def maskrcnn_upXconv_head(feature, num_category, num_convs, norm=None):
"""
Args:
feature (NxCx s x s): size is 7 in C4 models and 14 in FPN models.
num_category(int):
num_convs (int): number of convolution layers
norm (str or None): either None or 'GN'
Returns:
mask_logits (N x num_category x 2s x 2s):
"""
assert norm in [None, 'GN'], norm
l = feature
with argscope([Conv2D, Conv2DTranspose],
data_format='channels_first',
kernel_initializer=tf.variance_scaling_initializer(
scale=2.0, mode='fan_out', distribution='normal')):
# c2's MSRAFill is fan_out
for k in range(num_convs):
l = Conv2D('fcn{}'.format(k),
l,
cfg.MRCNN.HEAD_DIM,
3,
activation=tf.nn.relu)
if norm is not None:
l = GroupNorm('gn{}'.format(k), l)
l = Conv2DTranspose('deconv',
l,
cfg.MRCNN.HEAD_DIM,
2,
strides=2,
activation=tf.nn.relu)
l = Conv2D('conv', l, num_category, 1)
return l
def fastrcnn_Xconv1fc_head(feature, num_convs, norm=None):
"""
Args:
feature (NCHW):
num_classes(int): num_category + 1
num_convs (int): number of conv layers
norm (str or None): either None or 'GN'
Returns:
2D head feature
"""
assert norm in [None, 'GN'], norm
l = feature
with argscope(Conv2D,
data_format='channels_first',
kernel_initializer=tf.variance_scaling_initializer(
scale=2.0, mode='fan_out', distribution='normal')):
for k in range(num_convs):
l = Conv2D('conv{}'.format(k),
l,
cfg.FPN.FRCNN_CONV_HEAD_DIM,
3,
activation=tf.nn.relu)
if norm is not None:
l = GroupNorm('gn{}'.format(k), l)
l = FullyConnected(
'fc',
l,
cfg.FPN.FRCNN_FC_HEAD_DIM,
kernel_initializer=tf.variance_scaling_initializer(),
activation=tf.nn.relu)
return l
def fpn_model(features):
"""
Args:
features ([tf.Tensor]): ResNet features c2-c5
Returns:
[tf.Tensor]: FPN features p2-p6
"""
assert len(features) == 4, features
num_channel = cfg.FPN.NUM_CHANNEL
use_gn = cfg.FPN.NORM == 'GN'
def upsample2x(name, x):
return FixedUnPooling(
name, x, 2, unpool_mat=np.ones((2, 2), dtype='float32'),
data_format='channels_first')
# tf.image.resize is, again, not aligned.
# with tf.name_scope(name):
# shape2d = tf.shape(x)[2:]
# x = tf.transpose(x, [0, 2, 3, 1])
# x = tf.image.resize_nearest_neighbor(x, shape2d * 2, align_corners=True)
# x = tf.transpose(x, [0, 3, 1, 2])
# return x
with argscope(Conv2D, data_format='channels_first',
activation=tf.identity, use_bias=True,
kernel_initializer=tf.variance_scaling_initializer(scale=1.)):
lat_2345 = [Conv2D('lateral_1x1_c{}'.format(i + 2), c, num_channel, 1)
for i, c in enumerate(features)]
if use_gn:
lat_2345 = [GroupNorm('gn_c{}'.format(i + 2), c) for i, c in enumerate(lat_2345)]
lat_sum_5432 = []
for idx, lat in enumerate(lat_2345[::-1]):
if idx == 0:
lat_sum_5432.append(lat)
else:
lat = lat + upsample2x('upsample_lat{}'.format(6 - idx), lat_sum_5432[-1])
lat_sum_5432.append(lat)
p2345 = [Conv2D('posthoc_3x3_p{}'.format(i + 2), c, num_channel, 3)
for i, c in enumerate(lat_sum_5432[::-1])]
if use_gn:
p2345 = [GroupNorm('gn_p{}'.format(i + 2), c) for i, c in enumerate(p2345)]
p6 = MaxPooling('maxpool_p6', p2345[-1], pool_size=1, strides=2, data_format='channels_first', padding='VALID')
return p2345 + [p6]
