diff --git a/mindspore-jina/MindsporeLeNet/lenet/.keep b/mindspore-jina/MindsporeLeNet/lenet/__init__.py similarity index 100% rename from mindspore-jina/MindsporeLeNet/lenet/.keep rename to mindspore-jina/MindsporeLeNet/lenet/__init__.py diff --git a/mindspore-jina/MindsporeLeNet/lenet/data/fashion/train/train-images-idx3-ubyte b/mindspore-jina/MindsporeLeNet/lenet/data/fashion/train/train-images-idx3-ubyte new file mode 100644 index 0000000..ff2f5a9 Binary files /dev/null and b/mindspore-jina/MindsporeLeNet/lenet/data/fashion/train/train-images-idx3-ubyte differ diff --git a/mindspore-jina/MindsporeLeNet/lenet/data/fashion/train/train-labels-idx1-ubyte b/mindspore-jina/MindsporeLeNet/lenet/data/fashion/train/train-labels-idx1-ubyte new file mode 100644 index 0000000..30424ca Binary files /dev/null and b/mindspore-jina/MindsporeLeNet/lenet/data/fashion/train/train-labels-idx1-ubyte differ diff --git a/mindspore-jina/MindsporeLeNet/lenet/src/__init__.py b/mindspore-jina/MindsporeLeNet/lenet/src/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/mindspore-jina/MindsporeLeNet/lenet/src/config.py b/mindspore-jina/MindsporeLeNet/lenet/src/config.py new file mode 100644 index 0000000..5c01352 --- /dev/null +++ b/mindspore-jina/MindsporeLeNet/lenet/src/config.py @@ -0,0 +1,33 @@ +# 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': 1, + 'batch_size': 32, + 'buffer_size': 1000, + 'image_height': 32, + 'image_width': 32, + 'save_checkpoint_steps': 1875, + 'keep_checkpoint_max': 10, + 'air_name': "lenet.air", +}) diff --git a/mindspore-jina/MindsporeLeNet/lenet/src/dataset.py b/mindspore-jina/MindsporeLeNet/lenet/src/dataset.py new file mode 100644 index 0000000..df9eecd --- /dev/null +++ b/mindspore-jina/MindsporeLeNet/lenet/src/dataset.py @@ -0,0 +1,60 @@ +# 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.vision.c_transforms as CV +import mindspore.dataset.transforms.c_transforms as C +from mindspore.dataset.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(operations=type_cast_op, input_columns="label", num_parallel_workers=num_parallel_workers) + mnist_ds = mnist_ds.map(operations=resize_op, input_columns="image", num_parallel_workers=num_parallel_workers) + mnist_ds = mnist_ds.map(operations=rescale_op, input_columns="image", num_parallel_workers=num_parallel_workers) + mnist_ds = mnist_ds.map(operations=rescale_nml_op, input_columns="image", num_parallel_workers=num_parallel_workers) + mnist_ds = mnist_ds.map(operations=hwc2chw_op, input_columns="image", 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 diff --git a/mindspore-jina/MindsporeLeNet/lenet/src/lenet.py b/mindspore-jina/MindsporeLeNet/lenet/src/lenet.py new file mode 100644 index 0000000..f34dedb --- /dev/null +++ b/mindspore-jina/MindsporeLeNet/lenet/src/lenet.py @@ -0,0 +1,61 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +"""LeNet.""" +import mindspore.nn as nn +from mindspore.common.initializer import Normal + + +class LeNet5(nn.Cell): + """ + Lenet network + + Args: + num_class (int): Number of classes. Default: 10. + num_channel (int): Number of channels. Default: 1. + + Returns: + Tensor, output tensor + Examples: + >>> LeNet(num_class=10) + + """ + def __init__(self, num_class=10, num_channel=1, include_top=True): + super(LeNet5, self).__init__() + self.conv1 = nn.Conv2d(num_channel, 6, 5, pad_mode='valid') + self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid') + self.relu = nn.ReLU() + self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2) + self.include_top = include_top + if self.include_top: + self.flatten = nn.Flatten() + self.fc1 = nn.Dense(16 * 5 * 5, 120, weight_init=Normal(0.02)) + self.fc2 = nn.Dense(120, 84, weight_init=Normal(0.02)) + self.fc3 = nn.Dense(84, num_class, weight_init=Normal(0.02)) + + + 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) + if not self.include_top: + return x + x = self.flatten(x) + x = self.relu(self.fc1(x)) + x = self.relu(self.fc2(x)) + x = self.fc3(x) + return x diff --git a/mindspore-jina/MindsporeLeNet/lenet/train.py b/mindspore-jina/MindsporeLeNet/lenet/train.py new file mode 100644 index 0000000..980b5e2 --- /dev/null +++ b/mindspore-jina/MindsporeLeNet/lenet/train.py @@ -0,0 +1,65 @@ +# 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 src.config import mnist_cfg as cfg +from src.dataset import create_dataset +from src.lenet import LeNet5 +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 mindspore.common import set_seed + + +parser = argparse.ArgumentParser(description='MindSpore Lenet Example') +parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend', 'GPU', 'CPU'], + help='device where the code will be implemented (default: Ascend)') +parser.add_argument('--data_path', type=str, default="./Data", + help='path where the dataset is saved') +parser.add_argument('--ckpt_path', type=str, default="./ckpt", help='if is test, must provide\ + path where the trained ckpt file') +args = parser.parse_args() +set_seed(1) + + +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) + if ds_train.get_dataset_size() == 0: + raise ValueError("Please check dataset size > 0 and batch_size <= dataset size") + + network = LeNet5(cfg.num_classes) + net_loss = nn.SoftmaxCrossEntropyWithLogits(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.save_checkpoint_steps, + keep_checkpoint_max=cfg.keep_checkpoint_max) + ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet", directory=args.ckpt_path, config=config_ck) + + if args.device_target != "Ascend": + model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()}) + else: + model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()}, amp_level="O2") + + print("============== Starting Training ==============") + model.train(cfg['epoch_size'], ds_train, callbacks=[time_cb, ckpoint_cb, LossMonitor()])