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274 lines
8.1 KiB
274 lines
8.1 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 paddle
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import paddle.nn as nn
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from paddle.utils.download import get_weights_path_from_url
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__all__ = ['MobileNetV1', 'mobilenet_v1']
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model_urls = {
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'mobilenetv1_1.0':
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('https://paddle-hapi.bj.bcebos.com/models/mobilenet_v1_x1.0.pdparams',
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'42a154c2f26f86e7457d6daded114e8c')
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}
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class ConvBNLayer(nn.Layer):
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def __init__(self,
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in_channels,
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out_channels,
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kernel_size,
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stride,
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padding,
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num_groups=1):
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super(ConvBNLayer, self).__init__()
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self._conv = nn.Conv2D(
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in_channels,
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out_channels,
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kernel_size,
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stride=stride,
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padding=padding,
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groups=num_groups,
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bias_attr=False)
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self._norm_layer = nn.BatchNorm2D(out_channels)
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self._act = nn.ReLU()
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def forward(self, x):
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x = self._conv(x)
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x = self._norm_layer(x)
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x = self._act(x)
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return x
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class DepthwiseSeparable(nn.Layer):
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def __init__(self, in_channels, out_channels1, out_channels2, num_groups,
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stride, scale):
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super(DepthwiseSeparable, self).__init__()
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self._depthwise_conv = ConvBNLayer(
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in_channels,
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int(out_channels1 * scale),
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kernel_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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self._pointwise_conv = ConvBNLayer(
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int(out_channels1 * scale),
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int(out_channels2 * scale),
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kernel_size=1,
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stride=1,
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padding=0)
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def forward(self, x):
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x = self._depthwise_conv(x)
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x = self._pointwise_conv(x)
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return x
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class MobileNetV1(nn.Layer):
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"""MobileNetV1 model from
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`"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications" <https://arxiv.org/abs/1704.04861>`_.
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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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Examples:
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.. code-block:: python
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from paddle.vision.models import MobileNetV1
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model = MobileNetV1()
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"""
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def __init__(self, scale=1.0, num_classes=1000, with_pool=True):
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super(MobileNetV1, self).__init__()
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self.scale = scale
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self.dwsl = []
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self.num_classes = num_classes
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self.with_pool = with_pool
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self.conv1 = ConvBNLayer(
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in_channels=3,
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out_channels=int(32 * scale),
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kernel_size=3,
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stride=2,
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padding=1)
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dws21 = self.add_sublayer(
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sublayer=DepthwiseSeparable(
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in_channels=int(32 * scale),
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out_channels1=32,
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out_channels2=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.dwsl.append(dws21)
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dws22 = self.add_sublayer(
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sublayer=DepthwiseSeparable(
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in_channels=int(64 * scale),
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out_channels1=64,
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out_channels2=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.dwsl.append(dws22)
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dws31 = self.add_sublayer(
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sublayer=DepthwiseSeparable(
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in_channels=int(128 * scale),
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out_channels1=128,
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out_channels2=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.dwsl.append(dws31)
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dws32 = self.add_sublayer(
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sublayer=DepthwiseSeparable(
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in_channels=int(128 * scale),
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out_channels1=128,
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out_channels2=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.dwsl.append(dws32)
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dws41 = self.add_sublayer(
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sublayer=DepthwiseSeparable(
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in_channels=int(256 * scale),
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out_channels1=256,
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out_channels2=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.dwsl.append(dws41)
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dws42 = self.add_sublayer(
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sublayer=DepthwiseSeparable(
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in_channels=int(256 * scale),
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out_channels1=256,
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out_channels2=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.dwsl.append(dws42)
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for i in range(5):
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tmp = self.add_sublayer(
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sublayer=DepthwiseSeparable(
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in_channels=int(512 * scale),
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out_channels1=512,
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out_channels2=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.dwsl.append(tmp)
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dws56 = self.add_sublayer(
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sublayer=DepthwiseSeparable(
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in_channels=int(512 * scale),
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out_channels1=512,
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out_channels2=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.dwsl.append(dws56)
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dws6 = self.add_sublayer(
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sublayer=DepthwiseSeparable(
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in_channels=int(1024 * scale),
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out_channels1=1024,
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out_channels2=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.dwsl.append(dws6)
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if with_pool:
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self.pool2d_avg = nn.AdaptiveAvgPool2D(1)
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if num_classes > 0:
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self.fc = nn.Linear(int(1024 * scale), num_classes)
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def forward(self, x):
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x = self.conv1(x)
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for dws in self.dwsl:
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x = dws(x)
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if self.with_pool:
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x = self.pool2d_avg(x)
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if self.num_classes > 0:
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x = paddle.flatten(x, 1)
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x = self.fc(x)
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return x
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def _mobilenet(arch, pretrained=False, **kwargs):
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model = MobileNetV1(**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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param = paddle.load(weight_path)
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model.load_dict(param)
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return model
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def mobilenet_v1(pretrained=False, scale=1.0, **kwargs):
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"""MobileNetV1
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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_v1
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# build model
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model = mobilenet_v1()
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# build model and load imagenet pretrained weight
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# model = mobilenet_v1(pretrained=True)
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# build mobilenet v1 with scale=0.5
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model = mobilenet_v1(scale=0.5)
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"""
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model = _mobilenet(
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'mobilenetv1_' + str(scale), pretrained, scale=scale, **kwargs)
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return model
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