def post_init(self):
"""
Initialize offset tensors
"""
cond_lead, r_off = vconv.output_offsets(self.wavenet_vc['beg_grcc'],
self.vc)
assert r_off == 0
self.register_buffer('cond_lead', torch.tensor(cond_lead))
if self.vc == self.wavenet_vc['end_grcc']:
skip_lead = 0
else:
skip_lead, r_off = vconv.output_offsets(self.vc.child,
self.wavenet_vc['end_grcc'])
assert r_off == 0
self.register_buffer('skip_lead', torch.tensor(skip_lead))
self.register_buffer('left_wing_size', torch.tensor(self.vc.l_wing_sz))
def init_leads(self):
"""
Update skip_lead and cond_lead to reflect changed geometry
or chunk size. Call this after vconv.compute_inputs is called
"""
cond_lead, r_off = vconv.output_offsets(self.wavenet_vc['beg_grcc'],
self.vc)
assert r_off == 0
if self.vc == self.wavenet_vc['end_grcc']:
skip_lead = 0
else:
skip_lead, r_off = vconv.output_offsets(
self.vc.child, self.wavenet_vc['end_grcc'])
assert r_off == 0
self.leads[0] = cond_lead
self.leads[1] = skip_lead
self.leads[2] = self.vc.l_wing_sz
self.leads[3] = 0
self.global_rf = self.vc.in_len()
self.local_rf = self.vc.filter_size()
def __init__(self,
n_in_chan,
n_out_chan,
filter_sz,
stride=1,
do_res=True,
parent_vc=None,
name=None):
super(ConvReLURes, self).__init__()
self.n_in = n_in_chan
self.n_out = n_out_chan
self.conv = nn.Conv1d(n_in_chan,
n_out_chan,
filter_sz,
stride,
padding=0,
bias=True)
self.relu = nn.ReLU()
self.name = name
# self.bn = nn.BatchNorm1d(n_out_chan)
self.vc = vconv.VirtualConv(filter_info=filter_sz,
stride=stride,
parent=parent_vc,
name=name)
self.do_res = do_res
if self.do_res:
if stride != 1:
print('Stride must be 1 for residually connected convolution',
file=stderr)
raise ValueError
l_off, r_off = vconv.output_offsets(self.vc, self.vc)
self.register_buffer('residual_offsets',
torch.tensor([l_off, r_off]))
netmisc.xavier_init(self.conv)