feat: adding resnet and formatting updates

3_test_train_split.py

@@ -7,6 +7,7 @@ import multiprocessing
 train_dir = "./data/train/"
 test_dir = "./data/test/"
 val_dir = "./data/val/"
+
 train = .80
 test = .10
 val = .10

4_train_keras_model.py

@@ -53,7 +53,7 @@ def get_gen(path, dataset_type: DatasetType = DatasetType.TRAIN):
     )
 
 
-def train_model(model, model_name, train_gen, val_gen):
+def train_model(model, model_name, train_gen, val_gen, max_epochs):
     print(model)
     print(f"NOW TRAINING: {model_name}")
     checkpoint = keras.callbacks.ModelCheckpoint(
@@ -82,7 +82,7 @@ def train_model(model, model_name, train_gen, val_gen):
     model.fit(
         train_gen,
         validation_data=val_gen,
-        epochs=100,
+        epochs=max_epochs,
         batch_size=batch_size,
         shuffle=True,
         verbose=True,
@@ -130,7 +130,47 @@ if __name__ == "__main__":
             pre_trained=True,
             freeze_layers=True,
             freeze_batch_norm=True,
-            base_model_type=ImageClassModels.EFFICIENTNET_V2S,
+            base_model_type=ImageClassModels.MOBILENET_V2,
+            dense_layer_neurons=1024,
+            dropout_rate=.5,
+        ),        ImageClassModelBuilder(
+            input_shape=input_shape,
+            n_classes=807,
+            optimizer=keras.optimizers.Adam(learning_rate=.0001),
+            pre_trained=True,
+            freeze_layers=True,
+            freeze_batch_norm=True,
+            base_model_type=ImageClassModels.INCEPTION_RESNET_V2,
+            dense_layer_neurons=1024,
+            dropout_rate=.5,
+        ),        ImageClassModelBuilder(
+            input_shape=input_shape,
+            n_classes=807,
+            optimizer=keras.optimizers.Adam(learning_rate=.0001),
+            pre_trained=True,
+            freeze_layers=True,
+            freeze_batch_norm=True,
+            base_model_type=ImageClassModels.INCEPTION_V3,
+            dense_layer_neurons=1024,
+            dropout_rate=.5,
+        ),        ImageClassModelBuilder(
+            input_shape=input_shape,
+            n_classes=807,
+            optimizer=keras.optimizers.Adam(learning_rate=.0001),
+            pre_trained=True,
+            freeze_layers=True,
+            freeze_batch_norm=True,
+            base_model_type=ImageClassModels.XCEPTION,
+            dense_layer_neurons=1024,
+            dropout_rate=.5,
+        ),        ImageClassModelBuilder(
+            input_shape=input_shape,
+            n_classes=807,
+            optimizer=keras.optimizers.Adam(learning_rate=.0001),
+            pre_trained=True,
+            freeze_layers=True,
+            freeze_batch_norm=True,
+            base_model_type=ImageClassModels.DENSENET201,
             dense_layer_neurons=1024,
             dropout_rate=.5,
         )
@@ -141,17 +181,17 @@ if __name__ == "__main__":
         train_gen = get_gen('./data/train', dataset_type=DatasetType.TRAIN)
         val_gen = get_gen('./data/val', dataset_type=DatasetType.VAL)
         test_gen = get_gen('./data/test', dataset_type=DatasetType.TEST)
-        model = train_model(model, model_name, train_gen, val_gen)
-        for layer in model.layers[2].layers:
-            if not isinstance(layer, keras.layers.BatchNormalization):
-                layer.trainable = True
-        model.layers[2].trainable = True
-        print(model)
-        model.compile(
-            optimizer=keras.optimizers.Adam(learning_rate=.00001),
-            loss=keras.losses.CategoricalCrossentropy(),
-            metrics=['accuracy', 'categorical_crossentropy']
-        )
-        model.summary()
-        model = train_model(model, model_name + "-second_stage", train_gen, val_gen)
-        test_model(model, test_gen)
+        model = train_model(model, model_name, train_gen, val_gen, 1)
+        # for layer in model.layers[2].layers:
+        #     if not isinstance(layer, keras.layers.BatchNormalization):
+        #         layer.trainable = True
+        # model.layers[2].trainable = True
+        # print(model)
+        # model.compile(
+        #     optimizer=keras.optimizers.Adam(learning_rate=.00001),
+        #     loss=keras.losses.CategoricalCrossentropy(),
+        #     metrics=['accuracy', 'categorical_crossentropy']
+        # )
+        # model.summary()
+        # model = train_model(model, model_name + "-second_stage", train_gen, val_gen, 1)
+        # test_model(model, test_gen)

5_test_models.py

@@ -24,7 +24,7 @@ test_gen = ImageDataGenerator().flow_from_directory(
     batch_size=batch_size,
     shuffle=False
 )
-#
+
 single_gen = ImageDataGenerator().flow_from_directory(
     './single_image_test_set',
     target_size=(input_shape[0], input_shape[1]),
@@ -32,7 +32,6 @@ single_gen = ImageDataGenerator().flow_from_directory(
     shuffle=False
 )
 
-
 for file in glob("./models/keras/*.hdf5"):
     print(file)
     if file in metrics_df.values:

6_convert_keras_to_tflite.py

@@ -5,7 +5,7 @@ from pathlib import Path
 import pandas as pd
 import tensorflow as tf
 from keras.preprocessing.image import ImageDataGenerator
-from tensorflow import keras
+import tensorflow as tf
 
 # TODO: Move these to a config for the project
 input_shape = (224, 224, 3)
@@ -25,16 +25,19 @@ for file in glob("./models/keras/*.hdf5"):
     path = Path(file)
     tflite_file = f'./models/tflite/models/{path.name[:-5] + ".tflite"}'
     if not Path(tflite_file).exists():
-        keras_model = keras.models.load_model(file)
-
+        print(tflite_file)
+        keras_model = tf.keras.models.load_model(file)
+        keras_model.summary()
+        print(keras_model.input)
+        print(keras_model.layers)
         converter = tf.lite.TFLiteConverter.from_keras_model(keras_model)
         tflite_model = converter.convert()
         with open(tflite_file, 'wb') as f:
             f.write(tflite_model)
-        # TODO: Verify the model performance after converting to TFLITE
-        # interpreter = tf.lite.Interpreter(model_path=tflite_file)
-        # single_acc, single_ll = get_metrics(single_gen, keras_model)
-        # tf_single_acc, tf_single_ll = get_metrics(single_gen, tflite_model)
-        #
-        # print(single_acc, tf_single_acc)
-        # print(single_ll, tf_single_ll)
+    # TODO: Verify the model performance after converting to TFLITE
+    # interpreter = tf.lite.Interpreter(model_path=tflite_file)
+    # single_acc, single_ll = get_metrics(single_gen, keras_model)
+    # tf_single_acc, tf_single_ll = get_metrics(single_gen, tflite_model)
+    #
+    # print(single_acc, tf_single_acc)
+    # print(single_ll, tf_single_ll)

all_model_output.csv

@@ -2,4 +2,6 @@ model,test_acc,test_loss,single_acc,single_loss
 ./models/keras\pt-fl-fbn-efficientnet_v2b0-d1024-do0.5-l11.e-04-l21.e-04-5224-second_stage.hdf5,0.6720150708068079,1.7423864365349095,0.9893048128342246,0.4364729183409372
 ./models/keras\pt-fl-fbn-efficientnet_v2b0-d1024-do0.5-l11.e-04-l21.e-04-5224.hdf5,0.410029881772119,3.346152696366266,0.986096256684492,0.3234976000776315
 ./models/keras\pt-fl-fbn-efficientnet_v2s-d1024-do0.5-l11.e-04-l21.e-04-8105-second_stage.hdf5,0.6850721060153306,1.675868156533777,0.9967914438502674,0.3373779159304851
-./models/keras\pt-fl-fbn-efficientnet_v2s-d1024-do0.5-l11.e-04-l21.e-04-8105.hdf5,0.37553592308691697,3.5500588697038067,0.9540106951871657,0.47270425785037834
+./models/keras\pt-fl-fbn-efficientnet_v2s-d1024-do0.5-l11.e-04-l21.e-04-8105.hdf5,0.3755359230869169,3.5500588697038067,0.9540106951871656,0.4727042578503783
+./models/keras\pt-fl-fbn-efficientnet_v2s-d1024-do0.5-l11.e-04-l21.e-04-9317-second_stage.hdf5,0.6121780461172843,2.197206965588216,0.9946581196581196,0.2974041509252359
+./models/keras\pt-fl-fbn-efficientnet_v2s-d1024-do0.5-l11.e-04-l21.e-04-9317.hdf5,0.3702228787976106,3.601324427207316,0.9594017094017094,0.4877960320956891

labels_generator.py

@@ -1,3 +0,0 @@
-import pandas as pd
-import os
-

modeling_utils/image_class_builder.py

@@ -15,11 +15,21 @@ class ImageClassModels(Enum):
         keras.applications.inception_v3.preprocess_input,
         "inception_v3"
     )
+    INCEPTION_RESNET_V2 = ModelWrapper(
+        keras.applications.inception_resnet_v2.InceptionResNetV2,
+        keras.applications.inception_resnet_v2.preprocess_input,
+        "inception_resnet_v2"
+    )
     XCEPTION = ModelWrapper(
         keras.applications.xception.Xception,
         keras.applications.xception.preprocess_input,
         "xception"
     )
+    DENSENET201 = ModelWrapper(
+        keras.applications.densenet.DenseNet201,
+        keras.applications.densenet.preprocess_input,
+        "densenet201"
+    )
     MOBILENET_V2 = ModelWrapper(
         keras.applications.mobilenet_v2.MobileNetV2,
         keras.applications.mobilenet_v2.preprocess_input,
@@ -34,7 +44,6 @@ class ImageClassModels(Enum):
         keras.applications.efficientnet_v2.EfficientNetV2B0,
         tf.keras.applications.efficientnet_v2.preprocess_input,
         "efficientnet_v2b0"
-
     )
 
 

modeling_utils/model_wrapper.py

@@ -1,5 +1,4 @@
-from collections import Callable
-
+from typing import Callable
 
 class ModelWrapper(object):
     def __init__(self, model_func:Callable, model_preprocessor:Callable, name:str):

print_all_wrong.py

@@ -1,4 +1,5 @@
 import pandas as pd
+
 df = pd.read_csv("models/keras/pt-fl-fbn-efficientnet_v2s-d1024-do0.5-l11.e-04-l21.e-04-8105-second_stage.csv")
 
 print(df.loc[df["prediction"] != df["true_val"]])

show_incorrect_images.py

@@ -19,7 +19,6 @@ for index, row in df2.iterrows():
 incorrect = df[df["prediction"]!= df["true_val"]]
 
 total_same_fam = 0
-# TODO: Add in support for figuring out if the pokemon are related/evolutions of one another
 for index, row in incorrect.iterrows():
     img = mpimg.imread("./SingleImageTestSet/" + row['fname'])
     imgplot = plt.imshow(img)