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Commit 6a86555b authored by Fabrice Daian's avatar Fabrice Daian
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...@@ -5,7 +5,7 @@ ...@@ -5,7 +5,7 @@
Preprint BioRXiv [![DOI:10.1101/2024.10.25.620201](images/badge_preprint_mupix.svg)](https://doi.org/10.1101/2024.10.25.620201) Preprint BioRXiv [![DOI:10.1101/2024.10.25.620201](images/badge_preprint_mupix.svg)](https://doi.org/10.1101/2024.10.25.620201)
#### ```Dataset & pre-trained µPIX Models``` #### ```Dataset & pre-trained µPIX Models```
| | Dataset | pre-trained µPIX Models | | | Dataset | Pre-trained µPIX Models |
|--------------------------|--------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------| |--------------------------|--------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------|
| ```csbdeep``` | [download](https://sync.lis-lab.fr/index.php/s/sy3SrGgqNafbP5X/download) | [download](https://sync.lis-lab.fr/index.php/s/riqZbKBbYwjLK4m/download) | | ```csbdeep``` | [download](https://sync.lis-lab.fr/index.php/s/sy3SrGgqNafbP5X/download) | [download](https://sync.lis-lab.fr/index.php/s/riqZbKBbYwjLK4m/download) |
| ```rejuvenation``` | [download](https://sync.lis-lab.fr/index.php/s/kDyLKjCt48BfNMK/download) | [download](https://sync.lis-lab.fr/index.php/s/n2oJAw4LobTCeNZ/download) | | ```rejuvenation``` | [download](https://sync.lis-lab.fr/index.php/s/kDyLKjCt48BfNMK/download) | [download](https://sync.lis-lab.fr/index.php/s/n2oJAw4LobTCeNZ/download) |
...@@ -26,7 +26,7 @@ Preprint BioRXiv [![DOI:10.1101/2024.10.25.620201](images/badge_preprint_mupix.s ...@@ -26,7 +26,7 @@ Preprint BioRXiv [![DOI:10.1101/2024.10.25.620201](images/badge_preprint_mupix.s
<details> <details>
<summary>Click to expand</summary> <summary>Click to expand</summary>
| Requirements | | | Requirements | Vesrsion |
|--------------------------|--------------------------------------------------------------------------------------------------------------------------------| |--------------------------|--------------------------------------------------------------------------------------------------------------------------------|
| Python | 3.11 | | Python | 3.11 |
| Tensorflow | 2.14 | | Tensorflow | 2.14 |
...@@ -37,26 +37,27 @@ Preprint BioRXiv [![DOI:10.1101/2024.10.25.620201](images/badge_preprint_mupix.s ...@@ -37,26 +37,27 @@ Preprint BioRXiv [![DOI:10.1101/2024.10.25.620201](images/badge_preprint_mupix.s
If you don't have already miniconda installed on your system, please refere to the [officical miniconda documentation](https://docs.anaconda.com/miniconda/install/) and then : If you do not have already miniconda installed on your system, please refer to the [officical miniconda documentation](https://docs.anaconda.com/miniconda/install/). Then :
Create a miniconda environment and source it: 1- Create a miniconda environment and source it:
```bash ```bash
conda create -n mupix_env python=3.11 conda create -n mupix_env python=3.11
``` ```
```bash ```bash
source ~/.bashrc source ~/.bashrc
``` ```
Finally activate the environment: 2- Activate the environment:
```bash ```bash
conda activate mupix_env conda activate mupix_env
``` ```
Clone this repository to download µPIX sources : 3 - Clone this repository to download µPIX sources :
```bash ```bash
git clone https://gitlab.lis-lab.fr/sicomp/mupix git clone https://gitlab.lis-lab.fr/sicomp/mupix
``` ```
and then proceed to the installation of required Python packages:
4- Install the required Python packages:
```bash ```bash
pip install -r mupix/requirements.txt pip install -r mupix/requirements.txt
...@@ -67,12 +68,12 @@ pip install -r mupix/requirements.txt ...@@ -67,12 +68,12 @@ pip install -r mupix/requirements.txt
## Use Case 1 - Use a pre-trained µPIX model to denoise an image dataset ([Tutorial VIDEO](https://sync.lis-lab.fr/index.php/s/ftw9fdGacJyyfBq)) ## Use Case #1 - Use a pre-trained µPIX model to denoise an image dataset ([Tutorial VIDEO](https://sync.lis-lab.fr/index.php/s/ftw9fdGacJyyfBq))
<details> <details>
<summary>Click to expand</summary> <summary>Click to expand</summary>
µPIX comes with 3 ptre-trained models along with their respective datasets: µPIX comes with 3 Pre-trained models along with their respective datasets:
| | Dataset | pre-trained µPIX Models | | | Dataset | pre-trained µPIX Models |
|--------------------------|--------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------| |--------------------------|--------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------|
...@@ -81,7 +82,7 @@ pip install -r mupix/requirements.txt ...@@ -81,7 +82,7 @@ pip install -r mupix/requirements.txt
| ```metrology``` | [download](https://sync.lis-lab.fr/index.php/s/mYDRTeAQxMxNPPJ/download) | [download](https://sync.lis-lab.fr/index.php/s/degZsCxN7ZXxeB6/download) | | ```metrology``` | [download](https://sync.lis-lab.fr/index.php/s/mYDRTeAQxMxNPPJ/download) | [download](https://sync.lis-lab.fr/index.php/s/degZsCxN7ZXxeB6/download) |
For this example, we will use a pre-trained µPIX model on the ```metrology``` dataset and we will use it to denoise the ```metrology``` test dataset. For this example, we will use a pre-trained µPIX model on the ```metrology``` training dataset to denoise the ```metrology``` test dataset.
First, move to the `mupix` directory: First, move to the `mupix` directory:
```bash ```bash
...@@ -91,7 +92,7 @@ cd mupix ...@@ -91,7 +92,7 @@ cd mupix
### 1 - Download and unzip the pre-trained µPIX ```metrology``` model ### 1 - Download and unzip the pre-trained µPIX ```metrology``` model
To download the ```metrology``` model, type in your Terminal: To download the ```metrology``` model, type the following command in your Terminal:
```bash ```bash
curl -o metrology_model.zip https://sync.lis-lab.fr/index.php/s/degZsCxN7ZXxeB6/download -q curl -o metrology_model.zip https://sync.lis-lab.fr/index.php/s/degZsCxN7ZXxeB6/download -q
...@@ -101,7 +102,7 @@ curl -o metrology_model.zip https://sync.lis-lab.fr/index.php/s/degZsCxN7ZXxeB6/ ...@@ -101,7 +102,7 @@ curl -o metrology_model.zip https://sync.lis-lab.fr/index.php/s/degZsCxN7ZXxeB6/
unzip -qq metrology_model.zip -d metrology_model unzip -qq metrology_model.zip -d metrology_model
``` ```
The model is now stored inside ```./metrology_model``` folder. The model is now stored in the ```./metrology_model``` folder.
### 2 - Download and unzip the ```metrology``` dataset ### 2 - Download and unzip the ```metrology``` dataset
...@@ -139,10 +140,10 @@ The ```new_experiment.py``` script allows you to set up a new experiment by spec ...@@ -139,10 +140,10 @@ The ```new_experiment.py``` script allows you to set up a new experiment by spec
| `--test_data_path` *(optional)* | Path to the test dataset (if available). | | `--test_data_path` *(optional)* | Path to the test dataset (if available). |
In our case, as we want just to use the pre-trained µPIX ```metrology``` model in inference (no training), we don't need to provide the ```clean_data_path``` and ```noisy_data_path``` parameters so we left it empty. In this case, since we are using the pre-trained µPIX ```metrology``` model in inference (no training), we do not need to provide the ```clean_data_path``` and ```noisy_data_path``` parameters so we left it empty.
Nevertheless, we have to indicate the path that contains the test data image we want to denoise using µPIX using the ```test_data_path``` parameter, and the ```experiment_name``` we want to create as well as the location where the experiment will be stored using the ```experiment_path``` parameter. Nevertheless, we have to indicate the path that contains the test data image we want to denoise using µPIX using the ```test_data_path``` parameter, and the ```experiment_name``` we want to create as well as the location where the experiment will be stored using the ```experiment_path``` parameter.
Type the following to create experiment: Type the following command to create a µPIX experiment:
```bash ```bash
python new_experiment.py --experiment_name "metrology_experiment" --experiment_path "./experiments" --clean_data_path "" --noisy_data_path "" --test_data_path "./metrology/test" python new_experiment.py --experiment_name "metrology_experiment" --experiment_path "./experiments" --clean_data_path "" --noisy_data_path "" --test_data_path "./metrology/test"
...@@ -175,12 +176,12 @@ For our example, here is the command you need to type to start the inference usi ...@@ -175,12 +176,12 @@ For our example, here is the command you need to type to start the inference usi
python mupixinfer.py --experiment_path "./experiments/metrology_experiment/" python mupixinfer.py --experiment_path "./experiments/metrology_experiment/"
``` ```
Once finished, the denoised images are stored inside ```./experiments/metrology_experiment/predictions/``` directory. Once completed, the denoised images are stored in the ```./experiments/metrology_experiment/predictions/``` directory.
</details> </details>
## Use Case 2 - Train a µPIX model from scratch using a custom dataset ## Use Case #2 - Train a µPIX model from scratch using a custom dataset
<details> <details>
<summary>Click to expand</summary> <summary>Click to expand</summary>
...@@ -281,7 +282,7 @@ For information, here is the ```hyperparameters.json``` file created by default ...@@ -281,7 +282,7 @@ For information, here is the ```hyperparameters.json``` file created by default
| `batch_size` | `int` | Number of samples per batch. | | `batch_size` | `int` | Number of samples per batch. |
| `num_epochs` | `int` | Total number of training epochs. | | `num_epochs` | `int` | Total number of training epochs. |
| `loss_weight` | `int` | Weight factor for µPIX loss calculation. | | `loss_weight` | `int` | Weight factor for µPIX loss calculation. |
| `tile_size` | `int` | Size of image tiles used for training. | | `tile_size` | `int` | Size of the image tiles used for training. |
| `patience` | `int` | Number of epochs to wait before triggering µPIX early stopping if no improvement. | | `patience` | `int` | Number of epochs to wait before triggering µPIX early stopping if no improvement. |
| `valid_size` | `float` | Proportion of the dataset used for validation. | | `valid_size` | `float` | Proportion of the dataset used for validation. |
| `seed` | `int` | Random seed for reproducibility. | | `seed` | `int` | Random seed for reproducibility. |
...@@ -304,7 +305,7 @@ python mupixtraining.py --experiment_path EXPERIMENT_PATH [--retrain] ...@@ -304,7 +305,7 @@ python mupixtraining.py --experiment_path EXPERIMENT_PATH [--retrain]
| Argument | Description | | Argument | Description |
|--------------------------|-------------| |--------------------------|-------------|
| `--experiment_path` | Path to the previously created experiment. | | `--experiment_path` | Path to the previously created experiment. |
| `--retrain` *(optional)* | Use this flag to continue training an existing µPIX model located inside the experiment path. | | `--retrain` *(optional)* | Use this flag to continue training an existing µPIX model located in the experiment path. |
For our example, here is the command you need to type to start the training of a µPIX model for the ```metrology``` dataset: For our example, here is the command you need to type to start the training of a µPIX model for the ```metrology``` dataset:
...@@ -346,11 +347,11 @@ Step>3, Generator loss : 1.941e+05 ...@@ -346,11 +347,11 @@ Step>3, Generator loss : 1.941e+05
... ...
... ...
``` ```
By default, the model is trained for 100 epochs (see ```hyperparameters.json```) but contains an ```EarlyStopping``` mechanisms (See the paper methods section pour details) governed by the ```patience``` parameters (see ```hyperparameters.json```). By default, the model is trained for 100 epochs (see ```hyperparameters.json```) but it includes an ```EarlyStopping``` mechanism (See the paper methods section pour details) governed by the ```patience``` parameters (see ```hyperparameters.json```).
You can stop the training anytime. The best checkpoints of your model is available inside ```experiments/metrology_experiment/results/networks/``` You can stop the training at anytime. The best checkpoints of your model is available in the ```experiments/metrology_experiment/results/networks/``` directory.
If the training stops because it reached the maximum number of epochs defined inside the ```hyperparameters.json``` configuration file, and you want to continue to train your model for more epochs, you can use the ```--retrain``` parameters to resume the training where it stops: If the training stops because it has reached the maximum number of epochs defined into the ```hyperparameters.json``` configuration file, and you want to continue training your model for more epochs, you can use the ```--retrain``` parameters to resume the training where it stops:
```bash ```bash
python mupixtraining.py --experiment_path "./experiments/metrology_experiment" --retrain python mupixtraining.py --experiment_path "./experiments/metrology_experiment" --retrain
...@@ -358,7 +359,7 @@ python mupixtraining.py --experiment_path "./experiments/metrology_experiment" - ...@@ -358,7 +359,7 @@ python mupixtraining.py --experiment_path "./experiments/metrology_experiment" -
### 2.4 - Inference on the test dataset ### 2.4 - Inference on the test dataset
The `mupixinfer.py` script allows you to use a pre-trained µPIX model to denoise a dataset located in the `test` directory inside the experiment path. The `mupixinfer.py` script allows you to use a pre-trained µPIX model to denoise a dataset located within the `test` directory inside the experiment path.
#### Usage #### Usage
...@@ -372,7 +373,7 @@ For our example, here is the command you need to type to start the inference usi ...@@ -372,7 +373,7 @@ For our example, here is the command you need to type to start the inference usi
python mupixinfer.py --experiment_path "./experiments/metrology_experiment/" python mupixinfer.py --experiment_path "./experiments/metrology_experiment/"
``` ```
Once done you can see that the predictions has been stored inside ```./experiments/metrology_experiment/predictions/``` directory: Once done you can see that the predictions has been stored in the ```./experiments/metrology_experiment/predictions/``` directory:
``` ```
Saved weights to ./experiments/metrology_experiment/results/networks/Generator.h5 Saved weights to ./experiments/metrology_experiment/results/networks/Generator.h5
1/1 [==============================] - 7s 7s/step 1/1 [==============================] - 7s 7s/step
...@@ -404,4 +405,4 @@ If you would like to cite this work, please use the following BibTeX entry: ...@@ -404,4 +405,4 @@ If you would like to cite this work, please use the following BibTeX entry:
</details> </details>
## License ## License
This code repository is release under the [CC BY-NS-SA 4.0](https://gitlab.lis-lab.fr/sicomp/mupix/-/blob/main/LICENSE?ref_type=heads) This code repository is released under the [CC BY-NS-SA 4.0](https://gitlab.lis-lab.fr/sicomp/mupix/-/blob/main/LICENSE?ref_type=heads)
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