mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
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139 lines
5.4 KiB
Python
139 lines
5.4 KiB
Python
# 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 paddle import nn
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from ppocr.modeling.backbones.det_mobilenet_v3 import ResidualUnit, ConvBNLayer, make_divisible
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__all__ = ['MobileNetV3']
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class MobileNetV3(nn.Layer):
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def __init__(self,
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in_channels=3,
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model_name='small',
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scale=0.5,
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large_stride=None,
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small_stride=None,
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disable_se=False,
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**kwargs):
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super(MobileNetV3, self).__init__()
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self.disable_se = disable_se
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if small_stride is None:
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small_stride = [2, 2, 2, 2]
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if large_stride is None:
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large_stride = [1, 2, 2, 2]
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assert isinstance(large_stride, list), "large_stride type must " \
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"be list but got {}".format(type(large_stride))
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assert isinstance(small_stride, list), "small_stride type must " \
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"be list but got {}".format(type(small_stride))
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assert len(large_stride) == 4, "large_stride length must be " \
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"4 but got {}".format(len(large_stride))
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assert len(small_stride) == 4, "small_stride length must be " \
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"4 but got {}".format(len(small_stride))
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if model_name == "large":
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cfg = [
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# k, exp, c, se, nl, s,
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[3, 16, 16, False, 'relu', large_stride[0]],
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[3, 64, 24, False, 'relu', (large_stride[1], 1)],
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[3, 72, 24, False, 'relu', 1],
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[5, 72, 40, True, 'relu', (large_stride[2], 1)],
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[5, 120, 40, True, 'relu', 1],
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[5, 120, 40, True, 'relu', 1],
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[3, 240, 80, False, 'hardswish', 1],
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[3, 200, 80, False, 'hardswish', 1],
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[3, 184, 80, False, 'hardswish', 1],
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[3, 184, 80, False, 'hardswish', 1],
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[3, 480, 112, True, 'hardswish', 1],
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[3, 672, 112, True, 'hardswish', 1],
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[5, 672, 160, True, 'hardswish', (large_stride[3], 1)],
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[5, 960, 160, True, 'hardswish', 1],
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[5, 960, 160, True, 'hardswish', 1],
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]
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cls_ch_squeeze = 960
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elif model_name == "small":
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cfg = [
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# k, exp, c, se, nl, s,
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[3, 16, 16, True, 'relu', (small_stride[0], 1)],
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[3, 72, 24, False, 'relu', (small_stride[1], 1)],
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[3, 88, 24, False, 'relu', 1],
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[5, 96, 40, True, 'hardswish', (small_stride[2], 1)],
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[5, 240, 40, True, 'hardswish', 1],
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[5, 240, 40, True, 'hardswish', 1],
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[5, 120, 48, True, 'hardswish', 1],
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[5, 144, 48, True, 'hardswish', 1],
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[5, 288, 96, True, 'hardswish', (small_stride[3], 1)],
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[5, 576, 96, True, 'hardswish', 1],
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[5, 576, 96, True, 'hardswish', 1],
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]
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cls_ch_squeeze = 576
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else:
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raise NotImplementedError("mode[" + model_name +
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"_model] is not implemented!")
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supported_scale = [0.35, 0.5, 0.75, 1.0, 1.25]
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assert scale in supported_scale, \
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"supported scales are {} but input scale is {}".format(supported_scale, scale)
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inplanes = 16
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# conv1
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self.conv1 = ConvBNLayer(
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in_channels=in_channels,
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out_channels=make_divisible(inplanes * scale),
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kernel_size=3,
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stride=2,
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padding=1,
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groups=1,
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if_act=True,
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act='hardswish')
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i = 0
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block_list = []
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inplanes = make_divisible(inplanes * scale)
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for (k, exp, c, se, nl, s) in cfg:
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se = se and not self.disable_se
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block_list.append(
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ResidualUnit(
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in_channels=inplanes,
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mid_channels=make_divisible(scale * exp),
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out_channels=make_divisible(scale * c),
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kernel_size=k,
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stride=s,
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use_se=se,
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act=nl))
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inplanes = make_divisible(scale * c)
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i += 1
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self.blocks = nn.Sequential(*block_list)
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self.conv2 = ConvBNLayer(
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in_channels=inplanes,
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out_channels=make_divisible(scale * cls_ch_squeeze),
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kernel_size=1,
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stride=1,
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padding=0,
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groups=1,
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if_act=True,
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act='hardswish')
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self.pool = nn.MaxPool2D(kernel_size=2, stride=2, padding=0)
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self.out_channels = make_divisible(scale * cls_ch_squeeze)
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def forward(self, x):
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x = self.conv1(x)
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x = self.blocks(x)
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x = self.conv2(x)
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x = self.pool(x)
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return x
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