Tests: refactor cleanup (#1744)

* wip

* cleaning

* optim imports

* -

* default hparams

* fix restore

* fix imports

tests/base/utils.py

@@ -9,8 +9,7 @@ from pytorch_lightning import Trainer
 from pytorch_lightning.callbacks import ModelCheckpoint
 from pytorch_lightning.loggers import TensorBoardLogger
 from tests import TEMP_PATH, RANDOM_PORTS, RANDOM_SEEDS
-from tests.base import LightningTestModel, EvalModelTemplate
-from tests.base.datasets import PATH_DATASETS
+from tests.base.model_template import EvalModelTemplate
 
 
 def assert_speed_parity(pl_times, pt_times, num_epochs):
@@ -97,30 +96,6 @@ def run_model_test(trainer_options, model, on_gpu=True, version=None, with_hpc=T
         trainer.hpc_load(save_dir, on_gpu=on_gpu)
 
 
-def get_default_hparams(continue_training=False, hpc_exp_number=0):
-    args = {
-        'drop_prob': 0.2,
-        'batch_size': 32,
-        'in_features': 28 * 28,
-        'learning_rate': 0.001 * 8,
-        'optimizer_name': 'adam',
-        'data_root': PATH_DATASETS,
-        'out_features': 10,
-        'hidden_dim': 1000,
-        'b1': 0.5,
-        'b2': 0.999,
-    }
-
-    if continue_training:
-        args.update(
-            test_tube_do_checkpoint_load=True,
-            hpc_exp_number=hpc_exp_number,
-        )
-
-    hparams = Namespace(**args)
-    return hparams
-
-
 def get_default_logger(save_dir, version=None):
     # set up logger object without actually saving logs
     logger = TensorBoardLogger(save_dir, name='lightning_logs', version=version)
@@ -148,7 +123,7 @@ def get_data_path(expt_logger, path_dir=None):
     return path_expt
 
 
-def load_model(logger, root_weights_dir, module_class=LightningTestModel, path_expt=None):
+def load_model(logger, root_weights_dir, module_class=EvalModelTemplate, path_expt=None):
     # load trained model
     path_expt_dir = get_data_path(logger, path_dir=path_expt)
     tags_path = os.path.join(path_expt_dir, TensorBoardLogger.NAME_CSV_TAGS)
@@ -166,7 +141,7 @@ def load_model(logger, root_weights_dir, module_class=LightningTestModel, path_e
     return trained_model
 
 
-def load_model_from_checkpoint(root_weights_dir, module_class=LightningTestModel):
+def load_model_from_checkpoint(root_weights_dir, module_class=EvalModelTemplate):
     # load trained model
     checkpoints = [x for x in os.listdir(root_weights_dir) if '.ckpt' in x]
     weights_dir = os.path.join(root_weights_dir, checkpoints[0])