[Model] Support YOLOv8 (#1137)

* add GPL lisence

* add GPL-3.0 lisence

* add GPL-3.0 lisence

* add GPL-3.0 lisence

* support yolov8

* add pybind for yolov8

* add yolov8 readme

Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>

python/fastdeploy/vision/detection/__init__.py

@@ -25,5 +25,6 @@ from .contrib.yolov5lite import YOLOv5Lite
 from .contrib.yolov6 import YOLOv6
 from .contrib.yolov7end2end_trt import YOLOv7End2EndTRT
 from .contrib.yolov7end2end_ort import YOLOv7End2EndORT
+from .contrib.yolov8 import *
 from .ppdet import *
 from .contrib.rkyolo import *

python/fastdeploy/vision/detection/contrib/yolov5seg.py

@@ -180,7 +180,7 @@ class YOLOv5Seg(FastDeployModel):
             model_file, params_file, self._runtime_option, model_format)
         assert self.initialized, "YOLOv5Seg initialize failed."
 
-    def predict(self, input_image, conf_threshold=0.25, nms_iou_threshold=0.5):
+    def predict(self, input_image):
         """Detect an input image
 
         :param input_image: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
@@ -189,8 +189,6 @@ class YOLOv5Seg(FastDeployModel):
         :return: DetectionResult
         """
 
-        self.postprocessor.conf_threshold = conf_threshold
-        self.postprocessor.nms_threshold = nms_iou_threshold
         return self._model.predict(input_image)
 
     def batch_predict(self, images):

python/fastdeploy/vision/detection/contrib/yolov8.py

@@ -0,0 +1,217 @@
+# 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 YOLOv8Preprocessor:
+    def __init__(self):
+        """Create a preprocessor for YOLOv8
+        """
+        self._preprocessor = C.vision.detection.YOLOv8Preprocessor()
+
+    def run(self, input_ims):
+        """Preprocess input images for YOLOv8
+
+        :param: input_ims: (list of numpy.ndarray)The input image
+        :return: list of FDTensor
+        """
+        return self._preprocessor.run(input_ims)
+
+    @property
+    def size(self):
+        """
+        Argument for image preprocessing step, the preprocess image size, tuple of (width, height), default size = [640, 640]
+        """
+        return self._preprocessor.size
+
+    @property
+    def padding_value(self):
+        """
+        padding value for preprocessing, default [114.0, 114.0, 114.0]
+        """
+        #  padding value, size should be the same as channels
+        return self._preprocessor.padding_value
+
+    @property
+    def is_scale_up(self):
+        """
+        is_scale_up for preprocessing, the input image only can be zoom out, the maximum resize scale cannot exceed 1.0, default true
+        """
+        return self._preprocessor.is_scale_up
+
+    @property
+    def is_mini_pad(self):
+        """
+        is_mini_pad for preprocessing, pad to the minimum rectange which height and width is times of stride, default false
+        """
+        return self._preprocessor.is_mini_pad
+
+    @property
+    def stride(self):
+        """
+        stride for preprocessing, only for mini_pad mode, default 32
+        """
+        return self._preprocessor.stride
+
+    @size.setter
+    def size(self, wh):
+        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._preprocessor.size = wh
+
+    @padding_value.setter
+    def padding_value(self, value):
+        assert isinstance(
+            value,
+            list), "The value to set `padding_value` must be type of list."
+        self._preprocessor.padding_value = value
+
+    @is_scale_up.setter
+    def is_scale_up(self, value):
+        assert isinstance(
+            value,
+            bool), "The value to set `is_scale_up` must be type of bool."
+        self._preprocessor.is_scale_up = value
+
+    @is_mini_pad.setter
+    def is_mini_pad(self, value):
+        assert isinstance(
+            value,
+            bool), "The value to set `is_mini_pad` must be type of bool."
+        self._preprocessor.is_mini_pad = value
+
+    @stride.setter
+    def stride(self, value):
+        assert isinstance(
+            stride, int), "The value to set `stride` must be type of int."
+        self._preprocessor.stride = value
+
+
+class YOLOv8Postprocessor:
+    def __init__(self):
+        """Create a postprocessor for YOLOv8
+        """
+        self._postprocessor = C.vision.detection.YOLOv8Postprocessor()
+
+    def run(self, runtime_results, ims_info):
+        """Postprocess the runtime results for YOLOv8
+
+        :param: runtime_results: (list of FDTensor)The output FDTensor results from runtime
+        :param: ims_info: (list of dict)Record input_shape and output_shape
+        :return: list of DetectionResult(If the runtime_results is predict by batched samples, the length of this list equals to the batch size)
+        """
+        return self._postprocessor.run(runtime_results, ims_info)
+
+    @property
+    def conf_threshold(self):
+        """
+        confidence threshold for postprocessing, default is 0.25
+        """
+        return self._postprocessor.conf_threshold
+
+    @property
+    def nms_threshold(self):
+        """
+        nms threshold for postprocessing, default is 0.5
+        """
+        return self._postprocessor.nms_threshold
+
+    @property
+    def multi_label(self):
+        """
+        multi_label for postprocessing, set true for eval, default is True
+        """
+        return self._postprocessor.multi_label
+
+    @conf_threshold.setter
+    def conf_threshold(self, conf_threshold):
+        assert isinstance(conf_threshold, float),\
+            "The value to set `conf_threshold` must be type of float."
+        self._postprocessor.conf_threshold = conf_threshold
+
+    @nms_threshold.setter
+    def nms_threshold(self, nms_threshold):
+        assert isinstance(nms_threshold, float),\
+            "The value to set `nms_threshold` must be type of float."
+        self._postprocessor.nms_threshold = nms_threshold
+
+    @multi_label.setter
+    def multi_label(self, value):
+        assert isinstance(
+            value,
+            bool), "The value to set `multi_label` must be type of bool."
+        self._postprocessor.multi_label = value
+
+
+class YOLOv8(FastDeployModel):
+    def __init__(self,
+                 model_file,
+                 params_file="",
+                 runtime_option=None,
+                 model_format=ModelFormat.ONNX):
+        """Load a YOLOv8 model exported by YOLOv8.
+
+        :param model_file: (str)Path of model file, e.g ./yolov8s.onnx
+        :param params_file: (str)Path of parameters file, e.g yolox/model.pdiparams, 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
+        """
+        super(YOLOv8, self).__init__(runtime_option)
+
+        self._model = C.vision.detection.YOLOv8(
+            model_file, params_file, self._runtime_option, model_format)
+        assert self.initialized, "YOLOv8 initialize failed."
+
+    def predict(self, input_image):
+        """Detect an input image
+
+        :param input_image: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
+        :param conf_threshold: confidence threshold for postprocessing, default is 0.25
+        :param nms_iou_threshold: iou threshold for NMS, default is 0.5
+        :return: DetectionResult
+        """
+
+        return self._model.predict(input_image)
+
+    def batch_predict(self, images):
+        """Classify a batch of input image
+
+        :param im: (list of numpy.ndarray) The input image list, each element is a 3-D array with layout HWC, BGR format
+        :return list of DetectionResult
+        """
+
+        return self._model.batch_predict(images)
+
+    @property
+    def preprocessor(self):
+        """Get YOLOv8Preprocessor object of the loaded model
+
+        :return YOLOv8Preprocessor
+        """
+        return self._model.preprocessor
+
+    @property
+    def postprocessor(self):
+        """Get YOLOv8Postprocessor object of the loaded model
+
+        :return YOLOv8Postprocessor
+        """
+        return self._model.postprocessor