# Fast Depth TF

Tensorflow 2.0 Implementation of Fast Depth.

Original Implementation in PyTorch is here: https://github.com/dwofk/fast-depth

This code has been tested with Tensorflow 2.4.1, however any version of Tensorflow >2 should work

The model has also been successfully optimised using the OpenVINO model optimiser

## Basic Usage

To train and evaluate the model on the nyu_v2 dataset, simply run:

`python main.py`

WARNING: This will download nyu_v2 which is ~100gb when including archived and extracted files, plus another 70gb for
generated examples.

The following sample demonstrates creating a FastDepth model that can later be used for inference, training or
evaluation.

```python
import fast_depth_functional as fd

# No Pretrained weights
model = fd.mobilenet_nnconv5()

# Imagenet weights
model = fd.mobilenet_nnconv5(weights='imagenet')

# Load trained model from file
model = fd.load_model('my_fastdepth_model')
```

### Train

Training with the NYU dataset is as simple as running the following:
WARNING: This will download ~30gb and extra ~70gb if you haven't downloaded it already. It also takes a long time to
prepare the examples (>1 hour)

```python
import fast_depth_functional as fd

model = fd.mobilenet_nnconv5(weights='imagenet')

# Train then save the model as keras h5 format
fd.train(model, save_file='fast_depth')

# A custom dataset can be passed in if required
fd.train(model, dataset=my_dataset)
```

### Evaluate

Evaluation is similar to training. The nyu dataset validation split will be used by default, and if you trained as shown
above, the dataset will have already been downloaded.

```python
import fast_depth_functional as fd

model = fd.load_model('fast_depth')
fd.compile(model)
fd.evaluate(model)

# A custom dataset for evaluation is supported
fd.evaluate(model, dataset=my_evaluation_dataset)
```

## Troubleshooting

### Windows GPU Fix

If you are using Windows and encounter an error opening cudnn (you should see CUDNN_STATUS_ALLOC_FAILED somewhere before
the error), first check you have correctly installed CUDA toolkit and cuDNN. If you have, then run the Windows GPU fix
included in this repo:

```python
import fast_depth_functional as fd

# Windows GPU Fix
fd.fix_windows_gpu()
```

More information about this error can be found here:
https://forums.developer.nvidia.com/t/could-not-create-cudnn-handle-cudnn-status-alloc-failed/108261