Python datasets.task_evaluation.log_copy_paste_friendly_results() Examples

The following are 14 code examples of datasets.task_evaluation.log_copy_paste_friendly_results(). 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 datasets.task_evaluation , or try the search function .
Example #1
Source File: reval.py    From seg_every_thing with Apache License 2.0 6 votes vote down vote up 
def do_reval(dataset_name, output_dir, args):
    dataset = JsonDataset(dataset_name)
    with open(os.path.join(output_dir, 'detections.pkl'), 'rb') as f:
        dets = pickle.load(f)
    # Override config with the one saved in the detections file
    if args.cfg_file is not None:
        core.config.merge_cfg_from_cfg(yaml.load(dets['cfg']))
    else:
        core.config._merge_a_into_b(yaml.load(dets['cfg']), cfg)
    results = task_evaluation.evaluate_all(
        dataset,
        dets['all_boxes'],
        dets['all_segms'],
        dets['all_keyps'],
        output_dir,
        use_matlab=args.matlab_eval
    )
    task_evaluation.log_copy_paste_friendly_results(results)
Example #2
Source File: reval.py    From masktextspotter.caffe2 with Apache License 2.0 6 votes vote down vote up 
def do_reval(dataset_name, output_dir, args):
    dataset = JsonDataset(dataset_name)
    with open(os.path.join(output_dir, 'detections.pkl'), 'rb') as f:
        dets = pickle.load(f)
    # Override config with the one saved in the detections file
    if args.cfg_file is not None:
        core.config.merge_cfg_from_cfg(yaml.load(dets['cfg']))
    else:
        core.config._merge_a_into_b(yaml.load(dets['cfg']), cfg)
    results = task_evaluation.evaluate_all(
        dataset,
        dets['all_boxes'],
        dets['all_segms'],
        dets['all_keyps'],
        output_dir,
        use_matlab=args.matlab_eval
    )
    task_evaluation.log_copy_paste_friendly_results(results)
Example #3
Source File: reval.py    From NucleiDetectron with Apache License 2.0 6 votes vote down vote up 
def do_reval(dataset_name, output_dir, args):
    dataset = JsonDataset(dataset_name)
    with open(os.path.join(output_dir, 'detections.pkl'), 'rb') as f:
        dets = pickle.load(f)
    # Override config with the one saved in the detections file
    if args.cfg_file is not None:
        core.config.merge_cfg_from_cfg(yaml.load(dets['cfg']))
    else:
        core.config._merge_a_into_b(yaml.load(dets['cfg']), cfg)
    results = task_evaluation.evaluate_all(
        dataset,
        dets['all_boxes'],
        dets['all_segms'],
        dets['all_keyps'],
        output_dir,
        use_matlab=args.matlab_eval
    )
    task_evaluation.log_copy_paste_friendly_results(results)
Example #4
Source File: test_net.py    From NucleiDetectron with Apache License 2.0 5 votes vote down vote up 
def main(ind_range=None, multi_gpu_testing=False):
    output_dir = get_output_dir(training=False)
    all_results = run_inference(
        output_dir, ind_range=ind_range, multi_gpu_testing=multi_gpu_testing
    )
    if not ind_range:
        task_evaluation.check_expected_results(
            all_results,
            atol=cfg.EXPECTED_RESULTS_ATOL,
            rtol=cfg.EXPECTED_RESULTS_RTOL
        )
        import json
        json.dump(all_results, open(os.path.join(output_dir, 'bbox_results_all.json'), 'w'))
        task_evaluation.log_copy_paste_friendly_results(all_results)
Example #5
Source File: test_engine.py    From Detectron.pytorch with MIT License 4 votes vote down vote up 
def run_inference(
        args, ind_range=None,
        multi_gpu_testing=False, gpu_id=0,
        check_expected_results=False):
    parent_func, child_func = get_eval_functions()
    is_parent = ind_range is None

    def result_getter():
        if is_parent:
            # Parent case:
            # In this case we're either running inference on the entire dataset in a
            # single process or (if multi_gpu_testing is True) using this process to
            # launch subprocesses that each run inference on a range of the dataset
            all_results = {}
            for i in range(len(cfg.TEST.DATASETS)):
                dataset_name, proposal_file = get_inference_dataset(i)
                output_dir = args.output_dir
                results = parent_func(
                    args,
                    dataset_name,
                    proposal_file,
                    output_dir,
                    multi_gpu=multi_gpu_testing
                )
                all_results.update(results)

            return all_results
        else:
            # Subprocess child case:
            # In this case test_net was called via subprocess.Popen to execute on a
            # range of inputs on a single dataset
            dataset_name, proposal_file = get_inference_dataset(0, is_parent=False)
            output_dir = args.output_dir
            return child_func(
                args,
                dataset_name,
                proposal_file,
                output_dir,
                ind_range=ind_range,
                gpu_id=gpu_id
            )

    all_results = result_getter()
    if check_expected_results and is_parent:
        task_evaluation.check_expected_results(
            all_results,
            atol=cfg.EXPECTED_RESULTS_ATOL,
            rtol=cfg.EXPECTED_RESULTS_RTOL
        )
        task_evaluation.log_copy_paste_friendly_results(all_results)

    return all_results
Example #6
Source File: test_engine.py    From FPN-Pytorch with MIT License 4 votes vote down vote up 
def run_inference(
        args, ind_range=None,
        multi_gpu_testing=False, gpu_id=0,
        check_expected_results=False):
    parent_func, child_func = get_eval_functions()
    is_parent = ind_range is None

    def result_getter():
        if is_parent:
            # Parent case:
            # In this case we're either running inference on the entire dataset in a
            # single process or (if multi_gpu_testing is True) using this process to
            # launch subprocesses that each run inference on a range of the dataset
            all_results = {}
            for i in range(len(cfg.TEST.DATASETS)):
                dataset_name, proposal_file = get_inference_dataset(i)
                output_dir = args.output_dir
                results = parent_func(
                    args,
                    dataset_name,
                    proposal_file,
                    output_dir,
                    multi_gpu=multi_gpu_testing
                )
                all_results.update(results)

            return all_results
        else:
            # Subprocess child case:
            # In this case test_net was called via subprocess.Popen to execute on a
            # range of inputs on a single dataset
            dataset_name, proposal_file = get_inference_dataset(0, is_parent=False)
            output_dir = args.output_dir
            return child_func(
                args,
                dataset_name,
                proposal_file,
                output_dir,
                ind_range=ind_range,
                gpu_id=gpu_id
            )

    all_results = result_getter()
    if check_expected_results and is_parent:
        task_evaluation.check_expected_results(
            all_results,
            atol=cfg.EXPECTED_RESULTS_ATOL,
            rtol=cfg.EXPECTED_RESULTS_RTOL
        )
        task_evaluation.log_copy_paste_friendly_results(all_results)

    return all_results
Example #7
Source File: test_engine.py    From pcl.pytorch with MIT License 4 votes vote down vote up 
def run_inference(
        args, ind_range=None,
        multi_gpu_testing=False, gpu_id=0,
        check_expected_results=False):
    parent_func, child_func = get_eval_functions()
    is_parent = ind_range is None

    def result_getter():
        if is_parent:
            # Parent case:
            # In this case we're either running inference on the entire dataset in a
            # single process or (if multi_gpu_testing is True) using this process to
            # launch subprocesses that each run inference on a range of the dataset
            all_results = {}
            for i in range(len(cfg.TEST.DATASETS)):
                dataset_name, proposal_file = get_inference_dataset(i)
                output_dir = args.output_dir
                results = parent_func(
                    args,
                    dataset_name,
                    proposal_file,
                    output_dir,
                    multi_gpu=multi_gpu_testing
                )
                all_results.update(results)

            return all_results
        else:
            # Subprocess child case:
            # In this case test_net was called via subprocess.Popen to execute on a
            # range of inputs on a single dataset
            dataset_name, proposal_file = get_inference_dataset(0, is_parent=False)
            output_dir = args.output_dir
            return child_func(
                args,
                dataset_name,
                proposal_file,
                output_dir,
                ind_range=ind_range,
                gpu_id=gpu_id
            )

    all_results = result_getter()
    if check_expected_results and is_parent:
        task_evaluation.check_expected_results(
            all_results,
            atol=cfg.EXPECTED_RESULTS_ATOL,
            rtol=cfg.EXPECTED_RESULTS_RTOL
        )
        task_evaluation.log_copy_paste_friendly_results(all_results)

    return all_results
Example #8
Source File: test_engine.py    From Detectron.pytorch with MIT License 4 votes vote down vote up 
def run_inference(
        args, ind_range=None,
        multi_gpu_testing=False, gpu_id=0,
        check_expected_results=False):
    parent_func, child_func = get_eval_functions()
    is_parent = ind_range is None

    def result_getter():
        if is_parent:
            # Parent case:
            # In this case we're either running inference on the entire dataset in a
            # single process or (if multi_gpu_testing is True) using this process to
            # launch subprocesses that each run inference on a range of the dataset
            all_results = {}
            for i in range(len(cfg.TEST.DATASETS)):
                dataset_name, proposal_file = get_inference_dataset(i)
                output_dir = args.output_dir
                results = parent_func(
                    args,
                    dataset_name,
                    proposal_file,
                    output_dir,
                    multi_gpu=multi_gpu_testing
                )
                all_results.update(results)

            return all_results
        else:
            # Subprocess child case:
            # In this case test_net was called via subprocess.Popen to execute on a
            # range of inputs on a single dataset
            dataset_name, proposal_file = get_inference_dataset(0, is_parent=False)
            output_dir = args.output_dir
            return child_func(
                args,
                dataset_name,
                proposal_file,
                output_dir,
                ind_range=ind_range,
                gpu_id=gpu_id
            )

    all_results = result_getter()
    if check_expected_results and is_parent:
        task_evaluation.check_expected_results(
            all_results,
            atol=cfg.EXPECTED_RESULTS_ATOL,
            rtol=cfg.EXPECTED_RESULTS_RTOL
        )
        task_evaluation.log_copy_paste_friendly_results(all_results)

    return all_results
Example #9
Source File: test_engine.py    From Context-aware-ZSR with MIT License 4 votes vote down vote up 
def run_inference(
        args, ind_range=None,
        multi_gpu_testing=False, gpu_id=0,
        check_expected_results=False):
    parent_func, child_func = get_eval_functions()
    is_parent = ind_range is None

    def result_getter():
        if True: #is_parent:
            # Parent case:
            # In this case we're either running inference on the entire dataset in a
            # single process or (if multi_gpu_testing is True) using this process to
            # launch subprocesses that each run inference on a range of the dataset
            all_results = {}
            for i in range(len(cfg.TEST.DATASETS)):
                dataset_name, proposal_file = get_inference_dataset(i)
                output_dir = args.output_dir
                results = parent_func(
                    args,
                    dataset_name,
                    proposal_file,
                    output_dir,
                    ind_range=ind_range,
                    multi_gpu=multi_gpu_testing
                )
                all_results.update(results)

            return all_results
        else:
            # Subprocess child case:
            # In this case test_net was called via subprocess.Popen to execute on a
            # range of inputs on a single dataset
            dataset_name, proposal_file = get_inference_dataset(0, is_parent=False)
            output_dir = args.output_dir
            return child_func(
                args,
                dataset_name,
                proposal_file,
                output_dir,
                ind_range=ind_range,
                gpu_id=gpu_id
            )

    all_results = result_getter()
    if check_expected_results and is_parent:
        task_evaluation.check_expected_results(
            all_results,
            atol=cfg.EXPECTED_RESULTS_ATOL,
            rtol=cfg.EXPECTED_RESULTS_RTOL
        )
        task_evaluation.log_copy_paste_friendly_results(all_results)

    return all_results
Example #10
Source File: test_engine.py    From PANet with MIT License 4 votes vote down vote up 
def run_inference(
        args, ind_range=None,
        multi_gpu_testing=False, gpu_id=0,
        check_expected_results=False):
    parent_func, child_func = get_eval_functions()
    is_parent = ind_range is None

    def result_getter():
        if is_parent:
            # Parent case:
            # In this case we're either running inference on the entire dataset in a
            # single process or (if multi_gpu_testing is True) using this process to
            # launch subprocesses that each run inference on a range of the dataset
            all_results = {}
            for i in range(len(cfg.TEST.DATASETS)):
                dataset_name, proposal_file = get_inference_dataset(i)
                output_dir = args.output_dir
                results = parent_func(
                    args,
                    dataset_name,
                    proposal_file,
                    output_dir,
                    multi_gpu=multi_gpu_testing
                )
                all_results.update(results)

            return all_results
        else:
            # Subprocess child case:
            # In this case test_net was called via subprocess.Popen to execute on a
            # range of inputs on a single dataset
            dataset_name, proposal_file = get_inference_dataset(0, is_parent=False)
            output_dir = args.output_dir
            return child_func(
                args,
                dataset_name,
                proposal_file,
                output_dir,
                ind_range=ind_range,
                gpu_id=gpu_id
            )

    all_results = result_getter()
    if check_expected_results and is_parent:
        task_evaluation.check_expected_results(
            all_results,
            atol=cfg.EXPECTED_RESULTS_ATOL,
            rtol=cfg.EXPECTED_RESULTS_RTOL
        )
        task_evaluation.log_copy_paste_friendly_results(all_results)

    return all_results
Example #11
Source File: test_engine.py    From seg_every_thing with Apache License 2.0 4 votes vote down vote up 
def run_inference(
    weights_file, ind_range=None,
    multi_gpu_testing=False, gpu_id=0,
    check_expected_results=False,
):
    parent_func, child_func = get_eval_functions()
    is_parent = ind_range is None

    def result_getter():
        if is_parent:
            # Parent case:
            # In this case we're either running inference on the entire dataset in a
            # single process or (if multi_gpu_testing is True) using this process to
            # launch subprocesses that each run inference on a range of the dataset
            all_results = {}
            for i in range(len(cfg.TEST.DATASETS)):
                dataset_name, proposal_file = get_inference_dataset(i)
                output_dir = get_output_dir(dataset_name, training=False)
                results = parent_func(
                    weights_file,
                    dataset_name,
                    proposal_file,
                    output_dir,
                    multi_gpu=multi_gpu_testing
                )
                all_results.update(results)

            return all_results
        else:
            # Subprocess child case:
            # In this case test_net was called via subprocess.Popen to execute on a
            # range of inputs on a single dataset
            dataset_name, proposal_file = get_inference_dataset(0, is_parent=False)
            output_dir = get_output_dir(dataset_name, training=False)
            return child_func(
                weights_file,
                dataset_name,
                proposal_file,
                output_dir,
                ind_range=ind_range,
                gpu_id=gpu_id
            )

    all_results = result_getter()
    if check_expected_results and is_parent:
        task_evaluation.check_expected_results(
            all_results,
            atol=cfg.EXPECTED_RESULTS_ATOL,
            rtol=cfg.EXPECTED_RESULTS_RTOL
        )
        task_evaluation.log_copy_paste_friendly_results(all_results)

    return all_results
Example #12
Source File: test_engine.py    From Large-Scale-VRD.pytorch with MIT License 4 votes vote down vote up 
def run_inference(
        args, ind_range=None,
        multi_gpu_testing=False, gpu_id=0,
        check_expected_results=False):
    parent_func, child_func = get_eval_functions()
    is_parent = ind_range is None

    def result_getter():
        if is_parent:
            # Parent case:
            # In this case we're either running inference on the entire dataset in a
            # single process or (if multi_gpu_testing is True) using this process to
            # launch subprocesses that each run inference on a range of the dataset
            all_results = {}
            for i in range(len(cfg.TEST.DATASETS)):
                dataset_name, proposal_file = get_inference_dataset(i)
                output_dir = args.output_dir
                results = parent_func(
                    args,
                    dataset_name,
                    proposal_file,
                    output_dir,
                    multi_gpu=multi_gpu_testing
                )
                all_results.update(results)

            return all_results
        else:
            # Subprocess child case:
            # In this case test_net was called via subprocess.Popen to execute on a
            # range of inputs on a single dataset
            dataset_name, proposal_file = get_inference_dataset(0, is_parent=False)
            output_dir = args.output_dir
            return child_func(
                args,
                dataset_name,
                proposal_file,
                output_dir,
                ind_range=ind_range,
                gpu_id=gpu_id
            )

    all_results = result_getter()
    if check_expected_results and is_parent:
        task_evaluation.check_expected_results(
            all_results,
            atol=cfg.EXPECTED_RESULTS_ATOL,
            rtol=cfg.EXPECTED_RESULTS_RTOL
        )
        task_evaluation.log_copy_paste_friendly_results(all_results)

    return all_results
Example #13
Source File: test_engine.py    From detectron-self-train with MIT License 4 votes vote down vote up 
def run_inference(
        args, ind_range=None,
        multi_gpu_testing=False, gpu_id=0,
        check_expected_results=False):
    parent_func, child_func = get_eval_functions()
    is_parent = ind_range is None

    def result_getter():
        if is_parent:
            # Parent case:
            # In this case we're either running inference on the entire dataset in a
            # single process or (if multi_gpu_testing is True) using this process to
            # launch subprocesses that each run inference on a range of the dataset
            all_results = {}
            for i in range(len(cfg.TEST.DATASETS)):
                dataset_name, proposal_file = get_inference_dataset(i)
                output_dir = args.output_dir
                results = parent_func(
                    args,
                    dataset_name,
                    proposal_file,
                    output_dir,
                    multi_gpu=multi_gpu_testing
                )
                all_results.update(results)

            return all_results
        else:
            # Subprocess child case:
            # In this case test_net was called via subprocess.Popen to execute on a
            # range of inputs on a single dataset
            dataset_name, proposal_file = get_inference_dataset(0, is_parent=False)
            output_dir = args.output_dir
            return child_func(
                args,
                dataset_name,
                proposal_file,
                output_dir,
                ind_range=ind_range,
                gpu_id=gpu_id
            )

    all_results = result_getter()
    if check_expected_results and is_parent:
        task_evaluation.check_expected_results(
            all_results,
            atol=cfg.EXPECTED_RESULTS_ATOL,
            rtol=cfg.EXPECTED_RESULTS_RTOL
        )
        task_evaluation.log_copy_paste_friendly_results(all_results)

    return all_results
Example #14
Source File: test_engine.py    From DIoU-pytorch-detectron with GNU General Public License v3.0 4 votes vote down vote up 
def run_inference(
        args, ind_range=None,
        multi_gpu_testing=False, gpu_id=0,
        check_expected_results=False):
    parent_func, child_func = get_eval_functions()
    is_parent = ind_range is None

    def result_getter():
        if is_parent:
            # Parent case:
            # In this case we're either running inference on the entire dataset in a
            # single process or (if multi_gpu_testing is True) using this process to
            # launch subprocesses that each run inference on a range of the dataset
            all_results = {}
            for i in range(len(cfg.TEST.DATASETS)):
                dataset_name, proposal_file = get_inference_dataset(i)
                output_dir = args.output_dir
                results = parent_func(
                    args,
                    dataset_name,
                    proposal_file,
                    output_dir,
                    multi_gpu=multi_gpu_testing
                )
                all_results.update(results)

            return all_results
        else:
            # Subprocess child case:
            # In this case test_net was called via subprocess.Popen to execute on a
            # range of inputs on a single dataset
            dataset_name, proposal_file = get_inference_dataset(0, is_parent=False)
            output_dir = args.output_dir
            return child_func(
                args,
                dataset_name,
                proposal_file,
                output_dir,
                ind_range=ind_range,
                gpu_id=gpu_id
            )

    all_results = result_getter()
    if check_expected_results and is_parent:
        task_evaluation.check_expected_results(
            all_results,
            atol=cfg.EXPECTED_RESULTS_ATOL,
            rtol=cfg.EXPECTED_RESULTS_RTOL
        )
        task_evaluation.log_copy_paste_friendly_results(all_results)

    return all_results