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model.py
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model.py
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import torch.nn as nn
from score_module import *
import CRF
from s4.s4 import S4
def init_weights(m):
if isinstance(m, (nn.Conv2d, nn.Conv1d)):
nn.init.kaiming_normal_(m.weight)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm1d):
nn.init.constant_(m.weight, 1)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
if isinstance(m, nn.Linear):
nn.init.kaiming_uniform_(m.weight)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
class Block1(nn.Module):
def __init__(self, cin, cout, kernel_size=9, padding=1):
super().__init__()
self.block = nn.Sequential(
nn.Conv1d(cin,
cout,
kernel_size=kernel_size,
stride=1,
padding=padding),
nn.BatchNorm1d(cout),
nn.ReLU(inplace=True),
nn.Conv1d(cout, cout,
kernel_size=kernel_size,
stride=1,
padding=padding),
nn.BatchNorm1d(cout))
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
x_conv = self.block(x)
x = x + x_conv
outputs = self.relu(x)
return outputs
class Block2(nn.Module):
def __init__(self, cin, cout,
kernel_size=9,
stride_size=2,
pad_size=4):
super().__init__()
self.mainblock = nn.Sequential(
nn.Conv1d(cin, cout,
kernel_size=kernel_size,
stride=stride_size,
padding=pad_size),
nn.BatchNorm1d(cout),
nn.ReLU(inplace=True),
nn.Conv1d(cout,
cout,
kernel_size=kernel_size,
stride=1,
padding=pad_size),
nn.BatchNorm1d(cout))
self.sideblock = nn.Sequential(
nn.Conv1d(cin,
cout,
kernel_size=1,
stride=stride_size,
padding=0),
nn.BatchNorm1d(cout),
nn.ReLU(inplace=True))
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
x_conv1 = self.mainblock(x)
x_conv2 = self.sideblock(x)
x_conv = x_conv1 + x_conv2
outputs = self.relu(x_conv)
return outputs
class S4_layer(nn.Module):
def __init__(self, dim, state_dim, bidirectional, dropout=0.1):
super().__init__()
self.norm = nn.LayerNorm(dim)
self.S4 = S4(d_model=dim, d_state=state_dim,
bidirectional=bidirectional,
dropout=dropout)
def forward(self, x):
x = self.norm(x.transpose(-1, -2)).transpose(-1, -2)
x = self.S4(x)[0]
return x
class SEGBASELINE(nn.Module):
def __init__(self, inputdim, numclass=6, labelres=20,
backbone_type='crnn', nb_layers=4, **kwargs):
super().__init__()
self.inputdim = inputdim
self.backbone_type = backbone_type
if backbone_type == 's4':
d_layers = []
hidden_size = 64
for _ in range(nb_layers):
d_layers.append(S4_layer(dim=hidden_size, state_dim=64,
bidirectional=True))
self.s4_layer = nn.ModuleList(d_layers)
self.outputlayer = nn.Linear(hidden_size, numclass)
else:
hidden_size = 32
self.features = nn.Sequential(Block2(32, 48, stride_size=1),
Block2(48, 48, stride_size=1),
Block2(48, 64, stride_size=1),
Block2(64, 64, stride_size=1))
self.rnn = nn.LSTM(64, 64, bidirectional=True, batch_first=True)
self.features.apply(init_weights)
self.outputlayer = nn.Linear(128, numclass)
if labelres == 10:
self.conv1 = nn.Conv1d(inputdim, hidden_size, kernel_size=3, stride=1, padding=1)
self.apool = nn.AvgPool1d(10, stride=9, padding=0)
elif labelres == 20:
self.conv1 = nn.Conv1d(inputdim, hidden_size, kernel_size=3, stride=1, padding=3)
self.apool = nn.AvgPool1d(10, stride=5, padding=2)
# initialization
self.conv1.apply(init_weights)
self.outputlayer.apply(init_weights)
def forward(self, x):
x = self.conv1(x)
if self.backbone_type == 's4':
for layer in self.s4_layer:
skip = x
x = layer(x)
x = x + skip
else:
x = self.features(x).transpose(1, 2)
x, _ = self.rnn(x)
x = x.transpose(1, 2)
x = self.apool(x).transpose(1, 2)
x = self.outputlayer(x)
return x
class SEGCRF(nn.Module):
def __init__(self, inputdim, numclass=6, nb_layers=4,
skip_score=False, backbone_type='crnn',
**kwargs):
super().__init__()
# parameters
self.inputdim = inputdim
self.numclass = numclass
self.backbone_type = backbone_type
# choose backbone
if backbone_type == 'crnn':
hidden_size = 32
features = nn.ModuleList()
self.features = nn.Sequential(Block2(32, 48, stride_size=1),
Block2(48, 48, stride_size=1),
Block2(48, 64, stride_size=1),
Block2(64, 64, stride_size=1))
self.rnn = nn.LSTM(64, 64, bidirectional=True, batch_first=True)
self.pair_score = pairwise_score_module(128, numclass, skip_score=skip_score)
self.features.apply(init_weights)
elif backbone_type == 's4':
d_layers = []
hidden_size = 64
for _ in range(nb_layers):
d_layers.append(S4_layer(dim=hidden_size, state_dim=64,
bidirectional=True))
self.s4_layer = nn.ModuleList(d_layers)
self.pair_score = pairwise_score_module(64, numclass, skip_score=skip_score)
# n=20
self.conv1 = nn.Conv1d(inputdim, hidden_size, kernel_size=3, stride=1, padding=3)
self.apool = nn.AvgPool1d(10, stride=5, padding=2)
# initialization
self.conv1.apply(init_weights)
def forward(self, x, y=None):
x = self.conv1(x)
if self.backbone_type == 's4':
for layer in self.s4_layer:
skip = x
x = layer(x)
x = x + skip
elif self.backbone_type == 'crnn':
x = self.features(x).transpose(1, 2)
x, _ = self.rnn(x)
x = x.transpose(1, 2)
x = self.apool(x).transpose(1, 2)
# compute pair score
x, x_skip = self.pair_score(x)
# compute crf logp
crf = CRF.NeuralSemiCRFInterval(x, x_skip)
if y is not None:
y_flatten = sum(y, [])
assert(len(y_flatten) == len(y)* self.numclass)
pathScore = crf.evalPath(y_flatten)
logZ = crf.computeLogZ()
logProb = pathScore - logZ
logProb = logProb.view(len(y), -1)
else:
logProb = 0
return logProb, crf
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
if __name__ == '__main__':
import os
fillerModel = SEGBASELINE(inputdim=64, numclass=6, backbone_type='s4')
# fillerModel = SEGCRF(inputdim=64, numclass=6, backbone_type='s4')
print(count_parameters(fillerModel))