Adds shortcut for path to log

.pyrightconfig.json

@@ -30,6 +30,7 @@
     "pytorch_lightning/trainer/training_tricks.py",
     "pytorch_lightning/trainer/batch_size_scaling.py",
     "pytorch_lightning/trainer/distrib_data_parallel.py",
+    "pytorch_lightning/trainer/properties.py",
     "pytorch_lightning/trainer/lr_scheduler_connector.py",
     "pytorch_lightning/trainer/training_loop_temp.py",
     "pytorch_lightning/trainer/connectors/checkpoint_connector.py",

docs/source/loggers.rst

@@ -19,6 +19,15 @@ but you can pass to the :class:`~pytorch_lightning.trainer.trainer.Trainer` any
 
 Read more about :ref:`logging` options.
 
+To log arbitrary artifacts like images or audio samples use the `trainer.log_dir` property to resolve
+the path.
+
+.. code-block:: python
+
+    def training_step(self, batch, batch_idx):
+        img = ...
+        log_image(img, self.trainer.log_dir)
+
 Comet.ml
 ========
 

docs/source/trainer.rst

@@ -1666,6 +1666,17 @@ The metrics sent to the logger (visualizer).
     logged_metrics = trainer.logged_metrics
     assert logged_metrics['a_val'] == 2
 
+log_dir
+*******
+The directory for the current experiment. Use this to save images to, etc...
+
+.. code-block:: python
+
+    def training_step(self, batch, batch_idx):
+        img = ...
+        save_img(img, self.trainer.log_dir)
+
+
 
 is_global_zero
 **************

pytorch_lightning/callbacks/model_checkpoint.py

@@ -291,7 +291,7 @@ def __init_ckpt_dir(self, filepath, dirpath, filename, save_top_k):
         if dirpath and self._fs.protocol == 'file':
             dirpath = os.path.realpath(dirpath)
 
-        self.dirpath = dirpath or None
+        self.dirpath: Union[str, None] = dirpath or None
         self.filename = filename or None
 
     def __init_monitor_mode(self, monitor, mode):

pytorch_lightning/trainer/properties.py

@@ -26,6 +26,9 @@
 from pytorch_lightning.utilities import argparse_utils
 from pytorch_lightning.utilities.cloud_io import get_filesystem
 from pytorch_lightning.utilities.model_utils import is_overridden
+from pytorch_lightning.accelerators.accelerator import Accelerator
+from pytorch_lightning.loggers.base import LightningLoggerBase
+from pytorch_lightning.loggers.tensorboard import TensorBoardLogger
 
 
 class TrainerProperties(ABC):
@@ -44,10 +47,30 @@ class TrainerProperties(ABC):
     limit_val_batches: int
     _default_root_dir: str
     _weights_save_path: str
+    default_root_path: str
+    accelerator_backend: Accelerator
+    logger: LightningLoggerBase
     model_connector: ModelConnector
     checkpoint_connector: CheckpointConnector
     callbacks: List[Callback]
 
+    @property
+    def log_dir(self):
+        if self.checkpoint_callback is not None:
+            dir = self.checkpoint_callback.dirpath
+            dir = os.path.split(dir)[0]
+        elif self.logger is not None:
+            if isinstance(self.logger, TensorBoardLogger):
+                dir = self.logger.log_dir
+            else:
+                dir = self.logger.save_dir
+        else:
+            dir = self._default_root_dir
+
+        if self.accelerator_backend is not None:
+            dir = self.accelerator_backend.broadcast(dir)
+        return dir
+
     @property
     def use_amp(self) -> bool:
         return self.precision == 16

tests/trainer/properties/log_dir.py

@@ -0,0 +1,125 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import os
+import torch
+import pytest
+from tests.base.boring_model import BoringModel, RandomDataset
+from pytorch_lightning import Trainer
+from pytorch_lightning.utilities import APEX_AVAILABLE
+from pytorch_lightning.utilities.exceptions import MisconfigurationException
+
+
+def test_logdir(tmpdir):
+    """
+    Tests that the path is correct when checkpoint and loggers are used
+    """
+    class TestModel(BoringModel):
+        def training_step(self, batch, batch_idx):
+            output = self.layer(batch)
+            loss = self.loss(batch, output)
+
+            expected = os.path.join(self.trainer.default_root_dir, 'lightning_logs', 'version_0')
+            assert self.trainer.log_dir == expected
+            return {"loss": loss}
+
+    model = TestModel()
+
+    limit_train_batches = 2
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        limit_train_batches=limit_train_batches,
+        limit_val_batches=2,
+        max_epochs=1,
+    )
+
+    trainer.fit(model)
+
+
+def test_logdir_no_checkpoint_cb(tmpdir):
+    """
+    Tests that the path is correct with no checkpoint
+    """
+    class TestModel(BoringModel):
+        def training_step(self, batch, batch_idx):
+            output = self.layer(batch)
+            loss = self.loss(batch, output)
+            expected = os.path.join(self.trainer.default_root_dir, 'lightning_logs', 'version_0')
+            assert self.trainer.log_dir == expected
+            return {"loss": loss}
+
+    model = TestModel()
+
+    limit_train_batches = 2
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        limit_train_batches=limit_train_batches,
+        limit_val_batches=2,
+        max_epochs=1,
+        checkpoint_callback=False
+    )
+
+    trainer.fit(model)
+
+
+def test_logdir_no_logger(tmpdir):
+    """
+    Tests that the path is correct even when there is no logger
+    """
+    class TestModel(BoringModel):
+        def training_step(self, batch, batch_idx):
+            output = self.layer(batch)
+            loss = self.loss(batch, output)
+            expected = os.path.join(self.trainer.default_root_dir)
+            assert self.trainer.log_dir == expected
+            return {"loss": loss}
+
+    model = TestModel()
+
+    limit_train_batches = 2
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        limit_train_batches=limit_train_batches,
+        limit_val_batches=2,
+        max_epochs=1,
+        logger=False,
+    )
+
+    trainer.fit(model)
+
+
+def test_logdir_no_logger_no_checkpoint(tmpdir):
+    """
+    Tests that the path is correct even when there is no logger
+    """
+    class TestModel(BoringModel):
+        def training_step(self, batch, batch_idx):
+            output = self.layer(batch)
+            loss = self.loss(batch, output)
+            expected = os.path.join(self.trainer.default_root_dir)
+            assert self.trainer.log_dir == expected
+            return {"loss": loss}
+
+    model = TestModel()
+
+    limit_train_batches = 2
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        limit_train_batches=limit_train_batches,
+        limit_val_batches=2,
+        max_epochs=1,
+        logger=False,
+        checkpoint_callback=False
+    )
+
+    trainer.fit(model)