Understanding Convolution for Semantic Segmentation

Related tags

Deep Learning python deep-neural-networks deep-learning mxnet convolution semantic-segmentation cityscapes
Overview

TuSimple-DUC

by Panqu Wang, Pengfei Chen, Ye Yuan, Ding Liu, Zehua Huang, Xiaodi Hou, and Garrison Cottrell.

Introduction

This repository is for Understanding Convolution for Semantic Segmentation (WACV 2018), which achieved state-of-the-art result on the CityScapes, PASCAL VOC 2012, and Kitti Road benchmark.

Requirement

We tested our code on:

Ubuntu 16.04, Python 2.7 with

MXNet (0.11.0), numpy(1.13.1), cv2(3.2.0), PIL(4.2.1), and cython(0.25.2)

Usage

  1. Clone the repository:

    git clone [email protected]:TuSimple/TuSimple-DUC.git
python setup.py develop --user

  2. Download the pretrained model from Google Drive.

  3. Build MXNet (only tested on the TuSimple version):

    git clone --recursive [email protected]:TuSimple/mxnet.git
vim make/config.mk (we should have USE_CUDA = 1, modify USE_CUDA_PATH, and have USE_CUDNN = 1 to enable GPU usage.)
make -j
cd python
python setup.py develop --user

    For more MXNet tutorials, please refer to the official documentation.

  4. Training:

    cd train
python train_model.py ../configs/train/train_cityscapes.cfg

    The paths/dirs in the .cfg file need to be specified by the user.

  5. Testing

    cd test
python predict_full_image.py ../configs/test/test_full_image.cfg

    The paths/dirs in the .cfg file need to be specified by the user.

  6. Results:

    Modify the result_dir path in the config file to save the label map and visualizations. The expected scores are:

    (single scale testing denotes as 'ss' and multiple scale testing denotes as 'ms')

    • ResNet101-DUC-HDC on CityScapes testset (mIoU): 79.1(ss) / 80.1(ms)
    • ResNet152-DUC on VOC2012 (mIoU): 83.1(ss)

Citation

If you find the repository is useful for your research, please consider citing:

@article{wang2017understanding,
  title={Understanding convolution for semantic segmentation},
  author={Wang, Panqu and Chen, Pengfei and Yuan, Ye and Liu, Ding and Huang, Zehua and Hou, Xiaodi and Cottrell, Garrison},
  journal={arXiv preprint arXiv:1702.08502},
  year={2017}
}

Questions

Please contact [email protected] or [email protected] .

Comments
  • Training data list for Cityscapes

    Training data list for Cityscapes

    Hi,

    Thanks for the effort for making the training code publicly available. I would like to try training the cityscapes model from scratch by your code. I saw on the paper that you used some data augmentation trick to enlarge the number of training images. Could you provide the augmented data you used and some instruction for generating the data list? Thank you very much!

    opened by chienyiwang 12
  • Train from

    Train from "ImageNet pretrained weights"

    Hi,

    I would like to train the network from ImageNet pretrained weights using your network structure and configurations. However, I didn't see the pretrained weights in your provided model folder. Is it possible to provide the ImageNet pretrained weight you used for training from scratch? Thank you so much!

    opened by chienyiwang 10
  • how to set the val.lst

    how to set the val.lst

    Hi,_ when i use predict_full_image.py , I make my val.lst like this:

    111 /home/xxx/MXnetProjects/TuSimple-DUC/Data/cityscapes/gtFine/test/berlin/berlin_000000_000019_leftImg8bit.png_` But these is always a error, i debug it and found that this is a error when use im = cv.imread(img_path)[:, :, ::-1] in predict_single, for i set the vla.lst in wrong format. can you help me. THANKS!

    opened by ZzzjzzZ 4
  • Where is the ImageNet pretrained classification models?

    Where is the ImageNet pretrained classification models?

    In the link to the models (https://drive.google.com/drive/folders/0B72xLTlRb0SoREhISlhibFZTRmM), I can only find the segmentation pretrained models. Would you please tell me where can I find the classification model pretrained on ImageNet?

    opened by yzou2 4
  • Failed to load the BatchNorm Block in ResNet_DUC_HDC_CityScapes-symbol.json

    Failed to load the BatchNorm Block in ResNet_DUC_HDC_CityScapes-symbol.json

    Hi, I'm very interested in your work. But when I tried to repeat your experiment, a MXNetError appeared that Cannot find argument 'fix_linear_trans' when loading the ResNet_DUC_HDC_CityScapes-symbol.json. My current environment is Python 2.7, Mxnet version 1.0.0 under Win10. Is this error because of the operating system or the different Mxnet version ?

    opened by Serian1992 3
  • ImageNet Pretrained ResNet-101 Model

    ImageNet Pretrained ResNet-101 Model

    I tried to convert the deeplab model in #1, but failed due to error Message type "caffe.ImageDataParameter" has no field named "label_type" So would you please share the ImageNet pretrained ResNet-101 Model please? Thanks

    opened by ArsenLuca 3
  • simple_bind error during Cityscapes testing

    simple_bind error during Cityscapes testing

    Hi,

    I'm trying to run the Cityscapes classifier in test mode on an Amazon EC2 instance, but I'm running into this error:

    $ python predict_full_image.py ../configs/test/test_full_image.cfg
[06:22:55] src/nnvm/legacy_json_util.cc:190: Loading symbol saved by previous version v0.8.0. Attempting to upgrade...
[06:22:55] src/nnvm/legacy_json_util.cc:198: Symbol successfully upgraded!
/home/ubuntu/TuSimple-DUC/mxnet/python/mxnet/module/base_module.py:53: UserWarning: You created Module with Module(..., label_names=['softmax_label']) but input with name 'softmax_label' is not found in symbol.list_arguments(). Did you mean one of:
        data
        seg_loss_label
  warnings.warn(msg)
/home/ubuntu/TuSimple-DUC/mxnet/python/mxnet/module/base_module.py:65: UserWarning: Data provided by label_shapes don't match names specified by label_names ([] vs. ['softmax_label'])
  warnings.warn(msg)
[06:22:55] /home/ubuntu/TuSimple-DUC/mxnet/dmlc-core/include/dmlc/./logging.h:308: [06:22:55] src/storage/storage.cc:113: Compile with USE_CUDA=1 to enable GPU usage

Stack trace returned 10 entries:
[bt] (0) /home/ubuntu/TuSimple-DUC/mxnet/python/mxnet/../../lib/libmxnet.so(+0x12a7b3a) [0x7f95e0047b3a]
[bt] (1) /home/ubuntu/TuSimple-DUC/mxnet/python/mxnet/../../lib/libmxnet.so(_ZN5mxnet11StorageImpl5AllocEmNS_7ContextE+0x57) [0x7f95e0048317]
[bt] (2) /home/ubuntu/TuSimple-DUC/mxnet/python/mxnet/../../lib/libmxnet.so(+0x131869f) [0x7f95e00b869f]
[bt] (3) /home/ubuntu/TuSimple-DUC/mxnet/python/mxnet/../../lib/libmxnet.so(_ZN5mxnet4exec15ReshapeOrCreateERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEERKN4nnvm6TShapeEiRKNS_7ContextEPSt13unordered_mapIS6_NS_7NDArrayESt4hashIS6_ESt8equal_toIS6_ESaISt4pairIS7_SH_EEE+0xa4f) [0x7f95e00be58f]
[bt] (4) /home/ubuntu/TuSimple-DUC/mxnet/python/mxnet/../../lib/libmxnet.so(_ZN5mxnet4exec13GraphExecutor13InitArgumentsERKN4nnvm12IndexedGraphERKSt6vectorINS2_6TShapeESaIS7_EERKS6_IiSaIiEERKS6_INS_7ContextESaISG_EESK_SK_RKS6_INS_9OpReqTypeESaISL_EERKSt13unordered_setINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4hashISW_ESt8equal_toISW_ESaISW_EEPKNS_8ExecutorEPSt13unordered_mapISW_NS_7NDArrayESY_S10_SaISt4pairIKSW_S19_EEEPS6_IS19_SaIS19_EES1I_S1I_+0xaa4) [0x7f95e00c1f24]
[bt] (5) /home/ubuntu/TuSimple-DUC/mxnet/python/mxnet/../../lib/libmxnet.so(_ZN5mxnet4exec13GraphExecutor4InitEN4nnvm6SymbolERKNS_7ContextERKSt3mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEES4_St4lessISD_ESaISt4pairIKSD_S4_EEERKSt6vectorIS4_SaIS4_EESR_SR_RKSt13unordered_mapISD_NS2_6TShapeESt4hashISD_ESt8equal_toISD_ESaISG_ISH_ST_EEERKSS_ISD_iSV_SX_SaISG_ISH_iEEERKSN_INS_9OpReqTypeESaIS18_EERKSt13unordered_setISD_SV_SX_SaISD_EEPSN_INS_7NDArrayESaIS1I_EES1L_S1L_PSS_ISD_S1I_SV_SX_SaISG_ISH_S1I_EEEPNS_8ExecutorERKSS_INS2_9NodeEntryES1I_NS2_13NodeEntryHashENS2_14NodeEntryEqualESaISG_IKS1S_S1I_EEE+0x88b) [0x7f95e00c9edb]
[bt] (6) /home/ubuntu/TuSimple-DUC/mxnet/python/mxnet/../../lib/libmxnet.so(_ZN5mxnet8Executor10SimpleBindEN4nnvm6SymbolERKNS_7ContextERKSt3mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEES3_St4lessISC_ESaISt4pairIKSC_S3_EEERKSt6vectorIS3_SaIS3_EESQ_SQ_RKSt13unordered_mapISC_NS1_6TShapeESt4hashISC_ESt8equal_toISC_ESaISF_ISG_SS_EEERKSR_ISC_iSU_SW_SaISF_ISG_iEEERKSM_INS_9OpReqTypeESaIS17_EERKSt13unordered_setISC_SU_SW_SaISC_EEPSM_INS_7NDArrayESaIS1H_EES1K_S1K_PSR_ISC_S1H_SU_SW_SaISF_ISG_S1H_EEEPS0_+0x233) [0x7f95e00ca583]
[bt] (7) /home/ubuntu/TuSimple-DUC/mxnet/python/mxnet/../../lib/libmxnet.so(MXExecutorSimpleBind+0x2a67) [0x7f95e00888c7]
[bt] (8) /usr/lib/x86_64-linux-gnu/libffi.so.6(ffi_call_unix64+0x4c) [0x7f9603afae40]
[bt] (9) /usr/lib/x86_64-linux-gnu/libffi.so.6(ffi_call+0x2eb) [0x7f9603afa8ab]

Traceback (most recent call last):
  File "predict_full_image.py", line 100, in <module>
    tester = ImageListTester(config)
  File "predict_full_image.py", line 33, in __init__
    self.tester = Tester(self.config)
  File "/home/ubuntu/TuSimple-DUC/tusimple_duc/test/tester.py", line 42, in __init__
    predictor.bind(data_shapes=[('data', data_shape)], for_training=False)
  File "/home/ubuntu/TuSimple-DUC/mxnet/python/mxnet/module/module.py", line 417, in bind
    state_names=self._state_names)
  File "/home/ubuntu/TuSimple-DUC/mxnet/python/mxnet/module/executor_group.py", line 231, in __init__
    self.bind_exec(data_shapes, label_shapes, shared_group)
  File "/home/ubuntu/TuSimple-DUC/mxnet/python/mxnet/module/executor_group.py", line 327, in bind_exec
    shared_group))
  File "/home/ubuntu/TuSimple-DUC/mxnet/python/mxnet/module/executor_group.py", line 603, in _bind_ith_exec
    shared_buffer=shared_data_arrays, **input_shapes)
  File "/home/ubuntu/TuSimple-DUC/mxnet/python/mxnet/symbol.py", line 1479, in simple_bind
    raise RuntimeError(error_msg)
RuntimeError: simple_bind error. Arguments:
data: (1, 3, 1024, 2048)
[06:22:55] src/storage/storage.cc:113: Compile with USE_CUDA=1 to enable GPU usage

Stack trace returned 10 entries:
[bt] (0) /home/ubuntu/TuSimple-DUC/mxnet/python/mxnet/../../lib/libmxnet.so(+0x12a7b3a) [0x7f95e0047b3a]
[bt] (1) /home/ubuntu/TuSimple-DUC/mxnet/python/mxnet/../../lib/libmxnet.so(_ZN5mxnet11StorageImpl5AllocEmNS_7ContextE+0x57) [0x7f95e0048317]
[bt] (2) /home/ubuntu/TuSimple-DUC/mxnet/python/mxnet/../../lib/libmxnet.so(+0x131869f) [0x7f95e00b869f]
[bt] (3) /home/ubuntu/TuSimple-DUC/mxnet/python/mxnet/../../lib/libmxnet.so(_ZN5mxnet4exec15ReshapeOrCreateERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEERKN4nnvm6TShapeEiRKNS_7ContextEPSt13unordered_mapIS6_NS_7NDArrayESt4hashIS6_ESt8equal_toIS6_ESaISt4pairIS7_SH_EEE+0xa4f) [0x7f95e00be58f]
[bt] (4) /home/ubuntu/TuSimple-DUC/mxnet/python/mxnet/../../lib/libmxnet.so(_ZN5mxnet4exec13GraphExecutor13InitArgumentsERKN4nnvm12IndexedGraphERKSt6vectorINS2_6TShapeESaIS7_EERKS6_IiSaIiEERKS6_INS_7ContextESaISG_EESK_SK_RKS6_INS_9OpReqTypeESaISL_EERKSt13unordered_setINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4hashISW_ESt8equal_toISW_ESaISW_EEPKNS_8ExecutorEPSt13unordered_mapISW_NS_7NDArrayESY_S10_SaISt4pairIKSW_S19_EEEPS6_IS19_SaIS19_EES1I_S1I_+0xaa4) [0x7f95e00c1f24]
[bt] (5) /home/ubuntu/TuSimple-DUC/mxnet/python/mxnet/../../lib/libmxnet.so(_ZN5mxnet4exec13GraphExecutor4InitEN4nnvm6SymbolERKNS_7ContextERKSt3mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEES4_St4lessISD_ESaISt4pairIKSD_S4_EEERKSt6vectorIS4_SaIS4_EESR_SR_RKSt13unordered_mapISD_NS2_6TShapeESt4hashISD_ESt8equal_toISD_ESaISG_ISH_ST_EEERKSS_ISD_iSV_SX_SaISG_ISH_iEEERKSN_INS_9OpReqTypeESaIS18_EERKSt13unordered_setISD_SV_SX_SaISD_EEPSN_INS_7NDArrayESaIS1I_EES1L_S1L_PSS_ISD_S1I_SV_SX_SaISG_ISH_S1I_EEEPNS_8ExecutorERKSS_INS2_9NodeEntryES1I_NS2_13NodeEntryHashENS2_14NodeEntryEqualESaISG_IKS1S_S1I_EEE+0x88b) [0x7f95e00c9edb]
[bt] (6) /home/ubuntu/TuSimple-DUC/mxnet/python/mxnet/../../lib/libmxnet.so(_ZN5mxnet8Executor10SimpleBindEN4nnvm6SymbolERKNS_7ContextERKSt3mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEES3_St4lessISC_ESaISt4pairIKSC_S3_EEERKSt6vectorIS3_SaIS3_EESQ_SQ_RKSt13unordered_mapISC_NS1_6TShapeESt4hashISC_ESt8equal_toISC_ESaISF_ISG_SS_EEERKSR_ISC_iSU_SW_SaISF_ISG_iEEERKSM_INS_9OpReqTypeESaIS17_EERKSt13unordered_setISC_SU_SW_SaISC_EEPSM_INS_7NDArrayESaIS1H_EES1K_S1K_PSR_ISC_S1H_SU_SW_SaISF_ISG_S1H_EEEPS0_+0x233) [0x7f95e00ca583]
[bt] (7) /home/ubuntu/TuSimple-DUC/mxnet/python/mxnet/../../lib/libmxnet.so(MXExecutorSimpleBind+0x2a67) [0x7f95e00888c7]
[bt] (8) /usr/lib/x86_64-linux-gnu/libffi.so.6(ffi_call_unix64+0x4c) [0x7f9603afae40]
[bt] (9) /usr/lib/x86_64-linux-gnu/libffi.so.6(ffi_call+0x2eb) [0x7f9603afa8ab]

    Any idea what is causing it and how to fix it?

    System: Ubuntu 16.04.3 mxnet 0.11.0 numpy 1.13.3 cv2 3.2.0 PIL 1.1.7 cython 0.27.1

    Thanks!

    opened by atward424 3
  • Data prep script

    Data prep script

    I am trying to understand your pipeline so that I can run it on my own data. I see a script in data_prep/get_cityscapes_list.py but I cannot find from your readme how to run it. Can you please add a description of the expected format and/or document the data prep pipeline? Thank you

    opened by DSLituiev 2
  • What is the `test_scales` in multiple scale testing？

    What is the `test_scales` in multiple scale testing？

    I would like to use your open model to reproduce this evaluation:

    ResNet101-DUC-HDC on CityScapes testset (mIoU): 80.1(multiple scale testing )

    But I do not know what test_scales does you use in test_full_image.cfg, could you tell me? Thank you very much.

    opened by acgtyrant 2
  • How to apply CRFs?

    How to apply CRFs?

    Hi, I feel confused to CRFs. I'm sorry that i can't find the way you apply the CRFs to your ResNet-DUC. Could you please tell me how to do it? Thank you very much!!!!

    opened by reborm 2
  • nice to meet you,can you help me ?there is an error in you code,

    nice to meet you,can you help me ?there is an error in you code, 

    when I run your code of train_model , there is an error : config_path = sys.argv[1] => list index out of range , what I mistake?
                           thank you!
    opened by zhouyuan888888 1
  • troubles met in the procedure of testing

    troubles met in the procedure of testing

    Hello,first of allThanks for all the effort you have made for the segmentation. During the testing procedure, I have met some problems. When I build the repository, after executing make command , the terminal gives the tips 'can not find the '....../ps-lite/make/ps.mk'.Is ther any way to solve this issues?thanks for all

    opened by bowenroom 0
  • Evaluate PASCAL VOC 2012 test set

    Evaluate PASCAL VOC 2012 test set

    Hi! How do you evaluate the VOC 2012 test set? How did you test the segmentation results of VOC2012 test data set on PASCAL VOC official website? Looking forward to your reply. Thanks!

    opened by InstantWindy 0
  • A question about DUC

    A question about DUC

    If the orginal image is (600,800), After downsample 16X, the feature map is (37,50), How to use DUC to reproduce orginal size? upsample 16X is (592,800)

    opened by WangTianYuan 2
  • hi，I try your DUC method as decoder on my network(similiar to mobile net for high performance),but there are many grid on the predicted alpha

    hi，I try your DUC method as decoder on my network(similiar to mobile net for high performance),but there are many grid on the predicted alpha

    hi，I try your DUC method as decoder on my network(similiar to mobile net for high performance),but there are many grid on the predicted alpha. the loss seems normal. could you please figure it out?

    opened by leonardodora 1
  • Hello,I am interesting in your work,but I have a trouble

    Hello,I am interesting in your work,but I have a trouble

    I have read your papers(Understanding Convolution for semantic segmentation ),and run your net.Then I make an improvement.But When submit my results to pascal voc server,I always get an evaluation error. I use the command: tar -zcvf results.tgz results. My directory structure ：/results/VOC2012/Segmentation/comp6_test_cls/ The pixel of every image is similar [0,64,128]

    Could you give me some point?

    opened by mshmoon 0
  • Reproduce failed.

    Reproduce failed.

    I write resnet.symbol by myself and use it as pretrained model to train TuSimple-DUC, and keep cfg consistent with the paper except cropped size. See my fork.

    However, train_IoU is only 0.563 while I can not validate temporarily #21 , I think the val_IoU should not be too far away:

    01-03 02:23:28 Epoch[19] Train-acc_ignore=0.932954
01-03 02:23:28 Epoch[19] Train-IoU=0.563348
01-03 02:23:28 Epoch[19] Train-SoftmaxLoss=0.212817
01-03 02:23:28 Epoch[19] Time cost=10693.555
01-03 02:23:29 Saved checkpoint to "../models/ResNet_DUC_HDC_CityScapes/2017_12_31_15:19:05/ResNet_DUC_HDC_CityScapes-0020.params"

    Anyone reproduce successfully?

    opened by acgtyrant 2
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GitHub https://arxiv.org/abs/1702.08502
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