def _output_dataset_to_numpy(self):
dataset = []
plicy = None
if self._run_mode == ACL_HOST:
policy = ACL_MEMCPY_DEVICE_TO_HOST
else:
policy = ACL_MEMCPY_DEVICE_TO_DEVICE
num = acl.mdl.get_dataset_num_buffers(self.output_dataset)
for i in range(num):
buffer = acl.mdl.get_dataset_buffer(self.output_dataset, i)
data = acl.get_data_buffer_addr(buffer)
size = int(acl.get_data_buffer_size(buffer))
output_ptr = self._output_info[i]["ptr"]
output_tensor = self._output_info[i]["tensor"]
ret = acl.rt.memcpy(output_ptr,
output_tensor.size * output_tensor.itemsize,
data, size, policy)
if ret != ACL_ERROR_NONE:
print("Memcpy inference output to local failed")
return None
dataset.append(output_tensor)
return dataset
def _output_dataset_to_numpy(self):
dataset = []
output_tensor_list = self._gen_output_tensor()
num = acl.mdl.get_dataset_num_buffers(self._output_dataset)
for i in range(num):
buf = acl.mdl.get_dataset_buffer(self._output_dataset, i)
data = acl.get_data_buffer_addr(buf)
size = int(acl.get_data_buffer_size(buf))
output_ptr = output_tensor_list[i]["ptr"]
output_data = output_tensor_list[i]["tensor"]
if isinstance(output_data, bytes):
data_size = len(output_data)
else:
data_size = output_data.size * output_data.itemsize
ret = acl.rt.memcpy(output_ptr, data_size, data, size,
self._copy_policy)
if ret != const.ACL_SUCCESS:
log_error("Memcpy inference output to local failed")
return None
if isinstance(output_data, bytes):
output_data = np.frombuffer(
output_data, dtype=output_tensor_list[i]["dtype"]).reshape(
output_tensor_list[i]["shape"])
output_tensor = output_data.copy()
else:
output_tensor = output_data
dataset.append(output_tensor)
return dataset
def _output_dataset_to_numpy(self):
dataset = []
num = acl.mdl.get_dataset_num_buffers(self.output_dataset)
for i in range(num):
buffer = acl.mdl.get_dataset_buffer(self.output_dataset, i)
data = acl.get_data_buffer_addr(buffer)
size = acl.get_data_buffer_size(buffer)
if self._run_mode == ACL_HOST:
data = copy_data_device_to_host(data, size)
data_array = acl.util.ptr_to_numpy(data, (size, ), NPY_BYTE)
data_array = self._unpack_output_item(data_array,
self._output_info[i]["shape"],
self._output_info[i]["type"])
dataset.append(data_array)
return dataset
def _output_dataset_to_numpy(self):
dataset = []
num = acl.mdl.get_dataset_num_buffers(self.output_dataset)
for i in range(num):
outbuffer = acl.mdl.get_dataset_buffer(self.output_dataset, i)
data = acl.get_data_buffer_addr(outbuffer)
size = acl.get_data_buffer_size(outbuffer)
narray = np.zeros(size, dtype=np.byte)
narray_ptr = acl.util.numpy_to_ptr(narray)
ret = acl.rt.memcpy(narray_ptr, narray.size * narray.itemsize,
data, size, ACL_MEMCPY_DEVICE_TO_DEVICE)
if ret != ACL_ERROR_NONE:
print("Memcpy inference output to local failed")
return None
output_nparray = self._unpack_bytes_array(
narray, self._output_info[i]["shape"],
self._output_info[i]["type"])
dataset.append(output_nparray)
return dataset
def _output_dataset_to_numpy(self):
dataset = []
num = acl.mdl.get_dataset_num_buffers(self.output_dataset)
# iterative each output
for i in range(num):
# obtain memory address from output buffer
buffer = acl.mdl.get_dataset_buffer(self.output_dataset, i)
data = acl.get_data_buffer_addr(buffer)
size = int(acl.get_data_buffer_size(buffer))
output_ptr = self._output_info[i]["ptr"]
output_tensor = self._output_info[i]["tensor"]
ret = acl.rt.memcpy(output_ptr, output_tensor.size*output_tensor.itemsize,
data, size, ACL_MEMCPY_DEVICE_TO_DEVICE)
if ret != ACL_ERROR_NONE:
print("Memcpy inference output to local failed")
return None
dataset.append(output_tensor)
return dataset
def _output_dataset_to_numpy(self):
dataset = []
output_tensor_list = self._gen_output_tensor()
num = acl.mdl.get_dataset_num_buffers(self._output_dataset)
for i in range(num):
buf = acl.mdl.get_dataset_buffer(self._output_dataset, i)
data = acl.get_data_buffer_addr(buf)
size = int(acl.get_data_buffer_size(buf))
output_ptr = output_tensor_list[i]["ptr"]
output_tensor = output_tensor_list[i]["tensor"]
ret = acl.rt.memcpy(output_ptr,
output_tensor.size * output_tensor.itemsize,
data, size, self._copy_policy)
if ret != const.ACL_ERROR_NONE:
log_error("Memcpy inference output to local failed")
return None
dataset.append(output_tensor)
return dataset
def _output_dataset_to_numpy(self):
dataset = []
num = acl.mdl.get_dataset_num_buffers(self.output_dataset)
#遍历每个输出
for i in range(num):
#从输出buffer中获取输出数据内存地址
buffer = acl.mdl.get_dataset_buffer(self.output_dataset, i)
data = acl.get_data_buffer_addr(buffer)
size = acl.get_data_buffer_size(buffer)
#创建一个numpy数组用于拷贝输出内存数据
narray = np.zeros(size, dtype=np.byte)
narray_ptr = acl.util.numpy_to_ptr(narray)
ret = acl.rt.memcpy(narray_ptr, narray.size * narray.itemsize,
data, size, ACL_MEMCPY_DEVICE_TO_DEVICE)
if ret != ACL_ERROR_NONE:
print("Memcpy inference output to local failed")
return None
#根据模型输出的shape和数据类型,将内存数据解码为numpy数组
output_nparray = self._unpack_bytes_array(
narray, self._output_info[i]["shape"],
self._output_info[i]["type"])
dataset.append(output_nparray)
return dataset
