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- # copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
- #
- # 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.
-
- import paddle
- from paddle import ParamAttr
- import paddle.nn as nn
- import paddle.nn.functional as F
- from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
- from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
-
- from paddleseg.cvlibs import manager
- from paddleseg import utils
-
- __all__ = [
- "MobileNetV2_x0_25",
- "MobileNetV2_x0_5",
- "MobileNetV2_x0_75",
- "MobileNetV2_x1_0",
- "MobileNetV2_x1_5",
- "MobileNetV2_x2_0",
- ]
-
-
- class MobileNetV2(nn.Layer):
- """
- The MobileNetV2 implementation based on PaddlePaddle.
-
- The original article refers to
- Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen
- "MobileNetV2: Inverted Residuals and Linear Bottlenecks"
- (https://arxiv.org/abs/1801.04381).
-
- Args:
- scale (float, optional): The scale of channel. Default: 1.0
- in_channels (int, optional): The channels of input image. Default: 3.
- pretrained (str, optional): The path or url of pretrained model. Default: None
- """
-
- def __init__(self, scale=1.0, in_channels=3, pretrained=None):
- super().__init__()
- self.scale = scale
- self.pretrained = pretrained
- prefix_name = ""
-
- bottleneck_params_list = [
- (1, 16, 1, 1),
- (6, 24, 2, 2), # x4
- (6, 32, 3, 2), # x8
- (6, 64, 4, 2),
- (6, 96, 3, 1), # x16
- (6, 160, 3, 2),
- (6, 320, 1, 1), # x32
- ]
- self.out_index = [1, 2, 4, 6]
-
- self.conv1 = ConvBNLayer(
- num_channels=in_channels,
- num_filters=int(32 * scale),
- filter_size=3,
- stride=2,
- padding=1,
- name=prefix_name + "conv1_1")
-
- self.block_list = []
- i = 1
- in_c = int(32 * scale)
- for layer_setting in bottleneck_params_list:
- t, c, n, s = layer_setting
- i += 1
- block = self.add_sublayer(
- prefix_name + "conv" + str(i),
- sublayer=InvresiBlocks(
- in_c=in_c,
- t=t,
- c=int(c * scale),
- n=n,
- s=s,
- name=prefix_name + "conv" + str(i)))
- self.block_list.append(block)
- in_c = int(c * scale)
-
- out_channels = [
- bottleneck_params_list[idx][1] for idx in self.out_index
- ]
- self.feat_channels = [int(c * scale) for c in out_channels]
-
- self.init_weight()
-
- def forward(self, inputs):
- feat_list = []
-
- y = self.conv1(inputs, if_act=True)
- for idx, block in enumerate(self.block_list):
- y = block(y)
- if idx in self.out_index:
- feat_list.append(y)
-
- return feat_list
-
- def init_weight(self):
- if self.pretrained is not None:
- utils.load_entire_model(self, self.pretrained)
-
-
- class ConvBNLayer(nn.Layer):
- def __init__(self,
- num_channels,
- filter_size,
- num_filters,
- stride,
- padding,
- channels=None,
- num_groups=1,
- name=None,
- use_cudnn=True):
- super(ConvBNLayer, self).__init__()
-
- self._conv = Conv2D(
- in_channels=num_channels,
- out_channels=num_filters,
- kernel_size=filter_size,
- stride=stride,
- padding=padding,
- groups=num_groups,
- weight_attr=ParamAttr(name=name + "_weights"),
- bias_attr=False)
-
- self._batch_norm = BatchNorm(
- num_filters,
- param_attr=ParamAttr(name=name + "_bn_scale"),
- bias_attr=ParamAttr(name=name + "_bn_offset"),
- moving_mean_name=name + "_bn_mean",
- moving_variance_name=name + "_bn_variance")
-
- def forward(self, inputs, if_act=True):
- y = self._conv(inputs)
- y = self._batch_norm(y)
- if if_act:
- y = F.relu6(y)
- return y
-
-
- class InvertedResidualUnit(nn.Layer):
- def __init__(self, num_channels, num_in_filter, num_filters, stride,
- filter_size, padding, expansion_factor, name):
- super(InvertedResidualUnit, self).__init__()
- num_expfilter = int(round(num_in_filter * expansion_factor))
- self._expand_conv = ConvBNLayer(
- num_channels=num_channels,
- num_filters=num_expfilter,
- filter_size=1,
- stride=1,
- padding=0,
- num_groups=1,
- name=name + "_expand")
-
- self._bottleneck_conv = ConvBNLayer(
- num_channels=num_expfilter,
- num_filters=num_expfilter,
- filter_size=filter_size,
- stride=stride,
- padding=padding,
- num_groups=num_expfilter,
- use_cudnn=False,
- name=name + "_dwise")
-
- self._linear_conv = ConvBNLayer(
- num_channels=num_expfilter,
- num_filters=num_filters,
- filter_size=1,
- stride=1,
- padding=0,
- num_groups=1,
- name=name + "_linear")
-
- def forward(self, inputs, ifshortcut):
- y = self._expand_conv(inputs, if_act=True)
- y = self._bottleneck_conv(y, if_act=True)
- y = self._linear_conv(y, if_act=False)
- if ifshortcut:
- y = paddle.add(inputs, y)
- return y
-
-
- class InvresiBlocks(nn.Layer):
- def __init__(self, in_c, t, c, n, s, name):
- super(InvresiBlocks, self).__init__()
-
- self._first_block = InvertedResidualUnit(
- num_channels=in_c,
- num_in_filter=in_c,
- num_filters=c,
- stride=s,
- filter_size=3,
- padding=1,
- expansion_factor=t,
- name=name + "_1")
-
- self._block_list = []
- for i in range(1, n):
- block = self.add_sublayer(
- name + "_" + str(i + 1),
- sublayer=InvertedResidualUnit(
- num_channels=c,
- num_in_filter=c,
- num_filters=c,
- stride=1,
- filter_size=3,
- padding=1,
- expansion_factor=t,
- name=name + "_" + str(i + 1)))
- self._block_list.append(block)
-
- def forward(self, inputs):
- y = self._first_block(inputs, ifshortcut=False)
- for block in self._block_list:
- y = block(y, ifshortcut=True)
- return y
-
-
- @manager.BACKBONES.add_component
- def MobileNetV2_x0_25(**kwargs):
- model = MobileNetV2(scale=0.25, **kwargs)
- return model
-
-
- @manager.BACKBONES.add_component
- def MobileNetV2_x0_5(**kwargs):
- model = MobileNetV2(scale=0.5, **kwargs)
- return model
-
-
- @manager.BACKBONES.add_component
- def MobileNetV2_x0_75(**kwargs):
- model = MobileNetV2(scale=0.75, **kwargs)
- return model
-
-
- @manager.BACKBONES.add_component
- def MobileNetV2_x1_0(**kwargs):
- model = MobileNetV2(scale=1.0, **kwargs)
- return model
-
-
- @manager.BACKBONES.add_component
- def MobileNetV2_x1_5(**kwargs):
- model = MobileNetV2(scale=1.5, **kwargs)
- return model
-
-
- @manager.BACKBONES.add_component
- def MobileNetV2_x2_0(**kwargs):
- model = MobileNetV2(scale=2.0, **kwargs)
- return model
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