extorch.utils.stats

class extorch.utils.stats.AverageMeter[source]

Bases: object

is_empty() → bool[source]
reset() → None[source]
update(val: Union[float, int], n: int = 1) → None[source]
class extorch.utils.stats.OrderedStats[source]

Bases: object

avgs() → Optional[collections.OrderedDict][source]
items()[source]
update(stats, n: int = 1) → None[source]
class extorch.utils.stats.SegConfusionMatrix(num_classes: int)[source]

Bases: object

Confusion matrix as well as metric calculation for image segmentation.

Parameters

num_classes (int) – The number of classes, including the background.

compute()[source]
reset() → None[source]
update(output: torch.Tensor, target: torch.Tensor) → None[source]
extorch.utils.stats.accuracy(outputs: torch.Tensor, targets: torch.Tensor, topk: Tuple[int] = (1,)) → List[torch.Tensor][source]
extorch.utils.stats.cal_flops(model: torch.nn.modules.module.Module, inputs: torch.Tensor) → float[source]

Calculate FLOPs of the given model.

Parameters

  • model (nn.Module) – The model whose FLOPs is to be calculated.

  • inputs (Tensor) – Example inputs to the model.

Returns

FLOPs of the model.

Return type

flops (float)

Examples::
>>> import torch
>>> from extorch.nn import AuxiliaryHead
>>> module = AuxiliaryHead(3, 10)
>>> input = torch.randn((10, 3, 32, 32))
>>> flops = cal_flops(module, input) / 1.e6 # 32.109868
extorch.utils.stats.get_params(model: torch.nn.modules.module.Module, only_trainable: bool = False) → int[source]

Get the parameter number of the model.

Parameters

only_trainable (bool) – If only_trainable is true, only trainable parameters will be counted.