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import math
import torch
import torch.nn as nn
from torch.nn.utils import weight_norm
from typing import Any, Dict, List, Optional, Tuple, Type, Union
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.chomp1 = Chomp1d(padding)
self.relu1 = nn.ReLU()
self.dropout1 = nn.Dropout(dropout)
self.conv2 = weight_norm(nn.Conv1d(n_outputs, n_outputs, kernel_size, stride=stride, padding=padding, dilation=dilation))
self.chomp2 = Chomp1d(padding)
self.relu2 = nn.ReLU()
self.dropout2 = nn.Dropout(dropout)
self.net = nn.Sequential(self.conv1, self.chomp1, self.relu1, self.dropout1, self.conv2, self.chomp2, self.relu2, self.dropout2)
self.downsample = nn.Conv1d(n_inputs, n_outputs, 1) if n_inputs != n_outputs else None
self.relu = nn.ReLU()
self.init_weights()
def init_weights(self):
self.conv1.weight.data.normal_(0, 0.01)
self.conv2.weight.data.normal_(0, 0.01)
if self.downsample is not None:
self.downsample.weight.data.normal_(0, 0.01)
def forward(self, x):
out = self.net(x)
res = x if self.downsample is None else self.downsample(x)
return self.relu(out + res)
class TemporalConvNet(nn.Module):
def __init__(self, num_inputs, num_channels, kernel_size=2, dropout=0.2):
super(TemporalConvNet, self).__init__()
layers = []
num_levels = len(num_channels)
for i in range(num_levels):
dilation_size = 2**i
in_channels = num_inputs if i == 0 else num_channels[i - 1]
out_channels = num_channels[i]
layers += [
TemporalBlock(
in_channels,
out_channels,
kernel_size,
stride=1,
dilation=dilation_size,
padding=(kernel_size - 1) * dilation_size,
dropout=dropout,
)
]
self.network = nn.Sequential(*layers)
def forward(self, x):
return self.network(x)
class TextEncoderTCN(nn.Module):
"""
based on https://github.com/locuslab/TCN/blob/master/TCN/word_cnn/model.py
Licensed under: https://github.com/locuslab/TCN/blob/master/LICENSE
"""
def __init__(
self, args, n_words=11195, embed_size=300, pre_trained_embedding=None, kernel_size=2, dropout=0.3, emb_dropout=0.1, word_cache=False
):
super(TextEncoderTCN, self).__init__()
num_channels = [args.hidden_size] # * args.n_layer
self.tcn = TemporalConvNet(embed_size, num_channels, kernel_size, dropout=dropout)
self.decoder = nn.Linear(num_channels[-1], args.word_f)
self.drop = nn.Dropout(emb_dropout)
self.init_weights()
def init_weights(self):
self.decoder.bias.data.fill_(0)
self.decoder.weight.data.normal_(0, 0.01)
def forward(self, input):
y = self.tcn(input.transpose(1, 2)).transpose(1, 2)
y = self.decoder(y)
return y, torch.max(y, dim=1)[0]
def reparameterize(mu, logvar):
std = torch.exp(0.5 * logvar)
eps = torch.randn_like(std)
return mu + eps * std
def ConvNormRelu(in_channels, out_channels, downsample=False, padding=0, batchnorm=True):
if not downsample:
k = 3
s = 1
else:
k = 4
s = 2
conv_block = nn.Conv1d(in_channels, out_channels, kernel_size=k, stride=s, padding=padding)
norm_block = nn.BatchNorm1d(out_channels)
if batchnorm:
net = nn.Sequential(conv_block, norm_block, nn.LeakyReLU(0.2, True))
else:
net = nn.Sequential(conv_block, nn.LeakyReLU(0.2, True))
return net
class BasicBlock(nn.Module):
"""
based on timm: https://github.com/huggingface/pytorch-image-models/blob/f689c850b90b16a45cc119a7bc3b24375636fc63/timm/models/resnet.py#L34
Licensed under: https://github.com/huggingface/pytorch-image-models/blob/main/LICENSE
"""
def __init__(
self,
inplanes: int,
planes: int,
ker_size: int, # add kernel size
stride: int = 1,
downsample: Optional[nn.Module] = None,
cardinality: int = 1,
base_width: int = 64,
reduce_first: int = 1,
dilation: int = 1,
first_dilation: Optional[int] = None,
act_layer: Type[nn.Module] = nn.LeakyReLU, # change activation function from ReLU to LeakyReLU
norm_layer: Type[nn.Module] = nn.BatchNorm1d, # change norm layer from BatchNorm2d to BatchNorm1d
attn_layer: Optional[Type[nn.Module]] = None,
aa_layer: Optional[Type[nn.Module]] = None,
drop_block: Optional[Type[nn.Module]] = None,
drop_path: Optional[nn.Module] = None,
):
super(BasicBlock, self).__init__()
"""
Original Layer definition:
https://github.com/huggingface/pytorch-image-models/blob/f689c850b90b16a45cc119a7bc3b24375636fc63/timm/models/resnet.py#L75C1-L100C35
"""
# Revised from here
self.conv1 = nn.Conv1d(inplanes, planes, kernel_size=ker_size, stride=stride, padding=first_dilation, dilation=dilation, bias=True)
self.bn1 = norm_layer(planes)
self.act1 = act_layer(inplace=True)
self.conv2 = nn.Conv1d(planes, planes, kernel_size=ker_size, padding=ker_size // 2, dilation=dilation, bias=True)
self.bn2 = norm_layer(planes)
self.act2 = act_layer(inplace=True)
if downsample is not None:
self.downsample = nn.Sequential(
nn.Conv1d(inplanes, planes, stride=stride, kernel_size=ker_size, padding=first_dilation, dilation=dilation, bias=True),
norm_layer(planes),
)
else:
self.downsample = None
self.stride = stride
self.dilation = dilation
self.drop_block = drop_block
self.drop_path = drop_path
# Until here
def zero_init_last(self):
if getattr(self.bn2, "weight", None) is not None:
nn.init.zeros_(self.bn2.weight)
def forward(self, x: torch.Tensor) -> torch.Tensor:
shortcut = x
"""
Original Layer Sequence:
https://github.com/huggingface/pytorch-image-models/blob/f689c850b90b16a45cc119a7bc3b24375636fc63/timm/models/resnet.py#L209C1-L226C34
"""
# Revised from here
x = self.conv1(x)
x = self.bn1(x)
x = self.act1(x)
x = self.conv2(x)
x = self.bn2(x)
# Until here
if self.downsample is not None:
shortcut = self.downsample(shortcut)
x += shortcut
x = self.act2(x)
return x
def init_weight(m):
if isinstance(m, nn.Conv1d) or isinstance(m, nn.Linear) or isinstance(m, nn.ConvTranspose1d):
nn.init.xavier_normal_(m.weight)
# m.bias.data.fill_(0.01)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
def init_weight_skcnn(m):
if isinstance(m, nn.Conv1d) or isinstance(m, nn.Linear) or isinstance(m, nn.ConvTranspose1d):
nn.init.kaiming_uniform_(m.weight, a=math.sqrt(5))
# m.bias.data.fill_(0.01)
if m.bias is not None:
# nn.init.constant_(m.bias, 0)
fan_in, _ = nn.init._calculate_fan_in_and_fan_out(m.weight)
bound = 1 / math.sqrt(fan_in)
nn.init.uniform_(m.bias, -bound, bound)
class ResBlock(nn.Module):
def __init__(self, channel):
super(ResBlock, self).__init__()
self.model = nn.Sequential(
nn.Conv1d(channel, channel, kernel_size=3, stride=1, padding=1),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv1d(channel, channel, kernel_size=3, stride=1, padding=1),
)
def forward(self, x):
residual = x
out = self.model(x)
out += residual
return out
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