drop deprecated TrainResult (#5323)

* drop TrainResult

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CHANGELOG.md

@@ -26,10 +26,12 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - `stat_scores` metric now calculates stat scores over all classes and gains new parameters, in line with the new `StatScores` metric ([#4839](https://github.com/PyTorchLightning/pytorch-lightning/pull/4839))
 
+
 ### Deprecated
 
 - `stat_scores_multiple_classes` is deprecated in favor of `stat_scores` ([#4839](https://github.com/PyTorchLightning/pytorch-lightning/pull/4839))
 
+
 ### Removed
 
 - Removed deprecated checkpoint argument `filepath` ([#5321](https://github.com/PyTorchLightning/pytorch-lightning/pull/5321))
@@ -38,9 +40,13 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Removed deprecated `Fbeta`, `f1_score` and `fbeta_score` metrics ([#5322](https://github.com/PyTorchLightning/pytorch-lightning/pull/5322))
 
 
+- Removed deprecated `TrainResult` ([#5323](https://github.com/PyTorchLightning/pytorch-lightning/pull/5323))
+
+
 ### Fixed
 
 
+
 ## [1.1.0] - 2020-12-09
 
 ### Added

pytorch_lightning/core/step_result.py

@@ -681,157 +681,6 @@ def collate_tensors(items: Union[List, Tuple]) -> Union[Tensor, List, Tuple]:
     return items
 
 
-class TrainResult(Result):
-    def __init__(
-        self,
-        minimize: Optional[Tensor] = None,
-        early_stop_on: Optional[Tensor] = None,
-        checkpoint_on: Optional[Union[Tensor, bool]] = None,
-        hiddens: Optional[Tensor] = None,
-    ):
-        """
-        Tracks internal metrics aggregations
-
-        Args:
-            minimize: Metric currently being minimized.
-            early_stop_on: Metric to early stop on.
-                Should be a one element tensor if combined with default
-                :class:`~pytorch_lightning.callbacks.early_stopping.EarlyStopping`.
-                If this result is returned by
-                :meth:`~pytorch_lightning.core.lightning.LightningModule.training_step`,
-                the specified value will be averaged across all steps.
-            checkpoint_on: Metric to checkpoint on.
-                Should be a one element tensor if combined with default checkpoint callback.
-                If this result is returned by
-                :meth:`~pytorch_lightning.core.lightning.LightningModule.training_step`,
-                the specified value will be averaged across all steps.
-            hiddens:
-        """
-
-        super().__init__(minimize, early_stop_on, checkpoint_on, hiddens)
-
-    def log(
-        self,
-        name,
-        value,
-        prog_bar: bool = False,
-        logger: bool = True,
-        on_step: bool = True,
-        on_epoch: bool = False,
-        reduce_fx: Callable = torch.mean,
-        tbptt_reduce_fx: Callable = torch.mean,
-        tbptt_pad_token: int = 0,
-        enable_graph: bool = False,
-        sync_dist: bool = False,
-        sync_dist_op: Union[Any, str] = 'mean',
-        sync_dist_group: Optional[Any] = None,
-    ):
-        """
-        Log a key, value
-
-        Example::
-
-            result.log('train_loss', loss)
-
-            # defaults used
-            result.log(
-                name,
-                value,
-                on_step=True,
-                on_epoch=False,
-                logger=True,
-                prog_bar=False,
-                reduce_fx=torch.mean,
-                enable_graph=False
-            )
-
-
-        Args:
-            name: key name
-            value: value name
-            prog_bar: if True logs to the progress base
-            logger: if True logs to the logger
-            on_step: if True logs the output of validation_step or test_step
-            on_epoch: if True, logs the output of the training loop aggregated
-            reduce_fx: Torch.mean by default
-            tbptt_reduce_fx: function to reduce on truncated back prop
-            tbptt_pad_token: token to use for padding
-            enable_graph: if True, will not auto detach the graph
-            sync_dist: if True, reduces the metric across GPUs/TPUs
-            sync_dist_op: the op to sync across
-            sync_dist_group: the ddp group
-        """
-        super().log(
-            name=name,
-            value=value,
-            prog_bar=prog_bar,
-            logger=logger,
-            on_step=on_step,
-            on_epoch=on_epoch,
-            reduce_fx=reduce_fx,
-            enable_graph=enable_graph,
-            sync_dist=sync_dist,
-            sync_dist_group=sync_dist_group,
-            sync_dist_op=sync_dist_op,
-            tbptt_pad_token=tbptt_pad_token,
-            tbptt_reduce_fx=tbptt_reduce_fx,
-        )
-
-    def log_dict(
-        self,
-        dictionary: dict,
-        prog_bar: bool = False,
-        logger: bool = True,
-        on_step: bool = False,
-        on_epoch: bool = True,
-        reduce_fx: Callable = torch.mean,
-        tbptt_reduce_fx: Callable = torch.mean,
-        tbptt_pad_token: int = 0,
-        enable_graph: bool = False,
-        sync_dist: bool = False,
-        sync_dist_op: Union[Any, str] = 'mean',
-        sync_dist_group: Optional[Any] = None,
-    ):
-        """
-        Log a dictonary of values at once
-
-        Example::
-
-            values = {'loss': loss, 'acc': acc, ..., 'metric_n': metric_n}
-            result.log_dict(values)
-
-        Args:
-            dictionary: key value pairs (str, tensors)
-            prog_bar: if True logs to the progress base
-            logger: if True logs to the logger
-            on_step: if True logs the output of validation_step or test_step
-            on_epoch: if True, logs the output of the training loop aggregated
-            reduce_fx: Torch.mean by default
-            tbptt_reduce_fx: function to reduce on truncated back prop
-            tbptt_pad_token: token to use for padding
-            enable_graph: if True, will not auto detach the graph
-            sync_dist: if True, reduces the metric across GPUs/TPUs
-            sync_dist_op: the op to sync across
-            sync_dist_group: the ddp group:
-        """
-        for k, v in dictionary.items():
-            self.log(
-                name=k,
-                value=v,
-                prog_bar=prog_bar,
-                logger=logger,
-                on_step=on_step,
-                on_epoch=on_epoch,
-                reduce_fx=reduce_fx,
-                enable_graph=enable_graph,
-                sync_dist=sync_dist,
-                sync_dist_group=sync_dist_group,
-                sync_dist_op=sync_dist_op,
-                tbptt_pad_token=tbptt_pad_token,
-                tbptt_reduce_fx=tbptt_reduce_fx,
-            )
-
-
 class EvalResult(Result):
     def __init__(
         self,

pytorch_lightning/overrides/data_parallel.py

@@ -104,11 +104,7 @@ def __gather_structured_result(self, outputs):
 
         outputs = self.gather(outputs)
 
-        # pass minimize to constructor for TrainResult
-        if 'minimize' in outputs:
-            result = original_class(outputs['minimize'])
-        else:
-            result = original_class()
+        result = original_class()
 
         result.update(outputs)
         result['meta'] = meta

tests/base/model_train_steps.py

@@ -17,8 +17,6 @@
 
 import torch
 
-from pytorch_lightning.core.step_result import TrainResult
-
 
 class TrainingStepVariations(ABC):
     """
@@ -28,27 +26,27 @@ class TrainingStepVariations(ABC):
     test_step_inf_loss = float('inf')
 
     def training_step(self, batch, batch_idx, optimizer_idx=None):
+        """Lightning calls this inside the training loop"""
         self.training_step_called = True
 
-        """Lightning calls this inside the training loop"""
         # forward pass
         x, y = batch
         x = x.view(x.size(0), -1)
         y_hat = self(x)
 
         # calculate loss
-        loss_val = self.loss(y, y_hat)
-        log_val = loss_val
+        loss_train = self.loss(y, y_hat)
+        log_train = loss_train
 
         # alternate between tensors and scalars for "log" and "progress_bar"
         if batch_idx % 2 == 0:
-            log_val = log_val.item()
+            log_train = log_train.item()
 
         output = OrderedDict(
             {
-                'loss': loss_val,
-                'progress_bar': {'some_val': log_val * log_val},
-                'log': {'train_some_val': log_val * log_val},
+                'loss': loss_train,
+                'progress_bar': {'some_val': log_train * log_train},
+                'log': {'train_some_val': log_train * log_train},
             }
         )
         return output
@@ -62,24 +60,22 @@ def training_step__inf_loss(self, batch, batch_idx, optimizer_idx=None):
                 output /= 0
         return output
 
-    def training_step_result_obj_dp(self, batch, batch_idx, optimizer_idx=None):
+    def training_step__result_obj_dp(self, batch, batch_idx, optimizer_idx=None):
+
         # forward pass
         x, y = batch
         x = x.view(x.size(0), -1)
         y_hat = self(x.to(self.device))
 
         # calculate loss
-        loss_val = self.loss(y.to(y_hat.device), y_hat)
-        log_val = loss_val
+        loss_train = self.loss(y.to(y_hat.device), y_hat)
+        log_train = loss_train
 
         # alternate between tensors and scalars for "log" and "progress_bar"
         if batch_idx % 2 == 0:
-            log_val = log_val.item()
-
-        result = TrainResult(loss_val)
-        result.log('some_val', log_val * log_val, prog_bar=True, logger=False)
-        result.log('train_some_val', log_val * log_val)
+            log_train = log_train.item()
 
-        self.training_step_called = True
+        self.log('some_val', log_train * log_train, prog_bar=True, logger=False)
+        self.log('train_some_val', log_train * log_train)
 
-        return result
+        return loss_train

tests/base/model_valid_steps.py

@@ -47,7 +47,7 @@ def validation_step(self, batch, batch_idx, *args, **kwargs):
         })
         return output
 
-    def validation_step_result_obj_dp(self, batch, batch_idx, *args, **kwargs):
+    def validation_step__result_obj_dp(self, batch, batch_idx, *args, **kwargs):
         x, y = batch
         x = x.view(x.size(0), -1)
         y_hat = self(x.to(self.device))

tests/checkpointing/test_model_checkpoint.py

@@ -493,10 +493,9 @@ def training_step(self, batch, batch_idx):
             if batch_idx % 2 == 0:
                 log_val = log_val.item()
 
-            result = pl.core.step_result.TrainResult(loss_val)
-            result.log('some_val', log_val * log_val, prog_bar=True, logger=False)
-            result.log('train_some_val', log_val * log_val)
-            return result
+            self.log('some_val', log_val * log_val, prog_bar=True, logger=False)
+            self.log('train_some_val', log_val * log_val)
+            return loss_val
 
         def validation_step(self, batch, batch_idx):
             y_hat = self(batch)

tests/core/test_results.py

@@ -19,7 +19,7 @@
 import torch.distributed as dist
 import torch.multiprocessing as mp
 from pytorch_lightning import Trainer
-from pytorch_lightning.core.step_result import Result, TrainResult, EvalResult
+from pytorch_lightning.core.step_result import Result, EvalResult
 import tests.base.develop_utils as tutils
 
 from tests.base import EvalModelTemplate
@@ -45,7 +45,7 @@ def _ddp_test_fn(rank, worldsize, result_cls: Result):
     assert res["test_tensor"].item() == dist.get_world_size(), "Result-Log does not work properly with DDP and Tensors"
 
 
-@pytest.mark.parametrize("result_cls", [Result, TrainResult, EvalResult])
+@pytest.mark.parametrize("result_cls", [Result, EvalResult])
 @pytest.mark.skipif(sys.platform == "win32", reason="DDP not available on windows")
 def test_result_reduce_ddp(result_cls):
     """Make sure result logging works with DDP"""