def bottleneck(inputs,
depth,
depth_bottleneck,
stride,
rate=1,
outputs_collections=None,
scope=None):
"""Bottleneck residual unit variant with BN before convolutions.
This is the full preactivation residual unit variant proposed in [2]. See
Fig. 1(b) of [2] for its definition. Note that we use here the bottleneck
variant which has an extra bottleneck layer.
When putting together two consecutive ResNet blocks that use this unit, one
should use stride = 2 in the last unit of the first block.
Args:
inputs: A tensor of size [batch, height, width, channels].
depth: The depth of the ResNet unit output.
depth_bottleneck: The depth of the bottleneck layers.
stride: The ResNet unit's stride. Determines the amount of downsampling of
the units output compared to its input.
rate: An integer, rate for atrous convolution.
outputs_collections: Collection to add the ResNet unit output.
scope: Optional variable_scope.
Returns:
The ResNet unit's output.
"""
with variable_scope.variable_scope(scope, 'bottleneck_v2', [inputs]) as sc:
depth_in = utils.last_dimension(inputs.get_shape(), min_rank=4)
preact = layers.batch_norm(
inputs, activation_fn=nn_ops.relu, scope='preact')
if depth == depth_in:
shortcut = resnet_utils.subsample(inputs, stride, 'shortcut')
else:
shortcut = layers_lib.conv2d(
preact,
depth, [1, 1],
stride=stride,
normalizer_fn=None,
activation_fn=None,
scope='shortcut')
residual = layers_lib.conv2d(
preact, depth_bottleneck, [1, 1], stride=1, scope='conv1')
residual = resnet_utils.conv2d_same(
residual, depth_bottleneck, 3, stride, rate=rate, scope='conv2')
residual = layers_lib.conv2d(
residual,
depth, [1, 1],
stride=1,
normalizer_fn=None,
activation_fn=None,
scope='conv3')
output = shortcut + residual
return utils.collect_named_outputs(outputs_collections, sc.name, output)
def conv2d_same(inputs, num_outputs, kernel_size, stride, rate=1, scope=None):
"""Strided 2-D convolution with 'SAME' padding.
When stride > 1, then we do explicit zero-padding, followed by conv2d with
'VALID' padding.
Note that
net = conv2d_same(inputs, num_outputs, 3, stride=stride)
is equivalent to
net = tf.contrib.layers.conv2d(inputs, num_outputs, 3, stride=1,
padding='SAME')
net = subsample(net, factor=stride)
whereas
net = tf.contrib.layers.conv2d(inputs, num_outputs, 3, stride=stride,
padding='SAME')
is different when the input's height or width is even, which is why we add the
current function. For more details, see ResnetUtilsTest.testConv2DSameEven().
Args:
inputs: A 4-D tensor of size [batch, height_in, width_in, channels].
num_outputs: An integer, the number of output filters.
kernel_size: An int with the kernel_size of the filters.
stride: An integer, the output stride.
rate: An integer, rate for atrous convolution.
scope: Scope.
Returns:
output: A 4-D tensor of size [batch, height_out, width_out, channels] with
the convolution output.
"""
if stride == 1:
return layers_lib.conv2d(inputs,
num_outputs,
kernel_size,
stride=1,
rate=rate,
padding='SAME',
scope=scope)
else:
kernel_size_effective = kernel_size + (kernel_size - 1) * (rate - 1)
pad_total = kernel_size_effective - 1
pad_beg = pad_total // 2
pad_end = pad_total - pad_beg
inputs = array_ops.pad(
inputs, [[0, 0], [pad_beg, pad_end], [pad_beg, pad_end], [0, 0]])
return layers_lib.conv2d(inputs,
num_outputs,
kernel_size,
stride=stride,
rate=rate,
padding='VALID',
scope=scope)
def testConv2DSameOdd(self):
n, n2 = 5, 3
# Input image.
x = create_test_input(1, n, n, 1)
# Convolution kernel.
w = create_test_input(1, 3, 3, 1)
w = array_ops.reshape(w, [3, 3, 1, 1])
variable_scope.get_variable('Conv/weights', initializer=w)
variable_scope.get_variable('Conv/biases', initializer=array_ops.zeros([1]))
variable_scope.get_variable_scope().reuse_variables()
y1 = layers.conv2d(x, 1, [3, 3], stride=1, scope='Conv')
y1_expected = math_ops.cast([[14, 28, 43, 58, 34],
[28, 48, 66, 84, 46],
[43, 66, 84, 102, 55],
[58, 84, 102, 120, 64],
[34, 46, 55, 64, 30]],
dtypes.float32)
y1_expected = array_ops.reshape(y1_expected, [1, n, n, 1])
y2 = resnet_utils.subsample(y1, 2)
y2_expected = math_ops.cast([[14, 43, 34],
[43, 84, 55],
[34, 55, 30]],
dtypes.float32)
y2_expected = array_ops.reshape(y2_expected, [1, n2, n2, 1])
y3 = resnet_utils.conv2d_same(x, 1, 3, stride=2, scope='Conv')
y3_expected = y2_expected
y4 = layers.conv2d(x, 1, [3, 3], stride=2, scope='Conv')
y4_expected = y2_expected
with self.cached_session() as sess:
sess.run(variables.global_variables_initializer())
self.assertAllClose(y1.eval(), y1_expected.eval())
self.assertAllClose(y2.eval(), y2_expected.eval())
self.assertAllClose(y3.eval(), y3_expected.eval())
self.assertAllClose(y4.eval(), y4_expected.eval())
def overfeat(inputs,
num_classes=1000,
is_training=True,
dropout_keep_prob=0.5,
spatial_squeeze=True,
scope='overfeat'):
"""Contains the model definition for the OverFeat network.
The definition for the network was obtained from:
OverFeat: Integrated Recognition, Localization and Detection using
Convolutional Networks
Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus and
Yann LeCun, 2014
http://arxiv.org/abs/1312.6229
Note: All the fully_connected layers have been transformed to conv2d layers.
To use in classification mode, resize input to 231x231. To use in fully
convolutional mode, set spatial_squeeze to false.
Args:
inputs: a tensor of size [batch_size, height, width, channels].
num_classes: number of predicted classes.
is_training: whether or not the model is being trained.
dropout_keep_prob: the probability that activations are kept in the dropout
layers during training.
spatial_squeeze: whether or not should squeeze the spatial dimensions of the
outputs. Useful to remove unnecessary dimensions for classification.
scope: Optional scope for the variables.
Returns:
the last op containing the log predictions and end_points dict.
"""
with variable_scope.variable_scope(scope, 'overfeat', [inputs]) as sc:
end_points_collection = sc.name + '_end_points'
# Collect outputs for conv2d, fully_connected and max_pool2d
with arg_scope(
[layers.conv2d, layers_lib.fully_connected, layers_lib.max_pool2d],
outputs_collections=end_points_collection):
net = layers.conv2d(
inputs, 64, [11, 11], 4, padding='VALID', scope='conv1')
net = layers_lib.max_pool2d(net, [2, 2], scope='pool1')
net = layers.conv2d(net, 256, [5, 5], padding='VALID', scope='conv2')
net = layers_lib.max_pool2d(net, [2, 2], scope='pool2')
net = layers.conv2d(net, 512, [3, 3], scope='conv3')
net = layers.conv2d(net, 1024, [3, 3], scope='conv4')
net = layers.conv2d(net, 1024, [3, 3], scope='conv5')
net = layers_lib.max_pool2d(net, [2, 2], scope='pool5')
with arg_scope(
[layers.conv2d],
weights_initializer=trunc_normal(0.005),
biases_initializer=init_ops.constant_initializer(0.1)):
# Use conv2d instead of fully_connected layers.
net = layers.conv2d(net, 3072, [6, 6], padding='VALID', scope='fc6')
net = layers_lib.dropout(
net, dropout_keep_prob, is_training=is_training, scope='dropout6')
net = layers.conv2d(net, 4096, [1, 1], scope='fc7')
net = layers_lib.dropout(
net, dropout_keep_prob, is_training=is_training, scope='dropout7')
net = layers.conv2d(
net,
num_classes, [1, 1],
activation_fn=None,
normalizer_fn=None,
biases_initializer=init_ops.zeros_initializer(),
scope='fc8')
# Convert end_points_collection into a end_point dict.
end_points = utils.convert_collection_to_dict(end_points_collection)
if spatial_squeeze:
net = array_ops.squeeze(net, [1, 2], name='fc8/squeezed')
end_points[sc.name + '/fc8'] = net
return net, end_points
def inception_v1(inputs,
num_classes=1000,
is_training=True,
dropout_keep_prob=0.8,
prediction_fn=layers_lib.softmax,
spatial_squeeze=True,
reuse=None,
scope='InceptionV1'):
"""Defines the Inception V1 architecture.
This architecture is defined in:
Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed,
Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich.
http://arxiv.org/pdf/1409.4842v1.pdf.
The default image size used to train this network is 224x224.
Args:
inputs: a tensor of size [batch_size, height, width, channels].
num_classes: number of predicted classes.
is_training: whether is training or not.
dropout_keep_prob: the percentage of activation values that are retained.
prediction_fn: a function to get predictions out of logits.
spatial_squeeze: if True, logits is of shape is [B, C], if false logits is
of shape [B, 1, 1, C], where B is batch_size and C is number of classes.
reuse: whether or not the network and its variables should be reused. To be
able to reuse 'scope' must be given.
scope: Optional variable_scope.
Returns:
logits: the pre-softmax activations, a tensor of size
[batch_size, num_classes]
end_points: a dictionary from components of the network to the corresponding
activation.
"""
# Final pooling and prediction
with variable_scope.variable_scope(scope,
'InceptionV1', [inputs, num_classes],
reuse=reuse) as scope:
with arg_scope([layers_lib.batch_norm, layers_lib.dropout],
is_training=is_training):
net, end_points = inception_v1_base(inputs, scope=scope)
with variable_scope.variable_scope('Logits'):
net = layers_lib.avg_pool2d(net, [7, 7],
stride=1,
scope='MaxPool_0a_7x7')
net = layers_lib.dropout(net,
dropout_keep_prob,
scope='Dropout_0b')
logits = layers.conv2d(net,
num_classes, [1, 1],
activation_fn=None,
normalizer_fn=None,
scope='Conv2d_0c_1x1')
if spatial_squeeze:
logits = array_ops.squeeze(logits, [1, 2],
name='SpatialSqueeze')
end_points['Logits'] = logits
end_points['Predictions'] = prediction_fn(logits,
scope='Predictions')
return logits, end_points
def inception_v1_base(inputs, final_endpoint='Mixed_5c', scope='InceptionV1'):
"""Defines the Inception V1 base architecture.
This architecture is defined in:
Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed,
Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich.
http://arxiv.org/pdf/1409.4842v1.pdf.
Args:
inputs: a tensor of size [batch_size, height, width, channels].
final_endpoint: specifies the endpoint to construct the network up to. It
can be one of ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c',
'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d', 'Mixed_4e',
'Mixed_4f', 'MaxPool_5a_2x2', 'Mixed_5b', 'Mixed_5c']
scope: Optional variable_scope.
Returns:
A dictionary from components of the network to the corresponding activation.
Raises:
ValueError: if final_endpoint is not set to one of the predefined values.
"""
end_points = {}
with variable_scope.variable_scope(scope, 'InceptionV1', [inputs]):
with arg_scope([layers.conv2d, layers_lib.fully_connected],
weights_initializer=trunc_normal(0.01)):
with arg_scope([layers.conv2d, layers_lib.max_pool2d],
stride=1,
padding='SAME'):
end_point = 'Conv2d_1a_7x7'
net = layers.conv2d(inputs,
64, [7, 7],
stride=2,
scope=end_point)
end_points[end_point] = net
if final_endpoint == end_point:
return net, end_points
end_point = 'MaxPool_2a_3x3'
net = layers_lib.max_pool2d(net, [3, 3],
stride=2,
scope=end_point)
end_points[end_point] = net
if final_endpoint == end_point:
return net, end_points
end_point = 'Conv2d_2b_1x1'
net = layers.conv2d(net, 64, [1, 1], scope=end_point)
end_points[end_point] = net
if final_endpoint == end_point:
return net, end_points
end_point = 'Conv2d_2c_3x3'
net = layers.conv2d(net, 192, [3, 3], scope=end_point)
end_points[end_point] = net
if final_endpoint == end_point:
return net, end_points
end_point = 'MaxPool_3a_3x3'
net = layers_lib.max_pool2d(net, [3, 3],
stride=2,
scope=end_point)
end_points[end_point] = net
if final_endpoint == end_point:
return net, end_points
end_point = 'Mixed_3b'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net,
64, [1, 1],
scope='Conv2d_0a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(net,
96, [1, 1],
scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(branch_1,
128, [3, 3],
scope='Conv2d_0b_3x3')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(net,
16, [1, 1],
scope='Conv2d_0a_1x1')
branch_2 = layers.conv2d(branch_2,
32, [3, 3],
scope='Conv2d_0b_3x3')
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.max_pool2d(
net, [3, 3], scope='MaxPool_0a_3x3')
branch_3 = layers.conv2d(branch_3,
32, [1, 1],
scope='Conv2d_0b_1x1')
net = array_ops.concat(
[branch_0, branch_1, branch_2, branch_3], 3)
end_points[end_point] = net
if final_endpoint == end_point:
return net, end_points
end_point = 'Mixed_3c'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net,
128, [1, 1],
scope='Conv2d_0a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(net,
128, [1, 1],
scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(branch_1,
192, [3, 3],
scope='Conv2d_0b_3x3')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(net,
32, [1, 1],
scope='Conv2d_0a_1x1')
branch_2 = layers.conv2d(branch_2,
96, [3, 3],
scope='Conv2d_0b_3x3')
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.max_pool2d(
net, [3, 3], scope='MaxPool_0a_3x3')
branch_3 = layers.conv2d(branch_3,
64, [1, 1],
scope='Conv2d_0b_1x1')
net = array_ops.concat(
[branch_0, branch_1, branch_2, branch_3], 3)
end_points[end_point] = net
if final_endpoint == end_point:
return net, end_points
end_point = 'MaxPool_4a_3x3'
net = layers_lib.max_pool2d(net, [3, 3],
stride=2,
scope=end_point)
end_points[end_point] = net
if final_endpoint == end_point:
return net, end_points
end_point = 'Mixed_4b'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net,
192, [1, 1],
scope='Conv2d_0a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(net,
96, [1, 1],
scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(branch_1,
208, [3, 3],
scope='Conv2d_0b_3x3')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(net,
16, [1, 1],
scope='Conv2d_0a_1x1')
branch_2 = layers.conv2d(branch_2,
48, [3, 3],
scope='Conv2d_0b_3x3')
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.max_pool2d(
net, [3, 3], scope='MaxPool_0a_3x3')
branch_3 = layers.conv2d(branch_3,
64, [1, 1],
scope='Conv2d_0b_1x1')
net = array_ops.concat(
[branch_0, branch_1, branch_2, branch_3], 3)
end_points[end_point] = net
if final_endpoint == end_point:
return net, end_points
end_point = 'Mixed_4c'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net,
160, [1, 1],
scope='Conv2d_0a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(net,
112, [1, 1],
scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(branch_1,
224, [3, 3],
scope='Conv2d_0b_3x3')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(net,
24, [1, 1],
scope='Conv2d_0a_1x1')
branch_2 = layers.conv2d(branch_2,
64, [3, 3],
scope='Conv2d_0b_3x3')
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.max_pool2d(
net, [3, 3], scope='MaxPool_0a_3x3')
branch_3 = layers.conv2d(branch_3,
64, [1, 1],
scope='Conv2d_0b_1x1')
net = array_ops.concat(
[branch_0, branch_1, branch_2, branch_3], 3)
end_points[end_point] = net
if final_endpoint == end_point:
return net, end_points
end_point = 'Mixed_4d'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net,
128, [1, 1],
scope='Conv2d_0a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(net,
128, [1, 1],
scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(branch_1,
256, [3, 3],
scope='Conv2d_0b_3x3')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(net,
24, [1, 1],
scope='Conv2d_0a_1x1')
branch_2 = layers.conv2d(branch_2,
64, [3, 3],
scope='Conv2d_0b_3x3')
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.max_pool2d(
net, [3, 3], scope='MaxPool_0a_3x3')
branch_3 = layers.conv2d(branch_3,
64, [1, 1],
scope='Conv2d_0b_1x1')
net = array_ops.concat(
[branch_0, branch_1, branch_2, branch_3], 3)
end_points[end_point] = net
if final_endpoint == end_point:
return net, end_points
end_point = 'Mixed_4e'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net,
112, [1, 1],
scope='Conv2d_0a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(net,
144, [1, 1],
scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(branch_1,
288, [3, 3],
scope='Conv2d_0b_3x3')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(net,
32, [1, 1],
scope='Conv2d_0a_1x1')
branch_2 = layers.conv2d(branch_2,
64, [3, 3],
scope='Conv2d_0b_3x3')
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.max_pool2d(
net, [3, 3], scope='MaxPool_0a_3x3')
branch_3 = layers.conv2d(branch_3,
64, [1, 1],
scope='Conv2d_0b_1x1')
net = array_ops.concat(
[branch_0, branch_1, branch_2, branch_3], 3)
end_points[end_point] = net
if final_endpoint == end_point:
return net, end_points
end_point = 'Mixed_4f'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net,
256, [1, 1],
scope='Conv2d_0a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(net,
160, [1, 1],
scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(branch_1,
320, [3, 3],
scope='Conv2d_0b_3x3')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(net,
32, [1, 1],
scope='Conv2d_0a_1x1')
branch_2 = layers.conv2d(branch_2,
128, [3, 3],
scope='Conv2d_0b_3x3')
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.max_pool2d(
net, [3, 3], scope='MaxPool_0a_3x3')
branch_3 = layers.conv2d(branch_3,
128, [1, 1],
scope='Conv2d_0b_1x1')
net = array_ops.concat(
[branch_0, branch_1, branch_2, branch_3], 3)
end_points[end_point] = net
if final_endpoint == end_point:
return net, end_points
end_point = 'MaxPool_5a_2x2'
net = layers_lib.max_pool2d(net, [2, 2],
stride=2,
scope=end_point)
end_points[end_point] = net
if final_endpoint == end_point:
return net, end_points
end_point = 'Mixed_5b'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net,
256, [1, 1],
scope='Conv2d_0a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(net,
160, [1, 1],
scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(branch_1,
320, [3, 3],
scope='Conv2d_0b_3x3')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(net,
32, [1, 1],
scope='Conv2d_0a_1x1')
branch_2 = layers.conv2d(branch_2,
128, [3, 3],
scope='Conv2d_0a_3x3')
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.max_pool2d(
net, [3, 3], scope='MaxPool_0a_3x3')
branch_3 = layers.conv2d(branch_3,
128, [1, 1],
scope='Conv2d_0b_1x1')
net = array_ops.concat(
[branch_0, branch_1, branch_2, branch_3], 3)
end_points[end_point] = net
if final_endpoint == end_point:
return net, end_points
end_point = 'Mixed_5c'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net,
384, [1, 1],
scope='Conv2d_0a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(net,
192, [1, 1],
scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(branch_1,
384, [3, 3],
scope='Conv2d_0b_3x3')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(net,
48, [1, 1],
scope='Conv2d_0a_1x1')
branch_2 = layers.conv2d(branch_2,
128, [3, 3],
scope='Conv2d_0b_3x3')
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.max_pool2d(
net, [3, 3], scope='MaxPool_0a_3x3')
branch_3 = layers.conv2d(branch_3,
128, [1, 1],
scope='Conv2d_0b_1x1')
net = array_ops.concat(
[branch_0, branch_1, branch_2, branch_3], 3)
end_points[end_point] = net
if final_endpoint == end_point:
return net, end_points
raise ValueError('Unknown final endpoint %s' % final_endpoint)
def resnet_v2(inputs,
blocks,
num_classes=None,
is_training=True,
global_pool=True,
output_stride=None,
include_root_block=True,
reuse=None,
scope=None):
"""Generator for v2 (preactivation) ResNet models.
This function generates a family of ResNet v2 models. See the resnet_v2_*()
methods for specific model instantiations, obtained by selecting different
block instantiations that produce ResNets of various depths.
Training for image classification on Imagenet is usually done with [224, 224]
inputs, resulting in [7, 7] feature maps at the output of the last ResNet
block for the ResNets defined in [1] that have nominal stride equal to 32.
However, for dense prediction tasks we advise that one uses inputs with
spatial dimensions that are multiples of 32 plus 1, e.g., [321, 321]. In
this case the feature maps at the ResNet output will have spatial shape
[(height - 1) / output_stride + 1, (width - 1) / output_stride + 1]
and corners exactly aligned with the input image corners, which greatly
facilitates alignment of the features to the image. Using as input [225, 225]
images results in [8, 8] feature maps at the output of the last ResNet block.
For dense prediction tasks, the ResNet needs to run in fully-convolutional
(FCN) mode and global_pool needs to be set to False. The ResNets in [1, 2] all
have nominal stride equal to 32 and a good choice in FCN mode is to use
output_stride=16 in order to increase the density of the computed features at
small computational and memory overhead, cf. http://arxiv.org/abs/1606.00915.
Args:
inputs: A tensor of size [batch, height_in, width_in, channels].
blocks: A list of length equal to the number of ResNet blocks. Each element
is a resnet_utils.Block object describing the units in the block.
num_classes: Number of predicted classes for classification tasks. If None
we return the features before the logit layer.
is_training: whether batch_norm layers are in training mode.
global_pool: If True, we perform global average pooling before computing the
logits. Set to True for image classification, False for dense prediction.
output_stride: If None, then the output will be computed at the nominal
network stride. If output_stride is not None, it specifies the requested
ratio of input to output spatial resolution.
include_root_block: If True, include the initial convolution followed by
max-pooling, if False excludes it. If excluded, `inputs` should be the
results of an activation-less convolution.
reuse: whether or not the network and its variables should be reused. To be
able to reuse 'scope' must be given.
scope: Optional variable_scope.
Returns:
net: A rank-4 tensor of size [batch, height_out, width_out, channels_out].
If global_pool is False, then height_out and width_out are reduced by a
factor of output_stride compared to the respective height_in and width_in,
else both height_out and width_out equal one. If num_classes is None, then
net is the output of the last ResNet block, potentially after global
average pooling. If num_classes is not None, net contains the pre-softmax
activations.
end_points: A dictionary from components of the network to the corresponding
activation.
Raises:
ValueError: If the target output_stride is not valid.
"""
with variable_scope.variable_scope(
scope, 'resnet_v2', [inputs], reuse=reuse) as sc:
end_points_collection = sc.original_name_scope + '_end_points'
with arg_scope(
[layers_lib.conv2d, bottleneck, resnet_utils.stack_blocks_dense],
outputs_collections=end_points_collection):
with arg_scope([layers.batch_norm], is_training=is_training):
net = inputs
if include_root_block:
if output_stride is not None:
if output_stride % 4 != 0:
raise ValueError('The output_stride needs to be a multiple of 4.')
output_stride /= 4
# We do not include batch normalization or activation functions in
# conv1 because the first ResNet unit will perform these. Cf.
# Appendix of [2].
with arg_scope(
[layers_lib.conv2d], activation_fn=None, normalizer_fn=None):
net = resnet_utils.conv2d_same(net, 64, 7, stride=2, scope='conv1')
net = layers.max_pool2d(net, [3, 3], stride=2, scope='pool1')
net = resnet_utils.stack_blocks_dense(net, blocks, output_stride)
# This is needed because the pre-activation variant does not have batch
# normalization or activation functions in the residual unit output. See
# Appendix of [2].
net = layers.batch_norm(
net, activation_fn=nn_ops.relu, scope='postnorm')
if global_pool:
# Global average pooling.
net = math_ops.reduce_mean(net, [1, 2], name='pool5', keepdims=True)
if num_classes is not None:
net = layers_lib.conv2d(
net,
num_classes, [1, 1],
activation_fn=None,
normalizer_fn=None,
scope='logits')
# Convert end_points_collection into a dictionary of end_points.
end_points = utils.convert_collection_to_dict(end_points_collection)
if num_classes is not None:
end_points['predictions'] = layers.softmax(net, scope='predictions')
return net, end_points
def inception_v3(inputs,
num_classes=1000,
is_training=True,
dropout_keep_prob=0.8,
min_depth=16,
depth_multiplier=1.0,
prediction_fn=layers_lib.softmax,
spatial_squeeze=True,
reuse=None,
scope='InceptionV3'):
"""Inception model from http://arxiv.org/abs/1512.00567.
"Rethinking the Inception Architecture for Computer Vision"
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens,
Zbigniew Wojna.
With the default arguments this method constructs the exact model defined in
the paper. However, one can experiment with variations of the inception_v3
network by changing arguments dropout_keep_prob, min_depth and
depth_multiplier.
The default image size used to train this network is 299x299.
Args:
inputs: a tensor of size [batch_size, height, width, channels].
num_classes: number of predicted classes.
is_training: whether is training or not.
dropout_keep_prob: the percentage of activation values that are retained.
min_depth: Minimum depth value (number of channels) for all convolution ops.
Enforced when depth_multiplier < 1, and not an active constraint when
depth_multiplier >= 1.
depth_multiplier: Float multiplier for the depth (number of channels)
for all convolution ops. The value must be greater than zero. Typical
usage will be to set this value in (0, 1) to reduce the number of
parameters or computation cost of the model.
prediction_fn: a function to get predictions out of logits.
spatial_squeeze: if True, logits is of shape is [B, C], if false logits is
of shape [B, 1, 1, C], where B is batch_size and C is number of classes.
To use this parameter, the input images must be smaller
than 300x300 pixels, in which case the output logit layer
does not contain spatial information and can be removed.
reuse: whether or not the network and its variables should be reused. To be
able to reuse 'scope' must be given.
scope: Optional variable_scope.
Returns:
logits: the pre-softmax activations, a tensor of size
[batch_size, num_classes]
end_points: a dictionary from components of the network to the corresponding
activation.
Raises:
ValueError: if 'depth_multiplier' is less than or equal to zero.
"""
if depth_multiplier <= 0:
raise ValueError('depth_multiplier is not greater than zero.')
depth = lambda d: max(int(d * depth_multiplier), min_depth)
with variable_scope.variable_scope(scope,
'InceptionV3', [inputs, num_classes],
reuse=reuse) as scope:
with arg_scope([layers_lib.batch_norm, layers_lib.dropout],
is_training=is_training):
net, end_points = inception_v3_base(
inputs,
scope=scope,
min_depth=min_depth,
depth_multiplier=depth_multiplier)
# Auxiliary Head logits
with arg_scope(
[layers.conv2d, layers_lib.max_pool2d, layers_lib.avg_pool2d],
stride=1,
padding='SAME'):
aux_logits = end_points['Mixed_6e']
with variable_scope.variable_scope('AuxLogits'):
aux_logits = layers_lib.avg_pool2d(aux_logits, [5, 5],
stride=3,
padding='VALID',
scope='AvgPool_1a_5x5')
aux_logits = layers.conv2d(aux_logits,
depth(128), [1, 1],
scope='Conv2d_1b_1x1')
# Shape of feature map before the final layer.
kernel_size = _reduced_kernel_size_for_small_input(
aux_logits, [5, 5])
aux_logits = layers.conv2d(
aux_logits,
depth(768),
kernel_size,
weights_initializer=trunc_normal(0.01),
padding='VALID',
scope='Conv2d_2a_{}x{}'.format(*kernel_size))
aux_logits = layers.conv2d(
aux_logits,
num_classes, [1, 1],
activation_fn=None,
normalizer_fn=None,
weights_initializer=trunc_normal(0.001),
scope='Conv2d_2b_1x1')
if spatial_squeeze:
aux_logits = array_ops.squeeze(aux_logits, [1, 2],
name='SpatialSqueeze')
end_points['AuxLogits'] = aux_logits
# Final pooling and prediction
with variable_scope.variable_scope('Logits'):
kernel_size = _reduced_kernel_size_for_small_input(net, [8, 8])
net = layers_lib.avg_pool2d(
net,
kernel_size,
padding='VALID',
scope='AvgPool_1a_{}x{}'.format(*kernel_size))
# 1 x 1 x 2048
net = layers_lib.dropout(net,
keep_prob=dropout_keep_prob,
scope='Dropout_1b')
end_points['PreLogits'] = net
# 2048
logits = layers.conv2d(net,
num_classes, [1, 1],
activation_fn=None,
normalizer_fn=None,
scope='Conv2d_1c_1x1')
if spatial_squeeze:
logits = array_ops.squeeze(logits, [1, 2],
name='SpatialSqueeze')
# 1000
end_points['Logits'] = logits
end_points['Predictions'] = prediction_fn(logits,
scope='Predictions')
return logits, end_points
def inception_v3_base(inputs,
final_endpoint='Mixed_7c',
min_depth=16,
depth_multiplier=1.0,
scope=None):
"""Inception model from http://arxiv.org/abs/1512.00567.
Constructs an Inception v3 network from inputs to the given final endpoint.
This method can construct the network up to the final inception block
Mixed_7c.
Note that the names of the layers in the paper do not correspond to the names
of the endpoints registered by this function although they build the same
network.
Here is a mapping from the old_names to the new names:
Old name | New name
=======================================
conv0 | Conv2d_1a_3x3
conv1 | Conv2d_2a_3x3
conv2 | Conv2d_2b_3x3
pool1 | MaxPool_3a_3x3
conv3 | Conv2d_3b_1x1
conv4 | Conv2d_4a_3x3
pool2 | MaxPool_5a_3x3
mixed_35x35x256a | Mixed_5b
mixed_35x35x288a | Mixed_5c
mixed_35x35x288b | Mixed_5d
mixed_17x17x768a | Mixed_6a
mixed_17x17x768b | Mixed_6b
mixed_17x17x768c | Mixed_6c
mixed_17x17x768d | Mixed_6d
mixed_17x17x768e | Mixed_6e
mixed_8x8x1280a | Mixed_7a
mixed_8x8x2048a | Mixed_7b
mixed_8x8x2048b | Mixed_7c
Args:
inputs: a tensor of size [batch_size, height, width, channels].
final_endpoint: specifies the endpoint to construct the network up to. It
can be one of ['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'].
min_depth: Minimum depth value (number of channels) for all convolution ops.
Enforced when depth_multiplier < 1, and not an active constraint when
depth_multiplier >= 1.
depth_multiplier: Float multiplier for the depth (number of channels)
for all convolution ops. The value must be greater than zero. Typical
usage will be to set this value in (0, 1) to reduce the number of
parameters or computation cost of the model.
scope: Optional variable_scope.
Returns:
tensor_out: output tensor corresponding to the final_endpoint.
end_points: a set of activations for external use, for example summaries or
losses.
Raises:
ValueError: if final_endpoint is not set to one of the predefined values,
or depth_multiplier <= 0
"""
# end_points will collect relevant activations for external use, for example
# summaries or losses.
end_points = {}
if depth_multiplier <= 0:
raise ValueError('depth_multiplier is not greater than zero.')
depth = lambda d: max(int(d * depth_multiplier), min_depth)
with variable_scope.variable_scope(scope, 'InceptionV3', [inputs]):
with arg_scope(
[layers.conv2d, layers_lib.max_pool2d, layers_lib.avg_pool2d],
stride=1,
padding='VALID'):
# 299 x 299 x 3
end_point = 'Conv2d_1a_3x3'
net = layers.conv2d(inputs,
depth(32), [3, 3],
stride=2,
scope=end_point)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# 149 x 149 x 32
end_point = 'Conv2d_2a_3x3'
net = layers.conv2d(net, depth(32), [3, 3], scope=end_point)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# 147 x 147 x 32
end_point = 'Conv2d_2b_3x3'
net = layers.conv2d(net,
depth(64), [3, 3],
padding='SAME',
scope=end_point)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# 147 x 147 x 64
end_point = 'MaxPool_3a_3x3'
net = layers_lib.max_pool2d(net, [3, 3], stride=2, scope=end_point)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# 73 x 73 x 64
end_point = 'Conv2d_3b_1x1'
net = layers.conv2d(net, depth(80), [1, 1], scope=end_point)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# 73 x 73 x 80.
end_point = 'Conv2d_4a_3x3'
net = layers.conv2d(net, depth(192), [3, 3], scope=end_point)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# 71 x 71 x 192.
end_point = 'MaxPool_5a_3x3'
net = layers_lib.max_pool2d(net, [3, 3], stride=2, scope=end_point)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# 35 x 35 x 192.
# Inception blocks
with arg_scope(
[layers.conv2d, layers_lib.max_pool2d, layers_lib.avg_pool2d],
stride=1,
padding='SAME'):
# mixed: 35 x 35 x 256.
end_point = 'Mixed_5b'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net,
depth(64), [1, 1],
scope='Conv2d_0a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(net,
depth(48), [1, 1],
scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(branch_1,
depth(64), [5, 5],
scope='Conv2d_0b_5x5')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(net,
depth(64), [1, 1],
scope='Conv2d_0a_1x1')
branch_2 = layers.conv2d(branch_2,
depth(96), [3, 3],
scope='Conv2d_0b_3x3')
branch_2 = layers.conv2d(branch_2,
depth(96), [3, 3],
scope='Conv2d_0c_3x3')
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.avg_pool2d(net, [3, 3],
scope='AvgPool_0a_3x3')
branch_3 = layers.conv2d(branch_3,
depth(32), [1, 1],
scope='Conv2d_0b_1x1')
net = array_ops.concat(
[branch_0, branch_1, branch_2, branch_3], 3)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# mixed_1: 35 x 35 x 288.
end_point = 'Mixed_5c'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net,
depth(64), [1, 1],
scope='Conv2d_0a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(net,
depth(48), [1, 1],
scope='Conv2d_0b_1x1')
branch_1 = layers.conv2d(branch_1,
depth(64), [5, 5],
scope='Conv_1_0c_5x5')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(net,
depth(64), [1, 1],
scope='Conv2d_0a_1x1')
branch_2 = layers.conv2d(branch_2,
depth(96), [3, 3],
scope='Conv2d_0b_3x3')
branch_2 = layers.conv2d(branch_2,
depth(96), [3, 3],
scope='Conv2d_0c_3x3')
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.avg_pool2d(net, [3, 3],
scope='AvgPool_0a_3x3')
branch_3 = layers.conv2d(branch_3,
depth(64), [1, 1],
scope='Conv2d_0b_1x1')
net = array_ops.concat(
[branch_0, branch_1, branch_2, branch_3], 3)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# mixed_2: 35 x 35 x 288.
end_point = 'Mixed_5d'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net,
depth(64), [1, 1],
scope='Conv2d_0a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(net,
depth(48), [1, 1],
scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(branch_1,
depth(64), [5, 5],
scope='Conv2d_0b_5x5')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(net,
depth(64), [1, 1],
scope='Conv2d_0a_1x1')
branch_2 = layers.conv2d(branch_2,
depth(96), [3, 3],
scope='Conv2d_0b_3x3')
branch_2 = layers.conv2d(branch_2,
depth(96), [3, 3],
scope='Conv2d_0c_3x3')
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.avg_pool2d(net, [3, 3],
scope='AvgPool_0a_3x3')
branch_3 = layers.conv2d(branch_3,
depth(64), [1, 1],
scope='Conv2d_0b_1x1')
net = array_ops.concat(
[branch_0, branch_1, branch_2, branch_3], 3)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# mixed_3: 17 x 17 x 768.
end_point = 'Mixed_6a'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net,
depth(384), [3, 3],
stride=2,
padding='VALID',
scope='Conv2d_1a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(net,
depth(64), [1, 1],
scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(branch_1,
depth(96), [3, 3],
scope='Conv2d_0b_3x3')
branch_1 = layers.conv2d(branch_1,
depth(96), [3, 3],
stride=2,
padding='VALID',
scope='Conv2d_1a_1x1')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers_lib.max_pool2d(net, [3, 3],
stride=2,
padding='VALID',
scope='MaxPool_1a_3x3')
net = array_ops.concat([branch_0, branch_1, branch_2], 3)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# mixed4: 17 x 17 x 768.
end_point = 'Mixed_6b'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net,
depth(192), [1, 1],
scope='Conv2d_0a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(net,
depth(128), [1, 1],
scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(branch_1,
depth(128), [1, 7],
scope='Conv2d_0b_1x7')
branch_1 = layers.conv2d(branch_1,
depth(192), [7, 1],
scope='Conv2d_0c_7x1')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(net,
depth(128), [1, 1],
scope='Conv2d_0a_1x1')
branch_2 = layers.conv2d(branch_2,
depth(128), [7, 1],
scope='Conv2d_0b_7x1')
branch_2 = layers.conv2d(branch_2,
depth(128), [1, 7],
scope='Conv2d_0c_1x7')
branch_2 = layers.conv2d(branch_2,
depth(128), [7, 1],
scope='Conv2d_0d_7x1')
branch_2 = layers.conv2d(branch_2,
depth(192), [1, 7],
scope='Conv2d_0e_1x7')
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.avg_pool2d(net, [3, 3],
scope='AvgPool_0a_3x3')
branch_3 = layers.conv2d(branch_3,
depth(192), [1, 1],
scope='Conv2d_0b_1x1')
net = array_ops.concat(
[branch_0, branch_1, branch_2, branch_3], 3)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# mixed_5: 17 x 17 x 768.
end_point = 'Mixed_6c'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net,
depth(192), [1, 1],
scope='Conv2d_0a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(net,
depth(160), [1, 1],
scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(branch_1,
depth(160), [1, 7],
scope='Conv2d_0b_1x7')
branch_1 = layers.conv2d(branch_1,
depth(192), [7, 1],
scope='Conv2d_0c_7x1')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(net,
depth(160), [1, 1],
scope='Conv2d_0a_1x1')
branch_2 = layers.conv2d(branch_2,
depth(160), [7, 1],
scope='Conv2d_0b_7x1')
branch_2 = layers.conv2d(branch_2,
depth(160), [1, 7],
scope='Conv2d_0c_1x7')
branch_2 = layers.conv2d(branch_2,
depth(160), [7, 1],
scope='Conv2d_0d_7x1')
branch_2 = layers.conv2d(branch_2,
depth(192), [1, 7],
scope='Conv2d_0e_1x7')
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.avg_pool2d(net, [3, 3],
scope='AvgPool_0a_3x3')
branch_3 = layers.conv2d(branch_3,
depth(192), [1, 1],
scope='Conv2d_0b_1x1')
net = array_ops.concat(
[branch_0, branch_1, branch_2, branch_3], 3)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# mixed_6: 17 x 17 x 768.
end_point = 'Mixed_6d'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net,
depth(192), [1, 1],
scope='Conv2d_0a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(net,
depth(160), [1, 1],
scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(branch_1,
depth(160), [1, 7],
scope='Conv2d_0b_1x7')
branch_1 = layers.conv2d(branch_1,
depth(192), [7, 1],
scope='Conv2d_0c_7x1')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(net,
depth(160), [1, 1],
scope='Conv2d_0a_1x1')
branch_2 = layers.conv2d(branch_2,
depth(160), [7, 1],
scope='Conv2d_0b_7x1')
branch_2 = layers.conv2d(branch_2,
depth(160), [1, 7],
scope='Conv2d_0c_1x7')
branch_2 = layers.conv2d(branch_2,
depth(160), [7, 1],
scope='Conv2d_0d_7x1')
branch_2 = layers.conv2d(branch_2,
depth(192), [1, 7],
scope='Conv2d_0e_1x7')
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.avg_pool2d(net, [3, 3],
scope='AvgPool_0a_3x3')
branch_3 = layers.conv2d(branch_3,
depth(192), [1, 1],
scope='Conv2d_0b_1x1')
net = array_ops.concat(
[branch_0, branch_1, branch_2, branch_3], 3)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# mixed_7: 17 x 17 x 768.
end_point = 'Mixed_6e'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net,
depth(192), [1, 1],
scope='Conv2d_0a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(net,
depth(192), [1, 1],
scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(branch_1,
depth(192), [1, 7],
scope='Conv2d_0b_1x7')
branch_1 = layers.conv2d(branch_1,
depth(192), [7, 1],
scope='Conv2d_0c_7x1')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(net,
depth(192), [1, 1],
scope='Conv2d_0a_1x1')
branch_2 = layers.conv2d(branch_2,
depth(192), [7, 1],
scope='Conv2d_0b_7x1')
branch_2 = layers.conv2d(branch_2,
depth(192), [1, 7],
scope='Conv2d_0c_1x7')
branch_2 = layers.conv2d(branch_2,
depth(192), [7, 1],
scope='Conv2d_0d_7x1')
branch_2 = layers.conv2d(branch_2,
depth(192), [1, 7],
scope='Conv2d_0e_1x7')
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.avg_pool2d(net, [3, 3],
scope='AvgPool_0a_3x3')
branch_3 = layers.conv2d(branch_3,
depth(192), [1, 1],
scope='Conv2d_0b_1x1')
net = array_ops.concat(
[branch_0, branch_1, branch_2, branch_3], 3)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# mixed_8: 8 x 8 x 1280.
end_point = 'Mixed_7a'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net,
depth(192), [1, 1],
scope='Conv2d_0a_1x1')
branch_0 = layers.conv2d(branch_0,
depth(320), [3, 3],
stride=2,
padding='VALID',
scope='Conv2d_1a_3x3')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(net,
depth(192), [1, 1],
scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(branch_1,
depth(192), [1, 7],
scope='Conv2d_0b_1x7')
branch_1 = layers.conv2d(branch_1,
depth(192), [7, 1],
scope='Conv2d_0c_7x1')
branch_1 = layers.conv2d(branch_1,
depth(192), [3, 3],
stride=2,
padding='VALID',
scope='Conv2d_1a_3x3')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers_lib.max_pool2d(net, [3, 3],
stride=2,
padding='VALID',
scope='MaxPool_1a_3x3')
net = array_ops.concat([branch_0, branch_1, branch_2], 3)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# mixed_9: 8 x 8 x 2048.
end_point = 'Mixed_7b'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net,
depth(320), [1, 1],
scope='Conv2d_0a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(net,
depth(384), [1, 1],
scope='Conv2d_0a_1x1')
branch_1 = array_ops.concat([
layers.conv2d(branch_1,
depth(384), [1, 3],
scope='Conv2d_0b_1x3'),
layers.conv2d(branch_1,
depth(384), [3, 1],
scope='Conv2d_0b_3x1')
], 3)
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(net,
depth(448), [1, 1],
scope='Conv2d_0a_1x1')
branch_2 = layers.conv2d(branch_2,
depth(384), [3, 3],
scope='Conv2d_0b_3x3')
branch_2 = array_ops.concat([
layers.conv2d(branch_2,
depth(384), [1, 3],
scope='Conv2d_0c_1x3'),
layers.conv2d(branch_2,
depth(384), [3, 1],
scope='Conv2d_0d_3x1')
], 3)
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.avg_pool2d(net, [3, 3],
scope='AvgPool_0a_3x3')
branch_3 = layers.conv2d(branch_3,
depth(192), [1, 1],
scope='Conv2d_0b_1x1')
net = array_ops.concat(
[branch_0, branch_1, branch_2, branch_3], 3)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# mixed_10: 8 x 8 x 2048.
end_point = 'Mixed_7c'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net,
depth(320), [1, 1],
scope='Conv2d_0a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(net,
depth(384), [1, 1],
scope='Conv2d_0a_1x1')
branch_1 = array_ops.concat([
layers.conv2d(branch_1,
depth(384), [1, 3],
scope='Conv2d_0b_1x3'),
layers.conv2d(branch_1,
depth(384), [3, 1],
scope='Conv2d_0c_3x1')
], 3)
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(net,
depth(448), [1, 1],
scope='Conv2d_0a_1x1')
branch_2 = layers.conv2d(branch_2,
depth(384), [3, 3],
scope='Conv2d_0b_3x3')
branch_2 = array_ops.concat([
layers.conv2d(branch_2,
depth(384), [1, 3],
scope='Conv2d_0c_1x3'),
layers.conv2d(branch_2,
depth(384), [3, 1],
scope='Conv2d_0d_3x1')
], 3)
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.avg_pool2d(net, [3, 3],
scope='AvgPool_0a_3x3')
branch_3 = layers.conv2d(branch_3,
depth(192), [1, 1],
scope='Conv2d_0b_1x1')
net = array_ops.concat(
[branch_0, branch_1, branch_2, branch_3], 3)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
raise ValueError('Unknown final endpoint %s' % final_endpoint)
def alexnet_v2(inputs,
num_classes=1000,
is_training=True,
dropout_keep_prob=0.5,
spatial_squeeze=True,
scope='alexnet_v2'):
"""AlexNet version 2.
Described in: http://arxiv.org/pdf/1404.5997v2.pdf
Parameters from:
github.com/akrizhevsky/cuda-convnet2/blob/master/layers/
layers-imagenet-1gpu.cfg
Note: All the fully_connected layers have been transformed to conv2d layers.
To use in classification mode, resize input to 224x224. To use in fully
convolutional mode, set spatial_squeeze to false.
The LRN layers have been removed and change the initializers from
random_normal_initializer to xavier_initializer.
Args:
inputs: a tensor of size [batch_size, height, width, channels].
num_classes: number of predicted classes.
is_training: whether or not the model is being trained.
dropout_keep_prob: the probability that activations are kept in the dropout
layers during training.
spatial_squeeze: whether or not should squeeze the spatial dimensions of the
outputs. Useful to remove unnecessary dimensions for classification.
scope: Optional scope for the variables.
Returns:
the last op containing the log predictions and end_points dict.
"""
with variable_scope.variable_scope(scope, 'alexnet_v2', [inputs]) as sc:
end_points_collection = sc.original_name_scope + '_end_points'
# Collect outputs for conv2d, fully_connected and max_pool2d.
with arg_scope(
[layers.conv2d, layers_lib.fully_connected, layers_lib.max_pool2d],
outputs_collections=[end_points_collection]):
net = layers.conv2d(
inputs, 64, [11, 11], 4, padding='VALID', scope='conv1')
net = layers_lib.max_pool2d(net, [3, 3], 2, scope='pool1')
net = layers.conv2d(net, 192, [5, 5], scope='conv2')
net = layers_lib.max_pool2d(net, [3, 3], 2, scope='pool2')
net = layers.conv2d(net, 384, [3, 3], scope='conv3')
net = layers.conv2d(net, 384, [3, 3], scope='conv4')
net = layers.conv2d(net, 256, [3, 3], scope='conv5')
net = layers_lib.max_pool2d(net, [3, 3], 2, scope='pool5')
# Use conv2d instead of fully_connected layers.
with arg_scope(
[layers.conv2d],
weights_initializer=trunc_normal(0.005),
biases_initializer=init_ops.constant_initializer(0.1)):
net = layers.conv2d(net, 4096, [5, 5], padding='VALID', scope='fc6')
net = layers_lib.dropout(
net, dropout_keep_prob, is_training=is_training, scope='dropout6')
net = layers.conv2d(net, 4096, [1, 1], scope='fc7')
net = layers_lib.dropout(
net, dropout_keep_prob, is_training=is_training, scope='dropout7')
net = layers.conv2d(
net,
num_classes, [1, 1],
activation_fn=None,
normalizer_fn=None,
biases_initializer=init_ops.zeros_initializer(),
scope='fc8')
# Convert end_points_collection into a end_point dict.
end_points = utils.convert_collection_to_dict(end_points_collection)
if spatial_squeeze:
net = array_ops.squeeze(net, [1, 2], name='fc8/squeezed')
end_points[sc.name + '/fc8'] = net
return net, end_points
def inception_v2(inputs,
num_classes=1000,
is_training=True,
dropout_keep_prob=0.8,
min_depth=16,
depth_multiplier=1.0,
prediction_fn=layers_lib.softmax,
spatial_squeeze=True,
reuse=None,
scope='InceptionV2'):
"""Inception v2 model for classification.
Constructs an Inception v2 network for classification as described in
http://arxiv.org/abs/1502.03167.
The recommended image size used to train this network is 224x224. For image
sizes that differ substantially, it is recommended to use inception_v2_base()
and connect custom final layers to the output.
Args:
inputs: a tensor of shape [batch_size, height, width, channels].
num_classes: number of predicted classes.
is_training: whether is training or not.
dropout_keep_prob: the percentage of activation values that are retained.
min_depth: Minimum depth value (number of channels) for all convolution ops.
Enforced when depth_multiplier < 1, and not an active constraint when
depth_multiplier >= 1.
depth_multiplier: Float multiplier for the depth (number of channels)
for all convolution ops. The value must be greater than zero. Typical
usage will be to set this value in (0, 1) to reduce the number of
parameters or computation cost of the model.
prediction_fn: a function to get predictions out of logits.
spatial_squeeze: if True, logits is of shape [B, C], if false logits is
of shape [B, 1, 1, C], where B is batch_size and C is number of classes.
Note that input image sizes other than 224x224 might lead to different
spatial dimensions, and hence cannot be squeezed. In this event,
it is best to set spatial_squeeze as False, and perform a reduce_mean
over the resulting spatial dimensions with sizes exceeding 1.
reuse: whether or not the network and its variables should be reused. To be
able to reuse 'scope' must be given.
scope: Optional variable_scope.
Returns:
logits: the pre-softmax activations, a tensor of size
[batch_size, num_classes]
end_points: a dictionary from components of the network to the corresponding
activation.
Raises:
ValueError: if depth_multiplier <= 0.
"""
if depth_multiplier <= 0:
raise ValueError('depth_multiplier is not greater than zero.')
# Final pooling and prediction
with variable_scope.variable_scope(scope,
'InceptionV2', [inputs, num_classes],
reuse=reuse) as scope:
with arg_scope([layers_lib.batch_norm, layers_lib.dropout],
is_training=is_training):
net, end_points = inception_v2_base(
inputs,
scope=scope,
min_depth=min_depth,
depth_multiplier=depth_multiplier)
with variable_scope.variable_scope('Logits'):
kernel_size = _reduced_kernel_size_for_small_input(net, [7, 7])
net = layers_lib.avg_pool2d(
net,
kernel_size,
padding='VALID',
scope='AvgPool_1a_{}x{}'.format(*kernel_size))
# 1 x 1 x 1024
net = layers_lib.dropout(net,
keep_prob=dropout_keep_prob,
scope='Dropout_1b')
logits = layers.conv2d(net,
num_classes, [1, 1],
activation_fn=None,
normalizer_fn=None,
scope='Conv2d_1c_1x1')
if spatial_squeeze:
logits = array_ops.squeeze(logits, [1, 2],
name='SpatialSqueeze')
end_points['Logits'] = logits
end_points['Predictions'] = prediction_fn(logits,
scope='Predictions')
return logits, end_points
def inception_v2_base(inputs,
final_endpoint='Mixed_5c',
min_depth=16,
depth_multiplier=1.0,
scope=None):
"""Inception v2 (6a2).
Constructs an Inception v2 network from inputs to the given final endpoint.
This method can construct the network up to the layer inception(5b) as
described in http://arxiv.org/abs/1502.03167.
Args:
inputs: a tensor of shape [batch_size, height, width, channels].
final_endpoint: specifies the endpoint to construct the network up to. It
can be one of ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c', 'Mixed_4a',
'Mixed_4b', 'Mixed_4c', 'Mixed_4d', 'Mixed_4e', 'Mixed_5a', 'Mixed_5b',
'Mixed_5c'].
min_depth: Minimum depth value (number of channels) for all convolution ops.
Enforced when depth_multiplier < 1, and not an active constraint when
depth_multiplier >= 1.
depth_multiplier: Float multiplier for the depth (number of channels)
for all convolution ops. The value must be greater than zero. Typical
usage will be to set this value in (0, 1) to reduce the number of
parameters or computation cost of the model.
scope: Optional variable_scope.
Returns:
tensor_out: output tensor corresponding to the final_endpoint.
end_points: a set of activations for external use, for example summaries or
losses.
Raises:
ValueError: if final_endpoint is not set to one of the predefined values,
or depth_multiplier <= 0
"""
# end_points will collect relevant activations for external use, for example
# summaries or losses.
end_points = {}
# Used to find thinned depths for each layer.
if depth_multiplier <= 0:
raise ValueError('depth_multiplier is not greater than zero.')
depth = lambda d: max(int(d * depth_multiplier), min_depth)
with variable_scope.variable_scope(scope, 'InceptionV2', [inputs]):
with arg_scope([
layers.conv2d, layers_lib.max_pool2d, layers_lib.avg_pool2d,
layers.separable_conv2d
],
stride=1,
padding='SAME'):
# Note that sizes in the comments below assume an input spatial size of
# 224x224, however, the inputs can be of any size greater 32x32.
# 224 x 224 x 3
end_point = 'Conv2d_1a_7x7'
# depthwise_multiplier here is different from depth_multiplier.
# depthwise_multiplier determines the output channels of the initial
# depthwise conv (see docs for tf.nn.separable_conv2d), while
# depth_multiplier controls the # channels of the subsequent 1x1
# convolution. Must have
# in_channels * depthwise_multipler <= out_channels
# so that the separable convolution is not overparameterized.
depthwise_multiplier = min(int(depth(64) / 3), 8)
net = layers.separable_conv2d(
inputs,
depth(64), [7, 7],
depth_multiplier=depthwise_multiplier,
stride=2,
weights_initializer=trunc_normal(1.0),
scope=end_point)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# 112 x 112 x 64
end_point = 'MaxPool_2a_3x3'
net = layers_lib.max_pool2d(net, [3, 3], scope=end_point, stride=2)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# 56 x 56 x 64
end_point = 'Conv2d_2b_1x1'
net = layers.conv2d(net,
depth(64), [1, 1],
scope=end_point,
weights_initializer=trunc_normal(0.1))
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# 56 x 56 x 64
end_point = 'Conv2d_2c_3x3'
net = layers.conv2d(net, depth(192), [3, 3], scope=end_point)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# 56 x 56 x 192
end_point = 'MaxPool_3a_3x3'
net = layers_lib.max_pool2d(net, [3, 3], scope=end_point, stride=2)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# 28 x 28 x 192
# Inception module.
end_point = 'Mixed_3b'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net,
depth(64), [1, 1],
scope='Conv2d_0a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(
net,
depth(64), [1, 1],
weights_initializer=trunc_normal(0.09),
scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(branch_1,
depth(64), [3, 3],
scope='Conv2d_0b_3x3')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(
net,
depth(64), [1, 1],
weights_initializer=trunc_normal(0.09),
scope='Conv2d_0a_1x1')
branch_2 = layers.conv2d(branch_2,
depth(96), [3, 3],
scope='Conv2d_0b_3x3')
branch_2 = layers.conv2d(branch_2,
depth(96), [3, 3],
scope='Conv2d_0c_3x3')
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.avg_pool2d(net, [3, 3],
scope='AvgPool_0a_3x3')
branch_3 = layers.conv2d(
branch_3,
depth(32), [1, 1],
weights_initializer=trunc_normal(0.1),
scope='Conv2d_0b_1x1')
net = array_ops.concat(
[branch_0, branch_1, branch_2, branch_3], 3)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# 28 x 28 x 256
end_point = 'Mixed_3c'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net,
depth(64), [1, 1],
scope='Conv2d_0a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(
net,
depth(64), [1, 1],
weights_initializer=trunc_normal(0.09),
scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(branch_1,
depth(96), [3, 3],
scope='Conv2d_0b_3x3')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(
net,
depth(64), [1, 1],
weights_initializer=trunc_normal(0.09),
scope='Conv2d_0a_1x1')
branch_2 = layers.conv2d(branch_2,
depth(96), [3, 3],
scope='Conv2d_0b_3x3')
branch_2 = layers.conv2d(branch_2,
depth(96), [3, 3],
scope='Conv2d_0c_3x3')
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.avg_pool2d(net, [3, 3],
scope='AvgPool_0a_3x3')
branch_3 = layers.conv2d(
branch_3,
depth(64), [1, 1],
weights_initializer=trunc_normal(0.1),
scope='Conv2d_0b_1x1')
net = array_ops.concat(
[branch_0, branch_1, branch_2, branch_3], 3)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# 28 x 28 x 320
end_point = 'Mixed_4a'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(
net,
depth(128), [1, 1],
weights_initializer=trunc_normal(0.09),
scope='Conv2d_0a_1x1')
branch_0 = layers.conv2d(branch_0,
depth(160), [3, 3],
stride=2,
scope='Conv2d_1a_3x3')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(
net,
depth(64), [1, 1],
weights_initializer=trunc_normal(0.09),
scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(branch_1,
depth(96), [3, 3],
scope='Conv2d_0b_3x3')
branch_1 = layers.conv2d(branch_1,
depth(96), [3, 3],
stride=2,
scope='Conv2d_1a_3x3')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers_lib.max_pool2d(net, [3, 3],
stride=2,
scope='MaxPool_1a_3x3')
net = array_ops.concat([branch_0, branch_1, branch_2], 3)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# 14 x 14 x 576
end_point = 'Mixed_4b'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net,
depth(224), [1, 1],
scope='Conv2d_0a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(
net,
depth(64), [1, 1],
weights_initializer=trunc_normal(0.09),
scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(branch_1,
depth(96), [3, 3],
scope='Conv2d_0b_3x3')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(
net,
depth(96), [1, 1],
weights_initializer=trunc_normal(0.09),
scope='Conv2d_0a_1x1')
branch_2 = layers.conv2d(branch_2,
depth(128), [3, 3],
scope='Conv2d_0b_3x3')
branch_2 = layers.conv2d(branch_2,
depth(128), [3, 3],
scope='Conv2d_0c_3x3')
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.avg_pool2d(net, [3, 3],
scope='AvgPool_0a_3x3')
branch_3 = layers.conv2d(
branch_3,
depth(128), [1, 1],
weights_initializer=trunc_normal(0.1),
scope='Conv2d_0b_1x1')
net = array_ops.concat(
[branch_0, branch_1, branch_2, branch_3], 3)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# 14 x 14 x 576
end_point = 'Mixed_4c'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net,
depth(192), [1, 1],
scope='Conv2d_0a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(
net,
depth(96), [1, 1],
weights_initializer=trunc_normal(0.09),
scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(branch_1,
depth(128), [3, 3],
scope='Conv2d_0b_3x3')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(
net,
depth(96), [1, 1],
weights_initializer=trunc_normal(0.09),
scope='Conv2d_0a_1x1')
branch_2 = layers.conv2d(branch_2,
depth(128), [3, 3],
scope='Conv2d_0b_3x3')
branch_2 = layers.conv2d(branch_2,
depth(128), [3, 3],
scope='Conv2d_0c_3x3')
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.avg_pool2d(net, [3, 3],
scope='AvgPool_0a_3x3')
branch_3 = layers.conv2d(
branch_3,
depth(128), [1, 1],
weights_initializer=trunc_normal(0.1),
scope='Conv2d_0b_1x1')
net = array_ops.concat(
[branch_0, branch_1, branch_2, branch_3], 3)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# 14 x 14 x 576
end_point = 'Mixed_4d'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net,
depth(160), [1, 1],
scope='Conv2d_0a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(
net,
depth(128), [1, 1],
weights_initializer=trunc_normal(0.09),
scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(branch_1,
depth(160), [3, 3],
scope='Conv2d_0b_3x3')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(
net,
depth(128), [1, 1],
weights_initializer=trunc_normal(0.09),
scope='Conv2d_0a_1x1')
branch_2 = layers.conv2d(branch_2,
depth(160), [3, 3],
scope='Conv2d_0b_3x3')
branch_2 = layers.conv2d(branch_2,
depth(160), [3, 3],
scope='Conv2d_0c_3x3')
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.avg_pool2d(net, [3, 3],
scope='AvgPool_0a_3x3')
branch_3 = layers.conv2d(
branch_3,
depth(96), [1, 1],
weights_initializer=trunc_normal(0.1),
scope='Conv2d_0b_1x1')
net = array_ops.concat(
[branch_0, branch_1, branch_2, branch_3], 3)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# 14 x 14 x 576
end_point = 'Mixed_4e'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net,
depth(96), [1, 1],
scope='Conv2d_0a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(
net,
depth(128), [1, 1],
weights_initializer=trunc_normal(0.09),
scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(branch_1,
depth(192), [3, 3],
scope='Conv2d_0b_3x3')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(
net,
depth(160), [1, 1],
weights_initializer=trunc_normal(0.09),
scope='Conv2d_0a_1x1')
branch_2 = layers.conv2d(branch_2,
depth(192), [3, 3],
scope='Conv2d_0b_3x3')
branch_2 = layers.conv2d(branch_2,
depth(192), [3, 3],
scope='Conv2d_0c_3x3')
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.avg_pool2d(net, [3, 3],
scope='AvgPool_0a_3x3')
branch_3 = layers.conv2d(
branch_3,
depth(96), [1, 1],
weights_initializer=trunc_normal(0.1),
scope='Conv2d_0b_1x1')
net = array_ops.concat(
[branch_0, branch_1, branch_2, branch_3], 3)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# 14 x 14 x 576
end_point = 'Mixed_5a'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(
net,
depth(128), [1, 1],
weights_initializer=trunc_normal(0.09),
scope='Conv2d_0a_1x1')
branch_0 = layers.conv2d(branch_0,
depth(192), [3, 3],
stride=2,
scope='Conv2d_1a_3x3')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(
net,
depth(192), [1, 1],
weights_initializer=trunc_normal(0.09),
scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(branch_1,
depth(256), [3, 3],
scope='Conv2d_0b_3x3')
branch_1 = layers.conv2d(branch_1,
depth(256), [3, 3],
stride=2,
scope='Conv2d_1a_3x3')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers_lib.max_pool2d(net, [3, 3],
stride=2,
scope='MaxPool_1a_3x3')
net = array_ops.concat([branch_0, branch_1, branch_2], 3)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# 7 x 7 x 1024
end_point = 'Mixed_5b'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net,
depth(352), [1, 1],
scope='Conv2d_0a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(
net,
depth(192), [1, 1],
weights_initializer=trunc_normal(0.09),
scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(branch_1,
depth(320), [3, 3],
scope='Conv2d_0b_3x3')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(
net,
depth(160), [1, 1],
weights_initializer=trunc_normal(0.09),
scope='Conv2d_0a_1x1')
branch_2 = layers.conv2d(branch_2,
depth(224), [3, 3],
scope='Conv2d_0b_3x3')
branch_2 = layers.conv2d(branch_2,
depth(224), [3, 3],
scope='Conv2d_0c_3x3')
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.avg_pool2d(net, [3, 3],
scope='AvgPool_0a_3x3')
branch_3 = layers.conv2d(
branch_3,
depth(128), [1, 1],
weights_initializer=trunc_normal(0.1),
scope='Conv2d_0b_1x1')
net = array_ops.concat(
[branch_0, branch_1, branch_2, branch_3], 3)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# 7 x 7 x 1024
end_point = 'Mixed_5c'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net,
depth(352), [1, 1],
scope='Conv2d_0a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(
net,
depth(192), [1, 1],
weights_initializer=trunc_normal(0.09),
scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(branch_1,
depth(320), [3, 3],
scope='Conv2d_0b_3x3')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(
net,
depth(192), [1, 1],
weights_initializer=trunc_normal(0.09),
scope='Conv2d_0a_1x1')
branch_2 = layers.conv2d(branch_2,
depth(224), [3, 3],
scope='Conv2d_0b_3x3')
branch_2 = layers.conv2d(branch_2,
depth(224), [3, 3],
scope='Conv2d_0c_3x3')
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.max_pool2d(net, [3, 3],
scope='MaxPool_0a_3x3')
branch_3 = layers.conv2d(
branch_3,
depth(128), [1, 1],
weights_initializer=trunc_normal(0.1),
scope='Conv2d_0b_1x1')
net = array_ops.concat(
[branch_0, branch_1, branch_2, branch_3], 3)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
raise ValueError('Unknown final endpoint %s' % final_endpoint)
def vgg_a(inputs,
num_classes=1000,
is_training=True,
dropout_keep_prob=0.5,
spatial_squeeze=True,
scope='vgg_a'):
"""Oxford Net VGG 11-Layers version A Example.
Note: All the fully_connected layers have been transformed to conv2d layers.
To use in classification mode, resize input to 224x224.
Args:
inputs: a tensor of size [batch_size, height, width, channels].
num_classes: number of predicted classes.
is_training: whether or not the model is being trained.
dropout_keep_prob: the probability that activations are kept in the dropout
layers during training.
spatial_squeeze: whether or not should squeeze the spatial dimensions of the
outputs. Useful to remove unnecessary dimensions for classification.
scope: Optional scope for the variables.
Returns:
the last op containing the log predictions and end_points dict.
"""
with variable_scope.variable_scope(scope, 'vgg_a', [inputs]) as sc:
end_points_collection = sc.original_name_scope + '_end_points'
# Collect outputs for conv2d, fully_connected and max_pool2d.
with arg_scope([layers.conv2d, layers_lib.max_pool2d],
outputs_collections=end_points_collection):
net = layers_lib.repeat(inputs,
1,
layers.conv2d,
64, [3, 3],
scope='conv1')
net = layers_lib.max_pool2d(net, [2, 2], scope='pool1')
net = layers_lib.repeat(net,
1,
layers.conv2d,
128, [3, 3],
scope='conv2')
net = layers_lib.max_pool2d(net, [2, 2], scope='pool2')
net = layers_lib.repeat(net,
2,
layers.conv2d,
256, [3, 3],
scope='conv3')
net = layers_lib.max_pool2d(net, [2, 2], scope='pool3')
net = layers_lib.repeat(net,
2,
layers.conv2d,
512, [3, 3],
scope='conv4')
net = layers_lib.max_pool2d(net, [2, 2], scope='pool4')
net = layers_lib.repeat(net,
2,
layers.conv2d,
512, [3, 3],
scope='conv5')
net = layers_lib.max_pool2d(net, [2, 2], scope='pool5')
# Use conv2d instead of fully_connected layers.
net = layers.conv2d(net,
4096, [7, 7],
padding='VALID',
scope='fc6')
net = layers_lib.dropout(net,
dropout_keep_prob,
is_training=is_training,
scope='dropout6')
net = layers.conv2d(net, 4096, [1, 1], scope='fc7')
net = layers_lib.dropout(net,
dropout_keep_prob,
is_training=is_training,
scope='dropout7')
net = layers.conv2d(net,
num_classes, [1, 1],
activation_fn=None,
normalizer_fn=None,
scope='fc8')
# Convert end_points_collection into a end_point dict.
end_points = utils.convert_collection_to_dict(
end_points_collection)
if spatial_squeeze:
net = array_ops.squeeze(net, [1, 2], name='fc8/squeezed')
end_points[sc.name + '/fc8'] = net
return net, end_points
