- class ignite.metrics.VariableAccumulation(op, output_transform=<function VariableAccumulation.<lambda>>, device=device(type='cpu'))#
Single variable accumulator helper to compute (arithmetic, geometric, harmonic) average of a single variable.
updatemust receive output of the form x.
x can be a number or torch.Tensor.
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).
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
process_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, torch.device]) – specifies which device updates are accumulated on. Setting the metric’s device to be the same as your
updatearguments ensures the
updatemethod is non-blocking. By default, CPU.
Computes the metric based on it's accumulated state.
Resets the metric to it's initial state.
Updates the metric's state using the passed batch output.
Computes the metric based on it’s accumulated state.
By default, this is called at the end of each epoch.
- Return type
NotComputableError – raised when the metric cannot be computed.
- required_output_keys: Optional[Tuple] = None#
Resets the metric to it’s initial state.
By default, this is called at the start of each epoch.
- Return type