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Commit fffc4590 authored by Tiago de Freitas Pereira's avatar Tiago de Freitas Pereira
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Shutting down batch norm variables when trainable is equals to False for InceptionResnetv1

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bob/learn/tensorflow/network/InceptionResnetV1.py
+ 175
− 116
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......@@ -303,7 +303,7 @@ def inception_resnet_v1_batch_norm(inputs,
# force in-place updates of mean and variance estimates
'updates_collections': None,
# Moving averages ends up in the trainable variables collection
'variables_collections': [tf.GraphKeys.TRAINABLE_VARIABLES],
'variables_collections': [tf.GraphKeys.TRAINABLE_VARIABLES if mode==tf.estimator.ModeKeys.TRAIN else None],
}
with slim.arg_scope(
......@@ -363,7 +363,7 @@ def inception_resnet_v1(inputs,
with tf.variable_scope(scope, 'InceptionResnetV1', [inputs], reuse=reuse):
with slim.arg_scope(
[slim.batch_norm, slim.dropout],
[slim.dropout],
is_training=(mode == tf.estimator.ModeKeys.TRAIN)):
with slim.arg_scope(
......@@ -373,37 +373,53 @@ def inception_resnet_v1(inputs,
# 149 x 149 x 32
name = "Conv2d_1a_3x3"
trainable = is_trainable(name, trainable_variables)
net = slim.conv2d(
inputs,
32,
3,
stride=2,
padding='VALID',
scope=name,
trainable=trainable,
reuse=reuse)
end_points[name] = net
trainable = is_trainable(name, trainable_variables, mode=mode)
with slim.arg_scope(
[slim.batch_norm],
is_training=(mode == tf.estimator.ModeKeys.TRAIN),
trainable = trainable):
net = slim.conv2d(
inputs,
32,
3,
stride=2,
padding='VALID',
scope=name,
trainable=trainable,
reuse=reuse)
end_points[name] = net
# 147 x 147 x 32
name = "Conv2d_2a_3x3"
trainable = is_trainable(name, trainable_variables)
net = slim.conv2d(
net,
32,
3,
padding='VALID',
scope=name,
trainable=trainable,
reuse=reuse)
end_points[name] = net
trainable = is_trainable(name, trainable_variables, mode=mode)
with slim.arg_scope(
[slim.batch_norm],
is_training=(mode == tf.estimator.ModeKeys.TRAIN),
trainable = trainable):
net = slim.conv2d(
net,
32,
3,
padding='VALID',
scope=name,
trainable=trainable,
reuse=reuse)
end_points[name] = net
# 147 x 147 x 64
name = "Conv2d_2b_3x3"
trainable = is_trainable(name, trainable_variables)
net = slim.conv2d(
net, 64, 3, scope=name, trainable=trainable, reuse=reuse)
end_points[name] = net
trainable = is_trainable(name, trainable_variables, mode=mode)
with slim.arg_scope(
[slim.batch_norm],
is_training=(mode == tf.estimator.ModeKeys.TRAIN),
trainable = trainable):
net = slim.conv2d(
net, 64, 3, scope=name, trainable=trainable, reuse=reuse)
end_points[name] = net
# 73 x 73 x 64
net = slim.max_pool2d(
net, 3, stride=2, padding='VALID', scope='MaxPool_3a_3x3')
......@@ -411,110 +427,148 @@ def inception_resnet_v1(inputs,
# 73 x 73 x 80
name = "Conv2d_3b_1x1"
trainable = is_trainable(name, trainable_variables)
net = slim.conv2d(
net,
80,
1,
padding='VALID',
scope=name,
trainable=trainable,
reuse=reuse)
end_points[name] = net
trainable = is_trainable(name, trainable_variables, mode=mode)
with slim.arg_scope(
[slim.batch_norm],
is_training=(mode == tf.estimator.ModeKeys.TRAIN),
trainable = trainable):
net = slim.conv2d(
net,
80,
1,
padding='VALID',
scope=name,
trainable=trainable,
reuse=reuse)
end_points[name] = net
# 71 x 71 x 192
name = "Conv2d_4a_3x3"
trainable = is_trainable(name, trainable_variables)
net = slim.conv2d(
net,
192,
3,
padding='VALID',
scope=name,
trainable=trainable,
reuse=reuse)
end_points[name] = net
trainable = is_trainable(name, trainable_variables, mode=mode)
with slim.arg_scope(
[slim.batch_norm],
is_training=(mode == tf.estimator.ModeKeys.TRAIN),
trainable = trainable):
net = slim.conv2d(
net,
192,
3,
padding='VALID',
scope=name,
trainable=trainable,
reuse=reuse)
end_points[name] = net
# 35 x 35 x 256
name = "Conv2d_4b_3x3"
trainable = is_trainable(name, trainable_variables)
net = slim.conv2d(
net,
256,
3,
stride=2,
padding='VALID',
scope=name,
trainable=trainable,
reuse=reuse)
end_points[name] = net
trainable = is_trainable(name, trainable_variables, mode=mode)
with slim.arg_scope(
[slim.batch_norm],
is_training=(mode == tf.estimator.ModeKeys.TRAIN),
trainable = trainable):
net = slim.conv2d(
net,
256,
3,
stride=2,
padding='VALID',
scope=name,
trainable=trainable,
reuse=reuse)
end_points[name] = net
# 5 x Inception-resnet-A
name = "block35"
trainable = is_trainable(name, trainable_variables)
net = slim.repeat(
net,
5,
block35,
scale=0.17,
trainable_variables=trainable,
reuse=reuse)
end_points[name] = net
# Reduction-A
name = "Mixed_6a"
trainable = is_trainable(name, trainable_variables)
with tf.variable_scope(name):
net = reduction_a(
trainable = is_trainable(name, trainable_variables, mode=mode)
with slim.arg_scope(
[slim.batch_norm],
is_training=(mode == tf.estimator.ModeKeys.TRAIN),
trainable = trainable):
net = slim.repeat(
net,
192,
192,
256,
384,
5,
block35,
scale=0.17,
trainable_variables=trainable,
reuse=reuse)
end_points[name] = net
end_points[name] = net
# Reduction-A
name = "Mixed_6a"
trainable = is_trainable(name, trainable_variables, mode=mode)
with slim.arg_scope(
[slim.batch_norm],
is_training=(mode == tf.estimator.ModeKeys.TRAIN),
trainable = trainable):
with tf.variable_scope(name):
net = reduction_a(
net,
192,
192,
256,
384,
trainable_variables=trainable,
reuse=reuse)
end_points[name] = net
# 10 x Inception-Resnet-B
name = "block17"
trainable = is_trainable(name, trainable_variables)
net = slim.repeat(
net,
10,
block17,
scale=0.10,
trainable_variables=trainable,
reuse=reuse)
end_points[name] = net
trainable = is_trainable(name, trainable_variables, mode=mode)
with slim.arg_scope(
[slim.batch_norm],
is_training=(mode == tf.estimator.ModeKeys.TRAIN),
trainable = trainable):
net = slim.repeat(
net,
10,
block17,
scale=0.10,
trainable_variables=trainable,
reuse=reuse)
end_points[name] = net
# Reduction-B
name = "Mixed_7a"
trainable = is_trainable(name, trainable_variables)
with tf.variable_scope(name):
net = reduction_b(
net, trainable_variables=trainable, reuse=reuse)
end_points[name] = net
trainable = is_trainable(name, trainable_variables, mode=mode)
with slim.arg_scope(
[slim.batch_norm],
is_training=(mode == tf.estimator.ModeKeys.TRAIN),
trainable = trainable):
with tf.variable_scope(name):
net = reduction_b(
net, trainable_variables=trainable, reuse=reuse)
end_points[name] = net
# 5 x Inception-Resnet-C
name = "block8"
trainable = is_trainable(name, trainable_variables)
net = slim.repeat(
net,
5,
block8,
scale=0.20,
trainable_variables=trainable,
reuse=reuse)
end_points[name] = net
trainable = is_trainable(name, trainable_variables, mode=mode)
with slim.arg_scope(
[slim.batch_norm],
is_training=(mode == tf.estimator.ModeKeys.TRAIN),
trainable = trainable):
net = slim.repeat(
net,
5,
block8,
scale=0.20,
trainable_variables=trainable,
reuse=reuse)
end_points[name] = net
name = "Mixed_8b"
trainable = is_trainable(name, trainable_variables)
net = block8(
net,
activation_fn=None,
trainable_variables=trainable,
reuse=reuse)
end_points[name] = net
trainable = is_trainable(name, trainable_variables, mode=mode)
with slim.arg_scope(
[slim.batch_norm],
is_training=(mode == tf.estimator.ModeKeys.TRAIN),
trainable = trainable):
net = block8(
net,
activation_fn=None,
trainable_variables=trainable,
reuse=reuse)
end_points[name] = net
with tf.variable_scope('Logits'):
end_points['PrePool'] = net
......@@ -535,13 +589,18 @@ def inception_resnet_v1(inputs,
end_points['PreLogitsFlatten'] = net
name = "Bottleneck"
trainable = is_trainable(name, trainable_variables)
net = slim.fully_connected(
net,
bottleneck_layer_size,
activation_fn=None,
scope=name,
reuse=reuse,
trainable=trainable)
trainable = is_trainable(name, trainable_variables, mode=mode)
with slim.arg_scope(
[slim.batch_norm],
is_training=(mode == tf.estimator.ModeKeys.TRAIN),
trainable = trainable):
net = slim.fully_connected(
net,
bottleneck_layer_size,
activation_fn=None,
scope=name,
reuse=reuse,
trainable=trainable)
end_points[name] = net
return net, end_points
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