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[Feature]: Add onnx evaluation tool (open-mmlab#279)
* update doc for onnx eval tool * add ort eval tool * add mattor test support * update document * update doc * add format to html table * add onnx wraper test * fix lint * better onnx unit test * skip if torch==1.3.x Co-authored-by: q.yao <[email protected]>
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| @@ -0,0 +1,3 @@ | ||
| from .wrappers import ONNXRuntimeEditing | ||
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| __all__ = ['ONNXRuntimeEditing'] |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,133 @@ | ||
| import os.path as osp | ||
| import warnings | ||
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| import numpy as np | ||
| import onnxruntime as ort | ||
| import torch | ||
| from torch import nn | ||
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| from mmedit.models import BaseMattor, BasicRestorer, build_model | ||
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| def inference_with_session(sess, io_binding, output_names, input_tensor): | ||
| device_type = input_tensor.device.type | ||
| device_id = input_tensor.device.index | ||
| device_id = 0 if device_id is None else device_id | ||
| io_binding.bind_input( | ||
| name='input', | ||
| device_type=device_type, | ||
| device_id=device_id, | ||
| element_type=np.float32, | ||
| shape=input_tensor.shape, | ||
| buffer_ptr=input_tensor.data_ptr()) | ||
| for name in output_names: | ||
| io_binding.bind_output(name) | ||
| sess.run_with_iobinding(io_binding) | ||
| pred = io_binding.copy_outputs_to_cpu() | ||
| return pred | ||
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| class ONNXRuntimeMattor(nn.Module): | ||
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| def __init__(self, sess, io_binding, output_names, base_model): | ||
| super(ONNXRuntimeMattor, self).__init__() | ||
| self.sess = sess | ||
| self.io_binding = io_binding | ||
| self.output_names = output_names | ||
| self.base_model = base_model | ||
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| def forward(self, | ||
| merged, | ||
| trimap, | ||
| meta, | ||
| test_mode=False, | ||
| save_image=False, | ||
| save_path=None, | ||
| iteration=None): | ||
| input_tensor = torch.cat((merged, trimap), 1).contiguous() | ||
| pred_alpha = inference_with_session(self.sess, self.io_binding, | ||
| self.output_names, input_tensor)[0] | ||
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| pred_alpha = pred_alpha.squeeze() | ||
| pred_alpha = self.base_model.restore_shape(pred_alpha, meta) | ||
| eval_result = self.base_model.evaluate(pred_alpha, meta) | ||
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| if save_image: | ||
| self.base_model.save_image(pred_alpha, meta, save_path, iteration) | ||
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| return {'pred_alpha': pred_alpha, 'eval_result': eval_result} | ||
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| class RestorerGenerator(nn.Module): | ||
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| def __init__(self, sess, io_binding, output_names): | ||
| super(RestorerGenerator, self).__init__() | ||
| self.sess = sess | ||
| self.io_binding = io_binding | ||
| self.output_names = output_names | ||
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| def forward(self, x): | ||
| pred = inference_with_session(self.sess, self.io_binding, | ||
| self.output_names, x)[0] | ||
| pred = torch.from_numpy(pred) | ||
| return pred | ||
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| class ONNXRuntimeRestorer(nn.Module): | ||
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| def __init__(self, sess, io_binding, output_names, base_model): | ||
| super(ONNXRuntimeRestorer, self).__init__() | ||
| self.sess = sess | ||
| self.io_binding = io_binding | ||
| self.output_names = output_names | ||
| self.base_model = base_model | ||
| restorer_generator = RestorerGenerator(self.sess, self.io_binding, | ||
| self.output_names) | ||
| base_model.generator = restorer_generator | ||
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| def forward(self, lq, gt=None, test_mode=False, **kwargs): | ||
| return self.base_model(lq, gt=gt, test_mode=test_mode, **kwargs) | ||
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| class ONNXRuntimeEditing(nn.Module): | ||
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| def __init__(self, onnx_file, cfg, device_id): | ||
| super(ONNXRuntimeEditing, self).__init__() | ||
| 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.') | ||
| session_options = ort.SessionOptions() | ||
| # register custom op for onnxruntime | ||
| if osp.exists(ort_custom_op_path): | ||
| session_options.register_custom_ops_library(ort_custom_op_path) | ||
| sess = ort.InferenceSession(onnx_file, session_options) | ||
| providers = ['CPUExecutionProvider'] | ||
| options = [{}] | ||
| is_cuda_available = ort.get_device() == 'GPU' | ||
| if is_cuda_available: | ||
| providers.insert(0, 'CUDAExecutionProvider') | ||
| options.insert(0, {'device_id': device_id}) | ||
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| sess.set_providers(providers, options) | ||
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| self.sess = sess | ||
| self.device_id = device_id | ||
| self.io_binding = sess.io_binding() | ||
| self.output_names = [_.name for _ in sess.get_outputs()] | ||
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| base_model = build_model( | ||
| cfg.model, train_cfg=None, test_cfg=cfg.test_cfg) | ||
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| if isinstance(base_model, BaseMattor): | ||
| WraperClass = ONNXRuntimeMattor | ||
| elif isinstance(base_model, BasicRestorer): | ||
| WraperClass = ONNXRuntimeRestorer | ||
| self.wraper = WraperClass(self.sess, self.io_binding, | ||
| self.output_names, base_model) | ||
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| def forward(self, **kwargs): | ||
| return self.wraper(**kwargs) |
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| Original file line number | Diff line number | Diff line change |
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@@ -2,5 +2,6 @@ codecov | |
| flake8 | ||
| interrogate | ||
| isort==4.3.21 | ||
| onnxruntime | ||
| pytest | ||
| pytest-runner | ||
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