diff --git a/docs/zh_CN/algorithm_introduction/ImageNet_models.md b/docs/zh_CN/algorithm_introduction/ImageNet_models.md
index d8d0a5bb04115733c08492e8a7dcf3e064708b56..f5c46ef1db125e6f6925330dc085e9682e1282b2 100644
--- a/docs/zh_CN/algorithm_introduction/ImageNet_models.md
+++ b/docs/zh_CN/algorithm_introduction/ImageNet_models.md
@@ -82,6 +82,7 @@
| MobileNetV3_small_x1_0_ssld | 0.713 | 0.682 | 0.031 | 6.546 | 0.123 | 2.94 | 12 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x1_0_ssld_pretrained.pdparams) |
| GhostNet_x1_3_ssld | 0.794 | 0.757 | 0.037 | 19.983 | 0.44 | 7.3 | 29 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x1_3_ssld_pretrained.pdparams) |
+
#### Intel CPU端知识蒸馏模型
@@ -180,6 +181,10 @@ ResNet及其Vd系列模型的精度、速度指标如下表所示，更多关于
| GhostNet_
x1_0 | 0.7402 | 0.9165 | 13.5587 | 0.294 | 5.2 | 20 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x1_0_pretrained.pdparams) |
| GhostNet_
x1_3 | 0.7579 | 0.9254 | 19.9825 | 0.44 | 7.3 | 29 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x1_3_pretrained.pdparams) |
| GhostNet_
x1_3_ssld | 0.7938 | 0.9449 | 19.9825 | 0.44 | 7.3 | 29 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x1_3_ssld_pretrained.pdparams) |
+| ESNet_x0_25 | 62.48 | 83.46 || 0.031 | 2.83 | 11 |[下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ESNet_x0_25_pretrained.pdparams) |
+| ESNet_x0_5 | 68.82 | 88.04 || 0.067 | 3.25 | 13 |[下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ESNet_x0_5_pretrained.pdparams) |
+| ESNet_x0_75 | 72.24 | 90.45 || 0.124 | 3.87 | 15 |[下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ESNet_x0_75_pretrained.pdparams) |
+| ESNet_x1_0 | 73.92 | 91.40 || 0.197 | 4.64 | 18 |[下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ESNet_x1_0_pretrained.pdparams) |
diff --git a/docs/zh_CN/models/ESNet.md b/docs/zh_CN/models/ESNet.md
new file mode 100644
index 0000000000000000000000000000000000000000..bd5322b8587024c13e7f8567c2e76342dc0ed9de
--- /dev/null
+++ b/docs/zh_CN/models/ESNet.md
@@ -0,0 +1,16 @@
+# ESNet系列
+
+## 概述
+
+ESNet(Enhanced ShuffleNet)是百度自研的一个轻量级网络，该网络在ShuffleNetV2的基础上融合了MobileNetV3、GhostNet、PPLCNet的优点，组合成了一个在ARM设备上速度更快、精度更高的网络，由于其出色的表现，所以在PaddleDetection推出的[PP-PicoDet](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.3/configs/picodet)使用了该模型做backbone，配合更强的目标检测算法，最终的指标一举刷新了目标检测模型在ARM设备上的SOTA指标。
+
+## 精度、FLOPs和参数量
+
+| Models | Top1 | Top5 | FLOPs
(M) | Params
(M) |
+|:--:|:--:|:--:|:--:|:--:|
+| ESNet_x0_25 | 62.48 | 83.46 | 30.9 | 2.83 |
+| ESNet_x0_5 | 68.82 | 88.04 | 67.3 | 3.25 |
+| ESNet_x0_75 | 72.24 | 90.45 | 123.7 | 3.87 |
+| ESNet_x1_0 | 73.92 | 91.40 | 197.3 | 4.64 |
+
+关于Inference speed等信息，敬请期待。
diff --git a/ppcls/arch/backbone/__init__.py b/ppcls/arch/backbone/__init__.py
index 9dd929bf8872694624b511fce8fce01548760970..bd2b99b0e8c7039fa4e705d0c6b635c0bdd813d6 100644
--- a/ppcls/arch/backbone/__init__.py
+++ b/ppcls/arch/backbone/__init__.py
@@ -22,6 +22,7 @@ from ppcls.arch.backbone.legendary_models.vgg import VGG11, VGG13, VGG16, VGG19
from ppcls.arch.backbone.legendary_models.inception_v3 import InceptionV3
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
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
+from ppcls.arch.backbone.legendary_models.esnet import ESNet_x0_25, ESNet_x0_5, ESNet_x0_75, ESNet_x1_0
from ppcls.arch.backbone.model_zoo.resnet_vc import ResNet50_vc
from ppcls.arch.backbone.model_zoo.resnext import ResNeXt50_32x4d, ResNeXt50_64x4d, ResNeXt101_32x4d, ResNeXt101_64x4d, ResNeXt152_32x4d, ResNeXt152_64x4d
diff --git a/ppcls/arch/backbone/legendary_models/esnet.py b/ppcls/arch/backbone/legendary_models/esnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..cf9c9626ef144809ba1025416a71bc0ac5e1cce7
--- /dev/null
+++ b/ppcls/arch/backbone/legendary_models/esnet.py
@@ -0,0 +1,355 @@
+# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import, division, print_function
+import math
+import paddle
+from paddle import ParamAttr, reshape, transpose, concat, split
+import paddle.nn as nn
+from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
+from paddle.nn import AdaptiveAvgPool2D, MaxPool2D
+from paddle.nn.initializer import KaimingNormal
+from paddle.regularizer import L2Decay
+
+from ppcls.arch.backbone.base.theseus_layer import TheseusLayer
+from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+
+MODEL_URLS = {
+ "ESNet_x0_25":
+ "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ESNet_x0_25_pretrained.pdparams",
+ "ESNet_x0_5":
+ "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ESNet_x0_5_pretrained.pdparams",
+ "ESNet_x0_75":
+ "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ESNet_x0_75_pretrained.pdparams",
+ "ESNet_x1_0":
+ "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ESNet_x1_0_pretrained.pdparams",
+}
+
+__all__ = list(MODEL_URLS.keys())
+
+
+def channel_shuffle(x, groups):
+ batch_size, num_channels, height, width = x.shape[0:4]
+ channels_per_group = num_channels // groups
+ x = reshape(
+ x=x, shape=[batch_size, groups, channels_per_group, height, width])
+ x = transpose(x=x, perm=[0, 2, 1, 3, 4])
+ x = reshape(x=x, shape=[batch_size, num_channels, height, width])
+ return x
+
+
+def make_divisible(v, divisor=8, min_value=None):
+ if min_value is None:
+ min_value = divisor
+ new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
+ if new_v < 0.9 * v:
+ new_v += divisor
+ return new_v
+
+
+class ConvBNLayer(TheseusLayer):
+ def __init__(self,
+ in_channels,
+ out_channels,
+ kernel_size,
+ stride=1,
+ groups=1,
+ if_act=True):
+ super().__init__()
+ self.conv = Conv2D(
+ in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=kernel_size,
+ stride=stride,
+ padding=(kernel_size - 1) // 2,
+ groups=groups,
+ weight_attr=ParamAttr(initializer=KaimingNormal()),
+ bias_attr=False)
+
+ self.bn = BatchNorm(
+ out_channels,
+ param_attr=ParamAttr(regularizer=L2Decay(0.0)),
+ bias_attr=ParamAttr(regularizer=L2Decay(0.0)))
+ self.if_act = if_act
+ self.hardswish = nn.Hardswish()
+
+ def forward(self, x):
+ x = self.conv(x)
+ x = self.bn(x)
+ if self.if_act:
+ x = self.hardswish(x)
+ return x
+
+
+class SEModule(TheseusLayer):
+ def __init__(self, channel, reduction=4):
+ super().__init__()
+ self.avg_pool = AdaptiveAvgPool2D(1)
+ self.conv1 = Conv2D(
+ in_channels=channel,
+ out_channels=channel // reduction,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+ self.relu = nn.ReLU()
+ self.conv2 = Conv2D(
+ in_channels=channel // reduction,
+ out_channels=channel,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+ self.hardsigmoid = nn.Hardsigmoid()
+
+ def forward(self, x):
+ identity = x
+ x = self.avg_pool(x)
+ x = self.conv1(x)
+ x = self.relu(x)
+ x = self.conv2(x)
+ x = self.hardsigmoid(x)
+ x = paddle.multiply(x=identity, y=x)
+ return x
+
+
+class ESBlock1(TheseusLayer):
+ def __init__(self, in_channels, out_channels):
+ super().__init__()
+ self.pw_1_1 = ConvBNLayer(
+ in_channels=in_channels // 2,
+ out_channels=out_channels // 2,
+ kernel_size=1,
+ stride=1)
+ self.dw_1 = ConvBNLayer(
+ in_channels=out_channels // 2,
+ out_channels=out_channels // 2,
+ kernel_size=3,
+ stride=1,
+ groups=out_channels // 2,
+ if_act=False)
+ self.se = SEModule(out_channels)
+
+ self.pw_1_2 = ConvBNLayer(
+ in_channels=out_channels,
+ out_channels=out_channels // 2,
+ kernel_size=1,
+ stride=1)
+
+ def forward(self, x):
+ x1, x2 = split(
+ x, num_or_sections=[x.shape[1] // 2, x.shape[1] // 2], axis=1)
+ x2 = self.pw_1_1(x2)
+ x3 = self.dw_1(x2)
+ x3 = concat([x2, x3], axis=1)
+ x3 = self.se(x3)
+ x3 = self.pw_1_2(x3)
+ x = concat([x1, x3], axis=1)
+ return channel_shuffle(x, 2)
+
+
+class ESBlock2(TheseusLayer):
+ def __init__(self, in_channels, out_channels):
+ super().__init__()
+
+ # branch1
+ self.dw_1 = ConvBNLayer(
+ in_channels=in_channels,
+ out_channels=in_channels,
+ kernel_size=3,
+ stride=2,
+ groups=in_channels,
+ if_act=False)
+ self.pw_1 = ConvBNLayer(
+ in_channels=in_channels,
+ out_channels=out_channels // 2,
+ kernel_size=1,
+ stride=1)
+ # branch2
+ self.pw_2_1 = ConvBNLayer(
+ in_channels=in_channels,
+ out_channels=out_channels // 2,
+ kernel_size=1)
+ self.dw_2 = ConvBNLayer(
+ in_channels=out_channels // 2,
+ out_channels=out_channels // 2,
+ kernel_size=3,
+ stride=2,
+ groups=out_channels // 2,
+ if_act=False)
+ self.se = SEModule(out_channels // 2)
+ self.pw_2_2 = ConvBNLayer(
+ in_channels=out_channels // 2,
+ out_channels=out_channels // 2,
+ kernel_size=1)
+ self.concat_dw = ConvBNLayer(
+ in_channels=out_channels,
+ out_channels=out_channels,
+ kernel_size=3,
+ groups=out_channels)
+ self.concat_pw = ConvBNLayer(
+ in_channels=out_channels, out_channels=out_channels, kernel_size=1)
+
+ def forward(self, x):
+ x1 = self.dw_1(x)
+ x1 = self.pw_1(x1)
+ x2 = self.pw_2_1(x)
+ x2 = self.dw_2(x2)
+ x2 = self.se(x2)
+ x2 = self.pw_2_2(x2)
+ x = concat([x1, x2], axis=1)
+ x = self.concat_dw(x)
+ x = self.concat_pw(x)
+ return x
+
+
+class ESNet(TheseusLayer):
+ def __init__(self,
+ class_num=1000,
+ scale=1.0,
+ dropout_prob=0.2,
+ class_expand=1280):
+ super().__init__()
+ self.scale = scale
+ self.class_num = class_num
+ self.class_expand = class_expand
+ stage_repeats = [3, 7, 3]
+ stage_out_channels = [
+ -1, 24, make_divisible(116 * scale), make_divisible(232 * scale),
+ make_divisible(464 * scale), 1024
+ ]
+
+ self.conv1 = ConvBNLayer(
+ in_channels=3,
+ out_channels=stage_out_channels[1],
+ kernel_size=3,
+ stride=2)
+ self.max_pool = MaxPool2D(kernel_size=3, stride=2, padding=1)
+
+ block_list = []
+ for stage_id, num_repeat in enumerate(stage_repeats):
+ for i in range(num_repeat):
+ if i == 0:
+ block = ESBlock2(
+ in_channels=stage_out_channels[stage_id + 1],
+ out_channels=stage_out_channels[stage_id + 2])
+ else:
+ block = ESBlock1(
+ in_channels=stage_out_channels[stage_id + 2],
+ out_channels=stage_out_channels[stage_id + 2])
+ block_list.append(block)
+ self.blocks = nn.Sequential(*block_list)
+
+ self.conv2 = ConvBNLayer(
+ in_channels=stage_out_channels[-2],
+ out_channels=stage_out_channels[-1],
+ kernel_size=1)
+
+ self.avg_pool = AdaptiveAvgPool2D(1)
+
+ self.last_conv = Conv2D(
+ in_channels=stage_out_channels[-1],
+ out_channels=self.class_expand,
+ kernel_size=1,
+ stride=1,
+ padding=0,
+ bias_attr=False)
+ self.hardswish = nn.Hardswish()
+ self.dropout = Dropout(p=dropout_prob, mode="downscale_in_infer")
+ self.flatten = nn.Flatten(start_axis=1, stop_axis=-1)
+ self.fc = Linear(self.class_expand, self.class_num)
+
+ def forward(self, x):
+ x = self.conv1(x)
+ x = self.max_pool(x)
+ x = self.blocks(x)
+ x = self.conv2(x)
+ x = self.avg_pool(x)
+ x = self.last_conv(x)
+ x = self.hardswish(x)
+ x = self.dropout(x)
+ x = self.flatten(x)
+ x = self.fc(x)
+ return x
+
+
+def _load_pretrained(pretrained, model, model_url, use_ssld):
+ if pretrained is False:
+ pass
+ elif pretrained is True:
+ load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
+ elif isinstance(pretrained, str):
+ load_dygraph_pretrain(model, pretrained)
+ else:
+ raise RuntimeError(
+ "pretrained type is not available. Please use `string` or `boolean` type."
+ )
+
+
+def ESNet_x0_25(pretrained=False, use_ssld=False, **kwargs):
+ """
+ ESNet_x0_25
+ Args:
+ pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
+ If str, means the path of the pretrained model.
+ use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
+ Returns:
+ model: nn.Layer. Specific `ESNet_x0_25` model depends on args.
+ """
+ model = ESNet(scale=0.25, **kwargs)
+ _load_pretrained(pretrained, model, MODEL_URLS["ESNet_x0_25"], use_ssld)
+ return model
+
+
+def ESNet_x0_5(pretrained=False, use_ssld=False, **kwargs):
+ """
+ ESNet_x0_5
+ Args:
+ pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
+ If str, means the path of the pretrained model.
+ use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
+ Returns:
+ model: nn.Layer. Specific `ESNet_x0_5` model depends on args.
+ """
+ model = ESNet(scale=0.5, **kwargs)
+ _load_pretrained(pretrained, model, MODEL_URLS["ESNet_x0_5"], use_ssld)
+ return model
+
+
+def ESNet_x0_75(pretrained=False, use_ssld=False, **kwargs):
+ """
+ ESNet_x0_75
+ Args:
+ pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
+ If str, means the path of the pretrained model.
+ use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
+ Returns:
+ model: nn.Layer. Specific `ESNet_x0_75` model depends on args.
+ """
+ model = ESNet(scale=0.75, **kwargs)
+ _load_pretrained(pretrained, model, MODEL_URLS["ESNet_x0_75"], use_ssld)
+ return model
+
+
+def ESNet_x1_0(pretrained=False, use_ssld=False, **kwargs):
+ """
+ ESNet_x1_0
+ Args:
+ pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
+ If str, means the path of the pretrained model.
+ use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
+ Returns:
+ model: nn.Layer. Specific `ESNet_x1_0` model depends on args.
+ """
+ model = ESNet(scale=1.0, **kwargs)
+ _load_pretrained(pretrained, model, MODEL_URLS["ESNet_x1_0"], use_ssld)
+ return model
diff --git a/ppcls/configs/ImageNet/ESNet/ESNet_x0_25.yaml b/ppcls/configs/ImageNet/ESNet/ESNet_x0_25.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..e01e853ac06196c8b0bafe82fed5a973662280bd
--- /dev/null
+++ b/ppcls/configs/ImageNet/ESNet/ESNet_x0_25.yaml
@@ -0,0 +1,129 @@
+# global configs
+Global:
+ checkpoints: null
+ pretrained_model: null
+ output_dir: ./output/
+ device: gpu
+ class_num: 1000
+ save_interval: 1
+ eval_during_train: True
+ eval_interval: 1
+ epochs: 360
+ print_batch_step: 10
+ use_visualdl: False
+ # used for static mode and model export
+ image_shape: [3, 224, 224]
+ save_inference_dir: ./inference
+# model architecture
+Arch:
+ name: ESNet_x0_25
+
+# 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: 0.8
+ warmup_epoch: 5
+ regularizer:
+ name: 'L2'
+ coeff: 0.00003
+
+
+# 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
+ - RandFlipImage:
+ flip_code: 1
+ - 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: 512
+ drop_last: False
+ shuffle: True
+ loader:
+ num_workers: 4
+ 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: 256
+ - 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: 64
+ drop_last: False
+ shuffle: False
+ loader:
+ num_workers: 4
+ use_shared_memory: True
+
+Infer:
+ infer_imgs: docs/images/whl/demo.jpg
+ batch_size: 10
+ transforms:
+ - DecodeImage:
+ to_rgb: True
+ channel_first: False
+ - ResizeImage:
+ resize_short: 256
+ - 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]
diff --git a/ppcls/configs/ImageNet/ESNet/ESNet_x0_5.yaml b/ppcls/configs/ImageNet/ESNet/ESNet_x0_5.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..fc51351f361650c736ef4dc1b57ab046aa4f3cb0
--- /dev/null
+++ b/ppcls/configs/ImageNet/ESNet/ESNet_x0_5.yaml
@@ -0,0 +1,129 @@
+# global configs
+Global:
+ checkpoints: null
+ pretrained_model: null
+ output_dir: ./output/
+ device: gpu
+ class_num: 1000
+ save_interval: 1
+ eval_during_train: True
+ eval_interval: 1
+ epochs: 360
+ print_batch_step: 10
+ use_visualdl: False
+ # used for static mode and model export
+ image_shape: [3, 224, 224]
+ save_inference_dir: ./inference
+# model architecture
+Arch:
+ name: ESNet_x0_5
+
+# 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: 0.8
+ warmup_epoch: 5
+ regularizer:
+ name: 'L2'
+ coeff: 0.00003
+
+
+# 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
+ - RandFlipImage:
+ flip_code: 1
+ - 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: 512
+ drop_last: False
+ shuffle: True
+ loader:
+ num_workers: 4
+ 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: 256
+ - 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: 64
+ drop_last: False
+ shuffle: False
+ loader:
+ num_workers: 4
+ use_shared_memory: True
+
+Infer:
+ infer_imgs: docs/images/whl/demo.jpg
+ batch_size: 10
+ transforms:
+ - DecodeImage:
+ to_rgb: True
+ channel_first: False
+ - ResizeImage:
+ resize_short: 256
+ - 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]
diff --git a/ppcls/configs/ImageNet/ESNet/ESNet_x0_75.yaml b/ppcls/configs/ImageNet/ESNet/ESNet_x0_75.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..265a622d28dca21f1b805184a52be95d1d27a117
--- /dev/null
+++ b/ppcls/configs/ImageNet/ESNet/ESNet_x0_75.yaml
@@ -0,0 +1,129 @@
+# global configs
+Global:
+ checkpoints: null
+ pretrained_model: null
+ output_dir: ./output/
+ device: gpu
+ class_num: 1000
+ save_interval: 1
+ eval_during_train: True
+ eval_interval: 1
+ epochs: 360
+ print_batch_step: 10
+ use_visualdl: False
+ # used for static mode and model export
+ image_shape: [3, 224, 224]
+ save_inference_dir: ./inference
+# model architecture
+Arch:
+ name: ESNet_x0_75
+
+# 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: 0.8
+ warmup_epoch: 5
+ regularizer:
+ name: 'L2'
+ coeff: 0.00003
+
+
+# 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
+ - RandFlipImage:
+ flip_code: 1
+ - 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: 512
+ drop_last: False
+ shuffle: True
+ loader:
+ num_workers: 4
+ 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: 256
+ - 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: 64
+ drop_last: False
+ shuffle: False
+ loader:
+ num_workers: 4
+ use_shared_memory: True
+
+Infer:
+ infer_imgs: docs/images/whl/demo.jpg
+ batch_size: 10
+ transforms:
+ - DecodeImage:
+ to_rgb: True
+ channel_first: False
+ - ResizeImage:
+ resize_short: 256
+ - 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]
diff --git a/ppcls/configs/ImageNet/ESNet/ESNet_x1_0.yaml b/ppcls/configs/ImageNet/ESNet/ESNet_x1_0.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..44fd7d5f158ad81dcfbc8ab69fd44e00c6bf1558
--- /dev/null
+++ b/ppcls/configs/ImageNet/ESNet/ESNet_x1_0.yaml
@@ -0,0 +1,129 @@
+# global configs
+Global:
+ checkpoints: null
+ pretrained_model: null
+ output_dir: ./output/
+ device: gpu
+ class_num: 1000
+ save_interval: 1
+ eval_during_train: True
+ eval_interval: 1
+ epochs: 360
+ print_batch_step: 10
+ use_visualdl: False
+ # used for static mode and model export
+ image_shape: [3, 224, 224]
+ save_inference_dir: ./inference
+# model architecture
+Arch:
+ name: ESNet_x1_0
+
+# 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: 0.8
+ warmup_epoch: 5
+ regularizer:
+ name: 'L2'
+ coeff: 0.00003
+
+
+# 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
+ - RandFlipImage:
+ flip_code: 1
+ - 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: 512
+ drop_last: False
+ shuffle: True
+ loader:
+ num_workers: 4
+ 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: 256
+ - 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: 64
+ drop_last: False
+ shuffle: False
+ loader:
+ num_workers: 4
+ use_shared_memory: True
+
+Infer:
+ infer_imgs: docs/images/whl/demo.jpg
+ batch_size: 10
+ transforms:
+ - DecodeImage:
+ to_rgb: True
+ channel_first: False
+ - ResizeImage:
+ resize_short: 256
+ - 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]