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How to use _process_logs method in autotest

Best Python code snippet using autotest_python

callback_list.py

Source:callback_list.py

...86        self._delta_t_batch = 0.087        self._delta_ts = collections.defaultdict(88            lambda: collections.deque([], maxlen=self._queue_length)89        )90    def _process_logs(self, logs):91        """Turns tensors into numpy arrays or Python scalars."""92        if logs:93            return logs94        return {}95    def append(self, callback):96        self.callbacks.append(callback)97    def set_params(self, params):98        self.params = params99        for callback in self.callbacks:100            callback.set_params(params)101    def set_model(self, model):102        self.model = model103        if self._history:104            model.history = self._history105        for callback in self.callbacks:106            callback.set_model(model)107    def _call_batch_hook(self, mode, hook, batch, logs=None):108        """Helper function for all batch_{begin | end} methods."""109        if not self.callbacks:110            return111        hook_name = "on_{mode}_batch_{hook}".format(mode=mode, hook=hook)112        if hook == "begin":113            self._t_enter_batch = time.time()114        if hook == "end":115            # Batch is ending, calculate batch time.116            self._delta_t_batch = time.time() - self._t_enter_batch117        logs = logs or {}118        t_before_callbacks = time.time()119        for callback in self.callbacks:120            batch_hook = getattr(callback, hook_name)121            batch_hook(batch, logs)122        self._delta_ts[hook_name].append(time.time() - t_before_callbacks)123        # delta_t_median = np.median(self._delta_ts[hook_name])124        # if (125        #     self._delta_t_batch > 0.0126        #     and delta_t_median > 0.95 * self._delta_t_batch127        #     and delta_t_median > 0.1128        # ):129        #     logging.warning(130        #         "Method (%s) is slow compared "131        #         "to the batch update (%f). Check your callbacks.",132        #         hook_name,133        #         delta_t_median,134        #     )135    def _call_begin_hook(self, mode):136        """Helper function for on_{train|test|predict}_begin methods."""137        if mode == ModeKeys.TRAIN:138            self.on_train_begin()139        elif mode == ModeKeys.TEST:140            self.on_test_begin()141        else:142            self.on_predict_begin()143    def _call_end_hook(self, mode):144        """Helper function for on_{train|test|predict}_end methods."""145        if mode == ModeKeys.TRAIN:146            self.on_train_end()147        elif mode == ModeKeys.TEST:148            self.on_test_end()149        else:150            self.on_predict_end()151    def on_batch_begin(self, batch, logs=None):152        if self._should_call_train_batch_hooks:153            logs = self._process_logs(logs)154            self._call_batch_hook(ModeKeys.TRAIN, "begin", batch, logs=logs)155    def on_batch_end(self, batch, logs=None):156        if self._should_call_train_batch_hooks:157            logs = self._process_logs(logs)158            self._call_batch_hook(ModeKeys.TRAIN, "end", batch, logs=logs)159    def on_epoch_begin(self, epoch, logs=None):160        """Calls the `on_epoch_begin` methods of its callbacks.161        This function should only be called during TRAIN mode.162        Arguments:163            epoch: integer, index of epoch.164            logs: dict. Currently no data is passed to this argument for this method165                but that may change in the future.166        """167        logs = self._process_logs(logs)168        for callback in self.callbacks:169            callback.on_epoch_begin(epoch, logs)170        self._reset_batch_timing()171    def on_epoch_end(self, epoch, logs=None):172        """Calls the `on_epoch_end` methods of its callbacks.173        This function should only be called during TRAIN mode.174        Arguments:175            epoch: integer, index of epoch.176            logs: dict, metric results for this training epoch, and for the177                validation epoch if validation is performed. Validation result keys178                are prefixed with `val_`.179        """180        logs = self._process_logs(logs)181        for callback in self.callbacks:182            callback.on_epoch_end(epoch, logs)183    def on_train_batch_begin(self, batch, logs=None):184        """Calls the `on_train_batch_begin` methods of its callbacks.185        Arguments:186            batch: integer, index of batch within the current epoch.187            logs: dict. Has keys `batch` and `size` representing the current batch188                number and the size of the batch.189        """190        # TODO(b/150629188): Make ProgBarLogger callback not use batch hooks191        # when verbose != 1192        if self._should_call_train_batch_hooks:193            logs = self._process_logs(logs)194            self._call_batch_hook(ModeKeys.TRAIN, "begin", batch, logs=logs)195    def on_train_batch_end(self, batch, logs=None):196        """Calls the `on_train_batch_end` methods of its callbacks.197        Arguments:198            batch: integer, index of batch within the current epoch.199            logs: dict. Metric results for this batch.200        """201        if self._should_call_train_batch_hooks:202            logs = self._process_logs(logs)203            self._call_batch_hook(ModeKeys.TRAIN, "end", batch, logs=logs)204    def on_test_batch_begin(self, batch, logs=None):205        """Calls the `on_test_batch_begin` methods of its callbacks.206        Arguments:207            batch: integer, index of batch within the current epoch.208            logs: dict. Has keys `batch` and `size` representing the current batch209                number and the size of the batch.210        """211        if self._should_call_test_batch_hooks:212            logs = self._process_logs(logs)213            self._call_batch_hook(ModeKeys.TEST, "begin", batch, logs=logs)214    def on_test_batch_end(self, batch, logs=None):215        """Calls the `on_test_batch_end` methods of its callbacks.216        Arguments:217            batch: integer, index of batch within the current epoch.218            logs: dict. Metric results for this batch.219        """220        if self._should_call_test_batch_hooks:221            logs = self._process_logs(logs)222            self._call_batch_hook(ModeKeys.TEST, "end", batch, logs=logs)223    def on_predict_batch_begin(self, batch, logs=None):224        """Calls the `on_predict_batch_begin` methods of its callbacks.225        Arguments:226            batch: integer, index of batch within the current epoch.227            logs: dict. Has keys `batch` and `size` representing the current batch228                number and the size of the batch.229        """230        if self._should_call_predict_batch_hooks:231            logs = self._process_logs(logs)232            self._call_batch_hook(ModeKeys.PREDICT, "begin", batch, logs=logs)233    def on_predict_batch_end(self, batch, logs=None):234        """Calls the `on_predict_batch_end` methods of its callbacks.235        Arguments:236            batch: integer, index of batch within the current epoch.237            logs: dict. Metric results for this batch.238        """239        if self._should_call_predict_batch_hooks:240            logs = self._process_logs(logs)241            self._call_batch_hook(ModeKeys.PREDICT, "end", batch, logs=logs)242    def on_train_begin(self, logs=None):243        """Calls the `on_train_begin` methods of its callbacks.244        Arguments:245            logs: dict. Currently no data is passed to this argument for this method246                but that may change in the future.247        """248        logs = self._process_logs(logs)249        for callback in self.callbacks:250            callback.on_train_begin(logs)251    def on_train_end(self, logs=None):252        """Calls the `on_train_end` methods of its callbacks.253        Arguments:254            logs: dict. Currently no data is passed to this argument for this method255                but that may change in the future.256        """257        logs = self._process_logs(logs)258        for callback in self.callbacks:259            callback.on_train_end(logs)260    def on_test_begin(self, logs=None):261        """Calls the `on_test_begin` methods of its callbacks.262        Arguments:263            logs: dict. Currently no data is passed to this argument for this method264                but that may change in the future.265        """266        logs = self._process_logs(logs)267        for callback in self.callbacks:268            callback.on_test_begin(logs)269    def on_test_end(self, logs=None):270        """Calls the `on_test_end` methods of its callbacks.271        Arguments:272            logs: dict. Currently no data is passed to this argument for this method273                but that may change in the future.274        """275        logs = self._process_logs(logs)276        for callback in self.callbacks:277            callback.on_test_end(logs)278    def on_predict_begin(self, logs=None):279        """Calls the 'on_predict_begin` methods of its callbacks.280        Arguments:281            logs: dict. Currently no data is passed to this argument for this method282                but that may change in the future.283        """284        logs = self._process_logs(logs)285        for callback in self.callbacks:286            callback.on_predict_begin(logs)287    def on_predict_end(self, logs=None):288        """Calls the `on_predict_end` methods of its callbacks.289        Arguments:290            logs: dict. Currently no data is passed to this argument for this method291                but that may change in the future.292        """293        logs = self._process_logs(logs)294        for callback in self.callbacks:295            callback.on_predict_end(logs)296    def __iter__(self):...

fixpatches.py

Source:fixpatches.py

...40                x = t.numpy()41                return x.item() if np.ndim(x) == 0 else x42            return t  # Don't turn ragged or sparse tensors to NumPy.43        return nest.map_structure(_to_single_numpy_or_python_type, tensors)44    def _process_logs(self, logs):45        """Turns tensors into numpy arrays or Python scalars if necessary."""46        if logs is None:47            return {}48        return tf_utils.sync_to_numpy_or_python_type(logs)49    def _call_batch_hook_helper(self, hook_name, batch, logs):50        """Helper function for `on_*_batch_*` methods."""51        if self._check_timing:52            start_time = time.time()53        logs = self._process_logs(logs)54        for callback in self.callbacks:55            hook = getattr(callback, hook_name)56            hook(batch, logs)57        if self._check_timing:58            if hook_name not in self._hook_times:59                self._hook_times[hook_name] = []60            self._hook_times[hook_name].append(time.time() - start_time)61    def on_epoch_end(self: typing.Any, epoch: int, logs: dict = None) -> None:62        """63            Calls the `on_epoch_end` methods of its callbacks.64            This function should only be called during TRAIN mode.65            :param epoch: Integer, index of epoch.66            :param logs: Dict, metric results for this training epoch, and for67                the validation epoch if validation is performed. Validation68                result keys are prefixed with `val_`.69        """70        logs = self._process_logs(logs)71        for callback in self.callbacks:72            callback.on_epoch_end(epoch, logs)73    tf_utils.sync_to_numpy_or_python_type = sync_to_numpy_or_python_type74    callback_list_class = keras.callbacks.CallbackList75    callback_list_class._process_logs = _process_logs76    callback_list_class._call_batch_hook_helper = _call_batch_hook_helper...

logs.py

Source:logs.py

1from typing import List2from e2e.dockertest.file_system import FileSystem3def process_logs(docker_container, file_system: FileSystem) -> List[str]:4    return _process_logs(docker_container, file_system)5def _process_logs(container, file_system: FileSystem):6    actual_logs = container.logs()7    encoded = actual_logs.decode('utf-8')8    split = encoded.split('\n')9    logs = _remove_cat_logs(split, file_system)10    return logs11def _remove_cat_logs(current_logs: List[str], file_system: FileSystem):12    line_number_of_start_cat_logs = []13    line_number_of_end_cat_logs = []14    final_logs = current_logs15    for index, line in enumerate(current_logs, start=0):16        if line.startswith('start cat'):17            line_number_of_start_cat_logs.append(index)18        if line.startswith('end cat'):19            line_number_of_end_cat_logs.append(index)...

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