# AUTOMAP

**Repository Path**: Heconnor/AUTOMAP

## Basic Information

- **Project Name**: AUTOMAP
- **Description**: No description available
- **Primary Language**: Python
- **License**: GPL-3.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-09-14
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: reconstruction

## README

# AUTOMAP

Welcome to the official repository for AUTOMAP (Automated Transform by Manifold Approximation), a Tensorflow (> 2.0) implementation of the model described in:

[Article PDF](http://martinos.org/lfi/pdf/AUTOMAP_Nature_2018.pdf): B. Zhu, J. Z. Liu, S. F. Cauley, B. R. Rosen, and M. S. Rosen, “Image reconstruction by domain-transform manifold learning,” Nature, vol. 555, no. 7697, pp. 487 EP ––492, Mar. 2018

## Training

Training utilizes `automap_main_train.py` and a JSON config file.

```
python automap_main_train.py -c configs/train_64x64_ex.json
```

Some key config file entries:

"exp_name": Name of experiment. Intermediate model files will be stored in `experiments/{exp_name}`

"resume": Resume training (based on "model_load" file)". 1 or 0

"loadmodel_dir": File location of model to be loaded (for resume = 1). If not resuming a previous training run, it should be set to null.

"num_epochs": Number of training epochs

"learning_rate": Learning rate

"batch_size": Size of training mini-batch.

"fc_input_dim": Input dimensionality (# nodes)

"fc_hidden_dim": Fully-Connected Hidden layer dimensionality ((# nodes)

"fc_output_dim": Fully-Connected Output layer dimensionality (# nodes)

"im_h": 128: Output image size (height in pixels)

"im_w": 128: Input image size (height in pixels)

"data_dir": Directory of Training Data where "train_input" and "train_output" are located.

"train_input": Input data file (as .mat file)

"train_output": Output data file (as .mat file)


## Inference

Inference utilizes `automap_main_inference.py` and a JSON config file.

```
python automap_main_inference.py -c configs/inference_64x64_ex.json
```

The JSON inference config file uses similar entries as the training config file mentioned above, but instead of "train_input" and "train_output" it takes in "inference_input"