Annexure 7: PyTorch Templates and Boilerplates for All Common Tasks.

Prof.(Dr.) D G Mahto, Founder & CEO -

Abstract:

Below is the complete Annexure 7: PyTorch Templates and Boilerplates for All Common Tasks.

**ANNEXURE 7

PyTorch Templates and Boilerplates for All Common Tasks**

This annexure provides practical, ready-to-use templates for every major task performed in PyTorch—including data loading, model building, training loops, evaluation, saving/loading models, visualization, and deployment.

These templates are designed to be directly copy-paste ready for academic projects, production workflow, and fast prototyping.

1. Standard PyTorch Project Structure

A clean and reusable project folder format:

project/
│── data/
│── models/
│── utils/
│── train.py
│── inference.py
│── requirements.txt

2. Template: Device Configuration

import torch

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("Using device:", device)

3. Template: Dataset & DataLoader

3.1 Custom Dataset Template

from torch.utils.data import Dataset

class CustomDataset(Dataset):
    def __init__(self, data, labels, transform=None):
        self.data = data
        self.labels = labels
        self.transform = transform

    def __len__(self):
        return len(self.data)

    def __getitem__(self, idx):
        x = self.data[idx]
        y = self.labels[idx]
        if self.transform:
            x = self.transform(x)
        return x, y

3.2 Image DataLoader (torchvision)

from torchvision import datasets, transforms
from torch.utils.data import DataLoader

transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
])

train_ds = datasets.ImageFolder("data/train", transform=transform)
train_loader = DataLoader(train_ds, batch_size=32, shuffle=True)

3.3 Text DataLoader (torchtext)

from torchtext.data.utils import get_tokenizer
from torch.utils.data import DataLoader

tokenizer = get_tokenizer("basic_english")

def collate_batch(batch):
    texts, labels = zip(*batch)
    tokens = [tokenizer(t) for t in texts]
    return tokens, labels

loader = DataLoader(dataset, batch_size=16, collate_fn=collate_batch)

4. Template: Building Models

4.1 Basic Feedforward Neural Network

import torch.nn as nn

class FFN(nn.Module):
    def __init__(self):
        super().__init__()
        self.layers = nn.Sequential(
            nn.Linear(784, 256),
            nn.ReLU(),
            nn.Linear(256, 64),
            nn.ReLU(),
            nn.Linear(64, 10)
        )

    def forward(self, x):
        return self.layers(x)

4.2 CNN Template

class SimpleCNN(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(3, 32, 3, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(32, 64, 3, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(2)
        )
        self.fc = nn.Sequential(
            nn.Linear(64 * 56 * 56, 128),
            nn.ReLU(),
            nn.Linear(128, 10)
        )

    def forward(self, x):
        x = self.conv(x)
        x = x.view(x.size(0), -1)
        return self.fc(x)

4.3 RNN / LSTM Template

class LSTMClassifier(nn.Module):
    def __init__(self, vocab_size, embed_dim, hidden_size, num_classes):
        super().__init__()
        self.embed = nn.Embedding(vocab_size, embed_dim)
        self.lstm = nn.LSTM(embed_dim, hidden_size, batch_first=True)
        self.fc = nn.Linear(hidden_size, num_classes)

    def forward(self, x):
        x = self.embed(x)
        _, (h, _) = self.lstm(x)
        return self.fc(h[-1])

5. Template: Training Loop (Standard)

def train(model, loader, loss_fn, optimizer, device):
    model.train()
    running_loss = 0

    for batch_idx, (x, y) in enumerate(loader):
        x, y = x.to(device), y.to(device)

        optimizer.zero_grad()
        preds = model(x)
        loss = loss_fn(preds, y)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()

    return running_loss / len(loader)

6. Template: Evaluation Loop

def evaluate(model, loader, loss_fn, device):
    model.eval()
    total, correct = 0, 0
    running_loss = 0

    with torch.no_grad():
        for x, y in loader:
            x, y = x.to(device), y.to(device)
            preds = model(x)
            loss = loss_fn(preds, y)
            running_loss += loss.item()

            _, predicted = torch.max(preds.data, 1)
            total += y.size(0)
            correct += (predicted == y).sum().item()

    accuracy = 100 * correct / total
    return running_loss / len(loader), accuracy

7. Template: Training + Evaluation Together

for epoch in range(10):
    train_loss = train(model, train_loader, loss_fn, optimizer, device)
    val_loss, val_acc = evaluate(model, val_loader, loss_fn, device)

    print(f"Epoch {epoch+1}: Train Loss={train_loss:.4f} | Val Loss={val_loss:.4f} | Val Acc={val_acc:.2f}%")

8. Template: Saving and Loading Models

8.1 Save Model

torch.save(model.state_dict(), "model.pth")

8.2 Load Model

model = MyModel()
model.load_state_dict(torch.load("model.pth", map_location=device))
model.to(device)

9. Template: Using TensorBoard

from torch.utils.tensorboard import SummaryWriter

writer = SummaryWriter()

writer.add_scalar("Loss/train", train_loss, epoch)
writer.add_graph(model, images)
writer.close()

10. Template: Mixed Precision Training

scaler = torch.cuda.amp.GradScaler()

for x, y in loader:
    x, y = x.to(device), y.to(device)
    optimizer.zero_grad()

    with torch.cuda.amp.autocast():
        preds = model(x)
        loss = loss_fn(preds, y)

    scaler.scale(loss).backward()
    scaler.step(optimizer)
    scaler.update()

11. Template: Inference Script

model.eval()
with torch.no_grad():
    x = image.to(device)
    preds = model(x)
    predicted_class = preds.argmax(1)
    print("Prediction:", predicted_class.item())

12. Template: Creating Data Transforms

from torchvision import transforms

transform = transforms.Compose([
    transforms.Resize((128,128)),
    transforms.RandomHorizontalFlip(),
    transforms.RandomRotation(10),
    transforms.ToTensor()
])

13. Template: Exporting Model to ONNX

dummy = torch.randn(1, 3, 224, 224).to(device)
torch.onnx.export(model, dummy, "model.onnx")

14. Template: Deployment Using FastAPI

from fastapi import FastAPI
import torch

app = FastAPI()
model = torch.load("model.pth")
model.eval()

@app.post("/predict")
def predict(image_tensor):
    with torch.no_grad():
        preds = model(image_tensor)
    return {"prediction": preds.argmax().item()}

15. Template: Distributed Data Parallel (DDP)

import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP

dist.init_process_group("nccl")
model = DDP(model.to(device))

16. Conclusion

This annexure provides every essential PyTorch boilerplate code snippet needed for:

  • Fast development

  • Academic assignments

  • Production-ready ML systems

  • Model training and deployment

  • Reproducible workflows

You can combine these templates to create end-to-end ML systems rapidly.


Prof. (Dr.) D. G. Mahto: A Visionary Director, Professor, Leader & Educator With over 25 years of excellence, Prof. (Dr.) D. G. Mahto is a Director, Professor, Philanthropist, HEI Builder, writer, consultant, reviewer, and career advisor at "Career Education for Success—Discover, Apply, Succeed!" He has empowered individuals, brands, and institutions through insightful content, innovation, and mentorship. Holding a Ph.D. in Engineering from NIT Jamshedpur, he has pursued Post-Doctoral Programs at IITs, NITs, and IIITs, reinforcing his expertise. A member of prestigious global organizations like NPA (USA), IET (UK), ASME, IAENG, and MRSI, he actively contributes to engineering, research, and education. An accomplished writer and researcher, he shares knowledge via Career Education Success Now, Career Edus, and Discover Knowledge Wisely. His vision integrates technology, innovation, and leadership, bridging gaps between aspirations and achievements. Connect with him for insights into education, research, and professional growth.

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