No auto load weights (Lightning-AI#985)

* remove autoload

* remove autoload

* added weights loading docs

* checkpoint loading saving docs

* checkpoint loading saving docs

* checkpoint loading saving docs

* docs (Lightning-AI#1010)

* remove autoload

* remove autoload

* added weights loading docs

* checkpoint loading saving docs

* checkpoint loading saving docs

* checkpoint loading saving docs

* docs

* docs

* docs

* docs

* docs

* docs

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* docs

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docs/source/index.rst

@@ -45,7 +45,6 @@ PyTorch-Lightning Documentation
    :caption: Common Use Cases
 
    apex
-   checkpointing
    slurm
    debugging
    experiment_logging
@@ -54,6 +53,7 @@ PyTorch-Lightning Documentation
    fast_training
    hooks
    multi_gpu
+   weights_loading
    single_gpu
    sequences
    training_tricks

docs/source/weights_loading.rst

@@ -0,0 +1,76 @@
+Saving and loading weights
+==========================
+
+Lightning can automate saving and loading checkpoints.
+
+Checkpoint saving
+-----------------
+
+Checkpointing is enabled by default to the current working directory.
+To change the checkpoint path pass in:
+
+.. code-block:: python
+
+    Trainer(default_save_path='/your/path/to/save/checkpoints')
+
+To modify the behavior of checkpointing pass in your own callback.
+
+.. code-block:: python
+
+    from pytorch_lightning.callbacks import ModelCheckpoint
+
+    # DEFAULTS used by the Trainer
+    checkpoint_callback = ModelCheckpoint(
+        filepath=os.getcwd(),
+        save_best_only=True,
+        verbose=True,
+        monitor='val_loss',
+        mode='min',
+        prefix=''
+    )
+
+    trainer = Trainer(checkpoint_callback=checkpoint_callback)
+
+
+Or disable it by passing
+
+.. code-block:: python
+
+        trainer = Trainer(checkpoint_callback=False)
+
+
+The Lightning checkpoint also saves the hparams (hyperparams) passed into the LightningModule init.
+
+.. note:: hparams is a `Namespace <https://docs.python.org/2/library/argparse.html#argparse.Namespace>`_.
+
+.. code-block:: python
+   :emphasize-lines: 8
+
+   from argparse import Namespace
+
+   # usually these come from command line args
+   args = Namespace(**{'learning_rate':0.001})
+
+   # define you module to have hparams as the first arg
+   # this means your checkpoint will have everything that went into making
+   # this model (in this case, learning rate)
+   class MyLightningModule(pl.LightningModule):
+
+       def __init__(self, hparams, ...):
+           self.hparams = hparams
+
+Checkpoint Loading
+------------------
+
+You might want to not only load a model but also continue training it. Use this method to
+restore the trainer state as well. This will continue from the epoch and global step you last left off.
+However, the dataloaders will start from the first batch again (if you shuffled it shouldn't matter).
+
+.. code-block:: python
+
+    model = MyLightingModule.load_from_checkpoint(PATH)
+    model.eval()
+    y_hat = model(x)
+
+A LightningModule is no different than a nn.Module. This means you can load it and use it for
+predictions as you would a nn.Module.

pytorch_lightning/trainer/callback_config.py

@@ -62,7 +62,7 @@ def configure_checkpoint_callback(self):
             self.weights_save_path = self.default_save_path
 
     def configure_early_stopping(self, early_stop_callback):
-        if early_stop_callback is True:
+        if early_stop_callback is True or None:
             self.early_stop_callback = EarlyStopping(
                 monitor='val_loss',
                 patience=3,
@@ -71,15 +71,6 @@ def configure_early_stopping(self, early_stop_callback):
                 mode='min'
             )
             self.enable_early_stop = True
-        elif early_stop_callback is None:
-            self.early_stop_callback = EarlyStopping(
-                monitor='val_loss',
-                patience=3,
-                strict=False,
-                verbose=False,
-                mode='min'
-            )
-            self.enable_early_stop = True
         elif not early_stop_callback:
             self.early_stop_callback = None
             self.enable_early_stop = False

pytorch_lightning/trainer/evaluation_loop.py

@@ -347,6 +347,10 @@ def run_evaluation(self, test_mode: bool = False):
         # add metrics to prog bar
         self.add_tqdm_metrics(prog_bar_metrics)
 
+        # log results of test
+        if test_mode:
+            model.print(prog_bar_metrics)
+
         # log metrics
         self.log_metrics(log_metrics, {})
 

pytorch_lightning/trainer/trainer.py

@@ -1096,7 +1096,8 @@ def run_pretrain_routine(self, model: LightningModule):
         self.register_slurm_signal_handlers()
 
         # print model summary
-        if self.proc_rank == 0 and self.weights_summary is not None:
+        # TODO: remove self.testing condition because model.summarize() is wiping out the weights
+        if self.proc_rank == 0 and self.weights_summary is not None and not self.testing:
             if self.weights_summary in ['full', 'top']:
                 ref_model.summarize(mode=self.weights_summary)
             else:
@@ -1116,7 +1117,7 @@ def run_pretrain_routine(self, model: LightningModule):
         # when testing requested only run test and return
         if self.testing:
             # only load test dataloader for testing
-            self.reset_test_dataloader(ref_model)
+            # self.reset_test_dataloader(ref_model)
             self.run_evaluation(test_mode=True)
             return
 
@@ -1189,8 +1190,10 @@ def test(self, model: Optional[LightningModule] = None):
         """
         self.testing = True
         if model is not None:
+            self.model = model
             self.fit(model)
-        self.run_evaluation(test_mode=True)
+        else:
+            self.run_evaluation(test_mode=True)
 
 
 class _PatchDataLoader(object):