def test_batch_norm(self, is_training, strategy):
inputs = tf.random.uniform([8, 40, 40, 3])
expect_results = utils.batch_norm_act(
inputs, is_training, None, strategy=strategy)
# Call batch norm layer with is_training parameter.
bn_layer = util_keras.build_batch_norm(is_training, strategy=strategy)
self.assertAllClose(expect_results, bn_layer(inputs, is_training))
def class_net(images,
level,
num_classes,
num_anchors,
num_filters,
is_training,
act_type,
separable_conv=True,
repeats=4,
survival_prob=None,
strategy=None,
data_format='channels_last'):
"""Class prediction network."""
if separable_conv:
conv_op = functools.partial(
tf.layers.separable_conv2d,
depth_multiplier=1,
data_format=data_format,
pointwise_initializer=tf.initializers.variance_scaling(),
depthwise_initializer=tf.initializers.variance_scaling())
else:
conv_op = functools.partial(
tf.layers.conv2d,
data_format=data_format,
kernel_initializer=tf.random_normal_initializer(stddev=0.01))
for i in range(repeats):
orig_images = images
images = conv_op(images,
num_filters,
kernel_size=3,
bias_initializer=tf.zeros_initializer(),
activation=None,
padding='same',
name='class-%d' % i)
images = utils.batch_norm_act(images,
is_training,
act_type=act_type,
init_zero=False,
strategy=strategy,
data_format=data_format,
name='class-%d-bn-%d' % (i, level))
if i > 0 and survival_prob:
images = utils.drop_connect(images, is_training, survival_prob)
images = images + orig_images
classes = conv_op(
images,
num_classes * num_anchors,
kernel_size=3,
bias_initializer=tf.constant_initializer(-np.log((1 - 0.01) / 0.01)),
padding='same',
name='class-predict')
return classes
def _maybe_apply_1x1(feat):
"""Apply 1x1 conv to change layer width if necessary."""
if num_channels != target_num_channels:
feat = tf.layers.conv2d(feat,
filters=target_num_channels,
kernel_size=(1, 1),
padding='same',
data_format=data_format)
if apply_bn:
feat = utils.batch_norm_act(feat,
is_training_bn=is_training,
act_type=None,
data_format=data_format,
strategy=strategy,
name='bn')
return feat
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)
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,
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