eager_excuetion off

eval.py

@@ -1,7 +1,5 @@
 # -*- coding: utf-8 -*-
 import tensorflow as tf
-tf.compat.v1.enable_eager_execution()
-
 import argparse
 
 argparser = argparse.ArgumentParser(

pred.py

@@ -1,7 +1,6 @@
 # -*- coding: utf-8 -*-
 
 import tensorflow as tf
-tf.compat.v1.enable_eager_execution()
 import argparse
 import cv2
 import matplotlib.pyplot as plt

train_eager.py

@@ -1,8 +1,6 @@
 # -*- coding: utf-8 -*-
 
 import tensorflow as tf
-tf.compat.v1.enable_eager_execution()
-
 import argparse
 from yolo.train import train_fn
 from yolo.config import ConfigParser

yolo/dataset/generator.py

@@ -177,7 +177,6 @@ def normalize(image):
 
 
 if __name__ == '__main__':
-    tf.enable_eager_execution()
     import os
     import glob
     from yolo import PROJECT_ROOT

yolo/loss/calc_tensor.py

@@ -2,7 +2,6 @@
 
 import tensorflow as tf
 import numpy as np
-# tf.enable_eager_execution()
 from yolo.loss.utils import adjust_pred_tensor, adjust_true_tensor
 from yolo.loss.utils import conf_delta_tensor
 from yolo.loss.utils import loss_class_tensor, loss_conf_tensor, loss_coord_tensor, wh_scale_tensor
@@ -77,7 +76,6 @@ def run(self, y_true, y_pred, anchors=[90, 95, 92, 154, 139, 281]):
 if __name__ == '__main__':
     import os
     from yolo import PROJECT_ROOT
-    tf.enable_eager_execution()
     def test():
         yolo_1 = np.load(os.path.join(PROJECT_ROOT, "yolo_1.npy")).astype(np.float32)
         pred_yolo_1 = np.load(os.path.join(PROJECT_ROOT, "pred_yolo_1.npy")).astype(np.float32)

yolo/net/bodynet.py

@@ -180,7 +180,6 @@ def call(self, input_tensor, training=False):
 
 if __name__ == '__main__':
     import numpy as np
-    tf.enable_eager_execution()
     imgs = np.random.randn(1, 256, 256, 3).astype(np.float32)
     input_tensor = tf.constant(imgs)
 

yolo/net/headnet.py

@@ -147,7 +147,6 @@ def call(self, input_tensor, training=False):
 
 if __name__ == '__main__':
     import numpy as np
-    tf.enable_eager_execution()
     s3 = tf.constant(np.random.randn(1, 32, 32, 256).astype(np.float32))
     s4 = tf.constant(np.random.randn(1, 16, 16, 512).astype(np.float32))
     s5 = tf.constant(np.random.randn(1, 8, 8, 1024).astype(np.float32))

yolo/net/weights.py

@@ -69,10 +69,6 @@ def _load_4d_var(self, variable):
 
 
 if __name__ == '__main__':
-    # Todo : tensorflow를 import 했을 때 / 안했을 때 offset 결과가 다르다.
-#     import tensorflow as tf
-#     tf.enable_eager_execution()
-
     from yolo.net.yolonet import Yolonet
     from yolo import YOLOV3_WEIGHTS
     yolonet = Yolonet(18)

yolo/net/yolonet.py

@@ -66,7 +66,6 @@ def preprocess_input(image, net_size):
 
 
 if __name__ == '__main__':
-    tf.enable_eager_execution()
     inputs = tf.constant(np.random.randn(1, 256, 256, 3).astype(np.float32))
 
     # (1, 256, 256, 3) => (1, 8, 8, 1024)

yolo/train.py

@@ -82,5 +82,4 @@ def _grad_fn(model, images_tensor, list_y_trues):
 
 
 if __name__ == '__main__':
-    tf.compat.v1.enable_eager_execution()
     pass