# h1 = lrelu(tf.contrib.layers.batch_norm(conv2d(h0, self.df_dim*2, name='d_h1_conv'),decay=0.9,updates_collections=None,epsilon=0.00001,scale=True,scope="d_h1_conv"))

def __init__(self, epsilon=1e-5, momentum=0.9, name="batch_norm"):

with tf.variable_scope(name):

self.epsilon = epsilon

self.momentum = momentum

self.name = name

def __call__(self, x, train=True):

return tf.contrib.layers.batch_norm(x, decay=self.momentum, updates_collections=None, epsilon=self.epsilon,

downsample_strides=2, activation='relu', batch_norm=True,

bias=True, weights_init='variance_scaling',

bias_init='zeros', regularizer='L2', weight_decay=0.0001,

trainable=True, restore=True, reuse=False, scope=None,

name="ResidualBlock", is_training=True):

""" Residual Block.

A residual block as described in MSRA's Deep Residual Network paper.

Full pre-activation architecture is used here.

Input:

4-D Tensor [batch, height, width, in_channels].

Output:

4-D Tensor [batch, new height, new width, nb_filter].

Arguments:

incoming: `Tensor`. Incoming 4-D Layer.

nb_blocks: `int`. Number of layer blocks.

out_channels: `int`. The number of convolutional filters of the

convolution layers.

downsample: `bool`. If True, apply downsampling using

'downsample_strides' for strides.

downsample_strides: `int`. The strides to use when downsampling.

activation: `str` (name) or `function` (returning a `Tensor`).

Activation applied to this layer (see tflearn.activations).

Default: 'linear'.

batch_norm: `bool`. If True, apply batch normalization.

bias: `bool`. If True, a bias is used.

weights_init: `str` (name) or `Tensor`. Weights initialization.

(see tflearn.initializations) Default: 'uniform_scaling'.

bias_init: `str` (name) or `tf.Tensor`. Bias initialization.

(see tflearn.initializations) Default: 'zeros'.

regularizer: `str` (name) or `Tensor`. Add a regularizer to this

layer weights (see tflearn.regularizers). Default: None.

weight_decay: `float`. Regularizer decay parameter. Default: 0.001.

trainable: `bool`. If True, weights will be trainable.

restore: `bool`. If True, this layer weights will be restored when

loading a model.

reuse: `bool`. If True and 'scope' is provided, this layer variables

will be reused (shared).

scope: `str`. Define this layer scope (optional). A scope can be

used to share variables between layers. Note that scope will

override name.

name: A name for this layer (optional). Default: 'ShallowBottleneck'.

is_training: True for training mode and False for val or test mode.

References:

- Deep Residual Learning for Image Recognition. Kaiming He, Xiangyu

Zhang, Shaoqing Ren, Jian Sun. 2015.

- Identity Mappings in Deep Residual Networks. Kaiming He, Xiangyu

Zhang, Shaoqing Ren, Jian Sun. 2015.

Links:

- [http://arxiv.org/pdf/1512.03385v1.pdf]

(http://arxiv.org/pdf/1512.03385v1.pdf)

- [Identity Mappings in Deep Residual Networks]

(https://arxiv.org/pdf/1603.05027v2.pdf)

"""

resnet = incoming

in_channels = incoming.get_shape().as_list()[-1]

with tf.variable_scope(values=[incoming], name_or_scope=scope, default_name=name, reuse=reuse) as scope:

name = scope.name # TODO

for i in range(nb_blocks):

identity = resnet

if not downsample:

downsample_strides = 1

if batch_norm:

resnet = normalization.instance_norm(resnet, scope=name + '_bn_' + str(i) + '_1')

resnet = tflearn.activation(resnet, activation)

resnet = tflearn.conv_2d(resnet, out_channels, 3,

downsample_strides, 'same', 'linear',

bias, weights_init, bias_init,

regularizer, weight_decay, trainable,

restore)

if batch_norm:

resnet = normalization.instance_norm(resnet, scope=name + '_bn_' + str(i) + '_2')

resnet = tflearn.activation(resnet, activation)

resnet = tflearn.conv_2d(resnet, out_channels, 3, downsample_strides, 'same',

'linear', bias, weights_init,

bias_init, regularizer, weight_decay,

trainable, restore)

# Downsampling

if downsample_strides > 1:

identity = tflearn.avg_pool_2d(identity, 1,

downsample_strides)

# Projection to new dimension

'''

if in_channels != out_channels:

ch = (out_channels - in_channels)//2

identity = tf.pad(identity,

[[0, 0], [0, 0], [0, 0], [ch, ch]])

in_channels = out_channels

'''

resnet = resnet + identity

downsample_strides=2, activation='relu', batch_norm=True,

bias=True, weights_init='variance_scaling',

bias_init='zeros', regularizer='L2', weight_decay=0.0001,

trainable=True, restore=True, reuse=False, scope=None,

name="ResidualBlock", is_training=True):

""" Residual Block.

A residual block as described in MSRA's Deep Residual Network paper.

Full pre-activation architecture is used here.

Input:

4-D Tensor [batch, height, width, in_channels].

Output:

4-D Tensor [batch, new height, new width, nb_filter].

Arguments:

incoming: `Tensor`. Incoming 4-D Layer.

nb_blocks: `int`. Number of layer blocks.

out_channels: `int`. The number of convolutional filters of the

convolution layers.

downsample: `bool`. If True, apply downsampling using

'downsample_strides' for strides.

downsample_strides: `int`. The strides to use when downsampling.

activation: `str` (name) or `function` (returning a `Tensor`).

Activation applied to this layer (see tflearn.activations).

Default: 'linear'.

batch_norm: `bool`. If True, apply batch normalization.

bias: `bool`. If True, a bias is used.

weights_init: `str` (name) or `Tensor`. Weights initialization.

(see tflearn.initializations) Default: 'uniform_scaling'.

bias_init: `str` (name) or `tf.Tensor`. Bias initialization.

(see tflearn.initializations) Default: 'zeros'.

regularizer: `str` (name) or `Tensor`. Add a regularizer to this

layer weights (see tflearn.regularizers). Default: None.

weight_decay: `float`. Regularizer decay parameter. Default: 0.001.

trainable: `bool`. If True, weights will be trainable.

restore: `bool`. If True, this layer weights will be restored when

loading a model.

reuse: `bool`. If True and 'scope' is provided, this layer variables

will be reused (shared).

scope: `str`. Define this layer scope (optional). A scope can be

used to share variables between layers. Note that scope will

override name.

name: A name for this layer (optional). Default: 'ShallowBottleneck'.

is_training: True for training mode and False for val or test mode.

References:

- Deep Residual Learning for Image Recognition. Kaiming He, Xiangyu

Zhang, Shaoqing Ren, Jian Sun. 2015.

- Identity Mappings in Deep Residual Networks. Kaiming He, Xiangyu

Zhang, Shaoqing Ren, Jian Sun. 2015.

Links:

- [http://arxiv.org/pdf/1512.03385v1.pdf]

(http://arxiv.org/pdf/1512.03385v1.pdf)

- [Identity Mappings in Deep Residual Networks]

(https://arxiv.org/pdf/1603.05027v2.pdf)

"""

resnet = incoming

in_channels = incoming.get_shape().as_list()[-1]

with tf.variable_scope(values=[incoming], name_or_scope=scope, default_name=name, reuse=reuse) as scope:

name = scope.name # TODO

for i in range(nb_blocks):

identity = resnet

if not downsample:

downsample_strides = 1

if batch_norm:

resnet = batch_normalization(resnet, is_training, name=name + '_bn_' + str(i) + '_1')

resnet = tflearn.activation(resnet, activation)

resnet = tflearn.conv_2d(resnet, out_channels, 1,

downsample_strides, 'same', 'linear',

bias, weights_init, bias_init,

regularizer, weight_decay, trainable,

restore)

if batch_norm:

resnet = batch_normalization(resnet, is_training, name=name + '_bn_' + str(i) + '_2')

resnet = tflearn.activation(resnet, activation)

resnet = tflearn.conv_2d(resnet, out_channels, 1, downsample_strides, 'same',

'linear', bias, weights_init,

bias_init, regularizer, weight_decay,

trainable, restore)

# Downsampling

if downsample_strides > 1:

identity = tflearn.avg_pool_2d(identity, 1,

downsample_strides)

# Projection to new dimension

'''

if in_channels != out_channels:

ch = (out_channels - in_channels)//2

identity = tf.pad(identity,

[[0, 0], [0, 0], [0, 0], [ch, ch]])

in_channels = out_channels

'''

resnet = resnet + identity

downsample_strides=2, activation='relu', batch_norm=True,

bias=True, weights_init='variance_scaling',

bias_init='zeros', regularizer='L2', weight_decay=0.0001,

trainable=True, restore=True, reuse=False, scope=None,

name="ResidualBlock", is_training=True):

""" Residual Block.

A residual block as described in MSRA's Deep Residual Network paper.

Full pre-activation architecture is used here.

Input:

4-D Tensor [batch, height, width, in_channels].

Output:

4-D Tensor [batch, new height, new width, nb_filter].

Arguments:

incoming: `Tensor`. Incoming 4-D Layer.

nb_blocks: `int`. Number of layer blocks.

out_channels: `int`. The number of convolutional filters of the

convolution layers.

downsample: `bool`. If True, apply downsampling using

'downsample_strides' for strides.

downsample_strides: `int`. The strides to use when downsampling.

activation: `str` (name) or `function` (returning a `Tensor`).

Activation applied to this layer (see tflearn.activations).

Default: 'linear'.

batch_norm: `bool`. If True, apply batch normalization.

bias: `bool`. If True, a bias is used.

weights_init: `str` (name) or `Tensor`. Weights initialization.

(see tflearn.initializations) Default: 'uniform_scaling'.

bias_init: `str` (name) or `tf.Tensor`. Bias initialization.

(see tflearn.initializations) Default: 'zeros'.

regularizer: `str` (name) or `Tensor`. Add a regularizer to this

layer weights (see tflearn.regularizers). Default: None.

weight_decay: `float`. Regularizer decay parameter. Default: 0.001.

trainable: `bool`. If True, weights will be trainable.

restore: `bool`. If True, this layer weights will be restored when

loading a model.

reuse: `bool`. If True and 'scope' is provided, this layer variables

will be reused (shared).

scope: `str`. Define this layer scope (optional). A scope can be

used to share variables between layers. Note that scope will

override name.

name: A name for this layer (optional). Default: 'ShallowBottleneck'.

is_training: True for training mode and False for val or test mode.

References:

- Deep Residual Learning for Image Recognition. Kaiming He, Xiangyu

Zhang, Shaoqing Ren, Jian Sun. 2015.

- Identity Mappings in Deep Residual Networks. Kaiming He, Xiangyu

Zhang, Shaoqing Ren, Jian Sun. 2015.

Links:

- [http://arxiv.org/pdf/1512.03385v1.pdf]

(http://arxiv.org/pdf/1512.03385v1.pdf)

- [Identity Mappings in Deep Residual Networks]

(https://arxiv.org/pdf/1603.05027v2.pdf)

"""

resnet = incoming

in_channels = incoming.get_shape().as_list()[-1]

with tf.variable_scope(values=[incoming], name_or_scope=scope, default_name=name, reuse=reuse) as scope:

name = scope.name # TODO

for i in range(nb_blocks):

identity = resnet

if not downsample:

downsample_strides = 1

if batch_norm:

resnet = batch_normalization(resnet, is_training, name=name + '_bn_' + str(i) + '_1')

resnet = tflearn.activation(resnet, activation)

resnet = tflearn.conv_2d(resnet, out_channels, 3,

downsample_strides, 'same', 'linear',

bias, weights_init, bias_init,

regularizer, weight_decay, trainable,

restore)

if batch_norm:

resnet = batch_normalization(resnet, is_training, name=name + '_bn_' + str(i) + '_2')

resnet = tflearn.activation(resnet, activation)

resnet = tflearn.conv_2d(resnet, out_channels, 3, downsample_strides, 'same',

'linear', bias, weights_init,

bias_init, regularizer, weight_decay,

trainable, restore)

# Downsampling

if downsample_strides > 1:

identity = tflearn.avg_pool_2d(identity, 1,

downsample_strides)

# Projection to new dimension

'''

if in_channels != out_channels:

ch = (out_channels - in_channels)//2

identity = tf.pad(identity,

[[0, 0], [0, 0], [0, 0], [ch, ch]])

in_channels = out_channels

'''

resnet = resnet + identity

"""Computes binary cross entropy given `preds`.

For brevity, let `x = `, `z = targets`. The logistic loss is

loss(x, z) = - sum_i (x[i] * log(z[i]) + (1 - x[i]) * log(1 - z[i]))

Args:

preds: A `Tensor` of type `float32` or `float64`.

targets: A `Tensor` of the same type and shape as `preds`.

"""

eps = 1e-12

with ops.op_scope([preds, targets], name, "bce_loss") as name:

preds = ops.convert_to_tensor(preds, name="preds")

targets = ops.convert_to_tensor(targets, name="targets")

return tf.reduce_mean(-(targets * tf.log(preds + eps) +

"""Concatenate conditioning vector on feature map axis."""

x_shapes = x.get_shape()

y_shapes = y.get_shape()

k_h=3, k_w=3, d_h=2, d_w=2, stddev=0.02,

name="conv2d"):

with tf.variable_scope(name):

w = tf.get_variable('w', [k_h, k_w, input_.get_shape()[-1], output_dim],

initializer=tf.truncated_normal_initializer(stddev=stddev))

conv = tf.nn.conv2d(input_, w, strides=[1, d_h, d_w, 1], padding='SAME')

biases = tf.get_variable('biases', [output_dim], initializer=tf.constant_initializer(0.0))

conv = tf.reshape(tf.nn.bias_add(conv, biases),

[-1, conv.get_shape()[1], conv.get_shape()[2], conv.get_shape()[3]])

# conv = tf.nn.bias_add(conv, biases)

k_h=3, k_w=3, d_h=2, d_w=2, stddev=0.02,

name="deconv2d", with_w=False):

with tf.variable_scope(name):

# filter : [height, width, output_channels, in_channels]

w = tf.get_variable('w', [k_h, k_w, output_shape[-1], input_.get_shape()[-1]],

initializer=tf.random_normal_initializer(stddev=stddev))

try:

deconv = tf.nn.conv2d_transpose(input_, w, output_shape=output_shape,

strides=[1, d_h, d_w, 1])

# Support for verisons of TensorFlow before 0.7.0

except AttributeError:

deconv = tf.nn.deconv2d(input_, w, output_shape=output_shape,

strides=[1, d_h, d_w, 1])

biases = tf.get_variable('biases', [output_shape[-1]], initializer=tf.constant_initializer(0.0))

deconv = tf.reshape(tf.nn.bias_add(deconv, biases), deconv.get_shape())

if with_w:

return deconv, w, biases

else:

shape = input_.get_shape().as_list()

with tf.variable_scope(scope or "Linear"):

matrix = tf.get_variable("Matrix", [shape[1], output_size], tf.float32,

tf.random_normal_initializer(stddev=stddev))

bias = tf.get_variable("bias", [output_size],

initializer=tf.constant_initializer(bias_start))

if with_w:

return tf.matmul(input_, matrix) + bias, matrix, bias

else:

input_c = input.get_shape().as_list()[-1]

dw_conv_w = tf.Variable(tf.random_uniform((3, 3, input_c, 1)), dtype=tf.float32, name='dw_' + name)

net = tf.nn.depthwise_conv2d(input, dw_conv_w, [1, 1, 1, 1],

padding='SAME', name='dwconv_' + name) # [bs,1,4,num_point]

if activation_fn == 'sigmoid':

net = tf.nn.sigmoid(net)

else:

net = tf.nn.sigmoid(net)

return net