move gallery layer into extractor

ppcls/utils/gallery2fc.py

@@ -8,71 +8,75 @@ from ppcls.utils.config import parse_config, parse_args
 from ppcls.utils.save_load import load_dygraph_pretrain
 from ppcls.utils.logger import init_logger
 from ppcls.data import transform, create_operators
-
-
-def build_gallery_layer(configs, feature_extractor):
-    transform_configs = configs["IndexProcess"]["transform_ops"]
-    preprocess_ops = create_operators(transform_configs)
-
-    embedding_size = configs["Arch"]["Head"]["embedding_size"]
-    batch_size = configs["IndexProcess"]["batch_size"]
-    image_shape = configs["Global"]["image_shape"].copy()
-    image_shape.insert(0, batch_size)
-    input_tensor = paddle.zeros(image_shape)
-
-    image_root = configs["IndexProcess"]["image_root"]
-    data_file = configs["IndexProcess"]["data_file"]
-    delimiter = configs["IndexProcess"]["delimiter"]
-    gallery_images = []
-    gallery_docs = []
-    gallery_labels = []
-
-    with open(data_file, 'r', encoding='utf-8') as f:
-        lines = f.readlines()
-        for ori_line in lines:
-            line = ori_line.strip().split(delimiter)
-            text_num = len(line)
-            assert text_num >= 2, f"line({ori_line}) must be splitted into at least 2 parts, but got {text_num}"
-            image_file = os.path.join(image_root, line[0])
-
-            gallery_images.append(image_file)
-            gallery_docs.append(ori_line.strip())
-            gallery_labels.append(line[1].strip())
-    batch_index = 0
-    gallery_feature = paddle.zeros((len(gallery_images), embedding_size))
-    for i, image_path in enumerate(gallery_images):
-        image = cv2.imread(image_path)
-        for op in preprocess_ops:
-            image = op(image)
-        input_tensor[batch_index] = image
-        batch_index += 1
-        if batch_index == batch_size or i == len(gallery_images) - 1:
-            batch_feature = feature_extractor(input_tensor)["features"]
-            for j in range(batch_index):
-                feature = batch_feature[j]
-                norm_feature = paddle.nn.functional.normalize(feature, axis=0)
-                gallery_feature[i - batch_index + j] = norm_feature
-    gallery_layer = paddle.nn.Linear(embedding_size, len(gallery_images), bias_attr=False)
-    gallery_layer.set_state_dict({"weight": gallery_feature.T})
-    return gallery_layer
+from ppcls.arch.slim import quantize_model
 
 
 class GalleryLayer(paddle.nn.Layer):
-    def __init__(self, configs, feature_extractor):
+    def __init__(self, configs):
         super().__init__()
-        self.gallery_layer = build_gallery_layer(configs, feature_extractor)
+        self.configs = configs
+        embedding_size = self.configs["Arch"]["Head"]["embedding_size"]
+        self.batch_size = self.configs["IndexProcess"]["batch_size"]
+        self.image_shape = self.configs["Global"]["image_shape"].copy()
+        self.image_shape.insert(0, self.batch_size)
+
+        image_root = self.configs["IndexProcess"]["image_root"]
+        data_file = self.configs["IndexProcess"]["data_file"]
+        delimiter = self.configs["IndexProcess"]["delimiter"]
+        self.gallery_images = []
+        gallery_docs = []
+        gallery_labels = []
+
+        with open(data_file, 'r', encoding='utf-8') as f:
+            lines = f.readlines()
+            for ori_line in lines:
+                line = ori_line.strip().split(delimiter)
+                text_num = len(line)
+                assert text_num >= 2, f"line({ori_line}) must be splitted into at least 2 parts, but got {text_num}"
+                image_file = os.path.join(image_root, line[0])
+
+                self.gallery_images.append(image_file)
+                gallery_docs.append(ori_line.strip())
+                gallery_labels.append(line[1].strip())
+        self.gallery_layer = paddle.nn.Linear(embedding_size, len(self.gallery_images), bias_attr=False)
 
     def forward(self, x):
         x = paddle.nn.functional.normalize(x)
         x = self.gallery_layer(x)
         return x
 
+    def build_gallery_layer(self, feature_extractor):
+        transform_configs = self.configs["IndexProcess"]["transform_ops"]
+        preprocess_ops = create_operators(transform_configs)
+        embedding_size = self.configs["Arch"]["Head"]["embedding_size"]
+        batch_index = 0
+        input_tensor = paddle.zeros(self.image_shape)
+        gallery_feature = paddle.zeros((len(self.gallery_images), embedding_size))
+        for i, image_path in enumerate(self.gallery_images):
+            image = cv2.imread(image_path)
+            for op in preprocess_ops:
+                image = op(image)
+            input_tensor[batch_index] = image
+            batch_index += 1
+            if batch_index == self.batch_size or i == len(self.gallery_images) - 1:
+                batch_feature = feature_extractor(input_tensor)["features"]
+                for j in range(batch_index):
+                    feature = batch_feature[j]
+                    norm_feature = paddle.nn.functional.normalize(feature, axis=0)
+                    gallery_feature[i - batch_index + j] = norm_feature
+        self.gallery_layer.set_state_dict({"weight": gallery_feature.T})
+
 
 def export_fuse_model(configs):
+    slim_config = configs["Slim"].copy()
+    configs["Slim"] = None
     fuse_model = build_model(configs)
+    fuse_model.head = GalleryLayer(configs)
+    configs["slim"] = slim_config
+    quantize_model(configs, fuse_model)
     load_dygraph_pretrain(fuse_model, configs["Global"]["pretrained_model"])
     fuse_model.eval()
-    fuse_model.head = GalleryLayer(configs, fuse_model)
+    fuse_model.head.build_gallery_layer(fuse_model)
     save_path = configs["Global"]["save_inference_dir"]
     fuse_model.quanter.save_quantized_model(
         fuse_model,