Add YOLOv5-cls Model (#335)

* add yolov5cls

* fixed bugs

* fixed bugs

* fixed preprocess bug

* add yolov5cls readme

* deal with comments

* Add YOLOv5Cls Note

* add yolov5cls test

Co-authored-by: Jason <jiangjiajun@baidu.com>

python/fastdeploy/vision/classification/__init__.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 from __future__ import absolute_import
 
+from .contrib.yolov5cls import YOLOv5Cls
 from .ppcls import PaddleClasModel
 
 PPLCNet = PaddleClasModel

python/fastdeploy/vision/classification/contrib/__init__.py

@@ -0,0 +1,15 @@
+# Copyright (c) 2022 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.
+
+from __future__ import absolute_import

python/fastdeploy/vision/classification/contrib/yolov5cls.py

@@ -0,0 +1,69 @@
+# Copyright (c) 2022 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.
+
+from __future__ import absolute_import
+import logging
+from .... import FastDeployModel, ModelFormat
+from .... import c_lib_wrap as C
+
+
+class YOLOv5Cls(FastDeployModel):
+    def __init__(self,
+                 model_file,
+                 params_file="",
+                 runtime_option=None,
+                 model_format=ModelFormat.ONNX):
+        """Load a image classification model exported by YOLOv5.
+
+        :param model_file: (str)Path of model file, e.g yolov5cls/yolov5n-cls.onnx
+        :param params_file: (str)Path of parameters file, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
+        :param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
+        :param model_format: (fastdeploy.ModelForamt)Model format of the loaded model, default is ONNX
+        """
+
+        super(YOLOv5Cls, self).__init__(runtime_option)
+
+        assert model_format == ModelFormat.ONNX, "YOLOv5Cls only support model format of ModelFormat.ONNX now."
+        self._model = C.vision.classification.YOLOv5Cls(
+            model_file, params_file, self._runtime_option, model_format)
+        assert self.initialized, "YOLOv5Cls initialize failed."
+
+    def predict(self, input_image, topk=1):
+        """Classify an input image
+
+        :param im: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
+        :param topk: (int)The topk result by the classify confidence score, default 1
+        :return: ClassifyResult
+        """
+
+        return self._model.predict(input_image, topk)
+
+    @property
+    def size(self):
+        """
+        Returns the preprocess image size
+        """
+        return self._model.size
+
+    @size.setter
+    def size(self, wh):
+        """
+        Set the preprocess image size
+        """
+        assert isinstance(wh, (list, tuple)),\
+            "The value to set `size` must be type of tuple or list."
+        assert len(wh) == 2,\
+            "The value to set `size` must contatins 2 elements means [width, height], but now it contains {} elements.".format(
+            len(wh))
+        self._model.size = wh