Detectron2 is FAIR's next-generation platform for object detection and segmentation.

Summary of changes

This PR fixes both ONNX-only and Caffe2 ONNX exporters for the latest versions of this repo and PyTorch.

For ONNX-only, the main issue is that add_export_config(cfg) is not exposed when Caffe2 is not compiled along with PyTorch, but for ONNX-only scenarios, such dependency is not needed. Therefore, add_export_config is moved from detectron2/export/api.py to detectron2/export/__init__.py

A second contribution is a new test_export_onnx.py test file that export almost the same models as the test_export_tracing.py tests.

For the Caffe2-ONNX, the main issue was a dependency on ONNX optimizer pass which is deprecated in newer ONNX versions. This PR removes such dependency because fuse_bn_into_conv optimization pass is already performed by torch.onnx.export anyway.

Fixes https://github.com/facebookresearch/detectron2/issues/3488 Fixes https://github.com/pytorch/pytorch/issues/69674 (PyTorch repo)

How do I compute validation loss during training?

I'm trying to compute the loss on a validation dataset for each iteration during training. To do so, I've created my own hook:

class ValidationLoss(detectron2.engine.HookBase):
    def __init__(self, config, dataset_name):
        super(ValidationLoss, self).__init__()
        self._loader = detectron2.data.build_detection_test_loader(config, dataset_name)
        
    def after_step(self):
        for batch in self._loader:
            loss = self.trainer.model(batch)
            log.debug(f"validation loss: {loss}")

... which I register with a DefaultTrainer. The hook code is called during training, but fails with the following:

INFO:detectron2.engine.train_loop:Starting training from iteration 0
ERROR:detectron2.engine.train_loop:Exception during training:
Traceback (most recent call last):
  File "/ascldap/users/tshead/miniconda3/lib/python3.7/site-packages/detectron2/engine/train_loop.py", line 133, in train
    self.after_step()
  File "/ascldap/users/tshead/miniconda3/lib/python3.7/site-packages/detectron2/engine/train_loop.py", line 153, in after_step
    h.after_step()
  File "<ipython-input-6-63b308743b7d>", line 8, in after_step
    loss = self.trainer.model(batch)
  File "/ascldap/users/tshead/miniconda3/lib/python3.7/site-packages/torch/nn/modules/module.py", line 532, in __call__
    result = self.forward(*input, **kwargs)
  File "/ascldap/users/tshead/miniconda3/lib/python3.7/site-packages/detectron2/modeling/meta_arch/rcnn.py", line 123, in forward
    proposals, proposal_losses = self.proposal_generator(images, features, gt_instances)
  File "/ascldap/users/tshead/miniconda3/lib/python3.7/site-packages/torch/nn/modules/module.py", line 532, in __call__
    result = self.forward(*input, **kwargs)
  File "/ascldap/users/tshead/miniconda3/lib/python3.7/site-packages/detectron2/modeling/proposal_generator/rpn.py", line 164, in forward
    losses = {k: v * self.loss_weight for k, v in outputs.losses().items()}
  File "/ascldap/users/tshead/miniconda3/lib/python3.7/site-packages/detectron2/modeling/proposal_generator/rpn_outputs.py", line 322, in losses
    gt_objectness_logits, gt_anchor_deltas = self._get_ground_truth()
  File "/ascldap/users/tshead/miniconda3/lib/python3.7/site-packages/detectron2/modeling/proposal_generator/rpn_outputs.py", line 262, in _get_ground_truth
    for image_size_i, anchors_i, gt_boxes_i in zip(self.image_sizes, anchors, self.gt_boxes):
TypeError: zip argument #3 must support iteration
INFO:detectron2.engine.hooks:Total training time: 0:00:00 (0:00:00 on hooks)

The traceback seems to imply that ground truth data is missing, which made me think that the data loader was the problem. However, switching to a training loader produces a different error:

class ValidationLoss(detectron2.engine.HookBase):
    def __init__(self, config, dataset_name):
        super(ValidationLoss, self).__init__()
        self._loader = detectron2.data.build_detection_train_loader(config, dataset_name)
        
    def after_step(self):
        for batch in self._loader:
            loss = self.trainer.model(batch)
            log.debug(f"validation loss: {loss}")

INFO:detectron2.engine.train_loop:Starting training from iteration 0
ERROR:detectron2.engine.train_loop:Exception during training:
Traceback (most recent call last):
  File "/ascldap/users/tshead/miniconda3/lib/python3.7/site-packages/detectron2/engine/train_loop.py", line 133, in train
    self.after_step()
  File "/ascldap/users/tshead/miniconda3/lib/python3.7/site-packages/detectron2/engine/train_loop.py", line 153, in after_step
    h.after_step()
  File "<ipython-input-6-e0d2c509cc72>", line 7, in after_step
    for batch in self._loader:
  File "/ascldap/users/tshead/miniconda3/lib/python3.7/site-packages/detectron2/data/common.py", line 109, in __iter__
    for d in self.dataset:
  File "/ascldap/users/tshead/miniconda3/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 345, in __next__
    data = self._next_data()
  File "/ascldap/users/tshead/miniconda3/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 856, in _next_data
    return self._process_data(data)
  File "/ascldap/users/tshead/miniconda3/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 881, in _process_data
    data.reraise()
  File "/ascldap/users/tshead/miniconda3/lib/python3.7/site-packages/torch/_utils.py", line 394, in reraise
    raise self.exc_type(msg)
TypeError: Caught TypeError in DataLoader worker process 0.
Original Traceback (most recent call last):
  File "/ascldap/users/tshead/miniconda3/lib/python3.7/site-packages/torch/utils/data/_utils/worker.py", line 178, in _worker_loop
    data = fetcher.fetch(index)
  File "/ascldap/users/tshead/miniconda3/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/ascldap/users/tshead/miniconda3/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp>
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/ascldap/users/tshead/miniconda3/lib/python3.7/site-packages/detectron2/data/common.py", line 39, in __getitem__
    data = self._map_func(self._dataset[cur_idx])
  File "/ascldap/users/tshead/miniconda3/lib/python3.7/site-packages/detectron2/utils/serialize.py", line 23, in __call__
    return self._obj(*args, **kwargs)
TypeError: 'str' object is not callable

INFO:detectron2.engine.hooks:Total training time: 0:00:00 (0:00:00 on hooks)

As a sanity check, inference works just fine:

class ValidationLoss(detectron2.engine.HookBase):
    def __init__(self, config, dataset_name):
        super(ValidationLoss, self).__init__()
        self._loader = detectron2.data.build_detection_test_loader(config, dataset_name)
        
    def after_step(self):
        for batch in self._loader:
            with detectron2.evaluation.inference_context(self.trainer.model):
                loss = self.trainer.model(batch)
                log.debug(f"validation loss: {loss}")

INFO:detectron2.engine.train_loop:Starting training from iteration 0
DEBUG:root:validation loss: [{'instances': Instances(num_instances=100, image_height=720, image_width=720, fields=[pred_boxes = Boxes(tensor([[4.4867e+02, 1.9488e+02, 5.1496e+02, 3.9878e+02],
        [4.2163e+02, 1.1204e+02, 6.1118e+02, 5.5378e+02],
        [8.7323e-01, 3.0374e+02, 9.2917e+01, 3.8698e+02],
        [4.3202e+02, 2.0296e+02, 5.7938e+02, 3.6817e+02],
        ...

... but that isn't what I want, of course. Any thoughts?

Thanks in advance, Tim

Can any one tell me how to select only one class which is 'person' in my case from coco data for instance segmentation. Pre trained model. (mask_rcnn_R_50_FPN_3x.yaml)

I want to detect 'person' from the given image only.

Since onnx provides almost all ops needs by maskrcnn, it would be great if model can exported to onnx and would be benefit more from TensorRT acceleration for these large models.

📚 Documentation Improvements

In short

Concerning: https://detectron2.readthedocs.io/en/latest/tutorials/configs.html Problem: Documentation does not seem to have been updated to reflect the new config files (.py rather than .yaml) Solution: Update the documentation

Problem description

FAIR recently published new Mask R-CNN baselines and this was my first introduction to the new config file that no longer relies on YAML files but on 'raw' .py files. I am trying to load the new baselines using the config files mentioned in the MODEL_ZOO (see this table). For example:

from detectron2 import model_zoo
model = model_zoo.get("new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_400ep_LSJ.py", trained=True)

This gives

RuntimeError: new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_400ep_LSJ not available in Model Zoo!

I have installed Detectron2 using the installation instructions. When looking up the documentation on configs, it seems that this has not been updated to reflect the new configs and still solely mentions YAML files.

Proposed solution

It could be that the CONFIG_PATH_TO_URL_SUFFIX dictionary in _ModelZooUrls class still has to be updated and that this is actually a bug (see here), but I find it hard to estimate wheter this is meant behavior (i.e. the new config file should be loaded differently) or a bug due to my limited understanding of the new config files. Either way, I therefore feel like the documentation on readthedocs should be updated to reflect the change from .yaml to .py.

This PR is composed of different fixes to enable and end-to-end ONNX export functionality for detectron2 models

• add_export_config API is publicly available exposed even when caffe2 is not compiled along with PyTorch (that is the new default behavior on latest PyTorch). A warning message informing users about its deprecation on future versions is also added

• tensor.shape[0] replaces len(tensor) and for idx, img in enumerate(tensors) replaces for tmp_var1, tmp_var2 in zip(tensors, batched_imgs) so that the tracer does not lose reference to the user input on the graphs.

  • Before the changes above, the graph (see below) does not have an actual input. Instead, the input is exported as a model weight
  • After the fix, the user images are properly acknowledged as model's input (see below) during ONNX export

• Added unit tests (tests/torch_export_onnx.py) for detectron2 models

• ONNX is added as dependency for the CI to be able to run the aforementioned tests

• Added custom symbolic functions to allow CI pipelines to succeed. The symbolics are needed because PyTorch 1.8, 1.9 and 1.10 adopted by detectron2 have several bugs. They can be removed when 1.11+ is adopted by detectron2's CI infra

Fixes https://github.com/facebookresearch/detectron2/issues/3488 Fixes https://github.com/pytorch/pytorch/issues/69674 (PyTorch repo)

From my perspective a docker-compose file has several benefits. On the one hand, it increases the comfort and on the other hand it is a way to supply users with useful tweaks. The commited docker-compose file addresses several issues and tweaks:

1. It fixes potential problems with dataloaders (see #384).
2. It includes Multi-GPU and performance tweaks as suggested by NVIDIA (see https://docs.nvidia.com/deeplearning/frameworks/user-guide/index.html#caffeovr).
3. It adds GUI support (see #379).
4. It enables caching of downloaded models (see #382).
5. It makes docker run with the UID of the host user.

Of course, all this can be accomplished with a long docker command. However, a docker-compose file give a central place to gather all recommendations for running detectron2 with docker, without bloating the Dockerfile with comments.

Hello,

I am trying to convert a Detectron2 model to ONNX format and make inference without use detectron2 dependence in inference stage.

Even is possible to find some information about that here : https://detectron2.readthedocs.io/en/latest/tutorials/deployment.html The implementation of this task is constantly being updated and the information found in this documentation is not clear enough to carry out this task .

Some one can help me with some Demo/Tutorial of how make it ?

@thiagocrepaldi

Some information:

My model was trained using pre-trained weight from:

'faster_rcnn_50': { 'model_path': 'COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml', 'weights_path': 'model_final_280758.pkl' },

I have 4 classes.

Of course now i have my our weight. My model was saved in .pth forrmat.

I used my our dataset, with image ( .png )

Code in Python

Hi, I am going through the google colab example tutorial.

I am trying to apply mask_rcnn segmentation on a random youtube balloon-video instead of an balloon-image to detect balloon only (one class).

How can I assign .yaml and .pkl files that were generated using images earlier in the tutorial to a random video? thanks

I tried the foolowing but it didn't work. I think I am having trouble assign the trained config and model files.

!cd detectron2_repo && python demo/demo.py --config-file configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml --video-input ../video-clip_b.mp4 --confidence-threshold 0.6 --output ../video-clip_b_testing1.mkv \
  --opts MODEL.WEIGHTS detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl

Currently all Caffe2 export tests (under tests/test_export_caffe2.py) fail because the latest onnx releases do not have onnx.optimizer submodule anymore (instead, a new module onnxoptimizer was created from it)

However, fuse_bn_into_conv optimization previously implemented within onnx.optimizer is already performed by torch.onnx.export too ruing ONNX export. Therefore onnx.optimizer dependency can be safely removed from detectron2 code.

Depends on pytorch/pytorch#75718 Fixes #3488 Fixes pytorch/pytorch#69674 (PyTorch repo)

ps: Although Caffe2 support is/will be deprecated, this PR relies on the fact that contributions are welcome as stated at docs/tutorials/deployment.md

❓ Questions and Help

Hi, I am trying to train on my dataset with just 4 classes. When I run it, I get an error as below: The scripts are interlinked a lot and therefore a bit difficult to debug. How to resolve this?

Thanks.

📚 Documentation Issue

I'm struggling to load the pre-trained model defined by new_baselines/mask_rcnn_R_101_FPN_400ep_LSJ.py. I've found relevant documentation here, here and issue #3225. However none of these clearly elucidate my error.

I'm trying to load the configuration with:

cfg = LazyConfig.load("detectron2/configs/new_baselines/mask_rcnn_R_101_FPN_400ep_LSJ.py")
cfg = setup_cfg(args)

This produces the following traceback:

Traceback (most recent call last):
  File "quality_test.py", line 97, in <module>
    results_ls = get_person_seg_masks(img_path, model_family, model)
  File "detectron2_wrapper.py", line 107, in get_person_seg_masks
    cfg = setup_cfg(args)
  File "detectron2/demo/demo.py", line 29, in setup_cfg
    cfg.merge_from_file(args.config_file)
  File "/home/appuser/detectron2_repo/detectron2/config/config.py", line 46, in merge_from_file
    loaded_cfg = self.load_yaml_with_base(cfg_filename, allow_unsafe=allow_unsafe)
  File "/home/appuser/.local/lib/python3.8/site-packages/fvcore/common/config.py", line 61, in load_yaml_with_base
    cfg = yaml.safe_load(f)
  File "/home/appuser/.local/lib/python3.8/site-packages/yaml/__init__.py", line 125, in safe_load
    return load(stream, SafeLoader)
  File "/home/appuser/.local/lib/python3.8/site-packages/yaml/__init__.py", line 81, in load
    return loader.get_single_data()
  File "/home/appuser/.local/lib/python3.8/site-packages/yaml/constructor.py", line 49, in get_single_data
    node = self.get_single_node()
  File "/home/appuser/.local/lib/python3.8/site-packages/yaml/composer.py", line 39, in get_single_node
    if not self.check_event(StreamEndEvent):
  File "/home/appuser/.local/lib/python3.8/site-packages/yaml/parser.py", line 98, in check_event
    self.current_event = self.state()
  File "/home/appuser/.local/lib/python3.8/site-packages/yaml/parser.py", line 171, in parse_document_start
    raise ParserError(None, None,
yaml.parser.ParserError: expected '<document start>', but found '<scalar>'
  in "detectron2/configs/new_baselines/mask_rcnn_R_101_FPN_400ep_LSJ.py", line 11, column 1

I am trying to implement semantic segmentation on Google Colab by the instructions of the Deeplab project of Detectron2 but when I want to visualize the segments on an image, I face a problem that I cannot solve it.

** "Instructions To Reproduce the Issue and Full Logs":** `!pip install pyyaml==5.1 !pip install exif==1.3.5 !pip install torch==1.8.1+cu111 torchvision==0.9.1+cu111 torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html !git clone --branch v0.6 https://github.com/facebookresearch/detectron2.git detectron2_repo !pip install -e detectron2_repo import detectron2 from detectron2.utils.logger import setup_logger setup_logger() import numpy as np import cv2 import torch from google.colab.patches import cv2_imshow from detectron2 import model_zoo from detectron2.engine import DefaultPredictor from detectron2.config import get_cfg from detectron2.utils.visualizer import Visualizer, ColorMode from detectron2.data import MetadataCatalog coco_metadata = MetadataCatalog.get("coco_2017_val") from detectron2.projects import point_rend from detectron2.projects import deeplab from detectron2.projects.deeplab import add_deeplab_config !pip install 'git+https://github.com/facebookresearch/fvcore.git@d39b53de2c1e363e2ceb27d321d403b4bdb2b7a7' im=cv2.imread("./aachen_000005_000019_leftImg8bit.png") cv2_imshow(im) from detectron2.projects.deeplab.build_solver import build_lr_scheduler from detectron2 import checkpoint from fvcore.common.checkpoint import Checkpointer cfg = get_cfg() deeplab.add_deeplab_config(cfg) cfg.load_yaml_with_base("detectron2_repo/projects/DeepLab/configs/Cityscapes-SemanticSegmentation/deeplab_v3_plus_R_103_os16_mg124_poly_90k_bs16.yaml") cfg.merge_from_file("detectron2_repo/projects/DeepLab/configs/Cityscapes-SemanticSegmentation/deeplab_v3_plus_R_103_os16_mg124_poly_90k_bs16.yaml") cfg.MODEL.WEIGHTS = "https://dl.fbaipublicfiles.com/detectron2/DeepLab/Cityscapes-SemanticSegmentation/deeplab_v3_plus_R_103_os16_mg124_poly_90k_bs16/28054032/model_final_a8a355.pkl" predictor = DefaultPredictor(cfg) outputs = predictor(im)

viz1=Visualizer(im[:,:,::-1], MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), scale=1.0, instance_mode=ColorMode.SEGMENTATION)

output=viz1.draw_sem_seg(outputs["sem_seg"].to("cpu"))

image2 = output.get_image()[:,:,::-1] cv2_imshow(image2)`

The error that I faced is:

TypeError Traceback (most recent call last) in 1 viz1=Visualizer(im[:,:,::-1], MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), scale=1.0, instance_mode=ColorMode.SEGMENTATION) ----> 2 output=viz1.draw_sem_seg(outputs["sem_seg"].to("cpu"))

/content/detectron2_repo/detectron2/utils/visualizer.py in draw_sem_seg(self, sem_seg, area_threshold, alpha) 449 if isinstance(sem_seg, torch.Tensor): 450 sem_seg = sem_seg.numpy() --> 451 labels, areas = np.unique(sem_seg, return_counts=True) 452 sorted_idxs = np.argsort(-areas).tolist() 453 labels = labels[sorted_idxs]

TypeError: list indices must be integers or slices, not numpy.float32

Expected behavior: I expected that I could draw semantic segmentation on the image.

Environment

2023-01-03 21:56:28 URL:https://raw.githubusercontent.com/facebookresearch/detectron2/main/detectron2/utils/collect_env.py [8391/8391] -> "collect_env.py" [1]

sys.platform linux Python 3.8.16 (default, Dec 7 2022, 01:12:13) [GCC 7.5.0] numpy 1.21.6 detectron2 failed to import detectron2._C not built correctly: No module named 'detectron2' Compiler ($CXX) c++ (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 CUDA compiler Build cuda_11.2.r11.2/compiler.29618528_0 DETECTRON2_ENV_MODULE PyTorch 1.13.0+cu116 @/usr/local/lib/python3.8/dist-packages/torch PyTorch debug build False GPU available Yes GPU 0 Tesla T4 (arch=7.5) Driver version 460.32.03 CUDA_HOME /usr/local/cuda Pillow 7.1.2 torchvision 0.14.0+cu116 @/usr/local/lib/python3.8/dist-packages/torchvision torchvision arch flags 3.5, 5.0, 6.0, 7.0, 7.5, 8.0, 8.6 cv2 4.6.0

PyTorch built with:

• GCC 9.3
• C++ Version: 201402
• Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications
• Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)
• OpenMP 201511 (a.k.a. OpenMP 4.5)
• LAPACK is enabled (usually provided by MKL)
• NNPACK is enabled
• CPU capability usage: AVX2
• CUDA Runtime 11.6
• NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86
• CuDNN 8.3.2 (built against CUDA 11.5)
• Magma 2.6.1
• Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.6, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.13.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF,

This is my program so far: `register_coco_instances("mesh_train", {}, "../mesh_coco_Train.json", "../Data/Train/Images") register_coco_instances("mesh_test", {}, "../mesh_coco_Test.json", "../Data/Test/Images")

MetadataCatalog.get("mesh_train").keypoint_names = ["joints"] MetadataCatalog.get("mesh_train").keypoint_flip_map = [] train_dicts = DatasetCatalog.get("mesh_train") test_dicts = DatasetCatalog.get("mesh_test") mesh_metadata = MetadataCatalog.get("mesh_train")

def cv2_imshow(im): im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB) plt.figure(), plt.imshow(im), plt.axis('off') plt.show()

for d in random.sample(train_dicts, 5): print(d["file_name"]) img = cv2.imread(d["file_name"]) visualizer = Visualizer(img[:, :, ::-1], metadata=mesh_metadata, scale=0.5) vis = visualizer.draw_dataset_dict(d) cv2_imshow(vis.get_image()[:, :, ::-1])

cfg = get_cfg() cfg.merge_from_file(model_zoo.get_config_file("COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml")) cfg.DATASETS.TRAIN = ("mesh_train",) cfg.DATASETS.TEST = ("mesh_test",) cfg.DATALOADER.NUM_WORKERS = 4 cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml") cfg.SOLVER.IMS_PER_BATCH = 2 cfg.SOLVER.BASE_LR = 0.001 cfg.SOLVER.MAX_ITER = 300 cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 128 cfg.MODEL.ROI_HEADS.NUM_CLASSES = 1

os.makedirs(cfg.OUTPUT_DIR, exist_ok=True) trainer = DefaultTrainer(cfg) trainer.resume_or_load(resume=False)

print("training....") trainer.train()`

But when I run it, I'm getting the following error:

[01/02 13:47:51 d2.engine.train_loop]: Starting training from iteration 0 ERROR [01/02 13:47:51 d2.engine.train_loop]: Exception during training: Traceback (most recent call last): File "C:\Users\Singh Automation\AppData\Local\Programs\Python\Python310\lib\site-packages\detectron2\engine\train_loop.py", line 149, in train self.run_step() File "C:\Users\Singh Automation\AppData\Local\Programs\Python\Python310\lib\site-packages\detectron2\engine\defaults.py", line 494, in run_step self._trainer.run_step() File "C:\Users\Singh Automation\AppData\Local\Programs\Python\Python310\lib\site-packages\detectron2\engine\train_loop.py", line 268, in run_step data = next(self._data_loader_iter) File "C:\Users\Singh Automation\AppData\Local\Programs\Python\Python310\lib\site-packages\detectron2\data\common.py", line 283, in iter for d in self.dataset: File "C:\Users\Singh Automation\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\utils\data\dataloader.py", line 435, in iter return self._get_iterator() File "C:\Users\Singh Automation\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\utils\data\dataloader.py", line 381, in _get_iterator return _MultiProcessingDataLoaderIter(self) File "C:\Users\Singh Automation\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\utils\data\dataloader.py", line 1034, in init w.start() File "C:\Users\Singh Automation\AppData\Local\Programs\Python\Python310\lib\multiprocessing\process.py", line 121, in start self._popen = self._Popen(self) File "C:\Users\Singh Automation\AppData\Local\Programs\Python\Python310\lib\multiprocessing\context.py", line 224, in _Popen return _default_context.get_context().Process._Popen(process_obj) File "C:\Users\Singh Automation\AppData\Local\Programs\Python\Python310\lib\multiprocessing\context.py", line 336, in _Popen return Popen(process_obj) File "C:\Users\Singh Automation\AppData\Local\Programs\Python\Python310\lib\multiprocessing\popen_spawn_win32.py", line 45, in init prep_data = spawn.get_preparation_data(process_obj._name) File "C:\Users\Singh Automation\AppData\Local\Programs\Python\Python310\lib\multiprocessing\spawn.py", line 154, in get_preparation_data _check_not_importing_main() File "C:\Users\Singh Automation\AppData\Local\Programs\Python\Python310\lib\multiprocessing\spawn.py", line 134, in _check_not_importing_main raise RuntimeError(''' RuntimeError: An attempt has been made to start a new process before the current process has finished its bootstrapping phase.

    This probably means that you are not using fork to start your
    child processes and you have forgotten to use the proper idiom
    in the main module:

        if __name__ == '__main__':
            freeze_support()
            ...

    The "freeze_support()" line can be omitted if the program
    is not going to be frozen to produce an executable.

[01/02 13:47:51 d2.engine.hooks]: Total training time: 0:00:00 (0:00:00 on hooks) [01/02 13:47:51 d2.utils.events]: iter: 0 lr: N/A max_mem: 228M

Hello

There is a problem that the training time is very slow when learning the model with detectron2 using two machines

I use A6000 RTX with 4 GPUs per node and train my models with the two nodes. Two nodes are on Ubuntu 20.04. Training is normally working and the log.txt file is also generated well.

I set the environment variables as follows

Node1 setting(189) export NCCL_DEBUG="INFO" export NCCL_SOCKET_IFNAME="enp36s0f1" export GLOO_SOCKET_IFNAME="enp36s0f1"

Node2 setting export NCCL_DEBUG="INFO" export NCCL_SOCKET_IFNAME="enp4s0" export GLOO_SOCKET_IFNAME="enp4s0"

First, when I only set NCCL environment variables (not set GLOO), I got these errors

-- Process 0 terminated with the following error:
Traceback (most recent call last):
File "/opt/conda/lib/python3.8/site-packages/torch/multiprocessing/spawn.py", line 19, in _wrap
fn(i, *args)
File "/root/xgy/experiments/detectron2/detectron2/engine/launch.py", line 125, in _distributed_worker
main_func(*args)
File "/root/xgy/experiments/distributed-pytorch/MaskRCNN/train/train_net.py", line 141, in main
trainer = Trainer(cfg)
File "/root/xgy/experiments/detectron2/detectron2/engine/defaults.py", line 383, in init
data_loader = self.build_train_loader(cfg)
File "/root/xgy/experiments/detectron2/detectron2/engine/defaults.py", line 543, in build_train_loader
return build_detection_train_loader(cfg)
File "/root/xgy/experiments/detectron2/detectron2/config/config.py", line 192, in wrapped
explicit_args = _get_args_from_config(from_config, *args, **kwargs)
File "/root/xgy/experiments/detectron2/detectron2/config/config.py", line 229, in _get_args_from_config
ret = from_config_func(*args, **kwargs)
File "/root/xgy/experiments/detectron2/detectron2/data/build.py", line 328, in _train_loader_from_config
sampler = TrainingSampler(len(dataset))
File "/root/xgy/experiments/detectron2/detectron2/data/samplers/distributed_sampler.py", line 37, in init
seed = comm.shared_random_seed()
File "/root/xgy/experiments/detectron2/detectron2/utils/comm.py", line 230, in shared_random_seed
all_ints = all_gather(ints)
File "/root/xgy/experiments/detectron2/detectron2/utils/comm.py", line 154, in all_gather
group = _get_global_gloo_group()
File "/root/xgy/experiments/detectron2/detectron2/utils/comm.py", line 89, in _get_global_gloo_group
return dist.new_group(backend="gloo")
File "/opt/conda/lib/python3.8/site-packages/torch/distributed/distributed_c10d.py", line 2019, in new_group
pg = _new_process_group_helper(group_world_size,
File "/opt/conda/lib/python3.8/site-packages/torch/distributed/distributed_c10d.py", line 504, in _new_process_group_helper
pg = ProcessGroupGloo(
RuntimeError: [/pytorch/third_party/gloo/gloo/transport/tcp/pair.cc:769] connect [127.0.0.1]:7602: Connection refused

After I set export GLOO_SOCKET_IFNAME="enp4s0" and export GLOO_SOCKET_IFNAME="enp36s0f1" respectively, The training worked but the time is too slow. This is my NCCL BUG Report

cvlab189-System-Product-Name:1379562:1379562 [0] NCCL INFO Bootstrap : Using enp36s0f1:168.188.129.189<0>
cvlab189-System-Product-Name:1379562:1379562 [0] NCCL INFO NET/Plugin : No plugin found (libnccl-net.so), using internal implementation

cvlab189-System-Product-Name:1379562:1379562 [0] misc/ibvwrap.cc:63 NCCL WARN Failed to open libibverbs.so[.1]
cvlab189-System-Product-Name:1379562:1379562 [0] NCCL INFO NET/Socket : Using [0]enp36s0f1:168.188.129.189<0>
cvlab189-System-Product-Name:1379562:1379562 [0] NCCL INFO Using network Socket
NCCL version 2.10.3+cuda11.3
cvlab189-System-Product-Name:1379564:1379564 [2] NCCL INFO Bootstrap : Using enp36s0f1:168.188.129.189<0>
cvlab189-System-Product-Name:1379564:1379564 [2] NCCL INFO NET/Plugin : No plugin found (libnccl-net.so), using internal implementation

cvlab189-System-Product-Name:1379564:1379564 [2] misc/ibvwrap.cc:63 NCCL WARN Failed to open libibverbs.so[.1]
cvlab189-System-Product-Name:1379564:1379564 [2] NCCL INFO NET/Socket : Using [0]enp36s0f1:168.188.129.189<0>
cvlab189-System-Product-Name:1379564:1379564 [2] NCCL INFO Using network Socket
cvlab189-System-Product-Name:1379565:1379565 [3] NCCL INFO Bootstrap : Using enp36s0f1:168.188.129.189<0>
cvlab189-System-Product-Name:1379565:1379565 [3] NCCL INFO NET/Plugin : No plugin found (libnccl-net.so), using internal implementation

cvlab189-System-Product-Name:1379565:1379565 [3] misc/ibvwrap.cc:63 NCCL WARN Failed to open libibverbs.so[.1]
cvlab189-System-Product-Name:1379563:1379563 [1] NCCL INFO Bootstrap : Using enp36s0f1:168.188.129.189<0>
cvlab189-System-Product-Name:1379565:1379565 [3] NCCL INFO NET/Socket : Using [0]enp36s0f1:168.188.129.189<0>
cvlab189-System-Product-Name:1379565:1379565 [3] NCCL INFO Using network Socket
cvlab189-System-Product-Name:1379563:1379563 [1] NCCL INFO NET/Plugin : No plugin found (libnccl-net.so), using internal implementation

cvlab189-System-Product-Name:1379563:1379563 [1] misc/ibvwrap.cc:63 NCCL WARN Failed to open libibverbs.so[.1]
cvlab189-System-Product-Name:1379563:1379563 [1] NCCL INFO NET/Socket : Using [0]enp36s0f1:168.188.129.189<0>
cvlab189-System-Product-Name:1379563:1379563 [1] NCCL INFO Using network Socket
cvlab189-System-Product-Name:1379565:1379737 [3] NCCL INFO Trees [0] -1/-1/-1->3->2 [1] -1/-1/-1->3->2
cvlab189-System-Product-Name:1379562:1379735 [0] NCCL INFO Channel 00/02 :    0   1   2   3   4   5   6   7
cvlab189-System-Product-Name:1379562:1379735 [0] NCCL INFO Channel 01/02 :    0   1   2   3   4   5   6   7
cvlab189-System-Product-Name:1379562:1379735 [0] NCCL INFO Trees [0] 1/4/-1->0->-1 [1] 1/-1/-1->0->4
cvlab189-System-Product-Name:1379563:1379738 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0
cvlab189-System-Product-Name:1379564:1379736 [2] NCCL INFO Trees [0] 3/-1/-1->2->1 [1] 3/-1/-1->2->1
cvlab189-System-Product-Name:1379564:1379736 [2] NCCL INFO Channel 00 : 2[41000] -> 3[61000] via P2P/IPC
cvlab189-System-Product-Name:1379563:1379738 [1] NCCL INFO Channel 00 : 1[2c000] -> 2[41000] via P2P/IPC
cvlab189-System-Product-Name:1379564:1379736 [2] NCCL INFO Channel 01 : 2[41000] -> 3[61000] via P2P/IPC
cvlab189-System-Product-Name:1379563:1379738 [1] NCCL INFO Channel 01 : 1[2c000] -> 2[41000] via P2P/IPC
cvlab189-System-Product-Name:1379562:1379735 [0] NCCL INFO Channel 00 : 7[68000] -> 0[1000] [receive] via NET/Socket/0
cvlab189-System-Product-Name:1379565:1379737 [3] NCCL INFO Channel 00 : 3[61000] -> 4[19000] [send] via NET/Socket/0
cvlab189-System-Product-Name:1379562:1379735 [0] NCCL INFO Channel 01 : 7[68000] -> 0[1000] [receive] via NET/Socket/0
cvlab189-System-Product-Name:1379562:1379735 [0] NCCL INFO Channel 00 : 0[1000] -> 1[2c000] via P2P/IPC
cvlab189-System-Product-Name:1379562:1379735 [0] NCCL INFO Channel 01 : 0[1000] -> 1[2c000] via P2P/IPC
cvlab189-System-Product-Name:1379565:1379737 [3] NCCL INFO Channel 01 : 3[61000] -> 4[19000] [send] via NET/Socket/0
cvlab189-System-Product-Name:1379565:1379737 [3] NCCL INFO Connected all rings
cvlab189-System-Product-Name:1379565:1379737 [3] NCCL INFO Channel 00 : 3[61000] -> 2[41000] via P2P/IPC
cvlab189-System-Product-Name:1379565:1379737 [3] NCCL INFO Channel 01 : 3[61000] -> 2[41000] via P2P/IPC
cvlab189-System-Product-Name:1379564:1379736 [2] NCCL INFO Connected all rings
cvlab189-System-Product-Name:1379562:1379735 [0] NCCL INFO Connected all rings
cvlab189-System-Product-Name:1379563:1379738 [1] NCCL INFO Connected all rings
cvlab189-System-Product-Name:1379564:1379736 [2] NCCL INFO Channel 00 : 2[41000] -> 1[2c000] via P2P/IPC
cvlab189-System-Product-Name:1379563:1379738 [1] NCCL INFO Channel 00 : 1[2c000] -> 0[1000] via P2P/IPC
cvlab189-System-Product-Name:1379564:1379736 [2] NCCL INFO Channel 01 : 2[41000] -> 1[2c000] via P2P/IPC
cvlab189-System-Product-Name:1379563:1379738 [1] NCCL INFO Channel 01 : 1[2c000] -> 0[1000] via P2P/IPC
cvlab189-System-Product-Name:1379565:1379737 [3] NCCL INFO Connected all trees
cvlab189-System-Product-Name:1379565:1379737 [3] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 8/8/512
cvlab189-System-Product-Name:1379565:1379737 [3] NCCL INFO 2 coll channels, 2 p2p channels, 1 p2p channels per peer
cvlab189-System-Product-Name:1379562:1379735 [0] NCCL INFO Channel 00 : 4[19000] -> 0[1000] [receive] via NET/Socket/0
cvlab189-System-Product-Name:1379564:1379736 [2] NCCL INFO Connected all trees
cvlab189-System-Product-Name:1379564:1379736 [2] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 8/8/512
cvlab189-System-Product-Name:1379564:1379736 [2] NCCL INFO 2 coll channels, 2 p2p channels, 1 p2p channels per peer
cvlab189-System-Product-Name:1379562:1379735 [0] NCCL INFO Channel 01 : 4[19000] -> 0[1000] [receive] via NET/Socket/0
cvlab189-System-Product-Name:1379562:1379735 [0] NCCL INFO Channel 00 : 0[1000] -> 4[19000] [send] via NET/Socket/0
cvlab189-System-Product-Name:1379562:1379735 [0] NCCL INFO Channel 01 : 0[1000] -> 4[19000] [send] via NET/Socket/0
cvlab189-System-Product-Name:1379562:1379735 [0] NCCL INFO Connected all trees
cvlab189-System-Product-Name:1379562:1379735 [0] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 8/8/512
cvlab189-System-Product-Name:1379562:1379735 [0] NCCL INFO 2 coll channels, 2 p2p channels, 1 p2p channels per peer
cvlab189-System-Product-Name:1379563:1379738 [1] NCCL INFO Connected all trees
cvlab189-System-Product-Name:1379563:1379738 [1] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 8/8/512
cvlab189-System-Product-Name:1379563:1379738 [1] NCCL INFO 2 coll channels, 2 p2p channels, 1 p2p channels per peer
cvlab189-System-Product-Name:1379562:1379735 [0] NCCL INFO comm 0x7f7590002fb0 rank 0 nranks 8 cudaDev 0 busId 1000 - Init COMPLETE
cvlab189-System-Product-Name:1379563:1379738 [1] NCCL INFO comm 0x7f3928002fb0 rank 1 nranks 8 cudaDev 1 busId 2c000 - Init COMPLETE
cvlab189-System-Product-Name:1379565:1379737 [3] NCCL INFO comm 0x7f9f00002fb0 rank 3 nranks 8 cudaDev 3 busId 61000 - Init COMPLETE
cvlab189-System-Product-Name:1379564:1379736 [2] NCCL INFO comm 0x7fe414002fb0 rank 2 nranks 8 cudaDev 2 busId 41000 - Init COMPLETE
cvlab189-System-Product-Name:1379562:1379562 [0] NCCL INFO Launch mode Parallel

For the record, according to this guide https://pytorch.org/docs/stable/distributed.html, "If you encounter any problem with NCCL, use Gloo as the fallback option. (Note that Gloo currently runs slower than NCCL for GPUs." distributed_sampler in detectron2 uses gloo backend.

When I type this command python -c "import torch;print(torch.cuda.nccl.version())"(NCCL Version check in Conda virtual Enviroment) (2, 10, 3) for both two machines I additionally didn't install NCCL (only installed Pytorch) What should I do?

Non-determinism of MaskRCNN

There have been a lot of discussions and inquiries in this repo about a fully deterministic MaskRCNN e.g. #4260, #3203 , #2615, #2480, and also on other detection repositories (e.g. MMDetection here and here and also torchvision here). Unfortunately, even after seeding everything and setting Pytorch's deterministic flags, results are still non-repeatable.

It boils down to the fact that some of the used Pytorch / torchvision ops doesn't have a deterministic GPU implementation (most notably, due to using atomicAdd in the backward pass). So, the only solution is to train for as long as possible to reduce variance in the results. It is worth noting that not only training, but also evaluation (see #2480) of MaskRCNN (and actually most detectron2 models) is not deterministic

Based on the minimal example in #4260, I made an analysis on the ops used for MaskRCNN and found that the main reason of non-determinism is the backward pass of ROIAlign (see here).

Proposed solution

I am here proposing a simple trick that makes ROIAlign practically fully reproducible, without touching the cuda kernel!! it introduces trivial additional memory and computation. It can be summarized as:

• Truncate the input to a smaller datatype, this gives a starting point with a very small number of significand bits used
• Then, cast to a larger data-type just before doing the computations that involve atomicAdd

In terms of code, this is translated to simply modifying this function call to

return roi_align(
    input.half().double(),
    rois.half().double(),
    self.output_size,
    self.spatial_scale,
    self.sampling_ratio,
    self.aligned,
).to(dtype=input.dtype)

Test

The conversion to double results in a trivial increase in memory & computation, but performing it after the truncation, significantly increases reproducibility.

This solution was tested and found fully deterministic (losses values, and evaluation results on COCO) upto tens of thousands of steps (using same code as in #4260) for:

• MaskRCNN based on ResNet-50 bakbone
• MaskRCNN based on ResNeXt-101 bakbone
• Wide range of batch sizes
• Mixed-precision training
• Single and Multi-GPU training
• A100's & V100's

Note on A100

Ampere by default uses TF32 format for tensor-core computations, which means that the above truncation is done implicitly! so on Ampere based devices it is enough just to cast to double, i.e.

return roi_align(
    input.double(),
    rois.double(),
    self.output_size,
    self.spatial_scale,
    self.sampling_ratio,
    self.aligned,
).to(dtype=input.dtype)

Note: This is the default mode for PyTorch, but if TF32 is disabled for some reason (i.e. torch.backends.cudnn.allow_tf32 = False) then the above truncation with .half() is still necessary

Note

• This solution was tested and found to work well for other non-deterministic Pytorch ops, including: F.interpolate and F.grid_sample
• This is not a general solution to the problem of random-order reproducible floating point summation, but a practical mitigation that works well for this setup / scenario
• At least in theory, this should work even better if applied inside the kernel right before atomicAdd
• The only alternative currently is training each experiment for very long, which isn't practical in many setups, and still isn't fully reproducible

Would love to hear what people think about this! @ppwwyyxx @fmassa

Hi,

During the training, I see that only loss and epochs-related info is stored in log file but what if any error happens in the training then it does not save such errors in the log file.

Can you please share if this feature already exists or is there any way to achieve that?