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Converted slim models to TF 1.0 and fixed a few issues
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@nealwu
nealwu committed Mar 14, 2017
1 parent f21c427 commit a9d0e6e
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Showing 18 changed files with 97 additions and 96 deletions.
15 changes: 6 additions & 9 deletions slim/deployment/model_deploy.py
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Original file line number Diff line number Diff line change
Expand Up @@ -232,11 +232,9 @@ def _gather_clone_loss(clone, num_clones, regularization_losses):
sum_loss = tf.add_n(all_losses)
# Add the summaries out of the clone device block.
if clone_loss is not None:
tf.scalar_summary(clone.scope + '/clone_loss', clone_loss,
name='clone_loss')
tf.summary.scalar('clone_loss', clone_loss)
if regularization_loss is not None:
tf.scalar_summary('regularization_loss', regularization_loss,
name='regularization_loss')
tf.summary.scalar('regularization_loss', regularization_loss)
return sum_loss


Expand Down Expand Up @@ -404,12 +402,11 @@ def deploy(config,

if total_loss is not None:
# Add total_loss to summary.
summaries.add(tf.scalar_summary('total_loss', total_loss,
name='total_loss'))
summaries.add(tf.summary.scalar('total_loss', total_loss))

if summaries:
# Merge all summaries together.
summary_op = tf.merge_summary(list(summaries), name='summary_op')
summary_op = tf.summary.merge(list(summaries), name='summary_op')
else:
summary_op = None

Expand Down Expand Up @@ -467,9 +464,9 @@ def _add_gradients_summaries(grads_and_vars):
grad_values = grad.values
else:
grad_values = grad
summaries.append(tf.histogram_summary(var.op.name + ':gradient',
summaries.append(tf.summary.histogram(var.op.name + ':gradient',
grad_values))
summaries.append(tf.histogram_summary(var.op.name + ':gradient_norm',
summaries.append(tf.summary.histogram(var.op.name + ':gradient_norm',
tf.global_norm([grad_values])))
else:
tf.logging.info('Var %s has no gradient', var.op.name)
Expand Down
2 changes: 1 addition & 1 deletion slim/eval_image_classifier.py
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Original file line number Diff line number Diff line change
Expand Up @@ -160,7 +160,7 @@ def main(_):
# Print the summaries to screen.
for name, value in names_to_values.iteritems():
summary_name = 'eval/%s' % name
op = tf.scalar_summary(summary_name, value, collections=[])
op = tf.summary.scalar(summary_name, value, collections=[])
op = tf.Print(op, [value], summary_name)
tf.add_to_collection(tf.GraphKeys.SUMMARIES, op)

Expand Down
2 changes: 1 addition & 1 deletion slim/nets/alexnet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -113,7 +113,7 @@ def alexnet_v2(inputs,
net = slim.conv2d(net, num_classes, [1, 1],
activation_fn=None,
normalizer_fn=None,
biases_initializer=tf.zeros_initializer,
biases_initializer=tf.zeros_initializer(),
scope='fc8')

# Convert end_points_collection into a end_point dict.
Expand Down
2 changes: 1 addition & 1 deletion slim/nets/cifarnet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -77,7 +77,7 @@ def cifarnet(images, num_classes=10, is_training=False,
net = slim.fully_connected(net, 192, scope='fc4')
end_points['fc4'] = net
logits = slim.fully_connected(net, num_classes,
biases_initializer=tf.zeros_initializer,
biases_initializer=tf.zeros_initializer(),
weights_initializer=trunc_normal(1/192.0),
weights_regularizer=None,
activation_fn=None,
Expand Down
18 changes: 9 additions & 9 deletions slim/nets/inception_v1.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -93,7 +93,7 @@ def inception_v1_base(inputs,
with tf.variable_scope('Branch_3'):
branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, 32, [1, 1], scope='Conv2d_0b_1x1')
net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if final_endpoint == end_point: return net, end_points

Expand All @@ -110,7 +110,7 @@ def inception_v1_base(inputs,
with tf.variable_scope('Branch_3'):
branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if final_endpoint == end_point: return net, end_points

Expand All @@ -132,7 +132,7 @@ def inception_v1_base(inputs,
with tf.variable_scope('Branch_3'):
branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if final_endpoint == end_point: return net, end_points

Expand All @@ -149,7 +149,7 @@ def inception_v1_base(inputs,
with tf.variable_scope('Branch_3'):
branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if final_endpoint == end_point: return net, end_points

Expand All @@ -166,7 +166,7 @@ def inception_v1_base(inputs,
with tf.variable_scope('Branch_3'):
branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if final_endpoint == end_point: return net, end_points

Expand All @@ -183,7 +183,7 @@ def inception_v1_base(inputs,
with tf.variable_scope('Branch_3'):
branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if final_endpoint == end_point: return net, end_points

Expand All @@ -200,7 +200,7 @@ def inception_v1_base(inputs,
with tf.variable_scope('Branch_3'):
branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1')
net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if final_endpoint == end_point: return net, end_points

Expand All @@ -222,7 +222,7 @@ def inception_v1_base(inputs,
with tf.variable_scope('Branch_3'):
branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1')
net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if final_endpoint == end_point: return net, end_points

Expand All @@ -239,7 +239,7 @@ def inception_v1_base(inputs,
with tf.variable_scope('Branch_3'):
branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1')
net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if final_endpoint == end_point: return net, end_points
raise ValueError('Unknown final endpoint %s' % final_endpoint)
Expand Down
20 changes: 10 additions & 10 deletions slim/nets/inception_v2.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -145,7 +145,7 @@ def inception_v2_base(inputs,
branch_3, depth(32), [1, 1],
weights_initializer=trunc_normal(0.1),
scope='Conv2d_0b_1x1')
net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points
# 28 x 28 x 256
Expand Down Expand Up @@ -175,7 +175,7 @@ def inception_v2_base(inputs,
branch_3, depth(64), [1, 1],
weights_initializer=trunc_normal(0.1),
scope='Conv2d_0b_1x1')
net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points
# 28 x 28 x 320
Expand All @@ -200,7 +200,7 @@ def inception_v2_base(inputs,
with tf.variable_scope('Branch_2'):
branch_2 = slim.max_pool2d(
net, [3, 3], stride=2, scope='MaxPool_1a_3x3')
net = tf.concat(3, [branch_0, branch_1, branch_2])
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2])
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points
# 14 x 14 x 576
Expand Down Expand Up @@ -230,7 +230,7 @@ def inception_v2_base(inputs,
branch_3, depth(128), [1, 1],
weights_initializer=trunc_normal(0.1),
scope='Conv2d_0b_1x1')
net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points
# 14 x 14 x 576
Expand Down Expand Up @@ -260,7 +260,7 @@ def inception_v2_base(inputs,
branch_3, depth(128), [1, 1],
weights_initializer=trunc_normal(0.1),
scope='Conv2d_0b_1x1')
net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points
# 14 x 14 x 576
Expand Down Expand Up @@ -290,7 +290,7 @@ def inception_v2_base(inputs,
branch_3, depth(96), [1, 1],
weights_initializer=trunc_normal(0.1),
scope='Conv2d_0b_1x1')
net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points

Expand Down Expand Up @@ -321,7 +321,7 @@ def inception_v2_base(inputs,
branch_3, depth(96), [1, 1],
weights_initializer=trunc_normal(0.1),
scope='Conv2d_0b_1x1')
net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points
# 14 x 14 x 576
Expand All @@ -346,7 +346,7 @@ def inception_v2_base(inputs,
with tf.variable_scope('Branch_2'):
branch_2 = slim.max_pool2d(net, [3, 3], stride=2,
scope='MaxPool_1a_3x3')
net = tf.concat(3, [branch_0, branch_1, branch_2])
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2])
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points
# 7 x 7 x 1024
Expand Down Expand Up @@ -376,7 +376,7 @@ def inception_v2_base(inputs,
branch_3, depth(128), [1, 1],
weights_initializer=trunc_normal(0.1),
scope='Conv2d_0b_1x1')
net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points

Expand Down Expand Up @@ -407,7 +407,7 @@ def inception_v2_base(inputs,
branch_3, depth(128), [1, 1],
weights_initializer=trunc_normal(0.1),
scope='Conv2d_0b_1x1')
net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points
raise ValueError('Unknown final endpoint %s' % final_endpoint)
Expand Down
30 changes: 15 additions & 15 deletions slim/nets/inception_v3.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -158,7 +158,7 @@ def inception_v3_base(inputs,
branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, depth(32), [1, 1],
scope='Conv2d_0b_1x1')
net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points

Expand All @@ -182,7 +182,7 @@ def inception_v3_base(inputs,
branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, depth(64), [1, 1],
scope='Conv2d_0b_1x1')
net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points

Expand All @@ -205,7 +205,7 @@ def inception_v3_base(inputs,
branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, depth(64), [1, 1],
scope='Conv2d_0b_1x1')
net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points

Expand All @@ -224,7 +224,7 @@ def inception_v3_base(inputs,
with tf.variable_scope('Branch_2'):
branch_2 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID',
scope='MaxPool_1a_3x3')
net = tf.concat(3, [branch_0, branch_1, branch_2])
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2])
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points

Expand Down Expand Up @@ -253,7 +253,7 @@ def inception_v3_base(inputs,
branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, depth(192), [1, 1],
scope='Conv2d_0b_1x1')
net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points

Expand Down Expand Up @@ -282,7 +282,7 @@ def inception_v3_base(inputs,
branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, depth(192), [1, 1],
scope='Conv2d_0b_1x1')
net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points
# mixed_6: 17 x 17 x 768.
Expand Down Expand Up @@ -310,7 +310,7 @@ def inception_v3_base(inputs,
branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, depth(192), [1, 1],
scope='Conv2d_0b_1x1')
net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points

Expand Down Expand Up @@ -339,7 +339,7 @@ def inception_v3_base(inputs,
branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, depth(192), [1, 1],
scope='Conv2d_0b_1x1')
net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points

Expand All @@ -361,7 +361,7 @@ def inception_v3_base(inputs,
with tf.variable_scope('Branch_2'):
branch_2 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID',
scope='MaxPool_1a_3x3')
net = tf.concat(3, [branch_0, branch_1, branch_2])
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2])
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points
# mixed_9: 8 x 8 x 2048.
Expand All @@ -371,21 +371,21 @@ def inception_v3_base(inputs,
branch_0 = slim.conv2d(net, depth(320), [1, 1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, depth(384), [1, 1], scope='Conv2d_0a_1x1')
branch_1 = tf.concat(3, [
branch_1 = tf.concat(axis=3, values=[
slim.conv2d(branch_1, depth(384), [1, 3], scope='Conv2d_0b_1x3'),
slim.conv2d(branch_1, depth(384), [3, 1], scope='Conv2d_0b_3x1')])
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, depth(448), [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(
branch_2, depth(384), [3, 3], scope='Conv2d_0b_3x3')
branch_2 = tf.concat(3, [
branch_2 = tf.concat(axis=3, values=[
slim.conv2d(branch_2, depth(384), [1, 3], scope='Conv2d_0c_1x3'),
slim.conv2d(branch_2, depth(384), [3, 1], scope='Conv2d_0d_3x1')])
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(
branch_3, depth(192), [1, 1], scope='Conv2d_0b_1x1')
net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points

Expand All @@ -396,21 +396,21 @@ def inception_v3_base(inputs,
branch_0 = slim.conv2d(net, depth(320), [1, 1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, depth(384), [1, 1], scope='Conv2d_0a_1x1')
branch_1 = tf.concat(3, [
branch_1 = tf.concat(axis=3, values=[
slim.conv2d(branch_1, depth(384), [1, 3], scope='Conv2d_0b_1x3'),
slim.conv2d(branch_1, depth(384), [3, 1], scope='Conv2d_0c_3x1')])
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, depth(448), [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(
branch_2, depth(384), [3, 3], scope='Conv2d_0b_3x3')
branch_2 = tf.concat(3, [
branch_2 = tf.concat(axis=3, values=[
slim.conv2d(branch_2, depth(384), [1, 3], scope='Conv2d_0c_1x3'),
slim.conv2d(branch_2, depth(384), [3, 1], scope='Conv2d_0d_3x1')])
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(
branch_3, depth(192), [1, 1], scope='Conv2d_0b_1x1')
net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if end_point == final_endpoint: return net, end_points
raise ValueError('Unknown final endpoint %s' % final_endpoint)
Expand Down
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