File size: 6,795 Bytes
b13b124 |
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 |
import argparse
from functools import partial
import mmcv
import numpy as np
import onnxruntime as rt
import torch
import torch._C
import torch.serialization
from mmcv.onnx import register_extra_symbolics
from mmcv.runner import load_checkpoint
from torch import nn
from mmseg.models import build_segmentor
torch.manual_seed(3)
def _convert_batchnorm(module):
module_output = module
if isinstance(module, torch.nn.SyncBatchNorm):
module_output = torch.nn.BatchNorm2d(module.num_features, module.eps,
module.momentum, module.affine,
module.track_running_stats)
if module.affine:
module_output.weight.data = module.weight.data.clone().detach()
module_output.bias.data = module.bias.data.clone().detach()
# keep requires_grad unchanged
module_output.weight.requires_grad = module.weight.requires_grad
module_output.bias.requires_grad = module.bias.requires_grad
module_output.running_mean = module.running_mean
module_output.running_var = module.running_var
module_output.num_batches_tracked = module.num_batches_tracked
for name, child in module.named_children():
module_output.add_module(name, _convert_batchnorm(child))
del module
return module_output
def _demo_mm_inputs(input_shape, num_classes):
"""Create a superset of inputs needed to run test or train batches.
Args:
input_shape (tuple):
input batch dimensions
num_classes (int):
number of semantic classes
"""
(N, C, H, W) = input_shape
rng = np.random.RandomState(0)
imgs = rng.rand(*input_shape)
segs = rng.randint(
low=0, high=num_classes - 1, size=(N, 1, H, W)).astype(np.uint8)
img_metas = [{
'img_shape': (H, W, C),
'ori_shape': (H, W, C),
'pad_shape': (H, W, C),
'filename': '<demo>.png',
'scale_factor': 1.0,
'flip': False,
} for _ in range(N)]
mm_inputs = {
'imgs': torch.FloatTensor(imgs).requires_grad_(True),
'img_metas': img_metas,
'gt_semantic_seg': torch.LongTensor(segs)
}
return mm_inputs
def pytorch2onnx(model,
input_shape,
opset_version=11,
show=False,
output_file='tmp.onnx',
verify=False):
"""Export Pytorch model to ONNX model and verify the outputs are same
between Pytorch and ONNX.
Args:
model (nn.Module): Pytorch model we want to export.
input_shape (tuple): Use this input shape to construct
the corresponding dummy input and execute the model.
opset_version (int): The onnx op version. Default: 11.
show (bool): Whether print the computation graph. Default: False.
output_file (string): The path to where we store the output ONNX model.
Default: `tmp.onnx`.
verify (bool): Whether compare the outputs between Pytorch and ONNX.
Default: False.
"""
model.cpu().eval()
if isinstance(model.decode_head, nn.ModuleList):
num_classes = model.decode_head[-1].num_classes
else:
num_classes = model.decode_head.num_classes
mm_inputs = _demo_mm_inputs(input_shape, num_classes)
imgs = mm_inputs.pop('imgs')
img_metas = mm_inputs.pop('img_metas')
img_list = [img[None, :] for img in imgs]
img_meta_list = [[img_meta] for img_meta in img_metas]
# replace original forward function
origin_forward = model.forward
model.forward = partial(
model.forward, img_metas=img_meta_list, return_loss=False)
register_extra_symbolics(opset_version)
with torch.no_grad():
torch.onnx.export(
model, (img_list, ),
output_file,
export_params=True,
keep_initializers_as_inputs=True,
verbose=show,
opset_version=opset_version)
print(f'Successfully exported ONNX model: {output_file}')
model.forward = origin_forward
if verify:
# check by onnx
import onnx
onnx_model = onnx.load(output_file)
onnx.checker.check_model(onnx_model)
# check the numerical value
# get pytorch output
pytorch_result = model(img_list, img_meta_list, return_loss=False)[0]
# get onnx output
input_all = [node.name for node in onnx_model.graph.input]
input_initializer = [
node.name for node in onnx_model.graph.initializer
]
net_feed_input = list(set(input_all) - set(input_initializer))
assert (len(net_feed_input) == 1)
sess = rt.InferenceSession(output_file)
onnx_result = sess.run(
None, {net_feed_input[0]: img_list[0].detach().numpy()})[0]
if not np.allclose(pytorch_result, onnx_result):
raise ValueError(
'The outputs are different between Pytorch and ONNX')
print('The outputs are same between Pytorch and ONNX')
def parse_args():
parser = argparse.ArgumentParser(description='Convert MMSeg to ONNX')
parser.add_argument('config', help='test config file path')
parser.add_argument('--checkpoint', help='checkpoint file', default=None)
parser.add_argument('--show', action='store_true', help='show onnx graph')
parser.add_argument(
'--verify', action='store_true', help='verify the onnx model')
parser.add_argument('--output-file', type=str, default='tmp.onnx')
parser.add_argument('--opset-version', type=int, default=11)
parser.add_argument(
'--shape',
type=int,
nargs='+',
default=[256, 256],
help='input image size')
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_args()
if len(args.shape) == 1:
input_shape = (1, 3, args.shape[0], args.shape[0])
elif len(args.shape) == 2:
input_shape = (
1,
3,
) + tuple(args.shape)
else:
raise ValueError('invalid input shape')
cfg = mmcv.Config.fromfile(args.config)
cfg.model.pretrained = None
# build the model and load checkpoint
cfg.model.train_cfg = None
segmentor = build_segmentor(
cfg.model, train_cfg=None, test_cfg=cfg.get('test_cfg'))
# convert SyncBN to BN
segmentor = _convert_batchnorm(segmentor)
if args.checkpoint:
load_checkpoint(segmentor, args.checkpoint, map_location='cpu')
# conver model to onnx file
pytorch2onnx(
segmentor,
input_shape,
opset_version=args.opset_version,
show=args.show,
output_file=args.output_file,
verify=args.verify)
|