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290 lines
9.3 KiB
290 lines
9.3 KiB
# Copyright (c) 2022 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 |
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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.nn import Conv2D, MaxPool2D, AdaptiveAvgPool2D |
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from paddle.nn.initializer import KaimingNormal |
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from paddle.regularizer import L2Decay |
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from paddlers.models.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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from paddlers.models.ppdet.modeling.ops import channel_shuffle |
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from paddlers.models.ppdet.modeling.backbones.shufflenet_v2 import ConvBNLayer |
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__all__ = ['ESNet'] |
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def make_divisible(v, divisor=16, min_value=None): |
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if min_value is None: |
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min_value = divisor |
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new_v = max(min_value, int(v + divisor / 2) // divisor * divisor) |
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if new_v < 0.9 * v: |
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new_v += divisor |
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return new_v |
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class SEModule(nn.Layer): |
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def __init__(self, channel, reduction=4): |
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super(SEModule, self).__init__() |
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self.avg_pool = AdaptiveAvgPool2D(1) |
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self.conv1 = Conv2D( |
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in_channels=channel, |
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out_channels=channel // reduction, |
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kernel_size=1, |
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stride=1, |
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padding=0, |
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weight_attr=ParamAttr(), |
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bias_attr=ParamAttr()) |
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self.conv2 = Conv2D( |
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in_channels=channel // reduction, |
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out_channels=channel, |
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kernel_size=1, |
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stride=1, |
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padding=0, |
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weight_attr=ParamAttr(), |
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bias_attr=ParamAttr()) |
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def forward(self, inputs): |
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outputs = self.avg_pool(inputs) |
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outputs = self.conv1(outputs) |
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outputs = F.relu(outputs) |
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outputs = self.conv2(outputs) |
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outputs = F.hardsigmoid(outputs) |
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return paddle.multiply(x=inputs, y=outputs) |
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class InvertedResidual(nn.Layer): |
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def __init__(self, |
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in_channels, |
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mid_channels, |
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out_channels, |
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stride, |
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act="relu"): |
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super(InvertedResidual, self).__init__() |
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self._conv_pw = ConvBNLayer( |
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in_channels=in_channels // 2, |
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out_channels=mid_channels // 2, |
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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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act=act) |
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self._conv_dw = ConvBNLayer( |
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in_channels=mid_channels // 2, |
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out_channels=mid_channels // 2, |
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kernel_size=3, |
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stride=stride, |
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padding=1, |
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groups=mid_channels // 2, |
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act=None) |
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self._se = SEModule(mid_channels) |
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self._conv_linear = ConvBNLayer( |
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in_channels=mid_channels, |
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out_channels=out_channels // 2, |
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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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act=act) |
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def forward(self, inputs): |
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x1, x2 = paddle.split( |
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inputs, |
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num_or_sections=[inputs.shape[1] // 2, inputs.shape[1] // 2], |
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axis=1) |
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x2 = self._conv_pw(x2) |
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x3 = self._conv_dw(x2) |
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x3 = paddle.concat([x2, x3], axis=1) |
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x3 = self._se(x3) |
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x3 = self._conv_linear(x3) |
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out = paddle.concat([x1, x3], axis=1) |
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return channel_shuffle(out, 2) |
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class InvertedResidualDS(nn.Layer): |
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def __init__(self, |
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in_channels, |
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mid_channels, |
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out_channels, |
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stride, |
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act="relu"): |
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super(InvertedResidualDS, self).__init__() |
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# branch1 |
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self._conv_dw_1 = ConvBNLayer( |
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in_channels=in_channels, |
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out_channels=in_channels, |
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kernel_size=3, |
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stride=stride, |
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padding=1, |
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groups=in_channels, |
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act=None) |
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self._conv_linear_1 = ConvBNLayer( |
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in_channels=in_channels, |
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out_channels=out_channels // 2, |
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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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act=act) |
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# branch2 |
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self._conv_pw_2 = ConvBNLayer( |
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in_channels=in_channels, |
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out_channels=mid_channels // 2, |
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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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act=act) |
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self._conv_dw_2 = ConvBNLayer( |
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in_channels=mid_channels // 2, |
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out_channels=mid_channels // 2, |
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kernel_size=3, |
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stride=stride, |
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padding=1, |
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groups=mid_channels // 2, |
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act=None) |
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self._se = SEModule(mid_channels // 2) |
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self._conv_linear_2 = ConvBNLayer( |
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in_channels=mid_channels // 2, |
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out_channels=out_channels // 2, |
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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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act=act) |
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self._conv_dw_mv1 = ConvBNLayer( |
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in_channels=out_channels, |
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out_channels=out_channels, |
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kernel_size=3, |
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stride=1, |
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padding=1, |
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groups=out_channels, |
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act="hard_swish") |
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self._conv_pw_mv1 = ConvBNLayer( |
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in_channels=out_channels, |
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out_channels=out_channels, |
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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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act="hard_swish") |
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def forward(self, inputs): |
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x1 = self._conv_dw_1(inputs) |
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x1 = self._conv_linear_1(x1) |
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x2 = self._conv_pw_2(inputs) |
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x2 = self._conv_dw_2(x2) |
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x2 = self._se(x2) |
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x2 = self._conv_linear_2(x2) |
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out = paddle.concat([x1, x2], axis=1) |
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out = self._conv_dw_mv1(out) |
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out = self._conv_pw_mv1(out) |
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return out |
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@register |
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@serializable |
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class ESNet(nn.Layer): |
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def __init__(self, |
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scale=1.0, |
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act="hard_swish", |
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feature_maps=[4, 11, 14], |
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channel_ratio=[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]): |
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super(ESNet, self).__init__() |
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self.scale = scale |
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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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stage_repeats = [3, 7, 3] |
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stage_out_channels = [ |
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-1, 24, make_divisible(128 * scale), make_divisible(256 * scale), |
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make_divisible(512 * scale), 1024 |
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] |
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self._out_channels = [] |
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self._feature_idx = 0 |
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# 1. conv1 |
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self._conv1 = ConvBNLayer( |
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in_channels=3, |
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out_channels=stage_out_channels[1], |
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kernel_size=3, |
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stride=2, |
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padding=1, |
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act=act) |
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self._max_pool = MaxPool2D(kernel_size=3, stride=2, padding=1) |
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self._feature_idx += 1 |
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# 2. bottleneck sequences |
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self._block_list = [] |
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arch_idx = 0 |
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for stage_id, num_repeat in enumerate(stage_repeats): |
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for i in range(num_repeat): |
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channels_scales = channel_ratio[arch_idx] |
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mid_c = make_divisible( |
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int(stage_out_channels[stage_id + 2] * channels_scales), |
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divisor=8) |
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if i == 0: |
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block = self.add_sublayer( |
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name=str(stage_id + 2) + '_' + str(i + 1), |
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sublayer=InvertedResidualDS( |
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in_channels=stage_out_channels[stage_id + 1], |
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mid_channels=mid_c, |
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out_channels=stage_out_channels[stage_id + 2], |
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stride=2, |
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act=act)) |
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else: |
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block = self.add_sublayer( |
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name=str(stage_id + 2) + '_' + str(i + 1), |
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sublayer=InvertedResidual( |
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in_channels=stage_out_channels[stage_id + 2], |
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mid_channels=mid_c, |
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out_channels=stage_out_channels[stage_id + 2], |
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stride=1, |
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act=act)) |
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self._block_list.append(block) |
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arch_idx += 1 |
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self._feature_idx += 1 |
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self._update_out_channels(stage_out_channels[stage_id + 2], |
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self._feature_idx, self.feature_maps) |
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def _update_out_channels(self, channel, feature_idx, feature_maps): |
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if feature_idx in feature_maps: |
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self._out_channels.append(channel) |
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def forward(self, inputs): |
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y = self._conv1(inputs['image']) |
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y = self._max_pool(y) |
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outs = [] |
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for i, inv in enumerate(self._block_list): |
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y = inv(y) |
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if i + 2 in self.feature_maps: |
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outs.append(y) |
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return outs |
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@property |
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def out_shape(self): |
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return [ShapeSpec(channels=c) for c in self._out_channels]
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