Adds the option of saving the last model on checkpoint

pytorch_lightning/callbacks/model_checkpoint.py

@@ -43,6 +43,7 @@ class ModelCheckpoint(Callback):
 
         monitor: quantity to monitor.
         verbose: verbosity mode. Default: ``False``.
+        save_last: always saves the model at the end of the epoch. Default: ``False``.
         save_top_k: if `save_top_k == k`,
             the best k models according to
             the quantity monitored will be saved.
@@ -83,7 +84,7 @@ class ModelCheckpoint(Callback):
     """
 
     def __init__(self, filepath: Optional[str] = None, monitor: str = 'val_loss', verbose: bool = False,
-                 save_top_k: int = 1, save_weights_only: bool = False,
+                 save_last: bool = False, save_top_k: int = 1, save_weights_only: bool = False,
                  mode: str = 'auto', period: int = 1, prefix: str = ''):
         super().__init__()
         if save_top_k > 0 and filepath is not None and os.path.isdir(filepath) and len(os.listdir(filepath)) > 0:
@@ -103,6 +104,7 @@ def __init__(self, filepath: Optional[str] = None, monitor: str = 'val_loss', ve
             else:
                 self.dirpath, self.filename = os.path.split(filepath)
             os.makedirs(self.dirpath, exist_ok=True)
+        self.save_last = save_last
         self.save_top_k = save_top_k
         self.save_weights_only = save_weights_only
         self.period = period
@@ -217,6 +219,10 @@ def on_validation_end(self, trainer, pl_module):
 
         self.epoch_last_check = epoch
 
+        if self.save_last:
+            filepath = os.path.join(self.dirpath, self.prefix + 'last.ckpt')
+            self._save_model(filepath)
+
         filepath = self.format_checkpoint_name(epoch, metrics)
         version_cnt = 0
         while os.path.isfile(filepath):

tests/trainer/test_trainer.py

@@ -255,19 +255,21 @@ def test_dp_output_reduce():
     assert reduced['b']['c'] == out['b']['c']
 
 
-@pytest.mark.parametrize(["save_top_k", "file_prefix", "expected_files"], [
-    pytest.param(-1, '', {'epoch=4.ckpt', 'epoch=3.ckpt', 'epoch=2.ckpt', 'epoch=1.ckpt', 'epoch=0.ckpt'},
+@pytest.mark.parametrize(["save_top_k", "save_last", "file_prefix", "expected_files"], [
+    pytest.param(-1, False, '', {'epoch=4.ckpt', 'epoch=3.ckpt', 'epoch=2.ckpt', 'epoch=1.ckpt', 'epoch=0.ckpt'},
                  id="CASE K=-1  (all)"),
-    pytest.param(1, 'test_prefix_', {'test_prefix_epoch=4.ckpt'},
+    pytest.param(1, False, 'test_prefix_', {'test_prefix_epoch=4.ckpt'},
                  id="CASE K=1 (2.5, epoch 4)"),
-    pytest.param(2, '', {'epoch=4.ckpt', 'epoch=2.ckpt'},
+    pytest.param(2, False, '', {'epoch=4.ckpt', 'epoch=2.ckpt'},
                  id="CASE K=2 (2.5 epoch 4, 2.8 epoch 2)"),
-    pytest.param(4, '', {'epoch=1.ckpt', 'epoch=4.ckpt', 'epoch=3.ckpt', 'epoch=2.ckpt'},
+    pytest.param(4, False, '', {'epoch=1.ckpt', 'epoch=4.ckpt', 'epoch=3.ckpt', 'epoch=2.ckpt'},
                  id="CASE K=4 (save all 4 base)"),
-    pytest.param(3, '', {'epoch=2.ckpt', 'epoch=3.ckpt', 'epoch=4.ckpt'},
+    pytest.param(3, False, '', {'epoch=2.ckpt', 'epoch=3.ckpt', 'epoch=4.ckpt'},
+                 id="CASE K=3 (save the 2nd, 3rd, 4th model)"),
+    pytest.param(1, True, '', {'epoch=4.ckpt', 'last.ckpt'},
                  id="CASE K=3 (save the 2nd, 3rd, 4th model)"),
 ])
-def test_model_checkpoint_options(tmpdir, save_top_k, file_prefix, expected_files):
+def test_model_checkpoint_options(tmpdir, save_top_k, save_last, file_prefix, expected_files):
     """Test ModelCheckpoint options."""
 
     def mock_save_function(filepath, *args):
@@ -276,7 +278,8 @@ def mock_save_function(filepath, *args):
     # simulated losses
     losses = [10, 9, 2.8, 5, 2.5]
 
-    checkpoint_callback = ModelCheckpoint(tmpdir, save_top_k=save_top_k, prefix=file_prefix, verbose=1)
+    checkpoint_callback = ModelCheckpoint(tmpdir, save_top_k=save_top_k, save_last=save_last,
+                                          prefix=file_prefix, verbose=1)
     checkpoint_callback.save_function = mock_save_function
     trainer = Trainer()