def __init__(self, n_inputs, n_shortcut, n_outputs, kernel_size, stride,
depth, conv_type, res):
super(UpsamplingBlock, self).__init__()
assert (stride > 1)
# CONV 1 for UPSAMPLING
if res == "fixed":
self.upconv = Resample1d(n_inputs, 15, stride, transpose=True)
else:
self.upconv = ConvLayer(n_inputs,
n_inputs,
kernel_size,
stride,
conv_type,
transpose=True)
self.pre_shortcut_convs = nn.ModuleList(
[ConvLayer(n_inputs, n_outputs, kernel_size, 1, conv_type)] + [
ConvLayer(n_outputs, n_outputs, kernel_size, 1, conv_type)
for _ in range(depth - 1)
])
# CONVS to combine high- with low-level information (from shortcut)
self.post_shortcut_convs = nn.ModuleList([
ConvLayer(n_outputs +
n_shortcut, n_outputs, kernel_size, 1, conv_type)
] + [
ConvLayer(n_outputs, n_outputs, kernel_size, 1, conv_type)
for _ in range(depth - 1)
])
def __init__(self, n_inputs, n_shortcut, n_outputs, kernel_size, stride,
depth, conv_type, res):
super(DownsamplingBlock, self).__init__()
assert (stride > 1)
self.kernel_size = kernel_size
self.stride = stride
# CONV 1
self.pre_shortcut_convs = nn.ModuleList(
[ConvLayer(n_inputs, n_shortcut, kernel_size, 1, conv_type)] + [
ConvLayer(n_shortcut, n_shortcut, kernel_size, 1, conv_type)
for _ in range(depth - 1)
])
self.post_shortcut_convs = nn.ModuleList(
[ConvLayer(n_shortcut, n_outputs, kernel_size, 1, conv_type)] + [
ConvLayer(n_outputs, n_outputs, kernel_size, 1, conv_type)
for _ in range(depth - 1)
])
# CONV 2 with decimation
if res == "fixed":
self.downconv = Resample1d(
n_outputs, 15,
stride) # Resampling with fixed-size sinc lowpass filter
else:
self.downconv = ConvLayer(n_outputs, n_outputs, kernel_size,
stride, conv_type)