feat: Add ModelSummary Callback

pytorch_lightning/callbacks/__init__.py

@@ -19,6 +19,7 @@
 from pytorch_lightning.callbacks.lambda_function import LambdaCallback
 from pytorch_lightning.callbacks.lr_monitor import LearningRateMonitor
 from pytorch_lightning.callbacks.model_checkpoint import ModelCheckpoint
+from pytorch_lightning.callbacks.model_summary import ModelSummary
 from pytorch_lightning.callbacks.prediction_writer import BasePredictionWriter
 from pytorch_lightning.callbacks.progress import ProgressBar, ProgressBarBase, RichProgressBar
 from pytorch_lightning.callbacks.pruning import ModelPruning
@@ -38,6 +39,7 @@
     "LambdaCallback",
     "LearningRateMonitor",
     "ModelCheckpoint",
+    "ModelSummary",
     "ModelPruning",
     "BasePredictionWriter",
     "ProgressBar",

pytorch_lightning/callbacks/model_summary.py

@@ -0,0 +1,32 @@
+# 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.
+"""
+Model Summary
+=============
+
+Generates a summary of all layers in a :class:`~pytorch_lightning.core.lightning.LightningModule`.
+
+The string representation of this summary prints a table with columns containing
+the name, type and number of parameters for each layer.
+
+"""
+from typing import Optional
+
+from pytorch_lightning.callbacks.base import Callback
+
+
+class ModelSummary(Callback):
+    def __init__(self, mode: Optional[str] = None, max_depth: Optional[int] = 1):
+        self._mode = mode
+        self._max_depth = max_depth

pytorch_lightning/trainer/connectors/callback_connector.py

@@ -15,7 +15,7 @@
 from datetime import timedelta
 from typing import Dict, List, Optional, Union
 
-from pytorch_lightning.callbacks import Callback, ModelCheckpoint, ProgressBar, ProgressBarBase
+from pytorch_lightning.callbacks import Callback, ModelCheckpoint, ModelSummary, ProgressBar, ProgressBarBase
 from pytorch_lightning.callbacks.timer import Timer
 from pytorch_lightning.utilities import rank_zero_info
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
@@ -34,6 +34,7 @@ def on_trainer_init(
         process_position: int,
         default_root_dir: Optional[str],
         weights_save_path: Optional[str],
+        weights_summary: Optional[str],
         stochastic_weight_avg: bool,
         max_time: Optional[Union[str, timedelta, Dict[str, int]]] = None,
     ):
@@ -58,6 +59,8 @@ def on_trainer_init(
         # responsible to stop the training when max_time is reached.
         self._configure_timer_callback(max_time)
 
+        self._configure_model_summary_callback(weights_summary)
+
         # init progress bar
         if process_position != 0:
             rank_zero_deprecation(
@@ -89,6 +92,12 @@ def _configure_checkpoint_callbacks(self, checkpoint_callback: bool) -> None:
         if not self._trainer_has_checkpoint_callbacks() and checkpoint_callback is True:
             self.trainer.callbacks.append(ModelCheckpoint())
 
+    def _configure_model_summary_callback(self, weights_summary: Optional[str] = None) -> None:
+        if any(isinstance(cb, ModelSummary) for cb in self.trainer.callbacks):
+            return
+        model_summary = ModelSummary(mode=weights_summary)
+        self.trainer.callbacks.append(model_summary)
+
     def _configure_swa_callbacks(self):
         if not self.trainer._stochastic_weight_avg:
             return