homura.metrics package¶
Submodules¶
homura.metrics.commons module¶
- homura.metrics.commons.accuracy(input, target, top_k=1)[source]¶
- Parameters
input (torch.Tensor) –
target (torch.Tensor) –
top_k (int) –
- Return type
torch.Tensor
- homura.metrics.commons.classwise_accuracy(input, target)[source]¶
Calculate class wise accuracy
- Parameters
input (torch.Tensor) – output of network, expected to be BxCx(OPTIONAL DIMENSIONS)
target (torch.Tensor) – target, expected to be Bx(OPTIONAL DIMENSIONS)
- Returns
class wise accuracy in float tensor of C
- Return type
torch.Tensor
- homura.metrics.commons.confusion_matrix(input, target)[source]¶
Calculate confusion matrix
- Parameters
input (torch.Tensor) – output of network, expected to be BxCx(OPTIONAL DIMENSIONS)
target (torch.Tensor) – target, expected to be Bx(OPTIONAL DIMENSIONS)
- Returns
confusion matrix in long tensor of CxC
- Return type
torch.Tensor
- homura.metrics.commons.f1_score(input, target)[source]¶
Calculate f1 score
- Parameters
input (torch.Tensor) – output of network, expected to be BxCx(OPTIONAL DIMENSIONS)
target (torch.Tensor) – target, expected to be Bx(OPTIONAL DIMENSIONS)
- Returns
f1 score in float tensor of C
- Return type
torch.Tensor
- homura.metrics.commons.false_negative(input, target)[source]¶
Calculate false negative :param input: output of network, expected to be BxCx(OPTIONAL DIMENSIONS) :param target: target, expected to be Bx(OPTIONAL DIMENSIONS) :return: false negative in float tensor of C
- Parameters
input (torch.Tensor) –
target (torch.Tensor) –
- Return type
torch.Tensor
- homura.metrics.commons.false_positive(input, target)[source]¶
Calculate false positive
- Parameters
input (torch.Tensor) – output of network, expected to be BxCx(OPTIONAL DIMENSIONS)
target (torch.Tensor) – target, expected to be Bx(OPTIONAL DIMENSIONS)
- Returns
false positive in float tensor of C
- Return type
torch.Tensor
- homura.metrics.commons.precision(input, target)[source]¶
Calculate precision
- Parameters
input (torch.Tensor) – output of network, expected to be BxCx(OPTIONAL DIMENSIONS)
target (torch.Tensor) – target, expected to be Bx(OPTIONAL DIMENSIONS)
- Returns
precision in float tensor of C
- Return type
torch.Tensor
- homura.metrics.commons.recall(input, target)[source]¶
Calculate recall
- Parameters
input (torch.Tensor) – output of network, expected to be BxCx(OPTIONAL DIMENSIONS)
target (torch.Tensor) – target, expected to be Bx(OPTIONAL DIMENSIONS)
- Returns
recall in float tensor of C
- Return type
torch.Tensor
- homura.metrics.commons.specificity(input, target)[source]¶
Calculate specificity
- Parameters
input (torch.Tensor) – output of network, expected to be BxCx(OPTIONAL DIMENSIONS)
target (torch.Tensor) – target, expected to be Bx(OPTIONAL DIMENSIONS)
- Returns
specificity in float tensor of C
- Return type
torch.Tensor
- homura.metrics.commons.true_negative(input, target)[source]¶
Calculate true negative
- Parameters
input (torch.Tensor) – output of network, expected to be BxCx(OPTIONAL DIMENSIONS)
target (torch.Tensor) – target, expected to be Bx(OPTIONAL DIMENSIONS)
- Returns
true negative in float tensor of C
- Return type
torch.Tensor
- homura.metrics.commons.true_positive(input, target)[source]¶
Calculate true positive
- Parameters
input (torch.Tensor) – output of network, expected to be BxCx(OPTIONAL DIMENSIONS)
target (torch.Tensor) – target, expected to be Bx(OPTIONAL DIMENSIONS)
- Returns
true positive in float tensor of C
- Return type
torch.Tensor
homura.metrics.generative module¶
Metrics for generated images
homura.metrics.segmentation module¶
- homura.metrics.segmentation.binary_as_multiclass(input, threshold)[source]¶
Convert Bx1xHxW outputs to BxCxHxW.
- Parameters
input (torch.Tensor) –
threshold (float) –
- Returns
- Return type
torch.Tensor
- homura.metrics.segmentation.classwise_iou(input, target)[source]¶
Class-wise IoU
- Parameters
input (torch.Tensor) – logits (BxCxHxW)
target (torch.Tensor) – target in LongTensor (BxHxW)
- Returns
- Return type
torch.Tensor