Python tensorflow.contrib.slim.convolution2d() Examples
The following are 9
code examples of tensorflow.contrib.slim.convolution2d().
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Example #1
| Source File: network_base.py From tf-pose with Apache License 2.0 | 5 votes |
|
def separable_conv(self, input, k_h, k_w, c_o, stride, name, relu=True, set_bias=True):
with slim.arg_scope([slim.batch_norm], decay=0.999, fused=common.batchnorm_fused, is_training=self.trainable):
output = slim.separable_convolution2d(input,
num_outputs=None,
stride=stride,
trainable=self.trainable,
depth_multiplier=1.0,
kernel_size=[k_h, k_w],
# activation_fn=common.activation_fn if relu else None,
activation_fn=None,
# normalizer_fn=slim.batch_norm,
weights_initializer=_init_xavier,
# weights_initializer=_init_norm,
weights_regularizer=_l2_regularizer_00004,
biases_initializer=None,
padding=DEFAULT_PADDING,
scope=name + '_depthwise')
output = slim.convolution2d(output,
c_o,
stride=1,
kernel_size=[1, 1],
activation_fn=common.activation_fn if relu else None,
weights_initializer=_init_xavier,
# weights_initializer=_init_norm,
biases_initializer=_init_zero if set_bias else None,
normalizer_fn=slim.batch_norm,
trainable=self.trainable,
weights_regularizer=None,
scope=name + '_pointwise')
return output
Example #2
| Source File: network_base.py From tf-pose with Apache License 2.0 | 5 votes |
|
def convb(self, input, k_h, k_w, c_o, stride, name, relu=True, set_bias=True, set_tanh=False):
with slim.arg_scope([slim.batch_norm], decay=0.999, fused=common.batchnorm_fused, is_training=self.trainable):
output = slim.convolution2d(input, c_o, kernel_size=[k_h, k_w],
stride=stride,
normalizer_fn=slim.batch_norm,
weights_regularizer=_l2_regularizer_convb,
weights_initializer=_init_xavier,
# weights_initializer=tf.truncated_normal_initializer(stddev=0.01),
biases_initializer=_init_zero if set_bias else None,
trainable=self.trainable,
activation_fn=common.activation_fn if relu else None,
scope=name)
if set_tanh:
output = tf.nn.sigmoid(output, name=name + '_extra_acv')
return output
Example #3
| Source File: network_base.py From MobileNetV2-PoseEstimation with MIT License | 5 votes |
|
def separable_conv(self, input, k_h, k_w, c_o, stride, name, relu=True, set_bias=True):
with slim.arg_scope([slim.batch_norm], decay=0.999, fused=common.batchnorm_fused, is_training=self.trainable):
output = slim.separable_convolution2d(input,
num_outputs=None,
stride=stride,
trainable=self.trainable,
depth_multiplier=1.0,
kernel_size=[k_h, k_w],
# activation_fn=common.activation_fn if relu else None,
activation_fn=None,
# normalizer_fn=slim.batch_norm,
weights_initializer=_init_xavier,
# weights_initializer=_init_norm,
weights_regularizer=_l2_regularizer_00004,
biases_initializer=None,
padding=DEFAULT_PADDING,
scope=name + '_depthwise')
output = slim.convolution2d(output,
c_o,
stride=1,
kernel_size=[1, 1],
activation_fn=common.activation_fn if relu else None,
weights_initializer=_init_xavier,
# weights_initializer=_init_norm,
biases_initializer=_init_zero if set_bias else None,
normalizer_fn=slim.batch_norm,
trainable=self.trainable,
weights_regularizer=None,
scope=name + '_pointwise')
return output
Example #4
| Source File: network_base.py From MobileNetV2-PoseEstimation with MIT License | 5 votes |
|
def convb(self, input, k_h, k_w, c_o, stride, name, relu=True, set_bias=True, set_tanh=False):
with slim.arg_scope([slim.batch_norm], decay=0.999, fused=common.batchnorm_fused, is_training=self.trainable):
output = slim.convolution2d(input, c_o, kernel_size=[k_h, k_w],
stride=stride,
normalizer_fn=slim.batch_norm,
weights_regularizer=_l2_regularizer_convb,
weights_initializer=_init_xavier,
# weights_initializer=tf.truncated_normal_initializer(stddev=0.01),
biases_initializer=_init_zero if set_bias else None,
trainable=self.trainable,
activation_fn=common.activation_fn if relu else None,
scope=name)
if set_tanh:
output = tf.nn.sigmoid(output, name=name + '_extra_acv')
return output
Example #5
| Source File: mobilenetdet.py From MobileNet with Apache License 2.0 | 5 votes |
|
def mobilenet_arg_scope(weight_decay=0.0):
"""Defines the default mobilenet argument scope.
Args:
weight_decay: The weight decay to use for regularizing the model.
Returns:
An `arg_scope` to use for the MobileNet model.
"""
with slim.arg_scope(
[slim.convolution2d, slim.separable_convolution2d],
weights_initializer=slim.initializers.xavier_initializer(),
biases_initializer=slim.init_ops.zeros_initializer(),
weights_regularizer=slim.l2_regularizer(weight_decay)) as sc:
return sc
Example #6
| Source File: mobilenet.py From MobileNet with Apache License 2.0 | 5 votes |
|
def mobilenet_arg_scope(weight_decay=0.0):
"""Defines the default mobilenet argument scope.
Args:
weight_decay: The weight decay to use for regularizing the model.
Returns:
An `arg_scope` to use for the MobileNet model.
"""
with slim.arg_scope(
[slim.convolution2d, slim.separable_convolution2d],
weights_initializer=slim.initializers.xavier_initializer(),
biases_initializer=slim.init_ops.zeros_initializer(),
weights_regularizer=slim.l2_regularizer(weight_decay)) as sc:
return sc
Example #7
| Source File: network_base.py From tf-pose-estimation with Apache License 2.0 | 5 votes |
|
def separable_conv(self, input, k_h, k_w, c_o, stride, name, relu=True, set_bias=True):
with slim.arg_scope([slim.batch_norm], decay=0.999, fused=common.batchnorm_fused, is_training=self.trainable):
output = slim.separable_convolution2d(input,
num_outputs=None,
stride=stride,
trainable=self.trainable,
depth_multiplier=1.0,
kernel_size=[k_h, k_w],
# activation_fn=common.activation_fn if relu else None,
activation_fn=None,
# normalizer_fn=slim.batch_norm,
weights_initializer=_init_xavier,
# weights_initializer=_init_norm,
weights_regularizer=_l2_regularizer_00004,
biases_initializer=None,
padding=DEFAULT_PADDING,
scope=name + '_depthwise')
output = slim.convolution2d(output,
c_o,
stride=1,
kernel_size=[1, 1],
activation_fn=common.activation_fn if relu else None,
weights_initializer=_init_xavier,
# weights_initializer=_init_norm,
biases_initializer=_init_zero if set_bias else None,
normalizer_fn=slim.batch_norm,
trainable=self.trainable,
weights_regularizer=None,
scope=name + '_pointwise')
return output
Example #8
| Source File: network_base.py From tf-pose-estimation with Apache License 2.0 | 5 votes |
|
def convb(self, input, k_h, k_w, c_o, stride, name, relu=True, set_bias=True, set_tanh=False):
with slim.arg_scope([slim.batch_norm], decay=0.999, fused=common.batchnorm_fused, is_training=self.trainable):
output = slim.convolution2d(input, c_o, kernel_size=[k_h, k_w],
stride=stride,
normalizer_fn=slim.batch_norm,
weights_regularizer=_l2_regularizer_convb,
weights_initializer=_init_xavier,
# weights_initializer=tf.truncated_normal_initializer(stddev=0.01),
biases_initializer=_init_zero if set_bias else None,
trainable=self.trainable,
activation_fn=common.activation_fn if relu else None,
scope=name)
if set_tanh:
output = tf.nn.sigmoid(output, name=name + '_extra_acv')
return output
Example #9
| Source File: mobilenetdet.py From MobileNet with Apache License 2.0 | 4 votes |
|
def mobilenet(inputs,
is_training=True,
width_multiplier=1,
scope='MobileNet'):
def _depthwise_separable_conv(inputs,
num_pwc_filters,
width_multiplier,
sc,
downsample=False):
""" Helper function to build the depth-wise separable convolution layer.
"""
num_pwc_filters = round(num_pwc_filters * width_multiplier)
_stride = 2 if downsample else 1
# skip pointwise by setting num_outputs=None
depthwise_conv = slim.separable_convolution2d(inputs,
num_outputs=None,
stride=_stride,
depth_multiplier=1,
kernel_size=[3, 3],
scope=sc+'/depthwise_conv')
bn = slim.batch_norm(depthwise_conv, scope=sc+'/dw_batch_norm')
pointwise_conv = slim.convolution2d(bn,
num_pwc_filters,
kernel_size=[1, 1],
scope=sc+'/pointwise_conv')
bn = slim.batch_norm(pointwise_conv, scope=sc+'/pw_batch_norm')
return bn
with tf.variable_scope(scope) as sc:
end_points_collection = sc.name + '_end_points'
with slim.arg_scope([slim.convolution2d, slim.separable_convolution2d],
activation_fn=None,
outputs_collections=[end_points_collection]):
with slim.arg_scope([slim.batch_norm],
is_training=is_training,
activation_fn=tf.nn.relu):
net = slim.convolution2d(inputs, round(32 * width_multiplier), [3, 3], stride=2, padding='SAME', scope='conv_1')
net = slim.batch_norm(net, scope='conv_1/batch_norm')
net = _depthwise_separable_conv(net, 64, width_multiplier, sc='conv_ds_2')
net = _depthwise_separable_conv(net, 128, width_multiplier, downsample=True, sc='conv_ds_3')
net = _depthwise_separable_conv(net, 128, width_multiplier, sc='conv_ds_4')
net = _depthwise_separable_conv(net, 256, width_multiplier, downsample=True, sc='conv_ds_5')
net = _depthwise_separable_conv(net, 256, width_multiplier, sc='conv_ds_6')
net = _depthwise_separable_conv(net, 512, width_multiplier, downsample=True, sc='conv_ds_7')
net = _depthwise_separable_conv(net, 512, width_multiplier, sc='conv_ds_8')
net = _depthwise_separable_conv(net, 512, width_multiplier, sc='conv_ds_9')
net = _depthwise_separable_conv(net, 512, width_multiplier, sc='conv_ds_10')
net = _depthwise_separable_conv(net, 512, width_multiplier, sc='conv_ds_11')
net = _depthwise_separable_conv(net, 512, width_multiplier, sc='conv_ds_12')
net = _depthwise_separable_conv(net, 1024, width_multiplier, downsample=True, sc='conv_ds_13')
net = _depthwise_separable_conv(net, 1024, width_multiplier, sc='conv_ds_14')
end_points = slim.utils.convert_collection_to_dict(end_points_collection)
return end_points
