Python workspace.CreateNet方法代碼示例

# 需要導入模塊: from caffe2.python import workspace [as 別名]
# 或者: from caffe2.python.workspace import CreateNet [as 別名]
def load_model_pb(net_file, init_file=None, is_run_init=True, is_create_net=True):
    net = core.Net("net")
    if net_file is not None:
        net.Proto().ParseFromString(open(net_file, "rb").read())

    if init_file is None:
        fn, ext = os.path.splitext(net_file)
        init_file = fn + "_init" + ext

    init_net = caffe2_pb2.NetDef()
    init_net.ParseFromString(open(init_file, "rb").read())

    if is_run_init:
        workspace.RunNetOnce(init_net)
        create_blobs_if_not_existed(net.external_inputs)
        if net.Proto().name == "":
            net.Proto().name = "net"
    if is_create_net:
        workspace.CreateNet(net)

    return (net, init_net) 

# 需要導入模塊: from caffe2.python import workspace [as 別名]
# 或者: from caffe2.python.workspace import CreateNet [as 別名]
def setup_model_for_training(model, weights_file, output_dir):
    """Loaded saved weights and create the network in the C2 workspace."""
    logger = logging.getLogger(__name__)
    add_model_training_inputs(model)

    if weights_file:
        # Override random weight initialization with weights from a saved model
        nu.initialize_gpu_from_weights_file(model, weights_file, gpu_id=0)
    # Even if we're randomly initializing we still need to synchronize
    # parameters across GPUs
    nu.broadcast_parameters(model)
    workspace.CreateNet(model.net)

    logger.info('Outputs saved to: {:s}'.format(os.path.abspath(output_dir)))
    dump_proto_files(model, output_dir)

    # Start loading mini-batches and enqueuing blobs
    model.roi_data_loader.register_sigint_handler()
    model.roi_data_loader.start(prefill=True)
    return output_dir 

# 需要導入模塊: from caffe2.python import workspace [as 別名]
# 或者: from caffe2.python.workspace import CreateNet [as 別名]
def _run_speed_test(self, iters=5, N=1024):
        """This function provides an example of how to benchmark custom
        operators using the Caffe2 'prof_dag' network execution type. Please
        note that for 'prof_dag' to work, Caffe2 must be compiled with profiling
        support using the `-DUSE_PROF=ON` option passed to `cmake` when building
        Caffe2.
        """
        net = core.Net('test')
        net.Proto().type = 'prof_dag'
        net.Proto().num_workers = 2
        Y = net.BatchPermutation(['X', 'I'], 'Y')
        Y_flat = net.FlattenToVec([Y], 'Y_flat')
        loss = net.AveragedLoss([Y_flat], 'loss')
        net.AddGradientOperators([loss])
        workspace.CreateNet(net)

        X = np.random.randn(N, 256, 14, 14)
        for _i in range(iters):
            I = np.random.permutation(N)
            workspace.FeedBlob('X', X.astype(np.float32))
            workspace.FeedBlob('I', I.astype(np.int32))
            workspace.RunNet(net.Proto().name)
            np.testing.assert_allclose(
                workspace.FetchBlob('Y'), X[I], rtol=1e-5, atol=1e-08
            ) 

# 需要導入模塊: from caffe2.python import workspace [as 別名]
# 或者: from caffe2.python.workspace import CreateNet [as 別名]
def initialize_model_from_cfg(weights_file, gpu_id=0):
    """Initialize a model from the global cfg. Loads test-time weights and
    creates the networks in the Caffe2 workspace.
    """
    model = model_builder.create(cfg.MODEL.TYPE, train=False, gpu_id=gpu_id)
    net_utils.initialize_gpu_from_weights_file(
        model, weights_file, gpu_id=gpu_id,
    )
    model_builder.add_inference_inputs(model)
    workspace.CreateNet(model.net)
    workspace.CreateNet(model.conv_body_net)
    if cfg.MODEL.MASK_ON:
        workspace.CreateNet(model.mask_net)
    if cfg.MODEL.KEYPOINTS_ON:
        workspace.CreateNet(model.keypoint_net)
    return model 

# 需要導入模塊: from caffe2.python import workspace [as 別名]
# 或者: from caffe2.python.workspace import CreateNet [as 別名]
def create_model(self, X, S_lengths, S_indices, T):
        #setup tril indices for the interactions
        offset = 1 if self.arch_interaction_itself else 0
        num_fea = len(self.emb_l) + 1
        tril_indices = np.array([j + i * num_fea
                                 for i in range(num_fea) for j in range(i + offset)])
        self.FeedBlobWrapper(self.tint + "_tril_indices", tril_indices)

        # create compute graph
        if T is not None:
            # WARNING: RunNetOnce call is needed only if we use brew and ConstantFill.
            # We could use direct calls to self.model functions above to avoid it
            workspace.RunNetOnce(self.model.param_init_net)
            workspace.CreateNet(self.model.net)
            if self.test_net is not None:
                workspace.CreateNet(self.test_net) 

# 需要導入模塊: from caffe2.python import workspace [as 別名]
# 或者: from caffe2.python.workspace import CreateNet [as 別名]
def initialize_model_from_cfg():
    """Initialize a model from the global cfg. Loads test-time weights and
    creates the networks in the Caffe2 workspace.
    """
    model = model_builder.create(cfg.MODEL.TYPE, train=False)
    net_utils.initialize_from_weights_file(
        model, cfg.TEST.WEIGHTS, broadcast=False
    )
    model_builder.add_inference_inputs(model)
    workspace.CreateNet(model.net)
    workspace.CreateNet(model.conv_body_net)
    if cfg.MODEL.MASK_ON:
        workspace.CreateNet(model.mask_net)
    if cfg.MODEL.KEYPOINTS_ON:
        workspace.CreateNet(model.keypoint_net)
    return model 

# 需要導入模塊: from caffe2.python import workspace [as 別名]
# 或者: from caffe2.python.workspace import CreateNet [as 別名]
def setup_model_for_training(model, output_dir):
    """Loaded saved weights and create the network in the C2 workspace."""
    logger = logging.getLogger(__name__)
    add_model_training_inputs(model)

    if cfg.TRAIN.WEIGHTS:
        # Override random weight initialization with weights from a saved model
        nu.initialize_gpu_0_from_weights_file(model, cfg.TRAIN.WEIGHTS)
    # Even if we're randomly initializing we still need to synchronize
    # parameters across GPUs
    nu.broadcast_parameters(model)
    workspace.CreateNet(model.net)

    logger.info('Outputs saved to: {:s}'.format(os.path.abspath(output_dir)))
    dump_proto_files(model, output_dir)

    # Start loading mini-batches and enqueuing blobs
    model.roi_data_loader.register_sigint_handler()
    model.roi_data_loader.start(prefill=True)
    return output_dir 

# 需要導入模塊: from caffe2.python import workspace [as 別名]
# 或者: from caffe2.python.workspace import CreateNet [as 別名]
def setup_model_for_training(model, weights_file, output_dir):
    """Loaded saved weights and create the network in the C2 workspace."""
    logger = logging.getLogger(__name__)
    if cfg.TRAIN.DOMAIN_ADAPTATION:
        add_model_da_training_inputs(model)
    else:
        add_model_training_inputs(model)

    if weights_file:
        # Override random weight initialization with weights from a saved model
        nu.initialize_gpu_from_weights_file(model, weights_file, gpu_id=0)
    # Even if we're randomly initializing we still need to synchronize
    # parameters across GPUs
    nu.broadcast_parameters(model)
    workspace.CreateNet(model.net)

    logger.info('Outputs saved to: {:s}'.format(os.path.abspath(output_dir)))
    dump_proto_files(model, output_dir)

    # Start loading mini-batches and enqueuing blobs
    model.roi_data_loader.register_sigint_handler()
    model.roi_data_loader.start(prefill=True)
    return output_dir 

# 需要導入模塊: from caffe2.python import workspace [as 別名]
# 或者: from caffe2.python.workspace import CreateNet [as 別名]
def setup_model_for_training(model, weights_file, output_dir):
    """Loaded saved weights and create the network in the C2 workspace."""
    logger = logging.getLogger(__name__)
    add_model_training_inputs(model)

    if weights_file:
        # Override random weight initialization with weights from a saved model
        nu.initialize_gpu_from_weights_file(model, weights_file, gpu_id=0)
        nu.initialize_gpu_from_old_weights_file(model, weights_file, gpu_id=0)
    # Even if we're randomly initializing we still need to synchronize
    # parameters across GPUs
    nu.broadcast_parameters(model)
    workspace.CreateNet(model.net)

    logger.info('Outputs saved to: {:s}'.format(os.path.abspath(output_dir)))
    dump_proto_files(model, output_dir)

    # Start loading mini-batches and enqueuing blobs
    model.roi_data_loader.register_sigint_handler()
    model.roi_data_loader.start(prefill=True)
    return output_dir 

# 需要導入模塊: from caffe2.python import workspace [as 別名]
# 或者: from caffe2.python.workspace import CreateNet [as 別名]
def initialize_model_from_cfg(gpu_id=0):
    """Initialize a model from the global cfg. Loads test-time weights and
    creates the networks in the Caffe2 workspace.
    """
    model = model_builder.create(cfg.MODEL.TYPE, train=False, gpu_id=gpu_id)
    net_utils.initialize_gpu_from_weights_file(
        model, cfg.TEST.WEIGHTS, gpu_id=gpu_id,
    )
    model_builder.add_inference_inputs(model)
    workspace.CreateNet(model.net)
    workspace.CreateNet(model.conv_body_net)
    if cfg.MODEL.MASK_ON:
        workspace.CreateNet(model.mask_net)
    if cfg.MODEL.KEYPOINTS_ON:
        workspace.CreateNet(model.keypoint_net)
    return model 

# 需要導入模塊: from caffe2.python import workspace [as 別名]
# 或者: from caffe2.python.workspace import CreateNet [as 別名]
def setup_model_for_training(model, output_dir):
    """Loaded saved weights and create the network in the C2 workspace."""
    logger = logging.getLogger(__name__)
    add_model_training_inputs(model)

    if cfg.TRAIN.WEIGHTS:
        # Override random weight initialization with weights from a saved model
        nu.initialize_gpu_from_weights_file(model, cfg.TRAIN.WEIGHTS, gpu_id=0)
    # Even if we're randomly initializing we still need to synchronize
    # parameters across GPUs
    nu.broadcast_parameters(model)
    workspace.CreateNet(model.net)

    logger.info('Outputs saved to: {:s}'.format(os.path.abspath(output_dir)))
    dump_proto_files(model, output_dir)

    # Start loading mini-batches and enqueuing blobs
    model.roi_data_loader.register_sigint_handler()
    model.roi_data_loader.start(prefill=True)
    return output_dir