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Lightning-AI · Aug 13, 2021 · 77e7027 · 77e7027
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commit 77e7027
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Showing 2 changed files with 14 additions and 6 deletions.
diff --git a/tests/checkpointing/test_model_checkpoint.py b/tests/checkpointing/test_model_checkpoint.py
@@ -47,7 +47,8 @@ def test_model_checkpoint_state_id():
     early_stopping = ModelCheckpoint(monitor="val_loss")
     assert (
         early_stopping.state_id
-        == "ModelCheckpoint{'monitor': 'val_loss', 'mode': 'min', 'every_n_train_steps': 0, 'every_n_epochs': 1, 'train_time_interval': None, 'save_on_train_epoch_end': None}"
+        == "ModelCheckpoint{'monitor': 'val_loss', 'mode': 'min', 'every_n_train_steps': 0, 'every_n_epochs': 1,"
+        " 'train_time_interval': None, 'save_on_train_epoch_end': None}"
     )
 
 
@@ -157,7 +158,8 @@ def on_validation_epoch_end(self):
         assert chk["global_step"] == limit_train_batches * (epoch + 1)
 
         mc_specific_data = chk["callbacks"][
-            f"ModelCheckpoint{{'monitor': '{monitor}', 'mode': 'min', 'every_n_train_steps': 0, 'every_n_epochs': 1, 'train_time_interval': None, 'save_on_train_epoch_end': True}}"
+            f"ModelCheckpoint{{'monitor': '{monitor}', 'mode': 'min', 'every_n_train_steps': 0, 'every_n_epochs': 1,"
+            " 'train_time_interval': None, 'save_on_train_epoch_end': True}"
         ]
         assert mc_specific_data["dirpath"] == checkpoint.dirpath
         assert mc_specific_data["monitor"] == monitor
@@ -270,7 +272,8 @@ def _make_assertions(epoch, ix, version=""):
         assert chk["global_step"] == expected_global_step
 
         mc_specific_data = chk["callbacks"][
-            f"ModelCheckpoint{{'monitor': '{monitor}', 'mode': 'min', 'every_n_train_steps': 0, 'every_n_epochs': 1, 'train_time_interval': None, 'save_on_train_epoch_end': False}}"
+            f"ModelCheckpoint{{'monitor': '{monitor}', 'mode': 'min', 'every_n_train_steps': 0, 'every_n_epochs': 1,"
+            " 'train_time_interval': None, 'save_on_train_epoch_end': False}"
         ]
         assert mc_specific_data["dirpath"] == checkpoint.dirpath
         assert mc_specific_data["monitor"] == monitor
@@ -870,7 +873,10 @@ def test_model_checkpoint_save_last_checkpoint_contents(tmpdir):
     assert ckpt_last_epoch["epoch"] == ckpt_last["epoch"]
     assert ckpt_last_epoch["global_step"] == ckpt_last["global_step"]
 
-    ckpt_id = "ModelCheckpoint{'monitor': 'early_stop_on', 'mode': 'min', 'every_n_train_steps': 0, 'every_n_epochs': 1, 'train_time_interval': None, 'save_on_train_epoch_end': True}"
+    ckpt_id = (
+        "ModelCheckpoint{'monitor': 'early_stop_on', 'mode': 'min', 'every_n_train_steps': 0, 'every_n_epochs': 1,"
+        " 'train_time_interval': None, 'save_on_train_epoch_end': True}"
+    )
     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
@@ -1111,7 +1117,8 @@ def training_step(self, *args):
     ckpts = [torch.load(str(ckpt)) for ckpt in tmpdir.listdir()]
     ckpts = [
         ckpt["callbacks"][
-            "ModelCheckpoint{'monitor': 'foo', 'mode': 'min', 'every_n_train_steps': 0, 'every_n_epochs': 1, 'train_time_interval': None, 'save_on_train_epoch_end': True}"
+            "ModelCheckpoint{'monitor': 'foo', 'mode': 'min', 'every_n_train_steps': 0, 'every_n_epochs': 1,"
+            " 'train_time_interval': None, 'save_on_train_epoch_end': True}"
         ]
         for ckpt in ckpts
     ]

diff --git a/tests/trainer/connectors/test_callback_connector.py b/tests/trainer/connectors/test_callback_connector.py
@@ -107,7 +107,8 @@ def test_all_callback_states_saved_before_checkpoint_callback(tmpdir):
     assert "content1" in state1 and state1["content1"] == "one"
     assert "content1" in state2 and state2["content1"] == "two"
     assert (
-        "ModelCheckpoint{'monitor': None, 'mode': 'min', 'every_n_train_steps': 0, 'every_n_epochs': 1, 'train_time_interval': None, 'save_on_train_epoch_end': True}"
+        "ModelCheckpoint{'monitor': None, 'mode': 'min', 'every_n_train_steps': 0, 'every_n_epochs': 1,"
+        " 'train_time_interval': None, 'save_on_train_epoch_end': True}"
         in ckpt["callbacks"]
     )