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258 lines
7.5 KiB
258 lines
7.5 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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from paddle import ParamAttr |
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from paddle.nn import AdaptiveAvgPool2D, Conv2D |
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from paddle.regularizer import L2Decay |
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from paddle.nn.initializer import KaimingNormal |
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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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__all__ = ['LCNet'] |
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NET_CONFIG = { |
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"blocks2": |
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#k, in_c, out_c, s, use_se |
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[[3, 16, 32, 1, False], ], |
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"blocks3": [ |
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[3, 32, 64, 2, False], |
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[3, 64, 64, 1, False], |
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], |
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"blocks4": [ |
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[3, 64, 128, 2, False], |
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[3, 128, 128, 1, False], |
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], |
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"blocks5": [ |
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[3, 128, 256, 2, False], |
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[5, 256, 256, 1, False], |
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[5, 256, 256, 1, False], |
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[5, 256, 256, 1, False], |
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[5, 256, 256, 1, False], |
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[5, 256, 256, 1, False], |
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], |
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"blocks6": [[5, 256, 512, 2, True], [5, 512, 512, 1, True]] |
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} |
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def make_divisible(v, divisor=8, 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 ConvBNLayer(nn.Layer): |
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def __init__(self, |
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num_channels, |
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filter_size, |
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num_filters, |
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stride, |
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num_groups=1): |
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super().__init__() |
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self.conv = Conv2D( |
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in_channels=num_channels, |
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out_channels=num_filters, |
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kernel_size=filter_size, |
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stride=stride, |
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padding=(filter_size - 1) // 2, |
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groups=num_groups, |
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weight_attr=ParamAttr(initializer=KaimingNormal()), |
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bias_attr=False) |
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self.bn = nn.BatchNorm2D( |
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num_filters, |
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weight_attr=ParamAttr(regularizer=L2Decay(0.0)), |
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bias_attr=ParamAttr(regularizer=L2Decay(0.0))) |
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self.hardswish = nn.Hardswish() |
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def forward(self, x): |
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x = self.conv(x) |
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x = self.bn(x) |
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x = self.hardswish(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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num_channels, |
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num_filters, |
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stride, |
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dw_size=3, |
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use_se=False): |
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super().__init__() |
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self.use_se = use_se |
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self.dw_conv = ConvBNLayer( |
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num_channels=num_channels, |
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num_filters=num_channels, |
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filter_size=dw_size, |
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stride=stride, |
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num_groups=num_channels) |
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if use_se: |
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self.se = SEModule(num_channels) |
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self.pw_conv = ConvBNLayer( |
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num_channels=num_channels, |
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filter_size=1, |
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num_filters=num_filters, |
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stride=1) |
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def forward(self, x): |
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x = self.dw_conv(x) |
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if self.use_se: |
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x = self.se(x) |
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x = self.pw_conv(x) |
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return x |
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class SEModule(nn.Layer): |
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def __init__(self, channel, reduction=4): |
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super().__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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self.relu = nn.ReLU() |
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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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self.hardsigmoid = nn.Hardsigmoid() |
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def forward(self, x): |
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identity = x |
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x = self.avg_pool(x) |
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x = self.conv1(x) |
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x = self.relu(x) |
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x = self.conv2(x) |
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x = self.hardsigmoid(x) |
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x = paddle.multiply(x=identity, y=x) |
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return x |
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@register |
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@serializable |
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class LCNet(nn.Layer): |
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def __init__(self, scale=1.0, feature_maps=[3, 4, 5]): |
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super().__init__() |
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self.scale = scale |
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self.feature_maps = feature_maps |
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out_channels = [] |
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self.conv1 = ConvBNLayer( |
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num_channels=3, |
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filter_size=3, |
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num_filters=make_divisible(16 * scale), |
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stride=2) |
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self.blocks2 = nn.Sequential(*[ |
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DepthwiseSeparable( |
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num_channels=make_divisible(in_c * scale), |
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num_filters=make_divisible(out_c * scale), |
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dw_size=k, |
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stride=s, |
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use_se=se) |
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for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks2"]) |
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]) |
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self.blocks3 = nn.Sequential(*[ |
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DepthwiseSeparable( |
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num_channels=make_divisible(in_c * scale), |
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num_filters=make_divisible(out_c * scale), |
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dw_size=k, |
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stride=s, |
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use_se=se) |
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for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks3"]) |
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]) |
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out_channels.append( |
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make_divisible(NET_CONFIG["blocks3"][-1][2] * scale)) |
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self.blocks4 = nn.Sequential(*[ |
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DepthwiseSeparable( |
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num_channels=make_divisible(in_c * scale), |
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num_filters=make_divisible(out_c * scale), |
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dw_size=k, |
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stride=s, |
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use_se=se) |
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for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks4"]) |
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]) |
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out_channels.append( |
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make_divisible(NET_CONFIG["blocks4"][-1][2] * scale)) |
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self.blocks5 = nn.Sequential(*[ |
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DepthwiseSeparable( |
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num_channels=make_divisible(in_c * scale), |
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num_filters=make_divisible(out_c * scale), |
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dw_size=k, |
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stride=s, |
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use_se=se) |
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for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks5"]) |
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]) |
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out_channels.append( |
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make_divisible(NET_CONFIG["blocks5"][-1][2] * scale)) |
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self.blocks6 = nn.Sequential(*[ |
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DepthwiseSeparable( |
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num_channels=make_divisible(in_c * scale), |
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num_filters=make_divisible(out_c * scale), |
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dw_size=k, |
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stride=s, |
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use_se=se) |
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for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks6"]) |
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]) |
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out_channels.append( |
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make_divisible(NET_CONFIG["blocks6"][-1][2] * scale)) |
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self._out_channels = [ |
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ch for idx, ch in enumerate(out_channels) if idx + 2 in feature_maps |
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] |
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def forward(self, inputs): |
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x = inputs['image'] |
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outs = [] |
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x = self.conv1(x) |
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x = self.blocks2(x) |
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x = self.blocks3(x) |
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outs.append(x) |
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x = self.blocks4(x) |
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outs.append(x) |
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x = self.blocks5(x) |
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outs.append(x) |
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x = self.blocks6(x) |
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outs.append(x) |
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outs = [o for i, o in enumerate(outs) if i + 2 in self.feature_maps] |
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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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