更换文档检测模型

paddle_detection/ppdet/model_zoo/model_zoo.py

@@ -0,0 +1,84 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. 
+#   
+# Licensed under the Apache License, Version 2.0 (the "License");   
+# you may not use this file except in compliance with the License.  
+# You may obtain a copy of the License at   
+#   
+#     http://www.apache.org/licenses/LICENSE-2.0    
+#   
+# Unless required by applicable law or agreed to in writing, software   
+# distributed under the License is distributed on an "AS IS" BASIS, 
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  
+# See the License for the specific language governing permissions and   
+# limitations under the License.
+
+import os.path as osp
+import pkg_resources
+
+try:
+    from collections.abc import Sequence
+except:
+    from collections import Sequence
+
+from ppdet.core.workspace import load_config, create
+from ppdet.utils.checkpoint import load_weight
+from ppdet.utils.download import get_config_path
+
+from ppdet.utils.logger import setup_logger
+logger = setup_logger(__name__)
+
+__all__ = [
+    'list_model', 'get_config_file', 'get_weights_url', 'get_model',
+    'MODEL_ZOO_FILENAME'
+]
+
+MODEL_ZOO_FILENAME = 'MODEL_ZOO'
+
+
+def list_model(filters=[]):
+    model_zoo_file = pkg_resources.resource_filename('ppdet.model_zoo',
+                                                     MODEL_ZOO_FILENAME)
+    with open(model_zoo_file) as f:
+        model_names = f.read().splitlines()
+
+    # filter model_name
+    def filt(name):
+        for f in filters:
+            if name.find(f) < 0:
+                return False
+        return True
+
+    if isinstance(filters, str) or not isinstance(filters, Sequence):
+        filters = [filters]
+    model_names = [name for name in model_names if filt(name)]
+    if len(model_names) == 0 and len(filters) > 0:
+        raise ValueError("no model found, please check filters seeting, "
+                         "filters can be set as following kinds:\n"
+                         "\tDataset: coco, voc ...\n"
+                         "\tArchitecture: yolo, rcnn, ssd ...\n"
+                         "\tBackbone: resnet, vgg, darknet ...\n")
+
+    model_str = "Available Models:\n"
+    for model_name in model_names:
+        model_str += "\t{}\n".format(model_name)
+    logger.info(model_str)
+
+
+# models and configs save on bcebos under dygraph directory
+def get_config_file(model_name):
+    return get_config_path("ppdet://configs/{}.yml".format(model_name))
+
+
+def get_weights_url(model_name):
+    return "ppdet://models/{}.pdparams".format(osp.split(model_name)[-1])
+
+
+def get_model(model_name, pretrained=True):
+    cfg_file = get_config_file(model_name)
+    cfg = load_config(cfg_file)
+    model = create(cfg.architecture)
+
+    if pretrained:
+        load_weight(model, get_weights_url(model_name))
+
+    return model