| Homography Regression by Gradient Descent | |
| ========================================= | |
| This examples show how to use the `HomographyWarper` in order to do a regression where the parameter to optimize in this case is the homography driven by the gradient from a photometric loss. | |
| Quick overview | |
| -------------- | |
| .. code:: python | |
| # load the data | |
| img_src = load_image(os.path.join(args.input_dir, 'img1.ppm')) | |
| img_dst = load_image(os.path.join(args.input_dir, 'img2.ppm')) | |
| # instantiate the homography warper from `kornia` | |
| height, width = img_src.shape[-2:] | |
| warper = dgm.HomographyWarper(height, width) | |
| # create the homography as the parameter to be optimized | |
| # NOTE: MyHomography is an inherited nn.Module class | |
| dst_homo_src = MyHomography().to(device) | |
| # create optimizer | |
| optimizer = optim.Adam(dst_homo_src.parameters(), lr=args.lr) | |
| # main training loop | |
| for iter_idx in range(args.num_iterations): | |
| # send data to device | |
| img_src, img_dst = img_src.to(device), img_dst.to(device) | |
| # warp the reference image to the destiny with current homography | |
| img_src_to_dst = warper(img_src, dst_homo_src()) | |
| # compute the photometric loss | |
| loss = F.l1_loss(img_src_to_dst, img_dst) | |
| # compute gradient and update optimizer parameters | |
| optimizer.zero_grad() | |
| loss.backward() | |
| optimizer.step() | |
| Downloading the data | |
| ==================== | |
| You can download the data by running: ``./download_data.sh`` | |
| Usage | |
| ===== | |
| 1. From root, run the docker development or build first if needed: ``//kornia/dev_en.sh`` | |
| 2. Browse to ``cd /code/kornia/examples/homography_regression`` | |
| 3. Install the dependencies by running: ``./install_dependencies.sh`` | |
| 4. Now you can run the example followingthe instructions below: | |
| .. code:: bash | |
| python main.py --input-dir ./data --output-dir ./out --num-iterations 1000 --log-interval-vis 200 --cuda --lr 1e-3 | |
| .. code:: bash | |
| main.py [-h] --input-dir INPUT_DIR --output-dir OUTPUT_DIR | |
| [--num-iterations N] [--lr LR] [--momentum M] [--cuda] | |
| [--seed S] [--log-interval N] | |
| Homography Regression with perception loss. | |
| optional arguments: | |
| -h, --help show this help message and exit | |
| --input-dir INPUT_DIR | |
| the path to the directory with the input data. | |
| --output-dir OUTPUT_DIR | |
| the path to output the results. | |
| --num-iterations N number of training iterations (default: 1000) | |
| --lr LR learning rate (default: 1e-3) | |
| --cuda enables CUDA training | |
| --seed S random seed (default: 666) | |
| --log-interval N how many batches to wait before logging training | |
| status | |
| --log-interval-vis N how many batches to wait before visual logging | |
| training status | |