import numpy as np
import torch
from atk.configs.dataset_config import InputDataset
from atk.configs.results_config import TaskResult
from atk.tasks.api_execute import register
from atk.tasks.api_execute.base_api import BaseApi
from atk.tasks.dataset.base_dataset import OpsDataset
from atk.tasks.backends.lib_interface.acl_wrapper import AclFormat
from atk.tasks.api_execute.aclnn_base_api import AclnnBaseApi
from atk.tasks.backends.lib_interface.acl_wrapper import TensorPtr
@register("function_aclnn_minimum")
class FunctionApi(BaseApi):
def __call__(self, input_data: InputDataset, with_output: bool = False):
if self.device == "cpu":
output = torch.minimum(input_data.kwargs["self"], input_data.kwargs["other"])
return output
@register("aclnn_minimum")
class exec_minimum(AclnnBaseApi):
def get_format(self, input_data:InputDataset, index=None, name=None):
if input_data.kwargs["format"] == "NCL":
return AclFormat.ACL_FORMAT_NCL
if input_data.kwargs["format"] == "NCHW":
return AclFormat.ACL_FORMAT_NCHW
if input_data.kwargs["format"] == "NCDHW":
return AclFormat.ACL_FORMAT_NCDHW
if input_data.kwargs["format"] == "ND":
return AclFormat.ACL_FORMAT_ND
return AclFormat.ACL_FORMAT_ND