added test for model loading and predicting

tests/debug.py

@@ -54,6 +54,41 @@ def clear_save_dir():
         shutil.rmtree(save_dir)
 
 
+def load_model(exp, save_dir):
+
+    # load trained model
+    tags_path = exp.get_data_path(exp.name, exp.version)
+    tags_path = os.path.join(tags_path, 'meta_tags.csv')
+
+    checkpoints = [x for x in os.listdir(save_dir) if '.ckpt' in x]
+    weights_dir = os.path.join(save_dir, checkpoints[0])
+
+    trained_model = LightningTemplateModel.load_from_metrics(weights_path=weights_dir, tags_csv=tags_path, on_gpu=True)
+
+    assert trained_model is not None, 'loading model failed'
+
+    return trained_model
+
+
+def run_prediction(dataloader, trained_model):
+    # run prediction on 1 batch
+    for batch in dataloader:
+        break
+
+    x, y = batch
+    x = x.view(x.size(0), -1)
+
+    y_hat = trained_model(x)
+
+    # acc
+    labels_hat = torch.argmax(y_hat, dim=1)
+    val_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)
+    val_acc = torch.tensor(val_acc)
+    val_acc = val_acc.item()
+
+    assert val_acc > 0.70, 'this model is expected to get > 0.7 in test set'
+
+
 def main():
 
     save_dir = init_save_dir()
@@ -72,7 +107,7 @@ def main():
         progress_bar=True,
         experiment=exp,
         max_nb_epochs=1,
-        train_percent_check=0.1,
+        train_percent_check=0.2,
         val_percent_check=0.1,
         gpus=[0, 1],
         distributed_backend='ddp',
@@ -84,32 +119,11 @@ def main():
     # correct result and ok accuracy
     assert result == 1, 'amp + ddp model failed to complete'
 
-    # load trained model
-    pdb.set_trace()
-    tags_path = exp.get_data_path(exp.name, exp.version)
-    tags_path = os.path.join(tags_path, 'meta_tags.csv')
+    # test model loading
+    pretrained_model = load_model(exp, save_dir)
 
-    pdb.set_trace()
-    checkpoints = [x for x in os.listdir(save_dir) if '.ckpt' in x]
-    weights_dir = os.path.join(save_dir, checkpoints[0])
-
-    trained_model = LightningTemplateModel.load_from_metrics(weights_path=weights_dir, tags_csv=tags_path, on_gpu=True)
-
-    # run prediction
-    for batch in model.test_dataloader:
-        break
-
-    x, y = batch
-    x = x.view(x.size(0), -1)
-
-    y_hat = trained_model(x)
-
-    # acc
-    labels_hat = torch.argmax(y_hat, dim=1)
-    val_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)
-    val_acc = torch.tensor(val_acc)
-
-    print(val_acc)
+    # test model preds
+    run_prediction(model.test_dataloader, pretrained_model)
 
     clear_save_dir()