TinyFlowNetPytorch

forked from ClementPinard/FlowNetPytorch

This code is mainly inspired from official imagenet example. It has not been tested for multiple GPU, but it should work just as in original code.

The code provides a training example, using the flying chair dataset , with data augmentation. An implementation for Scene Flow Datasets may be added in the future.

Prerequisite

these modules can be installed with pip

torch>=1.2
torchvision
numpy
spatial-correlation-sampler>=0.2.1
tensorboard
imageio
argparse
path
tqdm
scipy

or

pip install -r requirements.txt

Training on Flying Chair Dataset

First, you need to download the the flying chair dataset . It is ~64GB big and we recommend you put it in a SSD Drive.

Default HyperParameters provided in main.py are the same as in the caffe training scripts.

• Example usage for FlowNetS :

python main.py /path/to/flying_chairs/ -b8 -j8 -a flownets

We recommend you set j (number of data threads) to high if you use DataAugmentation as to avoid data loading to slow the training.

For further help you can type

python main.py -h

Both the latest epoch and best epoch networks are saved. The best epoch network is also converted to .onnx.

Visualizing training

Tensorboard-pytorch is used for logging. To visualize result, simply type

tensorboard --logdir=/path/to/checkoints

Running inference on a set of image pairs

Your folder needs to have all the images pairs in the same location, with the name pattern

{image_name}1.{ext}
{image_name}2.{ext}

python3 run_inference.py /path/to/images/folder /path/to/pretrained

As for the main.py script, a help menu is available for additional options.

Note on transform functions

In order to have coherent transformations between inputs and target, we must define new transformations that take both input and target, as a new random variable is defined each time a random transformation is called.

Flow Transformations

To allow data augmentation, we have considered rotation and translations for inputs and their result on target flow Map. Here is a set of things to take care of in order to achieve a proper data augmentation

The Flow Map is directly linked to img1

If you apply a transformation on img1, you have to apply the very same to Flow Map, to get coherent origin points for flow.

Translation between img1 and img2

Given a translation (tx,ty) applied on img2, we will have

flow[:,:,0] += tx
flow[:,:,1] += ty

Scale

A scale applied on both img1 and img2 with a zoom parameters alpha multiplies the flow by the same amount

flow *= alpha

Rotation applied on both images

A rotation applied on both images by an angle theta also rotates flow vectors (flow[i,j]) by the same angle

\for_all i,j flow[i,j] = rotate(flow[i,j], theta)

rotate: x,y,theta ->  (x*cos(theta)-x*sin(theta), y*cos(theta), x*sin(theta))

Rotation applied on img2

We consider the angle theta small enough to linearize cos(theta) to 1 and sin(theta) to theta .

x flow map ( flow[:,:,0] ) will get a shift proportional to distance from center horizontal axis j-h/2

y flow map ( flow[:,:,1] ) will get a shift proportional to distance from center vertical axis i-w/2

\for_all i,j flow[i,j] += theta*(j-h/2), theta*(i-w/2)