def test_simplify():
from mmcv.onnx.simplify import simplify
# only support PyTorch >= 1.5.0
if version.parse(torch.__version__) < version.parse('1.5.0'):
pytest.skip('mmcv.onnx.simplify only support with PyTorch >= 1.5.0')
def foo(x):
y = x.view((x.shape[0], x.shape[1], x.shape[3], x.shape[2]))
return y
net = WrapFunction(foo)
dummy_input = torch.randn(2, 3, 4, 5)
torch.onnx.export(net, dummy_input, onnx_file, input_names=['input'])
ori_onnx_model = onnx.load(onnx_file)
feed_input = [{'input': dummy_input.detach().cpu().numpy()}]
slim_onnx_model = simplify(ori_onnx_model, feed_input, onnx_file)
numel_before = len(ori_onnx_model.graph.node)
numel_after = len(slim_onnx_model.graph.node)
os.remove(onnx_file)
assert numel_before == 18 and numel_after == 1, 'Simplify failed.'
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):
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 = ['boxes']
if model.with_bbox:
output_names.append('labels')
if model.with_mask:
output_names.append('masks')
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)
model.forward = orig_model.forward
# simplify onnx model
if do_simplify:
from mmdet import digit_version
import mmcv
min_required_version = '1.2.5'
assert digit_version(mmcv.__version__) >= digit_version(
min_required_version
), f'Requires to install mmcv>={min_required_version}'
from mmcv.onnx.simplify import simplify
input_dic = {'input': one_img.detach().cpu().numpy()}
_ = simplify(output_file, [input_dic], output_file)
print(f'Successfully exported ONNX model: {output_file}')
if verify:
from mmdet.core import get_classes, bbox2result
from mmdet.apis import show_result_pyplot
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.')
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 test_img is not None:
input_config['input_path'] = test_img
one_img, one_meta = preprocess_example_input(input_config)
tensor_data = [one_img]
# check the numerical value
# 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 onnxruntime
if osp.exists(ort_custom_op_path):
session_options.register_custom_ops_library(ort_custom_op_path)
sess = rt.InferenceSession(output_file, session_options)
onnx_outputs = sess.run(None,
{net_feed_input[0]: one_img.detach().numpy()})
output_names = [_.name for _ in sess.get_outputs()]
output_shapes = [_.shape for _ in onnx_outputs]
print(f'onnxruntime output names: {output_names}, \
output shapes: {output_shapes}')
nrof_out = len(onnx_outputs)
assert nrof_out > 0, 'Must have output'
with_mask = nrof_out == 3
if nrof_out == 1:
onnx_results = onnx_outputs[0]
else:
det_bboxes, det_labels = onnx_outputs[:2]
onnx_results = bbox2result(det_bboxes, det_labels, num_classes)
if with_mask:
segm_results = onnx_outputs[2].squeeze(1)
cls_segms = [[] for _ in range(num_classes)]
for i in range(det_bboxes.shape[0]):
cls_segms[det_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='ONNX')
# compare a part of result
if with_mask:
compare_pairs = list(zip(onnx_results, pytorch_results))
else:
compare_pairs = [(onnx_results, pytorch_results)]
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,
)
print('The numerical values are the same between Pytorch and ONNX')