Merge pull request #936 from PaddlePaddle/develop

update qrcode

README.md

@@ -41,7 +41,7 @@ Res2Net200_vd预训练模型Top-1精度高达85.1%。
 * 您可以扫描下面的微信群二维码， 加入PaddleClas 微信交流群。获得更高效的问题答疑，与各行各业开发者充分交流，期待您的加入。
 
 <div align="center">
-<img src="./docs/images/wx_group.png"  width = "200" />
+<img src="./docs/images/wx_group.jpeg"  width = "200" />
 </div>
 
 ## 快速体验

README_en.md

@@ -46,7 +46,7 @@ Four sample solutions are provided, including product recognition, vehicle recog
 * You can also scan the QR code below to join the PaddleClas WeChat group to get more efficient answers to your questions and to communicate with developers from all walks of life. We look forward to hearing from you.
 
 <div align="center">
-<img src="./docs/images/wx_group.png"  width = "200" />
+<img src="./docs/images/wx_group.jpeg"  width = "200" />
 </div>
 
 ## Quick Start 

docs/zh_CN/ImageNet_models_cn.md

@@ -6,7 +6,7 @@
 <a name="模型库概览图"></a>
 ### 模型库概览图
 
-基于ImageNet1k分类数据集，PaddleClas支持24种系列分类网络结构以及对应的122个图像分类预训练模型，训练技巧、每个系列网络结构的简单介绍和性能评估将在相应章节展现，下面所有的速度指标评估环境如下：
+基于ImageNet1k分类数据集，PaddleClas支持35个系列分类网络结构以及对应的164个图像分类预训练模型，训练技巧、每个系列网络结构的简单介绍和性能评估将在相应章节展现，下面所有的速度指标评估环境如下：
 * CPU的评估环境基于骁龙855（SD855）。
 * GPU评估环境基于T4机器，在FP32+TensorRT配置下运行500次测得（去除前10次的warmup时间）。
 

ppcls/arch/__init__.py

@@ -16,11 +16,14 @@ import copy
 import importlib
 
 import paddle.nn as nn
+from paddle.jit import to_static
+from paddle.static import InputSpec
 
 from . import backbone, gears
 from .backbone import *
 from .gears import build_gear
 from .utils import *
+from ppcls.utils import logger
 from ppcls.utils.save_load import load_dygraph_pretrain
 
 __all__ = ["build_model", "RecModel", "DistillationModel"]
@@ -34,6 +37,19 @@ def build_model(config):
     return arch
 
 
+def apply_to_static(config, model):
+    support_to_static = config['Global'].get('to_static', False)
+
+    if support_to_static:
+        specs = None
+        if 'image_shape' in config['Global']:
+            specs = [InputSpec([None] + config['Global']['image_shape'])]
+        model = to_static(model, input_spec=specs)
+        logger.info("Successfully to apply @to_static with specs: {}".format(
+            specs))
+    return model
+
+
 class RecModel(nn.Layer):
     def __init__(self, **config):
         super().__init__()

ppcls/configs/ImageNet/MobileNetV1/MobileNetV1.yaml

@@ -14,6 +14,8 @@ Global:
   # used for static mode and model export
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
+  # training model under @to_static
+  to_static: False
 
 # model architecture
 Arch:

ppcls/configs/ImageNet/MobileNetV2/MobileNetV2.yaml

@@ -14,6 +14,8 @@ Global:
   # used for static mode and model export
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
+  # training model under @to_static
+  to_static: False
 
 # model architecture
 Arch:

ppcls/configs/ImageNet/ResNet/ResNet101.yaml

@@ -14,6 +14,8 @@ Global:
   # used for static mode and model export
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
+  # training model under @to_static
+  to_static: False
 
 # model architecture
 Arch:

ppcls/configs/ImageNet/ResNet/ResNet152.yaml

@@ -14,6 +14,8 @@ Global:
   # used for static mode and model export
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
+  # training model under @to_static
+  to_static: False
 
 # model architecture
 Arch:

ppcls/configs/ImageNet/ResNet/ResNet50.yaml

@@ -14,6 +14,8 @@ Global:
   # used for static mode and model export
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
+  # training model under @to_static
+  to_static: False
 
 # model architecture
 Arch:

ppcls/engine/trainer.py

@@ -34,6 +34,7 @@ from ppcls.utils.logger import init_logger
 from ppcls.utils.config import print_config
 from ppcls.data import build_dataloader
 from ppcls.arch import build_model
+from ppcls.arch import apply_to_static
 from ppcls.loss import build_loss
 from ppcls.metric import build_metrics
 from ppcls.optimizer import build_optimizer
@@ -73,6 +74,8 @@ class Trainer(object):
             self.is_rec = False
 
         self.model = build_model(self.config["Arch"])
+        # set @to_static for benchmark, skip this by default.
+        apply_to_static(self.config, self.model)
 
         if self.config["Global"]["pretrained_model"] is not None:
             load_dygraph_pretrain(self.model,