tf_manager.py 文件源码

def execute(self,
                dataset: Dataset,
                execution_scripts,
                train=False,
                compute_losses=True,
                summaries=True,
                batch_size=None,
                log_progress: int = 0) -> List[ExecutionResult]:
        if batch_size is None:
            batch_size = len(dataset)
        batched_dataset = dataset.batch_dataset(batch_size)
        last_log_time = time.process_time()

        batch_results = [
            [] for _ in execution_scripts]  # type: List[List[ExecutionResult]]
        for batch_id, batch in enumerate(batched_dataset):
            if (time.process_time() - last_log_time > log_progress
                    and log_progress > 0):
                log("Processed {} examples.".format(batch_id * batch_size))
                last_log_time = time.process_time()
            executables = [s.get_executable(compute_losses=compute_losses,
                                            summaries=summaries,
                                            num_sessions=len(self.sessions))
                           for s in execution_scripts]

            while not all(ex.result is not None for ex in executables):
                self._run_executables(batch, executables, train)

            for script_list, executable in zip(batch_results, executables):
                script_list.append(executable.result)

        collected_results = []  # type: List[ExecutionResult]
        for result_list in batch_results:
            collected_results.append(reduce_execution_results(result_list))

        return collected_results