Converting a PyTorch YOLACT Model¶
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:
Go to a writable directory and create a
YOLACT_onnx_export.patchfile.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 compatability, 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 --
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:
Clone the YOLACT repository:
git clone https://github.com/dbolya/yolact
Check out the necessary commit:
git checkout 57b8f2d95e62e2e649b382f516ab41f949b57239
Set up the environment as described in
README.md.
Step 2. Download a pretrained 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.
Apply the
YOLACT_onnx_export.patchpatch 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
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=FalseThe script may fail, but you should get
yolact.onnxfile.
Step 4. Convert the model to the IR:
mo --input_model /path/to/yolact.onnxStep 4. 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 options of the Model Optimizer (MO):
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