From b214459bc9ee76b5ceb4ea754a7af9ca127de122 Mon Sep 17 00:00:00 2001
From: Matteo Bunino <matteo.bunino@gmail.com>
Date: Wed, 18 Oct 2023 17:49:13 +0200
Subject: [PATCH] FIX: duplicated code in TF Trainer

---
 src/itwinai/tensorflow/trainer.py | 146 +++++++++++++++---------------
 1 file changed, 73 insertions(+), 73 deletions(-)

diff --git a/src/itwinai/tensorflow/trainer.py b/src/itwinai/tensorflow/trainer.py
index ec0f4e09..3f51f000 100644
--- a/src/itwinai/tensorflow/trainer.py
+++ b/src/itwinai/tensorflow/trainer.py
@@ -138,76 +138,76 @@ def train(self, train_dataset, validation_dataset):
         return history
 
 
-class TensorflowTrainer2(Trainer):
-    def __init__(
-            self,
-            epochs,
-            batch_size,
-            callbacks,
-            model_dict: Dict,
-            compile_conf,
-            strategy
-    ):
-        super().__init__()
-        self.strategy = strategy
-        self.epochs = epochs
-        self.batch_size = batch_size
-        self.callbacks = callbacks
-
-        # Handle the parsing
-        model_class = import_class(model_dict["class_path"])
-        parser = ArgumentParser()
-        parser.add_subclass_arguments(model_class, "model")
-        model_dict = {"model": model_dict}
-
-        # Create distributed TF vars
-        if self.strategy:
-            with self.strategy.scope():
-                self.model = parser.instantiate_classes(model_dict).model
-                print(self.model)
-                self.model.compile(**compile_conf)
-                # TODO: move loss, optimizer and metrics instantiation under
-                # here
-                # Ref:
-                # https://www.tensorflow.org/guide/distributed_training#use_tfdistributestrategy_with_keras_modelfit
-        else:
-            self.model = parser.instantiate_classes(model_dict).model
-            self.model.compile(**compile_conf)
-
-        self.num_devices = (
-            self.strategy.num_replicas_in_sync if self.strategy else 1)
-        print(f"Strategy is working with: {self.num_devices} devices")
-
-    def train(self, train_dataset, validation_dataset):
-        # TODO: FIX Steps sizes in model.fit
-        train, test = train_dataset, validation_dataset
-
-        # Set batch size to the dataset
-        train = train.batch(self.batch_size, drop_remainder=True)
-        test = test.batch(self.batch_size, drop_remainder=True)
-
-        # Number of samples
-        n_train = train.cardinality().numpy()
-        n_test = test.cardinality().numpy()
-
-        # TODO: read
-        # https://github.com/tensorflow/tensorflow/issues/56773#issuecomment-1188693881
-        # https://www.tensorflow.org/guide/distributed_training#use_tfdistributestrategy_with_keras_modelfit
-
-        # Distribute dataset
-        if self.strategy:
-            train = self.strategy.experimental_distribute_dataset(train)
-            test = self.strategy.experimental_distribute_dataset(test)
-
-        # train the model
-        history = self.model.fit(
-            train,
-            validation_data=test,
-            steps_per_epoch=int(n_train // self.num_devices),
-            validation_steps=int(n_test // self.num_devices),
-            epochs=self.epochs,
-            callbacks=self.callbacks,
-        )
-
-        print("Model trained")
-        return history
+# class TensorflowTrainer2(Trainer):
+#     def __init__(
+#             self,
+#             epochs,
+#             batch_size,
+#             callbacks,
+#             model_dict: Dict,
+#             compile_conf,
+#             strategy
+#     ):
+#         super().__init__()
+#         self.strategy = strategy
+#         self.epochs = epochs
+#         self.batch_size = batch_size
+#         self.callbacks = callbacks
+
+#         # Handle the parsing
+#         model_class = import_class(model_dict["class_path"])
+#         parser = ArgumentParser()
+#         parser.add_subclass_arguments(model_class, "model")
+#         model_dict = {"model": model_dict}
+
+#         # Create distributed TF vars
+#         if self.strategy:
+#             with self.strategy.scope():
+#                 self.model = parser.instantiate_classes(model_dict).model
+#                 print(self.model)
+#                 self.model.compile(**compile_conf)
+#                 # TODO: move loss, optimizer and metrics instantiation under
+#                 # here
+#                 # Ref:
+#                 # https://www.tensorflow.org/guide/distributed_training#use_tfdistributestrategy_with_keras_modelfit
+#         else:
+#             self.model = parser.instantiate_classes(model_dict).model
+#             self.model.compile(**compile_conf)
+
+#         self.num_devices = (
+#             self.strategy.num_replicas_in_sync if self.strategy else 1)
+#         print(f"Strategy is working with: {self.num_devices} devices")
+
+#     def train(self, train_dataset, validation_dataset):
+#         # TODO: FIX Steps sizes in model.fit
+#         train, test = train_dataset, validation_dataset
+
+#         # Set batch size to the dataset
+#         train = train.batch(self.batch_size, drop_remainder=True)
+#         test = test.batch(self.batch_size, drop_remainder=True)
+
+#         # Number of samples
+#         n_train = train.cardinality().numpy()
+#         n_test = test.cardinality().numpy()
+
+#         # TODO: read
+#         # https://github.com/tensorflow/tensorflow/issues/56773#issuecomment-1188693881
+#         # https://www.tensorflow.org/guide/distributed_training#use_tfdistributestrategy_with_keras_modelfit
+
+#         # Distribute dataset
+#         if self.strategy:
+#             train = self.strategy.experimental_distribute_dataset(train)
+#             test = self.strategy.experimental_distribute_dataset(test)
+
+#         # train the model
+#         history = self.model.fit(
+#             train,
+#             validation_data=test,
+#             steps_per_epoch=int(n_train // self.num_devices),
+#             validation_steps=int(n_test // self.num_devices),
+#             epochs=self.epochs,
+#             callbacks=self.callbacks,
+#         )
+
+#         print("Model trained")
+#         return history