def __init__(self, frame_size, dim, q_levels, weight_norm):
super().__init__()
self.q_levels = q_levels
self.input = torch.nn.Conv1d(in_channels=frame_size,
out_channels=dim,
kernel_size=1,
bias=False)
init.kaiming_uniform(self.input.weight)
if weight_norm:
self.input = torch.nn.utils.weight_norm(self.input)
self.hidden = torch.nn.Conv1d(in_channels=dim,
out_channels=dim,
kernel_size=1)
init.kaiming_uniform(self.hidden.weight)
init.constant(self.hidden.bias, 0)
if weight_norm:
self.hidden = torch.nn.utils.weight_norm(self.hidden)
self.output = torch.nn.Conv1d(
in_channels=dim,
out_channels=2, # mu, log_sigma
kernel_size=1)
lecun_uniform(self.output.weight)
init.constant(self.output.bias, 0)
if weight_norm:
self.output = torch.nn.utils.weight_norm(self.output)
def __init__(self, frame_size, dim, q_levels, weight_norm):
super().__init__()
self.q_levels = q_levels
self.embedding = torch.nn.Embedding(self.q_levels, self.q_levels)
self.input = torch.nn.Conv1d(in_channels=q_levels,
out_channels=dim,
kernel_size=frame_size,
bias=False)
init.kaiming_uniform_(self.input.weight)
if weight_norm:
self.input = torch.nn.utils.weight_norm(self.input)
self.hidden = torch.nn.Conv1d(in_channels=dim,
out_channels=dim,
kernel_size=1)
init.kaiming_uniform_(self.hidden.weight)
init.constant_(self.hidden.bias, 0)
if weight_norm:
self.hidden = torch.nn.utils.weight_norm(self.hidden)
self.output = torch.nn.Conv1d(in_channels=dim,
out_channels=q_levels,
kernel_size=1)
nn.lecun_uniform(self.output.weight)
init.constant_(self.output.bias, 0)
if weight_norm:
self.output = torch.nn.utils.weight_norm(self.output)
def __init__(self, frame_size, dim, q_levels, weight_norm):
super().__init__()
self.q_levels = q_levels
self.frame_size = frame_size
self.dim = dim
self.embedding = torch.nn.Embedding(self.q_levels, self.q_levels)
self.last_out_shape = q_levels
# self.input = torch.nn.Conv1d(
# in_channels=q_levels,
# out_channels=dim,
# kernel_size=frame_size,
# bias=False
# )
self.input = torch.nn.Linear(self.frame_size * self.last_out_shape,
self.dim,
bias=False)
init.kaiming_uniform_(self.input.weight)
if weight_norm:
self.input = torch.nn.utils.weight_norm(self.input)
# self.hidden = torch.nn.Conv1d(
# in_channels=dim,
# out_channels=dim,
# kernel_size=1
# )
self.hidden = torch.nn.Linear(self.dim, self.dim)
init.kaiming_uniform_(self.hidden.weight)
init.constant_(self.hidden.bias, 0)
if weight_norm:
self.hidden = torch.nn.utils.weight_norm(self.hidden)
self.hidden_2 = torch.nn.Linear(self.dim, self.dim)
init.kaiming_uniform_(self.hidden_2.weight)
init.constant_(self.hidden_2.bias, 0)
# self.output = torch.nn.Conv1d(
# in_channels=dim,
# out_channels=q_levels,
# kernel_size=1
# )
self.output = torch.nn.Linear(self.dim, self.q_levels)
nn.lecun_uniform(self.output.weight)
init.constant_(self.output.bias, 0)
if weight_norm:
self.output = torch.nn.utils.weight_norm(self.output)
