def _combine_conv(inputs,
layer,
training,
out_channels,
padding='VALID',
is_batchnorm=True):
conv = conv2d(inputs,
kernel=3,
out_channels=out_channels,
stride=1,
padding=padding,
name='combine_conv' + layer)
print('conv layer: %s, shape: %s' % (layer, conv.get_shape()))
if is_batchnorm:
bn = tf.layers.batch_normalization(conv,
axis=1,
center=True,
scale=False,
training=training,
name='bn' + layer)
else:
bn = conv
relu = tf.nn.relu(bn, name='relu' + layer)
print('layer: %s, shape: %s' % (layer, relu.get_shape()))
return relu
def _combine_conv(inputs, layer, training, in_channels, out_channels, kernel=3, padding='SAME', is_batchnorm=False):
def _weights_initializer(weights):
if weights.shape != (kernel, kernel, in_channels, out_channels):
weights = np.reshape(weights, (kernel, kernel, in_channels, out_channels))
return tf.constant_initializer(weights)
conv = conv2d(
inputs, kernel=kernel,
out_channels=out_channels, stride=1,
weights_regularizer=tf.contrib.layers.l2_regularizer(scale=0.0005),
weights_initializer=_weights_initializer(init_weights['conv' + layer + '_W']),
biases_initializer=tf.constant_initializer(init_weights['conv' + layer + '_b']),
padding=padding, name='combine_conv' + layer
)
print('conv layer: %s, shape: %s' % (layer, conv.get_shape()))
if is_batchnorm:
bn = tf.layers.batch_normalization(conv, axis=1, center=True, scale=False,
training=training, name='bn' + layer)
else:
bn = conv
relu = tf.nn.relu(bn, name='relu' + layer)
print('layer: %s, shape: %s' % (layer, relu.get_shape()))
return relu
def _combine_conv(inputs,
layer,
training,
kernel=5,
padding='VALID',
out_channels=32):
conv = conv2d(inputs,
kernel=kernel,
out_channels=out_channels,
stride=1,
padding=padding,
name='combine_conv' + layer)
print('conv layer: %s, shape: %s' % (layer, conv.get_shape()))
bn = tf.layers.batch_normalization(conv,
axis=1,
center=True,
scale=False,
training=training,
name='bn' + layer)
relu = tf.nn.relu(bn, name='relu' + layer)
pool = tf.layers.max_pooling2d(inputs=relu,
pool_size=[2, 2],
strides=[2, 2],
data_format="channels_first",
name='pool' + layer)
print('layer: %s, shape: %s\n' % (layer, pool.get_shape()))
return pool
def _combine_deconv(inputs,
layer,
training,
conv_val,
kernel_deconv=5,
kernel_conv=3,
deconv_out_channels=32,
conv_out_channels=32,
conv_padding='SAME'):
deconv1 = deconv(inputs,
kernel=kernel_deconv,
out_channels=deconv_out_channels,
stride=2,
data_format='NCHW',
activation_fn=tf.nn.relu,
name='deconv' + layer)
print('deconv layer: %s, deconv shape: %s' % (layer, deconv1.get_shape()))
if conv_val is not None:
print('conv layer to be concatenated, shape: %s' %
(conv_val.get_shape()))
concat1 = tf.concat([conv_val, deconv1], axis=1, name='concat' + layer)
else:
concat1 = deconv1
conv = conv2d(concat1,
kernel=kernel_conv,
out_channels=conv_out_channels,
stride=1,
data_format='NCHW',
padding=conv_padding,
name='deconv_conv' + layer)
print('deconv layer: %s, conv shape: %s' % (layer, conv.get_shape()))
bn = tf.layers.batch_normalization(conv,
axis=-1,
center=True,
scale=False,
training=training,
name='deconv_bn' + layer)
print('deconv layer: %s, final shape: %s\n' % (layer, bn.get_shape()))
return bn
def inference(inputs, num_classes, training=False, name='unet'):
with tf.variable_scope(name) as scope:
conv1 = conv2d(inputs,
kernel=3,
out_channels=32,
stride=1,
padding='SAME',
activation_fn=tf.nn.relu,
normalizer_fn=tf.contrib.layers.batch_norm,
name='relu_conv1')
print('conv1 shape: %s' % conv1.get_shape())
pool1 = tf.nn.max_pool(conv1,
ksize=[1, 1, 2, 2],
strides=[1, 1, 2, 2],
padding='VALID',
data_format='NCHW',
name='pool1')
print('pool1 shape: %s' % pool1.get_shape())
conv2 = conv2d(pool1,
kernel=3,
out_channels=64,
stride=1,
padding='SAME',
activation_fn=tf.nn.relu,
normalizer_fn=tf.contrib.layers.batch_norm,
name='relu_conv2')
print('conv2 shape: %s' % conv2.get_shape())
pool2 = tf.nn.max_pool(conv2,
ksize=[1, 1, 2, 2],
strides=[1, 1, 2, 2],
padding='VALID',
data_format='NCHW',
name='pool2')
print('pool2 shape: %s' % pool2.get_shape())
conv3 = conv2d(pool2,
kernel=3,
out_channels=128,
stride=1,
padding='SAME',
activation_fn=tf.nn.relu,
normalizer_fn=tf.contrib.layers.batch_norm,
name='relu_conv3')
print('conv3 shape: %s' % conv3.get_shape())
pool3 = tf.nn.max_pool(conv3,
ksize=[1, 1, 2, 2],
strides=[1, 1, 2, 2],
padding='VALID',
data_format='NCHW',
name='pool3')
print('pool3 shape: %s' % pool3.get_shape())
pool3_dropout = tf.layers.dropout(pool3,
0.5,
training=training,
name='pool3_dropout')
deconv1 = deconv(pool3_dropout,
kernel=2,
out_channels=128,
stride=2,
data_format='NCHW',
activation_fn=tf.nn.relu,
normalizer_fn=tf.contrib.layers.batch_norm,
name='deconv1')
print('deconv1 shape: %s' % deconv1.get_shape())
deconv1_conv = conv2d(deconv1,
kernel=3,
out_channels=128,
stride=1,
padding='SAME',
activation_fn=tf.nn.relu,
normalizer_fn=tf.contrib.layers.batch_norm,
name='deconv1_conv')
print('deconv1_conv shape: %s' % deconv1_conv.get_shape())
concat1 = tf.concat([pool2, deconv1_conv], axis=1, name='concat1')
dropout1 = tf.layers.dropout(concat1,
0.5,
training=training,
name='dropout1')
concat1_conv = conv2d(dropout1,
kernel=3,
out_channels=128,
stride=1,
padding='SAME',
activation_fn=tf.nn.relu,
normalizer_fn=tf.contrib.layers.batch_norm,
name='concat1_conv')
print('concat1_conv shape: %s' % concat1_conv.get_shape())
deconv2 = deconv(concat1_conv,
kernel=2,
out_channels=128,
stride=2,
data_format='NCHW',
activation_fn=tf.nn.relu,
normalizer_fn=tf.contrib.layers.batch_norm,
name='deconv2')
print('deconv2 shape: %s' % deconv2.get_shape())
deconv2_conv = conv2d(deconv2,
kernel=3,
out_channels=128,
stride=1,
padding='SAME',
activation_fn=tf.nn.relu,
normalizer_fn=tf.contrib.layers.batch_norm,
name='deconv2_conv')
print('deconv2_conv shape: %s' % deconv2_conv.get_shape())
concat2 = tf.concat([pool1, deconv2_conv], axis=1, name='concat2')
dropout2 = tf.layers.dropout(concat2,
0.5,
training=training,
name='dropout2')
concat2_conv = conv2d(dropout2,
kernel=3,
out_channels=128,
stride=1,
padding='SAME',
activation_fn=tf.nn.relu,
normalizer_fn=tf.contrib.layers.batch_norm,
name='concat2_conv')
print('concat2_conv shape: %s' % concat2_conv.get_shape())
deconv3 = deconv(concat2_conv,
kernel=2,
out_channels=128,
stride=2,
data_format='NCHW',
activation_fn=tf.nn.relu,
normalizer_fn=tf.contrib.layers.batch_norm,
name='deconv3')
print('deconv3 shape: %s' % deconv3.get_shape())
deconv3_conv = conv2d(deconv3,
kernel=3,
out_channels=128,
stride=1,
padding='SAME',
activation_fn=tf.nn.relu,
name='deconv3_conv')
print('deconv3_conv shape: %s' % deconv3_conv.get_shape())
class_conv = conv2d(deconv3_conv,
kernel=3,
out_channels=num_classes,
stride=1,
padding='SAME',
name='class_conv')
print('class_conv shape: %s' % class_conv.get_shape())
label_logits = tf.transpose(class_conv, perm=[0, 2, 3, 1])
label_logits = tf.check_numerics(
label_logits, message="nan or inf from: label_logits")
print('label_logits shape: %s' % label_logits.get_shape())
return label_logits
def inference(inputs, num_classes, name='unet'):
with tf.variable_scope(name) as scope:
conv1 = conv2d(inputs,
kernel=5,
out_channels=128,
stride=1,
padding='VALID',
activation_fn=tf.nn.relu,
name='relu_conv1')
print('conv1 shape: %s' % conv1.get_shape())
# max_pool1 = tf.nn.max_pool(
# conv1,
# ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1],
# padding='VALID', data_format='NCHW', name='max_pool1'
# )
conv2 = conv2d(conv1,
kernel=5,
out_channels=128,
stride=1,
padding='VALID',
activation_fn=tf.nn.relu,
name='relu_conv2')
print('conv2 shape: %s' % conv2.get_shape())
# max_pool2 = tf.nn.max_pool(
# conv2,
# ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1],
# padding='VALID', data_format='NCHW', name='max_pool2'
# )
conv3 = conv2d(conv2,
kernel=3,
out_channels=256,
stride=2,
padding='VALID',
activation_fn=tf.nn.relu,
name='relu_conv3')
print('conv3 shape: %s' % conv3.get_shape())
# max_pool3 = tf.nn.max_pool(
# conv3,
# ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1],
# padding='VALID', data_format='NCHW', name='max_pool1'
# )
conv3 = tf.check_numerics(conv3, message="nan or inf from: conv3")
deconv1 = deconv(conv3,
kernel=4,
out_channels=128,
stride=2,
data_format='NCHW',
activation_fn=tf.nn.relu,
name='deconv1')
print('deconv1 shape: %s' % deconv1.get_shape())
concat1 = tf.concat([conv2, deconv1], axis=1, name='concat1')
# print('concat1 shape: %s' % concat1.get_shape())
deconv1_conv = conv2d(concat1,
kernel=3,
out_channels=128,
stride=1,
padding='SAME',
activation_fn=tf.nn.relu,
name='deconv1_conv')
deconv2 = deconv(deconv1_conv,
kernel=5,
out_channels=128,
stride=1,
data_format='NCHW',
activation_fn=tf.nn.relu,
name='deconv2')
print('deconv2 shape: %s' % deconv2.get_shape())
concat2 = tf.concat([conv1, deconv2], axis=1, name='concat2')
# print('concat2 shape: %s' % concat2.get_shape())
deconv2_conv = conv2d(concat2,
kernel=3,
out_channels=128,
stride=1,
padding='SAME',
activation_fn=tf.nn.relu,
name='deconv2_conv')
deconv3 = deconv(deconv2_conv,
kernel=5,
out_channels=num_classes,
stride=1,
data_format='NCHW',
activation_fn=tf.nn.relu,
name='deconv3')
# print('deconv3 shape: %s' % deconv3.get_shape())
deconv3_conv = conv2d(deconv3,
kernel=3,
out_channels=num_classes,
stride=1,
padding='SAME',
activation_fn=tf.nn.relu,
name='deconv3_conv')
label_logits = tf.transpose(deconv3_conv, perm=[0, 2, 3, 1])
label_logits = tf.check_numerics(
label_logits, message="nan or inf from: label_logits")
print('label_logits shape: %s' % label_logits.get_shape())
return label_logits
def _decode(activations, capsule_num, coupling_coeffs, num_classes, batch_size,
pool1, pool2, training):
capsule_probs = tf.norm(activations,
axis=-1) # # (b, 32, 4, 20, 8) -> (b, 32, 4, 20)
caps_probs_tiled = tf.tile(tf.expand_dims(capsule_probs, -1),
[1, 1, 1, 1, num_classes]) # (b, 32, 4, 20, 2)
# caps_probs_tiled = tf.check_numerics(caps_probs_tiled, message="nan or inf from: caps_probs_tiled")
print('coupling_coeffs shape: %s' % coupling_coeffs.get_shape())
activations_shape = activations.get_shape()
height, width = activations_shape[2].value, activations_shape[3].value
coupling_coeff_reshaped = tf.reshape(
coupling_coeffs, [batch_size, capsule_num, height, width, num_classes
]) # (b, 32, 4, 20, 2)
# coupling_coeff_reshaped = tf.check_numerics(coupling_coeff_reshaped, message="nan or inf from: coupling_coeff_reshaped")
primary_labels = tf.reduce_sum(coupling_coeff_reshaped * caps_probs_tiled,
1) # (b, 4, 20, 2)
# class_labels = tf.Print(class_labels, [tf.constant("class_labels"), class_labels])
# class_labels = tf.check_numerics(class_labels, message="nan or inf from: class_labels")
# primary_labels = tf.reduce_sum(caps_probs_tiled, 1)
# deconv1 = deconv(
# class_labels,
# kernel=3, out_channels=num_classes, stride=1,
# activation_fn=tf.nn.relu, name='deconv1'
# )
# deconv1 = tf.Print(deconv1, [tf.constant("deconv1"), deconv1])
print('primary_labels shape: %s' % primary_labels.get_shape())
primary_conv = conv2d(tf.transpose(primary_labels, perm=[0, 3, 1, 2]),
kernel=3,
out_channels=256,
stride=1,
padding='SAME',
activation_fn=tf.nn.relu,
name='primary_conv')
print('primary_conv shape: %s' % primary_conv.get_shape())
deconv1 = deconv(primary_conv,
kernel=3,
out_channels=128,
stride=1,
data_format='NCHW',
activation_fn=tf.nn.relu,
normalizer_fn=tf.contrib.layers.batch_norm,
name='deconv1')
print('deconv1 shape: %s' % deconv1.get_shape())
# deconv1_conv = conv2d(
# deconv1,
# kernel=3, out_channels=128, stride=1, padding='SAME',
# activation_fn=tf.nn.relu, normalizer_fn=tf.contrib.layers.batch_norm,
# name='deconv1_conv'
# )
# print('deconv1_conv shape: %s' % deconv1_conv.get_shape())
concat1 = tf.concat([pool2, deconv1], axis=1, name='concat1')
dropout1 = tf.layers.dropout(concat1,
0.5,
training=training,
name='dropout1')
concat1_conv = conv2d(dropout1,
kernel=2,
out_channels=128,
stride=1,
padding='VALID',
activation_fn=tf.nn.relu,
normalizer_fn=tf.contrib.layers.batch_norm,
name='concat1_conv')
print('concat1_conv shape: %s' % concat1_conv.get_shape())
deconv2 = deconv(concat1_conv,
kernel=4,
out_channels=128,
stride=2,
data_format='NCHW',
activation_fn=tf.nn.relu,
normalizer_fn=tf.contrib.layers.batch_norm,
name='deconv2')
print('deconv2 shape: %s' % deconv2.get_shape())
# deconv2_conv = conv2d(
# deconv2,
# kernel=3, out_channels=128, stride=1, padding='SAME',
# activation_fn=tf.nn.relu, normalizer_fn=tf.contrib.layers.batch_norm,
# name='deconv2_conv'
# )
# print('deconv2_conv shape: %s' % deconv2_conv.get_shape())
concat2 = tf.concat([pool1, deconv2], axis=1, name='concat2')
dropout2 = tf.layers.dropout(concat2,
0.5,
training=training,
name='dropout2')
concat2_conv = conv2d(dropout2,
kernel=2,
out_channels=128,
stride=1,
padding='VALID',
activation_fn=tf.nn.relu,
normalizer_fn=tf.contrib.layers.batch_norm,
name='concat2_conv')
print('concat2_conv shape: %s' % concat2_conv.get_shape())
deconv3 = deconv(concat2_conv,
kernel=4,
out_channels=128,
stride=2,
data_format='NCHW',
activation_fn=tf.nn.relu,
normalizer_fn=tf.contrib.layers.batch_norm,
name='deconv3')
print('deconv3 shape: %s' % deconv3.get_shape())
# deconv3_conv = conv2d(
# deconv3,
# kernel=3, out_channels=128, stride=1, padding='SAME',
# activation_fn=tf.nn.relu, name='deconv3_conv'
# )
# print('deconv3_conv shape: %s' % deconv3_conv.get_shape())
class_conv = conv2d(deconv3,
kernel=3,
out_channels=num_classes,
stride=1,
padding='SAME',
name='class_conv')
print('class_conv shape: %s' % class_conv.get_shape())
label_logits = tf.transpose(class_conv, perm=[0, 2, 3, 1])
print('label_logits shape: %s' % label_logits.get_shape())
# label_logits = tf.Print(label_logits, [tf.constant("label_logits"), label_logits])
return label_logits
def inference(inputs,
num_classes,
routing_ites=4,
remake=False,
training=False,
name='capsnet_1d'):
"""
:param inputs:
:param num_classes:
:param routing_ites:
:param remake:
:param name:
:return:
"""
with tf.variable_scope(name) as scope:
inputs_shape = inputs.get_shape()
batch_size = inputs_shape[0].value
image_height = inputs_shape[2].value
image_width = inputs_shape[3].value
# ReLU Conv1
# Images shape (b, 1, 24, 56) -> conv 5x5 filters, 32 output channels, strides 2 with padding, ReLU
# nets -> (b, 256, 16, 48)
print('inputs shape: %s' % inputs.get_shape())
inputs = tf.check_numerics(inputs, message="nan or inf from: inputs")
conv1 = conv2d(inputs,
kernel=3,
out_channels=32,
stride=1,
padding='SAME',
activation_fn=tf.nn.relu,
normalizer_fn=tf.contrib.layers.batch_norm,
name='relu_conv1')
print('conv1 shape: %s' % conv1.get_shape())
pool1 = tf.nn.max_pool(conv1,
ksize=[1, 1, 2, 2],
strides=[1, 1, 2, 2],
padding='VALID',
data_format='NCHW',
name='pool1')
print('pool1 shape: %s' % pool1.get_shape())
conv2 = conv2d(pool1,
kernel=3,
out_channels=64,
stride=1,
padding='SAME',
activation_fn=tf.nn.relu,
normalizer_fn=tf.contrib.layers.batch_norm,
name='relu_conv2')
print('conv2 shape: %s' % conv2.get_shape())
pool2 = tf.nn.max_pool(conv2,
ksize=[1, 1, 2, 2],
strides=[1, 1, 2, 2],
padding='VALID',
data_format='NCHW',
name='pool2')
print('pool2 shape: %s' % pool2.get_shape())
pool2_dropout = tf.layers.dropout(pool2,
0.5,
training=training,
name='pool2_dropout')
# conv3 = conv2d(
# pool2,
# kernel=3, out_channels=128, stride=1, padding='SAME',
# activation_fn=tf.nn.relu, normalizer_fn=tf.contrib.layers.batch_norm,
# name='relu_conv3'
# )
# print('conv3 shape: %s' % conv3.get_shape())
# pool3 = tf.nn.max_pool(
# conv3,
# ksize=[1, 1, 2, 2], strides=[1, 1, 2, 2],
# padding='VALID', data_format='NCHW', name='pool3'
# )
# print('pool3 shape: %s' % pool3.get_shape())
print("\nprimary layer:")
primary_out_capsules = 32
primary_caps_activations, conv_primary = primary_caps1d(
pool2_dropout,
kernel_size=3,
out_capsules=primary_out_capsules,
stride=1,
padding='VALID',
activation_length=8,
name='primary_caps') # (b, 32, 4, 20, 8)
print("\nclass capsule layer:")
class_caps_activations, class_coupling_coeffs = class_caps1d(
primary_caps_activations,
num_classes=num_classes,
activation_length=16,
routing_ites=routing_ites,
batch_size=batch_size,
name='class_capsules')
# class_coupling_coeffs = tf.Print(class_coupling_coeffs, [class_coupling_coeffs], summarize=50)
# class_caps_activations = tf.check_numerics(class_caps_activations, message="nan or inf from: class_caps_activations")
print('class_coupling_coeffs shape: %s' %
class_coupling_coeffs.get_shape())
print('class_caps_activations shape: %s' %
class_caps_activations.get_shape())
if remake:
remakes_flatten = _remake(class_caps_activations,
image_height * image_width)
else:
remakes_flatten = None
print("\ndecode layers:")
label_logits = _decode(primary_caps_activations,
primary_out_capsules,
coupling_coeffs=class_coupling_coeffs,
num_classes=num_classes,
batch_size=batch_size,
pool1=pool1,
pool2=pool2,
training=training)
# label_logits = tf.Print(label_logits, [tf.constant("label_logits"), label_logits[0]], summarize=100)
# label_logits = tf.check_numerics(label_logits, message="nan or inf from: label_logits")
labels2d = tf.argmax(label_logits, axis=3)
labels2d_expanded = tf.expand_dims(labels2d, -1)
tf.summary.image('labels', tf.cast(labels2d_expanded, tf.uint8))
return class_caps_activations, remakes_flatten, label_logits
def inference(inputs, num_classes, routing_ites=3, remake=False, training=False, name='capsnet_1d'):
"""
:param inputs:
:param num_classes:
:param routing_ites:
:param remake:
:param name:
:return:
"""
with tf.variable_scope(name) as scope:
inputs_shape = inputs.get_shape()
batch_size = inputs_shape[0].value
image_height = inputs_shape[2].value
image_width = inputs_shape[3].value
# ReLU Conv1
# Images shape (b, 1, 24, 56) -> conv 5x5 filters, 32 output channels, strides 2 with padding, ReLU
# nets -> (b, 256, 16, 48)
print('inputs shape: %s' % inputs.get_shape())
inputs = tf.check_numerics(inputs, message="nan or inf from: inputs")
conv1 = conv2d(
inputs,
kernel=3, out_channels=32, stride=1, padding='SAME',
activation_fn=tf.nn.relu, normalizer_fn=tf.contrib.layers.batch_norm,
name='relu_conv1'
)
print('conv1 shape: %s' % conv1.get_shape())
pool1 = tf.nn.max_pool(
conv1,
ksize=[1, 1, 2, 2], strides=[1, 1, 2, 2],
padding='VALID', data_format='NCHW', name='pool1'
)
print('pool1 shape: %s' % pool1.get_shape())
# pool1_dropout = tf.layers.dropout(pool1, 0.5, training=training, name='pool1_dropout')
# conv2 = conv2d(
# pool1,
# kernel=3, out_channels=64, stride=1, padding='VALID',
# activation_fn=tf.nn.relu, normalizer_fn=tf.contrib.layers.batch_norm,
# name='relu_conv2'
# )
# print('conv2 shape: %s' % conv2.get_shape())
# pool2 = tf.nn.max_pool(
# conv2,
# ksize=[1, 1, 2, 2], strides=[1, 1, 2, 2],
# padding='VALID', data_format='NCHW', name='pool2'
# )
# print('pool2 shape: %s' % pool2.get_shape())
# pool2_dropout = tf.layers.dropout(pool2, 0.5, training=training, name='pool2_dropout')
# conv3 = conv2d(
# pool2,
# kernel=3, out_channels=128, stride=1, padding='SAME',
# activation_fn=tf.nn.relu, normalizer_fn=tf.contrib.layers.batch_norm,
# name='relu_conv3'
# )
# print('conv3 shape: %s' % conv3.get_shape())
# pool3 = tf.nn.max_pool(
# conv3,
# ksize=[1, 1, 2, 2], strides=[1, 1, 2, 2],
# padding='VALID', data_format='NCHW', name='pool3'
# )
# print('pool3 shape: %s' % pool3.get_shape())
print("\nprimary layer:")
primary_out_capsules = 64
primary_caps_activations, _ = primary_caps1d(
pool1,
kernel_size=3, out_capsules=primary_out_capsules, stride=1,
padding='VALID', activation_length=3, name='primary_caps'
) # (b, 32, 4, 20, 8)
# primary_caps_activations = tf.check_numerics(primary_caps_activations,
# message="nan or inf from: primary_caps_activations")
print("\nconvolutional capsule layer 1:")
conv_out_capsules_1 = 32
conv_kernel_size_1, conv_stride_1 = 4, 2
conv_caps_activations_1, conv_coupling_coeffs_1 = conv_capsule1d(
primary_caps_activations,
kernel_size=conv_kernel_size_1, stride=conv_stride_1, routing_ites=3,
in_capsules=primary_out_capsules, out_capsules=conv_out_capsules_1,
activation_length=4, training=training, name="conv_caps_1"
) # (b, 32, 6, 6, 8), (b*6*6, 32*9, 32)
conv_caps_activations_1 = tf.check_numerics(conv_caps_activations_1,
message="nan or inf from: conv_caps_activations_1")
print("\nconvolutional capsule layer 2:")
conv_out_capsules_2 = 24
conv_kernel_size_2, conv_stride_2 = 3, 1
conv_caps_activations_2, conv_coupling_coeffs_2 = conv_capsule1d(
conv_caps_activations_1,
kernel_size=conv_kernel_size_2, stride=conv_stride_2, routing_ites=3,
in_capsules=conv_out_capsules_1, out_capsules=conv_out_capsules_2,
activation_length=8, training=training, name="conv_caps_2"
) # (b, 32, 6, 6, 8), (b*6*6, 32*9, 32)
conv_caps_activations_2 = tf.check_numerics(conv_caps_activations_2,
message="nan or inf from: conv_caps_activations_2")
print("\nclass capsule layer:")
class_caps_activations, class_coupling_coeffs = class_caps1d(
conv_caps_activations_2,
num_classes=num_classes, activation_length=16, routing_ites=routing_ites,
batch_size=batch_size, training=training, name='class_capsules')
# class_coupling_coeffs = tf.Print(class_coupling_coeffs, [class_coupling_coeffs], summarize=50)
class_caps_activations = tf.check_numerics(class_caps_activations,
message="nan or inf from: class_caps_activations")
print('class_coupling_coeffs shape: %s' % class_coupling_coeffs.get_shape())
print('class_caps_activations shape: %s' % class_caps_activations.get_shape())
if remake:
remakes_flatten = _remake(class_caps_activations, image_height * image_width)
else:
remakes_flatten = None
print("\ntraceback layer 1:")
conv_activations_2_shape = conv_caps_activations_2.get_shape() # (b, 32, 4, 20, 8),
conv_height_2, conv_width_2 = conv_activations_2_shape[2].value, conv_activations_2_shape[3].value
conv_1_cond_prob = trace_conv_cond_prob(class_coupling_coeffs, conv_coupling_coeffs_2,
conv_height_2, conv_width_2, conv_kernel_size_2, conv_stride_2,
conv_out_capsules_2, conv_out_capsules_1)
print("\ntraceback layer 2:")
conv_activations_1_shape = conv_caps_activations_1.get_shape() # (b, 32, 4, 20, 8),
conv_height_1, conv_width_1 = conv_activations_1_shape[2].value, conv_activations_1_shape[3].value
primary_cond_prob = trace_conv_cond_prob(conv_1_cond_prob, conv_coupling_coeffs_1,
conv_height_1, conv_width_1, conv_kernel_size_1, conv_stride_1,
conv_out_capsules_1, primary_out_capsules)
primary_labels = trace_labels(primary_caps_activations, primary_cond_prob, num_classes)
print('primary_caps_labels shape: %s' % primary_labels.get_shape())
# class_labels = tf.Print(class_labels, [tf.constant("class_labels"), class_labels])
# class_labels = tf.check_numerics(class_labels, message="nan or inf from: class_labels")
# primary_labels = tf.reduce_sum(caps_probs_tiled, 1)
print("\ndeconv layers:")
primary_conv = conv2d(
tf.transpose(primary_labels, perm=[0, 3, 1, 2]),
kernel=3, out_channels=256, stride=1, padding='SAME',
activation_fn=tf.nn.relu, name='primary_conv'
)
print('primary_conv shape: %s' % primary_conv.get_shape())
deconv1 = deconv(
primary_conv,
kernel=3, out_channels=128, stride=1, data_format='NCHW',
activation_fn=tf.nn.relu, normalizer_fn=tf.contrib.layers.batch_norm,
name='deconv1'
)
print('deconv2 shape: %s' % deconv1.get_shape())
concat1 = tf.concat([pool1, deconv1], axis=1, name='concat1')
# dropout2 = tf.layers.dropout(concat2, 0.5, training=training, name='dropout2')
concat1_conv = conv2d(
concat1,
kernel=3, out_channels=128, stride=1, padding='SAME',
activation_fn=tf.nn.relu, normalizer_fn=tf.contrib.layers.batch_norm,
name='concat1_conv'
)
print('deconv1_conv shape: %s' % concat1_conv.get_shape())
deconv2 = deconv(
concat1_conv,
kernel=4, out_channels=128, stride=2, data_format='NCHW',
activation_fn=tf.nn.relu, normalizer_fn=tf.contrib.layers.batch_norm,
name='deconv2'
)
print('deconv2 shape: %s' % deconv2.get_shape())
class_conv = conv2d(
deconv2,
kernel=3, out_channels=num_classes, stride=1, padding='VALID',
name='class_conv'
)
print('class_conv shape: %s' % class_conv.get_shape())
label_logits = tf.transpose(class_conv, perm=[0, 2, 3, 1])
print('label_logits shape: %s' % label_logits.get_shape())
# label_logits = tf.Print(label_logits, [tf.constant("label_logits"), label_logits])
labels2d = tf.argmax(label_logits, axis=3)
labels2d_expanded = tf.expand_dims(labels2d, -1)
tf.summary.image('labels', tf.cast(labels2d_expanded, tf.uint8))
return class_caps_activations, remakes_flatten, label_logits
def _decode(activations, capsule_num, coupling_coeffs, num_classes, batch_size,
conv1, conv2):
capsule_probs = tf.norm(activations,
axis=-1) # # (b, 32, 4, 20, 8) -> (b, 32, 4, 20)
caps_probs_tiled = tf.tile(tf.expand_dims(capsule_probs, -1),
[1, 1, 1, 1, num_classes]) # (b, 32, 4, 20, 2)
# caps_probs_tiled = tf.check_numerics(caps_probs_tiled, message="nan or inf from: caps_probs_tiled")
print('coupling_coeffs shape: %s' % coupling_coeffs.get_shape())
activations_shape = activations.get_shape()
height, width = activations_shape[2].value, activations_shape[3].value
coupling_coeff_reshaped = tf.reshape(
coupling_coeffs, [batch_size, capsule_num, height, width, num_classes
]) # (b, 32, 4, 20, 2)
# coupling_coeff_reshaped = tf.check_numerics(coupling_coeff_reshaped, message="nan or inf from: coupling_coeff_reshaped")
primary_labels = tf.reduce_sum(coupling_coeff_reshaped * caps_probs_tiled,
1) # (b, 4, 20, 2)
# class_labels = tf.Print(class_labels, [tf.constant("class_labels"), class_labels])
# class_labels = tf.check_numerics(class_labels, message="nan or inf from: class_labels")
# primary_labels = tf.reduce_sum(caps_probs_tiled, 1)
# deconv1 = deconv(
# class_labels,
# kernel=3, out_channels=num_classes, stride=1,
# activation_fn=tf.nn.relu, name='deconv1'
# )
# deconv1 = tf.Print(deconv1, [tf.constant("deconv1"), deconv1])
print('primary_labels shape: %s' % primary_labels.get_shape())
concat1 = tf.concat(
[tf.transpose(conv2, perm=[0, 2, 3, 1]), primary_labels],
axis=3,
name='concat1')
primary_conv = conv2d(concat1,
kernel=3,
out_channels=128,
stride=1,
padding='SAME',
activation_fn=tf.nn.relu,
data_format='NHWC',
name='primary_conv')
deconv2 = deconv(primary_conv,
kernel=8,
out_channels=128,
stride=2,
activation_fn=tf.nn.relu,
name='deconv2')
print('deconv2 shape: %s' % deconv2.get_shape())
concat2 = tf.concat([tf.transpose(conv1, perm=[0, 2, 3, 1]), deconv2],
axis=3,
name='concat2')
# deconv2 = tf.Print(deconv2, [tf.constant("deconv2"), deconv2])
deconv2_conv = conv2d(concat2,
kernel=5,
out_channels=128,
stride=1,
padding='SAME',
activation_fn=tf.nn.relu,
data_format='NHWC',
name='deconv2_conv')
print('deconv2_conv shape: %s' % deconv2_conv.get_shape())
# deconv3 = deconv(
# class_labels,
# kernel=9, out_channels=num_classes, stride=1,
# activation_fn=tf.nn.relu, name='deconv3'
# )
# deconv3 = tf.Print(deconv3, [tf.constant("deconv3"), deconv3])
deconv3 = deconv(deconv2_conv,
kernel=9,
out_channels=num_classes,
stride=1,
activation_fn=tf.nn.relu,
name='deconv3')
deconv3_conv = conv2d(deconv3,
kernel=3,
out_channels=num_classes,
stride=1,
padding='SAME',
activation_fn=tf.nn.relu,
data_format='NHWC',
name='deconv3_conv')
label_logits = deconv3_conv
print('label_logits shape: %s' % label_logits.get_shape())
# label_logits = tf.Print(label_logits, [tf.constant("label_logits"), label_logits])
return label_logits
def inference(inputs,
num_classes,
routing_ites=3,
remake=False,
training=False,
name='capsnet_1d'):
"""
:param inputs:
:param num_classes:
:param routing_ites:
:param remake:
:param name:
:return:
"""
with tf.variable_scope(name) as scope:
inputs_shape = inputs.get_shape()
batch_size = inputs_shape[0].value
image_height = inputs_shape[2].value
image_width = inputs_shape[3].value
# ReLU Conv1
# Images shape (b, 1, 24, 56) -> conv 5x5 filters, 32 output channels, strides 2 with padding, ReLU
# nets -> (b, 256, 16, 48)
print('inputs shape: %s' % inputs.get_shape())
inputs = tf.check_numerics(inputs, message="nan or inf from: inputs")
print("\nconv1 layer:")
conv1 = conv2d(inputs,
kernel=9,
out_channels=256,
stride=1,
padding='VALID',
activation_fn=tf.nn.relu,
name='relu_conv1')
# conv1 = tf.check_numerics(conv1, message="nan or inf from: conv1")
print('conv1 shape: %s' % conv1.get_shape())
# print("\nconv2 layer:")
# conv2 = conv2d(
# conv1,
# kernel=5, out_channels=256, stride=1, padding='VALID',
# activation_fn=tf.nn.relu, name='relu_conv2'
# )
# # conv2 = tf.check_numerics(conv2, message="nan or inf from: conv2")
# print('conv2 shape: %s' % conv2.get_shape())
# PrimaryCaps
# (b, 256, 16, 48) -> capsule 1x1 filter, 32 output capsule, strides 1 without padding
# nets -> activations (?, 14, 14, 32))
print("\nprimary layer:")
primary_out_capsules = 24
primary_caps_activations, conv2 = primary_caps1d(
conv1,
kernel_size=7,
out_capsules=primary_out_capsules,
stride=2,
padding='VALID',
activation_length=8,
name='primary_caps') # (b, 32, 4, 20, 8)
# (b, 32, 4, 20, 8) -> # (b, 32*4*20, 2*64)
print("\nclass capsule layer:")
class_caps_activations, class_coupling_coeffs = class_caps1d(
primary_caps_activations,
num_classes=num_classes,
activation_length=16,
routing_ites=routing_ites,
batch_size=batch_size,
name='class_capsules')
# class_coupling_coeffs = tf.Print(class_coupling_coeffs, [class_coupling_coeffs], summarize=50)
# class_caps_activations = tf.check_numerics(class_caps_activations, message="nan or inf from: class_caps_activations")
print('class_coupling_coeffs shape: %s' %
class_coupling_coeffs.get_shape())
print('class_caps_activations shape: %s' %
class_caps_activations.get_shape())
if remake:
remakes_flatten = _remake(class_caps_activations,
image_height * image_width)
else:
remakes_flatten = None
print("\ndecode layers:")
label_logits = _decode(primary_caps_activations,
primary_out_capsules,
coupling_coeffs=class_coupling_coeffs,
num_classes=num_classes,
batch_size=batch_size,
conv1=conv1,
conv2=conv2)
# label_logits = tf.Print(label_logits, [tf.constant("label_logits"), label_logits[0]], summarize=100)
# label_logits = tf.check_numerics(label_logits, message="nan or inf from: label_logits")
labels2d = tf.argmax(label_logits, axis=3)
labels2d_expanded = tf.expand_dims(labels2d, -1)
tf.summary.image('labels', tf.cast(labels2d_expanded, tf.uint8))
return class_caps_activations, remakes_flatten, label_logits
def inference(inputs,
num_classes,
feature_scale=2,
training=True,
name='unet'):
with tf.variable_scope(name) as scope:
filters = [64, 128, 256, 512, 1024]
filters = [int(x / feature_scale) for x in filters]
combine_conv1_1 = _combine_conv(inputs,
'1_1',
training,
out_channels=filters[0])
combine_conv1_2 = _combine_conv(combine_conv1_1,
'1_2',
training,
out_channels=filters[0])
pool1 = tf.layers.max_pooling2d(inputs=combine_conv1_2,
pool_size=[2, 2],
strides=[2, 2],
data_format="channels_first",
name='pool1')
print('pool1 shape: %s\n' % (pool1.get_shape()))
combine_conv2_1 = _combine_conv(pool1,
'2_1',
training,
out_channels=filters[1])
combine_conv2_2 = _combine_conv(combine_conv2_1,
'2_2',
training,
out_channels=filters[1])
pool2 = tf.layers.max_pooling2d(inputs=combine_conv2_2,
pool_size=[2, 2],
strides=[2, 2],
data_format="channels_first",
name='pool2')
print('pool2 shape: %s\n' % (pool2.get_shape()))
combine_conv3_1 = _combine_conv(pool2,
'3_1',
training,
out_channels=filters[2])
combine_conv3_2 = _combine_conv(combine_conv3_1,
'3_2',
training,
out_channels=filters[2])
pool3 = tf.layers.max_pooling2d(inputs=combine_conv3_2,
pool_size=[2, 2],
strides=[2, 2],
data_format="channels_first",
name='pool3')
print('pool3 shape: %s\n' % (pool3.get_shape()))
combine_conv4_1 = _combine_conv(pool3,
'4_1',
training,
out_channels=filters[3])
combine_conv4_2 = _combine_conv(combine_conv4_1,
'4_2',
training,
out_channels=filters[3])
# combine_conv4_2 = tf.nn.dropout(combine_conv4_2, 0.5)
pool4 = tf.layers.max_pooling2d(inputs=combine_conv4_2,
pool_size=[2, 2],
strides=[2, 2],
data_format="channels_first",
name='pool4')
print('pool4 shape: %s\n' % (pool4.get_shape()))
center = _combine_conv(pool4, '5', training, out_channels=filters[4])
# center = tf.nn.dropout(center, 0.5)
combine_deconv4 = _combine_deconv(center,
'4',
training,
combine_conv4_2,
deconv_out_channels=filters[3])
combine_deconv3 = _combine_deconv(combine_deconv4,
'3',
training,
combine_conv3_2,
deconv_out_channels=filters[2])
combine_deconv2 = _combine_deconv(combine_deconv3,
'2',
training,
combine_conv2_2,
deconv_out_channels=filters[1])
combine_deconv1 = _combine_deconv(combine_deconv2,
'1',
training,
combine_conv1_2,
deconv_out_channels=filters[0])
final_deconv = deconv(combine_deconv1,
kernel=5,
out_channels=num_classes,
stride=1,
data_format='NCHW',
name='final_deconv')
final = conv2d(final_deconv,
kernel=1,
out_channels=num_classes,
stride=1,
padding='SAME',
name='final_conv')
label_logits = tf.transpose(final, perm=[0, 2, 3, 1])
print('label_logits shape: %s' % label_logits.get_shape())
return label_logits
def inference(inputs, num_classes, feature_scale=1, training=True, name='unet'):
with tf.variable_scope(name) as scope:
filters = [64, 128, 256, 512, 512]
filters = [int(x / feature_scale) for x in filters]
padded_input = tf.pad(inputs, tf.constant([[0, 0], [0, 0], [100, 100], [100, 100]]), "CONSTANT")
combine_conv1_1 = _combine_conv(padded_input, '1_1', training, in_channels=3, out_channels=filters[0])
combine_conv1_2 = _combine_conv(combine_conv1_1, '1_2', training,
in_channels=filters[0], out_channels=filters[0])
pool1 = tf.layers.max_pooling2d(inputs=combine_conv1_2, pool_size=[2, 2], strides=[2, 2],
data_format="channels_first", name='pool1')
print('pool1 shape: %s\n' % (pool1.get_shape()))
combine_conv2_1 = _combine_conv(pool1, '2_1', training, in_channels=filters[0], out_channels=filters[1])
combine_conv2_2 = _combine_conv(combine_conv2_1, '2_2', training,
in_channels=filters[1], out_channels=filters[1])
pool2 = tf.layers.max_pooling2d(inputs=combine_conv2_2, pool_size=[2, 2], strides=[2, 2],
data_format="channels_first", name='pool2')
print('pool2 shape: %s\n' % (pool2.get_shape()))
combine_conv3_1 = _combine_conv(pool2, '3_1', training,
in_channels=filters[1], out_channels=filters[2])
combine_conv3_2 = _combine_conv(combine_conv3_1, '3_2', training,
in_channels=filters[2], out_channels=filters[2])
combine_conv3_3 = _combine_conv(combine_conv3_2, '3_3', training,
in_channels=filters[2], out_channels=filters[2])
pool3 = tf.layers.max_pooling2d(inputs=combine_conv3_3, pool_size=[2, 2], strides=[2, 2],
data_format="channels_first", name='pool3')
print('pool3 shape: %s\n' % (pool3.get_shape()))
combine_conv4_1 = _combine_conv(pool3, '4_1', training, in_channels=filters[2], out_channels=filters[3])
combine_conv4_2 = _combine_conv(combine_conv4_1, '4_2', training,
in_channels=filters[3], out_channels=filters[3])
combine_conv4_3 = _combine_conv(combine_conv4_2, '4_3', training,
in_channels=filters[3], out_channels=filters[3])
pool4 = tf.layers.max_pooling2d(inputs=combine_conv4_3, pool_size=[2, 2], strides=[2, 2],
data_format="channels_first", name='pool4')
print('pool4 shape: %s\n' % (pool4.get_shape()))
combine_conv5_1 = _combine_conv(pool4, '5_1', training, in_channels=filters[3], out_channels=filters[4])
combine_conv5_2 = _combine_conv(combine_conv5_1, '5_2', training,
in_channels=filters[4], out_channels=filters[4])
combine_conv5_3 = _combine_conv(combine_conv5_2, '5_3', training,
in_channels=filters[4], out_channels=filters[4])
pool5 = tf.layers.max_pooling2d(inputs=combine_conv5_3, pool_size=[2, 2], strides=[2, 2],
data_format="channels_first", name='pool5')
print('pool5 shape: %s\n' % (pool5.get_shape()))
combine_conv6 = _combine_conv(pool5, '6', training, kernel=7,
in_channels=filters[4], out_channels=4096, padding='VALID')
drop6 = tf.nn.dropout(combine_conv6, 0.5)
combine_conv7 = _combine_conv(drop6, '7', training, kernel=1,
in_channels=4096, out_channels=4096, padding='VALID')
drop7 = tf.nn.dropout(combine_conv7, 0.5)
score_fr = conv2d(
drop7, kernel=1,
out_channels=num_classes, stride=1,
padding='VALID', name='score_fr',
weights_regularizer=tf.contrib.layers.l2_regularizer(scale=0.0005)
)
kernel_upscore2 = 4
upscore2 = deconv(
score_fr,
kernel=kernel_upscore2, out_channels=num_classes, stride=2,
weights_regularizer=tf.contrib.layers.l2_regularizer(scale=0.0005),
weights_initializer=_deconv_intializer(kernel_upscore2),
data_format='NCHW', activation_fn=None, name='upscore2'
)
print('upscore2 shape: %s\n' % (upscore2.get_shape()))
score_pool4 = conv2d(
pool4, kernel=1,
out_channels=num_classes, stride=1,
padding='VALID', name='score_pool4',
weights_regularizer=tf.contrib.layers.l2_regularizer(scale=0.0005)
)
print('score_pool4 shape: %s' % (score_pool4.get_shape()))
score_pool4c = _crop(score_pool4, upscore2) # need crop here
print('score_pool4c shape: %s' % (score_pool4c.get_shape()))
fuse_pool4 = upscore2 + score_pool4c
kernel_upscore_pool4 = 4
upscore_pool4 = deconv(
fuse_pool4,
kernel=kernel_upscore_pool4, out_channels=num_classes, stride=2,
weights_regularizer=tf.contrib.layers.l2_regularizer(scale=0.0005),
weights_initializer=_deconv_intializer(kernel_upscore_pool4),
data_format='NCHW', activation_fn=None, name='upscore_pool4'
)
print('upscore_pool4 shape: %s\n' % (upscore_pool4.get_shape()))
score_pool3 = conv2d(
pool3, kernel=1,
out_channels=num_classes, stride=1,
padding='VALID', name='score_pool3',
weights_regularizer=tf.contrib.layers.l2_regularizer(scale=0.0005)
)
score_pool3c = _crop(score_pool3, upscore_pool4) # need crop here
print('score_pool3c shape: %s' % (score_pool3c.get_shape()))
fuse_pool3 = upscore_pool4 + score_pool3c
kernel_upscore_pool8 = 16
upscore_pool8 = deconv(
fuse_pool3,
kernel=kernel_upscore_pool8, out_channels=num_classes, stride=8,
data_format='NCHW', activation_fn=None, name='upscore_pool8',
weights_regularizer=tf.contrib.layers.l2_regularizer(scale=0.0005),
weights_initializer=_deconv_intializer(kernel_upscore_pool8)
)
print('upscore_pool8 shape: %s\n' % (upscore_pool8.get_shape()))
score = _crop(upscore_pool8, inputs) # need crop here
print('score shape: %s\n' % (score.get_shape()))
label_logits = tf.transpose(score, perm=[0, 2, 3, 1])
print('label_logits shape: %s' % label_logits.get_shape())
return label_logits
def inference(inputs,
num_classes,
training=True,
routing_ites=3,
name='unet_pascal'):
with tf.variable_scope(name) as scope:
inputs_shape = inputs.get_shape()
batch_size = inputs_shape[0].value
combine_conv1 = _combine_conv(inputs, '1', training, out_channels=16)
combine_conv2 = _combine_conv(combine_conv1,
'2',
training,
out_channels=32)
combine_conv3 = _combine_conv(combine_conv2,
'3',
training,
out_channels=64)
combine_conv4 = _combine_conv(combine_conv3,
'4',
training,
out_channels=128)
print("\nprimary layer:")
primary_out_capsules = 32
primary_caps_activations, primary_conv = primary_caps1d(
combine_conv4,
kernel_size=5,
out_capsules=primary_out_capsules,
stride=2,
padding='VALID',
activation_length=8,
name='primary_caps')
print('primary_conv shape: %s' % primary_conv.get_shape())
print("\nclass capsule layer:")
class_caps_activations, class_coupling_coeffs = class_caps1d(
primary_caps_activations,
num_classes=num_classes,
activation_length=16,
routing_ites=routing_ites,
batch_size=batch_size,
name='class_capsules')
print('class_coupling_coeffs shape: %s' %
class_coupling_coeffs.get_shape())
print('class_caps_activations shape: %s' %
class_caps_activations.get_shape())
capsule_probs = tf.norm(primary_caps_activations, axis=-1)
caps_probs_tiled = tf.tile(tf.expand_dims(capsule_probs, -1),
[1, 1, 1, 1, num_classes])
primary_activations_shape = primary_caps_activations.get_shape()
height, width = primary_activations_shape[
2].value, primary_activations_shape[3].value
coupling_coeff_reshaped = tf.reshape(
class_coupling_coeffs,
[batch_size, primary_out_capsules, height, width, num_classes])
primary_labels = tf.reduce_sum(
coupling_coeff_reshaped * caps_probs_tiled, 1)
print('\nprimary_labels shape: %s\n' % primary_labels.get_shape())
concat1 = tf.concat(
[primary_conv,
tf.transpose(primary_labels, perm=[0, 3, 1, 2])],
axis=1,
name='concat1')
primary_label_conv = conv2d(concat1,
kernel=3,
out_channels=128,
stride=1,
padding='SAME',
activation_fn=tf.nn.relu,
data_format='NCHW',
name='primary_label_conv')
combine_deconv5 = _combine_deconv(primary_label_conv,
'5',
training,
combine_conv4,
deconv_out_channels=256)
combine_deconv4 = _combine_deconv(combine_deconv5,
'4',
training,
combine_conv3,
kernel_deconv=[7, 7],
deconv_out_channels=128)
combine_deconv3 = _combine_deconv(combine_deconv4,
'3',
training,
combine_conv2,
kernel_deconv=[6, 6],
deconv_out_channels=64)
combine_deconv2 = _combine_deconv(combine_deconv3,
'2',
training,
combine_conv1,
kernel_deconv=[6, 7],
deconv_out_channels=64)
combine_deconv1 = _combine_deconv(combine_deconv2,
'1',
training,
None,
kernel_deconv=[6, 7],
deconv_out_channels=32)
conv = conv2d(combine_deconv1,
kernel=3,
out_channels=num_classes,
stride=1,
padding='SAME',
name='label_conv')
label_logits = tf.transpose(conv, perm=[0, 2, 3, 1])
# label_logits = tf.check_numerics(label_logits, message="nan or inf from: label_logits")
print('label_logits shape: %s' % label_logits.get_shape())
return class_caps_activations, None, label_logits
