forked from mindspore-Ecosystem/mindspore
!21 update lenet , add alexnet in example
Merge pull request !21 from wukesong/add_lenet_alexnet
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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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alexnet_cfg = edict({
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'num_classes': 10,
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'learning_rate': 0.002,
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'momentum': 0.9,
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'epoch_size': 1,
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'batch_size': 32,
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'buffer_size': 1000,
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'image_height': 227,
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'image_width': 227,
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'save_checkpoint_steps': 1562,
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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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from config import alexnet_cfg as cfg
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import mindspore.dataset as ds
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import mindspore.dataset.transforms.c_transforms as C
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import mindspore.dataset.transforms.vision.c_transforms as CV
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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, status="train"):
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"""
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create dataset for train or test
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"""
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cifar_ds = ds.Cifar10Dataset(data_path)
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rescale = 1.0 / 255.0
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shift = 0.0
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resize_op = CV.Resize((cfg.image_height, cfg.image_width))
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rescale_op = CV.Rescale(rescale, shift)
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normalize_op = CV.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
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if status == "train":
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random_crop_op = CV.RandomCrop([32, 32], [4, 4, 4, 4])
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random_horizontal_op = CV.RandomHorizontalFlip()
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channel_swap_op = CV.HWC2CHW()
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typecast_op = C.TypeCast(mstype.int32)
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cifar_ds = cifar_ds.map(input_columns="label", operations=typecast_op)
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if status == "train":
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cifar_ds = cifar_ds.map(input_columns="image", operations=random_crop_op)
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cifar_ds = cifar_ds.map(input_columns="image", operations=random_horizontal_op)
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cifar_ds = cifar_ds.map(input_columns="image", operations=resize_op)
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cifar_ds = cifar_ds.map(input_columns="image", operations=rescale_op)
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cifar_ds = cifar_ds.map(input_columns="image", operations=normalize_op)
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cifar_ds = cifar_ds.map(input_columns="image", operations=channel_swap_op)
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cifar_ds = cifar_ds.shuffle(buffer_size=cfg.buffer_size)
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cifar_ds = cifar_ds.batch(batch_size, drop_remainder=True)
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cifar_ds = cifar_ds.repeat(repeat_size)
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return cifar_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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"""
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######################## eval alexnet example ########################
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eval alexnet 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 argparse
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from config import alexnet_cfg as cfg
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from dataset import create_dataset
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import mindspore.nn as nn
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from mindspore import context
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from mindspore.model_zoo.alexnet import AlexNet
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from mindspore.train import Model
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from mindspore.nn.metrics import Accuracy
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description='MindSpore AlexNet Example')
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parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend', 'GPU'],
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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="./", help='path where the dataset is saved')
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parser.add_argument('--ckpt_path', type=str, default="./ckpt", help='if is test, must provide\
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path where the trained ckpt file')
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parser.add_argument('--dataset_sink_mode', type=bool, default=False, help='dataset_sink_mode is False or True')
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args = parser.parse_args()
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context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, enable_mem_reuse=False)
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network = AlexNet(cfg.num_classes)
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loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
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repeat_size = cfg.epoch_size
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opt = nn.Momentum(network.trainable_params(), cfg.learning_rate, cfg.momentum)
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model = Model(network, loss, opt, metrics={"Accuracy": Accuracy()}) # test
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print("============== Starting Testing ==============")
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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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ds_eval = create_dataset(args.data_path,
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cfg.batch_size,
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1,
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"test")
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acc = model.eval(ds_eval, dataset_sink_mode=args.dataset_sink_mode)
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print("============== Accuracy:{} ==============".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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######################## train alexnet example ########################
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train alexnet and get network model files(.ckpt) :
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python train.py --data_path /YourDataPath
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"""
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import argparse
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from config import alexnet_cfg as cfg
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from dataset import create_dataset
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import mindspore.nn as nn
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from mindspore import context
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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.model_zoo.alexnet import AlexNet
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from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description='MindSpore AlexNet Example')
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parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend', 'GPU'],
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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="./", help='path where the dataset is saved')
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parser.add_argument('--ckpt_path', type=str, default="./ckpt", help='if is test, must provide\
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path where the trained ckpt file')
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parser.add_argument('--dataset_sink_mode', type=bool, default=False, help='dataset_sink_mode is False or True')
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args = parser.parse_args()
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context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, enable_mem_reuse=False)
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network = AlexNet(cfg.num_classes)
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loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
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opt = nn.Momentum(network.trainable_params(), cfg.learning_rate, cfg.momentum)
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model = Model(network, loss, opt, metrics={"Accuracy": Accuracy()}) # test
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print("============== Starting Training ==============")
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ds_train = create_dataset(args.data_path,
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cfg.batch_size,
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cfg.epoch_size,
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"train")
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config_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_checkpoint_steps,
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keep_checkpoint_max=cfg.keep_checkpoint_max)
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ckpoint_cb = ModelCheckpoint(prefix="checkpoint_alexnet", directory=args.ckpt_path, config=config_ck)
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model.train(cfg.epoch_size, ds_train, callbacks=[ckpoint_cb, LossMonitor()],
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dataset_sink_mode=args.dataset_sink_mode)
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@ -1,125 +0,0 @@
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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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######################## train and test lenet example ########################
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1. train lenet and get network model files(.ckpt) :
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python main.py --data_path /home/workspace/mindspore_dataset/Tutorial_Network/Lenet/MNIST_Data
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2. test lenet according to model file:
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python main.py --data_path /home/workspace/mindspore_dataset/Tutorial_Network/Lenet/MNIST_Data
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--mode test --ckpt_path checkpoint_lenet_1-1_1875.ckpt
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"""
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import os
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import argparse
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from config import mnist_cfg as cfg
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import mindspore.dataengine as de
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import mindspore.nn as nn
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from mindspore.model_zoo.lenet import LeNet5
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from mindspore import context, Tensor
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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, LossMonitor
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from mindspore.train import Model
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import mindspore.ops.operations as P
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import mindspore.transforms.c_transforms as C
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from mindspore.transforms import Inter
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from mindspore.nn.metrics import Accuracy
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from mindspore.ops import functional as F
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from mindspore.common import dtype as mstype
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class CrossEntropyLoss(nn.Cell):
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"""
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Define loss for network
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"""
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def __init__(self):
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super(CrossEntropyLoss, self).__init__()
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self.cross_entropy = P.SoftmaxCrossEntropyWithLogits()
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self.mean = P.ReduceMean()
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self.one_hot = P.OneHot()
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self.on_value = Tensor(1.0, mstype.float32)
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self.off_value = Tensor(0.0, mstype.float32)
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def construct(self, logits, label):
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label = self.one_hot(label, F.shape(logits)[1], self.on_value, self.off_value)
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loss = self.cross_entropy(logits, label)[0]
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loss = self.mean(loss, (-1,))
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return loss
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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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ds1 = de.MnistDataset(data_path)
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# apply map operations on images
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ds1 = ds1.map(input_columns="label", operations=C.TypeCast(mstype.int32))
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ds1 = ds1.map(input_columns="image", operations=C.Resize((cfg.image_height, cfg.image_width),
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interpolation=Inter.LINEAR),
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num_parallel_workers=num_parallel_workers)
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ds1 = ds1.map(input_columns="image", operations=C.Rescale(1 / 0.3081, -1 * 0.1307 / 0.3081),
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num_parallel_workers=num_parallel_workers)
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ds1 = ds1.map(input_columns="image", operations=C.Rescale(1.0 / 255.0, 0.0),
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num_parallel_workers=num_parallel_workers)
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ds1 = ds1.map(input_columns="image", operations=C.HWC2CHW(), num_parallel_workers=num_parallel_workers)
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# apply DatasetOps
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ds1 = ds1.shuffle(buffer_size=cfg.buffer_size) # 10000 as in LeNet train script
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ds1 = ds1.batch(batch_size, drop_remainder=True)
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ds1 = ds1.repeat(repeat_size)
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return ds1
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description='MindSpore MNIST Example')
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parser.add_argument('--device_target', type=str, default="Ascend", 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('--mode', type=str, default="train", choices=['train', 'test'],
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help='implement phase, set to train or test')
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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="", help='if mode is test, must provide\
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path where the trained ckpt file')
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args = parser.parse_args()
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context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
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network = LeNet5(cfg.num_classes)
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network.set_train()
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# net_loss = nn.SoftmaxCrossEntropyWithLogits() # support this loss soon
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net_loss = CrossEntropyLoss()
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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.save_checkpoint_steps,
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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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if args.mode == 'train': # train
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ds = create_dataset(os.path.join(args.data_path, args.mode), batch_size=cfg.batch_size,
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repeat_size=cfg.epoch_size)
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print("============== Starting Training ==============")
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model.train(cfg['epoch_size'], ds, callbacks=[ckpoint_cb, LossMonitor()], dataset_sink_mode=False)
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elif args.mode == 'test': # test
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print("============== Starting Testing ==============")
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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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ds_eval = create_dataset(os.path.join(args.data_path, "test"), 32, 1)
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acc = model.eval(ds_eval, dataset_sink_mode=False)
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print("============== Accuracy:{} ==============".format(acc))
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else:
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raise RuntimeError('mode should be train or test, rather than {}'.format(args.mode))
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@ -13,8 +13,9 @@
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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 main.py
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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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@ -23,7 +24,6 @@ mnist_cfg = edict({
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'momentum': 0.9,
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'epoch_size': 1,
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'batch_size': 32,
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'repeat_size': 1,
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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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|
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@ -0,0 +1,60 @@
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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");
|
||||
# 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.
|
||||
# ============================================================================
|
||||
"""
|
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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)
|
||||
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
|
||||
|
|
@ -0,0 +1,64 @@
|
|||
# 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
|
||||
from dataset import create_dataset
|
||||
from config import mnist_cfg as cfg
|
||||
import mindspore.nn as nn
|
||||
from mindspore.model_zoo.lenet import LeNet5
|
||||
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
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
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=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, enable_mem_reuse=False)
|
||||
|
||||
network = LeNet5(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.save_checkpoint_steps,
|
||||
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 Testing ==============")
|
||||
param_dict = load_checkpoint(args.ckpt_path)
|
||||
load_param_into_net(network, param_dict)
|
||||
ds_eval = create_dataset(os.path.join(args.data_path, "test"),
|
||||
cfg.batch_size,
|
||||
1)
|
||||
acc = model.eval(ds_eval, dataset_sink_mode=args.dataset_sink_mode)
|
||||
print("============== Accuracy:{} ==============".format(acc))
|
||||
|
|
@ -0,0 +1,58 @@
|
|||
# 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
|
||||
from config import mnist_cfg as cfg
|
||||
from dataset import create_dataset
|
||||
import mindspore.nn as nn
|
||||
from mindspore.model_zoo.lenet import LeNet5
|
||||
from mindspore import context
|
||||
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor
|
||||
from mindspore.train import Model
|
||||
from mindspore.nn.metrics import Accuracy
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
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('--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, enable_mem_reuse=False)
|
||||
|
||||
network = LeNet5(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)
|
||||
config_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_checkpoint_steps,
|
||||
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()})
|
||||
|
||||
ds_train = create_dataset(os.path.join(args.data_path, "train"),
|
||||
cfg.batch_size,
|
||||
cfg.epoch_size)
|
||||
print("============== Starting Training ==============")
|
||||
model.train(cfg['epoch_size'], ds_train, callbacks=[ckpoint_cb, LossMonitor()],
|
||||
dataset_sink_mode=args.dataset_sink_mode)
|
||||
|
|
@ -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
|
||||
|
||||
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) # 0.02
|
||||
|
||||
|
||||
class AlexNet(nn.Cell):
|
||||
"""
|
||||
Alexnet
|
||||
"""
|
||||
def __init__(self, num_classes=10):
|
||||
super(AlexNet, self).__init__()
|
||||
self.batch_size = 32
|
||||
self.conv1 = conv(3, 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 = nn.MaxPool2d(kernel_size=3, stride=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
|
||||
|
|
@ -13,7 +13,6 @@
|
|||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""LeNet."""
|
||||
import mindspore.ops.operations as P
|
||||
import mindspore.nn as nn
|
||||
from mindspore.common.initializer import TruncatedNormal
|
||||
|
||||
|
|
@ -62,7 +61,7 @@ class LeNet5(nn.Cell):
|
|||
self.fc3 = fc_with_initialize(84, self.num_class)
|
||||
self.relu = nn.ReLU()
|
||||
self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
|
||||
self.reshape = P.Reshape()
|
||||
self.flatten = nn.Flatten()
|
||||
|
||||
def construct(self, x):
|
||||
x = self.conv1(x)
|
||||
|
|
@ -71,7 +70,7 @@ class LeNet5(nn.Cell):
|
|||
x = self.conv2(x)
|
||||
x = self.relu(x)
|
||||
x = self.max_pool2d(x)
|
||||
x = self.reshape(x, (self.batch_size, -1))
|
||||
x = self.flatten(x)
|
||||
x = self.fc1(x)
|
||||
x = self.relu(x)
|
||||
x = self.fc2(x)
|
||||
|
|
|
|||
Loading…
Reference in New Issue