From d957c74aceb787c9198d0549a0992ccdbec76e31 Mon Sep 17 00:00:00 2001
From: Amir MOHAMMADI <amir.mohammadi@idiap.ch>
Date: Fri, 7 Feb 2020 16:07:40 +0100
Subject: [PATCH] make sure densenet layer names are consistent
---
bob/learn/tensorflow/models/densenet.py | 44 ++++++++++++++++---------
1 file changed, 29 insertions(+), 15 deletions(-)
diff --git a/bob/learn/tensorflow/models/densenet.py b/bob/learn/tensorflow/models/densenet.py
index 0cda6666..e1f70d07 100644
--- a/bob/learn/tensorflow/models/densenet.py
+++ b/bob/learn/tensorflow/models/densenet.py
@@ -5,10 +5,6 @@ Densely Connected Convolutional Networks](https://arxiv.org/abs/1608.06993)
"""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
import tensorflow as tf
from bob.extension import rc
@@ -27,9 +23,15 @@ class ConvBlock(tf.keras.Model):
"""
def __init__(
- self, num_filters, data_format, bottleneck, weight_decay=1e-4, dropout_rate=0
+ self,
+ num_filters,
+ data_format,
+ bottleneck,
+ weight_decay=1e-4,
+ dropout_rate=0,
+ **kwargs,
):
- super().__init__()
+ super().__init__(**kwargs)
self.bottleneck = bottleneck
axis = -1 if data_format == "channels_last" else 1
@@ -47,7 +49,7 @@ class ConvBlock(tf.keras.Model):
kernel_regularizer=l2(weight_decay),
name="conv1",
)
- self.norm2 = tf.keras.layers.BatchNormalization(axis=axis)
+ self.norm2 = tf.keras.layers.BatchNormalization(axis=axis, name="norm2")
self.relu2 = tf.keras.layers.Activation("relu", name="relu2")
# don't forget to set use_bias=False when using batchnorm
@@ -103,16 +105,22 @@ class DenseBlock(tf.keras.Model):
bottleneck,
weight_decay=1e-4,
dropout_rate=0,
+ **kwargs,
):
- super(DenseBlock, self).__init__()
+ super().__init__(**kwargs)
self.num_layers = num_layers
self.axis = -1 if data_format == "channels_last" else 1
self.blocks = []
- for _ in range(int(self.num_layers)):
+ for i in range(int(self.num_layers)):
self.blocks.append(
ConvBlock(
- growth_rate, data_format, bottleneck, weight_decay, dropout_rate
+ growth_rate,
+ data_format,
+ bottleneck,
+ weight_decay,
+ dropout_rate,
+ name=f"conv_block_{i+1}",
)
)
@@ -134,8 +142,10 @@ class TransitionBlock(tf.keras.Model):
dropout_rate: dropout rate.
"""
- def __init__(self, num_filters, data_format, weight_decay=1e-4, dropout_rate=0):
- super(TransitionBlock, self).__init__()
+ def __init__(
+ self, num_filters, data_format, weight_decay=1e-4, dropout_rate=0, **kwargs
+ ):
+ super().__init__(**kwargs)
axis = -1 if data_format == "channels_last" else 1
self.norm = tf.keras.layers.BatchNormalization(axis=axis, name="norm")
@@ -200,8 +210,10 @@ class DenseNet(tf.keras.Model):
dropout_rate=0,
pool_initial=False,
include_top=True,
+ name="DenseNet",
+ **kwargs,
):
- super(DenseNet, self).__init__()
+ super().__init__(name=name, **kwargs)
self.depth_of_model = depth_of_model
self.growth_rate = growth_rate
self.num_of_blocks = num_of_blocks
@@ -302,6 +314,7 @@ class DenseNet(tf.keras.Model):
self.bottleneck,
self.weight_decay,
self.dropout_rate,
+ name=f"dense_block_{i+1}",
)
)
if i + 1 < self.num_of_blocks:
@@ -311,6 +324,7 @@ class DenseNet(tf.keras.Model):
self.data_format,
self.weight_decay,
self.dropout_rate,
+ name=f"transition_block_{i+1}",
)
)
@@ -408,15 +422,15 @@ class DeepPixBiS(tf.keras.Model):
tf.keras.layers.Conv2D(
filters=1,
kernel_size=1,
- name="dec",
kernel_initializer="he_normal",
kernel_regularizer=l2(weight_decay),
data_format=data_format,
+ name="dec",
),
tf.keras.layers.Flatten(
data_format=data_format, name="Pixel_Logits_Flatten"
),
- tf.keras.layers.Activation("sigmoid"),
+ tf.keras.layers.Activation("sigmoid", name="activation"),
]
def call(self, x, training=None):
--
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