compiled models

.python-version

@@ -0,0 +1 @@
+3.11.1

binary-compile.py

@@ -1,136 +1,34 @@
-import numpy as np
+from tflite_model_maker import model_spec
-import matplotlib.pyplot as plt
+from tflite_model_maker import image_classifier
-import tensorflow as tf 
+from tflite_model_maker.config import ExportFormat
-import os 
+from tflite_model_maker.config import QuantizationConfig
+from tflite_model_maker.image_classifier import DataLoader
 
-DIR = "data/binary-classification"
+DATA = "data/binary-classification/"
+MODELS = ["mobilenet_v2", "efficientnet_lite3", "efficientnet_lite4"]
 
-#Import Data
+train_data = DataLoader.from_folder(DATA + "train")
-PATH = os.path.join(os.getcwd(), DIR)
+test_data = DataLoader.from_folder(DATA + "test")
-
+train_data, valid_data = train_data.split(0.8)
-training_data = os.path.join(PATH, "train")
-validation_data = os.path.join(PATH, "valid")
-test_data = os.path.join(PATH, "test")
-
-BATCH_SIZE = 64
-IMG_SIZE = (224,224)
-
-#TODO: Import data from both directories, then resplit into test, train, and validation
-
-print(f"Train: {len(training_data)}\nValid: {len(validation_data)}\nTest: {len(test_data)}")
 
 
-#View Data
+for i in range(len(MODELS)):
-plt.figure(figsize=(10,10))
+    model = image_classifier.create(train_data,
-for images, labels in training_data.take(1):
+                                    validation_data=valid_data,
-    for i in range(9):
+                                    model_spec=model_spec.get(MODELS[i]),
-        ax = plt.subplot(3, 3, i+1)
+                                    epochs=50,
-        plt.imshow(images[i].numpy().astype("uint8"))
+                                    learning_rate=0.0001,
-        plt.title(class_names[labels[i]])
+                                    dropout_rate=0.2,
-        plt.axis("off")
+                                    batch_size=64,
-plt.show()
+                                    use_augmentation=True)
+    model.summary()
 
+    loss, accuracy = model.evaluate(test_data)
 
-#Init Prefetching
+    config = QuantizationConfig.for_float16()
-AUTOTUNE = tf.data.AUTOTUNE
+    filename = f"dermy-binary-classification-{MODELS[i]}.tflite"
-training_data = training_data.prefetch(buffer_size=AUTOTUNE)
-validation_data = validation_data.prefetch(buffer_size=AUTOTUNE)
-test_data = test_data.prefetch(buffer_size=AUTOTUNE)
 
-
+    model.export(export_dir="./models",
-#Data Augmentation
+                 export_format=ExportFormat.TFLITE,
-data_augmentation = tf.keras.Sequential([
+                 tflite_filename=filename,
-    tf.keras.layers.RandomFlip('horizontal'),
+                 quantization_config=config)
-    tf.keras.layers.RandomRotation(0.2)
-    ])
-
-#Create Base Model From MobileNetV3
-IMG_SHAPE = IMG_SIZE + (3,)
-base_model = tf.keras.applications.MobileNetV3Large(
-        input_shape=IMG_SHAPE,
-        include_top=False,
-        weights="imagenet"
-        )
-
-image_batch, label_batch = next(iter(training_data))
-feature_batch = base_model(image_batch)
-
-base_model.trainable = False
-
-
-#View Base Model
-base_model.summary()
-
-#Add Classification Header
-
-
-global_avg_layer = tf.keras.layers.GlobalAveragePooling2D()
-feature_batch_avg = global_avg_layer(feature_batch)
-
-prediction_layer = tf.keras.layers.Dense(38, activation="softmax")
-predication_batch = prediction_layer(feature_batch_avg)
-
-inputs = tf.keras.Input(shape=(160,160,3))
-x = data_augmentation(inputs)
-x = base_model(x, training=False)
-x = global_avg_layer(x)
-x = tf.keras.layers.Dropout(0.2)(x)
-outputs = prediction_layer(x)
-
-model = tf.keras.Model(inputs, outputs)
-
-#View Model with Classification Head
-model.summary()
-
-
-#Compile the Model
-base_learning_rate = 0.0001
-
-training_data = training_data.map(lambda x,y: (x, tf.one_hot(y,38)))
-validation_data = validation_data.map(lambda x,y: (x, tf.one_hot(y,38)))
-test_data = test_data.map(lambda x,y: (x, tf.one_hot(y,38)))
-
-optimizer = tf.keras.optimizers.Adam(learning_rate=base_learning_rate)
-loss = tf.keras.losses.CategoricalCrossentropy()
-metrics = [tf.keras.metrics.CategoricalAccuracy()]
-
-
-
-model.compile(optimizer=optimizer, loss=loss, metrics=metrics)
-
-#Train the Model
-initial_epochs = 50
-
-loss0, accuracy0 = model.evaluate(validation_data)
-
-print(f"initial loss: {loss0}")
-print(f"initial accuracy: {accuracy0}")
-
-lr_schedule = tf.keras.callbacks.ReduceLROnPlateau(
-        monitor="val_loss",
-        factor=0.1,
-        patience=5,
-        min_lr=1e-6
-        )
-
-early_stopping = tf.keras.callbacks.EarlyStopping(
-        monitor="val_loss",
-        patience=10,
-        restore_best_weights=True
-        )
-
-history = model.fit(training_data,
-                    epochs=initial_epochs,
-                    validation_data=validation_data,
-                    callbacks=[lr_schedule, early_stopping])
-
-model.save("crop-classifier-better-test.keras")
-
-#Evaluate Model
-results = model.evaluate(validation_data)
-print(f"Validation Loss: {results[0]}")
-print(f"Validation Accuracy: {results[1]}")
-
-results = model.evaluate(test_data)
-print(f"Test Loss: {results[0]}")
-print(f"Test Accuracy: {results[1]}")

convert.py

@@ -0,0 +1,17 @@
+import tensorflow as tf
+
+MODEL = "models/dermy-binary-classifier"
+
+
+model = tf.keras.models.load_model(MODEL + ".keras")
+
+converter = tf.lite.TFLiteConverter.from_keras_model(model)
+
+converter.allow_custom_ops = True
+converter.experimental_enable_resource_variables = True
+converter.experimental_new_converter = True
+
+converted_model = converter.convert()
+
+with open(MODEL + ".tflite", "wb") as file:
+    file.write(converted_model)

pyproject.toml

@@ -1,20 +0,0 @@
-[build-system]
-requires = ["setuptools", "wheel"]
-build-backend = "setuptools.build_meta"
-
-[project]
-name = "dermy-model"
-version = "0.1.0"
-description = "A Image Classification Model for classifying Moles"
-authors = [{ name = "r0r5chach", email = "r0r-5chach.xyz@proton.me" }]
-readme = "README.md"
-license = { file = "LICENSE" }
-dependencies = [
-	"matplotlib",
-	"numpy",
-	"tensorflow",
-]
-
-[too.setuptools]
-packages = ["dermy-model"]
-include_package_data = true

requirements.txt

@@ -0,0 +1,29 @@
+tf-models-official==2.3.0
+# tensorflow-hub is to load Hub model. Specific version is required by TFJS.
+tensorflow-hub>=0.7.0,<0.10; python_version < "3"
+tensorflow-hub>=0.7.0,<0.13; python_version >= "3"
+numpy>=1.17.3,<1.23.4
+pillow>=7.0.0
+sentencepiece>=0.1.91
+tensorflow-datasets>=2.1.0
+fire>=0.3.1
+flatbuffers>=2.0
+absl-py>=0.10.0
+urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1
+tflite-support>=0.4.2
+tensorflowjs>=2.4.0,<3.19.0
+tensorflow>=2.6.0
+# b/196287362: This Numba + Librosa combination works for numpy 1.19, introduced
+# by TensorFlow 2.6.0.
+numba>=0.53
+librosa==0.8.1
+lxml>=4.6.1
+PyYAML>=5.1
+# The following are the requirements of efficientdet.
+matplotlib>=3.0.3,<3.5.0
+six>=1.12.0
+tensorflow-addons>=0.11.2
+neural-structured-learning>=1.3.1
+tensorflow-model-optimization>=0.5
+Cython>=0.29.13
+scann==1.2.6