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| import math | |
| import torch | |
| from torch.nn import functional as F | |
| import torch.jit | |
| def script_method(fn, _rcb=None): | |
| return fn | |
| def script(obj, optimize=True, _frames_up=0, _rcb=None): | |
| return obj | |
| torch.jit.script_method = script_method | |
| torch.jit.script = script | |
| def init_weights(m, mean=0.0, std=0.01): | |
| classname = m.__class__.__name__ | |
| if classname.find("Conv") != -1: | |
| m.weight.data.normal_(mean, std) | |
| def get_padding(kernel_size, dilation=1): | |
| return int((kernel_size*dilation - dilation)/2) | |
| def intersperse(lst, item): | |
| result = [item] * (len(lst) * 2 + 1) | |
| result[1::2] = lst | |
| return result | |
| def slice_segments(x, ids_str, segment_size=4): | |
| ret = torch.zeros_like(x[:, :, :segment_size]) | |
| for i in range(x.size(0)): | |
| idx_str = ids_str[i] | |
| idx_end = idx_str + segment_size | |
| ret[i] = x[i, :, idx_str:idx_end] | |
| return ret | |
| def rand_slice_segments(x, x_lengths=None, segment_size=4): | |
| b, d, t = x.size() | |
| if x_lengths is None: | |
| x_lengths = t | |
| ids_str_max = x_lengths - segment_size + 1 | |
| ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long) | |
| ret = slice_segments(x, ids_str, segment_size) | |
| return ret, ids_str | |
| def subsequent_mask(length): | |
| mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0) | |
| return mask | |
| def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels): | |
| n_channels_int = n_channels[0] | |
| in_act = input_a + input_b | |
| t_act = torch.tanh(in_act[:, :n_channels_int, :]) | |
| s_act = torch.sigmoid(in_act[:, n_channels_int:, :]) | |
| acts = t_act * s_act | |
| return acts | |
| def convert_pad_shape(pad_shape): | |
| l = pad_shape[::-1] | |
| pad_shape = [item for sublist in l for item in sublist] | |
| return pad_shape | |
| def sequence_mask(length, max_length=None): | |
| if max_length is None: | |
| max_length = length.max() | |
| x = torch.arange(max_length, dtype=length.dtype, device=length.device) | |
| return x.unsqueeze(0) < length.unsqueeze(1) | |
| def generate_path(duration, mask): | |
| """ | |
| duration: [b, 1, t_x] | |
| mask: [b, 1, t_y, t_x] | |
| """ | |
| device = duration.device | |
| b, _, t_y, t_x = mask.shape | |
| cum_duration = torch.cumsum(duration, -1) | |
| cum_duration_flat = cum_duration.view(b * t_x) | |
| path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype) | |
| path = path.view(b, t_x, t_y) | |
| path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1] | |
| path = path.unsqueeze(1).transpose(2,3) * mask | |
| return path | |