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Merge branch 'master' into Feature-weights-hist-handler-issue-2328
2 parents aef9697 + 69ea3c8 commit 42e00db

15 files changed

+81
-83
lines changed

‎ignite/metrics/accumulation.py

Lines changed: 7 additions & 7 deletions
Original file line numberDiff line numberDiff line change
@@ -122,7 +122,7 @@ class Average(VariableAccumulation):
122122
metric = Average()
123123
metric.attach(default_evaluator, 'avg')
124124
# Case 1. input is er
125-
data = torch.Tensor([0, 1, 2, 3, 4])
125+
data = torch.tensor([0, 1, 2, 3, 4])
126126
state = default_evaluator.run(data)
127127
print(state.metrics['avg'])
128128
@@ -135,7 +135,7 @@ class Average(VariableAccumulation):
135135
metric = Average()
136136
metric.attach(default_evaluator, 'avg')
137137
# Case 2. input is a 1D torch.Tensor
138-
data = torch.Tensor([
138+
data = torch.tensor([
139139
[0, 0, 0],
140140
[1, 1, 1],
141141
[2, 2, 2],
@@ -154,8 +154,8 @@ class Average(VariableAccumulation):
154154
metric.attach(default_evaluator, 'avg')
155155
# Case 3. input is a ND torch.Tensor
156156
data = [
157-
torch.Tensor([[0, 0, 0], [1, 1, 1]]),
158-
torch.Tensor([[2, 2, 2], [3, 3, 3]])
157+
torch.tensor([[0, 0, 0], [1, 1, 1]]),
158+
torch.tensor([[2, 2, 2], [3, 3, 3]])
159159
]
160160
state = default_evaluator.run(data)
161161
print(state.metrics['avg'])
@@ -238,7 +238,7 @@ class GeometricAverage(VariableAccumulation):
238238
metric = GeometricAverage()
239239
metric.attach(default_evaluator, 'avg')
240240
# Case 2. input is a 1D torch.Tensor
241-
data = torch.Tensor([
241+
data = torch.tensor([
242242
[1, 1, 1],
243243
[2, 2, 2],
244244
[3, 3, 3],
@@ -257,8 +257,8 @@ class GeometricAverage(VariableAccumulation):
257257
metric.attach(default_evaluator, 'avg')
258258
# Case 3. input is a ND torch.Tensor
259259
data = [
260-
torch.Tensor([[1, 1, 1], [2, 2, 2]]),
261-
torch.Tensor([[3, 3, 3], [4, 4, 4]])
260+
torch.tensor([[1, 1, 1], [2, 2, 2]]),
261+
torch.tensor([[3, 3, 3], [4, 4, 4]])
262262
]
263263
state = default_evaluator.run(data)
264264
print(state.metrics['avg'])

‎ignite/metrics/accuracy.py

Lines changed: 8 additions & 8 deletions
Original file line numberDiff line numberDiff line change
@@ -128,8 +128,8 @@ class Accuracy(_BaseClassification):
128128
129129
metric = Accuracy()
130130
metric.attach(default_evaluator, "accuracy")
131-
y_true = torch.Tensor([1, 0, 1, 1, 0, 1])
132-
y_pred = torch.Tensor([1, 0, 1, 0, 1, 1])
131+
y_true = torch.tensor([1, 0, 1, 1, 0, 1])
132+
y_pred = torch.tensor([1, 0, 1, 0, 1, 1])
133133
state = default_evaluator.run([[y_pred, y_true]])
134134
print(state.metrics["accuracy"])
135135
@@ -143,8 +143,8 @@ class Accuracy(_BaseClassification):
143143
144144
metric = Accuracy()
145145
metric.attach(default_evaluator, "accuracy")
146-
y_true = torch.Tensor([2, 0, 2, 1, 0, 1]).long()
147-
y_pred = torch.Tensor([
146+
y_true = torch.tensor([2, 0, 2, 1, 0, 1])
147+
y_pred = torch.tensor([
148148
[0.0266, 0.1719, 0.3055],
149149
[0.6886, 0.3978, 0.8176],
150150
[0.9230, 0.0197, 0.8395],
@@ -165,14 +165,14 @@ class Accuracy(_BaseClassification):
165165
166166
metric = Accuracy(is_multilabel=True)
167167
metric.attach(default_evaluator, "accuracy")
168-
y_true = torch.Tensor([
168+
y_true = torch.tensor([
169169
[0, 0, 1, 0, 1],
170170
[1, 0, 1, 0, 0],
171171
[0, 0, 0, 0, 1],
172172
[1, 0, 0, 0, 1],
173173
[0, 1, 1, 0, 1],
174174
])
175-
y_pred = torch.Tensor([
175+
y_pred = torch.tensor([
176176
[1, 1, 0, 0, 0],
177177
[1, 0, 1, 0, 0],
178178
[1, 0, 0, 0, 0],
@@ -198,8 +198,8 @@ def thresholded_output_transform(output):
198198
199199
metric = Accuracy(output_transform=thresholded_output_transform)
200200
metric.attach(default_evaluator, "accuracy")
201-
y_true = torch.Tensor([1, 0, 1, 1, 0, 1])
202-
y_pred = torch.Tensor([0.6, 0.2, 0.9, 0.4, 0.7, 0.65])
201+
y_true = torch.tensor([1, 0, 1, 1, 0, 1])
202+
y_pred = torch.tensor([0.6, 0.2, 0.9, 0.4, 0.7, 0.65])
203203
state = default_evaluator.run([[y_pred, y_true]])
204204
print(state.metrics["accuracy"])
205205

‎ignite/metrics/classification_report.py

Lines changed: 4 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -49,8 +49,8 @@ def ClassificationReport(
4949
5050
metric = ClassificationReport(output_dict=True)
5151
metric.attach(default_evaluator, "cr")
52-
y_true = torch.Tensor([2, 0, 2, 1, 0, 1]).long()
53-
y_pred = torch.Tensor([
52+
y_true = torch.tensor([2, 0, 2, 1, 0, 1])
53+
y_pred = torch.tensor([
5454
[0.0266, 0.1719, 0.3055],
5555
[0.6886, 0.3978, 0.8176],
5656
[0.9230, 0.0197, 0.8395],
@@ -79,14 +79,14 @@ def ClassificationReport(
7979
8080
metric = ClassificationReport(output_dict=True, is_multilabel=True)
8181
metric.attach(default_evaluator, "cr")
82-
y_true = torch.Tensor([
82+
y_true = torch.tensor([
8383
[0, 0, 1],
8484
[0, 0, 0],
8585
[0, 0, 0],
8686
[1, 0, 0],
8787
[0, 1, 1],
8888
]).unsqueeze(0)
89-
y_pred = torch.Tensor([
89+
y_pred = torch.tensor([
9090
[1, 1, 0],
9191
[1, 0, 1],
9292
[1, 0, 0],

‎ignite/metrics/confusion_matrix.py

Lines changed: 12 additions & 12 deletions
Original file line numberDiff line numberDiff line change
@@ -56,8 +56,8 @@ class ConfusionMatrix(Metric):
5656
5757
metric = ConfusionMatrix(num_classes=3)
5858
metric.attach(default_evaluator, 'cm')
59-
y_true = torch.Tensor([0, 1, 0, 1, 2]).long()
60-
y_pred = torch.Tensor([
59+
y_true = torch.tensor([0, 1, 0, 1, 2])
60+
y_pred = torch.tensor([
6161
[0.0, 1.0, 0.0],
6262
[0.0, 1.0, 0.0],
6363
[1.0, 0.0, 0.0],
@@ -88,8 +88,8 @@ def binary_one_hot_output_transform(output):
8888
8989
metric = ConfusionMatrix(num_classes=2, output_transform=binary_one_hot_output_transform)
9090
metric.attach(default_evaluator, 'cm')
91-
y_true = torch.Tensor([0, 1, 0, 1, 0]).long()
92-
y_pred = torch.Tensor([0, 0, 1, 1, 0])
91+
y_true = torch.tensor([0, 1, 0, 1, 0])
92+
y_pred = torch.tensor([0, 0, 1, 1, 0])
9393
state = default_evaluator.run([[y_pred, y_true]])
9494
print(state.metrics['cm'])
9595
@@ -216,8 +216,8 @@ def IoU(cm: ConfusionMatrix, ignore_index: Optional[int] = None) -> MetricsLambd
216216
cm = ConfusionMatrix(num_classes=3)
217217
metric = IoU(cm)
218218
metric.attach(default_evaluator, 'iou')
219-
y_true = torch.Tensor([0, 1, 0, 1, 2]).long()
220-
y_pred = torch.Tensor([
219+
y_true = torch.tensor([0, 1, 0, 1, 2])
220+
y_pred = torch.tensor([
221221
[0.0, 1.0, 0.0],
222222
[0.0, 1.0, 0.0],
223223
[1.0, 0.0, 0.0],
@@ -281,8 +281,8 @@ def mIoU(cm: ConfusionMatrix, ignore_index: Optional[int] = None) -> MetricsLamb
281281
cm = ConfusionMatrix(num_classes=3)
282282
metric = mIoU(cm, ignore_index=0)
283283
metric.attach(default_evaluator, 'miou')
284-
y_true = torch.Tensor([0, 1, 0, 1, 2]).long()
285-
y_pred = torch.Tensor([
284+
y_true = torch.tensor([0, 1, 0, 1, 2])
285+
y_pred = torch.tensor([
286286
[0.0, 1.0, 0.0],
287287
[0.0, 1.0, 0.0],
288288
[1.0, 0.0, 0.0],
@@ -372,8 +372,8 @@ def DiceCoefficient(cm: ConfusionMatrix, ignore_index: Optional[int] = None) ->
372372
cm = ConfusionMatrix(num_classes=3)
373373
metric = DiceCoefficient(cm, ignore_index=0)
374374
metric.attach(default_evaluator, 'dice')
375-
y_true = torch.Tensor([0, 1, 0, 1, 2]).long()
376-
y_pred = torch.Tensor([
375+
y_true = torch.tensor([0, 1, 0, 1, 2])
376+
y_pred = torch.tensor([
377377
[0.0, 1.0, 0.0],
378378
[0.0, 1.0, 0.0],
379379
[1.0, 0.0, 0.0],
@@ -441,8 +441,8 @@ def JaccardIndex(cm: ConfusionMatrix, ignore_index: Optional[int] = None) -> Met
441441
cm = ConfusionMatrix(num_classes=3)
442442
metric = JaccardIndex(cm, ignore_index=0)
443443
metric.attach(default_evaluator, 'jac')
444-
y_true = torch.Tensor([0, 1, 0, 1, 2]).long()
445-
y_pred = torch.Tensor([
444+
y_true = torch.tensor([0, 1, 0, 1, 2])
445+
y_pred = torch.tensor([
446446
[0.0, 1.0, 0.0],
447447
[0.0, 1.0, 0.0],
448448
[1.0, 0.0, 0.0],

‎ignite/metrics/epoch_metric.py

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -54,7 +54,7 @@ def mse_fn(y_preds, y_targets):
5454
5555
metric = EpochMetric(mse_fn)
5656
metric.attach(default_evaluator, "mse")
57-
y_true = torch.Tensor([0, 1, 2, 3, 4, 5])
57+
y_true = torch.tensor([0, 1, 2, 3, 4, 5])
5858
y_pred = y_true * 0.75
5959
state = default_evaluator.run([[y_pred, y_true]])
6060
print(state.metrics["mse"])

‎ignite/metrics/fbeta.py

Lines changed: 8 additions & 8 deletions
Original file line numberDiff line numberDiff line change
@@ -60,8 +60,8 @@ def Fbeta(
6060
R = Recall(average=False)
6161
metric = Fbeta(beta=1.0, precision=P, recall=R)
6262
metric.attach(default_evaluator, "f-beta")
63-
y_true = torch.Tensor([1, 0, 1, 1, 0, 1])
64-
y_pred = torch.Tensor([1, 0, 1, 0, 1, 1])
63+
y_true = torch.tensor([1, 0, 1, 1, 0, 1])
64+
y_pred = torch.tensor([1, 0, 1, 0, 1, 1])
6565
state = default_evaluator.run([[y_pred, y_true]])
6666
print(state.metrics["f-beta"])
6767
@@ -77,8 +77,8 @@ def Fbeta(
7777
R = Recall(average=False)
7878
metric = Fbeta(beta=1.0, precision=P, recall=R)
7979
metric.attach(default_evaluator, "f-beta")
80-
y_true = torch.Tensor([2, 0, 2, 1, 0, 1]).long()
81-
y_pred = torch.Tensor([
80+
y_true = torch.tensor([2, 0, 2, 1, 0, 1])
81+
y_pred = torch.tensor([
8282
[0.0266, 0.1719, 0.3055],
8383
[0.6886, 0.3978, 0.8176],
8484
[0.9230, 0.0197, 0.8395],
@@ -101,8 +101,8 @@ def Fbeta(
101101
R = Recall(average=False)
102102
metric = Fbeta(beta=1.0, average=False, precision=P, recall=R)
103103
metric.attach(default_evaluator, "f-beta")
104-
y_true = torch.Tensor([2, 0, 2, 1, 0, 1]).long()
105-
y_pred = torch.Tensor([
104+
y_true = torch.tensor([2, 0, 2, 1, 0, 1])
105+
y_pred = torch.tensor([
106106
[0.0266, 0.1719, 0.3055],
107107
[0.6886, 0.3978, 0.8176],
108108
[0.9230, 0.0197, 0.8395],
@@ -131,8 +131,8 @@ def thresholded_output_transform(output):
131131
R = Recall(average=False, output_transform=thresholded_output_transform)
132132
metric = Fbeta(beta=1.0, precision=P, recall=R)
133133
metric.attach(default_evaluator, "f-beta")
134-
y_true = torch.Tensor([1, 0, 1, 1, 0, 1])
135-
y_pred = torch.Tensor([0.6, 0.2, 0.9, 0.4, 0.7, 0.65])
134+
y_true = torch.tensor([1, 0, 1, 1, 0, 1])
135+
y_pred = torch.tensor([0.6, 0.2, 0.9, 0.4, 0.7, 0.65])
136136
state = default_evaluator.run([[y_pred, y_true]])
137137
print(state.metrics["f-beta"])
138138

‎ignite/metrics/mean_absolute_error.py

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -44,7 +44,7 @@ class MeanAbsoluteError(Metric):
4444
4545
metric = MeanAbsoluteError()
4646
metric.attach(default_evaluator, 'mae')
47-
preds = torch.Tensor([
47+
preds = torch.tensor([
4848
[1, 2, 4, 1],
4949
[2, 3, 1, 5],
5050
[1, 3, 5, 1],

‎ignite/metrics/mean_squared_error.py

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -11,7 +11,7 @@
1111
class MeanSquaredError(Metric):
1212
r"""Calculates the `mean squared error <https://en.wikipedia.org/wiki/Mean_squared_error>`_.
1313
14-
.. math:: \text{MSE} = \frac{1}{N} \sum_{i=1}^N \left(y_{i} - x_{i} \right)^2
14+
.. math:: \text{MSE} = \frac{1}{N} \sum_{i=1}^N \|y_{i} - x_{i}\|^2
1515
1616
where :math:`y_{i}` is the prediction tensor and :math:`x_{i}` is ground true tensor.
1717
@@ -44,7 +44,7 @@ class MeanSquaredError(Metric):
4444
4545
metric = MeanSquaredError()
4646
metric.attach(default_evaluator, 'mse')
47-
preds = torch.Tensor([
47+
preds = torch.tensor([
4848
[1, 2, 4, 1],
4949
[2, 3, 1, 5],
5050
[1, 3, 5, 1],

‎ignite/metrics/metrics_lambda.py

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -52,8 +52,8 @@ def Fbeta(r, p, beta):
5252
F3.attach(default_evaluator, "F3")
5353
F4.attach(default_evaluator, "F4")
5454
55-
y_true = torch.Tensor([1, 0, 1, 0, 0, 1])
56-
y_pred = torch.Tensor([1, 0, 1, 0, 1, 1])
55+
y_true = torch.tensor([1, 0, 1, 0, 0, 1])
56+
y_pred = torch.tensor([1, 0, 1, 0, 1, 1])
5757
state = default_evaluator.run([[y_pred, y_true]])
5858
print(state.metrics["F1"])
5959
print(state.metrics["F2"])

‎ignite/metrics/multilabel_confusion_matrix.py

Lines changed: 4 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -49,20 +49,20 @@ class MultiLabelConfusionMatrix(Metric):
4949
5050
metric = MultiLabelConfusionMatrix(num_classes=3)
5151
metric.attach(default_evaluator, "mlcm")
52-
y_true = torch.Tensor([
52+
y_true = torch.tensor([
5353
[0, 0, 1],
5454
[0, 0, 0],
5555
[0, 0, 0],
5656
[1, 0, 0],
5757
[0, 1, 1],
58-
]).long()
59-
y_pred = torch.Tensor([
58+
])
59+
y_pred = torch.tensor([
6060
[1, 1, 0],
6161
[1, 0, 1],
6262
[1, 0, 0],
6363
[1, 0, 1],
6464
[1, 1, 0],
65-
]).long()
65+
])
6666
state = default_evaluator.run([[y_pred, y_true]])
6767
print(state.metrics["mlcm"])
6868

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