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https://github.com/pytorch/pytorch.git
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958 lines
30 KiB
Python
958 lines
30 KiB
Python
import sys
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import torch
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import torch._C as _C
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from collections import OrderedDict
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import torch.sparse as sparse
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import torch.utils.hooks as hooks
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import warnings
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import weakref
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class Variable(_C._VariableBase):
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"""Wraps a tensor and records the operations applied to it.
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Variable is a thin wrapper around a Tensor object, that also holds
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the gradient w.r.t. to it, and a reference to a function that created it.
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This reference allows retracing the whole chain of operations that
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created the data. If the Variable has been created by the user, its grad_fn
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will be ``None`` and we call such objects *leaf* Variables.
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Since autograd only supports scalar valued function differentiation, grad
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size always matches the data size. Also, grad is normally only allocated
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for leaf variables, and will be always zero otherwise.
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Attributes:
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data: Wrapped tensor of any type.
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grad: Variable holding the gradient of type and location matching
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the ``.data``. This attribute is lazily allocated and can't
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be reassigned.
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requires_grad: Boolean indicating whether the Variable has been
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created by a subgraph containing any Variable, that requires it.
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See :ref:`excluding-subgraphs` for more details.
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Can be changed only on leaf Variables.
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volatile: Boolean indicating that the Variable should be used in
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inference mode, i.e. don't save the history. See
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:ref:`excluding-subgraphs` for more details.
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Can be changed only on leaf Variables.
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is_leaf: Boolean indicating if the Variable is a graph leaf (i.e
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if it was created by the user).
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grad_fn: Gradient function graph trace.
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Parameters:
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data (any tensor class): Tensor to wrap.
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requires_grad (bool): Value of the requires_grad flag. **Keyword only.**
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volatile (bool): Value of the volatile flag. **Keyword only.**
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"""
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_fallthrough_methods = {
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'size',
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'stride',
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'nelement',
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'ndimension',
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'element_size',
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'is_contiguous',
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'is_set_to',
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'is_signed',
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'numel',
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'dim',
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'get_device',
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'is_cuda',
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'shape'
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}
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def __getattr__(self, name):
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if name in self._fallthrough_methods:
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return getattr(self.data, name)
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return object.__getattribute__(self, name)
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def __getitem__(self, key):
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if torch.is_tensor(key):
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key = Variable(key) # auto-wrap tensors
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if isinstance(key, Variable):
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if type(key.data).__name__ == 'ByteTensor':
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return MaskedSelect.apply(self, key)
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elif type(key.data).__name__ == 'LongTensor':
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return IndexSelect.apply(self, 0, key)
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# else fall through and raise an error in Index
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return Index.apply(self, key)
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def __setitem__(self, key, value):
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if isinstance(key, Variable) and type(key.data).__name__ == 'ByteTensor':
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if isinstance(value, Variable):
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return MaskedScatter.apply(self, key, value, True)
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else:
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return MaskedFill.apply(self, key, value, True)
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else:
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return SetItem.apply(self, key, value)
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def __deepcopy__(self, memo):
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if not self.is_leaf:
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raise RuntimeError("Only Variables created explicitly by the user "
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"(graph leaves) support the deepcopy protocol at the moment")
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result = type(self)(self.data.clone())
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result.requires_grad = self.requires_grad
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result.volatile = self.volatile
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memo[id(self)] = result
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return result
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def __reduce_ex__(self, proto):
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state = (self.requires_grad, self.volatile, self._backward_hooks)
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if proto > 1:
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return type(self), (self.data,), state
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if sys.version_info[0] == 2:
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from copy_reg import __newobj__
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else:
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from copyreg import __newobj__
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return __newobj__, (type(self), self.data), state
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def __setstate__(self, state):
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if len(state) == 5:
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# legacy serialization of Variable
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self.data = state[0]
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state = (state[3], state[4], state[2])
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if not self.is_leaf:
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raise RuntimeError('__setstate__ can be only called on leaf variables')
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self.requires_grad, self.volatile, self._backward_hooks = state
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def __repr__(self):
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return 'Variable containing:' + self.data.__repr__()
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def __bool__(self):
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if self.data.numel() == 0:
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return False
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raise RuntimeError("bool value of Variable objects containing non-empty " +
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torch.typename(self.data) + " is ambiguous")
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__nonzero__ = __bool__
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def backward(self, gradient=None, retain_graph=None, create_graph=None, retain_variables=None):
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"""Computes the gradient of current variable w.r.t. graph leaves.
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The graph is differentiated using the chain rule. If the variable is
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non-scalar (i.e. its data has more than one element) and requires
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gradient, the function additionaly requires specifying ``gradient``.
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It should be a tensor of matching type and location, that contains
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the gradient of the differentiated function w.r.t. ``self``.
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This function accumulates gradients in the leaves - you might need to
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zero them before calling it.
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Arguments:
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grad_variables (Tensor, Variable or None): Gradient w.r.t. the
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variable. If it is a tensor, it will be automatically converted
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to a Variable that is volatile unless ``create_graph`` is True.
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None values can be specified for scalar Variables or ones that
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don't require grad. If a None value would be acceptable then
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this argument is optional.
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retain_graph (bool, optional): If False, the graph used to compute
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the grads will be freed. Note that in nearly all cases setting
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this option to True is not needed and often can be worked around
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in a much more efficient way. Defaults to the value of
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``create_graph``.
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create_graph (bool, optional): If true, graph of the derivative will
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be constructed, allowing to compute higher order derivative
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products. Defaults to False, unless ``gradient`` is a volatile
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Variable.
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"""
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torch.autograd.backward(self, gradient, retain_graph, create_graph, retain_variables)
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def register_hook(self, hook):
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"""Registers a backward hook.
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The hook will be called every time a gradient with respect to the
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variable is computed. The hook should have the following signature::
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hook(grad) -> Variable or None
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The hook should not modify its argument, but it can optionally return
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a new gradient which will be used in place of :attr:`grad`.
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This function returns a handle with a method ``handle.remove()``
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that removes the hook from the module.
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Example:
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>>> v = Variable(torch.Tensor([0, 0, 0]), requires_grad=True)
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>>> h = v.register_hook(lambda grad: grad * 2) # double the gradient
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>>> v.backward(torch.Tensor([1, 1, 1]))
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>>> v.grad.data
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2
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2
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2
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[torch.FloatTensor of size 3]
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>>> h.remove() # removes the hook
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"""
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if self.volatile:
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raise RuntimeError("cannot register a hook on a volatile variable")
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if not self.requires_grad:
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raise RuntimeError("cannot register a hook on a variable that "
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"doesn't require gradient")
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if self._backward_hooks is None:
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self._backward_hooks = OrderedDict()
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if self.grad_fn is not None:
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self.grad_fn._register_hook_dict(self)
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handle = hooks.RemovableHandle(self._backward_hooks)
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self._backward_hooks[handle.id] = hook
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return handle
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def reinforce(self, reward):
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"""Registers a reward obtained as a result of a stochastic process.
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Differentiating stochastic nodes requires providing them with reward
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value. If your graph contains any stochastic operations, you should
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call this function on their outputs. Otherwise an error will be raised.
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Parameters:
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reward(Tensor): Tensor with per-element rewards. It has to match
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the device location and shape of Variable's data.
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"""
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if not isinstance(self.grad_fn, StochasticFunction):
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raise RuntimeError("reinforce() can be only called on outputs "
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"of stochastic functions")
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self.grad_fn._reinforce(reward)
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def detach(self):
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"""Returns a new Variable, detached from the current graph.
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Result will never require gradient. If the input is volatile, the output
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will be volatile too.
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.. note::
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Returned Variable uses the same data tensor, as the original one, and
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in-place modifications on either of them will be seen, and may trigger
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errors in correctness checks.
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"""
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result = NoGrad()(self) # this is needed, because it merges version counters
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result._grad_fn = None
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return result
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def detach_(self):
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"""Detaches the Variable from the graph that created it, making it a
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leaf.
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"""
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self._grad_fn = None
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self.requires_grad = False
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def retain_grad(self):
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"""Enables .grad attribute for non-leaf Variables."""
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if self.grad_fn is None: # no-op for leaves
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return
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if not self.requires_grad:
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raise RuntimeError("can't retain_grad on Variable that has requires_grad=False")
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if hasattr(self, 'retains_grad'):
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return
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weak_self = weakref.ref(self)
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def retain_grad_hook(grad):
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var = weak_self()
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if var is None:
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return
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if var._grad is None:
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var._grad = grad.clone()
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else:
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var._grad = var._grad + grad
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self.register_hook(retain_grad_hook)
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self.retains_grad = True
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def contiguous(self):
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self.data = self.data.contiguous()
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return self
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def clone(self):
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return Clone.apply(self)
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def type(self, t):
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if t != type(self.data):
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return Type.apply(self, t)
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return self
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def type_as(self, t):
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if isinstance(t, Variable):
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t = t.data
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return self.type(type(t))
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def _get_type(self, name):
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module = torch._import_dotted_name(self.data.__module__)
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return getattr(module, name)
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def cuda(self, device_id=None, async=False):
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return CudaTransfer.apply(self, device_id, async)
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def cpu(self):
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return self.type(getattr(torch, type(self.data).__name__))
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def double(self):
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return self.type(self._get_type('DoubleTensor'))
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def float(self):
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return self.type(self._get_type('FloatTensor'))
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def half(self):
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return self.type(self._get_type('HalfTensor'))
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def long(self):
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return self.type(self._get_type('LongTensor'))
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def int(self):
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return self.type(self._get_type('IntTensor'))
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def short(self):
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return self.type(self._get_type('ShortTensor'))
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def char(self):
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return self.type(self._get_type('CharTensor'))
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def byte(self):
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return self.type(self._get_type('ByteTensor'))
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def is_same_size(self, other_var):
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return self.data.is_same_size(other_var.data)
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def _add(self, other, inplace):
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if isinstance(other, Variable):
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return Add.apply(self, other, inplace)
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else:
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assert not torch.is_tensor(other)
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return AddConstant.apply(self, other, inplace)
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def add(self, other):
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return self._add(other, False)
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def add_(self, other):
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return self._add(other, True)
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def _sub(self, other, inplace):
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if isinstance(other, Variable):
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return Sub.apply(self, other, inplace)
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else:
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assert not torch.is_tensor(other)
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return SubConstant.apply(self, other, inplace)
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def sub(self, other):
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return self._sub(other, False)
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def sub_(self, other):
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return self._sub(other, True)
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def mul(self, other):
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if isinstance(other, Variable):
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return Mul.apply(self, other)
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else:
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assert not torch.is_tensor(other)
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return MulConstant.apply(self, other)
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def mul_(self, other):
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if not isinstance(other, Variable) and not torch.is_tensor(other):
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return MulConstant.apply(self, other, True)
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raise RuntimeError("mul_ only supports scalar multiplication")
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def div(self, other):
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if isinstance(other, Variable):
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return Div.apply(self, other)
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else:
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assert not torch.is_tensor(other)
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return DivConstant.apply(self, other)
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def div_(self, other):
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if not isinstance(other, Variable) and not torch.is_tensor(other):
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return DivConstant.apply(self, other, True)
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raise RuntimeError("div_ only supports scalar multiplication")
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def pow(self, other):
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if isinstance(other, Variable):
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return Pow.apply(self, other)
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else:
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assert not torch.is_tensor(other)
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return PowConstant.apply(self, other)
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def exp(self):
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return Exp.apply(self)
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def exp_(self):
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return Exp.apply(self, True)
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def log(self):
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return Log.apply(self)
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def log1p(self):
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return Log1p.apply(self)
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def neg(self):
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return Negate.apply(self)
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def neg_(self):
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return Negate.apply(self, True)
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def tanh(self):
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return Tanh.apply(self)
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def tanh_(self):
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return Tanh.apply(self, True)
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def sigmoid(self):
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return Sigmoid.apply(self)
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def sigmoid_(self):
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return Sigmoid.apply(self, True)
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def sin(self):
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return Sin.apply(self)
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def cos(self):
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return Cos.apply(self)
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def tan(self):
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return Tan.apply(self)
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def asin(self):
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return Asin.apply(self)
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def acos(self):
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return Acos.apply(self)
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def atan(self):
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return Atan.apply(self)
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def atan2(self, x):
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return Atan2.apply(self, x)
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def sinh(self):
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return Sinh.apply(self)
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def cosh(self):
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return Cosh.apply(self)
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def abs(self):
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return Abs.apply(self)
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def clamp(self, min=None, max=None):
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if min is None and max is None:
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raise ValueError("clamp requires specifying at least one of "
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"min and max arguments")
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elif min is None and max is not None:
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return CminConstant.apply(self, max)
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elif min is not None and max is None:
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return CmaxConstant.apply(self, min)
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else:
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return Clamp.apply(self, min, max)
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def reciprocal(self):
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return Reciprocal.apply(self)
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def floor(self):
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return Floor.apply(self)
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def ceil(self):
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return Ceil.apply(self)
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def frac(self):
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return Frac.apply(self)
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def sqrt(self):
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return Sqrt.apply(self)
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def round(self):
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return Round.apply(self)
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def sign(self):
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return Sign.apply(self)
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def trunc(self):
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return Trunc.apply(self)
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def fmod(self, value):
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return Fmod.apply(self, value)
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def remainder(self, value):
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return Remainder.apply(self, value)
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def lerp(self, tensor, weight):
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return Lerp.apply(self, tensor, weight)
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def rsqrt(self):
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return Rsqrt.apply(self)
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def sum(self, dim=None, keepdim=None):
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return Sum.apply(self, dim, keepdim)
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def prod(self, dim=None, keepdim=None):
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return Prod.apply(self, dim, keepdim)
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def mean(self, dim=None, keepdim=None):
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return Mean.apply(self, dim, keepdim)
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def max(self, dim=None, keepdim=None):
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if isinstance(dim, Variable):
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return Cmax.apply(self, dim)
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return Max.apply(self, dim, keepdim)
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def min(self, dim=None, keepdim=None):
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if isinstance(dim, Variable):
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return Cmin.apply(self, dim)
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return Min.apply(self, dim, keepdim)
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def mode(self, dim=None, keepdim=None):
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return Mode.apply(self, dim, keepdim)
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def median(self, dim=None, keepdim=None):
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return Median.apply(self, dim, keepdim)
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def kthvalue(self, k, dim=None, keepdim=None):
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return Kthvalue.apply(self, k, dim, keepdim)
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def sort(self, dim=None, descending=False):
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return Sort.apply(self, dim, descending, True)
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def topk(self, k, dim=None, largest=True, sorted=True):
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return Topk.apply(self, k, dim, largest, sorted, True)
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def view(self, *sizes):
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return View.apply(self, sizes)
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def view_as(self, tensor):
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return View.apply(self, tensor.size())
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def split(self, split_size, dim=0):
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return torch.split(self, split_size, dim)
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def repeat(self, *repeats):
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if len(repeats) == 1 and isinstance(repeats[0], torch.Size):
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repeats = repeats[0]
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else:
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repeats = torch.Size(repeats)
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return Repeat.apply(self, repeats)
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def cumsum(self, dim):
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return Cumsum.apply(self, dim)
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def cumprod(self, dim):
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return Cumprod.apply(self, dim)
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def unfold(self, dim, size, step):
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return Unfold.apply(self, dim, size, step)
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def var(self, dim=None, keepdim=None, unbiased=True):
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keepdim_ = False if keepdim is None else keepdim
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mean = self.mean(dim, keepdim)
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if dim is None:
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mean = mean.view(*(1 for s in self.size()))
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# we could just set keepdim to True, but this preserves some fidelity
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elif keepdim_ is False and self.dim() != 1:
|
|
mean = mean.unsqueeze(dim)
|
|
mean_expanded = mean.expand_as(self)
|
|
zero_centered = self.sub(mean_expanded)
|
|
var = zero_centered.mul(zero_centered).sum(dim, keepdim=keepdim_)
|
|
numel = self.numel() if dim is None else self.size(dim)
|
|
return var.div(numel - int(unbiased))
|
|
|
|
def std(self, dim=None, keepdim=None, unbiased=True):
|
|
return self.var(dim, keepdim, unbiased).sqrt()
|
|
|
|
def renorm(self, p, dim, maxnorm):
|
|
t = self.transpose(dim, 0)
|
|
flat = t.contiguous().view(self.size(0), -1)
|
|
norms = flat.norm(p, 1, True)
|
|
norms = norms.clamp(max=maxnorm).div(norms.add(1e-7))
|
|
flat_out = flat.mul(norms.expand_as(flat))
|
|
return flat_out.view(t.size()).transpose(dim, 0)
|
|
|
|
def matmul(self, other):
|
|
return torch.matmul(self, other)
|
|
|
|
@staticmethod
|
|
def _static_blas(cls, args, inplace):
|
|
num_args = len(args)
|
|
alpha = beta = 1
|
|
if num_args > 5:
|
|
raise RuntimeError("too many args")
|
|
if num_args == 5:
|
|
alpha, beta = args[1:3]
|
|
if num_args == 4:
|
|
alpha = args[1]
|
|
return cls.apply(*(args[:1] + args[-2:] + (alpha, beta, inplace)))
|
|
|
|
def _blas(self, cls, args, inplace):
|
|
return self._static_blas(cls, (self,) + args, inplace)
|
|
|
|
def mm(self, matrix):
|
|
output = Variable(self.data.new(self.data.size(0), matrix.data.size(1)))
|
|
return Addmm.apply(output, self, matrix, 0, 1, True)
|
|
|
|
def bmm(self, batch):
|
|
output = Variable(self.data.new(self.data.size(0), self.data.size(1),
|
|
batch.data.size(2)))
|
|
return self._static_blas(Baddbmm, (output, 0, 1, self, batch), False)
|
|
|
|
def mv(self, vector):
|
|
output = Variable(self.data.new(self.data.size(0)))
|
|
return self._static_blas(Addmv, (output, 0, 1, self, vector), False)
|
|
|
|
def ger(self, vector):
|
|
output = Variable(self.data.new(self.data.size(0), vector.data.size(0)))
|
|
return self._static_blas(Addr, (output, 0, 1, self, vector), False)
|
|
|
|
def resize(self, *sizes):
|
|
return Resize.apply(self, sizes)
|
|
|
|
def resize_as(self, variable):
|
|
return Resize.apply(self, variable.size())
|
|
|
|
def addmm(self, *args):
|
|
return self._blas(Addmm, args, False)
|
|
|
|
def addmm_(self, *args):
|
|
return self._blas(Addmm, args, True)
|
|
|
|
def addbmm(self, *args):
|
|
return self._blas(Addbmm, args, False)
|
|
|
|
def addbmm_(self, *args):
|
|
return self._blas(Addbmm, args, True)
|
|
|
|
def baddbmm(self, *args):
|
|
return self._blas(Baddbmm, args, False)
|
|
|
|
def baddbmm_(self, *args):
|
|
return self._blas(Baddbmm, args, True)
|
|
|
|
def addmv(self, *args):
|
|
return self._blas(Addmv, args, False)
|
|
|
|
def addmv_(self, *args):
|
|
return self._blas(Addmv, args, True)
|
|
|
|
def addr(self, *args):
|
|
return self._blas(Addr, args, False)
|
|
|
|
def addr_(self, *args):
|
|
return self._blas(Addr, args, True)
|
|
|
|
def dot(self, other):
|
|
return Dot.apply(self, other)
|
|
|
|
def _addcop(self, op, args, inplace):
|
|
if len(args) == 3:
|
|
# args == [scale, tensor1, tensor2]
|
|
return op.apply(self, args[1], args[2], args[0], inplace)
|
|
else:
|
|
# args == [tensor1, tensor2]
|
|
return op.apply(self, args[0], args[1], 1.0, inplace)
|
|
|
|
def addcmul(self, *args):
|
|
return self._addcop(Addcmul, args, False)
|
|
|
|
def addcdiv(self, *args):
|
|
return self._addcop(Addcdiv, args, False)
|
|
|
|
def addcmul_(self, *args):
|
|
return self._addcop(Addcmul, args, True)
|
|
|
|
def addcdiv_(self, *args):
|
|
return self._addcop(Addcdiv, args, True)
|
|
|
|
def norm(self, p=2, dim=None, keepdim=None):
|
|
return Norm.apply(self, p, dim, keepdim)
|
|
|
|
def dist(self, tensor, p=2):
|
|
return Norm.apply(self - tensor, p)
|
|
|
|
def index_add(self, dim, index, tensor):
|
|
return IndexAdd.apply(self, dim, index, tensor)
|
|
|
|
def _advanced_index_add(self, index, tensor):
|
|
return AdvancedIndexAdd.apply(self, index, tensor)
|
|
|
|
def index_add_(self, dim, index, tensor):
|
|
return IndexAdd.apply(self, dim, index, tensor, True)
|
|
|
|
def index_copy(self, dim, index, tensor):
|
|
return IndexCopy.apply(self, dim, index, tensor)
|
|
|
|
def index_copy_(self, dim, index, tensor):
|
|
return IndexCopy.apply(self, dim, index, tensor, True)
|
|
|
|
def index_fill(self, dim, index, value):
|
|
return IndexFill.apply(self, dim, index, value)
|
|
|
|
def index_fill_(self, dim, index, value):
|
|
return IndexFill.apply(self, dim, index, value, True)
|
|
|
|
def index_select(self, dim, index):
|
|
return IndexSelect.apply(self, dim, index)
|
|
|
|
def gather(self, dim, index):
|
|
return Gather.apply(self, dim, index)
|
|
|
|
def scatter(self, dim, index, source):
|
|
return Scatter.apply(self, dim, index, source)
|
|
|
|
def scatter_(self, dim, index, source):
|
|
return Scatter.apply(self, dim, index, source, True)
|
|
|
|
def scatter_add(self, dim, index, source):
|
|
return ScatterAdd.apply(self, dim, index, source)
|
|
|
|
def scatter_add_(self, dim, index, source):
|
|
return ScatterAdd.apply(self, dim, index, source, True)
|
|
|
|
def masked_copy(self, mask, variable):
|
|
warnings.warn("masked_copy is deprecated and renamed to masked_scatter, and will be removed in v0.3")
|
|
return MaskedScatter.apply(self, mask, variable)
|
|
|
|
def masked_copy_(self, mask, variable):
|
|
warnings.warn("masked_copy_ is deprecated and renamed to masked_scatter_, and will be removed in v0.3")
|
|
return MaskedScatter.apply(self, mask, variable, True)
|
|
|
|
def masked_scatter(self, mask, variable):
|
|
return MaskedScatter.apply(self, mask, variable)
|
|
|
|
def masked_scatter_(self, mask, variable):
|
|
return MaskedScatter.apply(self, mask, variable, True)
|
|
|
|
def masked_fill(self, mask, value):
|
|
return MaskedFill.apply(self, mask, value)
|
|
|
|
def masked_fill_(self, mask, value):
|
|
return MaskedFill.apply(self, mask, value, True)
|
|
|
|
def masked_select(self, mask):
|
|
return MaskedSelect.apply(self, mask)
|
|
|
|
def expand(self, *sizes):
|
|
return Expand.apply(self, sizes)
|
|
|
|
def expand_as(self, tensor):
|
|
return Expand.apply(self, (tensor.size(),))
|
|
|
|
def t(self):
|
|
if self.dim() != 2:
|
|
raise RuntimeError("t() expects a 2D Variable, but self is {}D".format(self.dim()))
|
|
return Transpose.apply(self, 0, 1)
|
|
|
|
def transpose(self, dim1, dim2):
|
|
return Transpose.apply(self, dim1, dim2)
|
|
|
|
def select(self, dim, _index):
|
|
dim = dim if dim >= 0 else dim + self.dim()
|
|
index = tuple(slice(None, None) for _ in range(dim)) + (_index,)
|
|
return Index.apply(self, index)
|
|
|
|
def narrow(self, dim, start_index, length):
|
|
dim = dim if dim >= 0 else dim + self.dim()
|
|
index = tuple(slice(None, None) for _ in range(dim)) + \
|
|
(slice(start_index, start_index + length),)
|
|
return Index.apply(self, index)
|
|
|
|
def chunk(self, num_chunks, dim=0):
|
|
return Chunk.apply(self, num_chunks, dim)
|
|
|
|
def squeeze(self, dim=None):
|
|
return Squeeze.apply(self, dim)
|
|
|
|
def squeeze_(self, dim=None):
|
|
return Squeeze.apply(self, dim, True)
|
|
|
|
def unsqueeze(self, dim):
|
|
return Unsqueeze.apply(self, dim)
|
|
|
|
def permute(self, *permutation):
|
|
return Permute.apply(self, permutation)
|
|
|
|
def diag(self, diagonal=0):
|
|
return Diag.apply(self, diagonal)
|
|
|
|
def tril(self, diagonal=0):
|
|
return Tril.apply(self, diagonal)
|
|
|
|
def triu(self, diagonal=0):
|
|
return Triu.apply(self, diagonal)
|
|
|
|
def trace(self):
|
|
return Trace.apply(self)
|
|
|
|
def cross(self, other, dim=-1):
|
|
return Cross.apply(self, other)
|
|
|
|
def inverse(self):
|
|
return Inverse.apply(self)
|
|
|
|
def gesv(self, a):
|
|
return Gesv.apply(self, a)
|
|
|
|
def multinomial(self, num_samples=1, replacement=False):
|
|
return Multinomial(num_samples, replacement)(self)
|
|
|
|
def bernoulli(self):
|
|
return Bernoulli()(self)
|
|
|
|
def eq(self, other):
|
|
assert not torch.is_tensor(other), "can't compare Variable and tensor"
|
|
return Eq.apply(self, other)
|
|
|
|
def ne(self, other):
|
|
assert not torch.is_tensor(other), "can't compare Variable and tensor"
|
|
return Ne.apply(self, other)
|
|
|
|
def gt(self, other):
|
|
assert not torch.is_tensor(other), "can't compare Variable and tensor"
|
|
return Gt.apply(self, other)
|
|
|
|
def ge(self, other):
|
|
assert not torch.is_tensor(other), "can't compare Variable and tensor"
|
|
return Ge.apply(self, other)
|
|
|
|
def lt(self, other):
|
|
assert not torch.is_tensor(other), "can't compare Variable and tensor"
|
|
return Lt.apply(self, other)
|
|
|
|
def le(self, other):
|
|
assert not torch.is_tensor(other), "can't compare Variable and tensor"
|
|
return Le.apply(self, other)
|
|
|
|
def __add__(self, other):
|
|
return self.add(other)
|
|
__radd__ = __add__
|
|
|
|
def __iadd__(self, other):
|
|
return self.add_(other)
|
|
|
|
def __sub__(self, other):
|
|
return self.sub(other)
|
|
|
|
def __isub__(self, other):
|
|
return self.sub_(other)
|
|
|
|
def __rsub__(self, other):
|
|
return SubConstant.apply(other, self)
|
|
|
|
def __mul__(self, other):
|
|
return self.mul(other)
|
|
__rmul__ = __mul__
|
|
|
|
def __imul__(self, other):
|
|
return self.mul_(other)
|
|
|
|
def __matmul__(self, other):
|
|
if not isinstance(other, Variable):
|
|
return NotImplemented
|
|
return self.matmul(other)
|
|
|
|
def __div__(self, other):
|
|
return self.div(other)
|
|
__truediv__ = __div__
|
|
|
|
def __rdiv__(self, other):
|
|
return DivConstant.apply(other, self)
|
|
__rtruediv__ = __rdiv__
|
|
|
|
def __idiv__(self, other):
|
|
return self.div_(other)
|
|
|
|
def __pow__(self, other):
|
|
return self.pow(other)
|
|
|
|
def __ipow__(self, other):
|
|
raise NotImplementedError("in-place pow not implemented")
|
|
|
|
def __rpow__(self, other):
|
|
return PowConstant.apply(other, self)
|
|
|
|
def __neg__(self):
|
|
return Negate.apply(self)
|
|
|
|
def __len__(self):
|
|
return len(self.data)
|
|
|
|
def __iter__(self):
|
|
return iter(map(lambda i: self[i], range(self.size(0))))
|
|
|
|
def __mod__(self, other):
|
|
return self.remainder(other)
|
|
|
|
def __eq__(self, other):
|
|
return self.eq(other)
|
|
|
|
def __ne__(self, other):
|
|
return self.ne(other)
|
|
|
|
def __lt__(self, other):
|
|
return self.lt(other)
|
|
|
|
def __le__(self, other):
|
|
return self.le(other)
|
|
|
|
def __gt__(self, other):
|
|
return self.gt(other)
|
|
|
|
def __ge__(self, other):
|
|
return self.ge(other)
|
|
|
|
def __hash__(self):
|
|
return id(self)
|
|
|
|
class _torch(object):
|
|
|
|
@staticmethod
|
|
def cat(iterable, dim=0):
|
|
return Concat.apply(dim, *iterable)
|
|
|
|
@staticmethod
|
|
def normal(means, std=1):
|
|
if isinstance(std, Variable):
|
|
return Normal()(means, std)
|
|
else:
|
|
return Normal(std)(means)
|
|
|
|
@staticmethod
|
|
def _blas(cls, args, inplace):
|
|
num_args = len(args)
|
|
alpha = beta = 1
|
|
if num_args > 5:
|
|
raise RuntimeError("too many args")
|
|
if num_args == 5:
|
|
alpha, beta = args[0], args[2]
|
|
tensors = args[1:2] + args[3:]
|
|
elif num_args == 4:
|
|
alpha = args[0]
|
|
tensors = args[1:]
|
|
else:
|
|
tensors = args
|
|
return cls.apply(*(tensors + (alpha, beta, inplace)))
|
|
|
|
@classmethod
|
|
def addmm(cls, *args):
|
|
return cls._blas(Addmm, args, False)
|
|
|
|
@classmethod
|
|
def addbmm(cls, *args):
|
|
return cls._blas(Addbmm, args, False)
|
|
|
|
@classmethod
|
|
def baddbmm(cls, *args):
|
|
return cls._blas(Baddbmm, args, False)
|
|
|
|
@classmethod
|
|
def addmv(cls, *args):
|
|
return cls._blas(Addmv, args, False)
|
|
|
|
@classmethod
|
|
def addr(cls, *args):
|
|
return cls._blas(Addr, args, False)
|
|
|
|
|
|
for method in dir(Variable):
|
|
# This will also wrap some methods that normally aren't part of the
|
|
# funcitonal interface, but we don't care, as they won't ever be used
|
|
if method.startswith('_') or method.endswith('_'):
|
|
continue
|
|
if hasattr(Variable._torch, method):
|
|
continue
|
|
as_static = staticmethod(getattr(Variable, method))
|
|
setattr(Variable._torch, method, as_static)
|
|
|
|
|
|
from ._functions import *
|
|
from torch._C import _ImperativeEngine as ImperativeEngine
|
|
Variable._execution_engine = ImperativeEngine()
|