Converting a PyTorch YOLACT Model

Danger

The code described here has been deprecated! Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but you should not use it in contemporary applications.

This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the Python tutorials.

You Only Look At CoefficienTs (YOLACT) is a simple, fully convolutional model for real-time instance segmentation. The PyTorch implementation is publicly available in this GitHub repository. The YOLACT++ model is not supported, because it uses deformable convolutional layers that cannot be represented in ONNX format.

Creating a Patch File

Before converting the model, create a patch file for the repository. The patch modifies the framework code by adding a special command-line argument to the framework options. The argument enables inference graph dumping:

1. Go to a writable directory and create a YOLACT_onnx_export.patch file.

2. Copy the following diff code to the file:

   From 76deb67d4f09f29feda1a633358caa18335d9e9f Mon Sep 17 00:00:00 2001
   From: "OpenVINO" <openvino@intel.com>
   Date: Fri, 12 Mar 2021 00:27:35 +0300
   Subject: [PATCH] Add export to ONNX
   
   ---
    eval.py                |  5 ++++-
    utils/augmentations.py |  7 +++++--
    yolact.py              | 29 +++++++++++++++++++----------
    3 files changed, 28 insertions(+), 13 deletions(-)
   
   diff --git a/eval.py b/eval.py
   index 547bc0a..bde0680 100644
   --- a/eval.py
   +++ b/eval.py
   @@ -593,9 +593,12 @@ def badhash(x):
        return x
   
    def evalimage(net:Yolact, path:str, save_path:str=None):
   -    frame = torch.from_numpy(cv2.imread(path)).cuda().float()
   +    frame = torch.from_numpy(cv2.imread(path)).float()
   +    if torch.cuda.is_available():
   +        frame = frame.cuda()
        batch = FastBaseTransform()(frame.unsqueeze(0))
        preds = net(batch)
   +    torch.onnx.export(net, batch, "yolact.onnx", opset_version=11)
   
        img_numpy = prep_display(preds, frame, None, None, undo_transform=False)
   
   diff --git a/utils/augmentations.py b/utils/augmentations.py
   index cc7a73a..2420603 100644
   --- a/utils/augmentations.py
   +++ b/utils/augmentations.py
   @@ -623,8 +623,11 @@ class FastBaseTransform(torch.nn.Module):
        def __init__(self):
            super().__init__()
   
   -        self.mean = torch.Tensor(MEANS).float().cuda()[None, :, None, None]
   -        self.std  = torch.Tensor( STD ).float().cuda()[None, :, None, None]
   +        self.mean = torch.Tensor(MEANS).float()[None, :, None, None]
   +        self.std  = torch.Tensor( STD ).float()[None, :, None, None]
   +        if torch.cuda.is_available():
   +            self.mean.cuda()
   +            self.std.cuda()
            self.transform = cfg.backbone.transform
   
        def forward(self, img):
   diff --git a/yolact.py b/yolact.py
   index d83703b..f8c787c 100644
   --- a/yolact.py
   +++ b/yolact.py
   @@ -17,19 +17,22 @@ import torch.backends.cudnn as cudnn
    from utils import timer
    from utils.functions import MovingAverage, make_net
   
   -# This is required for Pytorch 1.0.1 on Windows to initialize Cuda on some driver versions.
   -# See the bug report here: https://github.com/pytorch/pytorch/issues/17108
   -torch.cuda.current_device()
   -
   -# As of March 10, 2019, Pytorch DataParallel still doesn't support JIT Script Modules
   -use_jit = torch.cuda.device_count() <= 1
   -if not use_jit:
   -    print('Multiple GPUs detected! Turning off JIT.')
   +use_jit = False
   
    ScriptModuleWrapper = torch.jit.ScriptModule if use_jit else nn.Module
    script_method_wrapper = torch.jit.script_method if use_jit else lambda fn, _rcn=None: fn
   
   
   +def decode(loc, priors):
   +    variances = [0.1, 0.2]
   +    boxes = torch.cat((priors[:, :2] + loc[:, :, :2] * variances[0] * priors[:, 2:], priors[:, 2:] * torch.exp(loc[:, :, 2:] * variances[1])), 2)
   +
   +    boxes_result1 = boxes[:, :, :2] - boxes[:, :, 2:] / 2
   +    boxes_result2 = boxes[:, :, 2:] + boxes_result1
   +    boxes_result = torch.cat((boxes_result1, boxes_result2), 2)
   +
   +    return boxes_result
   +
   
    class Concat(nn.Module):
        def __init__(self, nets, extra_params):
   @@ -476,7 +479,10 @@ class Yolact(nn.Module):
   
        def load_weights(self, path):
            """ Loads weights from a compressed save file. """
   -        state_dict = torch.load(path)
   +        if torch.cuda.is_available():
   +            state_dict = torch.load(path)
   +        else:
   +            state_dict = torch.load(path, map_location=torch.device('cpu'))
   
            # For backward compatibility, remove these (the new variable is called layers)
            for key in list(state_dict.keys()):
   @@ -673,8 +679,11 @@ class Yolact(nn.Module):
                    else:
                        pred_outs['conf'] = F.softmax(pred_outs['conf'], -1)
   
   -            return self.detect(pred_outs, self)
   +            pred_outs['boxes'] = decode(pred_outs['loc'], pred_outs['priors']) # decode output boxes
   
   +            pred_outs.pop('priors') # remove unused in postprocessing layers
   +            pred_outs.pop('loc') # remove unused in postprocessing layers
   +            return pred_outs
   
   
   
   --
   

3. Save and close the file.

Converting a YOLACT Model to the OpenVINO IR format

Step 1. Clone the GitHub repository and check out the commit:

1. Clone the YOLACT repository:

   git clone https://github.com/dbolya/yolact
   

2. Check out the necessary commit:

   git checkout 57b8f2d95e62e2e649b382f516ab41f949b57239
   

3. Set up the environment as described in README.md.

Step 2. Download a pre-trained model from the list attached in the Evaluation section of README.md document, for example yolact_base_54_800000.pth.

Step 3. Export the model to ONNX format.

1. Apply the YOLACT_onnx_export.patch patch to the repository. Refer to the Create a Patch File instructions if you do not have it:

   git apply /path/to/patch/YOLACT_onnx_export.patch
   

2. Evaluate the YOLACT model to export it to ONNX format:

   python3 eval.py \
       --trained_model=/path/to/yolact_base_54_800000.pth \
       --score_threshold=0.3 \
       --top_k=10 \
       --image=/path/to/image.jpg \
       --cuda=False
   

3. The script may fail, but you should get yolact.onnx file.

Step 4. Convert the model to the IR:

mo --input_model /path/to/yolact.onnx

Step 5. Embed input preprocessing into the IR:

To get performance gain by offloading to the OpenVINO application of mean/scale values and RGB->BGR conversion, use the following model conversion API parameters:

• If the backbone of the model is Resnet50-FPN or Resnet101-FPN, use the following MO command line:

  mo \
      --input_model /path/to/yolact.onnx \
      --reverse_input_channels \
      --mean_values "[123.68, 116.78, 103.94]" \
      --scale_values "[58.40, 57.12, 57.38]"
  

• If the backbone of the model is Darknet53-FPN, use the following MO command line:

  mo \
      --input_model /path/to/yolact.onnx \
      --reverse_input_channels \
      --scale 255