forked from PulseFocusPlatform/PulseFocusPlatform
410 lines
13 KiB
Python
410 lines
13 KiB
Python
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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 paddle.nn as nn
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import paddle.nn.functional as F
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from paddle import ParamAttr
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from paddle.regularizer import L2Decay
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from paddle.nn.initializer import KaimingNormal
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from ppdet.core.workspace import register, serializable
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from numbers import Integral
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from ..shape_spec import ShapeSpec
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__all__ = ['MobileNet']
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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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act='relu',
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conv_lr=1.,
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conv_decay=0.,
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norm_decay=0.,
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norm_type='bn',
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name=None):
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super(ConvBNLayer, self).__init__()
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self.act = act
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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=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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weight_attr=ParamAttr(
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learning_rate=conv_lr,
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initializer=KaimingNormal(),
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regularizer=L2Decay(conv_decay)),
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bias_attr=False)
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param_attr = ParamAttr(regularizer=L2Decay(norm_decay))
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bias_attr = ParamAttr(regularizer=L2Decay(norm_decay))
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if norm_type == 'sync_bn':
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self._batch_norm = nn.SyncBatchNorm(
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out_channels, weight_attr=param_attr, bias_attr=bias_attr)
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else:
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self._batch_norm = nn.BatchNorm(
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out_channels,
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act=None,
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param_attr=param_attr,
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bias_attr=bias_attr,
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use_global_stats=False)
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def forward(self, x):
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x = self._conv(x)
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x = self._batch_norm(x)
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if self.act == "relu":
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x = F.relu(x)
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elif self.act == "relu6":
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x = F.relu6(x)
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return x
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class DepthwiseSeparable(nn.Layer):
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def __init__(self,
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in_channels,
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out_channels1,
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out_channels2,
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num_groups,
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stride,
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scale,
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conv_lr=1.,
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conv_decay=0.,
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norm_decay=0.,
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norm_type='bn',
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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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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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conv_lr=conv_lr,
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conv_decay=conv_decay,
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norm_decay=norm_decay,
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norm_type=norm_type,
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name=name + "_dw")
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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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conv_lr=conv_lr,
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conv_decay=conv_decay,
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norm_decay=norm_decay,
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norm_type=norm_type,
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name=name + "_sep")
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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 ExtraBlock(nn.Layer):
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def __init__(self,
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in_channels,
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out_channels1,
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out_channels2,
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num_groups=1,
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stride=2,
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conv_lr=1.,
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conv_decay=0.,
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norm_decay=0.,
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norm_type='bn',
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name=None):
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super(ExtraBlock, self).__init__()
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self.pointwise_conv = ConvBNLayer(
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in_channels,
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int(out_channels1),
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kernel_size=1,
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stride=1,
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padding=0,
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num_groups=int(num_groups),
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act='relu6',
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conv_lr=conv_lr,
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conv_decay=conv_decay,
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norm_decay=norm_decay,
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norm_type=norm_type,
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name=name + "_extra1")
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self.normal_conv = ConvBNLayer(
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int(out_channels1),
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int(out_channels2),
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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),
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act='relu6',
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conv_lr=conv_lr,
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conv_decay=conv_decay,
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norm_decay=norm_decay,
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norm_type=norm_type,
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name=name + "_extra2")
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def forward(self, x):
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x = self.pointwise_conv(x)
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x = self.normal_conv(x)
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return x
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@register
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@serializable
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class MobileNet(nn.Layer):
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__shared__ = ['norm_type']
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def __init__(self,
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norm_type='bn',
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norm_decay=0.,
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conv_decay=0.,
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scale=1,
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conv_learning_rate=1.0,
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feature_maps=[4, 6, 13],
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with_extra_blocks=False,
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extra_block_filters=[[256, 512], [128, 256], [128, 256],
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[64, 128]]):
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super(MobileNet, self).__init__()
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if isinstance(feature_maps, Integral):
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feature_maps = [feature_maps]
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self.feature_maps = feature_maps
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self.with_extra_blocks = with_extra_blocks
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self.extra_block_filters = extra_block_filters
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self._out_channels = []
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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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conv_lr=conv_learning_rate,
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conv_decay=conv_decay,
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norm_decay=norm_decay,
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norm_type=norm_type,
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name="conv1")
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self.dwsl = []
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dws21 = self.add_sublayer(
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"conv2_1",
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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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conv_lr=conv_learning_rate,
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conv_decay=conv_decay,
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norm_decay=norm_decay,
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norm_type=norm_type,
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name="conv2_1"))
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self.dwsl.append(dws21)
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self._update_out_channels(64, len(self.dwsl), feature_maps)
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dws22 = self.add_sublayer(
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"conv2_2",
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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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conv_lr=conv_learning_rate,
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conv_decay=conv_decay,
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norm_decay=norm_decay,
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norm_type=norm_type,
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name="conv2_2"))
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self.dwsl.append(dws22)
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self._update_out_channels(128, len(self.dwsl), feature_maps)
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# 1/4
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dws31 = self.add_sublayer(
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"conv3_1",
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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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conv_lr=conv_learning_rate,
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conv_decay=conv_decay,
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norm_decay=norm_decay,
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norm_type=norm_type,
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name="conv3_1"))
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self.dwsl.append(dws31)
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self._update_out_channels(128, len(self.dwsl), feature_maps)
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dws32 = self.add_sublayer(
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"conv3_2",
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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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conv_lr=conv_learning_rate,
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conv_decay=conv_decay,
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norm_decay=norm_decay,
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norm_type=norm_type,
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name="conv3_2"))
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self.dwsl.append(dws32)
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self._update_out_channels(256, len(self.dwsl), feature_maps)
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# 1/8
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dws41 = self.add_sublayer(
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"conv4_1",
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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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conv_lr=conv_learning_rate,
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conv_decay=conv_decay,
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norm_decay=norm_decay,
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norm_type=norm_type,
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name="conv4_1"))
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self.dwsl.append(dws41)
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self._update_out_channels(256, len(self.dwsl), feature_maps)
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dws42 = self.add_sublayer(
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"conv4_2",
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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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conv_lr=conv_learning_rate,
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conv_decay=conv_decay,
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norm_decay=norm_decay,
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norm_type=norm_type,
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name="conv4_2"))
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self.dwsl.append(dws42)
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self._update_out_channels(512, len(self.dwsl), feature_maps)
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# 1/16
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for i in range(5):
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tmp = self.add_sublayer(
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"conv5_" + str(i + 1),
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sublayer=DepthwiseSeparable(
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in_channels=512,
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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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conv_lr=conv_learning_rate,
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conv_decay=conv_decay,
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norm_decay=norm_decay,
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norm_type=norm_type,
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name="conv5_" + str(i + 1)))
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self.dwsl.append(tmp)
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self._update_out_channels(512, len(self.dwsl), feature_maps)
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dws56 = self.add_sublayer(
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"conv5_6",
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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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conv_lr=conv_learning_rate,
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conv_decay=conv_decay,
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norm_decay=norm_decay,
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norm_type=norm_type,
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name="conv5_6"))
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self.dwsl.append(dws56)
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self._update_out_channels(1024, len(self.dwsl), feature_maps)
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# 1/32
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dws6 = self.add_sublayer(
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"conv6",
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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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conv_lr=conv_learning_rate,
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conv_decay=conv_decay,
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norm_decay=norm_decay,
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norm_type=norm_type,
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name="conv6"))
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self.dwsl.append(dws6)
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self._update_out_channels(1024, len(self.dwsl), feature_maps)
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if self.with_extra_blocks:
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self.extra_blocks = []
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for i, block_filter in enumerate(self.extra_block_filters):
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in_c = 1024 if i == 0 else self.extra_block_filters[i - 1][1]
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conv_extra = self.add_sublayer(
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"conv7_" + str(i + 1),
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sublayer=ExtraBlock(
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in_c,
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block_filter[0],
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block_filter[1],
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conv_lr=conv_learning_rate,
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conv_decay=conv_decay,
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norm_decay=norm_decay,
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norm_type=norm_type,
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name="conv7_" + str(i + 1)))
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|
|
self.extra_blocks.append(conv_extra)
|
||
|
|
self._update_out_channels(
|
||
|
|
block_filter[1],
|
||
|
|
len(self.dwsl) + len(self.extra_blocks), feature_maps)
|
||
|
|
|
||
|
|
def _update_out_channels(self, channel, feature_idx, feature_maps):
|
||
|
|
if feature_idx in feature_maps:
|
||
|
|
self._out_channels.append(channel)
|
||
|
|
|
||
|
|
def forward(self, inputs):
|
||
|
|
outs = []
|
||
|
|
y = self.conv1(inputs['image'])
|
||
|
|
for i, block in enumerate(self.dwsl):
|
||
|
|
y = block(y)
|
||
|
|
if i + 1 in self.feature_maps:
|
||
|
|
outs.append(y)
|
||
|
|
|
||
|
|
if not self.with_extra_blocks:
|
||
|
|
return outs
|
||
|
|
|
||
|
|
y = outs[-1]
|
||
|
|
for i, block in enumerate(self.extra_blocks):
|
||
|
|
idx = i + len(self.dwsl)
|
||
|
|
y = block(y)
|
||
|
|
if idx + 1 in self.feature_maps:
|
||
|
|
outs.append(y)
|
||
|
|
return outs
|
||
|
|
|
||
|
|
@property
|
||
|
|
def out_shape(self):
|
||
|
|
return [ShapeSpec(channels=c) for c in self._out_channels]
|