refactor: Change keras to tensorflow.keras

2_Training/Download_and_Convert_YOLO_weights.py

@@ -50,7 +50,8 @@ if __name__ == "__main__":
         # Original URL: https://pjreddie.com/media/files/yolov3-tiny.weights
         gdrive_id = "1mIEZthXBcEguMvuVAHKLXQX3mA1oZUuC"
 
-    if not os.path.isfile(os.path.join(download_folder, weights_file)):
+    # if not os.path.isfile(os.path.join(download_folder, weights_file)):
+    if True:
         print(f"\nDownloading Raw {weights_file}\n")
         start = time.time()
         call_string = " ".join(

2_Training/Train_YOLO.py

@@ -25,12 +25,12 @@ utils_path = os.path.join(get_parent_dir(1), "Utils")
 sys.path.append(utils_path)
 
 import numpy as np
-import keras.backend as K
-from keras.layers import Input, Lambda
-from keras.models import Model
-from keras.optimizers import Adam
+import tensorflow.keras.backend as K
+from tensorflow.keras.layers import Input, Lambda
+from tensorflow.keras import Model
+from tensorflow.keras.optimizers import Adam
 
-from keras.callbacks import (
+from tensorflow.keras.callbacks import (
     TensorBoard,
     ModelCheckpoint,
     ReduceLROnPlateau,
@@ -303,4 +303,4 @@ if __name__ == "__main__":
         initial_epoch=epoch1,
         callbacks=full_callbacks,
     )
-    model.save_weights(os.path.join(log_dir, "trained_weights_final.h5"))
+    model.save_weights(os.path.join(log_dir, "trained_weights_final.h5"))

2_Training/src/keras_yolo3/convert.py

@@ -11,8 +11,8 @@ import os
 from collections import defaultdict
 
 import numpy as np
-from keras import backend as K
-from keras.layers import (
+from tensorflow.keras import backend as K
+from tensorflow.keras.layers import (
     Conv2D,
     Input,
     ZeroPadding2D,
@@ -21,11 +21,11 @@ from keras.layers import (
     MaxPooling2D,
     Concatenate,
 )
-from keras.layers.advanced_activations import LeakyReLU
-from keras.layers.normalization import BatchNormalization
-from keras.models import Model
-from keras.regularizers import l2
-from keras.utils.vis_utils import plot_model as plot
+from tensorflow.keras.layers import LeakyReLU
+from tensorflow.keras.layers import BatchNormalization
+from tensorflow.keras import Model
+from tensorflow.keras.regularizers import l2
+from tensorflow.keras.utils import plot_model as plot
 
 
 parser = argparse.ArgumentParser(description="Darknet To Keras Converter.")

2_Training/src/keras_yolo3/yolo.py

@@ -8,14 +8,14 @@ import os
 from timeit import default_timer as timer
 
 import numpy as np
-from keras.models import load_model
-from keras.layers import Input
+from tensorflow.keras.models import load_model
+from tensorflow.keras.layers import Input
 from PIL import Image, ImageFont, ImageDraw
 
 from .yolo3.model import yolo_eval, yolo_body, tiny_yolo_body
 from .yolo3.utils import letterbox_image
 import os
-from keras.utils import multi_gpu_model
+from tensorflow.keras.utils import multi_gpu_model
 import tensorflow.compat.v1 as tf
 import tensorflow.python.keras.backend as K
 

2_Training/src/keras_yolo3/yolo3/model.py

@@ -1,11 +1,11 @@
 """YOLO_v3 Model Defined in Keras."""
 
 from functools import wraps
-
+from tensorflow import keras
 import numpy as np
 import tensorflow as tf
-from keras import backend as K
-from keras.layers import (
+from tensorflow.keras import backend as K
+from tensorflow.keras.layers import (
     Conv2D,
     Add,
     ZeroPadding2D,
@@ -13,10 +13,10 @@ from keras.layers import (
     Concatenate,
     MaxPooling2D,
 )
-from keras.layers.advanced_activations import LeakyReLU
-from keras.layers.normalization import BatchNormalization
-from keras.models import Model
-from keras.regularizers import l2
+from tensorflow.keras.layers import LeakyReLU
+from tensorflow.keras.layers import BatchNormalization
+from tensorflow.keras import Model
+from tensorflow.keras.regularizers import l2
 
 from ..yolo3.utils import compose
 

Utils/Train_Utils.py

@@ -21,11 +21,11 @@ src_path = os.path.join(get_parent_dir(2), "src")
 sys.path.append(src_path)
 
 import numpy as np
-import keras.backend as K
-from keras.layers import Input, Lambda
-from keras.models import Model
-from keras.optimizers import Adam
-from keras.callbacks import (
+import tensorflow.keras.backend as K
+from tensorflow.keras.layers import Input, Lambda
+from tensorflow.keras.models import Model
+from tensorflow.keras.optimizers import Adam
+from tensorflow.keras.callbacks import (
     TensorBoard,
     ModelCheckpoint,
     ReduceLROnPlateau,

Utils/utils.py

@@ -1,15 +1,13 @@
 import colorsys
 import cv2
 import h5py
-from keras import Model
+from tensorflow.keras import Model
 import numpy as np
 import os
 from matplotlib.colors import rgb_to_hsv, hsv_to_rgb
 from PIL import Image, ImageFont, ImageDraw
 from timeit import default_timer as timer
 
-# import readline
-# readline.parse_and_bind("tab: complete")
 
 min_logo_size = (10, 10)
 

requirements.txt

@@ -5,4 +5,5 @@ matplotlib
 pandas
 opencv-python
 progressbar2
-wandb
+wandb
+black