Python caffe2.python.workspace.GlobalInit() Examples

def main():
    args = parse_args()

    num_threads = 8 if not args.parallel else 1
    workspace.GlobalInit(
        [
            "caffe2",
            "--caffe2_log_level=2",
            "--caffe2_omp_num_threads={}".format(num_threads),
            "--caffe2_mkl_num_threads={}".format(num_threads),
        ]
    )

    net = load_model(args)
    eval_segm_cpu(args, net) 

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) 

def main():
    args = parse_args()

    num_threads = 8
    workspace.GlobalInit(
        [
            "caffe2",
            "--caffe2_log_level=2",
            "--caffe2_omp_num_threads={}".format(num_threads),
            "--caffe2_mkl_num_threads={}".format(num_threads),
        ]
    )

    net = load_model(args)
    eval_segm_cpu(args, net) 

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) 

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('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()
    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 = utils.train.train_model()
    # Test the trained model
    if not args.skip_test:
        test_model(checkpoints['final'], args.multi_gpu_testing, args.opts) 

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('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()
    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 = train_model()
    # Test the trained model
    if not args.skip_test:
        test_model(checkpoints['final'], args.multi_gpu_testing, args.opts) 

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) 

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) 

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) 

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) 

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('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()
    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 = train_model()
    # Test the trained model
    if not args.skip_test:
        test_model(checkpoints['final'], args.multi_gpu_testing, args.opts) 

def __init__(
        self,
        m_spa,
        ln_emb,
        ln_bot,
        ln_top,
        arch_interaction_op,
        arch_interaction_itself=False,
        sigmoid_bot=-1,
        sigmoid_top=-1,
        save_onnx=False,
        model=None,
        tag=None,
        ndevices=-1,
        forward_ops=True,
        enable_prof=False,
    ):
        super(DLRM_Net, self).__init__()

        # init model
        if model is None:
            global_init_opt = ["caffe2", "--caffe2_log_level=0"]
            if enable_prof:
                global_init_opt += [
                    "--logtostderr=0",
                    "--log_dir=$HOME",
                    "--caffe2_logging_print_net_summary=1",
                ]
            workspace.GlobalInit(global_init_opt)
            self.set_tags()
            self.model = model_helper.ModelHelper(name="DLRM", init_params=True)
        else:
            # WARNING: assume that workspace and tags have been initialized elsewhere
            self.set_tags(tag[0], tag[1], tag[2], tag[3], tag[4], tag[5], tag[6],
                          tag[7], tag[8], tag[9])
            self.model = model

        # save arguments
        self.m_spa = m_spa
        self.ln_emb = ln_emb
        self.ln_bot = ln_bot
        self.ln_top = ln_top
        self.arch_interaction_op = arch_interaction_op
        self.arch_interaction_itself = arch_interaction_itself
        self.sigmoid_bot = sigmoid_bot
        self.sigmoid_top = sigmoid_top
        self.save_onnx = save_onnx
        self.ndevices = ndevices
        # onnx types and shapes dictionary
        if self.save_onnx:
            self.onnx_tsd = {}
        # create forward operators
        if forward_ops:
            if self.ndevices <= 1:
                return self.create_sequential_forward_ops()
            else:
                return self.create_parallel_forward_ops() 

def __init__(
        self,
        m_spa,
        ln_emb,
        ln_bot,
        ln_top,
        arch_interaction_op,
        arch_interaction_itself=False,
        sigmoid_bot=-1,
        sigmoid_top=-1,
        save_onnx=False,
        model=None,
        test_net=None,
        tag=None,
        ndevices=-1,
        forward_ops=True,
        enable_prof=False,
    ):
        super(DLRM_Net, self).__init__()

        # init model
        if model is None:
            global_init_opt = ["caffe2", "--caffe2_log_level=0"]
            if enable_prof:
                global_init_opt += [
                    "--logtostderr=0",
                    "--log_dir=$HOME",
                    "--caffe2_logging_print_net_summary=1",
                ]
            workspace.GlobalInit(global_init_opt)
            self.set_tags()
            self.model = model_helper.ModelHelper(name="DLRM", init_params=True)
            self.test_net = None
        else:
            # WARNING: assume that workspace and tags have been initialized elsewhere
            self.set_tags(tag[0], tag[1], tag[2], tag[3], tag[4], tag[5], tag[6],
                          tag[7], tag[8], tag[9])
            self.model = model
            self.test_net = test_net

        # save arguments
        self.m_spa = m_spa
        self.ln_emb = ln_emb
        self.ln_bot = ln_bot
        self.ln_top = ln_top
        self.arch_interaction_op = arch_interaction_op
        self.arch_interaction_itself = arch_interaction_itself
        self.sigmoid_bot = sigmoid_bot
        self.sigmoid_top = sigmoid_top
        self.save_onnx = save_onnx
        self.ndevices = ndevices
        # onnx types and shapes dictionary
        if self.save_onnx:
            self.onnx_tsd = {}
        # create forward operators
        if forward_ops:
            if self.ndevices <= 1:
                return self.create_sequential_forward_ops()
            else:
                return self.create_parallel_forward_ops() 

def main():
    workspace.GlobalInit(['caffe2', '--caffe2_log_level=0'])
    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)
    cfg.NUM_GPUS = 1
    assert_and_infer_cfg()
    logger.info('Conerting model with config:')
    logger.info(pprint.pformat(cfg))

    assert not cfg.MODEL.KEYPOINTS_ON, "Keypoint model not supported."
    assert not cfg.MODEL.MASK_ON, "Mask model not supported."
    assert not cfg.FPN.FPN_ON, "FPN not supported."
    assert not cfg.RETINANET.RETINANET_ON, "RetinaNet model not supported."

    # load model from cfg
    model, blobs = load_model(args)

    net = core.Net('')
    net.Proto().op.extend(copy.deepcopy(model.net.Proto().op))
    net.Proto().external_input.extend(
        copy.deepcopy(model.net.Proto().external_input))
    net.Proto().external_output.extend(
        copy.deepcopy(model.net.Proto().external_output))
    net.Proto().type = args.net_execution_type
    net.Proto().num_workers = 1 if args.net_execution_type == 'simple' else 4

    # Reset the device_option, change to unscope name and replace python operators
    convert_net(args, net.Proto(), blobs)

    # add operators for bbox
    add_bbox_ops(args, net, blobs)

    if args.fuse_af:
        print('Fusing affine channel...')
        net, blobs = mutils.fuse_net_affine(
            net, blobs)

    if args.use_nnpack:
        mutils.update_mobile_engines(net.Proto())

    # generate init net
    empty_blobs = ['data', 'im_info']
    init_net = gen_init_net(net, blobs, empty_blobs)

    if args.device == 'gpu':
        [net, init_net] = convert_model_gpu(args, net, init_net)

    net.Proto().name = args.net_name
    init_net.Proto().name = args.net_name + "_init"

    if args.test_img is not None:
        verify_model(args, [net, init_net], args.test_img)

    _save_models(net, init_net, args) 

def main():
    workspace.GlobalInit(['caffe2', '--caffe2_log_level=0'])
    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)
    cfg.NUM_GPUS = 1
    assert_and_infer_cfg()
    logger.info('Conerting model with config:')
    logger.info(pprint.pformat(cfg))

    assert not cfg.MODEL.KEYPOINTS_ON, "Keypoint model not supported."
    assert not cfg.MODEL.MASK_ON, "Mask model not supported."
    assert not cfg.FPN.FPN_ON, "FPN not supported."
    assert not cfg.RETINANET.RETINANET_ON, "RetinaNet model not supported."

    # load model from cfg
    model, blobs = load_model(args)

    net = core.Net('')
    net.Proto().op.extend(copy.deepcopy(model.net.Proto().op))
    net.Proto().external_input.extend(
        copy.deepcopy(model.net.Proto().external_input))
    net.Proto().external_output.extend(
        copy.deepcopy(model.net.Proto().external_output))
    net.Proto().type = args.net_execution_type
    net.Proto().num_workers = 1 if args.net_execution_type == 'simple' else 4

    # Reset the device_option, change to unscope name and replace python operators
    convert_net(args, net.Proto(), blobs)

    # add operators for bbox
    add_bbox_ops(args, net, blobs)

    if args.fuse_af:
        print('Fusing affine channel...')
        net, blobs = mutils.fuse_net_affine(
            net, blobs)

    if args.use_nnpack:
        mutils.update_mobile_engines(net.Proto())

    # generate init net
    empty_blobs = ['data', 'im_info']
    init_net = gen_init_net(net, blobs, empty_blobs)

    if args.device == 'gpu':
        [net, init_net] = convert_model_gpu(args, net, init_net)

    net.Proto().name = args.net_name
    init_net.Proto().name = args.net_name + "_init"

    if args.test_img is not None:
        verify_model(args, [net, init_net], args.test_img)

    _save_models(net, init_net, args)