feat: support Twins & PPLCNet

__init__.py

@@ -14,3 +14,4 @@
 
 __all__ = ['PaddleClas']
 from .paddleclas import PaddleClas
+from ppcls.arch.backbone import *

paddleclas.py

@@ -38,6 +38,7 @@ from deploy.utils.get_image_list import get_image_list
 from deploy.utils import config
 
 from ppcls.arch.backbone import *
+from ppcls.utils.logger import init_logger
 
 __all__ = ["PaddleClas"]
 
@@ -89,6 +90,10 @@ MODEL_SERIES = {
         "MobileNetV3_large_x1_0", "MobileNetV3_large_x1_25",
         "MobileNetV3_small_x1_0_ssld", "MobileNetV3_large_x1_0_ssld"
     ],
+    "PPLCNet": [
+        "PPLCNet_x0_25", "PPLCNet_x0_35", "PPLCNet_x0_5", "PPLCNet_x0_75",
+        "PPLCNet_x1_0", "PPLCNet_x1_5", "PPLCNet_x2_0", "PPLCNet_x2_5"
+    ],
     "RegNet": ["RegNetX_4GF"],
     "Res2Net": [
         "Res2Net50_14w_8s", "Res2Net50_26w_4s", "Res2Net50_vd_26w_4s",
@@ -134,6 +139,10 @@ MODEL_SERIES = {
         "SwinTransformer_small_patch4_window7_224",
         "SwinTransformer_tiny_patch4_window7_224"
     ],
+    "Twins": [
+        "pcpvt_small", "pcpvt_base", "pcpvt_large", "alt_gvt_small",
+        "alt_gvt_base", "alt_gvt_large"
+    ],
     "VGG": ["VGG11", "VGG13", "VGG16", "VGG19"],
     "VisionTransformer": [
         "ViT_base_patch16_224", "ViT_base_patch16_384", "ViT_base_patch32_384",
@@ -399,6 +408,7 @@ class PaddleClas(object):
     """PaddleClas.
     """
 
+    init_logger(name='root')
     print_info()
 
     def __init__(self,
@@ -465,7 +475,7 @@ class PaddleClas(object):
         """Predict input_data.
 
         Args:
-            input_data (Union[str, np.array]): 
+            input_data (Union[str, np.array]):
                 When the type is str, it is the path of image, or the directory containing images, or the URL of image from Internet.
                 When the type is np.array, it is the image data whose channel order is RGB.
             print_pred (bool, optional): Whether print the prediction result. Defaults to False. Defaults to False.
@@ -474,9 +484,9 @@ class PaddleClas(object):
             ImageTypeError: Illegal input_data.
 
         Yields:
-            Generator[list, None, None]: 
-                The prediction result(s) of input_data by batch_size. For every one image, 
-                prediction result(s) is zipped as a dict, that includs topk "class_ids", "scores" and "label_names". 
+            Generator[list, None, None]:
+                The prediction result(s) of input_data by batch_size. For every one image,
+                prediction result(s) is zipped as a dict, that includs topk "class_ids", "scores" and "label_names".
                 The format is as follow: [{"class_ids": [...], "scores": [...], "label_names": [...]}, ...]
         """