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419 lines
14 KiB
419 lines
14 KiB
# 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, BatchNorm, 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 ppcls.arch.backbone.base.theseus_layer import TheseusLayer |
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from ppcls.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": |
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[[3, 128, 256, 2, False], [5, 256, 256, 1, False], [5, 256, 256, 1, False], |
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[5, 256, 256, 1, False], [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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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 = BatchNorm( |
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num_filters, |
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param_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(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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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(TheseusLayer): |
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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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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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return_patterns=None, |
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return_stages=None): |
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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.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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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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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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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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self.avg_pool = AdaptiveAvgPool2D(1) |
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self.last_conv = Conv2D( |
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in_channels=make_divisible(NET_CONFIG["blocks6"][-1][2] * 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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self.flatten = nn.Flatten(start_axis=1, stop_axis=-1) |
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self.fc = Linear(self.class_expand, 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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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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