359 lines
11 KiB
Python
359 lines
11 KiB
Python
# copyright (c) 2024 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, division, print_function
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import os
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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 AdaptiveAvgPool2D, BatchNorm2D, Conv2D, Dropout, Linear
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from paddle.regularizer import L2Decay
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from paddle.nn.initializer import KaimingNormal
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from paddle.utils.download import get_path_from_url
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MODEL_URLS = {
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"PPLCNetV2_small": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNetV2_small_ssld_pretrained.pdparams",
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"PPLCNetV2_base": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNetV2_base_ssld_pretrained.pdparams",
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"PPLCNetV2_large": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNetV2_large_ssld_pretrained.pdparams",
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}
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__all__ = list(MODEL_URLS.keys())
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NET_CONFIG = {
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# in_channels, kernel_size, split_pw, use_rep, use_se, use_shortcut
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"stage1": [64, 3, False, False, False, False],
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"stage2": [128, 3, False, False, False, False],
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"stage3": [256, 5, True, True, True, False],
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"stage4": [512, 5, False, True, False, 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__(
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self, in_channels, out_channels, kernel_size, stride, groups=1, use_act=True
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):
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super().__init__()
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self.use_act = use_act
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self.conv = Conv2D(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=kernel_size,
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stride=stride,
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padding=(kernel_size - 1) // 2,
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groups=groups,
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weight_attr=ParamAttr(initializer=KaimingNormal()),
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bias_attr=False,
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)
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self.bn = BatchNorm2D(
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out_channels,
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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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)
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if self.use_act:
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self.act = nn.ReLU()
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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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if self.use_act:
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x = self.act(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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)
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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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)
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self.hardsigmoid = nn.Sigmoid()
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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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class RepDepthwiseSeparable(nn.Layer):
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def __init__(
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self,
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in_channels,
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out_channels,
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stride,
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dw_size=3,
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split_pw=False,
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use_rep=False,
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use_se=False,
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use_shortcut=False,
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):
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super().__init__()
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.is_repped = False
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self.dw_size = dw_size
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self.split_pw = split_pw
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self.use_rep = use_rep
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self.use_se = use_se
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self.use_shortcut = (
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True
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if use_shortcut and stride == 1 and in_channels == out_channels
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else False
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)
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if self.use_rep:
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self.dw_conv_list = nn.LayerList()
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for kernel_size in range(self.dw_size, 0, -2):
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if kernel_size == 1 and stride != 1:
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continue
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dw_conv = ConvBNLayer(
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in_channels=in_channels,
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out_channels=in_channels,
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kernel_size=kernel_size,
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stride=stride,
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groups=in_channels,
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use_act=False,
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)
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self.dw_conv_list.append(dw_conv)
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self.dw_conv = nn.Conv2D(
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in_channels=in_channels,
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out_channels=in_channels,
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kernel_size=dw_size,
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stride=stride,
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padding=(dw_size - 1) // 2,
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groups=in_channels,
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)
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else:
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self.dw_conv = ConvBNLayer(
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in_channels=in_channels,
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out_channels=in_channels,
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kernel_size=dw_size,
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stride=stride,
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groups=in_channels,
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)
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self.act = nn.ReLU()
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if use_se:
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self.se = SEModule(in_channels)
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if self.split_pw:
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pw_ratio = 0.5
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self.pw_conv_1 = ConvBNLayer(
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in_channels=in_channels,
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kernel_size=1,
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out_channels=int(out_channels * pw_ratio),
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stride=1,
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)
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self.pw_conv_2 = ConvBNLayer(
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in_channels=int(out_channels * pw_ratio),
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kernel_size=1,
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out_channels=out_channels,
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stride=1,
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)
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else:
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self.pw_conv = ConvBNLayer(
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in_channels=in_channels,
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kernel_size=1,
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out_channels=out_channels,
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stride=1,
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)
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def forward(self, x):
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if self.use_rep:
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input_x = x
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if self.is_repped:
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x = self.act(self.dw_conv(x))
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else:
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y = self.dw_conv_list[0](x)
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for dw_conv in self.dw_conv_list[1:]:
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y += dw_conv(x)
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x = self.act(y)
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else:
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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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if self.split_pw:
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x = self.pw_conv_1(x)
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x = self.pw_conv_2(x)
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else:
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x = self.pw_conv(x)
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if self.use_shortcut:
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x = x + input_x
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return x
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def re_parameterize(self):
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if self.use_rep:
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self.is_repped = True
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kernel, bias = self._get_equivalent_kernel_bias()
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self.dw_conv.weight.set_value(kernel)
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self.dw_conv.bias.set_value(bias)
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def _get_equivalent_kernel_bias(self):
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kernel_sum = 0
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bias_sum = 0
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for dw_conv in self.dw_conv_list:
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kernel, bias = self._fuse_bn_tensor(dw_conv)
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kernel = self._pad_tensor(kernel, to_size=self.dw_size)
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kernel_sum += kernel
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bias_sum += bias
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return kernel_sum, bias_sum
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def _fuse_bn_tensor(self, branch):
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kernel = branch.conv.weight
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running_mean = branch.bn._mean
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running_var = branch.bn._variance
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gamma = branch.bn.weight
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beta = branch.bn.bias
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eps = branch.bn._epsilon
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std = (running_var + eps).sqrt()
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t = (gamma / std).reshape((-1, 1, 1, 1))
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return kernel * t, beta - running_mean * gamma / std
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def _pad_tensor(self, tensor, to_size):
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from_size = tensor.shape[-1]
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if from_size == to_size:
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return tensor
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pad = (to_size - from_size) // 2
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return F.pad(tensor, [pad, pad, pad, pad])
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class PPLCNetV2(nn.Layer):
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def __init__(self, scale, depths, out_indx=[1, 2, 3, 4], **kwargs):
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super().__init__(**kwargs)
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self.scale = scale
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self.out_channels = [
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# int(NET_CONFIG["blocks3"][-1][2] * scale),
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int(NET_CONFIG["stage1"][0] * scale * 2),
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int(NET_CONFIG["stage2"][0] * scale * 2),
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int(NET_CONFIG["stage3"][0] * scale * 2),
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int(NET_CONFIG["stage4"][0] * scale * 2),
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]
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self.stem = nn.Sequential(
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*[
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ConvBNLayer(
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in_channels=3,
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kernel_size=3,
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out_channels=make_divisible(32 * scale),
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stride=2,
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),
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RepDepthwiseSeparable(
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in_channels=make_divisible(32 * scale),
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out_channels=make_divisible(64 * scale),
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stride=1,
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dw_size=3,
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),
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]
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)
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self.out_indx = out_indx
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# stages
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self.stages = nn.LayerList()
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for depth_idx, k in enumerate(NET_CONFIG):
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(
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in_channels,
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kernel_size,
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split_pw,
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use_rep,
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use_se,
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use_shortcut,
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) = NET_CONFIG[k]
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self.stages.append(
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nn.Sequential(
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*[
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RepDepthwiseSeparable(
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in_channels=make_divisible(
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(in_channels if i == 0 else in_channels * 2) * scale
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),
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out_channels=make_divisible(in_channels * 2 * scale),
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stride=2 if i == 0 else 1,
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dw_size=kernel_size,
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split_pw=split_pw,
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use_rep=use_rep,
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use_se=use_se,
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use_shortcut=use_shortcut,
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)
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for i in range(depths[depth_idx])
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]
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)
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)
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# if pretrained:
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self._load_pretrained(MODEL_URLS["PPLCNetV2_base"], use_ssld=True)
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def forward(self, x):
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x = self.stem(x)
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i = 1
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outs = []
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for stage in self.stages:
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x = stage(x)
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if i in self.out_indx:
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outs.append(x)
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i += 1
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return outs
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def _load_pretrained(self, pretrained_url, use_ssld=False):
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print(pretrained_url)
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local_weight_path = get_path_from_url(
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pretrained_url, os.path.expanduser("~/.paddleclas/weights")
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)
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param_state_dict = paddle.load(local_weight_path)
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self.set_dict(param_state_dict)
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print("load pretrain ssd success!")
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return
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def PPLCNetV2_base(in_channels=3, **kwargs):
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"""
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PPLCNetV2_base
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Args:
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pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
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If str, means the path of the pretrained model.
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use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
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Returns:
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model: nn.Layer. Specific `PPLCNetV2_base` model depends on args.
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"""
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model = PPLCNetV2(scale=1.0, depths=[2, 2, 6, 2], **kwargs)
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return model
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