tests

Author: Borda

tests/accelerators/legacy/test_ddp_spawn.py

@@ -21,6 +21,9 @@
 from pytorch_lightning.trainer import Trainer
 from pytorch_lightning.trainer.states import TrainerState
 from tests.base import EvalModelTemplate
+from tests.helpers import BoringModel
+from tests.helpers.datamodules import ClassifDataModule
+from tests.helpers.simple_models import ClassificationModel
 
 
 @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
@@ -38,8 +41,9 @@ def test_multi_gpu_early_stop_ddp_spawn(tmpdir):
         accelerator='ddp_spawn',
     )
 
-    model = EvalModelTemplate()
-    tpipes.run_model_test(trainer_options, model)
+    dm = ClassifDataModule()
+    model = ClassificationModel()
+    tpipes.run_model_test(trainer_options, model, dm)
 
 
 @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
@@ -56,7 +60,7 @@ def test_multi_gpu_model_ddp_spawn(tmpdir):
         progress_bar_refresh_rate=0,
     )
 
-    model = EvalModelTemplate()
+    model = BoringModel()
 
     tpipes.run_model_test(trainer_options, model)
 

tests/accelerators/legacy/test_dp.py

@@ -20,6 +20,9 @@
 from pytorch_lightning.callbacks import EarlyStopping
 from pytorch_lightning.core import memory
 from tests.base import EvalModelTemplate
+from tests.helpers import BoringModel
+from tests.helpers.datamodules import ClassifDataModule
+from tests.helpers.simple_models import ClassificationModel
 
 PRETEND_N_OF_GPUS = 16
 
@@ -39,8 +42,9 @@ def test_multi_gpu_early_stop_dp(tmpdir):
         accelerator='dp',
     )
 
-    model = EvalModelTemplate()
-    tpipes.run_model_test(trainer_options, model)
+    dm = ClassifDataModule()
+    model = ClassificationModel()
+    tpipes.run_model_test(trainer_options, model, dm)
 
 
 @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
@@ -57,7 +61,7 @@ def test_multi_gpu_model_dp(tmpdir):
         progress_bar_refresh_rate=0,
     )
 
-    model = EvalModelTemplate()
+    model = BoringModel()
 
     tpipes.run_model_test(trainer_options, model)
 

tests/helpers/pipelines.py

@@ -54,7 +54,7 @@ def run_model_test_without_loggers(
 
 def run_model_test(
     trainer_options,
-    model,
+    model: LightningModule,
     data: LightningDataModule = None,
     on_gpu: bool = True,
     version=None,

tests/models/test_gpu.py

@@ -25,6 +25,7 @@
 from pytorch_lightning.utilities import device_parser
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from tests.helpers import BoringModel
+from tests.helpers.datamodules import ClassifDataModule
 from tests.helpers.simple_models import ClassificationModel
 
 PRETEND_N_OF_GPUS = 16
@@ -43,8 +44,9 @@ def test_multi_gpu_none_backend(tmpdir):
         gpus=2,
     )
 
+    dm = ClassifDataModule()
     model = ClassificationModel()
-    tpipes.run_model_test(trainer_options, model)
+    tpipes.run_model_test(trainer_options, model, dm)
 
 
 @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")

tests/models/test_tpu.py

@@ -219,12 +219,11 @@ def test_tpu_grad_norm(tmpdir):
 @pl_multi_process_test
 def test_dataloaders_passed_to_fit(tmpdir):
     """Test if dataloaders passed to trainer works on TPU"""
-
     tutils.reset_seed()
     model = BoringModel()
 
-    trainer = Trainer(default_root_dir=tmpdir, max_epochs=1, tpu_cores=8)
-    trainer.fit(model, train_dataloader=model.train_dataloader(), val_dataloaders=model.val_dataloader())
+    trainer = Trainer(default_root_dir=tmpdir, max_epochs=1, tpu_cores=8,)
+    trainer.fit(model, train_dataloader=model.train_dataloader(), val_dataloaders=model.val_dataloader(),)
     assert trainer.state == TrainerState.FINISHED, f"Training failed with {trainer.state}"