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import torch
def init_weights(m, mean=0.0, std=0.01):
if m.__class__.__name__.find("Conv") != -1: m.weight.data.normal_(mean, std)
def get_padding(kernel_size, dilation=1):
return int((kernel_size * dilation - dilation) / 2)
def convert_pad_shape(pad_shape):
return [item for sublist in pad_shape[::-1] for item in sublist]
def slice_segments(x, ids_str, segment_size = 4, dim = 2):
if dim == 2: ret = torch.zeros_like(x[:, :segment_size])
elif dim == 3: ret = torch.zeros_like(x[:, :, :segment_size])
for i in range(x.size(0)):
idx_str = ids_str[i].item()
idx_end = idx_str + segment_size
if dim == 2: ret[i] = x[i, idx_str:idx_end]
else: ret[i] = x[i, :, idx_str:idx_end]
return ret
def rand_slice_segments(x, x_lengths=None, segment_size=4):
b, _, t = x.size()
if x_lengths is None: x_lengths = t
ids_str = (torch.rand([b]).to(device=x.device) * (x_lengths - segment_size + 1)).to(dtype=torch.long)
return slice_segments(x, ids_str, segment_size, dim=3), ids_str
@torch.jit.script
def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
n_channels_int = n_channels[0]
in_act = input_a + input_b
return torch.tanh(in_act[:, :n_channels_int, :]) * torch.sigmoid(in_act[:, n_channels_int:, :])
def sequence_mask(length, max_length = None):
if max_length is None: max_length = length.max()
return torch.arange(max_length, dtype=length.dtype, device=length.device).unsqueeze(0) < length.unsqueeze(1)
def clip_grad_value(parameters, clip_value, norm_type=2):
if isinstance(parameters, torch.Tensor): parameters = [parameters]
norm_type = float(norm_type)
if clip_value is not None: clip_value = float(clip_value)
total_norm = 0
for p in list(filter(lambda p: p.grad is not None, parameters)):
total_norm += (p.grad.data.norm(norm_type)).item() ** norm_type
if clip_value is not None: p.grad.data.clamp_(min=-clip_value, max=clip_value)
return total_norm ** (1.0 / norm_type) |