Python torch 模块，_storage_classes() 实例源码

def test_print(self):
        for t in torch._tensor_classes:
            if t.is_cuda and not torch.cuda.is_available():
                continue
            obj = t(100, 100).fill_(1)
            obj.__repr__()
            str(obj)
        for t in torch._storage_classes:
            if  t.is_cuda and not torch.cuda.is_available():
                continue
            obj = t(100).fill_(1)
            obj.__repr__()
            str(obj)

        x = torch.Tensor([4, float('inf'), 1.5, float('-inf'), 0, float('nan'), 1])
        x.__repr__()
        str(x)

def test_print(self):
        for t in torch._tensor_classes:
            if t in torch.sparse._sparse_tensor_classes:
                continue
            if t.is_cuda and not torch.cuda.is_available():
                continue
            obj = t(100, 100).fill_(1)
            obj.__repr__()
            str(obj)
        for t in torch._storage_classes:
            if t.is_cuda and not torch.cuda.is_available():
                continue
            obj = t(100).fill_(1)
            obj.__repr__()
            str(obj)

        x = torch.Tensor([4, float('inf'), 1.5, float('-inf'), 0, float('nan'), 1])
        x.__repr__()
        str(x)

def test_print(self):
        for t in torch._tensor_classes:
            if t in torch.sparse._sparse_tensor_classes:
                continue
            if t.is_cuda and not torch.cuda.is_available():
                continue
            obj = t(100, 100).fill_(1)
            obj.__repr__()
            str(obj)
        for t in torch._storage_classes:
            if t.is_cuda and not torch.cuda.is_available():
                continue
            obj = t(100).fill_(1)
            obj.__repr__()
            str(obj)

        x = torch.Tensor([4, float('inf'), 1.5, float('-inf'), 0, float('nan'), 1])
        x.__repr__()
        str(x)

def test_print(self):
        for t in torch._tensor_classes:
            if t == torch.HalfTensor:
                continue  # HalfTensor does not support fill
            if t in torch.sparse._sparse_tensor_classes:
                continue
            if t.is_cuda and not torch.cuda.is_available():
                continue
            obj = t(100, 100).fill_(1)
            obj.__repr__()
            str(obj)
        for t in torch._storage_classes:
            if t.is_cuda and not torch.cuda.is_available():
                continue
            obj = t(100).fill_(1)
            obj.__repr__()
            str(obj)

        x = torch.Tensor([4, float('inf'), 1.5, float('-inf'), 0, float('nan'), 1])
        x.__repr__()
        str(x)

def __init__(self, context=None, reducers=None):
        if context is None:
            context = multiprocessing
        if reducers is None:
            reducers = {}

        for t in torch._tensor_classes:
            reducers.setdefault(t, reduce_tensor)
        for t in torch._storage_classes:
            reducers.setdefault(t, reduce_storage)

        super(Queue, self).__init__(context, reducers)

def init_reductions():
    ForkingPickler.register(torch.cuda.Event, reduce_event)

    for t in torch._storage_classes:
        ForkingPickler.register(t, reduce_storage)

    for t in torch._tensor_classes:
        ForkingPickler.register(t, reduce_tensor)

def init_reductions():
    ForkingPickler.register(torch.cuda.Event, reduce_event)

    for t in torch._storage_classes:
        ForkingPickler.register(t, reduce_storage)

    for t in torch._tensor_classes:
        ForkingPickler.register(t, reduce_tensor)

def init_reductions():
    ForkingPickler.register(torch.cuda.Event, reduce_event)

    for t in torch._storage_classes:
        ForkingPickler.register(t, reduce_storage)

    for t in torch._tensor_classes:
        ForkingPickler.register(t, reduce_tensor)

def init_reductions():
    ForkingPickler.register(torch.cuda.Event, reduce_event)

    for t in torch._storage_classes:
        ForkingPickler.register(t, reduce_storage)

    for t in torch._tensor_classes:
        ForkingPickler.register(t, reduce_tensor)

def test_print(self):
        for t in torch._tensor_classes:
            if IS_WINDOWS and t in [torch.cuda.sparse.HalfTensor, torch.cuda.HalfTensor]:
                return  # CUDA HalfTensor is not supported on Windows yet
            if t == torch.HalfTensor:
                continue  # HalfTensor does not support fill
            if t in torch.sparse._sparse_tensor_classes:
                continue
            if t.is_cuda and not torch.cuda.is_available():
                continue
            obj = t(100, 100).fill_(1)
            obj.__repr__()
            str(obj)
        for t in torch._storage_classes:
            if t.is_cuda and not torch.cuda.is_available():
                continue
            obj = t(100).fill_(1)
            obj.__repr__()
            str(obj)

        x = torch.Tensor([4, float('inf'), 1.5, float('-inf'), 0, float('nan'), 1])
        x.__repr__()
        str(x)

        x = torch.DoubleTensor([1e-324, 1e-323, 1e-322, 1e307, 1e308, 1e309])
        x.__repr__()
        str(x),