def __init__(self, layer_num, ch_out, name=None):
super(ShortCut, self).__init__()
shortcut_conv = Sequential()
ch_in = ch_out * 2
for i in range(layer_num):
fan_out = 3 * 3 * ch_out
std = math.sqrt(2. / fan_out)
in_channels = ch_in if i == 0 else ch_out
shortcut_name = name + '.conv.{}'.format(i)
shortcut_conv.add_sublayer(
shortcut_name,
Conv2D(in_channels=in_channels,
out_channels=ch_out,
kernel_size=3,
padding=1,
weight_attr=ParamAttr(initializer=Normal(0, std)),
bias_attr=ParamAttr(learning_rate=2.,
regularizer=L2Decay(0.))))
if i < layer_num - 1:
shortcut_conv.add_sublayer(shortcut_name + '.act', ReLU())
self.shortcut = self.add_sublayer('short', shortcut_conv)
def __init__(self,
mask_roi_extractor=None,
num_convs=0,
feat_in=2048,
feat_out=256,
mask_num_stages=1,
share_bbox_feat=False):
super(MaskFeat, self).__init__()
self.num_convs = num_convs
self.feat_in = feat_in
self.feat_out = feat_out
self.mask_roi_extractor = mask_roi_extractor
self.mask_num_stages = mask_num_stages
self.share_bbox_feat = share_bbox_feat
self.upsample_module = []
fan_conv = feat_out * 3 * 3
fan_deconv = feat_out * 2 * 2
for i in range(self.mask_num_stages):
name = 'stage_{}'.format(i)
mask_conv = Sequential()
for j in range(self.num_convs):
conv_name = 'mask_inter_feat_{}'.format(j + 1)
mask_conv.add_sublayer(
conv_name,
Conv2D(in_channels=feat_in if j == 0 else feat_out,
out_channels=feat_out,
kernel_size=3,
padding=1,
weight_attr=ParamAttr(initializer=KaimingNormal(
fan_in=fan_conv)),
bias_attr=ParamAttr(learning_rate=2.,
regularizer=L2Decay(0.))))
mask_conv.add_sublayer(conv_name + 'act', ReLU())
mask_conv.add_sublayer(
'conv5_mask',
Conv2DTranspose(
in_channels=self.feat_in,
out_channels=self.feat_out,
kernel_size=2,
stride=2,
weight_attr=ParamAttr(initializer=KaimingNormal(
fan_in=fan_deconv)),
bias_attr=ParamAttr(learning_rate=2.,
regularizer=L2Decay(0.))))
mask_conv.add_sublayer('conv5_mask' + 'act', ReLU())
upsample = self.add_sublayer(name, mask_conv)
self.upsample_module.append(upsample)