def parse_args(): parser = argparse.ArgumentParser( "Submitit script", parents=[classification.get_args_parser()]) parser.add_argument("--ngpus", default=8, type=int, help="Number of gpus to request on each node") parser.add_argument("--nodes", default=1, type=int, help="Number of nodes to request") parser.add_argument("--timeout", default=2800, type=int, help="Duration of the job") parser.add_argument("--job_dir", default="", type=str, help="Job dir. Leave empty for automatic.") parser.add_argument("--partition", default="learnlab", type=str, help="Partition where to submit") parser.add_argument("--use_volta32", action='store_true', help="Big models? Use this") parser.add_argument( '--comment', default="", type=str, help='Comment to pass to scheduler, e.g. priority message') return parser.parse_args()
def parse_args(): detection_parser = detection.get_args_parser() parser = argparse.ArgumentParser("Submitit for detection", parents=[detection_parser]) parser.add_argument("--ngpus", default=2, type=int, help="Number of gpus to request on each node") parser.add_argument("--nodes", default=2, type=int, help="Number of nodes to request") parser.add_argument("--timeout", default=3000, type=int, help="Duration of the job") parser.add_argument("--job_dir", default="", type=str, help="Job dir. Leave empty for automatic.") parser.add_argument( "--mail", default="", type=str, help="Email this user when the job finishes if specified") return parser.parse_args()
def get_args_parser(): detection_parser = detection.get_args_parser() parser = argparse.ArgumentParser( "Get predictions for GQA and dump to file", parents=[detection_parser], add_help=False) parser.add_argument("--split", type=str, default="testdev", choices=("testdev", "test", "challenge", "submission")) return parser
def local_get_args_parser(): detection_parser = detection.get_args_parser() parser = argparse.ArgumentParser( "Get predictions for clevr and dump to file", parents=[detection_parser], add_help=False) parser.add_argument("--split", type=str, default="val", choices=("val", "test", "testA", "testB", "valA", "valB")) parser.add_argument("--clevr_eval_path", type=str, default="") return parser
def parse_args(): detection_parser = detection.get_args_parser() parser = argparse.ArgumentParser('Submitit for detection', parents=[ detection_parser]) parser.add_argument('--ngpus', default=8, type=int, help=\ 'Number of gpus to request on each node') parser.add_argument('--nodes', default=4, type=int, help=\ 'Number of nodes to request') parser.add_argument('--timeout', default=60, type=int, help=\ 'Duration of the job') parser.add_argument('--job_dir', default='', type=str, help=\ 'Job dir. Leave empty for automatic.') return parser.parse_args()
def parse_args(): detection_parser = detection.get_args_parser() parser = argparse.ArgumentParser( "Submitit for detection", parents=[detection_parser] ) parser.add_argument( "--ngpus", default=4, type=int, help="Number of gpus to request on each node" ) parser.add_argument( "--nodes", default=1, type=int, help="Number of nodes to request" ) parser.add_argument( "--hours", default=12, type=int, help="Duration of the job in hours" ) parser.add_argument( "--job_dir", default="", type=str, help="Job dir. Leave empty for automatic." ) parser.add_argument("--experiment_name", type=str, default="test_experiment_delete") parser.add_argument("--constraint", type=str, default=None) return parser.parse_args()
from PIL import Image import requests import matplotlib.pyplot as plt import cv2 import torch from torch import nn from torchvision.models import resnet50 from models import build import torchvision.transforms as T from main import get_args_parser import numpy as np torch.set_grad_enabled(False) parser = get_args_parser() # for output bounding box post-processing def box_cxcywh_to_xyxy(x): x_c, y_c, w, h = x.unbind(1) b = [(x_c - 0.5 * w), (y_c - 0.5 * h), (x_c + 0.5 * w), (y_c + 0.5 * h)] return torch.stack(b, dim=1) def rescale_bboxes(out_bbox, size): img_w, img_h = size b = box_cxcywh_to_xyxy(out_bbox) b = b * torch.tensor([img_w, img_h, img_w, img_h], dtype=torch.float32) return b
self.write_results(txt_path=os.path.join(self.predict_path, f'{self.seq_num}.txt'), frame_id=(i + 1), bbox_xyxy=tracker_outputs[:, :4], identities=tracker_outputs[:, 5]) filter_pub_det( os.path.join(self.predict_path, f'{self.seq_num}.txt'), f'/data/Dataset/mot/MOT17/images/test/{self.seq_num}/det/det.txt') print("totally {} dts {} occlusion dts".format(total_dts, total_occlusion_dts)) if __name__ == '__main__': parser = argparse.ArgumentParser('DETR training and evaluation script', parents=[get_args_parser()]) args = parser.parse_args() if args.output_dir: Path(args.output_dir).mkdir(parents=True, exist_ok=True) # load model and weights detr, _, _ = build_model(args) checkpoint = torch.load(args.resume, map_location='cpu') detr = load_model(detr, args.resume) detr.eval() detr = detr.cuda() # '''for MOT17 submit''' sub_dir = 'MOT17/images/test' seq_nums = [ 'MOT17-01-SDP', 'MOT17-03-SDP', 'MOT17-06-SDP', 'MOT17-07-SDP',
# for visual # self.visualize_img_with_bbox(cur_vis_img_path, ori_img, dt_instances, ref_pts=all_ref_pts, gt_boxes=gt_boxes) tracker_outputs = self.tr_tracker.update(dt_instances) self.write_results(txt_path=os.path.join(self.predict_path, f'{self.seq_num}.txt'), frame_id=(i + 1), bbox_xyxy=tracker_outputs[:, :4], identities=tracker_outputs[:, 5]) filter_pub_det(os.path.join(self.predict_path, f'{self.seq_num}.txt'), f'/data/Dataset/mot/MOT17/images/test/{self.seq_num}/det/det.txt') print("totally {} dts {} occlusion dts".format(total_dts, total_occlusion_dts)) if __name__ == '__main__': parser = argparse.ArgumentParser('DETR training and evaluation script', parents=[get_args_parser()]) args = parser.parse_args() if args.output_dir: Path(args.output_dir).mkdir(parents=True, exist_ok=True) # load model and weights detr, _, _ = build_model(args) checkpoint = torch.load(args.resume, map_location='cpu') detr = load_model(detr, args.resume) detr.eval() detr = detr.cuda() # '''for MOT17 submit''' sub_dir = 'MOT17/images/test' seq_nums = ['MOT17-01-SDP', 'MOT17-03-SDP',
def get_args_parser(): detection_parser = detection.get_args_parser() parser = argparse.ArgumentParser("Evaluate MDETR on LVIS detection", parents=[detection_parser], add_help=False) parser.add_argument("--lvis_minival_path", type=str, default="") return parser