(dev) nehal@device:~/dgp$ git push nehaldgp feat/nehal/point-line-polygon-3d-proto
************* Module .pylintrc
.pylintrc:1: [E0015(unrecognized-option), ] Unrecognized option found: accept-no-param-doc, accept-no-return-doc, accept-no-yields-doc
Aborting push due to files with lint.
error: failed to push some refs to '[email protected]:nehalmamgain/dgp.git'

Step 38/47 : RUN git clone https://github.com/TRI-ML/dgp.git     && cd dgp     && pip3 install -r requirements.txt
 ---> Running in 8fe2dbd1205b
Cloning into 'dgp'...
Requirement already satisfied: torch==1.4.0 in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 17)) (1.4.0)
Requirement already satisfied: torchvision==0.5.0 in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 18)) (0.5.0)
Collecting attrs==19.1.0
  Downloading attrs-19.1.0-py2.py3-none-any.whl (35 kB)
Collecting awscli==1.16.192
  Downloading awscli-1.16.192-py2.py3-none-any.whl (1.7 MB)
Requirement already satisfied: docutils>=0.10 in /usr/local/lib/python3.6/dist-packages (from awscli==1.16.192->-r requirements.txt (line 2)) (0.15.2)
INFO: pip is looking at multiple versions of <Python from Requires-Python> to determine which version is compatible with other requirements. This could take a while.
INFO: pip is looking at multiple versions of attrs to determine which version is compatible with other requirements. This could take a while.
ERROR: Cannot install -r requirements.txt (line 2) and botocore==1.12.79 because these package versions have conflicting dependencies.
ERROR: ResolutionImpossible: for help visit https://pip.pypa.io/en/latest/user_guide/#fixing-conflicting-dependencies

The conflict is caused by:
    The user requested botocore==1.12.79
    awscli 1.16.192 depends on botocore==1.12.182

To fix this you could try to:
1. loosen the range of package versions you've specified
2. remove package versions to allow pip attempt to solve the dependency conflict

The command '/bin/bash -cu git clone https://github.com/TRI-ML/dgp.git     && cd dgp     && pip3 install -r requirements.txt' returned a non-zero code: 1

FrameSceneDataset(
/usr/local/lib/python3.8/dist-packages/dgp/datasets/frame_dataset.py:211: in __init__
    dataset_metadata = DatasetMetadata.from_scene_containers(scenes, requested_annotations, requested_autolabels)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

cls = <class 'dgp.datasets.base_dataset.DatasetMetadata'>
scene_containers = [SceneContainer[<path_to_scene>][Samples: 100], SceneContainer[<path_to_scene>][Samples: 100], SceneContainer[<path_to_scene>][Samples: 100], ...]
requested_annotations = ['key_line_2d'], requested_autolabels = []

    @classmethod
    def from_scene_containers(cls, scene_containers, requested_annotations=None, requested_autolabels=None):
        """Load DatasetMetadata from Scene Dataset JSON.
    
        Parameters
        ----------
        scene_containers: list of SceneContainer
            List of SceneContainer objects.
    
        requested_annotations: List(str)
            List of annotations, such as ['bounding_box_3d', 'bounding_box_2d']
    
        requested_autolabels: List(str)
            List of autolabels, such as['model_a/bounding_box_3d', 'model_a/bounding_box_2d']
        """
        assert len(scene_containers), 'SceneContainers is empty.'
        requested_annotations = [] if requested_annotations is None else requested_annotations
        requested_autolabels = [] if requested_autolabels is None else requested_autolabels
    
        if not requested_annotations and not requested_autolabels:
            # Return empty ontology table
            return cls(scene_containers, directory=os.path.dirname(scene_containers[0].directory), ontology_table={})
        # For each annotation type, we enforce a consistent ontology across the
        # dataset (i.e. 2 different `bounding_box_3d` ontologies are not
        # permitted). However, an autolabel may support a different ontology
        # for the same annotation type. For example, the following
        # ontology_table is valid:
        # {
        #   "bounding_box_3d": BoundingBoxOntology,
        #   "bounding_box_2d": BoundingBoxOntology,
        #   "my_autolabel_model/bounding_box_3d": BoundingBoxOntology
        # }
        dataset_ontology_table = {}
        logging.info('Building ontology table.')
        st = time.time()
    
        # Determine scenes with unique ontologies based on the ontology file basename.
        unique_scenes = {
            os.path.basename(f): scene_container
            for scene_container in scene_containers
            for _, _, filenames in os.walk(os.path.join(scene_container.directory, ONTOLOGY_FOLDER)) for f in filenames
        }
        # Parse through relevant scenes that have unique ontology keys.
        for _, scene_container in unique_scenes.items():
            for ontology_key, ontology_file in scene_container.ontology_files.items():
                # Keys in `ontology_files` may correspond to autolabels,
                # so we strip those prefixes when instantiating `Ontology` objects
                _autolabel_model, annotation_key = os.path.split(ontology_key)
    
                # Look up ontology for specific annotation type
                if annotation_key in ONTOLOGY_REGISTRY:
    
                    # Skip if we don't require this annotation/autolabel
                    if _autolabel_model:
                        if ontology_key not in requested_autolabels:
                            continue
                    else:
                        if annotation_key not in requested_annotations:
                            continue
    
                    ontology_spec = ONTOLOGY_REGISTRY[annotation_key]
    
                    # No need to add ontology-less tasks to the ontology table.
                    if ontology_spec is None:
                        continue
    
                    # If ontology and key have not been added to the table, add it.
                    if ontology_key not in dataset_ontology_table:
                        dataset_ontology_table[ontology_key] = ontology_spec.load(ontology_file)
    
                    # If we've already loaded an ontology for this annotation type, make sure other scenes have the same ontology
                    else:
                        assert dataset_ontology_table[ontology_key] == ontology_spec.load(
                            ontology_file
                        ), "Inconsistent ontology for key {}.".format(ontology_key)
    
                # In case an ontology type is not implemented yet
                else:
>                   raise Exception(f"Ontology for key {ontology_key} not found in registry!")
E                   Exception: Ontology for key key_line_2d not found in registry!

/usr/local/lib/python3.8/dist-packages/dgp/datasets/base_dataset.py:592: Exception

-rw-r--r--  1 root  root     73 Dec  7 11:46 COMMIT_EDITMSG
-rw-r--r--  1 root  root      0 Dec  7 12:34 FETCH_HEAD
-rw-r--r--  1 root  root     23 Dec  7 12:28 HEAD
-rw-r--r--  1 root  root    322 Dec  7 12:30 config
-rw-r--r--  1 root  root  39355 Dec  7 12:28 index

~/dgp$ git pull
error: cannot open .git/FETCH_HEAD: Permission denied

/home/dgp# git pull
Bad owner or permissions on /root/.ssh/config
fatal: Could not read from remote repository.

Please make sure you have the correct access rights
and the repository exists.

docker pull ghcr.io/tri-ml/dgp:master
docker image tag ghcr.io/tri-ml/dgp:master dgp:latest
make docker-start-interactive
# either of the following fail
make build-proto
make test

root@hostname:/home/dgp# make test
python3 setup.py clean && \
rm -rf build dist && \
find . -name "*.pyc" | xargs rm -f && \
find . -name "__pycache__" | xargs rm -rf
Traceback (most recent call last):
  File "setup.py", line 5, in <module>
    from setuptools import find_packages, setup
ModuleNotFoundError: No module named 'setuptools'
Makefile:33: recipe for target 'clean' failed
make: *** [clean] Error 1

root@hostname:/home/dgp# python3
Python 3.6.9 (default, Jun 29 2022, 11:45:57)

root@hostname:/home/dgp# pip show setuptools
Name: setuptools
Version: 63.2.0
Summary: Easily download, build, install, upgrade, and uninstall Python packages
Home-page: https://github.com/pypa/setuptools
Author: Python Packaging Authority
Author-email: [email protected]
License: 
Location: /usr/local/lib/python3.7/dist-packages
Requires: 
Required-by: astroid, grpcio-tools, xarray

DDAD_TRAIN_VAL_JSON_PATH = '/DDAD_tiny/ddad_tiny.json' 
DATUMS = ['camera_01']
ddad_train = SynchronizedSceneDataset(
    DDAD_TRAIN_VAL_JSON_PATH,
    split='train',
    datum_names=DATUMS,
    generate_depth_from_datum='lidar'
)

multiprocessing.pool.RemoteTraceback: 
"""
Traceback (most recent call last):
  File "/home1/wangyufei/anaconda3/envs/tri/lib/python3.6/multiprocessing/pool.py", line 119, in worker
    result = (True, func(*args, **kwds))
  File "/home1/wangyufei/anaconda3/envs/tri/lib/python3.6/multiprocessing/pool.py", line 44, in mapstar
    return list(map(*args))
  File "/home4/user_from_home1/wangyufei/dgp-1.0/dgp/datasets/base_dataset.py", line 1071, in _datum_index_for_scene
    return scene.datum_index
  File "/home4/user_from_home1/wangyufei/dgp-1.0/dgp/datasets/base_dataset.py", line 375, in datum_index
    assert len(datum_key_to_idx_in_scene) == bad_datums + num_datums, "Duplicated datum_key"
AssertionError: Duplicated datum_key
"""
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
  File "/home1/wangyufei/anaconda3/envs/tri/lib/python3.6/code.py", line 91, in runcode
    exec(code, self.locals)
  File "<input>", line 1, in <module>
  File "/home1/wangyufei/.pycharm_helpers/pydev/_pydev_bundle/pydev_umd.py", line 198, in runfile
    pydev_imports.execfile(filename, global_vars, local_vars)  # execute the script
  File "/home1/wangyufei/.pycharm_helpers/pydev/_pydev_imps/_pydev_execfile.py", line 18, in execfile
    exec(compile(contents+"\n", file, 'exec'), glob, loc)
  File "/home4/user_from_home1/wangyufei/dc/packnet-sfm/test_dataset.py", line 30, in <module>
    generate_depth_from_datum='lidar'
  File "/home4/user_from_home1/wangyufei/dgp-1.0/dgp/datasets/synchronized_dataset.py", line 424, in __init__
    only_annotated_datums=only_annotated_datums
  File "/home4/user_from_home1/wangyufei/dgp-1.0/dgp/datasets/synchronized_dataset.py", line 83, in __init__
    requested_autolabels=requested_autolabels
  File "/home4/user_from_home1/wangyufei/dgp-1.0/dgp/datasets/base_dataset.py", line 704, in __init__
    self.datum_index = self._build_datum_index()
  File "/home4/user_from_home1/wangyufei/dgp-1.0/dgp/datasets/base_dataset.py", line 1080, in _build_datum_index
    datum_index = list(proc.map(BaseDataset._datum_index_for_scene, self.scenes))
  File "/home1/wangyufei/anaconda3/envs/tri/lib/python3.6/multiprocessing/pool.py", line 266, in map
    return self._map_async(func, iterable, mapstar, chunksize).get()
  File "/home1/wangyufei/anaconda3/envs/tri/lib/python3.6/multiprocessing/pool.py", line 644, in get
    raise self._value
AssertionError: Duplicated datum_key

#4 [internal] load metadata for docker.io/nvidia/cuda:11.1-devel-ubuntu18.04
#4 ERROR: docker.io/nvidia/cuda:11.1-devel-ubuntu18.04: not found
------
 > [internal] load metadata for docker.io/nvidia/cuda:11.1-devel-ubuntu18.04:
------
error: failed to solve: rpc error: code = Unknown desc = failed to solve with frontend dockerfile.v0: failed to create LLB definition: docker.io/nvidia/cuda:11.1-devel-ubuntu18.04: not found
Error: buildx failed with: error: failed to solve: rpc error: code = Unknown desc = failed to solve with frontend dockerfile.v0: failed to create LLB definition: docker.io/nvidia/cuda:11.1-devel-ubuntu18.04: not found

FROM nvidia/cuda:11.1-devel-ubuntu18.04

FROM nvidia/cuda:11.1.1-devel-ubuntu18.04