fastreid.evaluation.testing Namespace Reference

Functions
	print_csv_format (results)
	verify_results (cfg, results)
	flatten_results_dict (results)

Variables
	logger = logging.getLogger(__name__)

Function Documentation

flatten_results_dict()

fastreid.evaluation.testing.flatten_results_dict

(

results

)

Expand a hierarchical dict of scalars into a flat dict of scalars.
If results[k1][k2][k3] = v, the returned dict will have the entry
{"k1/k2/k3": v}.
Args:
    results (dict):

Definition at line 73 of file testing.py.

def flatten_results_dict(results):
    """
    Expand a hierarchical dict of scalars into a flat dict of scalars.
    If results[k1][k2][k3] = v, the returned dict will have the entry
    {"k1/k2/k3": v}.
    Args:
        results (dict):
    """
    r = {}
    for k, v in results.items():
        if isinstance(v, Mapping):
            v = flatten_results_dict(v)
            for kk, vv in v.items():
                r[k + "/" + kk] = vv
        else:
            r[k] = v
    return r

print_csv_format()

fastreid.evaluation.testing.print_csv_format

(

results

)

Print main metrics in a format similar to Detectron,
so that they are easy to copypaste into a spreadsheet.
Args:
    results (OrderedDict[dict]): task_name -> {metric -> score}

Definition at line 14 of file testing.py.

def print_csv_format(results):
    """
    Print main metrics in a format similar to Detectron,
    so that they are easy to copypaste into a spreadsheet.
    Args:
        results (OrderedDict[dict]): task_name -> {metric -> score}
    """
    assert isinstance(results, OrderedDict), results  # unordered results cannot be properly printed
    task = list(results.keys())[0]
    metrics = ["Datasets"] + [k for k in results[task]]
 
    csv_results = []
    for task, res in results.items():
        csv_results.append((task, *list(res.values())))
 
    # tabulate it
    table = tabulate(
        csv_results,
        tablefmt="pipe",
        floatfmt=".2%",
        headers=metrics,
        numalign="left",
    )
 
    logger.info("Evaluation results in csv format: \n" + colored(table, "cyan"))
 
 

verify_results()

fastreid.evaluation.testing.verify_results	(		cfg,
			results )

Args:
    results (OrderedDict[dict]): task_name -> {metric -> score}
Returns:
    bool: whether the verification succeeds or not

Definition at line 41 of file testing.py.

def verify_results(cfg, results):
    """
    Args:
        results (OrderedDict[dict]): task_name -> {metric -> score}
    Returns:
        bool: whether the verification succeeds or not
    """
    expected_results = cfg.TEST.EXPECTED_RESULTS
    if not len(expected_results):
        return True
 
    ok = True
    for task, metric, expected, tolerance in expected_results:
        actual = results[task][metric]
        if not np.isfinite(actual):
            ok = False
        diff = abs(actual - expected)
        if diff > tolerance:
            ok = False
 
    logger = logging.getLogger(__name__)
    if not ok:
        logger.error("Result verification failed!")
        logger.error("Expected Results: " + str(expected_results))
        logger.error("Actual Results: " + pprint.pformat(results))
 
        sys.exit(1)
    else:
        logger.info("Results verification passed.")
    return ok
 
 

Variable Documentation

logger

fastreid.evaluation.testing.logger = logging.getLogger(__name__)

Definition at line 11 of file testing.py.