470 lines
17 KiB
Python
470 lines
17 KiB
Python
# copyright (c) 2021 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 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, 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 ..base.theseus_layer import TheseusLayer
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from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
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MODEL_URLS = {
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"PPLCNet_x0_25":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_25_pretrained.pdparams",
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"PPLCNet_x0_35":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_35_pretrained.pdparams",
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"PPLCNet_x0_5":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_5_pretrained.pdparams",
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"PPLCNet_x0_75":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_75_pretrained.pdparams",
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"PPLCNet_x1_0":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x1_0_pretrained.pdparams",
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"PPLCNet_x1_5":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x1_5_pretrained.pdparams",
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"PPLCNet_x2_0":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x2_0_pretrained.pdparams",
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"PPLCNet_x2_5":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x2_5_pretrained.pdparams"
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}
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MODEL_STAGES_PATTERN = {
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"PPLCNet": ["blocks2", "blocks3", "blocks4", "blocks5", "blocks6"]
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}
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__all__ = list(MODEL_URLS.keys())
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# Each element(list) represents a depthwise block, which is composed of k, in_c, out_c, s, use_se.
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# k: kernel_size
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# in_c: input channel number in depthwise block
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# out_c: output channel number in depthwise block
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# s: stride in depthwise block
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# use_se: whether to use SE block
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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": [[3, 32, 64, 2, False], [3, 64, 64, 1, False]],
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"blocks4": [[3, 64, 128, 2, False], [3, 128, 128, 1, False]],
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"blocks5": [[3, 128, 256, 2, False], [5, 256, 256, 1, False],
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[5, 256, 256, 1, False], [5, 256, 256, 1, False],
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[5, 256, 256, 1, False], [5, 256, 256, 1, False]],
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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(TheseusLayer):
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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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lr_mult=1.0):
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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(
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initializer=KaimingNormal(), learning_rate=lr_mult),
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bias_attr=False)
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self.bn = BatchNorm2D(
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num_filters,
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weight_attr=ParamAttr(
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regularizer=L2Decay(0.0), learning_rate=lr_mult),
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bias_attr=ParamAttr(
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regularizer=L2Decay(0.0), learning_rate=lr_mult))
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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(TheseusLayer):
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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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lr_mult=1.0):
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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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lr_mult=lr_mult)
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if use_se:
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self.se = SEModule(num_channels, lr_mult=lr_mult)
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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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lr_mult=lr_mult)
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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(TheseusLayer):
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def __init__(self, channel, reduction=4, lr_mult=1.0):
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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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weight_attr=ParamAttr(learning_rate=lr_mult),
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bias_attr=ParamAttr(learning_rate=lr_mult))
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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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weight_attr=ParamAttr(learning_rate=lr_mult),
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bias_attr=ParamAttr(learning_rate=lr_mult))
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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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class PPLCNet(TheseusLayer):
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def __init__(self,
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stages_pattern,
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scale=1.0,
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class_num=1000,
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dropout_prob=0.2,
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class_expand=1280,
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lr_mult_list=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
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stride_list=[2, 2, 2, 2, 2],
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use_last_conv=True,
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return_patterns=None,
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return_stages=None,
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**kwargs):
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super().__init__()
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self.scale = scale
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self.class_expand = class_expand
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self.lr_mult_list = lr_mult_list
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self.use_last_conv = use_last_conv
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self.stride_list = stride_list
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self.net_config = NET_CONFIG
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if isinstance(self.lr_mult_list, str):
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self.lr_mult_list = eval(self.lr_mult_list)
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assert isinstance(self.lr_mult_list, (
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list, tuple
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)), "lr_mult_list should be in (list, tuple) but got {}".format(
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type(self.lr_mult_list))
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assert len(self.lr_mult_list
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) == 6, "lr_mult_list length should be 6 but got {}".format(
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len(self.lr_mult_list))
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assert isinstance(self.stride_list, (
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list, tuple
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)), "stride_list should be in (list, tuple) but got {}".format(
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type(self.stride_list))
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assert len(self.stride_list
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) == 5, "stride_list length should be 5 but got {}".format(
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len(self.stride_list))
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for i, stride in enumerate(stride_list[1:]):
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self.net_config["blocks{}".format(i + 3)][0][3] = stride
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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=stride_list[0],
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lr_mult=self.lr_mult_list[0])
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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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lr_mult=self.lr_mult_list[1]) for i, (k, in_c, out_c, s, se) in
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enumerate(self.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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lr_mult=self.lr_mult_list[2]) for i, (k, in_c, out_c, s, se) in
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enumerate(self.net_config["blocks3"])
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])
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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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lr_mult=self.lr_mult_list[3]) for i, (k, in_c, out_c, s, se) in
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enumerate(self.net_config["blocks4"])
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])
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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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lr_mult=self.lr_mult_list[4]) for i, (k, in_c, out_c, s, se) in
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enumerate(self.net_config["blocks5"])
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])
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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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lr_mult=self.lr_mult_list[5]) for i, (k, in_c, out_c, s, se) in
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enumerate(self.net_config["blocks6"])
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])
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self.avg_pool = AdaptiveAvgPool2D(1)
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if self.use_last_conv:
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self.last_conv = Conv2D(
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in_channels=make_divisible(self.net_config["blocks6"][-1][2] *
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scale),
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out_channels=self.class_expand,
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kernel_size=1,
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stride=1,
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padding=0,
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bias_attr=False)
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self.hardswish = nn.Hardswish()
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self.dropout = Dropout(p=dropout_prob, mode="downscale_in_infer")
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else:
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self.last_conv = None
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self.flatten = nn.Flatten(start_axis=1, stop_axis=-1)
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self.fc = Linear(
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self.class_expand if self.use_last_conv else
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make_divisible(self.net_config["blocks6"][-1][2]), class_num)
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super().init_res(
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stages_pattern,
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return_patterns=return_patterns,
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return_stages=return_stages)
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def forward(self, x):
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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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x = self.blocks4(x)
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x = self.blocks5(x)
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x = self.blocks6(x)
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x = self.avg_pool(x)
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if self.last_conv is not None:
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x = self.last_conv(x)
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x = self.hardswish(x)
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x = self.dropout(x)
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x = self.flatten(x)
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x = self.fc(x)
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return x
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def _load_pretrained(pretrained, model, model_url, use_ssld):
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if pretrained is False:
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pass
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elif pretrained is True:
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load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
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elif isinstance(pretrained, str):
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load_dygraph_pretrain(model, pretrained)
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else:
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raise RuntimeError(
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"pretrained type is not available. Please use `string` or `boolean` type."
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)
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def PPLCNet_x0_25(pretrained=False, use_ssld=False, **kwargs):
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"""
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PPLCNet_x0_25
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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 `PPLCNet_x0_25` model depends on args.
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"""
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model = PPLCNet(
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scale=0.25, stages_pattern=MODEL_STAGES_PATTERN["PPLCNet"], **kwargs)
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_load_pretrained(pretrained, model, MODEL_URLS["PPLCNet_x0_25"], use_ssld)
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return model
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def PPLCNet_x0_35(pretrained=False, use_ssld=False, **kwargs):
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"""
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PPLCNet_x0_35
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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 `PPLCNet_x0_35` model depends on args.
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"""
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model = PPLCNet(
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scale=0.35, stages_pattern=MODEL_STAGES_PATTERN["PPLCNet"], **kwargs)
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_load_pretrained(pretrained, model, MODEL_URLS["PPLCNet_x0_35"], use_ssld)
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return model
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def PPLCNet_x0_5(pretrained=False, use_ssld=False, **kwargs):
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"""
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PPLCNet_x0_5
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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 `PPLCNet_x0_5` model depends on args.
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"""
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model = PPLCNet(
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scale=0.5, stages_pattern=MODEL_STAGES_PATTERN["PPLCNet"], **kwargs)
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_load_pretrained(pretrained, model, MODEL_URLS["PPLCNet_x0_5"], use_ssld)
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return model
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def PPLCNet_x0_75(pretrained=False, use_ssld=False, **kwargs):
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"""
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PPLCNet_x0_75
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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 `PPLCNet_x0_75` model depends on args.
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"""
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model = PPLCNet(
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scale=0.75, stages_pattern=MODEL_STAGES_PATTERN["PPLCNet"], **kwargs)
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_load_pretrained(pretrained, model, MODEL_URLS["PPLCNet_x0_75"], use_ssld)
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return model
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def PPLCNet_x1_0(pretrained=False, use_ssld=False, **kwargs):
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"""
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PPLCNet_x1_0
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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 `PPLCNet_x1_0` model depends on args.
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"""
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model = PPLCNet(
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scale=1.0, stages_pattern=MODEL_STAGES_PATTERN["PPLCNet"], **kwargs)
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_load_pretrained(pretrained, model, MODEL_URLS["PPLCNet_x1_0"], use_ssld)
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return model
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def PPLCNet_x1_5(pretrained=False, use_ssld=False, **kwargs):
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"""
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PPLCNet_x1_5
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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 `PPLCNet_x1_5` model depends on args.
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"""
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model = PPLCNet(
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scale=1.5, stages_pattern=MODEL_STAGES_PATTERN["PPLCNet"], **kwargs)
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_load_pretrained(pretrained, model, MODEL_URLS["PPLCNet_x1_5"], use_ssld)
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return model
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def PPLCNet_x2_0(pretrained=False, use_ssld=False, **kwargs):
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"""
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PPLCNet_x2_0
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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 `PPLCNet_x2_0` model depends on args.
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"""
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model = PPLCNet(
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scale=2.0, stages_pattern=MODEL_STAGES_PATTERN["PPLCNet"], **kwargs)
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_load_pretrained(pretrained, model, MODEL_URLS["PPLCNet_x2_0"], use_ssld)
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return model
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def PPLCNet_x2_5(pretrained=False, use_ssld=False, **kwargs):
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"""
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PPLCNet_x2_5
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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 `PPLCNet_x2_5` model depends on args.
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"""
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model = PPLCNet(
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scale=2.5, stages_pattern=MODEL_STAGES_PATTERN["PPLCNet"], **kwargs)
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_load_pretrained(pretrained, model, MODEL_URLS["PPLCNet_x2_5"], use_ssld)
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return model
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