exporting faster_rcnn_fbnetv3g_fpn get a NotImplementedError: Could not run 'aten::_slow_conv2d_forward' with arguments from the 'QuantizedCPU' backend

I used the demo to train and export faster_rcnn_fbnetv3g_fpn model using customized COCO datasets, got this error, all the other detection models works, code and error logs is as follows:

import os
import cv2
from detectron2.data.datasets import register_coco_instances
from detectron2.data import MetadataCatalog, DatasetCatalog
from detectron2.utils.visualizer import Visualizer, ColorMode
from d2go.model_zoo import model_zoo
import matplotlib.pyplot as plt
import copy
from detectron2.data import build_detection_test_loader
from d2go.export.api import convert_and_export_predictor
from d2go.utils.testing.data_loader_helper import create_fake_detection_data_loader
from d2go.export.d2_meta_arch import patch_d2_meta_arch
from mobile_cv.predictor.api import create_predictor
import logging
from d2go.utils.demo_predictor import DemoPredictor
from d2go.runner import GeneralizedRCNNRunner

register_coco_instances("my_dataset_train", {}, "/home/liupeng/sdb1/Image/ScaleDetection/Train/humanDetec/V4/Annotations/coco_info.json",
                        "/home/liupeng/sdb1/Image/ScaleDetection/Train/humanDetec/V4/AugImages")

model_name = "faster_rcnn_fbnetv3g_fpn.yaml"

def prepare_for_launch():
    runner = GeneralizedRCNNRunner()
    cfg = runner.get_default_cfg()
    cfg.merge_from_file(model_zoo.get_config_file(model_name))
    cfg.MODEL_EMA.ENABLED = False
    cfg.DATASETS.TRAIN = ("my_dataset_train",)
    cfg.DATASETS.TEST = ("my_dataset_train",)
    cfg.DATALOADER.NUM_WORKERS = 2
    cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(model_name)  # Let training initialize from model zoo
    cfg.SOLVER.IMS_PER_BATCH = 1
    cfg.SOLVER.BASE_LR = 0.00025  # pick a good LR
    cfg.SOLVER.MAX_ITER = 1    # 600 iterations seems good enough for this toy dataset; you will need to train longer for a practical dataset
    cfg.SOLVER.STEPS = []        # do not decay learning rate
    cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 128   # faster, and good enough for this toy dataset (default: 512)
    cfg.MODEL.ROI_HEADS.NUM_CLASSES = 1  # only has one class (ballon). (see https://detectron2.readthedocs.io/tutorials/datasets.html#update-the-config-for-new-datasets)
    # NOTE: this config means the number of classes, but a few popular unofficial tutorials incorrect uses num_classes+1 here.
    os.makedirs(cfg.OUTPUT_DIR, exist_ok=True)
    return cfg, runner

cfg, runner = prepare_for_launch()
model = runner.build_model(cfg)
runner.do_train(cfg, model, resume=False)

# # disable all the warnings
previous_level = logging.root.manager.disable
logging.disable(logging.INFO)

patch_d2_meta_arch()

cfg_name = model_name
pytorch_model = model_zoo.get(cfg_name, trained=True)
pytorch_model.cpu()

with create_fake_detection_data_loader(224, 320, is_train=False) as data_loader:
    predictor_path = convert_and_export_predictor(
            model_zoo.get_config(cfg_name),
            copy.deepcopy(pytorch_model),
            "torchscript_int8@tracing",
            './',
            data_loader,
        )

# recover the logging level
logging.disable(previous_level)

model = create_predictor(predictor_path)

im = cv2.imread("/home/liupeng/sdb1/PythonProject/scaleDetection/data/images/12_20_09_24_087.jpg")
predictor = DemoPredictor(model)
outputs = predictor(im)
v = Visualizer(im,
                    MetadataCatalog.get("my_dataset_train"),
                    scale=1,
                    instance_mode=ColorMode.IMAGE
                    )
out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
plt.imshow(out.get_image()[:, :, ::-1])
plt.show()

Traceback (most recent call last): File “/home/liupeng/sdb1/PythonProject/scaleDetection/detectron2go/fasterrcnn/train.py”, line 114, in <module> File “/home/liupeng/anaconda3/envs/scaleDetection/lib/python3.9/site-packages/d2go/export/api.py”, line 151, in convert_and_export_predictor return export_predictor(cfg, pytorch_model, predictor_type, output_dir, data_loader) File “/home/liupeng/anaconda3/envs/scaleDetection/lib/python3.9/site-packages/d2go/export/api.py”, line 174, in export_predictor return default_export_predictor( File “/home/liupeng/anaconda3/envs/scaleDetection/lib/python3.9/site-packages/d2go/export/api.py”, line 262, in default_export_predictor model_info = _export_single_model( File “/home/liupeng/anaconda3/envs/scaleDetection/lib/python3.9/site-packages/d2go/export/api.py”, line 196, in _export_single_model load_kwargs = model_export_method.export( File “/home/liupeng/anaconda3/envs/scaleDetection/lib/python3.9/site-packages/d2go/export/torchscript.py”, line 348, in new_f return old_f(cls, model, input_args, save_path, export_method, **export_kwargs) File “/home/liupeng/anaconda3/envs/scaleDetection/lib/python3.9/site-packages/d2go/export/torchscript.py”, line 209, in new_f outputs = model(*input_args) File “/home/liupeng/anaconda3/envs/scaleDetection/lib/python3.9/site-packages/torch/nn/modules/module.py”, line 1110, in _call_impl return forward_call(*input, **kwargs) File “/home/liupeng/anaconda3/envs/scaleDetection/lib/python3.9/site-packages/d2go/modeling/meta_arch/rcnn.py”, line 388, in forward return self.model.inference(inputs, do_postprocess=False)[0] File “/home/liupeng/anaconda3/envs/scaleDetection/lib/python3.9/site-packages/detectron2/modeling/meta_arch/rcnn.py”, line 200, in inference features = self.backbone(images.tensor) File “/home/liupeng/anaconda3/envs/scaleDetection/lib/python3.9/site-packages/torch/nn/modules/module.py”, line 1110, in _call_impl return forward_call(*input, **kwargs) File “/home/liupeng/anaconda3/envs/scaleDetection/lib/python3.9/site-packages/mobile_cv/arch/utils/quantize_utils.py”, line 345, in wrapped_method q_outputs = getattr(ModuleType, wrapped_method_name)(self, *q_args, **q_kwargs) File “/home/liupeng/anaconda3/envs/scaleDetection/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py”, line 128, in forward prev_features = self.lateral_convs0 File “/home/liupeng/anaconda3/envs/scaleDetection/lib/python3.9/site-packages/torch/nn/modules/module.py”, line 1110, in _call_impl return forward_call(*input, **kwargs) File “/home/liupeng/anaconda3/envs/scaleDetection/lib/python3.9/site-packages/detectron2/layers/wrappers.py”, line 106, in forward x = F.conv2d( NotImplementedError: Could not run ‘aten::_slow_conv2d_forward’ with arguments from the ‘QuantizedCPU’ backend. This could be because the operator doesn’t exist for this backend, or was omitted during the selective/custom build process (if using custom build). If you are a Facebook employee using PyTorch on mobile, please visit https://fburl.com/ptmfixes for possible resolutions. ‘aten::_slow_conv2d_forward’ is only available for these backends: [CPU, CUDA, BackendSelect, Python, Named, Conjugate, Negative, ZeroTensor, ADInplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradXLA, AutogradLazy, AutogradXPU, AutogradMLC, AutogradHPU, AutogradNestedTensor, AutogradPrivateUse1, AutogradPrivateUse2, AutogradPrivateUse3, Tracer, UNKNOWN_TENSOR_TYPE_ID, Autocast, Batched, VmapMode, Functionalize].

Expected behavior:

is it a config error?

[wat3rBro]

@wat3rBro Any thoughts about this issue? Is that because of using FBNetV2FpnBackbone

@zhanghang1989 yes, it looks like issue from FPN, https://github.com/facebookresearch/d2go/pull/163 should fix it.

[zhanghang1989]

Please change the https://github.com/facebookresearch/d2go/blob/main/configs/faster_rcnn_fbnetv3g_fpn.yaml#L38 to “MaskRCNNConvUpsampleHead”