111 lines
4.2 KiB
Python
111 lines
4.2 KiB
Python
# 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.
|
|
# ============================================================================
|
|
"""dataset base and LeNet."""
|
|
import os
|
|
|
|
from mindspore import dataset as ds
|
|
from mindspore.common import dtype as mstype
|
|
from mindspore.dataset.transforms import c_transforms as C
|
|
from mindspore.dataset.vision import Inter
|
|
from mindspore.dataset.vision import c_transforms as CV
|
|
from mindspore import nn, Tensor
|
|
from mindspore.common.initializer import Normal
|
|
from mindspore.ops import operations as P
|
|
|
|
def create_mnist_dataset(mode='train', num_samples=2, batch_size=2):
|
|
"""create dataset for train or test"""
|
|
mnist_path = '/home/workspace/mindspore_dataset/mnist'
|
|
num_parallel_workers = 1
|
|
|
|
# define dataset
|
|
mnist_ds = ds.MnistDataset(os.path.join(mnist_path, mode), num_samples=num_samples, shuffle=False)
|
|
|
|
resize_height, resize_width = 32, 32
|
|
|
|
# define map operations
|
|
resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR) # Bilinear mode
|
|
rescale_nml_op = CV.Rescale(1 / 0.3081, -1 * 0.1307 / 0.3081)
|
|
rescale_op = CV.Rescale(1.0 / 255.0, shift=0.0)
|
|
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
|
|
mnist_ds = mnist_ds.batch(batch_size=batch_size, drop_remainder=True)
|
|
|
|
return mnist_ds
|
|
|
|
|
|
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))
|
|
|
|
self.scalar_summary = P.ScalarSummary()
|
|
self.image_summary = P.ImageSummary()
|
|
self.histogram_summary = P.HistogramSummary()
|
|
self.tensor_summary = P.TensorSummary()
|
|
self.channel = Tensor(num_channel)
|
|
|
|
def construct(self, x):
|
|
"""construct."""
|
|
self.image_summary('image', x)
|
|
x = self.conv1(x)
|
|
self.histogram_summary('histogram', x)
|
|
x = self.relu(x)
|
|
self.tensor_summary('tensor', x)
|
|
x = self.relu(x)
|
|
x = self.max_pool2d(x)
|
|
self.scalar_summary('scalar', self.channel)
|
|
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
|