1. 模块列表
  2. 函数列表
  3. torch.is_storage()

Python torch 模块，is_storage() 实例源码

我们从Python开源项目中，提取了以下11个代码示例，用于说明如何使用torch.is_storage()。

项目：pytorch-dist    作者：apaszke    | 项目源码 | 文件源码 
def _wrap_function(function, ffi):
    @wraps(function)
    def safe_call(*args, **kwargs):
        args = tuple(ffi.cast(_torch_to_cffi.get(type(arg), 'void') + '*', arg._cdata)
                if torch.is_tensor(arg) or torch.is_storage(arg)
                else arg
                for arg in args)
        args = (function,) + args
        result = torch._C._safe_call(*args, **kwargs)
        if isinstance(result, ffi.CData):
            typeof = ffi.typeof(result)
            if typeof.kind == 'pointer':
                cdata = int(ffi.cast('uintptr_t', result))
                cname = typeof.item.cname
                if cname in _cffi_to_torch:
                    return _cffi_to_torch[cname](cdata=cdata)
        return result
    return safe_call
项目：pytorch-dist    作者：apaszke    | 项目源码 | 文件源码 
def to_gpu(obj, type_map={}):
    if torch.is_tensor(obj):
        t = type_map.get(type(obj), get_gpu_type(type(obj)))
        return obj.clone().type(t)
    elif torch.is_storage(obj):
        return obj.new().resize_(obj.size()).copy_(obj)
    elif isinstance(obj, Variable):
        assert obj.creator is None
        t = type_map.get(type(obj.data), get_gpu_type(type(obj.data)))
        return Variable(obj.data.clone().type(t), requires_grad=obj.requires_grad)
    elif isinstance(obj, list):
        return [to_gpu(o, type_map) for o in obj]
    elif isinstance(obj, tuple):
        return tuple(to_gpu(o, type_map) for o in obj)
    else:
        return deepcopy(obj)
项目：pyro    作者：uber    | 项目源码 | 文件源码 
def to_gpu(obj, type_map={}):
    if torch.is_tensor(obj):
        t = type_map.get(type(obj), get_gpu_type(type(obj)))
        return obj.clone().type(t)
    elif torch.is_storage(obj):
        return obj.new().resize_(obj.size()).copy_(obj)
    elif isinstance(obj, Variable):
        assert obj.is_leaf
        t = type_map.get(type(obj.data), get_gpu_type(type(obj.data)))
        return Variable(obj.data.clone().type(
            t), requires_grad=obj.requires_grad)
    elif isinstance(obj, list):
        return [to_gpu(o, type_map) for o in obj]
    elif isinstance(obj, tuple):
        return tuple(to_gpu(o, type_map) for o in obj)
    else:
        return deepcopy(obj)
项目：pytorch    作者：tylergenter    | 项目源码 | 文件源码 
def _wrap_function(function, ffi):
    @wraps(function)
    def safe_call(*args, **kwargs):
        args = tuple(ffi.cast(_torch_to_cffi.get(type(arg), 'void') + '*', arg._cdata)
                     if torch.is_tensor(arg) or torch.is_storage(arg)
                     else arg
                     for arg in args)
        args = (function,) + args
        result = torch._C._safe_call(*args, **kwargs)
        if isinstance(result, ffi.CData):
            typeof = ffi.typeof(result)
            if typeof.kind == 'pointer':
                cdata = int(ffi.cast('uintptr_t', result))
                cname = typeof.item.cname
                if cname in _cffi_to_torch:
                    return _cffi_to_torch[cname](cdata=cdata)
        return result
    return safe_call
项目：pytorch    作者：tylergenter    | 项目源码 | 文件源码 
def to_gpu(obj, type_map={}):
    if torch.is_tensor(obj):
        t = type_map.get(type(obj), get_gpu_type(type(obj)))
        return obj.clone().type(t)
    elif torch.is_storage(obj):
        return obj.new().resize_(obj.size()).copy_(obj)
    elif isinstance(obj, Variable):
        assert obj.is_leaf
        t = type_map.get(type(obj.data), get_gpu_type(type(obj.data)))
        return Variable(obj.data.clone().type(t), requires_grad=obj.requires_grad)
    elif isinstance(obj, list):
        return [to_gpu(o, type_map) for o in obj]
    elif isinstance(obj, tuple):
        return tuple(to_gpu(o, type_map) for o in obj)
    else:
        return deepcopy(obj)
项目：pytorch-coriander    作者：hughperkins    | 项目源码 | 文件源码 
def _wrap_function(function, ffi):
    @wraps(function)
    def safe_call(*args, **kwargs):
        args = tuple(ffi.cast(_torch_to_cffi.get(type(arg), 'void') + '*', arg._cdata)
                     if torch.is_tensor(arg) or torch.is_storage(arg)
                     else arg
                     for arg in args)
        args = (function,) + args
        result = torch._C._safe_call(*args, **kwargs)
        if isinstance(result, ffi.CData):
            typeof = ffi.typeof(result)
            if typeof.kind == 'pointer':
                cdata = int(ffi.cast('uintptr_t', result))
                cname = typeof.item.cname
                if cname in _cffi_to_torch:
                    return _cffi_to_torch[cname](cdata=cdata)
        return result
    return safe_call
项目：pytorch-coriander    作者：hughperkins    | 项目源码 | 文件源码 
def to_gpu(obj, type_map={}):
    if torch.is_tensor(obj):
        t = type_map.get(type(obj), get_gpu_type(type(obj)))
        return obj.clone().type(t)
    elif torch.is_storage(obj):
        return obj.new().resize_(obj.size()).copy_(obj)
    elif isinstance(obj, Variable):
        assert obj.is_leaf
        t = type_map.get(type(obj.data), get_gpu_type(type(obj.data)))
        return Variable(obj.data.clone().type(t), requires_grad=obj.requires_grad)
    elif isinstance(obj, list):
        return [to_gpu(o, type_map) for o in obj]
    elif isinstance(obj, tuple):
        return tuple(to_gpu(o, type_map) for o in obj)
    else:
        return deepcopy(obj)
项目：pytorch    作者：ezyang    | 项目源码 | 文件源码 
def _wrap_function(function, ffi):
    @wraps(function)
    def safe_call(*args, **kwargs):
        args = tuple(ffi.cast(_torch_to_cffi.get(type(arg), 'void') + '*', arg._cdata)
                     if torch.is_tensor(arg) or torch.is_storage(arg)
                     else arg
                     for arg in args)
        args = (function,) + args
        result = torch._C._safe_call(*args, **kwargs)
        if isinstance(result, ffi.CData):
            typeof = ffi.typeof(result)
            if typeof.kind == 'pointer':
                cdata = int(ffi.cast('uintptr_t', result))
                cname = typeof.item.cname
                if cname in _cffi_to_torch:
                    return _cffi_to_torch[cname](cdata=cdata)
        return result
    return safe_call
项目：pytorch    作者：ezyang    | 项目源码 | 文件源码 
def to_gpu(obj, type_map={}):
    if torch.is_tensor(obj):
        t = type_map.get(type(obj), get_gpu_type(type(obj)))
        return obj.clone().type(t)
    elif torch.is_storage(obj):
        return obj.new().resize_(obj.size()).copy_(obj)
    elif isinstance(obj, Variable):
        assert obj.is_leaf
        t = type_map.get(type(obj.data), get_gpu_type(type(obj.data)))
        return Variable(obj.data.clone().type(t), requires_grad=obj.requires_grad)
    elif isinstance(obj, list):
        return [to_gpu(o, type_map) for o in obj]
    elif isinstance(obj, tuple):
        return tuple(to_gpu(o, type_map) for o in obj)
    else:
        return deepcopy(obj)
项目：pytorch    作者：pytorch    | 项目源码 | 文件源码 
def _wrap_function(function, ffi):
    @wraps(function)
    def safe_call(*args, **kwargs):
        args = tuple(ffi.cast(_torch_to_cffi.get(type(arg), 'void') + '*', arg._cdata)
                     if torch.is_tensor(arg) or torch.is_storage(arg)
                     else arg
                     for arg in args)
        args = (function,) + args
        result = torch._C._safe_call(*args, **kwargs)
        if isinstance(result, ffi.CData):
            typeof = ffi.typeof(result)
            if typeof.kind == 'pointer':
                cdata = int(ffi.cast('uintptr_t', result))
                cname = typeof.item.cname
                if cname in _cffi_to_torch:
                    return _cffi_to_torch[cname](cdata=cdata)
        return result
    return safe_call
项目：pytorch    作者：pytorch    | 项目源码 | 文件源码 
def to_gpu(obj, type_map={}):
    if torch.is_tensor(obj):
        t = type_map.get(type(obj), get_gpu_type(type(obj)))
        return obj.clone().type(t)
    elif torch.is_storage(obj):
        return obj.new().resize_(obj.size()).copy_(obj)
    elif isinstance(obj, Variable):
        assert obj.is_leaf
        t = type_map.get(type(obj.data), get_gpu_type(type(obj.data)))
        return Variable(obj.data.clone().type(t), requires_grad=obj.requires_grad)
    elif isinstance(obj, list):
        return [to_gpu(o, type_map) for o in obj]
    elif isinstance(obj, tuple):
        return tuple(to_gpu(o, type_map) for o in obj)
    else:
        return deepcopy(obj)