[ECCV2020] Content-Consistent Matching for Domain Adaptive Semantic Segmentation

Guangrui Li

Last update: Aug 25, 2022

Overview

[ECCV20] Content-Consistent Matching for Domain Adaptive Semantic Segmentation

This is a PyTorch implementation of CCM.

News: GTA-4K list is available!

A smaller subset of GTA5 dataset that shares higher layout similarites with Cityscapes.

Prerequisites

To install requirements:

pip install -r requirements.txt

Python 3.6
GPU Memory: 24GB for the first stage(Source-only Model), and 12GB for the second stage
Pytorch 1.4.0

Getting Started

Download the dataset GTA5 and Cityscapes.
Download the ImageNet-pretrained Model [Link].
Download the Source-only Model Link.

Training

To train the source-only model:

CUDA_VISIBLE_DEVICES=0 python so_run.py

To train the adaptation model:

CUDA_VISIBLE_DEVICES=0 python run.py

Evaluation

To perform evaluation on a multiple models under a directory:

python eval.py --frm your_dir

To perform evaluation on single model:

python eval.py --frm model.pth --single

Citation

If you find it helpful, please consider citing:

@inproceedings{li2020content,
  title={Content-consistent matching for domain adaptive semantic segmentation},
  author={Li, Guangrui and Kang, Guoliang and Liu, Wu and Wei, Yunchao and Yang, Yi},
  booktitle={European Conference on Computer Vision},
  pages={440--456},
  year={2020},
  organization={Springer}
}

Comments

Source only model

Can I use the provided source only model to start training adaptation? (https://drive.google.com/file/d/1-52RggreImwr_BVcGzm41j0mchxclwwu/view) So that I can skip the first step where a large memory GPU is required to train the model.

opened by xdeng7 4
Questions about the two models

Hi I am a little confused about these two models, the ImageNet-pretrained Model and the Source-only Model. Why does the Source-only Model appear in this ccm_config.yml file？CCM and Source only, aren't they two different methods ? https://github.com/Solacex/CCM/blob/4e80b98007219e64d577fb105a35140cb2d13238/config/ccm_config.yml#L70 In addition, from the section 4.2 of paper, "We start from DeepLabV2-Res101with the backbone pretrained on the ImageNet. Then we ﬁrstly ﬁnetune the whole network on the source data and use such a source-trained network to initialize the target (adaptation) model.", The ImageNet-pretrained Model should be the adaption model. I am a little confused. THANKS! PS: There is a bug with 'News: GTA-4K list is avalible!'.

opened by ArcticOc 2
A little question about piece of code in the ccm_config.yml

I'm a beginner. I am not sure why the following parameters are set. Why is it 1191 or 0.9 ?
Thanks!

https://github.com/Solacex/CCM/blob/b7bc3ff5d132490d15d4e2cdaca16b43394c5a57/config/ccm_config.yml#L17-L21

opened by ArcticOc 2
neptune.exceptions.MissingApiToken: Missing API token.

for the below three rows:
if self.config.neptune: neptune.init(project_qualified_name="solacex/segmentation-DA") neptune.create_experiment(params=self.config, name=self.config["note"])

get the below rseult: neptune.exceptions.MissingApiToken: Missing API token. Use "NEPTUNE_API_TOKEN" environment variable or pass it as an argument

it's so strange for me, can you tell me how to tackle this problem?

opened by gong-lei 2
Bump pillow from 8.2.0 to 9.0.1
Bumps pillow from 8.2.0 to 9.0.1.

Release notes

Sourced from pillow's releases.

9.0.1

https://pillow.readthedocs.io/en/stable/releasenotes/9.0.1.html

Changes

In show_file, use os.remove to remove temporary images. CVE-2022-24303 #6010 [@radarhere, @hugovk]

Restrict builtins within lambdas for ImageMath.eval. CVE-2022-22817 #6009 [radarhere]

9.0.0

https://pillow.readthedocs.io/en/stable/releasenotes/9.0.0.html

Changes

Restrict builtins for ImageMath.eval() #5923 [@radarhere]

Ensure JpegImagePlugin stops at the end of a truncated file #5921 [@radarhere]

Fixed ImagePath.Path array handling #5920 [@radarhere]

Remove consecutive duplicate tiles that only differ by their offset #5919 [@radarhere]

Removed redundant part of condition #5915 [@radarhere]

Explicitly enable strip chopping for large uncompressed TIFFs #5517 [@kmilos]

Use the Windows method to get TCL functions on Cygwin #5807 [@DWesl]

Changed error type to allow for incremental WebP parsing #5404 [@radarhere]

Improved I;16 operations on big endian #5901 [@radarhere]

Ensure that BMP pixel data offset does not ignore palette #5899 [@radarhere]

Limit quantized palette to number of colors #5879 [@radarhere]

Use latin1 encoding to decode bytes #5870 [@radarhere]

Fixed palette index for zeroed color in FASTOCTREE quantize #5869 [@radarhere]

When saving RGBA to GIF, make use of first transparent palette entry #5859 [@radarhere]

Pass SAMPLEFORMAT to libtiff #5848 [@radarhere]

Added rounding when converting P and PA #5824 [@radarhere]

Improved putdata() documentation and data handling #5910 [@radarhere]

Exclude carriage return in PDF regex to help prevent ReDoS #5912 [@radarhere]

Image.NONE is only used for resampling and dithers #5908 [@radarhere]

Fixed freeing pointer in ImageDraw.Outline.transform #5909 [@radarhere]

Add Tidelift alignment action and badge #5763 [@aclark4life]

Replaced further direct invocations of setup.py #5906 [@radarhere]

Added ImageShow support for xdg-open #5897 [@m-shinder]

Fixed typo #5902 [@radarhere]

Switched from deprecated "setup.py install" to "pip install ." #5896 [@radarhere]

Support 16-bit grayscale ImageQt conversion #5856 [@cmbruns]

Fixed raising OSError in _safe_read when size is greater than SAFEBLOCK #5872 [@radarhere]

Convert subsequent GIF frames to RGB or RGBA #5857 [@radarhere]

WebP: Fix memory leak during decoding on failure #5798 [@ilai-deutel]

Do not prematurely return in ImageFile when saving to stdout #5665 [@infmagic2047]

Added support for top right and bottom right TGA orientations #5829 [@radarhere]

Corrected ICNS file length in header #5845 [@radarhere]

Block tile TIFF tags when saving #5839 [@radarhere]

Added line width argument to ImageDraw polygon #5694 [@radarhere]

Do not redeclare class each time when converting to NumPy #5844 [@radarhere]

Only prevent repeated polygon pixels when drawing with transparency #5835 [@radarhere]

... (truncated)

Changelog

Sourced from pillow's changelog.

9.0.1 (2022-02-03)

In show_file, use os.remove to remove temporary images. CVE-2022-24303 #6010 [radarhere, hugovk]

Restrict builtins within lambdas for ImageMath.eval. CVE-2022-22817 #6009 [radarhere]

9.0.0 (2022-01-02)

Restrict builtins for ImageMath.eval(). CVE-2022-22817 #5923 [radarhere]

Ensure JpegImagePlugin stops at the end of a truncated file #5921 [radarhere]

Fixed ImagePath.Path array handling. CVE-2022-22815, CVE-2022-22816 #5920 [radarhere]

Remove consecutive duplicate tiles that only differ by their offset #5919 [radarhere]

Improved I;16 operations on big endian #5901 [radarhere]

Limit quantized palette to number of colors #5879 [radarhere]

Fixed palette index for zeroed color in FASTOCTREE quantize #5869 [radarhere]

When saving RGBA to GIF, make use of first transparent palette entry #5859 [radarhere]

Pass SAMPLEFORMAT to libtiff #5848 [radarhere]

Added rounding when converting P and PA #5824 [radarhere]

Improved putdata() documentation and data handling #5910 [radarhere]

Exclude carriage return in PDF regex to help prevent ReDoS #5912 [hugovk]

Fixed freeing pointer in ImageDraw.Outline.transform #5909 [radarhere]

... (truncated)

Commits

6deac9e 9.0.1 version bump

c04d812 Update CHANGES.rst [ci skip]

4fabec3 Added release notes for 9.0.1

02affaa Added delay after opening image with xdg-open

ca0b585 Updated formatting

427221e In show_file, use os.remove to remove temporary images

c930be0 Restrict builtins within lambdas for ImageMath.eval

75b69dd Dont need to pin for GHA

cd938a7 Autolink CWE numbers with sphinx-issues

2e9c461 Add CVE IDs

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dependencies
opened by dependabot[bot] 1
Bump pillow from 8.2.0 to 9.0.0
Bumps pillow from 8.2.0 to 9.0.0.

Release notes

Sourced from pillow's releases.

9.0.0

https://pillow.readthedocs.io/en/stable/releasenotes/9.0.0.html

Changes

Restrict builtins for ImageMath.eval() #5923 [@radarhere]

Ensure JpegImagePlugin stops at the end of a truncated file #5921 [@radarhere]

Fixed ImagePath.Path array handling #5920 [@radarhere]

Remove consecutive duplicate tiles that only differ by their offset #5919 [@radarhere]

Removed redundant part of condition #5915 [@radarhere]

Explicitly enable strip chopping for large uncompressed TIFFs #5517 [@kmilos]

Use the Windows method to get TCL functions on Cygwin #5807 [@DWesl]

Changed error type to allow for incremental WebP parsing #5404 [@radarhere]

Improved I;16 operations on big endian #5901 [@radarhere]

Ensure that BMP pixel data offset does not ignore palette #5899 [@radarhere]

Limit quantized palette to number of colors #5879 [@radarhere]

Use latin1 encoding to decode bytes #5870 [@radarhere]

Fixed palette index for zeroed color in FASTOCTREE quantize #5869 [@radarhere]

When saving RGBA to GIF, make use of first transparent palette entry #5859 [@radarhere]

Pass SAMPLEFORMAT to libtiff #5848 [@radarhere]

Added rounding when converting P and PA #5824 [@radarhere]

Improved putdata() documentation and data handling #5910 [@radarhere]

Exclude carriage return in PDF regex to help prevent ReDoS #5912 [@radarhere]

Image.NONE is only used for resampling and dithers #5908 [@radarhere]

Fixed freeing pointer in ImageDraw.Outline.transform #5909 [@radarhere]

Add Tidelift alignment action and badge #5763 [@aclark4life]

Replaced further direct invocations of setup.py #5906 [@radarhere]

Added ImageShow support for xdg-open #5897 [@m-shinder]

Fixed typo #5902 [@radarhere]

Switched from deprecated "setup.py install" to "pip install ." #5896 [@radarhere]

Support 16-bit grayscale ImageQt conversion #5856 [@cmbruns]

Fixed raising OSError in _safe_read when size is greater than SAFEBLOCK #5872 [@radarhere]

Convert subsequent GIF frames to RGB or RGBA #5857 [@radarhere]

WebP: Fix memory leak during decoding on failure #5798 [@ilai-deutel]

Do not prematurely return in ImageFile when saving to stdout #5665 [@infmagic2047]

Added support for top right and bottom right TGA orientations #5829 [@radarhere]

Corrected ICNS file length in header #5845 [@radarhere]

Block tile TIFF tags when saving #5839 [@radarhere]

Added line width argument to ImageDraw polygon #5694 [@radarhere]

Do not redeclare class each time when converting to NumPy #5844 [@radarhere]

Only prevent repeated polygon pixels when drawing with transparency #5835 [@radarhere]

Fix pushes_fd method signature #5833 [@hoodmane]

Add support for pickling TrueType fonts #5826 [@hugovk]

Only prefer command line tools SDK on macOS over default MacOSX SDK #5828 [@radarhere]

Fix compilation on 64-bit Termux #5793 [@landfillbaby]

Replace 'setup.py sdist' with '-m build --sdist' #5785 [@hugovk]

Use declarative package configuration #5784 [@hugovk]

Use title for display in ImageShow #5788 [@radarhere]

Fix for PyQt6 #5775 [@hugovk]

... (truncated)

Changelog

Sourced from pillow's changelog.

9.0.0 (2022-01-02)

Restrict builtins for ImageMath.eval(). CVE-2022-22817 #5923 [radarhere]

Ensure JpegImagePlugin stops at the end of a truncated file #5921 [radarhere]

Fixed ImagePath.Path array handling. CVE-2022-22815, CVE-2022-22816 #5920 [radarhere]

Remove consecutive duplicate tiles that only differ by their offset #5919 [radarhere]

Improved I;16 operations on big endian #5901 [radarhere]

Limit quantized palette to number of colors #5879 [radarhere]

Fixed palette index for zeroed color in FASTOCTREE quantize #5869 [radarhere]

When saving RGBA to GIF, make use of first transparent palette entry #5859 [radarhere]

Pass SAMPLEFORMAT to libtiff #5848 [radarhere]

Added rounding when converting P and PA #5824 [radarhere]

Improved putdata() documentation and data handling #5910 [radarhere]

Exclude carriage return in PDF regex to help prevent ReDoS #5912 [hugovk]

Fixed freeing pointer in ImageDraw.Outline.transform #5909 [radarhere]

Added ImageShow support for xdg-open #5897 [m-shinder, radarhere]

Support 16-bit grayscale ImageQt conversion #5856 [cmbruns, radarhere]

Convert subsequent GIF frames to RGB or RGBA #5857 [radarhere]

... (truncated)

Commits

82541b6 9.0.0 version bump

cae5ac4 Merge pull request #5924 from radarhere/cves

ed4cf78 CVEs TBD

d7f60d1 Merge pull request #5923 from radarhere/imagemath_eval

8531b01 Restrict builtins for ImageMath.eval

1efb1d9 Merge pull request #5922 from radarhere/releasenotes

f6c7871 Added release notes for #5919, #5920 and #5921

032d2dc Update CHANGES.rst [ci skip]

baae9ec Merge pull request #5921 from radarhere/jpeg_eoi

1059eb5 If appended EOI did not work, do not keep trying

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dependencies
opened by dependabot[bot] 1
about the label of target domain

hi, I 'm a beginner. I noticed that in https://github.com/2669531054/CCM/blob/2fcd9cb3b1d2070dd403613f00ca55ef872b3104/trainer/ccm_trainer.py#L203 , you use the label of the train set of target domain to calculate the accuracy of the pseudo label. I wonder whether this accuracy influence the training process. And if I do not have the label of the target domain, can I skip this step?

opened by 2669531054 1
Bump pillow from 8.2.0 to 8.3.2
Bumps pillow from 8.2.0 to 8.3.2.

Release notes

Sourced from pillow's releases.

8.3.2

https://pillow.readthedocs.io/en/stable/releasenotes/8.3.2.html

Security

CVE-2021-23437 Raise ValueError if color specifier is too long [hugovk, radarhere]

Fix 6-byte OOB read in FliDecode [wiredfool]

Python 3.10 wheels

Add support for Python 3.10 #5569, #5570 [hugovk, radarhere]

Fixed regressions

Ensure TIFF RowsPerStrip is multiple of 8 for JPEG compression #5588 [kmilos, radarhere]

Updates for ImagePalette channel order #5599 [radarhere]

Hide FriBiDi shim symbols to avoid conflict with real FriBiDi library #5651 [nulano]

8.3.1

https://pillow.readthedocs.io/en/stable/releasenotes/8.3.1.html

Changes

Catch OSError when checking if fp is sys.stdout #5585 [@radarhere]

Handle removing orientation from alternate types of EXIF data #5584 [@radarhere]

Make Image.array take optional dtype argument #5572 [@t-vi]

8.3.0

https://pillow.readthedocs.io/en/stable/releasenotes/8.3.0.html

Changes

Use snprintf instead of sprintf #5567 [@radarhere]

Limit TIFF strip size when saving with LibTIFF #5514 [@kmilos]

Allow ICNS save on all operating systems #4526 [@newpanjing]

De-zigzag JPEG's DQT when loading; deprecate convert_dict_qtables #4989 [@gofr]

Do not use background or transparency index for new color #5564 [@radarhere]

Simplified code #5315 [@radarhere]

Replaced xml.etree.ElementTree #5565 [@radarhere]

... (truncated)

Changelog

Sourced from pillow's changelog.

8.3.2 (2021-09-02)

CVE-2021-23437 Raise ValueError if color specifier is too long [hugovk, radarhere]

Fix 6-byte OOB read in FliDecode [wiredfool]

Add support for Python 3.10 #5569, #5570 [hugovk, radarhere]

Ensure TIFF RowsPerStrip is multiple of 8 for JPEG compression #5588 [kmilos, radarhere]

Updates for ImagePalette channel order #5599 [radarhere]

Hide FriBiDi shim symbols to avoid conflict with real FriBiDi library #5651 [nulano]

8.3.1 (2021-07-06)

Catch OSError when checking if fp is sys.stdout #5585 [radarhere]

Handle removing orientation from alternate types of EXIF data #5584 [radarhere]

Make Image.array take optional dtype argument #5572 [t-vi, radarhere]

8.3.0 (2021-07-01)

Use snprintf instead of sprintf. CVE-2021-34552 #5567 [radarhere]

Limit TIFF strip size when saving with LibTIFF #5514 [kmilos]

Allow ICNS save on all operating systems #4526 [baletu, radarhere, newpanjing, hugovk]

De-zigzag JPEG's DQT when loading; deprecate convert_dict_qtables #4989 [gofr, radarhere]

Replaced xml.etree.ElementTree #5565 [radarhere]

... (truncated)

Commits

8013f13 8.3.2 version bump

23c7ca8 Update CHANGES.rst

8450366 Update release notes

a0afe89 Update test case

9e08eb8 Raise ValueError if color specifier is too long

bd5cf7d FLI tests for Oss-fuzz crash.

94a0cf1 Fix 6-byte OOB read in FliDecode

cece64f Add 8.3.2 (2021-09-02) [CI skip]

e422386 Add release notes for Pillow 8.3.2

08dcbb8 Pillow 8.3.2 supports Python 3.10 [ci skip]

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dependencies
opened by dependabot[bot] 1
The random selection of 'pool_prop' before matching and selection

Hi there,

Thanks for your code!

I ran the code and only got 48.8 mIoU which is 1 mIoU inferior to the reported one in the paper.

I found that the code randomly selects part of the source dataset at each round before conducting image selection. And 'pool_prop' in ccm_config.yml determines this proportion, namely, 20% in default. Then around 1k images are selected from the remained 20% images.

Would this random selection process affect the performance? And is it enough to use only 1k source images for the training during each epoch? What is the best set of these hyper-parameters?

opened by RogerZhangzz 1

Source Only Train

Hi, I'm trying to run your code from source only train but there is no "list" folder in the dataset directory.

gta5: {
        data_dir: '/media/NAS/nas_187/datasets/gta/',
        data_list: './dataset/list/gta5_list.txt',
        input_size: [1280, 720]
        }
synthia: {
        data_dir: '/home/guangrui/data/synthia/',
        data_list: './dataset/list/synthia_list.txt',
        input_size: [1280, 760]
        }
cityscapes: {
        data_dir: '/media/NAS/nas_187/datasets/synthia',
        data_list: './dataset/list/cityscapes_train.txt',
        input_size: [1024, 512]
        }

Could you please add the missing files?

Also, there is a line trying to add nonexistent files in model/__init__.py

opened by miguel-dgist 1

thres in gene_thres

A small question!

Your ccm_trainer.py

159 index = int(cls_total * prop) 160 cls_thres = cls_prob[-index] 161 cls_thres2 = cls_prob[index] 162 thres[k] = cls_thres

For what consideration, you set your thres be the value of the last ten percent of the probability value?

last bacause of (-index), ten percent because of (prop=0.1)

opened by Lufei-github 1
Bump pillow from 8.2.0 to 9.3.0
Bumps pillow from 8.2.0 to 9.3.0.

Release notes

Sourced from pillow's releases.

9.3.0

https://pillow.readthedocs.io/en/stable/releasenotes/9.3.0.html

Changes

Initialize libtiff buffer when saving #6699 [@radarhere]

Limit SAMPLESPERPIXEL to avoid runtime DOS #6700 [@wiredfool]

Inline fname2char to fix memory leak #6329 [@nulano]

Fix memory leaks related to text features #6330 [@nulano]

Use double quotes for version check on old CPython on Windows #6695 [@hugovk]

GHA: replace deprecated set-output command with GITHUB_OUTPUT file #6697 [@nulano]

Remove backup implementation of Round for Windows platforms #6693 [@cgohlke]

Upload fribidi.dll to GitHub Actions #6532 [@nulano]

Fixed set_variation_by_name offset #6445 [@radarhere]

Windows build improvements #6562 [@nulano]

Fix malloc in _imagingft.c:font_setvaraxes #6690 [@cgohlke]

Only use ASCII characters in C source file #6691 [@cgohlke]

Release Python GIL when converting images using matrix operations #6418 [@hmaarrfk]

Added ExifTags enums #6630 [@radarhere]

Do not modify previous frame when calculating delta in PNG #6683 [@radarhere]

Added support for reading BMP images with RLE4 compression #6674 [@npjg]

Decode JPEG compressed BLP1 data in original mode #6678 [@radarhere]

pylint warnings #6659 [@marksmayo]

Added GPS TIFF tag info #6661 [@radarhere]

Added conversion between RGB/RGBA/RGBX and LAB #6647 [@radarhere]

Do not attempt normalization if mode is already normal #6644 [@radarhere]

Fixed seeking to an L frame in a GIF #6576 [@radarhere]

Consider all frames when selecting mode for PNG save_all #6610 [@radarhere]

Don't reassign crc on ChunkStream close #6627 [@radarhere]

Raise a warning if NumPy failed to raise an error during conversion #6594 [@radarhere]

Only read a maximum of 100 bytes at a time in IMT header #6623 [@radarhere]

Show all frames in ImageShow #6611 [@radarhere]

Allow FLI palette chunk to not be first #6626 [@radarhere]

If first GIF frame has transparency for RGB_ALWAYS loading strategy, use RGBA mode #6592 [@radarhere]

Round box position to integer when pasting embedded color #6517 [@radarhere]

Removed EXIF prefix when saving WebP #6582 [@radarhere]

Pad IM palette to 768 bytes when saving #6579 [@radarhere]

Added DDS BC6H reading #6449 [@ShadelessFox]

Added support for opening WhiteIsZero 16-bit integer TIFF images #6642 [@JayWiz]

Raise an error when allocating translucent color to RGB palette #6654 [@jsbueno]

Moved mode check outside of loops #6650 [@radarhere]

Added reading of TIFF child images #6569 [@radarhere]

Improved ImageOps palette handling #6596 [@PososikTeam]

Defer parsing of palette into colors #6567 [@radarhere]

Apply transparency to P images in ImageTk.PhotoImage #6559 [@radarhere]

Use rounding in ImageOps contain() and pad() #6522 [@bibinhashley]

Fixed GIF remapping to palette with duplicate entries #6548 [@radarhere]

Allow remap_palette() to return an image with less than 256 palette entries #6543 [@radarhere]

Corrected BMP and TGA palette size when saving #6500 [@radarhere]

... (truncated)

Changelog

Sourced from pillow's changelog.

9.3.0 (2022-10-29)

Limit SAMPLESPERPIXEL to avoid runtime DOS #6700 [wiredfool]

Initialize libtiff buffer when saving #6699 [radarhere]

Inline fname2char to fix memory leak #6329 [nulano]

Fix memory leaks related to text features #6330 [nulano]

Use double quotes for version check on old CPython on Windows #6695 [hugovk]

Remove backup implementation of Round for Windows platforms #6693 [cgohlke]

Fixed set_variation_by_name offset #6445 [radarhere]

Fix malloc in _imagingft.c:font_setvaraxes #6690 [cgohlke]

Release Python GIL when converting images using matrix operations #6418 [hmaarrfk]

Added ExifTags enums #6630 [radarhere]

Do not modify previous frame when calculating delta in PNG #6683 [radarhere]

Added support for reading BMP images with RLE4 compression #6674 [npjg, radarhere]

Decode JPEG compressed BLP1 data in original mode #6678 [radarhere]

Added GPS TIFF tag info #6661 [radarhere]

Added conversion between RGB/RGBA/RGBX and LAB #6647 [radarhere]

Do not attempt normalization if mode is already normal #6644 [radarhere]

... (truncated)

Commits

d594f4c Update CHANGES.rst [ci skip]

909dc64 9.3.0 version bump

1a51ce7 Merge pull request #6699 from hugovk/security-libtiff_buffer

2444cdd Merge pull request #6700 from hugovk/security-samples_per_pixel-sec

744f455 Added release notes

0846bfa Add to release notes

799a6a0 Fix linting

00b25fd Hide UserWarning in logs

05b175e Tighter test case

13f2c5a Prevent DOS with large SAMPLESPERPIXEL in Tiff IFD

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dependencies
opened by dependabot[bot] 0
Bump numpy from 1.18.1 to 1.22.0
Bumps numpy from 1.18.1 to 1.22.0.

Release notes

Sourced from numpy's releases.

v1.22.0

NumPy 1.22.0 Release Notes

NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are:

Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release.

A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across application such as CuPy and JAX.

NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data.

New methods for quantile, percentile, and related functions. The new methods provide a complete set of the methods commonly found in the literature.

A new configurable allocator for use by downstream projects.

These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation.

The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays.

Expired deprecations

Deprecated numeric style dtype strings have been removed

Using the strings "Bytes0", "Datetime64", "Str0", "Uint32", and "Uint64" as a dtype will now raise a TypeError.

(gh-19539)

Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

numpy.loads was deprecated in v1.15, with the recommendation that users use pickle.loads instead. ndfromtxt and mafromtxt were both deprecated in v1.17 - users should use numpy.genfromtxt instead with the appropriate value for the usemask parameter.

(gh-19615)

... (truncated)

Commits

4adc87d Merge pull request #20685 from charris/prepare-for-1.22.0-release

fd66547 REL: Prepare for the NumPy 1.22.0 release.

125304b wip

c283859 Merge pull request #20682 from charris/backport-20416

5399c03 Merge pull request #20681 from charris/backport-20954

f9c45f8 Merge pull request #20680 from charris/backport-20663

794b36f Update armccompiler.py

d93b14e Update test_public_api.py

7662c07 Update init.py

311ab52 Update armccompiler.py

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dependencies
opened by dependabot[bot] 0
Training error: RuntimeError: For non-complex input tensors, argument alpha must not be a complex number.

Hi, thanks for your great jobs! When I try to train a model, there was an error like that:

Traceback (most recent call last): File "so_run.py", line 51, in main() File "so_run.py", line 43, in main trainer.train() File "/home/CCM/trainer/source_only_trainer.py", line 58, in train self.optim.step() File /home/anaconda3/envs/torch1.9/lib/python3.8/site-packages/torch/optim/optimizer.py", line 88, in wrapper return func(*args, **kwargs) File "/home/anaconda3/envs/torch1.9/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 28, in decorate_context return func(*args, **kwargs) File "/home/anaconda3/envs/torch1.9/lib/python3.8/site-packages/torch/optim/sgd.py", line 110, in step F.sgd(params_with_grad, File "/home/anaconda3/envs/torch1.9/lib/python3.8/site-packages/torch/optim/functional.py", line 180, in sgd param.add(d_p, alpha=-lr) RuntimeError: For non-complex input tensors, argument alpha must not be a complex number.

How should I fix it? Thank you. And my config used to train is:

note: 'train'

configs of data

model: 'deeplab' train: True multigpu: False fixbn: True fix_seed: True

Optimizaers

learning_rate: 7.5e-5 num_steps: 5000 epochs: 2 weight_decay: 0.0005 momentum: 0.9 power: 0.9 round: 6

Logging

print_freq: 1 save_freq: 2000 tensorboard: False neptune: False screen: True val: False val_freq: 300

Dataset

source: 'gta5' target: 'cityscapes' worker: 0 batch_size: 2

#Transforms input_src: 720 input_tgt: 720 crop_src: 600 crop_tgt: 600 mirror: True scale_min: 0.5 scale_max: 1.5 rec: False

Model hypers

init_weight: './pretrained/DeepLab_resnet_pretrained_init-f81d91e8.pth' restore_from: None

snapshot: './Data/snapshot/' result: './miou_result/' log: './log/' plabel: './plabel' gta5: { data_dir: '/home/data/datasets/GTA5/', data_list: './dataset/list/gta5_list.txt', input_size: [1280, 720] } synthia: { data_dir: '/home/guangrui/data/synthia/', data_list: './dataset/list/synthia_list.txt', input_size: [1280, 760] } cityscapes: { data_dir: '/home/data/datasets/Cityscapes', data_list: './dataset/list/cityscapes_train.txt', input_size: [1024, 512] }

opened by hosea7456 5

Owner

Guangrui Li

A Ph.D. Candidate at UTS

GitHub

Official implementation of "DSP: Dual Soft-Paste for Unsupervised Domain Adaptive Semantic Segmentation"

DSP Official implementation of "DSP: Dual Soft-Paste for Unsupervised Domain Adaptive Semantic Segmentation". Accepted by ACM Multimedia 2021. Authors

20 Oct 24, 2022

Pytorch Implementation for NeurIPS (oral) paper: Pixel Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation

Pixel-Level Cycle Association This is the Pytorch implementation of our NeurIPS 2020 Oral paper Pixel-Level Cycle Association: A New Perspective for D

87 Oct 19, 2022

LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation (NeurIPS2021 Benchmark and Dataset Track)

LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation by Junjue Wang, Zhuo Zheng, Ailong Ma, Xiaoyan Lu, and Yanfei Zh

174 Dec 22, 2022

LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation

LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation by Junjue Wang, Zhuo Zheng, Ailong Ma, Xiaoyan Lu, and Yanfei Zh

8 Nov 21, 2022

Official Implementation of HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation

HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation by Lukas Hoyer, Dengxin Dai, and Luc Van Gool [Arxiv] [Paper] Overview Unsup

149 Dec 28, 2022

TF2 implementation of knowledge distillation using the "function matching" hypothesis from the paper Knowledge distillation: A good teacher is patient and consistent by Beyer et al.

FunMatch-Distillation TF2 implementation of knowledge distillation using the "function matching" hypothesis from the paper Knowledge distillation: A g

67 Dec 20, 2022

Cycle Consistent Adversarial Domain Adaptation (CyCADA)

Cycle Consistent Adversarial Domain Adaptation (CyCADA) A pytorch implementation of CyCADA. If you use this code in your research please consider citi

2 Jan 10, 2022

Recall Loss for Semantic Segmentation (This repo implements the paper: Recall Loss for Semantic Segmentation)

Recall Loss for Semantic Segmentation (This repo implements the paper: Recall Loss for Semantic Segmentation) Download Synthia dataset The model uses

32 Sep 21, 2022

Code for the ECCV2020 paper "A Differentiable Recurrent Surface for Asynchronous Event-Based Data"

A Differentiable Recurrent Surface for Asynchronous Event-Based Data Code for the ECCV2020 paper "A Differentiable Recurrent Surface for Asynchronous

21 Oct 5, 2022

Official implementation of "Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervision" ECCV2020

XDVioDet Official implementation of "Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervision" ECCV2020. The proj

64 Dec 12, 2022

[ECCV2020] Content-Consistent Matching for Domain Adaptive Semantic Segmentation

Related tags

Overview

[ECCV20] Content-Consistent Matching for Domain Adaptive Semantic Segmentation

News: GTA-4K list is available!

Prerequisites

Getting Started

Training

Evaluation

Citation

Comments

9.0.1

Changes

9.0.0

Changes

9.0.1 (2022-02-03)

9.0.0 (2022-01-02)

9.0.0

Changes

9.0.0 (2022-01-02)

8.3.2

Security

Python 3.10 wheels

Fixed regressions

8.3.1

Changes

8.3.0

Changes

8.3.2 (2021-09-02)

8.3.1 (2021-07-06)

8.3.0 (2021-07-01)

9.3.0

Changes

9.3.0 (2022-10-29)

v1.22.0

NumPy 1.22.0 Release Notes

Expired deprecations

Deprecated numeric style dtype strings have been removed

Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

configs of data

Optimizaers

Logging

Dataset

Model hypers

Owner

Guangrui Li

Official implementation of "DSP: Dual Soft-Paste for Unsupervised Domain Adaptive Semantic Segmentation"

Pytorch Implementation for NeurIPS (oral) paper: Pixel Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation

LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation (NeurIPS2021 Benchmark and Dataset Track)

LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation

Official Implementation of HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation

TF2 implementation of knowledge distillation using the "function matching" hypothesis from the paper Knowledge distillation: A good teacher is patient and consistent by Beyer et al.

Cycle Consistent Adversarial Domain Adaptation (CyCADA)

Recall Loss for Semantic Segmentation (This repo implements the paper: Recall Loss for Semantic Segmentation)

Code for the ECCV2020 paper "A Differentiable Recurrent Surface for Asynchronous Event-Based Data"

Official implementation of "Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervision" ECCV2020

Evaluation Pipeline for our ECCV2020: Journey Towards Tiny Perceptual Super-Resolution.

PyTorch reimplementation of hand-biomechanical-constraints (ECCV2020)

Code for "Learning Canonical Representations for Scene Graph to Image Generation", Herzig & Bar et al., ECCV2020

Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion (CVPR 2021)

Adaptive Pyramid Context Network for Semantic Segmentation (APCNet CVPR'2019)

Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging

Code for C2-Matching (CVPR2021). Paper: Robust Reference-based Super-Resolution via C2-Matching.

A Python implementation of the Locality Preserving Matching (LPM) method for pruning outliers in image matching.

(CVPR2021) DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation

Expired deprecations for `loads`, `ndfromtxt`, and `mafromtxt` in npyio