Python detectron.utils.vis.convert_from_cls_format() Examples

The following are 6 code examples of detectron.utils.vis.convert_from_cls_format(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module detectron.utils.vis , or try the search function .
Example #1
Source File: convert_pkl_to_pb.py    From KL-Loss with Apache License 2.0 5 votes vote down vote up 
def run_model_cfg(args, im, check_blobs):
    workspace.ResetWorkspace()
    model, _ = load_model(args)
    with c2_utils.NamedCudaScope(0):
        cls_boxes, cls_segms, cls_keyps = test_engine.im_detect_all(
            model, im, None, None,
        )

    boxes, segms, keypoints, classes = vis_utils.convert_from_cls_format(
        cls_boxes, cls_segms, cls_keyps)

    # sort the results based on score for comparision
    boxes, segms, keypoints, classes = _sort_results(
        boxes, segms, keypoints, classes)

    # write final results back to workspace
    def _ornone(res):
        return np.array(res) if res is not None else np.array([], dtype=np.float32)
    with c2_utils.NamedCudaScope(0):
        workspace.FeedBlob(core.ScopedName('result_boxes'), _ornone(boxes))
        workspace.FeedBlob(core.ScopedName('result_segms'), _ornone(segms))
        workspace.FeedBlob(core.ScopedName('result_keypoints'), _ornone(keypoints))
        workspace.FeedBlob(core.ScopedName('result_classids'), _ornone(classes))

    # get result blobs
    with c2_utils.NamedCudaScope(0):
        ret = _get_result_blobs(check_blobs)

    return ret
Example #2
Source File: convert_pkl_to_pb.py    From Clustered-Object-Detection-in-Aerial-Image with Apache License 2.0 5 votes vote down vote up 
def run_model_cfg(args, im, check_blobs):
    workspace.ResetWorkspace()
    model, _ = load_model(args)
    with c2_utils.NamedCudaScope(0):
        cls_boxes, cls_segms, cls_keyps = test_engine.im_detect_all(
            model, im, None, None,
        )

    boxes, segms, keypoints, classes = vis_utils.convert_from_cls_format(
        cls_boxes, cls_segms, cls_keyps)

    # sort the results based on score for comparision
    boxes, segms, keypoints, classes = _sort_results(
        boxes, segms, keypoints, classes)

    # write final results back to workspace
    def _ornone(res):
        return np.array(res) if res is not None else np.array([], dtype=np.float32)
    with c2_utils.NamedCudaScope(0):
        workspace.FeedBlob(core.ScopedName('result_boxes'), _ornone(boxes))
        workspace.FeedBlob(core.ScopedName('result_segms'), _ornone(segms))
        workspace.FeedBlob(core.ScopedName('result_keypoints'), _ornone(keypoints))
        workspace.FeedBlob(core.ScopedName('result_classids'), _ornone(classes))

    # get result blobs
    with c2_utils.NamedCudaScope(0):
        ret = _get_result_blobs(check_blobs)

    return ret
Example #3
Source File: convert_pkl_to_pb.py    From Detectron-Cascade-RCNN with Apache License 2.0 5 votes vote down vote up 
def run_model_cfg(args, im, check_blobs):
    workspace.ResetWorkspace()
    model, _ = load_model(args)
    with c2_utils.NamedCudaScope(0):
        cls_boxes, cls_segms, cls_keyps = test_engine.im_detect_all(
            model, im, None, None,
        )

    boxes, segms, keypoints, classes = vis_utils.convert_from_cls_format(
        cls_boxes, cls_segms, cls_keyps)

    # sort the results based on score for comparision
    boxes, segms, keypoints, classes = _sort_results(
        boxes, segms, keypoints, classes)

    # write final results back to workspace
    def _ornone(res):
        return np.array(res) if res is not None else np.array([], dtype=np.float32)
    with c2_utils.NamedCudaScope(0):
        workspace.FeedBlob(core.ScopedName('result_boxes'), _ornone(boxes))
        workspace.FeedBlob(core.ScopedName('result_segms'), _ornone(segms))
        workspace.FeedBlob(core.ScopedName('result_keypoints'), _ornone(keypoints))
        workspace.FeedBlob(core.ScopedName('result_classids'), _ornone(classes))

    # get result blobs
    with c2_utils.NamedCudaScope(0):
        ret = _get_result_blobs(check_blobs)

    return ret
Example #4
Source File: convert_pkl_to_pb.py    From Detectron with Apache License 2.0 5 votes vote down vote up 
def run_model_cfg(args, im, check_blobs):
    workspace.ResetWorkspace()
    model, _ = load_model(args)
    with c2_utils.NamedCudaScope(0):
        cls_boxes, cls_segms, cls_keyps = test_engine.im_detect_all(
            model, im, None, None
        )

    boxes, segms, keypoints, classes = vis_utils.convert_from_cls_format(
        cls_boxes, cls_segms, cls_keyps
    )

    # sort the results based on score for comparision
    boxes, segms, keypoints, classes = _sort_results(boxes, segms, keypoints, classes)

    # write final results back to workspace
    def _ornone(res):
        return np.array(res) if res is not None else np.array([], dtype=np.float32)

    with c2_utils.NamedCudaScope(0):
        workspace.FeedBlob(core.ScopedName("result_boxes"), _ornone(boxes))
        workspace.FeedBlob(core.ScopedName("result_segms"), _ornone(segms))
        workspace.FeedBlob(core.ScopedName("result_keypoints"), _ornone(keypoints))
        workspace.FeedBlob(core.ScopedName("result_classids"), _ornone(classes))

    # get result blobs
    with c2_utils.NamedCudaScope(0):
        ret = _get_result_blobs(check_blobs)

    return ret
Example #5
Source File: convert_pkl_to_pb.py    From Detectron-DA-Faster-RCNN with Apache License 2.0 5 votes vote down vote up 
def run_model_cfg(args, im, check_blobs):
    workspace.ResetWorkspace()
    model, _ = load_model(args)
    with c2_utils.NamedCudaScope(0):
        cls_boxes, cls_segms, cls_keyps = test_engine.im_detect_all(
            model, im, None, None,
        )

    boxes, segms, keypoints, classes = vis_utils.convert_from_cls_format(
        cls_boxes, cls_segms, cls_keyps)

    # sort the results based on score for comparision
    boxes, segms, keypoints, classes = _sort_results(
        boxes, segms, keypoints, classes)

    # write final results back to workspace
    def _ornone(res):
        return np.array(res) if res is not None else np.array([], dtype=np.float32)
    with c2_utils.NamedCudaScope(0):
        workspace.FeedBlob(core.ScopedName('result_boxes'), _ornone(boxes))
        workspace.FeedBlob(core.ScopedName('result_segms'), _ornone(segms))
        workspace.FeedBlob(core.ScopedName('result_keypoints'), _ornone(keypoints))
        workspace.FeedBlob(core.ScopedName('result_classids'), _ornone(classes))

    # get result blobs
    with c2_utils.NamedCudaScope(0):
        ret = _get_result_blobs(check_blobs)

    return ret
Example #6
Source File: convert_pkl_to_pb.py    From CBNet with Apache License 2.0 5 votes vote down vote up 
def run_model_cfg(args, im, check_blobs):
    workspace.ResetWorkspace()
    model, _ = load_model(args)
    with c2_utils.NamedCudaScope(0):
        cls_boxes, cls_segms, cls_keyps = test_engine.im_detect_all(
            model, im, None, None,
        )

    boxes, segms, keypoints, classes = vis_utils.convert_from_cls_format(
        cls_boxes, cls_segms, cls_keyps)

    # sort the results based on score for comparision
    boxes, segms, keypoints, classes = _sort_results(
        boxes, segms, keypoints, classes)

    # write final results back to workspace
    def _ornone(res):
        return np.array(res) if res is not None else np.array([], dtype=np.float32)
    with c2_utils.NamedCudaScope(0):
        workspace.FeedBlob(core.ScopedName('result_boxes'), _ornone(boxes))
        workspace.FeedBlob(core.ScopedName('result_segms'), _ornone(segms))
        workspace.FeedBlob(core.ScopedName('result_keypoints'), _ornone(keypoints))
        workspace.FeedBlob(core.ScopedName('result_classids'), _ornone(classes))

    # get result blobs
    with c2_utils.NamedCudaScope(0):
        ret = _get_result_blobs(check_blobs)

    return ret