How ando-khachatryan/hidden works
<explanation>
1. Identify the project type and purpose
- This is a deep‑learning research codebase (“HiDDeN”), implemented in Python/PyTorch.
- Its goal is to train and evaluate an encoder–noise–decoder pipeline that hides and recovers data in images under various distortions.
2. Analyze the file structure
- Top‑level scripts:
• main.py (entry point; CLI for “new” or “continue” experiments)
• train.py / validate‑trained‑models.py / test_model.py (training, validation, testing)
• noise_argparser.py / options.py (argument parsing and experiment configuration)
- Core logic:
• model/ (encoder, decoder, discriminator submodules, conv_bn_relu layers)
• noise_layers/ (crop, cropout, dropout, jpeg_compression, resize, quantization, identity, noiser)
• vgg_loss.py (perceptual loss)
- Utilities:
• utils.py, average_meter.py (metrics aggregation), tensorboard_logger.py
- Experiments & outputs:
• experiments/ (per‑experiment checkpoints, CSV logs, images, tensorboard events)
• runfiles/ (.sh scripts to launch runs)
- Configuration & environment:
• requirements via README (PyTorch, TorchVision, TensorboardX)
3. Examine the README for additional insights
- Describes the high‑level pipeline: watermark image via encoder → apply stochastic noise layer(s) → decode hidden bits
- Experiment management: runs in ./runs/<name‑timestamp> and summaries under ./experiments
4. How to create the system design diagram
a. Main components
1. CLI / Experiment Manager
- main.py
- noise_argparser.py / options.py
- runfiles/*.sh
2. Data Loading
- torchvision DataLoader reading <data_root>/train and /val
3. Model Pipeline
- Encoder (model/encoder.py + sub‑layers)
- Noise Layers (random pick from noise_layers/*)
- Decoder (model/decoder.py)
- (Optional) Discriminator (if adversarial loss used)
4. Loss Computation
- Reconstruction loss (MSE, BCE)
- Perceptual loss (vgg_loss.py)
5. Optimizers & Training Loop
- train.py invoked by main.py
- average_meter.py aggregates metrics
- tensorboard_logger.py logs metrics & images
6. Checkpointing & Outputs
- model/*.pyt saved in experiments/<run>/checkpoints
- CSV summaries in experiments/<run>/train.csv, validation.csv
- Plots via experiments/make_plots.py
7. Experiment Archive
- experiments/<name‑date>/images, logs, pickle configs
b. Relationships & interactions
- CLI Manager → instantiates DataLoader, Model, Noise Layers, Loss, Optimizer
- Training Loop: for each batch, data flows → Encoder → Noise Layer → Decoder → Loss → Backprop → Optimizer step
- Logger taps into loop to record scalar metrics and sample images
- Checkpointer saves model weights at intervals
- Validation loop parallels training but with deterministic center crops
c. Architectural patterns & design principles
- Pipeline pattern: sequential data transformations (encode → noise → decode)
- Modular separation:
• Model definition separated from noise transforms
• Configuration management (options.py) decoupled from business logic
• Logging & checkpointing abstracted into utility modules
- Experiment reproducibility: explicit run directories, config pickles
d. Key technologies, frameworks, libraries
- PyTorch 1.x (torch.nn, torch.optim, DataLoader)
- TorchVision (datasets, transforms)
- TensorboardX & Tensorboard (visualizations)
- Python standard libs (argparse, pickle, logging)
5. Guidelines for tailoring the diagram to this ML project
- Emphasize the sequential pipeline in the center: Encoder → Noise Layer(s) → Decoder
- Show DataLoader feeding raw images into the pipeline
- Display Loss modules branching off the Decoder output
- Depict Training Loop orchestrating forward/backward passes
- Include Experiment Manager (CLI) at the top driving the workflow
- Show output sinks: Checkpoint storage (experiments/…), Tensorboard logs
6. Diagram elements to include
- Clear component boxes labeled: “CLI Manager,” “DataLoader,” “Encoder,” “Noise Layers,” “Decoder,” “Losses,” “Optimizer & Training Loop,” “Logger,” “Checkpoint Store”
- Directional arrows for data flow (solid) and control flow (dashed)
- Color‑code:
• Blue for data components (DataLoader, images)
• Green for model components (Encoder, Decoder, Noise Layers)
• Orange for training infrastructure (Losses, Optimizer, Logger)
• Gray for external/IO (CLI, file system, Tensorboard)
- Shapes: rectangles for compute units, cylinders for storage (checkpoints, CSVs), document icons for logs
7. Emphasize detail without over‑splitting
- Represent each noise layer category as a single “Noise Layer” module with an annotation “select one at random per batch”
- Group all experiment artifacts under one “Experiments Folder” storage icon
- Call out that model sub‑components (conv_bn_relu, hidden) are internal to Encoder/Decoder boxes
By following these guidelines, you’ll produce a detailed, accurate architecture diagram that maps the HiDDeN codebase’s ML training pipeline, module interactions, and experiment management flow.