mode=ParallelMode.DATA_PARALLEL,
+                                              mirror_mean=True)
+            ckpt_save_dir = cfg.save_checkpoint_path + "ckpt_" + str(get_rank()) + "/"
+
+
+    data_path = args.data_path
+    do_train = True
+
+    ds_train = create_dataset(data_path=data_path, batch_size=cfg.batch_size, do_train=do_train,
+                              target=target)
+    step_size = ds_train.get_dataset_size()
+
+    # define net
+    network = AlexNet(cfg.num_classes)
+
+    # init weight
+    if args.pre_trained:
+        param_dict = load_checkpoint(args.pre_trained)
+        load_param_into_net(network, param_dict)
+    else:
+        for _, cell in network.cells_and_names():
+            if isinstance(cell, nn.Conv2d):
+                cell.weight.default_input = weight_init.initializer(weight_init.XavierUniform(),
+                                                                    cell.weight.default_input.shape,
+                                                                    cell.weight.default_input.dtype).to_tensor()
+            if isinstance(cell, nn.Dense):
+                cell.weight.default_input = weight_init.initializer(weight_init.TruncatedNormal(),
+                                                                    cell.weight.default_input.shape,
+                                                                    cell.weight.default_input.dtype).to_tensor()
+
+
+    {% if loss=='SoftmaxCrossEntropyWithLogits' %}
+    net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
+    {% elif loss=='SoftmaxCrossEntropyExpand' %}
+    net_loss = nn.SoftmaxCrossEntropyExpand(sparse=True)
+    {% endif %}
+    lr = Tensor(get_lr(0, cfg.lr, cfg.epoch_size, ds_train.get_dataset_size()))
+    {% if optimizer=='Lamb' %}
+    net_opt = nn.Lamb(network.trainable_params(), learning_rate=lr)
+    {% elif optimizer=='Momentum' %}
+    net_opt = nn.Momentum(network.trainable_params(), learning_rate=lr, momentum=cfg.momentum)
+    {% endif %}
+    model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
+
+    # define callbacks
+    time_cb = TimeMonitor(data_size=step_size)
+    loss_cb = LossMonitor()
+    cb = [time_cb, loss_cb]
+    if cfg.save_checkpoint:
+        cfg_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_checkpoint_epochs * step_size,
+                                     keep_checkpoint_max=cfg.keep_checkpoint_max)
+        ckpt_cb = ModelCheckpoint(prefix="alexnet", directory=ckpt_save_dir, config=cfg_ck)
+        cb += [ckpt_cb]
+
+    print("============== Starting Training ==============")
+    model.train(cfg.epoch_size, ds_train, callbacks=cb, dataset_sink_mode=dataset_sink_mode)

mindinsight/wizard/conf/templates/network/lenet/dataset/mnist/dataset.py-tpl

@@ -0,0 +1,98 @@
+# 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.
+# ============================================================================
+"""
+Produce the dataset
+"""
+import os
+
+import mindspore.dataset as ds
+import mindspore.dataset.transforms.vision.c_transforms as CV
+import mindspore.dataset.transforms.c_transforms as C
+from mindspore.dataset.transforms.vision import Inter
+from mindspore.common import dtype as mstype
+from mindspore.communication.management import init, get_rank, get_group_size
+
+from .config import cfg
+
+
+def create_dataset(data_path, batch_size=32, repeat_size=1, do_train=True, target='Ascend'):
+    """
+    create dataset for train or test
+    """
+
+    if do_train:
+        data_path = os.path.join(data_path, "train")
+    else:
+        data_path = os.path.join(data_path, "test")
+
+    if target == 'Ascend':
+        device_num, rank_id = _get_rank_info()
+    elif target == 'GPU':
+        init("nccl")
+        rank_id = get_rank()
+        device_num = get_group_size()
+    else:
+        device_num = 1
+
+    # define dataset
+    if device_num == 1:
+        mnist_ds = ds.MnistDataset(data_path)
+    else:
+        mnist_ds = ds.MnistDataset(data_path, num_parallel_workers=8, shuffle=True,
+                                   num_shards=device_num, shard_id=rank_id)
+
+    resize_height, resize_width = cfg.image_height, cfg.image_width
+    rescale = 1.0 / 255.0
+    shift = 0.0
+    rescale_nml = 1 / 0.3081
+    shift_nml = -1 * 0.1307 / 0.3081
+
+    # define map operations
+    resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR)  # Bilinear mode
+    rescale_nml_op = CV.Rescale(rescale_nml, shift_nml)
+    rescale_op = CV.Rescale(rescale, shift)
+    hwc2chw_op = CV.HWC2CHW()
+    type_cast_op = C.TypeCast(mstype.int32)
+
+    # apply map operations on images
+    mnist_ds = mnist_ds.map(input_columns="label", operations=type_cast_op)
+    mnist_ds = mnist_ds.map(input_columns="image", operations=resize_op)
+    mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_op)
+    mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_nml_op)
+    mnist_ds = mnist_ds.map(input_columns="image", operations=hwc2chw_op)
+
+    # apply DatasetOps
+    buffer_size = 10000
+    mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size)
+    mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True)
+    mnist_ds = mnist_ds.repeat(repeat_size)
+
+    return mnist_ds
+
+
+def _get_rank_info():
+    """
+    get rank size and rank id
+    """
+    rank_size = int(os.environ.get("RANK_SIZE", 1))
+
+    if rank_size > 1:
+        rank_size = get_group_size()
+        rank_id = get_rank()
+    else:
+        rank_size = 1
+        rank_id = 0
+
+    return rank_size, rank_id

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

@@ -0,0 +1,67 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""
+######################## eval lenet example ########################
+eval lenet according to model file:
+python eval.py --data_path /YourDataPath --ckpt_path Your.ckpt
+"""
+import argparse
+
+import mindspore.nn as nn
+from mindspore import context
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from mindspore.train import Model
+from mindspore.nn.metrics import Accuracy
+
+from src.dataset import create_dataset
+from src.config import cfg
+from src.lenet import LeNet5
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='MindSpore Lenet Example')
+    parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend', 'GPU', 'CPU'],
+                        help='device where the code will be implemented (default: Ascend)')
+    parser.add_argument('--dataset_path', type=str, default="./Data",
+                        help='path where the dataset is saved')
+    parser.add_argument('--checkpoint_path', type=str, default="", help='if mode is test, must provide\
+                        path where the trained ckpt file')
+    parser.add_argument('--dataset_sink_mode', type=bool, default=False, help='dataset_sink_mode is False or True')
+
+    args = parser.parse_args()
+
+    context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
+
+    network = LeNet5(cfg.num_classes)
+    {% if loss=='SoftmaxCrossEntropyWithLogits' %}
+    net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
+    {% elif loss=='SoftmaxCrossEntropyExpand' %}
+    net_loss = nn.SoftmaxCrossEntropyExpand(sparse=True)
+    {% endif %}
+    {% if optimizer=='Lamb' %}
+    net_opt = nn.Lamb(network.trainable_params(), learning_rate=cfg.lr)
+    {% elif optimizer=='Momentum' %}
+    net_opt = nn.Momentum(network.trainable_params(), learning_rate=cfg.lr, momentum=cfg.momentum)
+    {% endif %}
+    model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
+
+    print("============== Starting Testing ==============")
+    param_dict = load_checkpoint(args.checkpoint_path)
+    load_param_into_net(network, param_dict)
+    data_path = args.dataset_path
+    do_train = False
+    ds_eval = create_dataset(data_path=data_path, do_train=do_train, batch_size=cfg.batch_size,
+                             target=args.device_target)
+    acc = model.eval(ds_eval, dataset_sink_mode=args.dataset_sink_mode)
+    print("============== {} ==============".format(acc))

mindinsight/wizard/conf/templates/network/lenet/scripts/run_distribute_train.sh

@@ -0,0 +1,87 @@
+#!/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 [ $# != 2 ] && [ $# != 3 ]
+then 
+	echo "Usage: sh run_distribute_train.sh [RANK_TABLE_FILE] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)"
+exit 1
+fi
+
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+
+PATH1=$(get_real_path $1)
+PATH2=$(get_real_path $2)
+
+if [ ! -f $PATH1 ]
+then 
+    echo "error: RANK_TABLE_FILE=$PATH1 is not a file"
+exit 1
+fi 
+
+if [ ! -d $PATH2 ]
+then 
+    echo "error: DATASET_PATH=$PATH2 is not a directory"
+exit 1
+fi
+
+if [ $# == 3 ]
+then
+    PATH3=$(get_real_path $3)
+fi
+
+if [ $# == 3 ] && [ ! -f $PATH3 ]
+then
+    echo "error: PRETRAINED_CKPT_PATH=$PATH3 is not a file"
+exit 1
+fi
+
+ulimit -u unlimited
+export DEVICE_NUM=8
+export RANK_SIZE=$DEVICE_NUM
+export RANK_TABLE_FILE=$PATH1
+
+export SERVER_ID=0
+rank_start=$((DEVICE_NUM * SERVER_ID))
+
+for((i=0; i<DEVICE_NUM; i++))
+do
+    export DEVICE_ID=$i
+    export RANK_ID=$((rank_start + i))
+    rm -rf ./train_parallel$i
+    mkdir ./train_parallel$i
+    cp ../*.py ./train_parallel$i
+    cp *.sh ./train_parallel$i
+    cp -r ../src ./train_parallel$i
+    cd ./train_parallel$i || exit
+    echo "start training for rank $RANK_ID, device $DEVICE_ID"
+    env > env.log
+    if [ $# == 2 ]
+    then
+        python train.py --run_distribute=True --device_num=$DEVICE_NUM --dataset_path=$PATH2 &> log &
+
+    else
+        python train.py --run_distribute=True --device_num=$DEVICE_NUM --dataset_path=$PATH2 --pre_trained=$PATH3 &> log &
+    fi
+
+    cd ..
+done

mindinsight/wizard/conf/templates/network/lenet/scripts/run_distribute_train_gpu.sh

@@ -0,0 +1,72 @@
+#!/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 [ $# != 1 ]&& [ $# != 2 ]
+then 
+	echo "Usage: sh run_distribute_train_gpu.sh [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)"
+exit 1
+fi
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+
+PATH1=$(get_real_path $1)
+
+if [ ! -d $PATH1 ]
+then 
+    echo "error: DATASET_PATH=$PATH1 is not a directory"
+exit 1
+fi
+
+if [ $# == 2 ]
+then
+    PATH2=$(get_real_path $2)
+fi
+
+if [ $# == 2 ] && [ ! -f $PATH2 ]
+then
+    echo "error: PRETRAINED_CKPT_PATH=$PATH2 is not a file"
+exit 1
+fi
+
+ulimit -u unlimited
+export DEVICE_NUM=8
+export RANK_SIZE=$DEVICE_NUM
+
+rm -rf ./train_parallel
+mkdir ./train_parallel
+cp ../*.py ./train_parallel
+cp *.sh ./train_parallel
+cp -r ../src ./train_parallel
+cd ./train_parallel || exit
+
+if [ $# == 1 ]
+then
+  mpirun --allow-run-as-root -n $RANK_SIZE \
+  python train.py --run_distribute=True \
+  --device_num=$DEVICE_NUM --device_target="GPU" --dataset_path=$PATH1 &> log &
+fi
+
+if [ $# == 2 ]
+then
+  mpirun --allow-run-as-root -n $RANK_SIZE \
+  python train.py --run_distribute=True \
+  --device_num=$DEVICE_NUM --device_target="GPU" --dataset_path=$PATH1 --pre_trained=$PATH2 &> log &
+fi

mindinsight/wizard/conf/templates/network/lenet/scripts/run_eval.sh

@@ -0,0 +1,65 @@
+#!/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 [ $# != 2 ]
+then 
+    echo "Usage: sh run_eval.sh [DATASET_PATH] [CHECKPOINT_PATH]"
+exit 1
+fi
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+
+PATH1=$(get_real_path $1)
+PATH2=$(get_real_path $2)
+
+
+if [ ! -d $PATH1 ]
+then 
+    echo "error: DATASET_PATH=$PATH1 is not a directory"
+exit 1
+fi 
+
+if [ ! -f $PATH2 ]
+then 
+    echo "error: CHECKPOINT_PATH=$PATH2 is not a file"
+exit 1
+fi 
+
+ulimit -u unlimited
+export DEVICE_NUM=1
+export DEVICE_ID=0
+export RANK_SIZE=$DEVICE_NUM
+export RANK_ID=0
+
+if [ -d "eval" ];
+then
+    rm -rf ./eval
+fi
+mkdir ./eval
+cp ../*.py ./eval
+cp *.sh ./eval
+cp -r ../src ./eval
+cd ./eval || exit
+env > env.log
+echo "start evaluation for device $DEVICE_ID"
+python eval.py --dataset_path=$PATH1 --checkpoint_path=$PATH2 &> log &
+cd ..

mindinsight/wizard/conf/templates/network/lenet/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 [ $# != 2 ]
+then 
+    echo "Usage: sh run_eval_gpu.sh [DATASET_PATH] [CHECKPOINT_PATH]"
+exit 1
+fi
+
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+
+PATH1=$(get_real_path $1)
+PATH2=$(get_real_path $2)
+
+
+if [ ! -d $PATH1 ]
+then 
+    echo "error: DATASET_PATH=$PATH1 is not a directory"
+exit 1
+fi 
+
+if [ ! -f $PATH2 ]
+then 
+    echo "error: CHECKPOINT_PATH=$PATH2 is not a file"
+exit 1
+fi 
+
+ulimit -u unlimited
+export DEVICE_NUM=1
+export DEVICE_ID=0
+export RANK_SIZE=$DEVICE_NUM
+export RANK_ID=0
+
+if [ -d "eval" ];
+then
+    rm -rf ./eval
+fi
+mkdir ./eval
+cp ../*.py ./eval
+cp *.sh ./eval
+cp -r ../src ./eval
+cd ./eval || exit
+env > env.log
+echo "start evaluation for device $DEVICE_ID"
+python eval.py --dataset_path=$PATH1 --checkpoint_path=$PATH2 --device_target="GPU" &> log &
+cd ..

mindinsight/wizard/conf/templates/network/lenet/scripts/run_standalone_train.sh

@@ -0,0 +1,79 @@
+#!/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 [ $# != 1 ] && [ $# != 2 ]
+then 
+    echo "Usage: sh run_standalone_train.sh [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)"
+exit 1
+fi
+
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+
+PATH1=$(get_real_path $1)
+
+if [ ! -d $PATH1 ]
+then 
+    echo "error: DATASET_PATH=$PATH1 is not a directory"
+exit 1
+fi
+
+if [ $# == 2 ]
+then
+    PATH2=$(get_real_path $2)
+fi
+
+if [ $# == 2 ] && [ ! -f $PATH2 ]
+then
+    echo "error: PRETRAINED_CKPT_PATH=$PATH2 is not a file"
+exit 1
+fi
+
+ulimit -u unlimited
+export DEVICE_NUM=1
+export DEVICE_ID=0
+export RANK_ID=0
+export RANK_SIZE=1
+
+if [ -d "train" ];
+then
+    rm -rf ./train
+fi
+mkdir ./train
+cp ../*.py ./train
+cp *.sh ./train
+cp -r ../src ./train
+cd ./train || exit
+echo "start training for device $DEVICE_ID"
+env > env.log
+
+if [ $# == 1 ]
+then
+  python train.py --dataset_path=$PATH1 &> log &
+fi
+
+if [ $# == 2 ]
+then
+  python train.py --dataset_path=$PATH1 --pre_trained=$PATH2 &> log &
+fi
+
+cd ..

mindinsight/wizard/conf/templates/network/lenet/scripts/run_standalone_train_gpu.sh

@@ -0,0 +1,78 @@
+#!/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 [ $# != 1 ] && [ $# != 2 ]
+then 
+    echo "Usage: sh run_standalone_train_gpu.sh [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)"
+exit 1
+fi
+
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+
+PATH1=$(get_real_path $1)
+
+
+if [ ! -d $PATH1 ]
+then 
+    echo "error: DATASET_PATH=$PATH1 is not a directory"
+exit 1
+fi
+
+if [ $# == 2 ]
+then
+    PATH2=$(get_real_path $2)
+fi
+
+if [ $# == 2 ] && [ ! -f $PATH2 ]
+then
+    echo "error: PRETRAINED_CKPT_PATH=$PATH2 is not a file"
+exit 1
+fi
+
+ulimit -u unlimited
+export DEVICE_NUM=1
+export DEVICE_ID=0
+export RANK_ID=0
+export RANK_SIZE=1
+
+if [ -d "train" ];
+then
+    rm -rf ./train
+fi
+mkdir ./train
+cp ../*.py ./train
+cp *.sh ./train
+cp -r ../src ./train
+cd ./train || exit
+
+if [ $# == 1 ]
+then
+  python train.py --device_target="GPU" --dataset_path=$PATH1 &> log &
+fi
+
+if [ $# == 2 ]
+then
+  python train.py --device_target="GPU" --dataset_path=$PATH1 --pre_trained=$PATH2 &> log &
+fi
+
+cd ..

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

@@ -0,0 +1,14 @@
+# 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.
+# ============================================================================

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

@@ -0,0 +1,43 @@
+# 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
+
+cfg = edict({
+    {% if dataset=='MNIST' %}
+    'num_classes': 10,
+    {% elif dataset=='Cifar10' %}
+    'num_classes': 10,
+    {% elif dataset=='ImageNet' %}
+    'num_classes': 1001,
+    {% endif %}
+    {% if dataset=='Momentum' %}
+    'lr': 0.01,
+    {% else %}
+    'lr': 0.001,
+    {% endif %}
+    {% if optimizer=='Momentum' %}
+    "momentum": 0.9,
+    {% endif %}
+    'epoch_size': 1,
+    'batch_size': 32,
+    'buffer_size': 1000,
+    'image_height': 32,
+    'image_width': 32,
+    'save_checkpoint_steps': 1875,
+    'keep_checkpoint_max': 10,
+})

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

@@ -0,0 +1,78 @@
+# 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.
+# ============================================================================
+"""LeNet."""
+import mindspore.nn as nn
+from mindspore.common.initializer import TruncatedNormal
+
+
+def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):
+    """weight initial for conv layer"""
+    weight = weight_variable()
+    return nn.Conv2d(in_channels, out_channels,
+                     kernel_size=kernel_size, stride=stride, padding=padding,
+                     weight_init=weight, has_bias=False, pad_mode="valid")
+
+
+def fc_with_initialize(input_channels, out_channels):
+    """weight initial for fc layer"""
+    weight = weight_variable()
+    bias = weight_variable()
+    return nn.Dense(input_channels, out_channels, weight, bias)
+
+
+def weight_variable():
+    """weight initial"""
+    return TruncatedNormal(0.02)
+
+
+class LeNet5(nn.Cell):
+    """
+    Lenet network
+
+    Args:
+        num_class (int): Num classes. Default: 10.
+
+    Returns:
+        Tensor, output tensor
+    Examples:
+        >>> LeNet(num_class=10)
+
+    """
+    def __init__(self, num_class=10, channel=1):
+        super(LeNet5, self).__init__()
+        self.num_class = num_class
+        self.conv1 = conv(channel, 6, 5)
+        self.conv2 = conv(6, 16, 5)
+        self.fc1 = fc_with_initialize(16 * 5 * 5, 120)
+        self.fc2 = fc_with_initialize(120, 84)
+        self.fc3 = fc_with_initialize(84, self.num_class)
+        self.relu = nn.ReLU()
+        self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.flatten = nn.Flatten()
+
+    def construct(self, x):
+        x = self.conv1(x)
+        x = self.relu(x)
+        x = self.max_pool2d(x)
+        x = self.conv2(x)
+        x = self.relu(x)
+        x = self.max_pool2d(x)
+        x = self.flatten(x)
+        x = self.fc1(x)
+        x = self.relu(x)
+        x = self.fc2(x)
+        x = self.relu(x)
+        x = self.fc3(x)
+        return x

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

@@ -0,0 +1,95 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""
+######################## train lenet example ########################
+train lenet and get network model files(.ckpt) :
+python train.py --data_path /YourDataPath
+"""
+import os
+import argparse
+
+import mindspore.nn as nn
+from mindspore import context, ParallelMode
+from mindspore.communication.management import init, get_rank
+from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
+from mindspore.train import Model
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from mindspore.nn.metrics import Accuracy
+
+from src.config import cfg
+from src.dataset import create_dataset
+from src.lenet import LeNet5
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='MindSpore Lenet Example')
+    parser.add_argument('--run_distribute', type=bool, default=False, help='Run distribute')
+    parser.add_argument('--device_num', type=int, default=1, help='Device num.')
+    parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend', 'GPU', 'CPU'],
+                        help='device where the code will be implemented (default: Ascend)')
+    parser.add_argument('--dataset_path', type=str, default="./Data",
+                        help='path where the dataset is saved')
+    parser.add_argument('--pre_trained', type=str, default=None, help='Pre-trained checkpoint path')
+    parser.add_argument('--dataset_sink', action='store_true', help='enable dataset sink or not')
+
+    args = parser.parse_args()
+
+    if args.device_target == "CPU":
+        args.dataset_sink = False
+
+    ckpt_save_dir = './'
+    if args.run_distribute:
+        if args.device_target == 'Ascend':
+            device_id = int(os.getenv('DEVICE_ID'))
+            context.set_context(device_id=device_id)
+            init()
+        elif args.device_target == "GPU":
+            init("nccl")
+            ckpt_save_dir = os.path.join(ckpt_save_dir, 'ckpt_' + str(get_rank()))
+        else:
+            raise ValueError('Distribute running is no supported on %s' % args.device_target)
+        context.reset_auto_parallel_context()
+        context.set_auto_parallel_context(device_num=args.device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
+                                          mirror_mean=True)
+    context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
+
+    data_path = args.dataset_path
+    do_train = True
+
+    ds_train = create_dataset(data_path=data_path, do_train=do_train,
+                              batch_size=cfg.batch_size, target=args.device_target)
+
+    network = LeNet5(cfg.num_classes)
+    if args.pre_trained:
+        param_dict = load_checkpoint(args.pre_trained)
+        load_param_into_net(network, param_dict)
+    {% if loss=='SoftmaxCrossEntropyWithLogits' %}
+    net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
+    {% elif loss=='SoftmaxCrossEntropyExpand' %}
+    net_loss = nn.SoftmaxCrossEntropyExpand(sparse=True)
+    {% endif %}
+    {% if optimizer=='Lamb' %}
+    net_opt = nn.Lamb(network.trainable_params(), learning_rate=cfg.lr)
+    {% elif optimizer=='Momentum' %}
+    net_opt = nn.Momentum(network.trainable_params(), learning_rate=cfg.lr, momentum=cfg.momentum)
+    {% endif %}
+    time_cb = TimeMonitor(data_size=ds_train.get_dataset_size())
+    config_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_checkpoint_steps,
+                                 keep_checkpoint_max=cfg.keep_checkpoint_max)
+    ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet", directory=ckpt_save_dir, config=config_ck)
+    model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
+
+    print("============== Starting Training ==============")
+    model.train(cfg['epoch_size'], ds_train, callbacks=[time_cb, ckpoint_cb, LossMonitor()],
+                dataset_sink_mode=args.dataset_sink)

mindinsight/wizard/conf/templates/network/resnet50/dataset/cifar10/dataset.py-tpl

@@ -0,0 +1,87 @@
+# 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.
+# ============================================================================
+"""
+Produce the dataset
+"""
+import os
+
+import mindspore.dataset as ds
+import mindspore.dataset.transforms.c_transforms as C
+import mindspore.dataset.transforms.vision.c_transforms as CV
+from mindspore.common import dtype as mstype
+from mindspore.communication.management import init, get_rank, get_group_size
+
+from .config import cfg
+
+
+def create_dataset(data_path, batch_size=32, repeat_size=1, do_train=True, target="Ascend"):
+    """
+    create dataset for train or test
+    """
+    if target == "Ascend":
+         device_num, rank_id = _get_rank_info()
+    elif target == "GPU":
+        init("nccl")
+        rank_id = get_rank()
+        device_num = get_group_size()
+    else:
+        device_num = 1
+
+    if device_num == 1:
+        cifar_ds = ds.Cifar10Dataset(data_path, num_parallel_workers=8)
+    else:
+        cifar_ds = ds.Cifar10Dataset(data_path, num_parallel_workers=8, num_shards=device_num, shard_id=rank_id)
+
+    rescale = 1.0 / 255.0
+    shift = 0.0
+    rescale = 1.0 / 255.0
+    shift = 0.0
+
+    resize_op = CV.Resize((cfg.image_height, cfg.image_width))
+    rescale_op = CV.Rescale(rescale, shift)
+    normalize_op = CV.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
+    if do_train:
+        random_crop_op = CV.RandomCrop([32, 32], [4, 4, 4, 4])
+        random_horizontal_op = CV.RandomHorizontalFlip()
+    channel_swap_op = CV.HWC2CHW()
+    typecast_op = C.TypeCast(mstype.int32)
+    cifar_ds = cifar_ds.map(input_columns="label", operations=typecast_op)
+    if do_train:
+        cifar_ds = cifar_ds.map(input_columns="image", operations=random_crop_op)
+        cifar_ds = cifar_ds.map(input_columns="image", operations=random_horizontal_op)
+    cifar_ds = cifar_ds.map(input_columns="image", operations=resize_op)
+    cifar_ds = cifar_ds.map(input_columns="image", operations=rescale_op)
+    cifar_ds = cifar_ds.map(input_columns="image", operations=normalize_op)
+    cifar_ds = cifar_ds.map(input_columns="image", operations=channel_swap_op)
+
+    cifar_ds = cifar_ds.shuffle(buffer_size=1000)
+    cifar_ds = cifar_ds.batch(batch_size, drop_remainder=True)
+    cifar_ds = cifar_ds.repeat(repeat_size)
+    return cifar_ds
+
+def _get_rank_info():
+    """
+    get rank size and rank id
+    """
+    rank_size = int(os.environ.get("RANK_SIZE", 1))
+
+    if rank_size > 1:
+        rank_size = get_group_size()
+        rank_id = get_rank()
+    else:
+        rank_size = 1
+        rank_id = 0
+
+    return rank_size, rank_id

mindinsight/wizard/conf/templates/network/resnet50/dataset/imagenet/dataset.py-tpl

@@ -0,0 +1,104 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""
+create train or eval dataset.
+"""
+import os
+import mindspore.common.dtype as mstype
+import mindspore.dataset.engine as de
+import mindspore.dataset.transforms.vision.c_transforms as C
+import mindspore.dataset.transforms.c_transforms as C2
+from mindspore.communication.management import init, get_rank, get_group_size
+
+from .config import cfg
+
+def create_dataset(data_path, batch_size=32, repeat_size=1, do_train=True, target="Ascend"):
+    """
+    create a train or eval imagenet 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
+        target(string): the target of device. Default: "Ascend"
+
+    Returns:
+        dataset
+    """
+
+    if target == "Ascend":
+         device_num, rank_id = _get_rank_info()
+    elif target == "GPU":
+        init("nccl")
+        rank_id = get_rank()
+        device_num = get_group_size()
+    else:
+        device_num = 1
+
+    if device_num == 1:
+        ds = de.ImageFolderDatasetV2(data_path, num_parallel_workers=8, shuffle=True)
+    else:
+        ds = de.ImageFolderDatasetV2(data_path, num_parallel_workers=8, shuffle=True,
+                                     num_shards=device_num, shard_id=rank_id)
+
+    image_size = cfg.image_height
+    mean = [0.485 * 255, 0.456 * 255, 0.406 * 255]
+    std = [0.229 * 255, 0.224 * 255, 0.225 * 255]
+
+    # define map operations
+    if do_train:
+        trans = [
+            C.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
+            C.RandomHorizontalFlip(prob=0.5),
+            C.Normalize(mean=mean, std=std),
+            C.HWC2CHW()
+        ]
+    else:
+        trans = [
+            C.Decode(),
+            C.Resize(256),
+            C.CenterCrop(image_size),
+            C.Normalize(mean=mean, std=std),
+            C.HWC2CHW()
+        ]
+
+    type_cast_op = C2.TypeCast(mstype.int32)
+
+    ds = ds.map(input_columns="image", num_parallel_workers=8, operations=trans)
+    ds = ds.map(input_columns="label", num_parallel_workers=8, operations=type_cast_op)
+
+    # apply batch operations
+    ds = ds.batch(batch_size, drop_remainder=True)
+
+    # apply dataset repeat operation
+    ds = ds.repeat(repeat_size)
+
+    return ds
+
+def _get_rank_info():
+    """
+    get rank size and rank id
+    """
+    rank_size = int(os.environ.get("RANK_SIZE", 1))
+
+    if rank_size > 1:
+        rank_size = get_group_size()
+        rank_id = get_rank()
+    else:
+        rank_size = 1
+        rank_id = 0
+
+    return rank_size, rank_id

mindinsight/wizard/conf/templates/network/resnet50/dataset/mnist/dataset.py-tpl

@@ -0,0 +1,98 @@
+# 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.
+# ============================================================================
+"""
+Produce the dataset
+"""
+import os
+
+import mindspore.dataset as ds
+import mindspore.dataset.transforms.vision.c_transforms as CV
+import mindspore.dataset.transforms.c_transforms as C
+from mindspore.dataset.transforms.vision import Inter
+from mindspore.common import dtype as mstype
+from mindspore.communication.management import init, get_rank, get_group_size
+
+from .config import cfg
+
+
+def create_dataset(data_path, batch_size=32, repeat_size=1, do_train=True, target='Ascend'):
+    """
+    create dataset for train or test
+    """
+
+    if do_train:
+        data_path = os.path.join(data_path, "train")
+    else:
+        data_path = os.path.join(data_path, "test")
+
+    if target == 'Ascend':
+        device_num, rank_id = _get_rank_info()
+    elif target == 'GPU':
+        init("nccl")
+        rank_id = get_rank()
+        device_num = get_group_size()
+    else:
+        device_num = 1
+
+    # define dataset
+    if device_num == 1:
+        mnist_ds = ds.MnistDataset(data_path)
+    else:
+        mnist_ds = ds.MnistDataset(data_path, num_parallel_workers=8, shuffle=True,
+                                   num_shards=device_num, shard_id=rank_id)
+
+    resize_height, resize_width = cfg.image_height, cfg.image_width
+    rescale = 1.0 / 255.0
+    shift = 0.0
+    rescale_nml = 1 / 0.3081
+    shift_nml = -1 * 0.1307 / 0.3081
+
+    # define map operations
+    resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR)  # Bilinear mode
+    rescale_nml_op = CV.Rescale(rescale_nml, shift_nml)
+    rescale_op = CV.Rescale(rescale, shift)
+    hwc2chw_op = CV.HWC2CHW()
+    type_cast_op = C.TypeCast(mstype.int32)
+
+    # apply map operations on images
+    mnist_ds = mnist_ds.map(input_columns="label", operations=type_cast_op)
+    mnist_ds = mnist_ds.map(input_columns="image", operations=resize_op)
+    mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_op)
+    mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_nml_op)
+    mnist_ds = mnist_ds.map(input_columns="image", operations=hwc2chw_op)
+
+    # apply DatasetOps
+    buffer_size = 10000
+    mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size)
+    mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True)
+    mnist_ds = mnist_ds.repeat(repeat_size)
+
+    return mnist_ds
+
+
+def _get_rank_info():
+    """
+    get rank size and rank id
+    """
+    rank_size = int(os.environ.get("RANK_SIZE", 1))
+
+    if rank_size > 1:
+        rank_size = get_group_size()
+        rank_id = get_rank()
+    else:
+        rank_size = 1
+        rank_id = 0
+
+    return rank_size, rank_id

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

@@ -0,0 +1,85 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""train resnet."""
+import os
+import random
+import argparse
+import numpy as np
+import mindspore.nn as nn
+from mindspore import context
+from mindspore import dataset as de
+from mindspore.train.model import Model
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from src.crossentropy import CrossEntropy
+
+parser = argparse.ArgumentParser(description='Image classification')
+
+parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
+parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
+parser.add_argument('--device_target', type=str, default='Ascend', help='Device target')
+parser.add_argument('--dataset_sink_mode', type=str, default='True', choices = ['True', 'False'],
+                    help='DataSet sink mode is True or False')
+args_opt = parser.parse_args()
+
+random.seed(1)
+np.random.seed(1)
+de.config.set_seed(1)
+
+
+from src.resnet50 import resnet50 as resnet
+
+
+from src.config import cfg
+from src.dataset import create_dataset
+
+
+if __name__ == '__main__':
+    target = args_opt.device_target
+
+    # init context
+    device_id = int(os.getenv('DEVICE_ID'))
+    context.set_context(mode=context.GRAPH_MODE, device_target=target, save_graphs=False, device_id=device_id)
+    dataset_sink_mode = args_opt.dataset_sink_mode=='True'
+
+    # create dataset
+    dataset = create_dataset(data_path=args_opt.dataset_path, do_train=False, batch_size=cfg.batch_size, target=target)
+    step_size = dataset.get_dataset_size()
+
+    # define net
+    net = resnet(class_num=cfg.num_classes)
+
+    # load checkpoint
+    param_dict = load_checkpoint(args_opt.checkpoint_path)
+    load_param_into_net(net, param_dict)
+    net.set_train(False)
+
+    # define loss, model
+    {% if dataset=='ImageNet' %}
+    if not cfg.use_label_smooth:
+        cfg.label_smooth_factor = 0.0
+    loss = CrossEntropy(smooth_factor=cfg.label_smooth_factor, num_classes=cfg.num_classes)
+    {% else %}
+    {% if loss=='SoftmaxCrossEntropyWithLogits' %}
+    loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
+    {% elif loss=='SoftmaxCrossEntropyExpand' %}
+    loss = nn.SoftmaxCrossEntropyExpand(sparse=True)
+    {% endif %}
+    {% endif %}
+    # define model
+    model = Model(net, loss_fn=loss, metrics={'top_1_accuracy', 'top_5_accuracy'})
+
+    # eval model
+    res = model.eval(dataset, dataset_sink_mode=dataset_sink_mode)
+    print("result:", res, "ckpt=", args_opt.checkpoint_path)

mindinsight/wizard/conf/templates/network/resnet50/scripts/run_distribute_train.sh

@@ -0,0 +1,88 @@
+#!/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 [ $# != 2 ] && [ $# != 3 ]
+then
+	echo "Usage: sh run_distribute_train.sh [DATASET_PATH] [MINDSPORE_HCCL_CONFIG_PATH] [PRETRAINED_CKPT_PATH](optional)"
+exit 1
+fi
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+
+PATH1=$(get_real_path $1)
+PATH2=$(get_real_path $2)
+
+if [ ! -d $PATH1 ]
+then
+    echo "error: DATASET_PATH=$PATH1 is not a directory"
+exit 1
+fi
+
+if [ ! -f $PATH2 ]
+then
+    echo "error: MINDSPORE_HCCL_CONFIG_PATH=$PATH2 is not a file"
+exit 1
+fi
+
+if [ $# == 3 ]
+then
+    PATH3=$(get_real_path $3)
+fi
+
+if [ $# == 3 ] && [ ! -f $PATH3 ]
+then
+    echo "error: PRETRAINED_CKPT_PATH=$PATH3 is not a file"
+exit 1
+fi
+
+ulimit -u unlimited
+export DEVICE_NUM=8
+export RANK_SIZE=8
+export MINDSPORE_HCCL_CONFIG_PATH=$PATH2
+export RANK_TABLE_FILE=$PATH2
+export SERVER_ID=0
+rank_start=$((DEVICE_NUM * SERVER_ID))
+
+for((i=0; i<DEVICE_NUM; i++))
+do
+    export DEVICE_ID=$i
+    export RANK_ID=$((rank_start + i))
+    rm -rf ./train_parallel$i
+    mkdir ./train_parallel$i
+    cp ../*.py ./train_parallel$i
+    cp *.sh ./train_parallel$i
+    cp -r ../src ./train_parallel$i
+    cd ./train_parallel$i || exit
+    echo "start training for rank $RANK_ID, device $DEVICE_ID"
+    env > env.log
+    if [ $# == 2 ]
+    then
+        python train.py --run_distribute=True --device_num=$DEVICE_NUM --dataset_path=$PATH1 --dataset_sink_mode=False &> log &
+    fi
+
+    if [ $# == 3 ]
+    then
+        python train.py --run_distribute=True --device_num=$DEVICE_NUM --dataset_path=$PATH1 --pre_trained=$PATH2 --dataset_sink_mode=False &> log &
+    fi
+
+    cd ..
+done

mindinsight/wizard/conf/templates/network/resnet50/scripts/run_distribute_train_gpu.sh

@@ -0,0 +1,53 @@
+#!/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 [ $# != 1 ]
+then
+	echo "Usage: sh run_distribute_train_gpu.sh [DATASET_PATH]"
+exit 1
+fi
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+
+PATH1=$(get_real_path $1)
+
+if [ ! -d $PATH1 ]
+then
+    echo "error: DATASET_PATH=$PATH1 is not a directory"
+exit 1
+fi
+
+
+ulimit -u unlimited
+export DEVICE_NUM=4
+export RANK_SIZE=4
+
+rm -rf ./train_parallel
+mkdir ./train_parallel
+cp ../*.py ./train_parallel
+cp *.sh ./train_parallel
+cp -r ../src ./train_parallel
+cd ./train_parallel || exit
+
+mpirun --allow-run-as-root -n $RANK_SIZE \
+python train.py --run_distribute=True \
+--device_num=$DEVICE_NUM --device_target="GPU" --dataset_path=$PATH1 &> log &

mindinsight/wizard/conf/templates/network/resnet50/scripts/run_eval.sh

@@ -0,0 +1,65 @@
+#!/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 [ $# != 2 ]
+then
+    echo "Usage: sh run_eval.sh [DATASET_PATH] [CHECKPOINT_PATH]"
+exit 1
+fi
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+
+PATH1=$(get_real_path $1)
+PATH2=$(get_real_path $2)
+
+
+if [ ! -d $PATH1 ]
+then
+    echo "error: DATASET_PATH=$PATH1 is not a directory"
+exit 1
+fi
+
+if [ ! -f $PATH2 ]
+then
+    echo "error: CHECKPOINT_PATH=$PATH2 is not a file"
+exit 1
+fi
+
+ulimit -u unlimited
+export DEVICE_NUM=1
+export DEVICE_ID=0
+export RANK_SIZE=$DEVICE_NUM
+export RANK_ID=0
+
+if [ -d "eval" ];
+then
+    rm -rf ./eval
+fi
+mkdir ./eval
+cp ../*.py ./eval
+cp *.sh ./eval
+cp -r ../src ./eval
+cd ./eval || exit
+env > env.log
+echo "start evaluation for device $DEVICE_ID"
+python eval.py --dataset_path=$PATH1 --checkpoint_path=$PATH2 &> log &
+cd ..

mindinsight/wizard/conf/templates/network/resnet50/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 [ $# != 2 ]
+then
+    echo "Usage: sh run_eval_gpu.sh [DATASET_PATH] [CHECKPOINT_PATH]"
+exit 1
+fi
+
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+
+PATH1=$(get_real_path $1)
+PATH2=$(get_real_path $2)
+
+
+if [ ! -d $PATH1 ]
+then
+    echo "error: DATASET_PATH=$PATH1 is not a directory"
+exit 1
+fi
+
+if [ ! -f $PATH2 ]
+then
+    echo "error: CHECKPOINT_PATH=$PATH2 is not a file"
+exit 1
+fi
+
+ulimit -u unlimited
+export DEVICE_NUM=1
+export DEVICE_ID=0
+export RANK_SIZE=$DEVICE_NUM
+export RANK_ID=0
+
+if [ -d "eval" ];
+then
+    rm -rf ./eval
+fi
+mkdir ./eval
+cp ../*.py ./eval
+cp *.sh ./eval
+cp -r ../src ./eval
+cd ./eval || exit
+env > env.log
+echo "start evaluation for device $DEVICE_ID"
+python eval.py --dataset_path=$PATH1 --checkpoint_path=$PATH2 --device_target="GPU" &> log &
+cd ..

mindinsight/wizard/conf/templates/network/resnet50/scripts/run_standalone_train.sh

@@ -0,0 +1,76 @@
+#!/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 [ $# != 1 ] && [ $# != 2 ]
+then
+    echo "Usage: sh run_standalone_train.sh [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)"
+exit 1
+fi
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+
+PATH1=$(get_real_path $1)
+
+if [ $# == 2 ]
+then
+    PATH2=$(get_real_path $2)
+fi
+
+if [ ! -d $PATH1 ]
+then
+    echo "error: DATASET_PATH=$PATH1 is not a directory"
+exit 1
+fi
+
+if [ $# == 2 ] && [ ! -f $PATH2 ]
+then
+    echo "error: PRETRAINED_CKPT_PATH=$PATH2 is not a file"
+exit 1
+fi
+
+ulimit -u unlimited
+export DEVICE_NUM=1
+export DEVICE_ID=0
+export RANK_ID=0
+export RANK_SIZE=1
+
+if [ -d "train" ];
+then
+    rm -rf ./train
+fi
+mkdir ./train
+cp ../*.py ./train
+cp *.sh ./train
+cp -r ../src ./train
+cd ./train || exit
+echo "start training for device $DEVICE_ID"
+env > env.log
+if [ $# == 1 ]
+then
+    python train.py --dataset_path=$PATH1 --dataset_sink_mode=False &> log &
+fi
+
+if [ $# == 2 ]
+then
+    python train.py --dataset_path=$PATH1 --pre_trained=$PATH2 --dataset_sink_mode=False &> log &
+fi
+cd ..

mindinsight/wizard/conf/templates/network/resnet50/scripts/run_standalone_train_gpu.sh

@@ -0,0 +1,59 @@
+#!/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 [ $# != 1 ]
+then
+    echo "Usage: sh run_standalone_train_gpu.sh [DATASET_PATH]"
+exit 1
+fi
+
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+
+PATH1=$(get_real_path $1)
+
+
+if [ ! -d $PATH1 ]
+then
+    echo "error: DATASET_PATH=$PATH1 is not a directory"
+exit 1
+fi
+
+
+ulimit -u unlimited
+export DEVICE_NUM=1
+export DEVICE_ID=0
+export RANK_ID=0
+export RANK_SIZE=1
+
+if [ -d "train" ];
+then
+    rm -rf ./train
+fi
+mkdir ./train
+cp ../*.py ./train
+cp *.sh ./train
+cp -r ../src ./train
+cd ./train || exit
+python train.py --device_target="GPU" --dataset_path=$PATH1 &> log &
+
+cd ..

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

@@ -0,0 +1,14 @@
+# 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.
+# ============================================================================

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

@@ -0,0 +1,55 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""
+network config setting, will be used in train.py and eval.py
+"""
+from easydict import EasyDict as ed
+
+cfg = ed({
+    {% if dataset=='MNIST' %}
+    'num_classes': 10,
+    {% elif dataset=='Cifar10' %}
+    'num_classes': 10,
+    {% elif dataset=='ImageNet' %}
+    'num_classes': 1001,
+    {% endif %}
+    "batch_size": 32,
+    "loss_scale": 1024,
+    {% if optimizer=='Momentum' %}
+    "momentum": 0.9,
+    {% 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": "./",
+    {% if dataset=='ImageNet' %}
+    "warmup_epochs": 0,
+    "lr_decay_mode": "cosine",
+    {% else %}
+    "warmup_epochs": 5,
+    "lr_decay_mode": "poly",
+    {% endif %}
+    "use_label_smooth": True,
+    "label_smooth_factor": 0.1,
+    "lr": 0.01,
+    "lr_init": 0.01,
+    "lr_end": 0.00001,
+    "lr_max": 0.1
+})

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

@@ -0,0 +1,39 @@
+# 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/src/lr_generator.py-tpl

@@ -0,0 +1,116 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""learning rate generator"""
+import math
+import numpy as np
+
+
+def get_lr(lr_init, lr_end, lr_max, warmup_epochs, total_epochs, steps_per_epoch, lr_decay_mode):
+    """
+    generate learning rate array
+
+    Args:
+       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
+       lr_decay_mode(string): learning rate decay mode, including steps, poly or default
+
+    Returns:
+       np.array, learning rate array
+    """
+    lr_each_step = []
+    total_steps = steps_per_epoch * total_epochs
+    warmup_steps = steps_per_epoch * warmup_epochs
+    if lr_decay_mode == 'steps':
+        decay_epoch_index = [0.3 * total_steps, 0.6 * total_steps, 0.8 * total_steps]
+        for i in range(total_steps):
+            if i < decay_epoch_index[0]:
+                lr = lr_max
+            elif i < decay_epoch_index[1]:
+                lr = lr_max * 0.1
+            elif i < decay_epoch_index[2]:
+                lr = lr_max * 0.01
+            else:
+                lr = lr_max * 0.001
+            lr_each_step.append(lr)
+    elif lr_decay_mode == 'poly':
+        if warmup_steps != 0:
+            inc_each_step = (float(lr_max) - float(lr_init)) / float(warmup_steps)
+        else:
+            inc_each_step = 0
+        for i in range(total_steps):
+            if i < warmup_steps:
+                lr = float(lr_init) + inc_each_step * float(i)
+            else:
+                base = (1.0 - (float(i) - float(warmup_steps)) / (float(total_steps) - float(warmup_steps)))
+                lr = float(lr_max) * base * base
+                if lr < 0.0:
+                    lr = 0.0
+            lr_each_step.append(lr)
+    else:
+        for i in range(total_steps):
+            if i < warmup_steps:
+                lr = lr_init + (lr_max - lr_init) * i / warmup_steps
+            else:
+                lr = lr_max - (lr_max - lr_end) * (i - warmup_steps) / (total_steps - warmup_steps)
+            lr_each_step.append(lr)
+
+    lr_each_step = np.array(lr_each_step).astype(np.float32)
+
+    return lr_each_step
+
+
+def linear_warmup_lr(current_step, warmup_steps, base_lr, init_lr):
+    lr_inc = (float(base_lr) - float(init_lr)) / float(warmup_steps)
+    lr = float(init_lr) + lr_inc * current_step
+    return lr
+
+
+def warmup_cosine_annealing_lr(lr, steps_per_epoch, warmup_epochs, max_epoch=120, global_step=0):
+    """
+    generate learning rate array with cosine
+
+    Args:
+       lr(float): base learning rate
+       steps_per_epoch(int): steps size of one epoch
+       warmup_epochs(int): number of warmup epochs
+       max_epoch(int): total epochs of training
+       global_step(int): the current start index of lr array
+    Returns:
+       np.array, learning rate array
+    """
+    base_lr = lr
+    warmup_init_lr = 0
+    total_steps = int(max_epoch * steps_per_epoch)
+    warmup_steps = int(warmup_epochs * steps_per_epoch)
+    decay_steps = total_steps - warmup_steps
+
+    lr_each_step = []
+    for i in range(total_steps):
+        if i < warmup_steps:
+            lr = linear_warmup_lr(i + 1, warmup_steps, base_lr, warmup_init_lr)
+        else:
+            linear_decay = (total_steps - i) / decay_steps
+            cosine_decay = 0.5 * (1 + math.cos(math.pi * 2 * 0.47 * i / decay_steps))
+            decayed = linear_decay * cosine_decay + 0.00001
+            lr = base_lr * decayed
+        lr_each_step.append(lr)
+
+    lr_each_step = np.array(lr_each_step).astype(np.float32)
+    learning_rate = lr_each_step[global_step:]
+    return learning_rate

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

@@ -0,0 +1,262 @@
+# 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.
+# ============================================================================
+"""ResNet."""
+import numpy as np
+import mindspore.nn as nn
+from mindspore.ops import operations as P
+from mindspore.common.tensor import Tensor
+
+
+def _weight_variable(shape, factor=0.01):
+    init_value = np.random.randn(*shape).astype(np.float32) * factor
+    return Tensor(init_value)
+
+
+def _conv3x3(in_channel, out_channel, stride=1):
+    weight_shape = (out_channel, in_channel, 3, 3)
+    weight = _weight_variable(weight_shape)
+    return nn.Conv2d(in_channel, out_channel,
+                     kernel_size=3, stride=stride, padding=0, pad_mode='same', weight_init=weight)
+
+
+def _conv1x1(in_channel, out_channel, stride=1):
+    weight_shape = (out_channel, in_channel, 1, 1)
+    weight = _weight_variable(weight_shape)
+    return nn.Conv2d(in_channel, out_channel,
+                     kernel_size=1, stride=stride, padding=0, pad_mode='same', weight_init=weight)
+
+
+def _conv7x7(in_channel, out_channel, stride=1):
+    weight_shape = (out_channel, in_channel, 7, 7)
+    weight = _weight_variable(weight_shape)
+    return nn.Conv2d(in_channel, out_channel,
+                     kernel_size=7, stride=stride, padding=0, pad_mode='same', weight_init=weight)
+
+
+def _bn(channel):
+    return nn.BatchNorm2d(channel, eps=1e-4, momentum=0.9,
+                          gamma_init=1, beta_init=0, moving_mean_init=0, moving_var_init=1)
+
+
+def _bn_last(channel):
+    return nn.BatchNorm2d(channel, eps=1e-4, momentum=0.9,
+                          gamma_init=0, beta_init=0, moving_mean_init=0, moving_var_init=1)
+
+
+def _fc(in_channel, out_channel):
+    weight_shape = (out_channel, in_channel)
+    weight = _weight_variable(weight_shape)
+    return nn.Dense(in_channel, out_channel, has_bias=True, weight_init=weight, bias_init=0)
+
+
+class ResidualBlock(nn.Cell):
+    """
+    ResNet V1 residual block definition.
+
+    Args:
+        in_channel (int): Input channel.
+        out_channel (int): Output channel.
+        stride (int): Stride size for the first convolutional layer. Default: 1.
+
+    Returns:
+        Tensor, output tensor.
+
+    Examples:
+        >>> ResidualBlock(3, 256, stride=2)
+    """
+    expansion = 4
+
+    def __init__(self,
+                 in_channel,
+                 out_channel,
+                 stride=1):
+        super(ResidualBlock, self).__init__()
+
+        channel = out_channel // self.expansion
+        self.conv1 = _conv1x1(in_channel, channel, stride=1)
+        self.bn1 = _bn(channel)
+
+        self.conv2 = _conv3x3(channel, channel, stride=stride)
+        self.bn2 = _bn(channel)
+
+        self.conv3 = _conv1x1(channel, out_channel, stride=1)
+        self.bn3 = _bn_last(out_channel)
+
+        self.relu = nn.ReLU()
+
+        self.down_sample = False
+
+        if stride != 1 or in_channel != out_channel:
+            self.down_sample = True
+        self.down_sample_layer = None
+
+        if self.down_sample:
+            self.down_sample_layer = nn.SequentialCell([_conv1x1(in_channel, out_channel, stride),
+                                                        _bn(out_channel)])
+        self.add = P.TensorAdd()
+
+    def construct(self, x):
+        identity = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.down_sample:
+            identity = self.down_sample_layer(identity)
+
+        out = self.add(out, identity)
+        out = self.relu(out)
+
+        return out
+
+
+class ResNet(nn.Cell):
+    """
+    ResNet architecture.
+
+    Args:
+        block (Cell): Block for network.
+        layer_nums (list): Numbers of block in different layers.
+        in_channels (list): Input channel in each layer.
+        out_channels (list): Output channel in each layer.
+        strides (list):  Stride size in each layer.
+        num_classes (int): The number of classes that the training images are belonging to.
+    Returns:
+        Tensor, output tensor.
+
+    Examples:
+        >>> ResNet(ResidualBlock,
+        >>>        [3, 4, 6, 3],
+        >>>        [64, 256, 512, 1024],
+        >>>        [256, 512, 1024, 2048],
+        >>>        [1, 2, 2, 2],
+        >>>        10)
+    """
+
+    def __init__(self,
+                 block,
+                 layer_nums,
+                 in_channels,
+                 out_channels,
+                 strides,
+                 num_classes):
+        super(ResNet, self).__init__()
+
+        if not len(layer_nums) == len(in_channels) == len(out_channels) == 4:
+            raise ValueError("the length of layer_num, in_channels, out_channels list must be 4!")
+
+        self.conv1 = _conv7x7(3, 64, stride=2)
+        self.bn1 = _bn(64)
+        self.relu = P.ReLU()
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode="same")
+
+        self.layer1 = self._make_layer(block,
+                                       layer_nums[0],
+                                       in_channel=in_channels[0],
+                                       out_channel=out_channels[0],
+                                       stride=strides[0])
+        self.layer2 = self._make_layer(block,
+                                       layer_nums[1],
+                                       in_channel=in_channels[1],
+                                       out_channel=out_channels[1],
+                                       stride=strides[1])
+        self.layer3 = self._make_layer(block,
+                                       layer_nums[2],
+                                       in_channel=in_channels[2],
+                                       out_channel=out_channels[2],
+                                       stride=strides[2])
+        self.layer4 = self._make_layer(block,
+                                       layer_nums[3],
+                                       in_channel=in_channels[3],
+                                       out_channel=out_channels[3],
+                                       stride=strides[3])
+
+        self.mean = P.ReduceMean(keep_dims=True)
+        self.flatten = nn.Flatten()
+        self.end_point = _fc(out_channels[3], num_classes)
+
+    def _make_layer(self, block, layer_num, in_channel, out_channel, stride):
+        """
+        Make stage network of ResNet.
+
+        Args:
+            block (Cell): Resnet block.
+            layer_num (int): Layer number.
+            in_channel (int): Input channel.
+            out_channel (int): Output channel.
+            stride (int): Stride size for the first convolutional layer.
+
+        Returns:
+            SequentialCell, the output layer.
+
+        Examples:
+            >>> _make_layer(ResidualBlock, 3, 128, 256, 2)
+        """
+        layers = []
+
+        resnet_block = block(in_channel, out_channel, stride=stride)
+        layers.append(resnet_block)
+
+        for _ in range(1, layer_num):
+            resnet_block = block(out_channel, out_channel, stride=1)
+            layers.append(resnet_block)
+
+        return nn.SequentialCell(layers)
+
+    def construct(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        c1 = self.maxpool(x)
+
+        c2 = self.layer1(c1)
+        c3 = self.layer2(c2)
+        c4 = self.layer3(c3)
+        c5 = self.layer4(c4)
+
+        out = self.mean(c5, (2, 3))
+        out = self.flatten(out)
+        out = self.end_point(out)
+
+        return out
+
+
+def resnet50(class_num=10):
+    """
+    Get ResNet50 neural network.
+
+    Args:
+        class_num (int): Class number.
+
+    Returns:
+        Cell, cell instance of ResNet50 neural network.
+
+    Examples:
+        >>> net = resnet50(10)
+    """
+    return ResNet(ResidualBlock,
+                  [3, 4, 6, 3],
+                  [64, 256, 512, 1024],
+                  [256, 512, 1024, 2048],
+                  [1, 2, 2, 2],
+                  class_num)

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

@@ -0,0 +1,164 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""train resnet."""
+import os
+import random
+import argparse
+import numpy as np
+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.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
+from mindspore.communication.management import init, get_rank, get_group_size
+import mindspore.nn as nn
+import mindspore.common.initializer as weight_init
+from src.lr_generator import get_lr, warmup_cosine_annealing_lr
+from src.crossentropy import CrossEntropy
+
+parser = argparse.ArgumentParser(description='Image classification')
+parser.add_argument('--run_distribute', type=bool, default=False, help='Run distribute')
+parser.add_argument('--device_num', type=int, default=1, help='Device num.')
+
+parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
+parser.add_argument('--device_target', type=str, default='Ascend', help='Device target: "Ascend", "GPU", "CPU"')
+parser.add_argument('--pre_trained', type=str, default=None, help='Pretrained checkpoint path')
+parser.add_argument('--dataset_sink_mode', type=str, default='True', choices = ['True', 'False'],
+                    help='DataSet sink mode is True or False')
+args_opt = parser.parse_args()
+
+random.seed(1)
+np.random.seed(1)
+de.config.set_seed(1)
+
+
+from src.resnet50 import resnet50 as resnet
+from src.config import cfg
+from src.dataset import create_dataset
+
+
+if __name__ == '__main__':
+    target = args_opt.device_target
+    ckpt_save_dir = cfg.save_checkpoint_path
+    dataset_sink_mode = args_opt.dataset_sink_mode=='True'
+
+    # init context
+    context.set_context(mode=context.GRAPH_MODE, device_target=target, save_graphs=False)
+    if args_opt.run_distribute:
+        if target == "Ascend":
+            device_id = int(os.getenv('DEVICE_ID'))
+            context.set_context(device_id=device_id, enable_auto_mixed_precision=True)
+            context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
+                                              mirror_mean=True)
+            auto_parallel_context().set_all_reduce_fusion_split_indices([107, 160])
+
+            init()
+        # GPU target
+        else:
+            init("nccl")
+            context.set_auto_parallel_context(device_num=get_group_size(), parallel_mode=ParallelMode.DATA_PARALLEL,
+                                              mirror_mean=True)
+            ckpt_save_dir = cfg.save_checkpoint_path + "ckpt_" + str(get_rank()) + "/"
+
+    # create dataset
+    dataset = create_dataset(data_path=args_opt.dataset_path, do_train=True, batch_size=cfg.batch_size, target=target)
+
+    step_size = dataset.get_dataset_size()
+
+    # define net
+    net = resnet(class_num=cfg.num_classes)
+
+    # init weight
+    if args_opt.pre_trained:
+        param_dict = load_checkpoint(args_opt.pre_trained)
+        load_param_into_net(net, param_dict)
+    else:
+        for _, cell in net.cells_and_names():
+            if isinstance(cell, nn.Conv2d):
+                cell.weight.default_input = weight_init.initializer(weight_init.XavierUniform(),
+                                                                    cell.weight.default_input.shape,
+                                                                    cell.weight.default_input.dtype).to_tensor()
+            if isinstance(cell, nn.Dense):
+                cell.weight.default_input = weight_init.initializer(weight_init.TruncatedNormal(),
+                                                                    cell.weight.default_input.shape,
+                                                                    cell.weight.default_input.dtype).to_tensor()
+
+    # init lr
+
+    {% if dataset=='Cifar10' %}
+    lr = get_lr(lr_init=cfg.lr_init, lr_end=cfg.lr_end, lr_max=cfg.lr_max,
+                warmup_epochs=cfg.warmup_epochs, total_epochs=cfg.epoch_size, steps_per_epoch=step_size,
+                lr_decay_mode='poly')
+    {% else %}
+    lr = get_lr(lr_init=cfg.lr_init, lr_end=0.0, lr_max=cfg.lr_max, warmup_epochs=cfg.warmup_epochs,
+                total_epochs=cfg.epoch_size, steps_per_epoch=step_size, lr_decay_mode='cosine')
+    {% endif %}
+
+    lr = Tensor(lr)
+
+    # define opt
+    {% if optimizer=='Lamb' %}
+    opt = nn.Lamb(filter(lambda x: x.requires_grad, net.get_parameters()), learning_rate=lr,
+                  weight_decay=cfg.weight_decay)
+    {% elif optimizer=='Momentum' %}
+    opt = nn.Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), learning_rate=lr, momentum=cfg.momentum,
+                      weight_decay=cfg.weight_decay, loss_scale=cfg.loss_scale)
+    {% endif %}
+
+    # define loss, model
+    if target == "Ascend":
+        {% if dataset=='ImageNet' %}
+        if not cfg.use_label_smooth:
+            cfg.label_smooth_factor = 0.0
+        loss = CrossEntropy(smooth_factor=cfg.label_smooth_factor, num_classes=cfg.num_classes)
+        {% else %}
+        {% if loss=='SoftmaxCrossEntropyWithLogits' %}
+        loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
+        {% elif loss=='SoftmaxCrossEntropyExpand' %}
+        loss = nn.SoftmaxCrossEntropyExpand(sparse=True)
+        {% endif %}
+        {% endif %}
+        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=False)
+    else:
+        # GPU target
+        {% if loss=='SoftmaxCrossEntropyWithLogits' %}
+        loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, is_grad=False, reduction='mean')
+        {% elif loss=='SoftmaxCrossEntropyExpand' %}
+        loss = nn.SoftmaxCrossEntropyExpand(sparse=True)
+        {% endif %}
+        {% if optimizer=='Lamb' %}
+        opt = nn.Lamb(filter(lambda x: x.requires_grad, net.get_parameters()), learning_rate=lr)
+        {% elif optimizer=='Momentum' %}
+        opt = nn.Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), learning_rate=lr, momentum=cfg.momentum)
+        {% endif %}
+        model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'})
+
+    # define callbacks
+    time_cb = TimeMonitor(data_size=step_size)
+    loss_cb = LossMonitor()
+    cb = [time_cb, loss_cb]
+    if cfg.save_checkpoint:
+        cfg_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_checkpoint_epochs * step_size,
+                                     keep_checkpoint_max=cfg.keep_checkpoint_max)
+        ckpt_cb = ModelCheckpoint(prefix="resnet", directory=ckpt_save_dir, config=cfg_ck)
+        cb += [ckpt_cb]
+
+    # train model
+    model.train(cfg.epoch_size, dataset, callbacks=cb, dataset_sink_mode=dataset_sink_mode)

mindinsight/wizard/create_project.py

@@ -0,0 +1,119 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""Create project command module."""
+import os
+import re
+import sys
+import textwrap
+from pathlib import Path
+
+import click
+
+from mindinsight.utils.command import BaseCommand
+from mindinsight.wizard.base.utility import find_network_maker_names, load_network_maker, process_prompt_choice
+from mindinsight.wizard.common.exceptions import CommandError
+from mindinsight.wizard.conf.constants import SUPPORT_MINDSPORE_VERSION, QUESTION_START
+
+
+class CreateProject(BaseCommand):
+    """Create project class."""
+    name = 'createproject'
+    description = 'create project'
+
+    def __init__(self):
+        self._network_types = find_network_maker_names()
+
+    def add_arguments(self, parser):
+        """
+        Add arguments to parser.
+
+        Args:
+            parser (ArgumentParser): Specify parser to which arguments are added.
+        """
+        parser.add_argument(
+            'name',
+            type=str,
+            help='Specify the new project name.')
+
+    def _make_project_dir(self, project_name):
+        self._check_project_dir(project_name)
+        permissions = os.R_OK | os.W_OK | os.X_OK
+        mode = permissions << 6
+        project_dir = os.path.join(os.getcwd(), project_name)
+        os.makedirs(project_dir, mode=mode, exist_ok=True)
+        return project_dir
+
+    @staticmethod
+    def _check_project_dir(project_name):
+        """Check project directory whether empty or exist."""
+        if not re.search('^[A-Za-z0-9][A-Za-z0-9._-]*$', project_name):
+            raise CommandError("'%s' is not a valid project name. Please input a valid name" % project_name)
+        project_dir = os.path.join(os.getcwd(), project_name)
+        if os.path.exists(project_dir):
+            output_path = Path(project_dir)
+            if output_path.is_dir():
+                if os.path.os.listdir(project_dir):
+                    raise CommandError('%s already exists, %s is not empty directory, please try another name.'
+                                       % (project_name, project_dir))
+            else:
+                CommandError('There is a file in the current directory has the same name as the project %s, '
+                             'please try another name.' % project_name)
+        return True
+
+    def ask_network(self):
+        """Ask user question for selecting a network to create."""
+        network_type_choices = self._network_types[:]
+        network_type_choices.sort(reverse=False)
+        prompt_msg = '{}:\n{}\n'.format(
+            '%sPlease select a network' % QUESTION_START,
+            '\n'.join(f'{idx: >4}: {choice}' for idx, choice in enumerate(network_type_choices, start=1))
+        )
+        prompt_type = click.IntRange(min=1, max=len(network_type_choices))
+        choice = click.prompt(prompt_msg, type=prompt_type, hide_input=False, show_choices=False,
+                              confirmation_prompt=False,
+                              value_proc=lambda x: process_prompt_choice(x, prompt_type))
+        return network_type_choices[choice - 1]
+
+    @staticmethod
+    def echo_notice():
+        """Echo notice for depending environment."""
+        click.secho(textwrap.dedent("""
+        [NOTICE] To ensure the final generated scripts run under specific environment with the following 
+        
+        mindspore   : %s
+        """ % SUPPORT_MINDSPORE_VERSION), fg='red')
+
+    def run(self, args):
+        """Override run method to start."""
+        project_name = args.get('name')
+        try:
+            self._check_project_dir(project_name)
+        except CommandError as error:
+            click.secho(error.message, fg='red')
+            sys.exit(1)
+        try:
+            self.echo_notice()
+            network_maker_name = self.ask_network()
+            network_maker = load_network_maker(network_maker_name)
+            network_maker.configure()
+        except click.exceptions.Abort:
+            sys.exit(1)
+
+        project_dir = self._make_project_dir(project_name)
+        source_files = network_maker.generate(**args)
+        for source_file in source_files:
+            source_file.write(project_dir)
+
+        click.secho(f"{project_name} is generated in {project_dir}")

mindinsight/wizard/dataset/__init__.py

@@ -0,0 +1,14 @@
+# 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.
+# ============================================================================

mindinsight/wizard/dataset/cifar10.py

@@ -0,0 +1,47 @@
+# 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.
+# ============================================================================
+"""BaseDataset module."""
+import os
+
+from mindinsight.wizard.base.dataset import BaseDataset
+from mindinsight.wizard.base.templates import TemplateManager
+from mindinsight.wizard.conf.constants import TEMPLATES_BASE_DIR
+
+
+class Dataset(BaseDataset):
+    """BaseDataset code generator."""
+    name = 'Cifar10'
+
+    def __init__(self):
+        super(Dataset, self).__init__()
+        self._network = None
+        self.template_manager = None
+
+    def set_network(self, network_maker):
+        self._network = network_maker
+        template_dir = os.path.join(TEMPLATES_BASE_DIR,
+                                    'network',
+                                    network_maker.name.lower(),
+                                    'dataset',
+                                    self.name.lower())
+        self.template_manager = TemplateManager(template_dir)
+
+    def configure(self):
+        """Configure the network options."""
+        return self.settings
+
+    def generate(self, **options):
+        source_files = self.template_manager.render(**options)
+        return source_files

mindinsight/wizard/dataset/imagenet.py

@@ -0,0 +1,47 @@
+# 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.
+# ============================================================================
+"""BaseDataset module."""
+import os
+
+from mindinsight.wizard.base.dataset import BaseDataset
+from mindinsight.wizard.base.templates import TemplateManager
+from mindinsight.wizard.conf.constants import TEMPLATES_BASE_DIR
+
+
+class Dataset(BaseDataset):
+    """BaseDataset code generator."""
+    name = 'ImageNet'
+
+    def __init__(self):
+        super(Dataset, self).__init__()
+        self._network = None
+        self.template_manager = None
+
+    def set_network(self, network_maker):
+        self._network = network_maker
+        template_dir = os.path.join(TEMPLATES_BASE_DIR,
+                                    'network',
+                                    network_maker.name.lower(),
+                                    'dataset',
+                                    self.name.lower())
+        self.template_manager = TemplateManager(template_dir)
+
+    def configure(self):
+        """Configure the network options."""
+        return self.settings
+
+    def generate(self, **options):
+        source_files = self.template_manager.render(**options)
+        return source_files

mindinsight/wizard/dataset/mnist.py

@@ -0,0 +1,47 @@
+# 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.
+# ============================================================================
+"""BaseDataset module."""
+import os
+
+from mindinsight.wizard.base.dataset import BaseDataset
+from mindinsight.wizard.base.templates import TemplateManager
+from mindinsight.wizard.conf.constants import TEMPLATES_BASE_DIR
+
+
+class Dataset(BaseDataset):
+    """BaseDataset code generator."""
+    name = 'MNIST'
+
+    def __init__(self):
+        super(Dataset, self).__init__()
+        self._network = None
+        self.template_manager = None
+
+    def set_network(self, network_maker):
+        self._network = network_maker
+        template_dir = os.path.join(TEMPLATES_BASE_DIR,
+                                    'network',
+                                    network_maker.name.lower(),
+                                    'dataset',
+                                    self.name.lower())
+        self.template_manager = TemplateManager(template_dir)
+
+    def configure(self):
+        """Configure the network options."""
+        return self.settings
+
+    def generate(self, **options):
+        source_files = self.template_manager.render(**options)
+        return source_files

mindinsight/wizard/network/__init__.py

@@ -0,0 +1,14 @@
+# 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.
+# ============================================================================

mindinsight/wizard/network/alexnet.py

@@ -0,0 +1,18 @@
+# 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.
+# ============================================================================
+"""alexnet module."""
+from mindinsight.wizard.network.generic_network import GenericNetwork
+
+
+class Network(GenericNetwork):
+    """Network code generator."""
+    name = 'alexnet'
+    supported_datasets = ['Cifar10', 'ImageNet']
+    supported_loss_functions = ['SoftmaxCrossEntropyWithLogits', 'SoftmaxCrossEntropyExpand']
+    supported_optimizers = ['Momentum', 'Lamb']

mindinsight/wizard/network/generic_network.py

@@ -0,0 +1,143 @@
+# 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.
+# ============================================================================
+"""GenericNetwork module."""
+import os
+
+import click
+
+from mindinsight.wizard.base.network import BaseNetwork
+from mindinsight.wizard.base.templates import TemplateManager
+from mindinsight.wizard.base.utility import process_prompt_choice, load_dataset_maker
+from mindinsight.wizard.conf.constants import TEMPLATES_BASE_DIR
+from mindinsight.wizard.conf.constants import QUESTION_START
+
+
+class GenericNetwork(BaseNetwork):
+    """BaseNetwork code generator."""
+    name = 'GenericNetwork'
+    supported_datasets = []
+    supported_loss_functions = []
+    supported_optimizers = []
+
+    def __init__(self):
+        self._dataset_maker = None
+        template_dir = os.path.join(TEMPLATES_BASE_DIR, 'network', self.name.lower())
+        self.network_template_manager = TemplateManager(os.path.join(template_dir, 'src'))
+        self.common_template_manager = TemplateManager(template_dir, ['src', 'dataset'])
+
+    def configure(self, settings=None):
+        """
+        Configure the network options.
+
+        If settings is not None, then use the input settings to configure the network.
+
+        Args:
+            settings (dict): Settings to configure, format is {'options': value}.
+                Example:
+                    {
+                        "loss": "SoftmaxCrossEntropyWithLogits",
+                        "optimizer": "Momentum",
+                        "dataset": "Cifar10"
+                    }
+
+        Returns:
+            dict, configuration value to network.
+        """
+        if settings:
+            config = {'loss': settings['loss'],
+                      'optimizer': settings['optimizer'],
+                      'dataset': settings['dataset']}
+            self.settings.update(config)
+            return config
+        loss = self.ask_loss_function()
+        optimizer = self.ask_optimizer()
+        dataset = self.ask_dataset()
+        self._dataset_maker = load_dataset_maker(dataset)
+        self._dataset_maker.set_network(self)
+        dataset_config = self._dataset_maker.configure()
+
+        config = {'loss': loss,
+                  'optimizer': optimizer,
+                  'dataset': dataset}
+        config.update(dataset_config)
+        self.settings.update(config)
+        return config
+
+    @staticmethod
+    def ask_choice(prompt_head, content_list, default_value=None):
+        """Ask user to get selected result."""
+        if default_value is None:
+            default_choice = 1  # start from 1 in prompt message.
+            default_value = content_list[default_choice - 1]
+
+        choice_contents = content_list[:]
+        choice_contents.sort(reverse=False)
+        default_choice = choice_contents.index(default_value) + 1  # start from 1 in prompt message.
+
+        prompt_msg = '{}:\n{}\n'.format(
+            prompt_head,
+            '\n'.join(f'{idx: >4}: {choice}' for idx, choice in enumerate(choice_contents, start=1))
+        )
+        prompt_type = click.IntRange(min=1, max=len(choice_contents))
+        choice = click.prompt(prompt_msg, type=prompt_type, hide_input=False, show_choices=False,
+                              confirmation_prompt=False, default=default_choice,
+                              value_proc=lambda x: process_prompt_choice(x, prompt_type))
+        return choice_contents[choice - 1]
+
+    def ask_loss_function(self):
+        """Select loss function by user."""
+        return self.ask_choice('%sPlease select a loss function' % QUESTION_START, self.supported_loss_functions)
+
+    def ask_optimizer(self):
+        """Select optimizer by user."""
+        return self.ask_choice('%sPlease select an optimizer' % QUESTION_START, self.supported_optimizers)
+
+    def ask_dataset(self):
+        """Select dataset by user."""
+        return self.ask_choice('%sPlease select a dataset' % QUESTION_START, self.supported_datasets)
+
+    def generate(self, **options):
+        """Generate network definition scripts."""
+        context = self.get_generate_context(**options)
+        network_source_files = self.network_template_manager.render(**context)
+        for source_file in network_source_files:
+            source_file.file_relative_path = os.path.join('src', source_file.file_relative_path)
+        dataset_source_files = self._dataset_maker.generate(**options)
+        for source_file in dataset_source_files:
+            source_file.file_relative_path = os.path.join('src', source_file.file_relative_path)
+
+        assemble_files = self._assemble(**options)
+        source_files = network_source_files + dataset_source_files + assemble_files
+        return source_files
+
+    def get_generate_context(self, **options):
+        """Get detailed info based on settings to network files."""
+        context = dict(options)
+        context.update(self.settings)
+        return context
+
+    def get_assemble_context(self, **options):
+        """Get detailed info based on settings to assemble files."""
+        context = dict(options)
+        context.update(self.settings)
+        return context
+
+    def _assemble(self, **options):
+        # generate train.py & eval.py & assemble scripts.
+        assemble_files = []
+        context = self.get_assemble_context(**options)
+        common_source_files = self.common_template_manager.render(**context)
+        assemble_files.extend(common_source_files)
+        return assemble_files

mindinsight/wizard/network/lenet.py

@@ -0,0 +1,24 @@
+# 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.
+# ============================================================================
+"""lenet module."""
+from mindinsight.wizard.network.generic_network import GenericNetwork
+
+
+class Network(GenericNetwork):
+    """Network code generator."""
+    name = 'lenet'
+    supported_datasets = ['MNIST']
+    supported_loss_functions = ['SoftmaxCrossEntropyWithLogits', 'SoftmaxCrossEntropyExpand']
+    supported_optimizers = ['Momentum', 'Lamb']

mindinsight/wizard/network/resnet50.py

@@ -0,0 +1,18 @@
+# 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.
+# ============================================================================
+"""alexnet module."""
+from mindinsight.wizard.network.generic_network import GenericNetwork
+
+
+class Network(GenericNetwork):
+    """Network code generator."""
+    name = 'resnet50'
+    supported_datasets = ['Cifar10', 'ImageNet']
+    supported_loss_functions = ['SoftmaxCrossEntropyWithLogits', 'SoftmaxCrossEntropyExpand']
+    supported_optimizers = ['Momentum', 'Lamb']

setup.py

@@ -207,6 +207,7 @@ if __name__ == '__main__':
             'console_scripts': [
                 'mindinsight=mindinsight.utils.command:main',
                 'mindconverter=mindinsight.mindconverter.cli:cli_entry',
+                'mindwizard=mindinsight.wizard.cli:cli_entry',
             ],
         },
         python_requires='>=3.7',