def __init__(self, opt, c_in):
super(Regressor, self).__init__()
batchNorm = opt['use_BN']
self.deconv1 = model_utils.conv_layer(batchNorm,
128,
128,
k=3,
stride=1,
pad=1)
self.deconv2 = model_utils.conv_layer(batchNorm,
128,
128,
k=3,
stride=1,
pad=1)
self.deconv3 = model_utils.deconv_layer(128, 64)
self.est_normal = self._make_output(64, 3, k=3, stride=1, pad=1)
def __init__(self, opt, c_in=4, c_out=256):
super(FeatExtractor, self).__init__()
batchNorm = opt['use_BN']
self.conv1 = model_utils.conv_layer(batchNorm,
c_in,
32,
k=3,
stride=2,
pad=1,
afunc='LReLU')
self.conv2 = model_utils.conv_layer(batchNorm,
32,
64,
k=3,
stride=2,
pad=1)
self.conv3 = model_utils.conv_layer(batchNorm,
64,
64,
k=3,
stride=1,
pad=1)
self.conv4 = model_utils.conv_layer(batchNorm,
64,
128,
k=3,
stride=2,
pad=1)
self.conv5 = model_utils.conv_layer(batchNorm,
128,
128,
k=3,
stride=1,
pad=1)
self.conv6 = model_utils.conv_layer(batchNorm,
128,
128,
k=3,
stride=2,
pad=1)
self.conv7 = model_utils.conv_layer(batchNorm,
128,
256,
k=3,
stride=1,
pad=1)
def __init__(self, opt, c_in):
super(Classifier, self).__init__()
batchNorm = opt['use_BN']
self.conv1 = model_utils.conv_layer(batchNorm,
512,
128,
k=3,
stride=1,
pad=1)
self.conv2 = model_utils.conv_layer(batchNorm,
128,
128,
k=3,
stride=2,
pad=1)
self.conv3 = model_utils.conv_layer(batchNorm,
128,
128,
k=3,
stride=2,
pad=1)
self.conv4 = model_utils.conv_layer(batchNorm,
128,
128,
k=3,
stride=2,
pad=1)
self.opt = opt
self.dir_x_est = nn.Sequential(
model_utils.conv_layer(batchNorm, 128, 64, k=1, stride=1, pad=0),
model_utils.output_conv(64, opt['dirs_cls'], k=1, stride=1, pad=0))
self.dir_y_est = nn.Sequential(
model_utils.conv_layer(batchNorm, 128, 64, k=1, stride=1, pad=0),
model_utils.output_conv(64, opt['dirs_cls'], k=1, stride=1, pad=0))
self.int_est = nn.Sequential(
model_utils.conv_layer(batchNorm, 128, 64, k=1, stride=1, pad=0),
model_utils.output_conv(64, opt['ints_cls'], k=1, stride=1, pad=0))
def __init__(self, opt, c_in=6, c_out=128):
super(FeatExtractor, self).__init__()
batchNorm = opt['use_BN']
self.conv1 = model_utils.conv_layer(batchNorm,
c_in,
16,
k=3,
stride=1,
pad=1)
self.conv2 = model_utils.conv_layer(batchNorm,
16,
32,
k=3,
stride=2,
pad=1)
self.conv3 = model_utils.conv_layer(batchNorm,
32,
64,
k=3,
stride=1,
pad=1)
self.conv4 = model_utils.conv_layer(batchNorm,
64,
128,
k=3,
stride=2,
pad=1)
self.conv5 = model_utils.conv_layer(batchNorm,
128,
128,
k=3,
stride=1,
pad=1)
self.conv6 = model_utils.deconv_layer(128, 128)
self.conv7 = model_utils.conv_layer(batchNorm,
128,
128,
k=3,
stride=1,
pad=1)