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3.11
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# Cell Segmentator
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## Overview
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This repository provides two main scripts to configure and run a cell segmentation workflow:
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* **generate\_config.py**: Interactive script to create JSON configuration files for training or prediction.
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* **main.py**: Entry point to train, test, or predict using the generated configuration.
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## Installation
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0. **Install uv**:
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Follow the official guide at [https://docs.astral.sh/uv/](https://docs.astral.sh/uv/)
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**Linux / macOS**
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```bash
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curl -LsSf https://astral.sh/uv/install.sh | sh
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```
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**Windows**
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```powershell
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powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
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```
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```bash
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uv --version
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```
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1. **Clone the repository**:
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```bash
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git clone https://git.ai.infran.ru/ilyukhin/model-v
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cd model-v
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```
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2. **Install dependencies**:
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```bash
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uv sync
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```
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## Dataset Structure
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Your data directory must follow this hierarchy:
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```
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path_to_data_folder/
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├── images/ # Input images
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│ ├── img1.tif
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│ ├── img2.tif
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│ └── ...
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└── masks/ # Ground-truth instance masks
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├── mask1.tif
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├── mask2.tif
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└── ...
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```
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If your dataset contains multiple classes (e.g., class A and B) and you prefer not to duplicate images, you can organize masks into class-specific subdirectories:
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```
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path_to_data_folder/
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├── images/
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│ └── img1.tif
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└── masks/
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├── A/ # Masks for class A
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│ ├── img1_mask.tif
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│ └── ...
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└── B/ # Masks for class B
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├── img1_mask.tif
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└── ...
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```
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In this case, set the `masks_subdir` field in your dataset configuration to the name of the mask subdirectory (e.g., `"A"` or `"B"`).
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**Mask format**: Instance masks should be provided for multi-label segmentation with channel-last ordering, i.e., each mask array must have shape `(H, W, C)`.
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## generate\_config.py
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This script guides you through creating a JSON configuration for either training or prediction.
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### Usage
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```bash
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python generate_config.py
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```
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1. **Training mode?** Select `y` or `n`.
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2. **Model selection**: Choose from available models in the registry.
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3. **(If training)**
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* Criterion selection
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* Optimizer selection
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* Scheduler selection
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4. Configuration is saved under `config/templates/train/` or `config/templates/predict/` with a unique filename.
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Generated config includes sections:
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* `model`: Model component and parameters
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* `dataset_config`: Paths, training flag, and mask subdirectory (if any)
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* `wandb_config`: Weights & Biases integration settings
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* *(If training)* `criterion`, `optimizer`, `scheduler`
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## main.py
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Entrypoint to run training, testing, or prediction using a config file.
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### Command-line Arguments
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```bash
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python main.py [-c CONFIG] [-m {train,test,predict}] [--no-save-masks] [--only-masks]
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```
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* `-c, --config` : Path to JSON config file (default: `config/templates/train/...json`).
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* `-m, --mode` : `train`, `test`, or `predict` (default: `train`).
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* `--no-save-masks` : Disable saving predicted masks.
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* `--only-masks` : Save only raw predicted masks (no visual overlays).
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### Workflow
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1. **Load config** and verify mode consistency.
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2. **Initialize** Weights & Biases if enabled.
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3. **Create** `CellSegmentator` and dataloaders with appropriate transforms.
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4. **Print** dataset info for the first batch.
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5. **Run** training or inference (`.run()`).
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6. **Save** model checkpoint and upload to W\&B if in training mode.
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## Configurable Parameters
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A brief overview of the key parameters you can adjust in your JSON config:
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### Common Settings (`common`)
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* `seed` (int): Random seed for data splitting and reproducibility (default: `0`).
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* `device` (str): Compute device to use, e.g., `'cuda:0'` or `'cpu'` (default: `'cuda:0'`).
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* `use_amp` (bool): Enable Automatic Mixed Precision for faster training (default: `false`).
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* `masks_subdir` (str): Name of subdirectory under `masks/` containing the instance masks (default: `""`).
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* `predictions_dir` (str): Output directory for saving predicted masks (default: `"."`).
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* `pretrained_weights` (str): Path to pretrained model weights (default: `""`).
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### Training Settings (`training`)
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* `is_split` (bool): Whether your data is already split (`true`) or needs splitting (`false`, default).
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* `split` / `pre_split`: Directories for data when pre-split or unsplit.
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* `train_size`, `valid_size`, `test_size` (int/float): Size or ratio of your splits (e.g., `0.7`, `0.1`, `0.2`).
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* `batch_size` (int): Number of samples per training batch (default: `1`).
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* `num_epochs` (int): Total training epochs (default: `100`).
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* `val_freq` (int): Frequency (in epochs) to run validation (default: `1`).
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### Testing Settings (`testing`)
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* `test_dir` (str): Directory containing test data (default: `"."`).
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* `test_size` (int/float): Portion or count of data for testing (default: `1.0`).
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* `shuffle` (bool): Shuffle test data before evaluation (default: `true`).
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> **Batch size note:** Validation, testing, and prediction runs always use a batch size of `1`, regardless of the `batch_size` setting in the training configuration.
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## Examples
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### Generate a training config
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```bash
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python generate_config.py
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# Follow prompts to select model, criterion, optimizer, scheduler
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# Output saved to config/templates/train/YourConfig.json
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```
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### Train a model
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```bash
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python main.py -c config/templates/train/YourConfig.json -m train
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```
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### Predict on new data
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```bash
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python main.py -c config/templates/predict/YourConfig.json -m predict
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```
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---
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@ -0,0 +1,37 @@
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[project]
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name = "model-v"
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version = "1.0.0"
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description = "Add your description here"
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readme = "README.md"
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requires-python = ">=3.11"
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dependencies = [
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"colorlog>=6.9.0",
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"fastremap>=1.16.1",
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"fill-voids>=2.0.8",
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"imagecodecs>=2025.3.30",
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"matplotlib>=3.10.3",
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"monai>=1.4.0",
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"numba>=0.61.2",
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"numpy>=1.26.4",
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"pydantic>=2.11.4",
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"scikit-image>=0.25.2",
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"scipy>=1.15.3",
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"segmentation-models-pytorch>=0.5.0",
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"tabulate>=0.9.0",
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"tifffile>=2025.3.30",
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"torch==2.5.1+cu124",
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"torchaudio==2.5.1+cu124",
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"torchvision==0.20.1+cu124",
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"tqdm>=4.67.1",
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"wandb>=0.19.11",
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]
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[tool.uv.sources]
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torch = { index = "pytorch-cu124" }
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torchvision = { index = "pytorch-cu124" }
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torchaudio = { index = "pytorch-cu124" }
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[[tool.uv.index]]
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name = "pytorch-cu124"
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url = "https://download.pytorch.org/whl/cu124"
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explicit = true
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