def main(): args = parse_args() cfg = mmcv.Config.fromfile(args.config) # Load output_config from cfg output_config = cfg.get('output_config', {}) # Overwrite output_config from args.out output_config = merge_configs(output_config, dict(out=args.out)) # Load eval_config from cfg eval_config = cfg.get('eval_config', {}) # Overwrite eval_config from args.eval eval_config = merge_configs(eval_config, dict(metrics=args.eval)) # Add options from args.option eval_config = merge_configs(eval_config, args.options) assert output_config or eval_config, \ ('Please specify at least one operation (save or eval the ' 'results) with the argument "--out" or "--eval"') # set cudnn benchmark if cfg.get('cudnn_benchmark', False): torch.backends.cudnn.benchmark = True cfg.data.test.test_mode = True if cfg.test_cfg is None: cfg.test_cfg = dict(average_clips=args.average_clips) else: cfg.test_cfg.average_clips = args.average_clips # init distributed env first, since logger depends on the dist info. if args.launcher == 'none': distributed = False else: distributed = True init_dist(args.launcher, **cfg.dist_params) # build the dataloader dataset = build_dataset(cfg.data.test, dict(test_mode=True)) data_loader = build_dataloader( dataset, videos_per_gpu=1, workers_per_gpu=cfg.data.workers_per_gpu, dist=distributed, shuffle=False) # build the model and load checkpoint model = build_model(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg) fp16_cfg = cfg.get('fp16', None) if fp16_cfg is not None: wrap_fp16_model(model) load_checkpoint(model, args.checkpoint, map_location='cpu') if args.fuse_conv_bn: model = fuse_conv_bn(model) if not distributed: model = MMDataParallel(model, device_ids=[0]) outputs = single_gpu_test(model, data_loader) else: model = MMDistributedDataParallel( model.cuda(), device_ids=[torch.cuda.current_device()], broadcast_buffers=False) outputs = multi_gpu_test(model, data_loader, args.tmpdir, args.gpu_collect) rank, _ = get_dist_info() if rank == 0: if output_config: out = output_config['out'] print(f'\nwriting results to {out}') dataset.dump_results(outputs, **output_config) if eval_config: eval_res = dataset.evaluate(outputs, **eval_config) for name, val in eval_res.items(): if 'confusion' not in name: print(f'{name}: {val:.04f}') elif output_config: if 'fig' in name: confmat_dir = os.path.dirname(output_config['out']) val.savefig(os.path.join(confmat_dir, name+'.jpg'), format='jpg')
def main(): args = parse_args() cfg = mmcv.Config.fromfile(args.config) input_cfg = mmcv.load(args.pipeline_config) my_cfg = update_configs(input_cfg) cfg = merge_from_mycfg(my_cfg, cfg) # set cudnn_benchmark if cfg.get('cudnn_benchmark', False): torch.backends.cudnn.benchmark = True cfg.model.pretrained = None cfg.data.test.test_mode = True # init distributed env first, since logger depends on the dist info. if args.launcher == 'none': distributed = False else: distributed = True init_dist(args.launcher, **cfg.dist_params) # build the dataloader dataset = build_dataset(cfg.data.test) data_loader = build_dataloader(dataset, samples_per_gpu=cfg.data.samples_per_gpu, workers_per_gpu=cfg.data.workers_per_gpu, dist=distributed, shuffle=False, round_up=False) # build the model and load checkpoint model = build_classifier(cfg.model) fp16_cfg = cfg.get('fp16', None) if fp16_cfg is not None: wrap_fp16_model(model) args.checkpoint = os.path.join(cfg.work_dir, 'latest.pth') _ = load_checkpoint(model, args.checkpoint, map_location='cpu') if not distributed: model = MMDataParallel(model, device_ids=[0]) outputs = single_gpu_test(model, data_loader) else: model = MMDistributedDataParallel( model.cuda(), device_ids=[torch.cuda.current_device()], broadcast_buffers=False) outputs = multi_gpu_test(model, data_loader, args.tmpdir, args.gpu_collect) rank, _ = get_dist_info() if rank == 0: nums = [] results = {} for output in outputs: nums.append(output['num_samples'].item()) for topk, v in output['accuracy'].items(): if topk not in results: results[topk] = [] results[topk].append(v.item()) assert sum(nums) == len(dataset) for topk, accs in results.items(): avg_acc = np.average(accs, weights=nums) print(f'\nevaluation_results/{topk} Accuracy[{avg_acc:.2f}]') args.out = os.path.join(cfg.work_dir, 'eval_result.pkl') if args.out and rank == 0: print(f'\nwriting results to {args.out}') mmcv.dump(outputs, args.out)
def main(): args = parse_args() assert args.out or args.eval or args.format_only or args.show, \ ('Please specify at least one operation (save/eval/format/show the ' 'results) with the argument "--out", "--eval", "--format_only" ' 'or "--show"') if args.eval and args.format_only: raise ValueError('--eval and --format_only cannot be both specified') if args.out is not None and not args.out.endswith(('.pkl', '.pickle')): raise ValueError('The output file must be a pkl file.') cfg = mmcv.Config.fromfile(args.config) # set cudnn_benchmark if cfg.get('cudnn_benchmark', False): torch.backends.cudnn.benchmark = True cfg.model.pretrained = None cfg.data.test.test_mode = True # init distributed env first, since logger depends on the dist info. if args.launcher == 'none': distributed = False else: distributed = True init_dist(args.launcher, **cfg.dist_params) # build the dataloader # TODO: support multiple images per gpu (only minor changes are needed) dataset = build_dataset(cfg.data.test) data_loader = build_dataloader(dataset, imgs_per_gpu=1, workers_per_gpu=cfg.data.workers_per_gpu, dist=distributed, shuffle=False) # # build the model and load checkpoint # model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg) # fp16_cfg = cfg.get('fp16', None) # if fp16_cfg is not None: # wrap_fp16_model(model) # checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu') # # old versions did not save class info in checkpoints, this walkaround is # # for backward compatibility # if 'CLASSES' in checkpoint['meta']: # model.CLASSES = checkpoint['meta']['CLASSES'] # else: # model.CLASSES = dataset.CLASSES prune_model = '/home/stella/zx/mmdetection-master/result_out/prune/pruned.pth' model = torch.load(prune_model) if not distributed: model = MMDataParallel(model, device_ids=[0]) outputs = single_gpu_test(model, data_loader, args.show) else: model = MMDistributedDataParallel( model.cuda(), device_ids=[torch.cuda.current_device()], broadcast_buffers=False) outputs = multi_gpu_test(model, data_loader, args.tmpdir, args.gpu_collect) rank, _ = get_dist_info() if rank == 0: if args.out: print('\nwriting results to {}'.format(args.out)) mmcv.dump(outputs, args.out) kwargs = {} if args.options is None else args.options if args.format_only: dataset.format_results(outputs, **kwargs) if args.eval: dataset.evaluate(outputs, args.eval, **kwargs)
def main(): args = parse_args() if args.out is not None and not args.out.endswith(('.json', '.pickle')): raise ValueError('The output file must be a pkl file.') for i in range(args.checkpoint_start, args.checkpoint_end): cfg = mmcv.Config.fromfile(args.config) # set cudnn_benchmark if cfg.get('cudnn_benchmark', False): torch.backends.cudnn.benchmark = True cfg.model.pretrained = None cfg.data.test.test_mode = True # init distributed env first, since logger depends on the dist info. if args.launcher == 'none': distributed = False else: distributed = True init_dist(args.launcher, **cfg.dist_params) # build the dataloader # TODO: support multiple images per gpu (only minor changes are needed) dataset = build_dataset(cfg.data.test) data_loader = build_dataloader( dataset, imgs_per_gpu=1, workers_per_gpu=cfg.data.workers_per_gpu, dist=distributed, shuffle=False) # build the model and load checkpoint model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg) fp16_cfg = cfg.get('fp16', None) if fp16_cfg is not None: wrap_fp16_model(model) if not args.mean_teacher: while not osp.exists(args.checkpoint + str(i) + '.pth'): time.sleep(5) while i + 1 != args.checkpoint_end and not osp.exists( args.checkpoint + str(i + 1) + '.pth'): time.sleep(5) checkpoint = load_checkpoint(model, args.checkpoint + str(i) + '.pth', map_location='cpu') model.CLASSES = dataset.CLASSES else: while not osp.exists(args.checkpoint + str(i) + '.pth.stu'): time.sleep(5) while i + 1 != args.checkpoint_end and not osp.exists( args.checkpoint + str(i + 1) + '.pth.stu'): time.sleep(5) checkpoint = load_checkpoint(model, args.checkpoint + str(i) + '.pth.stu', map_location='cpu') checkpoint['meta'] = dict() if 'CLASSES' in checkpoint['meta']: model.CLASSES = checkpoint['meta']['CLASSES'] else: model.CLASSES = dataset.CLASSES # old versions did not save class info in checkpoints, this walkaround is # for backward compatibility if not distributed: model = MMDataParallel(model, device_ids=[0]) outputs = single_gpu_test(model, data_loader, args.show, args.save_img, args.save_img_dir) else: model = MMDistributedDataParallel(model.cuda()) outputs = multi_gpu_test(model, data_loader, args.tmpdir) res = [] for id, boxes in enumerate(outputs): boxes = boxes[0] if type(boxes) == list: boxes = boxes[0] boxes[:, [2, 3]] -= boxes[:, [0, 1]] if len(boxes) > 0: for box in boxes: # box[:4] = box[:4] / 0.6 temp = dict() temp['image_id'] = id + 1 temp['category_id'] = 1 temp['bbox'] = box[:4].tolist() temp['score'] = float(box[4]) res.append(temp) with open(args.out, 'w') as f: json.dump(res, f) MRs = validate('datasets/CityPersons/val_gt.json', args.out) print( 'Checkpoint %d: [Reasonable: %.2f%%], [Reasonable_Small: %.2f%%], [Heavy: %.2f%%], [All: %.2f%%]' % (i, MRs[0] * 100, MRs[1] * 100, MRs[2] * 100, MRs[3] * 100))
def main(): args = parse_args() #scale = args.scale #args = my_fn() #print(args) #pdb.set_trace() assert args.out or args.eval or args.format_only or args.show, \ ('Please specify at least one operation (save/eval/format/show the ' 'results) with the argument "--out", "--eval", "--format_only" ' 'or "--show"') if args.eval and args.format_only: raise ValueError('--eval and --format_only cannot be both specified') if args.out is not None and not args.out.endswith(('.pkl', '.pickle')): raise ValueError('The output file must be a pkl file.') cfg = mmcv.Config.fromfile(args.config) # set cudnn_benchmark if cfg.get('cudnn_benchmark', False): torch.backends.cudnn.benchmark = True cfg.model.pretrained = None cfg.data.test.test_mode = True # init distributed env first, since logger depends on the dist info. if args.launcher == 'none': distributed = False else: distributed = True init_dist(args.launcher, **cfg.dist_params) # build the dataloader # TODO: support multiple images per gpu (only minor changes are needed) #cfg.test_pipeline[1]['img_scale'] = (int(640*scale),int(640*scale)) cfg.data.test[ 'ann_file'] = "/data/mengtial/COCO/annotations/instances_val2017.json" cfg.data.test['img_prefix'] = "/data/mengtial/COCO/val2017/" #print("-------------------------",) dataset = build_dataset(cfg.data.test) data_loader = build_dataloader( dataset, imgs_per_gpu=1, # Batch size workers_per_gpu=cfg.data.workers_per_gpu, dist=distributed, shuffle=False) # build the model and load checkpoint cfg.model['mask_head'] = None model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg) #print("Model",model) fp16_cfg = cfg.get('fp16', None) #print("----------------------",fp16_cfg) if fp16_cfg is not None: print("Inside") #pdb.set_trace() wrap_fp16_model(model) checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu') # old versions did not save class info in checkpoints, this walkaround is # for backward compatibility if 'CLASSES' in checkpoint['meta']: model.CLASSES = checkpoint['meta']['CLASSES'] else: model.CLASSES = dataset.CLASSES if not distributed: model = MMDataParallel(model, device_ids=[0]) outputs = single_gpu_test(model, data_loader, args.show) else: model = MMDistributedDataParallel( model.cuda(), device_ids=[torch.cuda.current_device()], broadcast_buffers=False) outputs = multi_gpu_test(model, data_loader, args.tmpdir, args.gpu_collect) rank, _ = get_dist_info() if rank == 0: if args.out: print('\nwriting results to {}'.format(args.out)) mmcv.dump(outputs, args.out) kwargs = {} if args.options is None else args.options if args.format_only: dataset.format_results(outputs, **kwargs) if args.eval: dataset.evaluate(outputs, args.eval, **kwargs)
def main(): args = parse_args() assert args.out or args.eval or args.format_only or args.show \ or args.show_dir, \ ('Please specify at least one operation (save/eval/format/show the ' 'results / save the results) with the argument "--out", "--eval"' ', "--format-only", "--show" or "--show-dir"') if args.eval and args.format_only: raise ValueError('--eval and --format_only cannot be both specified') if args.out is not None and not args.out.endswith(('.pkl', '.pickle')): raise ValueError('The output file must be a pkl file.') cfg = mmcv.Config.fromfile(args.config) if args.options is not None: cfg.merge_from_dict(args.options) # set cudnn_benchmark if cfg.get('cudnn_benchmark', False): torch.backends.cudnn.benchmark = True if args.aug_test: # hard code index cfg.data.test.pipeline[1].img_ratios = [ 0.5, 0.75, 1.0, 1.25, 1.5, 1.75 ] cfg.data.test.pipeline[1].flip = True cfg.model.pretrained = None cfg.data.test.test_mode = True # init distributed env first, since logger depends on the dist info. if args.launcher == 'none': distributed = False else: distributed = True init_dist(args.launcher, **cfg.dist_params) # build the dataloader # TODO: support multiple images per gpu (only minor changes are needed) dataset = build_dataset(cfg.data.test) data_loader = build_dataloader(dataset, samples_per_gpu=2, workers_per_gpu=cfg.data.workers_per_gpu, dist=distributed, shuffle=False) # build the model and load checkpoint cfg.model.train_cfg = None model = build_segmentor(cfg.model, test_cfg=cfg.get('test_cfg')) fp16_cfg = cfg.get('fp16', None) if fp16_cfg is not None: wrap_fp16_model(model) checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu') model.CLASSES = checkpoint['meta']['CLASSES'] model.PALETTE = checkpoint['meta']['PALETTE'] efficient_test = False if args.eval_options is not None: efficient_test = args.eval_options.get('efficient_test', False) if not distributed: model = MMDataParallel(model, device_ids=[0]) outputs = single_gpu_test(model, data_loader, args.show, args.show_dir, efficient_test, args.opacity) else: model = MMDistributedDataParallel( model.cuda(), device_ids=[torch.cuda.current_device()], broadcast_buffers=False) outputs = multi_gpu_test(model, data_loader, args.tmpdir, args.gpu_collect, efficient_test) rank, _ = get_dist_info() if rank == 0: if args.out: print(f'\nwriting results to {args.out}') mmcv.dump(outputs, args.out) kwargs = {} if args.eval_options is None else args.eval_options if args.format_only: dataset.format_results(outputs, **kwargs) if args.eval: dataset.evaluate(outputs, args.eval, **kwargs)
def main(): args = parse_args() assert args.out or args.show or args.show_dir, \ ('Please specify at least one operation (save or show the results) ' 'with the argument "--out", "--show" or "show-dir"') if args.out is not None and not args.out.endswith(('.pkl', '.pickle')): raise ValueError('The output file must be a pkl file.') cfg = mmcv.Config.fromfile(args.config) if args.cfg_options is not None: cfg.merge_from_dict(args.cfg_options) # import modules from string list. if cfg.get('custom_imports', None): from mmcv.utils import import_modules_from_strings import_modules_from_strings(**cfg['custom_imports']) # set cudnn_benchmark if cfg.get('cudnn_benchmark', False): torch.backends.cudnn.benchmark = True cfg.model.pretrained = None cfg.data.test.test_mode = True if args.workers == 0: args.workers = cfg.data.workers_per_gpu # init distributed env first, since logger depends on the dist info. if args.launcher == 'none': distributed = False else: distributed = True init_dist(args.launcher, **cfg.dist_params) # set random seeds if args.seed is not None: set_random_seed(args.seed) if 'all' in args.corruptions: corruptions = [ 'gaussian_noise', 'shot_noise', 'impulse_noise', 'defocus_blur', 'glass_blur', 'motion_blur', 'zoom_blur', 'snow', 'frost', 'fog', 'brightness', 'contrast', 'elastic_transform', 'pixelate', 'jpeg_compression', 'speckle_noise', 'gaussian_blur', 'spatter', 'saturate' ] elif 'benchmark' in args.corruptions: corruptions = [ 'gaussian_noise', 'shot_noise', 'impulse_noise', 'defocus_blur', 'glass_blur', 'motion_blur', 'zoom_blur', 'snow', 'frost', 'fog', 'brightness', 'contrast', 'elastic_transform', 'pixelate', 'jpeg_compression' ] elif 'noise' in args.corruptions: corruptions = ['gaussian_noise', 'shot_noise', 'impulse_noise'] elif 'blur' in args.corruptions: corruptions = [ 'defocus_blur', 'glass_blur', 'motion_blur', 'zoom_blur' ] elif 'weather' in args.corruptions: corruptions = ['snow', 'frost', 'fog', 'brightness'] elif 'digital' in args.corruptions: corruptions = [ 'contrast', 'elastic_transform', 'pixelate', 'jpeg_compression' ] elif 'holdout' in args.corruptions: corruptions = ['speckle_noise', 'gaussian_blur', 'spatter', 'saturate'] elif 'None' in args.corruptions: corruptions = ['None'] args.severities = [0] else: corruptions = args.corruptions rank, _ = get_dist_info() aggregated_results = {} for corr_i, corruption in enumerate(corruptions): aggregated_results[corruption] = {} for sev_i, corruption_severity in enumerate(args.severities): # evaluate severity 0 (= no corruption) only once if corr_i > 0 and corruption_severity == 0: aggregated_results[corruption][0] = \ aggregated_results[corruptions[0]][0] continue test_data_cfg = copy.deepcopy(cfg.data.test) # assign corruption and severity if corruption_severity > 0: corruption_trans = dict(type='Corrupt', corruption=corruption, severity=corruption_severity) # TODO: hard coded "1", we assume that the first step is # loading images, which needs to be fixed in the future test_data_cfg['pipeline'].insert(1, corruption_trans) # print info print(f'\nTesting {corruption} at severity {corruption_severity}') # build the dataloader # TODO: support multiple images per gpu # (only minor changes are needed) dataset = build_dataset(test_data_cfg) data_loader = build_dataloader(dataset, samples_per_gpu=1, workers_per_gpu=args.workers, dist=distributed, shuffle=False) # build the model and load checkpoint cfg.model.train_cfg = None model = build_detector(cfg.model, test_cfg=cfg.get('test_cfg')) fp16_cfg = cfg.get('fp16', None) if fp16_cfg is not None: wrap_fp16_model(model) checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu') # old versions did not save class info in checkpoints, # this walkaround is for backward compatibility if 'CLASSES' in checkpoint.get('meta', {}): model.CLASSES = checkpoint['meta']['CLASSES'] else: model.CLASSES = dataset.CLASSES if not distributed: model = MMDataParallel(model, device_ids=[0]) show_dir = args.show_dir if show_dir is not None: show_dir = osp.join(show_dir, corruption) show_dir = osp.join(show_dir, str(corruption_severity)) if not osp.exists(show_dir): osp.makedirs(show_dir) outputs = single_gpu_test(model, data_loader, args.show, show_dir, args.show_score_thr) else: model = MMDistributedDataParallel( model.cuda(), device_ids=[torch.cuda.current_device()], broadcast_buffers=False) outputs = multi_gpu_test(model, data_loader, args.tmpdir) if args.out and rank == 0: eval_results_filename = (osp.splitext(args.out)[0] + '_results' + osp.splitext(args.out)[1]) mmcv.dump(outputs, args.out) eval_types = args.eval if cfg.dataset_type == 'VOCDataset': if eval_types: for eval_type in eval_types: if eval_type == 'bbox': test_dataset = mmcv.runner.obj_from_dict( cfg.data.test, datasets) logger = 'print' if args.summaries else None mean_ap, eval_results = \ voc_eval_with_return( args.out, test_dataset, args.iou_thr, logger) aggregated_results[corruption][ corruption_severity] = eval_results else: print('\nOnly "bbox" evaluation \ is supported for pascal voc') else: if eval_types: print(f'Starting evaluate {" and ".join(eval_types)}') if eval_types == ['proposal_fast']: result_file = args.out else: if not isinstance(outputs[0], dict): result_files = dataset.results2json( outputs, args.out) else: for name in outputs[0]: print(f'\nEvaluating {name}') outputs_ = [out[name] for out in outputs] result_file = args.out + f'.{name}' result_files = dataset.results2json( outputs_, result_file) eval_results = coco_eval_with_return( result_files, eval_types, dataset.coco) aggregated_results[corruption][ corruption_severity] = eval_results else: print('\nNo task was selected for evaluation;' '\nUse --eval to select a task') # save results after each evaluation mmcv.dump(aggregated_results, eval_results_filename) if rank == 0: # print final results print('\nAggregated results:') prints = args.final_prints aggregate = args.final_prints_aggregate if cfg.dataset_type == 'VOCDataset': get_results(eval_results_filename, dataset='voc', prints=prints, aggregate=aggregate) else: get_results(eval_results_filename, dataset='coco', prints=prints, aggregate=aggregate)
def main(): # args = ['./DOTA_configs/DOTA_hbb/faster_rcnn_r50_fpn_2x_dota.py', # './results/faster_rcnn_hbb_tv/epoch_24.pth', # '--out', './results/faster_rcnn_hbb_tv/results.pkl', # '--eval', 'bbox' # ] # args = ['./DOTA_configs/DOTA_hbb/faster_rcnn_r50_fpn_2x_dota.py', # './results/faster_rcnn_hbb_tv/epoch_24.pth', # '--out', './results/faster_rcnn_hbb_tv/results.pkl', # ] args = [ './DOTA_configs/DOTA_obb/retinanet_r50_fpn_2x_ad.py', './results/DOTA_retina_ad_obb_tv/epoch_24.pth', '--out', './results/DOTA_retina_ad_obb_tv/results.pkl', ] # # # args = ['./DOTA_configs/DIOR/retinanet_r50_fpn_2x.py', # './results/DIOR_retinanet_full/epoch_24.pth', # '--out', './results/DIOR_retinanet_full/results.pkl', # '--eval', 'bbox' # ] # args = ['./DOTA_configs/DOTA_obb/s2anet_r50_fpn_1x_dota.py', # './results/DOTA_s2anet_obb_tv/epoch_24.pth', # '--out', './results/DOTA_s2anet_obb_tv/results.pkl', # '--eval', 'bbox' # ] # args = ['./DOTA_configs/DIOR_voc_test/retinanet_r50_fpn_2x.py', # './results/DIOR_retinanet_full/epoch_24.pth', # '--out', './results/DIOR_retinanet_full/results.pkl', # '--eval', 'mAP' # ] args = [ './DOTA_configs/DOTA_obb/faster_rcnn_RoITrans_r50_fpn_1x_dota.py', './results/DOTA_faster_rcnn_RoITrans_tv/epoch_12.pth', '--out', './results/DOTA_faster_rcnn_RoITrans_tv/results.pkl', ] args = parse_args(args) print(args) assert args.out or args.eval or args.format_only or args.show \ or args.show_dir, \ ('Please specify at least one operation (save/eval/format/show the ' 'results / save the results) with the argument "--out", "--eval"' ', "--format-only", "--show" or "--show-dir"') if args.eval and args.format_only: raise ValueError('--eval and --format_only cannot be both specified') if args.out is not None and not args.out.endswith(('.pkl', '.pickle')): raise ValueError('The output file must be a pkl file.') cfg = Config.fromfile(args.config) print(cfg.pretty_text) if args.cfg_options is not None: cfg.merge_from_dict(args.cfg_options) # import modules from string list. if cfg.get('custom_imports', None): from mmcv.utils import import_modules_from_strings import_modules_from_strings(**cfg['custom_imports']) # set cudnn_benchmark if cfg.get('cudnn_benchmark', False): torch.backends.cudnn.benchmark = True cfg.model.pretrained = None if cfg.model.get('neck'): if isinstance(cfg.model.neck, list): for neck_cfg in cfg.model.neck: if neck_cfg.get('rfp_backbone'): if neck_cfg.rfp_backbone.get('pretrained'): neck_cfg.rfp_backbone.pretrained = None elif cfg.model.neck.get('rfp_backbone'): if cfg.model.neck.rfp_backbone.get('pretrained'): cfg.model.neck.rfp_backbone.pretrained = None # in case the test dataset is concatenated if isinstance(cfg.data.test, dict): cfg.data.test.test_mode = True elif isinstance(cfg.data.test, list): for ds_cfg in cfg.data.test: ds_cfg.test_mode = True # init distributed env first, since logger depends on the dist info. if args.launcher == 'none': distributed = False else: distributed = True init_dist(args.launcher, **cfg.dist_params) # build the dataloader samples_per_gpu = cfg.data.test.pop('samples_per_gpu', 1) if samples_per_gpu > 1: # Replace 'ImageToTensor' to 'DefaultFormatBundle' cfg.data.test.pipeline = replace_ImageToTensor(cfg.data.test.pipeline) dataset = build_dataset(cfg.data.test) data_loader = build_dataloader(dataset, samples_per_gpu=samples_per_gpu, workers_per_gpu=cfg.data.workers_per_gpu, dist=distributed, shuffle=False) # build the model and load checkpoint model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg) fp16_cfg = cfg.get('fp16', None) if fp16_cfg is not None: wrap_fp16_model(model) checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu') if args.fuse_conv_bn: model = fuse_conv_bn(model) # old versions did not save class info in checkpoints, this walkaround is # for backward compatibility if 'CLASSES' in checkpoint['meta']: model.CLASSES = checkpoint['meta']['CLASSES'] else: model.CLASSES = dataset.CLASSES ########################################################################### if not LOAD_RESULT: if not distributed: model = MMDataParallel(model, device_ids=[0]) outputs = single_gpu_test(model, data_loader, args.show, args.show_dir, args.show_score_thr) else: model = MMDistributedDataParallel( model.cuda(), device_ids=[torch.cuda.current_device()], broadcast_buffers=False) outputs = multi_gpu_test(model, data_loader, args.tmpdir, args.gpu_collect) else: import pickle as pkl with open(str(args.out), 'rb') as f: outputs = pkl.load(f) a = 0 ########################################################################### rank, _ = get_dist_info() if rank == 0: if args.out: print(f'\nwriting results to {args.out}') mmcv.dump(outputs, args.out) kwargs = {} if args.eval_options is None else args.eval_options if args.format_only: dataset.format_results(outputs, **kwargs) if args.eval: eval_kwargs = cfg.get('evaluation', {}).copy() # hard-code way to remove EvalHook args for key in ['interval', 'tmpdir', 'start', 'gpu_collect']: eval_kwargs.pop(key, None) eval_kwargs.update(dict(metric=args.eval, **kwargs)) ###################################### # add class wise # eval_kwargs['classwise']=True # eval_kwargs['proposal_nums'] = (100, 300, 1000) s = str(dataset.evaluate(outputs, **eval_kwargs)) work_dir = os.path.split(str(args.out))[0] eval_file = os.path.join(work_dir, 'eval_results.txt') with open(eval_file, 'wt+') as f: f.write(str(s)) print(s)
def main(): args = parse_args() assert args.out or args.eval or args.format_only or args.show \ or args.show_dir, \ ('Please specify at least one operation (save/eval/format/show the ' 'results / save the results) with the argument "--out", "--eval"' ', "--format-only", "--show" or "--show-dir"') if args.eval and args.format_only: raise ValueError('--eval and --format_only cannot be both specified') if args.out is not None and not args.out.endswith(('.pkl', '.pickle')): raise ValueError('The output file must be a pkl file.') cfg = mmcv.Config.fromfile(args.config) if args.cfg_options is not None: cfg.merge_from_dict(args.cfg_options) # set multi-process settings setup_multi_processes(cfg) # set cudnn_benchmark if cfg.get('cudnn_benchmark', False): torch.backends.cudnn.benchmark = True if args.aug_test: # hard code index cfg.data.test.pipeline[1].img_ratios = [ 0.5, 0.75, 1.0, 1.25, 1.5, 1.75 ] cfg.data.test.pipeline[1].flip = True cfg.model.pretrained = None cfg.data.test.test_mode = True if args.gpu_id is not None: cfg.gpu_ids = [args.gpu_id] # init distributed env first, since logger depends on the dist info. if args.launcher == 'none': cfg.gpu_ids = [args.gpu_id] distributed = False if len(cfg.gpu_ids) > 1: warnings.warn(f'The gpu-ids is reset from {cfg.gpu_ids} to ' f'{cfg.gpu_ids[0:1]} to avoid potential error in ' 'non-distribute testing time.') cfg.gpu_ids = cfg.gpu_ids[0:1] else: distributed = True init_dist(args.launcher, **cfg.dist_params) rank, _ = get_dist_info() # allows not to create if args.work_dir is not None and rank == 0: mmcv.mkdir_or_exist(osp.abspath(args.work_dir)) timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime()) if args.aug_test: json_file = osp.join(args.work_dir, f'eval_multi_scale_{timestamp}.json') else: json_file = osp.join(args.work_dir, f'eval_single_scale_{timestamp}.json') elif rank == 0: work_dir = osp.join('./work_dirs', osp.splitext(osp.basename(args.config))[0]) mmcv.mkdir_or_exist(osp.abspath(work_dir)) timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime()) if args.aug_test: json_file = osp.join(work_dir, f'eval_multi_scale_{timestamp}.json') else: json_file = osp.join(work_dir, f'eval_single_scale_{timestamp}.json') # build the dataloader # TODO: support multiple images per gpu (only minor changes are needed) dataset = build_dataset(cfg.data.test) # The default loader config loader_cfg = dict( # cfg.gpus will be ignored if distributed num_gpus=len(cfg.gpu_ids), dist=distributed, shuffle=False) # The overall dataloader settings loader_cfg.update({ k: v for k, v in cfg.data.items() if k not in [ 'train', 'val', 'test', 'train_dataloader', 'val_dataloader', 'test_dataloader' ] }) test_loader_cfg = { **loader_cfg, 'samples_per_gpu': 1, 'shuffle': False, # Not shuffle by default **cfg.data.get('test_dataloader', {}) } # build the dataloader data_loader = build_dataloader(dataset, **test_loader_cfg) # build the model and load checkpoint cfg.model.train_cfg = None model = build_segmentor(cfg.model, test_cfg=cfg.get('test_cfg')) fp16_cfg = cfg.get('fp16', None) if fp16_cfg is not None: wrap_fp16_model(model) checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu') if 'CLASSES' in checkpoint.get('meta', {}): model.CLASSES = checkpoint['meta']['CLASSES'] else: print('"CLASSES" not found in meta, use dataset.CLASSES instead') model.CLASSES = dataset.CLASSES if 'PALETTE' in checkpoint.get('meta', {}): model.PALETTE = checkpoint['meta']['PALETTE'] else: print('"PALETTE" not found in meta, use dataset.PALETTE instead') model.PALETTE = dataset.PALETTE # clean gpu memory when starting a new evaluation. torch.cuda.empty_cache() eval_kwargs = {} if args.eval_options is None else args.eval_options # Deprecated efficient_test = eval_kwargs.get('efficient_test', False) if efficient_test: warnings.warn( '``efficient_test=True`` does not have effect in tools/test.py, ' 'the evaluation and format results are CPU memory efficient by ' 'default') eval_on_format_results = (args.eval is not None and 'cityscapes' in args.eval) if eval_on_format_results: assert len(args.eval) == 1, 'eval on format results is not ' \ 'applicable for metrics other than ' \ 'cityscapes' if args.format_only or eval_on_format_results: if 'imgfile_prefix' in eval_kwargs: tmpdir = eval_kwargs['imgfile_prefix'] else: tmpdir = '.format_cityscapes' eval_kwargs.setdefault('imgfile_prefix', tmpdir) mmcv.mkdir_or_exist(tmpdir) else: tmpdir = None if not distributed: warnings.warn( 'SyncBN is only supported with DDP. To be compatible with DP, ' 'we convert SyncBN to BN. Please use dist_train.sh which can ' 'avoid this error.') if not torch.cuda.is_available(): assert digit_version(mmcv.__version__) >= digit_version('1.4.4'), \ 'Please use MMCV >= 1.4.4 for CPU training!' model = revert_sync_batchnorm(model) model = MMDataParallel(model, device_ids=cfg.gpu_ids) results = single_gpu_test(model, data_loader, args.show, args.show_dir, False, args.opacity, pre_eval=args.eval is not None and not eval_on_format_results, format_only=args.format_only or eval_on_format_results, format_args=eval_kwargs) else: model = MMDistributedDataParallel( model.cuda(), device_ids=[torch.cuda.current_device()], broadcast_buffers=False) results = multi_gpu_test(model, data_loader, args.tmpdir, args.gpu_collect, False, pre_eval=args.eval is not None and not eval_on_format_results, format_only=args.format_only or eval_on_format_results, format_args=eval_kwargs) rank, _ = get_dist_info() if rank == 0: if args.out: warnings.warn( 'The behavior of ``args.out`` has been changed since MMSeg ' 'v0.16, the pickled outputs could be seg map as type of ' 'np.array, pre-eval results or file paths for ' '``dataset.format_results()``.') print(f'\nwriting results to {args.out}') mmcv.dump(results, args.out) if args.eval: eval_kwargs.update(metric=args.eval) metric = dataset.evaluate(results, **eval_kwargs) metric_dict = dict(config=args.config, metric=metric) mmcv.dump(metric_dict, json_file, indent=4) if tmpdir is not None and eval_on_format_results: # remove tmp dir when cityscapes evaluation shutil.rmtree(tmpdir)
def main(): args = parse_args() assert args.out or args.eval or args.format_only or args.show, \ ('Please specify at least one operation (save/eval/format/show the ' 'results) with the argument "--out", "--eval", "--format_only" ' 'or "--show"') if args.eval and args.format_only: raise ValueError('--eval and --format_only cannot be both specified') if args.out is not None and not args.out.endswith(('.pkl', '.pickle')): raise ValueError('The output file must be a pkl file.') cfg = Config.fromfile(args.config) # set cudnn_benchmark if cfg.get('cudnn_benchmark', False): torch.backends.cudnn.benchmark = True cfg.model.pretrained = None cfg.data.test.test_mode = True # init distributed env first, since logger depends on the dist info. if args.launcher == 'none': distributed = False else: distributed = True init_dist(args.launcher, **cfg.dist_params) # set random seeds if args.seed is not None: set_random_seed(args.seed, deterministic=args.deterministic) # build the dataloader samples_per_gpu = cfg.data.test.pop('samples_per_gpu', 1) dataset = build_dataset(cfg.data.test) data_loader = build_dataloader(dataset, samples_per_gpu=samples_per_gpu, workers_per_gpu=cfg.data.workers_per_gpu, dist=distributed, shuffle=False) # build the model and load checkpoint model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg) fp16_cfg = cfg.get('fp16', None) if fp16_cfg is not None: wrap_fp16_model(model) checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu') if args.fuse_conv_bn: model = fuse_module(model) # old versions did not save class info in checkpoints, this walkaround is # for backward compatibility if 'CLASSES' in checkpoint['meta']: model.CLASSES = checkpoint['meta']['CLASSES'] else: model.CLASSES = dataset.CLASSES if not distributed: model = MMDataParallel(model, device_ids=[0]) outputs = single_gpu_test(model, data_loader, args.show, args.show_dir) else: model = MMDistributedDataParallel( model.cuda(), device_ids=[torch.cuda.current_device()], broadcast_buffers=False) outputs = multi_gpu_test(model, data_loader, args.tmpdir, args.gpu_collect) rank, _ = get_dist_info() if rank == 0: if args.out: print(f'\nwriting results to {args.out}') mmcv.dump(outputs, args.out) kwargs = {} if args.options is None else args.options if args.format_only: dataset.format_results(outputs, **kwargs) if args.eval: dataset.evaluate(outputs, args.eval, **kwargs)
def main(): args = parse_args() assert args.out or args.eval or args.format_only or args.show, ( "Please specify at least one operation (save/eval/format/show the " 'results) with the argument "--out", "--eval", "--format_only" ' 'or "--show"') if args.eval and args.format_only: raise ValueError("--eval and --format_only cannot be both specified") if args.out is not None and not args.out.endswith((".pkl", ".pickle")): raise ValueError("The output file must be a pkl file.") cfg = Config.fromfile(args.config) # set cudnn_benchmark if cfg.get("cudnn_benchmark", False): torch.backends.cudnn.benchmark = True cfg.model.pretrained = None cfg.data.test.test_mode = True # init distributed env first, since logger depends on the dist info. if args.launcher == "none": distributed = False else: distributed = True init_dist(args.launcher, **cfg.dist_params) # set random seeds if args.seed is not None: set_random_seed(args.seed, deterministic=args.deterministic) # build the dataloader samples_per_gpu = cfg.data.test.pop("samples_per_gpu", 1) dataset = build_dataset(cfg.data.test) data_loader = build_dataloader( dataset, samples_per_gpu=samples_per_gpu, workers_per_gpu=cfg.data.workers_per_gpu, dist=distributed, shuffle=False, ) # build the model and load checkpoint cfg.model.train_cfg = None model = build_detector(cfg.model, test_cfg=cfg.get("test_cfg")) parameters = filter(lambda p: p.requires_grad, model.parameters()) model_engine, _, _, _ = initialize( args=args, model=model, model_parameters=parameters, ) _, client_state = model_engine.load_checkpoint(args.checkpoint, "ds") model = model_engine.module fp16_cfg = cfg.get("fp16", None) if fp16_cfg is not None: wrap_fp16_model(model) # checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu') if args.fuse_conv_bn: model = fuse_module(model) # old versions did not save class info in checkpoints, this walkaround is # for backward compatibility if "CLASSES" in client_state: model.CLASSES = client_state["CLASSES"] else: model.CLASSES = dataset.CLASSES if not distributed: model = MMDataParallel(model, device_ids=[0]) outputs = single_gpu_test(model, data_loader, args.show, args.show_dir) else: model = MMDistributedDataParallel( model.cuda(), device_ids=[torch.cuda.current_device()], broadcast_buffers=False, ) outputs = multi_gpu_test(model, data_loader, args.tmpdir, args.gpu_collect) rank, _ = get_dist_info() if rank == 0: if args.out: print(f"\nwriting results to {args.out}") mmcv.dump(outputs, args.out) kwargs = {} if args.options is None else args.options if args.format_only: dataset.format_results(outputs, **kwargs) if args.eval: dataset.evaluate(outputs, args.eval, **kwargs)
def main(): args = parse_args() assert args.out or args.eval or args.format_only or args.show \ or args.show_dir, \ ('Please specify at least one operation (save/eval/format/show the ' 'results / save the results) with the argument "--out", "--eval"' ', "--format-only", "--show" or "--show-dir"') if args.eval and args.format_only: raise ValueError('--eval and --format_only cannot be both specified') if args.out is not None and not args.out.endswith(('.pkl', '.pickle')): raise ValueError('The output file must be a pkl file.') cfg = Config.fromfile(args.config) if cfg.get('USE_MMDET', False): from mmdet.apis import multi_gpu_test, single_gpu_test from mmdet.datasets import build_dataloader from mmdet.models import build_detector as build_model else: from mmtrack.apis import multi_gpu_test, single_gpu_test from mmtrack.datasets import build_dataloader from mmtrack.models import build_model if args.cfg_options is not None: cfg.merge_from_dict(args.cfg_options) # set cudnn_benchmark if cfg.get('cudnn_benchmark', False): torch.backends.cudnn.benchmark = True # cfg.model.pretrains = None if hasattr(cfg.model, 'detector'): cfg.model.detector.pretrained = None cfg.data.test.test_mode = True # init distributed env first, since logger depends on the dist info. if args.launcher == 'none': distributed = False else: distributed = True init_dist(args.launcher, **cfg.dist_params) # build the dataloader dataset = build_dataset(cfg.data.test) data_loader = build_dataloader(dataset, samples_per_gpu=1, workers_per_gpu=cfg.data.workers_per_gpu, dist=distributed, shuffle=False) logger = get_logger('ParamsSearcher', log_file=args.log) # get all cases search_params = get_search_params(cfg.model.tracker, logger=logger) combinations = [p for p in product(*search_params.values())] search_cfgs = [] for c in combinations: search_cfg = dotty(cfg.model.tracker.copy()) for i, k in enumerate(search_params.keys()): search_cfg[k] = c[i] search_cfgs.append(dict(search_cfg)) print_log(f'Totally {len(search_cfgs)} cases.', logger) # init with the first one cfg.model.tracker = search_cfgs[0].copy() # build the model and load checkpoint if cfg.get('test_cfg', False): model = build_model(cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg) else: model = build_model(cfg.model) # We need call `init_weights()` to load pretained weights in MOT task. model.init_weights() fp16_cfg = cfg.get('fp16', None) if fp16_cfg is not None: wrap_fp16_model(model) if args.checkpoint is not None: checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu') if 'meta' in checkpoint and 'CLASSES' in checkpoint['meta']: model.CLASSES = checkpoint['meta']['CLASSES'] if not hasattr(model, 'CLASSES'): model.CLASSES = dataset.CLASSES if args.fuse_conv_bn: model = fuse_conv_bn(model) if not distributed: model = MMDataParallel(model, device_ids=[0]) else: model = MMDistributedDataParallel( model.cuda(), device_ids=[torch.cuda.current_device()], broadcast_buffers=False) print_log(f'Record {cfg.search_metrics}.', logger) for i, search_cfg in enumerate(search_cfgs): if not distributed: model.module.tracker = build_tracker(search_cfg) outputs = single_gpu_test(model, data_loader, args.show, args.show_dir) else: model.module.tracker = build_tracker(search_cfg) outputs = multi_gpu_test(model, data_loader, args.tmpdir, args.gpu_collect) rank, _ = get_dist_info() if rank == 0: if args.out: print(f'\nwriting results to {args.out}') mmcv.dump(outputs, args.out) kwargs = {} if args.eval_options is None else args.eval_options if args.format_only: dataset.format_results(outputs, **kwargs) if args.eval: eval_kwargs = cfg.get('evaluation', {}).copy() # hard-code way to remove EvalHook args for key in ['interval', 'tmpdir', 'start', 'gpu_collect']: eval_kwargs.pop(key, None) eval_kwargs.update(dict(metric=args.eval, **kwargs)) results = dataset.evaluate(outputs, **eval_kwargs) _records = [] for k in cfg.search_metrics: if isinstance(results[k], float): _records.append(f'{(results[k]):.3f}') else: _records.append(f'{(results[k])}') print_log(f'{combinations[i]}: {_records}', logger)
class TSN3D_adv_mt(BaseRecognizer): def __init__(self, backbone, flownet=None, spatial_temporal_module=None, segmental_consensus=None, cls_head=None, discriminator=None, train_cfg=None, test_cfg=None, seg_head=None, gpus=None, dist=False, train=True, weights=None, bb_weights=None): super(TSN3D_adv_mt, self).__init__() self.tsn3d_backbone = TSN3D_bb_mt(backbone, flownet, spatial_temporal_module, segmental_consensus, cls_head, seg_head, train_cfg, test_cfg, weights=bb_weights) if discriminator is not None: self.discriminator = builder.build_discriminator(discriminator) self.init_weights(weights) # put model on gpus #pdb.set_trace() if train == True: if dist == True: self.tsn3d_backbone = MMDistributedDataParallel( self.tsn3d_backbone.cuda()) self.discriminator = MMDistributedDataParallel( self.discriminator.cuda()) else: self.tsn3d_backbone = MMDataParallel( self.tsn3d_backbone, device_ids=range(gpus)).cuda() self.discriminator = MMDataParallel( self.discriminator, device_ids=range(gpus)).cuda() ''' assert gpus is not None self.backbone = MMDataParallel(self.backbone, device_ids=range(gpus)) self.spatial_temporal_module = MMDataParallel(self.spatial_temporal_module, device_ids=range(gpus)) self.segmental_consensus = MMDataParallel(self.segmental_consensus, device_ids=range(gpus)) self.cls_head = MMDataParallel(self.cls_head, device_ids=range(gpus)) self.discriminator = MMDataParallel(self.discriminator, device_ids=range(gpus)) self.train_cfg = train_cfg self.test_cfg = test_cfg ''' @property def with_discriminator(self): return hasattr(self, 'discriminator') and self.discriminator is not None def forward_train(self, num_modalities, img_meta, gt_label0, gt_label1, seg_lambda=0.1, **kwargs): #assert num_modalities == 1, '%s' % num_modalities img_group0 = kwargs['img_group_0'] seg = kwargs['img_group_seg'] img_group1 = kwargs['img_group_1'] # freeze discriminator self.discriminator.module.freeze(True) # Update generator # Src domain #feat0, loss_cls0 = self.tsn3d_backbone(img_group0, gt_label=gt_label0) # supervised feat0, loss_cls0 = self.tsn3d_backbone( img_group0, img_group_seg=seg, gt_label=gt_label0) # supervised multi task # Tgt domain #feat1, loss_cls1 = self.tsn3d_backbone(img_group1, gt_label=gt_label1) # supervised #feat1 = self.tsn3d_backbone(img_group1) # unsupervised losses = dict() # Src domain loss #loss_cls0['loss_cls'].mean().backward() loss_0 = loss_cls0['loss_cls'] + seg_lambda * loss_cls0['loss_seg'] losses['loss_seg'] = loss_cls0['loss_seg'] loss_0.mean().backward() losses['loss_cls0'] = loss_cls0['loss_cls'].mean() # Tgt domain loss #losses['loss_cls1'] = loss_cls1['loss_cls'].mean() #loss_1 = loss_cls1['loss_cls'] #loss_1.mean().backward() ''' Adversarial training outD_1 = self.discriminator(feat1) loss_D_1_fake = self.discriminator.module.loss(outD_1, 0) loss_1 = self.discriminator.module.lambda_adv_1 * loss_D_1_fake + loss_cls1['loss_cls'] #loss_1 = self.discriminator.module.lambda_adv_1 * loss_D_1_fake loss_1.mean().backward() losses['loss_D_1_fake'] = loss_D_1_fake.mean() # unfreeze discriminator self.discriminator.module.freeze(False) feat0, feat1 = feat0.detach(), feat1.detach() outD_0 = self.discriminator(feat0) loss_D_0_real = self.discriminator.module.loss(outD_0, 0) loss_D_0_real.mean().backward() outD_1 = self.discriminator(feat1) loss_D_1_real = self.discriminator.module.loss(outD_1, 1) loss_D_1_real.mean().backward() losses['loss_D_0_real'] = loss_D_0_real.mean() losses['loss_D_1_real'] = loss_D_1_real.mean() ''' return losses def forward_test(self, num_modalities, img_meta, **kwargs): #assert num_modalities == 1 img_group = kwargs['img_group_0'] return self.tsn3d_backbone(img_group, img_meta, test=True, output_seg=True) def init_weights(self, weights=None): if weights is not None: load_checkpoint_adv(self, weights, strict=False)
def main(): args = parse_args() if args.out is not None and not args.out.endswith((".pkl", ".pickle")): raise ValueError("The output file must be a pkl file.") cfg = mmcv.Config.fromfile(args.config) # set cudnn_benchmark if cfg.get("cudnn_benchmark", False): torch.backends.cudnn.benchmark = True cfg.model.pretrained = None cfg.data.test.test_mode = True # init distributed env first, since logger depends on the dist info. if args.launcher == "none": distributed = False else: distributed = True init_dist(args.launcher, **cfg.dist_params) # build the dataloader # TODO: support multiple images per gpu (only minor changes are needed) dataset = get_dataset(cfg.data.test) data_loader = build_dataloader( dataset, imgs_per_gpu=1, workers_per_gpu=cfg.data.workers_per_gpu, dist=distributed, shuffle=False, ) # build the model and load checkpoint model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg) load_checkpoint(model, args.checkpoint, map_location="cpu") if not distributed: model = MMDataParallel(model, device_ids=[0]) outputs = single_gpu_test(model, data_loader, args.show) else: model = MMDistributedDataParallel(model.cuda()) outputs = multi_gpu_test(model, data_loader, args.tmpdir) rank, _ = get_dist_info() if args.out and rank == 0: print("\nwriting results to {}".format(args.out)) mmcv.dump(outputs, args.out) eval_types = args.eval if eval_types: print("Starting evaluate {}".format(" and ".join(eval_types))) if eval_types == ["proposal_fast"]: result_file = args.out coco_eval(result_file, eval_types, dataset.coco) else: if not isinstance(outputs[0], dict): result_file = args.out + ".json" results2json(dataset, outputs, result_file) coco_eval(result_file, eval_types, dataset.coco) else: for name in outputs[0]: print("\nEvaluating {}".format(name)) outputs_ = [out[name] for out in outputs] result_file = args.out + ".{}.json".format(name) results2json(dataset, outputs_, result_file) coco_eval(result_file, eval_types, dataset.coco)
def evaluate_model(model_name, paper_arxiv_id, weights_url, weights_name, paper_results, config): print('---') print('Now Evaluating %s' % model_name) evaluator = COCOEvaluator(root='./.data/vision/coco', model_name=model_name, paper_arxiv_id=paper_arxiv_id, paper_results=paper_results) out = 'results.pkl' launcher = 'none' if out is not None and not out.endswith(('.pkl', '.pickle')): raise ValueError('The output file must be a pkl file.') cfg = mmcv.Config.fromfile(config) cfg.data.test[ 'ann_file'] = './.data/vision/coco/annotations/instances_val2017.json' cfg.data.test['img_prefix'] = './.data/vision/coco/val2017/' # set cudnn_benchmark if cfg.get('cudnn_benchmark', False): torch.backends.cudnn.benchmark = True cfg.model.pretrained = None cfg.data.test.test_mode = True # init distributed env first, since logger depends on the dist info. if launcher == 'none': distributed = False else: distributed = True init_dist(launcher, **cfg.dist_params) # build the dataloader # TODO: support multiple images per gpu (only minor changes are needed) dataset = build_dataset(cfg.data.test) data_loader = build_dataloader(dataset, imgs_per_gpu=1, workers_per_gpu=cfg.data.workers_per_gpu, dist=distributed, shuffle=False) # build the model and load checkpoint model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg) fp16_cfg = cfg.get('fp16', None) if fp16_cfg is not None: wrap_fp16_model(model) local_checkpoint, _ = urllib.request.urlretrieve( weights_url, '%s/.cache/torch/%s' % (str(Path.home()), weights_name)) print(local_checkpoint) # '/home/ubuntu/GCNet/mask_rcnn_r50_fpn_1x_20181010-069fa190.pth' checkpoint = load_checkpoint(model, local_checkpoint, map_location='cpu') # old versions did not save class info in checkpoints, this walkaround is # for backward compatibility if 'CLASSES' in checkpoint['meta']: model.CLASSES = checkpoint['meta']['CLASSES'] else: model.CLASSES = dataset.CLASSES if not distributed: model = MMDataParallel(model, device_ids=[0]) outputs, cache_exists = single_gpu_test(model, data_loader, False, evaluator) else: model = MMDistributedDataParallel(model.cuda()) outputs = multi_gpu_test(model, data_loader, args.tmpdir) if cache_exists: print('Cache exists: %s' % (evaluator.batch_hash)) evaluator.save() else: from mmdet.core import results2json rank, _ = get_dist_info() if out and rank == 0: print('\nwriting results to {}'.format(out)) mmcv.dump(outputs, out) eval_types = ['bbox'] if eval_types: print('Starting evaluate {}'.format(' and '.join(eval_types))) if eval_types == ['proposal_fast']: result_file = out else: if not isinstance(outputs[0], dict): result_files = results2json(dataset, outputs, out) else: for name in outputs[0]: print('\nEvaluating {}'.format(name)) outputs_ = [out[name] for out in outputs] result_file = out + '.{}'.format(name) result_files = results2json( dataset, outputs_, result_file) anns = json.load(open(result_files['bbox'])) evaluator.detections = [] evaluator.add(anns) evaluator.save()
def main(): args = parse_args() assert args.out or args.eval or args.format_only or args.show \ or args.show_dir, \ ('Please specify at least one operation (save/eval/format/show the ' 'results / save the results) with the argument "--out", "--eval"' ', "--format-only", "--show" or "--show-dir"') if args.eval and args.format_only: raise ValueError('--eval and --format_only cannot be both specified') cfg = Config.fromfile(args.config) if args.cfg_options is not None: cfg.merge_from_dict(args.cfg_options) # import modules from string list. if cfg.get('custom_imports', None): from mmcv.utils import import_modules_from_strings import_modules_from_strings(**cfg['custom_imports']) # set cudnn_benchmark if cfg.get('cudnn_benchmark', False): torch.backends.cudnn.benchmark = True cfg.model.pretrained = None if cfg.model.get('neck'): if isinstance(cfg.model.neck, list): for neck_cfg in cfg.model.neck: if neck_cfg.get('rfp_backbone'): if neck_cfg.rfp_backbone.get('pretrained'): neck_cfg.rfp_backbone.pretrained = None elif cfg.model.neck.get('rfp_backbone'): if cfg.model.neck.rfp_backbone.get('pretrained'): cfg.model.neck.rfp_backbone.pretrained = None # in case the test dataset is concatenated if isinstance(cfg.data.test, dict): cfg.data.test.test_mode = True elif isinstance(cfg.data.test, list): for ds_cfg in cfg.data.test: ds_cfg.test_mode = True # init distributed env first, since logger depends on the dist info. if args.launcher == 'none': distributed = False else: distributed = True init_dist(args.launcher, **cfg.dist_params) # build the dataloader samples_per_gpu = cfg.data.test.pop('samples_per_gpu', 1) if samples_per_gpu > 1: # Replace 'ImageToTensor' to 'DefaultFormatBundle' cfg.data.test.pipeline = replace_ImageToTensor(cfg.data.test.pipeline) dataset = build_dataset(cfg.data.test) data_loader = build_dataloader(dataset, samples_per_gpu=samples_per_gpu, workers_per_gpu=cfg.data.workers_per_gpu, dist=distributed, shuffle=False) # build the model and load checkpoint cfg.model.train_cfg = None model = build_detector(cfg.model, test_cfg=cfg.get('test_cfg')) fp16_cfg = cfg.get('fp16', None) if fp16_cfg is not None: wrap_fp16_model(model) checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu') if args.fuse_conv_bn: model = fuse_conv_bn(model) # old versions did not save class info in checkpoints, this walkaround is # for backward compatibility if 'CLASSES' in checkpoint['meta']: model.CLASSES = checkpoint['meta']['CLASSES'] else: model.CLASSES = dataset.CLASSES if not distributed: model = MMDataParallel(model, device_ids=[0]) outputs = single_gpu_test(model, data_loader, args.show, args.show_dir, args.show_score_thr) result_files = dataset.results2json(outputs, args.out_prefix) print(result_files) for metric in result_files: #print("here") #print(res_file) #cocoDt = cocoGt.loadRes(result_files[metric]) analyze_results(result_files[metric], args.ann, args.eval, args.out) else: model = MMDistributedDataParallel( model.cuda(), device_ids=[torch.cuda.current_device()], broadcast_buffers=False) outputs = multi_gpu_test(model, data_loader, args.tmpdir, args.gpu_collect) result_files = dataset.results2json(outputs, args.out_prefix) for res_file in result_files: analyze_results(res_file, args.ann, args.eval, args.out)
def process_dataset(cfg, cfg_data_test, args, distributed, json_out, args_out): dataset = build_dataset(cfg_data_test) data_loader = build_dataloader(dataset, imgs_per_gpu=1, workers_per_gpu=cfg.data.workers_per_gpu, dist=distributed, shuffle=False) # build the model and load checkpoint model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg) fp16_cfg = cfg.get('fp16', None) if fp16_cfg is not None: wrap_fp16_model(model) checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu') # old versions did not save class info in checkpoints, this walkaround is # for backward compatibility if 'CLASSES' in checkpoint['meta']: model.CLASSES = checkpoint['meta']['CLASSES'] else: model.CLASSES = dataset.CLASSES if not distributed: model = MMDataParallel(model, device_ids=[0]) outputs = single_gpu_test(model, data_loader, args.show) #outputs = single_gpu_test(model, data_loader, args.show) #outputs = single_gpu_test_seq_track_nms(model, data_loader, args.show) else: model = MMDistributedDataParallel(model.cuda()) outputs = multi_gpu_test(model, data_loader, args.tmpdir) rank, _ = get_dist_info() if args_out and rank == 0: print('\nwriting results to {}'.format(args_out)) #mmcv.dump(outputs, args_out) mmcv.dump(outputs[0], args_out.replace('.pkl', '_0.pkl')) mmcv.dump(outputs[1], args_out.replace('.pkl', '_1.pkl')) mmcv.dump(outputs[2], args_out.replace('.pkl', '_2.pkl')) eval_types = args.eval if eval_types: print('Starting evaluate {}'.format(' and '.join(eval_types))) if eval_types == ['proposal_fast']: result_file = args_out coco_eval(result_file, eval_types, dataset.coco) else: if not isinstance(outputs[0], dict): result_files = results2json(dataset, outputs, args_out) coco_eval(result_files, eval_types, dataset.coco) else: for name in outputs[0]: print('\nEvaluating {}'.format(name)) outputs_ = [out[name] for out in outputs] result_file = args_out + '.{}'.format(name) result_files = results2json(dataset, outputs_, result_file) coco_eval(result_files, eval_types, dataset.coco) # Save predictions in the COCO json format if json_out and rank == 0: if not isinstance(outputs[0], dict): print('\nwriting results to {}'.format(json_out)) results2json(dataset, outputs, json_out) else: for name in outputs[0]: outputs_ = [out[name] for out in outputs] result_file = json_out + '.{}'.format(name) results2json(dataset, outputs_, result_file)
def main(): args = parse_args() assert args.out or args.show or args.json_out, \ ('Please specify at least one operation (save or show the results) ' 'with the argument "--out" or "--show" or "--json_out"') if args.out is not None and not args.out.endswith(('.pkl', '.pickle')): raise ValueError('The output file must be a pkl file.') if args.json_out is not None and args.json_out.endswith('.json'): args.json_out = args.json_out[:-5] cfg = mmcv.Config.fromfile(args.config) # set cudnn_benchmark if cfg.get('cudnn_benchmark', False): torch.backends.cudnn.benchmark = True cfg.model.pretrained = None # cfg.model.rpn_pretrained = None # cfg.model.rcnn_pretrained = None cfg.data.test.test_mode = True # init distributed env first, since logger depends on the dist info. if args.launcher == 'none': distributed = False else: distributed = True init_dist(args.launcher, **cfg.dist_params) # build the dataloader # TODO: support multiple images per gpu (only minor changes are needed) dataset = build_dataset(cfg.data.test) data_loader = build_dataloader(dataset, imgs_per_gpu=1, workers_per_gpu=cfg.data.workers_per_gpu, dist=distributed, shuffle=False) # build the model and load checkpoint model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg) fp16_cfg = cfg.get('fp16', None) if fp16_cfg is not None: wrap_fp16_model(model) checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu') # old versions did not save class info in checkpoints, this walkaround is # for backward compatibility if 'CLASSES' in checkpoint['meta']: model.CLASSES = checkpoint['meta']['CLASSES'] else: model.CLASSES = dataset.CLASSES if not distributed: model = MMDataParallel(model, device_ids=[0]) outputs = single_gpu_test(model, data_loader, args.show) else: model = MMDistributedDataParallel(model.cuda()) outputs = multi_gpu_test(model, data_loader, args.tmpdir) rank, _ = get_dist_info() if args.out and rank == 0: print('\nwriting results to {}'.format(args.out)) mmcv.dump(outputs, args.out) eval_types = args.eval data_name = args.data if data_name == 'coco': if eval_types: print('Starting evaluate {}'.format(' and '.join(eval_types))) if eval_types == ['proposal_fast']: result_file = args.out coco_eval(result_file, eval_types, dataset.coco) else: if not isinstance(outputs[0], dict): result_files = results2json(dataset, outputs, args.out) coco_eval(result_files, eval_types, dataset.coco) else: for name in outputs[0]: print('\nEvaluating {}'.format(name)) outputs_ = [out[name] for out in outputs] result_file = args.out + '.{}'.format(name) result_files = results2json( dataset, outputs_, result_file) coco_eval(result_files, eval_types, dataset.coco) elif data_name in ['dota', 'hrsc2016']: eval_kwargs = cfg.get('evaluation', {}).copy() work_dir = osp.dirname(args.out) dataset.evaluate(outputs, work_dir, **eval_kwargs) # Save predictions in the COCO json format if args.json_out and rank == 0: if not isinstance(outputs[0], dict): results2json(dataset, outputs, args.json_out) else: for name in outputs[0]: outputs_ = [out[name] for out in outputs] result_file = args.json_out + '.{}'.format(name) results2json(dataset, outputs_, result_file)
def main(): args = parse_args() cfg = mmcv.Config.fromfile(args.config) if args.options is not None: cfg.merge_from_dict(args.options) # set cudnn_benchmark if cfg.get('cudnn_benchmark', False): torch.backends.cudnn.benchmark = True cfg.model.pretrained = None cfg.data.test.test_mode = True # init distributed env first, since logger depends on the dist info. if args.launcher == 'none': distributed = False else: distributed = True init_dist(args.launcher, **cfg.dist_params) # build the dataloader dataset = build_dataset(cfg.data.test) data_loader = build_dataloader(dataset, samples_per_gpu=cfg.data.samples_per_gpu, workers_per_gpu=cfg.data.workers_per_gpu, dist=distributed, shuffle=False, round_up=False) # build the model and load checkpoint model = build_classifier(cfg.model) fp16_cfg = cfg.get('fp16', None) if fp16_cfg is not None: wrap_fp16_model(model) checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu') if 'CLASSES' in checkpoint['meta']: CLASSES = checkpoint['meta']['CLASSES'] else: from mmcls.datasets import ImageNet warnings.simplefilter('once') warnings.warn('Class names are not saved in the checkpoint\'s ' 'meta data, use imagenet by default.') CLASSES = ImageNet.CLASSES if not distributed: model = MMDataParallel(model, device_ids=[0]) model.CLASSES = CLASSES show_kwargs = {} if args.show_options is None else args.show_options outputs = single_gpu_test(model, data_loader, args.show, args.show_dir, **show_kwargs) else: model = MMDistributedDataParallel( model.cuda(), device_ids=[torch.cuda.current_device()], broadcast_buffers=False) outputs = multi_gpu_test(model, data_loader, args.tmpdir, args.gpu_collect) rank, _ = get_dist_info() if rank == 0: if args.metrics: results = dataset.evaluate(outputs, args.metrics, args.metric_options) for k, v in results.items(): print(f'\n{k} : {v:.2f}') else: warnings.warn('Evaluation metrics are not specified.') scores = np.vstack(outputs) pred_score = np.max(scores, axis=1) pred_label = np.argmax(scores, axis=1) pred_class = [CLASSES[lb] for lb in pred_label] results = { 'pred_score': pred_score, 'pred_label': pred_label, 'pred_class': pred_class } if not args.out: print('\nthe predicted result for the first element is ' f'pred_score = {pred_score[0]:.2f}, ' f'pred_label = {pred_label[0]} ' f'and pred_class = {pred_class[0]}. ' 'Specify --out to save all results to files.') if args.out and rank == 0: print(f'\nwriting results to {args.out}') mmcv.dump(results, args.out)
def main(): args = parse_args() assert ( args.out or args.eval or args.format_only or args.show or args.show_dir), ( 'Please specify at least one operation (save/eval/format/show the ' 'results / save the results) with the argument "--out", "--eval"' ', "--format-only", "--show" or "--show-dir".') if args.eval and args.format_only: raise ValueError('--eval and --format_only cannot be both specified.') if args.out is not None and not args.out.endswith(('.pkl', '.pickle')): raise ValueError('The output file must be a pkl file.') cfg = Config.fromfile(args.config) if args.cfg_options is not None: cfg.merge_from_dict(args.cfg_options) setup_multi_processes(cfg) # set cudnn_benchmark if cfg.get('cudnn_benchmark', False): torch.backends.cudnn.benchmark = True if cfg.model.get('pretrained'): cfg.model.pretrained = None if cfg.model.get('neck'): if isinstance(cfg.model.neck, list): for neck_cfg in cfg.model.neck: if neck_cfg.get('rfp_backbone'): if neck_cfg.rfp_backbone.get('pretrained'): neck_cfg.rfp_backbone.pretrained = None elif cfg.model.neck.get('rfp_backbone'): if cfg.model.neck.rfp_backbone.get('pretrained'): cfg.model.neck.rfp_backbone.pretrained = None # in case the test dataset is concatenated samples_per_gpu = (cfg.data.get('test_dataloader', {})).get( 'samples_per_gpu', cfg.data.get('samples_per_gpu', 1)) if samples_per_gpu > 1: cfg = disable_text_recog_aug_test(cfg) cfg = replace_image_to_tensor(cfg) # init distributed env first, since logger depends on the dist info. if args.launcher == 'none': cfg.gpu_ids = [args.gpu_id] distributed = False else: distributed = True init_dist(args.launcher, **cfg.dist_params) # build the dataloader dataset = build_dataset(cfg.data.test, dict(test_mode=True)) # step 1: give default values and override (if exist) from cfg.data loader_cfg = { **dict(seed=cfg.get('seed'), drop_last=False, dist=distributed), **({} if torch.__version__ != 'parrots' else dict( prefetch_num=2, pin_memory=False, )), **dict((k, cfg.data[k]) for k in [ 'workers_per_gpu', 'seed', 'prefetch_num', 'pin_memory', 'persistent_workers', ] if k in cfg.data) } test_loader_cfg = { **loader_cfg, **dict(shuffle=False, drop_last=False), **cfg.data.get('test_dataloader', {}), **dict(samples_per_gpu=samples_per_gpu) } data_loader = build_dataloader(dataset, **test_loader_cfg) # build the model and load checkpoint cfg.model.train_cfg = None model = build_detector(cfg.model, test_cfg=cfg.get('test_cfg')) model = revert_sync_batchnorm(model) fp16_cfg = cfg.get('fp16', None) if fp16_cfg is not None: wrap_fp16_model(model) load_checkpoint(model, args.checkpoint, map_location='cpu') if args.fuse_conv_bn: model = fuse_conv_bn(model) if not distributed: model = MMDataParallel(model, device_ids=cfg.gpu_ids) is_kie = cfg.model.type in ['SDMGR'] outputs = single_gpu_test(model, data_loader, args.show, args.show_dir, is_kie, args.show_score_thr) else: model = MMDistributedDataParallel( model.cuda(), device_ids=[torch.cuda.current_device()], broadcast_buffers=False) outputs = multi_gpu_test(model, data_loader, args.tmpdir, args.gpu_collect) rank, _ = get_dist_info() if rank == 0: if args.out: print(f'\nwriting results to {args.out}') mmcv.dump(outputs, args.out) kwargs = {} if args.eval_options is None else args.eval_options if args.format_only: dataset.format_results(outputs, **kwargs) if args.eval: eval_kwargs = cfg.get('evaluation', {}).copy() # hard-code way to remove EvalHook args for key in [ 'interval', 'tmpdir', 'start', 'gpu_collect', 'save_best', 'rule' ]: eval_kwargs.pop(key, None) eval_kwargs.update(dict(metric=args.eval, **kwargs)) print(dataset.evaluate(outputs, **eval_kwargs))
def main(): args = parse_args() assert args.out or args.show, \ ('Please specify at least one operation (save or show the results) ' 'with the argument "--out" or "--show"') if args.out is not None and not args.out.endswith(('.pkl', '.pickle')): raise ValueError('The output file must be a pkl file.') cfg = mmcv.Config.fromfile(args.config) # set cudnn_benchmark if cfg.get('cudnn_benchmark', False): torch.backends.cudnn.benchmark = True cfg.model.pretrained = None # init distributed env first, since logger depends on the dist info. if args.launcher == 'none': distributed = False else: distributed = True init_dist(args.launcher, **cfg.dist_params) # build the dataloader # TODO: support multiple images per gpu (only minor changes are needed) if args.submit: dataset = build_dataset(cfg.data.submit) else: dataset = build_dataset(cfg.data.test) data_loader = build_dataloader(dataset, imgs_per_gpu=1, workers_per_gpu=cfg.data.workers_per_gpu, dist=distributed, shuffle=False) # build the model and load checkpoint model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg) fp16_cfg = cfg.get('fp16', None) if fp16_cfg is not None: wrap_fp16_model(model) checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu') # old versions did not save class info in checkpoints, this walkaround is # for backward compatibility if 'CLASSES' in checkpoint['meta']: model.CLASSES = checkpoint['meta']['CLASSES'] else: model.CLASSES = dataset.CLASSES if not distributed: model = MMDataParallel(model, device_ids=[0]) outputs = single_gpu_test(model, data_loader, args.show) else: model = MMDistributedDataParallel(model.cuda()) outputs = multi_gpu_test(model, data_loader, args.tmpdir) rank, _ = get_dist_info() if args.out and rank == 0: result_root = osp.dirname(args.out) if not osp.exists(result_root): os.makedirs(result_root) print('\nwriting results to {}'.format(args.out)) mmcv.dump(outputs, args.out) # eval_types = args.eval # if eval_types: # print('Starting evaluate {}'.format(' and '.join(eval_types))) # if eval_types == ['proposal_fast']: # result_file = args.out # coco_eval(result_file, eval_types, dataset.coco) # else: # if not isinstance(outputs[0], dict): # result_files = results2json(dataset, outputs, args.out) # coco_eval(result_files, eval_types, dataset.coco) # else: # for name in outputs[0]: # print('\nEvaluating {}'.format(name)) # outputs_ = [out[name] for out in outputs] # result_file = args.out + '.{}'.format(name) # result_files = results2json(dataset, outputs_, # result_file) # coco_eval(result_files, eval_types, dataset.coco) print('\ncreating result_file_instance and result_file_semantic...') result_file_instance = args.out + '.instance.json' # will be created as intermediate file by result2json() result_file_semantic = args.out + '.semantic.json' # will be created as intermediate file by result2json() result_file_aux = args.out + '.aux.json' # may be created results2json(dataset, outputs, result_file_instance, result_file_semantic, result_file_aux) logger = logger_init_pq(result_root) # evaluate mAP res_types = ['bbox', 'segm'] cocoGt = dataset.coco cocoDt = cocoGt.loadRes(result_file_instance) imgIds = cocoGt.getImgIds() for res_type in res_types: iou_type = res_type cocoEval = COCOeval(cocoGt, cocoDt, iou_type) cocoEval.params.imgIds = imgIds cocoEval.evaluate() cocoEval.accumulate() cocoEval.summarize() metrics = ['mAP', 'mAP_50', 'mAP_75', 'mAP_s', 'mAP_m', 'mAP_l'] for i in range(len(metrics)): key = '{}_{}'.format(res_type, metrics[i]) val = float('{:.3f}'.format(cocoEval.stats[i])) logger.info('{:20s}|{:10.3f}'.format(key, val)) # Evaluate PQ print('\ncombining instance and semantic predictions...') if not args.submit: combine_predictions( result_file_semantic, result_file_instance, result_file_aux, cfg.data.test.ann_file.replace( 'instances', 'panoptic'), # data/coco/annotations/panoptic_val2017.json cfg.data.test.cat_pan_file_discrete, "data/coco/annotations/panoptic_val2017", work_dir=result_root, mode='test', logger=logger) else: combine_predictions( result_file_semantic, result_file_instance, result_file_aux, cfg.data.submit. ann_file, # data/coco/annotations/image_info_test-dev2017.json cfg.data.submit.cat_pan_file_discrete, None, # no gt_folder work_dir=result_root, mode='submit', logger=logger)
def main(): args = parse_args() logger = get_root_logger('INFO') for arg in vars(args): logger.info(f"###################### {arg}: {getattr(args, arg)}") assert args.out or args.show or args.json_out, \ ('Please specify at least one operation (save or show the results) ' 'with the argument "--out" or "--show" or "--json_out"') if args.out is not None and not args.out.endswith(('.pkl', '.pickle')): raise ValueError('The output file must be a pkl file.') if args.json_out is not None and args.json_out.endswith('.json'): args.json_out = args.json_out[:-5] cfg = mmcv.Config.fromfile(args.config) # set cudnn_benchmark if cfg.get('cudnn_benchmark', False): torch.backends.cudnn.benchmark = True # import pdb; pdb.set_trace() cfg.model.pretrained = None cfg.data.test.test_mode = True cfg.test_cfg.rcnn.zsd = args.zsd cfg.test_cfg.rcnn.gzsd = args.gzsd cfg.test_cfg.rcnn.score_thr = 0.05 # init distributed env first, since logger depends on the dist info. if args.launcher == 'none': distributed = False else: distributed = True init_dist(args.launcher, **cfg.dist_params) # build the dataloader # TODO: support multiple images per gpu (only minor changes are needed) dataset = build_dataset(cfg.data.test) if cfg.test_cfg.rcnn.gzsd: dataset.cat_to_load = dataset.cat_ids data_loader = build_dataloader(dataset, imgs_per_gpu=1, workers_per_gpu=cfg.data.workers_per_gpu, dist=distributed, shuffle=False) # build the model and load checkpoint model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg) fp16_cfg = cfg.get('fp16', None) if fp16_cfg is not None: wrap_fp16_model(model) checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu') model.CLASSES = dataset.CLASSES logger.info(cfg.data.test.split) dataset_name = args.dataset if args.syn_weights: seen_bg_weight, seen_bg_bias = copy_synthesised_weights( model, args.syn_weights, dataset_name, split=cfg.data.test.split) model.bbox_head.seen_bg_weight = torch.from_numpy( seen_bg_weight).cuda() model.bbox_head.seen_bg_bias = torch.from_numpy(seen_bg_bias).cuda() if not distributed: model = MMDataParallel(model, device_ids=[0]) outputs = single_gpu_test(model, data_loader, args.show) else: model = MMDistributedDataParallel(model.cuda()) # if args.syn_weights: # seen_bg_weights, seen_bg_bias = copy_synthesised_weights(model, args.syn_weights, dataset_name, split=cfg.data.test.split) outputs = multi_gpu_test(model, data_loader, args.tmpdir) rank, _ = get_dist_info() if args.out and rank == 0: logger.info('\nwriting results to {}'.format(args.out)) mmcv.dump(outputs, args.out) mean_ap, _ = eval(args.out, dataset, dataset_name=dataset_name, split=cfg.data.test.split) f = open('results.txt', 'a') f.writelines(f"{mean_ap} -------- {args.syn_weights} \n" ) # import pdb; pdb.set_trace() f.close()
def main(): args = parse_args() cfg = mmcv.Config.fromfile(args.config) if args.update_config is not None: cfg.merge_from_dict(args.update_config) cfg = update_config(cfg, args) cfg = propagate_root_dir(cfg, args.data_dir) # Load output_config from cfg output_config = cfg.get('output_config', {}) # Overwrite output_config from args.out output_config = merge_configs(output_config, dict(out=args.out)) # Load eval_config from cfg eval_config = cfg.get('eval_config', {}) # Overwrite eval_config from args.eval eval_config = merge_configs(eval_config, dict(metrics=args.eval)) # Add options from args.option eval_config = merge_configs(eval_config, args.options) assert output_config or eval_config, \ ('Please specify at least one operation (save or eval the ' 'results) with the argument "--out" or "--eval"') # init distributed env first, since logger depends on the dist info. distributed = args.launcher != 'none' if distributed: init_dist(args.launcher, **cfg.dist_params) # get rank rank, _ = get_dist_info() if cfg.get('seed'): print(f'Set random seed to {cfg.seed}') set_random_seed(cfg.seed) # build the dataset dataset = build_dataset(cfg.data, 'test', dict(test_mode=True)) if cfg.get('classes'): dataset = dataset.filter(cfg.classes) if rank == 0: print(f'Test datasets:\n{str(dataset)}') # build the dataloader data_loader = build_dataloader( dataset, videos_per_gpu=1, workers_per_gpu=cfg.data.workers_per_gpu, dist=distributed, shuffle=False ) # build the model and load checkpoint model = build_model( cfg.model, train_cfg=None, test_cfg=cfg.test_cfg, class_sizes=dataset.class_sizes, class_maps=dataset.class_maps ) fp16_cfg = cfg.get('fp16', None) if fp16_cfg is not None: wrap_fp16_model(model) # load model weights load_checkpoint(model, args.checkpoint, map_location='cpu', force_matching=True) if args.fuse_conv_bn: model = fuse_conv_bn(model) if not distributed: model = MMDataParallel(model, device_ids=[0]) outputs = single_gpu_test(model, data_loader) else: model = MMDistributedDataParallel( model.cuda(), device_ids=[torch.cuda.current_device()], broadcast_buffers=False) outputs = multi_gpu_test(model, data_loader, args.tmpdir, args.gpu_collect) if rank == 0: if output_config: out = output_config['out'] print(f'\nwriting results to {out}') dataset.dump_results(outputs, **output_config) if eval_config: eval_res = dataset.evaluate(outputs, **eval_config) print('\nFinal metrics:') for name, val in eval_res.items(): print(f'{name}: {val:.04f}')
def main(): args = parse_args() cfg = Config.fromfile(args.config) cfg.merge_from_dict(args.cfg_options) # Load output_config from cfg output_config = cfg.get('output_config', {}) # Overwrite output_config from args.out output_config = Config._merge_a_into_b(dict(out=args.out), output_config) # Load eval_config from cfg eval_config = cfg.get('eval_config', {}) # Overwrite eval_config from args.eval eval_config = Config._merge_a_into_b(dict(metrics=args.eval), eval_config) # Add options from args.eval_options eval_config = Config._merge_a_into_b(args.eval_options, eval_config) assert output_config or eval_config, \ ('Please specify at least one operation (save or eval the ' 'results) with the argument "--out" or "--eval"') if output_config.get('out', None): out = output_config['out'] # make sure the dirname of the output path exists mmcv.mkdir_or_exist(osp.dirname(out)) _, suffix = osp.splitext(out) assert suffix in file_handlers, \ 'The format of the output file should be json, pickle or yaml' # set cudnn benchmark if cfg.get('cudnn_benchmark', False): torch.backends.cudnn.benchmark = True cfg.data.test.test_mode = True if cfg.test_cfg is None: cfg.test_cfg = dict(average_clips=args.average_clips) else: # You can set average_clips during testing, it will override the # original settting if args.average_clips is not None: cfg.test_cfg.average_clips = args.average_clips # init distributed env first, since logger depends on the dist info. if args.launcher == 'none': distributed = False else: distributed = True init_dist(args.launcher, **cfg.dist_params) # build the dataloader dataset = build_dataset(cfg.data.test, dict(test_mode=True)) dataloader_setting = dict( videos_per_gpu=cfg.data.get('videos_per_gpu', 2), workers_per_gpu=cfg.data.get('workers_per_gpu', 0), dist=distributed, shuffle=False) dataloader_setting = dict(dataloader_setting, **cfg.data.get('test_dataloader', {})) data_loader = build_dataloader(dataset, **dataloader_setting) # build the model and load checkpoint model = build_model(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg) fp16_cfg = cfg.get('fp16', None) if fp16_cfg is not None: wrap_fp16_model(model) load_checkpoint(model, args.checkpoint, map_location='cpu') if args.fuse_conv_bn: model = fuse_conv_bn(model) if not distributed: model = MMDataParallel(model, device_ids=[0]) outputs = single_gpu_test(model, data_loader) else: model = MMDistributedDataParallel( model.cuda(), device_ids=[torch.cuda.current_device()], broadcast_buffers=False) outputs = multi_gpu_test(model, data_loader, args.tmpdir, args.gpu_collect) rank, _ = get_dist_info() if rank == 0: if output_config.get('out', None): out = output_config['out'] print(f'\nwriting results to {out}') dataset.dump_results(outputs, **output_config) if eval_config: eval_res = dataset.evaluate(outputs, **eval_config) for name, val in eval_res.items(): print(f'{name}: {val:.04f}')
def main(): print("I am here") args = parse_args() assert args.out or args.show or args.json_out, \ ('Please specify at least one operation (save or show the results) ' 'with the argument "--out" or "--show" or "--json_out"') if args.out is not None and not args.out.endswith(('.pkl', '.pickle')): raise ValueError('The output file must be a pkl file.') if args.json_out is not None and args.json_out.endswith('.json'): args.json_out = args.json_out[:-5] cfg = mmcv.Config.fromfile(args.config) # set cudnn_benchmark if cfg.get('cudnn_benchmark', False): torch.backends.cudnn.benchmark = True cfg.model.pretrained = None cfg.data.test.test_mode = True cfg.data.test.ann_file = 'test.json' cfg.data.test.img_prefix = test_images_path # init distributed env first, since logger depends on the dist info. if args.launcher == 'none': distributed = False else: distributed = True init_dist(args.launcher, **cfg.dist_params) # build the dataloader # TODO: support multiple images per gpu (only minor changes are needed) dataset = build_dataset(cfg.data.test) data_loader = build_dataloader( dataset, imgs_per_gpu=1, workers_per_gpu=cfg.data.workers_per_gpu, dist=distributed, shuffle=False) # build the model and load checkpoint model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg) fp16_cfg = cfg.get('fp16', None) if fp16_cfg is not None: wrap_fp16_model(model) checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu') # old versions did not save class info in checkpoints, this walkaround is # for backward compatibility model.CLASSES = [category['name'] for category in annotations['categories']] if not distributed: model = MMDataParallel(model, device_ids=[0]) outputs = single_gpu_test(model, data_loader, args.show) else: model = MMDistributedDataParallel(model.cuda()) outputs = multi_gpu_test(model, data_loader, args.tmpdir) rank, _ = get_dist_info() if args.out and rank == 0: print('\nwriting results to {}'.format(args.out)) mmcv.dump(outputs, args.out) eval_types = args.eval if eval_types: print('Starting evaluate {}'.format(' and '.join(eval_types))) if eval_types == ['proposal_fast']: result_file = args.out coco_eval(result_file, eval_types, dataset.coco) else: if not isinstance(outputs[0], dict): result_files = results2json(dataset, outputs, args.out) coco_eval(result_files, eval_types, dataset.coco) else: for name in outputs[0]: print('\nEvaluating {}'.format(name)) outputs_ = [out[name] for out in outputs] result_file = args.out + '.{}'.format(name) result_files = results2json(dataset, outputs_, result_file) coco_eval(result_files, eval_types, dataset.coco) print(args.json_out, rank) if outputs and args.json_out and rank == 0: print("inside json_out") print(outputs) if not isinstance(outputs[0], dict): print("inside is instance") response = results2json(dataset, outputs, args.json_out) else: print("inside the else") for name in outputs[0]: print("inside the output folder") outputs_ = [out[name] for out in outputs] result_file = args.json_out + '.{}'.format(name) response = results2json(dataset, outputs_, result_file) print(response, response['segm'], args.json_out) shutil.move(response['segm'], predictions_output_path)
def main(): args = parse_args() cfg = Config.fromfile(args.config) if args.cfg_options is not None: cfg.merge_from_dict(args.cfg_options) # set cudnn_benchmark if cfg.get('cudnn_benchmark', False): torch.backends.cudnn.benchmark = True cfg.model.pretrained = None cfg.data.test.test_mode = True args.work_dir = osp.join('./work_dirs', osp.splitext(osp.basename(args.config))[0]) mmcv.mkdir_or_exist(osp.abspath(args.work_dir)) # init distributed env first, since logger depends on the dist info. if args.launcher == 'none': distributed = False else: distributed = True init_dist(args.launcher, **cfg.dist_params) # build the dataloader dataset = build_dataset(cfg.data.test, dict(test_mode=True)) dataloader_setting = dict( samples_per_gpu=512, #1 512 workers_per_gpu=cfg.data.get('workers_per_gpu', 1), dist=distributed, shuffle=False, drop_last=False) dataloader_setting = dict(dataloader_setting, **cfg.data.get('test_dataloader', {})) data_loader = build_dataloader(dataset, **dataloader_setting) # build the model and load checkpoint model = build_posenet(cfg.model) # print(model) # model=model.cuda() # summary(model,input_size=(3, 256, 192)) # with open('../logs/dark-HRNet-w32.out','w+') as f: # f.write(model) # f.close() fp16_cfg = cfg.get('fp16', None) if fp16_cfg is not None: wrap_fp16_model(model) load_checkpoint(model, args.checkpoint, map_location='cpu') #结果是因为文件名写错了,只能说mmcv报的错误实在是不够明显。 # model.load_state_dict(torch.load(args.checkpoint, map_location='cpu')['state_dict']) #"meta", "state_dict", "optimizer" if args.fuse_conv_bn: model = fuse_conv_bn(model) if not distributed: model = MMDataParallel(model, device_ids=[0]) outputs = single_gpu_test(model, data_loader) else: model = MMDistributedDataParallel( model.cuda(), device_ids=[torch.cuda.current_device()], broadcast_buffers=False) outputs = multi_gpu_test(model, data_loader, args.tmpdir, args.gpu_collect) rank, _ = get_dist_info() eval_config = cfg.get('evaluation', {}) eval_config = merge_configs(eval_config, dict(metric=args.eval)) if rank == 0: if args.out: print(f'\nwriting results to {args.out}') mmcv.dump(outputs, args.out) results = dataset.evaluate(outputs, args.work_dir, **eval_config) for k, v in sorted(results.items()): print(f'{k}: {v}')
def main(): args = parse_args() if args.out is not None and not args.out.endswith(('.pkl', '.pickle')): raise ValueError('The output file must be a pkl file.') cfg = mmcv.Config.fromfile(args.config) # set cudnn_benchmark if cfg.get('cudnn_benchmark', False): torch.backends.cudnn.benchmark = True cfg.model.pretrained = None cfg.data.test.test_mode = True # init distributed env first, since logger depends on the dist info. if args.launcher == 'none': distributed = False else: distributed = True init_dist(args.launcher, **cfg.dist_params) # build the dataloader # TODO: support multiple images per gpu (only minor changes are needed) dataset = get_dataset(cfg.data.test) data_loader = build_dataloader(dataset, imgs_per_gpu=1, workers_per_gpu=cfg.data.workers_per_gpu, dist=distributed, shuffle=False) # build the model and load checkpoint model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg) checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu') # old versions did not save class info in checkpoints, this walkaround is # for backward compatibility if 'CLASSES' in checkpoint['meta']: model.CLASSES = checkpoint['meta']['CLASSES'] else: model.CLASSES = dataset.CLASSES if not distributed: model = MMDataParallel(model, device_ids=[0]) outputs = single_gpu_test(model, data_loader, args.show, args.log_dir) else: model = MMDistributedDataParallel(model.cuda()) outputs = multi_gpu_test(model, data_loader, args.tmpdir) rank, _ = get_dist_info() if args.out and rank == 0: print('\nwriting results to {}'.format(args.out)) mmcv.dump(outputs, args.out) eval_types = args.eval if eval_types: print('Starting evaluate {}'.format(' and '.join(eval_types))) if eval_types == ['proposal_fast']: result_file = args.out coco_eval(result_file, eval_types, dataset.coco) else: if not isinstance(outputs[0], dict): result_file = args.out + '.json' results2json(dataset, outputs, result_file) # print('!!!!', result_file) coco_eval(result_file, eval_types, dataset.coco) else: for name in outputs[0]: print('\nEvaluating {}'.format(name)) outputs_ = [out[name] for out in outputs] result_file = args.out + '.{}.json'.format(name) results2json(dataset, outputs_, result_file) coco_eval(result_file, eval_types, dataset.coco)
def test(model, dataset, cfg, logger): if cfg.load_from: print('load from {}'.format(cfg.load_from)) load_checkpoint(model, cfg.load_from, strict=True, logger=logger) losses = [] accs = [] pred_conns = [] max_lst = [] multi_max = [] if cfg.gpus == 1: data_loader = build_dataloader(dataset, cfg.batch_size_per_gpu, cfg.workers_per_gpu, train=False) size = len(data_loader) model = MMDataParallel(model, device_ids=range(cfg.gpus)) if cfg.cuda: model.cuda() model.eval() for i, data in enumerate(data_loader): with torch.no_grad(): output, loss = model(data, return_loss=True) if not dataset.ignore_label: labels = data[2].view(-1) if not cfg.regressor: acc = output_accuracy(output, labels) accs += [acc.item()] losses += [loss.item()] if not cfg.regressor: output = output[:, 1] if cfg.max_conn == 1: output_max = output.max() pred = (output == output_max).nonzero().view(-1) pred_size = len(pred) if pred_size > 1: multi_max.append(pred_size) pred_i = np.random.choice(np.arange(pred_size)) else: pred_i = 0 pred = [int(pred[pred_i].detach().cpu().numpy())] max_lst.append(output_max.detach().cpu().numpy()) elif cfg.max_conn > 1: output = output.detach().cpu().numpy() pred = output.argpartition(cfg.max_conn)[:cfg.max_conn] pred_conns.append(pred) if i % cfg.log_config.interval == 0: if dataset.ignore_label: logger.info('[Test] Iter {}/{}'.format(i, size)) else: logger.info('[Test] Iter {}/{}: Loss {:.4f}'.format( i, size, loss)) else: raise NotImplementedError if not dataset.ignore_label: avg_loss = sum(losses) / len(losses) logger.info('[Test] Overall Loss {:.4f}'.format(avg_loss)) if not cfg.regressor: avg_acc = sum(accs) / len(accs) logger.info('[Test] Overall Accuracy {:.4f}'.format(avg_acc)) if size > 0: logger.info('max val: mean({:.2f}), max({:.2f}), min({:.2f})'.format( sum(max_lst) / size, max(max_lst), min(max_lst))) multi_max_size = len(multi_max) if multi_max_size > 0: logger.info('multi-max({:.2f}): mean({:.1f}), max({}), min({})'.format( 1. * multi_max_size / size, sum(multi_max) / multi_max_size, max(multi_max), min(multi_max))) return np.array(pred_conns)
def main(): args = parse_args() assert args.out or args.eval or args.format_only or args.show \ or args.show_dir, \ ('Please specify at least one operation (save/eval/format/show the ' 'results / save the results) with the argument "--out", "--eval"' ', "--format-only", "--show" or "--show-dir"') if args.eval and args.format_only: raise ValueError('--eval and --format_only cannot be both specified') if args.out is not None and not args.out.endswith(('.pkl', '.pickle')): raise ValueError('The output file must be a pkl file.') cfg = Config.fromfile(args.config) if args.cfg_options is not None: cfg.merge_from_dict(args.cfg_options) # import modules from string list. if cfg.get('custom_imports', None): from mmcv.utils import import_modules_from_strings import_modules_from_strings(**cfg['custom_imports']) # set cudnn_benchmark if cfg.get('cudnn_benchmark', False): torch.backends.cudnn.benchmark = True cfg.model.pretrained = None if cfg.model.get('neck'): if isinstance(cfg.model.neck, list): for neck_cfg in cfg.model.neck: if neck_cfg.get('rfp_backbone'): if neck_cfg.rfp_backbone.get('pretrained'): neck_cfg.rfp_backbone.pretrained = None elif cfg.model.neck.get('rfp_backbone'): if cfg.model.neck.rfp_backbone.get('pretrained'): cfg.model.neck.rfp_backbone.pretrained = None # in case the test dataset is concatenated if isinstance(cfg.data.test, dict): cfg.data.test.test_mode = True elif isinstance(cfg.data.test, list): for ds_cfg in cfg.data.test: ds_cfg.test_mode = True # init distributed env first, since logger depends on the dist info. if args.launcher == 'none': distributed = False else: distributed = True init_dist(args.launcher, **cfg.dist_params) # build the dataloader samples_per_gpu = cfg.data.test.pop('samples_per_gpu', 1) if samples_per_gpu > 1: # Replace 'ImageToTensor' to 'DefaultFormatBundle' cfg.data.test.pipeline = replace_ImageToTensor(cfg.data.test.pipeline) dataset = build_dataset(cfg.data.test) data_loader = build_dataloader( dataset, samples_per_gpu=samples_per_gpu, workers_per_gpu=cfg.data.workers_per_gpu, dist=distributed, shuffle=False) # build the model and load checkpoint model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg) fp16_cfg = cfg.get('fp16', None) if fp16_cfg is not None: wrap_fp16_model(model) checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu') if args.fuse_conv_bn: model = fuse_conv_bn(model) # old versions did not save class info in checkpoints, this walkaround is # for backward compatibility if 'CLASSES' in checkpoint['meta']: model.CLASSES = checkpoint['meta']['CLASSES'] else: model.CLASSES = dataset.CLASSES if not distributed: model = MMDataParallel(model, device_ids=[0]) outputs = single_gpu_test(model, data_loader, args.show, args.show_dir, args.show_score_thr) else: model = MMDistributedDataParallel( model.cuda(), device_ids=[torch.cuda.current_device()], broadcast_buffers=False) outputs = multi_gpu_test(model, data_loader, args.tmpdir, args.gpu_collect) rank, _ = get_dist_info() if rank == 0: if args.out: print(f'\nwriting results to {args.out}') mmcv.dump(outputs, args.out) kwargs = {} if args.eval_options is None else args.eval_options if args.format_only: dataset.format_results(outputs, **kwargs) if args.eval: eval_kwargs = cfg.get('evaluation', {}).copy() # hard-code way to remove EvalHook args for key in ['interval', 'tmpdir', 'start', 'gpu_collect']: eval_kwargs.pop(key, None) eval_kwargs.update(dict(metric=args.eval, **kwargs)) print(dataset.evaluate(outputs, **eval_kwargs))
def test_cluster_det(model, cfg, logger): if cfg.load_from: logger.info('load pretrained model from: {}'.format(cfg.load_from)) load_checkpoint(model, cfg.load_from, strict=True, logger=logger) for k, v in cfg.model['kwargs'].items(): setattr(cfg.test_data, k, v) setattr(cfg.test_data, 'det_label', cfg.det_label) dataset = build_dataset(cfg.test_data) processor = build_processor(cfg.stage) losses = [] pred_scores = [] if cfg.gpus == 1: data_loader = build_dataloader(dataset, processor, cfg.test_batch_size_per_gpu, cfg.workers_per_gpu, train=False) model = MMDataParallel(model, device_ids=range(cfg.gpus)) if cfg.cuda: model.cuda() model.eval() for i, data in enumerate(data_loader): with torch.no_grad(): output, loss = model(data, return_loss=True) losses += [loss.item()] if i % cfg.log_config.interval == 0: if dataset.ignore_label: logger.info('[Test] Iter {}/{}'.format( i, len(data_loader))) else: logger.info('[Test] Iter {}/{}: Loss {:.4f}'.format( i, len(data_loader), loss)) if cfg.save_output: output = output.view(-1) prob = output.data.cpu().numpy() pred_scores.append(prob) else: raise NotImplementedError if not dataset.ignore_label: avg_loss = sum(losses) / len(losses) logger.info('[Test] Overall Loss {:.4f}'.format(avg_loss)) # save predicted scores if cfg.save_output: if cfg.load_from: fn = os.path.basename(cfg.load_from) else: fn = 'random' opath = os.path.join(cfg.work_dir, fn[:fn.rfind('.pth')] + '.npz') meta = { 'tot_inst_num': dataset.inst_num, 'proposal_folders': cfg.test_data.proposal_folders, } print('dump pred_score to {}'.format(opath)) pred_scores = np.concatenate(pred_scores).ravel() np.savez_compressed(opath, data=pred_scores, meta=meta) # de-overlap proposals = [fn_node for fn_node, _ in dataset.lst] pred_labels = deoverlap(pred_scores, proposals, dataset.inst_num, cfg.th_pos, cfg.th_iou) # save predicted labels if cfg.save_output: ofn_meta = os.path.join(cfg.work_dir, 'pred_labels.txt') print('save predicted labels to {}'.format(ofn_meta)) pred_idx2lb = list2dict(pred_labels, ignore_value=-1) write_meta(ofn_meta, pred_idx2lb, inst_num=dataset.inst_num) # evaluation if not dataset.ignore_label: print('==> evaluation') gt_labels = dataset.labels for metric in cfg.metrics: evaluate(gt_labels, pred_labels, metric)