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Published 20 hours ago • verified publisher icon project-codeflare

Bump the requirements-txt group across 2 directories with 3 updates

Open dependabot[bot] opened this issue 1 year ago • 1 comments

Bumps the requirements-txt group with 3 updates in the /demo-notebooks/guided-demos directory: pytorch-lightning, torchmetrics and torchvision. Bumps the requirements-txt group with 3 updates in the /tests/e2e directory: pytorch-lightning, torchmetrics and torchvision.

Updates pytorch-lightning from 1.9.5 to 2.4.0

Release notes

Sourced from pytorch-lightning's releases.

Lightning v2.4

Lightning AI :zap: is excited to announce the release of Lightning 2.4. This is mainly a compatibility upgrade for PyTorch 2.4 and Python 3.12, with a sprinkle of a few features and bug fixes.

Did you know? The Lightning philosophy extends beyond a boilerplate-free deep learning framework: We've been hard at work bringing you Lightning Studio. Code together, prototype, train, deploy, host AI web apps. All from your browser, with zero setup.

Changes

PyTorch Lightning

  • Made saving non-distributed checkpoints fully atomic (#20011)
  • Added dump_stats flag to AdvancedProfiler (#19703)
  • Added a flag verbose to the seed_everything() function (#20108)
  • Added support for PyTorch 2.4 (#20010)
  • Added support for Python 3.12 (20078)
  • The TQDMProgressBar now provides an option to retain prior training epoch bars (#19578)
  • Added the count of modules in train and eval mode to the printed ModelSummary table (#20159)
  • Triggering KeyboardInterrupt (Ctrl+C) during .fit(), .evaluate(), .test() or .predict() now terminates all processes launched by the Trainer and exits the program (#19976)
  • Changed the implementation of how seeds are chosen for dataloader workers when using seed_everything(..., workers=True) (#20055)
  • NumPy is no longer a required dependency (#20090)
  • Removed support for PyTorch 2.1 (#20009)
  • Removed support for Python 3.8 (#20071)
  • Avoid LightningCLI saving hyperparameters with class_path and init_args since this would be a breaking change (#20068)
  • Fixed an issue that would cause too many printouts of the seed info when using seed_everything() (#20108)
  • Fixed _LoggerConnector's _ResultMetric to move all registered keys to the device of the logged value if needed (#19814)
  • Fixed _optimizer_to_device logic for special 'step' key in optimizer state causing performance regression (#20019)
  • Fixed parameter counts in ModelSummary when model has distributed parameters (DTensor) (#20163)

Lightning Fabric

... (truncated)

Commits

Updates torchmetrics from 0.9.1 to 1.4.1

Release notes

Sourced from torchmetrics's releases.

Minor patch release

[1.4.1] - 2024-08-02

Changed

  • Calculate the text color of ConfusionMatrix plot based on luminance (#2590)
  • Updated _safe_divide to allow Accuracy to run on the GPU (#2640)
  • Improved better error messages for intersection detection metrics for wrong user input (#2577)

Removed

  • Dropped Chrf implementation due to licensing issues with the upstream package (#2668)

Fixed

  • Fixed bug in MetricCollection when using compute groups and compute is called more than once (#2571)
  • Fixed class order of panoptic_quality(..., return_per_class=True) output (#2548)
  • Fixed BootstrapWrapper not being reset correctly (#2574)
  • Fixed integration between ClasswiseWrapper and MetricCollection with custom _filter_kwargs method (#2575)
  • Fixed BertScore calculation: pred target misalignment (#2347)
  • Fixed _cumsum helper function in multi-gpu (#2636)
  • Fixed bug in MeanAveragePrecision.coco_to_tm (#2588)
  • Fixed missed f-strings in exceptions/warnings (#2667)

Key Contributors

@​Borda, @​gxy-gxy, @​i-aki-y, @​ndrwrbgs, @​relativityhd, @​SkafteNicki

If we forgot someone due to not matching commit email with GitHub account, let us know :]

Full Changelog: https://github.com/Lightning-AI/torchmetrics/compare/v1.4.0...v1.4.1

Minor dependency correction

Full Changelog: https://github.com/Lightning-AI/torchmetrics/compare/v1.4.0...v1.4.0.post0

Metrics for segmentation

In Torchmetrics v1.4, we are happy to introduce a new domain of metrics to the library: segmentation metrics. Segmentation metrics are used to evaluate how well segmentation algorithms are performing, e.g., algorithms that take in an image and pixel-by-pixel decide what kind of object it is. These kind of algorithms are necessary in applications such as self driven cars. Segmentations are closely related to classification metrics, but for now, in Torchmetrics, expect the input to be formatted differently; see the documentation for more info. For now, MeanIoU and GeneralizedDiceScore have been added to the subpackage, with many more to follow in upcoming releases of Torchmetrics. We are happy to receive any feedback on metrics to add in the future or the user interface for the new segmentation metrics.

Torchmetrics v1.3 adds new metrics to the classification and image subpackage and has multiple bug fixes and other quality-of-life improvements. We refer to the changelog for the complete list of changes.

[1.4.0] - 2024-05-03

Added

  • Added SensitivityAtSpecificity metric to classification subpackage (#2217)
  • Added QualityWithNoReference metric to image subpackage (#2288)

... (truncated)

Changelog

Sourced from torchmetrics's changelog.

[1.4.1] - 2024-08-02

Changed

  • Calculate text color of ConfusionMatrix plot based on luminance (#2590)
  • Updated _safe_divide to allow Accuracy to run on the GPU (#2640)
  • Improved error messages for intersection detection metrics for wrong user input (#2577)

Removed

  • Dropped Chrf implementation due to licensing issues with the upstream package (#2668)

Fixed

  • Fixed bug in MetricCollection when using compute groups and compute is called more than once (#2571)
  • Fixed class order of panoptic_quality(..., return_per_class=True) output (#2548)
  • Fixed BootstrapWrapper not being reset correctly (#2574)
  • Fixed integration between ClasswiseWrapper and MetricCollection with custom _filter_kwargs method (#2575)
  • Fixed BertScore calculation: pred target misalignment (#2347)
  • Fixed _cumsum helper function in multi-gpu (#2636)
  • Fixed bug in MeanAveragePrecision.coco_to_tm (#2588)
  • Fixed missed f-strings in exceptions/warnings (#2667)

[1.4.0] - 2024-05-03

Added

  • Added SensitivityAtSpecificity metric to classification subpackage (#2217)
  • Added QualityWithNoReference metric to image subpackage (#2288)
  • Added a new segmentation metric:
  • Added support for calculating segmentation quality and recognition quality in PanopticQuality metric (#2381)
  • Added pretty-errors for improving error prints (#2431)
  • Added support for torch.float weighted networks for FID and KID calculations (#2483)
  • Added zero_division argument to selected classification metrics (#2198)

Changed

  • Made __getattr__ and __setattr__ of ClasswiseWrapper more general (#2424)

Fixed

  • Fix getitem for metric collection when prefix/postfix is set (#2430)
  • Fixed axis names with Precision-Recall curve (#2462)
  • Fixed list synchronization with partly empty lists (#2468)
  • Fixed memory leak in metrics using list states (#2492)
  • Fixed bug in computation of ERGAS metric (#2498)
  • Fixed BootStrapper wrapper not working with kwargs provided argument (#2503)

... (truncated)

Commits
  • 59d67ef releasing 1.4.1
  • 1309b07 test: update after ChrF removed
  • d95c772 build(deps): update huggingface-hub requirement from <0.24 to <0.25 in /requi...
  • 683b620 text: temp drop Chrf implementation (#2668)
  • 83ff38d build(deps): update scipy requirement from <1.14.0,>1.0.0 to >1.0.0,<1.15.0 i...
  • 72e8304 build(deps): bump Lightning-AI/utilities from 0.11.3.post0 to 0.11.6 (#2666)
  • 7a66579 fix missed f-strings (#2667)
  • 98c5456 docs: fix broken link
  • 32279a6 Update _safe_divide to allow Accuracy to run on the GPU (#2640)
  • d214dbe build(deps): bump lightning-utilities from 0.11.3.post0 to 0.11.5 in /require...
  • Additional commits viewable in compare view

Updates torchvision from 0.12.0 to 0.19.0

Release notes

Sourced from torchvision's releases.

Torchvision 0.19 release

Highlights

Encoding / Decoding images

Torchvision is extending its encoding/decoding capabilities. For this version, we added a GIF decoder which is available as torchvision.io.decode_gif(raw_tensor), torchvision.io.decode_image(raw_tensor), and torchvision.io.read_image(path_to_image).

We also added support for jpeg GPU encoding in torchvision.io.encode_jpeg(). This is 10X faster than the existing CPU jpeg encoder.

Read more on the docs!

Stay tuned for more improvements coming in the next versions. We plan to improve jpeg GPU decoding, and add more image decoders (webp in particular).

Resizing according to the longest edge of an image

It is now possible to resize images by setting torchvision.transforms.v2.Resize(max_size=N): this will resize the longest edge of the image exactly to max_size, making sure the image dimension don't exceed this value. Read more on the docs!

Detailed changes

Bug Fixes

[datasets] SBDataset: Only download noval file when image_set='train_noval' (#8475) [datasets] Update the download url in class EMNIST (#8350) [io] Fix compilation error when there is no libjpeg (#8342) [reference scripts] Fix use of cutmix_alpha in classification training references (#8448) [utils] Allow K=1 in draw_keypoints (#8439)

New Features

[io] Add decoder for GIF images (decode_gif(), decode_image(),read_image()) (#8406, #8419) [transforms] Add GaussianNoise transform (#8381)

Improvements

[transforms] Allow v2 Resize to resize longer edge exactly to max_size (#8459) [transforms] Add min_area parameter to SanitizeBoundingBox (#7735) [transforms] Make adjust_hue() work with numpy 2.0 (#8463) [transforms] Enable one-hot-encoded labels in MixUp and CutMix (#8427) [transforms] Create kernel on-device for transforms.functional.gaussian_blur (#8426) [io] Adding GPU acceleration to encode_jpeg (10X faster than CPU encoder) (#8391) [io] read_video: accept BytesIO objects on pyav backend (#8442) [io] Add compatibility with FFMPEG 7.0 (#8408) [datasets] Add extra to install gdown (#8430) [datasets] Support encoded RLE format in for COCO segmentations (#8387) [datasets] Added binary cat vs dog classification target type to Oxford pet dataset (#8388) [datasets] Return labels for FER2013 if possible (#8452) [ops] Force use of torch.compile on deterministic roi_align implementation (#8436) [utils] add float support to utils.draw_bounding_boxes() (#8328)

... (truncated)

Commits

Updates pytorch-lightning from 1.9.5 to 2.4.0

Release notes

Sourced from pytorch-lightning's releases.

Lightning v2.4

Lightning AI :zap: is excited to announce the release of Lightning 2.4. This is mainly a compatibility upgrade for PyTorch 2.4 and Python 3.12, with a sprinkle of a few features and bug fixes.

Did you know? The Lightning philosophy extends beyond a boilerplate-free deep learning framework: We've been hard at work bringing you Lightning Studio. Code together, prototype, train, deploy, host AI web apps. All from your browser, with zero setup.

Changes

PyTorch Lightning

  • Made saving non-distributed checkpoints fully atomic (#20011)
  • Added dump_stats flag to AdvancedProfiler (#19703)
  • Added a flag verbose to the seed_everything() function (#20108)
  • Added support for PyTorch 2.4 (#20010)
  • Added support for Python 3.12 (20078)
  • The TQDMProgressBar now provides an option to retain prior training epoch bars (#19578)
  • Added the count of modules in train and eval mode to the printed ModelSummary table (#20159)
  • Triggering KeyboardInterrupt (Ctrl+C) during .fit(), .evaluate(), .test() or .predict() now terminates all processes launched by the Trainer and exits the program (#19976)
  • Changed the implementation of how seeds are chosen for dataloader workers when using seed_everything(..., workers=True) (#20055)
  • NumPy is no longer a required dependency (#20090)
  • Removed support for PyTorch 2.1 (#20009)
  • Removed support for Python 3.8 (#20071)
  • Avoid LightningCLI saving hyperparameters with class_path and init_args since this would be a breaking change (#20068)
  • Fixed an issue that would cause too many printouts of the seed info when using seed_everything() (#20108)
  • Fixed _LoggerConnector's _ResultMetric to move all registered keys to the device of the logged value if needed (#19814)
  • Fixed _optimizer_to_device logic for special 'step' key in optimizer state causing performance regression (#20019)
  • Fixed parameter counts in ModelSummary when model has distributed parameters (DTensor) (#20163)

Lightning Fabric

... (truncated)

Commits

Updates torchmetrics from 0.9.1 to 1.4.1

Release notes

Sourced from torchmetrics's releases.

Minor patch release

[1.4.1] - 2024-08-02

Changed

  • Calculate the text color of ConfusionMatrix plot based on luminance (#2590)
  • Updated _safe_divide to allow Accuracy to run on the GPU (#2640)
  • Improved better error messages for intersection detection metrics for wrong user input (#2577)

Removed

  • Dropped Chrf implementation due to licensing issues with the upstream package (#2668)

Fixed

  • Fixed bug in MetricCollection when using compute groups and compute is called more than once (#2571)
  • Fixed class order of panoptic_quality(..., return_per_class=True) output (#2548)
  • Fixed BootstrapWrapper not being reset correctly (#2574)
  • Fixed integration between ClasswiseWrapper and MetricCollection with custom _filter_kwargs method (#2575)
  • Fixed BertScore calculation: pred target misalignment (#2347)
  • Fixed _cumsum helper function in multi-gpu (#2636)
  • Fixed bug in MeanAveragePrecision.coco_to_tm (#2588)
  • Fixed missed f-strings in exceptions/warnings (#2667)

Key Contributors

@​Borda, @​gxy-gxy, @​i-aki-y, @​ndrwrbgs, @​relativityhd, @​SkafteNicki

If we forgot someone due to not matching commit email with GitHub account, let us know :]

Full Changelog: https://github.com/Lightning-AI/torchmetrics/compare/v1.4.0...v1.4.1

Minor dependency correction

Full Changelog: https://github.com/Lightning-AI/torchmetrics/compare/v1.4.0...v1.4.0.post0

Metrics for segmentation

In Torchmetrics v1.4, we are happy to introduce a new domain of metrics to the library: segmentation metrics. Segmentation metrics are used to evaluate how well segmentation algorithms are performing, e.g., algorithms that take in an image and pixel-by-pixel decide what kind of object it is. These kind of algorithms are necessary in applications such as self driven cars. Segmentations are closely related to classification metrics, but for now, in Torchmetrics, expect the input to be formatted differently; see the documentation for more info. For now, MeanIoU and GeneralizedDiceScore have been added to the subpackage, with many more to follow in upcoming releases of Torchmetrics. We are happy to receive any feedback on metrics to add in the future or the user interface for the new segmentation metrics.

Torchmetrics v1.3 adds new metrics to the classification and image subpackage and has multiple bug fixes and other quality-of-life improvements. We refer to the changelog for the complete list of changes.

[1.4.0] - 2024-05-03

Added

  • Added SensitivityAtSpecificity metric to classification subpackage (#2217)
  • Added QualityWithNoReference metric to image subpackage (#2288)

... (truncated)

Changelog

Sourced from torchmetrics's changelog.

[1.4.1] - 2024-08-02

Changed

  • Calculate text color of ConfusionMatrix plot based on luminance (#2590)
  • Updated _safe_divide to allow Accuracy to run on the GPU (#2640)
  • Improved error messages for intersection detection metrics for wrong user input (#2577)

Removed

  • Dropped Chrf implementation due to licensing issues with the upstream package (#2668)

Fixed

  • Fixed bug in MetricCollection when using compute groups and compute is called more than once (#2571)
  • Fixed class order of panoptic_quality(..., return_per_class=True) output (#2548)
  • Fixed BootstrapWrapper not being reset correctly (#2574)
  • Fixed integration between ClasswiseWrapper and MetricCollection with custom _filter_kwargs method (#2575)
  • Fixed BertScore calculation: pred target misalignment (#2347)
  • Fixed _cumsum helper function in multi-gpu (#2636)
  • Fixed bug in MeanAveragePrecision.coco_to_tm (#2588)
  • Fixed missed f-strings in exceptions/warnings (#2667)

[1.4.0] - 2024-05-03

Added

  • Added SensitivityAtSpecificity metric to classification subpackage (#2217)
  • Added QualityWithNoReference metric to image subpackage (#2288)
  • Added a new segmentation metric:
  • Added support for calculating segmentation quality and recognition quality in PanopticQuality metric (#2381)
  • Added pretty-errors for improving error prints (#2431)
  • Added support for torch.float weighted networks for FID and KID calculations (#2483)
  • Added zero_division argument to selected classification metrics (#2198)

Changed

  • Made __getattr__ and __setattr__ of ClasswiseWrapper more general (#2424)

Fixed

  • Fix getitem for metric collection when prefix/postfix is set (#2430)
  • Fixed axis names with Precision-Recall curve (#2462)
  • Fixed list synchronization with partly empty lists (#2468)
  • Fixed memory leak in metrics using list states (#2492)
  • Fixed bug in computation of ERGAS metric (#2498)
  • Fixed BootStrapper wrapper not working with kwargs provided argument (#2503)

... (truncated)

Commits
  • 59d67ef releasing 1.4.1
  • 1309b07 test: update after ChrF removed
  • d95c772 build(deps): update huggingface-hub requirement from <0.24 to <0.25 in /requi...
  • 683b620 text: temp drop Chrf implementation (#2668)
  • 83ff38d build(deps): update scipy requirement from <1.14.0,>1.0.0 to >1.0.0,<1.15.0 i...
  • 72e8304 build(deps): bump Lightning-AI/utilities from 0.11.3.post0 to 0.11.6 (#2666)
  • 7a66579 fix missed f-strings (#2667)
  • 98c5456 docs: fix broken link
  • 32279a6 Update _safe_divide to allow Accuracy to run on the GPU (#2640)
  • d214dbe build(deps): bump lightning-utilities from 0.11.3.post0 to 0.11.5 in /require...
  • Additional commits viewable in compare view

Updates torchvision from 0.12.0 to 0.19.0

Release notes

Sourced from torchvision's releases.

Torchvision 0.19 release

Highlights

Encoding / Decoding images

Torchvision is extending its encoding/decoding capabilities. For this version, we added a GIF decoder which is available as torchvision.io.decode_gif(raw_tensor), torchvision.io.decode_image(raw_tensor), and torchvision.io.read_image(path_to_image).

We also added support for jpeg GPU encoding in torchvision.io.encode_jpeg(). This is 10X faster than the existing CPU jpeg encoder.

Read more on the docs!

Stay tuned for more improvements coming in the next versions. We plan to improve jpeg GPU decoding, and add more image decoders (webp in particular).

Resizing according to the longest edge of an image

It is now possible to resize images by setting torchvision.transforms.v2.Resize(max_size=N): this will resize the longest edge of the image exactly to max_size, making sure the image dimension don't exceed this value. Read more on the docs!

Detailed changes

Bug Fixes

[datasets] SBDataset: Only download noval file when image_set='train_noval' (#8475) [datasets] Update the download url in class EMNIST (#8350) [io] Fix compilation error when there is no libjpeg (#8342) [reference scripts] Fix use of cutmix_alpha in classification training references (#8448) [utils] Allow K=1 in draw_keypoints (#8439)

New Features

[io] Add decoder for GIF images (decode_gif(), decode_image(),read_image()) (#8406, #8419) [transforms] Add GaussianNoise transform (#8381)

Improvements

[transforms] Allow v2 Resize to resize longer edge exactly to max_size (#8459) [transforms] Add min_area parameter to SanitizeBoundingBox (#7735) [transforms] Make adjust_hue() work with numpy 2.0 (#8463) [transforms] Enable one-hot-encoded labels in MixUp and CutMix (#8427) [transforms] Create kernel on-device for transforms.functional.gaussian_blur (#8426) [io] Adding GPU acceleration to encode_jpeg (10X faster than CPU encoder) (#8391) [io] read_video: accept BytesIO objects on pyav backend (#8442) [io] Add compatibility with FFMPEG 7.0 (#8408) [datasets] Add extra to install gdown (#8430) [datasets] Support encoded RLE format in for COCO segmentations (#8387) [datasets] Added binary cat vs dog classification target type to Oxford pet dataset (#8388) [datasets] Return labels for FER2013 if possible (#8452) [ops] Force use of torch.compile on deterministic roi_align implementation (#8436) [utils] add float support to utils.draw_bounding_boxes() (#8328)

... (truncated)

Commits

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dependabot[bot] avatar Aug 12 '24 22:08 dependabot[bot]

[APPROVALNOTIFIER] This PR is NOT APPROVED

This pull-request has been approved by: Once this PR has been reviewed and has the lgtm label, please assign dimakis for approval. For more information see the Kubernetes Code Review Process.

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openshift-ci[bot] avatar Aug 12 '24 22:08 openshift-ci[bot]

Superseded by #661.

dependabot[bot] avatar Sep 09 '24 22:09 dependabot[bot]