add mobilenet_v1.py to legendary models
Bin Lu
GitHub
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# 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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from paddle import ParamAttr
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import paddle.nn as nn
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from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
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from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
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from paddle.nn.initializer import KaimingNormal
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import math
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from ppcls.arch.backbone.base.theseus_layer import TheseusLayer
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__all__ = [
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"MobileNetV1_x0_25", "MobileNetV1_x0_5", "MobileNetV1_x0_75", "MobileNetV1"
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]
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class ConvBNLayer(TheseusLayer):
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def __init__(self,
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num_channels,
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filter_size,
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num_filters,
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stride,
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padding,
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channels=None,
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num_groups=1,
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act='relu',
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name=None):
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super(ConvBNLayer, self).__init__()
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self._conv = Conv2D(
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in_channels=num_channels,
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out_channels=num_filters,
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kernel_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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weight_attr=ParamAttr(
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initializer=KaimingNormal(), name=name + "_weights"),
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bias_attr=False)
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self._batch_norm = BatchNorm(
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num_filters,
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act=act,
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param_attr=ParamAttr(name + "_bn_scale"),
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bias_attr=ParamAttr(name + "_bn_offset"),
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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, inputs):
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y = self._conv(inputs)
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y = self._batch_norm(y)
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return y
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class DepthwiseSeparable(TheseusLayer):
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def __init__(self,
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num_channels,
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num_filters1,
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num_filters2,
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num_groups,
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stride,
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scale,
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name=None):
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super(DepthwiseSeparable, self).__init__()
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self._depthwise_conv = ConvBNLayer(
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num_channels=num_channels,
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num_filters=int(num_filters1 * scale),
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filter_size=3,
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stride=stride,
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padding=1,
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num_groups=int(num_groups * scale),
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name=name + "_dw")
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self._pointwise_conv = ConvBNLayer(
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num_channels=int(num_filters1 * scale),
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filter_size=1,
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num_filters=int(num_filters2 * scale),
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stride=1,
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padding=0,
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name=name + "_sep")
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def forward(self, inputs):
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y = self._depthwise_conv(inputs)
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y = self._pointwise_conv(y)
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return y
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class MobileNet(TheseusLayer):
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def __init__(self, scale=1.0, class_dim=1000):
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super(MobileNet, self).__init__()
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self.scale = scale
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self.block_list = []
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self.conv1 = ConvBNLayer(
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num_channels=3,
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filter_size=3,
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channels=3,
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num_filters=int(32 * scale),
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stride=2,
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padding=1,
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name="conv1")
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conv2_1 = self.add_sublayer(
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"conv2_1",
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sublayer=DepthwiseSeparable(
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num_channels=int(32 * scale),
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num_filters1=32,
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num_filters2=64,
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num_groups=32,
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stride=1,
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scale=scale,
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name="conv2_1"))
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self.block_list.append(conv2_1)
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conv2_2 = self.add_sublayer(
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"conv2_2",
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sublayer=DepthwiseSeparable(
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num_channels=int(64 * scale),
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num_filters1=64,
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num_filters2=128,
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num_groups=64,
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stride=2,
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scale=scale,
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name="conv2_2"))
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self.block_list.append(conv2_2)
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conv3_1 = self.add_sublayer(
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"conv3_1",
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sublayer=DepthwiseSeparable(
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num_channels=int(128 * scale),
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num_filters1=128,
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num_filters2=128,
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num_groups=128,
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stride=1,
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scale=scale,
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name="conv3_1"))
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self.block_list.append(conv3_1)
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conv3_2 = self.add_sublayer(
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"conv3_2",
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sublayer=DepthwiseSeparable(
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num_channels=int(128 * scale),
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num_filters1=128,
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num_filters2=256,
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num_groups=128,
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stride=2,
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scale=scale,
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name="conv3_2"))
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self.block_list.append(conv3_2)
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conv4_1 = self.add_sublayer(
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"conv4_1",
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sublayer=DepthwiseSeparable(
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num_channels=int(256 * scale),
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num_filters1=256,
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num_filters2=256,
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num_groups=256,
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stride=1,
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scale=scale,
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name="conv4_1"))
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self.block_list.append(conv4_1)
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conv4_2 = self.add_sublayer(
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"conv4_2",
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sublayer=DepthwiseSeparable(
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num_channels=int(256 * scale),
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num_filters1=256,
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num_filters2=512,
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num_groups=256,
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stride=2,
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scale=scale,
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name="conv4_2"))
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self.block_list.append(conv4_2)
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for i in range(5):
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conv5 = self.add_sublayer(
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"conv5_" + str(i + 1),
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sublayer=DepthwiseSeparable(
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num_channels=int(512 * scale),
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num_filters1=512,
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num_filters2=512,
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num_groups=512,
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stride=1,
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scale=scale,
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name="conv5_" + str(i + 1)))
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self.block_list.append(conv5)
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conv5_6 = self.add_sublayer(
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"conv5_6",
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sublayer=DepthwiseSeparable(
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num_channels=int(512 * scale),
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num_filters1=512,
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num_filters2=1024,
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num_groups=512,
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stride=2,
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scale=scale,
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name="conv5_6"))
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self.block_list.append(conv5_6)
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conv6 = self.add_sublayer(
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"conv6",
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sublayer=DepthwiseSeparable(
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num_channels=int(1024 * scale),
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num_filters1=1024,
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num_filters2=1024,
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num_groups=1024,
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stride=1,
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scale=scale,
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name="conv6"))
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self.block_list.append(conv6)
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self.pool2d_avg = AdaptiveAvgPool2D(1)
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self.out = Linear(
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int(1024 * scale),
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class_dim,
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weight_attr=ParamAttr(
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initializer=KaimingNormal(), name="fc7_weights"),
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bias_attr=ParamAttr(name="fc7_offset"))
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def forward(self, inputs):
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y = self.conv1(inputs)
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for block in self.block_list:
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y = block(y)
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y = self.pool2d_avg(y)
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y = paddle.flatten(y, start_axis=1, stop_axis=-1)
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y = self.out(y)
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return y
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def MobileNetV1_x0_25(**args):
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model = MobileNet(scale=0.25, **args)
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return model
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def MobileNetV1_x0_5(**args):
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model = MobileNet(scale=0.5, **args)
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return model
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def MobileNetV1_x0_75(**args):
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model = MobileNet(scale=0.75, **args)
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return model
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def MobileNetV1(**args):
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model = MobileNet(scale=1.0, **args)
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return model
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