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How to use identify_backend method in yandex-tank

Best Python code snippet using yandex-tank

pshape.py

Source:pshape.py

...48        metrics = deepcopy(DEFAULT_METRICS)49    if len(arrs) == 0:50        return51    if DeviceMetric in metrics and all(52        identify_backend(arr) != BackendType.PYTORCH for arr in arrs53    ):54        metrics.remove(DeviceMetric)55    try:56        frame = inspect.currentframe()57        previous_frame = inspect.getframeinfo(frame.f_back)58        func_name = inspect.getframeinfo(frame).function59        if previous_frame.code_context is None:60            raise InteractiveSourceError61        call_line = previous_frame.code_context[0].strip()62        if not call_line.startswith(func_name + "("):63            warnings.warn(64                "Please don't call pshape in a compounded function like my_func(pshape(...)), this makes parsing a nightmare.",65                category=UserWarning,66            )...

metrics.py

Source:metrics.py

...51    _default_value = "N/A"52class DtypeMetric(ArrayMetric):53    name = "dtype"54    def __str__(self) -> str:55        if identify_backend(self.arr) is BackendType.TENSORFLOW:56            return repr(self.value)57        else:58            return str(self.value)59class NumericMetric(ArrayMetric):60    name: str61    precision: int = 462    _default_value = np.nan63    def __str__(self) -> str:64        return f"{self.value:.{self.precision}f}"65class CallableMetric(ArrayMetric):66    def _get_value(self):67        return super()._get_value()()68class NumericCallableMetric(NumericMetric, CallableMetric):69    def _get_value(self):70        backend = identify_backend(self.arr)71        if backend is BackendType.TENSORFLOW:72            import tensorflow as tf73            return getattr(tf.math, "reduce_" + self.name)(self.arr).numpy()74        else:75            return super()._get_value()76    def is_compatible(self):77        return super().is_compatible() or (78            identify_backend(self.arr) is BackendType.TENSORFLOW79        )80class MinMetric(NumericCallableMetric):81    name = "min"82class MaxMetric(NumericCallableMetric):83    name = "max"84class MeanMetric(NumericCallableMetric):85    name = "mean"86    def _get_value(self):87        backend = identify_backend(self.arr)88        if backend is BackendType.PYTORCH:89            import torch90            # PyTorch can't compute mean of integer-like types91            self.arr = self.arr.to(torch.float)92        return super()._get_value()93# NameMetric is the default first metric and can't be used elsewhere94DEFAULT_METRICS = [ShapeMetric]95if PYTORCH_ENABLED:96    DEFAULT_METRICS += [DeviceMetric]97DEFAULT_METRICS += [DtypeMetric]98DEFAULT_METRICS += [MinMetric]99DEFAULT_METRICS += [MeanMetric]...

test_uploader_plugin.py

Source:test_uploader_plugin.py

...11        ('overload', BackendTypes.OVERLOAD),12        ('overload-01', BackendTypes.OVERLOAD)13    ])14    def test_identify(self, section_name, expected_type):15        assert BackendTypes.identify_backend(section_name) == expected_type16    @pytest.mark.parametrize('section_name', [17        'meta lunapark',18        'meta ',19        ' lunapark',20        'lp ',21        'meta-'22    ])23    def test_exception(self, section_name):24        with pytest.raises(KeyError) as excinfo:25            BackendTypes.identify_backend(section_name)...

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