re-enabled naming metrics in ckpt name (#3060)

* re-enabled naming metrics in ckpt name

* re-enabled naming metrics in ckpt name

* re-enabled naming metrics in ckpt name

* re-enabled naming metrics in ckpt name

* re-enabled naming metrics in ckpt name

* re-enabled naming metrics in ckpt name

pytorch_lightning/callbacks/model_checkpoint.py

@@ -339,10 +339,11 @@ def on_validation_end(self, trainer, pl_module):
 
         self.epoch_last_check = epoch
 
-        filepath = self.format_checkpoint_name(epoch, metrics)
+        ckpt_name_metrics = trainer.logged_metrics
+        filepath = self.format_checkpoint_name(epoch, ckpt_name_metrics)
         version_cnt = 0
         while gfile.exists(filepath):
-            filepath = self.format_checkpoint_name(epoch, metrics, ver=version_cnt)
+            filepath = self.format_checkpoint_name(epoch, ckpt_name_metrics, ver=version_cnt)
             # this epoch called before
             version_cnt += 1
 

pytorch_lightning/trainer/logging.py

@@ -24,6 +24,7 @@ class TrainerLoggingMixin(ABC):
     default_root_dir: str
     slurm_job_id: int
     num_gpus: int
+    logged_metrics: ...
 
     def configure_logger(self, logger):
         if logger is True:
@@ -75,6 +76,8 @@ def log_metrics(self, metrics, grad_norm_dic, step=None):
             self.logger.agg_and_log_metrics(scalar_metrics, step=step)
             self.logger.save()
 
+            # track the logged metrics
+            self.logged_metrics = scalar_metrics
             self.dev_debugger.track_logged_metrics_history(scalar_metrics)
 
     def add_progress_bar_metrics(self, metrics):

pytorch_lightning/trainer/trainer.py

@@ -374,6 +374,7 @@ def __init__(
         self.batch_idx = 0
         self.progress_bar_metrics = {}
         self.callback_metrics = {}
+        self.logged_metrics = {}
         self.num_training_batches = 0
         self.num_val_batches = []
         self.num_test_batches = []

tests/callbacks/test_model_checkpoint.py

@@ -1,4 +1,5 @@
 import os
+import re
 import pickle
 import platform
 from pathlib import Path
@@ -128,3 +129,58 @@ def test_model_checkpoint_save_last_checkpoint_contents(tmpdir):
     model_last = EvalModelTemplate.load_from_checkpoint(path_last)
     for w0, w1 in zip(model_last_epoch.parameters(), model_last.parameters()):
         assert w0.eq(w1).all()
+
+
+def test_ckpt_metric_names(tmpdir):
+    model = EvalModelTemplate()
+
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        max_epochs=1,
+        gradient_clip_val=1.0,
+        overfit_batches=0.20,
+        progress_bar_refresh_rate=0,
+        limit_train_batches=0.01,
+        limit_val_batches=0.01,
+        checkpoint_callback=ModelCheckpoint(filepath=tmpdir + '/{val_loss:.2f}')
+    )
+
+    trainer.fit(model)
+
+    # make sure the checkpoint we saved has the metric in the name
+    ckpts = os.listdir(tmpdir)
+    ckpts = [x for x in ckpts if 'val_loss' in x]
+    assert len(ckpts) == 1
+    val = re.sub('[^0-9.]', '', ckpts[0])
+    assert len(val) > 3
+
+
+def test_ckpt_metric_names_results(tmpdir):
+    model = EvalModelTemplate()
+    model.training_step = model.training_step_result_obj
+    model.training_step_end = None
+    model.training_epoch_end = None
+
+    model.validation_step = model.validation_step_result_obj
+    model.validation_step_end = None
+    model.validation_epoch_end = None
+
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        max_epochs=1,
+        gradient_clip_val=1.0,
+        overfit_batches=0.20,
+        progress_bar_refresh_rate=0,
+        limit_train_batches=0.01,
+        limit_val_batches=0.01,
+        checkpoint_callback=ModelCheckpoint(filepath=tmpdir + '/{val_loss:.2f}')
+    )
+
+    trainer.fit(model)
+
+    # make sure the checkpoint we saved has the metric in the name
+    ckpts = os.listdir(tmpdir)
+    ckpts = [x for x in ckpts if 'val_loss' in x]
+    assert len(ckpts) == 1
+    val = re.sub('[^0-9.]', '', ckpts[0])
+    assert len(val) > 3

tests/trainer/test_eval_loop_dict_return.py

@@ -136,7 +136,7 @@ def test_validation_step_dict_return(tmpdir):
         assert k in eval_results[1]
 
     # ensure all the keys ended up as candidates for callbacks
-    assert len(trainer.callback_metrics) == 8
+    assert len(trainer.callback_metrics) == 7
 
     # make sure correct steps were called
     assert model.validation_step_called
@@ -211,7 +211,7 @@ def test_val_step_step_end(tmpdir):
         assert k in eval_results[1]
 
     # ensure all the keys ended up as candidates for callbacks
-    assert len(trainer.callback_metrics) == 9
+    assert len(trainer.callback_metrics) == 8
 
     # make sure correct steps were called
     assert model.validation_step_called
@@ -254,7 +254,7 @@ def test_no_val_step_end(tmpdir):
         assert k in eval_results
 
     # ensure all the keys ended up as candidates for callbacks
-    assert len(trainer.callback_metrics) == 9
+    assert len(trainer.callback_metrics) == 8
 
     # make sure correct steps were called
     assert model.validation_step_called
@@ -297,7 +297,7 @@ def test_full_val_loop(tmpdir):
         assert k in eval_results
 
     # ensure all the keys ended up as candidates for callbacks
-    assert len(trainer.callback_metrics) == 10
+    assert len(trainer.callback_metrics) == 9
 
     # make sure correct steps were called
     assert model.validation_step_called

tests/trainer/test_trainer_steps_scalar_return.py

@@ -108,7 +108,7 @@ def test_full_training_loop_scalar(tmpdir):
     assert model.training_epoch_end_called
 
     # assert epoch end metrics were added
-    assert 'epoch' in trainer.callback_metrics and len(trainer.callback_metrics) == 1
+    assert len(trainer.callback_metrics) == 0
     assert len(trainer.progress_bar_metrics) == 0
 
     # make sure training outputs what is expected
@@ -151,7 +151,7 @@ def test_train_step_epoch_end_scalar(tmpdir):
     assert model.training_epoch_end_called
 
     # assert epoch end metrics were added
-    assert 'epoch' in trainer.callback_metrics and len(trainer.callback_metrics) == 1
+    assert len(trainer.callback_metrics) == 0
     assert len(trainer.progress_bar_metrics) == 0
 
     # make sure training outputs what is expected