def ones(*sizes, **kwargs):
"""Return a float tensor with values of ``1``.
Parameters
----------
sizes : tuple, list or int
The sizes indicating the shape of the output tensor.
out : vm.torch.Tensor
The optional output tensor.
Returns
-------
vm.torch.FloatTensor
The output tensor.
"""
arguments = {'value': 1.0, 'dims': sizes}
out = kwargs['out'] if 'out' in kwargs else None
if out is None:
out = LeafTensor(sizes, requires_grad=kwargs['requires_grad'] \
if 'requires_grad' in kwargs else False)
inputs = []
outputs = [out]
ctx = MakeContext(inputs, outputs)
meta = ('ONCE', 'Fill', ctx)
return RunOperator(inputs, outputs, meta, **arguments)
def zeros_like(input, out=None, **kwargs):
"""Return a float tensor with values of ``0``, shape as the input.
Parameters
----------
input : vm.torch.Tensor
The tensor for indicating shape.
out : vm.torch.Tensor
The optional output tensor.
Returns
-------
vm.torch.FloatTensor
The output tensor.
"""
if not hasattr(input, 'shape'):
raise ValueError('Input does not have the shape attribute.')
arguments = {'value': 0.0, 'dims': input.shape}
if out is None:
out = LeafTensor(input.shape, requires_grad=kwargs['requires_grad'] \
if 'requires_grad' in kwargs else False)
inputs = []
outputs = [out]
ctx = MakeContext(inputs, outputs)
meta = ('ONCE', 'Fill', ctx)
return RunOperator(inputs, outputs, meta, **arguments)
def randn(*sizes, **kwargs):
"""Return a float tensor with a normal distribution of N(0, 1).
Parameters
----------
sizes : tuple, list or int
The sizes indicating the shape of the output tensor.
out : vm.torch.Tensor
The optional output tensor.
Returns
-------
vm.torch.FloatTensor
The output tensor.
"""
arguments = {'mean': 0.0, 'std': 1.0, 'dims': sizes}
out = kwargs['out'] if 'out' in kwargs else None
if out is None:
out = LeafTensor(sizes, requires_grad=kwargs['requires_grad'] \
if 'requires_grad' in kwargs else False)
inputs = []
outputs = [out]
ctx = MakeContext(inputs, outputs)
meta = ('ONCE', 'RandomNormal', ctx)
return RunOperator(inputs, outputs, meta, **arguments)
def uniform_(self, low=0, high=1):
"""Fill self tensor with the specified uniform distribution.
Parameters
----------
low : numerical type
The lower bound.
high : numerical type
The higher bound.
Returns
-------
vm.torch.Tensor
The self.
"""
# TODO(PhyscalX): To support various dtypes, not only float32.
arguments = {'low': float(low), 'high': float(high), 'dims': self.shape}
inputs = []; outputs = [self]; ctx = MakeContext(inputs, outputs)
meta = ('ONCE', 'RandomUniform', ctx)
return RunOperator(inputs, outputs, meta, **arguments)
def normal_(self, mean=0, std=1):
"""Fill self tensor with the specified normal distribution.
Parameters
----------
mean : numerical type
The mean(mu) of normal distribution.
std : numerical type
The std(sigma) of normal distribution.
Returns
-------
vm.torch.Tensor
The self.
"""
# TODO(PhyscalX): To support various dtypes, not only float32.
arguments = {'mean': float(mean), 'std': float(std), 'dims': self.shape}
inputs = []; outputs = [self]; ctx = MakeContext(inputs, outputs)
meta = ('ONCE', 'RandomNormal', ctx)
return RunOperator(inputs, outputs, meta, **arguments)
def run(self, inputs, outputs, auto_grad=True):
meta = ('PERSISTENT', self.persistent_key, self.op)
return RunOperator(inputs, outputs, meta, auto_grad=auto_grad)