From d111cdae69e1e94be4c215409bb9ece1293fc25d Mon Sep 17 00:00:00 2001 From: Lucas Date: Fri, 3 Jun 2022 13:44:34 -0400 Subject: [PATCH] Updates to all parts of model building - moving to frozen transfer learning followed by slowed learning rate fine tuning using EfficientNets for final model. --- 4_train_keras_model.py | 40 ++++++--- 5_test_models.py | 84 ++++++++---------- 6_convert_keras_to_tflite.py | 41 +++++++++ all_model_output.csv | 3 + convert_keras_to_tflite.py | 11 --- labels_generator.py | 1 - {model_builder => modeling_utils}/__init__.py | 3 +- .../image_class_builder.py | 25 +++++- modeling_utils/model_testing.py | 23 +++++ .../model_wrapper.py | 0 print_all_wrong.py | 4 + .../latias/17-6308965103.png | Bin 65012 -> 0 bytes .../latios/19-5822306863.png | Bin 42119 -> 0 bytes 13 files changed, 156 insertions(+), 79 deletions(-) create mode 100644 6_convert_keras_to_tflite.py create mode 100644 all_model_output.csv delete mode 100644 convert_keras_to_tflite.py rename {model_builder => modeling_utils}/__init__.py (57%) rename {model_builder => modeling_utils}/image_class_builder.py (79%) create mode 100644 modeling_utils/model_testing.py rename {model_builder => modeling_utils}/model_wrapper.py (100%) create mode 100644 print_all_wrong.py delete mode 100644 single_image_test_set/latias/17-6308965103.png delete mode 100644 single_image_test_set/latios/19-5822306863.png diff --git a/4_train_keras_model.py b/4_train_keras_model.py index 9641b32..795f7ec 100644 --- a/4_train_keras_model.py +++ b/4_train_keras_model.py @@ -3,9 +3,10 @@ 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 +from tensorflow import keras + +from modeling_utils import ImageClassModelBuilder, ImageClassModels ImageFile.LOAD_TRUNCATED_IMAGES = True @@ -52,20 +53,18 @@ def get_gen(path, dataset_type: DatasetType = DatasetType.TRAIN): ) -def train_model(model_builder, train_gen, val_gen): - model = model_builder.create_model() - model_name = model_builder.get_name() +def train_model(model, model_name, train_gen, val_gen): print(model) print(f"NOW TRAINING: {model_name}") checkpoint = keras.callbacks.ModelCheckpoint( f"./models/keras/{model_name}.hdf5", - monitor='val_loss', + monitor='val_categorical_crossentropy', verbose=1, save_best_only=True, mode='min' ) early = keras.callbacks.EarlyStopping( - monitor="val_loss", + monitor="val_categorical_crossentropy", mode="auto", patience=4, restore_best_weights=True, @@ -80,10 +79,10 @@ def train_model(model_builder, train_gen, val_gen): profile_batch=2, embeddings_freq=1, ) - history = model.fit( + model.fit( train_gen, validation_data=val_gen, - epochs=8, + epochs=100, batch_size=batch_size, shuffle=True, verbose=True, @@ -91,7 +90,6 @@ def train_model(model_builder, train_gen, val_gen): callbacks=[checkpoint, early, tensorboard], max_queue_size=1000 ) - print(history) return model @@ -119,7 +117,6 @@ def test_model(model, test_gen): print("made dataframe") plt.figure(figsize=(10, 7)) print("made plot") - # sn.heatmap(df_cm, annot=True) print("showing plot") plt.show() @@ -131,15 +128,30 @@ if __name__ == "__main__": n_classes=807, optimizer=keras.optimizers.Adam(learning_rate=.0001), pre_trained=True, - fine_tune=True, - base_model_type=ImageClassModels.MOBILENET_V2, + freeze_layers=True, + freeze_batch_norm=True, + base_model_type=ImageClassModels.EFFICIENTNET_V2B0, dense_layer_neurons=1024, dropout_rate=.5, ) ] for mb in model_builders: + model = mb.create_model() + model_name = mb.get_name() 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) + 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) diff --git a/5_test_models.py b/5_test_models.py index 731a3a3..5ad9fd3 100644 --- a/5_test_models.py +++ b/5_test_models.py @@ -1,76 +1,64 @@ -import pandas as pd -import matplotlib.pyplot as plt -import numpy as np - -from keras.preprocessing.image import ImageDataGenerator -from keras.models import load_model -from sklearn.metrics import accuracy_score, confusion_matrix, classification_report from glob import glob +import matplotlib.pyplot as plt +import pandas as pd from PIL import ImageFile +from keras.models import load_model +from keras.preprocessing.image import ImageDataGenerator + +from modeling_utils import get_metrics ImageFile.LOAD_TRUNCATED_IMAGES = True - accuracies = [] losses = [] filenames = [] input_shape = (224, 224, 3) batch_size = 32 +metrics_df = pd.read_csv("all_model_output.csv") test_gen = ImageDataGenerator().flow_from_directory( './data/test', - # './single_image_test_set', + target_size=(input_shape[0], input_shape[1]), + batch_size=batch_size, + shuffle=False +) +# +single_gen = ImageDataGenerator().flow_from_directory( + './single_image_test_set', target_size=(input_shape[0], input_shape[1]), batch_size=batch_size, shuffle=False ) -for file in glob("./models/keras/*"): - filenames.append(file) + +for file in glob("./models/keras/*.hdf5"): + print(file) + print(metrics_df["model"]) + if file in metrics_df.values: + continue model = load_model(file) + test_acc, test_ll = get_metrics(test_gen, model) + single_acc, single_ll = get_metrics(single_gen, model, file[:-5] + ".csv") + metrics_df = metrics_df.append({ + "model": file, + "test_acc": test_acc, + "test_loss": test_ll, + "single_acc": single_acc, + "single_loss": single_ll, + }, ignore_index=True) - predictions = model.predict(test_gen, verbose=True, workers=12) - print(predictions) - print(type(predictions)) - print(predictions.shape) - # Process the predictions - predictions = np.argmax(predictions, - axis=1) - 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] +# Save the results - # Save the results - print(label_index) - print(test_gen.classes) - print(test_gen.classes.shape) - print(type(test_gen.classes)) - df = pd.DataFrame(columns=['fname', 'prediction', 'true_val']) - df['fname'] = [x for x in test_gen.filenames] - df['prediction'] = predictions - df["true_val"] = reals - df.to_csv("sub1_non_transfer.csv", index=False) - - # 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) - - accuracies.append(acc) - -overall_df = pd.DataFrame(list(zip(filenames, accuracies)), - columns =['model', 'acc']).sort_values('acc') - -print(overall_df) -overall_df.plot.bar(y="acc", rot=90) +metrics_df.to_csv("all_model_output.csv", index=False) +print(metrics_df) +metrics_df = metrics_df.sort_values('single_acc') +metrics_df.plot.bar(y=["test_acc", "single_acc"], rot=90) +metrics_df = metrics_df.sort_values('test_acc') +metrics_df.plot.bar(y=["test_acc", "single_acc"], rot=90) plt.tight_layout() plt.show() -overall_df.to_csv("all_model_output.csv") diff --git a/6_convert_keras_to_tflite.py b/6_convert_keras_to_tflite.py new file mode 100644 index 0000000..eed41cf --- /dev/null +++ b/6_convert_keras_to_tflite.py @@ -0,0 +1,41 @@ +import os +from glob import glob +from pathlib import Path + +import pandas as pd +import tensorflow as tf +from keras.preprocessing.image import ImageDataGenerator +from tensorflow import keras + +from modeling_utils import get_metrics + +# TODO: Move these to a config for the project +input_shape = (224, 224, 3) +batch_size = 32 + +single_gen = ImageDataGenerator().flow_from_directory( + './single_image_test_set', + target_size=(input_shape[0], input_shape[1]), + batch_size=batch_size, + shuffle=False +) + +pd.DataFrame(sorted([f.name for f in os.scandir("./data/train") if f.is_dir()])).to_csv("./models/tflite/labels.txt", + index=False, header=False) + +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) + + converter = tf.lite.TFLiteConverter.from_keras_model(keras_model) + tflite_model = converter.convert() + with open(tflite_file, 'wb') as f: + f.write(tflite_model) + # 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) diff --git a/all_model_output.csv b/all_model_output.csv new file mode 100644 index 0000000..9753e63 --- /dev/null +++ b/all_model_output.csv @@ -0,0 +1,3 @@ +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 diff --git a/convert_keras_to_tflite.py b/convert_keras_to_tflite.py deleted file mode 100644 index e9bb2e9..0000000 --- a/convert_keras_to_tflite.py +++ /dev/null @@ -1,11 +0,0 @@ -import tensorflow as tf -from tensorflow import keras -keras_file = "mobilenetv2.hdf5" -keras.models.load_model(keras_file) - -h5_model = keras.models.load_model(keras_file) -converter = tf.lite.TFLiteConverter.from_keras_model_file(keras_file) - -tflite_model = converter.convert() -with open('mobilenetv2.tflite', 'wb') as f: - f.write(tflite_model) diff --git a/labels_generator.py b/labels_generator.py index 75f6c70..4718cf9 100644 --- a/labels_generator.py +++ b/labels_generator.py @@ -1,4 +1,3 @@ import pandas as pd import os -pd.DataFrame(sorted([f.name for f in os.scandir("./data/train") if f.is_dir()])).to_csv("labels.txt", index=False, header=False) diff --git a/model_builder/__init__.py b/modeling_utils/__init__.py similarity index 57% rename from model_builder/__init__.py rename to modeling_utils/__init__.py index 8e019e8..cb2befa 100644 --- a/model_builder/__init__.py +++ b/modeling_utils/__init__.py @@ -1 +1,2 @@ -from .image_class_builder import ImageClassModelBuilder, ImageClassModels \ No newline at end of file +from .image_class_builder import ImageClassModelBuilder, ImageClassModels +from .model_testing import get_metrics \ No newline at end of file diff --git a/model_builder/image_class_builder.py b/modeling_utils/image_class_builder.py similarity index 79% rename from model_builder/image_class_builder.py rename to modeling_utils/image_class_builder.py index caec94d..db25400 100644 --- a/model_builder/image_class_builder.py +++ b/modeling_utils/image_class_builder.py @@ -25,6 +25,17 @@ class ImageClassModels(Enum): keras.applications.mobilenet_v2.preprocess_input, "mobilenet_v2" ) + EFFICIENTNET_V2S = ModelWrapper( + keras.applications.efficientnet_v2.EfficientNetV2S, + tf.keras.applications.efficientnet_v2.preprocess_input, + "efficientnet_v2s" + ) + EFFICIENTNET_V2B0 = ModelWrapper( + keras.applications.efficientnet_v2.EfficientNetV2B0, + tf.keras.applications.efficientnet_v2.preprocess_input, + "efficientnet_v2b0" + + ) class ImageClassModelBuilder(object): @@ -35,7 +46,8 @@ class ImageClassModelBuilder(object): optimizer: tf.keras.optimizers.Optimizer = keras.optimizers.Adam( learning_rate=.0001), pre_trained: bool = True, - fine_tune: bool = False, + freeze_batch_norm: bool = False, + freeze_layers: bool = False, base_model_type: ImageClassModels = ImageClassModels.MOBILENET_V2, dense_layer_neurons: int = 1024, dropout_rate: float = .5, @@ -45,7 +57,8 @@ class ImageClassModelBuilder(object): self.n_classes = n_classes self.optimizer = optimizer self.pre_trained = pre_trained - self.fine_tune = fine_tune + self.freeze_layers = freeze_layers + self.freeze_batch_norm = freeze_batch_norm self.dense_layer_neurons = dense_layer_neurons self.dropout_rate = dropout_rate self.l1 = l1 @@ -61,8 +74,12 @@ class ImageClassModelBuilder(object): ) def create_model(self): - if not self.fine_tune: + if self.freeze_layers: self.base_model.trainable = False + if self.freeze_batch_norm: + for layer in self.base_model.layers: + if isinstance(layer, keras.layers.BatchNormalization): + layer.trainable = False i = tf.keras.layers.Input([self.input_shape[0], self.input_shape[1], self.input_shape[2]], dtype=tf.float32) x = tf.cast(i, tf.float32) x = self.base_model_type.value.model_preprocessor(x) @@ -93,7 +110,7 @@ class ImageClassModelBuilder(object): return self.model def get_name(self): - return f"{'pt-' if self.pre_trained else ''}{'ft-' if self.fine_tune else ''}" \ + return f"{'pt-' if self.pre_trained else ''}{'fl-' if self.freeze_layers else ''}{'fbn-' if self.freeze_batch_norm else ''}" \ f"{self.base_model_type.value.name}-d{self.dense_layer_neurons}-do{self.dropout_rate}" \ f"{'-l1' + np.format_float_scientific(self.l1) if self.l1 > 0 else ''}{'-l2' + np.format_float_scientific(self.l2) if self.l2 > 0 else ''}" \ f"-{random.randint(1111, 9999)}" diff --git a/modeling_utils/model_testing.py b/modeling_utils/model_testing.py new file mode 100644 index 0000000..b03f39a --- /dev/null +++ b/modeling_utils/model_testing.py @@ -0,0 +1,23 @@ +import numpy as np +from sklearn.metrics import accuracy_score, confusion_matrix, log_loss + + +def get_metrics(gen, model, save_predictions_file=None): + model_output = model.predict(gen, verbose=True, workers=12) + prediction_indices = np.argmax(model_output, axis=1) + label_index = {v: k for k, v in gen.class_indices.items()} + predictions = [label_index[p] for p in prediction_indices] + reals = [label_index[p] for p in gen.classes] + acc = accuracy_score(reals, predictions) + ll = log_loss(gen.classes, model_output, labels=[l for l in label_index.keys()]) + conf_mat = confusion_matrix(reals, predictions, labels=[l for l in label_index.values()]) + # print(classification_report(reals, predictions, labels=[l for l in label_index.values()])) + print("Testing accuracy score is ", acc) + print("Confusion Matrix", conf_mat) + if save_predictions_file: + df = pd.DataFrame(columns=['fname', 'prediction', 'true_val']) + df['fname'] = [x for x in gen.filenames] + df['prediction'] = predictions + df["true_val"] = reals + df.to_csv(save_predictions_file, index=False) + return acc, ll diff --git a/model_builder/model_wrapper.py b/modeling_utils/model_wrapper.py similarity index 100% rename from model_builder/model_wrapper.py rename to modeling_utils/model_wrapper.py diff --git a/print_all_wrong.py b/print_all_wrong.py new file mode 100644 index 0000000..439b175 --- /dev/null +++ b/print_all_wrong.py @@ -0,0 +1,4 @@ +import pandas as pd +df = pd.read_csv("models/keras/pt-fl-fbn-efficientnet_v2b0-d1024-do0.5-l11.e-04-l21.e-04-5224-second_stage.csv") + +print(df.loc[df["prediction"] != df["true_val"]]) diff --git a/single_image_test_set/latias/17-6308965103.png b/single_image_test_set/latias/17-6308965103.png deleted file mode 100644 index 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