From f920ed2d5da4faa6e3ee90edf73a8a8c0c228ee0 Mon Sep 17 00:00:00 2001 From: JHLee0513 Date: Mon, 2 Mar 2020 18:29:57 -0800 Subject: [PATCH 01/15] added yolov3 submodule --- .gitmodules | 3 +++ uw_detection/src/yolov3 | 1 + 2 files changed, 4 insertions(+) create mode 100644 .gitmodules create mode 160000 uw_detection/src/yolov3 diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 0000000..f0c8d29 --- /dev/null +++ b/.gitmodules @@ -0,0 +1,3 @@ +[submodule "uw_detection/src/yolov3"] + path = uw_detection/src/yolov3 + url = https://github.com/homeskies/yolov3 diff --git a/uw_detection/src/yolov3 b/uw_detection/src/yolov3 new file mode 160000 index 0000000..878f74a --- /dev/null +++ b/uw_detection/src/yolov3 @@ -0,0 +1 @@ +Subproject commit 878f74a059e66d054c4c99c568832ed5c5e0cb1b From 7027ef265f3657537a803f72e32efc95d12113d2 Mon Sep 17 00:00:00 2001 From: JHLee0513 Date: Mon, 2 Mar 2020 18:37:07 -0800 Subject: [PATCH 02/15] updated yolov3 to be a python module --- uw_detection/src/yolov3 | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/uw_detection/src/yolov3 b/uw_detection/src/yolov3 index 878f74a..1646452 160000 --- a/uw_detection/src/yolov3 +++ b/uw_detection/src/yolov3 @@ -1 +1 @@ -Subproject commit 878f74a059e66d054c4c99c568832ed5c5e0cb1b +Subproject commit 164645294e5ef89a451f7791d69c47fd4da18c57 From 0027d8b5ed337abccee3ac4098ec209b01c06d40 Mon Sep 17 00:00:00 2001 From: Nick Walker Date: Tue, 3 Mar 2020 14:54:38 -0800 Subject: [PATCH 03/15] Make uw_detection a catkin package Wrap yolov3 submodule in a package, have the package use catkin python setup to provide the module to scripts and other catkin packages Paste camera demo with imports corrected --- uw_detection/CMakeLists.txt | 12 ++ uw_detection/README.md | 3 + uw_detection/package.xml | 17 ++ uw_detection/scripts/camera_detect_demo | 208 ++++++++++++++++++++++++ uw_detection/setup.py | 11 ++ 5 files changed, 251 insertions(+) create mode 100644 uw_detection/CMakeLists.txt create mode 100644 uw_detection/README.md create mode 100644 uw_detection/package.xml create mode 100755 uw_detection/scripts/camera_detect_demo create mode 100644 uw_detection/setup.py diff --git a/uw_detection/CMakeLists.txt b/uw_detection/CMakeLists.txt new file mode 100644 index 0000000..39764f1 --- /dev/null +++ b/uw_detection/CMakeLists.txt @@ -0,0 +1,12 @@ +cmake_minimum_required(VERSION 2.8.12) +project(uw_detection) + +find_package(catkin REQUIRED) + +catkin_python_setup() + +catkin_package() + +install(DIRECTORY maps + DESTINATION ${CATKIN_PACKAGE_SHARE_DESTINATION} +) \ No newline at end of file diff --git a/uw_detection/README.md b/uw_detection/README.md new file mode 100644 index 0000000..0d92fbc --- /dev/null +++ b/uw_detection/README.md @@ -0,0 +1,3 @@ +# uw_detection + +Wrappers for detection models. Tools for preparing data and training. diff --git a/uw_detection/package.xml b/uw_detection/package.xml new file mode 100644 index 0000000..196bdf3 --- /dev/null +++ b/uw_detection/package.xml @@ -0,0 +1,17 @@ + + uw_detection + 0.1.0 + + Detection models, as well as data prep, labeling, and training tools + + MIT + UW RoboCup Contributors + UW RoboCup Contributors + + + catkin + + catkin + rospack + + \ No newline at end of file diff --git a/uw_detection/scripts/camera_detect_demo b/uw_detection/scripts/camera_detect_demo new file mode 100755 index 0000000..ba8340c --- /dev/null +++ b/uw_detection/scripts/camera_detect_demo @@ -0,0 +1,208 @@ +#!/usr/bin/env python3 +import argparse +from sys import platform + +from yolov3.models import * # set ONNX_EXPORT in models.py +from yolov3.utils.datasets import * +from yolov3.utils.utils import * +import cv2 +import pyrealsense2 as rs +import torchvision.transforms as transforms + + +def detect(pipe=None, save_img=False): + img_size = (320, 192) if ONNX_EXPORT else opt.img_size # (320, 192) or (416, 256) or (608, 352) for (height, width) + out, source, weights, half, view_img, save_txt = opt.output, opt.source, opt.weights, opt.half, opt.view_img, opt.save_txt + webcam = source == '0' or source.startswith('rtsp') or source.startswith('http') or source.endswith('.txt') + + # Initialize + device = torch_utils.select_device(device='cpu' if ONNX_EXPORT else opt.device) + if os.path.exists(out): + shutil.rmtree(out) # delete output folder + os.makedirs(out) # make new output folder + + # Initialize model + model = Darknet(opt.cfg, img_size) + + # Load weights + attempt_download(weights) + if weights.endswith('.pt'): # pytorch format + model.load_state_dict(torch.load(weights, map_location=device)['model']) + else: # darknet format + load_darknet_weights(model, weights) + + # Fuse Conv2d + BatchNorm2d layers + # model.fuse() + # torch_utils.model_info(model, report='summary') # 'full' or 'summary' + + # Eval mode + model.to(device).eval() + + # Export mode + if ONNX_EXPORT: + model.fuse() + img = torch.zeros((1, 3) + img_size) # (1, 3, 320, 192) + torch.onnx.export(model, img, 'weights/export.onnx', verbose=False, opset_version=11) + + # Validate exported model + import onnx + model = onnx.load('weights/export.onnx') # Load the ONNX model + onnx.checker.check_model(model) # Check that the IR is well formed + print(onnx.helper.printable_graph(model.graph)) # Print a human readable representation of the graph + return + + # Half precision + half = half and device.type != 'cpu' # half precision only supported on CUDA + if half: + model.half() + + # Set Dataloader + vid_path, vid_writer = None, None + if webcam: + view_img = True + torch.backends.cudnn.benchmark = True # set True to speed up constant image size inference + # dataset = LoadStreams(source, img_size=img_size, half=half) + else: + save_img = True + # dataset = LoadImages(source, img_size=img_size, half=half) + + # Get names and colors + names = load_classes(opt.names) + colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(names))] + + # Run inference + t0 = time.time() + # for path, img, im0s, vid_cap in dataset: + while True: + t = time.time() + + # Get detections + img = pipe.wait_for_frames() + + # Align the depth frame to color frame + aligned_frames = align.process(img) + + # Get aligned frames + aligned_depth_frame = aligned_frames.get_depth_frame() # aligned_depth_frame is a 640x480 depth image + aligned_color_frame = aligned_frames.get_color_frame() + + aligned_color_frame = np.asanyarray(img.get_data()) + # im0 = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + # im0 = cv2.resize(im0, (640,480)) + aligned_color_frame = cv2.resize(img, (320, 320)) + im0 = cv2.cvtColor(aligned_color_frame, cv2.COLOR_RGB2BGR) # im0 is original image + img = transforms.ToTensor()(im0).to(device) + + aligned_depth_frame = cv2.resize(aligned_depth_frame, (320, 320)) + + # img = torch.from_numpy(img).to(device) + if img.ndimension() == 3: + img = img.unsqueeze(0) + pred = model(img)[0] + + if opt.half: + pred = pred.float() + + # Apply NMS + pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms) + + # Apply Classifier + # if classify: + # pred = apply_classifier(pred, modelc, img, im0s) + + # Process detections + for i, det in enumerate(pred): # detections per image + # depth mask for each of the objects + class_mask = np.zeros((320, 320, 21)) + + s = "" + # if webcam: # batch_size >= 1 + # p, s, im0 = " ", '%g: ' % i, im0s[i] + # else: + # p, s, im0 = path, '', im0s + + # save_path = str(Path(out) / Path(p).name) + s += '%gx%g ' % img.shape[2:] # print string + if det is not None and len(det): + # Rescale boxes from img_size to im0 size + det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round() + + # Print results + for c in det[:, -1].unique(): + n = (det[:, -1] == c).sum() # detections per class + s += '%g %ss, ' % (n, names[int(c)]) # add to string + + # Write results + for *xyxy, conf, cls in det: + if save_img or view_img: # Add bbox to image + label = '%s %.2f' % (names[int(cls)], conf) + plot_one_box(xyxy, im0, label=label, color=colors[int(cls)]) + # print(det) + + # fill in bounding box area for mask + # cv2.contour for each channel + # for each contour -> find the centroid + + # depth + color image combine to overlay + + # Stream results + if True: + print(1 / (time.time() - t)) + # cv2.imshow("output", im0) + # cv2.imshow("resized", cv2.resize(im0, (1080,720))) + + if cv2.waitKey(1) == ord('q'): # q to quit + raise StopIteration + + # Save results (image with detections) + # if save_img: + # if dataset.mode == 'images': + # cv2.imwrite(save_path, im0) + # else: + # if vid_path != save_path: # new video + # vid_path = save_path + # if isinstance(vid_writer, cv2.VideoWriter): + # vid_writer.release() # release previous video writer + + # fps = vid_cap.get(cv2.CAP_PROP_FPS) + # w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + # h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + # vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*opt.fourcc), fps, (w, h)) + # vid_writer.write(im0) + + # if save_txt or save_img: + # print('Results saved to %s' % os.getcwd() + os.sep + out) + # if platform == 'darwin': # MacOS + # os.system('open ' + out + ' ' + save_path) + + # print('Done. (%.3fs)' % (time.time() - t0)) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--cfg', type=str, default='cfg/yolov3-spp.cfg', help='*.cfg path') + parser.add_argument('--names', type=str, default='data/coco.names', help='*.names path') + parser.add_argument('--weights', type=str, default='weights/yolov3-spp-ultralytics.pt', help='weights path') + parser.add_argument('--source', type=str, default='data/samples', help='source') # input file/folder, 0 for webcam + parser.add_argument('--output', type=str, default='output', help='output folder') # output folder + parser.add_argument('--img-size', type=int, default=416, help='inference size (pixels)') + parser.add_argument('--conf-thres', type=float, default=0.3, help='object confidence threshold') + parser.add_argument('--iou-thres', type=float, default=0.6, help='IOU threshold for NMS') + parser.add_argument('--fourcc', type=str, default='mp4v', help='output video codec (verify ffmpeg support)') + parser.add_argument('--half', action='store_true', help='half precision FP16 inference') + parser.add_argument('--device', default='', help='device id (i.e. 0 or 0,1) or cpu') + parser.add_argument('--view-img', action='store_true', help='display results') + parser.add_argument('--save-txt', action='store_true', help='save results to *.txt') + parser.add_argument('--classes', nargs='+', type=int, help='filter by class') + parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS') + opt = parser.parse_args() + print(opt) + + align_to = rs.stream.color + align = rs.align(align_to) + + pipeline = rs.pipeline() + pipeline.start() + + with torch.no_grad(): + detect(pipe=pipeline) diff --git a/uw_detection/setup.py b/uw_detection/setup.py new file mode 100644 index 0000000..81d0f8c --- /dev/null +++ b/uw_detection/setup.py @@ -0,0 +1,11 @@ +# ! DO NOT MANUALLY INVOKE THIS setup.py, USE CATKIN INSTEAD + +from distutils.core import setup +from catkin_pkg.python_setup import generate_distutils_setup + +# fetch values from package.xml +setup_args = generate_distutils_setup( + packages=['yolov3'], + package_dir={'': 'src'}) + +setup(**setup_args) \ No newline at end of file From c39611b5202fad3f491d4c43427dbda114506557 Mon Sep 17 00:00:00 2001 From: JHLee0513 Date: Sat, 14 Mar 2020 15:46:29 -0700 Subject: [PATCH 04/15] merging centroid detection and frame conversion updated to yolo --- uw_detection/src/yolov3 | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/uw_detection/src/yolov3 b/uw_detection/src/yolov3 index 1646452..b8a1aa4 160000 --- a/uw_detection/src/yolov3 +++ b/uw_detection/src/yolov3 @@ -1 +1 @@ -Subproject commit 164645294e5ef89a451f7791d69c47fd4da18c57 +Subproject commit b8a1aa482a8688b0c24e70164bac24f48241e0ba From cf3c942c51f4c98be460a40010d145ea8a97e4b5 Mon Sep 17 00:00:00 2001 From: Brian Lee Date: Fri, 8 May 2020 17:16:09 -0700 Subject: [PATCH 05/15] Moved frame transformation script from yolo to uw_detection since it is a wrapper usage --- uw_detection/scripts/camera_to_baselink | 52 +++++++++++++++++++++++++ 1 file changed, 52 insertions(+) create mode 100755 uw_detection/scripts/camera_to_baselink diff --git a/uw_detection/scripts/camera_to_baselink b/uw_detection/scripts/camera_to_baselink new file mode 100755 index 0000000..1032c6f --- /dev/null +++ b/uw_detection/scripts/camera_to_baselink @@ -0,0 +1,52 @@ +#!/usr/bin/env python3 + +import tf +import rospy +from tf import TransformListener +from std_msgs.msg import Float32MultiArray, MultiArrayDimension +import numpy as np + +position = None + +def callback(data): + centroids_in_baselink = [] + if position is not None: + # Convert each centroid prediction in baselink frame from realsense frame + #rospy.loginfo("DATA: ", data) + #rospy.loginfo("POSITION: ", position) + # data is a 50x4 array, 50 samples of [x,y,z,class_idx] + data = np.array(data.data).reshape(50,4) + #print(position) + for i, row in enumerate(data): + new_x = row[0] + position[0] + new_y = row[1] + position[1] + new_z = row[2] + position[2] + data[i,:] = np.array([new_x, new_y, new_z, row[3]]) + # get row to be + # publish converted array + centroid_msg = Float32MultiArray() + centroid_msg.layout.data_offset = 0 + centroid_msg.layout.dim = [MultiArrayDimension(), MultiArrayDimension()] + centroid_msg.layout.dim[0].label = "channels" + centroid_msg.layout.dim[0].size = 4 + centroid_msg.layout.dim[0].stride = 200 + centroid_msg.layout.dim[1].label = "samples" + centroid_msg.layout.dim[1].size = 50 + centroid_msg.layout.dim[1].stride = 50 + centroid_msg.data = data.flatten() + pub.publish(centroid_msg) + rate.sleep() + +if __name__ == "__main__": + rospy.init_node('centroid_listener', anonymous = True) + rospy.Subscriber("object_centroids", Float32MultiArray, callback) + pub = rospy.Publisher('object_centroids_baselink', Float32MultiArray, queue_size = 10) + #rospy.init_node("baselink_tf_listener") + listener = TransformListener() + rate = rospy.Rate(100.) + while not rospy.is_shutdown(): + listener.waitForTransform("/base_link", "head_external_camera_depth_frame", rospy.Time(), rospy.Duration(3.0)) + #t = listener.getLatestCommonTime("/base_link", "/head_external_camera_aligned_depth_to_color_frame") + position, _ = listener.lookupTransform("/base_link", "/head_external_camera_depth_frame", rospy.Time()) + #rate.sleep() + rospy.spin() From 6392111996c6e91c664a99bd13e5736ce8847693 Mon Sep 17 00:00:00 2001 From: Nick Walker Date: Fri, 29 May 2020 13:24:55 -0500 Subject: [PATCH 06/15] Pull submodules in CI --- .github/workflows/ci.yaml | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index 4e0e4d4..5e98ea5 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -15,7 +15,9 @@ jobs: - {} runs-on: ubuntu-latest steps: - - uses: actions/checkout@v1 + - uses: actions/checkout@v2 + with: + submodules: true - uses: 'ros-industrial/industrial_ci@master' env: ${{matrix.env}} lint: From fd3b154ccadb533afa449c549866367ef716e5cc Mon Sep 17 00:00:00 2001 From: Nick Walker Date: Fri, 29 May 2020 13:35:39 -0500 Subject: [PATCH 07/15] Remove extra install directive --- uw_detection/CMakeLists.txt | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/uw_detection/CMakeLists.txt b/uw_detection/CMakeLists.txt index 39764f1..24b30a8 100644 --- a/uw_detection/CMakeLists.txt +++ b/uw_detection/CMakeLists.txt @@ -5,8 +5,4 @@ find_package(catkin REQUIRED) catkin_python_setup() -catkin_package() - -install(DIRECTORY maps - DESTINATION ${CATKIN_PACKAGE_SHARE_DESTINATION} -) \ No newline at end of file +catkin_package() \ No newline at end of file From a33a1fcf008b971681926fd0bbfd02fccdc50c71 Mon Sep 17 00:00:00 2001 From: Brian Lee Date: Tue, 23 Jun 2020 18:08:17 -0700 Subject: [PATCH 08/15] Trying to get a script working that: 1. subscribes to image from sim 2. predicts object bounding boxes 3. publishes prediction --- uw_detection/scripts/camera_detect_sim | 208 +++++++++++++++++++++++++ 1 file changed, 208 insertions(+) create mode 100644 uw_detection/scripts/camera_detect_sim diff --git a/uw_detection/scripts/camera_detect_sim b/uw_detection/scripts/camera_detect_sim new file mode 100644 index 0000000..ba8340c --- /dev/null +++ b/uw_detection/scripts/camera_detect_sim @@ -0,0 +1,208 @@ +#!/usr/bin/env python3 +import argparse +from sys import platform + +from yolov3.models import * # set ONNX_EXPORT in models.py +from yolov3.utils.datasets import * +from yolov3.utils.utils import * +import cv2 +import pyrealsense2 as rs +import torchvision.transforms as transforms + + +def detect(pipe=None, save_img=False): + img_size = (320, 192) if ONNX_EXPORT else opt.img_size # (320, 192) or (416, 256) or (608, 352) for (height, width) + out, source, weights, half, view_img, save_txt = opt.output, opt.source, opt.weights, opt.half, opt.view_img, opt.save_txt + webcam = source == '0' or source.startswith('rtsp') or source.startswith('http') or source.endswith('.txt') + + # Initialize + device = torch_utils.select_device(device='cpu' if ONNX_EXPORT else opt.device) + if os.path.exists(out): + shutil.rmtree(out) # delete output folder + os.makedirs(out) # make new output folder + + # Initialize model + model = Darknet(opt.cfg, img_size) + + # Load weights + attempt_download(weights) + if weights.endswith('.pt'): # pytorch format + model.load_state_dict(torch.load(weights, map_location=device)['model']) + else: # darknet format + load_darknet_weights(model, weights) + + # Fuse Conv2d + BatchNorm2d layers + # model.fuse() + # torch_utils.model_info(model, report='summary') # 'full' or 'summary' + + # Eval mode + model.to(device).eval() + + # Export mode + if ONNX_EXPORT: + model.fuse() + img = torch.zeros((1, 3) + img_size) # (1, 3, 320, 192) + torch.onnx.export(model, img, 'weights/export.onnx', verbose=False, opset_version=11) + + # Validate exported model + import onnx + model = onnx.load('weights/export.onnx') # Load the ONNX model + onnx.checker.check_model(model) # Check that the IR is well formed + print(onnx.helper.printable_graph(model.graph)) # Print a human readable representation of the graph + return + + # Half precision + half = half and device.type != 'cpu' # half precision only supported on CUDA + if half: + model.half() + + # Set Dataloader + vid_path, vid_writer = None, None + if webcam: + view_img = True + torch.backends.cudnn.benchmark = True # set True to speed up constant image size inference + # dataset = LoadStreams(source, img_size=img_size, half=half) + else: + save_img = True + # dataset = LoadImages(source, img_size=img_size, half=half) + + # Get names and colors + names = load_classes(opt.names) + colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(names))] + + # Run inference + t0 = time.time() + # for path, img, im0s, vid_cap in dataset: + while True: + t = time.time() + + # Get detections + img = pipe.wait_for_frames() + + # Align the depth frame to color frame + aligned_frames = align.process(img) + + # Get aligned frames + aligned_depth_frame = aligned_frames.get_depth_frame() # aligned_depth_frame is a 640x480 depth image + aligned_color_frame = aligned_frames.get_color_frame() + + aligned_color_frame = np.asanyarray(img.get_data()) + # im0 = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + # im0 = cv2.resize(im0, (640,480)) + aligned_color_frame = cv2.resize(img, (320, 320)) + im0 = cv2.cvtColor(aligned_color_frame, cv2.COLOR_RGB2BGR) # im0 is original image + img = transforms.ToTensor()(im0).to(device) + + aligned_depth_frame = cv2.resize(aligned_depth_frame, (320, 320)) + + # img = torch.from_numpy(img).to(device) + if img.ndimension() == 3: + img = img.unsqueeze(0) + pred = model(img)[0] + + if opt.half: + pred = pred.float() + + # Apply NMS + pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms) + + # Apply Classifier + # if classify: + # pred = apply_classifier(pred, modelc, img, im0s) + + # Process detections + for i, det in enumerate(pred): # detections per image + # depth mask for each of the objects + class_mask = np.zeros((320, 320, 21)) + + s = "" + # if webcam: # batch_size >= 1 + # p, s, im0 = " ", '%g: ' % i, im0s[i] + # else: + # p, s, im0 = path, '', im0s + + # save_path = str(Path(out) / Path(p).name) + s += '%gx%g ' % img.shape[2:] # print string + if det is not None and len(det): + # Rescale boxes from img_size to im0 size + det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round() + + # Print results + for c in det[:, -1].unique(): + n = (det[:, -1] == c).sum() # detections per class + s += '%g %ss, ' % (n, names[int(c)]) # add to string + + # Write results + for *xyxy, conf, cls in det: + if save_img or view_img: # Add bbox to image + label = '%s %.2f' % (names[int(cls)], conf) + plot_one_box(xyxy, im0, label=label, color=colors[int(cls)]) + # print(det) + + # fill in bounding box area for mask + # cv2.contour for each channel + # for each contour -> find the centroid + + # depth + color image combine to overlay + + # Stream results + if True: + print(1 / (time.time() - t)) + # cv2.imshow("output", im0) + # cv2.imshow("resized", cv2.resize(im0, (1080,720))) + + if cv2.waitKey(1) == ord('q'): # q to quit + raise StopIteration + + # Save results (image with detections) + # if save_img: + # if dataset.mode == 'images': + # cv2.imwrite(save_path, im0) + # else: + # if vid_path != save_path: # new video + # vid_path = save_path + # if isinstance(vid_writer, cv2.VideoWriter): + # vid_writer.release() # release previous video writer + + # fps = vid_cap.get(cv2.CAP_PROP_FPS) + # w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + # h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + # vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*opt.fourcc), fps, (w, h)) + # vid_writer.write(im0) + + # if save_txt or save_img: + # print('Results saved to %s' % os.getcwd() + os.sep + out) + # if platform == 'darwin': # MacOS + # os.system('open ' + out + ' ' + save_path) + + # print('Done. (%.3fs)' % (time.time() - t0)) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--cfg', type=str, default='cfg/yolov3-spp.cfg', help='*.cfg path') + parser.add_argument('--names', type=str, default='data/coco.names', help='*.names path') + parser.add_argument('--weights', type=str, default='weights/yolov3-spp-ultralytics.pt', help='weights path') + parser.add_argument('--source', type=str, default='data/samples', help='source') # input file/folder, 0 for webcam + parser.add_argument('--output', type=str, default='output', help='output folder') # output folder + parser.add_argument('--img-size', type=int, default=416, help='inference size (pixels)') + parser.add_argument('--conf-thres', type=float, default=0.3, help='object confidence threshold') + parser.add_argument('--iou-thres', type=float, default=0.6, help='IOU threshold for NMS') + parser.add_argument('--fourcc', type=str, default='mp4v', help='output video codec (verify ffmpeg support)') + parser.add_argument('--half', action='store_true', help='half precision FP16 inference') + parser.add_argument('--device', default='', help='device id (i.e. 0 or 0,1) or cpu') + parser.add_argument('--view-img', action='store_true', help='display results') + parser.add_argument('--save-txt', action='store_true', help='save results to *.txt') + parser.add_argument('--classes', nargs='+', type=int, help='filter by class') + parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS') + opt = parser.parse_args() + print(opt) + + align_to = rs.stream.color + align = rs.align(align_to) + + pipeline = rs.pipeline() + pipeline.start() + + with torch.no_grad(): + detect(pipe=pipeline) From 764063a7cebe3cb81e8094f9e6fac18a8e25cb58 Mon Sep 17 00:00:00 2001 From: Nick Walker Date: Tue, 21 Jul 2020 19:18:04 -0700 Subject: [PATCH 09/15] Add hacked together YOLOv3 TensorRT implementation Includes a copy of Xinyu Wang's TensorRT implementation of the Ultralytics YOLOv3 implementation Some headers headers shuffled out into the include directory so things build in a ROS environment A bit of CMake setup to make sure things only attempt to build when there's CUDA on the machine --- uw_detection/CMakeLists.txt | 62 +- uw_detection/include/uw_detection/Utils.h | 94 +++ uw_detection/include/uw_detection/logging.h | 503 +++++++++++++++ uw_detection/include/uw_detection/yololayer.h | 159 +++++ .../include/uw_detection/yolov3-spp.h | 13 + uw_detection/package.xml | 17 + uw_detection/src/yolo_ros_image_node.cpp | 105 ++++ uw_detection/src/yolov3-spp/.gitignore | 1 + uw_detection/src/yolov3-spp/README.md | 60 ++ uw_detection/src/yolov3-spp/gen_wts.py | 22 + uw_detection/src/yolov3-spp/samples/bus.jpg | Bin 0 -> 487438 bytes .../src/yolov3-spp/samples/zidane.jpg | Bin 0 -> 168949 bytes uw_detection/src/yolov3-spp/yololayer.cu | 259 ++++++++ uw_detection/src/yolov3-spp/yolov3-spp.cpp | 573 ++++++++++++++++++ uw_detection/src/yolov3_spp_prepare_plan.cpp | 35 ++ 15 files changed, 1901 insertions(+), 2 deletions(-) create mode 100644 uw_detection/include/uw_detection/Utils.h create mode 100644 uw_detection/include/uw_detection/logging.h create mode 100644 uw_detection/include/uw_detection/yololayer.h create mode 100644 uw_detection/include/uw_detection/yolov3-spp.h create mode 100644 uw_detection/src/yolo_ros_image_node.cpp create mode 100644 uw_detection/src/yolov3-spp/.gitignore create mode 100644 uw_detection/src/yolov3-spp/README.md create mode 100644 uw_detection/src/yolov3-spp/gen_wts.py create mode 100644 uw_detection/src/yolov3-spp/samples/bus.jpg create mode 100644 uw_detection/src/yolov3-spp/samples/zidane.jpg create mode 100644 uw_detection/src/yolov3-spp/yololayer.cu create mode 100644 uw_detection/src/yolov3-spp/yolov3-spp.cpp create mode 100644 uw_detection/src/yolov3_spp_prepare_plan.cpp diff --git a/uw_detection/CMakeLists.txt b/uw_detection/CMakeLists.txt index 24b30a8..be56afc 100644 --- a/uw_detection/CMakeLists.txt +++ b/uw_detection/CMakeLists.txt @@ -1,8 +1,66 @@ cmake_minimum_required(VERSION 2.8.12) project(uw_detection) -find_package(catkin REQUIRED) +find_package(catkin REQUIRED COMPONENTS cv_bridge + geometry_msgs + image_transport + knowledge_representation + message_generation + message_filters + message_runtime + roscpp + roslint + sensor_msgs + std_msgs + std_srvs + tf2 + tf2_eigen + visualization_msgs) catkin_python_setup() -catkin_package() \ No newline at end of file +catkin_package() + + +option(CUDA_USE_STATIC_CUDA_RUNTIME OFF) +set(CMAKE_CXX_STANDARD 11) +set(CMAKE_BUILD_TYPE Debug) + +find_package(OpenCV) +include_directories(OpenCV_INCLUDE_DIRS + ${catkin_INCLUDE_DIRS} + include) + +find_package(CUDA) + +if (CUDA_FOUND) +set(CUDA_NVCC_PLAGS ${CUDA_NVCC_PLAGS};-std=c++11;-g;-G;-gencode;arch=compute_30;code=sm_30) + + +if (CMAKE_SYSTEM_PROCESSOR MATCHES "aarch64") + message("embed_platform on") + include_directories(/usr/local/cuda/targets/aarch64-linux/include) + link_directories(/usr/local/cuda/targets/aarch64-linux/lib) +else() + message("embed_platform off") + include_directories(/usr/local/cuda/include) + link_directories(/usr/local/cuda/lib64) +endif() + + +set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -Wall -Ofast -Wfatal-errors -D_MWAITXINTRIN_H_INCLUDED") + +cuda_add_library(yololayer SHARED src/yolov3-spp/yololayer.cu) + +add_library(yolov3-spp src/yolov3-spp/yolov3-spp.cpp) +target_link_libraries(yolov3-spp nvinfer cudart yololayer ${OpenCV_LIBS}) + +add_executable(yolov3_prepare_plan src/yolov3_spp_prepare_plan.cpp) +target_link_libraries(yolov3_prepare_plan yolov3-spp nvinfer cudart yololayer ${OpenCV_LIBS} ${catkin_LIBRARIES}) + +add_executable(yolo_ros_image_node src/yolo_ros_image_node.cpp) +target_link_libraries(yolo_ros_image_node yolov3-spp nvinfer cudart yololayer ${OpenCV_LIBS} ${catkin_LIBRARIES}) + +add_definitions(-O2 -pthread) +endif() + diff --git a/uw_detection/include/uw_detection/Utils.h b/uw_detection/include/uw_detection/Utils.h new file mode 100644 index 0000000..0de663c --- /dev/null +++ b/uw_detection/include/uw_detection/Utils.h @@ -0,0 +1,94 @@ +#ifndef __TRT_UTILS_H_ +#define __TRT_UTILS_H_ + +#include +#include +#include +#include + +#ifndef CUDA_CHECK + +#define CUDA_CHECK(callstr) \ + { \ + cudaError_t error_code = callstr; \ + if (error_code != cudaSuccess) { \ + std::cerr << "CUDA error " << error_code << " at " << __FILE__ << ":" << __LINE__; \ + assert(0); \ + } \ + } + +#endif + +namespace Tn +{ + class Profiler : public nvinfer1::IProfiler + { + public: + void printLayerTimes(int itrationsTimes) + { + float totalTime = 0; + for (size_t i = 0; i < mProfile.size(); i++) + { + printf("%-40.40s %4.3fms\n", mProfile[i].first.c_str(), mProfile[i].second / itrationsTimes); + totalTime += mProfile[i].second; + } + printf("Time over all layers: %4.3f\n", totalTime / itrationsTimes); + } + private: + typedef std::pair Record; + std::vector mProfile; + + virtual void reportLayerTime(const char* layerName, float ms) + { + auto record = std::find_if(mProfile.begin(), mProfile.end(), [&](const Record& r){ return r.first == layerName; }); + if (record == mProfile.end()) + mProfile.push_back(std::make_pair(layerName, ms)); + else + record->second += ms; + } + }; + + //Logger for TensorRT info/warning/errors + class Logger : public nvinfer1::ILogger + { + public: + + Logger(): Logger(Severity::kWARNING) {} + + Logger(Severity severity): reportableSeverity(severity) {} + + void log(Severity severity, const char* msg) override + { + // suppress messages with severity enum value greater than the reportable + if (severity > reportableSeverity) return; + + switch (severity) + { + case Severity::kINTERNAL_ERROR: std::cerr << "INTERNAL_ERROR: "; break; + case Severity::kERROR: std::cerr << "ERROR: "; break; + case Severity::kWARNING: std::cerr << "WARNING: "; break; + case Severity::kINFO: std::cerr << "INFO: "; break; + default: std::cerr << "UNKNOWN: "; break; + } + std::cerr << msg << std::endl; + } + + Severity reportableSeverity{Severity::kWARNING}; + }; + + template + void write(char*& buffer, const T& val) + { + *reinterpret_cast(buffer) = val; + buffer += sizeof(T); + } + + template + void read(const char*& buffer, T& val) + { + val = *reinterpret_cast(buffer); + buffer += sizeof(T); + } +} + +#endif \ No newline at end of file diff --git a/uw_detection/include/uw_detection/logging.h b/uw_detection/include/uw_detection/logging.h new file mode 100644 index 0000000..602b69f --- /dev/null +++ b/uw_detection/include/uw_detection/logging.h @@ -0,0 +1,503 @@ +/* + * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef TENSORRT_LOGGING_H +#define TENSORRT_LOGGING_H + +#include "NvInferRuntimeCommon.h" +#include +#include +#include +#include +#include +#include +#include + +using Severity = nvinfer1::ILogger::Severity; + +class LogStreamConsumerBuffer : public std::stringbuf +{ +public: + LogStreamConsumerBuffer(std::ostream& stream, const std::string& prefix, bool shouldLog) + : mOutput(stream) + , mPrefix(prefix) + , mShouldLog(shouldLog) + { + } + + LogStreamConsumerBuffer(LogStreamConsumerBuffer&& other) + : mOutput(other.mOutput) + { + } + + ~LogStreamConsumerBuffer() + { + // std::streambuf::pbase() gives a pointer to the beginning of the buffered part of the output sequence + // std::streambuf::pptr() gives a pointer to the current position of the output sequence + // if the pointer to the beginning is not equal to the pointer to the current position, + // call putOutput() to log the output to the stream + if (pbase() != pptr()) + { + putOutput(); + } + } + + // synchronizes the stream buffer and returns 0 on success + // synchronizing the stream buffer consists of inserting the buffer contents into the stream, + // resetting the buffer and flushing the stream + virtual int sync() + { + putOutput(); + return 0; + } + + void putOutput() + { + if (mShouldLog) + { + // prepend timestamp + std::time_t timestamp = std::time(nullptr); + tm* tm_local = std::localtime(×tamp); + std::cout << "["; + std::cout << std::setw(2) << std::setfill('0') << 1 + tm_local->tm_mon << "/"; + std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_mday << "/"; + std::cout << std::setw(4) << std::setfill('0') << 1900 + tm_local->tm_year << "-"; + std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_hour << ":"; + std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_min << ":"; + std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_sec << "] "; + // std::stringbuf::str() gets the string contents of the buffer + // insert the buffer contents pre-appended by the appropriate prefix into the stream + mOutput << mPrefix << str(); + // set the buffer to empty + str(""); + // flush the stream + mOutput.flush(); + } + } + + void setShouldLog(bool shouldLog) + { + mShouldLog = shouldLog; + } + +private: + std::ostream& mOutput; + std::string mPrefix; + bool mShouldLog; +}; + +//! +//! \class LogStreamConsumerBase +//! \brief Convenience object used to initialize LogStreamConsumerBuffer before std::ostream in LogStreamConsumer +//! +class LogStreamConsumerBase +{ +public: + LogStreamConsumerBase(std::ostream& stream, const std::string& prefix, bool shouldLog) + : mBuffer(stream, prefix, shouldLog) + { + } + +protected: + LogStreamConsumerBuffer mBuffer; +}; + +//! +//! \class LogStreamConsumer +//! \brief Convenience object used to facilitate use of C++ stream syntax when logging messages. +//! Order of base classes is LogStreamConsumerBase and then std::ostream. +//! This is because the LogStreamConsumerBase class is used to initialize the LogStreamConsumerBuffer member field +//! in LogStreamConsumer and then the address of the buffer is passed to std::ostream. +//! This is necessary to prevent the address of an uninitialized buffer from being passed to std::ostream. +//! Please do not change the order of the parent classes. +//! +class LogStreamConsumer : protected LogStreamConsumerBase, public std::ostream +{ +public: + //! \brief Creates a LogStreamConsumer which logs messages with level severity. + //! Reportable severity determines if the messages are severe enough to be logged. + LogStreamConsumer(Severity reportableSeverity, Severity severity) + : LogStreamConsumerBase(severityOstream(severity), severityPrefix(severity), severity <= reportableSeverity) + , std::ostream(&mBuffer) // links the stream buffer with the stream + , mShouldLog(severity <= reportableSeverity) + , mSeverity(severity) + { + } + + LogStreamConsumer(LogStreamConsumer&& other) + : LogStreamConsumerBase(severityOstream(other.mSeverity), severityPrefix(other.mSeverity), other.mShouldLog) + , std::ostream(&mBuffer) // links the stream buffer with the stream + , mShouldLog(other.mShouldLog) + , mSeverity(other.mSeverity) + { + } + + void setReportableSeverity(Severity reportableSeverity) + { + mShouldLog = mSeverity <= reportableSeverity; + mBuffer.setShouldLog(mShouldLog); + } + +private: + static std::ostream& severityOstream(Severity severity) + { + return severity >= Severity::kINFO ? std::cout : std::cerr; + } + + static std::string severityPrefix(Severity severity) + { + switch (severity) + { + case Severity::kINTERNAL_ERROR: return "[F] "; + case Severity::kERROR: return "[E] "; + case Severity::kWARNING: return "[W] "; + case Severity::kINFO: return "[I] "; + case Severity::kVERBOSE: return "[V] "; + default: assert(0); return ""; + } + } + + bool mShouldLog; + Severity mSeverity; +}; + +//! \class Logger +//! +//! \brief Class which manages logging of TensorRT tools and samples +//! +//! \details This class provides a common interface for TensorRT tools and samples to log information to the console, +//! and supports logging two types of messages: +//! +//! - Debugging messages with an associated severity (info, warning, error, or internal error/fatal) +//! - Test pass/fail messages +//! +//! The advantage of having all samples use this class for logging as opposed to emitting directly to stdout/stderr is +//! that the logic for controlling the verbosity and formatting of sample output is centralized in one location. +//! +//! In the future, this class could be extended to support dumping test results to a file in some standard format +//! (for example, JUnit XML), and providing additional metadata (e.g. timing the duration of a test run). +//! +//! TODO: For backwards compatibility with existing samples, this class inherits directly from the nvinfer1::ILogger +//! interface, which is problematic since there isn't a clean separation between messages coming from the TensorRT +//! library and messages coming from the sample. +//! +//! In the future (once all samples are updated to use Logger::getTRTLogger() to access the ILogger) we can refactor the +//! class to eliminate the inheritance and instead make the nvinfer1::ILogger implementation a member of the Logger +//! object. + +class Logger : public nvinfer1::ILogger +{ +public: + Logger(Severity severity = Severity::kWARNING) + : mReportableSeverity(severity) + { + } + + //! + //! \enum TestResult + //! \brief Represents the state of a given test + //! + enum class TestResult + { + kRUNNING, //!< The test is running + kPASSED, //!< The test passed + kFAILED, //!< The test failed + kWAIVED //!< The test was waived + }; + + //! + //! \brief Forward-compatible method for retrieving the nvinfer::ILogger associated with this Logger + //! \return The nvinfer1::ILogger associated with this Logger + //! + //! TODO Once all samples are updated to use this method to register the logger with TensorRT, + //! we can eliminate the inheritance of Logger from ILogger + //! + nvinfer1::ILogger& getTRTLogger() + { + return *this; + } + + //! + //! \brief Implementation of the nvinfer1::ILogger::log() virtual method + //! + //! Note samples should not be calling this function directly; it will eventually go away once we eliminate the + //! inheritance from nvinfer1::ILogger + //! + void log(Severity severity, const char* msg) override + { + LogStreamConsumer(mReportableSeverity, severity) << "[TRT] " << std::string(msg) << std::endl; + } + + //! + //! \brief Method for controlling the verbosity of logging output + //! + //! \param severity The logger will only emit messages that have severity of this level or higher. + //! + void setReportableSeverity(Severity severity) + { + mReportableSeverity = severity; + } + + //! + //! \brief Opaque handle that holds logging information for a particular test + //! + //! This object is an opaque handle to information used by the Logger to print test results. + //! The sample must call Logger::defineTest() in order to obtain a TestAtom that can be used + //! with Logger::reportTest{Start,End}(). + //! + class TestAtom + { + public: + TestAtom(TestAtom&&) = default; + + private: + friend class Logger; + + TestAtom(bool started, const std::string& name, const std::string& cmdline) + : mStarted(started) + , mName(name) + , mCmdline(cmdline) + { + } + + bool mStarted; + std::string mName; + std::string mCmdline; + }; + + //! + //! \brief Define a test for logging + //! + //! \param[in] name The name of the test. This should be a string starting with + //! "TensorRT" and containing dot-separated strings containing + //! the characters [A-Za-z0-9_]. + //! For example, "TensorRT.sample_googlenet" + //! \param[in] cmdline The command line used to reproduce the test + // + //! \return a TestAtom that can be used in Logger::reportTest{Start,End}(). + //! + static TestAtom defineTest(const std::string& name, const std::string& cmdline) + { + return TestAtom(false, name, cmdline); + } + + //! + //! \brief A convenience overloaded version of defineTest() that accepts an array of command-line arguments + //! as input + //! + //! \param[in] name The name of the test + //! \param[in] argc The number of command-line arguments + //! \param[in] argv The array of command-line arguments (given as C strings) + //! + //! \return a TestAtom that can be used in Logger::reportTest{Start,End}(). + static TestAtom defineTest(const std::string& name, int argc, char const* const* argv) + { + auto cmdline = genCmdlineString(argc, argv); + return defineTest(name, cmdline); + } + + //! + //! \brief Report that a test has started. + //! + //! \pre reportTestStart() has not been called yet for the given testAtom + //! + //! \param[in] testAtom The handle to the test that has started + //! + static void reportTestStart(TestAtom& testAtom) + { + reportTestResult(testAtom, TestResult::kRUNNING); + assert(!testAtom.mStarted); + testAtom.mStarted = true; + } + + //! + //! \brief Report that a test has ended. + //! + //! \pre reportTestStart() has been called for the given testAtom + //! + //! \param[in] testAtom The handle to the test that has ended + //! \param[in] result The result of the test. Should be one of TestResult::kPASSED, + //! TestResult::kFAILED, TestResult::kWAIVED + //! + static void reportTestEnd(const TestAtom& testAtom, TestResult result) + { + assert(result != TestResult::kRUNNING); + assert(testAtom.mStarted); + reportTestResult(testAtom, result); + } + + static int reportPass(const TestAtom& testAtom) + { + reportTestEnd(testAtom, TestResult::kPASSED); + return EXIT_SUCCESS; + } + + static int reportFail(const TestAtom& testAtom) + { + reportTestEnd(testAtom, TestResult::kFAILED); + return EXIT_FAILURE; + } + + static int reportWaive(const TestAtom& testAtom) + { + reportTestEnd(testAtom, TestResult::kWAIVED); + return EXIT_SUCCESS; + } + + static int reportTest(const TestAtom& testAtom, bool pass) + { + return pass ? reportPass(testAtom) : reportFail(testAtom); + } + + Severity getReportableSeverity() const + { + return mReportableSeverity; + } + +private: + //! + //! \brief returns an appropriate string for prefixing a log message with the given severity + //! + static const char* severityPrefix(Severity severity) + { + switch (severity) + { + case Severity::kINTERNAL_ERROR: return "[F] "; + case Severity::kERROR: return "[E] "; + case Severity::kWARNING: return "[W] "; + case Severity::kINFO: return "[I] "; + case Severity::kVERBOSE: return "[V] "; + default: assert(0); return ""; + } + } + + //! + //! \brief returns an appropriate string for prefixing a test result message with the given result + //! + static const char* testResultString(TestResult result) + { + switch (result) + { + case TestResult::kRUNNING: return "RUNNING"; + case TestResult::kPASSED: return "PASSED"; + case TestResult::kFAILED: return "FAILED"; + case TestResult::kWAIVED: return "WAIVED"; + default: assert(0); return ""; + } + } + + //! + //! \brief returns an appropriate output stream (cout or cerr) to use with the given severity + //! + static std::ostream& severityOstream(Severity severity) + { + return severity >= Severity::kINFO ? std::cout : std::cerr; + } + + //! + //! \brief method that implements logging test results + //! + static void reportTestResult(const TestAtom& testAtom, TestResult result) + { + severityOstream(Severity::kINFO) << "&&&& " << testResultString(result) << " " << testAtom.mName << " # " + << testAtom.mCmdline << std::endl; + } + + //! + //! \brief generate a command line string from the given (argc, argv) values + //! + static std::string genCmdlineString(int argc, char const* const* argv) + { + std::stringstream ss; + for (int i = 0; i < argc; i++) + { + if (i > 0) + ss << " "; + ss << argv[i]; + } + return ss.str(); + } + + Severity mReportableSeverity; +}; + +namespace +{ + +//! +//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kVERBOSE +//! +//! Example usage: +//! +//! LOG_VERBOSE(logger) << "hello world" << std::endl; +//! +inline LogStreamConsumer LOG_VERBOSE(const Logger& logger) +{ + return LogStreamConsumer(logger.getReportableSeverity(), Severity::kVERBOSE); +} + +//! +//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kINFO +//! +//! Example usage: +//! +//! LOG_INFO(logger) << "hello world" << std::endl; +//! +inline LogStreamConsumer LOG_INFO(const Logger& logger) +{ + return LogStreamConsumer(logger.getReportableSeverity(), Severity::kINFO); +} + +//! +//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kWARNING +//! +//! Example usage: +//! +//! LOG_WARN(logger) << "hello world" << std::endl; +//! +inline LogStreamConsumer LOG_WARN(const Logger& logger) +{ + return LogStreamConsumer(logger.getReportableSeverity(), Severity::kWARNING); +} + +//! +//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kERROR +//! +//! Example usage: +//! +//! LOG_ERROR(logger) << "hello world" << std::endl; +//! +inline LogStreamConsumer LOG_ERROR(const Logger& logger) +{ + return LogStreamConsumer(logger.getReportableSeverity(), Severity::kERROR); +} + +//! +//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kINTERNAL_ERROR +// ("fatal" severity) +//! +//! Example usage: +//! +//! LOG_FATAL(logger) << "hello world" << std::endl; +//! +inline LogStreamConsumer LOG_FATAL(const Logger& logger) +{ + return LogStreamConsumer(logger.getReportableSeverity(), Severity::kINTERNAL_ERROR); +} + +} // anonymous namespace + +#endif // TENSORRT_LOGGING_H diff --git a/uw_detection/include/uw_detection/yololayer.h b/uw_detection/include/uw_detection/yololayer.h new file mode 100644 index 0000000..1ce9bad --- /dev/null +++ b/uw_detection/include/uw_detection/yololayer.h @@ -0,0 +1,159 @@ +#ifndef _YOLO_LAYER_H +#define _YOLO_LAYER_H + +#include +#include +#include +#include +#include "NvInfer.h" +#include +#include + +namespace Yolo +{ + static constexpr int CHECK_COUNT = 3; + static constexpr float IGNORE_THRESH = 0.1f; + static constexpr int MAX_OUTPUT_BBOX_COUNT = 1000; + static constexpr int CLASS_NUM = 80; + static constexpr int INPUT_H = 608; + static constexpr int INPUT_W = 608; + + struct YoloKernel + { + int width; + int height; + float anchors[CHECK_COUNT*2]; + }; + + static constexpr YoloKernel yolo1 = { + INPUT_W / 32, + INPUT_H / 32, + {116,90, 156,198, 373,326} + }; + static constexpr YoloKernel yolo2 = { + INPUT_W / 16, + INPUT_H / 16, + {30,61, 62,45, 59,119} + }; + static constexpr YoloKernel yolo3 = { + INPUT_W / 8, + INPUT_H / 8, + {10,13, 16,30, 33,23} + }; + + static constexpr int LOCATIONS = 4; + struct alignas(float) Detection{ + //x y w h + float bbox[LOCATIONS]; + float det_confidence; + float class_id; + float class_confidence; + }; +} + + +namespace nvinfer1 +{ + class YoloLayerPlugin: public IPluginV2IOExt + { + public: + explicit YoloLayerPlugin(); + YoloLayerPlugin(const void* data, size_t length); + + ~YoloLayerPlugin(); + + int getNbOutputs() const override + { + return 1; + } + + Dims getOutputDimensions(int index, const Dims* inputs, int nbInputDims) override; + + int initialize() override; + + virtual void terminate() override {}; + + virtual size_t getWorkspaceSize(int maxBatchSize) const override { return 0;} + + virtual int enqueue(int batchSize, const void*const * inputs, void** outputs, void* workspace, cudaStream_t stream) override; + + virtual size_t getSerializationSize() const override; + + virtual void serialize(void* buffer) const override; + + bool supportsFormatCombination(int pos, const PluginTensorDesc* inOut, int nbInputs, int nbOutputs) const override { + return inOut[pos].format == TensorFormat::kLINEAR && inOut[pos].type == DataType::kFLOAT; + } + + const char* getPluginType() const override; + + const char* getPluginVersion() const override; + + void destroy() override; + + IPluginV2IOExt* clone() const override; + + void setPluginNamespace(const char* pluginNamespace) override; + + const char* getPluginNamespace() const override; + + DataType getOutputDataType(int index, const nvinfer1::DataType* inputTypes, int nbInputs) const override; + + bool isOutputBroadcastAcrossBatch(int outputIndex, const bool* inputIsBroadcasted, int nbInputs) const override; + + bool canBroadcastInputAcrossBatch(int inputIndex) const override; + + void attachToContext( + cudnnContext* cudnnContext, cublasContext* cublasContext, IGpuAllocator* gpuAllocator) override; + + void configurePlugin(const PluginTensorDesc* in, int nbInput, const PluginTensorDesc* out, int nbOutput) override; + + void detachFromContext() override; + + private: + void forwardGpu(const float *const * inputs,float * output, cudaStream_t stream,int batchSize = 1); + int mClassCount; + int mKernelCount; + std::vector mYoloKernel; + int mThreadCount = 256; + const char* mPluginNamespace; + }; + + class YoloPluginCreator : public IPluginCreator + { + public: + YoloPluginCreator(); + + ~YoloPluginCreator() override = default; + + const char* getPluginName() const override; + + const char* getPluginVersion() const override; + + const PluginFieldCollection* getFieldNames() override; + + IPluginV2IOExt* createPlugin(const char* name, const PluginFieldCollection* fc) override; + + IPluginV2IOExt* deserializePlugin(const char* name, const void* serialData, size_t serialLength) override; + + void setPluginNamespace(const char* libNamespace) override + { + mNamespace = libNamespace; + } + + const char* getPluginNamespace() const override + { + return mNamespace.c_str(); + } + + private: + std::string mNamespace; + static PluginFieldCollection mFC; + static std::vector mPluginAttributes; + }; + + + +}; + +#endif diff --git a/uw_detection/include/uw_detection/yolov3-spp.h b/uw_detection/include/uw_detection/yolov3-spp.h new file mode 100644 index 0000000..1ba3cdb --- /dev/null +++ b/uw_detection/include/uw_detection/yolov3-spp.h @@ -0,0 +1,13 @@ +#pragma once +#include +#include "NvInfer.h" + +void APIToModel(unsigned int maxBatchSize, nvinfer1::IHostMemory** modelStream, const std::string& weights_path); + +void doInference(nvinfer1::IExecutionContext& context, float* input, float* output, int batchSize); + +cv::Mat preprocess_img(cv::Mat& img); + +cv::Rect get_rect(cv::Mat& img, float bbox[4]); + +void nms(std::vector& res, float *output, float nms_thresh); \ No newline at end of file diff --git a/uw_detection/package.xml b/uw_detection/package.xml index 196bdf3..57d5bef 100644 --- a/uw_detection/package.xml +++ b/uw_detection/package.xml @@ -14,4 +14,21 @@ catkin rospack + cv_bridge + roscpp + std_msgs + sensor_msgs + geometry_msgs + visualization_msgs + message_generation + message_runtime + + image_transport + octomap + roslint + std_srvs + tf2 + tf2_eigen + message_filters + \ No newline at end of file diff --git a/uw_detection/src/yolo_ros_image_node.cpp b/uw_detection/src/yolo_ros_image_node.cpp new file mode 100644 index 0000000..16e2838 --- /dev/null +++ b/uw_detection/src/yolo_ros_image_node.cpp @@ -0,0 +1,105 @@ +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#define NMS_THRESH 0.4 + +using namespace nvinfer1; + +// stuff we know about the network and the input/output blobs +static const int INPUT_H = Yolo::INPUT_H; +static const int INPUT_W = Yolo::INPUT_W; +static const int OUTPUT_SIZE = 1000 * 7 + 1; // we assume the yololayer outputs no more than 1000 boxes that conf >= 0.1 + +static Logger gLogger; +// prepare input data --------------------------- +static float data[3 * INPUT_H * INPUT_W]; +//for (int i = 0; i < 3 * INPUT_H * INPUT_W; i++) +// data[i] = 1.0; +static float prob[OUTPUT_SIZE]; + +static IExecutionContext* context; + +void imageCallback(const sensor_msgs::ImageConstPtr& msg) +{ + try + { + auto img = cv_bridge::toCvShare(msg, "bgr8")->image; + + cv::Mat pr_img = preprocess_img(img); + + for (int i = 0; i < INPUT_H * INPUT_W; i++) { + data[i] = pr_img.at(i)[2] / 255.0; + data[i + INPUT_H * INPUT_W] = pr_img.at(i)[1] / 255.0; + data[i + 2 * INPUT_H * INPUT_W] = pr_img.at(i)[0] / 255.0; + } + + auto start = std::chrono::system_clock::now(); + doInference(*context, data, prob, 1); + auto end = std::chrono::system_clock::now(); + std::cout << std::chrono::duration_cast(end - start).count() << "ms" << std::endl; + std::vector res; + nms(res, prob, NMS_THRESH); + for (int i=0; i<20; i++) { + std::cout << prob[i] << ","; + } + std::cout << res.size() << std::endl; + for (size_t j = 0; j < res.size(); j++) { + float *p = (float*)&res[j]; + for (size_t k = 0; k < 7; k++) { + std::cout << p[k] << ", "; + } + std::cout << std::endl; + cv::Rect r = get_rect(img, res[j].bbox); + cv::rectangle(img, r, cv::Scalar(0x27, 0xC1, 0x36), 2); + cv::putText(img, std::to_string((int)res[j].class_id), cv::Point(r.x, r.y - 1), cv::FONT_HERSHEY_PLAIN, 1.2, cv::Scalar(0xFF, 0xFF, 0xFF), 2); + } + cv::imshow("view", img); + } + catch (cv_bridge::Exception& e) + { + ROS_ERROR("Could not convert from '%s' to 'bgr8'.", msg->encoding.c_str()); + } +} + +int main(int argc, char **argv) +{ + cudaSetDevice(0); + size_t size{0}; + + char *trtModelStream{nullptr}; + + ros::init(argc, argv, "image_listener"); + std::string path = ros::package::getPath("uw_detection"); + std::ifstream file(path + "/share/yolov3-spp.engine", std::ios::binary); + if (file.good()) { + file.seekg(0, file.end); + size = file.tellg(); + file.seekg(0, file.beg); + trtModelStream = new char[size]; + assert(trtModelStream); + file.read(trtModelStream, size); + file.close(); + } + + IRuntime* runtime = createInferRuntime(gLogger); + assert(runtime != nullptr); + ICudaEngine* engine = runtime->deserializeCudaEngine(trtModelStream, size); + assert(engine != nullptr); + context = engine->createExecutionContext(); + assert(context != nullptr); + delete[] trtModelStream; + ros::NodeHandle nh; + cv::namedWindow("view"); + cv::startWindowThread(); + image_transport::ImageTransport it(nh); + image_transport::Subscriber sub = it.subscribe("/head_camera/rgb/image_raw", 1, imageCallback); + ros::spin(); + cv::destroyWindow("view"); +} \ No newline at end of file diff --git a/uw_detection/src/yolov3-spp/.gitignore b/uw_detection/src/yolov3-spp/.gitignore new file mode 100644 index 0000000..80bf80f --- /dev/null +++ b/uw_detection/src/yolov3-spp/.gitignore @@ -0,0 +1 @@ +yolov3 \ No newline at end of file diff --git a/uw_detection/src/yolov3-spp/README.md b/uw_detection/src/yolov3-spp/README.md new file mode 100644 index 0000000..986daf7 --- /dev/null +++ b/uw_detection/src/yolov3-spp/README.md @@ -0,0 +1,60 @@ +# yolov3-spp + +yolov4 is [here](../yolov4). + +The Pytorch implementation is [ultralytics/yolov3](https://github.com/ultralytics/yolov3). It provides two trained weights of yolov3-spp, `yolov3-spp.pt` and `yolov3-spp-ultralytics.pt`(originally named `ultralytics68.pt`). + +Following tricks are used in this yolov3-spp: + +- Yololayer plugin is different from the plugin used in [this repo's yolov3](https://github.com/wang-xinyu/tensorrtx/tree/master/yolov3). In this version, three yololayer are implemented in one plugin to improve speed, codes derived from [lewes6369/TensorRT-Yolov3](https://github.com/lewes6369/TensorRT-Yolov3) +- Batchnorm layer, implemented by scale layer. + +## Excute: + +``` +1. generate yolov3-spp_ultralytics68.wts from pytorch implementation with yolov3-spp.cfg and yolov3-spp-ultralytics.pt + +git clone https://github.com/wang-xinyu/tensorrtx.git +git clone https://github.com/ultralytics/yolov3.git +// download its weights 'yolov3-spp-ultralytics.pt' +cd yolov3 +cp ../tensorrtx/yolov3-spp/gen_wts.py . +python gen_wts.py yolov3-spp-ultralytics.pt +// a file 'yolov3-spp_ultralytics68.wts' will be generated. +// the master branch of yolov3 should work, if not, you can checkout 4ac60018f6e6c1e24b496485f126a660d9c793d8 + +2. put yolov3-spp_ultralytics68.wts into yolov3-spp, build and run + +mv yolov3-spp_ultralytics68.wts ../tensorrtx/yolov3-spp/ +cd ../tensorrtx/yolov3-spp +mkdir build +cd build +cmake .. +make +sudo ./yolov3-spp -s // serialize model to plan file i.e. 'yolov3-spp.engine' +sudo ./yolov3-spp -d ../samples // deserialize plan file and run inference, the images in samples will be processed. + +3. check the images generated, as follows. _zidane.jpg and _bus.jpg +``` + +

+ +

+ +

+ +

+ +## Config + +- Input shape defined in yololayer.h +- Number of classes defined in yololayer.h +- FP16/FP32 can be selected by the macro in yolov3-spp.cpp +- GPU id can be selected by the macro in yolov3-spp.cpp +- NMS thresh in yolov3-spp.cpp +- BBox confidence thresh in yolov3-spp.cpp + +## More Information + +See the readme in [home page.](https://github.com/wang-xinyu/tensorrtx) + diff --git a/uw_detection/src/yolov3-spp/gen_wts.py b/uw_detection/src/yolov3-spp/gen_wts.py new file mode 100644 index 0000000..7604c77 --- /dev/null +++ b/uw_detection/src/yolov3-spp/gen_wts.py @@ -0,0 +1,22 @@ +import struct +import sys +from models import * +from utils.utils import * + +model = Darknet('cfg/yolov3-spp.cfg', (416, 416)) +weights = sys.argv[1] +dev = '1' +device = torch_utils.select_device(dev) +model.load_state_dict(torch.load(weights, map_location=device)['model']) + + +f = open('yolov3-spp_ultralytics68.wts', 'w') +f.write('{}\n'.format(len(model.state_dict().keys()))) +for k, v in 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mYoloKernel.push_back(yolo1); + mYoloKernel.push_back(yolo2); + mYoloKernel.push_back(yolo3); + + mKernelCount = mYoloKernel.size(); + } + + YoloLayerPlugin::~YoloLayerPlugin() + { + } + + // create the plugin at runtime from a byte stream + YoloLayerPlugin::YoloLayerPlugin(const void* data, size_t length) + { + using namespace Tn; + const char *d = reinterpret_cast(data), *a = d; + read(d, mClassCount); + read(d, mThreadCount); + read(d, mKernelCount); + mYoloKernel.resize(mKernelCount); + auto kernelSize = mKernelCount*sizeof(YoloKernel); + memcpy(mYoloKernel.data(),d,kernelSize); + d += kernelSize; + + assert(d == a + length); + } + + void YoloLayerPlugin::serialize(void* buffer) const + { + using namespace Tn; + char* d = static_cast(buffer), *a = d; + write(d, mClassCount); + write(d, mThreadCount); + write(d, mKernelCount); + auto kernelSize = mKernelCount*sizeof(YoloKernel); + memcpy(d,mYoloKernel.data(),kernelSize); + d += kernelSize; + + assert(d == a + getSerializationSize()); + } + + size_t YoloLayerPlugin::getSerializationSize() const + { + return sizeof(mClassCount) + sizeof(mThreadCount) + sizeof(mKernelCount) + sizeof(Yolo::YoloKernel) * mYoloKernel.size(); + } + + int YoloLayerPlugin::initialize() + { + return 0; + } + + Dims YoloLayerPlugin::getOutputDimensions(int index, const Dims* inputs, int nbInputDims) + { + //output the result to channel + int totalsize = MAX_OUTPUT_BBOX_COUNT * sizeof(Detection) / sizeof(float); + + return Dims3(totalsize + 1, 1, 1); + } + + // Set plugin namespace + void YoloLayerPlugin::setPluginNamespace(const char* pluginNamespace) + { + mPluginNamespace = pluginNamespace; + } + + const char* YoloLayerPlugin::getPluginNamespace() const + { + return mPluginNamespace; + } + + // Return the DataType of the plugin output at the requested index + DataType YoloLayerPlugin::getOutputDataType(int index, const nvinfer1::DataType* inputTypes, int nbInputs) const + { + return DataType::kFLOAT; + } + + // Return true if output tensor is broadcast across a batch. + bool YoloLayerPlugin::isOutputBroadcastAcrossBatch(int outputIndex, const bool* inputIsBroadcasted, int nbInputs) const + { + return false; + } + + // Return true if plugin can use input that is broadcast across batch without replication. + bool YoloLayerPlugin::canBroadcastInputAcrossBatch(int inputIndex) const + { + return false; + } + + void YoloLayerPlugin::configurePlugin(const PluginTensorDesc* in, int nbInput, const PluginTensorDesc* out, int nbOutput) + { + } + + // Attach the plugin object to an execution context and grant the plugin the access to some context resource. + void YoloLayerPlugin::attachToContext(cudnnContext* cudnnContext, cublasContext* cublasContext, IGpuAllocator* gpuAllocator) + { + } + + // Detach the plugin object from its execution context. + void YoloLayerPlugin::detachFromContext() {} + + const char* YoloLayerPlugin::getPluginType() const + { + return "YoloLayer_TRT"; + } + + const char* YoloLayerPlugin::getPluginVersion() const + { + return "1"; + } + + void YoloLayerPlugin::destroy() + { + delete this; + } + + // Clone the plugin + IPluginV2IOExt* YoloLayerPlugin::clone() const + { + YoloLayerPlugin *p = new YoloLayerPlugin(); + p->setPluginNamespace(mPluginNamespace); + return p; + } + + __device__ float Logist(float data){ return 1.0f / (1.0f + expf(-data)); }; + + __global__ void CalDetection(const float *input, float *output,int noElements, + int yoloWidth,int yoloHeight,const float anchors[CHECK_COUNT*2],int classes,int outputElem) { + + int idx = threadIdx.x + blockDim.x * blockIdx.x; + if (idx >= noElements) return; + + int total_grid = yoloWidth * yoloHeight; + int bnIdx = idx / total_grid; + idx = idx - total_grid*bnIdx; + int info_len_i = 5 + classes; + const float* curInput = input + bnIdx * (info_len_i * total_grid * CHECK_COUNT); + + for (int k = 0; k < 3; ++k) { + int class_id = 0; + float max_cls_prob = 0.0; + for (int i = 5; i < info_len_i; ++i) { + float p = Logist(curInput[idx + k * info_len_i * total_grid + i * total_grid]); + if (p > max_cls_prob) { + max_cls_prob = p; + class_id = i - 5; + } + } + float box_prob = Logist(curInput[idx + k * info_len_i * total_grid + 4 * total_grid]); + if (max_cls_prob < IGNORE_THRESH || box_prob < IGNORE_THRESH) continue; + + float *res_count = output + bnIdx*outputElem; + int count = (int)atomicAdd(res_count, 1); + if (count >= MAX_OUTPUT_BBOX_COUNT) return; + char* data = (char * )res_count + sizeof(float) + count*sizeof(Detection); + Detection* det = (Detection*)(data); + + int row = idx / yoloWidth; + int col = idx % yoloWidth; + + //Location + det->bbox[0] = (col + Logist(curInput[idx + k * info_len_i * total_grid + 0 * total_grid])) * INPUT_W / yoloWidth; + det->bbox[1] = (row + Logist(curInput[idx + k * info_len_i * total_grid + 1 * total_grid])) * INPUT_H / yoloHeight; + det->bbox[2] = expf(curInput[idx + k * info_len_i * total_grid + 2 * total_grid]) * anchors[2*k]; + det->bbox[3] = expf(curInput[idx + k * info_len_i * total_grid + 3 * total_grid]) * anchors[2*k + 1]; + det->det_confidence = box_prob; + det->class_id = class_id; + det->class_confidence = max_cls_prob; + } + } + + void YoloLayerPlugin::forwardGpu(const float *const * inputs, float* output, cudaStream_t stream, int batchSize) { + void* devAnchor; + size_t AnchorLen = sizeof(float)* CHECK_COUNT*2; + CUDA_CHECK(cudaMalloc(&devAnchor,AnchorLen)); + + int outputElem = 1 + MAX_OUTPUT_BBOX_COUNT * sizeof(Detection) / sizeof(float); + + for(int idx = 0 ; idx < batchSize; ++idx) { + CUDA_CHECK(cudaMemset(output + idx*outputElem, 0, sizeof(float))); + } + int numElem = 0; + for (unsigned int i = 0;i< mYoloKernel.size();++i) + { + const auto& yolo = mYoloKernel[i]; + numElem = yolo.width*yolo.height*batchSize; + if (numElem < mThreadCount) + mThreadCount = numElem; + CUDA_CHECK(cudaMemcpy(devAnchor, yolo.anchors, AnchorLen, cudaMemcpyHostToDevice)); + CalDetection<<< (yolo.width*yolo.height*batchSize + mThreadCount - 1) / mThreadCount, mThreadCount>>> + (inputs[i],output, numElem, yolo.width, yolo.height, (float *)devAnchor, mClassCount ,outputElem); + } + + CUDA_CHECK(cudaFree(devAnchor)); + } + + + int YoloLayerPlugin::enqueue(int batchSize, const void*const * inputs, void** outputs, void* workspace, cudaStream_t stream) + { + //assert(batchSize == 1); + //GPU + //CUDA_CHECK(cudaStreamSynchronize(stream)); + forwardGpu((const float *const *)inputs, (float*)outputs[0], stream, batchSize); + + return 0; + } + + PluginFieldCollection YoloPluginCreator::mFC{}; + std::vector YoloPluginCreator::mPluginAttributes; + + YoloPluginCreator::YoloPluginCreator() + { + mPluginAttributes.clear(); + + mFC.nbFields = mPluginAttributes.size(); + mFC.fields = mPluginAttributes.data(); + } + + const char* YoloPluginCreator::getPluginName() const + { + return "YoloLayer_TRT"; + } + + const char* YoloPluginCreator::getPluginVersion() const + { + return "1"; + } + + const PluginFieldCollection* YoloPluginCreator::getFieldNames() + { + return &mFC; + } + + IPluginV2IOExt* YoloPluginCreator::createPlugin(const char* name, const PluginFieldCollection* fc) + { + YoloLayerPlugin* obj = new YoloLayerPlugin(); + obj->setPluginNamespace(mNamespace.c_str()); + return obj; + } + + IPluginV2IOExt* YoloPluginCreator::deserializePlugin(const char* name, const void* serialData, size_t serialLength) + { + // This object will be deleted when the network is destroyed, which will + // call MishPlugin::destroy() + YoloLayerPlugin* obj = new YoloLayerPlugin(serialData, serialLength); + obj->setPluginNamespace(mNamespace.c_str()); + return obj; + } + +} diff --git a/uw_detection/src/yolov3-spp/yolov3-spp.cpp b/uw_detection/src/yolov3-spp/yolov3-spp.cpp new file mode 100644 index 0000000..647f90d --- /dev/null +++ b/uw_detection/src/yolov3-spp/yolov3-spp.cpp @@ -0,0 +1,573 @@ +#include +#include +#include +#include +#include +#include +#include +#include +#include "NvInfer.h" +#include "cuda_runtime_api.h" +#include +#include + +#define CHECK(status) \ + do\ + {\ + auto ret = (status);\ + if (ret != 0)\ + {\ + std::cerr << "Cuda failure: " << ret << std::endl;\ + abort();\ + }\ + } while (0) + + +#define USE_FP16 // comment out this if want to use FP32 +#define DEVICE 0 // GPU id +#define NMS_THRESH 0.4 +#define BBOX_CONF_THRESH 0.5 + +using namespace nvinfer1; + +// stuff we know about the network and the input/output blobs +static const int INPUT_H = Yolo::INPUT_H; +static const int INPUT_W = Yolo::INPUT_W; +static const int OUTPUT_SIZE = 1000 * 7 + 1; // we assume the yololayer outputs no more than 1000 boxes that conf >= 0.1 +const char* INPUT_BLOB_NAME = "data"; +const char* OUTPUT_BLOB_NAME = "prob"; +static Logger gLogger; +REGISTER_TENSORRT_PLUGIN(YoloPluginCreator); + +cv::Mat preprocess_img(cv::Mat& img) { + int w, h, x, y; + float r_w = INPUT_W / (img.cols*1.0); + float r_h = INPUT_H / (img.rows*1.0); + if (r_h > r_w) { + w = INPUT_W; + h = r_w * img.rows; + x = 0; + y = (INPUT_H - h) / 2; + } else { + w = r_h* img.cols; + h = INPUT_H; + x = (INPUT_W - w) / 2; + y = 0; + } + cv::Mat re(h, w, CV_8UC3); + cv::resize(img, re, re.size(), 0, 0, cv::INTER_CUBIC); + cv::Mat out(INPUT_H, INPUT_W, CV_8UC3, cv::Scalar(128, 128, 128)); + re.copyTo(out(cv::Rect(x, y, re.cols, re.rows))); + return out; +} + +cv::Rect get_rect(cv::Mat& img, float bbox[4]) { + int l, r, t, b; + float r_w = INPUT_W / (img.cols * 1.0); + float r_h = INPUT_H / (img.rows * 1.0); + if (r_h > r_w) { + l = bbox[0] - bbox[2]/2.f; + r = bbox[0] + bbox[2]/2.f; + t = bbox[1] - bbox[3]/2.f - (INPUT_H - r_w * img.rows) / 2; + b = bbox[1] + bbox[3]/2.f - (INPUT_H - r_w * img.rows) / 2; + l = l / r_w; + r = r / r_w; + t = t / r_w; + b = b / r_w; + } else { + l = bbox[0] - bbox[2]/2.f - (INPUT_W - r_h * img.cols) / 2; + r = bbox[0] + bbox[2]/2.f - (INPUT_W - r_h * img.cols) / 2; + t = bbox[1] - bbox[3]/2.f; + b = bbox[1] + bbox[3]/2.f; + l = l / r_h; + r = r / r_h; + t = t / r_h; + b = b / r_h; + } + return cv::Rect(l, t, r-l, b-t); +} + +float iou(float lbox[4], float rbox[4]) { + float interBox[] = { + std::max(lbox[0] - lbox[2]/2.f , rbox[0] - rbox[2]/2.f), //left + std::min(lbox[0] + lbox[2]/2.f , rbox[0] + rbox[2]/2.f), //right + std::max(lbox[1] - lbox[3]/2.f , rbox[1] - rbox[3]/2.f), //top + std::min(lbox[1] + lbox[3]/2.f , rbox[1] + rbox[3]/2.f), //bottom + }; + + if(interBox[2] > interBox[3] || interBox[0] > interBox[1]) + return 0.0f; + + float interBoxS =(interBox[1]-interBox[0])*(interBox[3]-interBox[2]); + return interBoxS/(lbox[2]*lbox[3] + rbox[2]*rbox[3] -interBoxS); +} + +bool cmp(Yolo::Detection& a, Yolo::Detection& b) { + return a.det_confidence > b.det_confidence; +} + +void nms(std::vector& res, float *output, float nms_thresh = NMS_THRESH) { + std::map> m; + for (int i = 0; i < output[0] && i < 1000; i++) { + if (output[1 + 7 * i + 4] <= BBOX_CONF_THRESH) continue; + Yolo::Detection det; + memcpy(&det, &output[1 + 7 * i], 7 * sizeof(float)); + if (m.count(det.class_id) == 0) m.emplace(det.class_id, std::vector()); + m[det.class_id].push_back(det); + } + for (auto it = m.begin(); it != m.end(); it++) { + //std::cout << it->second[0].class_id << " --- " << std::endl; + auto& dets = it->second; + std::sort(dets.begin(), dets.end(), cmp); + for (size_t m = 0; m < dets.size(); ++m) { + auto& item = dets[m]; + res.push_back(item); + for (size_t n = m + 1; n < dets.size(); ++n) { + if (iou(item.bbox, dets[n].bbox) > nms_thresh) { + dets.erase(dets.begin()+n); + --n; + } + } + } + } +} + +// TensorRT weight files have a simple space delimited format: +// [type] [size] +std::map loadWeights(const std::string file) { + std::cout << "Loading weights: " << file << std::endl; + std::map weightMap; + + // Open weights file + std::ifstream input(file); + assert(input.is_open() && "Unable to load weight file."); + + // Read number of weight blobs + int32_t count; + input >> count; + assert(count > 0 && "Invalid weight map file."); + + while (count--) + { + Weights wt{DataType::kFLOAT, nullptr, 0}; + uint32_t size; + + // Read name and type of blob + std::string name; + input >> name >> std::dec >> size; + wt.type = DataType::kFLOAT; + + // Load blob + uint32_t* val = reinterpret_cast(malloc(sizeof(val) * size)); + for (uint32_t x = 0, y = size; x < y; ++x) + { + input >> std::hex >> val[x]; + } + wt.values = val; + + wt.count = size; + weightMap[name] = wt; + } + + return weightMap; +} + +IScaleLayer* addBatchNorm2d(INetworkDefinition *network, std::map& weightMap, ITensor& input, std::string lname, float eps) { + float *gamma = (float*)weightMap[lname + ".weight"].values; + float *beta = (float*)weightMap[lname + ".bias"].values; + float *mean = (float*)weightMap[lname + ".running_mean"].values; + float *var = (float*)weightMap[lname + ".running_var"].values; + int len = weightMap[lname + ".running_var"].count; + + float *scval = reinterpret_cast(malloc(sizeof(float) * len)); + for (int i = 0; i < len; i++) { + scval[i] = gamma[i] / sqrt(var[i] + eps); + } + Weights scale{DataType::kFLOAT, scval, len}; + + float *shval = reinterpret_cast(malloc(sizeof(float) * len)); + for (int i = 0; i < len; i++) { + shval[i] = beta[i] - mean[i] * gamma[i] / sqrt(var[i] + eps); + } + Weights shift{DataType::kFLOAT, shval, len}; + + float *pval = reinterpret_cast(malloc(sizeof(float) * len)); + for (int i = 0; i < len; i++) { + pval[i] = 1.0; + } + Weights power{DataType::kFLOAT, pval, len}; + + weightMap[lname + ".scale"] = scale; + weightMap[lname + ".shift"] = shift; + weightMap[lname + ".power"] = power; + IScaleLayer* scale_1 = network->addScale(input, ScaleMode::kCHANNEL, shift, scale, power); + assert(scale_1); + return scale_1; +} + +ILayer* convBnLeaky(INetworkDefinition *network, std::map& weightMap, ITensor& input, int outch, int ksize, int s, int p, int linx) { + Weights emptywts{DataType::kFLOAT, nullptr, 0}; + IConvolutionLayer* conv1 = network->addConvolutionNd(input, outch, DimsHW{ksize, ksize}, weightMap["module_list." + std::to_string(linx) + ".Conv2d.weight"], emptywts); + assert(conv1); + conv1->setStrideNd(DimsHW{s, s}); + conv1->setPaddingNd(DimsHW{p, p}); + + IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), "module_list." + std::to_string(linx) + ".BatchNorm2d", 1e-5); + + auto lr = network->addActivation(*bn1->getOutput(0), ActivationType::kLEAKY_RELU); + lr->setAlpha(0.1); + + return lr; +} + +// Creat the engine using only the API and not any parser. +ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt, const std::string& weights_path) { + INetworkDefinition* network = builder->createNetworkV2(0U); + + // Create input tensor of shape {3, INPUT_H, INPUT_W} with name INPUT_BLOB_NAME + ITensor* data = network->addInput(INPUT_BLOB_NAME, dt, Dims3{3, INPUT_H, INPUT_W}); + assert(data); + + std::map weightMap = loadWeights(weights_path); + Weights emptywts{DataType::kFLOAT, nullptr, 0}; + + // Yeah I am stupid, I just want to expand the complete arch of darknet.. + auto lr0 = convBnLeaky(network, weightMap, *data, 32, 3, 1, 1, 0); + auto lr1 = convBnLeaky(network, weightMap, *lr0->getOutput(0), 64, 3, 2, 1, 1); + auto lr2 = convBnLeaky(network, weightMap, *lr1->getOutput(0), 32, 1, 1, 0, 2); + auto lr3 = convBnLeaky(network, weightMap, *lr2->getOutput(0), 64, 3, 1, 1, 3); + auto ew4 = network->addElementWise(*lr3->getOutput(0), *lr1->getOutput(0), ElementWiseOperation::kSUM); + auto lr5 = convBnLeaky(network, weightMap, *ew4->getOutput(0), 128, 3, 2, 1, 5); + auto lr6 = convBnLeaky(network, weightMap, *lr5->getOutput(0), 64, 1, 1, 0, 6); + auto lr7 = convBnLeaky(network, weightMap, *lr6->getOutput(0), 128, 3, 1, 1, 7); + auto ew8 = network->addElementWise(*lr7->getOutput(0), *lr5->getOutput(0), ElementWiseOperation::kSUM); + auto lr9 = convBnLeaky(network, weightMap, *ew8->getOutput(0), 64, 1, 1, 0, 9); + auto lr10 = convBnLeaky(network, weightMap, *lr9->getOutput(0), 128, 3, 1, 1, 10); + auto ew11 = network->addElementWise(*lr10->getOutput(0), *ew8->getOutput(0), ElementWiseOperation::kSUM); + auto lr12 = convBnLeaky(network, weightMap, *ew11->getOutput(0), 256, 3, 2, 1, 12); + auto lr13 = convBnLeaky(network, weightMap, *lr12->getOutput(0), 128, 1, 1, 0, 13); + auto lr14 = convBnLeaky(network, weightMap, *lr13->getOutput(0), 256, 3, 1, 1, 14); + auto ew15 = network->addElementWise(*lr14->getOutput(0), *lr12->getOutput(0), ElementWiseOperation::kSUM); + auto lr16 = convBnLeaky(network, weightMap, *ew15->getOutput(0), 128, 1, 1, 0, 16); + auto lr17 = convBnLeaky(network, weightMap, *lr16->getOutput(0), 256, 3, 1, 1, 17); + auto ew18 = network->addElementWise(*lr17->getOutput(0), *ew15->getOutput(0), ElementWiseOperation::kSUM); + auto lr19 = convBnLeaky(network, weightMap, *ew18->getOutput(0), 128, 1, 1, 0, 19); + auto lr20 = convBnLeaky(network, weightMap, *lr19->getOutput(0), 256, 3, 1, 1, 20); + auto ew21 = network->addElementWise(*lr20->getOutput(0), *ew18->getOutput(0), ElementWiseOperation::kSUM); + auto lr22 = convBnLeaky(network, weightMap, *ew21->getOutput(0), 128, 1, 1, 0, 22); + auto lr23 = convBnLeaky(network, weightMap, *lr22->getOutput(0), 256, 3, 1, 1, 23); + auto ew24 = network->addElementWise(*lr23->getOutput(0), *ew21->getOutput(0), ElementWiseOperation::kSUM); + auto lr25 = convBnLeaky(network, weightMap, *ew24->getOutput(0), 128, 1, 1, 0, 25); + auto lr26 = convBnLeaky(network, weightMap, *lr25->getOutput(0), 256, 3, 1, 1, 26); + auto ew27 = network->addElementWise(*lr26->getOutput(0), *ew24->getOutput(0), ElementWiseOperation::kSUM); + auto lr28 = convBnLeaky(network, weightMap, *ew27->getOutput(0), 128, 1, 1, 0, 28); + auto lr29 = convBnLeaky(network, weightMap, *lr28->getOutput(0), 256, 3, 1, 1, 29); + auto ew30 = network->addElementWise(*lr29->getOutput(0), *ew27->getOutput(0), ElementWiseOperation::kSUM); + auto lr31 = convBnLeaky(network, weightMap, *ew30->getOutput(0), 128, 1, 1, 0, 31); + auto lr32 = convBnLeaky(network, weightMap, *lr31->getOutput(0), 256, 3, 1, 1, 32); + auto ew33 = network->addElementWise(*lr32->getOutput(0), *ew30->getOutput(0), ElementWiseOperation::kSUM); + auto lr34 = convBnLeaky(network, weightMap, *ew33->getOutput(0), 128, 1, 1, 0, 34); + auto lr35 = convBnLeaky(network, weightMap, *lr34->getOutput(0), 256, 3, 1, 1, 35); + auto ew36 = network->addElementWise(*lr35->getOutput(0), *ew33->getOutput(0), ElementWiseOperation::kSUM); + auto lr37 = convBnLeaky(network, weightMap, *ew36->getOutput(0), 512, 3, 2, 1, 37); + auto lr38 = convBnLeaky(network, weightMap, *lr37->getOutput(0), 256, 1, 1, 0, 38); + auto lr39 = convBnLeaky(network, weightMap, *lr38->getOutput(0), 512, 3, 1, 1, 39); + auto ew40 = network->addElementWise(*lr39->getOutput(0), *lr37->getOutput(0), ElementWiseOperation::kSUM); + auto lr41 = convBnLeaky(network, weightMap, *ew40->getOutput(0), 256, 1, 1, 0, 41); + auto lr42 = convBnLeaky(network, weightMap, *lr41->getOutput(0), 512, 3, 1, 1, 42); + auto ew43 = network->addElementWise(*lr42->getOutput(0), *ew40->getOutput(0), ElementWiseOperation::kSUM); + auto lr44 = convBnLeaky(network, weightMap, *ew43->getOutput(0), 256, 1, 1, 0, 44); + auto lr45 = convBnLeaky(network, weightMap, *lr44->getOutput(0), 512, 3, 1, 1, 45); + auto ew46 = network->addElementWise(*lr45->getOutput(0), *ew43->getOutput(0), ElementWiseOperation::kSUM); + auto lr47 = convBnLeaky(network, weightMap, *ew46->getOutput(0), 256, 1, 1, 0, 47); + auto lr48 = convBnLeaky(network, weightMap, *lr47->getOutput(0), 512, 3, 1, 1, 48); + auto ew49 = network->addElementWise(*lr48->getOutput(0), *ew46->getOutput(0), ElementWiseOperation::kSUM); + auto lr50 = convBnLeaky(network, weightMap, *ew49->getOutput(0), 256, 1, 1, 0, 50); + auto lr51 = convBnLeaky(network, weightMap, *lr50->getOutput(0), 512, 3, 1, 1, 51); + auto ew52 = network->addElementWise(*lr51->getOutput(0), *ew49->getOutput(0), ElementWiseOperation::kSUM); + auto lr53 = convBnLeaky(network, weightMap, *ew52->getOutput(0), 256, 1, 1, 0, 53); + auto lr54 = convBnLeaky(network, weightMap, *lr53->getOutput(0), 512, 3, 1, 1, 54); + auto ew55 = network->addElementWise(*lr54->getOutput(0), *ew52->getOutput(0), ElementWiseOperation::kSUM); + auto lr56 = convBnLeaky(network, weightMap, *ew55->getOutput(0), 256, 1, 1, 0, 56); + auto lr57 = convBnLeaky(network, weightMap, *lr56->getOutput(0), 512, 3, 1, 1, 57); + auto ew58 = network->addElementWise(*lr57->getOutput(0), *ew55->getOutput(0), ElementWiseOperation::kSUM); + auto lr59 = convBnLeaky(network, weightMap, *ew58->getOutput(0), 256, 1, 1, 0, 59); + auto lr60 = convBnLeaky(network, weightMap, *lr59->getOutput(0), 512, 3, 1, 1, 60); + auto ew61 = network->addElementWise(*lr60->getOutput(0), *ew58->getOutput(0), ElementWiseOperation::kSUM); + auto lr62 = convBnLeaky(network, weightMap, *ew61->getOutput(0), 1024, 3, 2, 1, 62); + auto lr63 = convBnLeaky(network, weightMap, *lr62->getOutput(0), 512, 1, 1, 0, 63); + auto lr64 = convBnLeaky(network, weightMap, *lr63->getOutput(0), 1024, 3, 1, 1, 64); + auto ew65 = network->addElementWise(*lr64->getOutput(0), *lr62->getOutput(0), ElementWiseOperation::kSUM); + auto lr66 = convBnLeaky(network, weightMap, *ew65->getOutput(0), 512, 1, 1, 0, 66); + auto lr67 = convBnLeaky(network, weightMap, *lr66->getOutput(0), 1024, 3, 1, 1, 67); + auto ew68 = network->addElementWise(*lr67->getOutput(0), *ew65->getOutput(0), ElementWiseOperation::kSUM); + auto lr69 = convBnLeaky(network, weightMap, *ew68->getOutput(0), 512, 1, 1, 0, 69); + auto lr70 = convBnLeaky(network, weightMap, *lr69->getOutput(0), 1024, 3, 1, 1, 70); + auto ew71 = network->addElementWise(*lr70->getOutput(0), *ew68->getOutput(0), ElementWiseOperation::kSUM); + auto lr72 = convBnLeaky(network, weightMap, *ew71->getOutput(0), 512, 1, 1, 0, 72); + auto lr73 = convBnLeaky(network, weightMap, *lr72->getOutput(0), 1024, 3, 1, 1, 73); + auto ew74 = network->addElementWise(*lr73->getOutput(0), *ew71->getOutput(0), ElementWiseOperation::kSUM); + auto lr75 = convBnLeaky(network, weightMap, *ew74->getOutput(0), 512, 1, 1, 0, 75); + auto lr76 = convBnLeaky(network, weightMap, *lr75->getOutput(0), 1024, 3, 1, 1, 76); + auto lr77 = convBnLeaky(network, weightMap, *lr76->getOutput(0), 512, 1, 1, 0, 77); + + auto pool78 = network->addPoolingNd(*lr77->getOutput(0), PoolingType::kMAX, DimsHW{5,5}); + pool78->setPaddingNd(DimsHW{2, 2}); + pool78->setStrideNd(DimsHW{1, 1}); + auto pool80 = network->addPoolingNd(*lr77->getOutput(0), PoolingType::kMAX, DimsHW{9,9}); + pool80->setPaddingNd(DimsHW{4, 4}); + pool80->setStrideNd(DimsHW{1, 1}); + auto pool82 = network->addPoolingNd(*lr77->getOutput(0), PoolingType::kMAX, DimsHW{13,13}); + pool82->setPaddingNd(DimsHW{6, 6}); + pool82->setStrideNd(DimsHW{1, 1}); + + ITensor* inputTensors83[] = {pool82->getOutput(0), pool80->getOutput(0), pool78->getOutput(0), lr77->getOutput(0)}; + auto cat83 = network->addConcatenation(inputTensors83, 4); + + auto lr84 = convBnLeaky(network, weightMap, *cat83->getOutput(0), 512, 1, 1, 0, 84); + auto lr85 = convBnLeaky(network, weightMap, *lr84->getOutput(0), 1024, 3, 1, 1, 85); + auto lr86 = convBnLeaky(network, weightMap, *lr85->getOutput(0), 512, 1, 1, 0, 86); + auto lr87 = convBnLeaky(network, weightMap, *lr86->getOutput(0), 1024, 3, 1, 1, 87); + IConvolutionLayer* conv88 = network->addConvolutionNd(*lr87->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{1, 1}, weightMap["module_list.88.Conv2d.weight"], weightMap["module_list.88.Conv2d.bias"]); + assert(conv88); + auto lr91 = convBnLeaky(network, weightMap, *lr86->getOutput(0), 256, 1, 1, 0, 91); + + float *deval = reinterpret_cast(malloc(sizeof(float) * 256 * 2 * 2)); + for (int i = 0; i < 256 * 2 * 2; i++) { + deval[i] = 1.0; + } + Weights deconvwts92{DataType::kFLOAT, deval, 256 * 2 * 2}; + IDeconvolutionLayer* deconv92 = network->addDeconvolutionNd(*lr91->getOutput(0), 256, DimsHW{2, 2}, deconvwts92, emptywts); + assert(deconv92); + deconv92->setStrideNd(DimsHW{2, 2}); + deconv92->setNbGroups(256); + weightMap["deconv92"] = deconvwts92; + + ITensor* inputTensors[] = {deconv92->getOutput(0), ew61->getOutput(0)}; + auto cat93 = network->addConcatenation(inputTensors, 2); + auto lr94 = convBnLeaky(network, weightMap, *cat93->getOutput(0), 256, 1, 1, 0, 94); + auto lr95 = convBnLeaky(network, weightMap, *lr94->getOutput(0), 512, 3, 1, 1, 95); + auto lr96 = convBnLeaky(network, weightMap, *lr95->getOutput(0), 256, 1, 1, 0, 96); + auto lr97 = convBnLeaky(network, weightMap, *lr96->getOutput(0), 512, 3, 1, 1, 97); + auto lr98 = convBnLeaky(network, weightMap, *lr97->getOutput(0), 256, 1, 1, 0, 98); + auto lr99 = convBnLeaky(network, weightMap, *lr98->getOutput(0), 512, 3, 1, 1, 99); + IConvolutionLayer* conv100 = network->addConvolutionNd(*lr99->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{1, 1}, weightMap["module_list.100.Conv2d.weight"], weightMap["module_list.100.Conv2d.bias"]); + assert(conv100); + auto lr103 = convBnLeaky(network, weightMap, *lr98->getOutput(0), 128, 1, 1, 0, 103); + Weights deconvwts104{DataType::kFLOAT, deval, 128 * 2 * 2}; + IDeconvolutionLayer* deconv104 = network->addDeconvolutionNd(*lr103->getOutput(0), 128, DimsHW{2, 2}, deconvwts104, emptywts); + assert(deconv104); + deconv104->setStrideNd(DimsHW{2, 2}); + deconv104->setNbGroups(128); + ITensor* inputTensors1[] = {deconv104->getOutput(0), ew36->getOutput(0)}; + auto cat105 = network->addConcatenation(inputTensors1, 2); + auto lr106 = convBnLeaky(network, weightMap, *cat105->getOutput(0), 128, 1, 1, 0, 106); + auto lr107 = convBnLeaky(network, weightMap, *lr106->getOutput(0), 256, 3, 1, 1, 107); + auto lr108 = convBnLeaky(network, weightMap, *lr107->getOutput(0), 128, 1, 1, 0, 108); + auto lr109 = convBnLeaky(network, weightMap, *lr108->getOutput(0), 256, 3, 1, 1, 109); + auto lr110 = convBnLeaky(network, weightMap, *lr109->getOutput(0), 128, 1, 1, 0, 110); + auto lr111 = convBnLeaky(network, weightMap, *lr110->getOutput(0), 256, 3, 1, 1, 111); + IConvolutionLayer* conv112 = network->addConvolutionNd(*lr111->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{1, 1}, weightMap["module_list.112.Conv2d.weight"], weightMap["module_list.112.Conv2d.bias"]); + assert(conv112); + + auto creator = getPluginRegistry()->getPluginCreator("YoloLayer_TRT", "1"); + const PluginFieldCollection* pluginData = creator->getFieldNames(); + IPluginV2 *pluginObj = creator->createPlugin("yololayer", pluginData); + ITensor* inputTensors_yolo[] = {conv88->getOutput(0), conv100->getOutput(0), conv112->getOutput(0)}; + auto yolo = network->addPluginV2(inputTensors_yolo, 3, *pluginObj); + + yolo->getOutput(0)->setName(OUTPUT_BLOB_NAME); + network->markOutput(*yolo->getOutput(0)); + + // Build engine + builder->setMaxBatchSize(maxBatchSize); + config->setMaxWorkspaceSize(16 * (1 << 20)); // 16MB +#ifdef USE_FP16 + config->setFlag(BuilderFlag::kFP16); +#endif + std::cout << "Building engine, please wait for a while..." << std::endl; + ICudaEngine* engine = builder->buildEngineWithConfig(*network, *config); + std::cout << "Build engine successfully!" << std::endl; + + // Don't need the network any more + network->destroy(); + + // Release host memory + for (auto& mem : weightMap) + { + free((void*) (mem.second.values)); + } + + return engine; +} + +void APIToModel(unsigned int maxBatchSize, IHostMemory** modelStream, const std::string& weights_path) { + // Create builder + IBuilder* builder = createInferBuilder(gLogger); + IBuilderConfig* config = builder->createBuilderConfig(); + + // Create model to populate the network, then set the outputs and create an engine + ICudaEngine* engine = createEngine(maxBatchSize, builder, config, DataType::kFLOAT, weights_path); + assert(engine != nullptr); + + // Serialize the engine + (*modelStream) = engine->serialize(); + + // Close everything down + engine->destroy(); + builder->destroy(); +} + +void doInference(IExecutionContext& context, float* input, float* output, int batchSize) { + const ICudaEngine& engine = context.getEngine(); + + // Pointers to input and output device buffers to pass to engine. + // Engine requires exactly IEngine::getNbBindings() number of buffers. + assert(engine.getNbBindings() == 2); + void* buffers[2]; + + // In order to bind the buffers, we need to know the names of the input and output tensors. + // Note that indices are guaranteed to be less than IEngine::getNbBindings() + const int inputIndex = engine.getBindingIndex(INPUT_BLOB_NAME); + const int outputIndex = engine.getBindingIndex(OUTPUT_BLOB_NAME); + + // Create GPU buffers on device + CHECK(cudaMalloc(&buffers[inputIndex], batchSize * 3 * INPUT_H * INPUT_W * sizeof(float))); + CHECK(cudaMalloc(&buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float))); + + // Create stream + cudaStream_t stream; + CHECK(cudaStreamCreate(&stream)); + + // DMA input batch data to device, infer on the batch asynchronously, and DMA output back to host + CHECK(cudaMemcpyAsync(buffers[inputIndex], input, batchSize * 3 * INPUT_H * INPUT_W * sizeof(float), cudaMemcpyHostToDevice, stream)); + context.enqueue(batchSize, buffers, stream, nullptr); + CHECK(cudaMemcpyAsync(output, buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float), cudaMemcpyDeviceToHost, stream)); + cudaStreamSynchronize(stream); + + // Release stream and buffers + cudaStreamDestroy(stream); + CHECK(cudaFree(buffers[inputIndex])); + CHECK(cudaFree(buffers[outputIndex])); +} + +int read_files_in_dir(const char *p_dir_name, std::vector &file_names) { + DIR *p_dir = opendir(p_dir_name); + if (p_dir == nullptr) { + return -1; + } + + struct dirent* p_file = nullptr; + while ((p_file = readdir(p_dir)) != nullptr) { + if (strcmp(p_file->d_name, ".") != 0 && + strcmp(p_file->d_name, "..") != 0) { + //std::string cur_file_name(p_dir_name); + //cur_file_name += "/"; + //cur_file_name += p_file->d_name; + std::string cur_file_name(p_file->d_name); + file_names.push_back(cur_file_name); + } + } + + closedir(p_dir); + return 0; +} + +int main(int argc, char** argv) { + cudaSetDevice(DEVICE); + // create a model using the API directly and serialize it to a stream + char *trtModelStream{nullptr}; + size_t size{0}; + + if (argc == 2 && std::string(argv[1]) == "-s") { + IHostMemory* modelStream{nullptr}; + APIToModel(1, &modelStream, "../yolov3-spp_ultralytics68.wts"); + assert(modelStream != nullptr); + std::ofstream p("yolov3-spp.engine", std::ios::binary); + if (!p) { + std::cerr << "could not open plan output file" << std::endl; + return -1; + } + p.write(reinterpret_cast(modelStream->data()), modelStream->size()); + modelStream->destroy(); + return 0; + } else if (argc == 3 && std::string(argv[1]) == "-d") { + std::ifstream file("yolov3-spp.engine", std::ios::binary); + if (file.good()) { + file.seekg(0, file.end); + size = file.tellg(); + file.seekg(0, file.beg); + trtModelStream = new char[size]; + assert(trtModelStream); + file.read(trtModelStream, size); + file.close(); + } + } else { + std::cerr << "arguments not right!" << std::endl; + std::cerr << "./yolov3-spp -s // serialize model to plan file" << std::endl; + std::cerr << "./yolov3-spp -d ../samples // deserialize plan file and run inference" << std::endl; + return -1; + } + + std::vector file_names; + if (read_files_in_dir(argv[2], file_names) < 0) { + std::cout << "read_files_in_dir failed." << std::endl; + return -1; + } + + // prepare input data --------------------------- + float data[3 * INPUT_H * INPUT_W]; + //for (int i = 0; i < 3 * INPUT_H * INPUT_W; i++) + // data[i] = 1.0; + static float prob[OUTPUT_SIZE]; + IRuntime* runtime = createInferRuntime(gLogger); + assert(runtime != nullptr); + ICudaEngine* engine = runtime->deserializeCudaEngine(trtModelStream, size); + assert(engine != nullptr); + IExecutionContext* context = engine->createExecutionContext(); + assert(context != nullptr); + delete[] trtModelStream; + + int fcount = 0; + for (auto f: file_names) { + fcount++; + std::cout << fcount << " " << f << std::endl; + cv::Mat img = cv::imread(std::string(argv[2]) + "/" + f); + if (img.empty()) continue; + cv::Mat pr_img = preprocess_img(img); + for (int i = 0; i < INPUT_H * INPUT_W; i++) { + data[i] = pr_img.at(i)[2] / 255.0; + data[i + INPUT_H * INPUT_W] = pr_img.at(i)[1] / 255.0; + data[i + 2 * INPUT_H * INPUT_W] = pr_img.at(i)[0] / 255.0; + } + + // Run inference + auto start = std::chrono::system_clock::now(); + doInference(*context, data, prob, 1); + auto end = std::chrono::system_clock::now(); + std::cout << std::chrono::duration_cast(end - start).count() << "ms" << std::endl; + std::vector res; + nms(res, prob); + for (int i=0; i<20; i++) { + std::cout << prob[i] << ","; + } + std::cout << res.size() << std::endl; + for (size_t j = 0; j < res.size(); j++) { + float *p = (float*)&res[j]; + for (size_t k = 0; k < 7; k++) { + std::cout << p[k] << ", "; + } + std::cout << std::endl; + cv::Rect r = get_rect(img, res[j].bbox); + cv::rectangle(img, r, cv::Scalar(0x27, 0xC1, 0x36), 2); + cv::putText(img, std::to_string((int)res[j].class_id), cv::Point(r.x, r.y - 1), cv::FONT_HERSHEY_PLAIN, 1.2, cv::Scalar(0xFF, 0xFF, 0xFF), 2); + } + cv::imwrite("_" + f, img); + } + + // Destroy the engine + context->destroy(); + engine->destroy(); + runtime->destroy(); + return 0; +} diff --git a/uw_detection/src/yolov3_spp_prepare_plan.cpp b/uw_detection/src/yolov3_spp_prepare_plan.cpp new file mode 100644 index 0000000..b87ab65 --- /dev/null +++ b/uw_detection/src/yolov3_spp_prepare_plan.cpp @@ -0,0 +1,35 @@ +#include +#include +#include +#include +#include +#include +#include +#include +#include "NvInfer.h" +#include "cuda_runtime_api.h" +#include "yolov3-spp/yololayer.h" +#include +#include + + +#define DEVICE 0 // GPU id + + +using namespace nvinfer1; + +int main(int argc, char** argv) { + cudaSetDevice(DEVICE); + IHostMemory *modelStream{nullptr}; + std::string path = ros::package::getPath("uw_detection"); + APIToModel(1, &modelStream, path + "/share/yolov3-spp_ultralytics68.wts"); + assert(modelStream != nullptr); + std::ofstream p(path+ "/share/yolov3-spp.engine", std::ios::binary); + if (!p) { + std::cerr << "could not open plan output file" << std::endl; + return -1; + } + p.write(reinterpret_cast(modelStream->data()), modelStream->size()); + modelStream->destroy(); + return 0; +} From 77a7f2c92df001406f942a48ef905e60c37fd43d Mon Sep 17 00:00:00 2001 From: Nick Walker Date: Sat, 1 Aug 2020 23:59:35 -0700 Subject: [PATCH 10/15] Copy in and patch plain YOLOv3 Change demo node to use plain model Add readme with usage notes --- uw_detection/CMakeLists.txt | 13 +- uw_detection/README.md | 14 + uw_detection/include/uw_detection/yolov3.h | 13 + uw_detection/share/.gitignore | 2 + uw_detection/src/yolo_ros_image_node.cpp | 4 +- uw_detection/src/yolov3-spp/README.md | 4 + uw_detection/src/yolov3-spp/gen_wts.py | 17 +- uw_detection/src/yolov3-spp/gen_wts_spp.py | 22 + uw_detection/src/yolov3-spp/yolov3-spp.cpp | 10 +- uw_detection/src/yolov3-spp/yolov3.cpp | 562 +++++++++++++++++++ uw_detection/src/yolov3_prepare_plan.cpp | 35 ++ uw_detection/src/yolov3_spp_prepare_plan.cpp | 2 +- 12 files changed, 678 insertions(+), 20 deletions(-) create mode 100644 uw_detection/include/uw_detection/yolov3.h create mode 100644 uw_detection/share/.gitignore create mode 100644 uw_detection/src/yolov3-spp/gen_wts_spp.py create mode 100644 uw_detection/src/yolov3-spp/yolov3.cpp create mode 100644 uw_detection/src/yolov3_prepare_plan.cpp diff --git a/uw_detection/CMakeLists.txt b/uw_detection/CMakeLists.txt index be56afc..f985885 100644 --- a/uw_detection/CMakeLists.txt +++ b/uw_detection/CMakeLists.txt @@ -4,7 +4,6 @@ project(uw_detection) find_package(catkin REQUIRED COMPONENTS cv_bridge geometry_msgs image_transport - knowledge_representation message_generation message_filters message_runtime @@ -55,11 +54,17 @@ cuda_add_library(yololayer SHARED src/yolov3-spp/yololayer.cu) add_library(yolov3-spp src/yolov3-spp/yolov3-spp.cpp) target_link_libraries(yolov3-spp nvinfer cudart yololayer ${OpenCV_LIBS}) -add_executable(yolov3_prepare_plan src/yolov3_spp_prepare_plan.cpp) -target_link_libraries(yolov3_prepare_plan yolov3-spp nvinfer cudart yololayer ${OpenCV_LIBS} ${catkin_LIBRARIES}) +add_library(yolov3 src/yolov3-spp/yolov3.cpp) +target_link_libraries(yolov3 nvinfer cudart yololayer ${OpenCV_LIBS}) + +add_executable(yolov3_spp_prepare_plan src/yolov3_spp_prepare_plan.cpp) +target_link_libraries(yolov3_spp_prepare_plan yolov3-spp nvinfer cudart yololayer ${OpenCV_LIBS} ${catkin_LIBRARIES}) + +add_executable(yolov3_prepare_plan src/yolov3_prepare_plan.cpp) +target_link_libraries(yolov3_prepare_plan yolov3 nvinfer cudart yololayer ${OpenCV_LIBS} ${catkin_LIBRARIES}) add_executable(yolo_ros_image_node src/yolo_ros_image_node.cpp) -target_link_libraries(yolo_ros_image_node yolov3-spp nvinfer cudart yololayer ${OpenCV_LIBS} ${catkin_LIBRARIES}) +target_link_libraries(yolo_ros_image_node yolov3 nvinfer cudart yololayer ${OpenCV_LIBS} ${catkin_LIBRARIES}) add_definitions(-O2 -pthread) endif() diff --git a/uw_detection/README.md b/uw_detection/README.md index 0d92fbc..87ac3c9 100644 --- a/uw_detection/README.md +++ b/uw_detection/README.md @@ -1,3 +1,17 @@ # uw_detection Wrappers for detection models. Tools for preparing data and training. + +TensorRT YOLOv3 implementations taken from [tensorrtx](https://github.com/wang-xinyu/tensorrtx). + +## Usage + +### YOLO + +All of the YOLO targets described here are only available when CMake can detect a working CUDA and TensorRT installation. Ensure these are set up correctly before beginning. They can't be run on the CPU + +These implementations come with a helper for loading weights from the Ultralytics PyTorch trained models. Follow the steps in [their readme](https://github.com/wang-xinyu/tensorrtx/blob/master/yolov3/README.md) (the process involves using one of the `gen_wts.py` scripts to load the Torch graph and dump it into the necessary format). You may need to change the CUDA device set in the script. + +TensorRT requires that you precompute an inference graph ("engine") before you can actually use the weights you've obtained. The `*_prepare_plan.cpp` targets will take care of this. They expect the weights file to be placed in the `share` folder at the root of the package. When they've run successfully (only required once), they'll output the serialized engine into the `share` folder. Note that if you're using a customized class list, you'll need to adjust the definition for the number of classes in "yololayer.h". + +Now you can test the model on a ROS image topic using `yolo_ros_image_node.cpp`. It's configured to take images from Fetch's head camera topic and feed them through YOLOv3, outputting the results to the console and drawing them in a CV window. It'll work in simulation without changes assuming you've followed the previous steps for the Ultralytics provided yolov3 checkpoint. \ No newline at end of file diff --git a/uw_detection/include/uw_detection/yolov3.h b/uw_detection/include/uw_detection/yolov3.h new file mode 100644 index 0000000..1ba3cdb --- /dev/null +++ b/uw_detection/include/uw_detection/yolov3.h @@ -0,0 +1,13 @@ +#pragma once +#include +#include "NvInfer.h" + +void APIToModel(unsigned int maxBatchSize, nvinfer1::IHostMemory** modelStream, const std::string& weights_path); + +void doInference(nvinfer1::IExecutionContext& context, float* input, float* output, int batchSize); + +cv::Mat preprocess_img(cv::Mat& img); + +cv::Rect get_rect(cv::Mat& img, float bbox[4]); + +void nms(std::vector& res, float *output, float nms_thresh); \ No newline at end of file diff --git a/uw_detection/share/.gitignore b/uw_detection/share/.gitignore new file mode 100644 index 0000000..d6b7ef3 --- /dev/null +++ b/uw_detection/share/.gitignore @@ -0,0 +1,2 @@ +* +!.gitignore diff --git a/uw_detection/src/yolo_ros_image_node.cpp b/uw_detection/src/yolo_ros_image_node.cpp index 16e2838..513839c 100644 --- a/uw_detection/src/yolo_ros_image_node.cpp +++ b/uw_detection/src/yolo_ros_image_node.cpp @@ -2,7 +2,7 @@ #include #include #include -#include +#include #include #include #include @@ -77,7 +77,7 @@ int main(int argc, char **argv) ros::init(argc, argv, "image_listener"); std::string path = ros::package::getPath("uw_detection"); - std::ifstream file(path + "/share/yolov3-spp.engine", std::ios::binary); + std::ifstream file(path + "/share/yolov3.engine", std::ios::binary); if (file.good()) { file.seekg(0, file.end); size = file.tellg(); diff --git a/uw_detection/src/yolov3-spp/README.md b/uw_detection/src/yolov3-spp/README.md index 986daf7..ec38033 100644 --- a/uw_detection/src/yolov3-spp/README.md +++ b/uw_detection/src/yolov3-spp/README.md @@ -1,3 +1,7 @@ +This directory contains both the SPP and regular YOLOv3 implementations copied from tensorrtx. The minimum changes required to catkinize the code were applied; headers were moved into the package include directory, and the model API was exposed and modified to accept a path to weights file. + + + # yolov3-spp yolov4 is [here](../yolov4). diff --git a/uw_detection/src/yolov3-spp/gen_wts.py b/uw_detection/src/yolov3-spp/gen_wts.py index 7604c77..52c8e1c 100644 --- a/uw_detection/src/yolov3-spp/gen_wts.py +++ b/uw_detection/src/yolov3-spp/gen_wts.py @@ -3,14 +3,16 @@ from models import * from utils.utils import * -model = Darknet('cfg/yolov3-spp.cfg', (416, 416)) +model = Darknet('cfg/yolov3.cfg', (608, 608)) weights = sys.argv[1] -dev = '1' -device = torch_utils.select_device(dev) -model.load_state_dict(torch.load(weights, map_location=device)['model']) +device = torch_utils.select_device('0') +if weights.endswith('.pt'): # pytorch format + model.load_state_dict(torch.load(weights, map_location=device)['model']) +else: # darknet format + load_darknet_weights(model, weights) +model = model.eval() - -f = open('yolov3-spp_ultralytics68.wts', 'w') +f = open('yolov3.wts', 'w') f.write('{}\n'.format(len(model.state_dict().keys()))) for k, v in model.state_dict().items(): vr = v.reshape(-1).cpu().numpy() @@ -18,5 +20,4 @@ for vv in vr: f.write(' ') f.write(struct.pack('>f',float(vv)).hex()) - f.write('\n') - + f.write('\n') \ No newline at end of file diff --git a/uw_detection/src/yolov3-spp/gen_wts_spp.py b/uw_detection/src/yolov3-spp/gen_wts_spp.py new file mode 100644 index 0000000..7604c77 --- /dev/null +++ b/uw_detection/src/yolov3-spp/gen_wts_spp.py @@ -0,0 +1,22 @@ +import struct +import sys +from models import * +from utils.utils import * + +model = Darknet('cfg/yolov3-spp.cfg', (416, 416)) +weights = sys.argv[1] +dev = '1' +device = torch_utils.select_device(dev) +model.load_state_dict(torch.load(weights, map_location=device)['model']) + + +f = open('yolov3-spp_ultralytics68.wts', 'w') +f.write('{}\n'.format(len(model.state_dict().keys()))) +for k, v in model.state_dict().items(): + vr = v.reshape(-1).cpu().numpy() + f.write('{} {} '.format(k, len(vr))) + for vv in vr: + f.write(' ') + f.write(struct.pack('>f',float(vv)).hex()) + f.write('\n') + diff --git a/uw_detection/src/yolov3-spp/yolov3-spp.cpp b/uw_detection/src/yolov3-spp/yolov3-spp.cpp index 647f90d..c576782 100644 --- a/uw_detection/src/yolov3-spp/yolov3-spp.cpp +++ b/uw_detection/src/yolov3-spp/yolov3-spp.cpp @@ -164,7 +164,7 @@ std::map loadWeights(const std::string file) { input >> std::hex >> val[x]; } wt.values = val; - + wt.count = size; weightMap[name] = wt; } @@ -184,7 +184,7 @@ IScaleLayer* addBatchNorm2d(INetworkDefinition *network, std::map(malloc(sizeof(float) * len)); for (int i = 0; i < len; i++) { shval[i] = beta[i] - mean[i] * gamma[i] / sqrt(var[i] + eps); @@ -310,7 +310,7 @@ ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, IBuilder auto lr75 = convBnLeaky(network, weightMap, *ew74->getOutput(0), 512, 1, 1, 0, 75); auto lr76 = convBnLeaky(network, weightMap, *lr75->getOutput(0), 1024, 3, 1, 1, 76); auto lr77 = convBnLeaky(network, weightMap, *lr76->getOutput(0), 512, 1, 1, 0, 77); - + auto pool78 = network->addPoolingNd(*lr77->getOutput(0), PoolingType::kMAX, DimsHW{5,5}); pool78->setPaddingNd(DimsHW{2, 2}); pool78->setStrideNd(DimsHW{1, 1}); @@ -342,7 +342,7 @@ ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, IBuilder deconv92->setStrideNd(DimsHW{2, 2}); deconv92->setNbGroups(256); weightMap["deconv92"] = deconvwts92; - + ITensor* inputTensors[] = {deconv92->getOutput(0), ew61->getOutput(0)}; auto cat93 = network->addConcatenation(inputTensors, 2); auto lr94 = convBnLeaky(network, weightMap, *cat93->getOutput(0), 256, 1, 1, 0, 94); @@ -481,7 +481,7 @@ int main(int argc, char** argv) { if (argc == 2 && std::string(argv[1]) == "-s") { IHostMemory* modelStream{nullptr}; - APIToModel(1, &modelStream, "../yolov3-spp_ultralytics68.wts"); + APIToModel(1, &modelStream, "yolov3-spp_ultralytics68.wts"); assert(modelStream != nullptr); std::ofstream p("yolov3-spp.engine", std::ios::binary); if (!p) { diff --git a/uw_detection/src/yolov3-spp/yolov3.cpp b/uw_detection/src/yolov3-spp/yolov3.cpp new file mode 100644 index 0000000..0ba978c --- /dev/null +++ b/uw_detection/src/yolov3-spp/yolov3.cpp @@ -0,0 +1,562 @@ +#include +#include +#include +#include +#include +#include +#include +#include +#include "NvInfer.h" +#include "cuda_runtime_api.h" +#include +#include + +#define CHECK(status) \ + do\ + {\ + auto ret = (status);\ + if (ret != 0)\ + {\ + std::cerr << "Cuda failure: " << ret << std::endl;\ + abort();\ + }\ + } while (0) + + +#define USE_FP16 // comment out this if want to use FP32 +#define DEVICE 0 // GPU id +#define NMS_THRESH 0.4 +#define BBOX_CONF_THRESH 0.5 + +using namespace nvinfer1; + +// stuff we know about the network and the input/output blobs +static const int INPUT_H = Yolo::INPUT_H; +static const int INPUT_W = Yolo::INPUT_W; +static const int OUTPUT_SIZE = 1000 * 7 + 1; // we assume the yololayer outputs no more than 1000 boxes that conf >= 0.1 +const char* INPUT_BLOB_NAME = "data"; +const char* OUTPUT_BLOB_NAME = "prob"; +static Logger gLogger; +REGISTER_TENSORRT_PLUGIN(YoloPluginCreator); + +cv::Mat preprocess_img(cv::Mat& img) { + int w, h, x, y; + float r_w = INPUT_W / (img.cols*1.0); + float r_h = INPUT_H / (img.rows*1.0); + if (r_h > r_w) { + w = INPUT_W; + h = r_w * img.rows; + x = 0; + y = (INPUT_H - h) / 2; + } else { + w = r_h* img.cols; + h = INPUT_H; + x = (INPUT_W - w) / 2; + y = 0; + } + cv::Mat re(h, w, CV_8UC3); + cv::resize(img, re, re.size(), 0, 0, cv::INTER_CUBIC); + cv::Mat out(INPUT_H, INPUT_W, CV_8UC3, cv::Scalar(128, 128, 128)); + re.copyTo(out(cv::Rect(x, y, re.cols, re.rows))); + return out; +} + +cv::Rect get_rect(cv::Mat& img, float bbox[4]) { + int l, r, t, b; + float r_w = INPUT_W / (img.cols * 1.0); + float r_h = INPUT_H / (img.rows * 1.0); + if (r_h > r_w) { + l = bbox[0] - bbox[2]/2.f; + r = bbox[0] + bbox[2]/2.f; + t = bbox[1] - bbox[3]/2.f - (INPUT_H - r_w * img.rows) / 2; + b = bbox[1] + bbox[3]/2.f - (INPUT_H - r_w * img.rows) / 2; + l = l / r_w; + r = r / r_w; + t = t / r_w; + b = b / r_w; + } else { + l = bbox[0] - bbox[2]/2.f - (INPUT_W - r_h * img.cols) / 2; + r = bbox[0] + bbox[2]/2.f - (INPUT_W - r_h * img.cols) / 2; + t = bbox[1] - bbox[3]/2.f; + b = bbox[1] + bbox[3]/2.f; + l = l / r_h; + r = r / r_h; + t = t / r_h; + b = b / r_h; + } + return cv::Rect(l, t, r-l, b-t); +} + +float iou(float lbox[4], float rbox[4]) { + float interBox[] = { + std::max(lbox[0] - lbox[2]/2.f , rbox[0] - rbox[2]/2.f), //left + std::min(lbox[0] + lbox[2]/2.f , rbox[0] + rbox[2]/2.f), //right + std::max(lbox[1] - lbox[3]/2.f , rbox[1] - rbox[3]/2.f), //top + std::min(lbox[1] + lbox[3]/2.f , rbox[1] + rbox[3]/2.f), //bottom + }; + + if(interBox[2] > interBox[3] || interBox[0] > interBox[1]) + return 0.0f; + + float interBoxS =(interBox[1]-interBox[0])*(interBox[3]-interBox[2]); + return interBoxS/(lbox[2]*lbox[3] + rbox[2]*rbox[3] -interBoxS); +} + +bool cmp(Yolo::Detection& a, Yolo::Detection& b) { + return a.det_confidence > b.det_confidence; +} + +void nms(std::vector& res, float *output, float nms_thresh = NMS_THRESH) { + std::map> m; + for (int i = 0; i < output[0] && i < 1000; i++) { + if (output[1 + 7 * i + 4] <= BBOX_CONF_THRESH) continue; + Yolo::Detection det; + memcpy(&det, &output[1 + 7 * i], 7 * sizeof(float)); + if (m.count(det.class_id) == 0) m.emplace(det.class_id, std::vector()); + m[det.class_id].push_back(det); + } + for (auto it = m.begin(); it != m.end(); it++) { + //std::cout << it->second[0].class_id << " --- " << std::endl; + auto& dets = it->second; + std::sort(dets.begin(), dets.end(), cmp); + for (size_t m = 0; m < dets.size(); ++m) { + auto& item = dets[m]; + res.push_back(item); + for (size_t n = m + 1; n < dets.size(); ++n) { + if (iou(item.bbox, dets[n].bbox) > nms_thresh) { + dets.erase(dets.begin()+n); + --n; + } + } + } + } +} + +// TensorRT weight files have a simple space delimited format: +// [type] [size] +std::map loadWeights(const std::string file) { + std::cout << "Loading weights: " << file << std::endl; + std::map weightMap; + + // Open weights file + std::ifstream input(file); + assert(input.is_open() && "Unable to load weight file."); + + // Read number of weight blobs + int32_t count; + input >> count; + assert(count > 0 && "Invalid weight map file."); + + while (count--) + { + Weights wt{DataType::kFLOAT, nullptr, 0}; + uint32_t size; + + // Read name and type of blob + std::string name; + input >> name >> std::dec >> size; + wt.type = DataType::kFLOAT; + + // Load blob + uint32_t* val = reinterpret_cast(malloc(sizeof(val) * size)); + for (uint32_t x = 0, y = size; x < y; ++x) + { + input >> std::hex >> val[x]; + } + wt.values = val; + + wt.count = size; + weightMap[name] = wt; + } + + return weightMap; +} + +IScaleLayer* addBatchNorm2d(INetworkDefinition *network, std::map& weightMap, ITensor& input, std::string lname, float eps) { + float *gamma = (float*)weightMap[lname + ".weight"].values; + float *beta = (float*)weightMap[lname + ".bias"].values; + float *mean = (float*)weightMap[lname + ".running_mean"].values; + float *var = (float*)weightMap[lname + ".running_var"].values; + int len = weightMap[lname + ".running_var"].count; + + float *scval = reinterpret_cast(malloc(sizeof(float) * len)); + for (int i = 0; i < len; i++) { + scval[i] = gamma[i] / sqrt(var[i] + eps); + } + Weights scale{DataType::kFLOAT, scval, len}; + + float *shval = reinterpret_cast(malloc(sizeof(float) * len)); + for (int i = 0; i < len; i++) { + shval[i] = beta[i] - mean[i] * gamma[i] / sqrt(var[i] + eps); + } + Weights shift{DataType::kFLOAT, shval, len}; + + float *pval = reinterpret_cast(malloc(sizeof(float) * len)); + for (int i = 0; i < len; i++) { + pval[i] = 1.0; + } + Weights power{DataType::kFLOAT, pval, len}; + + weightMap[lname + ".scale"] = scale; + weightMap[lname + ".shift"] = shift; + weightMap[lname + ".power"] = power; + IScaleLayer* scale_1 = network->addScale(input, ScaleMode::kCHANNEL, shift, scale, power); + assert(scale_1); + return scale_1; +} + +ILayer* convBnLeaky(INetworkDefinition *network, std::map& weightMap, ITensor& input, int outch, int ksize, int s, int p, int linx) { + Weights emptywts{DataType::kFLOAT, nullptr, 0}; + IConvolutionLayer* conv1 = network->addConvolutionNd(input, outch, DimsHW{ksize, ksize}, weightMap["module_list." + std::to_string(linx) + ".Conv2d.weight"], emptywts); + assert(conv1); + conv1->setStrideNd(DimsHW{s, s}); + conv1->setPaddingNd(DimsHW{p, p}); + + IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), "module_list." + std::to_string(linx) + ".BatchNorm2d", 1e-5); + + auto lr = network->addActivation(*bn1->getOutput(0), ActivationType::kLEAKY_RELU); + lr->setAlpha(0.1); + + return lr; +} + +// Creat the engine using only the API and not any parser. +ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt, const std::string& weights_path) { + INetworkDefinition* network = builder->createNetworkV2(0U); + + // Create input tensor of shape {3, INPUT_H, INPUT_W} with name INPUT_BLOB_NAME + ITensor* data = network->addInput(INPUT_BLOB_NAME, dt, Dims3{3, INPUT_H, INPUT_W}); + assert(data); + + std::map weightMap = loadWeights(weights_path); + Weights emptywts{DataType::kFLOAT, nullptr, 0}; + + // Yeah I am stupid, I just want to expand the complete arch of darknet.. + auto lr0 = convBnLeaky(network, weightMap, *data, 32, 3, 1, 1, 0); + auto lr1 = convBnLeaky(network, weightMap, *lr0->getOutput(0), 64, 3, 2, 1, 1); + auto lr2 = convBnLeaky(network, weightMap, *lr1->getOutput(0), 32, 1, 1, 0, 2); + auto lr3 = convBnLeaky(network, weightMap, *lr2->getOutput(0), 64, 3, 1, 1, 3); + auto ew4 = network->addElementWise(*lr3->getOutput(0), *lr1->getOutput(0), ElementWiseOperation::kSUM); + auto lr5 = convBnLeaky(network, weightMap, *ew4->getOutput(0), 128, 3, 2, 1, 5); + auto lr6 = convBnLeaky(network, weightMap, *lr5->getOutput(0), 64, 1, 1, 0, 6); + auto lr7 = convBnLeaky(network, weightMap, *lr6->getOutput(0), 128, 3, 1, 1, 7); + auto ew8 = network->addElementWise(*lr7->getOutput(0), *lr5->getOutput(0), ElementWiseOperation::kSUM); + auto lr9 = convBnLeaky(network, weightMap, *ew8->getOutput(0), 64, 1, 1, 0, 9); + auto lr10 = convBnLeaky(network, weightMap, *lr9->getOutput(0), 128, 3, 1, 1, 10); + auto ew11 = network->addElementWise(*lr10->getOutput(0), *ew8->getOutput(0), ElementWiseOperation::kSUM); + auto lr12 = convBnLeaky(network, weightMap, *ew11->getOutput(0), 256, 3, 2, 1, 12); + auto lr13 = convBnLeaky(network, weightMap, *lr12->getOutput(0), 128, 1, 1, 0, 13); + auto lr14 = convBnLeaky(network, weightMap, *lr13->getOutput(0), 256, 3, 1, 1, 14); + auto ew15 = network->addElementWise(*lr14->getOutput(0), *lr12->getOutput(0), ElementWiseOperation::kSUM); + auto lr16 = convBnLeaky(network, weightMap, *ew15->getOutput(0), 128, 1, 1, 0, 16); + auto lr17 = convBnLeaky(network, weightMap, *lr16->getOutput(0), 256, 3, 1, 1, 17); + auto ew18 = network->addElementWise(*lr17->getOutput(0), *ew15->getOutput(0), ElementWiseOperation::kSUM); + auto lr19 = convBnLeaky(network, weightMap, *ew18->getOutput(0), 128, 1, 1, 0, 19); + auto lr20 = convBnLeaky(network, weightMap, *lr19->getOutput(0), 256, 3, 1, 1, 20); + auto ew21 = network->addElementWise(*lr20->getOutput(0), *ew18->getOutput(0), ElementWiseOperation::kSUM); + auto lr22 = convBnLeaky(network, weightMap, *ew21->getOutput(0), 128, 1, 1, 0, 22); + auto lr23 = convBnLeaky(network, weightMap, *lr22->getOutput(0), 256, 3, 1, 1, 23); + auto ew24 = network->addElementWise(*lr23->getOutput(0), *ew21->getOutput(0), ElementWiseOperation::kSUM); + auto lr25 = convBnLeaky(network, weightMap, *ew24->getOutput(0), 128, 1, 1, 0, 25); + auto lr26 = convBnLeaky(network, weightMap, *lr25->getOutput(0), 256, 3, 1, 1, 26); + auto ew27 = network->addElementWise(*lr26->getOutput(0), *ew24->getOutput(0), ElementWiseOperation::kSUM); + auto lr28 = convBnLeaky(network, weightMap, *ew27->getOutput(0), 128, 1, 1, 0, 28); + auto lr29 = convBnLeaky(network, weightMap, *lr28->getOutput(0), 256, 3, 1, 1, 29); + auto ew30 = network->addElementWise(*lr29->getOutput(0), *ew27->getOutput(0), ElementWiseOperation::kSUM); + auto lr31 = convBnLeaky(network, weightMap, *ew30->getOutput(0), 128, 1, 1, 0, 31); + auto lr32 = convBnLeaky(network, weightMap, *lr31->getOutput(0), 256, 3, 1, 1, 32); + auto ew33 = network->addElementWise(*lr32->getOutput(0), *ew30->getOutput(0), ElementWiseOperation::kSUM); + auto lr34 = convBnLeaky(network, weightMap, *ew33->getOutput(0), 128, 1, 1, 0, 34); + auto lr35 = convBnLeaky(network, weightMap, *lr34->getOutput(0), 256, 3, 1, 1, 35); + auto ew36 = network->addElementWise(*lr35->getOutput(0), *ew33->getOutput(0), ElementWiseOperation::kSUM); + auto lr37 = convBnLeaky(network, weightMap, *ew36->getOutput(0), 512, 3, 2, 1, 37); + auto lr38 = convBnLeaky(network, weightMap, *lr37->getOutput(0), 256, 1, 1, 0, 38); + auto lr39 = convBnLeaky(network, weightMap, *lr38->getOutput(0), 512, 3, 1, 1, 39); + auto ew40 = network->addElementWise(*lr39->getOutput(0), *lr37->getOutput(0), ElementWiseOperation::kSUM); + auto lr41 = convBnLeaky(network, weightMap, *ew40->getOutput(0), 256, 1, 1, 0, 41); + auto lr42 = convBnLeaky(network, weightMap, *lr41->getOutput(0), 512, 3, 1, 1, 42); + auto ew43 = network->addElementWise(*lr42->getOutput(0), *ew40->getOutput(0), ElementWiseOperation::kSUM); + auto lr44 = convBnLeaky(network, weightMap, *ew43->getOutput(0), 256, 1, 1, 0, 44); + auto lr45 = convBnLeaky(network, weightMap, *lr44->getOutput(0), 512, 3, 1, 1, 45); + auto ew46 = network->addElementWise(*lr45->getOutput(0), *ew43->getOutput(0), ElementWiseOperation::kSUM); + auto lr47 = convBnLeaky(network, weightMap, *ew46->getOutput(0), 256, 1, 1, 0, 47); + auto lr48 = convBnLeaky(network, weightMap, *lr47->getOutput(0), 512, 3, 1, 1, 48); + auto ew49 = network->addElementWise(*lr48->getOutput(0), *ew46->getOutput(0), ElementWiseOperation::kSUM); + auto lr50 = convBnLeaky(network, weightMap, *ew49->getOutput(0), 256, 1, 1, 0, 50); + auto lr51 = convBnLeaky(network, weightMap, *lr50->getOutput(0), 512, 3, 1, 1, 51); + auto ew52 = network->addElementWise(*lr51->getOutput(0), *ew49->getOutput(0), ElementWiseOperation::kSUM); + auto lr53 = convBnLeaky(network, weightMap, *ew52->getOutput(0), 256, 1, 1, 0, 53); + auto lr54 = convBnLeaky(network, weightMap, *lr53->getOutput(0), 512, 3, 1, 1, 54); + auto ew55 = network->addElementWise(*lr54->getOutput(0), *ew52->getOutput(0), ElementWiseOperation::kSUM); + auto lr56 = convBnLeaky(network, weightMap, *ew55->getOutput(0), 256, 1, 1, 0, 56); + auto lr57 = convBnLeaky(network, weightMap, *lr56->getOutput(0), 512, 3, 1, 1, 57); + auto ew58 = network->addElementWise(*lr57->getOutput(0), *ew55->getOutput(0), ElementWiseOperation::kSUM); + auto lr59 = convBnLeaky(network, weightMap, *ew58->getOutput(0), 256, 1, 1, 0, 59); + auto lr60 = convBnLeaky(network, weightMap, *lr59->getOutput(0), 512, 3, 1, 1, 60); + auto ew61 = network->addElementWise(*lr60->getOutput(0), *ew58->getOutput(0), ElementWiseOperation::kSUM); + auto lr62 = convBnLeaky(network, weightMap, *ew61->getOutput(0), 1024, 3, 2, 1, 62); + auto lr63 = convBnLeaky(network, weightMap, *lr62->getOutput(0), 512, 1, 1, 0, 63); + auto lr64 = convBnLeaky(network, weightMap, *lr63->getOutput(0), 1024, 3, 1, 1, 64); + auto ew65 = network->addElementWise(*lr64->getOutput(0), *lr62->getOutput(0), ElementWiseOperation::kSUM); + auto lr66 = convBnLeaky(network, weightMap, *ew65->getOutput(0), 512, 1, 1, 0, 66); + auto lr67 = convBnLeaky(network, weightMap, *lr66->getOutput(0), 1024, 3, 1, 1, 67); + auto ew68 = network->addElementWise(*lr67->getOutput(0), *ew65->getOutput(0), ElementWiseOperation::kSUM); + auto lr69 = convBnLeaky(network, weightMap, *ew68->getOutput(0), 512, 1, 1, 0, 69); + auto lr70 = convBnLeaky(network, weightMap, *lr69->getOutput(0), 1024, 3, 1, 1, 70); + auto ew71 = network->addElementWise(*lr70->getOutput(0), *ew68->getOutput(0), ElementWiseOperation::kSUM); + auto lr72 = convBnLeaky(network, weightMap, *ew71->getOutput(0), 512, 1, 1, 0, 72); + auto lr73 = convBnLeaky(network, weightMap, *lr72->getOutput(0), 1024, 3, 1, 1, 73); + auto ew74 = network->addElementWise(*lr73->getOutput(0), *ew71->getOutput(0), ElementWiseOperation::kSUM); + auto lr75 = convBnLeaky(network, weightMap, *ew74->getOutput(0), 512, 1, 1, 0, 75); + auto lr76 = convBnLeaky(network, weightMap, *lr75->getOutput(0), 1024, 3, 1, 1, 76); + auto lr77 = convBnLeaky(network, weightMap, *lr76->getOutput(0), 512, 1, 1, 0, 77); + auto lr78 = convBnLeaky(network, weightMap, *lr77->getOutput(0), 1024, 3, 1, 1, 78); + auto lr79 = convBnLeaky(network, weightMap, *lr78->getOutput(0), 512, 1, 1, 0, 79); + auto lr80 = convBnLeaky(network, weightMap, *lr79->getOutput(0), 1024, 3, 1, 1, 80); + IConvolutionLayer* conv81 = network->addConvolutionNd(*lr80->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{1, 1}, weightMap["module_list.81.Conv2d.weight"], weightMap["module_list.81.Conv2d.bias"]); + assert(conv81); + // 82 is yolo + auto l83 = lr79; + auto lr84 = convBnLeaky(network, weightMap, *l83->getOutput(0), 256, 1, 1, 0, 84); + + float *deval = reinterpret_cast(malloc(sizeof(float) * 256 * 2 * 2)); + for (int i = 0; i < 256 * 2 * 2; i++) { + deval[i] = 1.0; + } + Weights deconvwts85{DataType::kFLOAT, deval, 256 * 2 * 2}; + IDeconvolutionLayer* deconv85 = network->addDeconvolutionNd(*lr84->getOutput(0), 256, DimsHW{2, 2}, deconvwts85, emptywts); + assert(deconv85); + deconv85->setStrideNd(DimsHW{2, 2}); + deconv85->setNbGroups(256); + weightMap["deconv85"] = deconvwts85; + + ITensor* inputTensors[] = {deconv85->getOutput(0), ew61->getOutput(0)}; + auto cat86 = network->addConcatenation(inputTensors, 2); + auto lr87 = convBnLeaky(network, weightMap, *cat86->getOutput(0), 256, 1, 1, 0, 87); + auto lr88 = convBnLeaky(network, weightMap, *lr87->getOutput(0), 512, 3, 1, 1, 88); + auto lr89 = convBnLeaky(network, weightMap, *lr88->getOutput(0), 256, 1, 1, 0, 89); + auto lr90 = convBnLeaky(network, weightMap, *lr89->getOutput(0), 512, 3, 1, 1, 90); + auto lr91 = convBnLeaky(network, weightMap, *lr90->getOutput(0), 256, 1, 1, 0, 91); + auto lr92 = convBnLeaky(network, weightMap, *lr91->getOutput(0), 512, 3, 1, 1, 92); + IConvolutionLayer* conv93 = network->addConvolutionNd(*lr92->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{1, 1}, weightMap["module_list.93.Conv2d.weight"], weightMap["module_list.93.Conv2d.bias"]); + assert(conv93); + // 94 is yolo + auto l95 = lr91; + auto lr96 = convBnLeaky(network, weightMap, *l95->getOutput(0), 128, 1, 1, 0, 96); + Weights deconvwts97{DataType::kFLOAT, deval, 128 * 2 * 2}; + IDeconvolutionLayer* deconv97 = network->addDeconvolutionNd(*lr96->getOutput(0), 128, DimsHW{2, 2}, deconvwts97, emptywts); + assert(deconv97); + deconv97->setStrideNd(DimsHW{2, 2}); + deconv97->setNbGroups(128); + ITensor* inputTensors1[] = {deconv97->getOutput(0), ew36->getOutput(0)}; + auto cat98 = network->addConcatenation(inputTensors1, 2); + auto lr99 = convBnLeaky(network, weightMap, *cat98->getOutput(0), 128, 1, 1, 0, 99); + auto lr100 = convBnLeaky(network, weightMap, *lr99->getOutput(0), 256, 3, 1, 1, 100); + auto lr101 = convBnLeaky(network, weightMap, *lr100->getOutput(0), 128, 1, 1, 0, 101); + auto lr102 = convBnLeaky(network, weightMap, *lr101->getOutput(0), 256, 3, 1, 1, 102); + auto lr103 = convBnLeaky(network, weightMap, *lr102->getOutput(0), 128, 1, 1, 0, 103); + auto lr104 = convBnLeaky(network, weightMap, *lr103->getOutput(0), 256, 3, 1, 1, 104); + IConvolutionLayer* conv105 = network->addConvolutionNd(*lr104->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{1, 1}, weightMap["module_list.105.Conv2d.weight"], weightMap["module_list.105.Conv2d.bias"]); + assert(conv105); + + auto creator = getPluginRegistry()->getPluginCreator("YoloLayer_TRT", "1"); + const PluginFieldCollection* pluginData = creator->getFieldNames(); + IPluginV2 *pluginObj = creator->createPlugin("yololayer", pluginData); + ITensor* inputTensors_yolo[] = {conv81->getOutput(0), conv93->getOutput(0), conv105->getOutput(0)}; + auto yolo = network->addPluginV2(inputTensors_yolo, 3, *pluginObj); + + yolo->getOutput(0)->setName(OUTPUT_BLOB_NAME); + network->markOutput(*yolo->getOutput(0)); + + // Build engine + builder->setMaxBatchSize(maxBatchSize); + config->setMaxWorkspaceSize(16 * (1 << 20)); // 16MB +#ifdef USE_FP16 + config->setFlag(BuilderFlag::kFP16); +#endif + std::cout << "Building engine, please wait for a while..." << std::endl; + ICudaEngine* engine = builder->buildEngineWithConfig(*network, *config); + std::cout << "Build engine successfully!" << std::endl; + + // Don't need the network any more + network->destroy(); + + // Release host memory + for (auto& mem : weightMap) + { + free((void*) (mem.second.values)); + } + + return engine; +} + +void APIToModel(unsigned int maxBatchSize, IHostMemory** modelStream, const std::string& weights_path) { + // Create builder + IBuilder* builder = createInferBuilder(gLogger); + IBuilderConfig* config = builder->createBuilderConfig(); + + // Create model to populate the network, then set the outputs and create an engine + ICudaEngine* engine = createEngine(maxBatchSize, builder, config, DataType::kFLOAT, weights_path); + assert(engine != nullptr); + + // Serialize the engine + (*modelStream) = engine->serialize(); + + // Close everything down + engine->destroy(); + builder->destroy(); +} + +void doInference(IExecutionContext& context, float* input, float* output, int batchSize) { + const ICudaEngine& engine = context.getEngine(); + + // Pointers to input and output device buffers to pass to engine. + // Engine requires exactly IEngine::getNbBindings() number of buffers. + assert(engine.getNbBindings() == 2); + void* buffers[2]; + + // In order to bind the buffers, we need to know the names of the input and output tensors. + // Note that indices are guaranteed to be less than IEngine::getNbBindings() + const int inputIndex = engine.getBindingIndex(INPUT_BLOB_NAME); + const int outputIndex = engine.getBindingIndex(OUTPUT_BLOB_NAME); + + // Create GPU buffers on device + CHECK(cudaMalloc(&buffers[inputIndex], batchSize * 3 * INPUT_H * INPUT_W * sizeof(float))); + CHECK(cudaMalloc(&buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float))); + + // Create stream + cudaStream_t stream; + CHECK(cudaStreamCreate(&stream)); + + // DMA input batch data to device, infer on the batch asynchronously, and DMA output back to host + CHECK(cudaMemcpyAsync(buffers[inputIndex], input, batchSize * 3 * INPUT_H * INPUT_W * sizeof(float), cudaMemcpyHostToDevice, stream)); + context.enqueue(batchSize, buffers, stream, nullptr); + CHECK(cudaMemcpyAsync(output, buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float), cudaMemcpyDeviceToHost, stream)); + cudaStreamSynchronize(stream); + + // Release stream and buffers + cudaStreamDestroy(stream); + CHECK(cudaFree(buffers[inputIndex])); + CHECK(cudaFree(buffers[outputIndex])); +} + +int read_files_in_dir(const char *p_dir_name, std::vector &file_names) { + DIR *p_dir = opendir(p_dir_name); + if (p_dir == nullptr) { + return -1; + } + + struct dirent* p_file = nullptr; + while ((p_file = readdir(p_dir)) != nullptr) { + if (strcmp(p_file->d_name, ".") != 0 && + strcmp(p_file->d_name, "..") != 0) { + //std::string cur_file_name(p_dir_name); + //cur_file_name += "/"; + //cur_file_name += p_file->d_name; + std::string cur_file_name(p_file->d_name); + file_names.push_back(cur_file_name); + } + } + + closedir(p_dir); + return 0; +} + +int main(int argc, char** argv) { + cudaSetDevice(DEVICE); + // create a model using the API directly and serialize it to a stream + char *trtModelStream{nullptr}; + size_t size{0}; + + if (argc == 2 && std::string(argv[1]) == "-s") { + IHostMemory* modelStream{nullptr}; + APIToModel(1, &modelStream, "yolov3.wts"); + assert(modelStream != nullptr); + std::ofstream p("yolov3.engine", std::ios::binary); + if (!p) { + std::cerr << "could not open plan output file" << std::endl; + return -1; + } + p.write(reinterpret_cast(modelStream->data()), modelStream->size()); + modelStream->destroy(); + return 0; + } else if (argc == 3 && std::string(argv[1]) == "-d") { + std::ifstream file("yolov3.engine", std::ios::binary); + if (file.good()) { + file.seekg(0, file.end); + size = file.tellg(); + file.seekg(0, file.beg); + trtModelStream = new char[size]; + assert(trtModelStream); + file.read(trtModelStream, size); + file.close(); + } + } else { + std::cerr << "arguments not right!" << std::endl; + std::cerr << "./yolov3 -s // serialize model to plan file" << std::endl; + std::cerr << "./yolov3 -d ../samples // deserialize plan file and run inference" << std::endl; + return -1; + } + + std::vector file_names; + if (read_files_in_dir(argv[2], file_names) < 0) { + std::cout << "read_files_in_dir failed." << std::endl; + return -1; + } + + // prepare input data --------------------------- + static float data[3 * INPUT_H * INPUT_W]; + //for (int i = 0; i < 3 * INPUT_H * INPUT_W; i++) + // data[i] = 1.0; + static float prob[OUTPUT_SIZE]; + IRuntime* runtime = createInferRuntime(gLogger); + assert(runtime != nullptr); + ICudaEngine* engine = runtime->deserializeCudaEngine(trtModelStream, size); + assert(engine != nullptr); + IExecutionContext* context = engine->createExecutionContext(); + assert(context != nullptr); + delete[] trtModelStream; + + int fcount = 0; + for (auto f: file_names) { + fcount++; + std::cout << fcount << " " << f << std::endl; + cv::Mat img = cv::imread(std::string(argv[2]) + "/" + f); + if (img.empty()) continue; + cv::Mat pr_img = preprocess_img(img); + for (int i = 0; i < INPUT_H * INPUT_W; i++) { + data[i] = pr_img.at(i)[2] / 255.0; + data[i + INPUT_H * INPUT_W] = pr_img.at(i)[1] / 255.0; + data[i + 2 * INPUT_H * INPUT_W] = pr_img.at(i)[0] / 255.0; + } + + // Run inference + auto start = std::chrono::system_clock::now(); + doInference(*context, data, prob, 1); + auto end = std::chrono::system_clock::now(); + std::cout << std::chrono::duration_cast(end - start).count() << "ms" << std::endl; + std::vector res; + nms(res, prob); + for (int i=0; i<20; i++) { + std::cout << prob[i] << ","; + } + std::cout << res.size() << std::endl; + for (size_t j = 0; j < res.size(); j++) { + float *p = (float*)&res[j]; + for (size_t k = 0; k < 7; k++) { + std::cout << p[k] << ", "; + } + std::cout << std::endl; + cv::Rect r = get_rect(img, res[j].bbox); + cv::rectangle(img, r, cv::Scalar(0x27, 0xC1, 0x36), 2); + cv::putText(img, std::to_string((int)res[j].class_id), cv::Point(r.x, r.y - 1), cv::FONT_HERSHEY_PLAIN, 1.2, cv::Scalar(0xFF, 0xFF, 0xFF), 2); + } + cv::imwrite("_" + f, img); + } + + // Destroy the engine + context->destroy(); + engine->destroy(); + runtime->destroy(); + return 0; +} \ No newline at end of file diff --git a/uw_detection/src/yolov3_prepare_plan.cpp b/uw_detection/src/yolov3_prepare_plan.cpp new file mode 100644 index 0000000..0ba00df --- /dev/null +++ b/uw_detection/src/yolov3_prepare_plan.cpp @@ -0,0 +1,35 @@ +#include +#include +#include +#include +#include +#include +#include +#include +#include "NvInfer.h" +#include "cuda_runtime_api.h" +#include +#include +#include + + +#define DEVICE 0 // GPU id + + +using namespace nvinfer1; + +int main(int argc, char** argv) { + cudaSetDevice(DEVICE); + IHostMemory *modelStream{nullptr}; + std::string path = ros::package::getPath("uw_detection"); + APIToModel(1, &modelStream, path + "/share/yolov3.wts"); + assert(modelStream != nullptr); + std::ofstream p(path+ "/share/yolov3.engine", std::ios::binary); + if (!p) { + std::cerr << "could not open plan output file" << std::endl; + return -1; + } + p.write(reinterpret_cast(modelStream->data()), modelStream->size()); + modelStream->destroy(); + return 0; +} diff --git a/uw_detection/src/yolov3_spp_prepare_plan.cpp b/uw_detection/src/yolov3_spp_prepare_plan.cpp index b87ab65..ba164a3 100644 --- a/uw_detection/src/yolov3_spp_prepare_plan.cpp +++ b/uw_detection/src/yolov3_spp_prepare_plan.cpp @@ -8,7 +8,7 @@ #include #include "NvInfer.h" #include "cuda_runtime_api.h" -#include "yolov3-spp/yololayer.h" +#include #include #include From 16507ad8ef4d74c9b3c0747b94b107f2795b0871 Mon Sep 17 00:00:00 2001 From: Nick Walker Date: Sun, 2 Aug 2020 00:07:17 -0700 Subject: [PATCH 11/15] Fix formatting Not touching the copy pasted code to make future diffing easier Add clang files --- .clang-format | 64 +++++++++++++++++++ .clang-tidy | 49 ++++++++++++++ .../include/uw_detection/yolov3-spp.h | 4 +- uw_detection/include/uw_detection/yolov3.h | 4 +- uw_detection/src/yolo_ros_image_node.cpp | 46 +++++++------ uw_detection/src/yolov3_prepare_plan.cpp | 28 ++++---- uw_detection/src/yolov3_spp_prepare_plan.cpp | 28 ++++---- 7 files changed, 172 insertions(+), 51 deletions(-) create mode 100644 .clang-format create mode 100644 .clang-tidy diff --git a/.clang-format b/.clang-format new file mode 100644 index 0000000..20d75f4 --- /dev/null +++ b/.clang-format @@ -0,0 +1,64 @@ +--- +BasedOnStyle: Google +AccessModifierOffset: -2 +ConstructorInitializerIndentWidth: 2 +AlignEscapedNewlinesLeft: false +AlignTrailingComments: true +AllowAllParametersOfDeclarationOnNextLine: false +AllowShortIfStatementsOnASingleLine: false +AllowShortLoopsOnASingleLine: false +AllowShortFunctionsOnASingleLine: None +AlwaysBreakTemplateDeclarations: true +AlwaysBreakBeforeMultilineStrings: false +BreakBeforeBinaryOperators: false +BreakBeforeTernaryOperators: false +BreakConstructorInitializersBeforeComma: true +BinPackParameters: true +ColumnLimit: 120 +ConstructorInitializerAllOnOneLineOrOnePerLine: true +DerivePointerBinding: false +PointerBindsToType: true +ExperimentalAutoDetectBinPacking: false +IndentCaseLabels: true +MaxEmptyLinesToKeep: 1 +NamespaceIndentation: None +ObjCSpaceBeforeProtocolList: true +PenaltyBreakBeforeFirstCallParameter: 19 +PenaltyBreakComment: 60 +PenaltyBreakString: 100 +PenaltyBreakFirstLessLess: 1000 +PenaltyExcessCharacter: 1000 +PenaltyReturnTypeOnItsOwnLine: 70 +SpacesBeforeTrailingComments: 2 +Cpp11BracedListStyle: false +Standard: Auto +IndentWidth: 2 +TabWidth: 2 +UseTab: Never +IndentFunctionDeclarationAfterType: false +SpacesInParentheses: false +SpacesInAngles: false +SpaceInEmptyParentheses: false +SpacesInCStyleCastParentheses: false +SpaceAfterControlStatementKeyword: true +SpaceBeforeAssignmentOperators: true +ContinuationIndentWidth: 4 +SortIncludes: false +SpaceAfterCStyleCast: false + +# Configure each individual brace in BraceWrapping +BreakBeforeBraces: Custom + +# Control of individual brace wrapping cases +BraceWrapping: { + AfterClass: 'true' + AfterControlStatement: 'true' + AfterEnum : 'true' + AfterFunction : 'true' + AfterNamespace : 'true' + AfterStruct : 'true' + AfterUnion : 'true' + BeforeCatch : 'true' + BeforeElse : 'true' + IndentBraces : 'false' +} \ No newline at end of file diff --git a/.clang-tidy b/.clang-tidy new file mode 100644 index 0000000..6f40a77 --- /dev/null +++ b/.clang-tidy @@ -0,0 +1,49 @@ +--- +Checks: '-*, + performance-*, + llvm-namespace-comment, + modernize-redundant-void-arg, + modernize-use-nullptr, + modernize-use-default, + modernize-use-override, + modernize-loop-convert, + readability-named-parameter, + readability-redundant-smartptr-get, + readability-redundant-string-cstr, + readability-simplify-boolean-expr, + readability-container-size-empty, + readability-identifier-naming, + ' +HeaderFilterRegex: '' +AnalyzeTemporaryDtors: false +CheckOptions: + - key: llvm-namespace-comment.ShortNamespaceLines + value: '10' + - key: llvm-namespace-comment.SpacesBeforeComments + value: '2' + - key: readability-braces-around-statements.ShortStatementLines + value: '2' + # type names + - key: readability-identifier-naming.ClassCase + value: CamelCase + - key: readability-identifier-naming.EnumCase + value: CamelCase + - key: readability-identifier-naming.UnionCase + value: CamelCase + # method names + - key: readability-identifier-naming.MethodCase + value: camelBack + # variable names + - key: readability-identifier-naming.VariableCase + value: lower_case + - key: readability-identifier-naming.ClassMemberSuffix + value: '_' + # const static or global variables are UPPER_CASE + - key: readability-identifier-naming.EnumConstantCase + value: UPPER_CASE + - key: readability-identifier-naming.StaticConstantCase + value: UPPER_CASE + - key: readability-identifier-naming.ClassConstantCase + value: UPPER_CASE + - key: readability-identifier-naming.GlobalVariableCase + value: UPPER_CASE \ No newline at end of file diff --git a/uw_detection/include/uw_detection/yolov3-spp.h b/uw_detection/include/uw_detection/yolov3-spp.h index 1ba3cdb..a39da6a 100644 --- a/uw_detection/include/uw_detection/yolov3-spp.h +++ b/uw_detection/include/uw_detection/yolov3-spp.h @@ -1,6 +1,6 @@ #pragma once -#include #include "NvInfer.h" +#include void APIToModel(unsigned int maxBatchSize, nvinfer1::IHostMemory** modelStream, const std::string& weights_path); @@ -10,4 +10,4 @@ cv::Mat preprocess_img(cv::Mat& img); cv::Rect get_rect(cv::Mat& img, float bbox[4]); -void nms(std::vector& res, float *output, float nms_thresh); \ No newline at end of file +void nms(std::vector& res, float* output, float nms_thresh); \ No newline at end of file diff --git a/uw_detection/include/uw_detection/yolov3.h b/uw_detection/include/uw_detection/yolov3.h index 1ba3cdb..a39da6a 100644 --- a/uw_detection/include/uw_detection/yolov3.h +++ b/uw_detection/include/uw_detection/yolov3.h @@ -1,6 +1,6 @@ #pragma once -#include #include "NvInfer.h" +#include void APIToModel(unsigned int maxBatchSize, nvinfer1::IHostMemory** modelStream, const std::string& weights_path); @@ -10,4 +10,4 @@ cv::Mat preprocess_img(cv::Mat& img); cv::Rect get_rect(cv::Mat& img, float bbox[4]); -void nms(std::vector& res, float *output, float nms_thresh); \ No newline at end of file +void nms(std::vector& res, float* output, float nms_thresh); \ No newline at end of file diff --git a/uw_detection/src/yolo_ros_image_node.cpp b/uw_detection/src/yolo_ros_image_node.cpp index 513839c..5166c7f 100644 --- a/uw_detection/src/yolo_ros_image_node.cpp +++ b/uw_detection/src/yolo_ros_image_node.cpp @@ -1,13 +1,13 @@ -#include +#include +#include +#include #include #include -#include -#include +#include +#include #include -#include #include -#include -#include +#include #define NMS_THRESH 0.4 using namespace nvinfer1; @@ -15,12 +15,14 @@ using namespace nvinfer1; // stuff we know about the network and the input/output blobs static const int INPUT_H = Yolo::INPUT_H; static const int INPUT_W = Yolo::INPUT_W; -static const int OUTPUT_SIZE = 1000 * 7 + 1; // we assume the yololayer outputs no more than 1000 boxes that conf >= 0.1 +static const int OUTPUT_SIZE = 1000 * 7 + 1; // we assume the yololayer outputs + // no more than 1000 boxes that + // conf >= 0.1 static Logger gLogger; // prepare input data --------------------------- static float data[3 * INPUT_H * INPUT_W]; -//for (int i = 0; i < 3 * INPUT_H * INPUT_W; i++) +// for (int i = 0; i < 3 * INPUT_H * INPUT_W; i++) // data[i] = 1.0; static float prob[OUTPUT_SIZE]; @@ -34,7 +36,8 @@ void imageCallback(const sensor_msgs::ImageConstPtr& msg) cv::Mat pr_img = preprocess_img(img); - for (int i = 0; i < INPUT_H * INPUT_W; i++) { + for (int i = 0; i < INPUT_H * INPUT_W; i++) + { data[i] = pr_img.at(i)[2] / 255.0; data[i + INPUT_H * INPUT_W] = pr_img.at(i)[1] / 255.0; data[i + 2 * INPUT_H * INPUT_W] = pr_img.at(i)[0] / 255.0; @@ -46,19 +49,23 @@ void imageCallback(const sensor_msgs::ImageConstPtr& msg) std::cout << std::chrono::duration_cast(end - start).count() << "ms" << std::endl; std::vector res; nms(res, prob, NMS_THRESH); - for (int i=0; i<20; i++) { + for (int i = 0; i < 20; i++) + { std::cout << prob[i] << ","; } std::cout << res.size() << std::endl; - for (size_t j = 0; j < res.size(); j++) { - float *p = (float*)&res[j]; - for (size_t k = 0; k < 7; k++) { + for (size_t j = 0; j < res.size(); j++) + { + float* p = (float*)&res[j]; + for (size_t k = 0; k < 7; k++) + { std::cout << p[k] << ", "; } std::cout << std::endl; cv::Rect r = get_rect(img, res[j].bbox); cv::rectangle(img, r, cv::Scalar(0x27, 0xC1, 0x36), 2); - cv::putText(img, std::to_string((int)res[j].class_id), cv::Point(r.x, r.y - 1), cv::FONT_HERSHEY_PLAIN, 1.2, cv::Scalar(0xFF, 0xFF, 0xFF), 2); + cv::putText(img, std::to_string((int)res[j].class_id), cv::Point(r.x, r.y - 1), cv::FONT_HERSHEY_PLAIN, 1.2, + cv::Scalar(0xFF, 0xFF, 0xFF), 2); } cv::imshow("view", img); } @@ -68,17 +75,18 @@ void imageCallback(const sensor_msgs::ImageConstPtr& msg) } } -int main(int argc, char **argv) +int main(int argc, char** argv) { cudaSetDevice(0); - size_t size{0}; + size_t size{ 0 }; - char *trtModelStream{nullptr}; + char* trtModelStream{ nullptr }; ros::init(argc, argv, "image_listener"); - std::string path = ros::package::getPath("uw_detection"); + std::string path = ros::package::getPath("uw_detection"); std::ifstream file(path + "/share/yolov3.engine", std::ios::binary); - if (file.good()) { + if (file.good()) + { file.seekg(0, file.end); size = file.tellg(); file.seekg(0, file.beg); diff --git a/uw_detection/src/yolov3_prepare_plan.cpp b/uw_detection/src/yolov3_prepare_plan.cpp index 0ba00df..1ee7079 100644 --- a/uw_detection/src/yolov3_prepare_plan.cpp +++ b/uw_detection/src/yolov3_prepare_plan.cpp @@ -1,35 +1,35 @@ +#include "NvInfer.h" +#include "cuda_runtime_api.h" +#include +#include #include #include #include -#include -#include -#include #include -#include -#include "NvInfer.h" -#include "cuda_runtime_api.h" +#include +#include #include #include -#include - +#include #define DEVICE 0 // GPU id - using namespace nvinfer1; -int main(int argc, char** argv) { +int main(int argc, char** argv) +{ cudaSetDevice(DEVICE); - IHostMemory *modelStream{nullptr}; + IHostMemory* modelStream{ nullptr }; std::string path = ros::package::getPath("uw_detection"); APIToModel(1, &modelStream, path + "/share/yolov3.wts"); assert(modelStream != nullptr); - std::ofstream p(path+ "/share/yolov3.engine", std::ios::binary); - if (!p) { + std::ofstream p(path + "/share/yolov3.engine", std::ios::binary); + if (!p) + { std::cerr << "could not open plan output file" << std::endl; return -1; } - p.write(reinterpret_cast(modelStream->data()), modelStream->size()); + p.write(reinterpret_cast(modelStream->data()), modelStream->size()); modelStream->destroy(); return 0; } diff --git a/uw_detection/src/yolov3_spp_prepare_plan.cpp b/uw_detection/src/yolov3_spp_prepare_plan.cpp index ba164a3..96df97b 100644 --- a/uw_detection/src/yolov3_spp_prepare_plan.cpp +++ b/uw_detection/src/yolov3_spp_prepare_plan.cpp @@ -1,35 +1,35 @@ +#include "NvInfer.h" +#include "cuda_runtime_api.h" +#include +#include #include #include #include -#include -#include -#include #include -#include -#include "NvInfer.h" -#include "cuda_runtime_api.h" +#include +#include #include #include -#include - +#include #define DEVICE 0 // GPU id - using namespace nvinfer1; -int main(int argc, char** argv) { +int main(int argc, char** argv) +{ cudaSetDevice(DEVICE); - IHostMemory *modelStream{nullptr}; + IHostMemory* modelStream{ nullptr }; std::string path = ros::package::getPath("uw_detection"); APIToModel(1, &modelStream, path + "/share/yolov3-spp_ultralytics68.wts"); assert(modelStream != nullptr); - std::ofstream p(path+ "/share/yolov3-spp.engine", std::ios::binary); - if (!p) { + std::ofstream p(path + "/share/yolov3-spp.engine", std::ios::binary); + if (!p) + { std::cerr << "could not open plan output file" << std::endl; return -1; } - p.write(reinterpret_cast(modelStream->data()), modelStream->size()); + p.write(reinterpret_cast(modelStream->data()), modelStream->size()); modelStream->destroy(); return 0; } From cf8516479d87003ae823a9cc512ebd8a1f3d3da7 Mon Sep 17 00:00:00 2001 From: Nick Walker Date: Fri, 14 Aug 2020 15:10:43 -0700 Subject: [PATCH 12/15] Remove YOLOv3 submodule --- .gitmodules | 3 --- uw_detection/src/yolov3 | 1 - 2 files changed, 4 deletions(-) delete mode 100644 .gitmodules delete mode 160000 uw_detection/src/yolov3 diff --git a/.gitmodules b/.gitmodules deleted file mode 100644 index f0c8d29..0000000 --- a/.gitmodules +++ /dev/null @@ -1,3 +0,0 @@ -[submodule "uw_detection/src/yolov3"] - path = uw_detection/src/yolov3 - url = https://github.com/homeskies/yolov3 diff --git a/uw_detection/src/yolov3 b/uw_detection/src/yolov3 deleted file mode 160000 index b8a1aa4..0000000 --- a/uw_detection/src/yolov3 +++ /dev/null @@ -1 +0,0 @@ -Subproject commit b8a1aa482a8688b0c24e70164bac24f48241e0ba From 5fc8ef5eddc2b6f56c8d66c1f83513c035951bcd Mon Sep 17 00:00:00 2001 From: Nick Walker Date: Fri, 14 Aug 2020 15:11:35 -0700 Subject: [PATCH 13/15] Rename TensorRT YOLO directory --- uw_detection/CMakeLists.txt | 16 ++++++++-------- .../src/{yolov3-spp => yolov3}/.gitignore | 0 .../src/{yolov3-spp => yolov3}/README.md | 0 .../src/{yolov3-spp => yolov3}/gen_wts.py | 0 .../src/{yolov3-spp => yolov3}/gen_wts_spp.py | 0 .../src/{yolov3-spp => yolov3}/samples/bus.jpg | Bin .../{yolov3-spp => yolov3}/samples/zidane.jpg | Bin .../src/{yolov3-spp => yolov3}/yololayer.cu | 0 .../src/{yolov3-spp => yolov3}/yolov3-spp.cpp | 0 .../src/{yolov3-spp => yolov3}/yolov3.cpp | 0 10 files changed, 8 insertions(+), 8 deletions(-) rename uw_detection/src/{yolov3-spp => yolov3}/.gitignore (100%) rename uw_detection/src/{yolov3-spp => yolov3}/README.md (100%) rename uw_detection/src/{yolov3-spp => yolov3}/gen_wts.py (100%) rename uw_detection/src/{yolov3-spp => yolov3}/gen_wts_spp.py (100%) rename uw_detection/src/{yolov3-spp => yolov3}/samples/bus.jpg (100%) rename uw_detection/src/{yolov3-spp => yolov3}/samples/zidane.jpg (100%) rename uw_detection/src/{yolov3-spp => yolov3}/yololayer.cu (100%) rename uw_detection/src/{yolov3-spp => yolov3}/yolov3-spp.cpp (100%) rename uw_detection/src/{yolov3-spp => yolov3}/yolov3.cpp (100%) diff --git a/uw_detection/CMakeLists.txt b/uw_detection/CMakeLists.txt index f985885..59889f2 100644 --- a/uw_detection/CMakeLists.txt +++ b/uw_detection/CMakeLists.txt @@ -1,7 +1,8 @@ cmake_minimum_required(VERSION 2.8.12) project(uw_detection) -find_package(catkin REQUIRED COMPONENTS cv_bridge +find_package(catkin REQUIRED COMPONENTS + cv_bridge geometry_msgs image_transport message_generation @@ -23,9 +24,8 @@ catkin_package() option(CUDA_USE_STATIC_CUDA_RUNTIME OFF) set(CMAKE_CXX_STANDARD 11) -set(CMAKE_BUILD_TYPE Debug) -find_package(OpenCV) +find_package(OpenCV REQUIRED) include_directories(OpenCV_INCLUDE_DIRS ${catkin_INCLUDE_DIRS} include) @@ -47,18 +47,18 @@ else() endif() -set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -Wall -Ofast -Wfatal-errors -D_MWAITXINTRIN_H_INCLUDED") +set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall -Ofast -Wfatal-errors -D_MWAITXINTRIN_H_INCLUDED") -cuda_add_library(yololayer SHARED src/yolov3-spp/yololayer.cu) +cuda_add_library(yololayer SHARED src/yolov3/yololayer.cu) -add_library(yolov3-spp src/yolov3-spp/yolov3-spp.cpp) +add_library(yolov3-spp src/yolov3/yolov3-spp.cpp) target_link_libraries(yolov3-spp nvinfer cudart yololayer ${OpenCV_LIBS}) -add_library(yolov3 src/yolov3-spp/yolov3.cpp) +add_library(yolov3 src/yolov3/yolov3.cpp) target_link_libraries(yolov3 nvinfer cudart yololayer ${OpenCV_LIBS}) add_executable(yolov3_spp_prepare_plan src/yolov3_spp_prepare_plan.cpp) -target_link_libraries(yolov3_spp_prepare_plan yolov3-spp nvinfer cudart yololayer ${OpenCV_LIBS} ${catkin_LIBRARIES}) +target_link_libraries(yolov3_spp_prepare_plan yolov3 nvinfer cudart yololayer ${OpenCV_LIBS} ${catkin_LIBRARIES}) add_executable(yolov3_prepare_plan src/yolov3_prepare_plan.cpp) target_link_libraries(yolov3_prepare_plan yolov3 nvinfer cudart yololayer ${OpenCV_LIBS} ${catkin_LIBRARIES}) diff --git a/uw_detection/src/yolov3-spp/.gitignore b/uw_detection/src/yolov3/.gitignore similarity index 100% rename from uw_detection/src/yolov3-spp/.gitignore rename to uw_detection/src/yolov3/.gitignore diff --git a/uw_detection/src/yolov3-spp/README.md b/uw_detection/src/yolov3/README.md similarity index 100% rename from uw_detection/src/yolov3-spp/README.md rename to uw_detection/src/yolov3/README.md diff --git a/uw_detection/src/yolov3-spp/gen_wts.py b/uw_detection/src/yolov3/gen_wts.py similarity index 100% rename from uw_detection/src/yolov3-spp/gen_wts.py rename to uw_detection/src/yolov3/gen_wts.py diff --git a/uw_detection/src/yolov3-spp/gen_wts_spp.py b/uw_detection/src/yolov3/gen_wts_spp.py similarity index 100% rename from uw_detection/src/yolov3-spp/gen_wts_spp.py rename to uw_detection/src/yolov3/gen_wts_spp.py diff --git a/uw_detection/src/yolov3-spp/samples/bus.jpg b/uw_detection/src/yolov3/samples/bus.jpg similarity index 100% rename from uw_detection/src/yolov3-spp/samples/bus.jpg rename to uw_detection/src/yolov3/samples/bus.jpg diff --git a/uw_detection/src/yolov3-spp/samples/zidane.jpg b/uw_detection/src/yolov3/samples/zidane.jpg similarity index 100% rename from uw_detection/src/yolov3-spp/samples/zidane.jpg rename to uw_detection/src/yolov3/samples/zidane.jpg diff --git a/uw_detection/src/yolov3-spp/yololayer.cu b/uw_detection/src/yolov3/yololayer.cu similarity index 100% rename from uw_detection/src/yolov3-spp/yololayer.cu rename to uw_detection/src/yolov3/yololayer.cu diff --git a/uw_detection/src/yolov3-spp/yolov3-spp.cpp b/uw_detection/src/yolov3/yolov3-spp.cpp similarity index 100% rename from uw_detection/src/yolov3-spp/yolov3-spp.cpp rename to uw_detection/src/yolov3/yolov3-spp.cpp diff --git a/uw_detection/src/yolov3-spp/yolov3.cpp b/uw_detection/src/yolov3/yolov3.cpp similarity index 100% rename from uw_detection/src/yolov3-spp/yolov3.cpp rename to uw_detection/src/yolov3/yolov3.cpp From d88e14364b439d71dd1256675c219752ba92f242 Mon Sep 17 00:00:00 2001 From: Nick Walker Date: Fri, 14 Aug 2020 15:12:20 -0700 Subject: [PATCH 14/15] Linter updates for YOLO Fix whitespace, disable roslint --- .flake8 | 2 +- .github/workflows/ci.yaml | 1 + uw_detection/CMakeLists.txt | 1 - uw_detection/package.xml | 1 - uw_detection/setup.py | 2 +- 5 files changed, 3 insertions(+), 4 deletions(-) diff --git a/.flake8 b/.flake8 index 1d0f20d..7877ccc 100644 --- a/.flake8 +++ b/.flake8 @@ -1,6 +1,6 @@ [flake8] max-line-length = 160 -exclude = tests/* +exclude = tests/* uw_detection/src/yolov3 max-complexity = 10 # We import modules here to make the API cleaner per-file-ignores = **/recognize_speech/__init__.py:F401 diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index 5e98ea5..0cf493d 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -7,6 +7,7 @@ jobs: env: AFTER_SCRIPT: 'cd $target_ws && rosenv && catkin build $(catkin list --depends-on roslint -u) --no-deps --no-status --verbose --make-args roslint' CATKIN_LINT: true + CATKIN_LINT_ARGS: "--ignore missing_directory --ignore external_link_directory" ROS_DISTRO: melodic ROS_REPO: main strategy: diff --git a/uw_detection/CMakeLists.txt b/uw_detection/CMakeLists.txt index 59889f2..836d796 100644 --- a/uw_detection/CMakeLists.txt +++ b/uw_detection/CMakeLists.txt @@ -9,7 +9,6 @@ find_package(catkin REQUIRED COMPONENTS message_filters message_runtime roscpp - roslint sensor_msgs std_msgs std_srvs diff --git a/uw_detection/package.xml b/uw_detection/package.xml index 57d5bef..772408d 100644 --- a/uw_detection/package.xml +++ b/uw_detection/package.xml @@ -25,7 +25,6 @@ image_transport octomap - roslint std_srvs tf2 tf2_eigen diff --git a/uw_detection/setup.py b/uw_detection/setup.py index 81d0f8c..756d253 100644 --- a/uw_detection/setup.py +++ b/uw_detection/setup.py @@ -8,4 +8,4 @@ packages=['yolov3'], package_dir={'': 'src'}) -setup(**setup_args) \ No newline at end of file +setup(**setup_args) From 69839bd75893d13fd09f6a7d985510d2f15b5cb1 Mon Sep 17 00:00:00 2001 From: Nick Walker Date: Tue, 1 Sep 2020 14:45:01 -0700 Subject: [PATCH 15/15] Make class num more configurable Use TensorRTs plugin options struct to pass in the number. Also copied over some boilerplate for making other parameters configurable this way but didn't hook them up fully Tweak plugin registration point to match an update in upstream --- uw_detection/README.md | 2 +- uw_detection/include/uw_detection/yololayer.h | 5 +- uw_detection/include/uw_detection/yolov3.h | 1 + uw_detection/src/yolov3/.gitignore | 1 - uw_detection/src/yolov3/yololayer.cu | 56 +++++++++++++++++-- uw_detection/src/yolov3/yolov3-spp.cpp | 3 +- uw_detection/src/yolov3/yolov3.cpp | 19 ++++--- 7 files changed, 69 insertions(+), 18 deletions(-) delete mode 100644 uw_detection/src/yolov3/.gitignore diff --git a/uw_detection/README.md b/uw_detection/README.md index 87ac3c9..67778c5 100644 --- a/uw_detection/README.md +++ b/uw_detection/README.md @@ -2,7 +2,7 @@ Wrappers for detection models. Tools for preparing data and training. -TensorRT YOLOv3 implementations taken from [tensorrtx](https://github.com/wang-xinyu/tensorrtx). +TensorRT YOLOv3 implementations taken from [tensorrtx](https://github.com/wang-xinyu/tensorrtx). Additional modifactions based on [this implementation](https://github.com/jkjung-avt/tensorrt_demos/blob/master/plugins/yolo_layer.cu#L39) ## Usage diff --git a/uw_detection/include/uw_detection/yololayer.h b/uw_detection/include/uw_detection/yololayer.h index 1ce9bad..91af21e 100644 --- a/uw_detection/include/uw_detection/yololayer.h +++ b/uw_detection/include/uw_detection/yololayer.h @@ -14,7 +14,6 @@ namespace Yolo static constexpr int CHECK_COUNT = 3; static constexpr float IGNORE_THRESH = 0.1f; static constexpr int MAX_OUTPUT_BBOX_COUNT = 1000; - static constexpr int CLASS_NUM = 80; static constexpr int INPUT_H = 608; static constexpr int INPUT_W = 608; @@ -57,7 +56,7 @@ namespace nvinfer1 class YoloLayerPlugin: public IPluginV2IOExt { public: - explicit YoloLayerPlugin(); + explicit YoloLayerPlugin(int num_classes); YoloLayerPlugin(const void* data, size_t length); ~YoloLayerPlugin(); @@ -151,7 +150,7 @@ namespace nvinfer1 static PluginFieldCollection mFC; static std::vector mPluginAttributes; }; - + REGISTER_TENSORRT_PLUGIN(YoloPluginCreator); }; diff --git a/uw_detection/include/uw_detection/yolov3.h b/uw_detection/include/uw_detection/yolov3.h index a39da6a..6cdc155 100644 --- a/uw_detection/include/uw_detection/yolov3.h +++ b/uw_detection/include/uw_detection/yolov3.h @@ -1,6 +1,7 @@ #pragma once #include "NvInfer.h" #include +#include void APIToModel(unsigned int maxBatchSize, nvinfer1::IHostMemory** modelStream, const std::string& weights_path); diff --git a/uw_detection/src/yolov3/.gitignore b/uw_detection/src/yolov3/.gitignore deleted file mode 100644 index 80bf80f..0000000 --- a/uw_detection/src/yolov3/.gitignore +++ /dev/null @@ -1 +0,0 @@ -yolov3 \ No newline at end of file diff --git a/uw_detection/src/yolov3/yololayer.cu b/uw_detection/src/yolov3/yololayer.cu index 5f8e253..8ff75b6 100644 --- a/uw_detection/src/yolov3/yololayer.cu +++ b/uw_detection/src/yolov3/yololayer.cu @@ -4,9 +4,9 @@ using namespace Yolo; namespace nvinfer1 { - YoloLayerPlugin::YoloLayerPlugin() + YoloLayerPlugin::YoloLayerPlugin(int num_classes) { - mClassCount = CLASS_NUM; + mClassCount = num_classes; mYoloKernel.clear(); mYoloKernel.push_back(yolo1); mYoloKernel.push_back(yolo2); @@ -126,7 +126,7 @@ namespace nvinfer1 // Clone the plugin IPluginV2IOExt* YoloLayerPlugin::clone() const { - YoloLayerPlugin *p = new YoloLayerPlugin(); + YoloLayerPlugin *p = new YoloLayerPlugin(mClassCount); p->setPluginNamespace(mPluginNamespace); return p; } @@ -220,6 +220,11 @@ namespace nvinfer1 YoloPluginCreator::YoloPluginCreator() { mPluginAttributes.clear(); + mPluginAttributes.emplace_back(PluginField("numClasses", nullptr, PluginFieldType::kINT32, 1)); + mPluginAttributes.emplace_back(PluginField("yoloWidth", nullptr, PluginFieldType::kINT32, 1)); + mPluginAttributes.emplace_back(PluginField("yoloHeight", nullptr, PluginFieldType::kINT32, 1)); + mPluginAttributes.emplace_back(PluginField("inputWidth", nullptr, PluginFieldType::kINT32, 1)); + mPluginAttributes.emplace_back(PluginField("inputHeight", nullptr, PluginFieldType::kINT32, 1)); mFC.nbFields = mPluginAttributes.size(); mFC.fields = mPluginAttributes.data(); @@ -242,7 +247,50 @@ namespace nvinfer1 IPluginV2IOExt* YoloPluginCreator::createPlugin(const char* name, const PluginFieldCollection* fc) { - YoloLayerPlugin* obj = new YoloLayerPlugin(); + //assert(!strcmp(name, getPluginName())); + const PluginField* fields = fc->fields; + int yolo_width, yolo_height, num_anchors = 0; + int num_classes; + int input_width, input_height; + + for (int i = 0; i < fc->nbFields; ++i) + { + const char* attrName = fields[i].name; + if (!strcmp(attrName, "yoloWidth")) + { + assert(fields[i].type == PluginFieldType::kINT32); + yolo_width = *(static_cast(fields[i].data)); + } + else if (!strcmp(attrName, "yoloHeight")) + { + assert(fields[i].type == PluginFieldType::kINT32); + yolo_height = *(static_cast(fields[i].data)); + } + else if (!strcmp(attrName, "numAnchors")) + { + assert(fields[i].type == PluginFieldType::kINT32); + num_anchors = *(static_cast(fields[i].data)); + } + else if (!strcmp(attrName, "numClasses")) + { + assert(fields[i].type == PluginFieldType::kINT32); + num_classes = *(static_cast(fields[i].data)); + } + else if (!strcmp(attrName, "inputWidth")) + { + assert(fields[i].type == PluginFieldType::kINT32); + input_width = *(static_cast(fields[i].data)); + } + else if (!strcmp(attrName, "inputHeight")) + { + assert(fields[i].type == PluginFieldType::kINT32); + input_height = *(static_cast(fields[i].data)); + } + } + //assert(yolo_width > 0 && yolo_height > 0); + //assert(num_classes > 0); + //assert(input_width > 0 && input_height > 0); + YoloLayerPlugin* obj = new YoloLayerPlugin(num_classes); obj->setPluginNamespace(mNamespace.c_str()); return obj; } diff --git a/uw_detection/src/yolov3/yolov3-spp.cpp b/uw_detection/src/yolov3/yolov3-spp.cpp index c576782..24e3aad 100644 --- a/uw_detection/src/yolov3/yolov3-spp.cpp +++ b/uw_detection/src/yolov3/yolov3-spp.cpp @@ -37,7 +37,6 @@ static const int OUTPUT_SIZE = 1000 * 7 + 1; // we assume the yololayer outputs const char* INPUT_BLOB_NAME = "data"; const char* OUTPUT_BLOB_NAME = "prob"; static Logger gLogger; -REGISTER_TENSORRT_PLUGIN(YoloPluginCreator); cv::Mat preprocess_img(cv::Mat& img) { int w, h, x, y; @@ -61,7 +60,7 @@ cv::Mat preprocess_img(cv::Mat& img) { return out; } -cv::Rect get_rect(cv::Mat& img, float bbox[4]) { +cv::Rect get_rect(const cv::Mat& img, float bbox[4]) { int l, r, t, b; float r_w = INPUT_W / (img.cols * 1.0); float r_h = INPUT_H / (img.rows * 1.0); diff --git a/uw_detection/src/yolov3/yolov3.cpp b/uw_detection/src/yolov3/yolov3.cpp index 0ba978c..13e63b8 100644 --- a/uw_detection/src/yolov3/yolov3.cpp +++ b/uw_detection/src/yolov3/yolov3.cpp @@ -27,6 +27,7 @@ #define DEVICE 0 // GPU id #define NMS_THRESH 0.4 #define BBOX_CONF_THRESH 0.5 +#define CLASS_NUM 21 using namespace nvinfer1; @@ -37,7 +38,6 @@ static const int OUTPUT_SIZE = 1000 * 7 + 1; // we assume the yololayer outputs const char* INPUT_BLOB_NAME = "data"; const char* OUTPUT_BLOB_NAME = "prob"; static Logger gLogger; -REGISTER_TENSORRT_PLUGIN(YoloPluginCreator); cv::Mat preprocess_img(cv::Mat& img) { int w, h, x, y; @@ -61,7 +61,7 @@ cv::Mat preprocess_img(cv::Mat& img) { return out; } -cv::Rect get_rect(cv::Mat& img, float bbox[4]) { +cv::Rect get_rect(const cv::Mat& img, float bbox[4]) { int l, r, t, b; float r_w = INPUT_W / (img.cols * 1.0); float r_h = INPUT_H / (img.rows * 1.0); @@ -313,7 +313,7 @@ ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, IBuilder auto lr78 = convBnLeaky(network, weightMap, *lr77->getOutput(0), 1024, 3, 1, 1, 78); auto lr79 = convBnLeaky(network, weightMap, *lr78->getOutput(0), 512, 1, 1, 0, 79); auto lr80 = convBnLeaky(network, weightMap, *lr79->getOutput(0), 1024, 3, 1, 1, 80); - IConvolutionLayer* conv81 = network->addConvolutionNd(*lr80->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{1, 1}, weightMap["module_list.81.Conv2d.weight"], weightMap["module_list.81.Conv2d.bias"]); + IConvolutionLayer* conv81 = network->addConvolutionNd(*lr80->getOutput(0), 3 * (CLASS_NUM + 5), DimsHW{1, 1}, weightMap["module_list.81.Conv2d.weight"], weightMap["module_list.81.Conv2d.bias"]); assert(conv81); // 82 is yolo auto l83 = lr79; @@ -338,7 +338,7 @@ ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, IBuilder auto lr90 = convBnLeaky(network, weightMap, *lr89->getOutput(0), 512, 3, 1, 1, 90); auto lr91 = convBnLeaky(network, weightMap, *lr90->getOutput(0), 256, 1, 1, 0, 91); auto lr92 = convBnLeaky(network, weightMap, *lr91->getOutput(0), 512, 3, 1, 1, 92); - IConvolutionLayer* conv93 = network->addConvolutionNd(*lr92->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{1, 1}, weightMap["module_list.93.Conv2d.weight"], weightMap["module_list.93.Conv2d.bias"]); + IConvolutionLayer* conv93 = network->addConvolutionNd(*lr92->getOutput(0), 3 * (CLASS_NUM + 5), DimsHW{1, 1}, weightMap["module_list.93.Conv2d.weight"], weightMap["module_list.93.Conv2d.bias"]); assert(conv93); // 94 is yolo auto l95 = lr91; @@ -356,12 +356,17 @@ ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, IBuilder auto lr102 = convBnLeaky(network, weightMap, *lr101->getOutput(0), 256, 3, 1, 1, 102); auto lr103 = convBnLeaky(network, weightMap, *lr102->getOutput(0), 128, 1, 1, 0, 103); auto lr104 = convBnLeaky(network, weightMap, *lr103->getOutput(0), 256, 3, 1, 1, 104); - IConvolutionLayer* conv105 = network->addConvolutionNd(*lr104->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{1, 1}, weightMap["module_list.105.Conv2d.weight"], weightMap["module_list.105.Conv2d.bias"]); + IConvolutionLayer* conv105 = network->addConvolutionNd(*lr104->getOutput(0), 3 * (CLASS_NUM + 5), DimsHW{1, 1}, weightMap["module_list.105.Conv2d.weight"], weightMap["module_list.105.Conv2d.bias"]); assert(conv105); auto creator = getPluginRegistry()->getPluginCreator("YoloLayer_TRT", "1"); - const PluginFieldCollection* pluginData = creator->getFieldNames(); - IPluginV2 *pluginObj = creator->createPlugin("yololayer", pluginData); + auto class_num = CLASS_NUM; + std::vector vpf{ + PluginField{"numClasses", &class_num, PluginFieldType::kINT32, 1} + }; + + PluginFieldCollection pfc{ (int)vpf.size(), vpf.data() }; + IPluginV2 *pluginObj = creator->createPlugin("yololayer", &pfc); ITensor* inputTensors_yolo[] = {conv81->getOutput(0), conv93->getOutput(0), conv105->getOutput(0)}; auto yolo = network->addPluginV2(inputTensors_yolo, 3, *pluginObj);