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Multi-Modal 1D ResNet + Dense Multi-Task Network V2.
Improvements: SE attention, deeper backbone, better regularization.
"""
import torch
import torch.nn as nn
from config import NUM_CLASSES, IN_CHANNELS, SEQ_LEN, RESNET_DIM, DENSE_DIM, FUSED_DIM, NUM_FIT_PARAMS
class SEBlock1d(nn.Module):
"""Squeeze-and-Excitation block for channel attention."""
def __init__(self, channels, reduction=4):
super().__init__()
self.squeeze = nn.AdaptiveAvgPool1d(1)
self.excitation = nn.Sequential(
nn.Linear(channels, channels // reduction),
nn.ReLU(inplace=True),
nn.Linear(channels // reduction, channels),
nn.Sigmoid(),
)
def forward(self, x):
b, c, _ = x.shape
w = self.squeeze(x).squeeze(-1) # (b, c)
w = self.excitation(w).unsqueeze(-1) # (b, c, 1)
return x * w
class ResBlock1d(nn.Module):
"""1D Residual Block with SE attention and optional downsampling."""
def __init__(self, in_channels, out_channels, stride=1, use_se=True):
super().__init__()
self.conv1 = nn.Conv1d(in_channels, out_channels, kernel_size=3,
stride=stride, padding=1, bias=False)
self.bn1 = nn.BatchNorm1d(out_channels)
self.relu = nn.ReLU(inplace=True)
self.conv2 = nn.Conv1d(out_channels, out_channels, kernel_size=3,
stride=1, padding=1, bias=False)
self.bn2 = nn.BatchNorm1d(out_channels)
self.se = SEBlock1d(out_channels) if use_se else nn.Identity()
self.shortcut = nn.Sequential()
if stride != 1 or in_channels != out_channels:
self.shortcut = nn.Sequential(
nn.Conv1d(in_channels, out_channels, kernel_size=1,
stride=stride, bias=False),
nn.BatchNorm1d(out_channels)
)
def forward(self, x):
out = self.relu(self.bn1(self.conv1(x)))
out = self.bn2(self.conv2(out))
out = self.se(out)
out += self.shortcut(x)
out = self.relu(out)
return out
class ResNetBackbone(nn.Module):
"""1D ResNet backbone with SE attention."""
def __init__(self, in_channels=IN_CHANNELS, base_dim=64, out_dim=RESNET_DIM):
super().__init__()
self.conv1 = nn.Conv1d(in_channels, base_dim, kernel_size=7,
stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm1d(base_dim)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool1d(kernel_size=3, stride=2, padding=1)
self.layer1 = nn.Sequential(
ResBlock1d(base_dim, base_dim),
ResBlock1d(base_dim, base_dim)
)
self.layer2 = nn.Sequential(
ResBlock1d(base_dim, base_dim * 2, stride=2),
ResBlock1d(base_dim * 2, base_dim * 2)
)
self.layer3 = nn.Sequential(
ResBlock1d(base_dim * 2, base_dim * 4, stride=2),
ResBlock1d(base_dim * 4, base_dim * 4)
)
self.avgpool = nn.AdaptiveAvgPool1d(1)
def forward(self, x):
x = self.relu(self.bn1(self.conv1(x)))
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.avgpool(x)
x = x.squeeze(-1)
return x
class DenseParamBranch(nn.Module):
"""Dense MLP branch for processing 4 fit parameters."""
def __init__(self, in_features=NUM_FIT_PARAMS, out_features=DENSE_DIM):
super().__init__()
self.net = nn.Sequential(
nn.Linear(in_features, 32),
nn.ReLU(inplace=True),
nn.BatchNorm1d(32),
nn.Dropout(0.1),
nn.Linear(32, out_features),
nn.ReLU(inplace=True),
nn.BatchNorm1d(out_features),
)
def forward(self, x):
return self.net(x)
class ClassificationHead(nn.Module):
"""Classification head with dropout."""
def __init__(self, in_features=FUSED_DIM, num_classes=NUM_CLASSES, dropout=0.4):
super().__init__()
self.net = nn.Sequential(
nn.Dropout(dropout),
nn.Linear(in_features, 128),
nn.ReLU(inplace=True),
nn.BatchNorm1d(128),
nn.Dropout(dropout * 0.5),
nn.Linear(128, num_classes),
)
def forward(self, x):
return self.net(x)
class RabiMultiTaskNet(nn.Module):
"""
Multi-Modal Multi-Task Network V2 with SE attention.
"""
def __init__(self):
super().__init__()
self.signal_backbone = ResNetBackbone()
self.param_branch = DenseParamBranch()
self.data_head = ClassificationHead()
self.fit_head = ClassificationHead()
def forward(self, signal, params):
signal_features = self.signal_backbone(signal)
param_features = self.param_branch(params)
fused = torch.cat([signal_features, param_features], dim=1)
data_quality = self.data_head(fused)
fit_quality = self.fit_head(fused)
return data_quality, fit_quality
def count_parameters(model):
"""Count total trainable parameters."""
return sum(p.numel() for p in model.parameters() if p.requires_grad)
if __name__ == '__main__':
from config import DEVICE
model = RabiMultiTaskNet()
print(f"Total parameters: {count_parameters(model):,}")
model = model.to(DEVICE)
x = torch.randn(4, 2, SEQ_LEN).to(DEVICE)
params = torch.randn(4, 4).to(DEVICE)
dq, fq = model(x, params)
print(f"Data quality output: {dq.shape}")
print(f"Fit quality output: {fq.shape}")
print(f"Device: {DEVICE}")
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