!2368 add auto create graph for aware quantization training demo
Merge pull request !2368 from chenzhongming/abc
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cb3bbf3c7a
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# LeNet Quantization Example
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## Description
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Training LeNet with MNIST dataset in MindSpore with aware quantization trainging.
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This is the simple and basic tutorial for constructing a network in MindSpore with quantization.
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## Requirements
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- Install [MindSpore](https://www.mindspore.cn/install/en).
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- Download the MNIST dataset, the directory structure is as follows:
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```
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└─MNIST_Data
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├─test
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│ t10k-images.idx3-ubyte
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│ t10k-labels.idx1-ubyte
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└─train
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train-images.idx3-ubyte
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train-labels.idx1-ubyte
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```
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## Running the example
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```python
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# train LeNet, hyperparameter setting in config.py
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python train.py --data_path MNIST_Data
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```
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You will get the loss value of each step as following:
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```bash
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Epoch: [ 1/ 10] step: [ 1 / 900], loss: [2.3040/2.5234], time: [1.300234]
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...
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Epoch: [ 10/ 10] step: [887 / 900], loss: [0.0113/0.0223], time: [1.300234]
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Epoch: [ 10/ 10] step: [888 / 900], loss: [0.0334/0.0223], time: [1.300234]
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Epoch: [ 10/ 10] step: [889 / 900], loss: [0.0233/0.0223], time: [1.300234]
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...
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```
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Then, evaluate LeNet according to network model
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```python
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python eval.py --data_path MNIST_Data --ckpt_path checkpoint_lenet-1_1875.ckpt
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```
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## Note
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Here are some optional parameters:
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```bash
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--device_target {Ascend,GPU,CPU}
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device where the code will be implemented (default: Ascend)
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--data_path DATA_PATH
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path where the dataset is saved
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--dataset_sink_mode DATASET_SINK_MODE
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dataset_sink_mode is False or True
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```
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You can run ```python train.py -h``` or ```python eval.py -h``` to get more information.
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""
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######################## eval lenet example ########################
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eval lenet according to model file:
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python eval.py --data_path /YourDataPath --ckpt_path Your.ckpt
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"""
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import os
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import argparse
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import mindspore.nn as nn
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from mindspore import context
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from mindspore.train.callback import ModelCheckpoint, CheckpointConfig
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from mindspore.train import Model
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from mindspore.nn.metrics import Accuracy
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from src.dataset import create_dataset
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from src.config import mnist_cfg as cfg
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from src.lenet_fusion import LeNet5 as LeNet5Fusion
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parser = argparse.ArgumentParser(description='MindSpore MNIST Example')
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parser.add_argument('--device_target', type=str, default="Ascend",
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choices=['Ascend', 'GPU', 'CPU'],
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help='device where the code will be implemented (default: Ascend)')
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parser.add_argument('--data_path', type=str, default="./MNIST_Data",
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help='path where the dataset is saved')
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parser.add_argument('--ckpt_path', type=str, default="",
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help='if mode is test, must provide path where the trained ckpt file')
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parser.add_argument('--dataset_sink_mode', type=bool, default=True,
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help='dataset_sink_mode is False or True')
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args = parser.parse_args()
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if __name__ == "__main__":
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context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
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ds_eval = create_dataset(os.path.join(args.data_path, "test"), cfg.batch_size, 1)
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step_size = ds_eval.get_dataset_size()
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network = LeNet5Fusion(cfg.num_classes)
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net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
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repeat_size = cfg.epoch_size
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net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum)
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config_ck = CheckpointConfig(save_checkpoint_steps=cfg.epoch_size * step_size,
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keep_checkpoint_max=cfg.keep_checkpoint_max)
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ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet", config=config_ck)
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model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
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param_dict = load_checkpoint(args.ckpt_path)
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load_param_into_net(network, param_dict)
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print("============== Starting Testing ==============")
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acc = model.eval(ds_eval, dataset_sink_mode=args.dataset_sink_mode)
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print("============== {} ==============".format(acc))
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""
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######################## eval lenet example ########################
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eval lenet according to model file:
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python eval.py --data_path /YourDataPath --ckpt_path Your.ckpt
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"""
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import os
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import argparse
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import mindspore.nn as nn
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from mindspore import context
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from mindspore.train.callback import ModelCheckpoint, CheckpointConfig
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from mindspore.train import Model
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from mindspore.nn.metrics import Accuracy
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from mindspore.train.quant import quant
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from src.dataset import create_dataset
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from src.config import mnist_cfg as cfg
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from src.lenet_fusion import LeNet5 as LeNet5Fusion
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parser = argparse.ArgumentParser(description='MindSpore MNIST Example')
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parser.add_argument('--device_target', type=str, default="Ascend",
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choices=['Ascend', 'GPU', 'CPU'],
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help='device where the code will be implemented (default: Ascend)')
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parser.add_argument('--data_path', type=str, default="./MNIST_Data",
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help='path where the dataset is saved')
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parser.add_argument('--ckpt_path', type=str, default="",
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help='if mode is test, must provide path where the trained ckpt file')
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parser.add_argument('--dataset_sink_mode', type=bool, default=True,
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help='dataset_sink_mode is False or True')
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args = parser.parse_args()
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if __name__ == "__main__":
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context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
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ds_eval = create_dataset(os.path.join(args.data_path, "test"), cfg.batch_size, 1)
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step_size = ds_eval.get_dataset_size()
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# define funsion network
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network = LeNet5Fusion(cfg.num_classes)
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# convert funsion netwrok to aware quantizaiton network
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network = quant.convert_quant_network(network, quant_delay=0, bn_fold=False, freeze_bn=10000)
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net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
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net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum)
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config_ck = CheckpointConfig(save_checkpoint_steps=cfg.epoch_size * step_size,
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keep_checkpoint_max=cfg.keep_checkpoint_max)
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ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet", config=config_ck)
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model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
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# load aware quantizaiton network checkpoint
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param_dict = load_checkpoint(args.ckpt_path)
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load_param_into_net(network, param_dict)
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print("============== Starting Testing ==============")
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acc = model.eval(ds_eval, dataset_sink_mode=args.dataset_sink_mode)
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print("============== {} ==============".format(acc))
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""
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network config setting, will be used in train.py
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"""
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from easydict import EasyDict as edict
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mnist_cfg = edict({
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'num_classes': 10,
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'lr': 0.01,
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'momentum': 0.9,
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'epoch_size': 10,
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'batch_size': 64,
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'buffer_size': 1000,
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'image_height': 32,
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'image_width': 32,
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'keep_checkpoint_max': 10,
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})
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""
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Produce the dataset
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"""
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import mindspore.dataset as ds
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import mindspore.dataset.transforms.vision.c_transforms as CV
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import mindspore.dataset.transforms.c_transforms as C
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from mindspore.dataset.transforms.vision import Inter
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from mindspore.common import dtype as mstype
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def create_dataset(data_path, batch_size=32, repeat_size=1,
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num_parallel_workers=1):
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"""
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create dataset for train or test
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"""
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# define dataset
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mnist_ds = ds.MnistDataset(data_path)
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resize_height, resize_width = 32, 32
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rescale = 1.0 / 255.0
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shift = 0.0
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rescale_nml = 1 / 0.3081
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shift_nml = -1 * 0.1307 / 0.3081
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# define map operations
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resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR) # Bilinear mode
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rescale_nml_op = CV.Rescale(rescale_nml, shift_nml)
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rescale_op = CV.Rescale(rescale, shift)
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hwc2chw_op = CV.HWC2CHW()
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type_cast_op = C.TypeCast(mstype.int32)
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# apply map operations on images
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mnist_ds = mnist_ds.map(input_columns="label", operations=type_cast_op, num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(input_columns="image", operations=resize_op, num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_op, num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_nml_op, num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(input_columns="image", operations=hwc2chw_op, num_parallel_workers=num_parallel_workers)
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# apply DatasetOps
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buffer_size = 10000
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mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size) # 10000 as in LeNet train script
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mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True)
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mnist_ds = mnist_ds.repeat(repeat_size)
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return mnist_ds
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
|
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""LeNet."""
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import mindspore.nn as nn
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class LeNet5(nn.Cell):
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"""
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Lenet network
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Args:
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num_class (int): Num classes. Default: 10.
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Returns:
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Tensor, output tensor
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Examples:
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>>> LeNet(num_class=10)
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"""
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def __init__(self, num_class=10, channel=1):
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super(LeNet5, self).__init__()
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self.num_class = num_class
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self.conv1 = nn.Conv2d(channel, 6, 5)
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self.conv2 = nn.Conv2d(6, 16, 5)
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self.fc1 = nn.Dense(16 * 5 * 5, 120)
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self.fc2 = nn.Dense(120, 84)
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self.fc3 = nn.Dense(84, self.num_class)
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self.relu = nn.ReLU()
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self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
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self.flatten = nn.Flatten()
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def construct(self, x):
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x = self.conv1(x)
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x = self.relu(x)
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x = self.max_pool2d(x)
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x = self.conv2(x)
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x = self.relu(x)
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x = self.max_pool2d(x)
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x = self.flatten(x)
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x = self.fc1(x)
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x = self.relu(x)
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x = self.fc2(x)
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x = self.relu(x)
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x = self.fc3(x)
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return x
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# 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
|
||||
#
|
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# 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.
|
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# ============================================================================
|
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"""LeNet."""
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import mindspore.nn as nn
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class LeNet5(nn.Cell):
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"""
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Lenet network
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Args:
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num_class (int): Num classes. Default: 10.
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Returns:
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Tensor, output tensor
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Examples:
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>>> LeNet(num_class=10)
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"""
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def __init__(self, num_class=10, channel=1):
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super(LeNet5, self).__init__()
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self.num_class = num_class
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# change `nn.Conv2d` to `nn.Conv2dBnAct`
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self.conv1 = nn.Conv2dBnAct(channel, 6, 5, activation='relu')
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self.conv2 = nn.Conv2dBnAct(6, 16, 5, activation='relu')
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# change `nn.Dense` to `nn.DenseBnAct`
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self.fc1 = nn.DenseBnAct(16 * 5 * 5, 120, activation='relu')
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self.fc2 = nn.DenseBnAct(120, 84, activation='relu')
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self.fc3 = nn.DenseBnAct(84, self.num_class)
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self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
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self.flatten = nn.Flatten()
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def construct(self, x):
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x = self.conv1(x)
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x = self.max_pool2d(x)
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x = self.conv2(x)
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x = self.max_pool2d(x)
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x = self.flatten(x)
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x = self.fc1(x)
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x = self.fc2(x)
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x = self.fc3(x)
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return x
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# Copyright 2020 Huawei Technologies Co., Ltd
|
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#
|
||||
# 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
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import argparse
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import mindspore.nn as nn
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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 ==============")
|
||||
|
|
@ -0,0 +1,70 @@
|
|||
# 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 ==============")
|
||||
Loading…
Reference in New Issue