def pytorch2onnx(model,
input_img,
input_shape,
normalize_cfg,
opset_version=11,
show=False,
output_file='tmp.onnx',
verify=False,
test_img=None,
do_simplify=False,
dynamic_export=None,
skip_postprocess=False):
input_config = {
'input_shape': input_shape,
'input_path': input_img,
'normalize_cfg': normalize_cfg
}
# prepare input
one_img, one_meta = preprocess_example_input(input_config)
img_list, img_meta_list = [one_img], [[one_meta]]
if skip_postprocess:
warnings.warn('Not all models support export onnx without post '
'process, especially two stage detectors!')
model.forward = model.forward_dummy
torch.onnx.export(model,
one_img,
output_file,
input_names=['input'],
export_params=True,
keep_initializers_as_inputs=True,
do_constant_folding=True,
verbose=show,
opset_version=opset_version)
print(f'Successfully exported ONNX model without '
f'post process: {output_file}')
return
# replace original forward function
origin_forward = model.forward
model.forward = partial(model.forward,
img_metas=img_meta_list,
return_loss=False,
rescale=False)
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,
img_list,
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 = origin_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:
import onnxsim
from mmdet import digit_version
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': img_list[0].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:
# check by onnx
onnx_model = onnx.load(output_file)
onnx.checker.check_model(onnx_model)
# wrap onnx model
onnx_model = ONNXRuntimeDetector(output_file, model.CLASSES, 0)
if dynamic_export:
# scale up to test dynamic shape
h, w = [int((_ * 1.5) // 32 * 32) for _ in input_shape[2:]]
h, w = min(1344, h), min(1344, w)
input_config['input_shape'] = (1, 3, h, w)
if test_img is None:
input_config['input_path'] = input_img
# prepare input once again
one_img, one_meta = preprocess_example_input(input_config)
img_list, img_meta_list = [one_img], [[one_meta]]
# get pytorch output
with torch.no_grad():
pytorch_results = model(img_list,
img_metas=img_meta_list,
return_loss=False,
rescale=True)[0]
img_list = [_.cuda().contiguous() for _ in img_list]
if dynamic_export:
img_list = img_list + [_.flip(-1).contiguous() for _ in img_list]
img_meta_list = img_meta_list * 2
# get onnx output
onnx_results = onnx_model(img_list,
img_metas=img_meta_list,
return_loss=False)[0]
# visualize predictions
score_thr = 0.3
if show:
out_file_ort, out_file_pt = None, None
else:
out_file_ort, out_file_pt = 'show-ort.png', 'show-pt.png'
show_img = one_meta['show_img']
model.show_result(show_img,
pytorch_results,
score_thr=score_thr,
show=True,
win_name='PyTorch',
out_file=out_file_pt)
onnx_model.show_result(show_img,
onnx_results,
score_thr=score_thr,
show=True,
win_name='ONNXRuntime',
out_file=out_file_ort)
# 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 onnx2tensorrt(onnx_file,
trt_file,
input_config,
verify=False,
show=False,
dataset='coco',
workspace_size=1,
verbose=False):
import tensorrt as trt
onnx_model = onnx.load(onnx_file)
input_shape = input_config['input_shape']
# create trt engine and wraper
opt_shape_dict = {'input': [input_shape, input_shape, input_shape]}
max_workspace_size = get_GiB(workspace_size)
trt_engine = onnx2trt(
onnx_model,
opt_shape_dict,
log_level=trt.Logger.VERBOSE if verbose else trt.Logger.ERROR,
fp16_mode=False,
max_workspace_size=max_workspace_size)
save_dir, _ = osp.split(trt_file)
if save_dir:
os.makedirs(save_dir, exist_ok=True)
save_trt_engine(trt_engine, trt_file)
print(f'Successfully created TensorRT engine: {trt_file}')
if verify:
one_img, one_meta = preprocess_example_input(input_config)
input_img_cpu = one_img.detach().cpu().numpy()
input_img_cuda = one_img.cuda()
img = one_meta['show_img']
# Get results from ONNXRuntime
ort_custom_op_path = get_onnxruntime_op_path()
session_options = ort.SessionOptions()
if osp.exists(ort_custom_op_path):
session_options.register_custom_ops_library(ort_custom_op_path)
sess = ort.InferenceSession(onnx_file, session_options)
output_names = [_.name for _ in sess.get_outputs()]
ort_outputs = sess.run(None, {
'input': input_img_cpu,
})
with_mask = len(output_names) == 3
ort_outputs = [_.squeeze(0) for _ in ort_outputs]
ort_dets, ort_labels = ort_outputs[:2]
ort_masks = ort_outputs[2] if with_mask else None
ort_shapes = [_.shape for _ in ort_outputs]
print(f'ONNX Runtime output names: {output_names}, \
output shapes: {ort_shapes}')
# Get results from TensorRT
trt_model = TRTWraper(trt_file, ['input'], output_names)
with torch.no_grad():
trt_outputs = trt_model({'input': input_img_cuda})
trt_outputs = [
trt_outputs[_].detach().cpu().numpy().squeeze(0)
for _ in output_names
]
trt_dets, trt_labels = trt_outputs[:2]
trt_shapes = [_.shape for _ in trt_outputs]
print(f'TensorRT output names: {output_names}, \
output shapes: {trt_shapes}')
trt_masks = trt_outputs[2] if with_mask else None
# Show detection outputs
if show:
CLASSES = get_classes(dataset)
score_thr = 0.35
imshow_det_bboxes(img.copy(),
trt_dets,
trt_labels,
segms=trt_masks,
class_names=CLASSES,
score_thr=score_thr,
win_name='TensorRT')
imshow_det_bboxes(img.copy(),
ort_dets,
ort_labels,
segms=ort_masks,
class_names=CLASSES,
score_thr=score_thr,
win_name='ONNXRuntime')
# Compare results
np.testing.assert_allclose(ort_dets, trt_dets, rtol=1e-03, atol=1e-05)
np.testing.assert_allclose(ort_labels, trt_labels)
if with_mask:
np.testing.assert_allclose(ort_masks,
trt_masks,
rtol=1e-03,
atol=1e-05)
print('The numerical values are the same ' +
'between ONNXRuntime and TensorRT')
def onnx2tensorrt(onnx_file,
trt_file,
input_config,
verify=False,
show=False,
workspace_size=1,
verbose=False):
import tensorrt as trt
onnx_model = onnx.load(onnx_file)
max_shape = input_config['max_shape']
min_shape = input_config['min_shape']
opt_shape = input_config['opt_shape']
fp16_mode = False
# create trt engine and wraper
opt_shape_dict = {'input': [min_shape, opt_shape, max_shape]}
max_workspace_size = get_GiB(workspace_size)
trt_engine = onnx2trt(
onnx_model,
opt_shape_dict,
log_level=trt.Logger.VERBOSE if verbose else trt.Logger.ERROR,
fp16_mode=fp16_mode,
max_workspace_size=max_workspace_size)
save_dir, _ = osp.split(trt_file)
if save_dir:
os.makedirs(save_dir, exist_ok=True)
save_trt_engine(trt_engine, trt_file)
print(f'Successfully created TensorRT engine: {trt_file}')
if verify:
# prepare input
one_img, one_meta = preprocess_example_input(input_config)
img_list, img_meta_list = [one_img], [[one_meta]]
img_list = [_.cuda().contiguous() for _ in img_list]
# wrap ONNX and TensorRT model
onnx_model = ONNXRuntimeDetector(onnx_file, CLASSES, device_id=0)
trt_model = TensorRTDetector(trt_file, CLASSES, device_id=0)
# inference with wrapped model
with torch.no_grad():
onnx_results = onnx_model(img_list,
img_metas=img_meta_list,
return_loss=False)[0]
trt_results = trt_model(img_list,
img_metas=img_meta_list,
return_loss=False)[0]
if show:
out_file_ort, out_file_trt = None, None
else:
out_file_ort, out_file_trt = 'show-ort.png', 'show-trt.png'
show_img = one_meta['show_img']
score_thr = 0.3
onnx_model.show_result(show_img,
onnx_results,
score_thr=score_thr,
show=True,
win_name='ONNXRuntime',
out_file=out_file_ort)
trt_model.show_result(show_img,
trt_results,
score_thr=score_thr,
show=True,
win_name='TensorRT',
out_file=out_file_trt)
with_mask = trt_model.with_masks
# compare a part of result
if with_mask:
compare_pairs = list(zip(onnx_results, trt_results))
else:
compare_pairs = [(onnx_results, trt_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 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')
opset_version=11,
show=False,
output_file='tmp.onnx',
verify=False,
test_img=None,
do_simplify=False,
dynamic_export=None,
skip_postprocess=False):
input_config = {
'input_shape': input_shape,
'input_path': input_img,
'normalize_cfg': normalize_cfg
}
# prepare input
one_img, one_meta = preprocess_example_input(input_config)
img_list, img_meta_list = [one_img], [[one_meta]]
if skip_postprocess:
warnings.warn('Not all models support export onnx without post '
'process, especially two stage detectors!')
model.forward = model.forward_dummy
torch.onnx.export(
model,
one_img,
output_file,
input_names=['input'],
export_params=True,
keep_initializers_as_inputs=True,
do_constant_folding=True,
verbose=show,