forked from huawei/mindspore2022
105 lines
3.6 KiB
Python
105 lines
3.6 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.
|
|
# ============================================================================
|
|
"""VGG."""
|
|
import mindspore.nn as nn
|
|
from mindspore.common.initializer import initializer
|
|
import mindspore.common.dtype as mstype
|
|
|
|
def _make_layer(base, batch_norm):
|
|
"""Make stage network of VGG."""
|
|
layers = []
|
|
in_channels = 3
|
|
for v in base:
|
|
if v == 'M':
|
|
layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
|
|
else:
|
|
weight_shape = (v, in_channels, 3, 3)
|
|
weight = initializer('XavierUniform', shape=weight_shape, dtype=mstype.float32).to_tensor()
|
|
conv2d = nn.Conv2d(in_channels=in_channels,
|
|
out_channels=v,
|
|
kernel_size=3,
|
|
padding=0,
|
|
pad_mode='same',
|
|
weight_init=weight)
|
|
if batch_norm:
|
|
layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU()]
|
|
else:
|
|
layers += [conv2d, nn.ReLU()]
|
|
in_channels = v
|
|
return nn.SequentialCell(layers)
|
|
|
|
|
|
class Vgg(nn.Cell):
|
|
"""
|
|
VGG network definition.
|
|
|
|
Args:
|
|
base (list): Configuration for different layers, mainly the channel number of Conv layer.
|
|
num_classes (int): Class numbers. Default: 1000.
|
|
batch_norm (bool): Whether to do the batchnorm. Default: False.
|
|
batch_size (int): Batch size. Default: 1.
|
|
|
|
Returns:
|
|
Tensor, infer output tensor.
|
|
|
|
Examples:
|
|
>>> Vgg([64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
|
|
>>> num_classes=1000, batch_norm=False, batch_size=1)
|
|
"""
|
|
|
|
def __init__(self, base, num_classes=1000, batch_norm=False, batch_size=1):
|
|
super(Vgg, self).__init__()
|
|
_ = batch_size
|
|
self.layers = _make_layer(base, batch_norm=batch_norm)
|
|
self.flatten = nn.Flatten()
|
|
self.classifier = nn.SequentialCell([
|
|
nn.Dense(512 * 7 * 7, 4096),
|
|
nn.ReLU(),
|
|
nn.Dense(4096, 4096),
|
|
nn.ReLU(),
|
|
nn.Dense(4096, num_classes)])
|
|
|
|
def construct(self, x):
|
|
x = self.layers(x)
|
|
x = self.flatten(x)
|
|
x = self.classifier(x)
|
|
return x
|
|
|
|
|
|
cfg = {
|
|
'11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
|
|
'13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
|
|
'16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
|
|
'19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
|
|
}
|
|
|
|
|
|
def vgg16(num_classes=1000):
|
|
"""
|
|
Get Vgg16 neural network with batch normalization.
|
|
|
|
Args:
|
|
num_classes (int): Class numbers. Default: 1000.
|
|
|
|
Returns:
|
|
Cell, cell instance of Vgg16 neural network with batch normalization.
|
|
|
|
Examples:
|
|
>>> vgg16(num_classes=1000)
|
|
"""
|
|
|
|
net = Vgg(cfg['16'], num_classes=num_classes, batch_norm=True)
|
|
return net
|