# from mmcv import Config, DictAction # from mmdet3d.apis import train_model # from mmdet3d.utils import collect_env, get_root_logger # from mmdet3d.apis import init_model # from mmdet3d.datasets import build_dataset # from mmdet3d.models import build_model # Copyright (c) OpenMMLab. All rights reserved. # Modifications Copyright 2021 Amazon.com, Inc. or its affiliates. All Rights Reserved. from __future__ import division import argparse import boto3 import copy import mmcv import os import time import torch import warnings from mmcv import Config, DictAction from mmcv.runner import get_dist_info, init_dist from os import path as osp from mmdet import __version__ as mmdet_version from mmdet3d import __version__ as mmdet3d_version from mmdet3d.apis import train_model from mmdet3d.datasets import build_dataset from mmdet3d.models import build_model from mmdet3d.utils import collect_env, get_root_logger from mmdet.apis import set_random_seed from mmseg import __version__ as mmseg_version from tools.data_converter import kitti_converter, kitti_data_utils # Check Pytorch installation import torch, torchvision print('torch version:', torch.__version__, torch.cuda.is_available()) print('torchvision version:', torchvision.__version__) # Check mmdet3d installation import mmdet3d print('mmdet3d version:', mmdet3d.__version__) # Check mmcv installation from mmcv.ops import get_compiling_cuda_version, get_compiler_version print('cuda version:', get_compiling_cuda_version()) print('compiler information:', get_compiler_version()) os.system('echo ----------') os.system('nvidia-smi') os.system('echo ----------') os.environ['MASTER_PORT'] = '12345' os.environ['MASTER_ADDR'] = 'algo-1' try: os.environ['WORLD_SIZE'] = os.environ['OMPI_COMM_WORLD_SIZE'] os.environ['RANK'] = os.environ['OMPI_COMM_WORLD_RANK'] except: if "WORLD_SIZE" not in os.environ: os.environ['WORLD_SIZE'] = "1" if "RANK" not in os.environ: os.environ['RANK'] = "0" #export WORLD_SIZE=OMPI_COMM_WORLD_SIZE #export RANK=OMPI_COMM_WORLD_RANK #export MASTER_ADDR=algo1 #export MASTER_PORT=12345 os.system('echo train folder contents') os.system('ls /opt/ml/input/data/train') def parse_args(): parser = argparse.ArgumentParser(description='Train a detector') parser.add_argument('--config', help='train config file path') parser.add_argument('--work-dir', help='the dir to save logs and models') parser.add_argument('--batch-size', help='training batch size', default=1) parser.add_argument('--epochs', type=int, default=5) parser.add_argument('--distributed', type=int, default=0) parser.add_argument('--instance-count', type=int, default=1) parser.add_argument( '--resume-from', help='the checkpoint file to resume from') parser.add_argument( '--no-validate', action='store_true', help='whether not to evaluate the checkpoint during training') parser.add_argument('--load-path', help='path to load model from') group_gpus = parser.add_mutually_exclusive_group() group_gpus.add_argument( '--gpus', type=int, help='number of gpus to use ' '(only applicable to non-distributed training)') group_gpus.add_argument( '--gpu-ids', type=int, nargs='+', help='ids of gpus to use ' '(only applicable to non-distributed training)') parser.add_argument('--seed', type=int, default=0, help='random seed') parser.add_argument( '--deterministic', action='store_true', help='whether to set deterministic options for CUDNN backend.') parser.add_argument( '--options', nargs='+', action=DictAction, help='override some settings in the used config, the key-value pair ' 'in xxx=yyy format will be merged into config file (deprecate), ' 'change to --cfg-options instead.') parser.add_argument( '--cfg-options', nargs='+', action=DictAction, help='override some settings in the used config, the key-value pair ' 'in xxx=yyy format will be merged into config file. If the value to ' 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' 'Note that the quotation marks are necessary and that no white space ' 'is allowed.') parser.add_argument( '--launcher', choices=['none', 'pytorch', 'slurm', 'mpi'], default='none', help='job launcher') parser.add_argument('--local_rank', type=int, default=0) parser.add_argument( '--autoscale-lr', action='store_true', help='automatically scale lr with the number of gpus') args = parser.parse_args() if 'LOCAL_RANK' not in os.environ: os.environ['LOCAL_RANK'] = str(args.local_rank) if 'RANK' not in os.environ: os.environ['RANK'] = str(args.local_rank) if args.options and args.cfg_options: raise ValueError( '--options and --cfg-options cannot be both specified, ' '--options is deprecated in favor of --cfg-options') if args.options: warnings.warn('--options is deprecated in favor of --cfg-options') args.cfg_options = args.options return args def main(): args = parse_args() print('Rank:',os.environ['RANK']) # check /opt/ml/code contents os.system('ls /opt/ml/code') cfg = Config.fromfile(args.config) num_gpus = torch.cuda.device_count() # int(os.environ['SM_NUM_GPUS']) 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 if args.load_path is not None: cfg.load_from = args.load_path # work_dir is determined in this priority: CLI > segment in file > 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(num_gpus) #range(1) if args.gpus is None else range(args.gpus) if args.autoscale_lr: # apply the linear scaling rule (https://arxiv.org/abs/1706.02677) cfg.optimizer['lr'] = cfg.optimizer['lr'] * len(cfg.gpu_ids) / 8 # init distributed env first, since logger depends on the dist info. if args.launcher == 'none': distributed = False else: distributed = True _, world_size = get_dist_info() if 'WORLD_SIZE' not in os.environ: os.environ['WORLD_SIZE'] = str(world_size) init_dist(args.launcher, **cfg.dist_params) # re-set gpu_ids with distributed training mode cfg.gpu_ids = range(world_size) print('world size:',os.environ['WORLD_SIZE']) # 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 the 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') # specify logger name, if we still use 'mmdet', the output info will be # filtered and won't be saved in the log_file # TODO: ugly workaround to judge whether we are training det or seg model if cfg.model.type in ['EncoderDecoder3D']: logger_name = 'mmseg' else: logger_name = 'mmdet' logger = get_root_logger( log_file=log_file, log_level=cfg.log_level, name=logger_name) # 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 meta['config'] = cfg.pretty_text # log some basic info logger.info(f'Distributed training: {distributed}') logger.info(f'Config:\n{cfg.pretty_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['exp_name'] = osp.basename(args.config) cfg['data_root'] = '/opt/ml/input/data/train/camera_lidar_semantic_bboxes' cfg.data.train.dataset.pipeline[4].db_sampler.data_root = '/opt/ml/input/data/train/camera_lidar_semantic_bboxes' cfg.data.train.dataset.pipeline[4].db_sampler.info_path = '/opt/ml/input/data/train/camera_lidar_semantic_bboxes/a2d2_dbinfos_train.pkl' cfg.data.train.dataset.data_root = '/opt/ml/input/data/train/camera_lidar_semantic_bboxes' cfg.data.train.dataset.ann_file = '/opt/ml/input/data/train/camera_lidar_semantic_bboxes/a2d2_infos_train.pkl' cfg.data.test.data_root = '/opt/ml/input/data/train/camera_lidar_semantic_bboxes' cfg.data.test.ann_file = '/opt/ml/input/data/train/camera_lidar_semantic_bboxes/a2d2_infos_test.pkl' cfg.data.val.data_root = '/opt/ml/input/data/train/camera_lidar_semantic_bboxes' cfg.data.val.ann_file = '/opt/ml/input/data/train/camera_lidar_semantic_bboxes/a2d2_infos_test.pkl' cfg.db_sampler.data_root = '/opt/ml/input/data/train/camera_lidar_semantic_bboxes' cfg.db_sampler.info_path = '/opt/ml/input/data/train/camera_lidar_semantic_bboxes/a2d2_dbinfos_train.pkl' cfg.dataset_type = 'A2D2Dataset' # need to set this to get it to register cfg.data.train.dataset.type = 'A2D2Dataset' cfg.data.test.type = 'A2D2Dataset' cfg.data.val.type = 'A2D2Dataset' # need to add dbinfos cfg.runner.max_epochs = int(args.epochs) cfg.data.samples_per_gpu = int(args.batch_size) print('trying to build dataset') datasets = [build_dataset(cfg.data.train)] print('test dataloader', datasets[0][0]) model = build_model( cfg.model, train_cfg=cfg.get('train_cfg'), test_cfg=cfg.get('test_cfg')) model.init_weights() logger.info(f'Model:\n{model}') print('model', model) # os.system('ls /opt/ml/input/data/train') print('config:',cfg) if len(cfg.workflow) == 2: val_dataset = copy.deepcopy(cfg.data.val) # in case we use a dataset wrapper if 'dataset' in cfg.data.train: val_dataset.pipeline = cfg.data.train.dataset.pipeline else: val_dataset.pipeline = cfg.data.train.pipeline # set test_mode=False here in deep copied config # which do not affect AP/AR calculation later # refer to https://mmdetection3d.readthedocs.io/en/latest/tutorials/customize_runtime.html#customize-workflow # noqa val_dataset.test_mode = False datasets.append(build_dataset(val_dataset)) if cfg.checkpoint_config is not None: # save mmdet version, config file content and class names in # checkpoints as meta data cfg.checkpoint_config.meta = dict( mmdet_version=mmdet_version, mmseg_version=mmseg_version, mmdet3d_version=mmdet3d_version, config=cfg.pretty_text, CLASSES=datasets[0].CLASSES, PALETTE=datasets[0].PALETTE # for segmentors if hasattr(datasets[0], 'PALETTE') else None) # add an attribute for visualization convenience model.CLASSES = datasets[0].CLASSES train_model( model, datasets, cfg, distributed=distributed, validate=(not args.no_validate), timestamp=timestamp, meta=meta) if __name__ == '__main__': main()