Spaces:
Build error
Build error
File size: 7,803 Bytes
1865436 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 |
# 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
|