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If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read \ No newline at end of file diff --git a/421_Gold-YOLO-Head/README.md b/421_Gold-YOLO-Head/README.md new file mode 100644 index 0000000000..0e30879d1d --- /dev/null +++ b/421_Gold-YOLO-Head/README.md @@ -0,0 +1 @@ +# Note diff --git a/421_Gold-YOLO-Head/demo/demo_goldyolo_onnx.py b/421_Gold-YOLO-Head/demo/demo_goldyolo_onnx.py new file mode 100644 index 0000000000..acbc057a48 --- /dev/null +++ b/421_Gold-YOLO-Head/demo/demo_goldyolo_onnx.py @@ -0,0 +1,350 @@ +#!/usr/bin/env python + +import copy +import cv2 +import time +import numpy as np +import onnxruntime +from argparse import ArgumentParser +from typing import Tuple, Optional, List + + +class GoldYOLOONNX(object): + def __init__( + self, + model_path: Optional[str] = 'gold_yolo_n_hand_post_0333_0.4040_1x3x480x640.onnx', + class_score_th: Optional[float] = 0.35, + providers: Optional[List] = [ + # ( + # 'TensorrtExecutionProvider', { + # 'trt_engine_cache_enable': True, + # 'trt_engine_cache_path': '.', + # 'trt_fp16_enable': True, + # } + # ), + 'CUDAExecutionProvider', + 'CPUExecutionProvider', + ], + ): + """GoldYOLOONNX + + Parameters + ---------- + model_path: Optional[str] + ONNX file path for GoldYOLO + + class_score_th: Optional[float] + Score threshold. Default: 0.35 + + providers: Optional[List] + Name of onnx execution providers + Default: + [ + ( + 'TensorrtExecutionProvider', { + 'trt_engine_cache_enable': True, + 'trt_engine_cache_path': '.', + 'trt_fp16_enable': True, + } + ), + 'CUDAExecutionProvider', + 'CPUExecutionProvider', + ] + """ + # Threshold + self.class_score_th = class_score_th + + # Model loading + session_option = onnxruntime.SessionOptions() + session_option.log_severity_level = 3 + self.onnx_session = onnxruntime.InferenceSession( + model_path, + sess_options=session_option, + providers=providers, + ) + self.providers = self.onnx_session.get_providers() + + self.input_shapes = [ + input.shape for input in self.onnx_session.get_inputs() + ] + self.input_names = [ + input.name for input in self.onnx_session.get_inputs() + ] + self.output_names = [ + output.name for output in self.onnx_session.get_outputs() + ] + + + def __call__( + self, + image: np.ndarray, + ) -> Tuple[np.ndarray, np.ndarray]: + """YOLOv7ONNX + + Parameters + ---------- + image: np.ndarray + Entire image + + Returns + ------- + boxes: np.ndarray + Predicted boxes: [N, y1, x1, y2, x2] + + scores: np.ndarray + Predicted box scores: [N, score] + """ + temp_image = copy.deepcopy(image) + + # PreProcess + resized_image = self.__preprocess( + temp_image, + ) + + # Inference + inferece_image = np.asarray([resized_image], dtype=np.float32) + boxes = self.onnx_session.run( + self.output_names, + {input_name: inferece_image for input_name in self.input_names}, + )[0] + + # PostProcess + result_boxes, result_scores = \ + self.__postprocess( + image=temp_image, + boxes=boxes, + ) + + return result_boxes, result_scores + + + def __preprocess( + self, + image: np.ndarray, + swap: Optional[Tuple[int,int,int]] = (2, 0, 1), + ) -> np.ndarray: + """__preprocess + + Parameters + ---------- + image: np.ndarray + Entire image + + swap: tuple + HWC to CHW: (2,0,1) + CHW to HWC: (1,2,0) + HWC to HWC: (0,1,2) + CHW to CHW: (0,1,2) + + Returns + ------- + resized_image: np.ndarray + Resized and normalized image. + """ + # Normalization + BGR->RGB + resized_image = cv2.resize( + image, + ( + int(self.input_shapes[0][3]), + int(self.input_shapes[0][2]), + ) + ) + resized_image = np.divide(resized_image, 255.0) + resized_image = resized_image[..., ::-1] + resized_image = resized_image.transpose(swap) + resized_image = np.ascontiguousarray( + resized_image, + dtype=np.float32, + ) + return resized_image + + + def __postprocess( + self, + image: np.ndarray, + boxes: np.ndarray, + ) -> Tuple[np.ndarray, np.ndarray]: + """__postprocess + + Parameters + ---------- + image: np.ndarray + Entire image. + + boxes: np.ndarray + float32[N, 7] + + Returns + ------- + result_boxes: np.ndarray + Predicted boxes: [N, y1, x1, y2, x2] + + result_scores: np.ndarray + Predicted box confs: [N, score] + """ + image_height = image.shape[0] + image_width = image.shape[1] + + """ + Detector is + N -> Number of boxes detected + batchno -> always 0: BatchNo.0 + + batchno_classid_y1x1y2x2_score: float32[N,7] + """ + result_boxes = [] + result_scores = [] + if len(boxes) > 0: + scores = boxes[:, 6:7] + keep_idxs = scores[:, 0] > self.class_score_th + scores_keep = scores[keep_idxs, :] + boxes_keep = boxes[keep_idxs, :] + + if len(boxes_keep) > 0: + for box, score in zip(boxes_keep, scores_keep): + x_min = int(max(box[2], 0) * image_width / self.input_shapes[0][3]) + y_min = int(max(box[3], 0) * image_height / self.input_shapes[0][2]) + x_max = int(min(box[4], self.input_shapes[0][3]) * image_width / self.input_shapes[0][3]) + y_max = int(min(box[5], self.input_shapes[0][2]) * image_height / self.input_shapes[0][2]) + + result_boxes.append( + [x_min, y_min, x_max, y_max] + ) + result_scores.append( + score + ) + + return np.asarray(result_boxes), np.asarray(result_scores) + + +def is_parsable_to_int(s): + try: + int(s) + return True + except ValueError: + return False + + +def main(): + parser = ArgumentParser() + parser.add_argument( + '-m', + '--model', + type=str, + default='gold_yolo_n_hand_post_0333_0.4040_1x3x480x640.onnx', + ) + parser.add_argument( + '-v', + '--video', + type=str, + default="0", + ) + args = parser.parse_args() + + model = GoldYOLOONNX( + model_path=args.model, + ) + + cap = cv2.VideoCapture( + int(args.video) if is_parsable_to_int(args.video) else args.video + ) + cap_fps = cap.get(cv2.CAP_PROP_FPS) + w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v') + video_writer = cv2.VideoWriter( + filename='output.mp4', + fourcc=fourcc, + fps=cap_fps, + frameSize=(w, h), + ) + + while cap.isOpened(): + res, image = cap.read() + if not res: + break + + debug_image = copy.deepcopy(image) + + start_time = time.time() + boxes, scores = model(debug_image) + elapsed_time = time.time() - start_time + fps = 1 / elapsed_time + cv2.putText( + debug_image, + f'{fps:.1f} FPS (inferece + post-process)', + (10, 30), + cv2.FONT_HERSHEY_SIMPLEX, + 0.7, + (255, 255, 255), + 2, + cv2.LINE_AA, + ) + cv2.putText( + debug_image, + f'{fps:.1f} FPS (inferece + post-process)', + (10, 30), + cv2.FONT_HERSHEY_SIMPLEX, + 0.7, + (0, 0, 255), + 1, + cv2.LINE_AA, + ) + + for box, score in zip(boxes, scores): + cv2.rectangle( + debug_image, + (box[0], box[1]), + (box[2], box[3]), + (255,255,255), + 2, + ) + cv2.rectangle( + debug_image, + (box[0], box[1]), + (box[2], box[3]), + (0,0,255), + 1, + ) + cv2.putText( + debug_image, + f'{score[0]:.2f}', + ( + box[0], + box[1]-10 if box[1]-10 > 0 else 10 + ), + cv2.FONT_HERSHEY_SIMPLEX, + 0.7, + (255, 255, 255), + 2, + cv2.LINE_AA, + ) + cv2.putText( + debug_image, + f'{score[0]:.2f}', + ( + box[0], + box[1]-10 if box[1]-10 > 0 else 10 + ), + cv2.FONT_HERSHEY_SIMPLEX, + 0.7, + (0, 0, 255), + 1, + cv2.LINE_AA, + ) + + key = cv2.waitKey(1) + if key == 27: # ESC + break + + cv2.imshow("test", debug_image) + video_writer.write(debug_image) + + if video_writer: + video_writer.release() + + if cap: + cap.release() + +if __name__ == "__main__": + main() diff --git a/421_Gold-YOLO-Head/demo/demo_goldyolo_onnx_image.py b/421_Gold-YOLO-Head/demo/demo_goldyolo_onnx_image.py new file mode 100644 index 0000000000..7536d66fba --- /dev/null +++ b/421_Gold-YOLO-Head/demo/demo_goldyolo_onnx_image.py @@ -0,0 +1,314 @@ +#!/usr/bin/env python + +import os +import copy +import cv2 +from tqdm import tqdm +import glob +import numpy as np +import onnxruntime +from argparse import ArgumentParser +from typing import Tuple, Optional, List + + +class GoldYOLOONNX(object): + def __init__( + self, + model_path: Optional[str] = 'gold_yolo_n_hand_post_0333_0.4040_1x3x480x640.onnx', + class_score_th: Optional[float] = 0.35, + providers: Optional[List] = [ + ( + 'TensorrtExecutionProvider', { + 'trt_engine_cache_enable': True, + 'trt_engine_cache_path': '.', + 'trt_fp16_enable': True, + } + ), + 'CUDAExecutionProvider', + 'CPUExecutionProvider', + ], + ): + """GoldYOLOONNX + + Parameters + ---------- + model_path: Optional[str] + ONNX file path for GoldYOLO + + class_score_th: Optional[float] + Score threshold. Default: 0.35 + + providers: Optional[List] + Name of onnx execution providers + Default: + [ + ( + 'TensorrtExecutionProvider', { + 'trt_engine_cache_enable': True, + 'trt_engine_cache_path': '.', + 'trt_fp16_enable': True, + } + ), + 'CUDAExecutionProvider', + 'CPUExecutionProvider', + ] + """ + # Threshold + self.class_score_th = class_score_th + + # Model loading + session_option = onnxruntime.SessionOptions() + session_option.log_severity_level = 3 + self.onnx_session = onnxruntime.InferenceSession( + model_path, + sess_options=session_option, + providers=providers, + ) + self.providers = self.onnx_session.get_providers() + + self.input_shapes = [ + input.shape for input in self.onnx_session.get_inputs() + ] + self.input_names = [ + input.name for input in self.onnx_session.get_inputs() + ] + self.output_names = [ + output.name for output in self.onnx_session.get_outputs() + ] + + + def __call__( + self, + image: np.ndarray, + ) -> Tuple[np.ndarray, np.ndarray]: + """YOLOv7ONNX + + Parameters + ---------- + image: np.ndarray + Entire image + + Returns + ------- + boxes: np.ndarray + Predicted boxes: [N, y1, x1, y2, x2] + + scores: np.ndarray + Predicted box scores: [N, score] + """ + temp_image = copy.deepcopy(image) + + # PreProcess + resized_image = self.__preprocess( + temp_image, + ) + + # Inference + inferece_image = np.asarray([resized_image], dtype=np.float32) + boxes = self.onnx_session.run( + self.output_names, + {input_name: inferece_image for input_name in self.input_names}, + )[0] + + # PostProcess + result_boxes, result_scores = \ + self.__postprocess( + image=temp_image, + boxes=boxes, + ) + + return result_boxes, result_scores + + + def __preprocess( + self, + image: np.ndarray, + swap: Optional[Tuple[int,int,int]] = (2, 0, 1), + ) -> np.ndarray: + """__preprocess + + Parameters + ---------- + image: np.ndarray + Entire image + + swap: tuple + HWC to CHW: (2,0,1) + CHW to HWC: (1,2,0) + HWC to HWC: (0,1,2) + CHW to CHW: (0,1,2) + + Returns + ------- + resized_image: np.ndarray + Resized and normalized image. + """ + # Normalization + BGR->RGB + resized_image = cv2.resize( + image, + ( + int(self.input_shapes[0][3]), + int(self.input_shapes[0][2]), + ) + ) + resized_image = np.divide(resized_image, 255.0) + resized_image = resized_image[..., ::-1] + resized_image = resized_image.transpose(swap) + resized_image = np.ascontiguousarray( + resized_image, + dtype=np.float32, + ) + return resized_image + + + def __postprocess( + self, + image: np.ndarray, + boxes: np.ndarray, + ) -> Tuple[np.ndarray, np.ndarray]: + """__postprocess + + Parameters + ---------- + image: np.ndarray + Entire image. + + boxes: np.ndarray + float32[N, 7] + + Returns + ------- + result_boxes: np.ndarray + Predicted boxes: [N, y1, x1, y2, x2] + + result_scores: np.ndarray + Predicted box confs: [N, score] + """ + image_height = image.shape[0] + image_width = image.shape[1] + + """ + Detector is + N -> Number of boxes detected + batchno -> always 0: BatchNo.0 + + batchno_classid_y1x1y2x2_score: float32[N,7] + """ + result_boxes = [] + result_scores = [] + if len(boxes) > 0: + scores = boxes[:, 6:7] + keep_idxs = scores[:, 0] > self.class_score_th + scores_keep = scores[keep_idxs, :] + boxes_keep = boxes[keep_idxs, :] + + if len(boxes_keep) > 0: + for box, score in zip(boxes_keep, scores_keep): + x_min = int(max(box[2], 0) * image_width / self.input_shapes[0][3]) + y_min = int(max(box[3], 0) * image_height / self.input_shapes[0][2]) + x_max = int(min(box[4], self.input_shapes[0][3]) * image_width / self.input_shapes[0][3]) + y_max = int(min(box[5], self.input_shapes[0][2]) * image_height / self.input_shapes[0][2]) + + result_boxes.append( + [x_min, y_min, x_max, y_max] + ) + result_scores.append( + score + ) + + return np.asarray(result_boxes), np.asarray(result_scores) + + +def is_parsable_to_int(s): + try: + int(s) + return True + except ValueError: + return False + + +def main(): + parser = ArgumentParser() + parser.add_argument( + '-m', + '--model', + type=str, + default='gold_yolo_n_hand_post_0333_0.4040_1x3x480x640.onnx', + ) + parser.add_argument( + '-i', + '--images_path', + type=str, + default="./00_COCO-Hand-S_base", + ) + parser.add_argument( + '-o', + '--output_path', + type=str, + default="./output", + ) + args = parser.parse_args() + + model = GoldYOLOONNX( + model_path=args.model, + ) + + files = sorted(glob.glob(f"{args.images_path}/*.jpg")) + os.makedirs(args.output_path, exist_ok=True) + + for file in tqdm(files, dynamic_ncols=True): + image = cv2.imread(file) + debug_image = copy.deepcopy(image) + boxes, scores = model(debug_image) + + for box, score in zip(boxes, scores): + cv2.rectangle( + debug_image, + (box[0], box[1]), + (box[2], box[3]), + (255,255,255), + 2, + ) + cv2.rectangle( + debug_image, + (box[0], box[1]), + (box[2], box[3]), + (0,0,255), + 1, + ) + cv2.putText( + debug_image, + f'{score[0]:.2f}', + ( + box[0], + box[1]-10 if box[1]-10 > 0 else 10 + ), + cv2.FONT_HERSHEY_SIMPLEX, + 0.7, + (255, 255, 255), + 2, + cv2.LINE_AA, + ) + cv2.putText( + debug_image, + f'{score[0]:.2f}', + ( + box[0], + box[1]-10 if box[1]-10 > 0 else 10 + ), + cv2.FONT_HERSHEY_SIMPLEX, + 0.7, + (0, 0, 255), + 1, + cv2.LINE_AA, + ) + cv2.imwrite(f'{args.output_path}/{os.path.basename(file)}', debug_image) + + key = cv2.waitKey(1) + if key == 27: # ESC + break + + cv2.imshow("test", debug_image) + +if __name__ == "__main__": + main() diff --git a/421_Gold-YOLO-Head/download_l.sh b/421_Gold-YOLO-Head/download_l.sh new file mode 100755 index 0000000000..1e35406ad5 --- /dev/null +++ b/421_Gold-YOLO-Head/download_l.sh @@ -0,0 +1,7 @@ +#!/bin/bash + +curl "https://s3.ap-northeast-2.wasabisys.com/pinto-model-zoo/421_Gold-YOLO-Head/resources_l.tar.gz" -o resources.tar.gz +tar -zxvf resources.tar.gz +rm resources.tar.gz + +echo Download finished. diff --git a/421_Gold-YOLO-Head/download_m.sh b/421_Gold-YOLO-Head/download_m.sh new file mode 100755 index 0000000000..295616a910 --- /dev/null +++ b/421_Gold-YOLO-Head/download_m.sh @@ -0,0 +1,7 @@ +#!/bin/bash + +curl "https://s3.ap-northeast-2.wasabisys.com/pinto-model-zoo/421_Gold-YOLO-Head/resources_m.tar.gz" -o resources.tar.gz +tar -zxvf resources.tar.gz +rm resources.tar.gz + +echo Download finished. diff --git a/421_Gold-YOLO-Head/download_n.sh b/421_Gold-YOLO-Head/download_n.sh new file mode 100755 index 0000000000..591c68db08 --- /dev/null +++ b/421_Gold-YOLO-Head/download_n.sh @@ -0,0 +1,7 @@ +#!/bin/bash + +curl "https://s3.ap-northeast-2.wasabisys.com/pinto-model-zoo/421_Gold-YOLO-Head/resources_n.tar.gz" -o resources.tar.gz +tar -zxvf resources.tar.gz +rm resources.tar.gz + +echo Download finished. diff --git a/421_Gold-YOLO-Head/download_s.sh b/421_Gold-YOLO-Head/download_s.sh new file mode 100755 index 0000000000..e8d6c5c473 --- /dev/null +++ b/421_Gold-YOLO-Head/download_s.sh @@ -0,0 +1,7 @@ +#!/bin/bash + +curl "https://s3.ap-northeast-2.wasabisys.com/pinto-model-zoo/421_Gold-YOLO-Head/resources_s.tar.gz" -o resources.tar.gz +tar -zxvf resources.tar.gz +rm resources.tar.gz + +echo Download finished. diff --git a/421_Gold-YOLO-Head/post_process_gen_tools/README.md b/421_Gold-YOLO-Head/post_process_gen_tools/README.md new file mode 100644 index 0000000000..6cf00fd90a --- /dev/null +++ b/421_Gold-YOLO-Head/post_process_gen_tools/README.md @@ -0,0 +1,209 @@ +# Note +- INPUTS + - `predictions`: `float32 [batches, boxes, 5 + classes]` + + * 5 = [center_x, center_y, width, height, score] +- OUTPUTS + - `batchno_classid_x1y1x2y2_score`: `float32 [final_boxes_count, 7]` + + * NMS boxes + * final_boxes_count (N) ≠ batches + * 7 = [batch_no, classid, x1, y1, x2, y2, score] + +![image](https://github.com/PINTO0309/PINTO_model_zoo/assets/33194443/9d4fecdf-c90e-4e0a-99a5-9c3e61a4cf41) + +# How to generate post-processing ONNX +Simply change the following parameters and run all shells. + +https://github.com/PINTO0309/PINTO_model_zoo/blob/main/420_Gold-YOLO-Hand/post_process_gen_tools/convert_script.sh +```bash +OPSET=11 +BATCHES=1 +BOXES=5040 +CLASSES=1 +``` + +```bash +sudo chmod +x ./convert_script.sh +./convert_script.sh +``` + +# How to change NMS parameters +![image](https://user-images.githubusercontent.com/33194443/178084918-af33bfcc-425f-496e-87fb-1331ef7b2b6e.png) + +https://github.com/PINTO0309/simple-onnx-processing-tools + +Run the script below to directly rewrite the parameters of the ONNX file. +```bash +### Number of output boxes for Gold-YOLO +BOXES=5040 + +### max_output_boxes_per_class +sam4onnx \ +--op_name main01_nonmaxsuppression11 \ +--input_onnx_file_path 30_nms_gold_yolo_m_hand_${BOXES}.onnx \ +--output_onnx_file_path 30_nms_gold_yolo_m_hand_${BOXES}.onnx \ +--input_constants main01_max_output_boxes_per_class int64 [10] + +### iou_threshold +sam4onnx \ +--op_name main01_nonmaxsuppression11 \ +--input_onnx_file_path 30_nms_gold_yolo_m_hand_${BOXES}.onnx \ +--output_onnx_file_path 30_nms_gold_yolo_m_hand_${BOXES}.onnx \ +--input_constants main01_iou_threshold float32 [0.05] + +### score_threshold +sam4onnx \ +--op_name main01_nonmaxsuppression11 \ +--input_onnx_file_path 30_nms_gold_yolo_m_hand_${BOXES}.onnx \ +--output_onnx_file_path 30_nms_gold_yolo_m_hand_${BOXES}.onnx \ +--input_constants main01_score_threshold float32 [0.25] +``` + +# How to merge post-processing into a Gold-YOLO model +Simply execute the following command. + +https://github.com/PINTO0309/simple-onnx-processing-tools + +```bash +################################################### Gold-YOLO + Post-Process +MODEL=gold_yolo +BOXES=5040 +H=256 +W=320 + +snc4onnx \ +--input_onnx_file_paths ${MODEL}_${H}x${W}.onnx 30_nms_gold_yolo_m_hand_${BOXES}.onnx \ +--srcop_destop output predictions \ +--output_onnx_file_path ${MODEL}_post_${H}x${W}.onnx +onnxsim ${MODEL}_post_${H}x${W}.onnx ${MODEL}_post_${H}x${W}.onnx +onnxsim ${MODEL}_post_${H}x${W}.onnx ${MODEL}_post_${H}x${W}.onnx + +################################################### 1 Batch + +MODEL=gold_yolo + +BOXES=5040 +H=256 +W=320 +snc4onnx \ +--input_onnx_file_paths ${MODEL}_${H}x${W}.onnx 30_nms_gold_yolo_m_hand_${BOXES}.onnx \ +--srcop_destop output predictions \ +--output_onnx_file_path ${MODEL}_post_${H}x${W}.onnx +onnxsim ${MODEL}_post_${H}x${W}.onnx ${MODEL}_post_${H}x${W}.onnx +onnxsim ${MODEL}_post_${H}x${W}.onnx ${MODEL}_post_${H}x${W}.onnx + +BOXES=7560 +H=256 +W=480 +snc4onnx \ +--input_onnx_file_paths ${MODEL}_${H}x${W}.onnx 30_nms_gold_yolo_m_hand_${BOXES}.onnx \ +--srcop_destop output predictions \ +--output_onnx_file_path ${MODEL}_post_${H}x${W}.onnx +onnxsim ${MODEL}_post_${H}x${W}.onnx ${MODEL}_post_${H}x${W}.onnx +onnxsim ${MODEL}_post_${H}x${W}.onnx ${MODEL}_post_${H}x${W}.onnx + +BOXES=10080 +H=256 +W=640 +snc4onnx \ +--input_onnx_file_paths ${MODEL}_${H}x${W}.onnx 30_nms_gold_yolo_m_hand_${BOXES}.onnx \ +--srcop_destop output predictions \ +--output_onnx_file_path ${MODEL}_post_${H}x${W}.onnx +onnxsim ${MODEL}_post_${H}x${W}.onnx ${MODEL}_post_${H}x${W}.onnx +onnxsim ${MODEL}_post_${H}x${W}.onnx ${MODEL}_post_${H}x${W}.onnx + +BOXES=15120 +H=384 +W=640 +snc4onnx \ +--input_onnx_file_paths ${MODEL}_${H}x${W}.onnx 30_nms_gold_yolo_m_hand_${BOXES}.onnx \ +--srcop_destop output predictions \ +--output_onnx_file_path ${MODEL}_post_${H}x${W}.onnx +onnxsim ${MODEL}_post_${H}x${W}.onnx ${MODEL}_post_${H}x${W}.onnx +onnxsim ${MODEL}_post_${H}x${W}.onnx ${MODEL}_post_${H}x${W}.onnx + +BOXES=18900 +H=480 +W=640 +snc4onnx \ +--input_onnx_file_paths ${MODEL}_${H}x${W}.onnx 30_nms_gold_yolo_m_hand_${BOXES}.onnx \ +--srcop_destop output predictions \ +--output_onnx_file_path ${MODEL}_post_${H}x${W}.onnx +onnxsim ${MODEL}_post_${H}x${W}.onnx ${MODEL}_post_${H}x${W}.onnx +onnxsim ${MODEL}_post_${H}x${W}.onnx ${MODEL}_post_${H}x${W}.onnx + +BOXES=57960 +H=736 +W=1280 +snc4onnx \ +--input_onnx_file_paths ${MODEL}_${H}x${W}.onnx 30_nms_gold_yolo_m_hand_${BOXES}.onnx \ +--srcop_destop output predictions \ +--output_onnx_file_path ${MODEL}_post_${H}x${W}.onnx +onnxsim ${MODEL}_post_${H}x${W}.onnx ${MODEL}_post_${H}x${W}.onnx +onnxsim ${MODEL}_post_${H}x${W}.onnx ${MODEL}_post_${H}x${W}.onnx + +################################################### N Batch + +MODEL=gold_yolo + +BOXES=5040 +H=256 +W=320 +snc4onnx \ +--input_onnx_file_paths ${MODEL}_Nx3x${H}x${W}.onnx 31_nms_gold_yolo_m_hand_N_${BOXES}.onnx \ +--srcop_destop output predictions \ +--output_onnx_file_path ${MODEL}_post_Nx3x${H}x${W}.onnx +onnxsim ${MODEL}_post_Nx3x${H}x${W}.onnx ${MODEL}_post_Nx3x${H}x${W}.onnx +onnxsim ${MODEL}_post_Nx3x${H}x${W}.onnx ${MODEL}_post_Nx3x${H}x${W}.onnx + +BOXES=7560 +H=256 +W=480 +snc4onnx \ +--input_onnx_file_paths ${MODEL}_Nx3x${H}x${W}.onnx 31_nms_gold_yolo_m_hand_N_${BOXES}.onnx \ +--srcop_destop output predictions \ +--output_onnx_file_path ${MODEL}_post_Nx3x${H}x${W}.onnx +onnxsim ${MODEL}_post_Nx3x${H}x${W}.onnx ${MODEL}_post_Nx3x${H}x${W}.onnx +onnxsim ${MODEL}_post_Nx3x${H}x${W}.onnx ${MODEL}_post_Nx3x${H}x${W}.onnx + +BOXES=10080 +H=256 +W=640 +snc4onnx \ +--input_onnx_file_paths ${MODEL}_Nx3x${H}x${W}.onnx 31_nms_gold_yolo_m_hand_N_${BOXES}.onnx \ +--srcop_destop output predictions \ +--output_onnx_file_path ${MODEL}_post_Nx3x${H}x${W}.onnx +onnxsim ${MODEL}_post_Nx3x${H}x${W}.onnx ${MODEL}_post_Nx3x${H}x${W}.onnx +onnxsim ${MODEL}_post_Nx3x${H}x${W}.onnx ${MODEL}_post_Nx3x${H}x${W}.onnx + +BOXES=15120 +H=384 +W=640 +snc4onnx \ +--input_onnx_file_paths ${MODEL}_Nx3x${H}x${W}.onnx 31_nms_gold_yolo_m_hand_N_${BOXES}.onnx \ +--srcop_destop output predictions \ +--output_onnx_file_path ${MODEL}_post_Nx3x${H}x${W}.onnx +onnxsim ${MODEL}_post_Nx3x${H}x${W}.onnx ${MODEL}_post_Nx3x${H}x${W}.onnx +onnxsim ${MODEL}_post_Nx3x${H}x${W}.onnx ${MODEL}_post_Nx3x${H}x${W}.onnx + +BOXES=18900 +H=480 +W=640 +snc4onnx \ +--input_onnx_file_paths ${MODEL}_Nx3x${H}x${W}.onnx 31_nms_gold_yolo_m_hand_N_${BOXES}.onnx \ +--srcop_destop output predictions \ +--output_onnx_file_path ${MODEL}_post_Nx3x${H}x${W}.onnx +onnxsim ${MODEL}_post_Nx3x${H}x${W}.onnx ${MODEL}_post_Nx3x${H}x${W}.onnx +onnxsim ${MODEL}_post_Nx3x${H}x${W}.onnx ${MODEL}_post_Nx3x${H}x${W}.onnx + +BOXES=57960 +H=736 +W=1280 +snc4onnx \ +--input_onnx_file_paths ${MODEL}_Nx3x${H}x${W}.onnx 31_nms_gold_yolo_m_hand_N_${BOXES}.onnx \ +--srcop_destop output predictions \ +--output_onnx_file_path ${MODEL}_post_Nx3x${H}x${W}.onnx +onnxsim ${MODEL}_post_Nx3x${H}x${W}.onnx ${MODEL}_post_Nx3x${H}x${W}.onnx +onnxsim ${MODEL}_post_Nx3x${H}x${W}.onnx ${MODEL}_post_Nx3x${H}x${W}.onnx +``` diff --git a/421_Gold-YOLO-Head/post_process_gen_tools/boxes_listup.py b/421_Gold-YOLO-Head/post_process_gen_tools/boxes_listup.py new file mode 100644 index 0000000000..4d6be5b33f --- /dev/null +++ b/421_Gold-YOLO-Head/post_process_gen_tools/boxes_listup.py @@ -0,0 +1,54 @@ +import onnxruntime as ort +from collections import OrderedDict + +MODELS = OrderedDict( + { + "01": "gold_yolo_n_hand_0423_0.2295_1x3x192x320.onnx", + "02": "gold_yolo_n_hand_0423_0.2295_1x3x192x416.onnx", + "03": "gold_yolo_n_hand_0423_0.2295_1x3x192x640.onnx", + "04": "gold_yolo_n_hand_0423_0.2295_1x3x192x800.onnx", + "05": "gold_yolo_n_hand_0423_0.2295_1x3x256x320.onnx", + "06": "gold_yolo_n_hand_0423_0.2295_1x3x256x416.onnx", + "07": "gold_yolo_n_hand_0423_0.2295_1x3x256x640.onnx", + "08": "gold_yolo_n_hand_0423_0.2295_1x3x256x800.onnx", + "09": "gold_yolo_n_hand_0423_0.2295_1x3x256x960.onnx", + "10": "gold_yolo_n_hand_0423_0.2295_1x3x288x1280.onnx", + "11": "gold_yolo_n_hand_0423_0.2295_1x3x288x480.onnx", + "12": "gold_yolo_n_hand_0423_0.2295_1x3x288x640.onnx", + "13": "gold_yolo_n_hand_0423_0.2295_1x3x288x800.onnx", + "14": "gold_yolo_n_hand_0423_0.2295_1x3x288x960.onnx", + "15": "gold_yolo_n_hand_0423_0.2295_1x3x320x320.onnx", + "16": "gold_yolo_n_hand_0423_0.2295_1x3x384x1280.onnx", + "17": "gold_yolo_n_hand_0423_0.2295_1x3x384x480.onnx", + "18": "gold_yolo_n_hand_0423_0.2295_1x3x384x640.onnx", + "19": "gold_yolo_n_hand_0423_0.2295_1x3x384x800.onnx", + "20": "gold_yolo_n_hand_0423_0.2295_1x3x384x960.onnx", + "21": "gold_yolo_n_hand_0423_0.2295_1x3x416x416.onnx", + "22": "gold_yolo_n_hand_0423_0.2295_1x3x480x1280.onnx", + "23": "gold_yolo_n_hand_0423_0.2295_1x3x480x640.onnx", + "24": "gold_yolo_n_hand_0423_0.2295_1x3x480x800.onnx", + "25": "gold_yolo_n_hand_0423_0.2295_1x3x480x960.onnx", + "26": "gold_yolo_n_hand_0423_0.2295_1x3x512x512.onnx", + "27": "gold_yolo_n_hand_0423_0.2295_1x3x512x640.onnx", + "28": "gold_yolo_n_hand_0423_0.2295_1x3x512x896.onnx", + "29": "gold_yolo_n_hand_0423_0.2295_1x3x544x1280.onnx", + "30": "gold_yolo_n_hand_0423_0.2295_1x3x544x800.onnx", + "31": "gold_yolo_n_hand_0423_0.2295_1x3x544x960.onnx", + "32": "gold_yolo_n_hand_0423_0.2295_1x3x640x640.onnx", + "33": "gold_yolo_n_hand_0423_0.2295_1x3x736x1280.onnx", + } +) + +box_sizes = [] +for k, v in MODELS.items(): + onnx_session = ort.InferenceSession( + path_or_bytes=v, + providers=['CPUExecutionProvider'], + ) + box_sizes.append([onnx_session.get_inputs()[0].shape[2], onnx_session.get_inputs()[0].shape[3], onnx_session.get_outputs()[0].shape[1]]) + +print(f'MODELS count: {len(MODELS)}') +print(f'BOX_SIZE count: {len(box_sizes)}') + +for box_size in box_sizes: + print(f'"{box_size[0]} {box_size[1]} {box_size[2]}"') \ No newline at end of file diff --git a/421_Gold-YOLO-Head/post_process_gen_tools/convert_script.sh b/421_Gold-YOLO-Head/post_process_gen_tools/convert_script.sh new file mode 100755 index 0000000000..17d43dd17e --- /dev/null +++ b/421_Gold-YOLO-Head/post_process_gen_tools/convert_script.sh @@ -0,0 +1,484 @@ +# pip install -U pip \ +# && pip install onnxsim==0.4.33 \ +# && pip install -U simple-onnx-processing-tools \ +# && pip install -U onnx \ +# && python3 -m pip install -U onnx_graphsurgeon --index-url https://pypi.ngc.nvidia.com + +MODEL_NAME=gold_yolo_n_hand +SUFFIX="0333_0.4040_1x3x" +OPSET=11 +BATCHES=1 +CLASSES=1 + +RESOLUTIONS=( + "192 320 1260" + "192 416 1638" + "192 640 2520" + "192 800 3150" + "256 320 1680" + "256 416 2184" + "256 640 3360" + "256 800 4200" + "256 960 5040" + "288 1280 7560" + "288 480 2835" + "288 640 3780" + "288 800 4725" + "288 960 5670" + "320 320 2100" + "384 1280 10080" + "384 480 3780" + "384 640 5040" + "384 800 6300" + "384 960 7560" + "416 416 3549" + "480 1280 12600" + "480 640 6300" + "480 800 7875" + "480 960 9450" + "512 512 5376" + "512 640 6720" + "512 896 9408" + "544 1280 14280" + "544 800 8925" + "544 960 10710" + "640 640 8400" + "736 1280 19320" +) + +for((i=0; i<${#RESOLUTIONS[@]}; i++)) +do + RESOLUTION=(`echo ${RESOLUTIONS[i]}`) + H=${RESOLUTION[0]} + W=${RESOLUTION[1]} + BOXES=${RESOLUTION[2]} + + ################################################### Boxes + Scores + python make_boxes_scores.py -o ${OPSET} -b ${BATCHES} -x ${BOXES} -c ${CLASSES} + python make_cxcywh_y1x1y2x2.py -o ${OPSET} -b ${BATCHES} -x ${BOXES} + + sor4onnx \ + --input_onnx_file_path 01_boxes_scores_${BOXES}.onnx \ + --old_new "/Constant" "boxes_scores_Constant" \ + --mode full \ + --search_mode prefix_match \ + --output_onnx_file_path 01_boxes_scores_${BOXES}.onnx + + sor4onnx \ + --input_onnx_file_path 02_cxcywh_y1x1y2x2_${BOXES}.onnx \ + --old_new "/Constant" "cxcywh_y1x1y2x2_Constant" \ + --mode full \ + --search_mode prefix_match \ + --output_onnx_file_path 02_cxcywh_y1x1y2x2_${BOXES}.onnx + + sor4onnx \ + --input_onnx_file_path 02_cxcywh_y1x1y2x2_${BOXES}.onnx \ + --old_new "/Slice" "cxcywh_y1x1y2x2_Slice" \ + --mode full \ + --search_mode prefix_match \ + --output_onnx_file_path 02_cxcywh_y1x1y2x2_${BOXES}.onnx + + snc4onnx \ + --input_onnx_file_paths 01_boxes_scores_${BOXES}.onnx 02_cxcywh_y1x1y2x2_${BOXES}.onnx \ + --srcop_destop boxes_cxcywh cxcywh \ + --output_onnx_file_path 03_boxes_y1x1y2x2_scores_${BOXES}.onnx + + + ################################################### NonMaxSuppression + sog4onnx \ + --op_type Constant \ + --opset ${OPSET} \ + --op_name max_output_boxes_per_class_const \ + --output_variables max_output_boxes_per_class int64 [1] \ + --attributes value int64 [20] \ + --output_onnx_file_path 04_Constant_max_output_boxes_per_class.onnx + + # N: iou_threshold_const=0.40, score_threshold_const=0.25 + # M: iou_threshold_const=0.40, score_threshold_const=0.25 + + sog4onnx \ + --op_type Constant \ + --opset ${OPSET} \ + --op_name iou_threshold_const \ + --output_variables iou_threshold float32 [1] \ + --attributes value float32 [0.40] \ + --output_onnx_file_path 05_Constant_iou_threshold.onnx + + sog4onnx \ + --op_type Constant \ + --opset ${OPSET} \ + --op_name score_threshold_const \ + --output_variables score_threshold float32 [1] \ + --attributes value float32 [0.25] \ + --output_onnx_file_path 06_Constant_score_threshold.onnx + + + OP=NonMaxSuppression + LOWEROP=${OP,,} + sog4onnx \ + --op_type ${OP} \ + --opset ${OPSET} \ + --op_name ${LOWEROP}${OPSET} \ + --input_variables boxes_var float32 [${BATCHES},${BOXES},4] \ + --input_variables scores_var float32 [${BATCHES},${CLASSES},${BOXES}] \ + --input_variables max_output_boxes_per_class_var int64 [1] \ + --input_variables iou_threshold_var float32 [1] \ + --input_variables score_threshold_var float32 [1] \ + --output_variables selected_indices int64 [\'N\',3] \ + --attributes center_point_box int64 0 \ + --output_onnx_file_path 07_${OP}${OPSET}.onnx + + + snc4onnx \ + --input_onnx_file_paths 04_Constant_max_output_boxes_per_class.onnx 07_${OP}${OPSET}.onnx \ + --srcop_destop max_output_boxes_per_class max_output_boxes_per_class_var \ + --output_onnx_file_path 07_${OP}${OPSET}.onnx + + snc4onnx \ + --input_onnx_file_paths 05_Constant_iou_threshold.onnx 07_${OP}${OPSET}.onnx \ + --srcop_destop iou_threshold iou_threshold_var \ + --output_onnx_file_path 07_${OP}${OPSET}.onnx + + snc4onnx \ + --input_onnx_file_paths 06_Constant_score_threshold.onnx 07_${OP}${OPSET}.onnx \ + --srcop_destop score_threshold score_threshold_var \ + --output_onnx_file_path 07_${OP}${OPSET}.onnx + + soc4onnx \ + --input_onnx_file_path 07_${OP}${OPSET}.onnx \ + --output_onnx_file_path 07_${OP}${OPSET}.onnx \ + --opset ${OPSET} + + + ################################################### Boxes + Scores + NonMaxSuppression + snc4onnx \ + --input_onnx_file_paths 03_boxes_y1x1y2x2_scores_${BOXES}.onnx 07_${OP}${OPSET}.onnx \ + --srcop_destop scores scores_var y1x1y2x2 boxes_var \ + --output_onnx_file_path 08_nms_${MODEL_NAME}_${BOXES}.onnx + + + ################################################### Myriad workaround Mul + OP=Mul + LOWEROP=${OP,,} + OPSET=${OPSET} + sog4onnx \ + --op_type ${OP} \ + --opset ${OPSET} \ + --op_name ${LOWEROP}${OPSET} \ + --input_variables workaround_mul_a int64 [\'N\',3] \ + --input_variables workaround_mul_b int64 [1] \ + --output_variables workaround_mul_out int64 [\'N\',3] \ + --output_onnx_file_path 09_${OP}${OPSET}_workaround.onnx + + ############ Myriad workaround Constant + sog4onnx \ + --op_type Constant \ + --opset ${OPSET} \ + --op_name workaround_mul_const_op \ + --output_variables workaround_mul_const int64 [1] \ + --attributes value int64 [1] \ + --output_onnx_file_path 10_Constant_workaround_mul.onnx + + ############ Myriad workaround Mul + Myriad workaround Constant + snc4onnx \ + --input_onnx_file_paths 10_Constant_workaround_mul.onnx 09_${OP}${OPSET}_workaround.onnx \ + --srcop_destop workaround_mul_const workaround_mul_b \ + --output_onnx_file_path 09_${OP}${OPSET}_workaround.onnx + + + + ################################################### NonMaxSuppression + Myriad workaround Mul + snc4onnx \ + --input_onnx_file_paths 08_nms_${MODEL_NAME}_${BOXES}.onnx 09_${OP}${OPSET}_workaround.onnx \ + --srcop_destop selected_indices workaround_mul_a \ + --output_onnx_file_path 11_nms_${MODEL_NAME}_${BOXES}.onnx \ + --disable_onnxsim + + ################################################### N batch NMS + sbi4onnx \ + --input_onnx_file_path 11_nms_${MODEL_NAME}_${BOXES}.onnx \ + --output_onnx_file_path 12_nms_${MODEL_NAME}_${BOXES}_batch.onnx \ + --initialization_character_string batch + + sio4onnx \ + --input_onnx_file_path 12_nms_${MODEL_NAME}_${BOXES}_batch.onnx \ + --output_onnx_file_path 12_nms_${MODEL_NAME}_${BOXES}_batch.onnx \ + --input_names "predictions" \ + --input_shapes "batch" ${BOXES} $((CLASSES+5)) \ + --output_names "x1y1x2y2" \ + --output_names "workaround_mul_out" \ + --output_shapes "batch" ${BOXES} 4 \ + --output_shapes "N" 3 + + + + + ################################################### Score GatherND + python make_score_gather_nd.py -b ${BATCHES} -x ${BOXES} -c ${CLASSES} + + python -m tf2onnx.convert \ + --opset ${OPSET} \ + --tflite saved_model_postprocess/nms_score_gather_nd.tflite \ + --output 13_nms_score_gather_nd.onnx + + sor4onnx \ + --input_onnx_file_path 13_nms_score_gather_nd.onnx \ + --old_new ":0" "" \ + --mode full \ + --search_mode partial_match \ + --output_onnx_file_path 13_nms_score_gather_nd.onnx + + sor4onnx \ + --input_onnx_file_path 13_nms_score_gather_nd.onnx \ + --old_new "serving_default_input_1" "gn_scores" \ + --output_onnx_file_path 13_nms_score_gather_nd.onnx \ + --mode inputs + + sor4onnx \ + --input_onnx_file_path 13_nms_score_gather_nd.onnx \ + --old_new "serving_default_input_2" "gn_selected_indices" \ + --output_onnx_file_path 13_nms_score_gather_nd.onnx \ + --mode inputs + + sor4onnx \ + --input_onnx_file_path 13_nms_score_gather_nd.onnx \ + --old_new "PartitionedCall" "final_scores" \ + --output_onnx_file_path 13_nms_score_gather_nd.onnx \ + --mode outputs + + python make_input_output_shape_update.py \ + --input_onnx_file_path 13_nms_score_gather_nd.onnx \ + --output_onnx_file_path 13_nms_score_gather_nd.onnx \ + --input_names gn_scores \ + --input_names gn_selected_indices \ + --input_shapes ${BATCHES} ${CLASSES} ${BOXES} \ + --input_shapes N 3 \ + --output_names final_scores \ + --output_shapes N 1 + + onnxsim 13_nms_score_gather_nd.onnx 13_nms_score_gather_nd.onnx + onnxsim 13_nms_score_gather_nd.onnx 13_nms_score_gather_nd.onnx + + sio4onnx \ + --input_onnx_file_path 13_nms_score_gather_nd.onnx \ + --output_onnx_file_path 14_nms_score_gather_nd_batch.onnx \ + --input_names "gn_scores" \ + --input_names "gn_selected_indices" \ + --input_shapes "batch" ${CLASSES} ${BOXES} \ + --input_shapes "N" 3 \ + --output_names "final_scores" \ + --output_shapes "N" 1 + + + ################################################### NonMaxSuppression + Score GatherND + snc4onnx \ + --input_onnx_file_paths 11_nms_${MODEL_NAME}_${BOXES}.onnx 13_nms_score_gather_nd.onnx \ + --srcop_destop scores gn_scores workaround_mul_out gn_selected_indices \ + --output_onnx_file_path 15_nms_${MODEL_NAME}_${BOXES}_nd.onnx + + onnxsim 15_nms_${MODEL_NAME}_${BOXES}_nd.onnx 15_nms_${MODEL_NAME}_${BOXES}_nd.onnx + onnxsim 15_nms_${MODEL_NAME}_${BOXES}_nd.onnx 15_nms_${MODEL_NAME}_${BOXES}_nd.onnx + + + snc4onnx \ + --input_onnx_file_paths 12_nms_${MODEL_NAME}_${BOXES}_batch.onnx 14_nms_score_gather_nd_batch.onnx \ + --srcop_destop scores gn_scores workaround_mul_out gn_selected_indices \ + --output_onnx_file_path 16_nms_${MODEL_NAME}_${BOXES}_nd_batch.onnx + + onnxsim 16_nms_${MODEL_NAME}_${BOXES}_nd_batch.onnx 16_nms_${MODEL_NAME}_${BOXES}_nd_batch.onnx + onnxsim 16_nms_${MODEL_NAME}_${BOXES}_nd_batch.onnx 16_nms_${MODEL_NAME}_${BOXES}_nd_batch.onnx + + + + + + + + ################################################### Final Batch Nums + python make_final_batch_nums_final_class_nums_final_box_nums.py + + + ################################################### Boxes GatherND + python make_box_gather_nd.py + + python -m tf2onnx.convert \ + --opset ${OPSET} \ + --tflite saved_model_postprocess/nms_box_gather_nd.tflite \ + --output 18_nms_box_gather_nd.onnx + + sor4onnx \ + --input_onnx_file_path 18_nms_box_gather_nd.onnx \ + --old_new ":0" "" \ + --mode full \ + --search_mode partial_match \ + --output_onnx_file_path 18_nms_box_gather_nd.onnx + + sor4onnx \ + --input_onnx_file_path 18_nms_box_gather_nd.onnx \ + --old_new "serving_default_input_1" "gn_boxes" \ + --output_onnx_file_path 18_nms_box_gather_nd.onnx \ + --mode inputs + + sor4onnx \ + --input_onnx_file_path 18_nms_box_gather_nd.onnx \ + --old_new "serving_default_input_2" "gn_box_selected_indices" \ + --output_onnx_file_path 18_nms_box_gather_nd.onnx \ + --mode inputs + + sor4onnx \ + --input_onnx_file_path 18_nms_box_gather_nd.onnx \ + --old_new "PartitionedCall" "final_boxes" \ + --output_onnx_file_path 18_nms_box_gather_nd.onnx \ + --mode outputs + + python make_input_output_shape_update.py \ + --input_onnx_file_path 18_nms_box_gather_nd.onnx \ + --output_onnx_file_path 18_nms_box_gather_nd.onnx \ + --input_names gn_boxes \ + --input_names gn_box_selected_indices \ + --input_shapes ${BATCHES} ${BOXES} 4 \ + --input_shapes N 2 \ + --output_names final_boxes \ + --output_shapes N 4 + + onnxsim 18_nms_box_gather_nd.onnx 18_nms_box_gather_nd.onnx + onnxsim 18_nms_box_gather_nd.onnx 18_nms_box_gather_nd.onnx + + sio4onnx \ + --input_onnx_file_path 18_nms_box_gather_nd.onnx \ + --output_onnx_file_path 19_nms_box_gather_nd_batch.onnx \ + --input_names "gn_boxes" \ + --input_names "gn_box_selected_indices" \ + --input_shapes "batch" ${BOXES} 4 \ + --input_shapes "N" 2 \ + --output_names "final_boxes" \ + --output_shapes "N" 4 + + + ################################################### nms_${MODEL_NAME}_xxx_nd + nms_final_batch_nums_final_class_nums_final_box_nums + snc4onnx \ + --input_onnx_file_paths 15_nms_${MODEL_NAME}_${BOXES}_nd.onnx 17_nms_final_batch_nums_final_class_nums_final_box_nums.onnx \ + --srcop_destop selected_indices bc_input \ + --op_prefixes_after_merging main01 sub01 \ + --output_onnx_file_path 20_nms_${MODEL_NAME}_${BOXES}_split.onnx + + snc4onnx \ + --input_onnx_file_paths 16_nms_${MODEL_NAME}_${BOXES}_nd_batch.onnx 17_nms_final_batch_nums_final_class_nums_final_box_nums.onnx \ + --srcop_destop workaround_mul_out bc_input \ + --op_prefixes_after_merging main01 sub01 \ + --output_onnx_file_path 21_nms_${MODEL_NAME}_${BOXES}_split_batch.onnx + + + + ################################################### nms_${MODEL_NAME}_${BOXES}_split + nms_box_gather_nd + snc4onnx \ + --input_onnx_file_paths 20_nms_${MODEL_NAME}_${BOXES}_split.onnx 18_nms_box_gather_nd.onnx \ + --srcop_destop x1y1x2y2 gn_boxes final_box_nums gn_box_selected_indices \ + --output_onnx_file_path 22_nms_${MODEL_NAME}_${BOXES}_merged.onnx + + onnxsim 22_nms_${MODEL_NAME}_${BOXES}_merged.onnx 22_nms_${MODEL_NAME}_${BOXES}_merged.onnx + onnxsim 22_nms_${MODEL_NAME}_${BOXES}_merged.onnx 22_nms_${MODEL_NAME}_${BOXES}_merged.onnx + + + snc4onnx \ + --input_onnx_file_paths 21_nms_${MODEL_NAME}_${BOXES}_split_batch.onnx 19_nms_box_gather_nd_batch.onnx \ + --srcop_destop x1y1x2y2 gn_boxes final_box_nums gn_box_selected_indices \ + --output_onnx_file_path 23_nms_${MODEL_NAME}_${BOXES}_merged_batch.onnx + + onnxsim 23_nms_${MODEL_NAME}_${BOXES}_merged_batch.onnx 23_nms_${MODEL_NAME}_${BOXES}_merged_batch.onnx + onnxsim 23_nms_${MODEL_NAME}_${BOXES}_merged_batch.onnx 23_nms_${MODEL_NAME}_${BOXES}_merged_batch.onnx + + + + + + ################################################### nms output op name Cleaning + sor4onnx \ + --input_onnx_file_path 22_nms_${MODEL_NAME}_${BOXES}_merged.onnx \ + --old_new "main01_final_scores" "final_scores" \ + --output_onnx_file_path 22_nms_${MODEL_NAME}_${BOXES}_merged.onnx \ + --mode outputs + + sor4onnx \ + --input_onnx_file_path 22_nms_${MODEL_NAME}_${BOXES}_merged.onnx \ + --old_new "sub01_final_batch_nums" "final_batch_nums" \ + --output_onnx_file_path 22_nms_${MODEL_NAME}_${BOXES}_merged.onnx \ + --mode outputs + + sor4onnx \ + --input_onnx_file_path 22_nms_${MODEL_NAME}_${BOXES}_merged.onnx \ + --old_new "sub01_final_class_nums" "final_class_nums" \ + --output_onnx_file_path 22_nms_${MODEL_NAME}_${BOXES}_merged.onnx \ + --mode outputs + + + sor4onnx \ + --input_onnx_file_path 23_nms_${MODEL_NAME}_${BOXES}_merged_batch.onnx \ + --old_new "main01_final_scores" "final_scores" \ + --output_onnx_file_path 23_nms_${MODEL_NAME}_${BOXES}_merged_batch.onnx \ + --mode outputs + + sor4onnx \ + --input_onnx_file_path 23_nms_${MODEL_NAME}_${BOXES}_merged_batch.onnx \ + --old_new "sub01_final_batch_nums" "final_batch_nums" \ + --output_onnx_file_path 23_nms_${MODEL_NAME}_${BOXES}_merged_batch.onnx \ + --mode outputs + + sor4onnx \ + --input_onnx_file_path 23_nms_${MODEL_NAME}_${BOXES}_merged_batch.onnx \ + --old_new "sub01_final_class_nums" "final_class_nums" \ + --output_onnx_file_path 23_nms_${MODEL_NAME}_${BOXES}_merged_batch.onnx \ + --mode outputs + + + + + + + + ################################################### nms output merge + python make_nms_outputs_merge.py + + onnxsim 24_nms_batchno_classid_x1y1x2y2_score_cat.onnx 24_nms_batchno_classid_x1y1x2y2_score_cat.onnx + + + ################################################### merge + snc4onnx \ + --input_onnx_file_paths 22_nms_${MODEL_NAME}_${BOXES}_merged.onnx 24_nms_batchno_classid_x1y1x2y2_score_cat.onnx \ + --srcop_destop final_batch_nums cat_batch final_class_nums cat_classid final_boxes cat_x1y1x2y2 final_scores cat_score \ + --output_onnx_file_path 30_nms_${MODEL_NAME}_${BOXES}.onnx + + sor4onnx \ + --input_onnx_file_path 30_nms_${MODEL_NAME}_${BOXES}.onnx \ + --old_new "final_scores" "score" \ + --output_onnx_file_path 30_nms_${MODEL_NAME}_${BOXES}.onnx \ + --mode outputs + + + + ################################################### merge + snc4onnx \ + --input_onnx_file_paths 23_nms_${MODEL_NAME}_${BOXES}_merged_batch.onnx 24_nms_batchno_classid_x1y1x2y2_score_cat.onnx \ + --srcop_destop final_batch_nums cat_batch final_class_nums cat_classid final_boxes cat_x1y1x2y2 final_scores cat_score \ + --output_onnx_file_path 31_nms_${MODEL_NAME}_N_${BOXES}.onnx + + sor4onnx \ + --input_onnx_file_path 31_nms_${MODEL_NAME}_N_${BOXES}.onnx \ + --old_new "final_scores" "score" \ + --output_onnx_file_path 31_nms_${MODEL_NAME}_N_${BOXES}.onnx \ + --mode outputs + + # ################################################### Cleaning + rm 0*.onnx + rm 1*.onnx + rm 2*.onnx + + + ################################################### ${MODEL_NAME} + Post-Process + snc4onnx \ + --input_onnx_file_paths ${MODEL_NAME}_${SUFFIX}${H}x${W}.onnx 30_nms_${MODEL_NAME}_${BOXES}.onnx \ + --srcop_destop output predictions \ + --output_onnx_file_path ${MODEL_NAME}_post_${SUFFIX}${H}x${W}.onnx + onnxsim ${MODEL_NAME}_post_${SUFFIX}${H}x${W}.onnx ${MODEL_NAME}_post_${SUFFIX}${H}x${W}.onnx + onnxsim ${MODEL_NAME}_post_${SUFFIX}${H}x${W}.onnx ${MODEL_NAME}_post_${SUFFIX}${H}x${W}.onnx +done diff --git a/421_Gold-YOLO-Head/post_process_gen_tools/demo_goldyolo_onnx.py b/421_Gold-YOLO-Head/post_process_gen_tools/demo_goldyolo_onnx.py new file mode 100644 index 0000000000..901db733c2 --- /dev/null +++ b/421_Gold-YOLO-Head/post_process_gen_tools/demo_goldyolo_onnx.py @@ -0,0 +1,340 @@ +#!/usr/bin/env python + +import copy +import cv2 +import time +import numpy as np +import onnxruntime +from argparse import ArgumentParser +from typing import Tuple, Optional, List + + +class GoldYOLOONNX(object): + def __init__( + self, + model_path: Optional[str] = 'gold_yolo_m_hand_post_0465_0.2501_1x3x480x640.onnx', + class_score_th: Optional[float] = 0.40, + providers: Optional[List] = [ + ( + 'TensorrtExecutionProvider', { + 'trt_engine_cache_enable': True, + 'trt_engine_cache_path': '.', + 'trt_fp16_enable': True, + } + ), + 'CUDAExecutionProvider', + 'CPUExecutionProvider', + ], + ): + """YOLOv7ONNX + + Parameters + ---------- + model_path: Optional[str] + ONNX file path for YOLOv7 + + class_score_th: Optional[float] + Score threshold. Default: 0.25 + + providers: Optional[List] + Name of onnx execution providers + Default: + [ + ( + 'TensorrtExecutionProvider', { + 'trt_engine_cache_enable': True, + 'trt_engine_cache_path': '.', + 'trt_fp16_enable': True, + } + ), + 'CUDAExecutionProvider', + 'CPUExecutionProvider', + ] + """ + # Threshold + self.class_score_th = class_score_th + + # Model loading + session_option = onnxruntime.SessionOptions() + session_option.log_severity_level = 3 + self.onnx_session = onnxruntime.InferenceSession( + model_path, + sess_options=session_option, + providers=providers, + ) + self.providers = self.onnx_session.get_providers() + + self.input_shapes = [ + input.shape for input in self.onnx_session.get_inputs() + ] + self.input_names = [ + input.name for input in self.onnx_session.get_inputs() + ] + self.output_names = [ + output.name for output in self.onnx_session.get_outputs() + ] + + + def __call__( + self, + image: np.ndarray, + ) -> Tuple[np.ndarray, np.ndarray]: + """YOLOv7ONNX + + Parameters + ---------- + image: np.ndarray + Entire image + + Returns + ------- + boxes: np.ndarray + Predicted boxes: [N, y1, x1, y2, x2] + + scores: np.ndarray + Predicted box scores: [N, score] + """ + temp_image = copy.deepcopy(image) + + # PreProcess + resized_image = self.__preprocess( + temp_image, + ) + + # Inference + inferece_image = np.asarray([resized_image], dtype=np.float32) + boxes = self.onnx_session.run( + self.output_names, + {input_name: inferece_image for input_name in self.input_names}, + )[0] + + # PostProcess + result_boxes, result_scores = \ + self.__postprocess( + image=temp_image, + boxes=boxes, + ) + + return result_boxes, result_scores + + + def __preprocess( + self, + image: np.ndarray, + swap: Optional[Tuple[int,int,int]] = (2, 0, 1), + ) -> np.ndarray: + """__preprocess + + Parameters + ---------- + image: np.ndarray + Entire image + + swap: tuple + HWC to CHW: (2,0,1) + CHW to HWC: (1,2,0) + HWC to HWC: (0,1,2) + CHW to CHW: (0,1,2) + + Returns + ------- + resized_image: np.ndarray + Resized and normalized image. + """ + # Normalization + BGR->RGB + resized_image = cv2.resize( + image, + ( + int(self.input_shapes[0][3]), + int(self.input_shapes[0][2]), + ) + ) + resized_image = np.divide(resized_image, 255.0) + resized_image = resized_image[..., ::-1] + resized_image = resized_image.transpose(swap) + resized_image = np.ascontiguousarray( + resized_image, + dtype=np.float32, + ) + return resized_image + + + def __postprocess( + self, + image: np.ndarray, + boxes: np.ndarray, + ) -> Tuple[np.ndarray, np.ndarray]: + """__postprocess + + Parameters + ---------- + image: np.ndarray + Entire image. + + boxes: np.ndarray + float32[N, 7] + + Returns + ------- + result_boxes: np.ndarray + Predicted boxes: [N, y1, x1, y2, x2] + + result_scores: np.ndarray + Predicted box confs: [N, score] + """ + image_height = image.shape[0] + image_width = image.shape[1] + + """ + Detector is + N -> Number of boxes detected + batchno -> always 0: BatchNo.0 + + batchno_classid_y1x1y2x2_score: float32[N,7] + """ + result_boxes = [] + result_scores = [] + if len(boxes) > 0: + scores = boxes[:, 6:7] + keep_idxs = scores[:, 0] > self.class_score_th + scores_keep = scores[keep_idxs, :] + boxes_keep = boxes[keep_idxs, :] + + if len(boxes_keep) > 0: + for box, score in zip(boxes_keep, scores_keep): + x_min = max(int(box[2]), 0) + y_min = max(int(box[3]), 0) + x_max = min(int(box[4]), image_width) + y_max = min(int(box[5]), image_height) + + result_boxes.append( + [x_min, y_min, x_max, y_max] + ) + result_scores.append( + score + ) + + return np.asarray(result_boxes), np.asarray(result_scores) + + +def main(): + parser = ArgumentParser() + parser.add_argument( + '-m', + '--model', + type=str, + default='gold_yolo_m_hand_post_0465_0.2501_1x3x480x640.onnx', + ) + parser.add_argument( + '-v', + '--video', + type=int, + default=0, + ) + args = parser.parse_args() + + model = GoldYOLOONNX( + model_path=args.model, + ) + + cap = cv2.VideoCapture(args.video) + cap_fps = cap.get(cv2.CAP_PROP_FPS) + w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v') + video_writer = cv2.VideoWriter( + filename='output.mp4', + fourcc=fourcc, + fps=cap_fps, + frameSize=(w, h), + ) + + while cap.isOpened(): + res, image = cap.read() + if not res: + break + + debug_image = copy.deepcopy(image) + + start_time = time.time() + boxes, scores = model(debug_image) + elapsed_time = time.time() - start_time + fps = 1 / elapsed_time + cv2.putText( + debug_image, + f'{fps:.1f} FPS (inferece + post-process)', + (10, 30), + cv2.FONT_HERSHEY_SIMPLEX, + 0.7, + (255, 255, 255), + 2, + cv2.LINE_AA, + ) + cv2.putText( + debug_image, + f'{fps:.1f} FPS (inferece + post-process)', + (10, 30), + cv2.FONT_HERSHEY_SIMPLEX, + 0.7, + (0, 0, 255), + 1, + cv2.LINE_AA, + ) + + for box, score in zip(boxes, scores): + cv2.rectangle( + debug_image, + (box[0], box[1]), + (box[2], box[3]), + (255,255,255), + 2, + ) + cv2.rectangle( + debug_image, + (box[0], box[1]), + (box[2], box[3]), + (0,0,255), + 1, + ) + cv2.putText( + debug_image, + f'{score[0]:.2f}', + ( + box[0], + box[1]-10 if box[1]-10 > 0 else 10 + ), + cv2.FONT_HERSHEY_SIMPLEX, + 0.7, + (255, 255, 255), + 2, + cv2.LINE_AA, + ) + cv2.putText( + debug_image, + f'{score[0]:.2f}', + ( + box[0], + box[1]-10 if box[1]-10 > 0 else 10 + ), + cv2.FONT_HERSHEY_SIMPLEX, + 0.7, + (0, 0, 255), + 1, + cv2.LINE_AA, + ) + + key = cv2.waitKey(1) + if key == 27: # ESC + break + + cv2.imshow("test", debug_image) + video_writer.write(debug_image) + + if video_writer: + video_writer.release() + + if cap: + cap.release() + +if __name__ == "__main__": + main() diff --git a/421_Gold-YOLO-Head/post_process_gen_tools/make_batch_initialize.py b/421_Gold-YOLO-Head/post_process_gen_tools/make_batch_initialize.py new file mode 100644 index 0000000000..50c6ddc6d8 --- /dev/null +++ b/421_Gold-YOLO-Head/post_process_gen_tools/make_batch_initialize.py @@ -0,0 +1,209 @@ +#! /usr/bin/env python + +import sys +import onnx +import onnx_graphsurgeon as gs +from typing import Optional +import struct +from argparse import ArgumentParser +from onnxsim import simplify + +class Color: + BLACK = '\033[30m' + RED = '\033[31m' + GREEN = '\033[32m' + YELLOW = '\033[33m' + BLUE = '\033[34m' + MAGENTA = '\033[35m' + CYAN = '\033[36m' + WHITE = '\033[37m' + COLOR_DEFAULT = '\033[39m' + BOLD = '\033[1m' + UNDERLINE = '\033[4m' + INVISIBLE = '\033[08m' + REVERCE = '\033[07m' + BG_BLACK = '\033[40m' + BG_RED = '\033[41m' + BG_GREEN = '\033[42m' + BG_YELLOW = '\033[43m' + BG_BLUE = '\033[44m' + BG_MAGENTA = '\033[45m' + BG_CYAN = '\033[46m' + BG_WHITE = '\033[47m' + BG_DEFAULT = '\033[49m' + RESET = '\033[0m' + + +TARGET_INPUTS = [ + 'predictions', +] + +TARGET_VALUE_INFO = [ + 'main01_boxes_cxcywh', + 'main01_onnx::Mul_15', + 'main01_onnx::Mul_10', + + 'main01_onnx::Div_28', + 'main01_onnx::Add_30', + 'main01_onnx::Add_26', + 'main01_onnx::Unsqueeze_31', + 'main01_onnx::Concat_32', + + 'main01_onnx::Div_20', + 'main01_onnx::Add_22', + 'main01_onnx::Add_18', + 'main01_onnx::Unsqueeze_23', + 'main01_onnx::Concat_24', + + 'main01_onnx::Div_12', + 'main01_onnx::Sub_14', + 'main01_onnx::Sub_10', + 'main01_onnx::Unsqueeze_15', + 'main01_onnx::Concat_16', + + 'main01_onnx::Div_4', + 'main01_onnx::Sub_6', + 'main01_onnx::Sub_2', + 'main01_onnx::Unsqueeze_7', + 'main01_onnx::Concat_8', + + 'main01_y1x1y2x2', + 'main01_onnx::Transpose_16', + 'main01_scores', +] + +def initialize( + input_onnx_file_path: Optional[str] = '', + onnx_graph: Optional[onnx.ModelProto] = None, + output_onnx_file_path: Optional[str] = '', + initialization_character_string: Optional[str] = 'batch', + non_verbose: Optional[bool] = False, +) -> onnx.ModelProto: + """ + Parameters + ---------- + input_onnx_file_path: Optional[str] + Input onnx file path.\n\ + Either input_onnx_file_path or onnx_graph must be specified.\n\ + Default: '' + onnx_graph: Optional[onnx.ModelProto] + onnx.ModelProto.\n\ + Either input_onnx_file_path or onnx_graph must be specified.\n\ + onnx_graph If specified, ignore input_onnx_file_path and process onnx_graph. + output_onnx_file_path: Optional[str] + Output onnx file path. If not specified, no ONNX file is output.\n\ + Default: '' + initialization_character_string: Optional[str] + String to initialize batch size. "-1" or "N" or "xxx", etc...\n + Default: 'batch' + non_verbose: Optional[bool] + Do not show all information logs. Only error logs are displayed.\n\ + Default: False + Returns + ------- + changed_graph: onnx.ModelProto + Changed onnx ModelProto. + """ + + # Unspecified check for input_onnx_file_path and onnx_graph + if not input_onnx_file_path and not onnx_graph: + print( + f'{Color.RED}ERROR:{Color.RESET} '+ + f'One of input_onnx_file_path or onnx_graph must be specified.' + ) + sys.exit(1) + + if not initialization_character_string: + print( + f'{Color.RED}ERROR:{Color.RESET} '+ + f'The initialization_character_string cannot be empty.' + ) + sys.exit(1) + + # Loading Graphs + # onnx_graph If specified, onnx_graph is processed first + if not onnx_graph: + onnx_graph = onnx.load(input_onnx_file_path) + try: + onnx_graph, _ = simplify(onnx_graph) + except: + pass + graph = gs.import_onnx(onnx_graph) + graph.cleanup().toposort() + target_model = gs.export_onnx(graph) + target_graph = target_model.graph + + for node in target_graph.input: + if node.name in TARGET_INPUTS: + if len(node.type.tensor_type.shape.dim)>0: + node.type.tensor_type.shape.dim[0].dim_param = initialization_character_string + + target_value_info = [value_info for value_info in target_graph.value_info if value_info.name in TARGET_VALUE_INFO] + for tensor in target_value_info: + if len(tensor.type.tensor_type.shape.dim)>0: + tensor.type.tensor_type.shape.dim[0].dim_param = initialization_character_string + + + # infer_shapes + target_model = onnx.shape_inference.infer_shapes(target_model) + + # Save + if output_onnx_file_path: + onnx.save(target_model, output_onnx_file_path) + + if not non_verbose: + print(f'{Color.GREEN}INFO:{Color.RESET} Finish!') + + # Return + return target_model + + +def main(): + parser = ArgumentParser() + parser.add_argument( + '--input_onnx_file_path', + type=str, + required=True, + help='Input onnx file path.' + ) + parser.add_argument( + '--output_onnx_file_path', + type=str, + required=True, + help='Output onnx file path.' + ) + parser.add_argument( + '--initialization_character_string', + type=str, + default='batch', + help=\ + 'String to initialize batch size. "-1" or "N" or "xxx", etc... \n'+ + 'Default: \'batch\'' + ) + parser.add_argument( + '--non_verbose', + action='store_true', + help='Do not show all information logs. Only error logs are displayed.' + ) + args = parser.parse_args() + + input_onnx_file_path = args.input_onnx_file_path + output_onnx_file_path = args.output_onnx_file_path + initialization_character_string = args.initialization_character_string + non_verbose = args.non_verbose + + # Load + onnx_graph = onnx.load(input_onnx_file_path) + + # Batchsize change + changed_graph = initialize( + input_onnx_file_path=None, + onnx_graph=onnx_graph, + output_onnx_file_path=output_onnx_file_path, + initialization_character_string=initialization_character_string, + non_verbose=non_verbose, + ) + + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/421_Gold-YOLO-Head/post_process_gen_tools/make_box_gather_nd.py b/421_Gold-YOLO-Head/post_process_gen_tools/make_box_gather_nd.py new file mode 100644 index 0000000000..7fd64f78c0 --- /dev/null +++ b/421_Gold-YOLO-Head/post_process_gen_tools/make_box_gather_nd.py @@ -0,0 +1,62 @@ +import os +os.environ['CUDA_VISIBLE_DEVICES'] = '-1' +import tensorflow as tf +import numpy as np +np.random.seed(0) +from argparse import ArgumentParser + + +if __name__ == "__main__": + parser = ArgumentParser() + parser.add_argument( + '-b', + '--batches', + type=int, + default=1, + help='batch size' + ) + parser.add_argument( + '-x', + '--boxes', + type=int, + default=5040, + help='boxes' + ) + args = parser.parse_args() + BATCHES = args.batches + BOXES = args.boxes + + # Create a model + boxes = tf.keras.layers.Input( + shape=[ + BOXES, + 4, + ], + batch_size=BATCHES, + dtype=tf.float32, + ) + + selected_indices = tf.keras.layers.Input( + shape=[ + 2, + ], + dtype=tf.int64, + ) + + gathered_boxes = tf.gather_nd( + boxes, + selected_indices, + batch_dims=0, + ) + + model = tf.keras.models.Model(inputs=[boxes, selected_indices], outputs=[gathered_boxes]) + model.summary() + output_path = 'saved_model_postprocess' + tf.saved_model.save(model, output_path) + converter = tf.lite.TFLiteConverter.from_keras_model(model) + converter.target_spec.supported_ops = [ + tf.lite.OpsSet.TFLITE_BUILTINS, + tf.lite.OpsSet.SELECT_TF_OPS + ] + tflite_model = converter.convert() + open(f"{output_path}/nms_box_gather_nd.tflite", "wb").write(tflite_model) \ No newline at end of file diff --git a/421_Gold-YOLO-Head/post_process_gen_tools/make_boxes_scores.py b/421_Gold-YOLO-Head/post_process_gen_tools/make_boxes_scores.py new file mode 100644 index 0000000000..054cfa5ab2 --- /dev/null +++ b/421_Gold-YOLO-Head/post_process_gen_tools/make_boxes_scores.py @@ -0,0 +1,97 @@ +#! /usr/bin/env python + +import torch +import torch.nn as nn +import onnx +from onnxsim import simplify +from argparse import ArgumentParser + +""" +prediction [1, 5040, 85] + +80 classes + +85 + +[0] -> center_x +[1] -> center_y +[2] -> width +[3] -> height +[4] -> box_score +[5]-[84] -> class_score +""" + + +class Model(nn.Module): + def __init__(self): + super(Model, self).__init__() + + def forward(self, x): + boxes = x[..., :4] # xywh [n, boxes, 4] + box_scores = x[..., 4:5] # [n, boxes, 1] + class_scores = x[..., 5:] # [n, boxes, 80] + scores = box_scores * class_scores + # scores = torch.sqrt(scores) + scores = scores.permute(0,2,1) + return boxes, scores + + +if __name__ == "__main__": + parser = ArgumentParser() + parser.add_argument( + '-o', + '--opset', + type=int, + default=11, + help='onnx opset' + ) + parser.add_argument( + '-b', + '--batches', + type=int, + default=1, + help='batch size' + ) + parser.add_argument( + '-x', + '--boxes', + type=int, + default=5040, + help='boxes' + ) + parser.add_argument( + '-c', + '--classes', + type=int, + default=80, + help='classes' + ) + args = parser.parse_args() + + model = Model() + + MODEL = f'01_boxes_scores' + OPSET=args.opset + BATCHES = args.batches + BOXES = args.boxes + CLASSES = args.classes + + onnx_file = f"{MODEL}_{BOXES}.onnx" + x = torch.randn(BATCHES, BOXES, CLASSES+5) + + torch.onnx.export( + model, + args=(x), + f=onnx_file, + opset_version=OPSET, + input_names = ['predictions'], + output_names=['boxes_cxcywh','scores'], + ) + model_onnx1 = onnx.load(onnx_file) + model_onnx1 = onnx.shape_inference.infer_shapes(model_onnx1) + onnx.save(model_onnx1, onnx_file) + + model_onnx2 = onnx.load(onnx_file) + model_simp, check = simplify(model_onnx2) + onnx.save(model_simp, onnx_file) + diff --git a/421_Gold-YOLO-Head/post_process_gen_tools/make_cxcywh_x1y1x2y2.py b/421_Gold-YOLO-Head/post_process_gen_tools/make_cxcywh_x1y1x2y2.py new file mode 100644 index 0000000000..3c564b2466 --- /dev/null +++ b/421_Gold-YOLO-Head/post_process_gen_tools/make_cxcywh_x1y1x2y2.py @@ -0,0 +1,72 @@ +#! /usr/bin/env python + +import torch +import torch.nn as nn +import numpy as np +import onnx +from onnxsim import simplify +from argparse import ArgumentParser + +class Model(nn.Module): + def __init__(self): + super(Model, self).__init__() + + def forward(self, cxcywh): + x1 = (cxcywh[..., 0:1] - cxcywh[..., 2:3] / 2) # top left x + y1 = (cxcywh[..., 1:2] - cxcywh[..., 3:4] / 2) # top left y + x2 = (cxcywh[..., 0:1] + cxcywh[..., 2:3] / 2) # bottom right x + y2 = (cxcywh[..., 1:2] + cxcywh[..., 3:4] / 2) # bottom right y + x1y1x2y2 = torch.cat([x1,y1,x2,y2], dim=2) + return x1y1x2y2 + + +if __name__ == "__main__": + parser = ArgumentParser() + parser.add_argument( + '-o', + '--opset', + type=int, + default=11, + help='onnx opset' + ) + parser.add_argument( + '-b', + '--batches', + type=int, + default=1, + help='batch size' + ) + parser.add_argument( + '-x', + '--boxes', + type=int, + default=5040, + help='boxes' + ) + args = parser.parse_args() + + model = Model() + + MODEL = f'cxcywh_x1y1x2y2' + OPSET=args.opset + BATCHES = args.batches + BOXES = args.boxes + + onnx_file = f"{MODEL}_{BOXES}.onnx" + cxcywh = torch.randn(BATCHES, BOXES, 4) + + torch.onnx.export( + model, + args=(cxcywh), + f=onnx_file, + opset_version=OPSET, + input_names = ['cxcywh'], + output_names=['x1y1x2y2'], + ) + model_onnx1 = onnx.load(onnx_file) + model_onnx1 = onnx.shape_inference.infer_shapes(model_onnx1) + onnx.save(model_onnx1, onnx_file) + + model_onnx2 = onnx.load(onnx_file) + model_simp, check = simplify(model_onnx2) + onnx.save(model_simp, onnx_file) diff --git a/421_Gold-YOLO-Head/post_process_gen_tools/make_cxcywh_y1x1y2x2.py b/421_Gold-YOLO-Head/post_process_gen_tools/make_cxcywh_y1x1y2x2.py new file mode 100644 index 0000000000..705ac5b0e0 --- /dev/null +++ b/421_Gold-YOLO-Head/post_process_gen_tools/make_cxcywh_y1x1y2x2.py @@ -0,0 +1,78 @@ +#! /usr/bin/env python + +import torch +import torch.nn as nn +import numpy as np +import onnx +from onnxsim import simplify +from argparse import ArgumentParser + +class Model(nn.Module): + def __init__(self): + super(Model, self).__init__() + + def forward(self, cxcywh): + x1 = (cxcywh[..., 0:1] - cxcywh[..., 2:3] / 2) # top left x + y1 = (cxcywh[..., 1:2] - cxcywh[..., 3:4] / 2) # top left y + x2 = (cxcywh[..., 0:1] + cxcywh[..., 2:3] / 2) # bottom right x + y2 = (cxcywh[..., 1:2] + cxcywh[..., 3:4] / 2) # bottom right y + y1x1y2x2 = torch.cat([y1,x1,y2,x2], dim=2) + x1y1x2y2 = torch.cat([x1,y1,x2,y2], dim=2) + return y1x1y2x2, x1y1x2y2 + + +if __name__ == "__main__": + parser = ArgumentParser() + parser.add_argument( + '-o', + '--opset', + type=int, + default=11, + help='onnx opset' + ) + parser.add_argument( + '-b', + '--batches', + type=int, + default=1, + help='batch size' + ) + parser.add_argument( + '-x', + '--boxes', + type=int, + default=5040, + help='boxes' + ) + args = parser.parse_args() + + model = Model() + + MODEL = f'02_cxcywh_y1x1y2x2' + OPSET=args.opset + BATCHES = args.batches + BOXES = args.boxes + + onnx_file = f"{MODEL}_{BOXES}.onnx" + cxcywh = torch.randn(BATCHES, BOXES, 4) + + torch.onnx.export( + model, + args=(cxcywh), + f=onnx_file, + opset_version=OPSET, + input_names = [ + 'cxcywh', + ], + output_names=[ + 'y1x1y2x2', + 'x1y1x2y2', + ], + ) + model_onnx1 = onnx.load(onnx_file) + model_onnx1 = onnx.shape_inference.infer_shapes(model_onnx1) + onnx.save(model_onnx1, onnx_file) + + model_onnx2 = onnx.load(onnx_file) + model_simp, check = simplify(model_onnx2) + onnx.save(model_simp, onnx_file) diff --git a/421_Gold-YOLO-Head/post_process_gen_tools/make_final_batch_nums_final_class_nums_final_box_nums.py b/421_Gold-YOLO-Head/post_process_gen_tools/make_final_batch_nums_final_class_nums_final_box_nums.py new file mode 100644 index 0000000000..dace921053 --- /dev/null +++ b/421_Gold-YOLO-Head/post_process_gen_tools/make_final_batch_nums_final_class_nums_final_box_nums.py @@ -0,0 +1,61 @@ +#! /usr/bin/env python + +import torch +import torch.nn as nn +import onnx +import numpy as np +from onnxsim import simplify +from argparse import ArgumentParser + +class Model(nn.Module): + def __init__(self): + super(Model, self).__init__() + + def forward(self, x): + batch_nums = x[:, 0:1].to(torch.float32) # batch number + class_nums = x[:, 1:2].to(torch.float32) # class ids + box_nums = x[:, [0,2]] # batch number + box number + return batch_nums, class_nums, box_nums + + +if __name__ == "__main__": + parser = ArgumentParser() + parser.add_argument( + '-o', + '--opset', + type=int, + default=11, + help='onnx opset' + ) + args = parser.parse_args() + + model = Model() + + MODEL = f'17_nms_final_batch_nums_final_class_nums_final_box_nums' + OPSET=args.opset + + onnx_file = f"{MODEL}.onnx" + x = torch.ones([1, 3], dtype=torch.int64) + + torch.onnx.export( + model, + args=(x), + f=onnx_file, + opset_version=OPSET, + input_names=['bc_input'], + output_names=['final_batch_nums','final_class_nums','final_box_nums'], + dynamic_axes={ + 'bc_input': {0: 'N'}, + 'final_batch_nums': {0: 'N'}, + 'final_class_nums': {0: 'N'}, + 'final_box_nums': {0: 'N'}, + } + ) + model_onnx1 = onnx.load(onnx_file) + model_onnx1 = onnx.shape_inference.infer_shapes(model_onnx1) + onnx.save(model_onnx1, onnx_file) + + model_onnx2 = onnx.load(onnx_file) + model_simp, check = simplify(model_onnx2) + onnx.save(model_simp, onnx_file) + diff --git a/421_Gold-YOLO-Head/post_process_gen_tools/make_input_output_shape_update.py b/421_Gold-YOLO-Head/post_process_gen_tools/make_input_output_shape_update.py new file mode 100644 index 0000000000..f00d8fc5dc --- /dev/null +++ b/421_Gold-YOLO-Head/post_process_gen_tools/make_input_output_shape_update.py @@ -0,0 +1,76 @@ +import onnx +from onnx.tools import update_model_dims +from argparse import ArgumentParser + + +if __name__ == "__main__": + parser = ArgumentParser() + parser.add_argument( + '-if', + '--input_onnx_file_path', + type=str, + required=True, + help='INPUT ONNX file path' + ) + parser.add_argument( + '-of', + '--output_onnx_file_path', + type=str, + required=True, + help='OUTPUT ONNX file path' + ) + parser.add_argument( + '-i', + '--input_names', + type=str, + action='append', + help='input names' + ) + parser.add_argument( + '-is', + '--input_shapes', + type=str, + nargs='+', + action='append', + help='input shapes' + ) + parser.add_argument( + '-o', + '--output_names', + type=str, + action='append', + help='output names' + ) + parser.add_argument( + '-os', + '--output_shapes', + type=str, + nargs='+', + action='append', + help='output shapes' + ) + + args = parser.parse_args() + INPUT_MODEL_PATH = args.input_onnx_file_path + OUTPUT_MODEL_PATH = args.output_onnx_file_path + INPUT_NAMES = args.input_names + INPUT_SHAPES = args.input_shapes + OUTPUT_NAMES = args.output_names + OUTPUT_SHAPES = args.output_shapes + + input_names = [name for name in INPUT_NAMES] + input_shapes = [shape for shape in INPUT_SHAPES] + output_names = [name for name in OUTPUT_NAMES] + output_shapes = [shape for shape in OUTPUT_SHAPES] + + input_dicts = {name:shape for (name, shape) in zip(input_names, input_shapes)} + output_dicts = {name:shape for (name, shape) in zip(output_names, output_shapes)} + + model = onnx.load(INPUT_MODEL_PATH) + updated_model = update_model_dims.update_inputs_outputs_dims( + model=model, + input_dims=input_dicts, + output_dims=output_dicts, + ) + + onnx.save(updated_model, OUTPUT_MODEL_PATH) \ No newline at end of file diff --git a/421_Gold-YOLO-Head/post_process_gen_tools/make_nms_outputs_merge.py b/421_Gold-YOLO-Head/post_process_gen_tools/make_nms_outputs_merge.py new file mode 100644 index 0000000000..296da32b96 --- /dev/null +++ b/421_Gold-YOLO-Head/post_process_gen_tools/make_nms_outputs_merge.py @@ -0,0 +1,64 @@ +#! /usr/bin/env python + +import torch +import torch.nn as nn +import onnx +from onnxsim import simplify +from argparse import ArgumentParser + + +class Model(nn.Module): + def __init__(self): + super(Model, self).__init__() + + def forward(self, batch, classid, x1y1x2y2, score): + batchno_classid_x1y1x2y2_score_cat = torch.cat([batch, classid, x1y1x2y2, score], dim=1) + return batchno_classid_x1y1x2y2_score_cat + + +if __name__ == "__main__": + parser = ArgumentParser() + parser.add_argument( + '-o', + '--opset', + type=int, + default=11, + help='onnx opset' + ) + args = parser.parse_args() + + model = Model() + + MODEL = f'24_nms_batchno_classid_x1y1x2y2_score_cat' + + onnx_file = f"{MODEL}.onnx" + OPSET=args.opset + + x1 = torch.ones([1, 1], dtype=torch.float32) + x2 = torch.ones([1, 1], dtype=torch.float32) + x3 = torch.ones([1, 4], dtype=torch.float32) + x4 = torch.ones([1, 1], dtype=torch.float32) + + torch.onnx.export( + model, + args=(x1,x2,x3,x4), + f=onnx_file, + opset_version=OPSET, + input_names=['cat_batch','cat_classid','cat_x1y1x2y2','cat_score'], + output_names=['batchno_classid_x1y1x2y2_score'], + dynamic_axes={ + 'cat_batch': {0: 'N'}, + 'cat_classid': {0: 'N'}, + 'cat_x1y1x2y2': {0: 'N'}, + 'cat_score': {0: 'N'}, + 'batchno_classid_x1y1x2y2_score': {0: 'N'}, + } + ) + model_onnx1 = onnx.load(onnx_file) + model_onnx1 = onnx.shape_inference.infer_shapes(model_onnx1) + onnx.save(model_onnx1, onnx_file) + + model_onnx2 = onnx.load(onnx_file) + model_simp, check = simplify(model_onnx2) + onnx.save(model_simp, onnx_file) + diff --git a/421_Gold-YOLO-Head/post_process_gen_tools/make_score_gather_nd.py b/421_Gold-YOLO-Head/post_process_gen_tools/make_score_gather_nd.py new file mode 100644 index 0000000000..c9cbc0f8a5 --- /dev/null +++ b/421_Gold-YOLO-Head/post_process_gen_tools/make_score_gather_nd.py @@ -0,0 +1,72 @@ +import os +os.environ['CUDA_VISIBLE_DEVICES'] = '-1' +import tensorflow as tf +import numpy as np +np.random.seed(0) +from argparse import ArgumentParser + + +if __name__ == "__main__": + parser = ArgumentParser() + parser.add_argument( + '-b', + '--batches', + type=int, + default=1, + help='batch size' + ) + parser.add_argument( + '-x', + '--boxes', + type=int, + default=5040, + help='boxes' + ) + parser.add_argument( + '-c', + '--classes', + type=int, + default=80, + help='classes' + ) + args = parser.parse_args() + BATCHES = args.batches + BOXES = args.boxes + CLASSES = args.classes + + + # Create a model + scores = tf.keras.layers.Input( + shape=[ + CLASSES, + BOXES, + ], + batch_size=BATCHES, + dtype=tf.float32, + ) + + selected_indices = tf.keras.layers.Input( + shape=[ + 3, + ], + dtype=tf.int64, + ) + + gathered_scores = tf.gather_nd( + scores, + selected_indices, + batch_dims=0, + ) + expands_scores = gathered_scores[:,np.newaxis] + + model = tf.keras.models.Model(inputs=[scores,selected_indices], outputs=[expands_scores]) + model.summary() + output_path = 'saved_model_postprocess' + tf.saved_model.save(model, output_path) + converter = tf.lite.TFLiteConverter.from_keras_model(model) + converter.target_spec.supported_ops = [ + tf.lite.OpsSet.TFLITE_BUILTINS, + tf.lite.OpsSet.SELECT_TF_OPS + ] + tflite_model = converter.convert() + open(f"{output_path}/nms_score_gather_nd.tflite", "wb").write(tflite_model) \ No newline at end of file diff --git a/421_Gold-YOLO-Head/url.txt b/421_Gold-YOLO-Head/url.txt new file mode 100644 index 0000000000..f3d10f3536 --- /dev/null +++ b/421_Gold-YOLO-Head/url.txt @@ -0,0 +1,5 @@ +https://github.com/huawei-noah/Efficient-Computing/tree/master/Detection/Gold-YOLO + +https://github.com/PINTO0309/onnx2tf +https://github.com/PINTO0309/simple-onnx-processing-tools + diff --git a/README.md b/README.md index f448ce4d83..df411697a4 100644 --- a/README.md +++ b/README.md @@ -217,6 +217,7 @@ I have been working on quantization of various models as a hobby, but I have ski |399|RetinaFace_MobileNetv2|[■■■](https://github.com/PINTO0309/PINTO_model_zoo/tree/main/399_RetinaFace_MobileNetv2)|||||||||||⚫|| |410|FaceMeshV2|[■■■](https://github.com/PINTO0309/PINTO_model_zoo/tree/main/410_FaceMeshV2)|⚫|⚫|⚫||⚫||⚫||||⚫|MediaPipe| |414|STAR|[■■■](https://github.com/PINTO0309/PINTO_model_zoo/tree/main/414_STAR)|⚫|⚫|⚫||⚫||⚫||||⚫|| +|421|Gold-YOLO-Head|[■■■](https://github.com/PINTO0309/PINTO_model_zoo/tree/main/421_Gold-YOLO-Head)|||||||||||⚫|Head (not Face)| ### 5. 2D/3D Hand Detection |No.|Model Name|Link|FP32|FP16|INT8|TPU|DQ|WQ|OV|CM|TFJS|TF-TRT|ONNX|Remarks| |:-|:-|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-|