facebookresearch / maskrcnn-benchmark Goto Github PK

Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch.

License: MIT License

Python 77.74% C++ 2.75% Cuda 17.48% Dockerfile 0.56% C 1.47%

maskrcnn-benchmark's Introduction

Faster R-CNN and Mask R-CNN in PyTorch 1.0

maskrcnn-benchmark has been deprecated. Please see detectron2, which includes implementations for all models in maskrcnn-benchmark

This project aims at providing the necessary building blocks for easily creating detection and segmentation models using PyTorch 1.0.

Highlights

PyTorch 1.0: RPN, Faster R-CNN and Mask R-CNN implementations that matches or exceeds Detectron accuracies
Very fast: up to 2x faster than Detectron and 30% faster than mmdetection during training. See MODEL_ZOO.md for more details.
Memory efficient: uses roughly 500MB less GPU memory than mmdetection during training
Multi-GPU training and inference
Mixed precision training: trains faster with less GPU memory on NVIDIA tensor cores.
Batched inference: can perform inference using multiple images per batch per GPU
CPU support for inference: runs on CPU in inference time. See our webcam demo for an example
Provides pre-trained models for almost all reference Mask R-CNN and Faster R-CNN configurations with 1x schedule.

Webcam and Jupyter notebook demo

We provide a simple webcam demo that illustrates how you can use maskrcnn_benchmark for inference:

cd demo
# by default, it runs on the GPU
# for best results, use min-image-size 800
python webcam.py --min-image-size 800
# can also run it on the CPU
python webcam.py --min-image-size 300 MODEL.DEVICE cpu
# or change the model that you want to use
python webcam.py --config-file ../configs/caffe2/e2e_mask_rcnn_R_101_FPN_1x_caffe2.yaml --min-image-size 300 MODEL.DEVICE cpu
# in order to see the probability heatmaps, pass --show-mask-heatmaps
python webcam.py --min-image-size 300 --show-mask-heatmaps MODEL.DEVICE cpu
# for the keypoint demo
python webcam.py --config-file ../configs/caffe2/e2e_keypoint_rcnn_R_50_FPN_1x_caffe2.yaml --min-image-size 300 MODEL.DEVICE cpu

A notebook with the demo can be found in demo/Mask_R-CNN_demo.ipynb.

Installation

Check INSTALL.md for installation instructions.

Model Zoo and Baselines

Pre-trained models, baselines and comparison with Detectron and mmdetection can be found in MODEL_ZOO.md

Inference in a few lines

We provide a helper class to simplify writing inference pipelines using pre-trained models. Here is how we would do it. Run this from the demo folder:

from maskrcnn_benchmark.config import cfg
from predictor import COCODemo

config_file = "../configs/caffe2/e2e_mask_rcnn_R_50_FPN_1x_caffe2.yaml"

# update the config options with the config file
cfg.merge_from_file(config_file)
# manual override some options
cfg.merge_from_list(["MODEL.DEVICE", "cpu"])

coco_demo = COCODemo(
    cfg,
    min_image_size=800,
    confidence_threshold=0.7,
)
# load image and then run prediction
image = ...
predictions = coco_demo.run_on_opencv_image(image)

Perform training on COCO dataset

For the following examples to work, you need to first install maskrcnn_benchmark.

You will also need to download the COCO dataset. We recommend to symlink the path to the coco dataset to datasets/ as follows

We use minival and valminusminival sets from Detectron

# symlink the coco dataset
cd ~/github/maskrcnn-benchmark
mkdir -p datasets/coco
ln -s /path_to_coco_dataset/annotations datasets/coco/annotations
ln -s /path_to_coco_dataset/train2014 datasets/coco/train2014
ln -s /path_to_coco_dataset/test2014 datasets/coco/test2014
ln -s /path_to_coco_dataset/val2014 datasets/coco/val2014
# or use COCO 2017 version
ln -s /path_to_coco_dataset/annotations datasets/coco/annotations
ln -s /path_to_coco_dataset/train2017 datasets/coco/train2017
ln -s /path_to_coco_dataset/test2017 datasets/coco/test2017
ln -s /path_to_coco_dataset/val2017 datasets/coco/val2017

# for pascal voc dataset:
ln -s /path_to_VOCdevkit_dir datasets/voc

P.S. COCO_2017_train = COCO_2014_train + valminusminival , COCO_2017_val = minival

You can also configure your own paths to the datasets. For that, all you need to do is to modify maskrcnn_benchmark/config/paths_catalog.py to point to the location where your dataset is stored. You can also create a new paths_catalog.py file which implements the same two classes, and pass it as a config argument PATHS_CATALOG during training.

Single GPU training

Most of the configuration files that we provide assume that we are running on 8 GPUs. In order to be able to run it on fewer GPUs, there are a few possibilities:

1. Run the following without modifications

python /path_to_maskrcnn_benchmark/tools/train_net.py --config-file "/path/to/config/file.yaml"

This should work out of the box and is very similar to what we should do for multi-GPU training. But the drawback is that it will use much more GPU memory. The reason is that we set in the configuration files a global batch size that is divided over the number of GPUs. So if we only have a single GPU, this means that the batch size for that GPU will be 8x larger, which might lead to out-of-memory errors.

If you have a lot of memory available, this is the easiest solution.

2. Modify the cfg parameters

If you experience out-of-memory errors, you can reduce the global batch size. But this means that you'll also need to change the learning rate, the number of iterations and the learning rate schedule.

Here is an example for Mask R-CNN R-50 FPN with the 1x schedule:

python tools/train_net.py --config-file "configs/e2e_mask_rcnn_R_50_FPN_1x.yaml" SOLVER.IMS_PER_BATCH 2 SOLVER.BASE_LR 0.0025 SOLVER.MAX_ITER 720000 SOLVER.STEPS "(480000, 640000)" TEST.IMS_PER_BATCH 1 MODEL.RPN.FPN_POST_NMS_TOP_N_TRAIN 2000

This follows the scheduling rules from Detectron. Note that we have multiplied the number of iterations by 8x (as well as the learning rate schedules), and we have divided the learning rate by 8x.

We also changed the batch size during testing, but that is generally not necessary because testing requires much less memory than training.

Furthermore, we set MODEL.RPN.FPN_POST_NMS_TOP_N_TRAIN 2000 as the proposals are selected for per the batch rather than per image in the default training. The value is calculated by 1000 x images-per-gpu. Here we have 2 images per GPU, therefore we set the number as 1000 x 2 = 2000. If we have 8 images per GPU, the value should be set as 8000. Note that this does not apply if MODEL.RPN.FPN_POST_NMS_PER_BATCH is set to False during training. See #672 for more details.

Multi-GPU training

We use internally torch.distributed.launch in order to launch multi-gpu training. This utility function from PyTorch spawns as many Python processes as the number of GPUs we want to use, and each Python process will only use a single GPU.

export NGPUS=8
python -m torch.distributed.launch --nproc_per_node=$NGPUS /path_to_maskrcnn_benchmark/tools/train_net.py --config-file "path/to/config/file.yaml" MODEL.RPN.FPN_POST_NMS_TOP_N_TRAIN images_per_gpu x 1000

Note we should set MODEL.RPN.FPN_POST_NMS_TOP_N_TRAIN follow the rule in Single-GPU training.

Mixed precision training

We currently use APEX to add Automatic Mixed Precision support. To enable, just do Single-GPU or Multi-GPU training and set DTYPE "float16".

export NGPUS=8
python -m torch.distributed.launch --nproc_per_node=$NGPUS /path_to_maskrcnn_benchmark/tools/train_net.py --config-file "path/to/config/file.yaml" MODEL.RPN.FPN_POST_NMS_TOP_N_TRAIN images_per_gpu x 1000 DTYPE "float16"

If you want more verbose logging, set AMP_VERBOSE True. See Mixed Precision Training guide for more details.

Evaluation

You can test your model directly on single or multiple gpus. Here is an example for Mask R-CNN R-50 FPN with the 1x schedule on 8 GPUS:

export NGPUS=8
python -m torch.distributed.launch --nproc_per_node=$NGPUS /path_to_maskrcnn_benchmark/tools/test_net.py --config-file "configs/e2e_mask_rcnn_R_50_FPN_1x.yaml" TEST.IMS_PER_BATCH 16

To calculate mAP for each class, you can simply modify a few lines in coco_eval.py. See #524 for more details.

Abstractions

For more information on some of the main abstractions in our implementation, see ABSTRACTIONS.md.

Adding your own dataset

This implementation adds support for COCO-style datasets. But adding support for training on a new dataset can be done as follows:

from maskrcnn_benchmark.structures.bounding_box import BoxList

class MyDataset(object):
    def __init__(self, ...):
        # as you would do normally

    def __getitem__(self, idx):
        # load the image as a PIL Image
        image = ...

        # load the bounding boxes as a list of list of boxes
        # in this case, for illustrative purposes, we use
        # x1, y1, x2, y2 order.
        boxes = [[0, 0, 10, 10], [10, 20, 50, 50]]
        # and labels
        labels = torch.tensor([10, 20])

        # create a BoxList from the boxes
        boxlist = BoxList(boxes, image.size, mode="xyxy")
        # add the labels to the boxlist
        boxlist.add_field("labels", labels)

        if self.transforms:
            image, boxlist = self.transforms(image, boxlist)

        # return the image, the boxlist and the idx in your dataset
        return image, boxlist, idx

    def get_img_info(self, idx):
        # get img_height and img_width. This is used if
        # we want to split the batches according to the aspect ratio
        # of the image, as it can be more efficient than loading the
        # image from disk
        return {"height": img_height, "width": img_width}

That's it. You can also add extra fields to the boxlist, such as segmentation masks (using structures.segmentation_mask.SegmentationMask), or even your own instance type.

For a full example of how the COCODataset is implemented, check maskrcnn_benchmark/data/datasets/coco.py.

Once you have created your dataset, it needs to be added in a couple of places:

maskrcnn_benchmark/data/datasets/__init__.py: add it to __all__
maskrcnn_benchmark/config/paths_catalog.py: DatasetCatalog.DATASETS and corresponding if clause in DatasetCatalog.get()

Testing

While the aforementioned example should work for training, we leverage the cocoApi for computing the accuracies during testing. Thus, test datasets should currently follow the cocoApi for now.

To enable your dataset for testing, add a corresponding if statement in maskrcnn_benchmark/data/datasets/evaluation/__init__.py:

if isinstance(dataset, datasets.MyDataset):
        return coco_evaluation(**args)

Finetuning from Detectron weights on custom datasets

Create a script tools/trim_detectron_model.py like here. You can decide which keys to be removed and which keys to be kept by modifying the script.

Then you can simply point the converted model path in the config file by changing MODEL.WEIGHT.

For further information, please refer to #15.

Troubleshooting

If you have issues running or compiling this code, we have compiled a list of common issues in TROUBLESHOOTING.md. If your issue is not present there, please feel free to open a new issue.

Citations

Please consider citing this project in your publications if it helps your research. The following is a BibTeX reference. The BibTeX entry requires the url LaTeX package.

@misc{massa2018mrcnn,
author = {Massa, Francisco and Girshick, Ross},
title = {{maskrcnn-benchmark: Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch}},
year = {2018},
howpublished = {\url{https://github.com/facebookresearch/maskrcnn-benchmark}},
note = {Accessed: [Insert date here]}
}

Projects using maskrcnn-benchmark

RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free. Cheng-Yang Fu, Mykhailo Shvets, and Alexander C. Berg. Tech report, arXiv,1901.03353.
FCOS: Fully Convolutional One-Stage Object Detection. Zhi Tian, Chunhua Shen, Hao Chen and Tong He. Tech report, arXiv,1904.01355. [code]
MULAN: Multitask Universal Lesion Analysis Network for Joint Lesion Detection, Tagging, and Segmentation. Ke Yan, Youbao Tang, Yifan Peng, Veit Sandfort, Mohammadhadi Bagheri, Zhiyong Lu, and Ronald M. Summers. MICCAI 2019. [code]
Is Sampling Heuristics Necessary in Training Deep Object Detectors? Joya Chen, Dong Liu, Tong Xu, Shilong Zhang, Shiwei Wu, Bin Luo, Xuezheng Peng, Enhong Chen. Tech report, arXiv,1909.04868. [code]

License

maskrcnn-benchmark is released under the MIT license. See LICENSE for additional details.

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maskrcnn-benchmark's Issues

How to finetune from pretrained detectron models with different number of classes?

❓ Questions and Help

Is there a config option to load pretrained coco models for finetuning? The last layers where the number of classes may be different, so those weights should not be loaded.

Support RLE format and binary masks.

🚀 Feature

Currently the code is based on polygon masks. RLE and binary masks should be supported for general datasets.

problem of installation

Environment
os: ubuntu 18.04
cuda 9.0
gcc 7.3

bug

the final instruction of install.md pythonsetup.py build develop

running build
running build_py
running build_ext
building 'maskrcnn_benchmark._C' extension
gcc -pthread -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -DWITH_CUDA -I/home/hzf/mask_rcnn/maskrcnn-benchmark-master/maskrcnn_benchmark/csrc -I/home/hzf/.local/lib/python3.6/site-packages/torch/lib/include -I/home/hzf/.local/lib/python3.6/site-packages/torch/lib/include/TH -I/home/hzf/.local/lib/python3.6/site-packages/torch/lib/include/THC -I/usr/local/cuda/include -I/home/hzf/anaconda3/envs/maskrcnn_pytorch/include/python3.6m -c /home/hzf/mask_rcnn/maskrcnn-benchmark-master/maskrcnn_benchmark/csrc/vision.cpp -o build/temp.linux-x86_64-3.6/home/hzf/mask_rcnn/maskrcnn-benchmark-master/maskrcnn_benchmark/csrc/vision.o -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++11
cc1plus: warning: command line option ‘-Wstrict-prototypes’ is valid for C/ObjC but not for C++
In file included from /home/hzf/mask_rcnn/maskrcnn-benchmark-master/maskrcnn_benchmark/csrc/nms.h:3:0,
from /home/hzf/mask_rcnn/maskrcnn-benchmark-master/maskrcnn_benchmark/csrc/vision.cpp:2:
/home/hzf/mask_rcnn/maskrcnn-benchmark-master/maskrcnn_benchmark/csrc/cpu/vision.h:3:10: fatal error: torch/extension.h: no such file or directory
#include <torch/extension.h>
^~~~~~~~~~~~~~~~~~~
compilation terminated.
error: command 'gcc' failed with exit status 1

guess
guess that you didn't push c++ extensions onto github

Will maskrcnn-benchmark support torch.jit.trace or torch.jit.script mode in the nearly future?

❓ Questions and Help

As we know,in pytorch1.0 Torch Script is a way to create serializable and optimizable models from PyTorch code. Any code written in Torch Script can be saved from Python process and loaded in a process where there is no Python dependency.
So will maskrcnn-benchmark support torch.jit.trace or torch.jit.script mode in the nearly future?

some trivial problems

❓ Questions and Help

Thank you for your excelent work.
I used the pretrained mask_rcnn_X_101_32x8d_FPN_1x model to infer this picture --->>>>"http://farm3.staticflickr.com/2469/3915380994_2e611b1779_z.jpg" . However the result is worse than the one shown in readme. Are there some parameters I need to change except confidence_threshold?
On the other hand, the font and mask displayed are ugly. Do you plan to optimize the display effect?
On the other other hand,I think the code structure is not very clear. Will there be any changes later?

ImportError: .../maskrcnn-benchmark/maskrcnn_benchmark/_C.cpython-36m-x86_64-linux-gnu.so: undefined symbol: __cudaPopCallConfiguration

❓ Questions and Help

Traceback (most recent call last):
File "webcam.py", line 6, in
from predictor import COCODemo
File "/home/laonb/github/maskrcnn-benchmark/demo/predictor.py", line 6, in
from maskrcnn_benchmark.modeling.detector import build_detection_model
File "/home/laonb/github/maskrcnn-benchmark/maskrcnn_benchmark/modeling/detector/init.py", line 2, in
from .detectors import build_detection_model
File "/home/laonb/github/maskrcnn-benchmark/maskrcnn_benchmark/modeling/detector/detectors.py", line 2, in
from .generalized_rcnn import GeneralizedRCNN
File "/home/laonb/github/maskrcnn-benchmark/maskrcnn_benchmark/modeling/detector/generalized_rcnn.py", line 11, in
from ..backbone import build_backbone
File "/home/laonb/github/maskrcnn-benchmark/maskrcnn_benchmark/modeling/backbone/init.py", line 2, in
from .backbone import build_backbone
File "/home/laonb/github/maskrcnn-benchmark/maskrcnn_benchmark/modeling/backbone/backbone.py", line 7, in
from . import resnet
File "/home/laonb/github/maskrcnn-benchmark/maskrcnn_benchmark/modeling/backbone/resnet.py", line 19, in
from maskrcnn_benchmark.layers import FrozenBatchNorm2d
File "/home/laonb/github/maskrcnn-benchmark/maskrcnn_benchmark/layers/init.py", line 8, in
from .nms import nms
File "/home/laonb/github/maskrcnn-benchmark/maskrcnn_benchmark/layers/nms.py", line 3, in
from maskrcnn_benchmark import _C
ImportError: /home/laonb/github/maskrcnn-benchmark/maskrcnn_benchmark/_C.cpython-36m-x86_64-linux-gnu.so: undefined symbol: __cudaPopCallConfiguration

Segmentation fault

🐛 Bug

Hi,

When trying to run the exact same code that appear in the notebook: demo/Mask_R-CNN_demo.ipynb
I'm getting a segmentation fault error caused by this line:

predictions = coco_demo.run_on_opencv_image(image)

Any idea why?

Environment

PyTorch version: 1.0.0.dev20181028
Is debug build: No
CUDA used to build PyTorch: 8.0.61

OS: Ubuntu 14.04.5 LTS
GCC version: (Ubuntu 4.8.4-2ubuntu1~14.04.3) 4.8.4
CMake version: version 2.8.12.2

Python version: 3.6
Is CUDA available: Yes
CUDA runtime version: Could not collect
GPU models and configuration:
GPU 0: Tesla P100-SXM2-16GB
GPU 1: Tesla P100-SXM2-16GB
GPU 2: Tesla P100-SXM2-16GB
GPU 3: Tesla P100-SXM2-16GB

Nvidia driver version: 375.39
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.5.1.10
/usr/local/cuda-8.0/lib64/libcudnn.so.5.0.5
/usr/local/cuda-8.0/lib64/libcudnn.so.5.1.5
/usr/local/cuda-8.0/lib64/libcudnn.so.6.0.21
/usr/local/cuda-8.0/lib64/libcudnn_static.a

Versions of relevant libraries:
[pip] numpy (1.15.3)
[pip] torch (1.0.0.dev20181028)
[pip] torchvision (0.2.1)
[conda] cuda80 1.0 h205658b_0 pytorch
[conda] pytorch-nightly 1.0.0.dev20181028 py3.6_cuda8.0.61_cudnn7.1.2_0 [cuda80] pytorch

Package name is too long

Thanks for a good implementation for research community. However, I suggest you use a shorter module's name to replace 'maskrcnn_benchmark', say, 'det' or 'detectron' before more people start to use it. It seems you originally use 'torch_detectron' (inferred from .gitignore).

Another suggestion is to include .idea(Pycharm setting files) in .gitignore.

Will the configs in Detectron be added?

Hello~The defaults.py has only a few configures in test-time compared with the detectron version. Will some choice like softnms be added soon?

DockerFile with nccl

Hi all, I was wondering if you guys have a sample DockerFile with CUDA, CUDNN and NCCL?
Would be really helpful for pushing things to production! Thanks!

small typo in demo instruction

❓ Questions and Help

There is a small typo in demo instruction sample command line :
correct launch is done with:
python webcam.py --config-file ../configs/caffe2/e2e_mask_rcnn_R_101_FPN_1x_caffe2.yaml --min-image-size 300 MODEL.DEVICE cpu
and not like written in https://github.com/facebookresearch/maskrcnn-benchmark with :
python webcam.py --config-file ../configs/caffe2/e2e_mask_rcnn_R_101_FPN_1x_caffe2.py --min-image-size 300 MODEL.DEVICE cpu

Thanks for such amazing performance !

How to convert .pth to caffe2 model(.pb)?

❓ Questions and Help

I download models from the modelzoo, but I want to use them in caffe2 for inference. Can I convert the .pth file to .pb file?

[Feature request] Make IoU computation more memory efficient

❓ Questions and Help

I'm experiencing high GPU memory usage. I made my own COCO dataset and started training with 2 separate models: e2e_faster_rcnn_R_50_FPN_1x.yaml, e2e_faster_rcnn_R_101_FPN_1x.yaml, e2e_faster_rcnn_X_101_32x8d_FPN_1x.yaml. I changed the number of GPUs to 1 and ran the single GPU training command.

I get well into the training, 100s or 1000s of iterations, then I receive the CUDA OOM message. The reported mem usage is around 7GB though nvidia-smi reports about 9.7GB for the ResNeXt model.

I'm running on a 1080Ti with 11GB of RAM, so it should be able to handle this amount of memory. It seems as though there are periodic peaks in the memory usage.

The error message for R_50_FPN looks like this:

2018-10-25 15:19:50,428 maskrcnn_benchmark.trainer INFO: eta: 4:07:54  iter: 2180  loss: 1.3242 (1.3455)  loss_classifier: 0.4725 (0.5510)  loss_box_reg: 0.2073 (0.1726)  loss_objectness: 0.3853 (0.4184)  loss_rpn_box_reg: 0.2073 (0.2034)  time: 0.1660 (0.1694)  data: 0.0022 (0.0023)  lr: 0.001000  max mem: 6457
Traceback (most recent call last):
  File "/home/sgoley/git/etegent/maskrcnn-benchmark/tools/train_net.py", line 170, in <module>
    main()
  File "/home/sgoley/git/etegent/maskrcnn-benchmark/tools/train_net.py", line 163, in main
    model = train(cfg, args.local_rank, args.distributed)
  File "/home/sgoley/git/etegent/maskrcnn-benchmark/tools/train_net.py", line 73, in train
    arguments,
  File "/home/sgoley/git/etegent/maskrcnn-benchmark/maskrcnn_benchmark/engine/trainer.py", line 67, in do_train
    loss_dict = model(images, targets)
  File "/home/sgoley/miniconda3/envs/pytorchv1/lib/python3.6/site-packages/torch/nn/modules/module.py", line 477, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/sgoley/git/etegent/maskrcnn-benchmark/maskrcnn_benchmark/modeling/detector/generalized_rcnn.py", line 50, in forward
    proposals, proposal_losses = self.rpn(images, features, targets)
  File "/home/sgoley/miniconda3/envs/pytorchv1/lib/python3.6/site-packages/torch/nn/modules/module.py", line 477, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/sgoley/git/etegent/maskrcnn-benchmark/maskrcnn_benchmark/modeling/rpn/rpn.py", line 94, in forward
    return self._forward_train(anchors, objectness, rpn_box_regression, targets)
  File "/home/sgoley/git/etegent/maskrcnn-benchmark/maskrcnn_benchmark/modeling/rpn/rpn.py", line 113, in _forward_train
    anchors, objectness, rpn_box_regression, targets
  File "/home/sgoley/git/etegent/maskrcnn-benchmark/maskrcnn_benchmark/modeling/rpn/loss.py", line 91, in __call__
    labels, regression_targets = self.prepare_targets(anchors, targets)
  File "/home/sgoley/git/etegent/maskrcnn-benchmark/maskrcnn_benchmark/modeling/rpn/loss.py", line 55, in prepare_targets
    anchors_per_image, targets_per_image
  File "/home/sgoley/git/etegent/maskrcnn-benchmark/maskrcnn_benchmark/modeling/rpn/loss.py", line 37, in match_targets_to_anchors
    match_quality_matrix = boxlist_iou(target, anchor)
  File "/home/sgoley/git/etegent/maskrcnn-benchmark/maskrcnn_benchmark/structures/boxlist_ops.py", line 87, in boxlist_iou
    iou = inter / (area1[:, None] + area2 - inter)
RuntimeError: CUDA error: out of memory

Note, I've trained with this dataset on Detectron.pytorch. Any suggestions?

Based on where the error occurs, is it possible that one of my images contains too many targets (potentially hundreds) and the iou calculation blows up?

Steve

from .nms import nms

❓ Questions and Help

  File "/home/idea/model/maskrcnn-benchmark/maskrcnn_benchmark/layers/nms.py", line 3, in <module>
    from maskrcnn_benchmark import _C
ImportError: cannot import name '_C'

python3 demo/webcam.py --min-image-size 800

TypeError: coercing to Unicode: need string or buffer, list found

🐛 Bug

File "/home/workplace/opt/anaconda2/lib/python2.7/distutils/unixccompiler.py", line 121, in _compile
self.spawn(compiler_so + cc_args + [src, '-o', obj] +
TypeError: coercing to Unicode: need string or buffer, list found

To Reproduce

Steps to reproduce the behavior:

1.python setup.py build_ext install

Expected behavior

Environment

Please copy and paste the output from the
environment collection script from PyTorch
(or fill out the checklist below manually).

You can get the script and run it with:

wget https://raw.githubusercontent.com/pytorch/pytorch/master/torch/utils/collect_env.py
# For security purposes, please check the contents of collect_env.py before running it.
python collect_env.py

PyTorch Version (e.g., 1.0): 1.0
OS (e.g., Linux):centos 7.0
How you installed PyTorch (conda, pip, source): source
Build command you used (if compiling from source): python setup.py build_ext install
Python version:2.7.14
CUDA/cuDNN version:9.0
GPU models and configuration:
Any other relevant information:

Additional context

No module named maskrcnn_benchmark.config

❓ Questions and Help

Hi
I have followed the instructions to install this package. However, when I run the notebook demo under './demo', it gives an error:

ImportError: No module named maskrcnn_benchmark.config.

aspect ratio grouping error

❓ Questions and Help

I added a new loss and it works fine if I use a single GPU.
However, it fails on "losses.backward()" if I use multiple GPUs. It seems this error relates to the "torch.distributed"
The error information is below:

File "tools/train_net.py", line 170, in <module>
    main()
  File "tools/train_net.py", line 163, in main
    model = train(cfg, args.local_rank, args.distributed)
  File "tools/train_net.py", line 73, in train
    arguments,
  File "/home/maskrcnn_benchmark/engine/trainer.py", line 77, in do_train
    losses.backward()
  File "/usr/local/lib/python3.5/dist-packages/torch/tensor.py", line 102, in backward
    torch.autograd.backward(self, gradient, retain_graph, create_graph)
  File "/usr/local/lib/python3.5/dist-packages/torch/autograd/__init__.py", line 90, in backward
    allow_unreachable=True)  # allow_unreachable flag
  File "/usr/local/lib/python3.5/dist-packages/torch/nn/parallel/deprecated/distributed.py", line 342, in reduction_fn_nccl
    group=self.nccl_reduction_group_id)
  File "/usr/local/lib/python3.5/dist-packages/torch/distributed/deprecated/__init__.py", line 317, in all_reduce_multigpu
    return torch._C._dist_all_reduce_multigpu(tensor_list, op, group)

untimeError: Not compiled with GPU support (nms at ./maskrcnn_benchmark/csrc/nms.h:22)

❓ Questions and Help

Why I can not compile gpu version nms following instructions?

fatal error: torch/extension.h

❓ Questions and Help

Hello greate guys,
I followed the installation instructions in INSTALL.md with a little modification by install pythoch through pip.
But when I run the final command to install the pytorch detection, it gave me a fatal error: torch/extension.h: No such file or directory.
Then I found that there has a torch/torch.h file in INCLUDE_PATH (site-packages/torch/lib/include/), which its content is same as the torch/extension.h found in the pytorch source codes.
However, there still has some missing files after I changed torch/extension.h to torch/torch.h, such like ATen/cuda/CUDAContext.h.

Follows are my installation commands:
$ virtualenv -p python3 maskrcnn-benchmark
$ source maskrcnn-benchmark/bin/activate
$ pip install ipython ninja yacs cython matplotlib
$ pip install http://download.pytorch.org/whl/cu80/torch-0.4.1-cp35-cp35m-linux_x86_64.whl
$ pip install torchvision
then follows the install pycocotools and pytorch detection.

Is the installation wrong?

ModuleNotFoundError: No module named 'maskrcnn_benchmark.config'

❓ Questions and Help

$ python webcam.py --min-image-size 800
Traceback (most recent call last):
File "webcam.py", line 5, in
from maskrcnn_benchmark.config import cfg
ModuleNotFoundError: No module named 'maskrcnn_benchmark.config'

Nan loss after several training iteration

❓ Questions and Help

Hi. I am training maskrcnn model with e2e_faster_rcnn_R_50_FPN_1x.yaml on coco dataset. However, after a few iterations (460), all the losses in this model become nan. I have checked the default number of class in config file, but the result (81) seems correct for coco dataset. Can anyone give me some suggestion about how to solve that? Many thanks!

Conda package

Following #25 (comment), a conda package would be a nice way to distribute precompiled package for Linux, Windows and Mac OSX.

For this, a conda recipe needs to be submitted as a PR to https://github.com/conda-forge/staged-recipes.

proposal_matcher problem

❓ Questions and Help

This code: matched_idxs = self.proposal_matcher(match_quality_matrix) lead to the bug, but i don't know how to get the Matcher self.proposal_matcher = proposal_matcher in the init.
how to fix it, and could u let me know what book or document have the infomation of class init in python.

I find the matcher,
maskrcnn_benchmark.modeling.matcher.Matcher
and it need the N*M tensor.

but the boxlist_iou(target, proposal) output shape is:

boxlist ops.py: iou shape is torch.Size([1, 16368])
boxlist ops.py: iou shape is torch.Size([1, 16368])
boxlist ops.py: iou shape is torch.Size([1, 16368])
boxlist ops.py: iou shape is torch.Size([1, 16368])
boxlist ops.py: iou shape is torch.Size([1, 16368])
boxlist ops.py: iou shape is torch.Size([1, 16368])
boxlist ops.py: iou shape is torch.Size([1, 16368])
boxlist ops.py: iou shape is torch.Size([1, 16368])
boxlist ops.py: iou shape is torch.Size([1, 16368])
boxlist ops.py: iou shape is torch.Size([1, 16368])
boxlist ops.py: iou shape is torch.Size([1, 16368])
boxlist ops.py: iou shape is torch.Size([1, 16368])
boxlist ops.py: iou shape is torch.Size([1, 16368])
boxlist ops.py: iou shape is torch.Size([1, 16368])
boxlist ops.py: iou shape is torch.Size([1, 16368])
boxlist ops.py: iou shape is torch.Size([1, 16368])
boxlist ops.py: iou shape is torch.Size([1, 112])
boxlist ops.py: iou shape is torch.Size([1, 150])
boxlist ops.py: iou shape is torch.Size([1, 117])
boxlist ops.py: iou shape is torch.Size([1, 169])
boxlist ops.py: iou shape is torch.Size([1, 123])
boxlist ops.py: iou shape is torch.Size([1, 110])
boxlist ops.py: iou shape is torch.Size([1, 108])
boxlist ops.py: iou shape is torch.Size([1, 140])
boxlist ops.py: iou shape is torch.Size([1, 126])
boxlist ops.py: iou shape is torch.Size([1, 103])
boxlist ops.py: iou shape is torch.Size([1, 141])
boxlist ops.py: iou shape is torch.Size([1, 138])
boxlist ops.py: iou shape is torch.Size([1, 142])
boxlist ops.py: iou shape is torch.Size([1, 118])
boxlist ops.py: iou shape is torch.Size([1, 102])
boxlist ops.py: iou shape is torch.Size([1, 117])
boxlist ops.py: iou shape is torch.Size([1, 1])

I haven't fully understanding the structure of the code.
I will study it, and find out the solution.

RuntimeError: The shape of the mask [230202] at index 0 does not match the shape of the indexed tensor [0] at index 0

❓ Questions and Help

Traceback (most recent call last):
File "github/maskrcnn-benchmark/tools/train_net.py", line 170, in
main()
File "github/maskrcnn-benchmark/tools/train_net.py", line 163, in main
model = train(cfg, args.local_rank, args.distributed)
File "github/maskrcnn-benchmark/tools/train_net.py", line 73, in train
arguments,
File "/home/v-minghl/github/maskrcnn-benchmark/maskrcnn_benchmark/engine/trainer.py", line 65, in do_train
loss_dict = model(images, targets)
File "/home/v-minghl/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py", line 477, in call
result = self.forward(*input, **kwargs)
File "/home/v-minghl/github/maskrcnn-benchmark/maskrcnn_benchmark/modeling/detector/generalized_rcnn.py", line 50, in forward
proposals, proposal_losses = self.rpn(images, features, targets)
File "/home/v-minghl/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py", line 477, in call
result = self.forward(*input, **kwargs)
File "/home/v-minghl/github/maskrcnn-benchmark/maskrcnn_benchmark/modeling/rpn/rpn.py", line 94, in forward
return self._forward_train(anchors, objectness, rpn_box_regression, targets)
File "/home/v-minghl/github/maskrcnn-benchmark/maskrcnn_benchmark/modeling/rpn/rpn.py", line 113, in _forward_train
anchors, objectness, rpn_box_regression, targets
File "/home/v-minghl/github/maskrcnn-benchmark/maskrcnn_benchmark/modeling/rpn/loss.py", line 91, in call
labels, regression_targets = self.prepare_targets(anchors, targets)
File "/home/v-minghl/github/maskrcnn-benchmark/maskrcnn_benchmark/modeling/rpn/loss.py", line 62, in prepare_targets
labels_per_image[~anchors_per_image.get_field("visibility")] = -1
RuntimeError: The shape of the mask [230202] at index 0 does not match the shape of the indexed tensor [0] at index 0

Hi,
I used my own data set in coco format to train. batchsize 2 and max iter is 15000. Train on P100 * 1.
It can succeed to run for 2540 iters and crashed with this error.
I succeed to train this data set with mmdetection.
I have no idea why the error happened, please give me some help.

need Retinanet!!

🚀 Feature

add RetinaNet

demo/Mask_R-CNN_demo.ipynb bug

❓ Questions and Help

ubuntu 18.04
cuda 9.0
cudnn 7.1.3

I test the demo/Mask_R-CNN_demo.ipynb on the ubuntu. Every time the code come to the last block:
predictions = coco_demo.run_on_opencv_image(image), the kernel will reboot without any erro infomation. I check the code step by step, and find out the No.200 : predictions = self.model(image_list) in the predictor.py is the source of the problem.
I can't solve it by myself, anyone could give me some suggestion.
Thanks.

i want to only train RPN for objectness detection, no need for classification, do there has any guide or experience?

❓ Questions and Help

in my project, i only want to detect out object but no need to know is label, so i want to use this code to train a rpn network, can share any guide or experience？

Small mistake in box predictor weight initalization

🐛 Bug

It's probably just a nitpick since it works well enough as it is, but in the roy_box_predictors weight initalization there is a mistake probably caused by some copy-pasting https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/maskrcnn_benchmark/modeling/roi_heads/box_head/roi_box_predictors.py#L19-L23 .
As you can see, self.cls_score.weight and self.bbox_pred.weight get initialized twice, which makes no sense.
It should be something like this unofficial implementation of Detectron https://github.com/roytseng-tw/Detectron.pytorch/blob/master/lib/modeling/fast_rcnn_heads.py#L24-L27 in which the bias is the one initialized to 0 (as per the paper).

AttributeError: module 'torch.distributed' has no attribute 'deprecated'

Installed everything as per INSTALL.md and on ubuntu 16.04, no CUDA and on a VM.
Any thoughts? :/

root@anshu-VirtualBox:~/github/maskrcnn-benchmark/demo# python webcam.py --min-image-size 300 MODEL.DEVICE cpu
Traceback (most recent call last):
  File "webcam.py", line 80, in <module>
    main()
  File "webcam.py", line 64, in main
    min_image_size=args.min_image_size,
  File "/root/github/maskrcnn-benchmark/demo/predictor.py", line 114, in __init__
    _ = checkpointer.load(cfg.MODEL.WEIGHT)
  File "/root/github/maskrcnn-benchmark/maskrcnn_benchmark/utils/checkpoint.py", line 61, in load
    checkpoint = self._load_file(f)
  File "/root/github/maskrcnn-benchmark/maskrcnn_benchmark/utils/checkpoint.py", line 128, in _load_file
    cached_f = cache_url(f)
  File "/root/github/maskrcnn-benchmark/maskrcnn_benchmark/utils/model_zoo.py", line 44, in cache_url
    if not os.path.exists(cached_file) and is_main_process():
  File "/root/github/maskrcnn-benchmark/maskrcnn_benchmark/utils/comm.py", line 22, in is_main_process
    if not torch.distributed.deprecated.is_initialized():
AttributeError: module 'torch.distributed' has no attribute 'deprecated'

If I want to merge pspnet into the faster rcnn,what should I do?

I want to use faster rcnn to do detection,and use pspnet to do segment,how could I merge these two model into one model?

Semantic Segmentation Support

Will the benchmark support only semantic segmentation task?

An illegal memory access was encountered

🐛 Bug

I just run the below script with 4 x P100.

PYTHON="/root/miniconda3/bin/python"
CONFIG="./configs/e2e_mask_rcnn_R_50_FPN_1x.yaml"

export NGPUS=4
${PYTHON} -m torch.distributed.launch --nproc_per_node=$NGPUS \
	./tools/train_net.py --config-file $CONFIG

Expected behavior

Here is the error information,

It seems that the first two few iterations are ok. (iter: 0, 20)

Then in the iter 40, the number in the bracket becomes nan. Then I got the error informing me that an illegal memory was encountered.

Environment

I just install all the enviroments follow the instructions

PyTorch Version 1.0
Linux 16.04
Python version: 3.6
CUDA/cuDNN version: 8.0
GPU models and configuration: 4 X P100

Fix two typos in the abstractions.md document: baseline to benchmark and BoxList argument size to image_size

Here is an example on how to create a BoxList from a list of coordinates:

from maskrcnn_baseline.structures.bounding_box import BoxList, FLIP_LEFT_RIGHT
from maskrcnn_benchmark.structures.bounding_box import BoxList, FLIP_LEFT_RIGHT

width = 100
height = 200
@ -49,7 +49,7 @@ boxes = [
  [10, 10, 50, 50]
]
# create a BoxList with 3 boxes
bbox = BoxList(boxes, size=(width, height), mode='xyxy')
bbox = BoxList(boxes, image_size=(width, height), mode='xyxy')

# perform some box transformations, has similar API as PIL.Image
bbox_scaled = bbox.resize((width * 2, height * 3))

Build error

I installed pytorch-nightly but just got this error:

/maskrcnn-benchmark/env/lib/python3.6/site-packages/torch/lib/include/ATen/core/TensorMethods.h:453:48:   required from here
/usr/include/c++/6/tuple:483:67: error: mismatched argument pack lengths while expanding ‘std::is_constructible<_Elements, _UElements&&>’
       return __and_<is_constructible<_Elements, _UElements&&>...>::value;
                                                                   ^~~~~
/usr/include/c++/6/tuple:484:1: error: body of constexpr function ‘static constexpr bool std::_TC<<anonymous>, _Elements>::_MoveConstructibleTuple() [with _UElements = {std::tuple<at::Tensor, at::Tensor, at::Tensor>}; bool <anonymous> = true; _Elements = {at::Tensor, at::Tensor, at::Tensor}]’ not a return-statement
     }
 ^

No idea what this about

there are problems when i use the methond writen in INSTALL.MD , i try every ways to install torchvision,but always be wrong

❓ Questions and Help

import torch
import torchvision
Traceback (most recent call last):
File "", line 1, in
File "/home/ziming/github/vision/torchvision/init.py", line 1, in
from torchvision import models
File "/home/ziming/github/vision/torchvision/models/init.py", line 1, in
from .alexnet import *
File "/home/ziming/github/vision/torchvision/models/alexnet.py", line 2, in
import torch.utils.model_zoo as model_zoo
ModuleNotFoundError: No module named 'torch.utils.model_zoo'

does pytorch1.0 support Synchronized BatchNorm?

🚀 Feature

does pytorch1.0 support Synchronized BatchNorm, and in this code the FrozenBatchNorm has the same function as Synchronized BN?

why use FrozenBatchNorm, as same thirdparty faster rcnn implement's backbone resnet do'nt has this feature

Non-local + MaskRCNN

🚀 Feature

I am wondering will you add the feature that employs the non-local block. As it can improves the performance for more than 1 point compared with the baseline.

Here I paste the related results reported in the non-local paper,

Motivation

I think the non-local is very hot recently. I want to further propose better module to optimize the current non-local method.

Additional context

Here I provide the paper of non-local.

Non-local Neural Networks

Converting e2e_faster_rcnn_X_101_32x8d_FPN_1x_caffe2.pkl to protobuf

❓ Questions and Help

Do we use the Detectron's script to convert the pkl config files to protobuf for C++ deployment?

I'm getting the following error when I try to convert the aforementioned file using that python script:

(result, consumed) = self._buffer_decode(data, self.errors, final)
UnicodeDecodeError: 'utf-8' codec can't decode byte 0x80 in position 0: invalid start byte

Why I got the map value -1

❓ Questions and Help

After training on coco2017 dataset, I got the following output:

2018-10-26 07:57:49,972 maskrcnn_benchmark.inference INFO: Total inference time: 0:30:09.844855 (0.08900146815007108 s / img per device, on 2 devices)
2018-10-26 07:57:57,928 maskrcnn_benchmark.inference INFO: Preparing results for COCO format
2018-10-26 07:57:57,928 maskrcnn_benchmark.inference INFO: Preparing bbox results
2018-10-26 07:58:06,302 maskrcnn_benchmark.inference INFO: Evaluating predictions
Loading and preparing results...
DONE (t=7.88s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=84.73s).
Accumulating evaluation results...
DONE (t=22.14s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = -1.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = -1.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = -1.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
2018-10-26 08:00:20,193 maskrcnn_benchmark.inference INFO: OrderedDict([('bbox', OrderedDict([('AP', -1.0), ('AP50', -1.0), ('AP75', -1.0), ('APs', -1.0), ('APm', -1.0), ('APl', -1.0)]))])

I used the file e2e_faster_rcnn_R_50_FPN_1x.yaml, and modified the lr to 0.01.

Can't build a model a second time

Consider the following code in a Jupyter notebook:

from maskrcnn_benchmark.config import cfg
from maskrcnn_benchmark.modeling.detector import build_detection_model

model = build_detection_model(cfg)

It works if you execute it a first time. If you try to execute it a second time without restarting the kernel, it raises the following error:

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-3-c924cbd11273> in <module>
      2 from maskrcnn_benchmark.modeling.detector import build_detection_model
      3 
----> 4 model = build_detection_model(cfg)

~/local/conda/envs/nn/lib/python3.6/site-packages/maskrcnn_benchmark/modeling/detector/detectors.py in build_detection_model(cfg)
      8 def build_detection_model(cfg):
      9     meta_arch = _DETECTION_META_ARCHITECTURES[cfg.MODEL.META_ARCHITECTURE]
---> 10     return meta_arch(cfg)

~/local/conda/envs/nn/lib/python3.6/site-packages/maskrcnn_benchmark/modeling/detector/generalized_rcnn.py in __init__(self, cfg)
     27         super(GeneralizedRCNN, self).__init__()
     28 
---> 29         self.backbone = build_backbone(cfg)
     30         self.rpn = build_rpn(cfg)
     31         self.roi_heads = build_roi_heads(cfg)

~/local/conda/envs/nn/lib/python3.6/site-packages/maskrcnn_benchmark/modeling/backbone/backbone.py in build_backbone(cfg)
     42     if cfg.MODEL.BACKBONE.CONV_BODY.endswith("-FPN"):
     43         return build_resnet_fpn_backbone(cfg)
---> 44     return build_resnet_backbone(cfg)

~/local/conda/envs/nn/lib/python3.6/site-packages/maskrcnn_benchmark/modeling/backbone/backbone.py in build_resnet_backbone(cfg)
      9 
     10 def build_resnet_backbone(cfg):
---> 11     body = resnet.ResNet(cfg)
     12     model = nn.Sequential(OrderedDict([("body", body)]))
     13     return model

~/local/conda/envs/nn/lib/python3.6/site-packages/maskrcnn_benchmark/modeling/backbone/resnet.py in __init__(self, cfg)
    101 
    102         # Optionally freeze (requires_grad=False) parts of the backbone
--> 103         self._freeze_backbone(cfg.MODEL.BACKBONE.FREEZE_CONV_BODY_AT)
    104 
    105     def _freeze_backbone(self, freeze_at):

~/local/conda/envs/nn/lib/python3.6/site-packages/maskrcnn_benchmark/modeling/backbone/resnet.py in _freeze_backbone(self, freeze_at)
    108                 m = self.stem  # stage 0 is the stem
    109             else:
--> 110                 m = getattr(self, "layer" + str(stage_index))
    111             for p in m.parameters():
    112                 p.requires_grad = False

~/local/conda/envs/nn/lib/python3.6/site-packages/torch/nn/modules/module.py in __getattr__(self, name)
    516                 return modules[name]
    517         raise AttributeError("'{}' object has no attribute '{}'".format(
--> 518             type(self).__name__, name))
    519 
    520     def __setattr__(self, name, value):

AttributeError: 'ResNet' object has no attribute 'layer1'

Version used: 3308cd9

Problem with multi-GPU training

Hello,

I have successfully built maskrcnn_benchmark on Ubuntu 16.04. My workstation has 4x1080Ti (CUDA 9.2, cuDNN 7, Nvidia drivers 410.48) and I tried to train on COCO dataset on multiple GPUs. I used the script provided in "Perform training on COCO dataset" section.

One GPU worked fine with:

python tools/train_net.py --config-file "configs/e2e_mask_rcnn_R_50_FPN_1x.yaml" SOLVER.IMS_PER_BATCH 2 SOLVER.BASE_LR 0.0025 SOLVER.MAX_ITER 720000 SOLVER.STEPS "(480000, 640000)" TEST.IMS_PER_BATCH 1

Then I used

export NGPUS=2
python -m torch.distributed.launch --nproc_per_node=$NGPUS tools/train_net.py --config-file "configs/e2e_mask_rcnn_R_50_FPN_1x.yaml" SOLVER.IMS_PER_BATCH 4 SOLVER.BASE_LR 0.0025 SOLVER.MAX_ITER 720000 SOLVER.STEPS "(480000, 640000)" TEST.IMS_PER_BATCH 1

(the same train_net.py file and the same config, changed images per batch to 4) and everything worked fine.

Next I tried the same thing for 3 GPUs (changed NGPUS=3, images per batch to 6) and the training gets stuck during the first interation. I have the following logging information and it does not change:

2018-10-29 17:20:55,722 maskrcnn_benchmark.trainer INFO: Start training
2018-10-29 17:20:58,453 maskrcnn_benchmark.trainer INFO: eta: 22 days, 18:04:18  iter: 0  loss: 6.7175 (6.7175)  loss_classifier: 4.4688 (4.4688)  loss_box_reg: 0.0044 (0.0044)  loss_mask: 1.4084 (1.4084)  loss_objectness: 0.7262 (0.7262)  loss_rpn_box_reg: 0.1097 (0.1097)  time: 2.7304 (2.7304)  data: 2.4296 (2.4296)  lr: 0.000833  max mem: 1749

The GPU memory is used, the temperature goes up, but nothing is happening (I tried multiple times and then gave up).

Any ideas? I'd be grateful for help.

will this benchmark plan to support retinanet?

🚀 Feature

retinanet report a high speed and mAP performance, will this benchmark support this

Motivation

Pitch

Find area of segmentation

❓ Questions and Help

Similar to how the original Mask_RCNN repo shows methods of displaying segmentation only, and not the classification bounding box, is there any way to extract the segmentation as a numpy array? My ultimate goal for a project is to find the number of pixels in each segmentation, and this would be extremely helpful!

Thanks so much.

support for the pretrained weights in Detectron

Hi, thanks for this great repo.

I wonder if you have any plan to support the use of the pretrained weights provided in Detectron Model_ZOO

Can I modify the library to make networks run faster?

❓ Questions and Help

Hello, thank you for amazing library!

I am working on an application that requires near realtime processing (~10 fps, i.e. 100 ms) on ~1050 GPU on ~ 1280x720 images. Is there anything I could easily modify that can make the object detection networks faster. I tried Resnet-50-FPN and got ~160 ms on 1070 GPU. I was thinking to modify the number of proposals (number of objects in my images are < 14, classes are < 6) that are processed by ROI heads or can I modify the architecture itself easily (e.g. number of filters)? What would you recommend? I don't mind to suffer a small decrease in mAP.

Cheers,

Why still use "from torch.distributed import deprecated as dist" in pytorch v1.0?

❓

Is there any bugs in the new version torch.distributed package?

when I try to change the interface to new version torch.distributed.DistributedDataParallel, I get an error:

site-packages/torch/nn/parallel/distributed.py", line 298, in distributed_data_parallel_hook
bucket[bucket_offset] = param.grad.data
IndexError: list assignment index out of range

Need help, thanks!

bug

Hi, Any idea about this?
everything in here are installed. https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/INSTALL.md

Traceback (most recent call last):
File "./tools/train_net.py", line 18, in
from maskrcnn_benchmark.engine.inference import inference
File "/home/lz/Workspace/maskrcnn-benchmark/maskrcnn_benchmark/engine/inference.py", line 20, in
from maskrcnn_benchmark.structures.boxlist_ops import boxlist_iou
File "/home/lz/Workspace/maskrcnn-benchmark/maskrcnn_benchmark/structures/boxlist_ops.py", line 6, in
from maskrcnn_benchmark.layers import nms as _box_nms
File "/home/lz/Workspace/maskrcnn-benchmark/maskrcnn_benchmark/layers/init.py", line 8, in
from .nms import nms
File "/home/lz/Workspace/maskrcnn-benchmark/maskrcnn_benchmark/layers/nms.py", line 3, in
from maskrcnn_benchmark import _C
ImportError: /home/lz/Workspace/maskrcnn-benchmark/maskrcnn_benchmark/_C.cpython-37m-x86_64-linux-gnu.so: undefined symbol: _ZN2at4cuda20getCurrentCUDAStreamEl

change to my own dataset, there is a problem.

❓ Questions and Help

File "/home/jason/github/maskrcnn-benchmark/maskrcnn_benchmark/data/collate_batch.py", line 17, in call
images = to_image_list(transposed_batch[0], self.size_divisible)
File "/home/jason/github/maskrcnn-benchmark/maskrcnn_benchmark/structures/image_list.py", line 62, in to_image_list
batched_imgs = tensors[0].new(*batch_shape).zero_()
AttributeError: 'numpy.ndarray' object has no attribute 'new'

I change the dataset, complete the dataset, yaml, paths_catalog, but this bug don't seem like the problem of the logic. Is that the problem of version of tools again?

Segmentation fault (core dumped)

It breaks by returning message 'Segmentation fault (core dumped)'.

I think I followed the installation guide correctly. But when I run the code (maskrcnn-benchmark/demo/webcam.py), it breaks by returning 'Segmentation fault (core dumped)'.

The specific location returning the message is
line 27 in the file 'boxlist_ops.py': keep = _box_nms(boxes, score, nms_thresh)
from
line 114 in the file 'inference.py': boxlist = boxlist_nms( .... )
from
line 138 in the file 'inference.py': sampled_boxes.append(self.forward_for_single_feature_map(a, o, b))
from
line 122 in the file 'rpn.py': boxes = self.box_selector_test(anchors, objectness, rpn_box_regression)
from
line 96 in the file 'rpn.py': return self._forward_test(anchors, objectness, rpn_box_regression)
from
line 50 in the file 'generalised_rcnn.py': proposals, proposal_losses = self.rpn(images, features,
targets)
from
line 205 in the file 'predictor.py': predictions = self.model(image_list)
from
file 'webcam.py': composite = coco_demo.run_on_opencv_image(img)

I thought it was because of my small GPU memory problem on my local machine (PC). But when I tried to run the same code on an available deep learning machine which has enough (about 11GB GPU) memory. It still returns the same message and breaks.

What is my problem? It would be grateful if someone has an answer. Thanks in advance.

Best,
Young

build error:error: command '/usr/local/cuda/bin/nvcc' failed with exit status 1

❓ Questions and Help

Hello Great guys,
When I tried to install maksrcnn-benchmark in my PC, I got the follow compilation error in the last step, anyone can have a look? Thanks

(maskrcnn_benchmark) gengfeng@ai-work-4:~/github/maskrcnn-benchmark$ python setup.py build develop
running build
running build_ext
building 'maskrcnn_benchmark._C' extension
gcc -pthread -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -DWITH_CUDA -I/home/gengfeng/github/maskrcnn-benchmark/maskrcnn_benchmark/csrc -I/home/gengfeng/anaconda3/lib/python3.6/site-packages/torch/lib/include -I/home/gengfeng/anaconda3/lib/python3.6/site-packages/torch/lib/include/torch/csrc/api/include -I/home/gengfeng/anaconda3/lib/python3.6/site-packages/torch/lib/include/TH -I/home/gengfeng/anaconda3/lib/python3.6/site-packages/torch/lib/include/THC -I/usr/local/cuda/include -I/home/gengfeng/anaconda3/envs/maskrcnn_benchmark/include/python3.6m -c /home/gengfeng/github/maskrcnn-benchmark/maskrcnn_benchmark/csrc/vision.cpp -o build/temp.linux-x86_64-3.6/home/gengfeng/github/maskrcnn-benchmark/maskrcnn_benchmark/csrc/vision.o -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++11
cc1plus: warning: command line option ‘-Wstrict-prototypes’ is valid for C/ObjC but not for C++
gcc -pthread -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -DWITH_CUDA -I/home/gengfeng/github/maskrcnn-benchmark/maskrcnn_benchmark/csrc -I/home/gengfeng/anaconda3/lib/python3.6/site-packages/torch/lib/include -I/home/gengfeng/anaconda3/lib/python3.6/site-packages/torch/lib/include/torch/csrc/api/include -I/home/gengfeng/anaconda3/lib/python3.6/site-packages/torch/lib/include/TH -I/home/gengfeng/anaconda3/lib/python3.6/site-packages/torch/lib/include/THC -I/usr/local/cuda/include -I/home/gengfeng/anaconda3/envs/maskrcnn_benchmark/include/python3.6m -c /home/gengfeng/github/maskrcnn-benchmark/maskrcnn_benchmark/csrc/cpu/nms_cpu.cpp -o build/temp.linux-x86_64-3.6/home/gengfeng/github/maskrcnn-benchmark/maskrcnn_benchmark/csrc/cpu/nms_cpu.o -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++11
cc1plus: warning: command line option ‘-Wstrict-prototypes’ is valid for C/ObjC but not for C++
gcc -pthread -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -DWITH_CUDA -I/home/gengfeng/github/maskrcnn-benchmark/maskrcnn_benchmark/csrc -I/home/gengfeng/anaconda3/lib/python3.6/site-packages/torch/lib/include -I/home/gengfeng/anaconda3/lib/python3.6/site-packages/torch/lib/include/torch/csrc/api/include -I/home/gengfeng/anaconda3/lib/python3.6/site-packages/torch/lib/include/TH -I/home/gengfeng/anaconda3/lib/python3.6/site-packages/torch/lib/include/THC -I/usr/local/cuda/include -I/home/gengfeng/anaconda3/envs/maskrcnn_benchmark/include/python3.6m -c /home/gengfeng/github/maskrcnn-benchmark/maskrcnn_benchmark/csrc/cpu/ROIAlign_cpu.cpp -o build/temp.linux-x86_64-3.6/home/gengfeng/github/maskrcnn-benchmark/maskrcnn_benchmark/csrc/cpu/ROIAlign_cpu.o -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++11
cc1plus: warning: command line option ‘-Wstrict-prototypes’ is valid for C/ObjC but not for C++
/usr/local/cuda/bin/nvcc -DWITH_CUDA -I/home/gengfeng/github/maskrcnn-benchmark/maskrcnn_benchmark/csrc -I/home/gengfeng/anaconda3/lib/python3.6/site-packages/torch/lib/include -I/home/gengfeng/anaconda3/lib/python3.6/site-packages/torch/lib/include/torch/csrc/api/include -I/home/gengfeng/anaconda3/lib/python3.6/site-packages/torch/lib/include/TH -I/home/gengfeng/anaconda3/lib/python3.6/site-packages/torch/lib/include/THC -I/usr/local/cuda/include -I/home/gengfeng/anaconda3/envs/maskrcnn_benchmark/include/python3.6m -c /home/gengfeng/github/maskrcnn-benchmark/maskrcnn_benchmark/csrc/cuda/ROIAlign_cuda.cu -o build/temp.linux-x86_64-3.6/home/gengfeng/github/maskrcnn-benchmark/maskrcnn_benchmark/csrc/cuda/ROIAlign_cuda.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --compiler-options '-fPIC' -DCUDA_HAS_FP16=1 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++11
/usr/include/c++/6/tuple: In instantiation of ‘static constexpr bool std::_TC<<anonymous>, _Elements>::_MoveConstructibleTuple() [with _UElements = {std::tuple<at::Tensor, at::Tensor, at::Tensor>}; bool <anonymous> = true; _Elements = {at::Tensor, at::Tensor, at::Tensor}]’:
/usr/include/c++/6/tuple:626:248:   required by substitution of ‘template<class ... _UElements, typename std::enable_if<(((std::_TC<(sizeof... (_UElements) == 1), at::Tensor, at::Tensor, at::Tensor>::_NotSameTuple<_UElements ...>() && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor>::_MoveConstructibleTuple<_UElements ...>()) && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor>::_ImplicitlyMoveConvertibleTuple<_UElements ...>()) && (3ul >= 1)), bool>::type <anonymous> > constexpr std::tuple< <template-parameter-1-1> >::tuple(_UElements&& ...) [with _UElements = {std::tuple<at::Tensor, at::Tensor, at::Tensor>}; typename std::enable_if<(((std::_TC<(sizeof... (_UElements) == 1), at::Tensor, at::Tensor, at::Tensor>::_NotSameTuple<_UElements ...>() && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor>::_MoveConstructibleTuple<_UElements ...>()) && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor>::_ImplicitlyMoveConvertibleTuple<_UElements ...>()) && (3ul >= 1)), bool>::type <anonymous> = <missing>]’
/home/gengfeng/anaconda3/lib/python3.6/site-packages/torch/lib/include/ATen/core/TensorMethods.h:453:36:   required from here
/usr/include/c++/6/tuple:483:67: error: mismatched argument pack lengths while expanding ‘std::is_constructible<_Elements, _UElements&&>’
       return __and_<is_constructible<_Elements, _UElements&&>...>::value;
                                                                   ^~~~~
/usr/include/c++/6/tuple:484:1: error: body of constexpr function ‘static constexpr bool std::_TC<<anonymous>, _Elements>::_MoveConstructibleTuple() [with _UElements = {std::tuple<at::Tensor, at::Tensor, at::Tensor>}; bool <anonymous> = true; _Elements = {at::Tensor, at::Tensor, at::Tensor}]’ not a return-statement
     }
 ^
/usr/include/c++/6/tuple: In instantiation of ‘static constexpr bool std::_TC<<anonymous>, _Elements>::_ImplicitlyMoveConvertibleTuple() [with _UElements = {std::tuple<at::Tensor, at::Tensor, at::Tensor>}; bool <anonymous> = true; _Elements = {at::Tensor, at::Tensor, at::Tensor}]’:
/usr/include/c++/6/tuple:626:362:   required by substitution of ‘template<class ... _UElements, typename std::enable_if<(((std::_TC<(sizeof... (_UElements) == 1), at::Tensor, at::Tensor, at::Tensor>::_NotSameTuple<_UElements ...>() && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor>::_MoveConstructibleTuple<_UElements ...>()) && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor>::_ImplicitlyMoveConvertibleTuple<_UElements ...>()) && (3ul >= 1)), bool>::type <anonymous> > constexpr std::tuple< <template-parameter-1-1> >::tuple(_UElements&& ...) [with _UElements = {std::tuple<at::Tensor, at::Tensor, at::Tensor>}; typename std::enable_if<(((std::_TC<(sizeof... (_UElements) == 1), at::Tensor, at::Tensor, at::Tensor>::_NotSameTuple<_UElements ...>() && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor>::_MoveConstructibleTuple<_UElements ...>()) && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor>::_ImplicitlyMoveConvertibleTuple<_UElements ...>()) && (3ul >= 1)), bool>::type <anonymous> = <missing>]’
/home/gengfeng/anaconda3/lib/python3.6/site-packages/torch/lib/include/ATen/core/TensorMethods.h:453:36:   required from here
/usr/include/c++/6/tuple:489:65: error: mismatched argument pack lengths while expanding ‘std::is_convertible<_UElements&&, _Elements>’
       return __and_<is_convertible<_UElements&&, _Elements>...>::value;
                                                                 ^~~~~
/usr/include/c++/6/tuple:490:1: error: body of constexpr function ‘static constexpr bool std::_TC<<anonymous>, _Elements>::_ImplicitlyMoveConvertibleTuple() [with _UElements = {std::tuple<at::Tensor, at::Tensor, at::Tensor>}; bool <anonymous> = true; _Elements = {at::Tensor, at::Tensor, at::Tensor}]’ not a return-statement
     }
 ^
/usr/include/c++/6/tuple: In instantiation of ‘static constexpr bool std::_TC<<anonymous>, _Elements>::_NonNestedTuple() [with _SrcTuple = const std::tuple<at::Tensor, at::Tensor, at::Tensor>&; bool <anonymous> = true; _Elements = {at::Tensor, at::Tensor, at::Tensor}]’:
/usr/include/c++/6/tuple:662:419:   required by substitution of ‘template<class ... _UElements, class _Dummy, typename std::enable_if<((std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor>::_ConstructibleTuple<_UElements ...>() && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor>::_ImplicitlyConvertibleTuple<_UElements ...>()) && std::_TC<(std::is_same<_Dummy, void>::value && (1ul == 1)), at::Tensor, at::Tensor, at::Tensor>::_NonNestedTuple<const tuple<_Elements ...>&>()), bool>::type <anonymous> > constexpr std::tuple< <template-parameter-1-1> >::tuple(const std::tuple<_Args1 ...>&) [with _UElements = {at::Tensor, at::Tensor, at::Tensor}; _Dummy = void; typename std::enable_if<((std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor>::_ConstructibleTuple<_UElements ...>() && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor>::_ImplicitlyConvertibleTuple<_UElements ...>()) && std::_TC<(std::is_same<_Dummy, void>::value && (1ul == 1)), at::Tensor, at::Tensor, at::Tensor>::_NonNestedTuple<const tuple<_Elements ...>&>()), bool>::type <anonymous> = <missing>]’
/home/gengfeng/anaconda3/lib/python3.6/site-packages/torch/lib/include/ATen/core/TensorMethods.h:453:36:   required from here
/usr/include/c++/6/tuple:495:244: error: wrong number of template arguments (4, should be 2)
       return  __and_<__not_<is_same<tuple<_Elements...>,
                                                                                                                                                                                                                                                    ^    
/usr/include/c++/6/type_traits:1558:8: note: provided for ‘template<class _From, class _To> struct std::is_convertible’
     struct is_convertible
        ^~~~~~~~~~~~~~
/usr/include/c++/6/tuple:502:1: error: body of constexpr function ‘static constexpr bool std::_TC<<anonymous>, _Elements>::_NonNestedTuple() [with _SrcTuple = const std::tuple<at::Tensor, at::Tensor, at::Tensor>&; bool <anonymous> = true; _Elements = {at::Tensor, at::Tensor, at::Tensor}]’ not a return-statement
     }
 ^
/usr/include/c++/6/tuple: In instantiation of ‘static constexpr bool std::_TC<<anonymous>, _Elements>::_NonNestedTuple() [with _SrcTuple = std::tuple<at::Tensor, at::Tensor, at::Tensor>&&; bool <anonymous> = true; _Elements = {at::Tensor, at::Tensor, at::Tensor}]’:
/usr/include/c++/6/tuple:686:422:   required by substitution of ‘template<class ... _UElements, class _Dummy, typename std::enable_if<((std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor>::_MoveConstructibleTuple<_UElements ...>() && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor>::_ImplicitlyMoveConvertibleTuple<_UElements ...>()) && std::_TC<(std::is_same<_Dummy, void>::value && (1ul == 1)), at::Tensor, at::Tensor, at::Tensor>::_NonNestedTuple<tuple<_Elements ...>&&>()), bool>::type <anonymous> > constexpr std::tuple< <template-parameter-1-1> >::tuple(std::tuple<_Args1 ...>&&) [with _UElements = {at::Tensor, at::Tensor, at::Tensor}; _Dummy = void; typename std::enable_if<((std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor>::_MoveConstructibleTuple<_UElements ...>() && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor>::_ImplicitlyMoveConvertibleTuple<_UElements ...>()) && std::_TC<(std::is_same<_Dummy, void>::value && (1ul == 1)), at::Tensor, at::Tensor, at::Tensor>::_NonNestedTuple<tuple<_Elements ...>&&>()), bool>::type <anonymous> = <missing>]’
/home/gengfeng/anaconda3/lib/python3.6/site-packages/torch/lib/include/ATen/core/TensorMethods.h:453:36:   required from here
/usr/include/c++/6/tuple:495:244: error: wrong number of template arguments (4, should be 2)
       return  __and_<__not_<is_same<tuple<_Elements...>,
                                                                                                                                                                                                                                                    ^    
/usr/include/c++/6/type_traits:1558:8: note: provided for ‘template<class _From, class _To> struct std::is_convertible’
     struct is_convertible
        ^~~~~~~~~~~~~~
/usr/include/c++/6/tuple:502:1: error: body of constexpr function ‘static constexpr bool std::_TC<<anonymous>, _Elements>::_NonNestedTuple() [with _SrcTuple = std::tuple<at::Tensor, at::Tensor, at::Tensor>&&; bool <anonymous> = true; _Elements = {at::Tensor, at::Tensor, at::Tensor}]’ not a return-statement
     }
 ^
/usr/include/c++/6/tuple: In instantiation of ‘static constexpr bool std::_TC<<anonymous>, _Elements>::_MoveConstructibleTuple() [with _UElements = {std::tuple<at::Tensor&, at::Tensor&, at::Tensor&>}; bool <anonymous> = true; _Elements = {at::Tensor&, at::Tensor&, at::Tensor&}]’:
/usr/include/c++/6/tuple:626:248:   required by substitution of ‘template<class ... _UElements, typename std::enable_if<(((std::_TC<(sizeof... (_UElements) == 1), at::Tensor&, at::Tensor&, at::Tensor&>::_NotSameTuple<_UElements ...>() && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor&, at::Tensor&, at::Tensor&>::_MoveConstructibleTuple<_UElements ...>()) && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor&, at::Tensor&, at::Tensor&>::_ImplicitlyMoveConvertibleTuple<_UElements ...>()) && (3ul >= 1)), bool>::type <anonymous> > constexpr std::tuple< <template-parameter-1-1> >::tuple(_UElements&& ...) [with _UElements = {std::tuple<at::Tensor&, at::Tensor&, at::Tensor&>}; typename std::enable_if<(((std::_TC<(sizeof... (_UElements) == 1), at::Tensor&, at::Tensor&, at::Tensor&>::_NotSameTuple<_UElements ...>() && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor&, at::Tensor&, at::Tensor&>::_MoveConstructibleTuple<_UElements ...>()) && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor&, at::Tensor&, at::Tensor&>::_ImplicitlyMoveConvertibleTuple<_UElements ...>()) && (3ul >= 1)), bool>::type <anonymous> = <missing>]’
/home/gengfeng/anaconda3/lib/python3.6/site-packages/torch/lib/include/ATen/Functions.h:1960:69:   required from here
/usr/include/c++/6/tuple:483:67: error: mismatched argument pack lengths while expanding ‘std::is_constructible<_Elements, _UElements&&>’
       return __and_<is_constructible<_Elements, _UElements&&>...>::value;
                                                                   ^~~~~
/usr/include/c++/6/tuple:484:1: error: body of constexpr function ‘static constexpr bool std::_TC<<anonymous>, _Elements>::_MoveConstructibleTuple() [with _UElements = {std::tuple<at::Tensor&, at::Tensor&, at::Tensor&>}; bool <anonymous> = true; _Elements = {at::Tensor&, at::Tensor&, at::Tensor&}]’ not a return-statement
     }
 ^
/usr/include/c++/6/tuple: In instantiation of ‘static constexpr bool std::_TC<<anonymous>, _Elements>::_ImplicitlyMoveConvertibleTuple() [with _UElements = {std::tuple<at::Tensor&, at::Tensor&, at::Tensor&>}; bool <anonymous> = true; _Elements = {at::Tensor&, at::Tensor&, at::Tensor&}]’:
/usr/include/c++/6/tuple:626:362:   required by substitution of ‘template<class ... _UElements, typename std::enable_if<(((std::_TC<(sizeof... (_UElements) == 1), at::Tensor&, at::Tensor&, at::Tensor&>::_NotSameTuple<_UElements ...>() && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor&, at::Tensor&, at::Tensor&>::_MoveConstructibleTuple<_UElements ...>()) && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor&, at::Tensor&, at::Tensor&>::_ImplicitlyMoveConvertibleTuple<_UElements ...>()) && (3ul >= 1)), bool>::type <anonymous> > constexpr std::tuple< <template-parameter-1-1> >::tuple(_UElements&& ...) [with _UElements = {std::tuple<at::Tensor&, at::Tensor&, at::Tensor&>}; typename std::enable_if<(((std::_TC<(sizeof... (_UElements) == 1), at::Tensor&, at::Tensor&, at::Tensor&>::_NotSameTuple<_UElements ...>() && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor&, at::Tensor&, at::Tensor&>::_MoveConstructibleTuple<_UElements ...>()) && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor&, at::Tensor&, at::Tensor&>::_ImplicitlyMoveConvertibleTuple<_UElements ...>()) && (3ul >= 1)), bool>::type <anonymous> = <missing>]’
/home/gengfeng/anaconda3/lib/python3.6/site-packages/torch/lib/include/ATen/Functions.h:1960:69:   required from here
/usr/include/c++/6/tuple:489:65: error: mismatched argument pack lengths while expanding ‘std::is_convertible<_UElements&&, _Elements>’
       return __and_<is_convertible<_UElements&&, _Elements>...>::value;
                                                                 ^~~~~
/usr/include/c++/6/tuple:490:1: error: body of constexpr function ‘static constexpr bool std::_TC<<anonymous>, _Elements>::_ImplicitlyMoveConvertibleTuple() [with _UElements = {std::tuple<at::Tensor&, at::Tensor&, at::Tensor&>}; bool <anonymous> = true; _Elements = {at::Tensor&, at::Tensor&, at::Tensor&}]’ not a return-statement
     }
 ^
/usr/include/c++/6/tuple: In instantiation of ‘static constexpr bool std::_TC<<anonymous>, _Elements>::_NonNestedTuple() [with _SrcTuple = const std::tuple<at::Tensor&, at::Tensor&, at::Tensor&>&; bool <anonymous> = true; _Elements = {at::Tensor&, at::Tensor&, at::Tensor&}]’:
/usr/include/c++/6/tuple:662:419:   required by substitution of ‘template<class ... _UElements, class _Dummy, typename std::enable_if<((std::_TC<(1ul == sizeof... (_UElements)), at::Tensor&, at::Tensor&, at::Tensor&>::_ConstructibleTuple<_UElements ...>() && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor&, at::Tensor&, at::Tensor&>::_ImplicitlyConvertibleTuple<_UElements ...>()) && std::_TC<(std::is_same<_Dummy, void>::value && (1ul == 1)), at::Tensor&, at::Tensor&, at::Tensor&>::_NonNestedTuple<const tuple<_Elements ...>&>()), bool>::type <anonymous> > constexpr std::tuple< <template-parameter-1-1> >::tuple(const std::tuple<_Args1 ...>&) [with _UElements = {at::Tensor&, at::Tensor&, at::Tensor&}; _Dummy = void; typename std::enable_if<((std::_TC<(1ul == sizeof... (_UElements)), at::Tensor&, at::Tensor&, at::Tensor&>::_ConstructibleTuple<_UElements ...>() && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor&, at::Tensor&, at::Tensor&>::_ImplicitlyConvertibleTuple<_UElements ...>()) && std::_TC<(std::is_same<_Dummy, void>::value && (1ul == 1)), at::Tensor&, at::Tensor&, at::Tensor&>::_NonNestedTuple<const tuple<_Elements ...>&>()), bool>::type <anonymous> = <missing>]’
/home/gengfeng/anaconda3/lib/python3.6/site-packages/torch/lib/include/ATen/Functions.h:1960:69:   required from here
/usr/include/c++/6/tuple:495:244: error: wrong number of template arguments (4, should be 2)
       return  __and_<__not_<is_same<tuple<_Elements...>,
                                                                                                                                                                                                                                                    ^    
/usr/include/c++/6/type_traits:1558:8: note: provided for ‘template<class _From, class _To> struct std::is_convertible’
     struct is_convertible
        ^~~~~~~~~~~~~~
/usr/include/c++/6/tuple:502:1: error: body of constexpr function ‘static constexpr bool std::_TC<<anonymous>, _Elements>::_NonNestedTuple() [with _SrcTuple = const std::tuple<at::Tensor&, at::Tensor&, at::Tensor&>&; bool <anonymous> = true; _Elements = {at::Tensor&, at::Tensor&, at::Tensor&}]’ not a return-statement
     }
 ^
/usr/include/c++/6/tuple: In instantiation of ‘static constexpr bool std::_TC<<anonymous>, _Elements>::_NonNestedTuple() [with _SrcTuple = std::tuple<at::Tensor&, at::Tensor&, at::Tensor&>&&; bool <anonymous> = true; _Elements = {at::Tensor&, at::Tensor&, at::Tensor&}]’:
/usr/include/c++/6/tuple:686:422:   required by substitution of ‘template<class ... _UElements, class _Dummy, typename std::enable_if<((std::_TC<(1ul == sizeof... (_UElements)), at::Tensor&, at::Tensor&, at::Tensor&>::_MoveConstructibleTuple<_UElements ...>() && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor&, at::Tensor&, at::Tensor&>::_ImplicitlyMoveConvertibleTuple<_UElements ...>()) && std::_TC<(std::is_same<_Dummy, void>::value && (1ul == 1)), at::Tensor&, at::Tensor&, at::Tensor&>::_NonNestedTuple<tuple<_Elements ...>&&>()), bool>::type <anonymous> > constexpr std::tuple< <template-parameter-1-1> >::tuple(std::tuple<_Args1 ...>&&) [with _UElements = {at::Tensor&, at::Tensor&, at::Tensor&}; _Dummy = void; typename std::enable_if<((std::_TC<(1ul == sizeof... (_UElements)), at::Tensor&, at::Tensor&, at::Tensor&>::_MoveConstructibleTuple<_UElements ...>() && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor&, at::Tensor&, at::Tensor&>::_ImplicitlyMoveConvertibleTuple<_UElements ...>()) && std::_TC<(std::is_same<_Dummy, void>::value && (1ul == 1)), at::Tensor&, at::Tensor&, at::Tensor&>::_NonNestedTuple<tuple<_Elements ...>&&>()), bool>::type <anonymous> = <missing>]’
/home/gengfeng/anaconda3/lib/python3.6/site-packages/torch/lib/include/ATen/Functions.h:1960:69:   required from here
/usr/include/c++/6/tuple:495:244: error: wrong number of template arguments (4, should be 2)
       return  __and_<__not_<is_same<tuple<_Elements...>,
                                                                                                                                                                                                                                                    ^    
/usr/include/c++/6/type_traits:1558:8: note: provided for ‘template<class _From, class _To> struct std::is_convertible’
     struct is_convertible
        ^~~~~~~~~~~~~~
/usr/include/c++/6/tuple:502:1: error: body of constexpr function ‘static constexpr bool std::_TC<<anonymous>, _Elements>::_NonNestedTuple() [with _SrcTuple = std::tuple<at::Tensor&, at::Tensor&, at::Tensor&>&&; bool <anonymous> = true; _Elements = {at::Tensor&, at::Tensor&, at::Tensor&}]’ not a return-statement
     }
 ^
/usr/include/c++/6/tuple: In instantiation of ‘static constexpr bool std::_TC<<anonymous>, _Elements>::_MoveConstructibleTuple() [with _UElements = {std::tuple<at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor>}; bool <anonymous> = true; _Elements = {at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor}]’:
/usr/include/c++/6/tuple:626:248:   required by substitution of ‘template<class ... _UElements, typename std::enable_if<(((std::_TC<(sizeof... (_UElements) == 1), at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_NotSameTuple<_UElements ...>() && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_MoveConstructibleTuple<_UElements ...>()) && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_ImplicitlyMoveConvertibleTuple<_UElements ...>()) && (5ul >= 1)), bool>::type <anonymous> > constexpr std::tuple< <template-parameter-1-1> >::tuple(_UElements&& ...) [with _UElements = {std::tuple<at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor>}; typename std::enable_if<(((std::_TC<(sizeof... (_UElements) == 1), at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_NotSameTuple<_UElements ...>() && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_MoveConstructibleTuple<_UElements ...>()) && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_ImplicitlyMoveConvertibleTuple<_UElements ...>()) && (5ul >= 1)), bool>::type <anonymous> = <missing>]’
/home/gengfeng/anaconda3/lib/python3.6/site-packages/torch/lib/include/ATen/Functions.h:3040:197:   required from here
/usr/include/c++/6/tuple:483:67: error: mismatched argument pack lengths while expanding ‘std::is_constructible<_Elements, _UElements&&>’
       return __and_<is_constructible<_Elements, _UElements&&>...>::value;
                                                                   ^~~~~
/usr/include/c++/6/tuple:484:1: error: body of constexpr function ‘static constexpr bool std::_TC<<anonymous>, _Elements>::_MoveConstructibleTuple() [with _UElements = {std::tuple<at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor>}; bool <anonymous> = true; _Elements = {at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor}]’ not a return-statement
     }
 ^
/usr/include/c++/6/tuple: In instantiation of ‘static constexpr bool std::_TC<<anonymous>, _Elements>::_ImplicitlyMoveConvertibleTuple() [with _UElements = {std::tuple<at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor>}; bool <anonymous> = true; _Elements = {at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor}]’:
/usr/include/c++/6/tuple:626:362:   required by substitution of ‘template<class ... _UElements, typename std::enable_if<(((std::_TC<(sizeof... (_UElements) == 1), at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_NotSameTuple<_UElements ...>() && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_MoveConstructibleTuple<_UElements ...>()) && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_ImplicitlyMoveConvertibleTuple<_UElements ...>()) && (5ul >= 1)), bool>::type <anonymous> > constexpr std::tuple< <template-parameter-1-1> >::tuple(_UElements&& ...) [with _UElements = {std::tuple<at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor>}; typename std::enable_if<(((std::_TC<(sizeof... (_UElements) == 1), at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_NotSameTuple<_UElements ...>() && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_MoveConstructibleTuple<_UElements ...>()) && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_ImplicitlyMoveConvertibleTuple<_UElements ...>()) && (5ul >= 1)), bool>::type <anonymous> = <missing>]’
/home/gengfeng/anaconda3/lib/python3.6/site-packages/torch/lib/include/ATen/Functions.h:3040:197:   required from here
/usr/include/c++/6/tuple:489:65: error: mismatched argument pack lengths while expanding ‘std::is_convertible<_UElements&&, _Elements>’
       return __and_<is_convertible<_UElements&&, _Elements>...>::value;
                                                                 ^~~~~
/usr/include/c++/6/tuple:490:1: error: body of constexpr function ‘static constexpr bool std::_TC<<anonymous>, _Elements>::_ImplicitlyMoveConvertibleTuple() [with _UElements = {std::tuple<at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor>}; bool <anonymous> = true; _Elements = {at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor}]’ not a return-statement
     }
 ^
/usr/include/c++/6/tuple: In instantiation of ‘static constexpr bool std::_TC<<anonymous>, _Elements>::_NonNestedTuple() [with _SrcTuple = const std::tuple<at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor>&; bool <anonymous> = true; _Elements = {at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor}]’:
/usr/include/c++/6/tuple:662:419:   required by substitution of ‘template<class ... _UElements, class _Dummy, typename std::enable_if<((std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_ConstructibleTuple<_UElements ...>() && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_ImplicitlyConvertibleTuple<_UElements ...>()) && std::_TC<(std::is_same<_Dummy, void>::value && (1ul == 1)), at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_NonNestedTuple<const tuple<_Elements ...>&>()), bool>::type <anonymous> > constexpr std::tuple< <template-parameter-1-1> >::tuple(const std::tuple<_Args1 ...>&) [with _UElements = {at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor}; _Dummy = void; typename std::enable_if<((std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_ConstructibleTuple<_UElements ...>() && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_ImplicitlyConvertibleTuple<_UElements ...>()) && std::_TC<(std::is_same<_Dummy, void>::value && (1ul == 1)), at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_NonNestedTuple<const tuple<_Elements ...>&>()), bool>::type <anonymous> = <missing>]’
/home/gengfeng/anaconda3/lib/python3.6/site-packages/torch/lib/include/ATen/Functions.h:3040:197:   required from here
/usr/include/c++/6/tuple:495:244: error: wrong number of template arguments (6, should be 2)
       return  __and_<__not_<is_same<tuple<_Elements...>,
                                                                                                                                                                                                                                                    ^    
/usr/include/c++/6/type_traits:1558:8: note: provided for ‘template<class _From, class _To> struct std::is_convertible’
     struct is_convertible
        ^~~~~~~~~~~~~~
/usr/include/c++/6/tuple:502:1: error: body of constexpr function ‘static constexpr bool std::_TC<<anonymous>, _Elements>::_NonNestedTuple() [with _SrcTuple = const std::tuple<at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor>&; bool <anonymous> = true; _Elements = {at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor}]’ not a return-statement
     }
 ^
/usr/include/c++/6/tuple: In instantiation of ‘static constexpr bool std::_TC<<anonymous>, _Elements>::_NonNestedTuple() [with _SrcTuple = std::tuple<at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor>&&; bool <anonymous> = true; _Elements = {at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor}]’:
/usr/include/c++/6/tuple:686:422:   required by substitution of ‘template<class ... _UElements, class _Dummy, typename std::enable_if<((std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_MoveConstructibleTuple<_UElements ...>() && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_ImplicitlyMoveConvertibleTuple<_UElements ...>()) && std::_TC<(std::is_same<_Dummy, void>::value && (1ul == 1)), at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_NonNestedTuple<tuple<_Elements ...>&&>()), bool>::type <anonymous> > constexpr std::tuple< <template-parameter-1-1> >::tuple(std::tuple<_Args1 ...>&&) [with _UElements = {at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor}; _Dummy = void; typename std::enable_if<((std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_MoveConstructibleTuple<_UElements ...>() && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_ImplicitlyMoveConvertibleTuple<_UElements ...>()) && std::_TC<(std::is_same<_Dummy, void>::value && (1ul == 1)), at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_NonNestedTuple<tuple<_Elements ...>&&>()), bool>::type <anonymous> = <missing>]’
/home/gengfeng/anaconda3/lib/python3.6/site-packages/torch/lib/include/ATen/Functions.h:3040:197:   required from here
/usr/include/c++/6/tuple:495:244: error: wrong number of template arguments (6, should be 2)
       return  __and_<__not_<is_same<tuple<_Elements...>,
                                                                                                                                                                                                                                                    ^    
/usr/include/c++/6/type_traits:1558:8: note: provided for ‘template<class _From, class _To> struct std::is_convertible’
     struct is_convertible
        ^~~~~~~~~~~~~~
/usr/include/c++/6/tuple:502:1: error: body of constexpr function ‘static constexpr bool std::_TC<<anonymous>, _Elements>::_NonNestedTuple() [with _SrcTuple = std::tuple<at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor>&&; bool <anonymous> = true; _Elements = {at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor}]’ not a return-statement
     }
 ^
/usr/include/c++/6/tuple: In instantiation of ‘static constexpr bool std::_TC<<anonymous>, _Elements>::_MoveConstructibleTuple() [with _UElements = {std::tuple<at::Tensor, at::Tensor, at::Tensor, std::vector<at::Tensor, std::allocator<at::Tensor> > >}; bool <anonymous> = true; _Elements = {at::Tensor, at::Tensor, at::Tensor, std::vector<at::Tensor, std::allocator<at::Tensor> >}]’:
/usr/include/c++/6/tuple:626:248:   required by substitution of ‘template<class ... _UElements, typename std::enable_if<(((std::_TC<(sizeof... (_UElements) == 1), at::Tensor, at::Tensor, at::Tensor, std::vector<at::Tensor, std::allocator<at::Tensor> > >::_NotSameTuple<_UElements ...>() && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, std::vector<at::Tensor, std::allocator<at::Tensor> > >::_MoveConstructibleTuple<_UElements ...>()) && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, std::vector<at::Tensor, std::allocator<at::Tensor> > >::_ImplicitlyMoveConvertibleTuple<_UElements ...>()) && (4ul >= 1)), bool>::type <anonymous> > constexpr std::tuple< <template-parameter-1-1> >::tuple(_UElements&& ...) [with _UElements = {std::tuple<at::Tensor, at::Tensor, at::Tensor, std::vector<at::Tensor, std::allocator<at::Tensor> > >}; typename std::enable_if<(((std::_TC<(sizeof... (_UElements) == 1), at::Tensor, at::Tensor, at::Tensor, std::vector<at::Tensor, std::allocator<at::Tensor> > >::_NotSameTuple<_UElements ...>() && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, std::vector<at::Tensor, std::allocator<at::Tensor> > >::_MoveConstructibleTuple<_UElements ...>()) && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, std::vector<at::Tensor, std::allocator<at::Tensor> > >::_ImplicitlyMoveConvertibleTuple<_UElements ...>()) && (4ul >= 1)), bool>::type <anonymous> = <missing>]’
/home/gengfeng/anaconda3/lib/python3.6/site-packages/torch/lib/include/ATen/Functions.h:3043:267:   required from here
/usr/include/c++/6/tuple:483:67: error: mismatched argument pack lengths while expanding ‘std::is_constructible<_Elements, _UElements&&>’
       return __and_<is_constructible<_Elements, _UElements&&>...>::value;
                                                                   ^~~~~
/usr/include/c++/6/tuple:484:1: error: body of constexpr function ‘static constexpr bool std::_TC<<anonymous>, _Elements>::_MoveConstructibleTuple() [with _UElements = {std::tuple<at::Tensor, at::Tensor, at::Tensor, std::vector<at::Tensor, std::allocator<at::Tensor> > >}; bool <anonymous> = true; _Elements = {at::Tensor, at::Tensor, at::Tensor, std::vector<at::Tensor, std::allocator<at::Tensor> >}]’ not a return-statement
     }
 ^
/usr/include/c++/6/tuple: In instantiation of ‘static constexpr bool std::_TC<<anonymous>, _Elements>::_ImplicitlyMoveConvertibleTuple() [with _UElements = {std::tuple<at::Tensor, at::Tensor, at::Tensor, std::vector<at::Tensor, std::allocator<at::Tensor> > >}; bool <anonymous> = true; _Elements = {at::Tensor, at::Tensor, at::Tensor, std::vector<at::Tensor, std::allocator<at::Tensor> >}]’:
/usr/include/c++/6/tuple:626:362:   required by substitution of ‘template<class ... _UElements, typename std::enable_if<(((std::_TC<(sizeof... (_UElements) == 1), at::Tensor, at::Tensor, at::Tensor, std::vector<at::Tensor, std::allocator<at::Tensor> > >::_NotSameTuple<_UElements ...>() && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, std::vector<at::Tensor, std::allocator<at::Tensor> > >::_MoveConstructibleTuple<_UElements ...>()) && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, std::vector<at::Tensor, std::allocator<at::Tensor> > >::_ImplicitlyMoveConvertibleTuple<_UElements ...>()) && (4ul >= 1)), bool>::type <anonymous> > constexpr std::tuple< <template-parameter-1-1> >::tuple(_UElements&& ...) [with _UElements = {std::tuple<at::Tensor, at::Tensor, at::Tensor, std::vector<at::Tensor, std::allocator<at::Tensor> > >}; typename std::enable_if<(((std::_TC<(sizeof... (_UElements) == 1), at::Tensor, at::Tensor, at::Tensor, std::vector<at::Tensor, std::allocator<at::Tensor> > >::_NotSameTuple<_UElements ...>() && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, std::vector<at::Tensor, std::allocator<at::Tensor> > >::_MoveConstructibleTuple<_UElements ...>()) && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, std::vector<at::Tensor, std::allocator<at::Tensor> > >::_ImplicitlyMoveConvertibleTuple<_UElements ...>()) && (4ul >= 1)), bool>::type <anonymous> = <missing>]’
/home/gengfeng/anaconda3/lib/python3.6/site-packages/torch/lib/include/ATen/Functions.h:3043:267:   required from here
/usr/include/c++/6/tuple:489:65: error: mismatched argument pack lengths while expanding ‘std::is_convertible<_UElements&&, _Elements>’
       return __and_<is_convertible<_UElements&&, _Elements>...>::value;
                                                                 ^~~~~
/usr/include/c++/6/tuple:490:1: error: body of constexpr function ‘static constexpr bool std::_TC<<anonymous>, _Elements>::_ImplicitlyMoveConvertibleTuple() [with _UElements = {std::tuple<at::Tensor, at::Tensor, at::Tensor, std::vector<at::Tensor, std::allocator<at::Tensor> > >}; bool <anonymous> = true; _Elements = {at::Tensor, at::Tensor, at::Tensor, std::vector<at::Tensor, std::allocator<at::Tensor> >}]’ not a return-statement
     }
 ^
/usr/include/c++/6/tuple: In instantiation of ‘static constexpr bool std::_TC<<anonymous>, _Elements>::_NonNestedTuple() [with _SrcTuple = const std::tuple<at::Tensor, at::Tensor, at::Tensor, std::vector<at::Tensor, std::allocator<at::Tensor> > >&; bool <anonymous> = true; _Elements = {at::Tensor, at::Tensor, at::Tensor, std::vector<at::Tensor, std::allocator<at::Tensor> >}]’:
/usr/include/c++/6/tuple:662:419:   required by substitution of ‘template<class ... _UElements, class _Dummy, typename std::enable_if<((std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, std::vector<at::Tensor, std::allocator<at::Tensor> > >::_ConstructibleTuple<_UElements ...>() && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, std::vector<at::Tensor, std::allocator<at::Tensor> > >::_ImplicitlyConvertibleTuple<_UElements ...>()) && std::_TC<(std::is_same<_Dummy, void>::value && (1ul == 1)), at::Tensor, at::Tensor, at::Tensor, std::vector<at::Tensor, std::allocator<at::Tensor> > >::_NonNestedTuple<const tuple<_Elements ...>&>()), bool>::type <anonymous> > constexpr std::tuple< <template-parameter-1-1> >::tuple(const std::tuple<_Args1 ...>&) [with _UElements = {at::Tensor, at::Tensor, at::Tensor, std::vector<at::Tensor, std::allocator<at::Tensor> >}; _Dummy = void; typename std::enable_if<((std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, std::vector<at::Tensor, std::allocator<at::Tensor> > >::_ConstructibleTuple<_UElements ...>() && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, std::vector<at::Tensor, std::allocator<at::Tensor> > >::_ImplicitlyConvertibleTuple<_UElements ...>()) && std::_TC<(std::is_same<_Dummy, void>::value && (1ul == 1)), at::Tensor, at::Tensor, at::Tensor, std::vector<at::Tensor, std::allocator<at::Tensor> > >::_NonNestedTuple<const tuple<_Elements ...>&>()), bool>::type <anonymous> = <missing>]’
/home/gengfeng/anaconda3/lib/python3.6/site-packages/torch/lib/include/ATen/Functions.h:3043:267:   required from here
/usr/include/c++/6/tuple:495:244: error: wrong number of template arguments (5, should be 2)
       return  __and_<__not_<is_same<tuple<_Elements...>,
                                                                                                                                                                                                                                                    ^    
/usr/include/c++/6/type_traits:1558:8: note: provided for ‘template<class _From, class _To> struct std::is_convertible’
     struct is_convertible
        ^~~~~~~~~~~~~~
/usr/include/c++/6/tuple:502:1: error: body of constexpr function ‘static constexpr bool std::_TC<<anonymous>, _Elements>::_NonNestedTuple() [with _SrcTuple = const std::tuple<at::Tensor, at::Tensor, at::Tensor, std::vector<at::Tensor, std::allocator<at::Tensor> > >&; bool <anonymous> = true; _Elements = {at::Tensor, at::Tensor, at::Tensor, std::vector<at::Tensor, std::allocator<at::Tensor> >}]’ not a return-statement
     }
 ^
/usr/include/c++/6/tuple: In instantiation of ‘static constexpr bool std::_TC<<anonymous>, _Elements>::_NonNestedTuple() [with _SrcTuple = std::tuple<at::Tensor, at::Tensor, at::Tensor, std::vector<at::Tensor, std::allocator<at::Tensor> > >&&; bool <anonymous> = true; _Elements = {at::Tensor, at::Tensor, at::Tensor, std::vector<at::Tensor, std::allocator<at::Tensor> >}]’:
/usr/include/c++/6/tuple:686:422:   required by substitution of ‘template<class ... _UElements, class _Dummy, typename std::enable_if<((std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, std::vector<at::Tensor, std::allocator<at::Tensor> > >::_MoveConstructibleTuple<_UElements ...>() && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, std::vector<at::Tensor, std::allocator<at::Tensor> > >::_ImplicitlyMoveConvertibleTuple<_UElements ...>()) && std::_TC<(std::is_same<_Dummy, void>::value && (1ul == 1)), at::Tensor, at::Tensor, at::Tensor, std::vector<at::Tensor, std::allocator<at::Tensor> > >::_NonNestedTuple<tuple<_Elements ...>&&>()), bool>::type <anonymous> > constexpr std::tuple< <template-parameter-1-1> >::tuple(std::tuple<_Args1 ...>&&) [with _UElements = {at::Tensor, at::Tensor, at::Tensor, std::vector<at::Tensor, std::allocator<at::Tensor> >}; _Dummy = void; typename std::enable_if<((std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, std::vector<at::Tensor, std::allocator<at::Tensor> > >::_MoveConstructibleTuple<_UElements ...>() && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, std::vector<at::Tensor, std::allocator<at::Tensor> > >::_ImplicitlyMoveConvertibleTuple<_UElements ...>()) && std::_TC<(std::is_same<_Dummy, void>::value && (1ul == 1)), at::Tensor, at::Tensor, at::Tensor, std::vector<at::Tensor, std::allocator<at::Tensor> > >::_NonNestedTuple<tuple<_Elements ...>&&>()), bool>::type <anonymous> = <missing>]’
/home/gengfeng/anaconda3/lib/python3.6/site-packages/torch/lib/include/ATen/Functions.h:3043:267:   required from here
/usr/include/c++/6/tuple:495:244: error: wrong number of template arguments (5, should be 2)
       return  __and_<__not_<is_same<tuple<_Elements...>,
                                                                                                                                                                                                                                                    ^    
/usr/include/c++/6/type_traits:1558:8: note: provided for ‘template<class _From, class _To> struct std::is_convertible’
     struct is_convertible
        ^~~~~~~~~~~~~~
/usr/include/c++/6/tuple:502:1: error: body of constexpr function ‘static constexpr bool std::_TC<<anonymous>, _Elements>::_NonNestedTuple() [with _SrcTuple = std::tuple<at::Tensor, at::Tensor, at::Tensor, std::vector<at::Tensor, std::allocator<at::Tensor> > >&&; bool <anonymous> = true; _Elements = {at::Tensor, at::Tensor, at::Tensor, std::vector<at::Tensor, std::allocator<at::Tensor> >}]’ not a return-statement
     }
 ^
/usr/include/c++/6/tuple: In instantiation of ‘static constexpr bool std::_TC<<anonymous>, _Elements>::_MoveConstructibleTuple() [with _UElements = {std::tuple<at::Tensor, at::Tensor, at::Tensor, at::Tensor>}; bool <anonymous> = true; _Elements = {at::Tensor, at::Tensor, at::Tensor, at::Tensor}]’:
/usr/include/c++/6/tuple:626:248:   required by substitution of ‘template<class ... _UElements, typename std::enable_if<(((std::_TC<(sizeof... (_UElements) == 1), at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_NotSameTuple<_UElements ...>() && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_MoveConstructibleTuple<_UElements ...>()) && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_ImplicitlyMoveConvertibleTuple<_UElements ...>()) && (4ul >= 1)), bool>::type <anonymous> > constexpr std::tuple< <template-parameter-1-1> >::tuple(_UElements&& ...) [with _UElements = {std::tuple<at::Tensor, at::Tensor, at::Tensor, at::Tensor>}; typename std::enable_if<(((std::_TC<(sizeof... (_UElements) == 1), at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_NotSameTuple<_UElements ...>() && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_MoveConstructibleTuple<_UElements ...>()) && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_ImplicitlyMoveConvertibleTuple<_UElements ...>()) && (4ul >= 1)), bool>::type <anonymous> = <missing>]’
/home/gengfeng/anaconda3/lib/python3.6/site-packages/torch/lib/include/ATen/Functions.h:3509:107:   required from here
/usr/include/c++/6/tuple:483:67: error: mismatched argument pack lengths while expanding ‘std::is_constructible<_Elements, _UElements&&>’
       return __and_<is_constructible<_Elements, _UElements&&>...>::value;
                                                                   ^~~~~
/usr/include/c++/6/tuple:484:1: error: body of constexpr function ‘static constexpr bool std::_TC<<anonymous>, _Elements>::_MoveConstructibleTuple() [with _UElements = {std::tuple<at::Tensor, at::Tensor, at::Tensor, at::Tensor>}; bool <anonymous> = true; _Elements = {at::Tensor, at::Tensor, at::Tensor, at::Tensor}]’ not a return-statement
     }
 ^
/usr/include/c++/6/tuple: In instantiation of ‘static constexpr bool std::_TC<<anonymous>, _Elements>::_ImplicitlyMoveConvertibleTuple() [with _UElements = {std::tuple<at::Tensor, at::Tensor, at::Tensor, at::Tensor>}; bool <anonymous> = true; _Elements = {at::Tensor, at::Tensor, at::Tensor, at::Tensor}]’:
/usr/include/c++/6/tuple:626:362:   required by substitution of ‘template<class ... _UElements, typename std::enable_if<(((std::_TC<(sizeof... (_UElements) == 1), at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_NotSameTuple<_UElements ...>() && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_MoveConstructibleTuple<_UElements ...>()) && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_ImplicitlyMoveConvertibleTuple<_UElements ...>()) && (4ul >= 1)), bool>::type <anonymous> > constexpr std::tuple< <template-parameter-1-1> >::tuple(_UElements&& ...) [with _UElements = {std::tuple<at::Tensor, at::Tensor, at::Tensor, at::Tensor>}; typename std::enable_if<(((std::_TC<(sizeof... (_UElements) == 1), at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_NotSameTuple<_UElements ...>() && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_MoveConstructibleTuple<_UElements ...>()) && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_ImplicitlyMoveConvertibleTuple<_UElements ...>()) && (4ul >= 1)), bool>::type <anonymous> = <missing>]’
/home/gengfeng/anaconda3/lib/python3.6/site-packages/torch/lib/include/ATen/Functions.h:3509:107:   required from here
/usr/include/c++/6/tuple:489:65: error: mismatched argument pack lengths while expanding ‘std::is_convertible<_UElements&&, _Elements>’
       return __and_<is_convertible<_UElements&&, _Elements>...>::value;
                                                                 ^~~~~
/usr/include/c++/6/tuple:490:1: error: body of constexpr function ‘static constexpr bool std::_TC<<anonymous>, _Elements>::_ImplicitlyMoveConvertibleTuple() [with _UElements = {std::tuple<at::Tensor, at::Tensor, at::Tensor, at::Tensor>}; bool <anonymous> = true; _Elements = {at::Tensor, at::Tensor, at::Tensor, at::Tensor}]’ not a return-statement
     }
 ^
/usr/include/c++/6/tuple: In instantiation of ‘static constexpr bool std::_TC<<anonymous>, _Elements>::_NonNestedTuple() [with _SrcTuple = const std::tuple<at::Tensor, at::Tensor, at::Tensor, at::Tensor>&; bool <anonymous> = true; _Elements = {at::Tensor, at::Tensor, at::Tensor, at::Tensor}]’:
/usr/include/c++/6/tuple:662:419:   required by substitution of ‘template<class ... _UElements, class _Dummy, typename std::enable_if<((std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_ConstructibleTuple<_UElements ...>() && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_ImplicitlyConvertibleTuple<_UElements ...>()) && std::_TC<(std::is_same<_Dummy, void>::value && (1ul == 1)), at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_NonNestedTuple<const tuple<_Elements ...>&>()), bool>::type <anonymous> > constexpr std::tuple< <template-parameter-1-1> >::tuple(const std::tuple<_Args1 ...>&) [with _UElements = {at::Tensor, at::Tensor, at::Tensor, at::Tensor}; _Dummy = void; typename std::enable_if<((std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_ConstructibleTuple<_UElements ...>() && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_ImplicitlyConvertibleTuple<_UElements ...>()) && std::_TC<(std::is_same<_Dummy, void>::value && (1ul == 1)), at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_NonNestedTuple<const tuple<_Elements ...>&>()), bool>::type <anonymous> = <missing>]’
/home/gengfeng/anaconda3/lib/python3.6/site-packages/torch/lib/include/ATen/Functions.h:3509:107:   required from here
/usr/include/c++/6/tuple:495:244: error: wrong number of template arguments (5, should be 2)
       return  __and_<__not_<is_same<tuple<_Elements...>,
                                                                                                                                                                                                                                                    ^    
/usr/include/c++/6/type_traits:1558:8: note: provided for ‘template<class _From, class _To> struct std::is_convertible’
     struct is_convertible
        ^~~~~~~~~~~~~~
/usr/include/c++/6/tuple:502:1: error: body of constexpr function ‘static constexpr bool std::_TC<<anonymous>, _Elements>::_NonNestedTuple() [with _SrcTuple = const std::tuple<at::Tensor, at::Tensor, at::Tensor, at::Tensor>&; bool <anonymous> = true; _Elements = {at::Tensor, at::Tensor, at::Tensor, at::Tensor}]’ not a return-statement
     }
 ^
/usr/include/c++/6/tuple: In instantiation of ‘static constexpr bool std::_TC<<anonymous>, _Elements>::_NonNestedTuple() [with _SrcTuple = std::tuple<at::Tensor, at::Tensor, at::Tensor, at::Tensor>&&; bool <anonymous> = true; _Elements = {at::Tensor, at::Tensor, at::Tensor, at::Tensor}]’:
/usr/include/c++/6/tuple:686:422:   required by substitution of ‘template<class ... _UElements, class _Dummy, typename std::enable_if<((std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_MoveConstructibleTuple<_UElements ...>() && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_ImplicitlyMoveConvertibleTuple<_UElements ...>()) && std::_TC<(std::is_same<_Dummy, void>::value && (1ul == 1)), at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_NonNestedTuple<tuple<_Elements ...>&&>()), bool>::type <anonymous> > constexpr std::tuple< <template-parameter-1-1> >::tuple(std::tuple<_Args1 ...>&&) [with _UElements = {at::Tensor, at::Tensor, at::Tensor, at::Tensor}; _Dummy = void; typename std::enable_if<((std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_MoveConstructibleTuple<_UElements ...>() && std::_TC<(1ul == sizeof... (_UElements)), at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_ImplicitlyMoveConvertibleTuple<_UElements ...>()) && std::_TC<(std::is_same<_Dummy, void>::value && (1ul == 1)), at::Tensor, at::Tensor, at::Tensor, at::Tensor>::_NonNestedTuple<tuple<_Elements ...>&&>()), bool>::type <anonymous> = <missing>]’
/home/gengfeng/anaconda3/lib/python3.6/site-packages/torch/lib/include/ATen/Functions.h:3509:107:   required from here
/usr/include/c++/6/tuple:495:244: error: wrong number of template arguments (5, should be 2)
       return  __and_<__not_<is_same<tuple<_Elements...>,
                                                                                                                                                                                                                                                    ^    
/usr/include/c++/6/type_traits:1558:8: note: provided for ‘template<class _From, class _To> struct std::is_convertible’
     struct is_convertible
        ^~~~~~~~~~~~~~
/usr/include/c++/6/tuple:502:1: error: body of constexpr function ‘static constexpr bool std::_TC<<anonymous>, _Elements>::_NonNestedTuple() [with _SrcTuple = std::tuple<at::Tensor, at::Tensor, at::Tensor, at::Tensor>&&; bool <anonymous> = true; _Elements = {at::Tensor, at::Tensor, at::Tensor, at::Tensor}]’ not a return-statement
     }
 ^
error: command '/usr/local/cuda/bin/nvcc' failed with exit status 1
(maskrcnn_benchmark) gengfeng@ai-work-4:~/github/maskrcnn-benchmark$ nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2017 NVIDIA Corporation
Built on Fri_Sep__1_21:08:03_CDT_2017
Cuda compilation tools, release 9.0, V9.0.176

facebookresearch / maskrcnn-benchmark Goto Github PK

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Faster R-CNN and Mask R-CNN in PyTorch 1.0

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