Remove forward slashes from layer names for backbones (#2037)

keras-team · Aug 24, 2023 · b038f58 · b038f58
1 parent d01aee4
commit b038f58
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Showing 2 changed files with 11 additions and 11 deletions.
diff --git a/keras_cv/models/backbones/densenet/densenet_backbone.py b/keras_cv/models/backbones/densenet/densenet_backbone.py
@@ -88,12 +88,12 @@ def __init__(
             x = keras.layers.Rescaling(1 / 255.0)(x)
 
         x = keras.layers.Conv2D(
-            64, 7, strides=2, use_bias=False, padding="same", name="conv1/conv"
+            64, 7, strides=2, use_bias=False, padding="same", name="conv1_conv"
         )(x)
         x = keras.layers.BatchNormalization(
-            axis=BN_AXIS, epsilon=BN_EPSILON, name="conv1/bn"
+            axis=BN_AXIS, epsilon=BN_EPSILON, name="conv1_bn"
         )(x)
-        x = keras.layers.Activation("relu", name="conv1/relu")(x)
+        x = keras.layers.Activation("relu", name="conv1_relu")(x)
         x = keras.layers.MaxPooling2D(
             3, strides=2, padding="same", name="pool1"
         )(x)

diff --git a/keras_cv/models/backbones/mobilenet_v3/mobilenet_v3_backbone.py b/keras_cv/models/backbones/mobilenet_v3/mobilenet_v3_backbone.py
@@ -126,7 +126,7 @@ def __init__(
             axis=CHANNEL_AXIS,
             epsilon=BN_EPSILON,
             momentum=BN_MOMENTUM,
-            name="Conv/BatchNorm",
+            name="Conv_BatchNorm",
         )(x)
         x = apply_hard_swish(x)
 
@@ -161,7 +161,7 @@ def __init__(
             axis=CHANNEL_AXIS,
             epsilon=BN_EPSILON,
             momentum=BN_MOMENTUM,
-            name="Conv_1/BatchNorm",
+            name="Conv_1_BatchNorm",
         )(x)
         x = apply_hard_swish(x)
 
@@ -291,11 +291,11 @@ def apply_inverted_res_block(
             activation = keras.activations.get(activation)
 
     shortcut = x
-    prefix = "expanded_conv/"
+    prefix = "expanded_conv_"
     infilters = x.shape[CHANNEL_AXIS]
 
     if expansion_index > 0:
-        prefix = f"expanded_conv_{expansion_index}/"
+        prefix = f"expanded_conv_{expansion_index}_"
 
         x = keras.layers.Conv2D(
             adjust_channels(infilters * expansion),
@@ -308,14 +308,14 @@ def apply_inverted_res_block(
             axis=CHANNEL_AXIS,
             epsilon=BN_EPSILON,
             momentum=BN_MOMENTUM,
-            name=prefix + "expand/BatchNorm",
+            name=prefix + "expand_BatchNorm",
         )(x)
         x = activation(x)
 
     if stride == 2:
         x = keras.layers.ZeroPadding2D(
             padding=utils.correct_pad_downsample(x, kernel_size),
-            name=prefix + "depthwise/pad",
+            name=prefix + "depthwise_pad",
         )(x)
 
     x = keras.layers.DepthwiseConv2D(
@@ -329,7 +329,7 @@ def apply_inverted_res_block(
         axis=CHANNEL_AXIS,
         epsilon=BN_EPSILON,
         momentum=BN_MOMENTUM,
-        name=prefix + "depthwise/BatchNorm",
+        name=prefix + "depthwise_BatchNorm",
     )(x)
     x = activation(x)
 
@@ -353,7 +353,7 @@ def apply_inverted_res_block(
         axis=CHANNEL_AXIS,
         epsilon=BN_EPSILON,
         momentum=BN_MOMENTUM,
-        name=prefix + "project/BatchNorm",
+        name=prefix + "project_BatchNorm",
     )(x)
 
     if stride == 1 and infilters == filters: