The Swiss-Army Neural Network

Nice — you want a “Swiss-Army-knife” neural network: one modular model/framework that can be extended to many tasks (classification, regression, segmentation, detection, language, multimodal, etc.). I’ll give you a practical, production-ready blueprint plus a clean PyTorch scaffold you can copy/run and extend. I’ll also include design rationale, recommended training/eval/deployment steps, config example, and extension notes.

I’ll assume PyTorch (most flexible for mixed heads) and keep the core small and modular so you can plug in heads. No clarifying Qs — I’ll deliver a best-effort full package below.

⸻

What this gives you
• Design: a single shared encoder + multiple task heads (classification, regression, segmentation, detection, text).
• Code: modular PyTorch implementation with:
• Backbone (simple ResNet-like),
• Head base class + example heads (classifier, regressor, segmentation upsample head),
• a SwissKnifeModel that routes features to active heads,
• a simple training loop and dummy dataset example so you can run right away.
• Config: YAML example for experiments.
• Notes: hyperparameters, multi-task loss weighting, data handling, scaling, deployment tips.

⸻

Design overview (short)
• Shared encoder: CNN / transformer / hybrid depending on modalities. Produces feature maps and/or pooled embeddings.
• Heads:
• Classification head: global-pooling → MLP → softmax
• Regression head: pooled features → MLP → linear output
• Segmentation head: decoder / upsampling + convs → pixel-wise prediction
• Detection head: anchor-free (centernet/FCOS style) or anchor-based; outputs boxes + classes
• Language head: transformer decoder/encoder (for pure text tasks)
• Multimodal fusion: project image and text embeddings to shared space, cross-attend
• Router/Controller: model receives task flags and routes features to relevant heads; heads registered dynamically.
• Training: multi-task loss sum/weighted sum with annealing or uncertainty-based weighting.

⸻

PyTorch scaffold (runnable minimal example)

The code below is a compact but complete starting point. It contains:
• a simple backbone,
• a classifier head,
• a segmentation head,
• SwissKnifeModel to hold them,
• a dummy training loop for classification on random data.

Copy into a file (e.g. swissknife.py) and run. You only need PyTorch installed.

“`

<h1>swissknife.py</h1>

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader

<h1>——– Backbone (small ResNet-like) ———-</h1>

class SimpleConvBackbone(nn.Module):
def <strong>init</strong>(self, in<em>channels=3, out_dim=256):
super().</em><em>init</em>_()
self.features = nn.Sequential(
nn.Conv2d(in_channels, 32, 3, padding=1, bias=False),
nn.BatchNorm2d(32),
nn.ReLU(inplace=True),
nn.MaxPool2d(2), # 32xH/2xW/2
nn.Conv2d(32, 64, 3, padding=1, bias=False),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.MaxPool2d(2), # 64xH/4xW/4
nn.Conv2d(64, 128, 3, padding=1, bias=False),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
nn.AdaptiveAvgPool2d((1,1))
)
self.fc = nn.Linear(128, out_dim)

<pre><code>def forward(self, x):
# returns both spatial feature map and pooled embedding
# for this tiny backbone we return pooled embedding and None for spatial map
fmap = None
x = self.features(x) # B x 128 x 1 x 1
x = x.view(x.size(0), -1) # B x 128
emb = self.fc(x) # B x out_dim
return emb, fmap
</code></pre>

<h1>——– Heads ——–</h1>

class Head(nn.Module):
"""Base head class."""
def forward(self, emb, fmap=None):
raise NotImplementedError

class ClassificationHead(Head):
def <strong>init</strong>(self, in<em>dim=256, n_classes=10, hidden=128):
super().</em><em>init</em>_()
self.net = nn.Sequential(
nn.Linear(in_dim, hidden),
nn.ReLU(inplace=True),
nn.Linear(hidden, n_classes)
)

<pre><code>def forward(self, emb, fmap=None):
return self.net(emb) # logits
</code></pre>

class RegressionHead(Head):
def <strong>init</strong>(self, in<em>dim=256, out_dim=1):
super().</em><em>init</em>_()
self.net = nn.Sequential(
nn.Linear(in_dim, in_dim//2),
nn.ReLU(inplace=True),
nn.Linear(in_dim//2, out_dim)
)

<pre><code>def forward(self, emb, fmap=None):
return self.net(emb)
</code></pre>

class SegmentationHead(Head):
def <strong>init</strong>(self, in<em>channels=128, n_classes=2):
super().</em><em>init</em>_()
# expects fmap (B x C x H x W). We'll upsample twice here
self.conv1 = nn.Conv2d(in_channels, 64, 3, padding=1)
self.conv2 = nn.Conv2d(64, n_classes, 1)

<pre><code>def forward(self, emb, fmap=None):
if fmap is None:
raise ValueError("Segmentation head requires fmap")
x = F.relu(self.conv1(fmap))
x = F.interpolate(x, scale_factor=4, mode='bilinear', align_corners=False)
x = self.conv2(x)
return x # logits per-pixel
</code></pre>

<h1>——– SwissKnifeModel ——–</h1>

class SwissKnifeModel(nn.Module):
def <strong>init</strong>(self, backbone, heads: dict):
"""
heads: dict of name -> nn.Module (head). e.g. {'clf': ClassificationHead(…)}
"""
super().<strong>init</strong>()
self.backbone = backbone
self.heads = nn.ModuleDict(heads)

<pre><code>def forward(self, x, task=None):
"""
If task is None: returns dict of all head outputs
If task is str: returns only that head output
"""
emb, fmap = self.backbone(x)
outputs = {}
for name, head in self.heads.items():
if task is None or task == name:
# segmentation head may expect fmap
try:
outputs[name] = head(emb, fmap)
except TypeError:
outputs[name] = head(emb)
if task is None:
return outputs
else:
return outputs[task]
</code></pre>

<h1>——– Dummy dataset and training example ——–</h1>

class DummyImageDataset(Dataset):
def <strong>init</strong>(self, n=1000, img_size=(3,32,32), n_classes=10):
self.n = n
self.img_size = img_size
self.n_classes = n_classes

<pre><code>def __len__(self):
return self.n

def __getitem__(self, idx):
x = torch.randn(self.img_size)
y = torch.randint(0, self.n_classes, (1,)).item()
return x, y
</code></pre>

def train_demo():
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
backbone = SimpleConvBackbone(in_channels=3, out_dim=256)
heads = {
'clf': ClassificationHead(in_dim=256, n_classes=10),
# more heads can be added here
}
model = SwissKnifeModel(backbone, heads).to(device)
opt = torch.optim.Adam(model.parameters(), lr=1e-3)
loss_fn = nn.CrossEntropyLoss()

<pre><code>ds = DummyImageDataset(n=2000, img_size=(3,32,32), n_classes=10)
dl = DataLoader(ds, batch_size=64, shuffle=True, num_workers=0)

model.train()
for epoch in range(3):
total_loss = 0.0
for xb, yb in dl:
xb, yb = xb.to(device), yb.to(device)
logits = model(xb, task='clf')
loss = loss_fn(logits, yb)
opt.zero_grad()
loss.backward()
opt.step()
total_loss += loss.item() * xb.size(0)
avg = total_loss / len(ds)
print(f"Epoch {epoch+1} avg loss {avg:.4f}")
</code></pre>

if <strong>name</strong> == "<strong>main</strong>":
train_demo()
“`
How to run
1. pip install torch (or pip install torch torchvision depending on your system).
2. python swissknife.py — this runs the tiny demo training loop.</p>

<p>⸻</p>

<p>Config example (YAML)</p>

<p>Use config files to control which heads are active, loss weights, optimizer settings, and dataset paths.</p>

<p>“`
model:
backbone: SimpleConvBackbone
backbone_params:
in_channels: 3
out_dim: 256
heads:
clf:
class: ClassificationHead
params:
in_dim: 256
n_classes: 100
seg:
class: SegmentationHead
params:
in_channels: 128
n_classes: 21

train:
optimizer:
type: Adam
lr: 1e-3
batch_size: 64
epochs: 50
loss_weights:
clf: 1.0
seg: 0.5
“`

⸻

Multi-task training & loss weighting
• Start with simple weighted sum: L = sum(w_i * L_i).
• Better: uncertainty weighting (Kendall et al., 2018) or dynamic weight averaging (DWA).
• Tip: normalize per-task losses (divide by initial loss scale) or use learnable log-variance parameters.

⸻

Data handling patterns
• Use task-specific data loaders and a scheduler that samples tasks (round-robin, proportional to dataset size, or task-priority sampling).
• For shared batches: if tasks differ in input modality or size, keep separate batches but update shared backbone with accumulated gradients (e.g., accumulate grads across tasks before optimizer.step()).
• Carefully separate data augmentation pipelines per task (segmentation needs spatially consistent augmentations).

⸻

Extending to other heads (notes)
• Detection: implement an anchor-free head (centernet/FCOS) or reuse Detectron2 style heads — detection requires spatial feature maps (FPN).
• Segmentation: use UNet or FPN decoder. Must return spatial fmap from backbone (not pooled).
• Language: keep a transformer encoder/decoder; if multimodal, use cross-attention modules or project image emb to token embeddings and append to text tokens.
• Multimodal: fuse by cross-attention, concatenation + projection, or contrastive pretraining (CLIP-style).

⸻

Deployment & interoperability
• Export options: torch.jit.script / torch.jit.trace, or export to ONNX (torch.onnx.export) with care for dynamic axes if inputs vary.
• Containerize model + inference server with FastAPI / TorchServe / Triton.
• For mobile: torchscript or onnx → convert to TensorFlow Lite / CoreML if needed.

⸻

Recommended training hyperparameters (starter)
• Optimizer: AdamW, lr 1e-4 to 1e-3 depending on model size.
• Batch size: 32–512 depending on GPU.
• LR schedule: CosineAnnealing or linear warmup + decay.
• Weight decay: 1e-4.
• Augmentation: RandAugment/Cutout/MixUp for classification; consistent geometric transforms for dense tasks.

⸻

Observability & monitoring
• Track per-head metrics (accuracy, mIoU, mAP, RMSE).
• Log weighted losses and per-task gradients to detect imbalance.
• Use TensorBoard, Weights & Biases or similar.

⸻

Safety, compute, and dataset considerations
• Multi-task models can overfit tasks with more data. Balance with loss weights and sampling.
• Be mindful of biased datasets when combining tasks (e.g., image-language).
• For production, prune unused heads or use conditional computation (only compute heads needed at inference time).

⸻

Next steps you can ask me to do (I can do now)
• Produce a more advanced backbone (ResNet50 with FPN) + detection head (FCOS-style) and show training code.
• Add example of multimodal fusion (image + text) with a small transformer text head and contrastive pretraining script.
• Create a full multi-task training loop that alternates tasks and demonstrates uncertainty-based weighting.
• Convert the above scaffold into a PyTorch Lightning module and provide config and trainer files.
• Build a minimal end-to-end notebook that trains on a real dataset (MNIST / CIFAR / Cityscapes sample) — I can create code for any of these right away.

⸻

If you want, tell me which of the following to build next (pick one — I’ll produce code immediately):
1. ResNet-FPN backbone + segmentation + detection heads (more real-world).
2. Multimodal image+text fusion with CLIP-style contrastive training.
3. Full multi-task training loop with dynamic loss weighting + YAML configs and logging.
4. Convert scaffold to PyTorch Lightning with data module + checkpoints.

Which do you want me to produce now?