FastDeploy/examples/vision/matting/rvm/export.md

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RobustVideoMatting supports TRT’s dynamic ONNX export

Environment Dependencies

• python >= 3.5
• pytorch 1.12.0
• onnx 1.10.0
• onnxsim 0.4.8

Step 1: Pull the RobustVideoMatting onnx branch code

git clone -b onnx https://github.com/PeterL1n/RobustVideoMatting.git
cd RobustVideoMatting

Step 2: Remove downsample_ratio dynamic input

Remove the downsample_ratio input in model/model.py.

def forward(self, src, r1, r2, r3, r4,
                # downsample_ratio: float = 0.25,
                segmentation_pass: bool = False):

    if torch.onnx.is_in_onnx_export():
        # src_sm = CustomOnnxResizeByFactorOp.apply(src, 0.25)
        src_sm = self._interpolate(src, scale_factor=0.25)
    elif downsample_ratio != 1:
        src_sm = self._interpolate(src, scale_factor=0.25)
    else:
        src_sm = src

    f1, f2, f3, f4 = self.backbone(src_sm)
    f4 = self.aspp(f4)
    hid, *rec = self.decoder(src_sm, f1, f2, f3, f4, r1, r2, r3, r4)

    if not segmentation_pass:
        fgr_residual, pha = self.project_mat(hid).split([3, 1], dim=-3)
        # if torch.onnx.is_in_onnx_export() or downsample_ratio != 1:
        if torch.onnx.is_in_onnx_export():
            fgr_residual, pha = self.refiner(src, src_sm, fgr_residual, pha, hid)
        fgr = fgr_residual + src
        fgr = fgr.clamp(0., 1.)
        pha = pha.clamp(0., 1.)
        return [fgr, pha, *rec]
    else:
        seg = self.project_seg(hid)
        return [seg, *rec]

Step 3: Modify the export ONNX script

Modify export_onnx.py script to remove the downsample_ratio input

def export(self):
    rec = (torch.zeros([1, 1, 1, 1]).to(self.args.device, self.precision),) * 4
    # src = torch.randn(1, 3, 1080, 1920).to(self.args.device, self.precision)
    src = torch.randn(1, 3, 1920, 1080).to(self.args.device, self.precision)
    # downsample_ratio = torch.tensor([0.25]).to(self.args.device)

    dynamic_spatial = {0: 'batch_size', 2: 'height', 3: 'width'}
    dynamic_everything = {0: 'batch_size', 1: 'channels', 2: 'height', 3: 'width'}

    torch.onnx.export(
        self.model,
        # (src, *rec, downsample_ratio),
        (src, *rec),
        self.args.output,
        export_params=True,
        opset_version=self.args.opset,
        do_constant_folding=True,
        # input_names=['src', 'r1i', 'r2i', 'r3i', 'r4i', 'downsample_ratio'],
        input_names=['src', 'r1i', 'r2i', 'r3i', 'r4i'],
        output_names=['fgr', 'pha', 'r1o', 'r2o', 'r3o', 'r4o'],
        dynamic_axes={
            'src': {0: 'batch_size0', 2: 'height0', 3: 'width0'},
            'fgr': {0: 'batch_size1', 2: 'height1', 3: 'width1'},
            'pha': {0: 'batch_size2', 2: 'height2', 3: 'width2'},
            'r1i': {0: 'batch_size3', 1: 'channels3', 2: 'height3', 3: 'width3'},
            'r2i': {0: 'batch_size4', 1: 'channels4', 2: 'height4', 3: 'width4'},
            'r3i': {0: 'batch_size5', 1: 'channels5', 2: 'height5', 3: 'width5'},
            'r4i': {0: 'batch_size6', 1: 'channels6', 2: 'height6', 3: 'width6'},
            'r1o': {0: 'batch_size7', 2: 'height7', 3: 'width7'},
            'r2o': {0: 'batch_size8', 2: 'height8', 3: 'width8'},
            'r3o': {0: 'batch_size9', 2: 'height9', 3: 'width9'},
            'r4o': {0: 'batch_size10', 2: 'height10', 3: 'width10'},
        })

Run the following commands

python export_onnx.py \
    --model-variant mobilenetv3 \
    --checkpoint rvm_mobilenetv3.pth \
    --precision float32 \
    --opset 12 \
    --device cuda \
    --output rvm_mobilenetv3.onnx

Note：

• For the dynamic shape of the multi-input ONNX model in trt, if the shapes of x0 and x1 are different, we cannot use height and width but height0 and height1 to differentiate them, otherwise, there will be some mistakes when building engine

Step 4: Simplify with onnxsim

Install onnxsim and simplify the ONNX model exported in step 3

pip install onnxsim
onnxsim rvm_mobilenetv3.onnx rvm_mobilenetv3_trt.onnx

rvm_mobilenetv3_trt.onnx: The ONNX model in dynamic shape that can run the TRT backend