def _resnet_plain(self, inputs, blocks, output_stride=None, scope=None):
"""A plain ResNet without extra layers before or after the ResNet blocks."""
with tf.variable_scope(scope, values=[inputs]):
with slim.arg_scope([slim.conv2d], outputs_collections='end_points'):
net = resnet_utils.stack_blocks_dense(inputs, blocks, output_stride)
end_points = slim.utils.convert_collection_to_dict('end_points')
return net, end_points
def resnet_v1(inputs,
blocks,
num_classes=None,
is_training=True,
global_pool=True,
output_stride=None,
include_root_block=True,
spatial_squeeze=True,
store_non_strided_activations=False,
reuse=None,
scope=None):
with tf.variable_scope(scope, 'resnet_v1', [inputs], reuse=reuse) as sc:
end_points_collection = sc.original_name_scope + '_end_points'
with slim.arg_scope(
[slim.conv2d, bottleneck, resnet_utils.stack_blocks_dense],
outputs_collections=end_points_collection):
with (slim.arg_scope([slim.batch_norm], is_training=is_training)
if is_training is not None else NoOpScope()):
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
net = resnet_utils.conv2d_same(net,
64,
7,
stride=2,
scope='conv1')
net = slim.max_pool2d(net, [3, 3], stride=2, scope='pool1')
net = resnet_utils.stack_blocks_dense(
net, blocks, output_stride, store_non_strided_activations)
# Convert end_points_collection into a dictionary of end_points.
end_points = slim.utils.convert_collection_to_dict(
end_points_collection)
if global_pool:
# Global average pooling.
net = tf.reduce_mean(input_tensor=net,
axis=[1, 2],
name='pool5',
keepdims=True)
end_points['global_pool'] = net
if num_classes:
net = slim.conv2d(net,
num_classes, [1, 1],
activation_fn=None,
normalizer_fn=None,
scope='logits')
end_points[sc.name + '/logits'] = net
if spatial_squeeze:
net = tf.squeeze(net, [1, 2], name='SpatialSqueeze')
end_points[sc.name + '/spatial_squeeze'] = net
end_points['predictions'] = slim.softmax(
net, scope='predictions')
return net, end_points
def resnet_v1(inputs,
blocks,
num_classes=None,
is_training=True,
global_pool=True,
output_stride=None,
include_root_block=True,
spatial_squeeze=True,
reuse=None,
scope=None):
with tf.variable_scope(scope, 'resnet_v1', [input], reuse=reuse) as sc:
end_points_collection = sc.name + '_end_points'
with slim.arg_scope(
[slim.conv2d, bottleneck, resnet_utils.stack_blocks_dense],
outputs_collections=end_points_collection):
with slim.arg_scope([slim.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
net = resnet_utils.conv2d_same(net,
64,
7,
stride=2,
scope='conv1')
net = slim.max_pool2d(net, [3, 3], stride=2, scope='pool1')
slim.utils.collect_named_outputs(end_points_collection,
'pool2', net)
net = resnet_utils.stack_blocks_dense(
net, blocks, output_stride=output_stride)
end_points = slim.utils.convert_collection_to_dict(
end_points_collection)
try:
end_points['pool3'] = end_points['resnet_v1_50/block1']
end_points['pool4'] = end_points['resnet_v1_50/block2']
except:
end_points['pool3'] = end_points[
'Detection/resnet_v1_50/block1']
end_points['pool4'] = end_points[
'Detection/resnet_v1_50/block2']
end_points['pool5'] = net
return net, end_points
def _atrousValues(self, bottleneck):
"""Verify the values of dense feature extraction by atrous convolution.
Make sure that dense feature extraction by stack_blocks_dense() followed by
subsampling gives identical results to feature extraction at the nominal
network output stride using the simple self._stack_blocks_nondense() above.
Args:
bottleneck: The bottleneck function.
"""
blocks = [
resnet_utils.Block('block1', bottleneck, [(4, 1, 1), (4, 1, 2)]),
resnet_utils.Block('block2', bottleneck, [(8, 2, 1), (8, 2, 2)]),
resnet_utils.Block('block3', bottleneck, [(16, 4, 1), (16, 4, 2)]),
resnet_utils.Block('block4', bottleneck, [(32, 8, 1), (32, 8, 1)])
]
nominal_stride = 8
# Test both odd and even input dimensions.
height = 30
width = 31
with slim.arg_scope(resnet_utils.resnet_arg_scope()):
with slim.arg_scope([slim.batch_norm], is_training=False):
for output_stride in [1, 2, 4, 8, None]:
with tf.Graph().as_default():
with self.test_session() as sess:
tf.set_random_seed(0)
inputs = create_test_input(1, height, width, 3)
# Dense feature extraction followed by subsampling.
output = resnet_utils.stack_blocks_dense(
inputs, blocks, output_stride)
if output_stride is None:
factor = 1
else:
factor = nominal_stride // output_stride
output = resnet_utils.subsample(output, factor)
# Make the two networks use the same weights.
tf.get_variable_scope().reuse_variables()
# Feature extraction at the nominal network rate.
expected = self._stack_blocks_nondense(
inputs, blocks)
sess.run(tf.global_variables_initializer())
output, expected = sess.run([output, expected])
self.assertAllClose(output,
expected,
atol=1e-4,
rtol=1e-4)
def resnet_v2(inputs,
blocks,
num_classes=None,
is_training=True,
global_pool=True,
output_stride=None,
include_root_block=True,
spatial_squeeze=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 is training or not.
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.
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.
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:
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 tf.variable_scope(scope, 'resnet_v2', [inputs], reuse=reuse) as sc:
end_points_collection = sc.name + '_end_points'
with slim.arg_scope(
[slim.conv2d, bottleneck, resnet_utils.stack_blocks_dense],
outputs_collections=end_points_collection):
with slim.arg_scope([slim.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 slim.arg_scope([slim.conv2d],
activation_fn=None,
normalizer_fn=None):
net = resnet_utils.conv2d_same(net,
64,
7,
stride=2,
scope='conv1')
net = slim.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 = slim.batch_norm(net,
activation_fn=tf.nn.relu,
scope='postnorm')
if global_pool:
# Global average pooling.
net = tf.reduce_mean(net, [1, 2],
name='pool5',
keep_dims=True)
if num_classes is not None:
net = slim.conv2d(net,
num_classes, [1, 1],
activation_fn=None,
normalizer_fn=None,
scope='logits')
if spatial_squeeze:
net = tf.squeeze(net, [1, 2], name='SpatialSqueeze')
# Convert end_points_collection into a dictionary of end_points.
end_points = slim.utils.convert_collection_to_dict(
end_points_collection)
if num_classes is not None:
end_points['predictions'] = slim.softmax(
net, scope='predictions')
return net, end_points
def resnet_v1(inputs,
blocks,
num_classes=None,
is_training=True,
global_pool=True,
output_stride=None,
include_root_block=True,
spatial_squeeze=True,
reuse=None,
scope=None):
"""Generator for v1 ResNet models.
This function generates a family of ResNet v1 models. See the resnet_v1_*()
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 is training or not.
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.
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.
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 tf.variable_scope(scope, 'resnet_v1', [inputs], reuse=reuse) as sc:
end_points_collection = sc.name + '_end_points'
with slim.arg_scope([slim.conv2d, bottleneck,
resnet_utils.stack_blocks_dense],
outputs_collections=end_points_collection):
with slim.arg_scope([slim.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
net = resnet_utils.conv2d_same(net, 64, 7, stride=2, scope='conv1')
net = slim.max_pool2d(net, [3, 3], stride=2, scope='pool1')
net = slim.utils.collect_named_outputs(end_points_collection, 'pool2', net)
net = resnet_utils.stack_blocks_dense(net, blocks, output_stride)
end_points = slim.utils.convert_collection_to_dict(end_points_collection)
# end_points['pool2'] = end_points['resnet_v1_50/pool1/MaxPool:0']
try:
end_points['pool3'] = end_points['resnet_v1_50/block1']
end_points['pool4'] = end_points['resnet_v1_50/block2']
except:
end_points['pool3'] = end_points['Detection/resnet_v1_50/block1']
end_points['pool4'] = end_points['Detection/resnet_v1_50/block2']
end_points['pool5'] = net
# if global_pool:
# # Global average pooling.
# net = tf.reduce_mean(net, [1, 2], name='pool5', keep_dims=True)
# if num_classes is not None:
# net = slim.conv2d(net, num_classes, [1, 1], activation_fn=None,
# normalizer_fn=None, scope='logits')
# if spatial_squeeze:
# logits = tf.squeeze(net, [1, 2], name='SpatialSqueeze')
# else:
# logits = net
# # Convert end_points_collection into a dictionary of end_points.
# end_points = slim.utils.convert_collection_to_dict(end_points_collection)
# if num_classes is not None:
# end_points['predictions'] = slim.softmax(logits, scope='predictions')
return net, end_points
def resnet_v1(inputs,
blocks,
num_classes=None,
is_training=True,
global_pool=True,
output_stride=None,
include_root_block=True,
spatial_squeeze=True,
store_non_strided_activations=False,
reuse=None,
scope=None):
"""Generator for v1 ResNet models.
This function generates a family of ResNet v1 models. See the resnet_v1_*()
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 0 or None, we return the features before the logit layer.
is_training: whether batch_norm layers are in training mode. If this is set
to None, the callers can specify slim.batch_norm's is_training parameter
from an outer slim.arg_scope.
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.
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.
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.
store_non_strided_activations: If True, we compute non-strided (undecimated)
activations at the last unit of each block and store them in the
`outputs_collections` before subsampling them. This gives us access to
higher resolution intermediate activations which are useful in some
dense prediction problems but increases 4x the computation and memory cost
at the last unit of each block.
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 0 or None,
then net is the output of the last ResNet block, potentially after global
average pooling. If num_classes a non-zero integer, 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 tf.variable_scope(scope, 'resnet_v1', [inputs], reuse=reuse) as sc:
end_points_collection = sc.original_name_scope + '_end_points'
with slim.arg_scope(
[slim.conv2d, bottleneck, resnet_utils.stack_blocks_dense],
outputs_collections=end_points_collection):
with (slim.arg_scope([slim.batch_norm], is_training=is_training)
if is_training is not None else NoOpScope()):
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
# Use slim.conv2d instead of resnet_utils.conv2d_same
# net = resnet_utils.conv2d_same(net, 64, 7, stride=2, scope='conv1')
# net = slim.max_pool2d(net, [3, 3], stride=2, scope='pool1')
net = slim.conv2d(net,
64, [3, 3],
stride=2,
padding='SAME')
net = resnet_utils.stack_blocks_dense(
net, blocks, output_stride, store_non_strided_activations)
# Convert end_points_collection into a dictionary of end_points.
end_points = slim.utils.convert_collection_to_dict(
end_points_collection)
if global_pool:
# Global average pooling. Use AvgPool instead of reduce_mean to insert fake_quant.
# net = tf.reduce_mean(net, [1, 2], name='pool5', keep_dims=True)
convout_shape = net.get_shape().as_list()
net = slim.avg_pool2d(net,
[convout_shape[1], convout_shape[2]],
padding='VALID',
scope='AvgPool')
end_points['global_pool'] = net
if num_classes:
net = slim.conv2d(net, num_classes, [1, 1], scope='logits')
end_points[sc.name + '/logits'] = net
if spatial_squeeze:
net = tf.squeeze(net, [1, 2], name='SpatialSqueeze')
end_points[sc.name + '/spatial_squeeze'] = net
end_points['predictions'] = slim.softmax(
net, scope='predictions')
return net, end_points
def resnet_v2(inputs,
blocks,
num_classes=None,
is_training=True,
global_pool=True,
output_stride=None,
include_root_block=True,
spatial_squeeze=True,
reuse=None,
scope=None):
with tf.variable_scope(scope, 'resnet_v2', [inputs], reuse=reuse) as sc:
end_points_collection = sc.name + '_end_points'
with slim.arg_scope(
[slim.conv2d, bottleneck, resnet_utils.stack_blocks_dense],
outputs_collections=end_points_collection):
with slim.arg_scope([slim.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 slim.arg_scope([slim.conv2d],
activation_fn=None,
normalizer_fn=None):
net = resnet_utils.conv2d_same(net,
64,
6,
stride=1,
scope='conv1')
net = slim.max_pool2d(net, [2, 2], 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 = slim.batch_norm(net,
activation_fn=tf.nn.relu,
scope='postnorm')
output0 = net
if global_pool:
# Global average pooling.
net = tf.reduce_mean(net, [1, 2],
name='pool5',
keep_dims=True)
output1 = net
if num_classes is not None:
net = slim.conv2d(net,
num_classes, [1, 1],
activation_fn=None,
normalizer_fn=None,
scope='logits')
if spatial_squeeze:
logits = tf.squeeze(net, [1, 2], name='SpatialSqueeze')
# Convert end_points_collection into a dictionary of end_points.
end_points = slim.utils.convert_collection_to_dict(
end_points_collection)
if num_classes is not None:
end_points['predictions'] = slim.softmax(
logits, scope='predictions')
return logits, end_points, output0, output1
def testStridingLastUnitVsSubsampleBlockEnd(self):
"""Compares subsampling at the block's last unit or block's end.
Makes sure that the final output is the same when we use a stride at the
last unit of a block vs. we subsample activations at the end of a block.
"""
block = resnet_v1.resnet_v1_block
blocks = [
block('block1', base_depth=1, num_units=2, stride=2),
block('block2', base_depth=2, num_units=2, stride=2),
block('block3', base_depth=4, num_units=2, stride=2),
block('block4', base_depth=8, num_units=2, stride=1),
]
# Test both odd and even input dimensions.
height = 30
width = 31
with slim.arg_scope(resnet_utils.resnet_arg_scope()):
with slim.arg_scope([slim.batch_norm], is_training=False):
for output_stride in [1, 2, 4, 8, None]:
with tf.Graph().as_default():
with self.test_session() as sess:
tf.set_random_seed(0)
inputs = create_test_input(1, height, width, 3)
# Subsampling at the last unit of the block.
output = resnet_utils.stack_blocks_dense(
inputs, blocks, output_stride,
store_non_strided_activations=False,
outputs_collections='output')
output_end_points = slim.utils.convert_collection_to_dict(
'output')
# Make the two networks use the same weights.
tf.get_variable_scope().reuse_variables()
# Subsample activations at the end of the blocks.
expected = resnet_utils.stack_blocks_dense(
inputs, blocks, output_stride,
store_non_strided_activations=True,
outputs_collections='expected')
expected_end_points = slim.utils.convert_collection_to_dict(
'expected')
sess.run(tf.global_variables_initializer())
# Make sure that the final output is the same.
output, expected = sess.run([output, expected])
self.assertAllClose(output, expected, atol=1e-4, rtol=1e-4)
# Make sure that intermediate block activations in
# output_end_points are subsampled versions of the corresponding
# ones in expected_end_points.
for i, block in enumerate(blocks[:-1:]):
output = output_end_points[block.scope]
expected = expected_end_points[block.scope]
atrous_activated = (output_stride is not None and
2 ** i >= output_stride)
if not atrous_activated:
expected = resnet_utils.subsample(expected, 2)
output, expected = sess.run([output, expected])
self.assertAllClose(output, expected, atol=1e-4, rtol=1e-4)
def _build_pyramid(self):
def _self_attention(U):
shape = tf.shape(U)
U = tf.transpose(U, [0, 3, 1, 2])
U = tf.reshape(U, [-1, shape[1] * shape[2]])
U = tf.nn.softmax(U)
U = tf.reshape(U, [-1, shape[3], shape[1], shape[2]])
U = tf.transpose(U, [0, 2, 3, 1])
return U
def _repadding_Up_to_C(Up, C):
shape_Up = Up.get_shape().as_list()
shape_C = C.get_shape().as_list()
kernel_shape = [
shape_Up[1] - shape_C[1] + 1, shape_Up[2] - shape_C[2] + 1
]
return kernel_shape
with tf.variable_scope('get_feature_maps'):
self.end_points[self.scope + '/C' +
str(4)] = self.end_points[self.scope + '/block' +
str(4)]
for i in range(3, 0, -1):
self.end_points[self.scope + '/C' + str(i)] = self.end_points[
self.scope + '/block' + str(i) + '/unit_' +
str(self.block_num[i - 1] - 1) + '/bottleneck_v2']
with tf.variable_scope('feature_pyramid'):
"""
net = self.end_points[self.scope + '/C5']
net = slim.batch_norm(net, activation_fn=tf.nn.relu, scope='P5_postnorm')
self.end_points[self.scope + '/P5'] = net
# self.end_points[self.scope + '/P5_visual'] = df._to_c_h_w_1(tf.image.resize_bicubic(net, [36, 60], name='build_P%d/visual' % 5))
self.predict_layers.append(self.scope + '/P5')
for layer in range(4, 5-self.build_pyramid_layers, -1):
P = self.end_points[self.scope + '/P' + str(layer + 1)]
C = self.end_points[self.scope + '/C' + str(layer)]
C = resnet_utils.stack_blocks_dense(
C,
self.reduce_dimension('build_P%d' % layer)
)
C = slim.batch_norm(C, activation_fn=tf.nn.relu, scope='build_P%d/reduce_dimension_postnorm' % layer)
# resize_image only can be operated by CPU
# Up_sample_shape = tf.shape(C)
# Up_sample = tf.image.resize_bicubic(P, [Up_sample_shape[1], Up_sample_shape[2]], name='build_P%d/up_sample_bicubic' % layer)
# conv2d_transpose
Up_sample = slim.conv2d_transpose(P, P.get_shape().as_list()[3], [2, 2], stride=2, padding='SAME', scope='build_P%d/up_sample_conv2d' % layer)
Up_sample = slim.conv2d(Up_sample, P.get_shape().as_list()[3], _repadding_Up_to_C(Up_sample, C), stride=1, padding='VALID', scope='build_P%d/up_sample_pad' % layer)
if self.fusion == 'sum':
# element_wise_sum
P = Up_sample + C
elif self.fusion == 'concat':
# concat
P = tf.concat([Up_sample, C], axis=2)
elif self.fusion == 'attention':
# self-attention & element_wise_dot
Up_sample = _self_attention(Up_sample)
P = Up_sample * C
else:
raise Exception('wrong fusion method!')
P = resnet_utils.stack_blocks_dense(
P,
self.reduce_confusion('build_P%d' % layer)
)
P = slim.batch_norm(P, activation_fn=tf.nn.relu, scope='build_P%d/reduce_confusion_postnorm' % layer)
self.end_points[self.scope + '/P' + str(layer)] = P
# self.end_points[self.scope + '/P' + str(layer) + '_visual'] = df._to_c_h_w_1(tf.image.resize_bicubic(P, [36, 60], name='build_P%d/visual' % layer))
self.predict_layers.append(self.scope + '/P' + str(layer))
"""
for layer in range(4, 4 - self.build_pyramid_layers, -1):
net = self.end_points[self.scope + '/C' + str(layer)]
net = resnet_utils.stack_blocks_dense(
net, self.reduce_dimension('build_P%d' % layer))
# net = slim.batch_norm(net, activation_fn=tf.nn.relu, scope='P%d_postnorm' % layer)
net = tf.nn.relu(net)
self.end_points[self.scope + '/P' + str(layer)] = net
# self.end_points[self.scope + '/P' + str(layer) + '_visual'] = df._to_c_h_w_1(tf.image.resize_bicubic(net, [36, 60], name='build_P%d/visual' % layer))
self.predict_layers.append(self.scope + '/P' + str(layer))
def resnet_v2(inputs,
blocks,
num_classes=None,
is_training=True,
global_pool=True,
output_stride=None,
include_root_block=True,
spatial_squeeze=True,
reuse=None,
scope=None):
with tf.variable_scope(scope, 'resnet_v2', [inputs], reuse=reuse) as sc:
end_points_collection = sc.name + '_end_points'
with slim.arg_scope(
[slim.conv2d, bottleneck, resnet_utils.stack_blocks_dense],
outputs_collections=end_points_collection):
with slim.arg_scope([slim.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
with slim.arg_scope([slim.conv2d],
activation_fn=None,
normalizer_fn=None):
net = resnet_utils.conv2d_same(net,
64,
6,
stride=1,
scope='conv1')
net = slim.max_pool2d(net, [2, 2], stride=2, scope='pool1')
net = resnet_utils.stack_blocks_dense(net, blocks,
output_stride)
net = slim.batch_norm(net,
activation_fn=tf.nn.relu,
scope='postnorm')
output0 = net
if global_pool:
net = tf.reduce_mean(net, [1, 2],
name='pool5',
keep_dims=True)
output1 = net
if num_classes is not None:
net = slim.conv2d(net,
num_classes, [1, 1],
activation_fn=None,
normalizer_fn=None,
scope='logits')
if spatial_squeeze:
logits = tf.squeeze(net, [1, 2], name='SpatialSqueeze')
end_points = slim.utils.convert_collection_to_dict(
end_points_collection)
if num_classes is not None:
end_points['predictions'] = slim.softmax(
logits, scope='predictions')
return logits, end_points, output0, output1
