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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)
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