mirror of
https://github.com/hero-y/BHRL.git
synced 2025-06-03 14:49:38 +08:00
279 lines
9.9 KiB
Python
279 lines
9.9 KiB
Python
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')
|
|
input_name = 'input'
|
|
dynamic_axes = None
|
|
if dynamic_export:
|
|
dynamic_axes = {
|
|
input_name: {
|
|
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_name],
|
|
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]
|
|
if segm_results.dtype != np.bool:
|
|
segm_results = (segm_results * 255).astype(np.uint8)
|
|
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)
|