Automotive reference implementation

automotive/3d-object-detection/README.md

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+## Reference implementation fo automotive 3D detection benchmark
+
+## TODO: Instructions for dataset download after it is uploaded somewhere appropriate
+
+## TODO: Instructions for checkpoints downloads after it is uploaded somewhere appropriate
+
+## Running with docker
+```
+docker build -t auto_inference -f dockerfile.gpu .
+
+docker run --gpus=all -it -v <directory to inference repo>/inference/:/inference -v <directory to waymo dataset>/waymo:/waymo --rm auto_inference
+
+cd /inference/automotive/3d-object-detection
+python main.py --dataset waymo --dataset-path /waymo/kitti_format/ --lidar-path <checkpoint_path>/pp_ep36.pth --segmentor-path <checkpoint_path>/best_deeplabv3plus_resnet50_waymo_os16.pth --mlperf_conf /inference/mlperf.conf

automotive/3d-object-detection/accuracy_waymo.py

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+"""
+Tool to calculate accuracy for loadgen accuracy output found in mlperf_log_accuracy.json
+We assume that loadgen's query index is in the same order as
+the images in coco's annotations/instances_val2017.json.
+"""
+
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import argparse
+import json
+import os
+
+import numpy as np
+from waymo import Waymo
+from tools.evaluate import do_eval
+# pylint: disable=missing-docstring
+CLASSES = Waymo.CLASSES
+LABEL2CLASSES = {v: k for k, v in CLASSES.items()}
+
+
+def get_args():
+    """Parse commandline."""
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--mlperf-accuracy-file",
+        required=True,
+        help="path to mlperf_log_accuracy.json")
+    parser.add_argument(
+        "--waymo-dir",
+        required=True,
+        help="waymo dataset directory")
+    parser.add_argument(
+        "--verbose",
+        action="store_true",
+        help="verbose messages")
+    parser.add_argument(
+        "--output-file",
+        default="openimages-results.json",
+        help="path to output file")
+    parser.add_argument(
+        "--use-inv-map",
+        action="store_true",
+        help="use inverse label map")
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = get_args()
+
+    with open(args.mlperf_accuracy_file, "r") as f:
+        results = json.load(f)
+
+    detections = {}
+    image_ids = set()
+    seen = set()
+    no_results = 0
+
+    val_dataset = Waymo(
+        data_root=args.waymo_dir,
+        split='val',
+        painted=True,
+        cam_sync=False)
+
+    for j in results:
+        idx = j['qsl_idx']
+        # de-dupe in case loadgen sends the same image multiple times
+        if idx in seen:
+            continue
+        seen.add(idx)
+
+        # reconstruct from mlperf accuracy log
+        # what is written by the benchmark is an array of float32's:
+        # id, box[0], box[1], box[2], box[3], score, detection_class
+        # note that id is a index into instances_val2017.json, not the actual
+        # image_id
+        data = np.frombuffer(bytes.fromhex(j['data']), np.float32)
+
+        for i in range(0, len(data), 14):
+            dimension = [float(x) for x in data[i:i + 3]]
+            location = [float(x) for x in data[i + 3:i + 6]]
+            rotation_y = float(data[i + 6])
+            bbox = [float(x) for x in data[i + 7:i + 11]]
+            label = int(data[i + 11])
+            score = float(data[i + 12])
+            image_idx = int(data[i + 13])
+            if image_idx not in detections:
+                detections[image_idx] = {
+                    'name': [],
+                    'dimensions': [],
+                    'location': [],
+                    'rotation_y': [],
+                    'bbox': [],
+                    'score': []
+                }
+
+            detections[image_idx]['name'].append(LABEL2CLASSES[label])
+            detections[image_idx]['dimensions'].append(dimension)
+            detections[image_idx]['location'].append(location)
+            detections[image_idx]['rotation_y'].append(rotation_y)
+            detections[image_idx]['bbox'].append(bbox)
+            detections[image_idx]['score'].append(score)
+            image_ids.add(image_idx)
+
+    with open(args.output_file, "w") as fp:
+        json.dump(detections, fp, sort_keys=True, indent=4)
+    format_results = {}
+    for key in detections.keys():
+        format_results[key] = {k: np.array(v)
+                               for k, v in detections[key].items()}
+    map_stats = do_eval(
+        format_results,
+        val_dataset.data_infos,
+        CLASSES,
+        cam_sync=False)
+
+    print(map_stats)
+    if args.verbose:
+        print("found {} results".format(len(results)))
+        print("found {} images".format(len(image_ids)))
+        print("found {} images with no results".format(no_results))
+        print("ignored {} dupes".format(len(results) - len(seen)))
+
+
+if __name__ == "__main__":
+    main()

automotive/3d-object-detection/backend.py

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+"""
+abstract backend class
+"""
+
+
+class Backend:
+    def __init__(self):
+        self.inputs = []
+        self.outputs = []
+
+    def version(self):
+        raise NotImplementedError("Backend:version")
+
+    def name(self):
+        raise NotImplementedError("Backend:name")
+
+    def load(self, model_path, inputs=None, outputs=None):
+        raise NotImplementedError("Backend:load")
+
+    def predict(self, feed):
+        raise NotImplementedError("Backend:predict")

automotive/3d-object-detection/backend_debug.py

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+import torch
+import backend
+
+
+class BackendDebug(backend.Backend):
+    def __init__(self, image_size=[3, 1024, 1024], **kwargs):
+        super(BackendDebug, self).__init__()
+        self.image_size = image_size
+
+    def version(self):
+        return torch.__version__
+
+    def name(self):
+        return "debug-SUT"
+
+    def image_format(self):
+        return "NCHW"
+
+    def load(self):
+        return self
+
+    def predict(self, prompts):
+        images = []
+        return images

automotive/3d-object-detection/backend_deploy.py

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+from typing import Optional, List, Union
+import os
+import torch
+import logging
+import backend
+from collections import namedtuple
+from model.painter import Painter
+from model.pointpillars import PointPillars
+import numpy as np
+from tools.process import keep_bbox_from_image_range
+from waymo import Waymo
+
+
+logging.basicConfig(level=logging.INFO)
+log = logging.getLogger("backend-pytorch")
+
+
+def change_calib_device(calib, cuda):
+    result = {}
+    if cuda:
+        device = 'cuda'
+    else:
+        device = 'cpu'
+    result['R0_rect'] = calib['R0_rect'].to(device=device, dtype=torch.float)
+    for i in range(5):
+        result['P' + str(i)] = calib['P' + str(i)
+                                     ].to(device=device, dtype=torch.float)
+        result['Tr_velo_to_cam_' +
+               str(i)] = calib['Tr_velo_to_cam_' +
+                               str(i)].to(device=device, dtype=torch.float)
+    return result
+
+
+class BackendDeploy(backend.Backend):
+    def __init__(
+        self,
+        segmentor_path,
+        lidar_detector_path,
+        data_path
+    ):
+        super(BackendDeploy, self).__init__()
+        self.segmentor_path = segmentor_path
+        self.lidar_detector_path = lidar_detector_path
+        # self.segmentation_classes = 18
+        self.detection_classes = 3
+        self.data_root = data_path
+        CLASSES = Waymo.CLASSES
+        self.LABEL2CLASSES = {v: k for k, v in CLASSES.items()}
+
+    def version(self):
+        return torch.__version__
+
+    def name(self):
+        return "python-SUT"
+
+    def load(self):
+        device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+        PaintArgs = namedtuple(
+            'PaintArgs', [
+                'training_path', 'model_path', 'cam_sync'])
+        painting_args = PaintArgs(
+            os.path.join(
+                self.data_root,
+                'training'),
+            self.segmentor_path,
+            False)
+        self.painter = Painter(painting_args)
+        self.segmentor = self.painter.model
+        model = PointPillars(
+            nclasses=self.detection_classes,
+            painted=True).to(
+            device=device)
+        model.eval()
+        checkpoint = torch.load(self.lidar_detector_path)
+        model.load_state_dict(checkpoint["model_state_dict"])
+        self.lidar_detector = model
+
+        return self
+
+    def predict(self, inputs):
+        # TODO: implement predict
+        dimensions, locations, rotation_y, box2d, class_labels, class_scores, ids = [
+        ], [], [], [], [], [], []
+        with torch.inference_mode():
+            device = torch.device(
+                "cuda:0" if torch.cuda.is_available() else "cpu")
+            format_results = {}
+            model_input = inputs[0]
+            batched_pts = model_input['pts']
+            scores_from_cam = []
+            for i in range(len(model_input['images'])):
+                segmentation_score = self.segmentor(
+                    model_input['images'][i].to(device))[0]
+                scores_from_cam.append(
+                    self.painter.get_score(segmentation_score).cpu())
+            points = self.painter.augment_lidar_class_scores_both(
+                scores_from_cam, batched_pts, model_input['calib_info'])
+            batch_results = self.lidar_detector(
+                batched_pts=[points.to(device=device)], mode='val')
+            for j, result in enumerate(batch_results):
+                format_result = {
+                    'class': [],
+                    'truncated': [],
+                    'occluded': [],
+                    'alpha': [],
+                    'bbox': [],
+                    'dimensions': [],
+                    'location': [],
+                    'rotation_y': [],
+                    'score': [],
+                    'idx': -1
+                }
+
+                calib_info = model_input['calib_info']
+                image_info = model_input['image_info']
+                idx = model_input['image_info']['image_idx']
+
+                calib_info = change_calib_device(calib_info, False)
+                result_filter = keep_bbox_from_image_range(
+                    result, calib_info, 5, image_info, False)
+
+                lidar_bboxes = result_filter['lidar_bboxes']
+                labels, scores = result_filter['labels'], result_filter['scores']
+                bboxes2d, camera_bboxes = result_filter['bboxes2d'], result_filter['camera_bboxes']
+                for lidar_bbox, label, score, bbox2d, camera_bbox in \
+                        zip(lidar_bboxes, labels, scores, bboxes2d, camera_bboxes):
+                    format_result['class'].append(label.item())
+                    format_result['truncated'].append(0.0)
+                    format_result['occluded'].append(0)
+                    alpha = camera_bbox[6] - \
+                        np.arctan2(camera_bbox[0], camera_bbox[2])
+                    format_result['alpha'].append(alpha.item())
+                    format_result['bbox'].append(bbox2d.tolist())
+                    format_result['dimensions'].append(camera_bbox[3:6])
+                    format_result['location'].append(camera_bbox[:3])
+                    format_result['rotation_y'].append(camera_bbox[6].item())
+                    format_result['score'].append(score.item())
+                    format_results['idx'] = idx
+
+                # write_label(format_result, os.path.join(saved_submit_path, f'{idx:06d}.txt'))
+
+                if len(format_result['dimensions']) > 0:
+                    format_result['dimensions'] = torch.stack(
+                        format_result['dimensions'])
+                    format_result['location'] = torch.stack(
+                        format_result['location'])
+                dimensions.append(format_result['dimensions'])
+                locations.append(format_result['location'])
+                rotation_y.append(format_result['rotation_y'])
+                class_labels.append(format_result['class'])
+                class_scores.append(format_result['score'])
+                box2d.append(format_result['bbox'])
+                ids.append(format_results['idx'])
+            # return Boxes, Classes, Scores # Change to desired output
+        return dimensions, locations, rotation_y, box2d, class_labels, class_scores, ids