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Remove forward slashes from layer names for backbones #2037

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merged 1 commit into from
Aug 24, 2023
Merged

Remove forward slashes from layer names for backbones #2037

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6 changes: 3 additions & 3 deletions keras_cv/models/backbones/densenet/densenet_backbone.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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)
Expand Down
16 changes: 8 additions & 8 deletions keras_cv/models/backbones/mobilenet_v3/mobilenet_v3_backbone.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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)

Expand Down Expand Up @@ -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)

Expand Down Expand Up @@ -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),
Expand All @@ -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(
Expand All @@ -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)

Expand All @@ -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:
Expand Down
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