def _call(inputs):
logging.info('Block %s input shape: %s', self.name, inputs.shape)
if self._block_args.expand_ratio != 1:
x = self._relu_fn(
self._bn0(self._expand_conv(inputs), training=training))
else:
x = inputs
logging.info('Expand shape: %s', x.shape)
self.endpoints = {'expansion_output': x}
x = self._bn1(self._project_conv(x), training=training)
# Add identity so that quantization-aware training can insert quantization
# ops correctly.
x = tf.identity(x)
if self._clip_projection_output:
x = tf.clip_by_value(x, -6, 6)
if self._block_args.id_skip:
if all(
s == 1 for s in self._block_args.strides
) and self._block_args.input_filters == self._block_args.output_filters:
# Apply only if skip connection presents.
if survival_prob:
x = utils.drop_connect(x, training, survival_prob)
x = tf.add(x, inputs)
logging.info('Project shape: %s', x.shape)
return x
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 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 _call(inputs):
logging.info('Block %s input shape: %s', self.name, inputs.shape)
x = inputs
# creates conv 2x2 kernel
if self.super_pixel:
x = self.super_pixel(x, training)
logging.info('SuperPixel %s: %s', self.name, x.shape)
if self._block_args.fused_conv:
# If use fused mbconv, skip expansion and use regular conv.
x = self._relu_fn(
self._bn1(self._fused_conv(x), training=training))
logging.info('Conv2D shape: %s', x.shape)
else:
# Otherwise, first apply expansion and then apply depthwise conv.
if self._block_args.expand_ratio != 1:
x = self._relu_fn(
self._bn0(self._expand_conv(x), training=training))
logging.info('Expand shape: %s', x.shape)
x = self._relu_fn(
self._bn1(self._depthwise_conv(x), training=training))
logging.info('DWConv shape: %s', x.shape)
if self._se:
x = self._se(x)
self.endpoints = {'expansion_output': x}
x = self._bn2(self._project_conv(x), training=training)
# Add identity so that quantization-aware training can insert quantization
# ops correctly.
x = tf.identity(x)
if self._clip_projection_output:
x = tf.clip_by_value(x, -6, 6)
if self._block_args.id_skip:
if all(
s == 1 for s in self._block_args.strides
) and self._block_args.input_filters == self._block_args.output_filters:
# Apply only if skip connection presents.
if survival_prob:
x = utils.drop_connect(x, training, survival_prob)
x = tf.add(x, inputs)
logging.info('Project shape: %s', x.shape)
return x