[Feature] add onnxruntime test tool

docs/tools_scripts.md

@@ -79,3 +79,39 @@ Description of arguments:
 - `--dynamic-export`: Determines whether to export ONNX model with dynamic input and output shapes. If not specified, it will be set to `False`.
 
 **Note**: This tool is still experimental. Some customized operators are not supported for now. And we only support `mattor` and `restorer` for now.
+
+### Evaluate ONNX model with ONNXRuntime
+
+We provide `tools/onnx_runtime_test.py` to evaluate ONNX model with ONNXRuntime backend.
+
+Install onnxruntime-gpu before you start the test.
+
+  ```bash
+  pip install onnxruntime-gpu
+  ```
+
+Usage:
+
+  ```bash
+  python ${MMEDIT_PATH}/tools/ort_test.py \
+        ${CFG_PATH} \
+        ${ONNX_PATH} \
+        --out ${OUT_PATH} \
+        --save-path ${SHOW_DIR}
+  ```
+
+Description of arguments:
+
+- `config` : The path of a model config file.
+- `model` : Input model file.
+- `--out` : Output result pickle file.
+- `--save-path` : Path to store images.
+
+**Note**: Only support `mattor` and `restorer` for now.
+
+Results and Models
+
+| Model  |               Config                | Dataset |   Metric    |    PyTorch    |  ONNXRuntime  |
+| :----: | :---------------------------------: | :-----: | :---------: | :-----------: | :-----------: |
+| SRCNN  |   srcnn_x4k915_g1_1000k_div2k.py    |  Set5   | PSNR / SSIM | 28.43 / 0.809 | 28.41 / 0.810 |
+| ESRGAN | esrgan_x4c64b23g32_g1_400k_div2k.py |  Set5   | PSNR / SSIM | 28.27 / 0.777 | 28.26 / 0.778 |

tools/onnx_runtime_test.py

@@ -0,0 +1,209 @@
+import argparse
+import os
+import os.path as osp
+import warnings
+
+import mmcv
+import numpy as np
+import onnxruntime as ort
+import torch
+from mmcv.parallel import MMDataParallel
+from mmcv.runner import get_dist_info
+from torch import nn
+
+from mmedit.apis import single_gpu_test
+from mmedit.datasets import build_dataloader, build_dataset
+from mmedit.models import BaseMattor, BasicRestorer, build_model
+
+
+def inference_with_session(sess, io_binding, output_names, input_tensor):
+    device_type = input_tensor.device.type
+    device_id = input_tensor.device.index
+    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
+
+
+class ONNXRuntimeMattor(nn.Module):
+
+    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
+
+    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]
+
+        pred_alpha = pred_alpha.squeeze()
+        pred_alpha = self.base_model.restore_shape(pred_alpha, meta)
+        eval_result = self.base_model.evaluate(pred_alpha, meta)
+
+        if save_image:
+            self.base_model.save_image(pred_alpha, meta, save_path, iteration)
+
+        return {'pred_alpha': pred_alpha, 'eval_result': eval_result}
+
+
+class RestorerGenerator(nn.Module):
+
+    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
+
+    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
+
+
+class ONNXRuntimeRestorer(nn.Module):
+
+    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
+
+    def forward(self, lq, gt=None, test_mode=False, **kwargs):
+        return self.base_model(lq, gt=gt, test_mode=test_mode, **kwargs)
+
+
+class ONNXRuntimeEditing(nn.Module):
+
+    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})
+
+        sess.set_providers(providers, options)
+
+        self.sess = sess
+        self.device_id = device_id
+        self.io_binding = sess.io_binding()
+        self.output_names = [_.name for _ in sess.get_outputs()]
+
+        base_model = build_model(
+            cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
+
+        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)
+
+    def forward(self, **kwargs):
+        return self.wraper(**kwargs)
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='mmediting tester')
+    parser.add_argument('config', help='test config file path')
+    parser.add_argument('model', help='Input model file')
+    parser.add_argument('--out', help='output result pickle file')
+    parser.add_argument(
+        '--save-path', default=None, type=str, help='path to store images')
+    parser.add_argument('--local_rank', type=int, default=0)
+    args = parser.parse_args()
+    if 'LOCAL_RANK' not in os.environ:
+        os.environ['LOCAL_RANK'] = str(args.local_rank)
+    return args
+
+
+def main():
+    args = parse_args()
+
+    cfg = mmcv.Config.fromfile(args.config)
+
+    cfg.model.pretrained = None
+
+    # init distributed env first, since logger depends on the dist info.
+    distributed = False
+
+    rank, _ = get_dist_info()
+
+    # build the dataloader
+    # TODO: support multiple images per gpu (only minor changes are needed)
+    dataset = build_dataset(cfg.data.test)
+
+    loader_cfg = {
+        **dict((k, cfg.data[k]) for k in ['workers_per_gpu'] if k in cfg.data),
+        **dict(
+            samples_per_gpu=1,
+            drop_last=False,
+            shuffle=False,
+            dist=distributed),
+        **cfg.data.get('test_dataloader', {})
+    }
+
+    data_loader = build_dataloader(dataset, **loader_cfg)
+
+    # build the model and load checkpoint
+    model = ONNXRuntimeEditing(args.model, cfg=cfg, device_id=0)
+
+    args.save_image = args.save_path is not None
+    model = MMDataParallel(model, device_ids=[0])
+    outputs = single_gpu_test(
+        model,
+        data_loader,
+        save_path=args.save_path,
+        save_image=args.save_image)
+
+    if rank == 0:
+        print('')
+        # print metrics
+        stats = dataset.evaluate(outputs)
+        for stat in stats:
+            print('Eval-{}: {}'.format(stat, stats[stat]))
+
+        # save result pickle
+        if args.out:
+            print('writing results to {}'.format(args.out))
+            mmcv.dump(outputs, args.out)
+
+
+if __name__ == '__main__':
+    main()