change to up to date packages

convert.py

@@ -14,8 +14,8 @@
 from keras import backend as K
 from keras.layers import (Conv2D, Input, ZeroPadding2D, Add,
                           UpSampling2D, MaxPooling2D, Concatenate)
-from keras.layers.advanced_activations import LeakyReLU
-from keras.layers.normalization import BatchNormalization
+from keras.layers import LeakyReLU
+from keras.layers import BatchNormalization
 from keras.models import Model
 from keras.regularizers import l2
 from keras.utils.vis_utils import plot_model as plot

train.py

@@ -56,7 +56,7 @@ def _main():
 
         batch_size = 32
         print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size))
-        model.fit_generator(data_generator_wrapper(lines[:num_train], batch_size, input_shape, anchors, num_classes),
+        model.fit(data_generator_wrapper(lines[:num_train], batch_size, input_shape, anchors, num_classes),
                 steps_per_epoch=max(1, num_train//batch_size),
                 validation_data=data_generator_wrapper(lines[num_train:], batch_size, input_shape, anchors, num_classes),
                 validation_steps=max(1, num_val//batch_size),

yolo3/model.py

@@ -6,11 +6,11 @@
 import tensorflow as tf
 from keras import backend as K
 from keras.layers import Conv2D, Add, ZeroPadding2D, UpSampling2D, Concatenate, MaxPooling2D
-from keras.layers.advanced_activations import LeakyReLU
-from keras.layers.normalization import BatchNormalization
+from keras.layers import LeakyReLU
+from keras.layers import BatchNormalization
 from keras.models import Model
 from keras.regularizers import l2
-
+from tensorflow.python.ops import control_flow_ops
 from yolo3.utils import compose
 
 
@@ -391,7 +391,7 @@ def loop_body(b, ignore_mask):
             best_iou = K.max(iou, axis=-1)
             ignore_mask = ignore_mask.write(b, K.cast(best_iou<ignore_thresh, K.dtype(true_box)))
             return b+1, ignore_mask
-        _, ignore_mask = K.control_flow_ops.while_loop(lambda b,*args: b<m, loop_body, [0, ignore_mask])
+        _, ignore_mask = control_flow_ops.while_loop(lambda b,*args: b<m, loop_body, [0, ignore_mask])
         ignore_mask = ignore_mask.stack()
         ignore_mask = K.expand_dims(ignore_mask, -1)