358 lines
11 KiB
Python
358 lines
11 KiB
Python
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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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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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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import paddle
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import paddle.fluid as fluid
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from paddle.fluid.param_attr import ParamAttr
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from paddle.fluid.layer_helper import LayerHelper
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from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear, Dropout
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import math
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__all__ = [
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"MobileNetV3_small_x0_35", "MobileNetV3_small_x0_5",
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"MobileNetV3_small_x0_75", "MobileNetV3_small_x1_0",
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"MobileNetV3_small_x1_25", "MobileNetV3_large_x0_35",
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"MobileNetV3_large_x0_5", "MobileNetV3_large_x0_75",
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"MobileNetV3_large_x1_0", "MobileNetV3_large_x1_25"
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]
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def make_divisible(v, divisor=8, min_value=None):
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if min_value is None:
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min_value = divisor
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new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
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if new_v < 0.9 * v:
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new_v += divisor
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return new_v
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class MobileNetV3(fluid.dygraph.Layer):
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def __init__(self, scale=1.0, model_name="small", class_dim=1000):
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super(MobileNetV3, self).__init__()
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inplanes = 16
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if model_name == "large":
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self.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, "hard_swish", 2],
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[3, 200, 80, False, "hard_swish", 1],
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[3, 184, 80, False, "hard_swish", 1],
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[3, 184, 80, False, "hard_swish", 1],
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[3, 480, 112, True, "hard_swish", 1],
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[3, 672, 112, True, "hard_swish", 1],
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[5, 672, 160, True, "hard_swish", 2],
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[5, 960, 160, True, "hard_swish", 1],
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[5, 960, 160, True, "hard_swish", 1],
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]
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self.cls_ch_squeeze = 960
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self.cls_ch_expand = 1280
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elif model_name == "small":
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self.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, "hard_swish", 2],
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[5, 240, 40, True, "hard_swish", 1],
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[5, 240, 40, True, "hard_swish", 1],
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[5, 120, 48, True, "hard_swish", 1],
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[5, 144, 48, True, "hard_swish", 1],
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[5, 288, 96, True, "hard_swish", 2],
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[5, 576, 96, True, "hard_swish", 1],
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[5, 576, 96, True, "hard_swish", 1],
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]
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self.cls_ch_squeeze = 576
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self.cls_ch_expand = 1280
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else:
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raise NotImplementedError(
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"mode[{}_model] is not implemented!".format(model_name))
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self.conv1 = ConvBNLayer(
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in_c=3,
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out_c=make_divisible(inplanes * scale),
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filter_size=3,
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stride=2,
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padding=1,
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num_groups=1,
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if_act=True,
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act="hard_swish",
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name="conv1")
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self.block_list = []
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i = 0
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inplanes = make_divisible(inplanes * scale)
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for (k, exp, c, se, nl, s) in self.cfg:
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self.block_list.append(
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ResidualUnit(
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in_c=inplanes,
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mid_c=make_divisible(scale * exp),
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out_c=make_divisible(scale * c),
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filter_size=k,
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stride=s,
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use_se=se,
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act=nl,
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name="conv" + str(i + 2)))
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self.add_sublayer(
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sublayer=self.block_list[-1], name="conv" + str(i + 2))
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inplanes = make_divisible(scale * c)
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i += 1
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self.last_second_conv = ConvBNLayer(
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in_c=inplanes,
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out_c=make_divisible(scale * self.cls_ch_squeeze),
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filter_size=1,
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stride=1,
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padding=0,
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num_groups=1,
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if_act=True,
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act="hard_swish",
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name="conv_last")
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self.pool = Pool2D(
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pool_type="avg", global_pooling=True, use_cudnn=False)
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self.last_conv = Conv2D(
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num_channels=make_divisible(scale * self.cls_ch_squeeze),
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num_filters=self.cls_ch_expand,
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filter_size=1,
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stride=1,
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padding=0,
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act=None,
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param_attr=ParamAttr(name="last_1x1_conv_weights"),
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bias_attr=False)
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self.out = Linear(
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input_dim=self.cls_ch_expand,
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output_dim=class_dim,
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param_attr=ParamAttr("fc_weights"),
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bias_attr=ParamAttr(name="fc_offset"))
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def forward(self, inputs, label=None, dropout_prob=0.2):
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x = self.conv1(inputs)
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for block in self.block_list:
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x = block(x)
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x = self.last_second_conv(x)
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x = self.pool(x)
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x = self.last_conv(x)
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x = fluid.layers.hard_swish(x)
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x = fluid.layers.dropout(x=x, dropout_prob=dropout_prob)
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x = fluid.layers.reshape(x, shape=[x.shape[0], x.shape[1]])
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x = self.out(x)
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return x
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class ConvBNLayer(fluid.dygraph.Layer):
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def __init__(self,
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in_c,
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out_c,
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filter_size,
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stride,
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padding,
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num_groups=1,
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if_act=True,
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act=None,
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use_cudnn=True,
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name=""):
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super(ConvBNLayer, self).__init__()
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self.if_act = if_act
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self.act = act
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self.conv = fluid.dygraph.Conv2D(
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num_channels=in_c,
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num_filters=out_c,
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filter_size=filter_size,
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stride=stride,
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padding=padding,
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groups=num_groups,
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param_attr=ParamAttr(name=name + "_weights"),
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bias_attr=False,
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use_cudnn=use_cudnn,
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act=None)
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self.bn = fluid.dygraph.BatchNorm(
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num_channels=out_c,
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act=None,
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param_attr=ParamAttr(
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name=name + "_bn_scale",
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regularizer=fluid.regularizer.L2DecayRegularizer(
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regularization_coeff=0.0)),
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bias_attr=ParamAttr(
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name=name + "_bn_offset",
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regularizer=fluid.regularizer.L2DecayRegularizer(
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regularization_coeff=0.0)),
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moving_mean_name=name + "_bn_mean",
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moving_variance_name=name + "_bn_variance")
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def forward(self, x):
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x = self.conv(x)
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x = self.bn(x)
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if self.if_act:
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if self.act == "relu":
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x = fluid.layers.relu(x)
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elif self.act == "hard_swish":
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x = fluid.layers.hard_swish(x)
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else:
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print("The activation function is selected incorrectly.")
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exit()
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return x
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class ResidualUnit(fluid.dygraph.Layer):
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def __init__(self,
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in_c,
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mid_c,
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out_c,
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filter_size,
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stride,
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use_se,
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act=None,
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name=''):
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super(ResidualUnit, self).__init__()
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self.if_shortcut = stride == 1 and in_c == out_c
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self.if_se = use_se
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self.expand_conv = ConvBNLayer(
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in_c=in_c,
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out_c=mid_c,
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filter_size=1,
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stride=1,
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padding=0,
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if_act=True,
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act=act,
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name=name + "_expand")
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self.bottleneck_conv = ConvBNLayer(
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in_c=mid_c,
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out_c=mid_c,
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filter_size=filter_size,
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stride=stride,
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padding=int((filter_size - 1) // 2),
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num_groups=mid_c,
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if_act=True,
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act=act,
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name=name + "_depthwise")
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if self.if_se:
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self.mid_se = SEModule(mid_c, name=name + "_se")
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self.linear_conv = ConvBNLayer(
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in_c=mid_c,
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out_c=out_c,
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filter_size=1,
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stride=1,
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padding=0,
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if_act=False,
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act=None,
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name=name + "_linear")
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def forward(self, inputs):
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x = self.expand_conv(inputs)
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x = self.bottleneck_conv(x)
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if self.if_se:
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x = self.mid_se(x)
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x = self.linear_conv(x)
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if self.if_shortcut:
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x = fluid.layers.elementwise_add(inputs, x)
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return x
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class SEModule(fluid.dygraph.Layer):
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def __init__(self, channel, reduction=4, name=""):
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super(SEModule, self).__init__()
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self.avg_pool = fluid.dygraph.Pool2D(
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pool_type="avg", global_pooling=True, use_cudnn=False)
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self.conv1 = fluid.dygraph.Conv2D(
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num_channels=channel,
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num_filters=channel // reduction,
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filter_size=1,
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stride=1,
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padding=0,
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act="relu",
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param_attr=ParamAttr(name=name + "_1_weights"),
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bias_attr=ParamAttr(name=name + "_1_offset"))
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self.conv2 = fluid.dygraph.Conv2D(
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num_channels=channel // reduction,
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num_filters=channel,
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filter_size=1,
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stride=1,
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padding=0,
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act=None,
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param_attr=ParamAttr(name + "_2_weights"),
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bias_attr=ParamAttr(name=name + "_2_offset"))
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def forward(self, inputs):
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outputs = self.avg_pool(inputs)
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outputs = self.conv1(outputs)
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outputs = self.conv2(outputs)
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outputs = fluid.layers.hard_sigmoid(outputs)
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return fluid.layers.elementwise_mul(x=inputs, y=outputs, axis=0)
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def MobileNetV3_small_x0_35(**args):
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model = MobileNetV3(model_name="small", scale=0.35, **args)
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return model
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def MobileNetV3_small_x0_5(**args):
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model = MobileNetV3(model_name="small", scale=0.5, **args)
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return model
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def MobileNetV3_small_x0_75(**args):
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model = MobileNetV3(model_name="small", scale=0.75, **args)
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return model
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def MobileNetV3_small_x1_0(**args):
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model = MobileNetV3(model_name="small", scale=1.0, **args)
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return model
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def MobileNetV3_small_x1_25(**args):
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model = MobileNetV3(model_name="small", scale=1.25, **args)
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return model
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def MobileNetV3_large_x0_35(**args):
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model = MobileNetV3(model_name="large", scale=0.35, **args)
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return model
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def MobileNetV3_large_x0_5(**args):
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model = MobileNetV3(model_name="large", scale=0.5, **args)
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return model
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def MobileNetV3_large_x0_75(**args):
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model = MobileNetV3(model_name="large", scale=0.75, **args)
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return model
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def MobileNetV3_large_x1_0(**args):
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model = MobileNetV3(model_name="large", scale=1.0, **args)
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return model
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def MobileNetV3_large_x1_25(**args):
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model = MobileNetV3(model_name="large", scale=1.25, **args)
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return model
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