homura.metrics package¶
Submodules¶
homura.metrics.commons module¶
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homura.metrics.commons.
accuracy
(input, target, top_k=1)[source]¶ - Parameters
input (torch.Tensor) –
target (torch.Tensor) –
top_k (int) –
- Return type
torch.Tensor
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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¶
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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
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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