Python mmcv.runner.get_dist_info() Examples
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Example #1
Source File: test.py From Feature-Selective-Anchor-Free-Module-for-Single-Shot-Object-Detection with Apache License 2.0 | 6 votes |
def multi_gpu_test(model, data_loader, tmpdir=None): model.eval() results = [] dataset = data_loader.dataset rank, world_size = get_dist_info() if rank == 0: prog_bar = mmcv.ProgressBar(len(dataset)) for i, data in enumerate(data_loader): with torch.no_grad(): result = model(return_loss=False, rescale=True, **data) results.append(result) if rank == 0: batch_size = data['img'][0].size(0) for _ in range(batch_size * world_size): prog_bar.update() # collect results from all ranks results = collect_results(results, len(dataset), tmpdir) return results
Example #2
Source File: test.py From kaggle-kuzushiji-recognition with MIT License | 6 votes |
def multi_gpu_test(model, data_loader, tmpdir=None): model.eval() results = [] dataset = data_loader.dataset rank, world_size = get_dist_info() if rank == 0: prog_bar = mmcv.ProgressBar(len(dataset)) for i, data in enumerate(data_loader): with torch.no_grad(): result = model(return_loss=False, rescale=True, **data) results.append(result) if rank == 0: batch_size = data['img'][0].size(0) for _ in range(batch_size * world_size): prog_bar.update() # collect results from all ranks results = collect_results(results, len(dataset), tmpdir) return results
Example #3
Source File: test_robustness.py From Cascade-RPN with Apache License 2.0 | 6 votes |
def multi_gpu_test(model, data_loader, tmpdir=None): model.eval() results = [] dataset = data_loader.dataset rank, world_size = get_dist_info() if rank == 0: prog_bar = mmcv.ProgressBar(len(dataset)) for i, data in enumerate(data_loader): with torch.no_grad(): result = model(return_loss=False, rescale=True, **data) results.append(result) if rank == 0: batch_size = data['img'][0].size(0) for _ in range(batch_size * world_size): prog_bar.update() # collect results from all ranks results = collect_results(results, len(dataset), tmpdir) return results
Example #4
Source File: test.py From mmdetection-annotated with Apache License 2.0 | 6 votes |
def multi_gpu_test(model, data_loader, tmpdir=None): model.eval() results = [] dataset = data_loader.dataset rank, world_size = get_dist_info() if rank == 0: prog_bar = mmcv.ProgressBar(len(dataset)) for i, data in enumerate(data_loader): with torch.no_grad(): result = model(return_loss=False, rescale=True, **data) results.append(result) if rank == 0: batch_size = data['img'][0].size(0) for _ in range(batch_size * world_size): prog_bar.update() # collect results from all ranks results = collect_results(results, len(dataset), tmpdir) return results
Example #5
Source File: test.py From FoveaBox with Apache License 2.0 | 6 votes |
def multi_gpu_test(model, data_loader, tmpdir=None): model.eval() results = [] dataset = data_loader.dataset rank, world_size = get_dist_info() if rank == 0: prog_bar = mmcv.ProgressBar(len(dataset)) for i, data in enumerate(data_loader): with torch.no_grad(): result = model(return_loss=False, rescale=True, **data) results.append(result) if rank == 0: batch_size = data['img'][0].size(0) for _ in range(batch_size * world_size): prog_bar.update() # collect results from all ranks results = collect_results(results, len(dataset), tmpdir) return results
Example #6
Source File: test.py From Cascade-RPN with Apache License 2.0 | 6 votes |
def multi_gpu_test(model, data_loader, tmpdir=None): model.eval() results = [] dataset = data_loader.dataset rank, world_size = get_dist_info() if rank == 0: prog_bar = mmcv.ProgressBar(len(dataset)) for i, data in enumerate(data_loader): with torch.no_grad(): result = model(return_loss=False, rescale=True, **data) results.append(result) if rank == 0: batch_size = data['img'][0].size(0) for _ in range(batch_size * world_size): prog_bar.update() # collect results from all ranks results = collect_results(results, len(dataset), tmpdir) return results
Example #7
Source File: test_robustness.py From kaggle-kuzushiji-recognition with MIT License | 6 votes |
def multi_gpu_test(model, data_loader, tmpdir=None): model.eval() results = [] dataset = data_loader.dataset rank, world_size = get_dist_info() if rank == 0: prog_bar = mmcv.ProgressBar(len(dataset)) for i, data in enumerate(data_loader): with torch.no_grad(): result = model(return_loss=False, rescale=True, **data) results.append(result) if rank == 0: batch_size = data['img'][0].size(0) for _ in range(batch_size * world_size): prog_bar.update() # collect results from all ranks results = collect_results(results, len(dataset), tmpdir) return results
Example #8
Source File: test.py From GCNet with Apache License 2.0 | 6 votes |
def multi_gpu_test(model, data_loader, tmpdir=None): model.eval() results = [] dataset = data_loader.dataset rank, world_size = get_dist_info() if rank == 0: prog_bar = mmcv.ProgressBar(len(dataset)) for i, data in enumerate(data_loader): with torch.no_grad(): result = model(return_loss=False, rescale=True, **data) results.append(result) if rank == 0: batch_size = data['img'][0].size(0) for _ in range(batch_size * world_size): prog_bar.update() # collect results from all ranks results = collect_results(results, len(dataset), tmpdir) return results
Example #9
Source File: test.py From Grid-R-CNN with Apache License 2.0 | 6 votes |
def multi_gpu_test(model, data_loader, tmpdir=None): model.eval() results = [] dataset = data_loader.dataset rank, world_size = get_dist_info() if rank == 0: prog_bar = mmcv.ProgressBar(len(dataset)) for i, data in enumerate(data_loader): with torch.no_grad(): result = model(return_loss=False, rescale=True, **data) results.append(result) if rank == 0: batch_size = data['img'][0].size(0) for _ in range(batch_size * world_size): prog_bar.update() # collect results from all ranks results = collect_results(results, len(dataset), tmpdir) return results
Example #10
Source File: test_robustness.py From Feature-Selective-Anchor-Free-Module-for-Single-Shot-Object-Detection with Apache License 2.0 | 6 votes |
def multi_gpu_test(model, data_loader, tmpdir=None): model.eval() results = [] dataset = data_loader.dataset rank, world_size = get_dist_info() if rank == 0: prog_bar = mmcv.ProgressBar(len(dataset)) for i, data in enumerate(data_loader): with torch.no_grad(): result = model(return_loss=False, rescale=True, **data) results.append(result) if rank == 0: batch_size = data['img'][0].size(0) for _ in range(batch_size * world_size): prog_bar.update() # collect results from all ranks results = collect_results(results, len(dataset), tmpdir) return results
Example #11
Source File: test_robustness.py From FoveaBox with Apache License 2.0 | 6 votes |
def multi_gpu_test(model, data_loader, tmpdir=None): model.eval() results = [] dataset = data_loader.dataset rank, world_size = get_dist_info() if rank == 0: prog_bar = mmcv.ProgressBar(len(dataset)) for i, data in enumerate(data_loader): with torch.no_grad(): result = model(return_loss=False, rescale=True, **data) results.append(result) if rank == 0: batch_size = data['img'][0].size(0) for _ in range(batch_size * world_size): prog_bar.update() # collect results from all ranks results = collect_results(results, len(dataset), tmpdir) return results
Example #12
Source File: env.py From DenseMatchingBenchmark with MIT License | 6 votes |
def get_root_logger(save_dir, log_level=logging.INFO, filename="log.txt"): logger = logging.getLogger() if not logger.hasHandlers(): logging.basicConfig( format='%(asctime)s - %(levelname)s - %(message)s', level=log_level) rank, _ = get_dist_info() if rank != 0: logger.setLevel('ERROR') if save_dir: fh = logging.FileHandler(os.path.join(save_dir, filename)) fh.setLevel(log_level) formatter = logging.Formatter("%(asctime)s - %(levelname)s: %(message)s") fh.setFormatter(formatter) logger.addHandler(fh) if rank != 0: fh.setLevel('ERROR') return logger
Example #13
Source File: test.py From AerialDetection with Apache License 2.0 | 6 votes |
def multi_gpu_test(model, data_loader, tmpdir=None): model.eval() results = [] dataset = data_loader.dataset rank, world_size = get_dist_info() if rank == 0: prog_bar = mmcv.ProgressBar(len(dataset)) for i, data in enumerate(data_loader): with torch.no_grad(): result = model(return_loss=False, rescale=True, **data) results.append(result) if rank == 0: batch_size = data['img'][0].size(0) for _ in range(batch_size * world_size): prog_bar.update() # collect results from all ranks results = collect_results(results, len(dataset), tmpdir) return results
Example #14
Source File: test.py From Libra_R-CNN with Apache License 2.0 | 6 votes |
def multi_gpu_test(model, data_loader, tmpdir=None): model.eval() results = [] dataset = data_loader.dataset rank, world_size = get_dist_info() if rank == 0: prog_bar = mmcv.ProgressBar(len(dataset)) for i, data in enumerate(data_loader): with torch.no_grad(): result = model(return_loss=False, rescale=True, **data) results.append(result) if rank == 0: batch_size = data['img'][0].size(0) for _ in range(batch_size * world_size): prog_bar.update() # collect results from all ranks results = collect_results(results, len(dataset), tmpdir) return results
Example #15
Source File: test_robustness.py From AerialDetection with Apache License 2.0 | 6 votes |
def multi_gpu_test(model, data_loader, tmpdir=None): model.eval() results = [] dataset = data_loader.dataset rank, world_size = get_dist_info() if rank == 0: prog_bar = mmcv.ProgressBar(len(dataset)) for i, data in enumerate(data_loader): with torch.no_grad(): result = model(return_loss=False, rescale=True, **data) results.append(result) if rank == 0: batch_size = data['img'][0].size(0) for _ in range(batch_size * world_size): prog_bar.update() # collect results from all ranks results = collect_results(results, len(dataset), tmpdir) return results
Example #16
Source File: test.py From PolarMask with Apache License 2.0 | 6 votes |
def multi_gpu_test(model, data_loader, tmpdir=None): model.eval() results = [] dataset = data_loader.dataset rank, world_size = get_dist_info() if rank == 0: prog_bar = mmcv.ProgressBar(len(dataset)) for i, data in enumerate(data_loader): with torch.no_grad(): result = model(return_loss=False, rescale=True, **data) results.append(result) if rank == 0: batch_size = data['img'][0].size(0) for _ in range(batch_size * world_size): prog_bar.update() # collect results from all ranks results = collect_results(results, len(dataset), tmpdir) return results
Example #17
Source File: test.py From kaggle-imaterialist with MIT License | 6 votes |
def multi_gpu_test(model, data_loader, tmpdir=None): model.eval() results = [] dataset = data_loader.dataset rank, world_size = get_dist_info() if rank == 0: prog_bar = mmcv.ProgressBar(len(dataset)) for i, data in enumerate(data_loader): with torch.no_grad(): result = model(return_loss=False, rescale=True, **data) results.append(result) if rank == 0: batch_size = data['img'][0].size(0) for _ in range(batch_size * world_size): prog_bar.update() # collect results from all ranks results = collect_results(results, len(dataset), tmpdir) return results
Example #18
Source File: test_robustness.py From RDSNet with Apache License 2.0 | 6 votes |
def multi_gpu_test(model, data_loader, tmpdir=None): model.eval() results = [] dataset = data_loader.dataset rank, world_size = get_dist_info() if rank == 0: prog_bar = mmcv.ProgressBar(len(dataset)) for i, data in enumerate(data_loader): with torch.no_grad(): result = model(return_loss=False, rescale=True, **data) results.append(result) if rank == 0: batch_size = data['img'][0].size(0) for _ in range(batch_size * world_size): prog_bar.update() # collect results from all ranks results = collect_results(results, len(dataset), tmpdir) return results
Example #19
Source File: test_ensemble.py From kaggle-imaterialist with MIT License | 6 votes |
def multi_gpu_test(model, data_loader, tmpdir=None): model.eval() results = [] dataset = data_loader.dataset rank, world_size = get_dist_info() if rank == 0: prog_bar = mmcv.ProgressBar(len(dataset)) for i, data in enumerate(data_loader): with torch.no_grad(): result = model(return_loss=False, rescale=True, **data) results.append(result) if rank == 0: batch_size = data['img'][0].size(0) for _ in range(batch_size * world_size): prog_bar.update() # collect results from all ranks results = collect_results(results, len(dataset), tmpdir) return results
Example #20
Source File: test_robustness.py From IoU-Uniform-R-CNN with Apache License 2.0 | 6 votes |
def multi_gpu_test(model, data_loader, tmpdir=None): model.eval() results = [] dataset = data_loader.dataset rank, world_size = get_dist_info() if rank == 0: prog_bar = mmcv.ProgressBar(len(dataset)) for i, data in enumerate(data_loader): with torch.no_grad(): result = model(return_loss=False, rescale=True, **data) results.append(result) if rank == 0: batch_size = data['img'][0].size(0) for _ in range(batch_size * world_size): prog_bar.update() # collect results from all ranks results = collect_results(results, len(dataset), tmpdir) return results
Example #21
Source File: test.py From RDSNet with Apache License 2.0 | 6 votes |
def multi_gpu_test(model, data_loader, tmpdir=None): model.eval() results = [] dataset = data_loader.dataset rank, world_size = get_dist_info() if rank == 0: prog_bar = mmcv.ProgressBar(len(dataset)) for i, data in enumerate(data_loader): with torch.no_grad(): result = model(return_loss=False, rescale=True, **data) results.append(result) if rank == 0: batch_size = data['img'][0].size(0) for _ in range(batch_size * world_size): prog_bar.update() # collect results from all ranks results = collect_results(results, len(dataset), tmpdir) return results
Example #22
Source File: env.py From Libra_R-CNN with Apache License 2.0 | 5 votes |
def get_root_logger(log_level=logging.INFO): logger = logging.getLogger() if not logger.hasHandlers(): logging.basicConfig( format='%(asctime)s - %(levelname)s - %(message)s', level=log_level) rank, _ = get_dist_info() if rank != 0: logger.setLevel('ERROR') return logger
Example #23
Source File: test.py From IoU-Uniform-R-CNN with Apache License 2.0 | 5 votes |
def multi_gpu_test(model, data_loader, tmpdir=None, gpu_collect=False): """Test model with multiple gpus. This method tests model with multiple gpus and collects the results under two different modes: gpu and cpu modes. By setting 'gpu_collect=True' it encodes results to gpu tensors and use gpu communication for results collection. On cpu mode it saves the results on different gpus to 'tmpdir' and collects them by the rank 0 worker. Args: model (nn.Module): Model to be tested. data_loader (nn.Dataloader): Pytorch data loader. tmpdir (str): Path of directory to save the temporary results from different gpus under cpu mode. gpu_collect (bool): Option to use either gpu or cpu to collect results. Returns: list: The prediction results. """ model.eval() results = [] dataset = data_loader.dataset rank, world_size = get_dist_info() if rank == 0: prog_bar = mmcv.ProgressBar(len(dataset)) for i, data in enumerate(data_loader): with torch.no_grad(): result = model(return_loss=False, rescale=True, **data) results.append(result) if rank == 0: batch_size = data['img'][0].size(0) for _ in range(batch_size * world_size): prog_bar.update() # collect results from all ranks if gpu_collect: results = collect_results_gpu(results, len(dataset)) else: results = collect_results_cpu(results, len(dataset), tmpdir) return results
Example #24
Source File: build_loader.py From Reasoning-RCNN with Apache License 2.0 | 5 votes |
def build_dataloader(dataset, imgs_per_gpu, workers_per_gpu, num_gpus=1, dist=True, **kwargs): if dist: rank, world_size = get_dist_info() sampler = DistributedGroupSampler(dataset, imgs_per_gpu, world_size, rank) batch_size = imgs_per_gpu num_workers = workers_per_gpu else: if not kwargs.get('shuffle', True): sampler = None else: sampler = GroupSampler(dataset, imgs_per_gpu) batch_size = num_gpus * imgs_per_gpu num_workers = num_gpus * workers_per_gpu data_loader = DataLoader( dataset, batch_size=batch_size, sampler=sampler, num_workers=num_workers, collate_fn=partial(collate, samples_per_gpu=imgs_per_gpu), pin_memory=False, **kwargs) return data_loader
Example #25
Source File: env.py From Reasoning-RCNN with Apache License 2.0 | 5 votes |
def get_root_logger(log_level=logging.INFO): logger = logging.getLogger() if not logger.hasHandlers(): logging.basicConfig( format='%(asctime)s - %(levelname)s - %(message)s', level=log_level) rank, _ = get_dist_info() if rank != 0: logger.setLevel('ERROR') return logger
Example #26
Source File: test.py From Libra_R-CNN with Apache License 2.0 | 5 votes |
def collect_results(result_part, size, tmpdir=None): rank, world_size = get_dist_info() # create a tmp dir if it is not specified if tmpdir is None: MAX_LEN = 512 # 32 is whitespace dir_tensor = torch.full((MAX_LEN, ), 32, dtype=torch.uint8, device='cuda') if rank == 0: tmpdir = tempfile.mkdtemp() tmpdir = torch.tensor( bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda') dir_tensor[:len(tmpdir)] = tmpdir dist.broadcast(dir_tensor, 0) tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip() else: mmcv.mkdir_or_exist(tmpdir) # dump the part result to the dir mmcv.dump(result_part, osp.join(tmpdir, 'part_{}.pkl'.format(rank))) dist.barrier() # collect all parts if rank != 0: return None else: # load results of all parts from tmp dir part_list = [] for i in range(world_size): part_file = osp.join(tmpdir, 'part_{}.pkl'.format(i)) part_list.append(mmcv.load(part_file)) # sort the results ordered_results = [] for res in zip(*part_list): ordered_results.extend(list(res)) # the dataloader may pad some samples ordered_results = ordered_results[:size] # remove tmp dir shutil.rmtree(tmpdir) return ordered_results
Example #27
Source File: test_robustness.py From IoU-Uniform-R-CNN with Apache License 2.0 | 5 votes |
def collect_results(result_part, size, tmpdir=None): rank, world_size = get_dist_info() # create a tmp dir if it is not specified if tmpdir is None: MAX_LEN = 512 # 32 is whitespace dir_tensor = torch.full((MAX_LEN, ), 32, dtype=torch.uint8, device='cuda') if rank == 0: tmpdir = tempfile.mkdtemp() tmpdir = torch.tensor( bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda') dir_tensor[:len(tmpdir)] = tmpdir dist.broadcast(dir_tensor, 0) tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip() else: mmcv.mkdir_or_exist(tmpdir) # dump the part result to the dir mmcv.dump(result_part, osp.join(tmpdir, 'part_{}.pkl'.format(rank))) dist.barrier() # collect all parts if rank != 0: return None else: # load results of all parts from tmp dir part_list = [] for i in range(world_size): part_file = osp.join(tmpdir, 'part_{}.pkl'.format(i)) part_list.append(mmcv.load(part_file)) # sort the results ordered_results = [] for res in zip(*part_list): ordered_results.extend(list(res)) # the dataloader may pad some samples ordered_results = ordered_results[:size] # remove tmp dir shutil.rmtree(tmpdir) return ordered_results
Example #28
Source File: build_loader.py From IoU-Uniform-R-CNN with Apache License 2.0 | 5 votes |
def build_dataloader(dataset, imgs_per_gpu, workers_per_gpu, num_gpus=1, dist=True, shuffle=True, **kwargs): if dist: rank, world_size = get_dist_info() if shuffle: sampler = DistributedGroupSampler(dataset, imgs_per_gpu, world_size, rank) else: sampler = DistributedSampler( dataset, world_size, rank, shuffle=False) batch_size = imgs_per_gpu num_workers = workers_per_gpu else: sampler = GroupSampler(dataset, imgs_per_gpu) if shuffle else None batch_size = num_gpus * imgs_per_gpu num_workers = num_gpus * workers_per_gpu data_loader = DataLoader( dataset, batch_size=batch_size, sampler=sampler, num_workers=num_workers, collate_fn=partial(collate, samples_per_gpu=imgs_per_gpu), pin_memory=False, **kwargs) return data_loader
Example #29
Source File: test.py From IoU-Uniform-R-CNN with Apache License 2.0 | 5 votes |
def collect_results_gpu(result_part, size): rank, world_size = get_dist_info() # dump result part to tensor with pickle part_tensor = torch.tensor( bytearray(pickle.dumps(result_part)), dtype=torch.uint8, device='cuda') # gather all result part tensor shape shape_tensor = torch.tensor(part_tensor.shape, device='cuda') shape_list = [shape_tensor.clone() for _ in range(world_size)] dist.all_gather(shape_list, shape_tensor) # padding result part tensor to max length shape_max = torch.tensor(shape_list).max() part_send = torch.zeros(shape_max, dtype=torch.uint8, device='cuda') part_send[:shape_tensor[0]] = part_tensor part_recv_list = [ part_tensor.new_zeros(shape_max) for _ in range(world_size) ] # gather all result part dist.all_gather(part_recv_list, part_send) if rank == 0: part_list = [] for recv, shape in zip(part_recv_list, shape_list): part_list.append( pickle.loads(recv[:shape[0]].cpu().numpy().tobytes())) # sort the results ordered_results = [] for res in zip(*part_list): ordered_results.extend(list(res)) # the dataloader may pad some samples ordered_results = ordered_results[:size] return ordered_results
Example #30
Source File: build_loader.py From Cascade-RPN with Apache License 2.0 | 5 votes |
def build_dataloader(dataset, imgs_per_gpu, workers_per_gpu, num_gpus=1, dist=True, **kwargs): shuffle = kwargs.get('shuffle', True) if dist: rank, world_size = get_dist_info() if shuffle: sampler = DistributedGroupSampler(dataset, imgs_per_gpu, world_size, rank) else: sampler = DistributedSampler( dataset, world_size, rank, shuffle=False) batch_size = imgs_per_gpu num_workers = workers_per_gpu else: sampler = GroupSampler(dataset, imgs_per_gpu) if shuffle else None batch_size = num_gpus * imgs_per_gpu num_workers = num_gpus * workers_per_gpu data_loader = DataLoader( dataset, batch_size=batch_size, sampler=sampler, num_workers=num_workers, collate_fn=partial(collate, samples_per_gpu=imgs_per_gpu), pin_memory=False, **kwargs) return data_loader