def main(): args = parse_args() cfg = Config.fromfile(args.config) # set cudnn_benchmark if cfg.get('cudnn_benchmark', False): torch.backends.cudnn.benchmark = True cfg.model.backbone.pretrained = None cfg.data.test.test_mode = True # 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=False, shuffle=False) # build the model and load checkpoint model = build_model( cfg.model, train_cfg=None, test_cfg=cfg.get('test_cfg')) fp16_cfg = cfg.get('fp16', None) if fp16_cfg is not None: wrap_fp16_model(model) if args.fuse_conv_bn: model = fuse_conv_bn(model) model = MMDataParallel(model, device_ids=[0]) model.eval() # the first several iterations may be very slow so skip them num_warmup = 5 pure_inf_time = 0 # benchmark with 2000 video and take the average for i, data in enumerate(data_loader): torch.cuda.synchronize() start_time = time.perf_counter() with torch.no_grad(): model(return_loss=False, **data) torch.cuda.synchronize() elapsed = time.perf_counter() - start_time if i >= num_warmup: pure_inf_time += elapsed if (i + 1) % args.log_interval == 0: fps = (i + 1 - num_warmup) / pure_inf_time print( f'Done video [{i + 1:<3}/ 2000], fps: {fps:.1f} video / s') if (i + 1) == 200: pure_inf_time += elapsed fps = (i + 1 - num_warmup) / pure_inf_time print(f'Overall fps: {fps:.1f} video / s') break
def main(args): # load config 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) 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)) print(f'Test datasets:\n{str(dataset)}') # build the dataloader data_loader = build_dataloader( dataset, videos_per_gpu=20, workers_per_gpu=cfg.data.workers_per_gpu, dist=False, shuffle=False ) # collect results mean_data, std_data = collect_stat(data_loader) # filter data mean_data, std_data = filter_stat(mean_data, std_data, min_value=1.0) # dump stat dump_stat(mean_data, std_data, args.out)
def get_phase_dataset(self, phase, modality_sampler): """ Get the dataset for the specific phase. """ rgb_mode_config = getattr(Config().data.multi_modal_configs.rgb, phase) flow_mode_config = getattr(Config().data.multi_modal_configs.flow, phase) audio_feature_mode_config = getattr( Config().data.multi_modal_configs.audio_feature, phase) rgb_mode_config = self.correct_current_config( loaded_plato_config=rgb_mode_config, mode=phase, modality_name="rgb") flow_mode_config = self.correct_current_config( loaded_plato_config=flow_mode_config, mode=phase, modality_name="flow") audio_feature_mode_config = self.correct_current_config( loaded_plato_config=audio_feature_mode_config, mode=phase, modality_name="audio_feature") # build a RawframeDataset rgb_mode_dataset = build_dataset(rgb_mode_config) flow_mode_dataset = build_dataset(flow_mode_config) audio_feature_mode_dataset = build_dataset(audio_feature_mode_config) multi_modal_mode_data = { "rgb": rgb_mode_dataset, "flow": flow_mode_dataset, "audio_feature": audio_feature_mode_dataset } multi_modal_mode_info = { "rgb": rgb_mode_config["ann_file"], "flow": flow_mode_config["ann_file"], "audio_feature": audio_feature_mode_config["ann_file"], "categories": self.categoty_anno_file_path } gym_mode_dataset = GymDataset( multimodal_data_holder=multi_modal_mode_data, phase="train", phase_info=multi_modal_mode_info, modality_sampler=modality_sampler) return gym_mode_dataset
def main(args): assert args.model.endswith('.xml') # load config 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 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 eval_config, 'Please specify at eval operation with the argument "--eval"' # build the dataset dataset = build_dataset(cfg.data, 'test', dict(test_mode=True)) assert dataset.num_datasets == 1 if cfg.get('classes'): dataset = dataset.filter(cfg.classes) 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=False, shuffle=False ) # build class mapping between model.classes and dataset.classes assert cfg.get('model_classes') is not None model_classes = {k: v for k, v in enumerate(cfg.model_classes)} class_map = build_class_map(dataset.class_maps[0], model_classes) # load model ie_core = load_ie_core() model = ActionRecognizer(args.model, ie_core, class_map) # collect results outputs = collect_results(model, data_loader) # get metrics 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 setup(self): print("loading dataset ..."); self.system_dict["local"]["datasets"] = [build_dataset(self.system_dict["local"]["cfg"].data.train)] print("loading_model ..."); self.system_dict["local"]["model"] = build_model(self.system_dict["local"]["cfg"].model, train_cfg=self.system_dict["local"]["cfg"].train_cfg, test_cfg=self.system_dict["local"]["cfg"].test_cfg) print("creating workspace directory ..."); mmcv.mkdir_or_exist(osp.abspath(self.system_dict["local"]["cfg"].work_dir)) print("Done");
def _update_long_cycle(self, runner): """Before every epoch, check if long cycle shape should change. If it should, change the pipelines accordingly. change dataloader and model's subbn3d(split_bn) """ base_b, base_t, base_s = self._get_schedule(runner.epoch) # rebuild dataset from mmaction.datasets import build_dataset resize_list = [] for trans in self.cfg.data.train.pipeline: if trans['type'] == 'SampleFrames': curr_t = trans['clip_len'] trans['clip_len'] = base_t trans['frame_interval'] = (curr_t * trans['frame_interval']) / base_t elif trans['type'] == 'Resize': resize_list.append(trans) resize_list[-1]['scale'] = _ntuple(2)(base_s) ds = build_dataset(self.cfg.data.train) from mmaction.datasets import build_dataloader dataloader = build_dataloader( ds, self.data_cfg.videos_per_gpu * base_b, self.data_cfg.workers_per_gpu, dist=True, num_gpus=len(self.cfg.gpu_ids), drop_last=True, seed=self.cfg.get('seed', None), ) runner.data_loader = dataloader self.logger.info('Rebuild runner.data_loader') # the self._max_epochs is changed, therefore update here runner._max_iters = runner._max_epochs * len(runner.data_loader) # rebuild all the sub_batch_bn layers num_modifies = modify_subbn3d_num_splits(self.logger, runner.model, base_b) self.logger.info(f'{num_modifies} subbns modified to {base_b}.')
def main(): parser = argparse.ArgumentParser(description='Benchmark dataloading') parser.add_argument('config', help='train config file path') args = parser.parse_args() cfg = Config.fromfile(args.config) # init logger before other steps logger = get_root_logger() logger.info(f'MMAction2 Version: {__version__}') logger.info(f'Config: {cfg.text}') # create bench data list ann_file_bench = 'benchlist.txt' if not os.path.exists(ann_file_bench): with open(cfg.ann_file_train) as f: lines = f.readlines()[:256] with open(ann_file_bench, 'w') as f1: f1.writelines(lines) cfg.data.train.ann_file = ann_file_bench dataset = build_dataset(cfg.data.train) data_loader = build_dataloader(dataset, videos_per_gpu=cfg.data.videos_per_gpu, workers_per_gpu=0, num_gpus=1, dist=False) # Start progress bar after first 5 batches prog_bar = mmcv.ProgressBar(len(dataset) - 5 * cfg.data.videos_per_gpu, start=False) for i, data in enumerate(data_loader): if i == 5: prog_bar.start() for img in data['imgs']: if i < 5: continue prog_bar.update()
def main(): args = parse_args() cfg = Config.fromfile(args.config) assert args.eval is not None if args.cfg_options is not None: cfg.merge_from_dict(args.cfg_options) cfg.data.test.test_mode = True dataset = build_dataset(cfg.data.test) outputs = mmcv.load(args.results) kwargs = {} if args.eval_options is None else args.eval_options eval_kwargs = cfg.get('evaluation', {}).copy() # hard-code way to remove EpochEvalHook args for key in [ 'interval', 'tmpdir', 'start', 'gpu_collect', 'save_best', 'rule', 'key_indicator' ]: eval_kwargs.pop(key, None) eval_kwargs.update(dict(metrics=args.eval, **kwargs)) print(dataset.evaluate(outputs, **eval_kwargs))
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) # map lable from txt to csv file df = pd.read_csv('/data2/phap/datasets/dataset3_test.txt', header=None) df.columns = ['full_name'] df['file_name'] = df['full_name'].apply(lambda x: x.rsplit(' ')[0]) df['true_label'] = df['full_name'].apply(lambda x: x.rsplit(' ')[-1]) # build the model and load checkpoint model = build_model(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) 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) # convert softmax output to one hot pred_arr = [] for i in outputs: pred = np.argmax(i) pred_arr.append(pred) # import output into csv df['pred_label_orig'] = outputs df['pred_label'] = pred_arr # save csv file df.to_csv('dataset3_test_pred_w_rwf_model.csv') print('\nSuccess, csv file saved') 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(): print(f'{name}: {val:.04f}')
opencv='OpenCV', pyav='PyAV') # read config file cfg = Config.fromfile(args.config) cfg.merge_from_dict(args.cfg_options) # build dataset dataset_type = cfg.data[args.split].type assert dataset_type == 'VideoDataset' cfg.data[args.split].pipeline = [ dict(type=decoder_to_pipeline_prefix[args.decoder] + 'Init'), dict(type='RandomSampleFrames'), dict(type=decoder_to_pipeline_prefix[args.decoder] + 'Decode') ] dataset = build_dataset(cfg.data[args.split], dict(test_mode=(args.split != 'train'))) # prepare for checking if os.path.exists(args.output_file): # remove exsiting output file os.remove(args.output_file) pool = Pool(args.num_processes) lock = Manager().Lock() worker_fn = partial(_do_check_videos, lock, dataset, args.output_file) ids = range(len(dataset)) # start checking prog_bar = mmcv.ProgressBar(len(dataset)) for _ in pool.imap_unordered(worker_fn, ids): prog_bar.update() pool.close()
def main(): # parse arguments args = parse_args() # load config cfg = 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) # init distributed env first, since logger depends on the dist info. distributed = args.launcher != 'none' if distributed: init_dist(args.launcher, **cfg.dist_params) # create work_dir mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir)) # init logger before other steps timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime()) log_file = osp.join(cfg.work_dir, f'{timestamp}.log') logger = get_root_logger(log_file=log_file, log_level=cfg.log_level) # init the meta dict to record some important information such as # environment info and seed, which will be logged meta = dict() # log env info env_info_dict = collect_env() env_info = '\n'.join([f'{k}: {v}' for k, v in env_info_dict.items()]) dash_line = '-' * 60 + '\n' logger.info('Environment info:\n' + dash_line + env_info + '\n' + dash_line) meta['env_info'] = env_info # log some basic info logger.info(f'Distributed training: {distributed}') logger.info(f'Config: {cfg.text}') if cfg.get('nncf_config'): check_nncf_is_enabled() logger.info('NNCF config: {}'.format(cfg.nncf_config)) meta.update(get_nncf_metadata()) # set random seeds cfg.seed = args.seed meta['seed'] = args.seed if cfg.get('seed'): logger.info(f'Set random seed to {cfg.seed}, deterministic: {args.deterministic}') set_random_seed(cfg.seed, deterministic=args.deterministic) # build datasets datasets = [build_dataset(cfg.data, 'train', dict(logger=logger))] logger.info(f'Train datasets:\n{str(datasets[0])}') if len(cfg.workflow) == 2: if not args.no_validate: warnings.warn('val workflow is duplicated with `--validate`, ' 'it is recommended to use `--validate`. see ' 'https://github.com/open-mmlab/mmaction2/pull/123') datasets.append(build_dataset(copy.deepcopy(cfg.data), 'val', dict(logger=logger))) logger.info(f'Val datasets:\n{str(datasets[-1])}') # filter dataset labels if cfg.get('classes'): datasets = [dataset.filter(cfg.classes) for dataset in datasets] # build model model = build_model( cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg, class_sizes=datasets[0].class_sizes, class_maps=datasets[0].class_maps ) # define ignore layers ignore_prefixes = [] if hasattr(cfg, 'reset_layer_prefixes') and isinstance(cfg.reset_layer_prefixes, (list, tuple)): ignore_prefixes += cfg.reset_layer_prefixes ignore_suffixes = ['num_batches_tracked'] if hasattr(cfg, 'reset_layer_suffixes') and isinstance(cfg.reset_layer_suffixes, (list, tuple)): ignore_suffixes += cfg.reset_layer_suffixes # train model train_model( model, datasets, cfg, distributed=distributed, validate=(not args.no_validate), timestamp=timestamp, meta=meta, ignore_prefixes=tuple(ignore_prefixes), ignore_suffixes=tuple(ignore_suffixes) )
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) if cfg.model['test_cfg'] is None: cfg.model['test_cfg'] = dict(feature_extraction=True) else: cfg.model['test_cfg']['feature_extraction'] = True # Load output_config from cfg output_config = cfg.get('output_config', {}) if args.out: # Overwrite output_config from args.out output_config = Config._merge_a_into_b(dict(out=args.out), output_config) assert output_config, 'Please specify output filename with --out.' dataset_type = cfg.data.test.type if output_config.get('out', None): if 'output_format' in output_config: # ugly workround to make recognition and localization the same warnings.warn( 'Skip checking `output_format` in localization task.') else: 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 dataset_type == 'VideoDataset' assert suffix[1:] 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 cfg.data.test.data_prefix = args.video_root # 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) rank, _ = get_dist_info() size = 256 fname_tensor = torch.zeros(size, dtype=torch.uint8).cuda() if rank == 0: videos = open(args.video_list).readlines() videos = [x.strip() for x in videos] timestamp = datetime.now().strftime('%Y%m%d_%H%M%S') fake_anno = f'fake_anno_{timestamp}.txt' with open(fake_anno, 'w') as fout: lines = [x + ' 0' for x in videos] fout.write('\n'.join(lines)) fname_tensor = text2tensor(fake_anno, size).cuda() if distributed: dist.broadcast(fname_tensor.cuda(), src=0) fname = tensor2text(fname_tensor) cfg.data.test.ann_file = fname # The flag is used to register module's hooks cfg.setdefault('module_hooks', []) # 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', 1), workers_per_gpu=cfg.data.get( 'workers_per_gpu', 1), dist=distributed, shuffle=False) dataloader_setting = dict(dataloader_setting, **cfg.data.get('test_dataloader', {})) data_loader = build_dataloader(dataset, **dataloader_setting) outputs = inference_pytorch(args, cfg, distributed, data_loader) 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) # remove the temporary file os.remove(fake_anno)
cfg.gpu_ids = range(1) # We can initialize the logger for training and have a look # at the final config used for training print(f'Config:\n{cfg.pretty_text}') import os.path as osp from mmaction.datasets import build_dataset from mmaction.models import build_model from mmaction.apis import train_model import mmcv # Build the dataset datasets = [build_dataset(cfg.data.train)] # Build the recognizer model = build_model(cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg) # Create work_dir mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir)) train_model(model, datasets, cfg, distributed=False, validate=True) from mmaction.apis import single_gpu_test from mmaction.datasets import build_dataloader from mmcv.parallel import MMDataParallel # Build a test dataloader dataset = build_dataset(cfg.data.test, dict(test_mode=True)) data_loader = build_dataloader(dataset,
def main(): args = parse_args() cfg = Config.fromfile(args.config) cfg.merge_from_dict(args.cfg_options) # set cudnn_benchmark if cfg.get('cudnn_benchmark', False): torch.backends.cudnn.benchmark = True # work_dir is determined in this priority: # CLI > config file > default (base filename) if args.work_dir is not None: # update configs according to CLI args if args.work_dir is not None cfg.work_dir = args.work_dir elif cfg.get('work_dir', None) is None: # use config filename as default work_dir if cfg.work_dir is None cfg.work_dir = osp.join('./work_dirs', osp.splitext(osp.basename(args.config))[0]) if args.resume_from is not None: cfg.resume_from = args.resume_from if args.gpu_ids is not None: cfg.gpu_ids = args.gpu_ids else: cfg.gpu_ids = range(1) if args.gpus is None else range(args.gpus) # 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) # The flag is used to determine whether it is omnisource training cfg.setdefault('omnisource', False) # The flag is used to register module's hooks cfg.setdefault('module_hooks', []) # create work_dir mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir)) # dump config cfg.dump(osp.join(cfg.work_dir, osp.basename(args.config))) # init logger before other steps timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime()) log_file = osp.join(cfg.work_dir, f'{timestamp}.log') logger = get_root_logger(log_file=log_file, log_level=cfg.log_level) # init the meta dict to record some important information such as # environment info and seed, which will be logged meta = dict() # log env info env_info_dict = collect_env() env_info = '\n'.join([f'{k}: {v}' for k, v in env_info_dict.items()]) dash_line = '-' * 60 + '\n' logger.info('Environment info:\n' + dash_line + env_info + '\n' + dash_line) meta['env_info'] = env_info # log some basic info logger.info(f'Distributed training: {distributed}') logger.info(f'Config: {cfg.text}') # set random seeds if args.seed is not None: logger.info(f'Set random seed to {args.seed}, ' f'deterministic: {args.deterministic}') set_random_seed(args.seed, deterministic=args.deterministic) cfg.seed = args.seed meta['seed'] = args.seed meta['config_name'] = osp.basename(args.config) meta['work_dir'] = osp.basename(cfg.work_dir.rstrip('/\\')) model = build_model(cfg.model, train_cfg=cfg.get('train_cfg'), test_cfg=cfg.get('test_cfg')) register_module_hooks(model.backbone, cfg.module_hooks) if cfg.omnisource: # If omnisource flag is set, cfg.data.train should be a list assert type(cfg.data.train) is list datasets = [build_dataset(dataset) for dataset in cfg.data.train] else: datasets = [build_dataset(cfg.data.train)] if len(cfg.workflow) == 2: # For simplicity, omnisource is not compatiable with val workflow, # we recommend you to use `--validate` assert not cfg.omnisource if args.validate: warnings.warn('val workflow is duplicated with `--validate`, ' 'it is recommended to use `--validate`. see ' 'https://github.com/open-mmlab/mmaction2/pull/123') val_dataset = copy.deepcopy(cfg.data.val) datasets.append(build_dataset(val_dataset)) if cfg.checkpoint_config is not None: # save mmaction version, config file content and class names in # checkpoints as meta data cfg.checkpoint_config.meta = dict(mmaction_version=__version__ + get_git_hash(digits=7), config=cfg.text) train_model(model, datasets, cfg, distributed=distributed, validate=args.validate, timestamp=timestamp, meta=meta)
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) if args.pred_result_path_1: model1_preds = np.load(args.pred_result_path_1, allow_pickle=True) print('Evaluating {} accuracy:'.format(args.pred_result_path_1)) eval_res = dataset.evaluate(model1_preds, **eval_config) if args.pred_result_path_2: model2_preds = np.load(args.pred_result_path_2, allow_pickle=True) print('Evaluating {} accuracy:'.format(args.pred_result_path_2)) eval_res = dataset.evaluate(model2_preds, **eval_config) if args.pred_result_path_3: model3_preds = np.load(args.pred_result_path_3, allow_pickle=True) print('Evaluating {} accuracy:'.format(args.pred_result_path_3)) eval_res = dataset.evaluate(model3_preds, **eval_config) if args.pred_result_path_3: all_preds = np.stack([model1_preds, model2_preds, model3_preds]) else: all_preds = np.stack([model1_preds, model2_preds]) ensemble_preds = np.mean(all_preds, axis=0).tolist() 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: print('Evaluating an ensemble accuracy:') eval_res = dataset.evaluate(ensemble_preds, **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) 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) # nncf model wrapper if is_checkpoint_nncf(args.checkpoint) and not cfg.get('nncf_config'): # reading NNCF config from checkpoint nncf_part = get_nncf_config_from_meta(args.checkpoint) for k, v in nncf_part.items(): cfg[k] = v if cfg.get('nncf_config'): check_nncf_is_enabled() if not is_checkpoint_nncf(args.checkpoint): raise RuntimeError( 'Trying to make testing with NNCF compression a model snapshot that was NOT trained with NNCF' ) cfg.load_from = args.checkpoint cfg.resume_from = None if torch.cuda.is_available(): model = model.cuda() _, model = wrap_nncf_model(model, cfg, None, get_fake_input) else: 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(): if 'invalid_info' in name: continue if isinstance(val, float): print(f'{name}: {val:.04f}') elif isinstance(val, str): print(f'{name}:\n{val}') else: print(f'{name}: {val}') invalid_info = { name: val for name, val in eval_res.items() if 'invalid_info' in name } if len(invalid_info) > 0: assert args.out_invalid is not None and args.out_invalid != '' if os.path.exists(args.out_invalid): shutil.rmtree(args.out_invalid) if not os.path.exists(args.out_invalid): os.makedirs(args.out_invalid) for name, invalid_record in invalid_info.items(): out_invalid_dir = os.path.join(args.out_invalid, name) item_gen = zip(invalid_record['ids'], invalid_record['conf'], invalid_record['pred']) for invalid_idx, pred_conf, pred_label in item_gen: record_info = dataset.get_info(invalid_idx) gt_label = record_info['label'] if 'filename' in record_info: src_data_path = record_info['filename'] in_record_name, record_extension = os.path.basename( src_data_path).split('.') out_record_name = f'{in_record_name}_gt{gt_label}_pred{pred_label}_conf{pred_conf:.3f}' trg_data_path = os.path.join( out_invalid_dir, f'{out_record_name}.{record_extension}') shutil.copyfile(src_data_path, trg_data_path) else: src_data_path = record_info['frame_dir'] in_record_name = os.path.basename(src_data_path) out_record_name = f'{in_record_name}_gt{gt_label}_pred{pred_label}_conf{pred_conf:.3f}' trg_data_path = os.path.join( out_invalid_dir, out_record_name) os.makedirs(trg_data_path) start_frame_id = record_info[ 'clip_start'] + dataset.start_index end_frame_id = record_info[ 'clip_end'] + dataset.start_index for frame_id in range(start_frame_id, end_frame_id): img_name = f'{frame_id:05}.jpg' shutil.copyfile( os.path.join(src_data_path, img_name), os.path.join(trg_data_path, img_name))
type='Mp4Word2VecDataset', ann_file='/mnt/lustre/jinliwei/bili_full/bili_anno_vec_train', data_prefix='/mnt/lustre/share_data/bilibili/sensebee_datalist_32109', pipeline=[ dict(type='DecordInit', io_backend='memcached', **mc_cfg), dict(type='SampleFrames', clip_len=1, frame_interval=1, num_clips=8), dict(type='DecordDecode'), dict(type='Resize', scale=(-1, 256), lazy=True), dict(type='MultiScaleCrop', input_size=224, scales=(1, 0.875, 0.75, 0.66), random_crop=False, max_wh_scale_gap=1, lazy=True), dict(type='Resize', scale=(224, 224), keep_ratio=False, lazy=True), dict(type='Flip', flip_ratio=0.5, lazy=True), dict(type='Fuse'), dict(type='Normalize', **img_norm_cfg), dict(type='FormatShape', input_format='NCHW'), dict(type='LoadWord2Vec'), dict(type='Collect', keys=['imgs', 'word2vec', 'weight'], meta_keys=[]), dict(type='ToTensor', keys=['imgs', 'word2vec', 'weight']) ]) bili_full_dataset = build_dataset(cfg) for i in range(10): d = bili_full_dataset[i] print(d)
def main(): args = parse_args() cfg = Config.fromfile(args.config) # set cudnn_benchmark if cfg.get('cudnn_benchmark', False): torch.backends.cudnn.benchmark = True # work_dir is determined in this priority: # CLI > config file > default (base filename) if args.work_dir is not None: # update configs according to CLI args if args.work_dir is not None cfg.work_dir = args.work_dir elif cfg.get('work_dir', None) is None: # use config filename as default work_dir if cfg.work_dir is None cfg.work_dir = osp.join('./work_dirs', osp.splitext(osp.basename(args.config))[0]) if args.resume_from is not None: cfg.resume_from = args.resume_from if args.gpu_ids is not None: cfg.gpu_ids = args.gpu_ids else: cfg.gpu_ids = range(1) if args.gpus is None else range(args.gpus) # 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) # create work_dir mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir)) # dump config cfg.dump(osp.join(cfg.work_dir, osp.basename(args.config))) # init logger before other steps timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime()) log_file = osp.join(cfg.work_dir, f'{timestamp}.log') logger = get_root_logger(log_file=log_file, log_level=cfg.log_level) # init the meta dict to record some important information such as # environment info and seed, which will be logged meta = dict() # log env info env_info_dict = collect_env() env_info = '\n'.join([f'{k}: {v}' for k, v in env_info_dict.items()]) dash_line = '-' * 60 + '\n' logger.info('Environment info:\n' + dash_line + env_info + '\n' + dash_line) meta['env_info'] = env_info # log some basic info logger.info(f'Distributed training: {distributed}') logger.info(f'Config: {cfg.text}') # set random seeds if args.seed is not None: logger.info('Set random seed to {}, deterministic: {}'.format( args.seed, args.deterministic)) set_random_seed(args.seed, deterministic=args.deterministic) cfg.seed = args.seed meta['seed'] = args.seed model = build_model(cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg) datasets = [build_dataset(cfg.data.train)] if len(cfg.workflow) == 2: val_dataset = copy.deepcopy(cfg.data.val) datasets.append(build_dataset(val_dataset)) if cfg.checkpoint_config is not None: # save mmaction version, config file content and class names in # checkpoints as meta data cfg.checkpoint_config.meta = dict(mmaction_version=__version__, config=cfg.text) train_model(model, datasets, cfg, distributed=distributed, validate=args.validate, timestamp=timestamp, meta=meta)
# The original learning rate (LR) is set for 8-GPU training. # We divide it by 8 since we only use one GPU. cfg.optimizer.lr = cfg.optimizer.lr / 8 / 16 cfg.total_epochs = 15 # We can set the checkpoint saving interval to reduce the storage cost cfg.checkpoint_config.interval = 10 # We can set the log print interval to reduce the the times of printing log cfg.log_config.interval = 5 # Set seed thus the results are more reproducible cfg.seed = 0 set_random_seed(0, deterministic=False) cfg.gpu_ids = range(1) # We can initialize the logger for training and have a look # at the final config used for training print(f'Config:\n{cfg.pretty_text}') # Build the dataset datasets = [build_dataset(cfg.data.train)] # Build the recognizer model = build_model(cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg) # Create work_dir mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir)) train_model(model, datasets, cfg, distributed=False, validate=True)
def main(): parser = ArgumentParser() parser.add_argument('--config', '-c', type=str, required=True) parser.add_argument('--checkpoint', '-w', type=str, required=True) parser.add_argument('--dataset_name', '-n', type=str, required=True) parser.add_argument('--data_dir', '-d', type=str, required=True) parser.add_argument('--predictions', '-p', type=str, required=True) parser.add_argument('--movements', '-m', type=str, required=True) parser.add_argument('--keypoints', '-k', type=str, required=True) parser.add_argument('--out_annotation', '-o', type=str, required=True) args = parser.parse_args() assert exists(args.config) assert exists(args.weights) assert exists(args.data_dir) assert exists(args.predictions) assert exists(args.movements) assert exists(args.keypoints) assert args.dataset_name is not None and args.dataset_name != '' assert args.out_annotation is not None and args.out_annotation != '' cfg = Config.fromfile(args.config) cfg = update_config(cfg, args, trg_name=args.dataset_name) cfg = propagate_root_dir(cfg, args.data_dir) dataset = build_dataset(cfg.data, 'train', dict(test_mode=True)) data_pipeline = Compose(dataset.pipeline.transforms[1:]) print('{} dataset:\n'.format(args.mode) + str(dataset)) model = build_model(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg) load_checkpoint(model, args.checkpoint, strict=False) model = MMDataParallel(model, device_ids=[0]) model.eval() annotation_path = join(args.data_dir, cfg.data.train.sources[0], cfg.data.train.ann_file) records = load_annotation(annotation_path) predictions = load_distributed_data(args.predictions, parse_predictions_file, 'txt') movements = load_distributed_data(args.movements, parse_movements_file, 'txt') hand_kpts = load_distributed_data(args.keypoints, parse_kpts_file, 'json') print('Loaded records: {}'.format(len(records))) invalid_stat = dict() all_candidates = [] ignore_candidates = get_ignore_candidates(records, IGNORE_LABELS) all_candidates += ignore_candidates static_candidates, static_invalids = get_regular_candidates( records, predictions, movements, hand_kpts, cfg.data.output.length, False, STATIC_LABELS, NEGATIVE_LABEL, NO_MOTION_LABEL, min_score=0.9, min_length=4, max_distance=1) all_candidates += static_candidates invalid_stat = update_stat(invalid_stat, static_invalids) print('Static candidates: {}'.format(len(static_candidates))) if len(invalid_stat) > 0: print('Ignored records after static analysis:') for ignore_label, ignore_values in invalid_stat.items(): print(' - {}: {}'.format(ignore_label.replace('_', ' '), len(ignore_values))) dynamic_candidates, dynamic_invalids = get_regular_candidates( records, predictions, movements, hand_kpts, cfg.data.output.length, True, DYNAMIC_LABELS, NEGATIVE_LABEL, NO_MOTION_LABEL, min_score=0.9, min_length=4, max_distance=1) all_candidates += dynamic_candidates invalid_stat = update_stat(invalid_stat, dynamic_invalids) print('Dynamic candidates: {}'.format(len(dynamic_candidates))) if len(invalid_stat) > 0: print('Ignored records after dynamic analysis:') for ignore_label, ignore_values in invalid_stat.items(): print(' - {}: {}'.format(ignore_label.replace('_', ' '), len(ignore_values))) fixed_records, fix_stat = find_best_match(all_candidates, model, dataset, NEGATIVE_LABEL) invalid_stat = update_stat(invalid_stat, fix_stat) print('Final records: {}'.format(len(fixed_records))) if len(invalid_stat) > 0: print('Final ignored records:') for ignore_label, ignore_values in invalid_stat.items(): print(' - {}: {}'.format(ignore_label.replace('_', ' '), len(ignore_values))) for ignored_record in ignore_values: print(' - {}'.format(ignored_record.path)) dump_records(fixed_records, args.out_annotation) print('Fixed annotation has been stored at: {}'.format( args.out_annotation))
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 = 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: # 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) # create work_dir mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir)) # 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: 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(): parser = ArgumentParser() parser.add_argument('--config', type=str, required=True, help='Test config file path') parser.add_argument('--checkpoint', type=str, required=True, help='Checkpoint file') parser.add_argument('--data_dir', type=str, required=True, help='The dir with dataset') parser.add_argument('--out_dir', type=str, required=True, help='Output directory') parser.add_argument('--dataset', type=str, required=True, help='Dataset name') parser.add_argument('--gpus', default=1, type=int, help='GPU number used for annotating') parser.add_argument('--proc_per_gpu', default=2, type=int, help='Number of processes per GPU') parser.add_argument('--mode', choices=['train', 'val', 'test'], default='train') args = parser.parse_args() assert exists(args.config) assert exists(args.checkpoint) assert exists(args.data_dir) cfg = Config.fromfile(args.config) cfg = update_config(cfg, args, trg_name=args.dataset) cfg = propagate_root_dir(cfg, args.data_dir) dataset = build_dataset(cfg.data, args.mode, dict(test_mode=True)) data_pipeline = Compose(dataset.pipeline.transforms[1:]) print('{} dataset:\n'.format(args.mode) + str(dataset)) tasks = prepare_tasks(dataset, cfg.input_clip_length) print('Prepared tasks: {}'.format(sum([len(v) for v in tasks.values()]))) if not exists(args.out_dir): makedirs(args.out_dir) model = build_model(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg) load_checkpoint(model, args.checkpoint, strict=False) batch_size = 4 * cfg.data.videos_per_gpu if args.gpus == 1: model = MMDataParallel(model, device_ids=[0]) model.eval() process_tasks(tasks, dataset, model, args.out_dir, batch_size, cfg.input_clip_length, data_pipeline) else: raise NotImplementedError
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', {}) if args.out: # 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', {}) if args.eval: # Overwrite eval_config from args.eval eval_config = Config._merge_a_into_b(dict(metrics=args.eval), eval_config) if args.eval_options: # 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"') dataset_type = cfg.data.test.type if output_config.get('out', None): if 'output_format' in output_config: # ugly workround to make recognition and localization the same warnings.warn( 'Skip checking `output_format` in localization task.') else: out = output_config['out'] # make sure the dirname of the output path exists mmcv.mkdir_or_exist(osp.dirname(out)) _, suffix = osp.splitext(out) if dataset_type == 'AVADataset': assert suffix[1:] == 'csv', ('For AVADataset, the format of ' 'the output file should be csv') else: assert suffix[1:] 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 args.average_clips is not None: # You can set average_clips during testing, it will override the # original setting if cfg.model.get('test_cfg') is None and cfg.get('test_cfg') is None: cfg.model.setdefault('test_cfg', dict(average_clips=args.average_clips)) else: if cfg.model.get('test_cfg') is not None: cfg.model.test_cfg.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) # The flag is used to register module's hooks cfg.setdefault('module_hooks', []) # 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', 1), workers_per_gpu=cfg.data.get( 'workers_per_gpu', 1), dist=distributed, shuffle=False) dataloader_setting = dict(dataloader_setting, **cfg.data.get('test_dataloader', {})) data_loader = build_dataloader(dataset, **dataloader_setting) # remove redundant pretrain steps for testing turn_off_pretrained(cfg.model) # build the model and load checkpoint model = build_model(cfg.model, train_cfg=None, test_cfg=cfg.get('test_cfg')) if len(cfg.module_hooks) > 0: register_module_hooks(model, cfg.module_hooks) 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}') #import pdb #pdb.set_trace() print(out[-4:]) if out[-4:] == 'json': result_dict = {} for result in outputs: video_name = result['video_name'] result_dict[video_name] = result['proposal_list'] output_dict = { 'version': 'VERSION 1.3', 'results': result_dict, 'external_data': {} } mmcv.dump(output_dict, out) else: 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(): args = parse_args() if args.tensorrt and args.onnx: raise ValueError( 'Cannot set onnx mode and tensorrt mode at the same time.') cfg = Config.fromfile(args.config) cfg.merge_from_dict(args.cfg_options) # Load output_config from cfg output_config = cfg.get('output_config', {}) if args.out: # 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', {}) if args.eval: # Overwrite eval_config from args.eval eval_config = Config._merge_a_into_b(dict(metrics=args.eval), eval_config) if args.eval_options: # 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"') dataset_type = cfg.data.test.type if output_config.get('out', None): if 'output_format' in output_config: # ugly workround to make recognition and localization the same warnings.warn( 'Skip checking `output_format` in localization task.') else: out = output_config['out'] # make sure the dirname of the output path exists mmcv.mkdir_or_exist(osp.dirname(out)) _, suffix = osp.splitext(out) if dataset_type == 'AVADataset': assert suffix[1:] == 'csv', ('For AVADataset, the format of ' 'the output file should be csv') else: assert suffix[1:] 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 # 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) # The flag is used to register module's hooks cfg.setdefault('module_hooks', []) # 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', 1), workers_per_gpu=cfg.data.get( 'workers_per_gpu', 1), dist=distributed, shuffle=False) dataloader_setting = dict(dataloader_setting, **cfg.data.get('test_dataloader', {})) data_loader = build_dataloader(dataset, **dataloader_setting) if args.tensorrt: outputs = inference_tensorrt(args.checkpoint, distributed, data_loader, dataloader_setting['videos_per_gpu']) elif args.onnx: outputs = inference_onnx(args.checkpoint, distributed, data_loader, dataloader_setting['videos_per_gpu']) else: outputs = inference_pytorch(args, cfg, distributed, data_loader) 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}')