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import argparse
import os.path as osp
import warnings
import numpy as np
import onnx
import onnxruntime as rt
import torch
from mmcv import DictAction
from mmdet.core.export import (build_model_from_cfg,
generate_inputs_and_wrap_model,
preprocess_example_input)
def pytorch2onnx(config_path,
checkpoint_path,
input_img,
input_shape,
opset_version=11,
show=False,
output_file='tmp.onnx',
verify=False,
normalize_cfg=None,
dataset='coco',
test_img=None,
do_simplify=False,
cfg_options=None,
dynamic_export=None):
input_config = {
'input_shape': input_shape,
'input_path': input_img,
'normalize_cfg': normalize_cfg
}
# prepare original model and meta for verifying the onnx model
orig_model = build_model_from_cfg(
config_path, checkpoint_path, cfg_options=cfg_options)
one_img, one_meta = preprocess_example_input(input_config)
model, tensor_data = generate_inputs_and_wrap_model(
config_path, checkpoint_path, input_config, cfg_options=cfg_options)
output_names = ['dets', 'labels']
if model.with_mask:
output_names.append('masks')
dynamic_axes = None
if dynamic_export:
dynamic_axes = {
'input': {
0: 'batch',
2: 'width',
3: 'height'
},
'dets': {
0: 'batch',
1: 'num_dets',
},
'labels': {
0: 'batch',
1: 'num_dets',
},
}
if model.with_mask:
dynamic_axes['masks'] = {0: 'batch', 1: 'num_dets'}
torch.onnx.export(
model,
tensor_data,
output_file,
input_names=['input'],
output_names=output_names,
export_params=True,
keep_initializers_as_inputs=True,
do_constant_folding=True,
verbose=show,
opset_version=opset_version,
dynamic_axes=dynamic_axes)
model.forward = orig_model.forward
# get the custom op path
ort_custom_op_path = ''
try:
from mmcv.ops import get_onnxruntime_op_path
ort_custom_op_path = get_onnxruntime_op_path()
except (ImportError, ModuleNotFoundError):
warnings.warn('If input model has custom op from mmcv, \
you may have to build mmcv with ONNXRuntime from source.')
if do_simplify:
from mmdet import digit_version
import onnxsim
min_required_version = '0.3.0'
assert digit_version(onnxsim.__version__) >= digit_version(
min_required_version
), f'Requires to install onnx-simplify>={min_required_version}'
input_dic = {'input': one_img.detach().cpu().numpy()}
onnxsim.simplify(
output_file, input_data=input_dic, custom_lib=ort_custom_op_path)
print(f'Successfully exported ONNX model: {output_file}')
if verify:
from mmdet.core import get_classes, bbox2result
from mmdet.apis import show_result_pyplot
model.CLASSES = get_classes(dataset)
num_classes = len(model.CLASSES)
# check by onnx
onnx_model = onnx.load(output_file)
onnx.checker.check_model(onnx_model)
if dynamic_export:
# scale up to test dynamic shape
h, w = [int((_ * 1.5) // 32 * 32) for _ in input_shape[2:]]
input_config['input_shape'] = (1, 3, h, w)
if test_img is not None:
input_config['input_path'] = test_img
one_img, one_meta = preprocess_example_input(input_config)
tensor_data = [one_img]
# get pytorch output
pytorch_results = model(tensor_data, [[one_meta]], return_loss=False)
pytorch_results = pytorch_results[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)
session_options = rt.SessionOptions()
# register custom op for ONNX Runtime
if osp.exists(ort_custom_op_path):
session_options.register_custom_ops_library(ort_custom_op_path)
feed_input_img = one_img.detach().numpy()
if dynamic_export:
# test batch with two input images
feed_input_img = np.vstack([feed_input_img, feed_input_img])
sess = rt.InferenceSession(output_file, session_options)
onnx_outputs = sess.run(None, {net_feed_input[0]: feed_input_img})
output_names = [_.name for _ in sess.get_outputs()]
output_shapes = [_.shape for _ in onnx_outputs]
print(f'ONNX Runtime output names: {output_names}, \
output shapes: {output_shapes}')
# get last image's outputs
onnx_outputs = [_[-1] for _ in onnx_outputs]
ort_dets, ort_labels = onnx_outputs[:2]
onnx_results = bbox2result(ort_dets, ort_labels, num_classes)
if model.with_mask:
segm_results = onnx_outputs[2]
cls_segms = [[] for _ in range(num_classes)]
for i in range(ort_dets.shape[0]):
cls_segms[ort_labels[i]].append(segm_results[i])
onnx_results = (onnx_results, cls_segms)
# visualize predictions
if show:
show_result_pyplot(
model, one_meta['show_img'], pytorch_results, title='Pytorch')
show_result_pyplot(
model, one_meta['show_img'], onnx_results, title='ONNXRuntime')
# compare a part of result
if model.with_mask:
compare_pairs = list(zip(onnx_results, pytorch_results))
else:
compare_pairs = [(onnx_results, pytorch_results)]
err_msg = 'The numerical values are different between Pytorch' + \
' and ONNX, but it does not necessarily mean the' + \
' exported ONNX model is problematic.'
# check the numerical value
for onnx_res, pytorch_res in compare_pairs:
for o_res, p_res in zip(onnx_res, pytorch_res):
np.testing.assert_allclose(
o_res, p_res, rtol=1e-03, atol=1e-05, err_msg=err_msg)
print('The numerical values are the same between Pytorch and ONNX')
def parse_args():
parser = argparse.ArgumentParser(
description='Convert MMDetection models to ONNX')
parser.add_argument('config', help='test config file path')
parser.add_argument('checkpoint', help='checkpoint file')
parser.add_argument('--input-img', type=str, help='Images for input')
parser.add_argument(
'--show',
action='store_true',
help='Show onnx graph and detection outputs')
parser.add_argument('--output-file', type=str, default='tmp.onnx')
parser.add_argument('--opset-version', type=int, default=11)
parser.add_argument(
'--test-img', type=str, default=None, help='Images for test')
parser.add_argument(
'--dataset', type=str, default='coco', help='Dataset name')
parser.add_argument(
'--verify',
action='store_true',
help='verify the onnx model output against pytorch output')
parser.add_argument(
'--simplify',
action='store_true',
help='Whether to simplify onnx model.')
parser.add_argument(
'--shape',
type=int,
nargs='+',
default=[800, 1216],
help='input image size')
parser.add_argument(
'--mean',
type=float,
nargs='+',
default=[123.675, 116.28, 103.53],
help='mean value used for preprocess input data')
parser.add_argument(
'--std',
type=float,
nargs='+',
default=[58.395, 57.12, 57.375],
help='variance value used for preprocess input data')
parser.add_argument(
'--cfg-options',
nargs='+',
action=DictAction,
help='Override some settings in the used config, the key-value pair '
'in xxx=yyy format will be merged into config file. If the value to '
'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
'Note that the quotation marks are necessary and that no white space '
'is allowed.')
parser.add_argument(
'--dynamic-export',
action='store_true',
help='Whether to export onnx with dynamic axis.')
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_args()
assert args.opset_version == 11, 'MMDet only support opset 11 now'
if not args.input_img:
args.input_img = osp.join(
osp.dirname(__file__), '../../tests/data/color.jpg')
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')
assert len(args.mean) == 3
assert len(args.std) == 3
normalize_cfg = {'mean': args.mean, 'std': args.std}
# convert model to onnx file
pytorch2onnx(
args.config,
args.checkpoint,
args.input_img,
input_shape,
opset_version=args.opset_version,
show=args.show,
output_file=args.output_file,
verify=args.verify,
normalize_cfg=normalize_cfg,
dataset=args.dataset,
test_img=args.test_img,
do_simplify=args.simplify,
cfg_options=args.cfg_options,
dynamic_export=args.dynamic_export)
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