def identity_bottleneck_1d(inputs, depth, depth_bottleneck, stride, v, rate=1, outputs_collections=None,
scope=None, use_bounded_activations=False, attention_module=None):
"""
Args:
inputs: A tensor of size [batch, height, width, channels].
depth、depth_bottleneck、stride三个参数是前面blocks类中的args
depth: 一个block中的某个unit中(第三个conv)输出的feature-map的个数
depth_bottleneck: 一个block中的某个unit中(前面两个conv)输出的feature-map个数
stride: 是short_cut路径对于para_inputs/pre_act(经过bn层的para_inputs)的subsample_2d的步长 -- (是否经过bn层主要看输入输出通道数是否一致)
以及unit中conv-2的步长
rate: An integer, rate for atrous convolution.
outputs_collections: 是收集end_points的collection
scope: 是这个unit的名称。
todo what is quantized inference??? what is bounded activations??? => must make it clear before any runs
use_bounded_activations: Whether or not to use bounded activations. Bounded
activations better lend themselves to quantized inference.
attention_module: SE-blocks or SESE-blocks
"""
with tf.variable_scope(scope, 'bottleneck_v1', [inputs]) as sc:
depth_in = slim.utils.last_dimension(inputs.get_shape(), min_rank=4)
# shortcut fine-tune
if depth == depth_in:
# pre se/sese in shortcut
if attention_module == 'se_block':
shortcut = se_block(inputs, name='se_block', ratio=2 if inputs.get_shape()[-1] <= 8 else 8)
elif attention_module == 'sese_block':
# todo ratio to be defined...
shortcut = sese_block(input_feature=inputs, name='sese_block', v=v, ratio=2 if inputs.get_shape()[-1] <= 8 else 8)
else:
shortcut = inputs
shortcut = subsample_1d(shortcut, stride, 'shortcut')
else:
# pre se/sese in shortcut
if attention_module == 'se_block':
shortcut = se_block(inputs, name='se_block', ratio=2 if inputs.get_shape()[-1] <= 8 else 8)
elif attention_module == 'sese_block':
# todo ratio to be defined...
shortcut = sese_block(input_feature=inputs, name='sese_block', v=v, ratio=2 if inputs.get_shape()[-1] <= 8 else 8)
else:
shortcut = inputs
shortcut = slim.conv2d(shortcut, depth, [1, 1], stride=stride, activation_fn=tf.nn.relu6 if use_bounded_activations else None,
scope='shortcut')
# convs
residual = slim.conv2d(inputs, depth_bottleneck, [1, 1], stride=1, scope='conv1')
residual = conv1d_same(residual, depth_bottleneck, 3, stride, rate=rate, scope='conv2')
residual = slim.conv2d(residual, depth, [1, 1], stride=1, activation_fn=None, scope='conv3')
# post-relu
if use_bounded_activations:
# Use clip_by_value to simulate bandpass activation.
# todo why using bandpass act?????
residual = tf.clip_by_value(residual, -6.0, 6.0)
output = tf.nn.relu6(shortcut + residual)
else:
output = tf.nn.relu(shortcut + residual)
return slim.utils.collect_named_outputs(outputs_collections, sc.name, output)
def pre_bottleneck_1d(inputs,
depth,
depth_bottleneck,
stride,
v,
rate=1,
outputs_collections=None,
scope=None,
attention_module=None):
"""
Args:
inputs: A tensor of size [batch, height, width, channels].
depth、depth_bottleneck、stride三个参数是前面blocks类中的args
depth: 一个block中的某个unit中(第三个conv)输出的feature-map的个数
depth_bottleneck: 一个block中的某个unit中(前面两个conv)输出的feature-map个数
stride: 是short_cut路径对于para_inputs/pre_act(经过bn层的para_inputs)的subsample_2d的步长 -- (是否经过bn层主要看输入输出通道数是否一致)
以及unit中conv-2的步长
rate: An integer, rate for atrous convolution.
outputs_collections: 是收集end_points的collection
scope: 是这个unit的名称
attention_module: SE-blocks or SESE-blocks
"""
with tf.variable_scope(scope, 'bottleneck_v2', [inputs]) as sc:
depth_in = slim.utils.last_dimension(inputs.get_shape(), min_rank=4)
# add se
if attention_module == 'se_block':
residual = se_block(inputs,
name='se_block',
ratio=2 if inputs.get_shape()[-1] <= 8 else 8)
# add sese
elif attention_module == 'sese_block':
# todo ratio to be defined...
residual = sese_block(
input_feature=inputs,
name='sese_block',
v=v,
ratio=2 if inputs.get_shape()[-1] <= 8 else 8)
# no other block implemented
else:
residual = inputs
# pre activate
preact = slim.batch_norm(inputs,
activation_fn=tf.nn.relu,
scope='preact')
# shortcut fine-tune
if depth == depth_in:
shortcut = subsample_1d(inputs, stride, 'shortcut')
else:
shortcut = slim.conv2d(preact,
depth, [1, 1],
stride=stride,
normalizer_fn=None,
activation_fn=None,
scope='shortcut')
# convs
residual = slim.conv2d(residual,
depth_bottleneck, [1, 1],
stride=1,
scope='conv1')
residual = conv1d_same(residual,
depth_bottleneck,
3,
stride,
rate=rate,
scope='conv2')
residual = slim.conv2d(residual,
depth, [1, 1],
stride=1,
normalizer_fn=None,
activation_fn=None,
scope='conv3')
# junction
output = shortcut + residual
return slim.utils.collect_named_outputs(outputs_collections, sc.name,
output)