Python core.NameScope方法代碼示例

# 需要導入模塊: from caffe2.python import core [as 別名]
# 或者: from caffe2.python.core import NameScope [as 別名]
def get_net(data_loader, name):
    logger = logging.getLogger(__name__)
    blob_names = data_loader.get_output_names()
    net = core.Net(name)
    net.type = 'dag'
    for gpu_id in range(cfg.NUM_GPUS):
        with core.NameScope('gpu_{}'.format(gpu_id)):
            with core.DeviceScope(muji.OnGPU(gpu_id)):
                for blob_name in blob_names:
                    blob = core.ScopedName(blob_name)
                    workspace.CreateBlob(blob)
                net.DequeueBlobs(
                    data_loader._blobs_queue_name, blob_names)
    logger.info("Protobuf:\n" + str(net.Proto()))

    return net 

# 需要導入模塊: from caffe2.python import core [as 別名]
# 或者: from caffe2.python.core import NameScope [as 別名]
def close_blobs_queues(self):
        """Close a BlobsQueue."""
        for gpu_id in range(self._num_gpus):
            with core.NameScope('gpu_{}'.format(gpu_id)):
                workspace.RunOperatorOnce(
                    core.CreateOperator(
                        'CloseBlobsQueue', [self._blobs_queue_name], []
                    )
                ) 

# 需要導入模塊: from caffe2.python import core [as 別名]
# 或者: from caffe2.python.core import NameScope [as 別名]
def GpuNameScope(gpu_id):
    """Create a name scope for GPU device `gpu_id`."""
    with core.NameScope('gpu_{:d}'.format(gpu_id)):
        yield 

# 需要導入模塊: from caffe2.python import core [as 別名]
# 或者: from caffe2.python.core import NameScope [as 別名]
def run_net(net):
    workspace.RunNetOnce(net)
    gpu_dev = core.DeviceOption(caffe2_pb2.CUDA, 0)
    name_scope = 'gpu_{}'.format(0)
    with core.NameScope(name_scope):
        with core.DeviceScope(gpu_dev):
            data = workspace.FetchBlob(core.ScopedName('data'))
            return data 

# 需要導入模塊: from caffe2.python import core [as 別名]
# 或者: from caffe2.python.core import NameScope [as 別名]
def test_simple_cnnmodel(self):
        model = cnn.CNNModelHelper("NCHW", name="overfeat")
        workspace.FeedBlob("data", np.random.randn(1, 3, 64, 64).astype(np.float32))
        workspace.FeedBlob("label", np.random.randn(1, 1000).astype(np.int))
        with core.NameScope("conv1"):
            conv1 = model.Conv("data", "conv1", 3, 96, 11, stride=4)
            relu1 = model.Relu(conv1, conv1)
            pool1 = model.MaxPool(relu1, "pool1", kernel=2, stride=2)
        with core.NameScope("classifier"):
            fc = model.FC(pool1, "fc", 4096, 1000)
            pred = model.Softmax(fc, "pred")
            xent = model.LabelCrossEntropy([pred, "label"], "xent")
            loss = model.AveragedLoss(xent, "loss")

        blob_name_tracker = {}
        graph = tb.model_to_graph_def(
            model,
            blob_name_tracker=blob_name_tracker,
            shapes={},
            show_simplified=False,
        )

        compare_proto(graph, self)

    # cnn.CNNModelHelper is deprecated, so we also test with
    # model_helper.ModelHelper. The model used in this test is taken from the
    # Caffe2 MNIST tutorial. Also use show_simplified=False here. 

# 需要導入模塊: from caffe2.python import core [as 別名]
# 或者: from caffe2.python.core import NameScope [as 別名]
def test_simple_model(self):
        model = model_helper.ModelHelper(name="mnist")
        # how come those inputs don't break the forward pass =.=a
        workspace.FeedBlob("data", np.random.randn(1, 3, 64, 64).astype(np.float32))
        workspace.FeedBlob("label", np.random.randn(1, 1000).astype(np.int))

        with core.NameScope("conv1"):
            conv1 = brew.conv(model, "data", 'conv1', dim_in=1, dim_out=20, kernel=5)
            # Image size: 24 x 24 -> 12 x 12
            pool1 = brew.max_pool(model, conv1, 'pool1', kernel=2, stride=2)
            # Image size: 12 x 12 -> 8 x 8
            conv2 = brew.conv(model, pool1, 'conv2', dim_in=20, dim_out=100, kernel=5)
            # Image size: 8 x 8 -> 4 x 4
            pool2 = brew.max_pool(model, conv2, 'pool2', kernel=2, stride=2)
        with core.NameScope("classifier"):
            # 50 * 4 * 4 stands for dim_out from previous layer multiplied by the image size
            fc3 = brew.fc(model, pool2, 'fc3', dim_in=100 * 4 * 4, dim_out=500)
            relu = brew.relu(model, fc3, fc3)
            pred = brew.fc(model, relu, 'pred', 500, 10)
            softmax = brew.softmax(model, pred, 'softmax')
            xent = model.LabelCrossEntropy([softmax, "label"], 'xent')
            # compute the expected loss
            loss = model.AveragedLoss(xent, "loss")
        model.net.RunAllOnMKL()
        model.param_init_net.RunAllOnMKL()
        model.AddGradientOperators([loss], skip=1)
        blob_name_tracker = {}
        graph = tb.model_to_graph_def(
            model,
            blob_name_tracker=blob_name_tracker,
            shapes={},
            show_simplified=False,
        )

        compare_proto(graph, self) 

# 需要導入模塊: from caffe2.python import core [as 別名]
# 或者: from caffe2.python.core import NameScope [as 別名]
def broadcast_parameters(model):
    num_gpus = cfg.NUM_GPUS
    if num_gpus == 1:
        return
    root_gpu_id = cfg.ROOT_GPU_ID
    all_model_params = model.GetAllParams('gpu_{}'.format(root_gpu_id))
    all_params_momentum = []
    if 'test' not in model.net.Name():
        for param in model.GetParams('gpu_{}'.format(root_gpu_id)):
            if param in model.TrainableParams():
                all_params_momentum.append(str(param) + '_momentum')
    all_params = all_model_params + all_params_momentum
    for param in all_params:
        data = workspace.FetchBlob(str(param))
        unscoped_param_name = misc.unscope_name(str(param))
        logger.info('Broadcasting {} to'.format(str(param)))
        for idx in range(root_gpu_id + 1, root_gpu_id + num_gpus):
            with core.NameScope('gpu_{}'.format(idx)):
                with core.DeviceScope(core.DeviceOption(caffe2_pb2.CUDA, idx)):
                    gpu_scoped_name = misc.scoped_name(unscoped_param_name)
                    logger.info(' |-> {}'.format(gpu_scoped_name))
                    workspace.FeedBlob(gpu_scoped_name, data)


# Initialize the model from a file and broadcast the parameters to all GPUs
# if num_gpus > 1. 

# 需要導入模塊: from caffe2.python import core [as 別名]
# 或者: from caffe2.python.core import NameScope [as 別名]
def _CorrectMomentum(self, correction):
        """The MomentumSGDUpdate op implements the update V as

            V := mu * V + lr * grad,

        where mu is the momentum factor, lr is the learning rate, and grad is the
        stochastic gradient. Since V is not defined independently of the learning
        rate (as it should ideally be), when the learning rate is changed we should
        scale the update history V in order to make it compatible in scale with
        lr * grad.
        """
        # Avoid noisy logging.
        if correction < 0.9 or correction > 1.1:
            logger.info('Scaling update history by {:.6f} (new/old lr)'.format(
                correction))

        root_gpu_id = cfg.ROOT_GPU_ID
        num_gpus = cfg.NUM_GPUS
        for i in range(root_gpu_id, root_gpu_id + num_gpus):
            with core.DeviceScope(core.DeviceOption(caffe2_pb2.CUDA, i)):
                with core.NameScope("gpu_{}".format(i)):
                    params = self.GetParams()
                    for param in params:
                        if param in self.TrainableParams():
                            op = core.CreateOperator(
                                'Scale', [param + '_momentum'],
                                [param + '_momentum'],
                                scale=correction)
                            workspace.RunOperatorOnce(op) 

# 需要導入模塊: from caffe2.python import core [as 別名]
# 或者: from caffe2.python.core import NameScope [as 別名]
def create_threads(self):
        # "worker" threads to construct (partial) minibatches and put them on
        # minibatch queue in CPU memory (limited by queue size).
        self._worker_ids = self.get_worker_ids()
        self._workers = [
            threading.Thread(
                target=self.minibatch_loader,
                name='worker_{}'.format(worker_id),
                args=[worker_id],
            ) for worker_id in self._worker_ids
        ]

        # Create one BlobsQueue per GPU which holds the training data in GPU
        # memory and feeds to the net.
        root_gpu_id = cfg.ROOT_GPU_ID
        for gpu_id in range(root_gpu_id, root_gpu_id + self._num_gpus):
            with core.NameScope('gpu_{}'.format(gpu_id)):
                self.create_blobs_queue(
                    queue_name=self._blobs_queue_name,
                    num_blobs=len(self._blobs_idx_map),
                    capacity=self._gpu_blobs_queue_capacity
                )

        # Launch enqueuer threads.
        blob_names = self._blobs_idx_map.keys()
        enqueue_blobs_names = [
            '{}_{}_enqueue'.format(self._split, blob_name)
            for blob_name in blob_names
        ]
        for gpu_id in range(root_gpu_id, root_gpu_id + self._num_gpus):
            with core.NameScope('gpu_{}'.format(gpu_id)):
                with core.DeviceScope(
                    core.DeviceOption(caffe2_pb2.CUDA, gpu_id)
                ):
                    for blob_list in enqueue_blobs_names:
                        for blob in blob_list:
                            scoped_blob_name = scope.CurrentNameScope() + blob
                            workspace.CreateBlob(scoped_blob_name)
        self._enqueuer = threading.Thread(
            target=self.enqueue_blobs_thread, args=(0, enqueue_blobs_names)
        ) 

# 需要導入模塊: from caffe2.python import core [as 別名]
# 或者: from caffe2.python.core import NameScope [as 別名]
def shutdown_dataloader(self):
        self.coordinator.request_stop()
        self.coordinator.wait_for_stop()
        root_gpu_id = cfg.ROOT_GPU_ID
        for idx in range(root_gpu_id, root_gpu_id + self._num_gpus):
            with core.NameScope("gpu_{}".format(idx)):
                self.close_blobs_queue()
        self.join()