133 lines
5.8 KiB
Python
133 lines
5.8 KiB
Python
import tensorflow as tf
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import os, tf2onnx, numpy, random
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from PIL import Image
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from tensorflow.keras import *
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####################################################################################################
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Image.MAX_IMAGE_PIXELS = None
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tf.keras.mixed_precision.set_global_policy('mixed_float16')
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####################################################################################################
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def get_file_list(directory):
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file_paths, labels, class_indices = [], [], {}
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for i, cls in enumerate(sorted([d for d in os.listdir(directory) if os.path.isdir(os.path.join(directory, d))])):
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class_indices[cls] = i
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cls_dir = os.path.join(directory, cls)
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for root, _, files in os.walk(cls_dir):
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for file in files:
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if file.lower().endswith(('.jpg', '.jpeg', '.png', '.bmp', '.gif')):
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file_paths.append(os.path.join(root, file))
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labels.append(i)
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return file_paths, labels, class_indices
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####################################################################################################
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def parse_fn(filename, label, image_size, channels, num_classes):
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img = tf.io.read_file(filename)
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img = tf.image.decode_image(img, channels=channels, expand_animations=False)
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img = tf.cond(
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tf.reduce_all(tf.equal(tf.shape(img)[:2], image_size)),
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lambda: tf.cast(img, tf.float32),
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lambda: tf.image.resize(img, image_size))
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img = tf.cast(img, tf.float32) / 255.0
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label = tf.one_hot(label, depth=num_classes)
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return img, label
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####################################################################################################
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def load_dataset(data_dir, image_size=(224, 224), val_split=0.00001, batch_size=128, color_mode="grayscale"):
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channels = 1 if color_mode == "grayscale" else 3
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file_paths, labels, class_indices = get_file_list(data_dir)
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combined = list(zip(file_paths, labels))
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random.shuffle(combined)
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file_paths[:], labels[:] = zip(*combined)
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num_val = int(val_split * len(file_paths))
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train_files = file_paths[num_val:]
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train_labels = labels[num_val:]
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val_files = file_paths[:num_val]
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val_labels = labels[:num_val]
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num_classes = len(class_indices)
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train_ds = tf.data.Dataset.from_tensor_slices((train_files, train_labels))
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train_ds = train_ds.map(lambda f, l: parse_fn(f, l, image_size, channels, num_classes), num_parallel_calls=tf.data.AUTOTUNE)
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train_ds = train_ds.batch(batch_size).prefetch(tf.data.AUTOTUNE)
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val_ds = tf.data.Dataset.from_tensor_slices((val_files, val_labels))
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val_ds = val_ds.map(lambda f, l: parse_fn(f, l, image_size, channels, num_classes), num_parallel_calls=tf.data.AUTOTUNE)
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val_ds = val_ds.batch(batch_size).prefetch(tf.data.AUTOTUNE)
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train_ds.class_indices = class_indices
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return train_ds, val_ds
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####################################################################################################
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def multi_conv(x, strides):
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convs = []
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for k in [1, 3, 5, 7]:
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c = layers.DepthwiseConv2D(kernel_size=k, strides=strides, padding='same', activation='gelu')(x)
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convs.append(c)
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x = layers.Concatenate()(convs)
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x = layers.BatchNormalization()(x)
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return layers.Activation('gelu')(x)
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def gated_conv(x, strides):
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c = layers.DepthwiseConv2D(3, strides=strides, padding='same', activation='gelu')(x)
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g = layers.DepthwiseConv2D(3, strides=strides, padding='same', activation='sigmoid')(x)
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return layers.Multiply()([c, g])
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def se_block(x, reduction):
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filters = x.shape[-1]
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s = layers.Conv2D(filters // reduction, 1, padding='same', activation='gelu')(x)
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s = layers.BatchNormalization()(s)
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s = layers.Conv2D(filters, 1, padding='same', activation='sigmoid')(s)
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return layers.multiply([x, s])
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def build_model(input_shape, num_classes):
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i = layers.Input(shape=input_shape)
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x = multi_conv(i, strides=1)
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x = gated_conv(x, strides=2)
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x = multi_conv(x, strides=2)
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x = multi_conv(x, strides=2)
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x = multi_conv(x, strides=2)
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x = multi_conv(x, strides=2)
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x = se_block(x, reduction=2)
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x = layers.GlobalMaxPooling2D()(x)
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o = layers.Dense(num_classes, activation='softmax', dtype='float32')(x)
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return models.Model(i, o)
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####################################################################################################
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def lr_scheduler(epoch, lr):
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return 1e-4 * (0.95 ** epoch)
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class CustomModelCheckpoint(callbacks.Callback):
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def on_epoch_end(self, epoch, logs=False):
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models.save_model(self.model, f"PYAS_Model_Epoch_{epoch + 1}.h5")
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spec = (tf.TensorSpec((None,) + train_ds.image_shape, tf.float32),)
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model_proto, _ = tf2onnx.convert.from_keras(self.model, input_signature=spec, opset=18)
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with open(f"PYAS_Model_Epoch_{epoch + 1}.onnx", "wb") as f:
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f.write(model_proto.SerializeToString())
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####################################################################################################
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train_ds, val_ds = load_dataset(r'.\Image_File')
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for imgs, _ in train_ds.take(1):
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train_ds.image_shape = imgs.shape[1:]
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break
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strategy = tf.distribute.MirroredStrategy(cross_device_ops=tf.distribute.HierarchicalCopyAllReduce())
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with strategy.scope():
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try:
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model = models.load_model('PYAS_Model_Epoch_0.h5')
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print("Load model from disk")
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except:
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print("Creating a new model")
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model = build_model(train_ds.image_shape, len(train_ds.class_indices))
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print(f"Total parameters: {model.count_params()}")
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model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
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model.fit(train_ds, epochs=30, callbacks=[CustomModelCheckpoint(), callbacks.LearningRateScheduler(lr_scheduler)], validation_data=val_ds)
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input('Training Complete')
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