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| # Copyright (c) Facebook, Inc. and its affiliates. | |
| import copy | |
| import io | |
| import logging | |
| import numpy as np | |
| from typing import List | |
| import onnx | |
| import torch | |
| from caffe2.proto import caffe2_pb2 | |
| from caffe2.python import core | |
| from caffe2.python.onnx.backend import Caffe2Backend | |
| from tabulate import tabulate | |
| from termcolor import colored | |
| from torch.onnx import OperatorExportTypes | |
| from .shared import ( | |
| ScopedWS, | |
| construct_init_net_from_params, | |
| fuse_alias_placeholder, | |
| fuse_copy_between_cpu_and_gpu, | |
| get_params_from_init_net, | |
| group_norm_replace_aten_with_caffe2, | |
| infer_device_type, | |
| remove_dead_end_ops, | |
| remove_reshape_for_fc, | |
| save_graph, | |
| ) | |
| logger = logging.getLogger(__name__) | |
| def export_onnx_model(model, inputs): | |
| """ | |
| Trace and export a model to onnx format. | |
| Args: | |
| model (nn.Module): | |
| inputs (tuple[args]): the model will be called by `model(*inputs)` | |
| Returns: | |
| an onnx model | |
| """ | |
| assert isinstance(model, torch.nn.Module) | |
| # make sure all modules are in eval mode, onnx may change the training state | |
| # of the module if the states are not consistent | |
| def _check_eval(module): | |
| assert not module.training | |
| model.apply(_check_eval) | |
| # Export the model to ONNX | |
| with torch.no_grad(): | |
| with io.BytesIO() as f: | |
| torch.onnx.export( | |
| model, | |
| inputs, | |
| f, | |
| operator_export_type=OperatorExportTypes.ONNX_ATEN_FALLBACK, | |
| # verbose=True, # NOTE: uncomment this for debugging | |
| # export_params=True, | |
| ) | |
| onnx_model = onnx.load_from_string(f.getvalue()) | |
| # Apply ONNX's Optimization | |
| all_passes = onnx.optimizer.get_available_passes() | |
| passes = ["fuse_bn_into_conv"] | |
| assert all(p in all_passes for p in passes) | |
| onnx_model = onnx.optimizer.optimize(onnx_model, passes) | |
| return onnx_model | |
| def _op_stats(net_def): | |
| type_count = {} | |
| for t in [op.type for op in net_def.op]: | |
| type_count[t] = type_count.get(t, 0) + 1 | |
| type_count_list = sorted(type_count.items(), key=lambda kv: kv[0]) # alphabet | |
| type_count_list = sorted(type_count_list, key=lambda kv: -kv[1]) # count | |
| return "\n".join("{:>4}x {}".format(count, name) for name, count in type_count_list) | |
| def _assign_device_option( | |
| predict_net: caffe2_pb2.NetDef, init_net: caffe2_pb2.NetDef, tensor_inputs: List[torch.Tensor] | |
| ): | |
| """ | |
| ONNX exported network doesn't have concept of device, assign necessary | |
| device option for each op in order to make it runable on GPU runtime. | |
| """ | |
| def _get_device_type(torch_tensor): | |
| assert torch_tensor.device.type in ["cpu", "cuda"] | |
| assert torch_tensor.device.index == 0 | |
| return torch_tensor.device.type | |
| def _assign_op_device_option(net_proto, net_ssa, blob_device_types): | |
| for op, ssa_i in zip(net_proto.op, net_ssa): | |
| if op.type in ["CopyCPUToGPU", "CopyGPUToCPU"]: | |
| op.device_option.CopyFrom(core.DeviceOption(caffe2_pb2.CUDA, 0)) | |
| else: | |
| devices = [blob_device_types[b] for b in ssa_i[0] + ssa_i[1]] | |
| assert all(d == devices[0] for d in devices) | |
| if devices[0] == "cuda": | |
| op.device_option.CopyFrom(core.DeviceOption(caffe2_pb2.CUDA, 0)) | |
| # update ops in predict_net | |
| predict_net_input_device_types = { | |
| (name, 0): _get_device_type(tensor) | |
| for name, tensor in zip(predict_net.external_input, tensor_inputs) | |
| } | |
| predict_net_device_types = infer_device_type( | |
| predict_net, known_status=predict_net_input_device_types, device_name_style="pytorch" | |
| ) | |
| predict_net_ssa, _ = core.get_ssa(predict_net) | |
| _assign_op_device_option(predict_net, predict_net_ssa, predict_net_device_types) | |
| # update ops in init_net | |
| init_net_ssa, versions = core.get_ssa(init_net) | |
| init_net_output_device_types = { | |
| (name, versions[name]): predict_net_device_types[(name, 0)] | |
| for name in init_net.external_output | |
| } | |
| init_net_device_types = infer_device_type( | |
| init_net, known_status=init_net_output_device_types, device_name_style="pytorch" | |
| ) | |
| _assign_op_device_option(init_net, init_net_ssa, init_net_device_types) | |
| def export_caffe2_detection_model(model: torch.nn.Module, tensor_inputs: List[torch.Tensor]): | |
| """ | |
| Export a caffe2-compatible Detectron2 model to caffe2 format via ONNX. | |
| Arg: | |
| model: a caffe2-compatible version of detectron2 model, defined in caffe2_modeling.py | |
| tensor_inputs: a list of tensors that caffe2 model takes as input. | |
| """ | |
| model = copy.deepcopy(model) | |
| assert isinstance(model, torch.nn.Module) | |
| assert hasattr(model, "encode_additional_info") | |
| # Export via ONNX | |
| logger.info( | |
| "Exporting a {} model via ONNX ...".format(type(model).__name__) | |
| + " Some warnings from ONNX are expected and are usually not to worry about." | |
| ) | |
| onnx_model = export_onnx_model(model, (tensor_inputs,)) | |
| # Convert ONNX model to Caffe2 protobuf | |
| init_net, predict_net = Caffe2Backend.onnx_graph_to_caffe2_net(onnx_model) | |
| ops_table = [[op.type, op.input, op.output] for op in predict_net.op] | |
| table = tabulate(ops_table, headers=["type", "input", "output"], tablefmt="pipe") | |
| logger.info( | |
| "ONNX export Done. Exported predict_net (before optimizations):\n" + colored(table, "cyan") | |
| ) | |
| # Apply protobuf optimization | |
| fuse_alias_placeholder(predict_net, init_net) | |
| if any(t.device.type != "cpu" for t in tensor_inputs): | |
| fuse_copy_between_cpu_and_gpu(predict_net) | |
| remove_dead_end_ops(init_net) | |
| _assign_device_option(predict_net, init_net, tensor_inputs) | |
| params, device_options = get_params_from_init_net(init_net) | |
| predict_net, params = remove_reshape_for_fc(predict_net, params) | |
| init_net = construct_init_net_from_params(params, device_options) | |
| group_norm_replace_aten_with_caffe2(predict_net) | |
| # Record necessary information for running the pb model in Detectron2 system. | |
| model.encode_additional_info(predict_net, init_net) | |
| logger.info("Operators used in predict_net: \n{}".format(_op_stats(predict_net))) | |
| logger.info("Operators used in init_net: \n{}".format(_op_stats(init_net))) | |
| return predict_net, init_net | |
| def run_and_save_graph(predict_net, init_net, tensor_inputs, graph_save_path): | |
| """ | |
| Run the caffe2 model on given inputs, recording the shape and draw the graph. | |
| predict_net/init_net: caffe2 model. | |
| tensor_inputs: a list of tensors that caffe2 model takes as input. | |
| graph_save_path: path for saving graph of exported model. | |
| """ | |
| logger.info("Saving graph of ONNX exported model to {} ...".format(graph_save_path)) | |
| save_graph(predict_net, graph_save_path, op_only=False) | |
| # Run the exported Caffe2 net | |
| logger.info("Running ONNX exported model ...") | |
| with ScopedWS("__ws_tmp__", True) as ws: | |
| ws.RunNetOnce(init_net) | |
| initialized_blobs = set(ws.Blobs()) | |
| uninitialized = [inp for inp in predict_net.external_input if inp not in initialized_blobs] | |
| for name, blob in zip(uninitialized, tensor_inputs): | |
| ws.FeedBlob(name, blob) | |
| try: | |
| ws.RunNetOnce(predict_net) | |
| except RuntimeError as e: | |
| logger.warning("Encountered RuntimeError: \n{}".format(str(e))) | |
| ws_blobs = {b: ws.FetchBlob(b) for b in ws.Blobs()} | |
| blob_sizes = {b: ws_blobs[b].shape for b in ws_blobs if isinstance(ws_blobs[b], np.ndarray)} | |
| logger.info("Saving graph with blob shapes to {} ...".format(graph_save_path)) | |
| save_graph(predict_net, graph_save_path, op_only=False, blob_sizes=blob_sizes) | |
| return ws_blobs | |