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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 | |
| 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) |