renaming all files - moving training to be a single file for transfer vs not transfer learning. Made the testing file test all models. Needs to be updated to only update with new models.

2022-06-01 17:46:55 -04:00
parent ab0b7a0a4a
commit 1b539d6945
960 changed files with 338 additions and 1411 deletions
@@ -0,0 +1,145 @@
+from enum import Enum
+
+import matplotlib.pyplot as plt
+import numpy as np
+from PIL import ImageFile
+from tensorflow import keras
+from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
+from model_builder import ImageClassModelBuilder, ImageClassModels
+
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+
+input_shape = (224, 224, 3)
+
+batch_size = 32
+
+training_idg = keras.preprocessing.image.ImageDataGenerator(
+    horizontal_flip=True,
+    rotation_range=30,
+    width_shift_range=[-.1, .1],
+    height_shift_range=[-.1, .1],
+)
+val_idg = keras.preprocessing.image.ImageDataGenerator(
+    horizontal_flip=True,
+)
+testing_idg = keras.preprocessing.image.ImageDataGenerator(
+    horizontal_flip=True,
+)
+
+
+class DatasetType(Enum):
+    TRAIN = 0
+    TEST = 1
+    VAL = 2
+
+
+def get_gen(path, dataset_type: DatasetType = DatasetType.TRAIN):
+    idg = None
+    if dataset_type is DatasetType.TRAIN:
+        idg = training_idg
+    if dataset_type is DatasetType.TEST:
+        idg = testing_idg
+    if dataset_type is DatasetType.VAL:
+        idg = val_idg
+
+    return idg.flow_from_directory(
+        path,
+        target_size=(input_shape[0], input_shape[1]),
+        batch_size=batch_size,
+        class_mode='categorical',
+        shuffle=True,
+        color_mode='rgb'
+    )
+
+
+def train_model(model_builder, train_gen, val_gen):
+    model = model_builder.create_model()
+    model_name = "rot-shift-" + model_builder.get_name()
+    print(model)
+    print(f"NOW TRAINING: {model_name}")
+    checkpoint = keras.callbacks.ModelCheckpoint(
+        f"./models/keras/{model_name}.hdf5",
+        monitor='val_loss',
+        verbose=1,
+        save_best_only=True,
+        mode='min'
+    )
+    early = keras.callbacks.EarlyStopping(
+        monitor="val_loss",
+        mode="auto",
+        patience=4,
+        restore_best_weights=True,
+        verbose=1,
+    )
+    tensorboard = keras.callbacks.TensorBoard(
+        log_dir="logs/" + model_name,
+        histogram_freq=1,
+        write_graph=True,
+        write_images=True,
+        update_freq=1,
+        profile_batch=2,
+        embeddings_freq=1,
+    )
+    history = model.fit(
+        train_gen,
+        validation_data=val_gen,
+        epochs=500,
+        batch_size=batch_size,
+        shuffle=True,
+        verbose=True,
+        workers=12,
+        callbacks=[checkpoint, early, tensorboard],
+        max_queue_size=1000
+    )
+    print(history)
+    return model
+
+
+def test_model(model, test_gen):
+    predictions = model.predict(test_gen, verbose=True, workers=1, steps=len(test_gen))
+
+    print(predictions)
+    print(type(predictions))
+    print(predictions.shape)
+    # Process the predictions
+    predictions = np.argmax(predictions,
+                            axis=1)
+    # test_gen.reset()
+    label_index = {v: k for k, v in test_gen.class_indices.items()}
+    predictions = [label_index[p] for p in predictions]
+    reals = [label_index[p] for p in test_gen.classes]
+
+    # Processed the saved results
+    acc = accuracy_score(reals, predictions)
+    conf_mat = confusion_matrix(reals, predictions)
+    print(classification_report(reals, predictions, labels=[l for l in label_index.values()]))
+    print("Testing accuracy score is ", acc)
+    print("Confusion Matrix", conf_mat)
+
+    print("made dataframe")
+    plt.figure(figsize=(10, 7))
+    print("made plot")
+    # sn.heatmap(df_cm, annot=True)
+    print("showing plot")
+    plt.show()
+
+
+if __name__ == "__main__":
+    model_builders = [
+        ImageClassModelBuilder(
+            input_shape=input_shape,
+            n_classes=807,
+            optimizer=keras.optimizers.Adam(learning_rate=.0001),
+            pre_trained=True,
+            fine_tune=True,
+            base_model_type=ImageClassModels.MOBILENET_V2,
+            dense_layer_neurons=1024,
+            dropout_rate=.33,
+        )
+    ]
+    for mb in model_builders:
+        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(mb, train_gen, val_gen)
+        test_model(model, test_gen)