relabeled 'Ground Animal' -> horse

relabeled as counter -> counter-other

(0,0) are matched once. (1,1) are matched once. (2,2) are matched once, giving us intersection [1,1,1] for those three classes. No way to compute

Bring label from training taxonomy (mapillary-public65) to the universal taxonomy. 21 is the motorcyclist class in mapillary-public65

Bring label from training taxonomy (mapillary-public65) to the universal taxonomy. 21 is the motorcyclist class in mapillary-public65

Make sure 255 stays mapped to 255 at each level (to be ignored in cross-entropy loss).

Test on dataset_config and on TaxonomyConverter Make sure tsv entries in a single column match EXACTLY to _names.txt file.

Note: ADE20K class "conveyer" is typo of "conveyor"

Many thanks to jmsteitz for noting this case. horizontal line https://github.com/mseg-dataset/mseg-api/issues/7

Implement simple matrix multiplication as 1x1 convolutions in PyTorch. [0] [1 0 1 0] [0] [2] = [0 1 0 1] [1] [2] [1 1 1 1] [0]

Implement simple matrix multiplication as 1x1 convolutions in PyTorch. [2] [1 0 1 0] [1] [2] = [0 1 0 1] [1] [4] [1 1 1 1] [1]

Implement simple matrix multiplication as 1x1 convolutions in PyTorch. Consider the following example with universal predictions at a single px:

grayscale -> grayscale label remapping.

Ensure demo script works correctly base_sizes=( #360 720 #1080 python -u mseg_semantic/tool/test_universal_tax.py --c

Consider predictions made within the universal taxonomy over a tiny 2x3 image. We use a linear mapping to bring these predictions into a test d

Consider predictions made within the universal taxonomy over a tiny 2x3 image. We use a linear mapping to bring these predictions into a test d

label map with four quadrants. | Sky | Road | ---------------- | Person | Horse |

Copy the ``.grad`` attribute from stored references to fp16 parameters to the ``.grad`` attribute of the fp32 master parameters that

Loop through all of the datasets and ensure that the absolute paths exist and are valid.

By using remap.py, we already have converted label img from original taxonomy, to universal taxonomy. Args: - num_processes: number of processe

Save every 1000th image of each dataset, with classnames embedded.

Dump one image per mask, for all masks of a specific class.

Dump one image per mask, for all masks of a specific class.

Dump one image per mask, for all masks of a specific class.

Dump one image per mask, for all masks of a specific class.

Each path resembles: gt_fpath = '/export/work/johnlamb/MTURK_IDD_COPY/IDD_Segmentation/gtFine/val/119/638495_gtFine_polygons.json'

By using remap.py, we already have converted label img from original taxonomy, to universal taxonomy. Args: - num_processes: number of processe

Args: - cse - split - instance_img_fpath - ignore_idx Returns: - None

:class:`GRU`

* Creates an empty pair */

Constructor: takes a const ref to the matrix object with the data points

* KDTree constructor * * Refer to docs in README.md or online in https://github.com/jlblancoc/nanoflann * * The KD-Tree point dimension (t

:class:`LSTM`

* Empty constructor */