def __init__(self,
n_in_chan,
n_out_chan,
filter_sz,
stride=1,
do_res=True,
parent_rf=None,
name=None):
super(ConvReLURes, self).__init__()
self.do_res = do_res
if self.do_res:
if stride != 1:
print('Stride must be 1 for residually connected convolution',
file=sys.stderr)
raise ValueError
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=False)
self.relu = nn.ReLU()
# self.bn = nn.BatchNorm1d(n_out_chan)
self.rf = rfield.Rfield(filter_info=filter_sz,
stride=stride,
parent=parent_rf,
name=name)
netmisc.xavier_init(self.conv)
def __init__(self, n_in, n_out, n_sam_per_datapoint=1, bias=True):
'''n_sam_per_datapoint is L from equation 7,
https://arxiv.org/pdf/1312.6114.pdf'''
super(VAE, self).__init__()
self.linear = nn.Conv1d(n_in, n_out * 2, 1, bias=False)
self.tanh = nn.Tanh()
self.linear2 = nn.Conv1d(n_out * 2, n_out * 2, 1, bias=bias)
self.n_sam_per_datapoint = n_sam_per_datapoint
netmisc.xavier_init(self.linear)
netmisc.xavier_init(self.linear2)
# Cache these values for later access by the objective function
self.mu = None
self.sigma = None
def __init__(self, n_in, n_out, vq_gamma, vq_n_embed):
super(VQ, self).__init__()
self.d = n_out
self.gamma = vq_gamma
self.k = vq_n_embed
self.linear = nn.Conv1d(n_in, self.d, 1, bias=False)
self.sg = StopGrad()
self.rg = ReplaceGrad()
self.ze = None
self.l2norm_min = None
self.register_buffer('ind_hist', torch.zeros(self.k))
self.circ_inds = None
self.emb = nn.Parameter(data=torch.empty(self.k, self.d))
netmisc.xavier_init(self.linear)
nn.init.xavier_uniform_(self.emb, gain=1)
def __init__(self, n_in, n_out, vq_gamma, vq_ema_gamma, vq_n_embed,
training):
super(VQEMA, self).__init__()
self.training = training
self.d = n_out
self.gamma = vq_gamma
self.ema_gamma = vq_ema_gamma
self.ema_gamma_comp = 1.0 - self.ema_gamma
self.k = vq_n_embed
self.linear = nn.Conv1d(n_in, self.d, 1, bias=False)
self.sg = StopGrad()
self.rg = ReplaceGrad()
self.ze = None
self.register_buffer('emb', torch.empty(self.k, self.d))
nn.init.xavier_uniform_(self.emb, gain=10)
if self.ema_gamma >= 1.0 or self.ema_gamma <= 0:
raise RuntimeError('VQEMA must use an EMA-gamma value in (0, 1)')
if self.training:
self.min_dist = None
self.circ_inds = None
self.register_buffer('ind_hist', torch.zeros(self.k))
self.register_buffer('ema_numer', torch.empty(self.k, self.d))
self.register_buffer('ema_denom', torch.empty(self.k))
self.register_buffer('z_sum', torch.empty(self.k, self.d))
self.register_buffer('n_sum', torch.empty(self.k))
self.register_buffer('n_sum_ones', torch.ones(self.k))
#self.ema_numer.detach_()
#self.ema_denom.detach_()
#self.z_sum.detach_()
#self.n_sum.detach_()
#self.emb.detach_()
#nn.init.ones_(self.ema_denom)
self.ema_numer = self.emb * self.ema_gamma_comp
self.ema_denom = self.n_sum_ones * self.ema_gamma_comp
netmisc.xavier_init(self.linear)
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)
def __init__(self, n_in, n_out, bias=True):
super(AE, self).__init__()
self.linear = nn.Conv1d(n_in, n_out, 1, bias=bias)
netmisc.xavier_init(self.linear)