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import math
import torch.nn as nn
import pdb
from espnet.nets.pytorch_backend.transformer.convolution import Swish
def conv3x3(in_planes, out_planes, stride=1):
"""conv3x3.
:param in_planes: int, number of channels in the input sequence.
:param out_planes: int, number of channels produced by the convolution.
:param stride: int, size of the convolving kernel.
"""
return nn.Conv1d(
in_planes,
out_planes,
kernel_size=3,
stride=stride,
padding=1,
bias=False,
)
def downsample_basic_block(inplanes, outplanes, stride):
"""downsample_basic_block.
:param inplanes: int, number of channels in the input sequence.
:param outplanes: int, number of channels produced by the convolution.
:param stride: int, size of the convolving kernel.
"""
return nn.Sequential(
nn.Conv1d(
inplanes,
outplanes,
kernel_size=1,
stride=stride,
bias=False,
),
nn.BatchNorm1d(outplanes),
)
class BasicBlock1D(nn.Module):
expansion = 1
def __init__(
self,
inplanes,
planes,
stride=1,
downsample=None,
relu_type="relu",
):
"""__init__.
:param inplanes: int, number of channels in the input sequence.
:param planes: int, number of channels produced by the convolution.
:param stride: int, size of the convolving kernel.
:param downsample: boolean, if True, the temporal resolution is downsampled.
:param relu_type: str, type of activation function.
"""
super(BasicBlock1D, self).__init__()
assert relu_type in ["relu","prelu", "swish"]
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm1d(planes)
# type of ReLU is an input option
if relu_type == "relu":
self.relu1 = nn.ReLU(inplace=True)
self.relu2 = nn.ReLU(inplace=True)
elif relu_type == "prelu":
self.relu1 = nn.PReLU(num_parameters=planes)
self.relu2 = nn.PReLU(num_parameters=planes)
elif relu_type == "swish":
self.relu1 = Swish()
self.relu2 = Swish()
else:
raise NotImplementedError
# --------
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm1d(planes)
self.downsample = downsample
self.stride = stride
def forward(self, x):
"""forward.
:param x: torch.Tensor, input tensor with input size (B, C, T)
"""
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu1(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu2(out)
return out
class ResNet1D(nn.Module):
def __init__(self,
block,
layers,
relu_type="swish",
a_upsample_ratio=1,
):
"""__init__.
:param block: torch.nn.Module, class of blocks.
:param layers: List, customised layers in each block.
:param relu_type: str, type of activation function.
:param a_upsample_ratio: int, The ratio related to the \
temporal resolution of output features of the frontend. \
a_upsample_ratio=1 produce features with a fps of 25.
"""
super(ResNet1D, self).__init__()
self.inplanes = 64
self.relu_type = relu_type
self.downsample_block = downsample_basic_block
self.a_upsample_ratio = a_upsample_ratio
self.conv1 = nn.Conv1d(
in_channels=1,
out_channels=self.inplanes,
kernel_size=80,
stride=4,
padding=38,
bias=False,
)
self.bn1 = nn.BatchNorm1d(self.inplanes)
if relu_type == "relu":
self.relu = nn.ReLU(inplace=True)
elif relu_type == "prelu":
self.relu = nn.PReLU(num_parameters=self.inplanes)
elif relu_type == "swish":
self.relu = Swish()
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
self.avgpool = nn.AvgPool1d(
kernel_size=20//self.a_upsample_ratio,
stride=20//self.a_upsample_ratio,
)
def _make_layer(self, block, planes, blocks, stride=1):
"""_make_layer.
:param block: torch.nn.Module, class of blocks.
:param planes: int, number of channels produced by the convolution.
:param blocks: int, number of layers in a block.
:param stride: int, size of the convolving kernel.
"""
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = self.downsample_block(
inplanes=self.inplanes,
outplanes=planes*block.expansion,
stride=stride,
)
layers = []
layers.append(
block(
self.inplanes,
planes,
stride,
downsample,
relu_type=self.relu_type,
)
)
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(
block(
self.inplanes,
planes,
relu_type=self.relu_type,
)
)
return nn.Sequential(*layers)
def forward(self, x):
"""forward.
:param x: torch.Tensor, input tensor with input size (B, C, T)
"""
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
return x
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