gpu推理时间3毫米
import os
import time
import torch
import torch.nn as nn
from torch.nn.utils import weight_norm
class Chomp1d(nn.Module):
def __init__(self, chomp_size):
super(Chomp1d, self).__init__()
self.chomp_size = chomp_size
def forward(self, x):
return x[:, :, :-self.chomp_size].contiguous()
class TemporalBlock(nn.Module):
def __init__(self, n_inputs, n_outputs, kernel_size, stride, dilation, padding, dropout=0.2):
super(TemporalBlock, self).__init__()
self.conv1 = weight_norm(nn.Conv1d(n_inputs, n_outputs, kernel_size,
stride=stride, padding=padding, dilation=dilation))
self.bn1 = nn.BatchNorm1d(n_outputs)
self.chomp1 = Chomp1d(padding)
self.relu1 = nn.ReLU()
self.dropout1 = nn.Dropout(dropout)
self.conv2 = weight_norm(nn.Conv1d(n_outputs, n_outputs, kernel_si