def testModelHasExpectedNumberOfParameters(self):
batch_size = 5
height, width = 299, 299
inputs = random_ops.random_uniform((batch_size, height, width, 3))
with arg_scope(inception_v3.inception_v3_arg_scope()):
inception_v3.inception_v3_base(inputs)
total_params, _ = model_analyzer.analyze_vars(
variables_lib.get_model_variables())
self.assertAlmostEqual(21802784, total_params)
def testBuildAndCheckAllEndPointsUptoMixed7c(self):
batch_size = 5
height, width = 299, 299
inputs = random_ops.random_uniform((batch_size, height, width, 3))
_, end_points = inception_v3.inception_v3_base(
inputs, final_endpoint='Mixed_7c')
endpoints_shapes = {
'Conv2d_1a_3x3': [batch_size, 149, 149, 32],
'Conv2d_2a_3x3': [batch_size, 147, 147, 32],
'Conv2d_2b_3x3': [batch_size, 147, 147, 64],
'MaxPool_3a_3x3': [batch_size, 73, 73, 64],
'Conv2d_3b_1x1': [batch_size, 73, 73, 80],
'Conv2d_4a_3x3': [batch_size, 71, 71, 192],
'MaxPool_5a_3x3': [batch_size, 35, 35, 192],
'Mixed_5b': [batch_size, 35, 35, 256],
'Mixed_5c': [batch_size, 35, 35, 288],
'Mixed_5d': [batch_size, 35, 35, 288],
'Mixed_6a': [batch_size, 17, 17, 768],
'Mixed_6b': [batch_size, 17, 17, 768],
'Mixed_6c': [batch_size, 17, 17, 768],
'Mixed_6d': [batch_size, 17, 17, 768],
'Mixed_6e': [batch_size, 17, 17, 768],
'Mixed_7a': [batch_size, 8, 8, 1280],
'Mixed_7b': [batch_size, 8, 8, 2048],
'Mixed_7c': [batch_size, 8, 8, 2048]
}
self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
for endpoint_name in endpoints_shapes:
expected_shape = endpoints_shapes[endpoint_name]
self.assertTrue(endpoint_name in end_points)
self.assertListEqual(
end_points[endpoint_name].get_shape().as_list(),
expected_shape)
def inception_v3(nlabels, images):
batch_norm_params = {
"is_training": False, "trainable": True, "decay": 0.9997,
"epsilon": 0.001,
"variables_collections": {
"beta": None,
"gamma": None,
"moving_mean": ["moving_vars"],
"moving_variance": ["moving_vars"],
}
}
weight_decay = 0.00004
stddev = 0.1
weights_regularizer = tf_slim.l2_regularizer(weight_decay)
args_for_scope = (
dict(list_ops_or_scope=[tf_slim.layers.conv2d, tf_slim.layers.fully_connected],
weights_regularizer=weights_regularizer, trainable=True),
dict(list_ops_or_scope=[tf_slim.layers.conv2d],
weights_initializer=tf1.truncated_normal_initializer(stddev=stddev),
activation_fn=tf1.nn.relu,
normalizer_fn=tf_slim.layers.batch_norm,
normalizer_params=batch_norm_params),
)
with tf1.variable_scope("InceptionV3", "InceptionV3", [images]) as scope, \
tf_slim.arg_scope(**args_for_scope[0]), \
tf_slim.arg_scope(**args_for_scope[1]):
net, end_points = inception_v3_base(images, scope=scope)
with tf1.variable_scope("logits"):
shape = net.get_shape()
net = tf_slim.layers.avg_pool2d(net, shape[1:3], padding="VALID",
scope="pool")
net = tf1.nn.dropout(net, 1, name='droplast')
net = tf_slim.layers.flatten(net, scope="flatten")
with tf1.variable_scope('output') as scope:
weights = tf1.Variable(
tf1.truncated_normal([2048, nlabels], mean=0.0, stddev=0.01),
name='weights')
biases = tf1.Variable(
tf1.constant(0.0, shape=[nlabels], dtype=tf1.float32), name='biases')
output = tf1.add(tf1.matmul(net, weights), biases, name=scope.name)
tensor_name = re.sub('tower_[0-9]*/', '', output.op.name)
tf1.summary.histogram(tensor_name + '/activations', output)
tf1.summary.scalar(tensor_name + '/sparsity', tf1.nn.zero_fraction(output))
return output
def testBuildBaseNetwork(self):
batch_size = 5
height, width = 299, 299
inputs = random_ops.random_uniform((batch_size, height, width, 3))
final_endpoint, end_points = inception_v3.inception_v3_base(inputs)
self.assertTrue(
final_endpoint.op.name.startswith('InceptionV3/Mixed_7c'))
self.assertListEqual(final_endpoint.get_shape().as_list(),
[batch_size, 8, 8, 2048])
expected_endpoints = [
'Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3',
'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3',
'MaxPool_5a_3x3', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d', 'Mixed_6a',
'Mixed_6b', 'Mixed_6c', 'Mixed_6d', 'Mixed_6e', 'Mixed_7a',
'Mixed_7b', 'Mixed_7c'
]
self.assertItemsEqual(end_points.keys(), expected_endpoints)
def testBuildOnlyUptoFinalEndpoint(self):
batch_size = 5
height, width = 299, 299
endpoints = [
'Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3',
'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3',
'MaxPool_5a_3x3', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d', 'Mixed_6a',
'Mixed_6b', 'Mixed_6c', 'Mixed_6d', 'Mixed_6e', 'Mixed_7a',
'Mixed_7b', 'Mixed_7c'
]
for index, endpoint in enumerate(endpoints):
with ops.Graph().as_default():
inputs = random_ops.random_uniform(
(batch_size, height, width, 3))
out_tensor, end_points = inception_v3.inception_v3_base(
inputs, final_endpoint=endpoint)
self.assertTrue(
out_tensor.op.name.startswith('InceptionV3/' + endpoint))
self.assertItemsEqual(endpoints[:index + 1], end_points)
def style_prediction(style_input_,
activation_names,
activation_depths,
is_training=True,
trainable=True,
inception_end_point='Mixed_6e',
style_prediction_bottleneck=100,
reuse=None):
"""Maps style images to the style embeddings (beta and gamma parameters).
Args:
style_input_: Tensor. Batch of style input images.
activation_names: string. Scope names of the activations of the transformer
network which are used to apply style normalization.
activation_depths: Shapes of the activations of the transformer network
which are used to apply style normalization.
is_training: bool. Is it training phase or not?
trainable: bool. Should the parameters be marked as trainable?
inception_end_point: string. Specifies the endpoint to construct the
inception_v3 network up to. This network is part of the style prediction
network.
style_prediction_bottleneck: int. Specifies the bottleneck size in the
number of parameters of the style embedding.
reuse: bool. Whether to reuse model parameters. Defaults to False.
Returns:
Tensor for the output of the style prediction network, Tensor for the
bottleneck of style parameters of the style prediction network.
"""
with tf.name_scope('style_prediction') and tf.variable_scope(
tf.get_variable_scope(), reuse=reuse):
with slim.arg_scope(_inception_v3_arg_scope(is_training=is_training)):
with slim.arg_scope(
[slim.conv2d, slim.fully_connected, slim.batch_norm],
trainable=trainable):
with slim.arg_scope([slim.batch_norm, slim.dropout],
is_training=is_training):
_, end_points = inception_v3.inception_v3_base(
style_input_,
scope='InceptionV3',
final_endpoint=inception_end_point)
# Shape of feat_convlayer is (batch_size, ?, ?, depth).
# For Mixed_6e end point, depth is 768, for input image size of 256x265
# width and height are 14x14.
feat_convlayer = end_points[inception_end_point]
with tf.name_scope('bottleneck'):
# (batch_size, 1, 1, depth).
bottleneck_feat = tf.reduce_mean(feat_convlayer,
axis=[1, 2],
keep_dims=True)
if style_prediction_bottleneck > 0:
with slim.arg_scope([slim.conv2d],
activation_fn=None,
normalizer_fn=None,
trainable=trainable):
# (batch_size, 1, 1, style_prediction_bottleneck).
bottleneck_feat = slim.conv2d(bottleneck_feat,
style_prediction_bottleneck,
[1, 1])
style_params = {}
with tf.variable_scope('style_params'):
for i in range(len(activation_depths)):
with tf.variable_scope(activation_names[i], reuse=reuse):
with slim.arg_scope([slim.conv2d],
activation_fn=None,
normalizer_fn=None,
trainable=trainable):
# Computing beta parameter of the style normalization for the
# activation_names[i] layer of the style transformer network.
# (batch_size, 1, 1, activation_depths[i])
beta = slim.conv2d(bottleneck_feat,
activation_depths[i], [1, 1])
# (batch_size, activation_depths[i])
beta = tf.squeeze(beta, [1, 2], name='SpatialSqueeze')
style_params['{}/beta'.format(
activation_names[i])] = beta
# Computing gamma parameter of the style normalization for the
# activation_names[i] layer of the style transformer network.
# (batch_size, 1, 1, activation_depths[i])
gamma = slim.conv2d(bottleneck_feat,
activation_depths[i], [1, 1])
# (batch_size, activation_depths[i])
gamma = tf.squeeze(gamma, [1, 2],
name='SpatialSqueeze')
style_params['{}/gamma'.format(
activation_names[i])] = gamma
return style_params, bottleneck_feat
def inception_v3(images,
trainable=True,
is_training=True,
weight_decay=0.00004,
stddev=0.1,
dropout_keep_prob=0.8,
use_batch_norm=True,
batch_norm_params=None,
add_summaries=True,
scope="InceptionV3"):
"""Builds an Inception V3 subgraph for image embeddings.
Args:
images: A float32 Tensor of shape [batch, height, width, channels].
trainable: Whether the inception submodel should be trainable or not.
is_training: Boolean indicating training mode or not.
weight_decay: Coefficient for weight regularization.
stddev: The standard deviation of the trunctated normal weight initializer.
dropout_keep_prob: Dropout keep probability.
use_batch_norm: Whether to use batch normalization.
batch_norm_params: Parameters for batch normalization. See
tf.contrib.layers.batch_norm for details.
add_summaries: Whether to add activation summaries.
scope: Optional Variable scope.
Returns:
end_points: A dictionary of activations from inception_v3 layers.
"""
# Only consider the inception model to be in training mode if it's trainable.
is_inception_model_training = trainable and is_training
if use_batch_norm:
# Default parameters for batch normalization.
if not batch_norm_params:
batch_norm_params = {
"is_training": is_inception_model_training,
"trainable": trainable,
# Decay for the moving averages.
"decay": 0.9997,
# Epsilon to prevent 0s in variance.
"epsilon": 0.001,
# Collection containing the moving mean and moving variance.
"variables_collections": {
"beta": None,
"gamma": None,
"moving_mean": ["moving_vars"],
"moving_variance": ["moving_vars"],
}
}
else:
batch_norm_params = None
if trainable:
weights_regularizer = tf.contrib.layers.l2_regularizer(weight_decay)
else:
weights_regularizer = None
with tf.compat.v1.variable_scope(scope, "InceptionV3", [images]) as scope:
with slim.arg_scope([slim.conv2d, slim.fully_connected],
weights_regularizer=weights_regularizer,
trainable=trainable):
with slim.arg_scope([slim.conv2d],
weights_initializer=tf.compat.v1.
truncated_normal_initializer(stddev=stddev),
activation_fn=tf.nn.relu,
normalizer_fn=slim.batch_norm,
normalizer_params=batch_norm_params):
net, end_points = inception_v3_base(images, scope=scope)
with tf.compat.v1.variable_scope("logits"):
shape = net.get_shape()
net = slim.avg_pool2d(net,
shape[1:3],
padding="VALID",
scope="pool")
net = slim.dropout(net,
keep_prob=dropout_keep_prob,
is_training=is_inception_model_training,
scope="dropout")
net = slim.flatten(net, scope="flatten")
# Add summaries.
if add_summaries:
for v in end_points.values():
slim.summarize_activation(v)
return net