tests: add default_root_dir=tmpdir

Author: Borda

tests/callbacks/test_callbacks.py

@@ -165,6 +165,7 @@ def on_test_end(self, trainer, pl_module):
         limit_val_batches=0.1,
         limit_train_batches=0.2,
         progress_bar_refresh_rate=0,
+        default_root_dir=tmpdir,
     )
 
     assert not test_callback.setup_called

tests/callbacks/test_progress_bar.py

@@ -54,7 +54,7 @@ def test_progress_bar_misconfiguration():
         Trainer(callbacks=callbacks)
 
 
-def test_progress_bar_totals():
+def test_progress_bar_totals(tmpdir):
     """Test that the progress finishes with the correct total steps processed."""
 
     model = EvalModelTemplate()
@@ -63,6 +63,7 @@ def test_progress_bar_totals():
         progress_bar_refresh_rate=1,
         limit_val_batches=1.0,
         max_epochs=1,
+        default_root_dir=tmpdir,
     )
     bar = trainer.progress_bar_callback
     assert 0 == bar.total_train_batches
@@ -136,7 +137,7 @@ def test_progress_bar_fast_dev_run():
 
 
 @pytest.mark.parametrize('refresh_rate', [0, 1, 50])
-def test_progress_bar_progress_refresh(refresh_rate):
+def test_progress_bar_progress_refresh(tmpdir, refresh_rate):
     """Test that the three progress bars get correctly updated when using different refresh rates."""
 
     model = EvalModelTemplate()
@@ -177,6 +178,7 @@ def on_test_batch_end(self, trainer, pl_module):
         limit_train_batches=1.0,
         num_sanity_val_steps=2,
         max_epochs=3,
+        default_root_dir=tmpdir,
     )
     assert trainer.progress_bar_callback.refresh_rate == refresh_rate
 

tests/loggers/test_all.py

@@ -54,6 +54,7 @@ def log_metrics(self, metrics, step):
         limit_train_batches=0.2,
         limit_val_batches=0.5,
         fast_dev_run=True,
+        default_root_dir=tmpdir,
     )
     trainer.fit(model)
 

tests/loggers/test_base.py

@@ -102,7 +102,7 @@ def test_multiple_loggers(tmpdir):
     assert logger2.finalized_status == "success"
 
 
-def test_multiple_loggers_pickle(tmpdir):
+def test_multiple_loggers_pickle():
     """Verify that pickling trainer with multiple loggers works."""
 
     logger1 = CustomLogger()

tests/loggers/test_tensorboard.py

@@ -17,7 +17,10 @@
 def test_tensorboard_hparams_reload(tmpdir):
     model = EvalModelTemplate()
 
-    trainer = Trainer(max_epochs=1, default_root_dir=tmpdir)
+    trainer = Trainer(
+        max_epochs=1,
+        default_root_dir=tmpdir,
+    )
     trainer.fit(model)
 
     folder_path = trainer.logger.log_dir

tests/models/test_amp.py

@@ -21,7 +21,7 @@ def test_amp_single_gpu(tmpdir, backend):
         max_epochs=1,
         gpus=1,
         distributed_backend=backend,
-        precision=16
+        precision=16,
     )
 
     model = EvalModelTemplate()
@@ -39,18 +39,15 @@ def test_amp_multi_gpu(tmpdir, backend):
     tutils.set_random_master_port()
 
     model = EvalModelTemplate()
-
-    trainer_options = dict(
+    # tutils.run_model_test(trainer_options, model)
+    trainer = Trainer(
         default_root_dir=tmpdir,
         max_epochs=1,
         # gpus=2,
         gpus='0, 1',  # test init with gpu string
         distributed_backend=backend,
         precision=16,
     )
-
-    # tutils.run_model_test(trainer_options, model)
-    trainer = Trainer(**trainer_options)
     result = trainer.fit(model)
     assert result
 
@@ -66,17 +63,15 @@ def test_multi_gpu_wandb(tmpdir, backend):
     model = EvalModelTemplate()
     logger = WandbLogger(name='utest')
 
-    trainer_options = dict(
+    # tutils.run_model_test(trainer_options, model)
+    trainer = Trainer(
         default_root_dir=tmpdir,
         max_epochs=1,
         gpus=2,
         distributed_backend=backend,
         precision=16,
         logger=logger,
-
     )
-    # tutils.run_model_test(trainer_options, model)
-    trainer = Trainer(**trainer_options)
     result = trainer.fit(model)
     assert result
     trainer.test(model)
@@ -106,6 +101,7 @@ def test_amp_gpu_ddp_slurm_managed(tmpdir):
         precision=16,
         checkpoint_callback=checkpoint,
         logger=logger,
+        default_root_dir=tmpdir,
     )
     trainer.is_slurm_managing_tasks = True
     result = trainer.fit(model)

tests/models/test_cpu.py

@@ -29,6 +29,7 @@ def test_cpu_slurm_save_load(tmpdir):
         limit_train_batches=0.2,
         limit_val_batches=0.2,
         checkpoint_callback=ModelCheckpoint(tmpdir),
+        default_root_dir=tmpdir,
     )
     result = trainer.fit(model)
     real_global_step = trainer.global_step
@@ -64,6 +65,7 @@ def test_cpu_slurm_save_load(tmpdir):
         max_epochs=1,
         logger=logger,
         checkpoint_callback=ModelCheckpoint(tmpdir),
+        default_root_dir=tmpdir,
     )
     model = EvalModelTemplate(**hparams)
 
@@ -220,6 +222,7 @@ def test_running_test_no_val(tmpdir):
         checkpoint_callback=checkpoint,
         logger=logger,
         early_stop_callback=False,
+        default_root_dir=tmpdir,
     )
     result = trainer.fit(model)
 

tests/models/test_gpu.py

@@ -38,18 +38,16 @@ def test_multi_gpu_model(tmpdir, backend):
     """Make sure DDP works."""
     tutils.set_random_master_port()
 
-    trainer_options = dict(
+    model = EvalModelTemplate()
+    # tutils.run_model_test(trainer_options, model)
+    trainer = Trainer(
         default_root_dir=tmpdir,
         max_epochs=1,
         limit_train_batches=0.4,
         limit_val_batches=0.2,
         gpus=[0, 1],
         distributed_backend=backend,
     )
-
-    model = EvalModelTemplate()
-    # tutils.run_model_test(trainer_options, model)
-    trainer = Trainer(**trainer_options)
     result = trainer.fit(model)
     assert result
 
@@ -63,7 +61,11 @@ def test_ddp_all_dataloaders_passed_to_fit(tmpdir):
     """Make sure DDP works with dataloaders passed to fit()"""
     tutils.set_random_master_port()
 
-    trainer_options = dict(
+    model = EvalModelTemplate()
+    fit_options = dict(train_dataloader=model.train_dataloader(),
+                       val_dataloaders=model.val_dataloader())
+
+    trainer = Trainer(
         default_root_dir=tmpdir,
         progress_bar_refresh_rate=0,
         max_epochs=1,
@@ -72,12 +74,6 @@ def test_ddp_all_dataloaders_passed_to_fit(tmpdir):
         gpus=[0, 1],
         distributed_backend='ddp'
     )
-
-    model = EvalModelTemplate()
-    fit_options = dict(train_dataloader=model.train_dataloader(),
-                       val_dataloaders=model.val_dataloader())
-
-    trainer = Trainer(**trainer_options)
     result = trainer.fit(model, **fit_options)
     assert result == 1, "DDP doesn't work with dataloaders passed to fit()."
 

tests/models/test_grad_norm.py

@@ -88,6 +88,7 @@ def test_grad_tracking(tmpdir, norm_type, rtol=5e-3):
         logger=logger,
         track_grad_norm=norm_type,
         row_log_interval=1,  # request grad_norms every batch
+        default_root_dir=tmpdir,
     )
     result = trainer.fit(model)
 

tests/models/test_hooks.py

@@ -8,7 +8,7 @@
 
 
 @pytest.mark.parametrize('max_steps', [1, 2, 3])
-def test_on_before_zero_grad_called(max_steps):
+def test_on_before_zero_grad_called(tmpdir, max_steps):
 
     class CurrentTestModel(EvalModelTemplate):
         on_before_zero_grad_called = 0
@@ -21,6 +21,7 @@ def on_before_zero_grad(self, optimizer):
     trainer = Trainer(
         max_steps=max_steps,
         num_sanity_val_steps=5,
+        default_root_dir=tmpdir,
     )
     assert 0 == model.on_before_zero_grad_called
     trainer.fit(model)