Remove old test artifacts (#14574)

tests/tests_pytorch/__init__.py

@@ -11,11 +11,8 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-import logging
 import os
 
-import numpy as np
-
 _TEST_ROOT = os.path.dirname(__file__)
 _PROJECT_ROOT = os.path.dirname(_TEST_ROOT)
 _TEMP_PATH = os.path.join(_PROJECT_ROOT, "test_temp")
@@ -27,10 +24,6 @@ if _PROJECT_ROOT not in os.getenv("PYTHONPATH", ""):
     splitter = ":" if os.environ.get("PYTHONPATH", "") else ""
     os.environ["PYTHONPATH"] = f'{_PROJECT_ROOT}{splitter}{os.environ.get("PYTHONPATH", "")}'
 
-# generate a list of random seeds for each test
-RANDOM_PORTS = list(np.random.randint(12000, 19000, 1000))
 
 if not os.path.isdir(_TEMP_PATH):
     os.mkdir(_TEMP_PATH)
-
-logging.basicConfig(level=logging.ERROR)

tests/tests_pytorch/helpers/pipelines.py

@@ -31,7 +31,7 @@ def run_model_test_without_loggers(
     # correct result and ok accuracy
     assert trainer.state.finished, f"Training failed with {trainer.state}"
 
-    model2 = load_model_from_checkpoint(trainer.logger, trainer.checkpoint_callback.best_model_path, type(model))
+    model2 = load_model_from_checkpoint(trainer.checkpoint_callback.best_model_path, type(model))
 
     # test new model accuracy
     test_loaders = model2.test_dataloader() if not data else data.test_dataloader()
@@ -68,7 +68,7 @@ def run_model_test(
     assert change_ratio > 0.03, f"the model is changed of {change_ratio}"
 
     # test model loading
-    _ = load_model_from_checkpoint(logger, trainer.checkpoint_callback.best_model_path, type(model))
+    _ = load_model_from_checkpoint(trainer.checkpoint_callback.best_model_path, type(model))
 
     # test new model accuracy
     test_loaders = model.test_dataloader() if not data else data.test_dataloader()

tests/tests_pytorch/helpers/utils.py

@@ -17,13 +17,14 @@ import re
 from contextlib import contextmanager
 from typing import Optional, Type
 
+import numpy as np
 import pytest
 
 from pytorch_lightning import seed_everything
 from pytorch_lightning.callbacks import ModelCheckpoint
 from pytorch_lightning.demos.boring_classes import BoringModel
 from pytorch_lightning.loggers import TensorBoardLogger
-from tests_pytorch import _TEMP_PATH, RANDOM_PORTS
+from tests_pytorch import _TEMP_PATH
 
 
 def get_default_logger(save_dir, version=None):
@@ -52,7 +53,7 @@ def get_data_path(expt_logger, path_dir=None):
     return path_expt
 
 
-def load_model_from_checkpoint(logger, root_weights_dir, module_class=BoringModel):
+def load_model_from_checkpoint(root_weights_dir, module_class=BoringModel):
     trained_model = module_class.load_from_checkpoint(root_weights_dir)
     assert trained_model is not None, "loading model failed"
     return trained_model
@@ -68,6 +69,10 @@ def reset_seed(seed=0):
     seed_everything(seed)
 
 
+# generate a list of random seeds for each test
+RANDOM_PORTS = list(np.random.randint(12000, 19000, 1000))
+
+
 def set_random_main_port():
     reset_seed()
     port = RANDOM_PORTS.pop()