Merge pull request #1436 from TingquanGao/dev/update_whl

update whl

__init__.py

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

paddleclas.py

@@ -38,6 +38,10 @@ 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
+
+# for building model with loading pretrained weights from backbone
+init_logger()
 
 __all__ = ["PaddleClas"]
 
@@ -58,20 +62,27 @@ MODEL_SERIES = {
         "DenseNet121", "DenseNet161", "DenseNet169", "DenseNet201",
         "DenseNet264"
     ],
+    "DLA": [
+        "DLA46_c", "DLA60x_c", "DLA34", "DLA60", "DLA60x", "DLA102", "DLA102x",
+        "DLA102x2", "DLA169"
+    ],
     "DPN": ["DPN68", "DPN92", "DPN98", "DPN107", "DPN131"],
     "EfficientNet": [
         "EfficientNetB0", "EfficientNetB0_small", "EfficientNetB1",
         "EfficientNetB2", "EfficientNetB3", "EfficientNetB4", "EfficientNetB5",
         "EfficientNetB6", "EfficientNetB7"
     ],
+    "ESNet": ["ESNet_x0_25", "ESNet_x0_5", "ESNet_x0_75", "ESNet_x1_0"],
     "GhostNet":
     ["GhostNet_x0_5", "GhostNet_x1_0", "GhostNet_x1_3", "GhostNet_x1_3_ssld"],
+    "HarDNet": ["HarDNet39_ds", "HarDNet68_ds", "HarDNet68", "HarDNet85"],
     "HRNet": [
         "HRNet_W18_C", "HRNet_W30_C", "HRNet_W32_C", "HRNet_W40_C",
         "HRNet_W44_C", "HRNet_W48_C", "HRNet_W64_C", "HRNet_W18_C_ssld",
         "HRNet_W48_C_ssld"
     ],
     "Inception": ["GoogLeNet", "InceptionV3", "InceptionV4"],
+    "MixNet": ["MixNet_S", "MixNet_M", "MixNet_L"],
     "MobileNetV1": [
         "MobileNetV1_x0_25", "MobileNetV1_x0_5", "MobileNetV1_x0_75",
         "MobileNetV1", "MobileNetV1_ssld"
@@ -89,6 +100,11 @@ 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"
+    ],
+    "RedNet": ["RedNet26", "RedNet38", "RedNet50", "RedNet101", "RedNet152"],
     "RegNet": ["RegNetX_4GF"],
     "Res2Net": [
         "Res2Net50_14w_8s", "Res2Net50_26w_4s", "Res2Net50_vd_26w_4s",
@@ -113,6 +129,8 @@ MODEL_SERIES = {
         "ResNeXt152_32x4d", "ResNeXt152_vd_32x4d", "ResNeXt152_64x4d",
         "ResNeXt152_vd_64x4d"
     ],
+    "ReXNet":
+    ["ReXNet_1_0", "ReXNet_1_3", "ReXNet_1_5", "ReXNet_2_0", "ReXNet_3_0"],
     "SENet": [
         "SENet154_vd", "SE_HRNet_W64_C_ssld", "SE_ResNet18_vd",
         "SE_ResNet34_vd", "SE_ResNet50_vd", "SE_ResNeXt50_32x4d",
@@ -134,6 +152,10 @@ 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",
@@ -465,24 +487,23 @@ 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.
+            print_pred (bool, optional): Whether print the prediction result. Defaults to False.
 
         Raises:
             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". 
-                The format is as follow: [{"class_ids": [...], "scores": [...], "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 of batch prediction result(s) is as follow: [{"class_ids": [...], "scores": [...], "label_names": [...]}, ...]
         """
 
         if isinstance(input_data, np.ndarray):
-            outputs = self.cls_predictor.predict(input_data)
-            yield self.cls_predictor.postprocess(outputs)
+            yield self.cls_predictor.predict(input_data)
         elif isinstance(input_data, str):
             if input_data.startswith("http") or input_data.startswith("https"):
                 image_storage_dir = partial(os.path.join, BASE_IMAGES_DIR)
@@ -497,7 +518,7 @@ class PaddleClas(object):
             image_list = get_image_list(input_data)
 
             batch_size = self._config.Global.get("batch_size", 1)
-            topk = self._config.PostProcess.get('topk', 1)
+            topk = self._config.PostProcess.Topk.get('topk', 1)
 
             img_list = []
             img_path_list = []
@@ -515,16 +536,15 @@ class PaddleClas(object):
                 cnt += 1
 
                 if cnt % batch_size == 0 or (idx + 1) == len(image_list):
-                    outputs = self.cls_predictor.predict(img_list)
-                    preds = self.cls_predictor.postprocess(outputs,
-                                                           img_path_list)
+                    preds = self.cls_predictor.predict(img_list)
+
                     if print_pred and preds:
-                        for pred in preds:
-                            filename = pred.pop("file_name")
+                        for idx, pred in enumerate(preds):
                             pred_str = ", ".join(
                                 [f"{k}: {pred[k]}" for k in pred])
                             print(
-                                f"filename: {filename}, top-{topk}, {pred_str}")
+                                f"filename: {img_path_list[idx]}, top-{topk}, {pred_str}"
+                            )
 
                     img_list = []
                     img_path_list = []

ppcls/arch/backbone/__init__.py

@@ -65,6 +65,7 @@ from ppcls.arch.backbone.variant_models.vgg_variant import VGG19Sigmoid
 from ppcls.arch.backbone.variant_models.pp_lcnet_variant import PPLCNet_x2_5_Tanh
 
 
+# help whl get all the models' api (class type) and components' api (func type)
 def get_apis():
     current_func = sys._getframe().f_code.co_name
     current_module = sys.modules[__name__]