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| # copyright (c) 2020 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. | |
| from paddle import nn | |
| from ppocr.modeling.backbones.det_mobilenet_v3 import ResidualUnit, ConvBNLayer, make_divisible | |
| __all__ = ['MobileNetV3'] | |
| class MobileNetV3(nn.Layer): | |
| def __init__(self, | |
| in_channels=3, | |
| model_name='small', | |
| scale=0.5, | |
| large_stride=None, | |
| small_stride=None, | |
| disable_se=False, | |
| **kwargs): | |
| super(MobileNetV3, self).__init__() | |
| self.disable_se = disable_se | |
| if small_stride is None: | |
| small_stride = [2, 2, 2, 2] | |
| if large_stride is None: | |
| large_stride = [1, 2, 2, 2] | |
| assert isinstance(large_stride, list), "large_stride type must " \ | |
| "be list but got {}".format(type(large_stride)) | |
| assert isinstance(small_stride, list), "small_stride type must " \ | |
| "be list but got {}".format(type(small_stride)) | |
| assert len(large_stride) == 4, "large_stride length must be " \ | |
| "4 but got {}".format(len(large_stride)) | |
| assert len(small_stride) == 4, "small_stride length must be " \ | |
| "4 but got {}".format(len(small_stride)) | |
| if model_name == "large": | |
| cfg = [ | |
| # k, exp, c, se, nl, s, | |
| [3, 16, 16, False, 'relu', large_stride[0]], | |
| [3, 64, 24, False, 'relu', (large_stride[1], 1)], | |
| [3, 72, 24, False, 'relu', 1], | |
| [5, 72, 40, True, 'relu', (large_stride[2], 1)], | |
| [5, 120, 40, True, 'relu', 1], | |
| [5, 120, 40, True, 'relu', 1], | |
| [3, 240, 80, False, 'hardswish', 1], | |
| [3, 200, 80, False, 'hardswish', 1], | |
| [3, 184, 80, False, 'hardswish', 1], | |
| [3, 184, 80, False, 'hardswish', 1], | |
| [3, 480, 112, True, 'hardswish', 1], | |
| [3, 672, 112, True, 'hardswish', 1], | |
| [5, 672, 160, True, 'hardswish', (large_stride[3], 1)], | |
| [5, 960, 160, True, 'hardswish', 1], | |
| [5, 960, 160, True, 'hardswish', 1], | |
| ] | |
| cls_ch_squeeze = 960 | |
| elif model_name == "small": | |
| cfg = [ | |
| # k, exp, c, se, nl, s, | |
| [3, 16, 16, True, 'relu', (small_stride[0], 1)], | |
| [3, 72, 24, False, 'relu', (small_stride[1], 1)], | |
| [3, 88, 24, False, 'relu', 1], | |
| [5, 96, 40, True, 'hardswish', (small_stride[2], 1)], | |
| [5, 240, 40, True, 'hardswish', 1], | |
| [5, 240, 40, True, 'hardswish', 1], | |
| [5, 120, 48, True, 'hardswish', 1], | |
| [5, 144, 48, True, 'hardswish', 1], | |
| [5, 288, 96, True, 'hardswish', (small_stride[3], 1)], | |
| [5, 576, 96, True, 'hardswish', 1], | |
| [5, 576, 96, True, 'hardswish', 1], | |
| ] | |
| cls_ch_squeeze = 576 | |
| else: | |
| raise NotImplementedError("mode[" + model_name + | |
| "_model] is not implemented!") | |
| supported_scale = [0.35, 0.5, 0.75, 1.0, 1.25] | |
| assert scale in supported_scale, \ | |
| "supported scales are {} but input scale is {}".format(supported_scale, scale) | |
| inplanes = 16 | |
| # conv1 | |
| self.conv1 = ConvBNLayer( | |
| in_channels=in_channels, | |
| out_channels=make_divisible(inplanes * scale), | |
| kernel_size=3, | |
| stride=2, | |
| padding=1, | |
| groups=1, | |
| if_act=True, | |
| act='hardswish') | |
| i = 0 | |
| block_list = [] | |
| inplanes = make_divisible(inplanes * scale) | |
| for (k, exp, c, se, nl, s) in cfg: | |
| se = se and not self.disable_se | |
| block_list.append( | |
| ResidualUnit( | |
| in_channels=inplanes, | |
| mid_channels=make_divisible(scale * exp), | |
| out_channels=make_divisible(scale * c), | |
| kernel_size=k, | |
| stride=s, | |
| use_se=se, | |
| act=nl)) | |
| inplanes = make_divisible(scale * c) | |
| i += 1 | |
| self.blocks = nn.Sequential(*block_list) | |
| self.conv2 = ConvBNLayer( | |
| in_channels=inplanes, | |
| out_channels=make_divisible(scale * cls_ch_squeeze), | |
| kernel_size=1, | |
| stride=1, | |
| padding=0, | |
| groups=1, | |
| if_act=True, | |
| act='hardswish') | |
| self.pool = nn.MaxPool2D(kernel_size=2, stride=2, padding=0) | |
| self.out_channels = make_divisible(scale * cls_ch_squeeze) | |
| def forward(self, x): | |
| x = self.conv1(x) | |
| x = self.blocks(x) | |
| x = self.conv2(x) | |
| x = self.pool(x) | |
| return x | |