Change 'torch.Tensor's to 'torch.tensor'

ignite/contrib/metrics/average_precision.py

@@ -48,7 +48,7 @@ def activated_output_transform(output):
 
         .. testcode::
 
-            y_pred = torch.Tensor([[0.79, 0.21], [0.30, 0.70], [0.46, 0.54], [0.16, 0.84]])
+            y_pred = torch.tensor([[0.79, 0.21], [0.30, 0.70], [0.46, 0.54], [0.16, 0.84]])
             y_true = torch.tensor([[1, 1], [1, 1], [0, 1], [0, 1]])
 
             avg_precision = AveragePrecision()

ignite/contrib/metrics/cohen_kappa.py

@@ -37,8 +37,8 @@ class CohenKappa(EpochMetric):
 
             metric = CohenKappa()
             metric.attach(default_evaluator, 'ck')
-            y_true = torch.Tensor([2, 0, 2, 2, 0, 1])
-            y_pred = torch.Tensor([0, 0, 2, 2, 0, 2])
+            y_true = torch.tensor([2, 0, 2, 2, 0, 1])
+            y_pred = torch.tensor([0, 0, 2, 2, 0, 2])
             state = default_evaluator.run([[y_pred, y_true]])
             print(state.metrics['ck'])
 

ignite/contrib/metrics/regression/canberra_metric.py

@@ -49,7 +49,7 @@ class CanberraMetric(_BaseRegression):
 
                 metric = CanberraMetric()
                 metric.attach(default_evaluator, 'canberra')
-                y_pred = torch.Tensor([[3.8], [9.9], [-5.4], [2.1]])
+                y_pred = torch.tensor([[3.8], [9.9], [-5.4], [2.1]])
                 y_true = y_pred * 1.5
                 state = default_evaluator.run([[y_pred, y_true]])
                 print(state.metrics['canberra'])

ignite/contrib/metrics/regression/fractional_absolute_error.py

@@ -46,7 +46,7 @@ class FractionalAbsoluteError(_BaseRegression):
 
                 metric = FractionalAbsoluteError()
                 metric.attach(default_evaluator, 'fractional_abs_error')
-                y_pred = torch.Tensor([[3.8], [9.9], [-5.4], [2.1]])
+                y_pred = torch.tensor([[3.8], [9.9], [-5.4], [2.1]])
                 y_true = y_pred * 0.8
                 state = default_evaluator.run([[y_pred, y_true]])
                 print(state.metrics['fractional_abs_error'])

ignite/contrib/metrics/regression/fractional_bias.py

@@ -46,7 +46,7 @@ class FractionalBias(_BaseRegression):
 
                 metric = FractionalBias()
                 metric.attach(default_evaluator, 'fractional_bias')
-                y_pred = torch.Tensor([[3.8], [9.9], [5.4], [2.1]])
+                y_pred = torch.tensor([[3.8], [9.9], [5.4], [2.1]])
                 y_true = y_pred * 1.5
                 state = default_evaluator.run([[y_pred, y_true]])
                 print(state.metrics['fractional_bias'])

ignite/contrib/metrics/regression/geometric_mean_absolute_error.py

@@ -46,7 +46,7 @@ class GeometricMeanAbsoluteError(_BaseRegression):
 
                 metric = GeometricMeanAbsoluteError()
                 metric.attach(default_evaluator, 'gmae')
-                y_pred = torch.Tensor([[3.8], [9.9], [-5.4], [2.1]])
+                y_pred = torch.tensor([[3.8], [9.9], [-5.4], [2.1]])
                 y_true = y_pred * 1.5
                 state = default_evaluator.run([[y_pred, y_true]])
                 print(state.metrics['gmae'])

ignite/contrib/metrics/regression/geometric_mean_relative_absolute_error.py

@@ -60,7 +60,7 @@ class GeometricMeanRelativeAbsoluteError(_BaseRegression):
 
             metric = GeometricMeanRelativeAbsoluteError()
             metric.attach(default_evaluator, 'gmare')
-            y_true = torch.Tensor([0, 1, 2, 3, 4, 5])
+            y_true = torch.tensor([0, 1, 2, 3, 4, 5])
             y_pred = y_true * 0.75
             state = default_evaluator.run([[y_pred, y_true]])
             print(state.metrics['gmare'])

ignite/contrib/metrics/regression/manhattan_distance.py

@@ -45,7 +45,7 @@ class ManhattanDistance(_BaseRegression):
 
             metric = ManhattanDistance()
             metric.attach(default_evaluator, 'manhattan')
-            y_true = torch.Tensor([0, 1, 2, 3, 4, 5])
+            y_true = torch.tensor([0, 1, 2, 3, 4, 5])
             y_pred = y_true * 0.75
             state = default_evaluator.run([[y_pred, y_true]])
             print(state.metrics['manhattan'])

ignite/contrib/metrics/regression/maximum_absolute_error.py

@@ -46,7 +46,7 @@ class MaximumAbsoluteError(_BaseRegression):
 
             metric = MaximumAbsoluteError()
             metric.attach(default_evaluator, 'mae')
-            y_true = torch.Tensor([0, 1, 2, 3, 4, 5])
+            y_true = torch.tensor([0, 1, 2, 3, 4, 5])
             y_pred = y_true * 0.75
             state = default_evaluator.run([[y_pred, y_true]])
             print(state.metrics['mae'])

ignite/contrib/metrics/regression/mean_absolute_relative_error.py

@@ -46,7 +46,7 @@ class MeanAbsoluteRelativeError(_BaseRegression):
 
             metric = MeanAbsoluteRelativeError()
             metric.attach(default_evaluator, 'mare')
-            y_true = torch.Tensor([1, 2, 3, 4, 5])
+            y_true = torch.tensor([1, 2, 3, 4, 5])
             y_pred = y_true * 0.75
             state = default_evaluator.run([[y_pred, y_true]])
             print(state.metrics['mare'])

ignite/contrib/metrics/regression/mean_error.py

@@ -46,7 +46,7 @@ class MeanError(_BaseRegression):
 
             metric = MeanError()
             metric.attach(default_evaluator, 'me')
-            y_true = torch.Tensor([0, 1, 2, 3, 4, 5])
+            y_true = torch.tensor([0, 1, 2, 3, 4, 5])
             y_pred = y_true * 0.75
             state = default_evaluator.run([[y_pred, y_true]])
             print(state.metrics['me'])

ignite/contrib/metrics/regression/mean_normalized_bias.py

@@ -46,7 +46,7 @@ class MeanNormalizedBias(_BaseRegression):
 
             metric = MeanNormalizedBias()
             metric.attach(default_evaluator, 'mnb')
-            y_true = torch.Tensor([1, 2, 3, 4, 5])
+            y_true = torch.tensor([1, 2, 3, 4, 5])
             y_pred = y_true * 0.75
             state = default_evaluator.run([[y_pred, y_true]])
             print(state.metrics['mnb'])

ignite/contrib/metrics/regression/median_absolute_error.py

@@ -52,7 +52,7 @@ class MedianAbsoluteError(EpochMetric):
 
             metric = MedianAbsoluteError()
             metric.attach(default_evaluator, 'mae')
-            y_true = torch.Tensor([0, 1, 2, 3, 4, 5])
+            y_true = torch.tensor([0, 1, 2, 3, 4, 5])
             y_pred = y_true * 0.75
             state = default_evaluator.run([[y_pred, y_true]])
             print(state.metrics['mae'])

ignite/contrib/metrics/regression/median_absolute_percentage_error.py

@@ -50,7 +50,7 @@ class MedianAbsolutePercentageError(EpochMetric):
 
             metric = MedianAbsolutePercentageError()
             metric.attach(default_evaluator, 'mape')
-            y_true = torch.Tensor([1, 2, 3, 4, 5])
+            y_true = torch.tensor([1, 2, 3, 4, 5])
             y_pred = y_true * 0.75
             state = default_evaluator.run([[y_pred, y_true]])
             print(state.metrics['mape'])

ignite/contrib/metrics/regression/median_relative_absolute_error.py

@@ -50,7 +50,7 @@ class MedianRelativeAbsoluteError(EpochMetric):
 
             metric = MedianRelativeAbsoluteError()
             metric.attach(default_evaluator, 'mrae')
-            y_true = torch.Tensor([0, 1, 2, 3, 4, 5])
+            y_true = torch.tensor([0, 1, 2, 3, 4, 5])
             y_pred = y_true * 0.75
             state = default_evaluator.run([[y_pred, y_true]])
             print(state.metrics['mrae'])

ignite/contrib/metrics/regression/r2_score.py

@@ -44,7 +44,7 @@ class R2Score(_BaseRegression):
 
             metric = R2Score()
             metric.attach(default_evaluator, 'r2')
-            y_true = torch.Tensor([0, 1, 2, 3, 4, 5])
+            y_true = torch.tensor([0, 1, 2, 3, 4, 5])
             y_pred = y_true * 0.75
             state = default_evaluator.run([[y_pred, y_true]])
             print(state.metrics['r2'])

ignite/contrib/metrics/regression/wave_hedges_distance.py

@@ -45,7 +45,7 @@ class WaveHedgesDistance(_BaseRegression):
 
             metric = WaveHedgesDistance()
             metric.attach(default_evaluator, 'whd')
-            y_true = torch.Tensor([0, 1, 2, 3, 4, 5])
+            y_true = torch.tensor([0, 1, 2, 3, 4, 5])
             y_pred = y_true * 0.75
             state = default_evaluator.run([[y_pred, y_true]])
             print(state.metrics['whd'])

ignite/metrics/accumulation.py

@@ -225,7 +225,7 @@ class GeometricAverage(VariableAccumulation):
             metric = GeometricAverage()
             metric.attach(default_evaluator, 'avg')
             # Case 1. input is er
-            data = torch.Tensor([1, 2, 3])
+            data = torch.tensor([1, 2, 3])
             state = default_evaluator.run(data)
             print(state.metrics['avg'])
 

ignite/metrics/mean_pairwise_distance.py

@@ -44,7 +44,7 @@ class MeanPairwiseDistance(Metric):
 
             metric = MeanPairwiseDistance(p=4)
             metric.attach(default_evaluator, 'mpd')
-            preds = torch.Tensor([
+            preds = torch.tensor([
                 [1, 2, 4, 1],
                 [2, 3, 1, 5],
                 [1, 3, 5, 1],

ignite/metrics/precision.py

@@ -286,7 +286,7 @@ class Precision(_BasePrecisionRecall):
             weighted_metric.attach(default_evaluator, "weighted precision")
 
             y_true = torch.tensor([2, 0, 2, 1, 0])
-            y_pred = torch.Tensor([
+            y_pred = torch.tensor([
                 [0.0266, 0.1719, 0.3055],
                 [0.6886, 0.3978, 0.8176],
                 [0.9230, 0.0197, 0.8395],
@@ -320,14 +320,14 @@ class Precision(_BasePrecisionRecall):
             weighted_metric.attach(default_evaluator, "weighted precision")
             samples_metric.attach(default_evaluator, "samples precision")
 
-            y_true = torch.Tensor([
+            y_true = torch.tensor([
                 [0, 0, 1],
                 [0, 0, 0],
                 [0, 0, 0],
                 [1, 0, 0],
                 [0, 1, 1],
             ])
-            y_pred = torch.Tensor([
+            y_pred = torch.tensor([
                 [1, 1, 0],
                 [1, 0, 1],
                 [1, 0, 0],
@@ -361,7 +361,7 @@ def thresholded_output_transform(output):
             metric = Precision(output_transform=thresholded_output_transform)
             metric.attach(default_evaluator, "precision")
             y_true = torch.tensor([1, 0, 1, 1, 0, 1])
-            y_pred = torch.Tensor([0.6, 0.2, 0.9, 0.4, 0.7, 0.65])
+            y_pred = torch.tensor([0.6, 0.2, 0.9, 0.4, 0.7, 0.65])
             state = default_evaluator.run([[y_pred, y_true]])
             print(state.metrics["precision"])
 

tests/ignite/contrib/handlers/test_clearml_logger.py

@@ -49,7 +49,7 @@ def test_optimizer_params_handler_wrong_setup():
 
 def test_optimizer_params():
 
-    optimizer = torch.optim.SGD([torch.Tensor(0)], lr=0.01)
+    optimizer = torch.optim.SGD([torch.tensor(0.0)], lr=0.01)
     wrapper = OptimizerParamsHandler(optimizer=optimizer, param_name="lr")
     mock_logger = MagicMock(spec=ClearMLLogger)
     mock_logger.clearml_logger = MagicMock()

tests/ignite/contrib/handlers/test_mlflow_logger.py

@@ -64,7 +64,7 @@ def test_output_handler_metric_names():
     wrapper = OutputHandler("tag", metric_names=["a"])
 
     mock_engine = MagicMock()
-    mock_engine.state = State(metrics={"a": torch.Tensor([0.0, 1.0, 2.0, 3.0])})
+    mock_engine.state = State(metrics={"a": torch.tensor([0.0, 1.0, 2.0, 3.0])})
     mock_engine.state.iteration = 5
 
     mock_logger = MagicMock(spec=MLflowLogger)
@@ -216,7 +216,7 @@ def test_optimizer_params_handler_wrong_setup():
 
 def test_optimizer_params():
 
-    optimizer = torch.optim.SGD([torch.Tensor(0)], lr=0.01)
+    optimizer = torch.optim.SGD([torch.tensor(0.0)], lr=0.01)
     wrapper = OptimizerParamsHandler(optimizer=optimizer, param_name="lr")
     mock_logger = MagicMock(spec=MLflowLogger)
     mock_logger.log_metrics = MagicMock()

tests/ignite/contrib/handlers/test_neptune_logger.py

@@ -34,7 +34,7 @@ def test_optimizer_params_handler_wrong_setup():
 
 
 def test_optimizer_params():
-    optimizer = torch.optim.SGD([torch.Tensor(0)], lr=0.01)
+    optimizer = torch.optim.SGD([torch.tensor(0.0)], lr=0.01)
     wrapper = OptimizerParamsHandler(optimizer=optimizer, param_name="lr")
     mock_logger = MagicMock(spec=NeptuneLogger)
     mock_logger.log_metric = MagicMock()
@@ -99,7 +99,7 @@ def test_output_handler_metric_names():
 
     mock_engine = MagicMock()
     mock_logger.log_metric = MagicMock()
-    mock_engine.state = State(metrics={"a": torch.Tensor([0.0, 1.0, 2.0, 3.0])})
+    mock_engine.state = State(metrics={"a": torch.tensor([0.0, 1.0, 2.0, 3.0])})
     mock_engine.state.iteration = 5
 
     mock_logger = MagicMock(spec=NeptuneLogger)

tests/ignite/contrib/handlers/test_polyaxon_logger.py

@@ -66,7 +66,7 @@ def test_output_handler_metric_names():
     wrapper = OutputHandler("tag", metric_names=["a"])
 
     mock_engine = MagicMock()
-    mock_engine.state = State(metrics={"a": torch.Tensor([0.0, 1.0, 2.0, 3.0])})
+    mock_engine.state = State(metrics={"a": torch.tensor([0.0, 1.0, 2.0, 3.0])})
     mock_engine.state.iteration = 5
 
     mock_logger = MagicMock(spec=PolyaxonLogger)
@@ -232,7 +232,7 @@ def test_optimizer_params_handler_wrong_setup():
 
 def test_optimizer_params():
 
-    optimizer = torch.optim.SGD([torch.Tensor(0)], lr=0.01)
+    optimizer = torch.optim.SGD([torch.tensor(0.0)], lr=0.01)
     wrapper = OptimizerParamsHandler(optimizer=optimizer, param_name="lr")
     mock_logger = MagicMock(spec=PolyaxonLogger)
     mock_logger.log_metrics = MagicMock()

tests/ignite/contrib/handlers/test_tensorboard_logger.py

@@ -34,7 +34,7 @@ def test_optimizer_params_handler_wrong_setup():
 
 def test_optimizer_params():
 
-    optimizer = torch.optim.SGD([torch.Tensor(0)], lr=0.01)
+    optimizer = torch.optim.SGD([torch.tensor(0.0)], lr=0.01)
     wrapper = OptimizerParamsHandler(optimizer=optimizer, param_name="lr")
     mock_logger = MagicMock(spec=TensorboardLogger)
     mock_logger.writer = MagicMock()
@@ -104,7 +104,7 @@ def test_output_handler_metric_names():
     wrapper = OutputHandler("tag", metric_names=["a"])
 
     mock_engine = MagicMock()
-    mock_engine.state = State(metrics={"a": torch.Tensor([0.0, 1.0, 2.0, 3.0])})
+    mock_engine.state = State(metrics={"a": torch.tensor([0.0, 1.0, 2.0, 3.0])})
     mock_engine.state.iteration = 5
 
     mock_logger = MagicMock(spec=TensorboardLogger)

tests/ignite/contrib/handlers/test_visdom_logger.py

@@ -32,7 +32,7 @@ def test_optimizer_params_handler_wrong_setup():
 
 def test_optimizer_params():
 
-    optimizer = torch.optim.SGD([torch.Tensor(0)], lr=0.01)
+    optimizer = torch.optim.SGD([torch.tensor(0.0)], lr=0.01)
     wrapper = OptimizerParamsHandler(optimizer=optimizer, param_name="lr")
     mock_logger = MagicMock(spec=VisdomLogger)
     mock_logger.vis = MagicMock()
@@ -181,7 +181,7 @@ def test_output_handler_metric_names(dirname):
     wrapper = OutputHandler("tag", metric_names=["a"])
 
     mock_engine = MagicMock()
-    mock_engine.state = State(metrics={"a": torch.Tensor([0.0, 1.0, 2.0, 3.0])})
+    mock_engine.state = State(metrics={"a": torch.tensor([0.0, 1.0, 2.0, 3.0])})
     mock_engine.state.iteration = 5
 
     mock_logger = MagicMock(spec=VisdomLogger)

tests/ignite/contrib/handlers/test_wandb_logger.py

@@ -26,7 +26,7 @@ def test_optimizer_params_handler_wrong_setup():
 
 
 def test_optimizer_params():
-    optimizer = torch.optim.SGD([torch.Tensor(0)], lr=0.01)
+    optimizer = torch.optim.SGD([torch.tensor(0.0)], lr=0.01)
     wrapper = OptimizerParamsHandler(optimizer=optimizer, param_name="lr")
     mock_logger = MagicMock(spec=WandBLogger)
     mock_logger.log = MagicMock()
@@ -272,7 +272,7 @@ def test_output_handler_state_attrs():
 
 
 def test_wandb_close():
-    optimizer = torch.optim.SGD([torch.Tensor(0)], lr=0.01)
+    optimizer = torch.optim.SGD([torch.tensor(0.0)], lr=0.01)
     wrapper = OptimizerParamsHandler(optimizer=optimizer, param_name="lr")
     mock_logger = MagicMock(spec=WandBLogger)
     mock_logger.log = MagicMock()

tests/ignite/metrics/gan/test_fid.py

@@ -110,7 +110,7 @@ def test_wrong_inputs():
         FID(num_features=-1, feature_extractor=torch.nn.Identity())
 
     with pytest.raises(ValueError, match=r"feature_extractor output must be a tensor of dim 2, got: 1"):
-        FID(num_features=1, feature_extractor=torch.nn.Identity()).update(torch.Tensor([[], []]))
+        FID(num_features=1, feature_extractor=torch.nn.Identity()).update(torch.tensor([[], []]))
 
     with pytest.raises(ValueError, match=r"Batch size should be greater than one, got: 0"):
         FID(num_features=1, feature_extractor=torch.nn.Identity()).update(torch.rand(2, 0, 0))

tests/ignite/metrics/test_loss.py

@@ -31,7 +31,7 @@ def update(self, output):
 
 
 def test_output_as_mapping_without_criterion_kwargs():
-    y_pred = torch.Tensor([[2.0], [-2.0]])
+    y_pred = torch.tensor([[2.0], [-2.0]])
     y = torch.zeros(2)
     criterion_kwargs = {}
 
@@ -42,7 +42,7 @@ def test_output_as_mapping_without_criterion_kwargs():
 
 
 def test_output_as_mapping_with_criterion_kwargs():
-    y_pred = torch.Tensor([[2.0], [-2.0]])
+    y_pred = torch.tensor([[2.0], [-2.0]])
     y = torch.zeros(2)
     criterion_kwargs = {"reduction": "sum"}
 

tests/ignite/metrics/test_mean_pairwise_distance.py

@@ -117,7 +117,7 @@ def _test_distrib_accumulator_device(device):
         for dev in [mpd._device, mpd._sum_of_distances.device]:
             assert dev == metric_device, f"{type(dev)}:{dev} vs {type(metric_device)}:{metric_device}"
 
-        y_pred = torch.Tensor([[3.0, 4.0], [-3.0, -4.0]])
+        y_pred = torch.tensor([[3.0, 4.0], [-3.0, -4.0]])
         y = torch.zeros(2, 2)
         mpd.update((y_pred, y))
 

tests/ignite/metrics/test_metric.py

@@ -30,7 +30,7 @@ def update(self, output):
 
 
 def test_no_transform():
-    y_pred = torch.Tensor([[2.0], [-2.0]])
+    y_pred = torch.tensor([[2.0], [-2.0]])
     y = torch.zeros(2)
 
     metric = DummyMetric1(true_output=(y_pred, y))
@@ -40,7 +40,7 @@ def test_no_transform():
 
 
 def test_transform():
-    y_pred = torch.Tensor([[2.0], [-2.0]])
+    y_pred = torch.tensor([[2.0], [-2.0]])
     y = torch.zeros(2)
 
     def transform(output):
@@ -87,7 +87,7 @@ def update(self, output):
 
 
 def test_output_as_mapping():
-    y_pred = torch.Tensor([[2.0], [-2.0]])
+    y_pred = torch.tensor([[2.0], [-2.0]])
     y = torch.zeros(2)
 
     metric = DummyMetric1(true_output=(y_pred, y))