include epotimingcallback into workflow

mlair/run_modules/model_setup.py

@@ -12,7 +12,7 @@ import keras
 import pandas as pd
 import tensorflow as tf
 
-from mlair.model_modules.keras_extensions import HistoryAdvanced, CallbackHandler
+from mlair.model_modules.keras_extensions import HistoryAdvanced, EpoTimingCallback, CallbackHandler
 from mlair.run_modules.run_environment import RunEnvironment
 from mlair.configuration import path_config
 
@@ -119,11 +119,14 @@ class ModelSetup(RunEnvironment):
         """
         lr = self.data_store.get_default("lr_decay", scope=self.scope, default=None)
         hist = HistoryAdvanced()
+        epo_timing = EpoTimingCallback()
         self.data_store.set("hist", hist, scope="model")
+        self.data_store.set("epo_timing", epo_timing, scope="model")
         callbacks = CallbackHandler()
         if lr is not None:
             callbacks.add_callback(lr, self.callbacks_name % "lr", "lr")
         callbacks.add_callback(hist, self.callbacks_name % "hist", "hist")
+        callbacks.add_callback(epo_timing, self.callbacks_name % "epo_timing", "epo_timing")
         callbacks.create_model_checkpoint(filepath=self.checkpoint_name, verbose=1, monitor='val_loss',
                                           save_best_only=True, mode='auto')
         self.data_store.set("callbacks", callbacks, self.scope)

mlair/run_modules/training.py

@@ -166,7 +166,11 @@ class Training(RunEnvironment):
             lr = self.callbacks.get_callback_by_name("lr")
         except IndexError:
             lr = None
-        self.save_callbacks_as_json(history, lr)
+        try:
+            epo_timing = self.callbacks.get_callback_by_name("epo_timing")
+        except IndexError:
+            epo_timing = None
+        self.save_callbacks_as_json(history, lr, epo_timing)
         self.load_best_model(checkpoint.filepath)
         self.create_monitoring_plots(history, lr)
 
@@ -190,7 +194,7 @@ class Training(RunEnvironment):
         except OSError:
             logging.info('no weights to reload...')
 
-    def save_callbacks_as_json(self, history: Callback, lr_sc: Callback) -> None:
+    def save_callbacks_as_json(self, history: Callback, lr_sc: Callback, epo_timing: Callback) -> None:
         """
         Save callbacks (history, learning rate) of training.
 
@@ -207,6 +211,9 @@ class Training(RunEnvironment):
         if lr_sc:
             with open(os.path.join(path, "history_lr.json"), "w") as f:
                 json.dump(lr_sc.lr, f)
+        if epo_timing is not None:
+            with open(os.path.join(path, "epo_timing.json"), "w") as f:
+                json.dump(epo_timing.epo_timing, f)
 
     def create_monitoring_plots(self, history: Callback, lr_sc: Callback) -> None:
         """

test/test_run_modules/test_model_setup.py

@@ -80,7 +80,7 @@ class TestModelSetup:
         setup._set_callbacks()
         assert "general.model" in setup.data_store.search_name("callbacks")
         callbacks = setup.data_store.get("callbacks", "general.model")
-        assert len(callbacks.get_callbacks()) == 3
+        assert len(callbacks.get_callbacks()) == 4
 
     def test_set_callbacks_no_lr_decay(self, setup):
         setup.data_store.set("lr_decay", None, "general.model")
@@ -88,7 +88,7 @@ class TestModelSetup:
         setup.checkpoint_name = "TestName"
         setup._set_callbacks()
         callbacks: CallbackHandler = setup.data_store.get("callbacks", "general.model")
-        assert len(callbacks.get_callbacks()) == 2
+        assert len(callbacks.get_callbacks()) == 3
         with pytest.raises(IndexError):
             callbacks.get_callback_by_name("lr_decay")
 

test/test_run_modules/test_training.py

@@ -13,7 +13,7 @@ from mlair.data_handler import DataCollection, KerasIterator, DefaultDataHandler
 from mlair.helpers import PyTestRegex
 from mlair.model_modules.flatten import flatten_tail
 from mlair.model_modules.inception_model import InceptionModelBase
-from mlair.model_modules.keras_extensions import LearningRateDecay, HistoryAdvanced, CallbackHandler
+from mlair.model_modules.keras_extensions import LearningRateDecay, HistoryAdvanced, CallbackHandler, EpoTimingCallback
 from mlair.run_modules.run_environment import RunEnvironment
 from mlair.run_modules.training import Training
 
@@ -100,6 +100,12 @@ class TestTraining:
         h.model = mock.MagicMock()
         return h
 
+    @pytest.fixture
+    def epo_timing(self):
+        epo_timing = EpoTimingCallback()
+        epo_timing.epoch = [0, 1]
+        epo_timing.epo_timing = {"epo_timing": [0.1, 0.2]}
+
     @pytest.fixture
     def path(self):
         return os.path.join(os.path.dirname(__file__), "TestExperiment")
@@ -144,9 +150,11 @@ class TestTraining:
     def callbacks(self, path):
         clbk = CallbackHandler()
         hist = HistoryAdvanced()
+        epo_timing = EpoTimingCallback()
         clbk.add_callback(hist, os.path.join(path, "hist_checkpoint.pickle"), "hist")
         lr = LearningRateDecay()
         clbk.add_callback(lr, os.path.join(path, "lr_checkpoint.pickle"), "lr")
+        clbk.add_callback(epo_timing, os.path.join(path, "epo_timing.pickle"), "epo_timing")
         clbk.create_model_checkpoint(filepath=os.path.join(path, "model_checkpoint"), monitor='val_loss',
                                      save_best_only=True)
         return clbk, hist, lr
@@ -256,22 +264,22 @@ class TestTraining:
         assert caplog.record_tuples[0] == ("root", 10, PyTestRegex("load best model: notExisting"))
         assert caplog.record_tuples[1] == ("root", 20, PyTestRegex("no weights to reload..."))
 
-    def test_save_callbacks_history_created(self, init_without_run, history, learning_rate, model_path):
-        init_without_run.save_callbacks_as_json(history, learning_rate)
+    def test_save_callbacks_history_created(self, init_without_run, history, learning_rate, epo_timing, model_path):
+        init_without_run.save_callbacks_as_json(history, learning_rate, epo_timing)
         assert "history.json" in os.listdir(model_path)
 
-    def test_save_callbacks_lr_created(self, init_without_run, history, learning_rate, model_path):
-        init_without_run.save_callbacks_as_json(history, learning_rate)
+    def test_save_callbacks_lr_created(self, init_without_run, history, learning_rate, epo_timing, model_path):
+        init_without_run.save_callbacks_as_json(history, learning_rate, epo_timing)
         assert "history_lr.json" in os.listdir(model_path)
 
-    def test_save_callbacks_inspect_history(self, init_without_run, history, learning_rate, model_path):
-        init_without_run.save_callbacks_as_json(history, learning_rate)
+    def test_save_callbacks_inspect_history(self, init_without_run, history, learning_rate, epo_timing, model_path):
+        init_without_run.save_callbacks_as_json(history, learning_rate, epo_timing)
         with open(os.path.join(model_path, "history.json")) as jfile:
             hist = json.load(jfile)
             assert hist == history.history
 
-    def test_save_callbacks_inspect_lr(self, init_without_run, history, learning_rate, model_path):
-        init_without_run.save_callbacks_as_json(history, learning_rate)
+    def test_save_callbacks_inspect_lr(self, init_without_run, history, learning_rate, epo_timing, model_path):
+        init_without_run.save_callbacks_as_json(history, learning_rate, epo_timing)
         with open(os.path.join(model_path, "history_lr.json")) as jfile:
             lr = json.load(jfile)
             assert lr == learning_rate.lr