!2368 add auto create graph for aware quantization training demo

Merge pull request !2368 from chenzhongming/abc

model_zoo/lenet_quant/README.md

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+# LeNet Quantization Example
+
+## Description
+
+Training LeNet with MNIST dataset in MindSpore with aware quantization trainging.
+
+This is the simple and basic tutorial for constructing a network in MindSpore with quantization.
+
+## Requirements
+
+- Install [MindSpore](https://www.mindspore.cn/install/en).
+
+- Download the MNIST dataset, the directory structure is as follows:
+
+```
+└─MNIST_Data
+    ├─test
+    │      t10k-images.idx3-ubyte
+    │      t10k-labels.idx1-ubyte
+    └─train
+           train-images.idx3-ubyte
+           train-labels.idx1-ubyte
+```
+
+## Running the example
+
+```python
+# train LeNet, hyperparameter setting in config.py
+python train.py --data_path MNIST_Data
+```
+
+You will get the loss value of each step as following:
+
+```bash
+Epoch: [  1/ 10] step: [  1 / 900], loss: [2.3040/2.5234], time: [1.300234]
+...
+Epoch: [ 10/ 10] step: [887 / 900], loss: [0.0113/0.0223], time: [1.300234]
+Epoch: [ 10/ 10] step: [888 / 900], loss: [0.0334/0.0223], time: [1.300234]
+Epoch: [ 10/ 10] step: [889 / 900], loss: [0.0233/0.0223], time: [1.300234]
+...
+```
+
+Then, evaluate LeNet according to network model
+
+```python
+python eval.py --data_path MNIST_Data --ckpt_path checkpoint_lenet-1_1875.ckpt
+```
+
+## Note
+Here are some optional parameters:
+
+```bash
+--device_target {Ascend,GPU,CPU}
+    device where the code will be implemented (default: Ascend)
+--data_path DATA_PATH
+    path where the dataset is saved
+--dataset_sink_mode DATASET_SINK_MODE
+    dataset_sink_mode is False or True
+```
+
+You can run ```python train.py -h``` or ```python eval.py -h``` to get more information.

model_zoo/lenet_quant/eval.py

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+# 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 os
+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.callback import ModelCheckpoint, CheckpointConfig
+from mindspore.train import Model
+from mindspore.nn.metrics import Accuracy
+from src.dataset import create_dataset
+from src.config import mnist_cfg as cfg
+from src.lenet_fusion import LeNet5 as LeNet5Fusion
+
+parser = argparse.ArgumentParser(description='MindSpore MNIST 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('--data_path', type=str, default="./MNIST_Data",
+                    help='path where the dataset is saved')
+parser.add_argument('--ckpt_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=True,
+                    help='dataset_sink_mode is False or True')
+args = parser.parse_args()
+
+if __name__ == "__main__":
+    context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
+    ds_eval = create_dataset(os.path.join(args.data_path, "test"), cfg.batch_size, 1)
+    step_size = ds_eval.get_dataset_size()
+
+    network = LeNet5Fusion(cfg.num_classes)
+    net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
+    repeat_size = cfg.epoch_size
+    net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum)
+    config_ck = CheckpointConfig(save_checkpoint_steps=cfg.epoch_size * step_size,
+                                 keep_checkpoint_max=cfg.keep_checkpoint_max)
+    ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet", config=config_ck)
+    model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
+
+    param_dict = load_checkpoint(args.ckpt_path)
+    load_param_into_net(network, param_dict)
+
+    print("============== Starting Testing ==============")
+    acc = model.eval(ds_eval, dataset_sink_mode=args.dataset_sink_mode)
+    print("============== {} ==============".format(acc))

model_zoo/lenet_quant/eval_quant.py

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+# 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 os
+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.callback import ModelCheckpoint, CheckpointConfig
+from mindspore.train import Model
+from mindspore.nn.metrics import Accuracy
+from mindspore.train.quant import quant
+from src.dataset import create_dataset
+from src.config import mnist_cfg as cfg
+from src.lenet_fusion import LeNet5 as LeNet5Fusion
+
+parser = argparse.ArgumentParser(description='MindSpore MNIST 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('--data_path', type=str, default="./MNIST_Data",
+                    help='path where the dataset is saved')
+parser.add_argument('--ckpt_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=True,
+                    help='dataset_sink_mode is False or True')
+args = parser.parse_args()
+
+if __name__ == "__main__":
+    context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
+    ds_eval = create_dataset(os.path.join(args.data_path, "test"), cfg.batch_size, 1)
+    step_size = ds_eval.get_dataset_size()
+
+    # define funsion network
+    network = LeNet5Fusion(cfg.num_classes)
+    # convert funsion netwrok to aware quantizaiton network
+    network = quant.convert_quant_network(network, quant_delay=0, bn_fold=False, freeze_bn=10000)
+
+    net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
+    net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum)
+    config_ck = CheckpointConfig(save_checkpoint_steps=cfg.epoch_size * step_size,
+                                 keep_checkpoint_max=cfg.keep_checkpoint_max)
+    ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet", config=config_ck)
+    model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
+
+    # load aware quantizaiton network checkpoint
+    param_dict = load_checkpoint(args.ckpt_path)
+    load_param_into_net(network, param_dict)
+
+    print("============== Starting Testing ==============")
+    acc = model.eval(ds_eval, dataset_sink_mode=args.dataset_sink_mode)
+    print("============== {} ==============".format(acc))

model_zoo/lenet_quant/src/config.py

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+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""
+network config setting, will be used in train.py
+"""
+
+from easydict import EasyDict as edict
+
+mnist_cfg = edict({
+    'num_classes': 10,
+    'lr': 0.01,
+    'momentum': 0.9,
+    'epoch_size': 10,
+    'batch_size': 64,
+    'buffer_size': 1000,
+    'image_height': 32,
+    'image_width': 32,
+    'keep_checkpoint_max': 10,
+})

model_zoo/lenet_quant/src/dataset.py

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+# 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 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
+
+
+def create_dataset(data_path, batch_size=32, repeat_size=1,
+                   num_parallel_workers=1):
+    """
+    create dataset for train or test
+    """
+    # define dataset
+    mnist_ds = ds.MnistDataset(data_path)
+
+    resize_height, resize_width = 32, 32
+    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, num_parallel_workers=num_parallel_workers)
+    mnist_ds = mnist_ds.map(input_columns="image", operations=resize_op, num_parallel_workers=num_parallel_workers)
+    mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_op, num_parallel_workers=num_parallel_workers)
+    mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_nml_op, num_parallel_workers=num_parallel_workers)
+    mnist_ds = mnist_ds.map(input_columns="image", operations=hwc2chw_op, num_parallel_workers=num_parallel_workers)
+
+    # apply DatasetOps
+    buffer_size = 10000
+    mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size)  # 10000 as in LeNet train script
+    mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True)
+    mnist_ds = mnist_ds.repeat(repeat_size)
+
+    return mnist_ds

model_zoo/lenet_quant/src/lenet.py

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+# 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
+
+
+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 = nn.Conv2d(channel, 6, 5)
+        self.conv2 = nn.Conv2d(6, 16, 5)
+        self.fc1 = nn.Dense(16 * 5 * 5, 120)
+        self.fc2 = nn.Dense(120, 84)
+        self.fc3 = nn.Dense(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

model_zoo/lenet_quant/src/lenet_fusion.py

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+# 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
+
+
+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
+
+        # change `nn.Conv2d` to `nn.Conv2dBnAct`
+        self.conv1 = nn.Conv2dBnAct(channel, 6, 5, activation='relu')
+        self.conv2 = nn.Conv2dBnAct(6, 16, 5, activation='relu')
+        # change `nn.Dense` to `nn.DenseBnAct`
+        self.fc1 = nn.DenseBnAct(16 * 5 * 5, 120, activation='relu')
+        self.fc2 = nn.DenseBnAct(120, 84, activation='relu')
+        self.fc3 = nn.DenseBnAct(84, self.num_class)
+
+        self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.flatten = nn.Flatten()
+
+    def construct(self, x):
+        x = self.conv1(x)
+        x = self.max_pool2d(x)
+        x = self.conv2(x)
+        x = self.max_pool2d(x)
+        x = self.flatten(x)
+        x = self.fc1(x)
+        x = self.fc2(x)
+        x = self.fc3(x)
+        return x

model_zoo/lenet_quant/train.py

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+# 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
+from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
+from mindspore.train import Model
+from mindspore.nn.metrics import Accuracy
+from src.dataset import create_dataset
+from src.config import mnist_cfg as cfg
+from src.lenet_fusion import LeNet5 as LeNet5Fusion
+
+parser = argparse.ArgumentParser(description='MindSpore MNIST 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('--data_path', type=str, default="./MNIST_Data",
+                    help='path where the dataset is saved')
+parser.add_argument('--ckpt_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=True,
+                    help='dataset_sink_mode is False or True')
+args = parser.parse_args()
+
+if __name__ == "__main__":
+    context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
+    ds_train = create_dataset(os.path.join(args.data_path, "train"), cfg.batch_size, cfg.epoch_size)
+    step_size = ds_train.get_dataset_size()
+
+    network = LeNet5Fusion(cfg.num_classes)
+    net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
+    net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum)
+    time_cb = TimeMonitor(data_size=ds_train.get_dataset_size())
+    config_ck = CheckpointConfig(save_checkpoint_steps=cfg.epoch_size * step_size,
+                                 keep_checkpoint_max=cfg.keep_checkpoint_max)
+    ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet", 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_mode)
+    print("============== End Training ==============")

model_zoo/lenet_quant/train_quant.py

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+# 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
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
+from mindspore.train import Model
+from mindspore.nn.metrics import Accuracy
+from mindspore.train.quant import quant
+from src.dataset import create_dataset
+from src.config import mnist_cfg as cfg
+from src.lenet_fusion import LeNet5 as LeNet5Fusion
+
+parser = argparse.ArgumentParser(description='MindSpore MNIST 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('--data_path', type=str, default="./MNIST_Data",
+                    help='path where the dataset is saved')
+parser.add_argument('--ckpt_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=True,
+                    help='dataset_sink_mode is False or True')
+args = parser.parse_args()
+
+if __name__ == "__main__":
+    context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
+    ds_train = create_dataset(os.path.join(args.data_path, "train"), cfg.batch_size, cfg.epoch_size)
+    step_size = ds_train.get_dataset_size()
+
+    # define funsion network
+    network = LeNet5Fusion(cfg.num_classes)
+    # load aware quantizaiton network checkpoint
+    param_dict = load_checkpoint(args.ckpt_path)
+    load_param_into_net(network, param_dict)
+    # convert funsion netwrok to aware quantizaiton network
+    network = quant.convert_quant_network(network, quant_delay=0, bn_fold=False, freeze_bn=10000)
+
+    net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
+    net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum)
+    time_cb = TimeMonitor(data_size=ds_train.get_dataset_size())
+    config_ck = CheckpointConfig(save_checkpoint_steps=cfg.epoch_size * step_size,
+                                 keep_checkpoint_max=cfg.keep_checkpoint_max)
+    ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet", 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_mode)
+    print("============== End Training ==============")