def call(self, new_node, training):
if not self.conv_bn_act_pattern:
new_node = utils.activation_fn(new_node, self.act_type)
new_node = self.conv_op(new_node)
new_node = self.bn(new_node, training=training)
if self.conv_bn_act_pattern:
new_node = utils.activation_fn(new_node, self.act_type)
return new_node
def _call(image):
original_image = image
image = conv_op(image)
image = bn(image, training=training)
if self.act_type:
image = utils.activation_fn(image, act_type)
if i > 0 and self.survival_prob:
image = utils.drop_connect(image, training, self.survival_prob)
image = image + original_image
return image
def call(self, feats, training):
x = feats[-1]
skips = list(reversed(feats[:-1]))
for con2d_t, con2d_t_bn, skip in zip(self.con2d_ts, self.con2d_t_bns,
skips):
x = con2d_t(x)
x = con2d_t_bn(x, training)
x = utils.activation_fn(x, self.act_type)
x = tf.concat([x, skip], axis=-1)
# This is the last layer of the model
return self.head_transpose(x) # 64x64 -> 128x128
def build_bifpn_layer(feats, feat_sizes, config):
"""Builds a feature pyramid given previous feature pyramid and config."""
p = config # use p to denote the network config.
if p.fpn_config:
fpn_config = p.fpn_config
else:
fpn_config = fpn_configs.get_fpn_config(p.fpn_name, p.min_level,
p.max_level,
p.fpn_weight_method)
num_output_connections = [0 for _ in feats]
for i, fnode in enumerate(fpn_config.nodes):
with tf.variable_scope('fnode{}'.format(i)):
logging.info('fnode %d : %s', i, fnode)
new_node_height = feat_sizes[fnode['feat_level']]['height']
new_node_width = feat_sizes[fnode['feat_level']]['width']
nodes = []
for idx, input_offset in enumerate(fnode['inputs_offsets']):
input_node = feats[input_offset]
num_output_connections[input_offset] += 1
input_node = resample_feature_map(
input_node,
'{}_{}_{}'.format(idx, input_offset, len(feats)),
new_node_height,
new_node_width,
p.fpn_num_filters,
p.apply_bn_for_resampling,
p.is_training_bn,
p.conv_after_downsample,
strategy=p.strategy,
data_format=config.data_format,
batch_norm_trainable=p.batch_norm_trainable)
nodes.append(input_node)
new_node = fuse_features(nodes, fpn_config.weight_method)
with tf.variable_scope('op_after_combine{}'.format(len(feats))):
if not p.conv_bn_act_pattern:
new_node = utils.activation_fn(new_node, p.act_type)
if p.separable_conv:
conv_op = functools.partial(tf.layers.separable_conv2d,
depth_multiplier=1)
else:
conv_op = tf.layers.conv2d
new_node = conv_op(new_node,
filters=p.fpn_num_filters,
kernel_size=(3, 3),
padding='same',
use_bias=not p.conv_bn_act_pattern,
data_format=config.data_format,
name='conv')
new_node = utils.batch_norm_act(
new_node,
is_training_bn=p.is_training_bn,
act_type=None if not p.conv_bn_act_pattern else p.act_type,
data_format=config.data_format,
strategy=p.strategy,
batch_norm_trainable=p.batch_norm_trainable,
name='bn')
feats.append(new_node)
num_output_connections.append(0)
output_feats = {}
for l in range(p.min_level, p.max_level + 1):
for i, fnode in enumerate(reversed(fpn_config.nodes)):
if fnode['feat_level'] == l:
output_feats[l] = feats[-1 - i]
break
return output_feats
