def concat_conv_bn_relu_drop(input_,
feature,
filter_,
strides,
activation_type=None,
norm_type=None,
is_train=True,
keep_prob=1,
replace=1,
resnet=True):
with tf.variable_scope('concat_conv_bn_relu_drop'):
x = crop_and_concat(input_, feature)
x = get_normalization_fn(x, norm_type, is_train)
for i in range(replace):
with tf.variable_scope('conv_' + str(i + 1)):
if i == 0:
_filter = [
_f if _i != len(filter_) - 2 else _f * 2
for _i, _f in enumerate(filter_)
]
x = convolution(x, _filter, strides=strides)
input_ = get_normalization_fn(x,
norm_type,
is_train,
scope='input')
else:
x = convolution(x, filter_, strides=strides)
x = get_normalization_fn(x, norm_type, is_train)
x = get_activation_fn(x, activation_type)
x = tf.nn.dropout(x, keep_prob=keep_prob)
if resnet and i == replace - 1:
x = resnet_add(x, input_)
return x
def _build_network(self):
with tf.variable_scope('vnet'):
with tf.variable_scope('input_layer'):
x = tf.tile(self.image_ph, [1, 1, 1, 1, self.init_filter])
x = get_normalization_fn(x, self.norm_type, self.is_train)
features = {}
for level_name, items in net_configs.items():
with tf.variable_scope(level_name):
for sub_name, _configs in items.items():
n_channels = get_num_channels(x)
if 'conv_block' == sub_name:
filter_ = _configs['kernel'] + [
n_channels, n_channels
]
x = conv_bn_relu_drop(
x, filter_, _configs['strides'],
self.activation_type, self.norm_type,
self.is_train, self.keep_prob,
_configs['replace'], _configs['resnet'])
features[level_name] = x
elif 'down_block' == sub_name:
filter_ = _configs['kernel'] + [
n_channels, n_channels * 2
]
x = down_conv_bn_relu(x, filter_,
_configs['strides'],
self.activation_type,
self.norm_type,
self.is_train)
elif 'up_block' == sub_name:
filter_ = _configs['kernel'] + [
n_channels // 2, n_channels
] # 32,32,2,128
x = deconv_bn_relu(x, filter_, _configs['strides'],
self.activation_type,
self.norm_type, self.is_train)
elif 'concat_conv_block' == sub_name:
filter_ = _configs['kernel'] + [
n_channels, n_channels
]
feature = features[_configs['feature']]
x = concat_conv_bn_relu_drop(
x, feature, filter_, _configs['strides'],
self.activation_type, self.norm_type,
self.is_train, self.keep_prob,
_configs['replace'], _configs['resnet'])
else:
raise Exception('找不到相应操作')
with tf.variable_scope('output_layer'):
x = convolution(x,
[1, 1, 1, self.init_filter, self.num_classes])
logits = get_normalization_fn(x, self.norm_type, self.is_train)
return logits
def deconv_bn_relu(x,
filter_,
strides,
activation_type=None,
norm_type=None,
is_train=True):
with tf.variable_scope('deconv_bn_relu'):
x = deconvolution(x, filter_, strides=strides)
x = get_normalization_fn(x, norm_type, is_train)
x = get_activation_fn(x, activation_type)
return x
def conv_bn_relu_drop(input_,
filter_,
strides=None,
activation_type=None,
norm_type=None,
is_train=True,
keep_prob=1,
replace=1,
resnet=True):
with tf.variable_scope('conv_bn_relu_drop'):
x = input_
for i in range(replace):
with tf.variable_scope('conv_' + str(i + 1)):
x = convolution(x,
filter_,
strides=strides,
activation_type=activation_type)
x = get_normalization_fn(x, norm_type, is_train)
x = get_activation_fn(x, activation_type)
x = tf.nn.dropout(x, keep_prob=keep_prob)
if resnet and i == replace - 1:
x = resnet_add(x, input_)
return x