update tests

tests/callbacks/test_early_stopping.py

@@ -81,7 +81,8 @@ def test_resume_early_stopping_from_checkpoint(tmpdir):
     # the checkpoint saves "epoch + 1"
     early_stop_callback_state = early_stop_callback.saved_states[checkpoint["epoch"] - 1]
     assert 4 == len(early_stop_callback.saved_states)
-    assert checkpoint["callbacks"]["EarlyStoppingTestRestore"] == early_stop_callback_state
+    print(checkpoint["callbacks"])
+    assert checkpoint["callbacks"]["EarlyStoppingTestRestore[monitor=train_loss]"] == early_stop_callback_state
 
     # ensure state is reloaded properly (assertion in the callback)
     early_stop_callback = EarlyStoppingTestRestore(early_stop_callback_state, monitor='train_loss')

tests/callbacks/test_lambda_function.py

@@ -28,7 +28,7 @@ def on_train_epoch_start(self):
                 raise KeyboardInterrupt
 
     checker = set()
-    hooks = [m for m, _ in inspect.getmembers(Callback, predicate=inspect.isfunction)]
+    hooks = [m for m, _ in inspect.getmembers(Callback, predicate=inspect.isfunction) if not m.startswith("_")]
     hooks_args = {h: (lambda x: lambda *_: checker.add(x))(h) for h in hooks}
     hooks_args["on_save_checkpoint"] = (lambda x: lambda *_: [checker.add(x)])("on_save_checkpoint")
 

tests/checkpointing/test_model_checkpoint.py

@@ -152,7 +152,7 @@ def configure_optimizers(self):
         assert chk['epoch'] == epoch + 1
         assert chk['global_step'] == limit_train_batches * (epoch + 1)
 
-        mc_specific_data = chk['callbacks']["ModelCheckpoint"]
+        mc_specific_data = chk['callbacks'][f"ModelCheckpoint[monitor={monitor}]"]
         assert mc_specific_data['dirpath'] == checkpoint.dirpath
         assert mc_specific_data['monitor'] == monitor
         assert mc_specific_data['current_score'] == score
@@ -269,7 +269,7 @@ def _make_assertions(epoch, ix, add=''):
         expected_global_step = per_epoch_steps * (global_ix + 1) + (left_over_steps * epoch_num)
         assert chk['global_step'] == expected_global_step
 
-        mc_specific_data = chk['callbacks']["ModelCheckpoint"]
+        mc_specific_data = chk['callbacks'][f"ModelCheckpoint[monitor={monitor}]"]
         assert mc_specific_data['dirpath'] == checkpoint.dirpath
         assert mc_specific_data['monitor'] == monitor
         assert mc_specific_data['current_score'] == score
@@ -870,8 +870,8 @@ def test_model_checkpoint_save_last_checkpoint_contents(tmpdir):
     ckpt_last = torch.load(path_last)
     assert all(ckpt_last_epoch[k] == ckpt_last[k] for k in ("epoch", "global_step"))
 
-    ch_type = "ModelCheckpoint"
-    assert ckpt_last["callbacks"][ch_type] == ckpt_last_epoch["callbacks"][ch_type]
+    ckpt_id = "ModelCheckpoint[monitor=early_stop_on]"
+    assert ckpt_last["callbacks"][ckpt_id] == ckpt_last_epoch["callbacks"][ckpt_id]
 
     # it is easier to load the model objects than to iterate over the raw dict of tensors
     model_last_epoch = LogInTwoMethods.load_from_checkpoint(path_last_epoch)
@@ -1128,7 +1128,7 @@ def training_step(self, *args):
     trainer.fit(TestModel())
     assert model_checkpoint.current_score == 0.3
     ckpts = [torch.load(str(ckpt)) for ckpt in tmpdir.listdir()]
-    ckpts = [ckpt["callbacks"]["ModelCheckpoint"] for ckpt in ckpts]
+    ckpts = [ckpt["callbacks"]["ModelCheckpoint[monitor=foo]"] for ckpt in ckpts]
     assert sorted(ckpt["current_score"] for ckpt in ckpts) == [0.1, 0.2, 0.3]