| ## @package onnx |
| #Module caffe2.python.onnx.onnxifi |
| |
| """ |
| ONNXIFI a Caffe2 net |
| """ |
| |
| from __future__ import absolute_import |
| from __future__ import division |
| from __future__ import print_function |
| from __future__ import unicode_literals |
| |
| from caffe2.proto import caffe2_pb2 |
| from caffe2.python import core, workspace |
| import caffe2.python._import_c_extension as C |
| import numpy as np |
| |
| |
| def onnxifi_caffe2_net( |
| pred_net, |
| input_shapes, |
| max_batch_size=1, |
| max_seq_size=1, |
| debug=False, |
| use_onnx=True, |
| adjust_batch=True, |
| black_list=None, |
| weight_names=None): |
| """ |
| Transform the caffe2_net by collapsing ONNXIFI-runnable nodes into Onnxifi c2 ops |
| """ |
| shape_hints = {} |
| for k, v in input_shapes.items(): |
| shape_hints[k] = v |
| pred_net_str = C.onnxifi(pred_net.SerializeToString(), |
| shape_hints, |
| black_list if black_list else [], |
| weight_names if weight_names is not None else [], |
| max_batch_size, |
| max_seq_size, |
| adjust_batch, |
| debug, |
| use_onnx) |
| pred_net_cut = caffe2_pb2.NetDef() |
| pred_net_cut.ParseFromString(pred_net_str) |
| return pred_net_cut |