51 lines
1.8 KiB
Python
51 lines
1.8 KiB
Python
import os
|
|
|
|
from image_prediction.redai_adapter.model_wrapper import ModelWrapper
|
|
|
|
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
|
|
|
|
import tensorflow as tf
|
|
|
|
|
|
class EfficientNetWrapper(ModelWrapper):
|
|
|
|
def __init__(self, classes, base_weights_path=None, weights_path=None):
|
|
self.__input_shape = (224, 224, 3)
|
|
super().__init__(classes=classes, base_weights_path=base_weights_path, weights_path=weights_path)
|
|
|
|
@property
|
|
def input_shape(self):
|
|
return self.__input_shape
|
|
|
|
def _ModelWrapper__preprocess_tensor(self, tensor):
|
|
return tf.keras.applications.efficientnet.preprocess_input(tensor)
|
|
|
|
def _ModelWrapper__build(self, base_weights=None) -> tf.keras.models.Model:
|
|
input_img = tf.keras.layers.Input(shape=self.input_shape)
|
|
|
|
pretrained = tf.keras.applications.efficientnet.EfficientNetB0(
|
|
include_top=False, input_tensor=tf.keras.layers.Input(shape=self.input_shape), weights=base_weights
|
|
)
|
|
|
|
pretrained.trainable = False
|
|
|
|
for layer in pretrained.layers:
|
|
layer.trainable = False
|
|
|
|
pretrained = pretrained(input_img)
|
|
|
|
finetuned = tf.keras.layers.Flatten()(pretrained)
|
|
finetuned = tf.keras.layers.Dense(512, activation="relu")(finetuned)
|
|
finetuned = tf.keras.layers.Dropout(0.2)(finetuned)
|
|
finetuned = tf.keras.layers.Dense(128, activation="relu")(finetuned)
|
|
finetuned = tf.keras.layers.Dropout(0.2)(finetuned)
|
|
finetuned = tf.keras.layers.Dense(32, activation="relu")(finetuned)
|
|
finetuned = tf.keras.layers.Dropout(0.2)(finetuned)
|
|
finetuned = tf.keras.layers.Dense(len(self.classes), activation="softmax")(finetuned)
|
|
|
|
model = tf.keras.models.Model(inputs=input_img, outputs=finetuned)
|
|
|
|
model.compile()
|
|
|
|
return model
|