def __init__(self, nhidden, dropout):
super(Concat, self).__init__()
self.conv1 = Conv2d(3 * nhidden, nhidden, 1)
self.conv2 = Conv2d(nhidden, 256, 1)
self.conv3 = Conv2d(256, 128, 1)
self.compress = FC(640, nhidden, relu=True)
self.compress_r = FC(640, nhidden, relu=True)
self.dropout = dropout
def __init__(self, nhidden):
super(DualMask, self).__init__()
self.conv1 = Conv2d(2, 96, kernel_size=5)
self.conv2 = Conv2d(96, 128, kernel_size=5)
self.conv3 = Conv2d(128, 64, kernel_size=8)
self.fc1_dm = FC(18496, nhidden, relu=True)
self.fc2_dm = FC(25600, 2 * nhidden, relu=True)
faster_rcnn.network.weights_normal_init(self.fc1_dm, 0.01)
faster_rcnn.network.weights_normal_init(self.fc2_dm, 0.01)
def __init__(self):
super(RPN, self).__init__()
self.features = VGG16(bn=False)
self.conv1 = Conv2d(512, 512, 3, same_padding=True)
self.score_conv = Conv2d(512,
len(self.anchor_scales) * 3 * 2,
1,
relu=False,
same_padding=False)
self.bbox_conv = Conv2d(512,
len(self.anchor_scales) * 3 * 4,
1,
relu=False,
same_padding=False)
# loss
self.cross_entropy = None
self.los_box = None
def __init__(self, debug=False):
super(RPN, self).__init__()
self.anchor_scales = cfg.ANCHOR_SCALES
self.anchor_ratios = cfg.ANCHOR_RATIOS
self.feat_stride = cfg.FEAT_STRIDE[0]
self._vgg16 = VGG16()
self.conv1 = Conv2d(512, 512, 3, same_padding=True)
self.score_conv = Conv2d(512,
len(self.anchor_scales) *
len(self.anchor_ratios) * 2,
1,
relu=False,
same_padding=False)
self.bbox_conv = Conv2d(512,
len(self.anchor_scales) *
len(self.anchor_ratios) * 4,
1,
relu=False,
same_padding=False)
# define proposal layer
self.proposal_layer = proposal_layer_py(self.feat_stride,
self.anchor_scales,
self.anchor_ratios)
# define anchor target layer
self.anchor_target_layer = anchor_target_layer_py(
self.feat_stride, self.anchor_scales, self.anchor_ratios)
# loss
self.cross_entropy = 0
self.loss_box = 0
# for log
self.debug = debug
def __init__(self, nhidden, dropout):
super(GraphicalModel, self).__init__()
self.f = FC(nhidden, nhidden, relu=True)
self.g = FC(nhidden, nhidden, relu=True)
self.conv1 = Conv2d(3 * nhidden, nhidden, 1)
self.conv2 = Conv2d(nhidden, 256, 1)
self.conv3 = Conv2d(256, 128, 1)
self.conv1_r = Conv2d(3 * nhidden, nhidden, 1)
self.conv2_r = Conv2d(nhidden, 256, 1)
self.conv3_r = Conv2d(256, 128, 1)
self.compress = FC(640, nhidden, relu=True)
self.compress_r = FC(640, nhidden, relu=True)
self.dropout = dropout
def __init__(self, bn=False):
super(VGG16, self).__init__()
self.conv1 = nn.Sequential(Conv2d(3, 64, 3, same_padding=True, bn=bn),
Conv2d(64, 64, 3, same_padding=True, bn=bn),
nn.MaxPool2d(2))
self.conv2 = nn.Sequential(
Conv2d(64, 128, 3, same_padding=True, bn=bn),
Conv2d(128, 128, 3, same_padding=True, bn=bn), nn.MaxPool2d(2))
faster_rcnn.network.set_trainable(self.conv1, requires_grad=False)
faster_rcnn.network.set_trainable(self.conv2, requires_grad=False)
self.conv3 = nn.Sequential(
Conv2d(128, 256, 3, same_padding=True, bn=bn),
Conv2d(256, 256, 3, same_padding=True, bn=bn),
Conv2d(256, 256, 3, same_padding=True, bn=bn), nn.MaxPool2d(2))
self.conv4 = nn.Sequential(
Conv2d(256, 512, 3, same_padding=True, bn=bn),
Conv2d(512, 512, 3, same_padding=True, bn=bn),
Conv2d(512, 512, 3, same_padding=True, bn=bn), nn.MaxPool2d(2))
self.conv5 = nn.Sequential(
Conv2d(512, 512, 3, same_padding=True, bn=bn),
Conv2d(512, 512, 3, same_padding=True, bn=bn),
Conv2d(512, 512, 3, same_padding=True, bn=bn))