Python detectron.utils.c2.get_nvidia_info() Examples

The following are 6 code examples of detectron.utils.c2.get_nvidia_info(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module detectron.utils.c2 , or try the search function .
Example #1
Source File: train_net.py    From KL-Loss with Apache License 2.0 5 votes vote down vote up 
def main():
    # Initialize C2
    workspace.GlobalInit(
        ['caffe2', '--caffe2_log_level=0', '--caffe2_gpu_memory_tracking=1']
    )
    # Set up logging and load config options
    logger = setup_logging(__name__)
    logging.getLogger('detectron.roi_data.loader').setLevel(logging.INFO)
    args = parse_args()
    logger.info('Called with args:')
    logger.info(args)
    if args.cfg_file is not None:
        merge_cfg_from_file(args.cfg_file)
    if args.opts is not None:
        merge_cfg_from_list(args.opts)
    assert_and_infer_cfg()
    smi_output, cuda_ver, cudnn_ver = c2_utils.get_nvidia_info()
    logger.info("cuda version : {}".format(cuda_ver))
    logger.info("cudnn version: {}".format(cudnn_ver))
    logger.info("nvidia-smi output:\n{}".format(smi_output))
    logger.info('Training with config:')
    logger.info(pprint.pformat(cfg))
    # Note that while we set the numpy random seed network training will not be
    # deterministic in general. There are sources of non-determinism that cannot
    # be removed with a reasonble execution-speed tradeoff (such as certain
    # non-deterministic cudnn functions).
    np.random.seed(cfg.RNG_SEED)
    # Execute the training run
    checkpoints = detectron.utils.train.train_model()
    # Test the trained model
    if not args.skip_test:
        test_model(checkpoints['final'], args.single_gpu_testing, args.opts)
Example #2
Source File: train_net.py    From Clustered-Object-Detection-in-Aerial-Image with Apache License 2.0 5 votes vote down vote up 
def main():
    # Initialize C2
    workspace.GlobalInit(
        ['caffe2', '--caffe2_log_level=0', '--caffe2_gpu_memory_tracking=1']
    )
    # Set up logging and load config options
    logger = setup_logging(__name__)
    logging.getLogger('detectron.roi_data.loader').setLevel(logging.INFO)
    args = parse_args()
    logger.info('Called with args:')
    logger.info(args)
    if args.cfg_file is not None:
        merge_cfg_from_file(args.cfg_file)
    if args.opts is not None:
        merge_cfg_from_list(args.opts)
    assert_and_infer_cfg()
    smi_output, cuda_ver, cudnn_ver = c2_utils.get_nvidia_info()
    logger.info("cuda version : {}".format(cuda_ver))
    logger.info("cudnn version: {}".format(cudnn_ver))
    logger.info("nvidia-smi output:\n{}".format(smi_output))
    logger.info('Training with config:')
    logger.info(pprint.pformat(cfg))
    # Note that while we set the numpy random seed network training will not be
    # deterministic in general. There are sources of non-determinism that cannot
    # be removed with a reasonble execution-speed tradeoff (such as certain
    # non-deterministic cudnn functions).
    np.random.seed(cfg.RNG_SEED)
    # Execute the training run
    checkpoints = detectron.utils.train.train_model()
    # Test the trained model
    if not args.skip_test:
        test_model(checkpoints['final'], args.multi_gpu_testing, args.opts)
Example #3
Source File: train_net.py    From Detectron-Cascade-RCNN with Apache License 2.0 5 votes vote down vote up 
def main():
    # Initialize C2
    workspace.GlobalInit(
        ['caffe2', '--caffe2_log_level=0', '--caffe2_gpu_memory_tracking=1']
    )
    # Set up logging and load config options
    logger = setup_logging(__name__)
    logging.getLogger('detectron.roi_data.loader').setLevel(logging.INFO)
    args = parse_args()
    logger.info('Called with args:')
    logger.info(args)
    if args.cfg_file is not None:
        merge_cfg_from_file(args.cfg_file)
    if args.opts is not None:
        merge_cfg_from_list(args.opts)
    assert_and_infer_cfg()
    smi_output, cuda_ver, cudnn_ver = c2_utils.get_nvidia_info()
    logger.info("cuda version : {}".format(cuda_ver))
    logger.info("cudnn version: {}".format(cudnn_ver))
    logger.info("nvidia-smi output:\n{}".format(smi_output))
    logger.info('Training with config:')
    logger.info(pprint.pformat(cfg))
    # Note that while we set the numpy random seed network training will not be
    # deterministic in general. There are sources of non-determinism that cannot
    # be removed with a reasonble execution-speed tradeoff (such as certain
    # non-deterministic cudnn functions).
    np.random.seed(cfg.RNG_SEED)
    # Execute the training run
    checkpoints = detectron.utils.train.train_model()
    # Test the trained model
    if not args.skip_test:
        test_model(checkpoints['final'], args.multi_gpu_testing, args.opts)
Example #4
Source File: train_net.py    From Detectron with Apache License 2.0 5 votes vote down vote up 
def main():
    # Initialize C2
    workspace.GlobalInit(
        ['caffe2', '--caffe2_log_level=0', '--caffe2_gpu_memory_tracking=1']
    )
    # Set up logging and load config options
    logger = setup_logging(__name__)
    logging.getLogger('detectron.roi_data.loader').setLevel(logging.INFO)
    args = parse_args()
    logger.info('Called with args:')
    logger.info(args)
    if args.cfg_file is not None:
        merge_cfg_from_file(args.cfg_file)
    if args.opts is not None:
        merge_cfg_from_list(args.opts)
    assert_and_infer_cfg()
    smi_output, cuda_ver, cudnn_ver = c2_utils.get_nvidia_info()
    logger.info("cuda version : {}".format(cuda_ver))
    logger.info("cudnn version: {}".format(cudnn_ver))
    logger.info("nvidia-smi output:\n{}".format(smi_output))
    logger.info('Training with config:')
    logger.info(pprint.pformat(cfg))
    # Note that while we set the numpy random seed network training will not be
    # deterministic in general. There are sources of non-determinism that cannot
    # be removed with a reasonble execution-speed tradeoff (such as certain
    # non-deterministic cudnn functions).
    np.random.seed(cfg.RNG_SEED)
    # Execute the training run
    checkpoints = detectron.utils.train.train_model()
    # Test the trained model
    if not args.skip_test:
        test_model(checkpoints['final'], args.multi_gpu_testing, args.opts)
Example #5
Source File: train_net.py    From Detectron-DA-Faster-RCNN with Apache License 2.0 5 votes vote down vote up 
def main():
    # Initialize C2
    workspace.GlobalInit(
        ['caffe2', '--caffe2_log_level=0', '--caffe2_gpu_memory_tracking=1']
    )
    # Set up logging and load config options
    logger = setup_logging(__name__)
    logging.getLogger('detectron.roi_data.loader').setLevel(logging.INFO)
    args = parse_args()
    logger.info('Called with args:')
    logger.info(args)
    if args.cfg_file is not None:
        merge_cfg_from_file(args.cfg_file)
    if args.opts is not None:
        merge_cfg_from_list(args.opts)
    assert_and_infer_cfg()
    smi_output, cuda_ver, cudnn_ver = c2_utils.get_nvidia_info()
    logger.info("cuda version : {}".format(cuda_ver))
    logger.info("cudnn version: {}".format(cudnn_ver))
    logger.info("nvidia-smi output:\n{}".format(smi_output))
    logger.info('Training with config:')
    logger.info(pprint.pformat(cfg))
    # Note that while we set the numpy random seed network training will not be
    # deterministic in general. There are sources of non-determinism that cannot
    # be removed with a reasonble execution-speed tradeoff (such as certain
    # non-deterministic cudnn functions).
    np.random.seed(cfg.RNG_SEED)
    # Execute the training run
    checkpoints = detectron.utils.train.train_model()
    # Test the trained model
    if not args.skip_test:
        test_model(checkpoints['final'], args.multi_gpu_testing, args.opts)
Example #6
Source File: train_net.py    From CBNet with Apache License 2.0 5 votes vote down vote up 
def main():
    # Initialize C2
    workspace.GlobalInit(
        ['caffe2', '--caffe2_log_level=0', '--caffe2_gpu_memory_tracking=1']
    )
    # Set up logging and load config options
    logger = setup_logging(__name__)
    logging.getLogger('detectron.roi_data.loader').setLevel(logging.INFO)
    args = parse_args()
    logger.info('Called with args:')
    logger.info(args)
    if args.cfg_file is not None:
        merge_cfg_from_file(args.cfg_file)
    if args.opts is not None:
        merge_cfg_from_list(args.opts)
    assert_and_infer_cfg()
    smi_output, cuda_ver, cudnn_ver = c2_utils.get_nvidia_info()
    logger.info("cuda version : {}".format(cuda_ver))
    logger.info("cudnn version: {}".format(cudnn_ver))
    logger.info("nvidia-smi output:\n{}".format(smi_output))
    logger.info('Training with config:')
    logger.info(pprint.pformat(cfg))
    # Note that while we set the numpy random seed network training will not be
    # deterministic in general. There are sources of non-determinism that cannot
    # be removed with a reasonble execution-speed tradeoff (such as certain
    # non-deterministic cudnn functions).
    np.random.seed(cfg.RNG_SEED)
    # Execute the training run
    checkpoints = detectron.utils.train.train_model()
    # Test the trained model
    if not args.skip_test:
        test_model(checkpoints['final'], args.multi_gpu_testing, args.opts)