(
self,
program=None,
dataset=None,
scope=None,
thread=0,
is_infer=False,
debug=False,
fetch_list=None,
fetch_info=None,
print_period=100,
fetch_handler=None,
use_program_cache=False,
)
| 2832 | return tmp_program |
| 2833 | |
| 2834 | def _run_pipeline( |
| 2835 | self, |
| 2836 | program=None, |
| 2837 | dataset=None, |
| 2838 | scope=None, |
| 2839 | thread=0, |
| 2840 | is_infer=False, |
| 2841 | debug=False, |
| 2842 | fetch_list=None, |
| 2843 | fetch_info=None, |
| 2844 | print_period=100, |
| 2845 | fetch_handler=None, |
| 2846 | use_program_cache=False, |
| 2847 | ): |
| 2848 | scope, real_fetch_list, trainer_instance = self._prepare_pipeline_ctx( |
| 2849 | program, |
| 2850 | dataset, |
| 2851 | scope, |
| 2852 | thread, |
| 2853 | is_infer, |
| 2854 | debug, |
| 2855 | fetch_list, |
| 2856 | fetch_info, |
| 2857 | print_period, |
| 2858 | fetch_handler, |
| 2859 | use_program_cache, |
| 2860 | ) |
| 2861 | |
| 2862 | from paddle.optimizer.lr import LRScheduler |
| 2863 | |
| 2864 | if hasattr(program, 'lr_scheduler'): |
| 2865 | lr_scheduler = program.lr_scheduler |
| 2866 | assert isinstance(lr_scheduler, LRScheduler), "must be LRScheduler" |
| 2867 | lr_value = lr_scheduler() |
| 2868 | lr_var = program.global_block().vars[lr_scheduler._var_name] |
| 2869 | data = np.array([lr_value]).astype(convert_dtype(lr_var.dtype)) |
| 2870 | tensor = core.get_variable_tensor(scope, lr_scheduler._var_name) |
| 2871 | tensor.set(data, self.place) |
| 2872 | |
| 2873 | self._default_executor.run_from_dataset(trainer_instance) |
| 2874 | |
| 2875 | if not use_program_cache: |
| 2876 | self._default_executor.release_trainer(trainer_instance) |
| 2877 | |
| 2878 | if real_fetch_list: |
| 2879 | arr = scope.find_var('fetch').get_fetch_list() |
| 2880 | tensors = arr._move_to_list() |
| 2881 | return as_numpy(tensors) |
| 2882 | |
| 2883 | return None |
| 2884 | |
| 2885 | def infer_from_dataset( |
| 2886 | self, |
nothing calls this directly
no test coverage detected