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284 lines
8.9 KiB
284 lines
8.9 KiB
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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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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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.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear
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from paddle.utils.download import get_weights_path_from_url
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__all__ = ['MobileNetV2', 'mobilenet_v2']
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model_urls = {
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'mobilenetv2_1.0':
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('https://paddle-hapi.bj.bcebos.com/models/mobilenet_v2_x1.0.pdparams',
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'8ff74f291f72533f2a7956a4efff9d88')
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}
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class ConvBNLayer(fluid.dygraph.Layer):
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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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use_cudnn=True):
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super(ConvBNLayer, self).__init__()
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tmp_param = ParamAttr(name=self.full_name() + "_weights")
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self._conv = Conv2D(
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num_channels=num_channels,
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num_filters=num_filters,
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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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act=None,
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use_cudnn=use_cudnn,
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param_attr=tmp_param,
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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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param_attr=ParamAttr(name=self.full_name() + "_bn" + "_scale"),
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bias_attr=ParamAttr(name=self.full_name() + "_bn" + "_offset"),
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moving_mean_name=self.full_name() + "_bn" + '_mean',
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moving_variance_name=self.full_name() + "_bn" + '_variance')
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def forward(self, inputs, if_act=True):
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y = self._conv(inputs)
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y = self._batch_norm(y)
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if if_act:
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y = fluid.layers.relu6(y)
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return y
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class InvertedResidualUnit(fluid.dygraph.Layer):
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def __init__(
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self,
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num_channels,
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num_in_filter,
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num_filters,
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stride,
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filter_size,
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padding,
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expansion_factor, ):
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super(InvertedResidualUnit, self).__init__()
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num_expfilter = int(round(num_in_filter * expansion_factor))
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self._expand_conv = ConvBNLayer(
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num_channels=num_channels,
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num_filters=num_expfilter,
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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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self._bottleneck_conv = ConvBNLayer(
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num_channels=num_expfilter,
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num_filters=num_expfilter,
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filter_size=filter_size,
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stride=stride,
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padding=padding,
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num_groups=num_expfilter,
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use_cudnn=False)
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self._linear_conv = ConvBNLayer(
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num_channels=num_expfilter,
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num_filters=num_filters,
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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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def forward(self, inputs, ifshortcut):
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y = self._expand_conv(inputs, if_act=True)
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y = self._bottleneck_conv(y, if_act=True)
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y = self._linear_conv(y, if_act=False)
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if ifshortcut:
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y = fluid.layers.elementwise_add(inputs, y)
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return y
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class InvresiBlocks(fluid.dygraph.Layer):
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def __init__(self, in_c, t, c, n, s):
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super(InvresiBlocks, self).__init__()
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self._first_block = InvertedResidualUnit(
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num_channels=in_c,
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num_in_filter=in_c,
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num_filters=c,
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stride=s,
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filter_size=3,
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padding=1,
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expansion_factor=t)
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self._inv_blocks = []
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for i in range(1, n):
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tmp = self.add_sublayer(
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sublayer=InvertedResidualUnit(
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num_channels=c,
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num_in_filter=c,
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num_filters=c,
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stride=1,
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filter_size=3,
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padding=1,
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expansion_factor=t),
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name=self.full_name() + "_" + str(i + 1))
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self._inv_blocks.append(tmp)
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def forward(self, inputs):
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y = self._first_block(inputs, ifshortcut=False)
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for inv_block in self._inv_blocks:
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y = inv_block(y, ifshortcut=True)
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return y
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class MobileNetV2(fluid.dygraph.Layer):
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"""MobileNetV2 model from
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`"MobileNetV2: Inverted Residuals and Linear Bottlenecks" <https://arxiv.org/abs/1801.04381>`_.
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Args:
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scale (float): scale of channels in each layer. Default: 1.0.
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num_classes (int): output dim of last fc layer. If num_classes <=0, last fc layer
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will not be defined. Default: 1000.
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with_pool (bool): use pool before the last fc layer or not. Default: True.
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classifier_activation (str): activation for the last fc layer. Default: 'softmax'.
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Examples:
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.. code-block:: python
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from paddle.vision.models import MobileNetV2
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model = MobileNetV2()
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"""
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def __init__(self,
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scale=1.0,
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num_classes=1000,
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with_pool=True,
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classifier_activation='softmax'):
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super(MobileNetV2, self).__init__()
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self.scale = scale
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self.num_classes = num_classes
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self.with_pool = with_pool
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bottleneck_params_list = [
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(1, 16, 1, 1),
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(6, 24, 2, 2),
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(6, 32, 3, 2),
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(6, 64, 4, 2),
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(6, 96, 3, 1),
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(6, 160, 3, 2),
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(6, 320, 1, 1),
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]
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self._conv1 = ConvBNLayer(
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num_channels=3,
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num_filters=int(32 * scale),
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filter_size=3,
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stride=2,
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padding=1)
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self._invl = []
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i = 1
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in_c = int(32 * scale)
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for layer_setting in bottleneck_params_list:
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t, c, n, s = layer_setting
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i += 1
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tmp = self.add_sublayer(
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sublayer=InvresiBlocks(
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in_c=in_c, t=t, c=int(c * scale), n=n, s=s),
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name='conv' + str(i))
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self._invl.append(tmp)
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in_c = int(c * scale)
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self._out_c = int(1280 * scale) if scale > 1.0 else 1280
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self._conv9 = ConvBNLayer(
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num_channels=in_c,
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num_filters=self._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 with_pool:
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self._pool2d_avg = Pool2D(pool_type='avg', global_pooling=True)
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if num_classes > 0:
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tmp_param = ParamAttr(name=self.full_name() + "fc10_weights")
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self._fc = Linear(
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self._out_c,
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num_classes,
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act=classifier_activation,
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param_attr=tmp_param,
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bias_attr=ParamAttr(name="fc10_offset"))
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def forward(self, inputs):
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y = self._conv1(inputs, if_act=True)
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for inv in self._invl:
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y = inv(y)
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y = self._conv9(y, if_act=True)
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if self.with_pool:
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y = self._pool2d_avg(y)
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if self.num_classes > 0:
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y = fluid.layers.reshape(y, shape=[-1, self._out_c])
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y = self._fc(y)
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return y
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def _mobilenet(arch, pretrained=False, **kwargs):
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model = MobileNetV2(**kwargs)
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if pretrained:
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assert arch in model_urls, "{} model do not have a pretrained model now, you should set pretrained=False".format(
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arch)
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weight_path = get_weights_path_from_url(model_urls[arch][0],
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model_urls[arch][1])
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assert weight_path.endswith(
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'.pdparams'), "suffix of weight must be .pdparams"
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param, _ = fluid.load_dygraph(weight_path)
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model.load_dict(param)
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return model
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def mobilenet_v2(pretrained=False, scale=1.0, **kwargs):
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"""MobileNetV2
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet. Default: False.
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scale: (float): scale of channels in each layer. Default: 1.0.
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Examples:
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.. code-block:: python
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from paddle.vision.models import mobilenet_v2
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# build model
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model = mobilenet_v2()
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# build model and load imagenet pretrained weight
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# model = mobilenet_v2(pretrained=True)
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# build mobilenet v2 with scale=0.5
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model = mobilenet_v2(scale=0.5)
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"""
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model = _mobilenet(
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'mobilenetv2_' + str(scale), pretrained, scale=scale, **kwargs)
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return model
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