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models.py
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import tensorflow as tf
import numpy as np
slim = tf.contrib.slim
# custom layers
def deconv_layer(net, up_scale, n_channel, method='transpose'):
nh = tf.shape(net)[-3] * up_scale
nw = tf.shape(net)[-2] * up_scale
if method == 'transpose':
net = slim.conv2d_transpose(net, n_channel, (up_scale, up_scale), (
up_scale, up_scale), activation_fn=None, padding='VALID')
elif method == 'transpose+conv':
net = slim.conv2d_transpose(net, n_channel, (up_scale, up_scale), (
up_scale, up_scale), activation_fn=None, padding='VALID')
net = slim.conv2d(net, n_channel, (3, 3), (1, 1))
elif method == 'transpose+conv+relu':
net = slim.conv2d_transpose(net, n_channel, (up_scale, up_scale), (
up_scale, up_scale), padding='VALID')
net = slim.conv2d(net, n_channel, (3, 3), (1, 1))
elif method == 'bilinear':
net = tf.image.resize_images(net, [nh, nw])
else:
raise Exception('Unrecognised Deconvolution Method: %s' % method)
return net
# arg scopes
def hourglass_arg_scope_torch(weight_decay=0.0001,
batch_norm_decay=0.997,
batch_norm_epsilon=1e-5,
batch_norm_scale=True):
"""Defines the default ResNet arg scope.
Args:
is_training: Whether or not we are training the parameters in the batch
normalization layers of the model.
weight_decay: The weight decay to use for regularizing the model.
batch_norm_decay: The moving average decay when estimating layer activation
statistics in batch normalization.
batch_norm_epsilon: Small constant to prevent division by zero when
normalizing activations by their variance in batch normalization.
batch_norm_scale: If True, uses an explicit `gamma` multiplier to scale the
activations in the batch normalization layer.
Returns:
An `arg_scope` to use for the resnet models.
"""
batch_norm_params = {
'decay': batch_norm_decay,
'epsilon': batch_norm_epsilon,
'scale': batch_norm_scale,
'updates_collections': tf.GraphKeys.UPDATE_OPS,
}
with slim.arg_scope(
[slim.conv2d],
weights_regularizer=slim.l2_regularizer(weight_decay),
weights_initializer=slim.variance_scaling_initializer(),
activation_fn=None,
normalizer_fn=None,
normalizer_params=None):
with slim.arg_scope([slim.batch_norm], **batch_norm_params):
with slim.arg_scope([slim.max_pool2d], padding='VALID') as arg_sc:
return arg_sc
def hourglass_arg_scope_tf(weight_decay=0.0001,
batch_norm_decay=0.997,
batch_norm_epsilon=1e-5,
batch_norm_scale=True):
"""Defines the default ResNet arg scope.
Args:
is_training: Whether or not we are training the parameters in the batch
normalization layers of the model.
weight_decay: The weight decay to use for regularizing the model.
batch_norm_decay: The moving average decay when estimating layer activation
statistics in batch normalization.
batch_norm_epsilon: Small constant to prevent division by zero when
normalizing activations by their variance in batch normalization.
batch_norm_scale: If True, uses an explicit `gamma` multiplier to scale the
activations in the batch normalization layer.
Returns:
An `arg_scope` to use for the resnet models.
"""
batch_norm_params = {
'decay': batch_norm_decay,
'epsilon': batch_norm_epsilon,
'scale': batch_norm_scale,
'updates_collections': tf.GraphKeys.UPDATE_OPS,
}
with slim.arg_scope(
[slim.conv2d],
weights_regularizer=slim.l2_regularizer(weight_decay),
weights_initializer=slim.variance_scaling_initializer(),
activation_fn=tf.nn.relu,
normalizer_fn=slim.batch_norm,
normalizer_params=batch_norm_params):
with slim.arg_scope([slim.batch_norm], **batch_norm_params):
with slim.arg_scope([slim.max_pool2d], padding='SAME') as arg_sc:
return arg_sc
# bottleneck_inception_SE
def bottleneck_inception_SE_module(
inputs,
out_channel=256,
res=None,
scope='inception_block'):
min_channel = out_channel // 8
with tf.variable_scope(scope):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(inputs, min_channel * 3,
[1, 1], scope='Conv2d_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(inputs, min_channel *
3 / 2, [1, 1], scope='Conv2d_1x1')
branch_1 = slim.conv2d(
branch_1, min_channel * 3, [3, 3], scope='Conv2d_3x3')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(inputs, min_channel //
3, [1, 1], scope='Conv2d_1x1')
branch_2 = slim.conv2d(
branch_2, min_channel, [3, 3], scope='Conv2d_3x3')
with tf.variable_scope('Branch_3'):
branch_3 = slim.max_pool2d(inputs, [3, 3], 1, scope='MaxPool_3x3')
branch_3 = slim.conv2d(
branch_3, min_channel, [1, 1], scope='Conv2d_1x1')
net = tf.concat(
axis=3, values=[branch_0, branch_1, branch_2, branch_3])
se_branch = tf.reduce_mean(net, axis=[1, 2])
se_branch = slim.fully_connected(se_branch, out_channel // 16)
se_branch = slim.fully_connected(
se_branch, out_channel, activation_fn=tf.sigmoid)
net = net * se_branch[:,None,None,:]
if res:
inputs = slim.conv2d(inputs, res, (1, 1),
scope='bn_res'.format(scope))
net += inputs
return net
# bottle neck modules
def bottleneck_inception_module(
inputs,
out_channel=256,
res=None,
scope='inception_block'):
min_channel = out_channel // 8
with tf.variable_scope(scope):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(inputs, min_channel * 3,
[1, 1], scope='Conv2d_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(inputs, min_channel *
3 / 2, [1, 1], scope='Conv2d_1x1')
branch_1 = slim.conv2d(
branch_1, min_channel * 3, [3, 3], scope='Conv2d_3x3')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(inputs, min_channel //
3, [1, 1], scope='Conv2d_1x1')
branch_2 = slim.conv2d(
branch_2, min_channel, [3, 3], scope='Conv2d_3x3')
with tf.variable_scope('Branch_3'):
branch_3 = slim.max_pool2d(inputs, [3, 3], 1, scope='MaxPool_3x3')
branch_3 = slim.conv2d(
branch_3, min_channel, [1, 1], scope='Conv2d_1x1')
net = tf.concat(
axis=3, values=[branch_0, branch_1, branch_2, branch_3])
if res:
inputs = slim.conv2d(inputs, res, (1, 1),
scope='bn_res'.format(scope))
net += inputs
return net
def bottleneck_module(inputs, out_channel=256, res=None, scope=''):
with tf.variable_scope(scope):
net = slim.stack(inputs, slim.conv2d, [
(out_channel // 2, [1, 1]), (out_channel // 2, [3, 3]), (out_channel, [1, 1])], scope='conv')
if res:
inputs = slim.conv2d(inputs, res, (1, 1),
scope='bn_res'.format(scope))
net += inputs
return net
# recursive hourglass definition
def hourglass_module(inputs, depth=0, deconv='bilinear', bottleneck='bottleneck'):
bm_fn = globals()['%s_module' % bottleneck]
with tf.variable_scope('depth_{}'.format(depth)):
# buttom up layers
net = slim.max_pool2d(inputs, [2, 2], scope='pool')
net = slim.stack(net, bm_fn, [
(256, None), (256, None), (256, None)], scope='buttom_up')
# connecting layers
if depth > 0:
net = hourglass_module(net, depth=depth - 1, deconv=deconv)
else:
net = bm_fn(
net, out_channel=512, res=512, scope='connecting')
# top down layers
net = bm_fn(net, out_channel=512,
res=512, scope='top_down')
net = deconv_layer(net, 2, 512, method=deconv)
# residual layers
net += slim.stack(inputs, bm_fn,
[(256, None), (256, None), (512, 512)], scope='res')
return net
def hourglass(inputs,
scale=1,
regression_channels=2,
classification_channels=22,
deconv='bilinear',
bottleneck='bottleneck'):
"""Defines a lightweight resnet based model for dense estimation tasks.
Args:
inputs: A `Tensor` with dimensions [num_batches, height, width, depth].
scale: A scalar which denotes the factor to subsample the current image.
output_channels: The number of output channels. E.g., for human pose
estimation this equals 13 channels.
Returns:
A `Tensor` of dimensions [num_batches, height, width, output_channels]."""
out_shape = tf.shape(inputs)[1:3]
if scale > 1:
inputs = tf.pad(inputs, ((0, 0), (1, 1), (1, 1), (0, 0)))
inputs = slim.layers.avg_pool2d(
inputs, (3, 3), (scale, scale), padding='VALID')
output_channels = regression_channels + classification_channels
with slim.arg_scope(hourglass_arg_scope_tf()):
# D1
net = slim.conv2d(inputs, 64, (7, 7), 2, scope='conv1')
net = bottleneck_module(net, out_channel=128,
res=128, scope='bottleneck1')
net = slim.max_pool2d(net, [2, 2], scope='pool1')
# D2
net = slim.stack(net, bottleneck_module, [
(128, None), (128, None), (256, 256)], scope='conv2')
# hourglasses (D3,D4,D5)
with tf.variable_scope('hourglass'):
net = hourglass_module(
net, depth=4, deconv=deconv, bottleneck=bottleneck)
# final layers (D6, D7)
net = slim.stack(net, slim.conv2d, [(512, [1, 1]), (256, [1, 1]),
(output_channels, [1, 1])
], scope='conv3')
net = deconv_layer(net, 4, output_channels, method=deconv)
net = slim.conv2d(net, output_channels, 1, scope='conv_last')
regression = slim.conv2d(
net, regression_channels, 1, activation_fn=None
) if regression_channels else None
logits = slim.conv2d(
net, classification_channels, 1, activation_fn=None
) if classification_channels else None
return regression, logits
def StackedHourglassTorch(inputs, out_channels=16, deconv='bilinear'):
net = inputs
with tf.name_scope('nn.Sequential'):
with tf.name_scope('nn.Sequential'):
net = tf.pad(net, np.array([[0, 0], [3, 3], [3, 3], [0, 0]]))
net = slim.conv2d(net, 64, (7, 7), (2, 2),
activation_fn=None, padding='VALID')
net = slim.batch_norm(net)
net = slim.nn.relu(net)
with tf.name_scope('nn.Sequential'):
with tf.name_scope('nn.ConcatTable'):
net0 = net
with tf.name_scope('nn.Sequential'):
net0 = slim.batch_norm(net0)
net0 = slim.nn.relu(net0)
net0 = tf.pad(net0, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net0 = slim.conv2d(
net0, 64, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net0 = slim.batch_norm(net0)
net0 = slim.nn.relu(net0)
net0 = tf.pad(net0, np.array(
[[0, 0], [1, 1], [1, 1], [0, 0]]))
net0 = slim.conv2d(
net0, 64, (3, 3), (1, 1), activation_fn=None, padding='VALID')
net0 = slim.batch_norm(net0)
net0 = slim.nn.relu(net0)
net0 = tf.pad(net0, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net0 = slim.conv2d(
net0, 128, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net1 = net
with tf.name_scope('nn.Sequential'):
net1 = tf.pad(net1, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net1 = slim.conv2d(
net1, 128, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net = tf.add_n([net0, net1])
net = tf.pad(net, np.array([[0, 0], [0, 0], [0, 0], [0, 0]]))
net = slim.max_pool2d(net, (2, 2), (2, 2))
with tf.name_scope('nn.Sequential'):
with tf.name_scope('nn.ConcatTable'):
net0 = net
with tf.name_scope('nn.Sequential'):
net0 = slim.batch_norm(net0)
net0 = slim.nn.relu(net0)
net0 = tf.pad(net0, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net0 = slim.conv2d(
net0, 64, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net0 = slim.batch_norm(net0)
net0 = slim.nn.relu(net0)
net0 = tf.pad(net0, np.array(
[[0, 0], [1, 1], [1, 1], [0, 0]]))
net0 = slim.conv2d(
net0, 64, (3, 3), (1, 1), activation_fn=None, padding='VALID')
net0 = slim.batch_norm(net0)
net0 = slim.nn.relu(net0)
net0 = tf.pad(net0, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net0 = slim.conv2d(
net0, 128, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net1 = net
net = tf.add_n([net0, net1])
with tf.name_scope('nn.Sequential'):
with tf.name_scope('nn.ConcatTable'):
net0 = net
with tf.name_scope('nn.Sequential'):
net0 = slim.batch_norm(net0)
net0 = slim.nn.relu(net0)
net0 = tf.pad(net0, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net0 = slim.conv2d(
net0, 64, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net0 = slim.batch_norm(net0)
net0 = slim.nn.relu(net0)
net0 = tf.pad(net0, np.array(
[[0, 0], [1, 1], [1, 1], [0, 0]]))
net0 = slim.conv2d(
net0, 64, (3, 3), (1, 1), activation_fn=None, padding='VALID')
net0 = slim.batch_norm(net0)
net0 = slim.nn.relu(net0)
net0 = tf.pad(net0, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net0 = slim.conv2d(
net0, 128, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net1 = net
net = tf.add_n([net0, net1])
with tf.name_scope('nn.Sequential'):
with tf.name_scope('nn.ConcatTable'):
net0 = net
with tf.name_scope('nn.Sequential'):
net0 = slim.batch_norm(net0)
net0 = slim.nn.relu(net0)
net0 = tf.pad(net0, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net0 = slim.conv2d(
net0, 128, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net0 = slim.batch_norm(net0)
net0 = slim.nn.relu(net0)
net0 = tf.pad(net0, np.array(
[[0, 0], [1, 1], [1, 1], [0, 0]]))
net0 = slim.conv2d(
net0, 128, (3, 3), (1, 1), activation_fn=None, padding='VALID')
net0 = slim.batch_norm(net0)
net0 = slim.nn.relu(net0)
net0 = tf.pad(net0, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net0 = slim.conv2d(
net0, 256, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net1 = net
with tf.name_scope('nn.Sequential'):
net1 = tf.pad(net1, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net1 = slim.conv2d(
net1, 256, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net = tf.add_n([net0, net1])
with tf.name_scope('nn.Sequential'):
with tf.name_scope('nn.ConcatTable'):
net0 = net
with tf.name_scope('nn.Sequential'):
net0 = tf.pad(net0, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net0 = slim.max_pool2d(net0, (2, 2), (2, 2))
with tf.name_scope('nn.Sequential'):
with tf.name_scope('nn.ConcatTable'):
net00 = net0
with tf.name_scope('nn.Sequential'):
net00 = slim.batch_norm(net00)
net00 = slim.nn.relu(net00)
net00 = tf.pad(net00, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net00 = slim.conv2d(
net00, 128, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net00 = slim.batch_norm(net00)
net00 = slim.nn.relu(net00)
net00 = tf.pad(net00, np.array(
[[0, 0], [1, 1], [1, 1], [0, 0]]))
net00 = slim.conv2d(
net00, 128, (3, 3), (1, 1), activation_fn=None, padding='VALID')
net00 = slim.batch_norm(net00)
net00 = slim.nn.relu(net00)
net00 = tf.pad(net00, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net00 = slim.conv2d(
net00, 256, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net01 = net0
net0 = tf.add_n([net00, net01])
with tf.name_scope('nn.Sequential'):
with tf.name_scope('nn.ConcatTable'):
net00 = net0
with tf.name_scope('nn.Sequential'):
net00 = slim.batch_norm(net00)
net00 = slim.nn.relu(net00)
net00 = tf.pad(net00, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net00 = slim.conv2d(
net00, 128, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net00 = slim.batch_norm(net00)
net00 = slim.nn.relu(net00)
net00 = tf.pad(net00, np.array(
[[0, 0], [1, 1], [1, 1], [0, 0]]))
net00 = slim.conv2d(
net00, 128, (3, 3), (1, 1), activation_fn=None, padding='VALID')
net00 = slim.batch_norm(net00)
net00 = slim.nn.relu(net00)
net00 = tf.pad(net00, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net00 = slim.conv2d(
net00, 256, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net01 = net0
net0 = tf.add_n([net00, net01])
with tf.name_scope('nn.Sequential'):
with tf.name_scope('nn.ConcatTable'):
net00 = net0
with tf.name_scope('nn.Sequential'):
net00 = slim.batch_norm(net00)
net00 = slim.nn.relu(net00)
net00 = tf.pad(net00, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net00 = slim.conv2d(
net00, 128, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net00 = slim.batch_norm(net00)
net00 = slim.nn.relu(net00)
net00 = tf.pad(net00, np.array(
[[0, 0], [1, 1], [1, 1], [0, 0]]))
net00 = slim.conv2d(
net00, 128, (3, 3), (1, 1), activation_fn=None, padding='VALID')
net00 = slim.batch_norm(net00)
net00 = slim.nn.relu(net00)
net00 = tf.pad(net00, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net00 = slim.conv2d(
net00, 256, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net01 = net0
net0 = tf.add_n([net00, net01])
with tf.name_scope('nn.Sequential'):
with tf.name_scope('nn.ConcatTable'):
net00 = net0
with tf.name_scope('nn.Sequential'):
net00 = tf.pad(net00, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net00 = slim.max_pool2d(
net00, (2, 2), (2, 2))
with tf.name_scope('nn.Sequential'):
with tf.name_scope('nn.ConcatTable'):
net000 = net00
with tf.name_scope('nn.Sequential'):
net000 = slim.batch_norm(
net000)
net000 = slim.nn.relu(net000)
net000 = tf.pad(net000, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net000 = slim.conv2d(
net000, 128, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net000 = slim.batch_norm(
net000)
net000 = slim.nn.relu(net000)
net000 = tf.pad(net000, np.array(
[[0, 0], [1, 1], [1, 1], [0, 0]]))
net000 = slim.conv2d(
net000, 128, (3, 3), (1, 1), activation_fn=None, padding='VALID')
net000 = slim.batch_norm(
net000)
net000 = slim.nn.relu(net000)
net000 = tf.pad(net000, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net000 = slim.conv2d(
net000, 256, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net001 = net00
net00 = tf.add_n([net000, net001])
with tf.name_scope('nn.Sequential'):
with tf.name_scope('nn.ConcatTable'):
net000 = net00
with tf.name_scope('nn.Sequential'):
net000 = slim.batch_norm(
net000)
net000 = slim.nn.relu(net000)
net000 = tf.pad(net000, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net000 = slim.conv2d(
net000, 128, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net000 = slim.batch_norm(
net000)
net000 = slim.nn.relu(net000)
net000 = tf.pad(net000, np.array(
[[0, 0], [1, 1], [1, 1], [0, 0]]))
net000 = slim.conv2d(
net000, 128, (3, 3), (1, 1), activation_fn=None, padding='VALID')
net000 = slim.batch_norm(
net000)
net000 = slim.nn.relu(net000)
net000 = tf.pad(net000, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net000 = slim.conv2d(
net000, 256, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net001 = net00
net00 = tf.add_n([net000, net001])
with tf.name_scope('nn.Sequential'):
with tf.name_scope('nn.ConcatTable'):
net000 = net00
with tf.name_scope('nn.Sequential'):
net000 = slim.batch_norm(
net000)
net000 = slim.nn.relu(net000)
net000 = tf.pad(net000, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net000 = slim.conv2d(
net000, 128, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net000 = slim.batch_norm(
net000)
net000 = slim.nn.relu(net000)
net000 = tf.pad(net000, np.array(
[[0, 0], [1, 1], [1, 1], [0, 0]]))
net000 = slim.conv2d(
net000, 128, (3, 3), (1, 1), activation_fn=None, padding='VALID')
net000 = slim.batch_norm(
net000)
net000 = slim.nn.relu(net000)
net000 = tf.pad(net000, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net000 = slim.conv2d(
net000, 256, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net001 = net00
net00 = tf.add_n([net000, net001])
with tf.name_scope('nn.Sequential'):
with tf.name_scope('nn.ConcatTable'):
net000 = net00
with tf.name_scope('nn.Sequential'):
net000 = tf.pad(net000, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net000 = slim.max_pool2d(
net000, (2, 2), (2, 2))
with tf.name_scope('nn.Sequential'):
with tf.name_scope('nn.ConcatTable'):
net0000 = net000
with tf.name_scope('nn.Sequential'):
net0000 = slim.batch_norm(
net0000)
net0000 = slim.nn.relu(
net0000)
net0000 = tf.pad(net0000, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net0000 = slim.conv2d(
net0000, 128, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net0000 = slim.batch_norm(
net0000)
net0000 = slim.nn.relu(
net0000)
net0000 = tf.pad(net0000, np.array(
[[0, 0], [1, 1], [1, 1], [0, 0]]))
net0000 = slim.conv2d(
net0000, 128, (3, 3), (1, 1), activation_fn=None, padding='VALID')
net0000 = slim.batch_norm(
net0000)
net0000 = slim.nn.relu(
net0000)
net0000 = tf.pad(net0000, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net0000 = slim.conv2d(
net0000, 256, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net0001 = net000
net000 = tf.add_n(
[net0000, net0001])
with tf.name_scope('nn.Sequential'):
with tf.name_scope('nn.ConcatTable'):
net0000 = net000
with tf.name_scope('nn.Sequential'):
net0000 = slim.batch_norm(
net0000)
net0000 = slim.nn.relu(
net0000)
net0000 = tf.pad(net0000, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net0000 = slim.conv2d(
net0000, 128, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net0000 = slim.batch_norm(
net0000)
net0000 = slim.nn.relu(
net0000)
net0000 = tf.pad(net0000, np.array(
[[0, 0], [1, 1], [1, 1], [0, 0]]))
net0000 = slim.conv2d(
net0000, 128, (3, 3), (1, 1), activation_fn=None, padding='VALID')
net0000 = slim.batch_norm(
net0000)
net0000 = slim.nn.relu(
net0000)
net0000 = tf.pad(net0000, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net0000 = slim.conv2d(
net0000, 256, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net0001 = net000
net000 = tf.add_n(
[net0000, net0001])
with tf.name_scope('nn.Sequential'):
with tf.name_scope('nn.ConcatTable'):
net0000 = net000
with tf.name_scope('nn.Sequential'):
net0000 = slim.batch_norm(
net0000)
net0000 = slim.nn.relu(
net0000)
net0000 = tf.pad(net0000, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net0000 = slim.conv2d(
net0000, 128, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net0000 = slim.batch_norm(
net0000)
net0000 = slim.nn.relu(
net0000)
net0000 = tf.pad(net0000, np.array(
[[0, 0], [1, 1], [1, 1], [0, 0]]))
net0000 = slim.conv2d(
net0000, 128, (3, 3), (1, 1), activation_fn=None, padding='VALID')
net0000 = slim.batch_norm(
net0000)
net0000 = slim.nn.relu(
net0000)
net0000 = tf.pad(net0000, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net0000 = slim.conv2d(
net0000, 256, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net0001 = net000
net000 = tf.add_n(
[net0000, net0001])
with tf.name_scope('nn.Sequential'):
with tf.name_scope('nn.ConcatTable'):
net0000 = net000
with tf.name_scope('nn.Sequential'):
net0000 = tf.pad(net0000, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net0000 = slim.max_pool2d(
net0000, (2, 2), (2, 2))
with tf.name_scope('nn.Sequential'):
with tf.name_scope('nn.ConcatTable'):
net00000 = net0000
with tf.name_scope('nn.Sequential'):
net00000 = slim.batch_norm(
net00000)
net00000 = slim.nn.relu(
net00000)
net00000 = tf.pad(net00000, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net00000 = slim.conv2d(
net00000, 128, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net00000 = slim.batch_norm(
net00000)
net00000 = slim.nn.relu(
net00000)
net00000 = tf.pad(net00000, np.array(
[[0, 0], [1, 1], [1, 1], [0, 0]]))
net00000 = slim.conv2d(
net00000, 128, (3, 3), (1, 1), activation_fn=None, padding='VALID')
net00000 = slim.batch_norm(
net00000)
net00000 = slim.nn.relu(
net00000)
net00000 = tf.pad(net00000, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net00000 = slim.conv2d(
net00000, 256, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net00001 = net0000
net0000 = tf.add_n(
[net00000, net00001])
with tf.name_scope('nn.Sequential'):
with tf.name_scope('nn.ConcatTable'):
net00000 = net0000
with tf.name_scope('nn.Sequential'):
net00000 = slim.batch_norm(
net00000)
net00000 = slim.nn.relu(
net00000)
net00000 = tf.pad(net00000, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net00000 = slim.conv2d(
net00000, 128, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net00000 = slim.batch_norm(
net00000)
net00000 = slim.nn.relu(
net00000)
net00000 = tf.pad(net00000, np.array(
[[0, 0], [1, 1], [1, 1], [0, 0]]))
net00000 = slim.conv2d(
net00000, 128, (3, 3), (1, 1), activation_fn=None, padding='VALID')
net00000 = slim.batch_norm(
net00000)
net00000 = slim.nn.relu(
net00000)
net00000 = tf.pad(net00000, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net00000 = slim.conv2d(
net00000, 256, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net00001 = net0000
net0000 = tf.add_n(
[net00000, net00001])
with tf.name_scope('nn.Sequential'):
with tf.name_scope('nn.ConcatTable'):
net00000 = net0000
with tf.name_scope('nn.Sequential'):
net00000 = slim.batch_norm(
net00000)
net00000 = slim.nn.relu(
net00000)
net00000 = tf.pad(net00000, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net00000 = slim.conv2d(
net00000, 128, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net00000 = slim.batch_norm(
net00000)
net00000 = slim.nn.relu(
net00000)
net00000 = tf.pad(net00000, np.array(
[[0, 0], [1, 1], [1, 1], [0, 0]]))
net00000 = slim.conv2d(
net00000, 128, (3, 3), (1, 1), activation_fn=None, padding='VALID')
net00000 = slim.batch_norm(
net00000)
net00000 = slim.nn.relu(
net00000)
net00000 = tf.pad(net00000, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net00000 = slim.conv2d(
net00000, 256, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net00001 = net0000
net0000 = tf.add_n(
[net00000, net00001])
with tf.name_scope('nn.Sequential'):
with tf.name_scope('nn.ConcatTable'):
net00000 = net0000
with tf.name_scope('nn.Sequential'):
net00000 = slim.batch_norm(
net00000)
net00000 = slim.nn.relu(
net00000)
net00000 = tf.pad(net00000, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net00000 = slim.conv2d(
net00000, 256, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net00000 = slim.batch_norm(
net00000)
net00000 = slim.nn.relu(
net00000)
net00000 = tf.pad(net00000, np.array(
[[0, 0], [1, 1], [1, 1], [0, 0]]))
net00000 = slim.conv2d(
net00000, 256, (3, 3), (1, 1), activation_fn=None, padding='VALID')
net00000 = slim.batch_norm(
net00000)
net00000 = slim.nn.relu(
net00000)
net00000 = tf.pad(net00000, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net00000 = slim.conv2d(
net00000, 512, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net00001 = net0000
with tf.name_scope('nn.Sequential'):
net00001 = tf.pad(net00001, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net00001 = slim.conv2d(
net00001, 512, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net0000 = tf.add_n(
[net00000, net00001])
with tf.name_scope('nn.Sequential'):
with tf.name_scope('nn.ConcatTable'):
net00000 = net0000
with tf.name_scope('nn.Sequential'):
net00000 = slim.batch_norm(
net00000)
net00000 = slim.nn.relu(
net00000)
net00000 = tf.pad(net00000, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net00000 = slim.conv2d(
net00000, 256, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net00000 = slim.batch_norm(
net00000)
net00000 = slim.nn.relu(
net00000)
net00000 = tf.pad(net00000, np.array(
[[0, 0], [1, 1], [1, 1], [0, 0]]))
net00000 = slim.conv2d(
net00000, 256, (3, 3), (1, 1), activation_fn=None, padding='VALID')
net00000 = slim.batch_norm(
net00000)
net00000 = slim.nn.relu(
net00000)
net00000 = tf.pad(net00000, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net00000 = slim.conv2d(
net00000, 512, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net00001 = net0000
net0000 = tf.add_n(
[net00000, net00001])
net0000 = tf.pad(net0000, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net0000 = deconv_layer(
net0000, 2, 512, method=deconv)
net0001 = net000
with tf.name_scope('nn.Sequential'):
with tf.name_scope('nn.Sequential'):
with tf.name_scope('nn.ConcatTable'):
net00010 = net0001
with tf.name_scope('nn.Sequential'):
net00010 = slim.batch_norm(
net00010)
net00010 = slim.nn.relu(
net00010)
net00010 = tf.pad(net00010, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net00010 = slim.conv2d(
net00010, 128, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net00010 = slim.batch_norm(
net00010)
net00010 = slim.nn.relu(
net00010)
net00010 = tf.pad(net00010, np.array(
[[0, 0], [1, 1], [1, 1], [0, 0]]))
net00010 = slim.conv2d(
net00010, 128, (3, 3), (1, 1), activation_fn=None, padding='VALID')
net00010 = slim.batch_norm(
net00010)
net00010 = slim.nn.relu(
net00010)
net00010 = tf.pad(net00010, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net00010 = slim.conv2d(
net00010, 256, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net00011 = net0001
net0001 = tf.add_n(
[net00010, net00011])
with tf.name_scope('nn.Sequential'):
with tf.name_scope('nn.ConcatTable'):
net00010 = net0001
with tf.name_scope('nn.Sequential'):
net00010 = slim.batch_norm(
net00010)
net00010 = slim.nn.relu(
net00010)
net00010 = tf.pad(net00010, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net00010 = slim.conv2d(
net00010, 128, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net00010 = slim.batch_norm(
net00010)
net00010 = slim.nn.relu(
net00010)
net00010 = tf.pad(net00010, np.array(
[[0, 0], [1, 1], [1, 1], [0, 0]]))
net00010 = slim.conv2d(
net00010, 128, (3, 3), (1, 1), activation_fn=None, padding='VALID')
net00010 = slim.batch_norm(
net00010)
net00010 = slim.nn.relu(
net00010)
net00010 = tf.pad(net00010, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net00010 = slim.conv2d(
net00010, 256, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net00011 = net0001
net0001 = tf.add_n(
[net00010, net00011])
with tf.name_scope('nn.Sequential'):
with tf.name_scope('nn.ConcatTable'):
net00010 = net0001
with tf.name_scope('nn.Sequential'):
net00010 = slim.batch_norm(
net00010)
net00010 = slim.nn.relu(
net00010)
net00010 = tf.pad(net00010, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net00010 = slim.conv2d(
net00010, 256, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net00010 = slim.batch_norm(
net00010)
net00010 = slim.nn.relu(
net00010)
net00010 = tf.pad(net00010, np.array(
[[0, 0], [1, 1], [1, 1], [0, 0]]))
net00010 = slim.conv2d(
net00010, 256, (3, 3), (1, 1), activation_fn=None, padding='VALID')
net00010 = slim.batch_norm(
net00010)
net00010 = slim.nn.relu(
net00010)
net00010 = tf.pad(net00010, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net00010 = slim.conv2d(
net00010, 512, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net00011 = net0001
with tf.name_scope('nn.Sequential'):
net00011 = tf.pad(net00011, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net00011 = slim.conv2d(
net00011, 512, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net0001 = tf.add_n(
[net00010, net00011])
net000 = tf.add_n(
[net0000, net0001])
with tf.name_scope('nn.Sequential'):
with tf.name_scope('nn.ConcatTable'):
net0000 = net000
with tf.name_scope('nn.Sequential'):
net0000 = slim.batch_norm(
net0000)
net0000 = slim.nn.relu(
net0000)
net0000 = tf.pad(net0000, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net0000 = slim.conv2d(
net0000, 256, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net0000 = slim.batch_norm(
net0000)
net0000 = slim.nn.relu(
net0000)
net0000 = tf.pad(net0000, np.array(
[[0, 0], [1, 1], [1, 1], [0, 0]]))
net0000 = slim.conv2d(
net0000, 256, (3, 3), (1, 1), activation_fn=None, padding='VALID')
net0000 = slim.batch_norm(
net0000)
net0000 = slim.nn.relu(
net0000)
net0000 = tf.pad(net0000, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net0000 = slim.conv2d(
net0000, 512, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net0001 = net000
net000 = tf.add_n(
[net0000, net0001])
net000 = tf.pad(net000, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net000 = deconv_layer(
net000, 2, 512, method=deconv)
net001 = net00
with tf.name_scope('nn.Sequential'):
with tf.name_scope('nn.Sequential'):
with tf.name_scope('nn.ConcatTable'):
net0010 = net001
with tf.name_scope('nn.Sequential'):
net0010 = slim.batch_norm(
net0010)
net0010 = slim.nn.relu(
net0010)
net0010 = tf.pad(net0010, np.array(
[[0, 0], [0, 0], [0, 0], [0, 0]]))
net0010 = slim.conv2d(
net0010, 128, (1, 1), (1, 1), activation_fn=None, padding='VALID')
net0010 = slim.batch_norm(
net0010)