o .wi @s~ddlmZddlmZmZmZddlmZddlm Z ddl m Z ddl m Z mZddlmZe s5dgZGd d d eZd S) )Sequence)AnyOptionalUnion)Tensor)Metric)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPE) WrapperMetric Running.plotcseZdZdZddededdffdd Zd ed eddfd d Zd ed edefd dZ defddZ dfdd Z dde e eeefde edefddZZS)Runninga Running wrapper for metrics. Using this wrapper allows for calculating metrics over a running window of values, instead of the whole history of values. This is beneficial when you want to get a better estimate of the metric during training and don't want to wait for the whole training to finish to get epoch level estimates. The running window is defined by the `window` argument. The window is a fixed size and this wrapper will store a duplicate of the underlying metric state for each value in the window. Thus memory usage will increase linearly with window size. Use accordingly. Also note that the running only works with metrics that have the `full_state_update` set to `False`. Importantly, the wrapper does not alter the value of the `forward` method of the underlying metric. Thus, forward will still return the value on the current batch. To get the running value call `compute` instead. Args: base_metric: The metric to wrap. window: The size of the running window. Example (single metric): >>> from torch import tensor >>> from torchmetrics.wrappers import Running >>> from torchmetrics.aggregation import SumMetric >>> metric = Running(SumMetric(), window=3) >>> for i in range(6): ... current_val = metric(tensor([i])) ... running_val = metric.compute() ... total_val = tensor(sum(list(range(i+1)))) # value we would get from `compute` without running ... print(f"{current_val=}, {running_val=}, {total_val=}") current_val=tensor(0.), running_val=tensor(0.), total_val=tensor(0) current_val=tensor(1.), running_val=tensor(1.), total_val=tensor(1) current_val=tensor(2.), running_val=tensor(3.), total_val=tensor(3) current_val=tensor(3.), running_val=tensor(6.), total_val=tensor(6) current_val=tensor(4.), running_val=tensor(9.), total_val=tensor(10) current_val=tensor(5.), running_val=tensor(12.), total_val=tensor(15) Example (metric collection): >>> from torch import tensor >>> from torchmetrics.wrappers import Running >>> from torchmetrics import MetricCollection >>> from torchmetrics.aggregation import SumMetric, MeanMetric >>> # note that running is input to collection, not the other way >>> metric = MetricCollection({"sum": Running(SumMetric(), 3), "mean": Running(MeanMetric(), 3)}) >>> for i in range(6): ... current_val = metric(tensor([i])) ... running_val = metric.compute() ... print(f"{current_val=}, {running_val=}") current_val={'mean': tensor(0.), 'sum': tensor(0.)}, running_val={'mean': tensor(0.), 'sum': tensor(0.)} current_val={'mean': tensor(1.), 'sum': tensor(1.)}, running_val={'mean': tensor(0.5000), 'sum': tensor(1.)} current_val={'mean': tensor(2.), 'sum': tensor(2.)}, running_val={'mean': tensor(1.), 'sum': tensor(3.)} current_val={'mean': tensor(3.), 'sum': tensor(3.)}, running_val={'mean': tensor(2.), 'sum': tensor(6.)} current_val={'mean': tensor(4.), 'sum': tensor(4.)}, running_val={'mean': tensor(3.), 'sum': tensor(9.)} current_val={'mean': tensor(5.), 'sum': tensor(5.)}, running_val={'mean': tensor(4.), 'sum': tensor(12.)}  base_metricwindowreturnNcstt|tstd|t|tr|dks!td|||_||_|jdur4td|jd|_ |j D]}t |D]}|j |d||j ||j |dq@q:dS)NzNExpected argument `metric` to be an instance of `torchmetrics.Metric` but got rzExpected attribute `full_state_update` set to `False` but got _)namedefaultdist_reduce_fx)super__init__ isinstancer ValueErrorintrrfull_state_update_num_vals_seen _defaultsrange add_state _reductions)selfrrkeyi __class__Z/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/wrappers/running.pyrTs*      zRunning.__init__argskwargscOsf|j|j}|jj|i||jjD]}t||d|t|j|q|j|jd7_dS)z7Update the underlying metric and save state afterwards.rN)rrrupdatersetattrgetattrreset)r!r(r)valr"r&r&r'r+ks    zRunning.updatecOsl|j|j}|jj|i|}|jjD]}t||d|t|j|q|j|jd7_d|_|S)zAForward input to the underlying metric and save state afterwards.rr*N) rrrforwardrr,r-r. _computed)r!r(r)r/resr"r&r&r'r0ts    zRunning.forwardcsRtjD]jfddjjDqjj_j}j|S)z+Compute the metric over the running window.cs"i|] }|t|dqS)r)r-).0r"r#r!r&r' s"z#Running.compute..) rrr_reduce_statesrr _update_countcomputer.)r!r/r&r4r'r8s "   zRunning.computecstd|_dS)z Reset metric.rN)rr.r)r!r$r&r'r.s  z Running.resetr/axcCs |||S)aePlot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> # Example plotting a single value >>> import torch >>> from torchmetrics.wrappers import Running >>> from torchmetrics.aggregation import SumMetric >>> metric = Running(SumMetric(), 2) >>> metric.update(torch.randn(20, 2)) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> import torch >>> from torchmetrics.wrappers import Running >>> from torchmetrics.aggregation import SumMetric >>> metric = Running(SumMetric(), 2) >>> values = [ ] >>> for _ in range(3): ... values.append(metric(torch.randn(20, 2))) >>> fig_, ax_ = metric.plot(values) )_plot)r!r/r9r&r&r'plots *r )r)rN)NN)__name__ __module__ __qualname____doc__rrrrr+r0r8r.rrrrr r r; __classcell__r&r&r$r'r s7   r N)collections.abcrtypingrrrtorchrtorchmetrics.metricrtorchmetrics.utilities.importsrtorchmetrics.utilities.plotr r torchmetrics.wrappers.abstractr __doctest_skip__r r&r&r&r's