!1497 add mobilenet file for ssd net

Merge pull request !1497 from chengxb7532/master
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mindspore-ci-bot 2020-05-27 15:11:31 +08:00 committed by Gitee
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# 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.
# ============================================================================
"""MobileNetV2 model define"""
import numpy as np
import mindspore.nn as nn
from mindspore.ops import operations as P
from mindspore.ops.operations import TensorAdd
from mindspore import Parameter, Tensor
from mindspore.common.initializer import initializer
__all__ = ['MobileNetV2', 'mobilenet_v2']
def _make_divisible(v, divisor, min_value=None):
"""
This function is taken from the original tf repo.
It ensures that all layers have a channel number that is divisible by 8
It can be seen here:
https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
:param v:
:param divisor:
:param min_value:
:return:
"""
if min_value is None:
min_value = divisor
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
# Make sure that round down does not go down by more than 10%.
if new_v < 0.9 * v:
new_v += divisor
return new_v
class GlobalAvgPooling(nn.Cell):
"""
Global avg pooling definition.
Args:
Returns:
Tensor, output tensor.
Examples:
>>> GlobalAvgPooling()
"""
def __init__(self):
super(GlobalAvgPooling, self).__init__()
self.mean = P.ReduceMean(keep_dims=False)
def construct(self, x):
x = self.mean(x, (2, 3))
return x
class DepthwiseConv(nn.Cell):
"""
Depthwise Convolution warpper definition.
Args:
in_planes (int): Input channel.
kernel_size (int): Input kernel size.
stride (int): Stride size.
pad_mode (str): pad mode in (pad, same, valid)
channel_multiplier (int): Output channel multiplier
has_bias (bool): has bias or not
Returns:
Tensor, output tensor.
Examples:
>>> DepthwiseConv(16, 3, 1, 'pad', 1, channel_multiplier=1)
"""
def __init__(self, in_planes, kernel_size, stride, pad_mode, pad, channel_multiplier=1, has_bias=False):
super(DepthwiseConv, self).__init__()
self.has_bias = has_bias
self.in_channels = in_planes
self.channel_multiplier = channel_multiplier
self.out_channels = in_planes * channel_multiplier
self.kernel_size = (kernel_size, kernel_size)
self.depthwise_conv = P.DepthwiseConv2dNative(channel_multiplier=channel_multiplier,
kernel_size=self.kernel_size,
stride=stride, pad_mode=pad_mode, pad=pad)
self.bias_add = P.BiasAdd()
weight_shape = [channel_multiplier, in_planes, *self.kernel_size]
self.weight = Parameter(initializer('ones', weight_shape), name='weight')
if has_bias:
bias_shape = [channel_multiplier * in_planes]
self.bias = Parameter(initializer('zeros', bias_shape), name='bias')
else:
self.bias = None
def construct(self, x):
output = self.depthwise_conv(x, self.weight)
if self.has_bias:
output = self.bias_add(output, self.bias)
return output
class ConvBNReLU(nn.Cell):
"""
Convolution/Depthwise fused with Batchnorm and ReLU block definition.
Args:
in_planes (int): Input channel.
out_planes (int): Output channel.
kernel_size (int): Input kernel size.
stride (int): Stride size for the first convolutional layer. Default: 1.
groups (int): channel group. Convolution is 1 while Depthiwse is input channel. Default: 1.
Returns:
Tensor, output tensor.
Examples:
>>> ConvBNReLU(16, 256, kernel_size=1, stride=1, groups=1)
"""
def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1):
super(ConvBNReLU, self).__init__()
padding = (kernel_size - 1) // 2
if groups == 1:
conv = nn.Conv2d(in_planes, out_planes, kernel_size, stride, pad_mode='pad',
padding=padding)
else:
conv = DepthwiseConv(in_planes, kernel_size, stride, pad_mode='pad', pad=padding)
layers = [conv, nn.BatchNorm2d(out_planes), nn.ReLU6()]
self.features = nn.SequentialCell(layers)
def construct(self, x):
output = self.features(x)
return output
class InvertedResidual(nn.Cell):
"""
Mobilenetv2 residual block definition.
Args:
inp (int): Input channel.
oup (int): Output channel.
stride (int): Stride size for the first convolutional layer. Default: 1.
expand_ratio (int): expand ration of input channel
Returns:
Tensor, output tensor.
Examples:
>>> ResidualBlock(3, 256, 1, 1)
"""
def __init__(self, inp, oup, stride, expand_ratio):
super(InvertedResidual, self).__init__()
assert stride in [1, 2]
hidden_dim = int(round(inp * expand_ratio))
self.use_res_connect = stride == 1 and inp == oup
layers = []
if expand_ratio != 1:
layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1))
layers.extend([
# dw
ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim),
# pw-linear
nn.Conv2d(hidden_dim, oup, kernel_size=1, stride=1, has_bias=False),
nn.BatchNorm2d(oup),
])
self.conv = nn.SequentialCell(layers)
self.add = TensorAdd()
self.cast = P.Cast()
def construct(self, x):
identity = x
x = self.conv(x)
if self.use_res_connect:
return self.add(identity, x)
return x
class MobileNetV2(nn.Cell):
"""
MobileNetV2 architecture.
Args:
class_num (Cell): number of classes.
width_mult (int): Channels multiplier for round to 8/16 and others. Default is 1.
has_dropout (bool): Is dropout used. Default is false
inverted_residual_setting (list): Inverted residual settings. Default is None
round_nearest (list): Channel round to . Default is 8
Returns:
Tensor, output tensor.
Examples:
>>> MobileNetV2(num_classes=1000)
"""
def __init__(self, num_classes=1000, width_mult=1.,
has_dropout=False, inverted_residual_setting=None, round_nearest=8):
super(MobileNetV2, self).__init__()
block = InvertedResidual
input_channel = 32
last_channel = 1280
# setting of inverted residual blocks
self.cfgs = inverted_residual_setting
if inverted_residual_setting is None:
self.cfgs = [
# t, c, n, s
[1, 16, 1, 1],
[6, 24, 2, 2],
[6, 32, 3, 2],
[6, 64, 4, 2],
[6, 96, 3, 1],
[6, 160, 3, 2],
[6, 320, 1, 1],
]
# building first layer
input_channel = _make_divisible(input_channel * width_mult, round_nearest)
self.out_channels = _make_divisible(last_channel * max(1.0, width_mult), round_nearest)
features = [ConvBNReLU(3, input_channel, stride=2)]
# building inverted residual blocks
for t, c, n, s in self.cfgs:
output_channel = _make_divisible(c * width_mult, round_nearest)
for i in range(n):
stride = s if i == 0 else 1
features.append(block(input_channel, output_channel, stride, expand_ratio=t))
input_channel = output_channel
# building last several layers
features.append(ConvBNReLU(input_channel, self.out_channels, kernel_size=1))
# make it nn.CellList
self.features = nn.SequentialCell(features)
# mobilenet head
head = ([GlobalAvgPooling(), nn.Dense(self.out_channels, num_classes, has_bias=True)] if not has_dropout else
[GlobalAvgPooling(), nn.Dropout(0.2), nn.Dense(self.out_channels, num_classes, has_bias=True)])
self.head = nn.SequentialCell(head)
self._initialize_weights()
def construct(self, x):
x = self.features(x)
x = self.head(x)
return x
def _initialize_weights(self):
"""
Initialize weights.
Args:
Returns:
None.
Examples:
>>> _initialize_weights()
"""
for _, m in self.cells_and_names():
if isinstance(m, (nn.Conv2d, DepthwiseConv)):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.set_parameter_data(Tensor(np.random.normal(0, np.sqrt(2. / n),
m.weight.data.shape()).astype("float32")))
if m.bias is not None:
m.bias.set_parameter_data(Tensor(np.zeros(m.bias.data.shape(), dtype="float32")))
elif isinstance(m, nn.BatchNorm2d):
m.gamma.set_parameter_data(Tensor(np.ones(m.gamma.data.shape(), dtype="float32")))
m.beta.set_parameter_data(Tensor(np.zeros(m.beta.data.shape(), dtype="float32")))
elif isinstance(m, nn.Dense):
m.weight.set_parameter_data(Tensor(np.random.normal(0, 0.01, m.weight.data.shape()).astype("float32")))
if m.bias is not None:
m.bias.set_parameter_data(Tensor(np.zeros(m.bias.data.shape(), dtype="float32")))
def mobilenet_v2(**kwargs):
"""
Constructs a MobileNet V2 model
"""
return MobileNetV2(**kwargs)