forked from huawei/mindspore2022
391 lines
13 KiB
Python
391 lines
13 KiB
Python
# 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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"""MobileNetV3 model define"""
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from functools import partial
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import numpy as np
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import mindspore.nn as nn
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from mindspore.ops import operations as P
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from mindspore import Tensor
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__all__ = ['mobilenet_v3_large',
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'mobilenet_v3_small']
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def _make_divisible(x, divisor=8):
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return int(np.ceil(x * 1. / divisor) * divisor)
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class Activation(nn.Cell):
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"""
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Activation definition.
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Args:
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act_func(string): activation name.
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Returns:
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Tensor, output tensor.
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"""
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def __init__(self, act_func):
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super(Activation, self).__init__()
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if act_func == 'relu':
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self.act = nn.ReLU()
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elif act_func == 'relu6':
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self.act = nn.ReLU6()
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elif act_func in ('hsigmoid', 'hard_sigmoid'):
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self.act = nn.HSigmoid()
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elif act_func in ('hswish', 'hard_swish'):
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self.act = nn.HSwish()
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else:
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raise NotImplementedError
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def construct(self, x):
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return self.act(x)
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class GlobalAvgPooling(nn.Cell):
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"""
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Global avg pooling definition.
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Args:
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Returns:
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Tensor, output tensor.
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Examples:
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>>> GlobalAvgPooling()
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"""
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def __init__(self, keep_dims=False):
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super(GlobalAvgPooling, self).__init__()
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self.mean = P.ReduceMean(keep_dims=keep_dims)
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def construct(self, x):
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x = self.mean(x, (2, 3))
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return x
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class SE(nn.Cell):
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"""
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SE warpper definition.
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Args:
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num_out (int): Output channel.
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ratio (int): middle output ratio.
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Returns:
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Tensor, output tensor.
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Examples:
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>>> SE(4)
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"""
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def __init__(self, num_out, ratio=4):
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super(SE, self).__init__()
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num_mid = _make_divisible(num_out // ratio)
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self.pool = GlobalAvgPooling(keep_dims=True)
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self.conv1 = nn.Conv2d(in_channels=num_out, out_channels=num_mid,
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kernel_size=1, has_bias=True, pad_mode='pad')
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self.act1 = Activation('relu')
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self.conv2 = nn.Conv2d(in_channels=num_mid, out_channels=num_out,
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kernel_size=1, has_bias=True, pad_mode='pad')
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self.act2 = Activation('hsigmoid')
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self.mul = P.Mul()
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def construct(self, x):
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out = self.pool(x)
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out = self.conv1(out)
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out = self.act1(out)
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out = self.conv2(out)
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out = self.act2(out)
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out = self.mul(x, out)
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return out
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class Unit(nn.Cell):
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"""
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Unit warpper definition.
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Args:
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num_in (int): Input channel.
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num_out (int): Output channel.
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kernel_size (int): Input kernel size.
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stride (int): Stride size.
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padding (int): Padding number.
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num_groups (int): Output num group.
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use_act (bool): Used activation or not.
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act_type (string): Activation type.
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Returns:
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Tensor, output tensor.
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Examples:
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>>> Unit(3, 3)
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"""
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def __init__(self, num_in, num_out, kernel_size=1, stride=1, padding=0, num_groups=1,
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use_act=True, act_type='relu'):
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super(Unit, self).__init__()
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self.conv = nn.Conv2d(in_channels=num_in,
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out_channels=num_out,
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kernel_size=kernel_size,
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stride=stride,
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padding=padding,
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group=num_groups,
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has_bias=False,
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pad_mode='pad')
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self.bn = nn.BatchNorm2d(num_out)
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self.use_act = use_act
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self.act = Activation(act_type) if use_act else None
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def construct(self, x):
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out = self.conv(x)
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out = self.bn(out)
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if self.use_act:
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out = self.act(out)
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return out
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class ResUnit(nn.Cell):
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"""
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ResUnit warpper definition.
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Args:
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num_in (int): Input channel.
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num_mid (int): Middle channel.
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num_out (int): Output channel.
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kernel_size (int): Input kernel size.
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stride (int): Stride size.
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act_type (str): Activation type.
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use_se (bool): Use SE warpper or not.
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Returns:
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Tensor, output tensor.
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Examples:
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>>> ResUnit(16, 3, 1, 1)
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"""
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def __init__(self, num_in, num_mid, num_out, kernel_size, stride=1, act_type='relu', use_se=False):
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super(ResUnit, self).__init__()
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self.use_se = use_se
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self.first_conv = (num_out != num_mid)
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self.use_short_cut_conv = True
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if self.first_conv:
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self.expand = Unit(num_in, num_mid, kernel_size=1,
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stride=1, padding=0, act_type=act_type)
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else:
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self.expand = None
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self.conv1 = Unit(num_mid, num_mid, kernel_size=kernel_size, stride=stride,
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padding=self._get_pad(kernel_size), act_type=act_type, num_groups=num_mid)
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if use_se:
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self.se = SE(num_mid)
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self.conv2 = Unit(num_mid, num_out, kernel_size=1, stride=1,
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padding=0, act_type=act_type, use_act=False)
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if num_in != num_out or stride != 1:
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self.use_short_cut_conv = False
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self.add = P.TensorAdd() if self.use_short_cut_conv else None
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def construct(self, x):
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if self.first_conv:
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out = self.expand(x)
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else:
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out = x
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out = self.conv1(out)
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if self.use_se:
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out = self.se(out)
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out = self.conv2(out)
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if self.use_short_cut_conv:
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out = self.add(x, out)
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return out
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def _get_pad(self, kernel_size):
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"""set the padding number"""
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pad = 0
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if kernel_size == 1:
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pad = 0
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elif kernel_size == 3:
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pad = 1
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elif kernel_size == 5:
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pad = 2
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elif kernel_size == 7:
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pad = 3
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else:
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raise NotImplementedError
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return pad
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class MobileNetV3(nn.Cell):
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"""
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MobileNetV3 architecture.
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Args:
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model_cfgs (Cell): number of classes.
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num_classes (int): Output number classes.
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multiplier (int): Channels multiplier for round to 8/16 and others. Default is 1.
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final_drop (float): Dropout number.
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round_nearest (list): Channel round to . Default is 8.
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Returns:
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Tensor, output tensor.
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Examples:
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>>> MobileNetV3(num_classes=1000)
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"""
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def __init__(self, model_cfgs, num_classes=1000, multiplier=1., final_drop=0., round_nearest=8):
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super(MobileNetV3, self).__init__()
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self.cfgs = model_cfgs['cfg']
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self.inplanes = 16
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self.features = []
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first_conv_in_channel = 3
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first_conv_out_channel = _make_divisible(multiplier * self.inplanes)
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self.features.append(nn.Conv2d(in_channels=first_conv_in_channel,
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out_channels=first_conv_out_channel,
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kernel_size=3, padding=1, stride=2,
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has_bias=False, pad_mode='pad'))
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self.features.append(nn.BatchNorm2d(first_conv_out_channel))
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self.features.append(Activation('hswish'))
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for layer_cfg in self.cfgs:
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self.features.append(self._make_layer(kernel_size=layer_cfg[0],
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exp_ch=_make_divisible(multiplier * layer_cfg[1]),
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out_channel=_make_divisible(multiplier * layer_cfg[2]),
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use_se=layer_cfg[3],
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act_func=layer_cfg[4],
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stride=layer_cfg[5]))
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output_channel = _make_divisible(multiplier * model_cfgs["cls_ch_squeeze"])
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self.features.append(nn.Conv2d(in_channels=_make_divisible(multiplier * self.cfgs[-1][2]),
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out_channels=output_channel,
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kernel_size=1, padding=0, stride=1,
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has_bias=False, pad_mode='pad'))
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self.features.append(nn.BatchNorm2d(output_channel))
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self.features.append(Activation('hswish'))
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self.features.append(GlobalAvgPooling(keep_dims=True))
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self.features.append(nn.Conv2d(in_channels=output_channel,
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out_channels=model_cfgs['cls_ch_expand'],
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kernel_size=1, padding=0, stride=1,
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has_bias=False, pad_mode='pad'))
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self.features.append(Activation('hswish'))
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if final_drop > 0:
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self.features.append((nn.Dropout(final_drop)))
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# make it nn.CellList
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self.features = nn.SequentialCell(self.features)
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self.output = nn.Conv2d(in_channels=model_cfgs['cls_ch_expand'],
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out_channels=num_classes,
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kernel_size=1, has_bias=True, pad_mode='pad')
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self.squeeze = P.Squeeze(axis=(2, 3))
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self._initialize_weights()
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def construct(self, x):
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x = self.features(x)
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x = self.output(x)
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x = self.squeeze(x)
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return x
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def _make_layer(self, kernel_size, exp_ch, out_channel, use_se, act_func, stride=1):
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mid_planes = exp_ch
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out_planes = out_channel
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#num_in, num_mid, num_out, kernel_size, stride=1, act_type='relu', use_se=False):
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layer = ResUnit(self.inplanes, mid_planes, out_planes,
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kernel_size, stride=stride, act_type=act_func, use_se=use_se)
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self.inplanes = out_planes
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return layer
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def _initialize_weights(self):
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"""
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Initialize weights.
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Args:
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Returns:
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None.
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Examples:
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>>> _initialize_weights()
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"""
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for _, m in self.cells_and_names():
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if isinstance(m, (nn.Conv2d)):
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n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
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m.weight.set_parameter_data(Tensor(np.random.normal(0, np.sqrt(2. / n),
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m.weight.data.shape).astype("float32")))
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if m.bias is not None:
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m.bias.set_parameter_data(
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Tensor(np.zeros(m.bias.data.shape, dtype="float32")))
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elif isinstance(m, nn.BatchNorm2d):
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m.gamma.set_parameter_data(
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Tensor(np.ones(m.gamma.data.shape, dtype="float32")))
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m.beta.set_parameter_data(
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Tensor(np.zeros(m.beta.data.shape, dtype="float32")))
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elif isinstance(m, nn.Dense):
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m.weight.set_parameter_data(Tensor(np.random.normal(
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0, 0.01, m.weight.data.shape).astype("float32")))
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if m.bias is not None:
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m.bias.set_parameter_data(
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Tensor(np.zeros(m.bias.data.shape, dtype="float32")))
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def mobilenet_v3(model_name, **kwargs):
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"""
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Constructs a MobileNet V2 model
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"""
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model_cfgs = {
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"large": {
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"cfg": [
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# k, exp, c, se, nl, s,
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[3, 16, 16, False, 'relu', 1],
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[3, 64, 24, False, 'relu', 2],
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[3, 72, 24, False, 'relu', 1],
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[5, 72, 40, True, 'relu', 2],
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[5, 120, 40, True, 'relu', 1],
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[5, 120, 40, True, 'relu', 1],
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[3, 240, 80, False, 'hswish', 2],
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[3, 200, 80, False, 'hswish', 1],
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[3, 184, 80, False, 'hswish', 1],
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[3, 184, 80, False, 'hswish', 1],
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[3, 480, 112, True, 'hswish', 1],
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[3, 672, 112, True, 'hswish', 1],
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[5, 672, 160, True, 'hswish', 2],
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[5, 960, 160, True, 'hswish', 1],
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[5, 960, 160, True, 'hswish', 1]],
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"cls_ch_squeeze": 960,
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"cls_ch_expand": 1280,
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},
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"small": {
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"cfg": [
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# k, exp, c, se, nl, s,
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[3, 16, 16, True, 'relu', 2],
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[3, 72, 24, False, 'relu', 2],
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[3, 88, 24, False, 'relu', 1],
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[5, 96, 40, True, 'hswish', 2],
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[5, 240, 40, True, 'hswish', 1],
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[5, 240, 40, True, 'hswish', 1],
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[5, 120, 48, True, 'hswish', 1],
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[5, 144, 48, True, 'hswish', 1],
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[5, 288, 96, True, 'hswish', 2],
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[5, 576, 96, True, 'hswish', 1],
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[5, 576, 96, True, 'hswish', 1]],
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"cls_ch_squeeze": 576,
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"cls_ch_expand": 1280,
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}
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}
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return MobileNetV3(model_cfgs[model_name], **kwargs)
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mobilenet_v3_large = partial(mobilenet_v3, model_name="large")
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mobilenet_v3_small = partial(mobilenet_v3, model_name="small")
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