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from tensorflow.keras.models import Sequential
from tensorflow.keras.models import Model
from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard, EarlyStopping
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.applications import MobileNetV2, Xception, VGG16, InceptionV3
from tensorflow.keras.layers import Conv2D, MaxPool2D, MaxPooling2D, Dropout, \
Flatten, Dense, BatchNormalization, \
SpatialDropout2D, AveragePooling2D, Input
import os
import cv2
import warnings
import argparse
import numpy as np
import pandas as pd
import tensorflow as tf
import matplotlib.pyplot as plt
tf.get_logger().setLevel('WARNING')
parser = argparse.ArgumentParser()
parser.add_argument('-d', '--data-dir', type=str, default='data/raw_dataset',
help="Directory of dataset")
parser.add_argument('-e', '--epochs', type=int, default=30,
help="Where to write the new data")
parser.add_argument("-m", "--model", type=str, default="mask_detector.model",
help="Path to output face mask detector model")
parser.add_argument('-s', '--size', type=int, default=64,
help="Size of input data")
parser.add_argument('-b', '--batch-size', type=int, default=32,
help="Bactch size of data generator")
parser.add_argument('-l', '--learning-rate', type=float, default=0.0001,
help="Learning rate value")
parser.add_argument('-sh', '--show-history', action='store_true',
help="Show training history")
parser.add_argument('-n', '--net-type', type=str, default='MobileNetV2',
choices=['CNN', 'MobileNetV2', 'VGG16','Xception'],
help="The network architecture, optional: CNN, MobileNetV2, VGG16, Xception")
def CNN_model(learning_rate, input_shape):
# Build model
model = Sequential()
model.add(Conv2D(filters=32, kernel_size=(3, 3), padding='same', input_shape=input_shape, activation='relu'))
model.add(Conv2D(filters=32, kernel_size=(3, 3), padding='same', input_shape=input_shape, activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.5))
model.add(Conv2D(filters=64, kernel_size=(3, 3), padding='same', activation='relu'))
model.add(Conv2D(filters=64, kernel_size=(3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.5))
model.add(Conv2D(filters=128, kernel_size=(3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.5))
model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(50, activation="relu"))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss="binary_crossentropy", metrics=["accuracy"], \
optimizer=Adam(learning_rate=learning_rate))
return model
def MobileNetV2_model(learning_rate, input_shape):
baseModel = MobileNetV2(include_top=False, input_tensor=Input(shape=input_shape))
for layer in baseModel.layers[:-4]:
layer.trainable = False
model = Sequential()
model.add(baseModel)
model.add(AveragePooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(512, activation="relu"))
model.add(Dropout(0.5))
model.add(Dense(50, activation="relu"))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))
# compile our model
model.compile(loss="binary_crossentropy", metrics=["accuracy"], \
optimizer=Adam(learning_rate=learning_rate))
return model
def VGG16_model(learning_rate, input_shape):
baseModel = VGG16(include_top=False, input_tensor=Input(shape=input_shape))
for layer in baseModel.layers:
layer.trainable = False
model = Sequential()
model.add(baseModel)
model.add(AveragePooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(512, activation="relu"))
model.add(Dropout(0.5))
model.add(Dense(50, activation="relu"))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))
# compile our model
model.compile(loss="binary_crossentropy", metrics=["accuracy"], \
optimizer=Adam(learning_rate=learning_rate))
return model
def Xception_model(learning_rate, input_shape):
baseModel = Xception(include_top=False, input_tensor=Input(shape=input_shape))
for layer in baseModel.layers:
layer.trainable = False
model = Sequential()
model.add(baseModel)
model.add(AveragePooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(512, activation="relu"))
model.add(Dropout(0.5))
model.add(Dense(50, activation="relu"))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))
# compile our model
model.compile(loss="binary_crossentropy", metrics=["accuracy"], \
optimizer=Adam(learning_rate=learning_rate))
return model
def keras_model_memory_usage_in_bytes(model, *, batch_size: int):
"""
Return the estimated memory usage of a given Keras model in bytes.
Ref: https://stackoverflow.com/a/64359137
"""
default_dtype = tf.keras.backend.floatx()
shapes_mem_count = 0
internal_model_mem_count = 0
for layer in model.layers:
if isinstance(layer, tf.keras.Model):
internal_model_mem_count += keras_model_memory_usage_in_bytes( layer, batch_size=batch_size)
single_layer_mem = tf.as_dtype(layer.dtype or default_dtype).size
out_shape = layer.output_shape
if isinstance(out_shape, list):
out_shape = out_shape[0]
for s in out_shape:
if s is None:
continue
single_layer_mem *= s
shapes_mem_count += single_layer_mem
trainable_count = sum([tf.keras.backend.count_params(p) for p in model.trainable_weights])
non_trainable_count = sum( [tf.keras.backend.count_params(p) for p in model.non_trainable_weights])
total_memory = ( batch_size * shapes_mem_count + internal_model_mem_count
+ trainable_count + non_trainable_count)
return total_memory
if __name__ == "__main__":
args = parser.parse_args()
bs = args.batch_size
lr = args.learning_rate
size = (args.size, args.size)
shape = (args.size, args.size, 3)
epochs = args.epochs
# Load and preprocess data
train_dir = os.path.join(args.data_dir, 'train')
test_dir = os.path.join(args.data_dir, 'test')
valid_dir = os.path.join(args.data_dir, 'validation')
train_datagen = ImageDataGenerator(rescale=1./255, rotation_range=5, zoom_range=0.2, \
shear_range=0.2, brightness_range=[0.9, 1.1], \
horizontal_flip=True)
valid_datagen = ImageDataGenerator(rescale=1./255, rotation_range=5, zoom_range=0.2, \
shear_range=0.2, brightness_range=[0.9, 1.1], \
horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(train_dir, target_size=size, shuffle=True,
batch_size=bs, class_mode='binary')
valid_generator = valid_datagen.flow_from_directory(valid_dir, target_size=size, shuffle=True,
batch_size=bs, class_mode='binary')
test_generator = test_datagen.flow_from_directory(test_dir, target_size=size, shuffle=True,
batch_size=bs, class_mode='binary')
print(train_generator.class_indices)
print(train_generator.image_shape)
# Build model
net_type_to_model = {
'CNN' : CNN_model,
'MobileNetV2': MobileNetV2_model,
'VGG16' : VGG16_model,
'Xception' : Xception_model
}
model_name = args.net_type
model_builder = net_type_to_model.get(model_name)
model = model_builder(lr, shape)
model.summary()
earlystop = EarlyStopping(monitor='val_loss', patience=5, mode='auto')
tensorboard = TensorBoard(log_dir=os.path.join("logs", model_name))
checkpoint = ModelCheckpoint(os.path.join("results", f"{model_name}" + f"-size-{size[0]}" + \
f"-bs-{bs}" + f"-lr-{lr}.h5"), \
monitor='val_loss',save_best_only=True, verbose=1)
# Train model
history = model.fit(train_generator, epochs=epochs, validation_data=valid_generator,
batch_size=bs, callbacks=[earlystop, tensorboard, checkpoint], shuffle=True)
test_loss, test_accuracy = model.evaluate(test_generator)
metrics = pd.DataFrame(history.history)
print(metrics.head(10))
print('test_loss: ', test_loss)
print('test_accuracy: ', test_accuracy)
print('Memory consumption: %s bytes' % keras_model_memory_usage_in_bytes(model, batch_size=bs))
# serialize the model to disk
print("saving mask detector model...")
model.save(args.model, save_format="h5")
if args.show_history:
plt.subplot(211)
plt.title('Loss')
plt.plot(history.history['loss'], label='train')
plt.plot(history.history['val_loss'], label='test')
plt.legend()
plt.subplot(212)
plt.title('Accuracy')
plt.plot(history.history['accuracy'], label='train')
plt.plot(history.history['val_accuracy'], label='test')
plt.legend()
plt.show()
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