add PPHGNet_base config

docs/zh_CN/algorithm_introduction/ImageNet_models.md

@@ -133,6 +133,8 @@ PP-LCNet 系列模型的精度、速度指标如下表所示，更多关于该
 
 **: 基于 Intel-Xeon-Gold-6271C 硬件平台与 OpenVINO 2021.4.2 推理平台。
 
+<a name="PPHGNet"></a>
+
 ## PP-HGNet 系列
 
 PP-HGNet 系列模型的精度、速度指标如下表所示，更多关于该系列的模型介绍可以参考：[PP-HGNet 系列模型文档](../models/PP-HGNet.md)。
@@ -140,7 +142,10 @@ PP-HGNet 系列模型的精度、速度指标如下表所示，更多关于该
 | 模型  | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
 | ---  | --- | --- | --- | --- | --- | --- | --- | --- | --- |
 | PPHGNet_tiny | 0.7983    |  0.9504    | 1.77            |       -     |  -       | 4.54        | 14.75        | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNet_tiny_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNet_tiny_infer.tar) |
+| PPHGNet_tiny_ssld | 0.8195    |  0.9612  |  1.77            |       -     |  -       | 4.54        | 14.75        | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNet_tiny_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNet_tiny_ssld_infer.tar) |
 | PPHGNet_small | 0.8151    |  0.9582    |  2.52            | -           |    -  | 8.53       | 24.38           | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNet_small_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNet_small_infer.tar) |
+| PPHGNet_small_ssld | 0.8382    |  0.9681  | 2.52            | -           |    -  | 8.53       | 24.38           | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNet_small_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNet_small_ssld_infer.tar) |
+| PPHGNet_base_ssld | 0.8500    |  0.9735  | 5.97            | -           |    -  | 25.14       | 71.62           | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNet_base_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNet_base_ssld_infer.tar) |
 
 <a name="ResNet"></a>
 

docs/zh_CN/models/PP-HGNet.md

@@ -46,6 +46,11 @@ PP-HGNet 与其他模型的比较如下，其中测试机器为 NVIDIA® Tesla®
 | SwinTransformer_tiny     | 81.2      | 95.5       | 6.59        |
 | <b>PPHGNet_small<b>      | <b>81.51<b>| <b>95.82<b> | <b>2.52<b>  |
 | <b>PPHGNet_small_ssld<b> | <b>83.82<b>| <b>96.81<b> | <b>2.52<b>  |
+| Res2Net200_vd_26w_4s_ssld| 85.13      | 97.42       | 11.45       |
+| ResNeXt101_32x48d_wsl    | 85.37      | 97.69       | 55.07       |
+| SwinTransformer_base     | 85.2       | 97.5        | 13.53       |  
+| <b>PPHGNet_base_ssld<b> | <b>85.00<b>| <b>97.35<b> | <b>5.97<b>   |
+
 
 
 关于更多 PP-HGNet 的介绍以及下游任务的表现，敬请期待。

ppcls/arch/backbone/legendary_models/pp_hgnet.py

@@ -345,7 +345,7 @@ def PPHGNet_small(pretrained=False, use_ssld=False, **kwargs):
     return model
 
 
-def PPHGNet_base(pretrained=False, use_ssld=False, **kwargs):
+def PPHGNet_base(pretrained=False, use_ssld=True, **kwargs):
     """
     PPHGNet_base
     Args:

ppcls/configs/ImageNet/PPHGNet/PPHGNet_base.yaml

+# 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_base
+  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-m15-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: 0.4
+            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: 0.4
+              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]