| |
| |
|
|
| """ |
| ONNXIFI a Caffe2 net |
| """ |
|
|
| from caffe2.proto import caffe2_pb2 |
| import caffe2.python._import_c_extension as C |
|
|
|
|
| def onnxifi_set_option(option_name, option_value): |
| """ |
| Set onnxifi option |
| """ |
| return C.onnxifi_set_option(option_name, str(option_value)) |
|
|
|
|
| def onnxifi_get_option(option_name): |
| """ |
| Get onnxifi option |
| """ |
| return C.onnxifi_get_option(option_name) |
|
|
| def onnxifi_caffe2_net( |
| pred_net, |
| input_shapes, |
| max_batch_size=1, |
| max_seq_size=1, |
| debug=False, |
| use_onnx=True, |
| merge_fp32_inputs_into_fp16=False, |
| adjust_batch=True, |
| block_list=None, |
| weight_names=None, |
| net_ssa_rewritten=False, |
| timeout=0): |
| """ |
| Transform the caffe2_net by collapsing ONNXIFI-runnable nodes into Onnxifi c2 ops |
| """ |
| shape_hints = caffe2_pb2.TensorBoundShapes() |
| if type(input_shapes) is caffe2_pb2.TensorBoundShapes: |
| shape_hints = input_shapes |
| elif type(input_shapes) is dict: |
| for k, v in input_shapes.items(): |
| tbs = caffe2_pb2.TensorBoundShape() |
| tbs.name = k |
| tbs.shape.dims.extend(v) |
| tbs.dim_type.extend([caffe2_pb2.TensorBoundShape.CONSTANT] * len(tbs.shape.dims)) |
| tbs.dim_type[0] = caffe2_pb2.TensorBoundShape.BATCH |
| shape_hints.shapes.extend([tbs]) |
| shape_hints.max_batch_size = max_batch_size |
| shape_hints.max_feature_len = max_seq_size |
| pred_net_str = C.onnxifi(pred_net.SerializeToString(), |
| shape_hints.SerializeToString(), |
| block_list if block_list else [], |
| weight_names if weight_names is not None else [], |
| max_batch_size, |
| max_seq_size, |
| timeout, |
| adjust_batch, |
| debug, |
| merge_fp32_inputs_into_fp16, |
| net_ssa_rewritten, |
| use_onnx) |
| pred_net_cut = caffe2_pb2.NetDef() |
| pred_net_cut.ParseFromString(pred_net_str) |
| return pred_net_cut |
|
|
