Merge pull request #1455 from cuicheng01/release/2.3

add esnet & [cherry-pick]fix class_num in quickstart

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)               |
 
+
 <a name="Intel-CPU端知识蒸馏模型"></a>
 #### 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_<br>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_<br>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_<br>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)               |
 
 
 <a name="SEResNeXt与Res2Net系列"></a>

docs/zh_CN/models/ESNet.md

+# 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<br>(M) | Params<br/>(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。

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

ppcls/arch/backbone/legendary_models/esnet.py

+# 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

ppcls/configs/ImageNet/ESNet/ESNet_x0_25.yaml

+# 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]

ppcls/configs/ImageNet/ESNet/ESNet_x0_5.yaml

+# 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]

ppcls/configs/ImageNet/ESNet/ESNet_x0_75.yaml

+# 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]

ppcls/configs/ImageNet/ESNet/ESNet_x1_0.yaml

+# 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]

ppcls/configs/quick_start/ResNet50_vd.yaml

@@ -4,7 +4,6 @@ Global:
   pretrained_model: null
   output_dir: ./output/
   device: gpu
-  class_num: 102
   save_interval: 1
   eval_during_train: True
   eval_interval: 1
@@ -17,8 +16,9 @@ Global:
 
 # model architecture
 Arch:
-  name: ResNet50_vd
- 
+  name: ResNet50_vd 
+  class_num: 102
+
 # loss function config for traing/eval process
 Loss:
   Train: