removed dummy d

tests/debug.py

@@ -3,6 +3,7 @@ from pytorch_lightning import Trainer
 from pytorch_lightning.examples.new_project_templates.lightning_module_template import LightningTemplateModel
 from argparse import Namespace
 from test_tube import Experiment
+from pytorch_lightning.callbacks import ModelCheckpoint
 import numpy as np
 import warnings
 import torch
@@ -27,28 +28,48 @@ def get_model():
     return model
 
 
-def get_exp():
+def get_exp(debug=True):
     # set up exp object without actually saving logs
     root_dir = os.path.dirname(os.path.realpath(__file__))
-    exp = Experiment(debug=True, save_dir=root_dir, name='tests_tt_dir')
+    exp = Experiment(debug=debug, save_dir=root_dir, name='tests_tt_dir')
     return exp
 
 
-def clear_tt_dir():
+def init_save_dir():
     root_dir = os.path.dirname(os.path.realpath(__file__))
-    tt_dir = os.path.join(root_dir, 'tests_tt_dir')
-    if os.path.exists(tt_dir):
-        shutil.rmtree(tt_dir)
+    save_dir = os.path.join(root_dir, 'save_dir')
+
+    if os.path.exists(save_dir):
+        shutil.rmtree(save_dir)
+
+    os.makedirs(save_dir, exist_ok=True)
+
+    return save_dir
+
+
+def clear_save_dir():
+    root_dir = os.path.dirname(os.path.realpath(__file__))
+    save_dir = os.path.join(root_dir, 'save_dir')
+    if os.path.exists(save_dir):
+        shutil.rmtree(save_dir)
 
 
 def main():
 
-    clear_tt_dir()
+    save_dir = init_save_dir()
     model = get_model()
 
+    # exp file to get meta
+    exp = get_exp(False)
+    exp.save()
+
+    # exp file to get weights
+    checkpoint = ModelCheckpoint(save_dir)
+
     trainer = Trainer(
+        checkpoint_callback=checkpoint,
         progress_bar=True,
-        experiment=get_exp(),
+        experiment=exp,
         max_nb_epochs=1,
         train_percent_check=0.1,
         val_percent_check=0.1,
@@ -62,22 +83,12 @@ def main():
     # correct result and ok accuracy
     assert result == 1, 'amp + ddp model failed to complete'
 
-    # test prediction
-    data = model.val_dataloader
-    for batch in data:
-        break
+    # load trained model
+    pdb.set_trace()
+    tags_path = exp.get_data_path(exp.name, exp.version)
+    LightningTemplateModel.load_from_metrics(weights_path=save_dir, tags_csv=)
 
-    x, y = batch
-    x = x.view(x.size(0), -1)
-    out = model(x)
-
-    labels_hat = torch.argmax(out, dim=1)
-    val_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)
-    val_acc = torch.tensor(val_acc)
-    print(val_acc)
-
-
-    clear_tt_dir()
+    clear_save_dir()
 
 
 if __name__ == '__main__':