cleaning tests (#2201)

tests/callbacks/test_callbacks.py

@@ -1,6 +1,7 @@
 from pathlib import Path
 
 import pytest
+import torch
 
 import tests.base.utils as tutils
 from pytorch_lightning import Callback
@@ -8,7 +9,6 @@ from pytorch_lightning import Trainer, LightningModule
 from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
 from pytorch_lightning.loggers import TensorBoardLogger
 from tests.base import EvalModelTemplate
-import torch
 
 
 def test_early_stopping_functionality(tmpdir):

tests/conftest.py

@@ -1,9 +1,9 @@
+import sys
+import threading
 from functools import wraps, partial
 from http.server import SimpleHTTPRequestHandler
 
-import sys
 import pytest
-import threading
 import torch.multiprocessing as mp
 
 

tests/core/test_decorators.py

@@ -1,8 +1,8 @@
 import pytest
 import torch
 
-from tests.base import EvalModelTemplate
 from pytorch_lightning.core.decorators import auto_move_data
+from tests.base import EvalModelTemplate
 
 
 @pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires GPU machine")

tests/metrics/functional/test_classification.py

@@ -1,6 +1,15 @@
+from functools import partial
+
 import pytest
 import torch
-from functools import partial
+from sklearn.metrics import (
+    accuracy_score as sk_accuracy,
+    precision_score as sk_precision,
+    recall_score as sk_recall,
+    f1_score as sk_f1_score,
+    fbeta_score as sk_fbeta_score,
+    confusion_matrix as sk_confusion_matrix
+)
 
 from pytorch_lightning import seed_everything
 from pytorch_lightning.metrics.functional.classification import (
@@ -24,15 +33,6 @@ from pytorch_lightning.metrics.functional.classification import (
     auc,
 )
 
-from sklearn.metrics import (
-    accuracy_score as sk_accuracy,
-    precision_score as sk_precision,
-    recall_score as sk_recall,
-    f1_score as sk_f1_score,
-    fbeta_score as sk_fbeta_score,
-    confusion_matrix as sk_confusion_matrix
-)
-
 
 @pytest.mark.parametrize(['sklearn_metric', 'torch_metric'], [
     (sk_accuracy, accuracy),

tests/models/test_cpu.py

@@ -1,6 +1,5 @@
 import os
 import platform
-from collections import namedtuple
 
 import pytest
 import torch
@@ -9,8 +8,8 @@ from packaging.version import parse as version_parse
 import tests.base.utils as tutils
 from pytorch_lightning import Trainer
 from pytorch_lightning.callbacks import EarlyStopping
-from tests.base import EvalModelTemplate
 from pytorch_lightning.callbacks import ModelCheckpoint
+from tests.base import EvalModelTemplate
 
 
 def test_cpu_slurm_save_load(tmpdir):
@@ -231,53 +230,6 @@ def test_running_test_no_val(tmpdir):
     tutils.assert_ok_model_acc(trainer)
 
 
-@pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires GPU machine")
-def test_single_gpu_batch_parse():
-    trainer = Trainer()
-
-    # batch is just a tensor
-    batch = torch.rand(2, 3)
-    batch = trainer.transfer_batch_to_gpu(batch, 0)
-    assert batch.device.index == 0 and batch.type() == 'torch.cuda.FloatTensor'
-
-    # tensor list
-    batch = [torch.rand(2, 3), torch.rand(2, 3)]
-    batch = trainer.transfer_batch_to_gpu(batch, 0)
-    assert batch[0].device.index == 0 and batch[0].type() == 'torch.cuda.FloatTensor'
-    assert batch[1].device.index == 0 and batch[1].type() == 'torch.cuda.FloatTensor'
-
-    # tensor list of lists
-    batch = [[torch.rand(2, 3), torch.rand(2, 3)]]
-    batch = trainer.transfer_batch_to_gpu(batch, 0)
-    assert batch[0][0].device.index == 0 and batch[0][0].type() == 'torch.cuda.FloatTensor'
-    assert batch[0][1].device.index == 0 and batch[0][1].type() == 'torch.cuda.FloatTensor'
-
-    # tensor dict
-    batch = [{'a': torch.rand(2, 3), 'b': torch.rand(2, 3)}]
-    batch = trainer.transfer_batch_to_gpu(batch, 0)
-    assert batch[0]['a'].device.index == 0 and batch[0]['a'].type() == 'torch.cuda.FloatTensor'
-    assert batch[0]['b'].device.index == 0 and batch[0]['b'].type() == 'torch.cuda.FloatTensor'
-
-    # tuple of tensor list and list of tensor dict
-    batch = ([torch.rand(2, 3) for _ in range(2)],
-             [{'a': torch.rand(2, 3), 'b': torch.rand(2, 3)} for _ in range(2)])
-    batch = trainer.transfer_batch_to_gpu(batch, 0)
-    assert batch[0][0].device.index == 0 and batch[0][0].type() == 'torch.cuda.FloatTensor'
-
-    assert batch[1][0]['a'].device.index == 0
-    assert batch[1][0]['a'].type() == 'torch.cuda.FloatTensor'
-
-    assert batch[1][0]['b'].device.index == 0
-    assert batch[1][0]['b'].type() == 'torch.cuda.FloatTensor'
-
-    # namedtuple of tensor
-    BatchType = namedtuple('BatchType', ['a', 'b'])
-    batch = [BatchType(a=torch.rand(2, 3), b=torch.rand(2, 3)) for _ in range(2)]
-    batch = trainer.transfer_batch_to_gpu(batch, 0)
-    assert batch[0].a.device.index == 0
-    assert batch[0].a.type() == 'torch.cuda.FloatTensor'
-
-
 def test_simple_cpu(tmpdir):
     """Verify continue training session on CPU."""
     model = EvalModelTemplate()

tests/models/test_gpu.py

@@ -1,4 +1,4 @@
-import os
+from collections import namedtuple
 
 import pytest
 import torch
@@ -239,3 +239,50 @@ def test_parse_gpu_fail_on_non_existent_id_2(mocked_device_count):
 def test_parse_gpu_returns_None_when_no_devices_are_available(mocked_device_count_0, gpus):
     with pytest.raises(MisconfigurationException):
         parse_gpu_ids(gpus)
+
+
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires GPU machine")
+def test_single_gpu_batch_parse():
+    trainer = Trainer()
+
+    # batch is just a tensor
+    batch = torch.rand(2, 3)
+    batch = trainer.transfer_batch_to_gpu(batch, 0)
+    assert batch.device.index == 0 and batch.type() == 'torch.cuda.FloatTensor'
+
+    # tensor list
+    batch = [torch.rand(2, 3), torch.rand(2, 3)]
+    batch = trainer.transfer_batch_to_gpu(batch, 0)
+    assert batch[0].device.index == 0 and batch[0].type() == 'torch.cuda.FloatTensor'
+    assert batch[1].device.index == 0 and batch[1].type() == 'torch.cuda.FloatTensor'
+
+    # tensor list of lists
+    batch = [[torch.rand(2, 3), torch.rand(2, 3)]]
+    batch = trainer.transfer_batch_to_gpu(batch, 0)
+    assert batch[0][0].device.index == 0 and batch[0][0].type() == 'torch.cuda.FloatTensor'
+    assert batch[0][1].device.index == 0 and batch[0][1].type() == 'torch.cuda.FloatTensor'
+
+    # tensor dict
+    batch = [{'a': torch.rand(2, 3), 'b': torch.rand(2, 3)}]
+    batch = trainer.transfer_batch_to_gpu(batch, 0)
+    assert batch[0]['a'].device.index == 0 and batch[0]['a'].type() == 'torch.cuda.FloatTensor'
+    assert batch[0]['b'].device.index == 0 and batch[0]['b'].type() == 'torch.cuda.FloatTensor'
+
+    # tuple of tensor list and list of tensor dict
+    batch = ([torch.rand(2, 3) for _ in range(2)],
+             [{'a': torch.rand(2, 3), 'b': torch.rand(2, 3)} for _ in range(2)])
+    batch = trainer.transfer_batch_to_gpu(batch, 0)
+    assert batch[0][0].device.index == 0 and batch[0][0].type() == 'torch.cuda.FloatTensor'
+
+    assert batch[1][0]['a'].device.index == 0
+    assert batch[1][0]['a'].type() == 'torch.cuda.FloatTensor'
+
+    assert batch[1][0]['b'].device.index == 0
+    assert batch[1][0]['b'].type() == 'torch.cuda.FloatTensor'
+
+    # namedtuple of tensor
+    BatchType = namedtuple('BatchType', ['a', 'b'])
+    batch = [BatchType(a=torch.rand(2, 3), b=torch.rand(2, 3)) for _ in range(2)]
+    batch = trainer.transfer_batch_to_gpu(batch, 0)
+    assert batch[0].a.device.index == 0
+    assert batch[0].a.type() == 'torch.cuda.FloatTensor'

tests/models/test_grad_norm.py

@@ -1,12 +1,9 @@
-import torch
-import pytest
 import numpy as np
+import pytest
 
-from pytorch_lightning import Trainer, seed_everything
-
+from pytorch_lightning import Trainer
 from pytorch_lightning.loggers import LightningLoggerBase
 from pytorch_lightning.utilities import rank_zero_only
-
 from tests.base import EvalModelTemplate
 from tests.base.utils import reset_seed
 

tests/models/test_hparams.py

@@ -1,7 +1,8 @@
 import os
-import sys
+import pickle
 from argparse import Namespace
 
+import cloudpickle
 import pytest
 import torch
 from omegaconf import OmegaConf, Container
@@ -9,8 +10,6 @@ from omegaconf import OmegaConf, Container
 from pytorch_lightning import Trainer, LightningModule
 from pytorch_lightning.utilities import AttributeDict
 from tests.base import EvalModelTemplate
-import pickle
-import cloudpickle
 
 
 class SaveHparamsModel(EvalModelTemplate):

tests/trainer/test_trainer.py

@@ -2,8 +2,8 @@ import glob
 import math
 import os
 import pickle
-import types
 import sys
+import types
 from argparse import Namespace
 from pathlib import Path
 
@@ -18,8 +18,8 @@ from pytorch_lightning.core.saving import (
     load_hparams_from_tags_csv, load_hparams_from_yaml, save_hparams_to_tags_csv)
 from pytorch_lightning.loggers import TensorBoardLogger
 from pytorch_lightning.trainer.logging import TrainerLoggingMixin
-from pytorch_lightning.utilities.io import load as pl_load
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
+from pytorch_lightning.utilities.io import load as pl_load
 from tests.base import EvalModelTemplate