Remove local_variables from on_step

classy_vision/hooks/classy_hook.py

@@ -51,7 +51,7 @@ def __init__(self, a, b):
     def __init__(self):
         self.state = ClassyHookState()
 
-    def _noop(self, task: "tasks.ClassyTask", local_variables: Dict[str, Any]) -> None:
+    def _noop(self, *args, **kwargs) -> None:
         """Derived classes can set their hook functions to this.
 
         This is useful if they want those hook functions to not do anything.
@@ -79,9 +79,7 @@ def on_phase_start(
         pass
 
     @abstractmethod
-    def on_step(
-        self, task: "tasks.ClassyTask", local_variables: Dict[str, Any]
-    ) -> None:
+    def on_step(self, task: "tasks.ClassyTask") -> None:
         """Called each time after parameters have been updated by the optimizer."""
         pass
 

classy_vision/hooks/exponential_moving_average_model_hook.py

@@ -103,7 +103,7 @@ def on_phase_end(self, task: ClassyTask, local_variables: Dict[str, Any]) -> Non
             # state in the test phase
             self._save_current_model_state(task.base_model, self.state.model_state)
 
-    def on_step(self, task: ClassyTask, local_variables: Dict[str, Any]) -> None:
+    def on_step(self, task: ClassyTask) -> None:
         if not task.train:
             return
 

classy_vision/hooks/loss_lr_meter_logging_hook.py

@@ -45,36 +45,30 @@ def on_phase_end(
             # trainer to implement an unsynced end of phase meter or
             # for meters to not provide a sync function.
             logging.info("End of phase metric values:")
-            self._log_loss_meters(task, local_variables)
+            self._log_loss_meters(task)
             if task.train:
-                self._log_lr(task, local_variables)
+                self._log_lr(task)
 
-    def on_step(
-        self, task: "tasks.ClassyTask", local_variables: Dict[str, Any]
-    ) -> None:
+    def on_step(self, task: "tasks.ClassyTask") -> None:
         """
         Log the LR every log_freq batches, if log_freq is not None.
         """
         if self.log_freq is None or not task.train:
             return
         batches = len(task.losses)
         if batches and batches % self.log_freq == 0:
-            self._log_lr(task, local_variables)
+            self._log_lr(task)
             logging.info("Local unsynced metric values:")
-            self._log_loss_meters(task, local_variables)
+            self._log_loss_meters(task)
 
-    def _log_lr(
-        self, task: "tasks.ClassyTask", local_variables: Dict[str, Any]
-    ) -> None:
+    def _log_lr(self, task: "tasks.ClassyTask") -> None:
         """
         Compute and log the optimizer LR.
         """
         optimizer_lr = task.optimizer.parameters.lr
         logging.info("Learning Rate: {}\n".format(optimizer_lr))
 
-    def _log_loss_meters(
-        self, task: "tasks.ClassyTask", local_variables: Dict[str, Any]
-    ) -> None:
+    def _log_loss_meters(self, task: "tasks.ClassyTask") -> None:
         """
         Compute and log the loss and meters.
         """

classy_vision/hooks/progress_bar_hook.py

@@ -51,9 +51,7 @@ def on_phase_start(
             self.progress_bar = progressbar.ProgressBar(self.bar_size)
             self.progress_bar.start()
 
-    def on_step(
-        self, task: "tasks.ClassyTask", local_variables: Dict[str, Any]
-    ) -> None:
+    def on_step(self, task: "tasks.ClassyTask") -> None:
         """Update the progress bar with the batch size."""
         if task.train and is_master() and self.progress_bar is not None:
             self.batches += 1

classy_vision/hooks/tensorboard_plot_hook.py

@@ -64,9 +64,7 @@ def on_phase_start(
         self.wall_times = []
         self.num_steps_global = []
 
-    def on_step(
-        self, task: "tasks.ClassyTask", local_variables: Dict[str, Any]
-    ) -> None:
+    def on_step(self, task: "tasks.ClassyTask") -> None:
         """Store the observed learning rates."""
         if self.learning_rates is None:
             logging.warning("learning_rates is not initialized")

classy_vision/hooks/time_metrics_hook.py

@@ -40,19 +40,17 @@ def on_phase_start(
         Initialize start time and reset perf stats
         """
         self.start_time = time.time()
-        local_variables["perf_stats"] = PerfStats()
+        task.perf_stats = PerfStats()
 
-    def on_step(
-        self, task: "tasks.ClassyTask", local_variables: Dict[str, Any]
-    ) -> None:
+    def on_step(self, task: "tasks.ClassyTask") -> None:
         """
         Log metrics every log_freq batches, if log_freq is not None.
         """
         if self.log_freq is None:
             return
         batches = len(task.losses)
         if batches and batches % self.log_freq == 0:
-            self._log_performance_metrics(task, local_variables)
+            self._log_performance_metrics(task)
 
     def on_phase_end(
         self, task: "tasks.ClassyTask", local_variables: Dict[str, Any]
@@ -62,11 +60,9 @@ def on_phase_end(
         """
         batches = len(task.losses)
         if batches:
-            self._log_performance_metrics(task, local_variables)
+            self._log_performance_metrics(task)
 
-    def _log_performance_metrics(
-        self, task: "tasks.ClassyTask", local_variables: Dict[str, Any]
-    ) -> None:
+    def _log_performance_metrics(self, task: "tasks.ClassyTask") -> None:
         """
         Compute and log performance metrics.
         """
@@ -85,11 +81,11 @@ def _log_performance_metrics(
             )
 
         # Train step time breakdown
-        if local_variables.get("perf_stats") is None:
-            logging.warning('"perf_stats" not set in local_variables')
+        if not hasattr(task, "perf_stats") or task.perf_stats is None:
+            logging.warning('"perf_stats" not set in task')
         elif task.train:
             logging.info(
                 "Train step time breakdown (rank {}):\n{}".format(
-                    get_rank(), local_variables["perf_stats"].report_str()
+                    get_rank(), task.perf_stats.report_str()
                 )
             )

classy_vision/tasks/classification_task.py

@@ -8,7 +8,7 @@
 import enum
 import logging
 import time
-from typing import Any, Dict, List, Optional, Union
+from typing import Any, Dict, List, NamedTuple, Optional, Union
 
 import torch
 from classy_vision.dataset import ClassyDataset, build_dataset
@@ -54,6 +54,13 @@ class BroadcastBuffersMode(enum.Enum):
     BEFORE_EVAL = enum.auto()
 
 
+class LastBatchInfo(NamedTuple):
+    loss: torch.Tensor
+    output: torch.Tensor
+    target: torch.Tensor
+    sample: Dict[str, Any]
+
+
 @register_task("classification_task")
 class ClassificationTask(ClassyTask):
     """Basic classification training task.
@@ -672,6 +679,14 @@ def eval_step(self, use_gpu, local_variables=None):
 
             self.update_meters(local_variables["output"], local_variables["sample"])
 
+        # Move some data to the task so hooks get a chance to access it
+        self.last_batch = LastBatchInfo(
+            loss=local_variables["loss"],
+            output=local_variables["output"],
+            target=local_variables["target"],
+            sample=local_variables["sample"],
+        )
+
     def train_step(self, use_gpu, local_variables=None):
         """Train step to be executed in train loop
 
@@ -684,6 +699,8 @@ def train_step(self, use_gpu, local_variables=None):
         if local_variables is None:
             local_variables = {}
 
+        self.last_batch = None
+
         # Process next sample
         sample = next(self.get_data_iterator())
         local_variables["sample"] = sample
@@ -738,6 +755,14 @@ def train_step(self, use_gpu, local_variables=None):
 
         self.num_updates += self.get_global_batchsize()
 
+        # Move some data to the task so hooks get a chance to access it
+        self.last_batch = LastBatchInfo(
+            loss=local_variables["loss"],
+            output=local_variables["output"],
+            target=local_variables["target"],
+            sample=local_variables["sample"],
+        )
+
     def compute_loss(self, model_output, sample):
         return self.loss(model_output, sample["target"])
 

classy_vision/tasks/classy_task.py

@@ -178,7 +178,8 @@ def step(self, use_gpu, local_variables: Optional[Dict] = None) -> None:
         else:
             self.eval_step(use_gpu, local_variables)
 
-        self.run_hooks(local_variables, ClassyHookFunctions.on_step.name)
+        for hook in self.hooks:
+            hook.on_step(self)
 
     def run_hooks(self, local_variables: Dict[str, Any], hook_function: str) -> None:
         """

test/hooks_exponential_moving_average_model_hook_test.py

@@ -53,7 +53,7 @@ def _test_exponential_moving_average_hook(self, model_device, hook_device):
         task.base_model.update_fc_weight()
         fc_weight = model.fc.weight.clone()
         for _ in range(num_updates):
-            exponential_moving_average_hook.on_step(task, local_variables)
+            exponential_moving_average_hook.on_step(task)
         exponential_moving_average_hook.on_phase_end(task, local_variables)
         # the model weights shouldn't have changed
         self.assertTrue(torch.allclose(model.fc.weight, fc_weight))

test/hooks_loss_lr_meter_logging_hook_test.py

@@ -51,27 +51,27 @@ def test_logging(self, mock_get_rank: mock.MagicMock) -> None:
 
                     for i in range(num_batches):
                         task.losses = list(range(i))
-                        loss_lr_meter_hook.on_step(task, local_variables)
+                        loss_lr_meter_hook.on_step(task)
                         if log_freq is not None and i and i % log_freq == 0:
-                            mock_fn.assert_called_with(task, local_variables)
+                            mock_fn.assert_called_with(task)
                             mock_fn.reset_mock()
-                            mock_lr_fn.assert_called_with(task, local_variables)
+                            mock_lr_fn.assert_called_with(task)
                             mock_lr_fn.reset_mock()
                             continue
                         mock_fn.assert_not_called()
                         mock_lr_fn.assert_not_called()
 
                     loss_lr_meter_hook.on_phase_end(task, local_variables)
-                    mock_fn.assert_called_with(task, local_variables)
+                    mock_fn.assert_called_with(task)
                     if task.train:
-                        mock_lr_fn.assert_called_with(task, local_variables)
+                        mock_lr_fn.assert_called_with(task)
 
             # test _log_loss_lr_meters()
             task.losses = losses
 
             with self.assertLogs():
-                loss_lr_meter_hook._log_loss_meters(task, local_variables)
-                loss_lr_meter_hook._log_lr(task, local_variables)
+                loss_lr_meter_hook._log_loss_meters(task)
+                loss_lr_meter_hook._log_lr(task)
 
             task.phase_idx += 1
 
@@ -95,7 +95,7 @@ def scheduler_mock(where):
         lr_order = [0.0, 1 / 6, 1 / 6, 2 / 6, 3 / 6, 3 / 6, 4 / 6, 5 / 6, 5 / 6]
         lr_list = []
 
-        def mock_log_lr(task: ClassyTask, local_variables) -> None:
+        def mock_log_lr(task: ClassyTask) -> None:
             lr_list.append(task.optimizer.parameters.lr)
 
         with mock.patch.object(

test/hooks_time_metrics_hook_test.py

@@ -49,7 +49,7 @@ def test_time_metrics(
             mock_time.return_value = start_time
             time_metrics_hook.on_phase_start(task, local_variables)
             self.assertEqual(time_metrics_hook.start_time, start_time)
-            self.assertTrue(isinstance(local_variables.get("perf_stats"), PerfStats))
+            self.assertTrue(isinstance(task.perf_stats, PerfStats))
 
             # test that the code doesn't raise an exception if losses is empty
             try:
@@ -66,15 +66,15 @@ def test_time_metrics(
 
                 for i in range(num_batches):
                     task.losses = list(range(i))
-                    time_metrics_hook.on_step(task, local_variables)
+                    time_metrics_hook.on_step(task)
                     if log_freq is not None and i and i % log_freq == 0:
-                        mock_fn.assert_called_with(task, local_variables)
+                        mock_fn.assert_called_with(task)
                         mock_fn.reset_mock()
                         continue
                     mock_fn.assert_not_called()
 
                 time_metrics_hook.on_phase_end(task, local_variables)
-                mock_fn.assert_called_with(task, local_variables)
+                mock_fn.assert_called_with(task)
 
             task.losses = [0.23, 0.45, 0.34, 0.67]
 
@@ -84,7 +84,7 @@ def test_time_metrics(
 
             # test _log_performance_metrics()
             with self.assertLogs() as log_watcher:
-                time_metrics_hook._log_performance_metrics(task, local_variables)
+                time_metrics_hook._log_performance_metrics(task)
 
             # there should 2 be info logs for train and 1 for test
             self.assertEqual(len(log_watcher.output), 2 if train else 1)
@@ -112,7 +112,7 @@ def test_time_metrics(
 
             # if on_phase_start() is not called, 2 warnings should be logged
             # create a new time metrics hook
-            local_variables = {}
+            task.perf_stats = None
             time_metrics_hook_new = TimeMetricsHook()
 
             with self.assertLogs() as log_watcher:

test/manual/hooks_progress_bar_hook_test.py

@@ -48,14 +48,14 @@ def test_progress_bar(
 
         # on_step should update the progress bar correctly
         for i in range(num_batches):
-            progress_bar_hook.on_step(task, local_variables)
+            progress_bar_hook.on_step(task)
             mock_progress_bar.update.assert_called_once_with(i + 1)
             mock_progress_bar.update.reset_mock()
 
         # check that even if on_step is called again, the progress bar is
         # only updated with num_batches
         for _ in range(num_batches):
-            progress_bar_hook.on_step(task, local_variables)
+            progress_bar_hook.on_step(task)
             mock_progress_bar.update.assert_called_once_with(num_batches)
             mock_progress_bar.update.reset_mock()
 
@@ -68,7 +68,7 @@ def test_progress_bar(
         # crash
         progress_bar_hook = ProgressBarHook()
         try:
-            progress_bar_hook.on_step(task, local_variables)
+            progress_bar_hook.on_step(task)
             progress_bar_hook.on_phase_end(task, local_variables)
         except Exception as e:
             self.fail(
@@ -81,7 +81,7 @@ def test_progress_bar(
         progress_bar_hook = ProgressBarHook()
         try:
             progress_bar_hook.on_phase_start(task, local_variables)
-            progress_bar_hook.on_step(task, local_variables)
+            progress_bar_hook.on_step(task)
             progress_bar_hook.on_phase_end(task, local_variables)
         except Exception as e:
             self.fail("Received Exception when is_master() is False: {}".format(e))

test/manual/hooks_tensorboard_plot_hook_test.py

@@ -62,7 +62,7 @@ def test_writer(self, mock_is_master_func: mock.MagicMock) -> None:
             # the writer if on_phase_start() is not called for initialization
             # before on_step() is called.
             with self.assertLogs() as log_watcher:
-                tensorboard_plot_hook.on_step(task, local_variables)
+                tensorboard_plot_hook.on_step(task)
 
             self.assertTrue(
                 len(log_watcher.records) == 1
@@ -88,7 +88,7 @@ def test_writer(self, mock_is_master_func: mock.MagicMock) -> None:
 
             for loss in losses:
                 task.losses.append(loss)
-                tensorboard_plot_hook.on_step(task, local_variables)
+                tensorboard_plot_hook.on_step(task)
 
             tensorboard_plot_hook.on_phase_end(task, local_variables)
 

test/optim_param_scheduler_test.py

@@ -207,7 +207,7 @@ class TestHook(ClassyHook):
             on_phase_end = ClassyHook._noop
             on_end = ClassyHook._noop
 
-            def on_step(self, task: ClassyTask, local_variables) -> None:
+            def on_step(self, task: ClassyTask) -> None:
                 if not task.train:
                     return