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Running
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Zero
File size: 3,334 Bytes
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import math
import pdb
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class DTCBlock(nn.Module):
def __init__(
self, input_dim, output_dim, kernel_size, stride, causal_conv, dilation, dropout_rate
):
super(DTCBlock, self).__init__()
self.input_dim = input_dim
self.output_dim = output_dim
self.kernel_size = kernel_size
self.stride = stride
self.dilation = dilation
if causal_conv:
self.padding = 0
self.lorder = (kernel_size - 1) * self.dilation
self.left_padding = nn.ConstantPad1d((self.lorder, 0), 0.0)
else:
assert (kernel_size - 1) % 2 == 0
self.padding = ((kernel_size - 1) // 2) * self.dilation
self.lorder = 0
self.causal_conv = causal_conv
self.depthwise_conv = nn.Conv1d(
self.input_dim,
self.input_dim,
self.kernel_size,
self.stride,
self.padding,
self.dilation,
groups=self.input_dim,
)
self.point_conv_1 = nn.Conv1d(self.input_dim, self.input_dim, 1, 1, self.padding)
self.point_conv_2 = nn.Conv1d(self.input_dim, self.input_dim, 1, 1, self.padding)
self.bn_1 = nn.BatchNorm1d(self.input_dim)
self.bn_2 = nn.BatchNorm1d(self.input_dim)
self.bn_3 = nn.BatchNorm1d(self.input_dim)
self.dropout = nn.Dropout(p=dropout_rate)
# buffer = 1, self.input_dim, self.lorder
self.lorder = (kernel_size - 1) * self.dilation - (self.stride - 1)
self.buffer_size = 1 * self.input_dim * self.lorder
@torch.jit.unused
def forward(self, x):
x_in = x
x_data = x_in.transpose(1, 2)
if self.causal_conv:
x_data_pad = self.left_padding(x_data)
else:
x_data_pad = x_data
x_depth = self.depthwise_conv(x_data_pad)
x_bn_1 = self.bn_1(x_depth)
x_point_1 = self.point_conv_1(x_bn_1)
x_bn_2 = self.bn_2(x_point_1)
x_relu_2 = torch.relu(x_bn_2)
x_point_2 = self.point_conv_2(x_relu_2)
x_bn_3 = self.bn_3(x_point_2)
x_bn_3 = x_bn_3.transpose(1, 2)
if self.stride == 1:
x_relu_3 = torch.relu(x_bn_3 + x_in)
else:
x_relu_3 = torch.relu(x_bn_3)
x_drop = self.dropout(x_relu_3)
return x_drop
@torch.jit.export
def infer(self, x, buffer, buffer_index, buffer_out):
# type: (Tensor, Tensor, Tensor) -> Tuple[Tensor, Tensor, Tensor]
x_in = x
x = x_in.transpose(1, 2)
cnn_buffer = buffer[buffer_index : buffer_index + self.buffer_size].reshape(
[1, self.input_dim, self.lorder]
)
x = torch.cat([cnn_buffer, x], dim=2)
buffer_out.append(x[:, :, -self.lorder :].reshape(-1))
buffer_index = buffer_index + self.buffer_size
x = self.depthwise_conv(x)
x = self.bn_1(x)
x = self.point_conv_1(x)
x = self.bn_2(x)
x = torch.relu(x)
x = self.point_conv_2(x)
x = self.bn_3(x)
x = x.transpose(1, 2)
if self.stride == 1:
x = torch.relu(x + x_in)
else:
x = torch.relu(x)
return x, buffer, buffer_index, buffer_out
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