TopKCategoricalAccuracy#
- class ignite.metrics.TopKCategoricalAccuracy(k=5, output_transform=<function TopKCategoricalAccuracy.<lambda>>, device=device(type='cpu'), skip_unrolling=False)[source]#
Calculates the top-k categorical accuracy.
update
must receive output of the form(y_pred, y)
.
- Parameters
k (int) – the k in “top-k”.
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. By default, metrics require the output as(y_pred, y)
or{'y_pred': y_pred, 'y': y}
.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.skip_unrolling (bool) – specifies whether output should be unrolled before being fed to update method. Should be true for multi-output model, for example, if
y_pred
contains multi-ouput as(y_pred_a, y_pred_b)
Alternatively,output_transform
can be used to handle this.
Examples
To use with
Engine
andprocess_function
, simply attach the metric instance to the engine. The output of the engine’sprocess_function
needs to be in the format of(y_pred, y)
or{'y_pred': y_pred, 'y': y, ...}
. If not,output_tranform
can be added to the metric to transform the output into the form expected by the metric.For more information on how metric works with
Engine
, visit Attach Engine API.from collections import OrderedDict import torch from torch import nn, optim from ignite.engine import * from ignite.handlers import * from ignite.metrics import * from ignite.metrics.regression import * from ignite.utils import * # create default evaluator for doctests def eval_step(engine, batch): return batch default_evaluator = Engine(eval_step) # create default optimizer for doctests param_tensor = torch.zeros([1], requires_grad=True) default_optimizer = torch.optim.SGD([param_tensor], lr=0.1) # create default trainer for doctests # as handlers could be attached to the trainer, # each test must define his own trainer using `.. testsetup:` def get_default_trainer(): def train_step(engine, batch): return batch return Engine(train_step) # create default model for doctests default_model = nn.Sequential(OrderedDict([ ('base', nn.Linear(4, 2)), ('fc', nn.Linear(2, 1)) ])) manual_seed(666)
def process_function(engine, batch): y_pred, y = batch return y_pred, y def one_hot_to_binary_output_transform(output): y_pred, y = output y = torch.argmax(y, dim=1) # one-hot vector to label index vector return y_pred, y engine = Engine(process_function) metric = TopKCategoricalAccuracy( k=2, output_transform=one_hot_to_binary_output_transform) metric.attach(engine, 'top_k_accuracy') preds = torch.tensor([ [0.7, 0.2, 0.05, 0.05], # 1 is in the top 2 [0.2, 0.3, 0.4, 0.1], # 0 is not in the top 2 [0.4, 0.4, 0.1, 0.1], # 0 is in the top 2 [0.7, 0.05, 0.2, 0.05] # 2 is in the top 2 ]) target = torch.tensor([ # targets as one-hot vectors [0, 1, 0, 0], [1, 0, 0, 0], [1, 0, 0, 0], [0, 0, 1, 0] ]) state = engine.run([[preds, target]]) print(state.metrics['top_k_accuracy'])
0.75
Changed in version 0.5.1:
skip_unrolling
argument is added.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.