[Model] add detection model : FastestDet (#842)

* model done, CLA fix

* remove letter_box and ConvertAndPermute, use resize hwc2chw and convert in preprocess

* remove useless values in preprocess

* remove useless values in preprocess

* fix reviewed problem

* fix reviewed problem pybind

* fix reviewed problem pybind

* postprocess fix

* add test_fastestdet.py, coco_val2017_500 fixed done, ready to review

* fix reviewed problem

* python/.../fastestdet.py

* fix infer.cc, preprocess, python/fastestdet.py

* fix examples/python/infer.py

python/fastdeploy/vision/detection/__init__.py

@@ -19,6 +19,7 @@ from .contrib.scaled_yolov4 import ScaledYOLOv4
 from .contrib.nanodet_plus import NanoDetPlus
 from .contrib.yolox import YOLOX
 from .contrib.yolov5 import *
+from .contrib.fastestdet import *
 from .contrib.yolov5lite import YOLOv5Lite
 from .contrib.yolov6 import YOLOv6
 from .contrib.yolov7end2end_trt import YOLOv7End2EndTRT

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

@@ -0,0 +1,149 @@
+# 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 FastestDetPreprocessor:
+    def __init__(self):
+        """Create a preprocessor for FastestDet
+        """
+        self._preprocessor = C.vision.detection.FastestDetPreprocessor()
+
+    def run(self, input_ims):
+        """Preprocess input images for FastestDet
+
+        :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 = [352, 352]
+        """
+        return self._preprocessor.size
+
+    @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
+
+
+class FastestDetPostprocessor:
+    def __init__(self):
+        """Create a postprocessor for FastestDet
+        """
+        self._postprocessor = C.vision.detection.FastestDetPostprocessor()
+
+    def run(self, runtime_results, ims_info):
+        """Postprocess the runtime results for FastestDet
+
+        :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 0.65
+        """
+        return self._postprocessor.conf_threshold
+
+    @property
+    def nms_threshold(self):
+        """
+        nms threshold for postprocessing, default is 0.45
+        """
+        return self._postprocessor.nms_threshold
+
+    @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
+
+
+class FastestDet(FastDeployModel):
+    def __init__(self,
+                 model_file,
+                 params_file="",
+                 runtime_option=None,
+                 model_format=ModelFormat.ONNX):
+        """Load a FastestDet model exported by FastestDet.
+
+        :param model_file: (str)Path of model file, e.g ./FastestDet.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(FastestDet, self).__init__(runtime_option)
+
+        assert model_format == ModelFormat.ONNX, "FastestDet only support model format of ModelFormat.ONNX now."
+        self._model = C.vision.detection.FastestDet(
+            model_file, params_file, self._runtime_option, model_format)
+
+        assert self.initialized, "FastestDet 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
+        :return: DetectionResult
+        """
+        assert input_image is not None, "Input image is None."
+        return self._model.predict(input_image)
+
+    def batch_predict(self, images):
+        assert len(images) == 1,"FastestDet is only support 1 image in batch_predict"
+        """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 FastestDetPreprocessor object of the loaded model
+
+        :return FastestDetPreprocessor
+        """
+        return self._model.preprocessor
+
+    @property
+    def postprocessor(self):
+        """Get FastestDetPostprocessor object of the loaded model
+
+        :return FastestDetPostprocessor
+        """
+        return self._model.postprocessor