Add PPHGNet code
cuicheng01
parent
50c1302bce
commit
8a760fb85f
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# PP-HGNet 系列
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---
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## 目录
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* [1. 概述](#1)
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* [2. 精度、FLOPs 和参数量](#2)
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<a name='1'></a>
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## 1. 概述
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PP-HGNet是百度自研的一个在 GPU 端上高性能的网络，该网络在 VOVNet 的基础上融合了 ResNet_vd、PPLCNet 的优点，使用了可学习的下采样层，组合成了一个在 GPU 设备上速度快、精度高的网络，超越其他 GPU 端 SOTA 模型。
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<a name='2'></a>
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## 2.精度、FLOPs 和参数量
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| Models | Top1 | Top5 | FLOPs<br>(G) | Params<br/>(M) |
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|:--:|:--:|:--:|:--:|:--:|
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| PPHGNet_tiny | 79.83 | 95.04 | 4.54 | 14.75 |
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| PPHGNet_tiny_ssld | 81.95 | 96.12 | 4.54 | 14.75 |
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| PPHGNet_small | 81.51 | 95.82 | 8.53 | 24.38 |
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关于 Inference speed 等信息，敬请期待。
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@ -23,6 +23,7 @@ from ppcls.arch.backbone.legendary_models.inception_v3 import InceptionV3
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from ppcls.arch.backbone.legendary_models.hrnet import HRNet_W18_C, HRNet_W30_C, HRNet_W32_C, HRNet_W40_C, HRNet_W44_C, HRNet_W48_C, HRNet_W60_C, HRNet_W64_C, SE_HRNet_W64_C
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from ppcls.arch.backbone.legendary_models.pp_lcnet import PPLCNet_x0_25, PPLCNet_x0_35, PPLCNet_x0_5, PPLCNet_x0_75, PPLCNet_x1_0, PPLCNet_x1_5, PPLCNet_x2_0, PPLCNet_x2_5
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from ppcls.arch.backbone.legendary_models.esnet import ESNet_x0_25, ESNet_x0_5, ESNet_x0_75, ESNet_x1_0
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from ppcls.arch.backbone.legendary_models.pp_hgnet import PPHGNet_tiny, PPHGNet_small, PPHGNet_base
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from ppcls.arch.backbone.model_zoo.resnet_vc import ResNet50_vc
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from ppcls.arch.backbone.model_zoo.resnext import ResNeXt50_32x4d, ResNeXt50_64x4d, ResNeXt101_32x4d, ResNeXt101_64x4d, ResNeXt152_32x4d, ResNeXt152_64x4d
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# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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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.nn.initializer import KaimingNormal, Constant
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from paddle.nn import Conv2D, BatchNorm2D, ReLU, AdaptiveAvgPool2D, MaxPool2D
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from paddle.regularizer import L2Decay
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from paddle import ParamAttr
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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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"PPHGNet_tiny":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNet_tiny_pretrained.pdparams",
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"PPHGNet_small":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNet_small_pretrained.pdparams"
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}
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__all__ = list(MODEL_URLS.keys())
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kaiming_normal_ = KaimingNormal()
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zeros_ = Constant(value=0.)
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ones_ = Constant(value=1.)
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class ConvBNAct(TheseusLayer):
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def __init__(self,
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in_channels,
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out_channels,
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kernel_size,
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stride,
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groups=1,
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use_act=True):
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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,
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out_channels,
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kernel_size,
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stride,
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padding=(kernel_size - 1) // 2,
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groups=groups,
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bias_attr=False)
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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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if self.use_act:
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self.act = 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 ESEModule(TheseusLayer):
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def __init__(self, channels):
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super().__init__()
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self.avg_pool = AdaptiveAvgPool2D(1)
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self.conv = Conv2D(
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in_channels=channels,
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out_channels=channels,
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kernel_size=1,
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stride=1,
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padding=0)
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self.sigmoid = 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.conv(x)
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x = self.sigmoid(x)
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return paddle.multiply(x=identity, y=x)
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class _HG_Block(TheseusLayer):
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def __init__(
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self,
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in_channels,
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mid_channels,
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out_channels,
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layer_num,
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identity=False, ):
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super().__init__()
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self.identity = identity
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self.layers = nn.LayerList()
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self.layers.append(
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ConvBNAct(
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in_channels=in_channels,
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out_channels=mid_channels,
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kernel_size=3,
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stride=1))
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for _ in range(layer_num - 1):
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self.layers.append(
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ConvBNAct(
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in_channels=mid_channels,
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out_channels=mid_channels,
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kernel_size=3,
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stride=1))
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# feature aggregation
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total_channels = in_channels + layer_num * mid_channels
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self.aggregation_conv = ConvBNAct(
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in_channels=total_channels,
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out_channels=out_channels,
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kernel_size=1,
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stride=1)
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self.att = ESEModule(out_channels)
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def forward(self, x):
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identity = x
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output = []
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output.append(x)
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for layer in self.layers:
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x = layer(x)
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output.append(x)
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x = paddle.concat(output, axis=1)
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x = self.aggregation_conv(x)
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x = self.att(x)
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if self.identity:
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x += identity
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return x
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class _HG_Stage(TheseusLayer):
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def __init__(self,
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in_channels,
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mid_channels,
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out_channels,
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block_num,
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layer_num,
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downsample=True):
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super().__init__()
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self.downsample = downsample
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if downsample:
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self.downsample = ConvBNAct(
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in_channels=in_channels,
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out_channels=in_channels,
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kernel_size=3,
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stride=2,
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groups=in_channels,
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use_act=False)
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blocks_list = []
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blocks_list.append(
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_HG_Block(
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in_channels,
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mid_channels,
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out_channels,
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layer_num,
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identity=False))
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for _ in range(block_num - 1):
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blocks_list.append(
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_HG_Block(
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out_channels,
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mid_channels,
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out_channels,
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layer_num,
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identity=True))
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self.blocks = nn.Sequential(*blocks_list)
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def forward(self, x):
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if self.downsample:
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x = self.downsample(x)
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x = self.blocks(x)
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return x
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class PPHGNet(TheseusLayer):
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"""
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PPHGNet
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Args:
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stem_channels: list. Stem channel list of PPHGNet.
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stage_config: dict. The configuration of each stage of PPHGNet. such as the number of channels, stride, etc.
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layer_num: int. Number of layers of HG_Block.
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use_last_conv: boolean. Whether to use a 1x1 convolutional layer before the classification layer.
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class_expand: int=2048. Number of channels for the last 1x1 convolutional layer.
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dropout_prob: float. Parameters of dropout, 0.0 means dropout is not used.
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class_num: int=1000. The number of classes.
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Returns:
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model: nn.Layer. Specific PPHGNet model depends on args.
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"""
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def __init__(self,
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stem_channels,
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stage_config,
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layer_num,
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use_last_conv=True,
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class_expand=2048,
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dropout_prob=0.0,
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class_num=1000):
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super().__init__()
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self.use_last_conv = use_last_conv
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self.class_expand = class_expand
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# stem
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stem_channels.insert(0, 3)
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self.stem = nn.Sequential(* [
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ConvBNAct(
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in_channels=stem_channels[i],
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out_channels=stem_channels[i + 1],
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kernel_size=3,
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stride=2 if i == 0 else 1) for i in range(
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len(stem_channels) - 1)
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])
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self.pool = nn.MaxPool2D(kernel_size=3, stride=2, padding=1)
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# stages
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self.stages = nn.LayerList()
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for k in stage_config:
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in_channels, mid_channels, out_channels, block_num, downsample = stage_config[
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k]
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self.stages.append(
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_HG_Stage(in_channels, mid_channels, out_channels, block_num,
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layer_num, downsample))
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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=out_channels,
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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.act = nn.ReLU()
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self.dropout = nn.Dropout(
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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 = nn.Linear(self.class_expand
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if self.use_last_conv else out_channels, class_num)
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self._init_weights()
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def _init_weights(self):
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for m in self.sublayers():
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if isinstance(m, nn.Conv2D):
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kaiming_normal_(m.weight)
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elif isinstance(m, (nn.BatchNorm2D)):
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ones_(m.weight)
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zeros_(m.bias)
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elif isinstance(m, nn.Linear):
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zeros_(m.bias)
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def forward(self, x):
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x = self.stem(x)
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x = self.pool(x)
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for stage in self.stages:
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x = stage(x)
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x = self.avg_pool(x)
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if self.use_last_conv:
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x = self.last_conv(x)
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x = self.act(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 PPHGNet_tiny(pretrained=False, use_ssld=False, **kwargs):
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"""
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PPHGNet_tiny
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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 `PPHGNet_tiny` model depends on args.
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"""
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stage_config = {
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# in_channels, mid_channels, out_channels, blocks, downsample
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"stage1": [96, 96, 224, 1, False],
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"stage2": [224, 128, 448, 1, True],
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"stage3": [448, 160, 512, 2, True],
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"stage4": [512, 192, 768, 1, True],
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}
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model = PPHGNet(
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stem_channels=[48, 48, 96],
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stage_config=stage_config,
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layer_num=5,
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**kwargs)
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_load_pretrained(pretrained, model, MODEL_URLS["PPHGNet_tiny"], use_ssld)
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return model
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def PPHGNet_small(pretrained=False, use_ssld=False, **kwargs):
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"""
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PPHGNet_small
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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 `PPHGNet_small` model depends on args.
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"""
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stage_config = {
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# in_channels, mid_channels, out_channels, blocks, downsample
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"stage1": [128, 128, 256, 1, False],
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"stage2": [256, 160, 512, 1, True],
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"stage3": [512, 192, 768, 2, True],
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"stage4": [768, 224, 1024, 1, True],
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}
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model = PPHGNet(
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stem_channels=[64, 64, 128],
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stage_config=stage_config,
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layer_num=6,
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**kwargs)
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_load_pretrained(pretrained, model, MODEL_URLS["PPHGNet_small"], use_ssld)
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return model
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def PPHGNet_base(pretrained=False, use_ssld=False, **kwargs):
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"""
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PPHGNet_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 `PPHGNet_base` model depends on args.
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"""
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stage_config = {
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# in_channels, mid_channels, out_channels, blocks, downsample
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"stage1": [160, 192, 320, 1, False],
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"stage2": [320, 224, 640, 2, True],
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"stage3": [640, 256, 960, 3, True],
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"stage4": [960, 288, 1280, 2, True],
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}
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model = PPHGNet(
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stem_channels=[96, 96, 160],
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stage_config=stage_config,
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layer_num=7,
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dropout_prob=0.2,
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**kwargs)
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_load_pretrained(pretrained, model, MODEL_URLS["PPHGNet_base"], use_ssld)
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return model
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@ -0,0 +1,164 @@
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# global configs
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Global:
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checkpoints: null
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pretrained_model: null
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output_dir: ./output/
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device: gpu
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save_interval: 1
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eval_during_train: True
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eval_interval: 1
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epochs: 600
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print_batch_step: 10
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use_visualdl: False
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# used for static mode and model export
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image_shape: [3, 224, 224]
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save_inference_dir: ./inference
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# training model under @to_static
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to_static: False
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use_dali: False
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# mixed precision training
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AMP:
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scale_loss: 128.0
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use_dynamic_loss_scaling: True
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# O1: mixed fp16
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level: O1
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# model architecture
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Arch:
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name: PPHGNet_small
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class_num: 1000
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# loss function config for traing/eval process
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Loss:
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Train:
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- CELoss:
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weight: 1.0
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epsilon: 0.1
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Eval:
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- CELoss:
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weight: 1.0
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Optimizer:
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name: Momentum
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momentum: 0.9
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lr:
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name: Cosine
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learning_rate: 0.5
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warmup_epoch: 5
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regularizer:
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name: 'L2'
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coeff: 0.00004
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# data loader for train and eval
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DataLoader:
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Train:
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dataset:
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name: ImageNetDataset
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image_root: ./dataset/ILSVRC2012/
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cls_label_path: ./dataset/ILSVRC2012/train_list.txt
|
||||
transform_ops:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
interpolation: bicubic
|
||||
backend: pil
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- TimmAutoAugment:
|
||||
config_str: rand-m7-mstd0.5-inc1
|
||||
interpolation: bicubic
|
||||
img_size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- RandomErasing:
|
||||
EPSILON: 0.25
|
||||
sl: 0.02
|
||||
sh: 1.0/3.0
|
||||
r1: 0.3
|
||||
attempt: 10
|
||||
use_log_aspect: True
|
||||
mode: pixel
|
||||
batch_transform_ops:
|
||||
- OpSampler:
|
||||
MixupOperator:
|
||||
alpha: 0.2
|
||||
prob: 0.5
|
||||
CutmixOperator:
|
||||
alpha: 1.0
|
||||
prob: 0.5
|
||||
|
||||
sampler:
|
||||
name: DistributedBatchSampler
|
||||
batch_size: 128
|
||||
drop_last: False
|
||||
shuffle: True
|
||||
loader:
|
||||
num_workers: 16
|
||||
use_shared_memory: True
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: ImageNetDataset
|
||||
image_root: ./dataset/ILSVRC2012/
|
||||
cls_label_path: ./dataset/ILSVRC2012/val_list.txt
|
||||
transform_ops:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 236
|
||||
interpolation: bicubic
|
||||
backend: pil
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
sampler:
|
||||
name: DistributedBatchSampler
|
||||
batch_size: 128
|
||||
drop_last: False
|
||||
shuffle: False
|
||||
loader:
|
||||
num_workers: 16
|
||||
use_shared_memory: True
|
||||
|
||||
Infer:
|
||||
infer_imgs: docs/images/inference_deployment/whl_demo.jpg
|
||||
batch_size: 10
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 236
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
PostProcess:
|
||||
name: Topk
|
||||
topk: 5
|
||||
class_id_map_file: ppcls/utils/imagenet1k_label_list.txt
|
||||
|
||||
Metric:
|
||||
Train:
|
||||
- TopkAcc:
|
||||
topk: [1, 5]
|
||||
Eval:
|
||||
- TopkAcc:
|
||||
topk: [1, 5]
|
||||
|
|
@ -0,0 +1,164 @@
|
|||
# global configs
|
||||
Global:
|
||||
checkpoints: null
|
||||
pretrained_model: null
|
||||
output_dir: ./output/
|
||||
device: gpu
|
||||
save_interval: 1
|
||||
eval_during_train: True
|
||||
eval_interval: 1
|
||||
epochs: 600
|
||||
print_batch_step: 10
|
||||
use_visualdl: False
|
||||
# used for static mode and model export
|
||||
image_shape: [3, 224, 224]
|
||||
save_inference_dir: ./inference
|
||||
# training model under @to_static
|
||||
to_static: False
|
||||
use_dali: False
|
||||
|
||||
# mixed precision training
|
||||
AMP:
|
||||
scale_loss: 128.0
|
||||
use_dynamic_loss_scaling: True
|
||||
# O1: mixed fp16
|
||||
level: O1
|
||||
|
||||
# model architecture
|
||||
Arch:
|
||||
name: PPHGNet_tiny
|
||||
class_num: 1000
|
||||
|
||||
# loss function config for traing/eval process
|
||||
Loss:
|
||||
Train:
|
||||
- CELoss:
|
||||
weight: 1.0
|
||||
epsilon: 0.1
|
||||
Eval:
|
||||
- CELoss:
|
||||
weight: 1.0
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: Momentum
|
||||
momentum: 0.9
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.5
|
||||
warmup_epoch: 5
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
coeff: 0.00004
|
||||
|
||||
|
||||
# data loader for train and eval
|
||||
DataLoader:
|
||||
Train:
|
||||
dataset:
|
||||
name: ImageNetDataset
|
||||
image_root: ./dataset/ILSVRC2012/
|
||||
cls_label_path: ./dataset/ILSVRC2012/train_list.txt
|
||||
transform_ops:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
interpolation: bicubic
|
||||
backend: pil
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- TimmAutoAugment:
|
||||
config_str: rand-m7-mstd0.5-inc1
|
||||
interpolation: bicubic
|
||||
img_size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- RandomErasing:
|
||||
EPSILON: 0.25
|
||||
sl: 0.02
|
||||
sh: 1.0/3.0
|
||||
r1: 0.3
|
||||
attempt: 10
|
||||
use_log_aspect: True
|
||||
mode: pixel
|
||||
batch_transform_ops:
|
||||
- OpSampler:
|
||||
MixupOperator:
|
||||
alpha: 0.2
|
||||
prob: 0.5
|
||||
CutmixOperator:
|
||||
alpha: 1.0
|
||||
prob: 0.5
|
||||
|
||||
sampler:
|
||||
name: DistributedBatchSampler
|
||||
batch_size: 128
|
||||
drop_last: False
|
||||
shuffle: True
|
||||
loader:
|
||||
num_workers: 16
|
||||
use_shared_memory: True
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: ImageNetDataset
|
||||
image_root: ./dataset/ILSVRC2012/
|
||||
cls_label_path: ./dataset/ILSVRC2012/val_list.txt
|
||||
transform_ops:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 232
|
||||
interpolation: bicubic
|
||||
backend: pil
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
sampler:
|
||||
name: DistributedBatchSampler
|
||||
batch_size: 128
|
||||
drop_last: False
|
||||
shuffle: False
|
||||
loader:
|
||||
num_workers: 16
|
||||
use_shared_memory: True
|
||||
|
||||
Infer:
|
||||
infer_imgs: docs/images/inference_deployment/whl_demo.jpg
|
||||
batch_size: 10
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 232
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
PostProcess:
|
||||
name: Topk
|
||||
topk: 5
|
||||
class_id_map_file: ppcls/utils/imagenet1k_label_list.txt
|
||||
|
||||
Metric:
|
||||
Train:
|
||||
- TopkAcc:
|
||||
topk: [1, 5]
|
||||
Eval:
|
||||
- TopkAcc:
|
||||
topk: [1, 5]
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
===========================train_params===========================
|
||||
model_name:PPHGNet_small
|
||||
python:python3.7
|
||||
gpu_list:0|0,1
|
||||
-o Global.device:gpu
|
||||
-o Global.auto_cast:null
|
||||
-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
|
||||
-o Global.output_dir:./output/
|
||||
-o DataLoader.Train.sampler.batch_size:8
|
||||
-o Global.pretrained_model:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:./dataset/ILSVRC2012/val
|
||||
null:null
|
||||
##
|
||||
trainer:norm_train
|
||||
norm_train:tools/train.py -c ppcls/configs/ImageNet/PPHGNet/PPHGNet_small.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
|
||||
pact_train:null
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPHGNet/PPHGNet_small.yaml
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:tools/export_model.py -c ppcls/configs/ImageNet/PPHGNet/PPHGNet_small.yaml
|
||||
quant_export:null
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:null
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNet_small_pretrained.pdparams
|
||||
infer_model:../inference/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.transform_ops.0.ResizeImage.resize_short=236
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:True|False
|
||||
-o Global.cpu_num_threads:1|6
|
||||
-o Global.batch_size:1|16
|
||||
-o Global.use_tensorrt:True|False
|
||||
-o Global.use_fp16:True|False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../dataset/ILSVRC2012/val
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:True
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
===========================train_params===========================
|
||||
model_name:PPHGNet_tiny
|
||||
python:python3.7
|
||||
gpu_list:0|0,1
|
||||
-o Global.device:gpu
|
||||
-o Global.auto_cast:null
|
||||
-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
|
||||
-o Global.output_dir:./output/
|
||||
-o DataLoader.Train.sampler.batch_size:8
|
||||
-o Global.pretrained_model:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:./dataset/ILSVRC2012/val
|
||||
null:null
|
||||
##
|
||||
trainer:norm_train
|
||||
norm_train:tools/train.py -c ppcls/configs/ImageNet/PPHGNet/PPHGNet_tiny.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
|
||||
pact_train:null
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPHGNet/PPHGNet_tiny.yaml
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:tools/export_model.py -c ppcls/configs/ImageNet/PPHGNet/PPHGNet_tiny.yaml
|
||||
quant_export:null
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:null
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNet_tiny_pretrained.pdparams
|
||||
infer_model:../inference/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.transform_ops.0.ResizeImage.resize_short=232
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:True|False
|
||||
-o Global.cpu_num_threads:1|6
|
||||
-o Global.batch_size:1|16
|
||||
-o Global.use_tensorrt:True|False
|
||||
-o Global.use_fp16:True|False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../dataset/ILSVRC2012/val
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:True
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
Loading…
Reference in New Issue