#python #openvino #openvino-notebooks #deeplearning #accelerated-inference #object-detection #onnx #onnx-runtime #openvino-onnx-runtime #openvino-execution-provider-for-onnx #tiny-yolov4
Object detection with YOLOv4 in Python using OpenVINO™ Execution Provider# pip3 install openvino # Install ONNX Runtime for OpenVINO™ Execution Provider # pip3 install onnxruntime-openvino==1.11.0 # pip3 install -r requirements.txt # Running the ONNXRuntime OpenVINO™ Execution Provider sample # python3 yolov4.py --device CPU_FP32 --video classroom.mp4 --model yolov4.onnx ''' Copyright (C) 2021-2022, Intel Corporation SPDX-License-Identifier: Apache-2.0 Major Portions of this code are copyright of their respective authors and released under the Apache License Version 2.0: - onnx, Copyright 2021-2022. For licensing see https://github.com/onnx/models/blob/master/LICENSE ''' import cv2 import numpy as np from onnx import numpy_helper import onnx import onnxruntime as rt import os from PIL import Image from scipy import special import colorsys import random import argparse import sys import time import platform if platform.system() == "Windows": from openvino import utils utils.add_openvino_libs_to_path() def image_preprocess(image, target_size, gt_boxes=None): ih, iw = target_size h, w, _ = image.shape scale = min(iw/w, ih/h) nw, nh = int(scale * w), int(scale * h) image_resized = cv2.resize(image, (nw, nh)) image_padded = np.full(shape=[ih, iw, 3], fill_value=128.0) dw, dh = (iw - nw) // 2, (ih-nh) // 2 image_padded[dh:nh+dh, dw:nw+dw, :] = image_resized image_padded = image_padded / 255. if gt_boxes is None: return image_padded else: gt_boxes[:, [0, 2]] = gt_boxes[:, [0, 2]] * scale + dw gt_boxes[:, [1, 3]] = gt_boxes[:, [1, 3]] * scale + dh return image_padded, gt_boxes def postprocess_bbbox(pred_bbox): '''define anchor boxes''' for i, pred in enumerate(pred_bbox): conv_shape = pred.shape output_size = conv_shape[1] conv_raw_dxdy = pred[:, :, :, :, 0:2] conv_raw_dwdh = pred[:, :, :, :, 2:4] xy_grid = np.meshgrid(np.arange(output_size), np.arange(output_size)) xy_grid = np.expand_dims(np.stack(xy_grid, axis=-1), axis=2) xy_grid = np.tile(np.expand_dims(xy_grid, axis=0), [1, 1, 1, 3, 1]) xy_grid = xy_grid.astype(float) pred_xy = ((special.expit(conv_raw_dxdy) * XYSCALE[i]) - 0.5 * (XYSCALE[i] - 1) + xy_grid) * STRIDES[i] pred_wh = (np.exp(conv_raw_dwdh) * ANCHORS[i]) pred[:, :, :, :, 0:4] = np.concatenate([pred_xy, pred_wh], axis=-1) pred_bbox = [np.reshape(x, (-1, np.shape(x)[-1])) for x in pred_bbox] pred_bbox = np.concatenate(pred_bbox, axis=0) return pred_bbox def postprocess_boxes(pred_bbox, org_img_shape, input_size, score_threshold): '''remove boundary boxs with a low detection probability''' valid_scale=[0, np.inf] pred_bbox = np.array(pred_bbox) pred_xywh = pred_bbox[:, 0:4] pred_conf = pred_bbox[:, 4] pred_prob = pred_bbox[:, 5:] # # (1) (x, y, w, h) --> (xmin, ymin, xmax, ymax) pred_coor = np.concatenate([pred_xywh[:, :2] - pred_xywh[:, 2:] * 0.5, pred_xywh[:, :2] + pred_xywh[:, 2:] * 0.5], axis=-1) # # (2) (xmin, ymin, xmax, ymax) -> (xmin_org, ymin_org, xmax_org, ymax_org) org_h, org_w = org_img_shape resize_ratio = min(input_size / org_w, input_size / org_h) dw = (input_size - resize_ratio * org_w) / 2 dh = (input_size - resize_ratio * org_h) / 2 pred_coor[:, 0::2] = 1.0 * (pred_coor[:, 0::2] - dw) / resize_ratio pred_coor[:, 1::2] = 1.0 * (pred_coor[:, 1::2] - dh) / resize_ratio # # (3) clip some boxes that are out of range pred_coor = np.concatenate([np.maximum(pred_coor[:, :2], [0, 0]), np.minimum(pred_coor[:, 2:], [org_w - 1, org_h - 1])], axis=-1) invalid_mask = np.logical_or((pred_coor[:, 0] > pred_coor[:, 2]), (pred_coor[:, 1] > pred_coor[:, 3])) pred_coor[invalid_mask] = 0 # # (4) discard some invalid boxes bboxes_scale = np.sqrt(np.multiply.reduce(pred_coor[:, 2:4] - pred_coor[:, 0:2], axis=-1)) scale_mask = np.logical_and((valid_scale[0] < bboxes_scale), (bboxes_scale < valid_scale[1])) # # (5) discard some boxes with low scores classes = np.argmax(pred_prob, axis=-1) scores = pred_conf * pred_prob[np.arange(len(pred_coor)), classes] score_mask = scores > score_threshold mask = np.logical_and(scale_mask, score_mask) coors, scores, classes = pred_coor[mask], scores[mask], classes[mask] return np.concatenate([coors, scores[:, np.newaxis], classes[:, np.newaxis]], axis=-1) def bboxes_iou(boxes1, boxes2): '''calculate the Intersection Over Union value''' boxes1 = np.array(boxes1) boxes2 = np.array(boxes2) boxes1_area = (boxes1[..., 2] - boxes1[..., 0]) * (boxes1[..., 3] - boxes1[..., 1]) boxes2_area = (boxes2[..., 2] - boxes2[..., 0]) * (boxes2[..., 3] - boxes2[..., 1]) left_up = np.maximum(boxes1[..., :2], boxes2[..., :2]) right_down = np.minimum(boxes1[..., 2:], boxes2[..., 2:]) inter_section = np.maximum(right_down - left_up, 0.0) inter_area = inter_section[..., 0] * inter_section[..., 1] union_area = boxes1_area + boxes2_area - inter_area ious = np.maximum(1.0 * inter_area / union_area, np.finfo(np.float32).eps) return ious def nms(bboxes, iou_threshold, sigma=0.3, method='nms'): """ :param bboxes: (xmin, ymin, xmax, ymax, score, class) Note: soft-nms, https://arxiv.org/pdf/1704.04503.pdf https://github.com/bharatsingh430/soft-nms """ classes_in_img = list(set(bboxes[:, 5])) best_bboxes = [] for cls in classes_in_img: cls_mask = (bboxes[:, 5] == cls) cls_bboxes = bboxes[cls_mask] while len(cls_bboxes) > 0: max_ind = np.argmax(cls_bboxes[:, 4]) best_bbox = cls_bboxes[max_ind] best_bboxes.append(best_bbox) cls_bboxes = np.concatenate([cls_bboxes[: max_ind], cls_bboxes[max_ind + 1:]]) iou = bboxes_iou(best_bbox[np.newaxis, :4], cls_bboxes[:, :4]) weight = np.ones((len(iou),), dtype=np.float32) assert method in ['nms', 'soft-nms'] if method == 'nms': iou_mask = iou > iou_threshold weight[iou_mask] = 0.0 if method == 'soft-nms': weight = np.exp(-(1.0 * iou ** 2 / sigma)) cls_bboxes[:, 4] = cls_bboxes[:, 4] * weight score_mask = cls_bboxes[:, 4] > 0. cls_bboxes = cls_bboxes[score_mask] return best_bboxes def read_class_names(class_file_name): '''loads class name from a file''' names = {} with open(class_file_name, 'r') as data: for ID, name in enumerate(data): names[ID] = name.strip('\n') return names def draw_bbox(image, bboxes, classes=read_class_names("coco.names"), show_label=True): """ bboxes: [x_min, y_min, x_max, y_max, probability, cls_id] format coordinates. """ num_classes = len(classes) image_h, image_w, _ = image.shape hsv_tuples = [(1.0 * x / num_classes, 1., 1.) for x in range(num_classes)] colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples)) colors = list(map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), colors)) random.seed(0) random.shuffle(colors) random.seed(None) for i, bbox in enumerate(bboxes): coor = np.array(bbox[:4], dtype=np.int32) fontScale = 0.5 score = bbox[4] class_ind = int(bbox[5]) bbox_color = colors[class_ind] bbox_thick = int(0.6 * (image_h + image_w) / 600) c1, c2 = (coor[0], coor[1]), (coor[2], coor[3]) cv2.rectangle(image, c1, c2, bbox_color, bbox_thick) if show_label: bbox_mess = '%s: %.2f' % (classes[class_ind], score) t_size = cv2.getTextSize(bbox_mess, 0, fontScale, thickness=bbox_thick//2)[0] cv2.rectangle(image, c1, (c1[0] + t_size[0], c1[1] - t_size[1] - 3), bbox_color, -1) cv2.putText(image, bbox_mess, (c1[0], c1[1]-2), cv2.FONT_HERSHEY_SIMPLEX, fontScale, (0, 0, 0), bbox_thick//2, lineType=cv2.LINE_AA) return image def get_anchors(anchors_path, tiny=False): '''loads the anchors from a file''' with open(anchors_path) as f: anchors = f.readline() anchors = np.array(anchors.split(','), dtype=np.float32) return anchors.reshape(3, 3, 2) #Specify the path to anchors file on your machine ANCHORS = "./yolov4_anchors.txt" STRIDES = [8, 16, 32] XYSCALE = [1.2, 1.1, 1.05] ANCHORS = get_anchors(ANCHORS) STRIDES = np.array(STRIDES) def parse_arguments(): parser = argparse.ArgumentParser(description='Object Detection using YOLOv4 in OPENCV using OpenVINO Execution Provider for ONNXRuntime') parser.add_argument('--device', default='CPU_FP32', help="Device to perform inference on 'cpu (MLAS)' or on devices supported by OpenVINO-EP [CPU_FP32, GPU_FP32, GPU_FP16, MYRIAD_FP16, VAD-M_FP16].") parser.add_argument('--image', help='Path to image file.') parser.add_argument('--video', help='Path to video file.') parser.add_argument('--model', help='Path to model.') args = parser.parse_args() return args def check_model_extension(fp): # Split the extension from the path and normalise it to lowercase. ext = os.path.splitext(fp)[-1].lower() # Now we can simply use != to check for inequality, no need for wildcards. if(ext != ".onnx"): raise Exception(fp, "is an unknown file format. Use the model ending with .onnx format") if not os.path.exists(fp): raise Exception("[ ERROR ] Path of the onnx model file is Invalid") def main(): # Process arguments args = parse_arguments() # Validate model file path check_model_extension(args.model) # Process inputs win_name = 'Object detection using ONNXRuntime OpenVINO Execution Provider using YoloV4 model' cv2.namedWindow(win_name, cv2.WINDOW_NORMAL) output_file = "yolo_out_py.avi" if (args.image): # Open the image file if not os.path.isfile(args.image): print("Input image file ", args.image, " doesn't exist") sys.exit(1) cap = cv2.VideoCapture(args.image) output_file = args.image[:-4]+'_yolo_out_py.jpg' elif (args.video): # Open the video file if not os.path.isfile(args.video): print("Input video file ", args.video, " doesn't exist") sys.exit(1) cap = cv2.VideoCapture(args.video) output_file = args.video[:-4]+'_yolo_out_py.avi' else: # Webcam input cap = cv2.VideoCapture(0) # Get the video writer initialized to save the output video if (not args.image): vid_writer = cv2.VideoWriter(output_file, cv2.VideoWriter_fourcc('M','J','P','G'), 30, (round(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),round(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)))) # Check the device information and create a session device = args.device so = rt.SessionOptions() so.log_severity_level = 3 if(args.device == 'cpu'): print("Device type selected is 'cpu' which is the default CPU Execution Provider (MLAS)") #Specify the path to the ONNX model on your machine and register the CPU EP sess = rt.InferenceSession(args.model, so, providers=['CPUExecutionProvider']) else: #Specify the path to the ONNX model on your machine and register the OpenVINO EP sess = rt.InferenceSession(args.model, so, providers=['OpenVINOExecutionProvider'], provider_options=[{'device_type' : device}]) print("Device type selected is: " + device + " using the OpenVINO Execution Provider") ''' other 'device_type' options are: (Any hardware target can be assigned if you have the access to it) 'CPU_FP32', 'GPU_FP32', 'GPU_FP16', 'MYRIAD_FP16', 'VAD-M_FP16' ''' input_name = sess.get_inputs()[0].name while cv2.waitKey(1) < 0: # get frame from the video has_frame, frame = cap.read() # Stop the program if reached end of video if not has_frame: print("Done processing !!!") print("Output file is stored as ", output_file) has_frame=False cv2.waitKey(3000) # Release device cap.release() break input_size = 416 original_image = frame original_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB) original_image_size = original_image.shape[:2] image_data = image_preprocess(np.copy(original_image), [input_size, input_size]) image_data = image_data[np.newaxis, ...].astype(np.float32) outputs = sess.get_outputs() output_names = list(map(lambda output: output.name, outputs)) start = time.time() detections = sess.run(output_names, {input_name: image_data}) end = time.time() inference_time = end - start pred_bbox = postprocess_bbbox(detections) bboxes = postprocess_boxes(pred_bbox, original_image_size, input_size, 0.25) bboxes = nms(bboxes, 0.213, method='nms') image = draw_bbox(original_image, bboxes) cv2.putText(image,device,(10,20),cv2.FONT_HERSHEY_COMPLEX,0.5,(255,255,255),1) cv2.putText(image,'FPS: {}'.format(1.0/inference_time),(10,40),cv2.FONT_HERSHEY_COMPLEX,0.5,(255,255,255),1) # Write the frame with the detection boxes if (args.image): cv2.imwrite(output_file, image.astype(np.uint8)) else: vid_writer.write(image.astype(np.uint8)) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) cv2.imshow(win_name, image) if __name__ == "__main__": main()
#python #openvino #openvino-notebooks #deeplearning #accelerated-inference #object-detection #onnx #onnx-runtime #openvino-onnx-runtime #yolov2 #openvino-execution-provider-for-onnx
Object detection with tinyYOLOv2 in Python using OpenVINO™ Execution Provider# pip3 install openvino # Install ONNX Runtime for OpenVINO™ Execution Provider # pip3 install onnxruntime-openvino==1.11.0 # pip3 install -r requirements.txt # How to run the sample # python3 tiny_yolov2_obj_detection_sample.py --h # Running the ONNXRuntime OpenVINO™ Execution Provider sample # python3 tiny_yolov2_obj_detection_sample.py --video face-demographics-walking-and-pause.mp4 --model tinyyolov2.onnx --device CPU_FP32 ''' Copyright (C) 2021-2022, Intel Corporation SPDX-License-Identifier: Apache-2.0 ''' import numpy as np import onnxruntime as rt import cv2 import time import os import argparse import platform if platform.system() == "Windows": from openvino import utils utils.add_openvino_libs_to_path() # color look up table for different classes for object detection sample clut = [(0,0,0),(255,0,0),(255,0,255),(0,0,255),(0,255,0),(0,255,128), (128,255,0),(128,128,0),(0,128,255),(128,0,128), (255,0,128),(128,0,255),(255,128,128),(128,255,128),(255,255,0), (255,128,128),(128,128,255),(255,128,128),(128,255,128),(128,255,128)] # 20 labels that the tiny-yolov2 model can do the object_detection on label = ["aeroplane","bicycle","bird","boat","bottle", "bus","car","cat","chair","cow","diningtable", "dog","horse","motorbike","person","pottedplant", "sheep","sofa","train","tvmonitor"] def parse_arguments(): parser = argparse.ArgumentParser(description='Object Detection using YOLOv2 in OPENCV using OpenVINO Execution Provider for ONNXRuntime') parser.add_argument('--device', default='CPU_FP32', help="Device to perform inference on 'cpu (MLAS)' or on devices supported by OpenVINO-EP [CPU_FP32, GPU_FP32, GPU_FP16, MYRIAD_FP16, VAD-M_FP16].") parser.add_argument('--video', help='Path to video file.') parser.add_argument('--model', help='Path to model.') args = parser.parse_args() return args def sigmoid(x, derivative=False): return x*(1-x) if derivative else 1/(1+np.exp(-x)) def softmax(x): score_mat_exp = np.exp(np.asarray(x)) return score_mat_exp / score_mat_exp.sum(0) def check_model_extension(fp): # Split the extension from the path and normalise it to lowercase. ext = os.path.splitext(fp)[-1].lower() # Now we can simply use != to check for inequality, no need for wildcards. if(ext != ".onnx"): raise Exception(fp, "is an unknown file format. Use the model ending with .onnx format") if not os.path.exists(fp): raise Exception("[ ERROR ] Path of the onnx model file is Invalid") def check_video_file_extension(fp): # Split the extension from the path and normalise it to lowercase. ext = os.path.splitext(fp)[-1].lower() # Now we can simply use != to check for inequality, no need for wildcards. if(ext == ".mp4" or ext == ".avi" or ext == ".mov"): pass else: raise Exception(fp, "is an unknown file format. Use the video file ending with .mp4 or .avi or .mov formats") if not os.path.exists(fp): raise Exception("[ ERROR ] Path of the video file is Invalid") def image_preprocess(frame): in_frame = cv2.resize(frame, (416, 416)) preprocessed_image = np.asarray(in_frame) preprocessed_image = preprocessed_image.astype(np.float32) preprocessed_image = preprocessed_image.transpose(2,0,1) #Reshaping the input array to align with the input shape of the model preprocessed_image = preprocessed_image.reshape(1,3,416,416) return preprocessed_image def postprocess_output(out, frame, x_scale, y_scale, i): out = out[0][0] num_classes = 20 anchors = [1.08, 1.19, 3.42, 4.41, 6.63, 11.38, 9.42, 5.11, 16.62, 10.52] existing_labels = {l: [] for l in label} #Inside this loop we compute the bounding box b for grid cell (cy, cx) for cy in range(0,13): for cx in range(0,13): for b in range(0,5): # First we read the tx, ty, width(tw), and height(th) for the bounding box from the out array, as well as the confidence score channel = b*(num_classes+5) tx = out[channel ][cy][cx] ty = out[channel+1][cy][cx] tw = out[channel+2][cy][cx] th = out[channel+3][cy][cx] tc = out[channel+4][cy][cx] x = (float(cx) + sigmoid(tx))*32 y = (float(cy) + sigmoid(ty))*32 w = np.exp(tw) * 32 * anchors[2*b] h = np.exp(th) * 32 * anchors[2*b+1] #calculating the confidence score confidence = sigmoid(tc) # The confidence value for the bounding box is given by tc classes = np.zeros(num_classes) for c in range(0,num_classes): classes[c] = out[channel + 5 +c][cy][cx] # we take the softmax to turn the array into a probability distribution. And then we pick the class with the largest score as the winner. classes = softmax(classes) detected_class = classes.argmax() # Now we can compute the final score for this bounding box and we only want to keep the ones whose combined score is over a certain threshold if 0.60 < classes[detected_class]*confidence: color =clut[detected_class] x = (x - w/2)*x_scale y = (y - h/2)*y_scale w *= x_scale h *= y_scale labelX = int((x+x+w)/2) labelY = int((y+y+h)/2) addLabel = True lab_threshold = 100 for point in existing_labels[label[detected_class]]: if labelX < point[0] + lab_threshold and labelX > point[0] - lab_threshold and \ labelY < point[1] + lab_threshold and labelY > point[1] - lab_threshold: addLabel = False #Adding class labels to the output of the frame and also drawing a rectangular bounding box around the object detected. if addLabel: cv2.rectangle(frame, (int(x),int(y)),(int(x+w),int(y+h)),color,2) cv2.rectangle(frame, (int(x),int(y-13)),(int(x)+9*len(label[detected_class]),int(y)),color,-1) cv2.putText(frame,label[detected_class],(int(x)+2,int(y)-3),cv2.FONT_HERSHEY_COMPLEX,0.4,(255,255,255),1) existing_labels[label[detected_class]].append((labelX,labelY)) print('{} detected in frame {}'.format(label[detected_class],i)) def show_bbox(device, frame, inference_time): cv2.putText(frame,device,(10,20),cv2.FONT_HERSHEY_COMPLEX,0.5,(255,255,255),1) cv2.putText(frame,'FPS: {}'.format(1.0/inference_time),(10,40),cv2.FONT_HERSHEY_COMPLEX,0.5,(255,255,255),1) frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) cv2.imshow('frame',frame) def main(): # Process arguments args = parse_arguments() # Validate model file path check_model_extension(args.model) so = rt.SessionOptions() so.log_severity_level = 3 if (args.device == 'cpu'): print("Device type selected is 'cpu' which is the default CPU Execution Provider (MLAS)") #Specify the path to the ONNX model on your machine and register the CPU EP sess = rt.InferenceSession(args.model, so, providers=['CPUExecutionProvider']) elif (args.device == 'CPU_FP32' or args.device == 'GPU_FP32' or args.device == 'GPU_FP16' or args.device == 'MYRIAD_FP16' or args.device == 'VADM_FP16'): #Specify the path to the ONNX model on your machine and register the OpenVINO EP sess = rt.InferenceSession(args.model, so, providers=['OpenVINOExecutionProvider'], provider_options=[{'device_type' : args.device}]) print("Device type selected is: " + args.device + " using the OpenVINO Execution Provider") ''' other 'device_type' options are: (Any hardware target can be assigned if you have the access to it) 'CPU_FP32', 'GPU_FP32', 'GPU_FP16', 'MYRIAD_FP16', 'VAD-M_FP16' ''' else: raise Exception("Device type selected is not [cpu, CPU_FP32, GPU_FP32, GPU_FP16, MYRIAD_FP16, VADM_FP16]") # Get the input name of the model input_name = sess.get_inputs()[0].name #validate video file input path check_video_file_extension(args.video) #Path to video file has to be provided cap = cv2.VideoCapture(args.video) # capturing different metrics of the image from the video fps = cap.get(cv2.CAP_PROP_FPS) width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) x_scale = float(width)/416.0 #In the document of tino-yolo-v2, input shape of this network is (1,3,416,416). y_scale = float(height)/416.0 # writing the inferencing output as a video to the local disk fourcc = cv2.VideoWriter_fourcc(*'XVID') output_video_name = args.device + "_output.avi" output_video = cv2.VideoWriter(output_video_name,fourcc, float(17.0), (640,360)) # capturing one frame at a time from the video feed and performing the inference i = 0 while cv2.waitKey(1) < 0: l_start = time.time() ret, frame = cap.read() if not ret: break initial_w = cap.get(3) initial_h = cap.get(4) # preprocessing the input frame and reshaping it. #In the document of tino-yolo-v2, input shape of this network is (1,3,416,416). so we resize the model frame w.r.t that size. preprocessed_image = image_preprocess(frame) start = time.time() #Running the session by passing in the input data of the model out = sess.run(None, {input_name: preprocessed_image}) end = time.time() inference_time = end - start #Get the output postprocess_output(out, frame, x_scale, y_scale, i) #Show the Output output_video.write(frame) show_bbox(args.device, frame, inference_time) #Press 'q' to quit the process print('Processed Frame {}'.format(i)) i += 1 l_end = time.time() print('Loop Time = {}'.format(l_end - l_start)) output_video.release() cv2.destroyAllWindows() if __name__ == "__main__": main()
#python #openvino #openvino-notebook #onnx #paddlepaddle
103-paddle-onnx-to-openvino: Convert a PaddlePaddle Model to ONNX and OpenVINO IR# Imports import os import time import cv2 import matplotlib.pyplot as plt import numpy as np import paddlehub as hub from IPython.display import Markdown, display from PIL import Image from openvino.runtime import Core from paddle.static import InputSpec from scipy.special import softmax # Settings IMAGE_FILENAME = "coco_close.png" MODEL_NAME = "mobilenet_v3_large_imagenet_ssld" hub.config.server = "https://paddlepaddle.org.cn/paddlehub" # Show Inference on PaddlePaddle Model classifier = hub.Module(name=MODEL_NAME) # Load image in BGR format, as specified in model documentation image = cv2.imread(filename=IMAGE_FILENAME) plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) result = classifier.classification(images=[image], top_k=3) for class_name, softmax_probability in result[0].items(): print(f"{class_name}, {softmax_probability:.5f}") from mobilenet_v3_large_imagenet_ssld.data_feed import process_image pil_image = Image.open(IMAGE_FILENAME) processed_image = process_image(pil_image) print(f"Processed image shape: {processed_image.shape}") # Processed image is in (C,H,W) format, convert to (H,W,C) to show the image plt.imshow(np.transpose(processed_image, (1, 2, 0))) # Preparation input_shape = list(classifier.cpu_predictor.get_input_tensor_shape().values()) print("input shape:", input_shape) print("mean:", classifier.get_pretrained_images_mean()) print("std:", classifier.get_pretrained_images_std()) # Convert PaddlePaddle Model to ONNX target_height, target_width = next(iter(input_shape))[2:] x_spec = InputSpec([1, 3, target_height, target_width], "float32", "x") print( "Exporting PaddlePaddle model to ONNX with target_height " f"{target_height} and target_width {target_width}" ) classifier.export_onnx_model(".", input_spec=[x_spec], opset_version=11) # Convert ONNX model to OpenVINO IR Format model_xml = f"{MODEL_NAME}.xml" if not os.path.exists(model_xml): mo_command = f'mo --input_model {MODEL_NAME}.onnx --input_shape "[1,3,{target_height},{target_width}]"' display(Markdown(f"Model Optimizer command to convert the ONNX model to IR: `{mo_command}`")) display(Markdown("_Converting model to IR. This may take a few minutes..._")) ! $mo_command else: print(f"{model_xml} already exists.") # Show Inference on OpenVINO Model # Load Inference Engine and IR model ie = Core() model = ie.read_model(model=f"{MODEL_NAME}.xml", weights=f"{MODEL_NAME}.bin") compiled_model = ie.compile_model(model=model, device_name="CPU") # Get model output output_layer = compiled_model.output(0) # Read, show, and preprocess input image # See the "Show Inference on PaddlePaddle Model" section for source of process_image image = Image.open(IMAGE_FILENAME) plt.imshow(image) input_image = process_image(image)[None,] # Do inference ie_result = compiled_model([input_image])[output_layer][0] # Compute softmax probabilities for the inference result and find the top three values softmax_result = softmax(ie_result) top_indices = np.argsort(softmax_result)[-3:][::-1] top_softmax = softmax_result[top_indices] # Convert the inference results to class names, using the same labels as the PaddlePaddle classifier for index, softmax_probability in zip(top_indices, top_softmax): print(f"{classifier.label_list[index]}, {softmax_probability:.5f}") # Timing and Comparison num_images = 50 # PaddlePaddle's classification method expects a BGR numpy array image = cv2.imread(filename=IMAGE_FILENAME) # The process_image function expects a PIL image pil_image = Image.open(fp=IMAGE_FILENAME) # Show CPU information ie = Core() print(f"CPU: {ie.get_property(device_name='CPU', name='FULL_DEVICE_NAME')}") # Show inference speed on PaddlePaddle model start = time.perf_counter() for _ in range(num_images): result = classifier.classification(images=[image], top_k=3) end = time.perf_counter() time_ir = end - start print( f"PaddlePaddle model on CPU: {time_ir/num_images:.4f} " f"seconds per image, FPS: {num_images/time_ir:.2f}\n" ) print("PaddlePaddle result:") for class_name, softmax_probability in result[0].items(): print(f"{class_name}, {softmax_probability:.5f}") plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)); # Show inference speed on OpenVINO IR model compiled_model = ie.compile_model(model=model, device_name="CPU") output_layer = compiled_model.output(0) start = time.perf_counter() input_image = process_image(pil_image)[None,] for _ in range(num_images): ie_result = compiled_model([input_image])[output_layer][0] result_index = np.argmax(ie_result) class_name = classifier.label_list[np.argmax(ie_result)] softmax_result = softmax(ie_result) top_indices = np.argsort(softmax_result)[-3:][::-1] top_softmax = softmax_result[top_indices] end = time.perf_counter() time_ir = end - start print( f"IR model in Inference Engine (CPU): {time_ir/num_images:.4f} " f"seconds per image, FPS: {num_images/time_ir:.2f}" ) print() print("OpenVINO result:") for index, softmax_probability in zip(top_indices, top_softmax): print(f"{classifier.label_list[index]}, {softmax_probability:.5f}") plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB));
#python #openvino #openvino-notebook #pytorch #onnx
102-pytorch-onnx-to-openvino: PyTorch to ONNX and OpenVINO IR Tutorial# Imports import sys import time from pathlib import Path import cv2 import numpy as np import torch from IPython.display import Markdown, display from fastseg import MobileV3Large from openvino.runtime import Core sys.path.append("../utils") from notebook_utils import CityScapesSegmentation, segmentation_map_to_image, viz_result_image # Settings IMAGE_WIDTH = 1024 # Suggested values: 2048, 1024 or 512. The minimum width is 512. # Set IMAGE_HEIGHT manually for custom input sizes. Minimum height is 512 IMAGE_HEIGHT = 1024 if IMAGE_WIDTH == 2048 else 512 DIRECTORY_NAME = "model" BASE_MODEL_NAME = DIRECTORY_NAME + f"/fastseg{IMAGE_WIDTH}" # Paths where PyTorch, ONNX and OpenVINO IR models will be stored model_path = Path(BASE_MODEL_NAME).with_suffix(".pth") onnx_path = model_path.with_suffix(".onnx") ir_path = model_path.with_suffix(".xml") # Download the Fastseg Model print("Downloading the Fastseg model (if it has not been downloaded before)....") model = MobileV3Large.from_pretrained().cpu().eval() print("Loaded PyTorch Fastseg model") # Save the model model_path.parent.mkdir(exist_ok=True) torch.save(model.state_dict(), str(model_path)) print(f"Model saved at {model_path}") # Convert PyTorch model to ONNX if not onnx_path.exists(): dummy_input = torch.randn(1, 3, IMAGE_HEIGHT, IMAGE_WIDTH) # For the Fastseg model, setting do_constant_folding to False is required # for PyTorch>1.5.1 torch.onnx.export( model, dummy_input, onnx_path, opset_version=11, do_constant_folding=False, ) print(f"ONNX model exported to {onnx_path}.") else: print(f"ONNX model {onnx_path} already exists.") # Convert ONNX Model to OpenVINO IR Format # Construct the command for Model Optimizer mo_command = f"""mo --input_model "{onnx_path}" --input_shape "[1,3, {IMAGE_HEIGHT}, {IMAGE_WIDTH}]" --mean_values="[123.675, 116.28 , 103.53]" --scale_values="[58.395, 57.12 , 57.375]" --data_type FP16 --output_dir "{model_path.parent}" """ mo_command = " ".join(mo_command.split()) print("Model Optimizer command to convert the ONNX model to OpenVINO:") display(Markdown(f"`{mo_command}`")) if not ir_path.exists(): print("Exporting ONNX model to IR... This may take a few minutes.") mo_result = %sx $mo_command print("\n".join(mo_result)) else: print(f"IR model {ir_path} already exists.") # Show results: Load and Preprocess an Input Image def normalize(image: np.ndarray) -> np.ndarray: """ Normalize the image to the given mean and standard deviation for CityScapes models. """ image = image.astype(np.float32) mean = (0.485, 0.456, 0.406) std = (0.229, 0.224, 0.225) image /= 255.0 image -= mean image /= std return image image_filename = "data/street.jpg" image = cv2.cvtColor(cv2.imread(image_filename), cv2.COLOR_BGR2RGB) resized_image = cv2.resize(image, (IMAGE_WIDTH, IMAGE_HEIGHT)) normalized_image = normalize(resized_image) # Convert the resized images to network input shape input_image = np.expand_dims(np.transpose(resized_image, (2, 0, 1)), 0) normalized_input_image = np.expand_dims(np.transpose(normalized_image, (2, 0, 1)), 0) # ONNX Model in Inference Engine # Load network to Inference Engine ie = Core() model_onnx = ie.read_model(model=onnx_path) compiled_model_onnx = ie.compile_model(model=model_onnx, device_name="CPU") output_layer_onnx = compiled_model_onnx.output(0) # Run inference on the input image res_onnx = compiled_model_onnx([normalized_input_image])[output_layer_onnx] # Convert network result to segmentation map and display the result result_mask_onnx = np.squeeze(np.argmax(res_onnx, axis=1)).astype(np.uint8) viz_result_image( image, segmentation_map_to_image(result_mask_onnx, CityScapesSegmentation.get_colormap()), resize=True, ) # IR Model in Inference Engine # Load the network in Inference Engine ie = Core() model_ir = ie.read_model(model=ir_path) compiled_model_ir = ie.compile_model(model=model_ir, device_name="CPU") # Get input and output layers output_layer_ir = compiled_model_ir.output(0) # Run inference on the input image res_ir = compiled_model_ir([input_image])[output_layer_ir] result_mask_ir = np.squeeze(np.argmax(res_ir, axis=1)).astype(np.uint8) viz_result_image( image, segmentation_map_to_image(result=result_mask_ir, colormap=CityScapesSegmentation.get_colormap()), resize=True, ) # PyTorch Comparison with torch.no_grad(): result_torch = model(torch.as_tensor(normalized_input_image).float()) result_mask_torch = torch.argmax(result_torch, dim=1).squeeze(0).numpy().astype(np.uint8) viz_result_image( image, segmentation_map_to_image(result=result_mask_torch, colormap=CityScapesSegmentation.get_colormap()), resize=True, ) # Performance Comparison num_images = 20 start = time.perf_counter() for _ in range(num_images): compiled_model_onnx([normalized_input_image]) end = time.perf_counter() time_onnx = end - start print( f"ONNX model in Inference Engine/CPU: {time_onnx/num_images:.3f} " f"seconds per image, FPS: {num_images/time_onnx:.2f}" ) start = time.perf_counter() for _ in range(num_images): compiled_model_ir([input_image]) end = time.perf_counter() time_ir = end - start print( f"IR model in Inference Engine/CPU: {time_ir/num_images:.3f} " f"seconds per image, FPS: {num_images/time_ir:.2f}" ) with torch.no_grad(): start = time.perf_counter() for _ in range(num_images): model(torch.as_tensor(input_image).float()) end = time.perf_counter() time_torch = end - start print( f"PyTorch model on CPU: {time_torch/num_images:.3f} seconds per image, " f"FPS: {num_images/time_torch:.2f}" ) if "GPU" in ie.available_devices: compiled_model_onnx_gpu = ie.compile_model(model=model_onnx, device_name="GPU") start = time.perf_counter() for _ in range(num_images): compiled_model_onnx_gpu([input_image]) end = time.perf_counter() time_onnx_gpu = end - start print( f"ONNX model in Inference Engine/GPU: {time_onnx_gpu/num_images:.3f} " f"seconds per image, FPS: {num_images/time_onnx_gpu:.2f}" ) compiled_model_ir_gpu = ie.compile_model(model=model_ir, device_name="GPU") start = time.perf_counter() for _ in range(num_images): compiled_model_ir_gpu([input_image]) end = time.perf_counter() time_ir_gpu = end - start print( f"IR model in Inference Engine/GPU: {time_ir_gpu/num_images:.3f} " f"seconds per image, FPS: {num_images/time_ir_gpu:.2f}" ) # Show Device Information devices = ie.available_devices for device in devices: device_name = ie.get_property(device_name=device, name="FULL_DEVICE_NAME") print(f"{device}: {device_name}")
#python #openvino #onnx #openvino-notebook #api #openvino-api
002-openvino-api: OpenVINO API tutorial# Load Inference Engine and Show Info from openvino.runtime import Core ie = Core() devices = ie.available_devices for device in devices: device_name = ie.get_property(device_name=device, name="FULL_DEVICE_NAME") print(f"{device}: {device_name}") # Loading a Model from openvino.runtime import Core ie = Core() classification_model_xml = "model/classification.xml" model = ie.read_model(model=classification_model_xml) compiled_model = ie.compile_model(model=model, device_name="CPU") from openvino.runtime import Core ie = Core() onnx_model_path = "model/segmentation.onnx" model_onnx = ie.read_model(model=onnx_model_path) compiled_model_onnx = ie.compile_model(model=model_onnx, device_name="CPU") from openvino.offline_transformations import serialize serialize(model=model_onnx, model_path="model/exported_onnx_model.xml", weights_path="model/exported_onnx_model.bin") # Getting Information about a Model from openvino.runtime import Core ie = Core() classification_model_xml = "model/classification.xml" model = ie.read_model(model=classification_model_xml) model.input(0).any_name input_layer = model.input(0) print(f"input precision: {input_layer.element_type}") print(f"input shape: {input_layer.shape}") from openvino.runtime import Core ie = Core() classification_model_xml = "model/classification.xml" model = ie.read_model(model=classification_model_xml) model.output(0).any_name output_layer = model.output(0) output_layer print(f"output precision: {output_layer.element_type}") print(f"output shape: {output_layer.shape}") from openvino.runtime import Core ie = Core() classification_model_xml = "model/classification.xml" model = ie.read_model(model=classification_model_xml) compiled_model = ie.compile_model(model=model, device_name="CPU") input_layer = compiled_model.input(0) output_layer = compiled_model.output(0) import cv2 image_filename = "data/coco_hollywood.jpg" image = cv2.imread(image_filename) image.shape # N,C,H,W = batch size, number of channels, height, width N, C, H, W = input_layer.shape # OpenCV resize expects the destination size as (width, height) resized_image = cv2.resize(src=image, dsize=(W, H)) resized_image.shape import numpy as np input_data = np.expand_dims(np.transpose(resized_image, (2, 0, 1)), 0).astype(np.float32) input_data.shape # Do Inference result = compiled_model([input_data])[output_layer] request = compiled_model.create_infer_request() request.infer(inputs={input_layer.any_name: input_data}) result = request.get_output_tensor(output_layer.index).data from openvino.runtime import Core, PartialShape ie = Core() segmentation_model_xml = "model/segmentation.xml" segmentation_model = ie.read_model(model=segmentation_model_xml) segmentation_input_layer = segmentation_model.input(0) segmentation_output_layer = segmentation_model.output(0) print("~~~~ ORIGINAL MODEL ~~~~") print(f"input shape: {segmentation_input_layer.shape}") print(f"output shape: {segmentation_output_layer.shape}") new_shape = PartialShape([1, 3, 544, 544]) segmentation_model.reshape({segmentation_input_layer.any_name: new_shape}) segmentation_compiled_model = ie.compile_model(model=segmentation_model, device_name="CPU") # help(segmentation_compiled_model) print("~~~~ RESHAPED MODEL ~~~~") print(f"model input shape: {segmentation_input_layer.shape}") print( f"compiled_model input shape: " f"{segmentation_compiled_model.input(index=0).shape}" ) print(f"compiled_model output shape: {segmentation_output_layer.shape}") # Change Batch Size from openvino.runtime import Core, PartialShape ie = Core() segmentation_model_xml = "model/segmentation.xml" segmentation_model = ie.read_model(model=segmentation_model_xml) segmentation_input_layer = segmentation_model.input(0) segmentation_output_layer = segmentation_model.output(0) new_shape = PartialShape([2, 3, 544, 544]) segmentation_model.reshape({segmentation_input_layer.any_name: new_shape}) segmentation_compiled_model = ie.compile_model(model=segmentation_model, device_name="CPU") print(f"input shape: {segmentation_input_layer.shape}") print(f"output shape: {segmentation_output_layer.shape}") import numpy as np from openvino.runtime import Core, PartialShape ie = Core() segmentation_model_xml = "model/segmentation.xml" segmentation_model = ie.read_model(model=segmentation_model_xml) segmentation_input_layer = segmentation_model.input(0) segmentation_output_layer = segmentation_model.output(0) new_shape = PartialShape([2, 3, 544, 544]) segmentation_model.reshape({segmentation_input_layer.any_name: new_shape}) segmentation_compiled_model = ie.compile_model(model=segmentation_model, device_name="CPU") input_data = np.random.rand(2, 3, 544, 544) output = segmentation_compiled_model([input_data]) print(f"input data shape: {input_data.shape}") print(f"result data data shape: {segmentation_output_layer.shape}") # Caching a Model import time from pathlib import Path from openvino.runtime import Core, PartialShape ie = Core() device_name = "GPU" # Model Caching is not available for CPU if device_name in ie.available_devices and device_name != "CPU": cache_path = Path("model/model_cache") cache_path.mkdir(exist_ok=True) # Enable caching for Inference Engine. To disable caching set enable_caching = False enable_caching = True config_dict = {"CACHE_DIR": str(cache_path)} if enable_caching else {} classification_model_xml = "model/classification.xml" model = ie.read_model(model=classification_model_xml) start_time = time.perf_counter() compiled_model = ie.compile_model(model=model, device_name=device_name, config=config_dict) end_time = time.perf_counter() print(f"Loading the network to the {device_name} device took {end_time-start_time:.2f} seconds.") else: print("Model caching is not available on CPU devices.") if device_name in ie.available_devices and device_name != "CPU": del compiled_model start_time = time.perf_counter() compiled_model = ie.compile_model(model=model, device_name=device_name, config=config_dict) end_time = time.perf_counter() print(f"Loading the network to the {device_name} device took {end_time-start_time:.2f} seconds.")
Fri Jun 17 2022 09:51:37 GMT+0000 (Coordinated Universal Time)
#python #openvino #openvino-notebooks #deeplearning #accelerated-inference #object-detection #onnx #onnx-runtime #openvino-onnx-runtime #openvino-execution-provider-for-onnx #tiny-yolov4Fri Jun 17 2022 09:49:43 GMT+0000 (Coordinated Universal Time) https://github.com/microsoft/onnxruntime-inference-examples/blob/main/python/OpenVINO_EP/tiny_yolo_v2_object_detection/tiny_yolov2_obj_detection_sample.py
#python #openvino #openvino-notebooks #deeplearning #accelerated-inference #object-detection #onnx #onnx-runtime #openvino-onnx-runtime #yolov2 #openvino-execution-provider-for-onnxThu Jun 09 2022 17:20:26 GMT+0000 (Coordinated Universal Time) https://github.com/openvinotoolkit/openvino_notebooks/blob/main/notebooks/103-paddle-onnx-to-openvino/103-paddle-onnx-to-openvino-classification.ipynb
#python #openvino #openvino-notebook #onnx #paddlepaddleThu Jun 09 2022 17:04:05 GMT+0000 (Coordinated Universal Time) https://github.com/openvinotoolkit/openvino_notebooks/blob/main/notebooks/102-pytorch-onnx-to-openvino/102-pytorch-onnx-to-openvino.ipynb
#python #openvino #openvino-notebook #pytorch #onnxThu Jun 09 2022 16:41:35 GMT+0000 (Coordinated Universal Time) https://github.com/openvinotoolkit/openvino_notebooks/blob/main/notebooks/002-openvino-api/002-openvino-api.ipynb
#python #openvino #onnx #openvino-notebook #api #openvino-api