VariableAccumulation#
- class ignite.metrics.VariableAccumulation(op, output_transform=<function VariableAccumulation.<lambda>>, device=device(type='cpu'))[source]#
Single variable accumulator helper to compute (arithmetic, geometric, harmonic) average of a single variable.
update
must receive output of the form x.x can be a number or torch.Tensor.
Note
The class stores input into two public variables: accumulator and num_examples. Number of samples is updated following the rule:
+1 if input is a number
+1 if input is a 1D torch.Tensor
+batch_size if input is a ND torch.Tensor. Batch size is the first dimension (shape[0]).
- Parameters
op (Callable) – a callable to update accumulator. Method’s signature is (accumulator, output). For example, to compute arithmetic mean value, op = lambda a, x: a + x.
output_transform (Callable) – a callable that is used to transform the
Engine
’sprocess_function
’s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs.device (Union[str, device]) – specifies which device updates are accumulated on. Setting the metric’s device to be the same as your
update
arguments ensures theupdate
method is non-blocking. By default, CPU.
Methods
Computes the metric based on its accumulated state.
Resets the metric to its initial state.
Updates the metric's state using the passed batch output.
- compute()[source]#
Computes the metric based on its accumulated state.
By default, this is called at the end of each epoch.
- Returns
- the actual quantity of interest. However, if a
Mapping
is returned, it will be (shallow) flattened into engine.state.metrics whencompleted()
is called. - Return type
Any
- Raises
NotComputableError – raised when the metric cannot be computed.
- required_output_keys: Optional[Tuple] = None#