From 7991f14dcf31877f0363f1f2a4392586a04d6eee Mon Sep 17 00:00:00 2001 From: Liezl Maree <38435167+roomrys@users.noreply.github.com> Date: Thu, 19 Dec 2024 09:47:25 -0800 Subject: [PATCH] Release 1.4.1 (#2068) * Remove no-op code from #1498 * Add options to set background color when exporting video (#1328) * implement #921 * simplified form / refractor * Add test function and update cli docs * Improve test function to check background color * Improve comments * Change background options to lowercase * Use coderabbitai suggested `fill` --------- Co-authored-by: Shrivaths Shyam <52810689+shrivaths16@users.noreply.github.com> Co-authored-by: Liezl Maree <38435167+roomrys@users.noreply.github.com> * Increase range on batch size (#1513) * Increase range on batch size * Set maximum to a factor of 2 * Set default callable for `match_lists_function` (#1520) * Set default for `match_lists_function` * Move test code to official tests * Check using expected values * Allow passing in `Labels` to `app.main` (#1524) * Allow passing in `Labels` to `app.main` * Load the labels object through command * Add warning when unable to switch back to CPU mode * Replace (broken) `--unrag` with `--ragged` (#1539) * Fix unrag always set to true in sleap-export * Replace unrag with ragged * Fix typos * Add function to create app (#1546) * Refactor `AddInstance` command (#1561) * Refactor AddInstance command * Add staticmethod wrappers * Return early from set_visible_nodes * Import DLC with uniquebodyparts, add Tracks (#1562) * Import DLC with uniquebodyparts, add Tracks * add tests * correct tests * Make the hdf5 videos store as int8 format (#1559) * make the hdf5 video dataset type as proper int8 by padding with zeros * add gzip compression * Scale new instances to new frame size (#1568) * Fix typehinting in `AddInstance` * brought over changes from my own branch * added suggestions * Ensured google style comments --------- Co-authored-by: roomrys <38435167+roomrys@users.noreply.github.com> Co-authored-by: sidharth srinath * Fix package export (#1619) Add check for empty videos * Add resize/scroll to training GUI (#1565) * Make resizable training GUI and add adaptive scroll bar * Set a maximum window size --------- Co-authored-by: Liezl Maree <38435167+roomrys@users.noreply.github.com> * support loading slp files with non-compound types and str in metadata (#1566) Co-authored-by: Liezl Maree <38435167+roomrys@users.noreply.github.com> * change inference pipeline option to tracking-only (#1666) change inference pipeline none option to tracking-only * Add ABL:AOC 2023 Workshop link (#1673) * Add ABL:AOC 2023 Workshop link * Trigger website build * Graceful failing with seeking errors (#1712) * Don't try to seek to faulty last frame on provider initialization * Catch seeking errors and pass * Lint * Fix IndexError for hdf5 file import for single instance analysis files (#1695) * Fix hdf5 read for single instance analysis files * Add test * Small test files * removing unneccessary fixtures * Replace imgaug with albumentations (#1623) What's the worst that could happen? * Initial commit * Fix augmentation * Update more deps requirements * Use pip for installing albumentations and avoid reinstalling OpenCV * Update other conda envs * Fix out of bounds albumentations issues and update dependencies (#1724) * Install albumentations using conda-forge in environment file * Conda install albumentations * Add ndx-pose to pypi requirements * Keep out of bounds points * Black * Add ndx-pose to conda install in environment file * Match environment file without cuda * Ordered dependencies * Add test * Delete comments * Add conda packages to mac environment file * Order dependencies in pypi requirements * Add tests with zeroes and NaNs for augmentation * Back * Black * Make comment one line * Add todo for later * Black * Update to new TensorFlow conda package (#1726) * Build conda package locally * Try 2.8.4 * Merge develop into branch to fix dependencies * Change tensorflow version to 2.7.4 in where conda packages are used * Make tensorflow requirements in pypi looser * Conda package has TensorFlow 2.7.0 and h5py and numpy installed via conda * Change tensorflow version in `environment_no_cuda.yml` to test using CI * Test new sleap/tensorflow package * Reset build number * Bump version * Update mac deps * Update to Arm64 Mac runners * pin `importlib-metadata` * Pin more stuff on mac * constrain `opencv` version due to new qt dependencies * Update more mac stuff * Patches to get to green * More mac skipping --------- Co-authored-by: Talmo Pereira Co-authored-by: Talmo Pereira * Fix CI on macosx-arm64 (#1734) * Build conda package locally * Try 2.8.4 * Merge develop into branch to fix dependencies * Change tensorflow version to 2.7.4 in where conda packages are used * Make tensorflow requirements in pypi looser * Conda package has TensorFlow 2.7.0 and h5py and numpy installed via conda * Change tensorflow version in `environment_no_cuda.yml` to test using CI * Test new sleap/tensorflow package * Reset build number * Bump version * Update mac deps * Update to Arm64 Mac runners * pin `importlib-metadata` * Pin more stuff on mac * constrain `opencv` version due to new qt dependencies * Update more mac stuff * Patches to get to green * More mac skipping * Re-enable mac tests * Handle GPU re-init * Fix mac build CI * Widen tolerance for movenet correctness test * Fix build ci * Try for manual build without upload * Try to reduce training CI time * Rework actions * Fix miniforge usage * Tweaks * Fix build ci * Disable manual build * Try merging CI coverage * GPU/CPU usage in tests * Lint * Clean up * Fix test skip condition * Remove scratch test --------- Co-authored-by: eberrigan * Add option to export to CSV via sleap-convert and API (#1730) * Add csv as a format option * Add analysis to format * Add csv suffix to output path * Add condition for csv analysis file * Add export function to Labels class * delete print statement * lint * Add `analysis.csv` as parametrize input for `sleap-convert` tests * test `export_csv` method added to `Labels` class * black formatting * use `Path` to construct filename * add `analysis.csv` to cli guide for `sleap-convert` --------- Co-authored-by: Talmo Pereira * Only propagate Transpose Tracks when propagate is checked (#1748) Fix always-propagate transpose tracks issue * View Hyperparameter nonetype fix (#1766) Pass config getter argument to fetch hyperparameters * Adding ragged metadata to `info.json` (#1765) Add ragged metadata to info.json file * Add batch size to GUI for inference (#1771) * Fix conda builds (#1776) * test conda packages in a test environment as part of CI * do not test sleap import using conda build * use github environment variables to define build path for each OS in the matrix and add print statements for testing * figure out paths one OS at a time * github environment variables work in subsequent steps not current step * use local builds first * print env info * try simple environment creation * try conda instead of mamba * fix windows build path * fix windows build path * add comment to reference pull request * remove test stage from conda build for macs and test instead by creating the environment in a workflow * test workflow by pushing to current branch * test conda package on macos runner * Mac build does not need nvidia channel * qudida and albumentations are conda installed now * add comment with original issue * use python 3.9 * use conda match specifications syntax * make print statements more readable for troubleshooting python versioning * clean up build file * update version for pre-release * add TODO * add tests for conda packages before uploading * update ci comments and branches * remove macos test of pip wheel since python 3.9 is not supported by setup-python action * Upgrade build actions for release (#1779) * update `build.yml` so it matches updates from `build_manual.yml` * test `build.yml` without uploading * test again using build_manual.yml * build pip wheel with Ubuntu and turn off caching so build.yml exactly matches build_manual.yml * `build.yml` on release only and upload * testing caching * `use-only-tar-bz2: true` makes environment unsolvable, change it back * Update .github/workflows/build_manual.yml Co-authored-by: Liezl Maree <38435167+roomrys@users.noreply.github.com> * Update .github/workflows/build.yml Co-authored-by: Liezl Maree <38435167+roomrys@users.noreply.github.com> * bump pre-release version * fix version for pre-release * run build and upload on release! * try setting `CACHE_NUMBER` to 1 with `use-only-tar-bz2` set to true * increasing the cache number to reset the cache does work when `use-only-tar-bz2` is set to true * publish and upload on release only --------- Co-authored-by: Liezl Maree <38435167+roomrys@users.noreply.github.com> * Add ZMQ support via GUI and CLI (#1780) * Add ZMQ support via GUI and CLI, automatic port handler, separate utils module for the functions * Change menu name to match deleting predictions beyond max instance (#1790) Change menu and function names * Fix website build and remove build cache across workflows (#1786) * test with build_manual on push * comment out caching in build manual * remove cache step from builad manual since environment resolves when this is commented out * comment out cache in build ci * remove cache from build on release * remove cache from website build * test website build on push * add name to checkout step * update checkout to v4 * update checkout to v4 in build ci * remove cache since build ci works without it * update upload-artifact to v4 in build ci * update second chechout to v4 in build ci * update setup-python to v5 in build ci * update download-artifact to v4 in build ci * update checkout to v4 in build ci * update checkout to v4 in website build * update setup-miniconda to v3.0.3 in website build * update actions-gh-pages to v4 in website build * update actions checkout and setup-python in ci * update checkout action in ci to v4 * pip install lxml[html_clean] because of error message during action * add error message to website to explain why pip install lxml[html_clean] * remove my branch for pull request * Bump to 1.4.1a1 (#1791) * bump versions to 1.4.1a1 * we can change the version on the installation page since this will be merged into the develop branch and not main * Fix windows conda package upload and build ci (#1792) * windows OS is 2022 not 2019 on runner * upload windows conda build manually but not pypi build * remove comment and run build ci * change build manual back so that it doesn't upload * remove branch from build manual * update installation docs for 1.4.1a1 * Fix zmq inference (#1800) * Ensure that we always pass in the zmq_port dict to LossViewer * Ensure zmq_ports has correct keys inside LossViewer * Use specified controller and publish ports for first attempted addresses * Add test for ports being set in LossViewer * Add max attempts to find unused port * Fix find free port loop and add for controller port also * Improve code readablility and reuse * Improve error message when unable to find free port * Set selected instance to None after removal (#1808) * Add test that selected instance set to None after removal * Set selected instance to None after removal * Add `InstancesList` class to handle backref to `LabeledFrame` (#1807) * Add InstancesList class to handle backref to LabeledFrame * Register structure/unstructure hooks for InstancesList * Add tests for the InstanceList class * Handle case where instance are passed in but labeled_frame is None * Add tests relevant methods in LabeledFrame * Delegate setting frame to InstancesList * Add test for PredictedInstance.frame after complex merge * Add todo comment to not use Instance.frame * Add rtest for InstasnceList.remove * Use normal list for informative `merged_instances` * Add test for copy and clear * Add copy and clear methods, use normal lists in merge method * Bump to v1.4.1a2 (#1835) bump to 1.4.1a2 * Updated trail length viewing options (#1822) * updated trail length optptions * Updated trail length options in the view menu * Updated `prefs` to include length info from `preferences.yaml` * Added trail length as method of `MainWindow` * Updated trail length documentation * black formatting --------- Co-authored-by: Keya Loding * Handle case when no frame selection for trail overlay (#1832) * Menu option to open preferences directory and update to util functions to pathlib (#1843) * Add menu to view preferences directory and update to pathlib * text formatting * Add `Keep visualizations` checkbox to training GUI (#1824) * Renamed save_visualizations to view_visualizations for clarity * Added Delete Visualizations button to the training pipeline gui, exposed del_viz_predictions config option to the user * Reverted view_ back to save_ and changed new training checkbox to Keep visualization images after training. * Fixed keep_viz config option state override bug and updated keep_viz doc description * Added test case for reading training CLI argument correctly * Removed unnecessary testing code * Creating test case to check for viz folder * Finished tests to check CLI argument reading and viz directory existence * Use empty string instead of None in cli args test * Use keep_viz_images false in most all test configs (except test to override config) --------- Co-authored-by: roomrys <38435167+roomrys@users.noreply.github.com> * Allowing inference on multiple videos via `sleap-track` (#1784) * implementing proposed code changes from issue #1777 * comments * configuring output_path to support multiple video inputs * fixing errors from preexisting test cases * Test case / code fixes * extending test cases for mp4 folders * test case for output directory * black and code rabbit fixes * code rabbit fixes * as_posix errors resolved * syntax error * adding test data * black * output error resolved * edited for push to dev branch * black * errors fixed, test cases implemented * invalid output test and invalid input test * deleting debugging statements * deleting print statements * black * deleting unnecessary test case * implemented tmpdir * deleting extraneous file * fixing broken test case * fixing test_sleap_track_invalid_output * removing support for multiple slp files * implementing talmo's comments * adding comments * Add object keypoint similarity method (#1003) * Add object keypoint similarity method * fix max_tracking * correct off-by-one error * correct off-by-one error * Generate suggestions using max point displacement threshold (#1862) * create function max_point_displacement, _max_point_displacement_video. Add to yaml file. Create test for new function . . . will need to edit * remove unnecessary for loop, calculate proper displacement, adjusted tests accordingly * Increase range for displacement threshold * Fix frames not found bug * Return the latter frame index * Lint --------- Co-authored-by: roomrys <38435167+roomrys@users.noreply.github.com> * Added Three Different Cases for Adding a New Instance (#1859) * implemented paste with offset * right click and then default will paste the new instance at the location of the cursor * modified the logics for creating new instance * refined the logic * fixed the logic for right click * refined logics for adding new instance at a specific location * Remove print statements * Comment code * Ensure that we choose a non nan reference node * Move OOB nodes to closest in-bounds position --------- Co-authored-by: roomrys <38435167+roomrys@users.noreply.github.com> * Allow csv and text file support on sleap track (#1875) * initial changes * csv support and test case * increased code coverage * Error fixing, black, deletion of (self-written) unused code * final edits * black * documentation changes * documentation changes * Fix GUI crash on scroll (#1883) * Only pass wheelEvent to children that can handle it * Add test for wheelEvent * Fix typo to allow rendering videos with mp4 (Mac) (#1892) Fix typo to allow rendering videos with mp4 * Do not apply offset when double clicking a `PredictedInstance` (#1888) * Add offset argument to newInstance and AddInstance * Apply offset of 10 for Add Instance menu button (Ctrl + I) * Add offset for docks Add Instance button * Make the QtVideoPlayer context menu unit-testable * Add test for creating a new instance * Add test for "New Instance" button in `InstancesDock` * Fix typo in docstring * Add docstrings and typehinting * Remove unused imports and sort imports * Refactor video writer to use imageio instead of skvideo (#1900) * modify `VideoWriter` to use imageio with ffmpeg backend * check to see if ffmpeg is present * use the new check for ffmpeg * import imageio.v2 * add imageio-ffmpeg to environments to test * using avi format for now * remove SKvideo videowriter * test `VideoWriterImageio` minimally * add more documentation for ffmpeg * default mp4 for ffmpeg should be mp4 * print using `IMAGEIO` when using ffmpeg * mp4 for ffmpeg * use mp4 ending in test * test `VideoWriterImageio` with avi file extension * test video with odd size * remove redundant filter since imageio-ffmpeg resizes automatically * black * remove unused import * use logging instead of print statement * import cv2 is needed for resize * remove logging * Use `Video.from_filename` when structuring videos (#1905) * Use Video.from_filename when structuring videos * Modify removal_test_labels to have extension in filename * Use | instead of + in key commands (#1907) * Use | instead of + in key commands * Lint * Replace QtDesktop widget in preparation for PySide6 (#1908) * Replace to-be-depreciated QDesktopWidget * Remove unused imports and sort remaining imports * Remove unsupported |= operand to prepare for PySide6 (#1910) Fixes TypeError: unsupported operand type(s) for |=: 'int' and 'Option' * Use positional argument for exception type (#1912) traceback.format_exception has changed it's first positional argument's name from etype to exc in python 3.7 to 3.10 * Replace all Video structuring with Video.cattr() (#1911) * Remove unused AsyncVideo class (#1917) Remove unused AsyncVideo * Refactor `LossViewer` to use matplotlib (#1899) * use updated syntax for QtAgg backend of matplotlib * start add features to `MplCanvas` to replace QtCharts features in `LossViewer` (untested) * remove QtCharts imports and replace with MplCanvas * remove QtCharts imports and replace with MplCanvas * start using MplCanvas in LossViwer instead of QtCharts (untested) * use updated syntax * Uncomment all commented out QtChart * Add debug code * Refactor monitor to use LossViewer._init_series method * Add monitor only debug code * Add methods for setting up axes and legend * Add the matplotlib canvas to the widget * Resize axis with data (no log support yet) * Try using PathCollection for "batch" * Get "batch" plotting with ax.scatter (no log support yet) * Add log support * Add a _resize_axis method * Modify init_series to work for ax.plot as well * Use matplotlib to plot epoch_loss line * Add method _add_data_to_scatter * Add _add_data_to_plot method * Add docstring to _resize_axes * Add matplotlib plot for val_loss * Add matplotlib scatter for val_loss_best * Avoid errors with setting log scale before any positive values * Add x and y axes labels * Set title (removing html tags) * Add legend * Adjust positioning of plot * Lint * Leave MplCanvas unchanged * Removed unused training_monitor.LossViewer * Resize fonts * Move legend outside of plot * Add debug code for montitor aesthetics * Use latex formatting to bold parts of title * Make axes aesthetic * Add midpoint grid lines * Set initial limits on x and y axes to be 0+ * Ensure x axis minimum is always resized to 0+ * Adjust plot to account for plateau patience title * Add debug code for plateau patience title line * Lint * Set thicker line width * Remove unused import * Set log axis on initialization * Make tick labels smaller * Move plot down a smidge * Move ylabel left a bit * Lint * Add class LossPlot * Refactor LossViewer to use LossPlot * Remove QtCharts code * Remove debug codes * Allocate space for figure items based on item's size * Refactor LossPlot to use underscores for internal methods * Ensure y_min, y_max not equal Otherwise we get an unnecessary teminal message: UserWarning: Attempting to set identical bottom == top == 3.0 results in singular transformations; automatically expanding. self.axes.set_ylim(y_min, y_max) --------- Co-authored-by: roomrys Co-authored-by: roomrys <38435167+roomrys@users.noreply.github.com> * Refactor `LossViewer` to use underscores for internal method names (#1919) Refactor LossViewer to use underscores for internal method names * Manually handle `Instance.from_predicted` structuring when not `None` (#1930) * Use `tf.math.mod` instead of `%` (#1931) * Option for Max Stride to be 128 (#1941) Co-authored-by: Max Weinberg * Add discussion comment workflow (#1945) * Add a bot to autocomment on workflow * Use github markdown warning syntax * Add a multiline warning * Change happy coding to happy SLEAPing Co-authored-by: Talmo Pereira --------- Co-authored-by: roomrys Co-authored-by: Talmo Pereira * Add comment on issue workflow (#1946) * Add workflow to test conda packages (#1935) * Add missing imageio-ffmpeg to meta.ymls (#1943) * Update installation docs 1.4.1 (#1810) * [wip] Updated installation docs * Add tabs for different OS installations * Move installation methods to tabs * Use tabs.css * FIx styling error (line under last tab in terminal hint) * Add installation instructions before TOC * Replace mamba with conda * Lint * Find good light colors not switching when change dark/light themes * Get color scheme switching with dark/light toggle button * Upgrade website build dependencies * Remove seemingly unneeded dependencies from workflow * Add myst-nb>=0.16.0 lower bound * Trigger dev website build * Fix minor typo in css * Add miniforge and one-liner installs for package managers --------- Co-authored-by: roomrys Co-authored-by: Talmo Pereira * Add imageio dependencies for pypi wheel (#1950) Add imagio dependencies for pypi wheel Co-authored-by: roomrys * Do not always color skeletons table black (#1952) Co-authored-by: roomrys * Remove no module named work error (#1956) * Do not always color skeletons table black * Remove offending (possibly unneeded) line that causes the no module named work error to print in terminal * Remove offending (possibly unneeded) line that causes the no module named work error to print in terminal * Remove accidentally added changes * Add (failing) test to ensure menu-item updates with state change * Reconnect callback for menu-item (using lambda) * Add (failing) test to ensure menu-item updates with state change Do not assume inital state * Reconnect callback for menu-item (using lambda) --------- Co-authored-by: roomrys * Add `normalized_instance_similarity` method (#1939) * Add normalize function * Expose normalization function * Fix tests * Expose object keypoint sim function * Fix tests * Handle skeleton decoding internally (#1961) * Reorganize (and add) imports * Add (and reorganize) imports * Modify decode_preview_image to return bytes if specified * Implement (minimally tested) replace_jsonpickle_decode * Add support for using idx_to_node map i.e. loading from Labels (slp file) * Ignore None items in reduce_list * Convert large function to SkeletonDecoder class * Update SkeletonDecoder.decode docstring * Move decode_preview_image to SkeletonDecoder * Use SkeletonDecoder instead of jsonpickle in tests * Remove unused imports * Add test for decoding dict vs tuple pystates * Handle skeleton encoding internally (#1970) * start class `SkeletonEncoder` * _encoded_objects need to be a dict to add to * add notebook for testing * format * fix type in docstring * finish classmethod for encoding Skeleton as a json string * test encoded Skeleton as json string by decoding it * add test for decoded encoded skeleton * update jupyter notebook for easy testing * constraining attrs in dev environment to make sure decode format is always the same locally * encode links first then encode source then target then type * save first enconding statically as an input to _get_or_assign_id so that we do not always get py/id * save first encoding statically * first encoding is passed to _get_or_assign_id * use first_encoding variable to determine if we should assign a py/id * add print statements for debugging * update notebook for easy testing * black * remove comment * adding attrs constraint to show this passes for certain attrs version only * add import * switch out jsonpickle.encode * oops remove import * can attrs be unconstrained? * forgot comma * pin attrs for testing * test Skeleton from json, template, with symmetries, and template * use SkeletonEncoder.encode * black * try removing None values in EdgeType reduced * Handle case when nodes are replaced by integer indices from caller * Remove prototyping notebook * Remove attrs pins * Remove sort keys (which flips the neccessary ordering of our py/ids) * Do not add extra indents to encoded file * Only append links after fully encoded (fat-finger) * Remove outdated comment * Lint --------- Co-authored-by: Talmo Pereira Co-authored-by: roomrys <38435167+roomrys@users.noreply.github.com> * Pin ndx-pose<0.2.0 (#1978) * Pin ndx-pose<0.2.0 * Typo * Sort encoded `Skeleton` dictionary for backwards compatibility (#1975) * Add failing test to check that encoded Skeleton is sorted * Sort Skeleton dictionary before encoding * Remove unused import * Disable comment bot for now * Fix COCO Dataset Loading for Invisible Keypoints (#2035) Update coco.py # Fix COCO Dataset Loading for Invisible Keypoints ## Issue When loading COCO datasets, keypoints marked as invisible (flag=0) are currently skipped and later placed randomly within the instance's bounding box. However, in COCO format, these keypoints may still have valid coordinate information that should be preserved (see toy_dataset for expected vs. current behavior). ## Changes Modified the COCO dataset loading logic to: - Check if invisible keypoints (flag=0) have non-zero coordinates - If coordinates are (0,0), skip the point (existing behavior) - If coordinates are not (0,0), create the point at those coordinates but mark it as not visible - Maintain existing behavior for visible (flag=2) and labeled * Lint * Add tracking score as seekbar header options (#2047) * Add `tracking_score` as a constructor arg for `PredictedInstance` * Add `tracking_score` to ID models * Add fixture with tracking scores * Add tracking score to seekbar header * Add bonsai guide for sleap docs (#2050) * [WIP] Add bonsai guide page * Add more information to the guide with images * add branch for website build * Typos * fix links * Include suggestions * Add more screenshots and refine the doc * Remove branch from website workflow * Completed documentation edits from PR made by reviewer + review bot. --------- Co-authored-by: Shrivaths Shyam <52810689+shrivaths16@users.noreply.github.com> Co-authored-by: Liezl Maree <38435167+roomrys@users.noreply.github.com> * Don't mark complete on instance scaling (#2049) * Add check for instances with track assigned before training ID models (#2053) * Add menu item for deleting instances beyond frame limit (#1797) * Add menu item for deleting instances beyond frame limit * Add test function to test the instances returned * typos * Update docstring * Add frame range form * Extend command to use frame range --------- Co-authored-by: Talmo Pereira * Highlight instance box on hover (#2055) * Make node marker and label sizes configurable via preferences (#2057) * Make node marker and label sizes configurable via preferences * Fix test * Enable touchpad pinch to zoom (#2058) * Fix import PySide2 -> qtpy (#2065) * Fix import PySide2 -> qtpy * Remove unnecessary print statements. * Add channels for pip conda env (#2067) * Add channels for pypi conda env * Trigger dev website build * Separate the video name and its filepath columns in `VideoTablesModel` (#2052) * add option to show video names with filepath * add doc * new feature added successfully * delete unnecessary code * remove attributes from video object * Update dataviews.py * remove all properties * delete toggle option * remove video show * fix the order of the columns * remove options * Update sleap/gui/dataviews.py Co-authored-by: Liezl Maree <38435167+roomrys@users.noreply.github.com> * Update sleap/gui/dataviews.py Co-authored-by: Liezl Maree <38435167+roomrys@users.noreply.github.com> * use pathlib instead of substrings * Update dataviews.py Co-authored-by: Liezl Maree <38435167+roomrys@users.noreply.github.com> * Use Path instead of pathlib.Path and sort imports and remove unused imports * Use item.filename instead of getattr --------- Co-authored-by: Liezl Maree <38435167+roomrys@users.noreply.github.com> * Make status bar dependent on UI mode (#2063) * remove bug for dark mode * fix toggle case --------- Co-authored-by: Liezl Maree <38435167+roomrys@users.noreply.github.com> * Bump version to 1.4.1 (#2062) * Bump version to 1.4.1 * Trigger conda/pypi builds (no upload) * Trigger website build * Add dev channel to installation instructions --------- Co-authored-by: Talmo Pereira * Add -c sleap/label/dev channel for win/linux - also trigger website build --------- Co-authored-by: Scott Yang <67733409+scott-yj-yang@users.noreply.github.com> Co-authored-by: Shrivaths Shyam <52810689+shrivaths16@users.noreply.github.com> Co-authored-by: getzze Co-authored-by: Lili Karashchuk Co-authored-by: Sidharth Srinath <69165889+ssrinath22@users.noreply.github.com> Co-authored-by: sidharth srinath Co-authored-by: Talmo Pereira Co-authored-by: KevinZ0217 <96039456+KevinZ0217@users.noreply.github.com> Co-authored-by: Elizabeth <106755962+eberrigan@users.noreply.github.com> Co-authored-by: Talmo Pereira Co-authored-by: eberrigan Co-authored-by: vaibhavtrip29 <82742234+vaibhavtrip29@users.noreply.github.com> Co-authored-by: Keya Loding <117320751+keyaloding@users.noreply.github.com> Co-authored-by: Keya Loding Co-authored-by: Hajin Park Co-authored-by: Elise Davis Co-authored-by: gqcpm <63070177+gqcpm@users.noreply.github.com> Co-authored-by: Andrew Park Co-authored-by: roomrys Co-authored-by: MweinbergUmass <143860933+MweinbergUmass@users.noreply.github.com> Co-authored-by: Max Weinberg Co-authored-by: DivyaSesh <64513125+gitttt-1234@users.noreply.github.com> Co-authored-by: Felipe Parodi <53239931+felipe-parodi@users.noreply.github.com> Co-authored-by: croblesMed --- .conda/README.md | 4 +- .conda/condarc.yaml | 1 + .conda/meta.yaml | 32 +- .conda_mac/build.sh | 1 - .conda_mac/condarc.yaml | 1 - .conda_mac/meta.yaml | 28 +- .github/ISSUE_TEMPLATE/bug_report.md | 16 +- .../workflows/archive/comment-template.yml | 71 + .github/workflows/archive/comment.yml | 24 + .github/workflows/build.yml | 121 +- .github/workflows/build_conda_ci.yml | 179 +++ .github/workflows/build_manual.yml | 213 ++- .../{build_ci.yml => build_pypi_ci.yml} | 74 +- .github/workflows/ci.yml | 81 +- .github/workflows/website.yml | 24 +- AUTHORS | 2 + README.rst | 4 +- dev_requirements.txt | 7 +- docs/_static/bonsai-connection.jpg | Bin 0 -> 113805 bytes docs/_static/bonsai-filecapture.jpg | Bin 0 -> 68990 bytes docs/_static/bonsai-predictcentroids.jpg | Bin 0 -> 76547 bytes docs/_static/bonsai-predictposeidentities.jpg | Bin 0 -> 85845 bytes docs/_static/bonsai-predictposes.jpg | Bin 0 -> 79334 bytes docs/_static/bonsai-workflow.jpg | Bin 0 -> 33481 bytes docs/_static/css/tabs.css | 91 ++ docs/conf.py | 16 +- docs/guides/bonsai.md | 75 ++ docs/guides/cli.md | 27 +- docs/guides/gui.md | 2 +- docs/guides/index.md | 5 + docs/guides/proofreading.md | 2 + docs/installation.md | 424 +++--- ..._and_inference_on_an_example_dataset.ipynb | 4 +- environment.yml | 13 +- environment_mac.yml | 12 +- environment_no_cuda.yml | 14 +- pypi_requirements.txt | 9 +- requirements.txt | 6 +- sleap/config/frame_range_form.yaml | 13 + sleap/config/labeled_clip_form.yaml | 4 + sleap/config/pipeline_form.yaml | 70 +- sleap/config/suggestions.yaml | 9 +- sleap/config/training_editor_form.yaml | 9 +- sleap/gui/app.py | 141 +- sleap/gui/commands.py | 445 +++++-- sleap/gui/dataviews.py | 53 +- sleap/gui/dialogs/export_clip.py | 8 +- sleap/gui/dialogs/filedialog.py | 3 +- sleap/gui/dialogs/frame_range.py | 42 + sleap/gui/dialogs/metrics.py | 3 +- sleap/gui/learning/dialog.py | 88 +- sleap/gui/learning/runners.py | 53 +- sleap/gui/overlays/base.py | 1 - sleap/gui/overlays/tracks.py | 23 +- sleap/gui/suggestions.py | 52 + sleap/gui/utils.py | 28 + sleap/gui/widgets/docks.py | 10 +- sleap/gui/widgets/monitor.py | 1176 ++++++++++++----- sleap/gui/widgets/mpl.py | 5 +- sleap/gui/widgets/training_monitor.py | 566 -------- sleap/gui/widgets/video.py | 143 +- sleap/info/feature_suggestions.py | 2 +- sleap/info/metrics.py | 181 +-- sleap/info/summary.py | 44 +- sleap/instance.py | 239 +++- sleap/io/asyncvideo.py | 218 --- sleap/io/convert.py | 22 +- sleap/io/dataset.py | 13 + sleap/io/format/coco.py | 3 + sleap/io/format/deeplabcut.py | 52 +- sleap/io/format/hdf5.py | 51 +- sleap/io/format/sleap_analysis.py | 2 +- sleap/io/video.py | 46 +- sleap/io/videowriter.py | 71 +- sleap/io/visuals.py | 34 +- sleap/nn/config/outputs.py | 6 +- sleap/nn/data/augmentation.py | 89 +- sleap/nn/data/pipelines.py | 16 +- sleap/nn/data/providers.py | 4 +- sleap/nn/inference.py | 411 ++++-- sleap/nn/peak_finding.py | 2 +- sleap/nn/system.py | 26 +- sleap/nn/tracker/components.py | 110 +- sleap/nn/tracking.py | 114 +- sleap/nn/training.py | 31 +- sleap/prefs.py | 12 +- sleap/skeleton.py | 528 +++++++- .../training_profiles/baseline.centroid.json | 1 + .../baseline_large_rf.bottomup.json | 1 + .../baseline_large_rf.single.json | 1 + .../baseline_large_rf.topdown.json | 1 + .../baseline_medium_rf.bottomup.json | 1 + .../baseline_medium_rf.single.json | 1 + .../baseline_medium_rf.topdown.json | 1 + .../pretrained.bottomup.json | 1 + .../pretrained.centroid.json | 1 + .../training_profiles/pretrained.single.json | 1 + .../training_profiles/pretrained.topdown.json | 1 + sleap/util.py | 48 +- sleap/version.py | 3 +- .../labeled-data/video/CollectedData_LM.csv | 14 +- .../labeled-data/video/maudlc_testdata.csv | 8 + .../labeled-data/video/maudlc_testdata_v2.csv | 8 + tests/data/dlc/madlc_230_config.yaml | 8 +- ..._robot.000_small_robot_3_frame.analysis.h5 | Bin 0 -> 26408 bytes .../initial_config.json | 1 + .../training_config.json | 1 + .../initial_config.json | 2 +- .../training_config.json | 2 +- .../initial_config.json | 1 + .../training_config.json | 1 + .../initial_config.json | 1 + .../training_config.json | 1 + .../initial_config.json | 1 + .../training_config.json | 1 + .../initial_config.json | 1 + .../training_config.json | 1 + .../fly_skeleton_legs_pystate_dict.json | 1 + tests/data/tracks/clip.predictions.slp | Bin 0 -> 396184 bytes tests/fixtures/datasets.py | 21 + tests/fixtures/skeletons.py | 15 +- tests/fixtures/videos.py | 9 + tests/gui/learning/test_dialog.py | 20 + tests/gui/test_app.py | 8 + tests/gui/test_commands.py | 126 +- tests/gui/test_dialogs.py | 1 - tests/gui/test_monitor.py | 23 +- tests/gui/test_suggestions.py | 14 + tests/gui/test_video_player.py | 44 +- tests/gui/widgets/test_docks.py | 36 +- tests/info/test_metrics.py | 55 + tests/info/test_summary.py | 15 +- tests/io/test_asyncvideo.py | 31 - tests/io/test_convert.py | 4 +- tests/io/test_dataset.py | 46 +- tests/io/test_formats.py | 86 +- tests/io/test_videowriter.py | 63 +- tests/io/test_visuals.py | 41 + tests/nn/architectures/test_common.py | 2 +- .../nn/architectures/test_encoder_decoder.py | 2 +- tests/nn/architectures/test_hourglass.py | 2 +- tests/nn/architectures/test_leap.py | 2 +- .../architectures/test_pretrained_encoders.py | 2 +- tests/nn/architectures/test_resnet.py | 2 +- tests/nn/architectures/test_unet.py | 2 +- tests/nn/config/test_config_utils.py | 2 +- tests/nn/data/test_augmentation.py | 114 +- tests/nn/data/test_data_training.py | 2 +- tests/nn/data/test_edge_maps.py | 2 +- tests/nn/data/test_identity.py | 2 +- tests/nn/data/test_instance_centroids.py | 2 +- tests/nn/data/test_instance_cropping.py | 2 +- tests/nn/data/test_normalization.py | 2 +- tests/nn/data/test_offset_regression.py | 2 +- tests/nn/data/test_pipelines.py | 2 +- tests/nn/data/test_providers.py | 2 +- tests/nn/data/test_resizing.py | 6 +- tests/nn/data/test_utils.py | 2 +- tests/nn/test_evals.py | 2 +- tests/nn/test_heads.py | 2 +- tests/nn/test_inference.py | 507 ++++++- tests/nn/test_inference_identity.py | 2 +- tests/nn/test_model.py | 2 +- tests/nn/test_nn_utils.py | 2 +- tests/nn/test_paf_grouping.py | 2 +- tests/nn/test_peak_finding.py | 2 +- tests/nn/test_system.py | 24 +- tests/nn/test_tracker_components.py | 83 +- tests/nn/test_tracking_integration.py | 4 +- tests/nn/test_training.py | 68 +- tests/test_instance.py | 231 +++- tests/test_skeleton.py | 98 +- tests/test_util.py | 8 - 173 files changed, 6329 insertions(+), 2492 deletions(-) create mode 100644 .github/workflows/archive/comment-template.yml create mode 100644 .github/workflows/archive/comment.yml create mode 100644 .github/workflows/build_conda_ci.yml rename .github/workflows/{build_ci.yml => build_pypi_ci.yml} (71%) create mode 100644 docs/_static/bonsai-connection.jpg create mode 100644 docs/_static/bonsai-filecapture.jpg create mode 100644 docs/_static/bonsai-predictcentroids.jpg create mode 100644 docs/_static/bonsai-predictposeidentities.jpg create mode 100644 docs/_static/bonsai-predictposes.jpg create mode 100644 docs/_static/bonsai-workflow.jpg create mode 100644 docs/_static/css/tabs.css create mode 100644 docs/guides/bonsai.md create mode 100644 sleap/config/frame_range_form.yaml create mode 100644 sleap/gui/dialogs/frame_range.py create mode 100644 sleap/gui/utils.py delete mode 100644 sleap/gui/widgets/training_monitor.py delete mode 100644 sleap/io/asyncvideo.py create mode 100644 tests/data/dlc/labeled-data/video/maudlc_testdata.csv create mode 100644 tests/data/dlc/labeled-data/video/maudlc_testdata_v2.csv create mode 100644 tests/data/hdf5_format_v1/small_robot.000_small_robot_3_frame.analysis.h5 create mode 100644 tests/data/skeleton/fly_skeleton_legs_pystate_dict.json create mode 100644 tests/data/tracks/clip.predictions.slp create mode 100644 tests/info/test_metrics.py delete mode 100644 tests/io/test_asyncvideo.py diff --git a/.conda/README.md b/.conda/README.md index 71a49d7f1..65fadd36e 100644 --- a/.conda/README.md +++ b/.conda/README.md @@ -3,7 +3,7 @@ This folder defines the conda package build for Linux and Windows. There are run To build, first go to the base repo directory and install the build environment: ``` -mamba env create -f environment_build.yml -n sleap_build && conda activate sleap_build +conda env create -f environment_build.yml -n sleap_build && conda activate sleap_build ``` And finally, run the build command pointing to this directory: @@ -15,7 +15,7 @@ conda build .conda --output-folder build -c conda-forge -c nvidia -c https://con To install the local package: ``` -mamba create -n sleap_0 -c conda-forge -c nvidia -c ./build -c https://conda.anaconda.org/sleap/ -c anaconda sleap=x.x.x +conda create -n sleap_0 -c conda-forge -c nvidia -c ./build -c https://conda.anaconda.org/sleap/ -c anaconda sleap=x.x.x ``` replacing x.x.x with the version of SLEAP that you just built. diff --git a/.conda/condarc.yaml b/.conda/condarc.yaml index f9ac6efbe..c5fbc2d96 100644 --- a/.conda/condarc.yaml +++ b/.conda/condarc.yaml @@ -1,5 +1,6 @@ channels: - conda-forge - nvidia + - https://conda.anaconda.org/sleap/label/dev - sleap - anaconda diff --git a/.conda/meta.yaml b/.conda/meta.yaml index caffe9fcb..c1781a3ee 100644 --- a/.conda/meta.yaml +++ b/.conda/meta.yaml @@ -16,7 +16,7 @@ source: path: ../ build: - number: 1 + number: 0 requirements: host: @@ -32,12 +32,12 @@ requirements: # unnecessary pypi packages are installed via the build script (bld.bat, build.sh) - conda-forge::attrs ==21.4.0 - conda-forge::cattrs ==1.1.1 - - conda-forge::h5py ==3.1 # [not win] - - conda-forge::imgaug ==0.4.0 + - conda-forge::h5py ==3.7.0 + - conda-forge::imageio-ffmpeg # Required for imageio to read/write videos with ffmpeg - conda-forge::jsmin - conda-forge::jsonpickle ==1.2 - conda-forge::networkx - - conda-forge::opencv + - conda-forge::opencv <4.9.0 - conda-forge::pandas - conda-forge::pillow >=8.3.2 - conda-forge::psutil @@ -53,20 +53,24 @@ requirements: - conda-forge::scikit-learn ==1.0 - conda-forge::scikit-video - conda-forge::seaborn + - conda-forge::qudida + - conda-forge::albumentations + - conda-forge::ndx-pose <0.2.0 + - conda-forge::importlib-metadata ==4.11.4 run: - conda-forge::python ==3.7.12 # Run into _MAX_WINDOWS_WORKERS not found if < - conda-forge::attrs ==21.4.0 - conda-forge::cattrs ==1.1.1 + - conda-forge::imageio-ffmpeg # Required for imageio to read/write videos with ffmpeg - conda-forge::cudatoolkit ==11.3.1 - conda-forge::cudnn=8.2.1 - nvidia::cuda-nvcc=11.3 - - conda-forge::h5py ==3.1 # [not win] - - conda-forge::imgaug ==0.4.0 + - conda-forge::h5py ==3.7.0 - conda-forge::jsmin - conda-forge::jsonpickle ==1.2 - conda-forge::networkx - numpy >=1.19.5,<1.23.0 # Linux likes anaconda, windows likes conda-forge - - conda-forge::opencv + - conda-forge::opencv <4.9.0 - conda-forge::pandas - conda-forge::pillow >=8.3.2 - conda-forge::psutil @@ -82,9 +86,15 @@ requirements: - conda-forge::scikit-learn ==1.0 - conda-forge::scikit-video - conda-forge::seaborn - - sleap::tensorflow >=2.6.3,<2.11 # No windows GPU support for >2.10, sleap channel has 2.6.3 + - sleap/label/dev::tensorflow ==2.7.0 # TODO: Switch to main label when updated - conda-forge::tensorflow-hub <0.14.0 # Causes pynwb conflicts on linux GH-1446 + - conda-forge::qudida + - conda-forge::albumentations + - conda-forge::ndx-pose <0.2.0 + - conda-forge::importlib-metadata ==4.11.4 -test: - imports: - - sleap \ No newline at end of file +# This no longer works so we have moved it to the build workflow +# https://github.com/talmolab/sleap/pull/1744 +# test: +# imports: +# - sleap \ No newline at end of file diff --git a/.conda_mac/build.sh b/.conda_mac/build.sh index 2036035f6..a68193560 100644 --- a/.conda_mac/build.sh +++ b/.conda_mac/build.sh @@ -2,7 +2,6 @@ # Install anything that didn't get conda installed via pip. # We need to turn pip index back on because Anaconda turns it off for some reason. - export PIP_NO_INDEX=False export PIP_NO_DEPENDENCIES=False export PIP_IGNORE_INSTALLED=False diff --git a/.conda_mac/condarc.yaml b/.conda_mac/condarc.yaml index df2727c74..c1be41bf1 100644 --- a/.conda_mac/condarc.yaml +++ b/.conda_mac/condarc.yaml @@ -1,4 +1,3 @@ -# This file is not used at the moment, but when github actions can be used to build the package, it needs to be listed. # https://github.com/github/roadmap/issues/528 channels: diff --git a/.conda_mac/meta.yaml b/.conda_mac/meta.yaml index 7496f2057..8f773badf 100644 --- a/.conda_mac/meta.yaml +++ b/.conda_mac/meta.yaml @@ -16,14 +16,14 @@ about: summary: {{ data.get('description') }} build: - number: 1 + number: 0 source: path: ../ requirements: host: - - conda-forge::python ~=3.9 + - conda-forge::python >=3.9.0, <3.10.0 - anaconda::numpy >=1.19.5,<1.23.0 - conda-forge::setuptools - conda-forge::packaging @@ -34,11 +34,11 @@ requirements: - conda-forge::attrs >=21.2.0 - conda-forge::cattrs ==1.1.1 - conda-forge::h5py - - conda-forge::imgaug ==0.4.0 + - conda-forge::imageio-ffmpeg # Required for imageio to read/write videos with ffmpeg - conda-forge::jsmin - conda-forge::jsonpickle ==1.2 - conda-forge::keras <2.10.0,>=2.9.0rc0 # Required by tensorflow-macos - - conda-forge::networkx + - conda-forge::networkx <3.3 - conda-forge::opencv - conda-forge::pandas - conda-forge::pillow @@ -55,17 +55,20 @@ requirements: - conda-forge::scikit-learn ==1.0 - conda-forge::scikit-video - conda-forge::seaborn + - conda-forge::qudida + - conda-forge::albumentations + - conda-forge::ndx-pose <0.2.0 run: - - conda-forge::python ~=3.9 + - conda-forge::python >=3.9.0, <3.10.0 - conda-forge::attrs >=21.2.0 - conda-forge::cattrs ==1.1.1 - conda-forge::h5py - - conda-forge::imgaug ==0.4.0 + - conda-forge::imageio-ffmpeg # Required for imageio to read/write videos with ffmpeg - conda-forge::jsmin - conda-forge::jsonpickle ==1.2 - conda-forge::keras <2.10.0,>=2.9.0rc0 # Required by tensorflow-macos - - conda-forge::networkx + - conda-forge::networkx <3.3 - anaconda::numpy >=1.19.5,<1.23.0 - conda-forge::opencv - conda-forge::pandas @@ -83,8 +86,11 @@ requirements: - conda-forge::scikit-learn ==1.0 - conda-forge::scikit-video - conda-forge::seaborn - - conda-forge::tensorflow-hub + # - conda-forge::tensorflow-hub # pulls in tensorflow cpu from conda-forge + - conda-forge::qudida + - conda-forge::albumentations + - conda-forge::ndx-pose <0.2.0 -test: - imports: - - sleap +# test: +# imports: +# - sleap diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md index 8c95f28dc..6a92c2e3b 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.md +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -25,15 +25,15 @@ Tell us a little about the system you're using. Please include information about how you installed. --> -- OS: +- OS: -- Version(s): - -- SLEAP installation method (listed [here](https://sleap.ai/installation.html#)): - - [ ] [Conda from package](https://sleap.ai/installation.html#conda-package) - - [ ] [Conda from source](https://sleap.ai/installation.html#conda-from-source) - - [ ] [pip package](https://sleap.ai/installation.html#pip-package) - - [ ] [Apple Silicon Macs](https://sleap.ai/installation.html#apple-silicon-macs) +- Version(s): + +- SLEAP installation method (listed [here](https://sleap.ai/installation.html#)): + - [ ] [Conda from package](https://sleap.ai/installation.html#conda-package) + - [ ] [Conda from source](https://sleap.ai/installation.html#conda-from-source) + - [ ] [pip package](https://sleap.ai/installation.html#pip-package) + - [ ] [Apple Silicon Macs](https://sleap.ai/installation.html#apple-silicon-macs)
Environment packages diff --git a/.github/workflows/archive/comment-template.yml b/.github/workflows/archive/comment-template.yml new file mode 100644 index 000000000..3bef84531 --- /dev/null +++ b/.github/workflows/archive/comment-template.yml @@ -0,0 +1,71 @@ +name: Reusable Comment Workflow + +on: + workflow_call: + inputs: + subject_id: + required: true + type: string + body_prefix: + required: true + type: string + comment_type: + required: true + type: string + +jobs: + comment: + runs-on: ubuntu-latest + steps: + - name: Post a comment + uses: actions/github-script@v6 + with: + script: | + const { owner, repo } = context.repo; + const subject_id = '${{ inputs.subject_id }}'; + const comment_type = '${{ inputs.comment_type }}'; + const baseBody = ` + We appreciate your input and will review it soon. + + > [!WARNING] + > A friendly reminder that this is a public forum. Please be cautious when clicking links, downloading files, or running scripts posted by others. + > + > - Always verify the credibility of links and code. + > - Avoid running scripts or installing files from untrusted sources. + > - If you're unsure, ask for clarification before proceeding. + + Stay safe and happy SLEAPing! + + Best regards, + The Team + `; + const body = `${{ inputs.body_prefix }}\n\n${baseBody}`; + + const mutation = comment_type === 'discussion' + ? ` + mutation($discussionId: ID!, $body: String!) { + addDiscussionComment(input: {discussionId: $discussionId, body: $body}) { + comment { + id + } + } + } + ` + : ` + mutation($issueId: ID!, $body: String!) { + addComment(input: {subjectId: $issueId, body: $body}) { + commentEdge { + node { + id + body + } + } + } + } + `; + + const variables = comment_type === 'discussion' + ? { discussionId: subject_id, body: body.trim() } + : { issueId: subject_id, body: body.trim() }; + + await github.graphql(mutation, variables); diff --git a/.github/workflows/archive/comment.yml b/.github/workflows/archive/comment.yml new file mode 100644 index 000000000..a24df018f --- /dev/null +++ b/.github/workflows/archive/comment.yml @@ -0,0 +1,24 @@ +name: Comment on New Discussions and Issues + +on: + discussion: + types: [created] + issues: + types: [opened] + +jobs: + comment_on_discussion: + if: github.event_name == 'discussion' + uses: ./.github/workflows/comment-template.yml + with: + subject_id: ${{ github.event.discussion.node_id }} + body_prefix: "Thank you for starting a new discussion!" + comment_type: "discussion" + + comment_on_issue: + if: github.event_name == 'issues' + uses: ./.github/workflows/comment-template.yml + with: + subject_id: ${{ github.event.issue.node_id }} + body_prefix: "Thank you for opening a new issue!" + comment_type: "issue" diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 02bc8798b..74203245c 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -13,39 +13,37 @@ jobs: strategy: fail-fast: false matrix: - os: ["ubuntu-22.04", "windows-2022", "macos-latest"] + os: ["ubuntu-22.04", "windows-2022", "macos-14"] # https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#jobsjob_idstrategymatrixinclude include: # Use this condarc as default - condarc: .conda/condarc.yaml + - pyver: "3.7" # Use special condarc if macos - - os: "macos-latest" + - os: "macos-14" condarc: .conda_mac/condarc.yaml + pyver: "3.9" steps: # Setup - - uses: actions/checkout@v2 - - name: Cache conda - uses: actions/cache@v1 - env: - # Increase this value to reset cache if environment_build.yml has not changed - CACHE_NUMBER: 0 - with: - path: ~/conda_pkgs_dir - key: ${{ runner.os }}-conda-${{ env.CACHE_NUMBER }}-${{ hashFiles('environment_build.yml', 'requirements.txt') }} + - name: Checkout + uses: actions/checkout@v4 + - name: Setup Miniconda - # https://github.com/conda-incubator/setup-miniconda - uses: conda-incubator/setup-miniconda@v2.0.1 + uses: conda-incubator/setup-miniconda@v3.0.3 with: - python-version: 3.7 - use-only-tar-bz2: true # IMPORTANT: This needs to be set for caching to work properly! - environment-file: environment_build.yml + miniforge-version: latest condarc-file: ${{ matrix.condarc }} + python-version: ${{ matrix.pyver }} + environment-file: environment_build.yml activate-environment: sleap_ci + conda-solver: "libmamba" + - name: Print environment info shell: bash -l {0} run: | which python conda info + conda list # Build pip wheel (Ubuntu) - name: Build pip wheel (Ubuntu) @@ -69,21 +67,92 @@ jobs: shell: bash -l {0} run: | conda build .conda --output-folder build + echo "BUILD_PATH=$(pwd)/build" >> "$GITHUB_ENV" - # Build conda package (Windows) + # Build conda package (Windows) - name: Build conda package (Windows) if: matrix.os == 'windows-2022' shell: powershell run: | conda build .conda --output-folder build + echo "BUILD_PATH=\$(pwd)\build" >> "$env:GITHUB_ENV" # Build conda package (Mac) - name: Build conda package (Mac) - if: matrix.os == 'macos-latest' + if: matrix.os == 'macos-14' shell: bash -l {0} run: | conda build .conda_mac --output-folder build + echo "BUILD_PATH=$(pwd)/build" >> "$GITHUB_ENV" + + # Test built conda package (Ubuntu and Windows) + - name: Test built conda package (Ubuntu and Windows) + if: matrix.os != 'macos-14' + shell: bash -l {0} + run: | + echo "Current build path: $BUILD_PATH" + conda deactivate + + echo "Python executable before activating environment:" + which python + echo "Python version before activating environment:" + python --version + echo "Conda info before activating environment:" + conda info + + echo "Creating and testing conda environment with sleap package..." + conda create -y -n sleap_test -c file://$BUILD_PATH -c sleap/label/dev -c conda-forge -c nvidia -c anaconda sleap + conda activate sleap_test + echo "Python executable after activating sleap_test environment:" + which python + echo "Python version after activating sleap_test environment:" + python --version + echo "Conda info after activating sleap_test environment:" + conda info + echo "List of installed conda packages in the sleap_test environment:" + conda list + echo "List of installed pip packages in the sleap_test environment:" + pip list + + echo "Testing sleap package installation..." + sleap_version=$(python -c "import sleap; print(sleap.__version__)") + echo "Test completed using sleap version: $sleap_version" + + # Test built conda package (Mac) + - name: Test built conda package (Mac) + if: matrix.os == 'macos-14' + shell: bash -l {0} + run: | + echo "Current build path: $BUILD_PATH" + conda deactivate + + echo "Python executable before activating environment:" + which python + echo "Python version before activating environment:" + python --version + echo "Conda info before activating environment:" + conda info + + echo "Creating and testing conda environment with sleap package..." + conda create -y -n sleap_test -c file://$BUILD_PATH -c conda-forge -c anaconda sleap + conda activate sleap_test + + echo "Python executable after activating sleap_test environment:" + which python + echo "Python version after activating sleap_test environment:" + python --version + echo "Conda info after activating sleap_test environment:" + conda info + echo "List of installed conda packages in the sleap_test environment:" + conda list + echo "List of installed pip packages in the sleap_test environment:" + pip list + + echo "Testing sleap package installation..." + sleap_version=$(python -c "import sleap; print(sleap.__version__)") + echo "Test completed using sleap version: $sleap_version" + # Login to conda (Ubuntu) - name: Login to Anaconda (Ubuntu) if: matrix.os == 'ubuntu-22.04' @@ -95,7 +164,7 @@ jobs: # Login to conda (Windows) - name: Login to Anaconda (Windows) - if: matrix.os == 'windows-2019' + if: matrix.os == 'windows-2022' env: ANACONDA_LOGIN: ${{ secrets.ANACONDA_LOGIN }} shell: powershell @@ -104,7 +173,7 @@ jobs: # Login to conda (Mac) - name: Login to Anaconda (Mac) - if: matrix.os == 'macos-latest' + if: matrix.os == 'macos-14' env: ANACONDA_LOGIN: ${{ secrets.ANACONDA_LOGIN }} shell: bash -l {0} @@ -113,12 +182,12 @@ jobs: # Upload conda package (Windows) - name: Upload conda package (Windows/main) - if: matrix.os == 'windows-2019' && !github.event.release.prerelease + if: matrix.os == 'windows-2022' && !github.event.release.prerelease shell: powershell run: | anaconda -v upload "build\win-64\*.tar.bz2" - name: Upload conda package (Windows/dev) - if: matrix.os == 'windows-2019' && github.event.release.prerelease + if: matrix.os == 'windows-2022' && github.event.release.prerelease shell: powershell run: | anaconda -v upload "build\win-64\*.tar.bz2" --label dev @@ -137,15 +206,15 @@ jobs: # Upload conda package (Mac) - name: Upload conda package (Mac/main) - if: matrix.os == 'macos-latest' && !github.event.release.prerelease + if: matrix.os == 'macos-14' && !github.event.release.prerelease shell: bash -l {0} run: | - anaconda -v upload build/osx-64/*.tar.bz2 --label dev + anaconda -v upload build/osx-arm64/*.tar.bz2 --label dev - name: Upload conda package (Mac/dev) - if: matrix.os == 'macos-latest' && github.event.release.prerelease + if: matrix.os == 'macos-14' && github.event.release.prerelease shell: bash -l {0} run: | - anaconda -v upload build/osx-64/*.tar.bz2 --label dev + anaconda -v upload build/osx-arm64/*.tar.bz2 --label dev # Logout - name: Logout from Anaconda diff --git a/.github/workflows/build_conda_ci.yml b/.github/workflows/build_conda_ci.yml new file mode 100644 index 000000000..3fd3d2b92 --- /dev/null +++ b/.github/workflows/build_conda_ci.yml @@ -0,0 +1,179 @@ +# Run tests using built conda packages. +name: Build Conda CI (no upload) + +# Run when changes to pip wheel +on: + push: + paths: + - ".conda/meta.yaml" + - ".conda_mac/meta.yaml" + - "setup.py" + - "requirements.txt" + - "dev_requirements.txt" + - "environment_build.yml" + - ".github/workflows/build_conda_ci.yml" # Run! + +# If RUN_BUILD_JOB is set to true, then RUN_ID will be overwritten to the current run id +env: + RUN_BUILD_JOB: true + RUN_ID: 10713717594 # Only used if RUN_BUILD_JOB is false (to dowload build artifact) + +jobs: + build: + name: Build package from push (${{ matrix.os }}) + runs-on: ${{ matrix.os }} + strategy: + fail-fast: false + matrix: + os: ["windows-2022", "ubuntu-22.04", "macos-14"] + # https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#jobsjob_idstrategymatrixinclude + include: + # Use these variables as defaults + - condarc: .conda/condarc.yaml + - conda-folder: .conda + - pyver: "3.10" + - build-prefix: win + - os: "ubuntu-22.04" + build-prefix: linux + # Use special condarc if macos + - os: "macos-14" + condarc: .conda_mac/condarc.yaml + conda-folder: .conda_mac + build-prefix: osx + + steps: + # Setup + - name: Checkout + if: env.RUN_BUILD_JOB == 'true' + uses: actions/checkout@v4 + + - name: Setup Miniconda + if: env.RUN_BUILD_JOB == 'true' + uses: conda-incubator/setup-miniconda@v3.0.4 + with: + miniforge-version: latest + condarc-file: ${{ matrix.condarc }} + python-version: ${{ matrix.pyver }} + environment-file: environment_build.yml + activate-environment: sleap_ci + conda-solver: "libmamba" + + - name: Print build environment info + if: env.RUN_BUILD_JOB == 'true' + shell: bash -l {0} + run: | + which python + conda list + pip freeze + + # Build conda package + - name: Build conda package + if: env.RUN_BUILD_JOB == 'true' + shell: bash -l {0} + run: | + conda build ${{ matrix.conda-folder }} --output-folder build + + # Upload artifact "tests" can use it + - name: Upload conda package artifact + if: env.RUN_BUILD_JOB == 'true' + uses: actions/upload-artifact@v4 + with: + name: sleap-build-${{ matrix.build-prefix }} + path: build # Upload entire build directory + retention-days: 1 + overwrite: true + + tests: + name: Run tests using package (${{ matrix.os }}) + runs-on: ${{ matrix.os }} + needs: build # Ensure the build job has completed before starting this job. + strategy: + fail-fast: false + matrix: + os: ["windows-2022", "ubuntu-22.04", "macos-14"] + # https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#jobsjob_idstrategymatrixinclude + include: + # Default values + - build-prefix: win + - build-suffix: 64 + - test_args: pytest --durations=-1 tests/ + - condarc: .conda/condarc.yaml + - pyver: "3.10" + - conda-channels: -c conda-forge -c nvidia -c anaconda + # Ubuntu specific values + - os: ubuntu-22.04 + build-prefix: linux + # Otherwise core dumped in github actions + test_args: | + sudo apt install xvfb libxkbcommon-x11-0 libxcb-icccm4 libxcb-image0 libxcb-keysyms1 libxcb-randr0 libxcb-render-util0 libxcb-xinerama0 libxcb-xfixes0 + sudo Xvfb :1 -screen 0 1024x768x24 > $GITHUB_ENV + + # https://github.com/actions/download-artifact?tab=readme-ov-file#usage + - name: Download conda package artifact + uses: actions/download-artifact@v4 + id: download + with: + name: sleap-build-${{ matrix.build-prefix }} + path: build + run-id: ${{ env.RUN_ID }} + github-token: ${{ secrets.GITHUB_TOKEN }} + + - name: List items in current directory + run: | + ls . + ls -R build + + - name: Setup Miniconda + uses: conda-incubator/setup-miniconda@v3.0.4 + with: + miniforge-version: latest + condarc-file: ${{ matrix.condarc }} + python-version: ${{ matrix.pyver }} + conda-solver: "libmamba" + + - name: Create conda environment + shell: bash -l {0} + run: conda create sleap -y -n sleap_ci -c ./build ${{ matrix.conda-channels }} + + - name: Install packages for testing + shell: bash -l {0} + run: | + conda activate sleap_ci + pip install -r "dev_requirements.txt" + + # Note: "conda activate" does not persist across steps + - name: Print environment info + shell: bash -l {0} + run: | + conda activate sleap_ci + conda info + conda list + pip freeze + + - name: Test package + shell: bash -l {0} + run: | + conda activate sleap_ci + ${{ matrix.test_args}} diff --git a/.github/workflows/build_manual.yml b/.github/workflows/build_manual.yml index ab689342d..7cba65d67 100644 --- a/.github/workflows/build_manual.yml +++ b/.github/workflows/build_manual.yml @@ -8,8 +8,10 @@ on: paths: - '.conda/meta.yaml' - '.conda_mac/meta.yaml' + - '.github/workflows/build_manual.yml' branches: - - develop + # - develop + - fakebranch jobs: build: @@ -18,39 +20,37 @@ jobs: strategy: fail-fast: false matrix: - os: ["ubuntu-22.04", "windows-2022", "macos-latest"] + os: ["ubuntu-22.04", "windows-2022", "macos-14"] # https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#jobsjob_idstrategymatrixinclude include: # Use this condarc as default - condarc: .conda/condarc.yaml + - pyver: "3.7" # Use special condarc if macos - - os: "macos-latest" + - os: "macos-14" condarc: .conda_mac/condarc.yaml + pyver: "3.9" steps: # Setup - - uses: actions/checkout@v2 - - name: Cache conda - uses: actions/cache@v1 - env: - # Increase this value to reset cache if environment_build.yml has not changed - CACHE_NUMBER: 0 - with: - path: ~/conda_pkgs_dir - key: ${{ runner.os }}-conda-${{ env.CACHE_NUMBER }}-${{ hashFiles('environment_build.yml', 'requirements.txt') }} + - name: Checkout + uses: actions/checkout@v4 + - name: Setup Miniconda - # https://github.com/conda-incubator/setup-miniconda - uses: conda-incubator/setup-miniconda@v2.0.1 + uses: conda-incubator/setup-miniconda@v3.0.3 with: - python-version: 3.7 - use-only-tar-bz2: true # IMPORTANT: This needs to be set for caching to work properly! - environment-file: environment_build.yml + miniforge-version: latest condarc-file: ${{ matrix.condarc }} + python-version: ${{ matrix.pyver }} + environment-file: environment_build.yml activate-environment: sleap_ci + conda-solver: "libmamba" + - name: Print environment info shell: bash -l {0} run: | which python conda info + conda list # Build pip wheel (Not Windows) - name: Build pip wheel (Not Windows) @@ -59,14 +59,14 @@ jobs: run: | python setup.py bdist_wheel - # Upload pip wheel (Ubuntu) - - name: Upload pip wheel (Ubuntu) - if: matrix.os == 'ubuntu-22.04' - env: - PYPI_TOKEN: ${{ secrets.PYPI_TOKEN }} - shell: bash -l {0} - run: | - twine upload -u __token__ -p "$PYPI_TOKEN" dist/* --non-interactive --skip-existing --disable-progress-bar + # # Upload pip wheel (Ubuntu) + # - name: Upload pip wheel (Ubuntu) + # if: matrix.os == 'ubuntu-22.04' + # env: + # PYPI_TOKEN: ${{ secrets.PYPI_TOKEN }} + # shell: bash -l {0} + # run: | + # twine upload -u __token__ -p "$PYPI_TOKEN" dist/* --non-interactive --skip-existing --disable-progress-bar # Build conda package (Ubuntu) - name: Build conda package (Ubuntu) @@ -74,70 +74,141 @@ jobs: shell: bash -l {0} run: | conda build .conda --output-folder build + echo "BUILD_PATH=$(pwd)/build" >> "$GITHUB_ENV" - # Build conda package (Windows) + # Build conda package (Windows) - name: Build conda package (Windows) if: matrix.os == 'windows-2022' shell: powershell run: | conda build .conda --output-folder build + echo "BUILD_PATH=\$(pwd)\build" >> "$env:GITHUB_ENV" # Build conda package (Mac) - name: Build conda package (Mac) - if: matrix.os == 'macos-latest' + if: matrix.os == 'macos-14' shell: bash -l {0} run: | conda build .conda_mac --output-folder build + echo "BUILD_PATH=$(pwd)/build" >> "$GITHUB_ENV" - # Login to conda (Ubuntu) - - name: Login to Anaconda (Ubuntu) - if: matrix.os == 'ubuntu-22.04' - env: - ANACONDA_LOGIN: ${{ secrets.ANACONDA_LOGIN }} + # Test built conda package (Ubuntu and Windows) + - name: Test built conda package (Ubuntu and Windows) + if: matrix.os != 'macos-14' shell: bash -l {0} run: | - yes 2>/dev/null | anaconda login --username sleap --password "$ANACONDA_LOGIN" || true - - # Login to conda (Windows) - - name: Login to Anaconda (Windows) - if: matrix.os == 'windows-2022' - env: - ANACONDA_LOGIN: ${{ secrets.ANACONDA_LOGIN }} - shell: powershell - run: | - echo "yes" | anaconda login --username sleap --password "$env:ANACONDA_LOGIN" - - # Login to conda (Mac) - - name: Login to Anaconda (Mac) - if: matrix.os == 'macos-latest' - env: - ANACONDA_LOGIN: ${{ secrets.ANACONDA_LOGIN }} + echo "Current build path: $BUILD_PATH" + conda deactivate + + echo "Python executable before activating environment:" + which python + echo "Python version before activating environment:" + python --version + echo "Conda info before activating environment:" + conda info + + echo "Creating and testing conda environment with sleap package..." + conda create -y -n sleap_test -c file://$BUILD_PATH -c sleap/label/dev -c conda-forge -c nvidia -c anaconda sleap + conda activate sleap_test + + echo "Python executable after activating sleap_test environment:" + which python + echo "Python version after activating sleap_test environment:" + python --version + echo "Conda info after activating sleap_test environment:" + conda info + echo "List of installed conda packages in the sleap_test environment:" + conda list + echo "List of installed pip packages in the sleap_test environment:" + pip list + + echo "Testing sleap package installation..." + sleap_version=$(python -c "import sleap; print(sleap.__version__)") + echo "Test completed using sleap version: $sleap_version" + + # Test built conda package (Mac) + - name: Test built conda package (Mac) + if: matrix.os == 'macos-14' shell: bash -l {0} run: | - yes 2>/dev/null | anaconda login --username sleap --password "$ANACONDA_LOGIN" || true + echo "Current build path: $BUILD_PATH" + conda deactivate + + echo "Python executable before activating environment:" + which python + echo "Python version before activating environment:" + python --version + echo "Conda info before activating environment:" + conda info + + echo "Creating and testing conda environment with sleap package..." + conda create -y -n sleap_test -c file://$BUILD_PATH -c conda-forge -c anaconda sleap + conda activate sleap_test + + echo "Python executable after activating sleap_test environment:" + which python + echo "Python version after activating sleap_test environment:" + python --version + echo "Conda info after activating sleap_test environment:" + conda info + echo "List of installed conda packages in the sleap_test environment:" + conda list + echo "List of installed pip packages in the sleap_test environment:" + pip list + + echo "Testing sleap package installation..." + sleap_version=$(python -c "import sleap; print(sleap.__version__)") + echo "Test completed using sleap version: $sleap_version" + + # # Login to conda (Ubuntu) + # - name: Login to Anaconda (Ubuntu) + # if: matrix.os == 'ubuntu-22.04' + # env: + # ANACONDA_LOGIN: ${{ secrets.ANACONDA_LOGIN }} + # shell: bash -l {0} + # run: | + # yes 2>/dev/null | anaconda login --username sleap --password "$ANACONDA_LOGIN" || true - # Upload conda package (Windows) - - name: Upload conda package (Windows/dev) - if: matrix.os == 'windows-2022' - shell: powershell - run: | - anaconda -v upload "build\win-64\*.tar.bz2" --label dev + # # Login to conda (Windows) + # - name: Login to Anaconda (Windows) + # if: matrix.os == 'windows-2022' + # env: + # ANACONDA_LOGIN: ${{ secrets.ANACONDA_LOGIN }} + # shell: powershell + # run: | + # echo "yes" | anaconda login --username sleap --password "$env:ANACONDA_LOGIN" - # Upload conda package (Ubuntu) - - name: Upload conda package (Ubuntu/dev) - if: matrix.os == 'ubuntu-22.04' - shell: bash -l {0} - run: | - anaconda -v upload build/linux-64/*.tar.bz2 --label dev + # # Login to conda (Mac) + # - name: Login to Anaconda (Mac) + # if: matrix.os == 'macos-14' + # env: + # ANACONDA_LOGIN: ${{ secrets.ANACONDA_LOGIN }} + # shell: bash -l {0} + # run: | + # yes 2>/dev/null | anaconda login --username sleap --password "$ANACONDA_LOGIN" || true - # Upload conda package (Mac) - - name: Upload conda package (Mac/dev) - if: matrix.os == 'macos-latest' - shell: bash -l {0} - run: | - anaconda -v upload build/osx-64/*.tar.bz2 --label dev + # # Upload conda package (Windows) + # - name: Upload conda package (Windows/dev) + # if: matrix.os == 'windows-2022' + # shell: powershell + # run: | + # anaconda -v upload "build\win-64\*.tar.bz2" --label dev - - name: Logout from Anaconda - shell: bash -l {0} - run: | - anaconda logout + # # Upload conda package (Ubuntu) + # - name: Upload conda package (Ubuntu/dev) + # if: matrix.os == 'ubuntu-22.04' + # shell: bash -l {0} + # run: | + # anaconda -v upload build/linux-64/*.tar.bz2 --label dev + + # # Upload conda package (Mac) + # - name: Upload conda package (Mac/dev) + # if: matrix.os == 'macos-14' + # shell: bash -l {0} + # run: | + # anaconda -v upload build/osx-arm64/*.tar.bz2 --label dev + + # - name: Logout from Anaconda + # shell: bash -l {0} + # run: | + # anaconda logout diff --git a/.github/workflows/build_ci.yml b/.github/workflows/build_pypi_ci.yml similarity index 71% rename from .github/workflows/build_ci.yml rename to .github/workflows/build_pypi_ci.yml index baf046295..68142b288 100644 --- a/.github/workflows/build_ci.yml +++ b/.github/workflows/build_pypi_ci.yml @@ -1,17 +1,17 @@ -# Run tests using built conda packages and wheels. -name: Build CI (no upload) +# Run tests using built wheels. +name: Build PyPI CI (no upload) # Run when changes to pip wheel on: push: paths: - - 'setup.py' - - 'requirements.txt' - - 'dev_requirements.txt' - - 'jupyter_requirements.txt' - - 'pypi_requirements.txt' - - 'environment_build.yml' - - '.github/workflows/build_ci.yml' + - "setup.py" + - "requirements.txt" + - "dev_requirements.txt" + - "jupyter_requirements.txt" + - "pypi_requirements.txt" + - "environment_build.yml" + - ".github/workflows/build_pypi_ci.yml" # Run! jobs: build: @@ -26,28 +26,21 @@ jobs: # Use this condarc as default - condarc: .conda/condarc.yaml - wheel_name: sleap-wheel-linux + - pyver: "3.7" steps: # Setup - - uses: actions/checkout@v2 + - name: Checkout + uses: actions/checkout@v4 - - name: Cache conda - uses: actions/cache@v1 - env: - # Increase this value to reset cache if environment_build.yml has not changed - CACHE_NUMBER: 0 + - name: Setup Miniconda + uses: conda-incubator/setup-miniconda@v3.0.3 with: - path: ~/conda_pkgs_dir - key: ${{ runner.os }}-conda-${{ env.CACHE_NUMBER }}-${{ hashFiles('environment_build.yml', 'pyproject.toml') }} - - - name: Setup Miniconda for Build - # https://github.com/conda-incubator/setup-miniconda - uses: conda-incubator/setup-miniconda@v2.0.1 - with: - python-version: 3.7 - use-only-tar-bz2: true # IMPORTANT: This needs to be set for caching to work properly! - environment-file: environment_build.yml + miniforge-version: latest condarc-file: ${{ matrix.condarc }} + python-version: ${{ matrix.pyver }} + environment-file: environment_build.yml activate-environment: sleap_ci + conda-solver: "libmamba" - name: Print build environment info shell: bash -l {0} @@ -61,10 +54,10 @@ jobs: shell: bash -l {0} run: | python setup.py bdist_wheel - + # Upload artifact "tests" can use it - name: Upload wheel artifact - uses: actions/upload-artifact@v3 + uses: actions/upload-artifact@v4 with: name: ${{ matrix.wheel_name }} path: dist/*.whl @@ -73,11 +66,12 @@ jobs: tests: name: Run tests using wheel (${{ matrix.os }}) runs-on: ${{ matrix.os }} - needs: build # Ensure the build job has completed before starting this job. + needs: build # Ensure the build job has completed before starting this job. strategy: fail-fast: false matrix: - os: ["ubuntu-22.04", "windows-2022", "macos-latest"] + os: ["ubuntu-22.04", "windows-2022"] + # os: ["ubuntu-22.04", "windows-2022", "macos-14"] # removing macos-14 for now since the setup-python action only support py>=3.10, which is breaking this CI. # https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#jobsjob_idstrategymatrixinclude include: # Default values @@ -89,9 +83,11 @@ jobs: pip install '$wheel_path'[dev] - test_args: pytest --durations=-1 tests/ - condarc: .conda/condarc.yaml + - pyver: "3.7" # Use special condarc if macos - - os: "macos-latest" + - os: "macos-14" condarc: .conda_mac/condarc.yaml + pyver: "3.10" # Ubuntu specific values - os: ubuntu-22.04 # Otherwise core dumped in github actions @@ -106,16 +102,16 @@ jobs: steps: - name: Checkout repo - uses: actions/checkout@v3 - - - name: Set up Python 3.7 - uses: actions/setup-python@v4 + uses: actions/checkout@v4 + + - name: Setup Python + uses: actions/setup-python@v5 with: - python-version: 3.7 - + python-version: ${{ matrix.pyver }} + # Download wheel - name: Download wheel artifact - uses: actions/download-artifact@v3 + uses: actions/download-artifact@v4 id: download with: name: ${{ matrix.wheel_name }} @@ -148,8 +144,8 @@ jobs: run: | which python pip freeze - + # Install and test the wheel - name: Test the built wheel run: | - ${{ matrix.test_args}} \ No newline at end of file + ${{ matrix.test_args}} diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index e1d193724..84b028fc3 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -11,45 +11,28 @@ on: - "environment_no_cuda.yml" - "requirements.txt" - "dev_requirements.txt" - push: - branches: - - master - - develop - paths: - - "sleap/**" - - "tests/**" - - ".github/workflows/ci.yml" - - "environment_no_cuda.yml" - - "requirements.txt" - - "dev_requirements.txt" + # push: + # branches: + # - main + # - develop + # paths: + # - "sleap/**" + # - "tests/**" + # - ".github/workflows/ci.yml" + # - "environment_no_cuda.yml" + # - "requirements.txt" + # - "dev_requirements.txt" jobs: - type_check: - name: Type Check - runs-on: "ubuntu-22.04" - steps: - - name: Checkout repo - uses: actions/checkout@v3 - - name: Set up Python 3.7 - uses: actions/setup-python@v4 - with: - python-version: 3.7 - - name: Install Dependencies - run: | - pip install mypy - - name: Run MyPy - # TODO: remove this once all MyPy errors get fixed - continue-on-error: true - run: | - mypy --follow-imports=skip --ignore-missing-imports sleap tests + # Lint lint: name: Lint runs-on: "ubuntu-22.04" steps: - name: Checkout repo - uses: actions/checkout@v3 + uses: actions/checkout@v4 - name: Set up Python 3.7 - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: python-version: 3.7 - name: Install Dependencies @@ -59,58 +42,52 @@ jobs: - name: Run Black run: | black --check sleap tests + + # Tests tests: name: Tests (${{ matrix.os }}) runs-on: ${{ matrix.os }} strategy: fail-fast: false matrix: - os: ["ubuntu-22.04", "windows-2022", "macos-latest"] + os: ["ubuntu-22.04", "windows-2022", "macos-14"] include: # Default values - env_file: environment_no_cuda.yml - - test_args: --durations=-1 tests/ # Mac specific values - - os: macos-latest + - os: macos-14 env_file: environment_mac.yml - # Ubuntu specific values - - os: ubuntu-22.04 - test_args: --cov=sleap --cov-report=xml --durations=-1 tests/ + steps: - name: Checkout repo - uses: actions/checkout@v3 + uses: actions/checkout@v4 - - name: Setup Micromamba - # https://github.com/mamba-org/setup-micromamba - uses: mamba-org/setup-micromamba@v1 + - name: Setup Conda + uses: conda-incubator/setup-miniconda@v3.0.3 with: - micromamba-version: '1.4.6-0' + miniforge-version: latest + conda-solver: "libmamba" environment-file: ${{ matrix.env_file }} - environment-name: sleap_ci - init-shell: >- - bash - powershell - post-cleanup: all + activate-environment: sleap_ci # Print environment info - name: Print environment info shell: bash -l {0} run: | which python - micromamba info - micromamba list + conda info + conda list pip freeze # Test environment - name: Test with pytest shell: bash -l {0} run: | - pytest ${{ matrix.test_args }} + pytest --cov=sleap --cov-report=xml --durations=-1 tests/ # Upload coverage - name: Upload coverage - uses: codecov/codecov-action@v1 - if: matrix.os == 'ubuntu-22.04' + uses: codecov/codecov-action@v4 with: token: ${{ secrets.CODECOV_TOKEN }} fail_ci_if_error: true diff --git a/.github/workflows/website.yml b/.github/workflows/website.yml index 7db6b4d74..36c1d6ad7 100644 --- a/.github/workflows/website.yml +++ b/.github/workflows/website.yml @@ -7,8 +7,8 @@ on: branches: # 'main' triggers updates to 'sleap.ai', all others to 'sleap.ai/develop' - main - - develop - - liezl/add-pip-extras + - develop # Run + - liezl/bump-to-1.4.1 paths: - "docs/**" - "README.rst" @@ -20,21 +20,13 @@ jobs: steps: # Setup - name: Checkout - uses: actions/checkout@v2 - - name: Cache conda - uses: actions/cache@v1 - env: - # Increase this value to reset cache if environment_build.yml has not changed - CACHE_NUMBER: 0 - with: - path: ~/conda_pkgs_dir - key: ${{ runner.os }}-conda-${{ env.CACHE_NUMBER }}-${{ hashFiles('environment_build.yml', 'requirements.txt') }} + uses: actions/checkout@v4 + - name: Setup Miniconda # https://github.com/conda-incubator/setup-miniconda - uses: conda-incubator/setup-miniconda@v2.0.1 + uses: conda-incubator/setup-miniconda@v3.0.3 with: python-version: 3.7 - use-only-tar-bz2: true # IMPORTANT: This needs to be set for caching to work properly! environment-file: environment_no_cuda.yml activate-environment: sleap_ci - name: Print environment info @@ -51,7 +43,7 @@ jobs: make html - name: Deploy (sleap.ai) - uses: peaceiris/actions-gh-pages@v3 + uses: peaceiris/actions-gh-pages@v4 if: github.ref == 'refs/heads/main' with: github_token: ${{ secrets.GITHUB_TOKEN }} @@ -60,10 +52,10 @@ jobs: keep_files: true - name: Deploy (test) - uses: peaceiris/actions-gh-pages@v3 + uses: peaceiris/actions-gh-pages@v4 if: github.ref != 'refs/heads/main' with: github_token: ${{ secrets.GITHUB_TOKEN }} publish_branch: gh-pages publish_dir: docs/build/html - destination_dir: develop \ No newline at end of file + destination_dir: develop diff --git a/AUTHORS b/AUTHORS index e6a78d2ba..11e40e839 100644 --- a/AUTHORS +++ b/AUTHORS @@ -11,3 +11,5 @@ John Smith Example Inc. Jeremy Delahanty The Salk Institute for Biological Studies + +Lili Karashchuk Allen Institute of Neural Dynamics diff --git a/README.rst b/README.rst index dbc5a7cac..f7a5acd6c 100644 --- a/README.rst +++ b/README.rst @@ -69,7 +69,7 @@ Quick install .. code-block:: bash - conda create -y -n sleap -c conda-forge -c nvidia -c sleap -c anaconda sleap + conda create -y -n sleap -c conda-forge -c nvidia -c sleap/label/dev -c sleap -c anaconda sleap `pip` **(any OS except Apple silicon)**: @@ -84,7 +84,7 @@ Learn to SLEAP -------------- - **Learn step-by-step**: `Tutorial `_ - **Learn more advanced usage**: `Guides `__ and `Notebooks `__ -- **Learn by watching**: `MIT CBMM Tutorial `_ +- **Learn by watching**: `ABL:AOC 2023 Workshop `_ and `MIT CBMM Tutorial `_ - **Learn by reading**: `Paper (Pereira et al., Nature Methods, 2022) `__ and `Review on behavioral quantification (Pereira et al., Nature Neuroscience, 2020) `_ - **Learn from others**: `Discussions on Github `_ diff --git a/dev_requirements.txt b/dev_requirements.txt index f7bb23643..709fb48fd 100644 --- a/dev_requirements.txt +++ b/dev_requirements.txt @@ -5,13 +5,14 @@ pytest-qt>=4.0.0 pytest-cov<=3.0.0 pytest-xvfb ipython -sphinx +sphinx>=5.0 # sphinxcontrib.applehelp extension needs at least Sphinx v5.0 # furo sphinx-book-theme sphinx-copybutton +sphinx-tabs nbformat==5.1.3 -myst-nb==0.13.2 -myst-parser==0.15.2 +myst-nb>=0.16.0 # sphinx>=5.0 needs myst-nb>=0.16.0 +myst-parser linkify-it-py sphinx-autobuild black==21.6b0 diff --git a/docs/_static/bonsai-connection.jpg b/docs/_static/bonsai-connection.jpg new file mode 100644 index 0000000000000000000000000000000000000000..32b725416e2ed32f12977cdf50c73b53ce9b3d9c GIT binary patch literal 113805 zcmeFZ2UJwgmp|A9MTwFXl$<0=R#J;dmZ;F=77@v`l5-r2$r&Uk z$*G&zK!@$`&QAC`vpchMcK^F)&ade@R8_xM@7`PWs&4pH;eO$k0kB*@WZ!^%{rlF&!`>PQGBK$qX*KS_Bc9V{RjDqg}_6PR^Kz)NSiugAX0SADP znt+I!0M`j%1po-H;i>(L;Q!-8K!~UDI?0Wjq-6LDYHtAu35bXYiHZKA8h^Dv{&xT| 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zo2TOXpco}?92FtFBcKu-4a5)P-y*~6$AX|@g`(v?AKc36K+_nRkXtmF8vS2F`sV!` zPZNJp^qzFx!6;4sfSAwLTC$7|U;K9Pt?XM6Gb^>Ii9@)+EIe8QxVKpA3% zAL`WE=;yu>s){wLi*wA>ov4fXDujQ~8U7A3*To#i@VJkk#<-?x@E&CiA`T`I5*pTN z*%*UuLrdZPW8C=g8c|{*G6>o!&Etqje!uEZ2_#d+65jc3x!=b5p7F&+-MJLdiqWyk z#C;DMFvYU$i(i;`hB4|wwIe&TuaB)}LRyly7{OEd(FfSCz5&CMUk^SGOS?7@G(Ktj z2=F-NZ4gh6#dk%Gyz8^K5Y1`u_lbx5EdL{jm12IJuh^Rw8KQe|kRrmom5;4g>jrX$ z0I;fr!5Jz;;LZCrFf?d$#!%^X%A?1yEr^$b zx?CBD?)J#&`24)7-q3d|Uu|opz2L&e;CkQTlgoK%UOauLINMx^Lw)_2(*1l$z&Ogl z%caD5l1FZAwMD`C(_0~WdeKZsdyW%>vU{GnKn39rKs#aMFk3B1s8`G?$P zEFRv!yYW2wX5P!+H8CC|FOq%;xPDB$q7XTmM9X<3J=5}t2r)SwXv~&>C`m1j{(9j1 z;4O>GrA*dysT7Va#$3DdEW0@`VC=-v#mJYiYXQC}yF3>d<%E*iqOkA`8(CQ-BeyYB z59jm%C6{ej8~6EkgL^KLQOX<;@WfEMg>xZKChFXwW+va1)Cyc+53;YJYN|%-(nac4 zTOMC$ym(>4dy8Z(~jpa@u30j^@>iwdq)*CvAl_gm~J&r?T ztR^E)d4accQxZ4t;^JXHvZXr7xcdElu7ji~r?~Go8O-HB_j2O9}v7iN104aHiJmeeo!PXzHtSpRPQsWi0P!QYeq%^qHq3z@wgn zsIc&MOh=wMb=oC-@5I=Dt0v9F8xm)2H5(84_e13X zxB;(hhUr{0jBy5C{o_Z|;Y4+X%RH!B4q13ond!(M&*v2=*~N3jZ74I_d8xMMBX>WA zyv*vU06?!~0f1~93Zhstf0{o=ig#VKfT&|S_PE~h&5bjw4fRP9Ax#WXbD!C-wqosvkD2Ltr^evn4O;D>q?Ui}z!Gglw zOKu?0jVsib$)fa&7aFO$b}qF>p{&Kq7RzR~Zpf46>=*mLikppQ=mexLQhv~4sBfvi zH1hE9F5g9rYPltgy^Ff9{I$K7w5%DO?Z%cG>m3K5K0O9hv)my+cD3kmZ;TWjO@sho zNtBad=W4`ZC&ngc2~O0k+^VOOAa)p|$ISFB3TR4j1vLQ-YM` below for more instructions. +````{tabs} + ```{group-tab} Windows and Linux + ```bash + conda create -y -n sleap -c conda-forge -c nvidia -c sleap/label/dev -c sleap -c anaconda sleap=1.4.1 + ``` + ``` + ```{group-tab} Mac OS + ```bash + conda create -y -n sleap -c conda-forge -c anaconda -c sleap sleap=1.4.1 + ``` + ``` +```` -The newest version of SLEAP can always be found in the [Releases page](https://github.com/talmolab/sleap/releases). +. For more in-depth installation instructions, see the [installation methods](installation-methods). The newest version of SLEAP can always be found in the [Releases page](https://github.com/talmolab/sleap/releases). ```{contents} Contents --- @@ -12,66 +23,30 @@ local: --- ``` -````{hint} -Installation requires entering commands in a terminal. To open one: - -**Windows:** Open the *Start menu* and search for the *Miniforge Prompt* (if using Mambaforge) or the *Command Prompt* if not. -```{note} -On Windows, our personal preference is to use alternative terminal apps like [Cmder](https://cmder.net) or [Windows Terminal](https://aka.ms/terminal). -``` - -**Linux:** Launch a new terminal by pressing Ctrl + Alt + T. - -**Mac:** Launch a new terminal by pressing Cmd + Space and searching for _Terminal_. - -```` - -(apple-silicon)= - -### Macs Pre-M1 (Pre-Installation) - -SLEAP can be installed on Macs by following these instructions: - -1. Make sure you're on **macOS Monterey** or later, i.e., version 12+. - -2. If you don't have it yet, [install **homebrew**](https://brew.sh/), a convenient package manager for Macs (skip this if you can run `brew` from the terminal): - - ```bash - /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)" - ``` - - This might take a little while since it'll also install Xcode (which we'll need later). Once it's finished, your terminal should give you two extra commands to run listed under **Next Steps**. - - ````{note} - We recommend running the commands given in your terminal which will be similar to (but may differ slightly) from the commands below: - ```bash - echo 'eval "$(/opt/homebrew/bin/brew shellenv)"' >> ~/.zprofile - ``` - - ```bash - eval "$(/opt/homebrew/bin/brew shellenv)" - ``` - +`````{hint} + Installation requires entering commands in a terminal. To open one: + ````{tabs} + ```{tab} Windows + Open the *Start menu* and search for the *Anaconda Prompt* (if using Miniconda) or the *Command Prompt* if not. + ```{note} + On Windows, our personal preference is to use alternative terminal apps like [Cmder](https://cmder.net) or [Windows Terminal](https://aka.ms/terminal). + ``` + ``` + ```{tab} Linux + Launch a new terminal by pressing Ctrl + Alt + T. + ``` + ```{group-tab} Mac OS + Launch a new terminal by pressing Cmd + Space and searching for _Terminal_. + ``` ```` +````` - Then, close and re-open the terminal for it to take effect. +## Package Manager -3. Install wget, a CLI downloading utility (also makes sure your homebrew setup worked): - - ```bash - brew install wget - ``` - -(mambaforge)= - -## Installing Mambaforge - -**Anaconda** is a Python environment manager that makes it easy to install SLEAP and its necessary dependencies without affecting other Python software on your computer. - -[**Mambaforge**](https://mamba.readthedocs.io/en/latest/installation.html) is a lightweight installer of Anaconda with speedy package resolution that we recommend. +SLEAP requires many complex dependencies, so we **strongly** recommend using a package manager such as [Miniforge](https://github.com/conda-forge/miniforge) or [Miniconda](https://docs.anaconda.com/free/miniconda/) to install SLEAP in its own isolated environment. ````{note} -If you already have Anaconda on your computer, then you can [set the solver to `libmamba`](https://www.anaconda.com/blog/a-faster-conda-for-a-growing-community) in the `base` environment (and skip the Mambaforge installation): +If you already have Anaconda on your computer (and it is an [older installation](https://conda.org/blog/2023-11-06-conda-23-10-0-release/)), then make sure to [set the solver to `libmamba`](https://www.anaconda.com/blog/a-faster-conda-for-a-growing-community) in the `base` environment. ```bash conda update -n base conda @@ -80,195 +55,220 @@ conda config --set solver libmamba ``` ```{warning} -Any subsequent `mamba` commands in the docs will need to be replaced with `conda` if you choose to use your existing Anaconda installation. +Any subsequent `conda` commands in the docs will need to be replaced with `mamba` if you have [Mamba](https://mamba.readthedocs.io/en/latest/) installed instead of Anaconda or Miniconda. ``` ```` -Otherwise, to install Mamba: - -**On Windows**, just click through the installation steps. - -1. Go to: https://github.com/conda-forge/miniforge#mambaforge -2. Download the latest version for your OS. -3. Follow the installer instructions. - -We recommend using the following settings: - -- Install for: All Users (requires admin privileges) -- Destination folder: `C:\mambaforge` -- Advanced Options: Add MambaForge to the system PATH environment variable -- Advanced Options: Register MambaForge as the system Python 3.X - These will make sure that MambaForge is easily accessible from most places on your computer. - -**On Linux**, it might be easier to do this straight from the terminal (Ctrl + Alt + T) with this one-liner: - -```bash -wget -nc https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-Linux-x86_64.sh && bash Mambaforge-Linux-x86_64.sh -b && ~/mambaforge/bin/conda init bash -``` - -Restart the terminal after running this command. - -```{note} -For other Linux architectures (arm64 and POWER8/9), replace the `.sh` filenames above with the correct installer name for your architecture. See the Download column in [this table](https://github.com/conda-forge/miniforge#mambaforge) for the correct filename. - -``` - -**On Macs (pre-M1)**, you can run the installer using this terminal command: +If you don't have a `conda` package manager installation, here are some quick install options: -```bash -wget -nc https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-MacOSX-x86_64.sh && bash Mambaforge-MacOSX-x86_64.sh -b && ~/mambaforge/bin/conda init zsh -``` +### Miniforge (recommended) -**On Macs (Apple Silicon)**, use this terminal command: +Miniforge is a minimal installer for conda that includes the `conda` package manager and is maintained by the [conda-forge](https://conda-forge.org) community. The only difference between Miniforge and Miniconda is that Miniforge uses the `conda-forge` channel by default, which provides a much wider selection of community-maintained packages. -```bash -curl -fsSL --compressed https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-MacOSX-arm64.sh -o Mambaforge3-MacOSX-arm64.sh && chmod +x Mambaforge3-MacOSX-arm64.sh && ./Mambaforge3-MacOSX-arm64.sh -b -p ~/mambaforge3 && rm Mambaforge3-MacOSX-arm64.sh && ~/mambaforge3/bin/conda init "$(basename "${SHELL}")" && source "$HOME/.$(basename "${SHELL}")rc" -``` +````{tabs} + ```{group-tab} Windows + Open a new PowerShell terminal (does not need to be admin) and enter: -## Installation methods - -SLEAP can be installed three different ways: via {ref}`conda package`, {ref}`conda from source`, or {ref}`pip package`. Select one of the methods below to install SLEAP. We recommend {ref}`conda package`. - -(condapackage)= - -### `conda` package - -**Windows** and **Linux** - -```bash -mamba create -y -n sleap -c conda-forge -c nvidia -c sleap -c anaconda sleap=1.3.3 -``` - -**Mac OS X** and **Apple Silicon** - -```bash -mamba create -y -n sleap -c conda-forge -c anaconda -c sleap sleap=1.3.3 -``` - -**This is the recommended installation method**. - -```{note} -- This comes with CUDA to enable GPU support. All you need is to have an NVIDIA GPU and [updated drivers](https://nvidia.com/drivers). -- If you already have CUDA installed on your system, this will not conflict with it. -- This will also work in CPU mode if you don't have a GPU on your machine. -``` - -(condasource)= - -### `conda` from source - -1. First, ensure git is installed: - - ```bash - git --version + ```bash + Invoke-WebRequest -Uri "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Windows-x86_64.exe" -OutFile "$env:UserProfile/Downloads/Miniforge3-Windows-x86_64.exe"; Start-Process -FilePath "$env:UserProfile/Downloads/Miniforge3-Windows-x86_64.exe" -ArgumentList "/InstallationType=JustMe /RegisterPython=1 /S" -Wait; Remove-Item -Path "$env:UserProfile/Downloads/Miniforge3-Windows-x86_64.exe" + ``` ``` + ```{group-tab} Linux + Open a new terminal and enter: - If 'git' is not recognized, then [install git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git). - -2. Then, clone the repository: - - ```bash - git clone https://github.com/talmolab/sleap && cd sleap + ```bash + curl -fsSL --compressed https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh -o "~/Downloads/Miniforge3-Linux-x86_64.sh" && chmod +x "~/Downloads/Miniforge3-Linux-x86_64.sh" && "~/Downloads/Miniforge3-Linux-x86_64.sh" -b -p ~/miniforge3 && rm "~/Downloads/Miniforge3-Linux-x86_64.sh" && ~/miniforge3/bin/conda init "$(basename "${SHELL}")" && source "$HOME/.$(basename "${SHELL}")rc" + ``` ``` + ```{group-tab} Mac (Apple Silicon) + Open a new terminal and enter: -3. Finally, install from the environment file (differs based on OS and GPU): - - **Windows** and **Linux** - - ```bash - mamba env create -f environment.yml -n sleap - ``` - - If you do not have a NVIDIA GPU, then you should use the no CUDA environment file: - - ```bash - mamba env create -f environment_no_cuda.yml -n sleap + ```bash + curl -fsSL --compressed https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-MacOSX-arm64.sh -o "~/Downloads/Miniforge3-MacOSX-arm64.sh" && chmod +x "~/Downloads/Miniforge3-MacOSX-arm64.sh" && "~/Downloads/Miniforge3-MacOSX-arm64.sh" -b -p ~/miniforge3 && rm "~/Downloads/Miniforge3-MacOSX-arm64.sh" && ~/miniforge3/bin/conda init "$(basename "${SHELL}")" && source "$HOME/.$(basename "${SHELL}")rc" + ``` ``` + ```{group-tab} Mac (Intel) + Open a new terminal and enter: - **Mac OS X** and **Apple Silicon** - - ```bash - mamba env create -f environment_mac.yml -n sleap + ```bash + curl -fsSL --compressed https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-MacOSX-x86_64.sh -o "~/Downloads/Miniforge3-MacOSX-x86_64.sh" && chmod +x "~/Downloads/Miniforge3-MacOSX-x86_64.sh" && "~/Downloads/Miniforge3-MacOSX-x86_64.sh" -b -p ~/miniforge3 && rm "~/Downloads/Miniforge3-MacOSX-x86_64.sh" && ~/miniforge3/bin/conda init "$(basename "${SHELL}")" && source "$HOME/.$(basename "${SHELL}")rc" + ``` ``` +```` - This is the **recommended method for development**. - -```{note} -- This installs SLEAP in development mode, which means that edits to the source code will be applied the next time you run SLEAP. -- Change the `-n sleap` in the command to create an environment with a different name (e.g., `-n sleap_develop`). -``` - -(pippackage)= - -### `pip` package - -Although you do not need Mambaforge installed to perform a `pip install`, we recommend {ref}`installing Mambaforge` to create a new environment where we can isolate the `pip install`. Alternatively, you can use a venv if you have an existing python installation. If you are working on **Google Colab**, skip to step 3 to perform the `pip install` without using a conda environment. +### Miniconda -1. Otherwise, create a new conda environment where we will `pip install sleap`: +This is a minimal installer for conda that includes the `conda` package manager and is maintained by the [Anaconda](https://www.anaconda.com) company. - either without GPU support: +````{tabs} + ```{group-tab} Windows + Open a new PowerShell terminal (does not need to be admin) and enter: - ```bash - mamba create --name sleap pip python=3.7.12 + ```bash + curl https://repo.anaconda.com/miniconda/Miniconda3-latest-Windows-x86_64.exe -o miniconda.exe; Start-Process -FilePath ".\miniconda.exe" -ArgumentList "/S" -Wait; del miniconda.exe + ``` ``` + ```{group-tab} Linux + Open a new terminal and enter: - or with GPU support: - - ```bash - mamba create --name sleap pip python=3.7.12 cudatoolkit=11.3 cudnn=8.2 + ```bash + mkdir -p ~/miniconda3 && wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh && bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3 && rm ~/miniconda3/miniconda.sh && ~/miniconda3/bin/conda init "$(basename "${SHELL}")" && source "$HOME/.$(basename "${SHELL}")rc" + ``` ``` + ```{group-tab} Mac (Apple Silicon) + Open a new terminal and enter: -2. Then activate the environment to isolate the `pip install` from other environments on your computer: - - ```bash - mamba activate sleap + ```bash + curl -fsSL --compressed https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-arm64.sh -o "~/Downloads/Miniconda3-latest-MacOSX-arm64.sh" && chmod +x "~/Downloads/Miniconda3-latest-MacOSX-arm64.sh" && "~/Downloads/Miniconda3-latest-MacOSX-arm64.sh" -b -u -p ~/miniconda3 && rm "~/Downloads/Miniconda3-latest-MacOSX-arm64.sh" && ~/miniconda3/bin/conda init "$(basename "${SHELL}")" && source "$HOME/.$(basename "${SHELL}")rc" + ``` ``` + ```{group-tab} Mac (Intel) + Open a new terminal and enter: - ```{warning} - Refrain from installing anything into the `base` environment. Always create a new environment to install new packages. + ```bash + curl -fsSL --compressed https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh -o "~/Downloads/Miniconda3-latest-MacOSX-x86_64.sh" && chmod +x "~/Downloads/Miniconda3-latest-MacOSX-x86_64.sh" && "~/Downloads/Miniconda3-latest-MacOSX-x86_64.sh" -b -u -p ~/miniconda3 && rm "~/Downloads/Miniconda3-latest-MacOSX-x86_64.sh" && ~/miniconda3/bin/conda init "$(basename "${SHELL}")" && source "$HOME/.$(basename "${SHELL}")rc" + ``` ``` +```` -3. Finally, we can perform the `pip install`: - - ```bash - pip install sleap[pypi]==1.3.3 - ``` +See the [Miniconda website](https://docs.anaconda.com/free/miniconda/) for up-to-date installation instructions if the above instructions don't work for your system. - This works on **any OS except Apple silicon** and on **Google Colab**. +(installation-methods)= - ```{note} - The pypi distributed package of SLEAP ships with the following extras: - - **pypi**: For installation without an mamba environment file. All dependencies come from PyPI. - - **jupyter**: This installs all *pypi* and jupyter lab dependencies. - - **dev**: This installs all *jupyter* dependencies and developement tools for testing and building docs. - - **conda_jupyter**: For installation using a mamba environment file included in the source code. Most dependencies are listed as conda packages in the environment file and only a few come from PyPI to allow jupyter lab support. - - **conda_dev**: For installation using [a mamba environment](https://github.com/search?q=repo%3Atalmolab%2Fsleap+path%3Aenvironment*.yml&type=code) with a few PyPI dependencies for development tools. - ``` +## Installation methods - ```{note} - - Requires Python 3.7 - - To enable GPU support, make sure that you have **CUDA Toolkit v11.3** and **cuDNN v8.2** installed. - ``` +SLEAP can be installed three different ways: via {ref}`conda package`, {ref}`conda from source`, or {ref}`pip package`. Select one of the methods below to install SLEAP. We recommend {ref}`conda package`. - ```{warning} - This will uninstall existing libraries and potentially install conflicting ones. +`````{tabs} + ```{tab} conda package + **This is the recommended installation method**. + ````{tabs} + ```{group-tab} Windows and Linux + ```bash + conda create -y -n sleap -c conda-forge -c nvidia -c sleap/label/dev -c sleap -c anaconda sleap=1.4.1 + ``` + ```{note} + - This comes with CUDA to enable GPU support. All you need is to have an NVIDIA GPU and [updated drivers](https://nvidia.com/drivers). + - If you already have CUDA installed on your system, this will not conflict with it. + - This will also work in CPU mode if you don't have a GPU on your machine. + ``` + ``` + ```{group-tab} Mac OS + ```bash + conda create -y -n sleap -c conda-forge -c anaconda -c sleap sleap=1.4.1 + ``` + ```{note} + This will also work in CPU mode if you don't have a GPU on your machine. + ``` + ``` + ```` - We strongly recommend that you **only use this method if you know what you're doing**! ``` + ```{tab} conda from source + This is the **recommended method for development**. + 1. First, ensure git is installed: + ```bash + git --version + ``` + If `git` is not recognized, then [install git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git). + 2. Then, clone the repository: + ```bash + git clone https://github.com/talmolab/sleap && cd sleap + ``` + 3. Finally, install SLEAP from the environment file: + ````{tabs} + ```{group-tab} Windows and Linux + ````{tabs} + ```{group-tab} NVIDIA GPU + ```bash + conda env create -f environment.yml -n sleap + ``` + ``` + ```{group-tab} CPU or other GPU + ```bash + conda env create -f environment_no_cuda.yml -n sleap + ``` + ``` + ```` + ``` + ```{group-tab} Mac OS + ```bash + conda env create -f environment_mac.yml -n sleap + ``` + ``` + ```` + ```{note} + - This installs SLEAP in development mode, which means that edits to the source code will be applied the next time you run SLEAP. + - Change the `-n sleap` in the command to create an environment with a different name (e.g., `-n sleap_develop`). + ``` + ``` + ```{tab} pip package + This is the **recommended method for Google Colab only**. + ```{warning} + This will uninstall existing libraries and potentially install conflicting ones. + + We strongly recommend that you **only use this method if you know what you're doing**! + ``` + ````{tabs} + ```{group-tab} Windows and Linux + ```{note} + - Requires Python 3.7 + - To enable GPU support, make sure that you have **CUDA Toolkit v11.3** and **cuDNN v8.2** installed. + ``` + Although you do not need Miniconda installed to perform a `pip install`, we recommend [installing Miniconda](https://docs.anaconda.com/free/miniconda/) to create a new environment where we can isolate the `pip install`. Alternatively, you can use a venv if you have an existing Python 3.7 installation. If you are working on **Google Colab**, skip to step 3 to perform the `pip install` without using a conda environment. + 1. Otherwise, create a new conda environment where we will `pip install sleap`: + ````{tabs} + ```{group-tab} NVIDIA GPU + ```bash + conda create --name sleap pip python=3.7.12 cudatoolkit=11.3 cudnn=8.2 -c conda-forge -c nvidia + ``` + ``` + ```{group-tab} CPU or other GPU + ```bash + conda create --name sleap pip python=3.7.12 + ``` + ``` + ```` + 2. Then activate the environment to isolate the `pip install` from other environments on your computer: + ```bash + conda activate sleap + ``` + ```{warning} + Refrain from installing anything into the `base` environment. Always create a new environment to install new packages. + ``` + 3. Finally, we can perform the `pip install`: + ```bash + pip install sleap[pypi]==1.4.1 + ``` + ```{note} + The pypi distributed package of SLEAP ships with the following extras: + - **pypi**: For installation without an conda environment file. All dependencies come from PyPI. + - **jupyter**: This installs all *pypi* and jupyter lab dependencies. + - **dev**: This installs all *jupyter* dependencies and developement tools for testing and building docs. + - **conda_jupyter**: For installation using a conda environment file included in the source code. Most dependencies are listed as conda packages in the environment file and only a few come from PyPI to allow jupyter lab support. + - **conda_dev**: For installation using [a conda environment](https://github.com/search?q=repo%3Atalmolab%2Fsleap+path%3Aenvironment*.yml&type=code) with a few PyPI dependencies for development tools. + ``` + ``` + ```{group-tab} Mac OS + Not supported. + ``` + ```` + ``` +````` ## Testing that things are working -If you installed using `mamba`, first activate the `sleap` environment by opening a terminal and typing: +If you installed using `conda`, first activate the `sleap` environment by opening a terminal and typing: ```bash -mamba activate sleap +conda activate sleap ``` ````{hint} -Not sure what `mamba` environments you already installed? You can get a list of the environments on your system with: +Not sure what `conda` environments you already installed? You can get a list of the environments on your system with: ``` -mamba env list +conda env list ``` ```` @@ -301,7 +301,7 @@ python -c "import sleap; sleap.versions()" ### GPU support -Assuming you installed using either of the `mamba`-based methods on Windows or Linux, SLEAP should automatically have GPU support enabled. +Assuming you installed using either of the `conda`-based methods on Windows or Linux, SLEAP should automatically have GPU support enabled. To check, verify that SLEAP can detect the GPUs on your system: @@ -362,7 +362,7 @@ file: No such file or directory then activate the environment: ```bash -mamba activate sleap +conda activate sleap ``` and run the commands: @@ -391,13 +391,13 @@ We **strongly recommend** installing SLEAP in a fresh environment when updating. To uninstall an existing environment named `sleap`: ```bash -mamba env remove -n sleap +conda env remove -n sleap ``` ````{hint} -Not sure what `mamba` environments you already installed? You can get a list of the environments on your system with: +Not sure what `conda` environments you already installed? You can get a list of the environments on your system with: ```bash -mamba env list +conda env list ``` ```` @@ -413,10 +413,10 @@ If you get any errors or the GUI fails to launch, try running the diagnostics to sleap-diagnostic ``` -If you were not able to get SLEAP installed, activate the mamba environment it is in and generate a list of the package versions installed: +If you were not able to get SLEAP installed, activate the conda environment it is in and generate a list of the package versions installed: ```bash -mamba list +conda list ``` Then, [open a new Issue](https://github.com/talmolab/sleap/issues) providing the versions from either command above, as well as any errors you saw in the console during the installation. Or [start a discussion](https://github.com/talmolab/sleap/discussions) to get help from the community. diff --git a/docs/notebooks/Training_and_inference_on_an_example_dataset.ipynb b/docs/notebooks/Training_and_inference_on_an_example_dataset.ipynb index b0211bbca..4e26cb286 100644 --- a/docs/notebooks/Training_and_inference_on_an_example_dataset.ipynb +++ b/docs/notebooks/Training_and_inference_on_an_example_dataset.ipynb @@ -335,7 +335,7 @@ " \"runs_folder\": \"models\",\n", " \"tags\": [],\n", " \"save_visualizations\": true,\n", - " \"delete_viz_images\": true,\n", + " \"keep_viz_images\": true,\n", " \"zip_outputs\": false,\n", " \"log_to_csv\": true,\n", " \"checkpointing\": {\n", @@ -727,7 +727,7 @@ " \"runs_folder\": \"models\",\n", " \"tags\": [],\n", " \"save_visualizations\": true,\n", - " \"delete_viz_images\": true,\n", + " \"keep_viz_images\": true,\n", " \"zip_outputs\": false,\n", " \"log_to_csv\": true,\n", " \"checkpointing\": {\n", diff --git a/environment.yml b/environment.yml index 67ed39d01..d8f752759 100644 --- a/environment.yml +++ b/environment.yml @@ -10,14 +10,15 @@ channels: dependencies: # Packages SLEAP uses directly - - conda-forge::attrs >=21.2.0 #,<=21.4.0 + - conda-forge::attrs >=21.2.0 - conda-forge::cattrs ==1.1.1 - - conda-forge::imgaug ==0.4.0 + - conda-forge::imageio-ffmpeg # Required for imageio to read/write videos with ffmpeg - conda-forge::jsmin - conda-forge::jsonpickle ==1.2 - conda-forge::networkx - anaconda::numpy >=1.19.5,<1.23.0 - - conda-forge::opencv + - conda-forge::opencv <4.9.0 + - conda-forge::h5py <=3.7.0 - conda-forge::pandas - conda-forge::pip - conda-forge::pillow #>=8.3.1,<=8.4.0 @@ -35,8 +36,11 @@ dependencies: - conda-forge::scikit-learn ==1.0 - conda-forge::scikit-video - conda-forge::seaborn - - sleap::tensorflow >=2.6.3,<2.11 # No windows GPU support for >2.10 + - sleap/label/dev::tensorflow ==2.7.0 # TODO: Switch to main label when updated - conda-forge::tensorflow-hub # Pinned in meta.yml, but no problems here... yet + - conda-forge::qudida + - conda-forge::albumentations + - conda-forge::ndx-pose <0.2.0 # Packages required by tensorflow to find/use GPUs - conda-forge::cudatoolkit ==11.3.1 @@ -46,4 +50,3 @@ dependencies: - pip: - "--editable=.[conda_dev]" - \ No newline at end of file diff --git a/environment_mac.yml b/environment_mac.yml index 85ef7d3b9..2026154fa 100644 --- a/environment_mac.yml +++ b/environment_mac.yml @@ -9,13 +9,14 @@ channels: dependencies: # Packages SLEAP uses directly - conda-forge::attrs >=21.2.0 + - conda-forge::importlib-metadata <7.1.0 - conda-forge::cattrs ==1.1.1 - conda-forge::h5py - - conda-forge::imgaug ==0.4.0 + - conda-forge::imageio-ffmpeg # Required for imageio to read/write videos with ffmpeg - conda-forge::jsmin - conda-forge::jsonpickle ==1.2 - conda-forge::keras <2.10.0,>=2.9.0rc0 # Required by tensorflow-macos - - conda-forge::networkx + - conda-forge::networkx <3.3 - anaconda::numpy >=1.19.5,<1.23.0 - conda-forge::opencv - conda-forge::pandas @@ -35,6 +36,9 @@ dependencies: - conda-forge::scikit-learn ==1.0 - conda-forge::scikit-video - conda-forge::seaborn - - conda-forge::tensorflow-hub + # - conda-forge::tensorflow-hub # pulls in tensorflow cpu from conda-forge + - conda-forge::qudida + - conda-forge::albumentations + - conda-forge::ndx-pose <0.2.0 - pip: - - "--editable=.[conda_dev]" + - "--editable=.[conda_dev]" \ No newline at end of file diff --git a/environment_no_cuda.yml b/environment_no_cuda.yml index 7e384b5f9..721c27fca 100644 --- a/environment_no_cuda.yml +++ b/environment_no_cuda.yml @@ -11,14 +11,14 @@ channels: dependencies: # Packages SLEAP uses directly - - conda-forge::attrs >=21.2.0 #,<=21.4.0 + - conda-forge::attrs >=21.2.0 - conda-forge::cattrs ==1.1.1 - - conda-forge::imgaug ==0.4.0 + - conda-forge::imageio-ffmpeg # Required for imageio to read/write videos with ffmpeg - conda-forge::jsmin - conda-forge::jsonpickle ==1.2 - conda-forge::networkx - anaconda::numpy >=1.19.5,<1.23.0 - - conda-forge::opencv + - conda-forge::opencv <4.9.0 - conda-forge::pandas - conda-forge::pip - conda-forge::pillow #>=8.3.1,<=8.4.0 @@ -36,8 +36,12 @@ dependencies: - conda-forge::scikit-learn ==1.0 - conda-forge::scikit-video - conda-forge::seaborn - - sleap::tensorflow >=2.6.3,<2.11 # No windows GPU support for >2.10 + # - sleap::tensorflow >=2.6.3,<2.11 # No windows GPU support for >2.10 + - sleap/label/dev::tensorflow ==2.7.0 - conda-forge::tensorflow-hub + - conda-forge::qudida + - conda-forge::albumentations + - conda-forge::ndx-pose <0.2.0 - pip: - - "--editable=.[conda_dev]" + - "--editable=.[conda_dev]" \ No newline at end of file diff --git a/pypi_requirements.txt b/pypi_requirements.txt index 33f419c9c..775ce584e 100644 --- a/pypi_requirements.txt +++ b/pypi_requirements.txt @@ -3,15 +3,17 @@ # setup.py, the packages in requirements.txt will also be installed when running # pip install sleap[pypi]. -# These are also distrubuted through conda and not pip installed when using conda. +# These are also distributed through conda and not pip installed when using conda. attrs>=21.2.0,<=21.4.0 cattrs==1.1.1 +imageio +imageio-ffmpeg # certifi>=2017.4.17,<=2021.10.8 jsmin jsonpickle==1.2 networkx numpy>=1.19.5,<1.23.0 -opencv-python>=4.2.0,<=4.6.0 +opencv-python>=4.2.0,<=4.7.0 pandas pillow>=8.3.1,<=8.4.0 psutil @@ -32,7 +34,10 @@ scikit-learn ==1.0.* scikit-video seaborn tensorflow>=2.6.3,<2.9; platform_machine != 'arm64' +# tensorflow ==2.7.4; platform_machine != 'arm64' tensorflow-hub<=0.14.0 +albumentations +ndx-pose<0.2.0 # These dependencies are untested since we do not offer a wheel for apple silicon atm. tensorflow-macos==2.9.2; sys_platform == 'darwin' and platform_machine == 'arm64' tensorflow-metal==0.5.0; sys_platform == 'darwin' and platform_machine == 'arm64' diff --git a/requirements.txt b/requirements.txt index cb0ef45c5..5db435ec8 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,14 +1,12 @@ # This file contains the minimal requirements to be installed via pip when using conda. # No conda packages for these -imgstore<0.3.0 # 0.3.3 results in https://github.com/O365/python-o365/issues/591 -ndx-pose +imgstore<0.3.0 # 0.3.3 results in https://github.com/O365/python-o365/issues/591 which is from https://github.com/regebro/tzlocal/issues/112 when tzlocal is v3.0 nixio>=1.5.3 # Constrain put on by @jgrewe from G-Node qimage2ndarray # ==1.9.0 segmentation-models tensorflow-macos==2.9.2; sys_platform == 'darwin' and platform_machine == 'arm64' tensorflow-metal==0.5.0; sys_platform == 'darwin' and platform_machine == 'arm64' +tensorflow-hub==0.12.0; sys_platform == 'darwin' and platform_machine == 'arm64' -# Conda installing results in https://github.com/h5py/h5py/issues/2037 -h5py<3.2; sys_platform == 'win32' # Newer versions result in error above, linking issue in Linux pynwb>=2.3.3 # 2.0.0 required by ndx-pose, 2.3.3 fixes importlib-metadata incompatibility diff --git a/sleap/config/frame_range_form.yaml b/sleap/config/frame_range_form.yaml new file mode 100644 index 000000000..3f01eade4 --- /dev/null +++ b/sleap/config/frame_range_form.yaml @@ -0,0 +1,13 @@ +main: + + - name: min_frame_idx + label: Minimum frame index + type: int + range: 1,1000000 + default: 1 + + - name: max_frame_idx + label: Maximum frame index + type: int + range: 1,1000000 + default: 1000 \ No newline at end of file diff --git a/sleap/config/labeled_clip_form.yaml b/sleap/config/labeled_clip_form.yaml index be0d64829..9236ad42b 100644 --- a/sleap/config/labeled_clip_form.yaml +++ b/sleap/config/labeled_clip_form.yaml @@ -18,6 +18,10 @@ main: label: Use GUI Visual Settings (colors, line widths) type: bool default: true + - name: background + label: Video Background + type: list + options: original,black,white,grey - name: open_when_done label: Open When Done Saving type: bool diff --git a/sleap/config/pipeline_form.yaml b/sleap/config/pipeline_form.yaml index cbcea2be5..1bb930e58 100644 --- a/sleap/config/pipeline_form.yaml +++ b/sleap/config/pipeline_form.yaml @@ -52,7 +52,7 @@ training: This pipeline uses two models: a "centroid" model to locate and crop around each animal in the frame, and a "centered-instance confidence map" model for predicted node locations - for each individual animal predicted by the centroid model.' + for each individual animal predicted by the centroid model.' - label: Max Instances name: max_instances type: optional_int @@ -211,6 +211,21 @@ training: options: ',RGB,grayscale' type: list +- type: text + text: 'ZMQ Options' + +- name: controller_port + label: Controller Port + type: int + default: 9000 + range: 1024,65535 + +- name: publish_port + label: Publish Port + type: int + default: 9001 + range: 1024,65535 + - type: text text: 'Output Options' @@ -271,6 +286,11 @@ training: type: bool default: true +- name: _keep_viz + label: Keep Prediction Visualization Images After Training + type: bool + default: false + - name: _predict_frames label: Predict On type: list @@ -287,7 +307,7 @@ inference: label: Training/Inference Pipeline Type type: stacked default: "multi-animal bottom-up " - options: "multi-animal bottom-up,multi-animal top-down,multi-animal bottom-up-id,multi-animal top-down-id,single animal,movenet-lightning,movenet-thunder,none" + options: "multi-animal bottom-up,multi-animal top-down,multi-animal bottom-up-id,multi-animal top-down-id,single animal,movenet-lightning,movenet-thunder,tracking-only" multi-animal bottom-up: - type: text @@ -365,7 +385,13 @@ inference: Note that this model is intended for human pose estimation. There is no support for videos containing more than one instance' - none: + tracking-only: + +- name: batch_size + label: Batch Size + type: int + default: 4 + range: 1,512 - name: tracking.tracker label: Tracker (cross-frame identity) Method @@ -413,7 +439,7 @@ inference: label: Similarity Method type: list default: instance - options: instance,centroid,iou + options: "instance,normalized_instance,centroid,iou,object keypoint" - name: tracking.match label: Matching Method type: list @@ -452,6 +478,22 @@ inference: label: Nodes to use for Tracking type: string default: 0,1,2 + - type: text + text: 'Object keypoint similarity options:
+ Only used if this similarity method is selected.' + - name: tracking.oks_errors + label: Keypoints errors in pixels + help: 'Standard error in pixels of the distance for each keypoint. + If the list is empty, defaults to 1. If singleton list, each keypoint has + the same error. Otherwise, the length should be the same as the number of + keypoints in the skeleton.' + type: string + default: + - name: tracking.oks_score_weighting + label: Use prediction score for weighting + help: 'Use prediction scores to weight the similarity of each keypoint' + type: bool + default: false - type: text text: 'Post-tracker data cleaning:' - name: tracking.post_connect_single_breaks @@ -495,8 +537,8 @@ inference: - name: tracking.similarity label: Similarity Method type: list - default: iou - options: instance,centroid,iou + default: instance + options: "instance,normalized_instance,centroid,iou,object keypoint" - name: tracking.match label: Matching Method type: list @@ -531,6 +573,22 @@ inference: label: Nodes to use for Tracking type: string default: 0,1,2 + - type: text + text: 'Object keypoint similarity options:
+ Only used if this similarity method is selected.' + - name: tracking.oks_errors + label: Keypoints errors in pixels + help: 'Standard error in pixels of the distance for each keypoint. + If the list is empty, defaults to 1. If singleton list, each keypoint has + the same error. Otherwise, the length should be the same as the number of + keypoints in the skeleton.' + type: string + default: + - name: tracking.oks_score_weighting + label: Use prediction score for weighting + help: 'Use prediction scores to weight the similarity of each keypoint' + type: bool + default: false - type: text text: 'Post-tracker data cleaning:' - name: tracking.post_connect_single_breaks diff --git a/sleap/config/suggestions.yaml b/sleap/config/suggestions.yaml index 8cf89728a..1440530fc 100644 --- a/sleap/config/suggestions.yaml +++ b/sleap/config/suggestions.yaml @@ -3,7 +3,7 @@ main: label: Method type: stacked default: " " - options: " ,image features,sample,prediction score,velocity,frame chunk" + options: " ,image features,sample,prediction score,velocity,frame chunk,max point displacement" " ": sample: @@ -175,6 +175,13 @@ main: type: double default: 0.1 range: 0.1,1.0 + + "max point displacement": + - name: displacement_threshold + label: Maximum Displacement Threshold + type: int + default: 10 + range: 0,999 - name: target label: Target diff --git a/sleap/config/training_editor_form.yaml b/sleap/config/training_editor_form.yaml index d10b840a0..7d7972892 100644 --- a/sleap/config/training_editor_form.yaml +++ b/sleap/config/training_editor_form.yaml @@ -44,7 +44,7 @@ model: label: Max Stride name: model.backbone.hourglass.max_stride type: list - options: 1,2,4,8,16,32,64 + options: 1,2,4,8,16,32,64,128 # - default: 4 # help: Determines the number of upsampling blocks in the network. # label: Output Stride @@ -81,7 +81,7 @@ model: label: Max Stride name: model.backbone.leap.max_stride type: list - options: 2,4,8,16,32,64 + options: 2,4,8,16,32,64,128 # - default: 1 # help: Determines the number of upsampling blocks in the network. # label: Output Stride @@ -190,7 +190,7 @@ model: label: Max Stride name: model.backbone.resnet.max_stride type: list - options: 2,4,8,16,32,64 + options: 2,4,8,16,32,64,128 # - default: 4 # help: Stride of the final output. If the upsampling branch is not defined, the # output stride is controlled via dilated convolutions or reduced pooling in the @@ -250,7 +250,7 @@ model: label: Max Stride name: model.backbone.unet.max_stride type: list - options: 2,4,8,16,32,64 + options: 2,4,8,16,32,64,128 # - default: 1 # help: Determines the number of upsampling blocks in the network. # label: Output Stride @@ -661,6 +661,7 @@ optimization: label: Batch Size name: optimization.batch_size type: int + range: 1,512 - default: 100 help: Maximum number of epochs to train for. Training can be stopped manually or automatically if early stopping is enabled and a plateau is detected. label: Epochs diff --git a/sleap/gui/app.py b/sleap/gui/app.py index de6ce9fbf..8b711c806 100644 --- a/sleap/gui/app.py +++ b/sleap/gui/app.py @@ -44,13 +44,16 @@ frame and instances listed in data view table. """ - import os import platform import random import re +import traceback +from logging import getLogger from pathlib import Path from typing import Callable, List, Optional, Tuple +import sys +import subprocess from qtpy import QtCore, QtGui from qtpy.QtCore import QEvent, Qt @@ -82,7 +85,10 @@ from sleap.io.video import available_video_exts from sleap.prefs import prefs from sleap.skeleton import Skeleton -from sleap.util import parse_uri_path +from sleap.util import parse_uri_path, get_config_file + + +logger = getLogger(__name__) class MainWindow(QMainWindow): @@ -101,6 +107,7 @@ class MainWindow(QMainWindow): def __init__( self, labels_path: Optional[str] = None, + labels: Optional[Labels] = None, reset: bool = False, no_usage_data: bool = False, *args, @@ -118,7 +125,7 @@ def __init__( self.setAcceptDrops(True) self.state = GuiState() - self.labels = Labels() + self.labels = labels or Labels() self.commands = CommandContext( state=self.state, app=self, update_callback=self.on_data_update @@ -145,6 +152,7 @@ def __init__( self.state["edge style"] = prefs["edge style"] self.state["fit"] = False self.state["color predicted"] = prefs["color predicted"] + self.state["trail_length"] = prefs["trail length"] self.state["trail_shade"] = prefs["trail shade"] self.state["marker size"] = prefs["marker size"] self.state["propagate track labels"] = prefs["propagate track labels"] @@ -175,8 +183,10 @@ def __init__( print("Restoring GUI state...") self.restoreState(prefs["window state"]) - if labels_path: + if labels_path is not None: self.commands.loadProjectFile(filename=labels_path) + elif labels is not None: + self.commands.loadLabelsObject(labels=labels) else: self.state["project_loaded"] = False @@ -213,6 +223,7 @@ def closeEvent(self, event): prefs["edge style"] = self.state["edge style"] prefs["propagate track labels"] = self.state["propagate track labels"] prefs["color predicted"] = self.state["color predicted"] + prefs["trail length"] = self.state["trail_length"] prefs["trail shade"] = self.state["trail_shade"] prefs["share usage data"] = self.state["share usage data"] @@ -254,7 +265,6 @@ def dragEnterEvent(self, event): event.acceptProposedAction() def dropEvent(self, event): - # Parse filenames filenames = event.mimeData().data("text/uri-list").data().decode() filenames = [parse_uri_path(f.strip()) for f in filenames.strip().split("\n")] @@ -367,7 +377,9 @@ def add_menu_item(menu, key: str, name: str, action: Callable): def connect_check(key): self._menu_actions[key].setCheckable(True) self._menu_actions[key].setChecked(self.state[key]) - self.state.connect(key, self._menu_actions[key].setChecked) + self.state.connect( + key, lambda checked: self._menu_actions[key].setChecked(checked) + ) # add checkable menu item connected to state variable def add_menu_check_item(menu, key: str, name: str): @@ -506,6 +518,13 @@ def add_submenu_choices(menu, title, options, key): fileMenu, "reset prefs", "Reset preferences to defaults...", self.resetPrefs ) + add_menu_item( + fileMenu, + "open preference directory", + "Open Preferences Directory...", + self.openPrefs, + ) + fileMenu.addSeparator() add_menu_item(fileMenu, "close", "Quit", self.close) @@ -637,17 +656,18 @@ def prev_vid(): key="edge style", ) + # XXX add_submenu_choices( menu=viewMenu, title="Node Marker Size", - options=(1, 2, 4, 6, 8, 12), + options=prefs["node marker sizes"], key="marker size", ) add_submenu_choices( menu=viewMenu, title="Node Label Size", - options=(6, 12, 18, 24, 36), + options=prefs["node label sizes"], key="node label size", ) @@ -686,13 +706,17 @@ def prev_vid(): ) def new_instance_menu_action(): + """Determine which action to use when using Ctrl + I or menu Add Instance. + + We always add an offset of 10. + """ method_key = [ key for (key, val) in instance_adding_methods.items() if val == self.state["instance_init_method"] ] if method_key: - self.commands.newInstance(init_method=method_key[0]) + self.commands.newInstance(init_method=method_key[0], offset=10) labelMenu = self.menuBar().addMenu("Labels") add_menu_item( @@ -735,12 +759,12 @@ def new_instance_menu_action(): labelMenu.addAction( "Copy Instance", self.commands.copyInstance, - Qt.CTRL + Qt.Key_C, + Qt.CTRL | Qt.Key_C, ) labelMenu.addAction( "Paste Instance", self.commands.pasteInstance, - Qt.CTRL + Qt.Key_V, + Qt.CTRL | Qt.Key_V, ) labelMenu.addSeparator() @@ -775,6 +799,12 @@ def new_instance_menu_action(): "Delete Predictions with Low Score...", self.commands.deleteLowScorePredictions, ) + add_menu_item( + labelMenu, + "delete max instance predictions", + "Delete Predictions beyond Max Instances...", + self.commands.deleteInstanceLimitPredictions, + ) add_menu_item( labelMenu, "delete frame limit predictions", @@ -834,12 +864,12 @@ def new_instance_menu_action(): tracksMenu.addAction( "Copy Instance Track", self.commands.copyInstanceTrack, - Qt.CTRL + Qt.SHIFT + Qt.Key_C, + Qt.CTRL | Qt.SHIFT | Qt.Key_C, ) tracksMenu.addAction( "Paste Instance Track", self.commands.pasteInstanceTrack, - Qt.CTRL + Qt.SHIFT + Qt.Key_V, + Qt.CTRL | Qt.SHIFT | Qt.Key_V, ) tracksMenu.addSeparator() @@ -850,6 +880,8 @@ def new_instance_menu_action(): "Point Displacement (max)", "Primary Point Displacement (sum)", "Primary Point Displacement (max)", + "Tracking Score (mean)", + "Tracking Score (min)", "Instance Score (sum)", "Instance Score (min)", "Point Score (sum)", @@ -1018,6 +1050,7 @@ def _load_overlays(self): labels=self.labels, player=self.player, trail_shade=self.state["trail_shade"], + trail_length=self.state["trail_length"], ) self.overlays["instance"] = InstanceOverlay( labels=self.labels, player=self.player, state=self.state @@ -1307,7 +1340,7 @@ def updateStatusMessage(self, message: Optional[str] = None): message += f" [Hidden] Press '{hide_key}' to toggle." self.statusBar().setStyleSheet("color: red") else: - self.statusBar().setStyleSheet("color: black") + self.statusBar().setStyleSheet("") self.statusBar().showMessage(message) @@ -1320,14 +1353,42 @@ def resetPrefs(self): ) msg.exec_() + def openPrefs(self): + """Open preference file directory""" + pref_path = get_config_file("preferences.yaml") + # Make sure the pref_path is a directory rather than a file + if pref_path.is_file(): + pref_path = pref_path.parent + # Open the file explorer at the folder containing the preferences.yaml file + if sys.platform == "win32": + subprocess.Popen(["explorer", str(pref_path)]) + elif sys.platform == "darwin": + subprocess.Popen(["open", str(pref_path)]) + else: + subprocess.Popen(["xdg-open", str(pref_path)]) + def _update_track_menu(self): """Updates track menu options.""" self.track_menu.clear() self.delete_tracks_menu.clear() + + # Create a dictionary mapping track indices to Qt.Key values + key_mapping = { + 0: Qt.Key_1, + 1: Qt.Key_2, + 2: Qt.Key_3, + 3: Qt.Key_4, + 4: Qt.Key_5, + 5: Qt.Key_6, + 6: Qt.Key_7, + 7: Qt.Key_8, + 8: Qt.Key_9, + 9: Qt.Key_0, + } for track_ind, track in enumerate(self.labels.tracks): key_command = "" if track_ind < 9: - key_command = Qt.CTRL + Qt.Key_0 + self.labels.tracks.index(track) + 1 + key_command = Qt.CTRL | key_mapping[track_ind] self.track_menu.addAction( f"{track.name}", lambda x=track: self.commands.setInstanceTrack(x), @@ -1337,7 +1398,7 @@ def _update_track_menu(self): f"{track.name}", lambda x=track: self.commands.deleteTrack(x) ) self.track_menu.addAction( - "New Track", self.commands.addTrack, Qt.CTRL + Qt.Key_0 + "New Track", self.commands.addTrack, Qt.CTRL | Qt.Key_0 ) def _update_seekbar_marks(self): @@ -1354,6 +1415,8 @@ def _set_seekbar_header(self, graph_name: str): "Point Displacement (max)": data_obj.get_point_displacement_series, "Primary Point Displacement (sum)": data_obj.get_primary_point_displacement_series, "Primary Point Displacement (max)": data_obj.get_primary_point_displacement_series, + "Tracking Score (mean)": data_obj.get_tracking_score_series, + "Tracking Score (min)": data_obj.get_tracking_score_series, "Instance Score (sum)": data_obj.get_instance_score_series, "Instance Score (min)": data_obj.get_instance_score_series, "Point Score (sum)": data_obj.get_point_score_series, @@ -1367,7 +1430,7 @@ def _set_seekbar_header(self, graph_name: str): else: if graph_name in header_functions: kwargs = dict(video=self.state["video"]) - reduction_name = re.search("\\((sum|max|min)\\)", graph_name) + reduction_name = re.search("\\((sum|max|min|mean)\\)", graph_name) if reduction_name is not None: kwargs["reduction"] = reduction_name.group(1) series = header_functions[graph_name](**kwargs) @@ -1594,8 +1657,12 @@ def _show_keyboard_shortcuts_window(self): ShortcutDialog().exec_() -def main(args: Optional[list] = None): - """Starts new instance of app.""" +def create_sleap_label_parser(): + """Creates parser for `sleap-label` command line arguments. + + Returns: + argparse.ArgumentParser: The parser. + """ import argparse @@ -1635,6 +1702,23 @@ def main(args: Optional[list] = None): default=False, ) + return parser + + +def create_app(): + """Creates Qt application.""" + + app = QApplication([]) + app.setApplicationName(f"SLEAP v{sleap.version.__version__}") + app.setWindowIcon(QtGui.QIcon(sleap.util.get_package_file("gui/icon.png"))) + + return app + + +def main(args: Optional[list] = None, labels: Optional[Labels] = None): + """Starts new instance of app.""" + + parser = create_sleap_label_parser() args = parser.parse_args(args) if args.nonnative: @@ -1646,17 +1730,26 @@ def main(args: Optional[list] = None): # https://stackoverflow.com/q/64818879 os.environ["QT_MAC_WANTS_LAYER"] = "1" - app = QApplication([]) - app.setApplicationName(f"SLEAP v{sleap.version.__version__}") - app.setWindowIcon(QtGui.QIcon(sleap.util.get_package_file("gui/icon.png"))) + app = create_app() window = MainWindow( - labels_path=args.labels_path, reset=args.reset, no_usage_data=args.no_usage_data + labels_path=args.labels_path, + labels=labels, + reset=args.reset, + no_usage_data=args.no_usage_data, ) window.showMaximized() # Disable GPU in GUI process. This does not affect subprocesses. - sleap.use_cpu_only() + try: + sleap.use_cpu_only() + except RuntimeError: # Visible devices cannot be modified after being initialized + logger.warning( + "Running processes on the GPU. Restarting your GUI should allow switching " + "back to CPU-only mode.\n" + "Received the following error when trying to switch back to CPU-only mode:" + ) + traceback.print_exc() # Print versions. print() diff --git a/sleap/gui/commands.py b/sleap/gui/commands.py index 698eed756..fca982327 100644 --- a/sleap/gui/commands.py +++ b/sleap/gui/commands.py @@ -36,7 +36,7 @@ class which inherits from `AppCommand` (or a more specialized class such as from enum import Enum from glob import glob from pathlib import Path, PurePath -from typing import Callable, Dict, Iterator, List, Optional, Tuple, Type +from typing import Callable, Dict, Iterator, List, Optional, Tuple, Type, Union, cast import attr import cv2 @@ -49,7 +49,7 @@ class which inherits from `AppCommand` (or a more specialized class such as from sleap.gui.dialogs.merge import MergeDialog, ReplaceSkeletonTableDialog from sleap.gui.dialogs.message import MessageDialog from sleap.gui.dialogs.missingfiles import MissingFilesDialog -from sleap.gui.dialogs.query import QueryDialog +from sleap.gui.dialogs.frame_range import FrameRangeDialog from sleap.gui.state import GuiState from sleap.gui.suggestions import VideoFrameSuggestions from sleap.instance import Instance, LabeledFrame, Point, PredictedInstance, Track @@ -260,16 +260,15 @@ def loadLabelsObject(self, labels: Labels, filename: Optional[str] = None): """ self.execute(LoadLabelsObject, labels=labels, filename=filename) - def loadProjectFile(self, filename: str): + def loadProjectFile(self, filename: Union[str, Labels]): """Loads given labels file into GUI. Args: - filename: The path to the saved labels dataset. If None, - then don't do anything. + filename: The path to the saved labels dataset or the `Labels` object. + If None, then don't do anything. Returns: None - """ self.execute(LoadProjectFile, filename=filename) @@ -492,8 +491,12 @@ def deleteLowScorePredictions(self): """Gui for deleting instances below some score threshold.""" self.execute(DeleteLowScorePredictions) - def deleteFrameLimitPredictions(self): + def deleteInstanceLimitPredictions(self): """Gui for deleting instances beyond some number in each frame.""" + self.execute(DeleteInstanceLimitPredictions) + + def deleteFrameLimitPredictions(self): + """Gui for deleting instances beyond some frame number.""" self.execute(DeleteFrameLimitPredictions) def completeInstanceNodes(self, instance: Instance): @@ -506,6 +509,7 @@ def newInstance( init_method: str = "best", location: Optional[QtCore.QPoint] = None, mark_complete: bool = False, + offset: int = 0, ): """Creates a new instance, copying node coordinates as appropriate. @@ -515,6 +519,8 @@ def newInstance( init_method: Method to use for positioning nodes. location: The location where instance should be added (if node init method supports custom location). + mark_complete: Whether to mark the instance as complete. + offset: Offset to apply to the location if given. """ self.execute( AddInstance, @@ -522,6 +528,7 @@ def newInstance( init_method=init_method, location=location, mark_complete=mark_complete, + offset=offset, ) def setPointLocations( @@ -647,9 +654,8 @@ def do_action(context: "CommandContext", params: dict): Returns: None. - """ - filename = params["filename"] + filename = params.get("filename", None) # If called with just a Labels object labels: Labels = params["labels"] context.state["labels"] = labels @@ -669,7 +675,9 @@ def do_action(context: "CommandContext", params: dict): context.state["video"] = labels.videos[0] context.state["project_loaded"] = True - context.state["has_changes"] = params.get("changed_on_load", False) + context.state["has_changes"] = params.get("changed_on_load", False) or ( + filename is None + ) # This is not listed as an edit command since we want a clean changestack context.app.on_data_update([UpdateTopic.project, UpdateTopic.all]) @@ -683,17 +691,16 @@ def ask(context: "CommandContext", params: dict): if len(filename) == 0: return - gui_video_callback = Labels.make_gui_video_callback( - search_paths=[os.path.dirname(filename)], context=params - ) - has_loaded = False labels = None - if type(filename) == Labels: + if isinstance(filename, Labels): labels = filename filename = None has_loaded = True else: + gui_video_callback = Labels.make_gui_video_callback( + search_paths=[os.path.dirname(filename)], context=params + ) try: labels = Labels.load_file(filename, video_search=gui_video_callback) has_loaded = True @@ -751,7 +758,6 @@ def ask(context: "CommandContext", params: dict) -> bool: class ImportAlphaTracker(AppCommand): @staticmethod def do_action(context: "CommandContext", params: dict): - video_path = params["video_path"] if "video_path" in params else None labels = Labels.load_alphatracker( @@ -791,7 +797,6 @@ def ask(context: "CommandContext", params: dict) -> bool: class ImportNWB(AppCommand): @staticmethod def do_action(context: "CommandContext", params: dict): - labels = Labels.load_nwb(filename=params["filename"]) new_window = context.app.__class__() @@ -824,7 +829,6 @@ def ask(context: "CommandContext", params: dict) -> bool: class ImportDeepPoseKit(AppCommand): @staticmethod def do_action(context: "CommandContext", params: dict): - labels = Labels.from_deepposekit( filename=params["filename"], video_path=params["video_path"], @@ -873,7 +877,6 @@ def ask(context: "CommandContext", params: dict) -> bool: class ImportLEAP(AppCommand): @staticmethod def do_action(context: "CommandContext", params: dict): - labels = Labels.load_leap_matlab( filename=params["filename"], ) @@ -904,7 +907,6 @@ def ask(context: "CommandContext", params: dict) -> bool: class ImportCoco(AppCommand): @staticmethod def do_action(context: "CommandContext", params: dict): - labels = Labels.load_coco( filename=params["filename"], img_dir=params["img_dir"], use_missing_gui=True ) @@ -936,7 +938,6 @@ def ask(context: "CommandContext", params: dict) -> bool: class ImportDeepLabCut(AppCommand): @staticmethod def do_action(context: "CommandContext", params: dict): - labels = Labels.load_deeplabcut(filename=params["filename"]) new_window = context.app.__class__() @@ -1295,6 +1296,7 @@ def do_action(context: CommandContext, params: dict): frames=list(params["frames"]), fps=params["fps"], color_manager=params["color_manager"], + background=params["background"], show_edges=params["show edges"], edge_is_wedge=params["edge_is_wedge"], marker_size=params["marker size"], @@ -1309,7 +1311,6 @@ def do_action(context: CommandContext, params: dict): @staticmethod def ask(context: CommandContext, params: dict) -> bool: - from sleap.gui.dialogs.export_clip import ExportClipDialog dialog = ExportClipDialog() @@ -1333,17 +1334,15 @@ def ask(context: CommandContext, params: dict) -> bool: # makes mp4's that most programs can't open (VLC can). default_out_filename = context.state["filename"] + ".avi" - # But if we can write mpegs using sci-kit video, use .mp4 - # since it has trouble writing .avi files. - if VideoWriter.can_use_skvideo(): + if VideoWriter.can_use_ffmpeg(): default_out_filename = context.state["filename"] + ".mp4" - # Ask where use wants to save video file + # Ask where user wants to save video file filename, _ = FileDialog.save( context.app, caption="Save Video As...", dir=default_out_filename, - filter="Video (*.avi *mp4)", + filter="Video (*.avi *.mp4)", ) # Check if user hit cancel @@ -1354,6 +1353,7 @@ def ask(context: CommandContext, params: dict) -> bool: params["fps"] = export_options["fps"] params["scale"] = export_options["scale"] params["open_when_done"] = export_options["open_when_done"] + params["background"] = export_options["background"] params["crop"] = None @@ -1584,7 +1584,6 @@ class GoNextSuggestedFrame(NavCommand): @classmethod def do_action(cls, context: CommandContext, params: dict): - next_suggestion_frame = context.labels.get_next_suggestion( context.state["video"], context.state["frame_idx"], cls.seek_direction ) @@ -1770,7 +1769,6 @@ class ReplaceVideo(EditCommand): @staticmethod def do_action(context: CommandContext, params: dict) -> bool: - import_list = params["import_list"] for import_item, video in import_list: @@ -1899,7 +1897,6 @@ def ask(context: CommandContext, params: dict) -> bool: video_file_names = [] total_num_labeled_frames = 0 for idx in row_idxs: - video = videos[idx] if video is None: return False @@ -1944,7 +1941,6 @@ def load_skeleton(filename: str): def compare_skeletons( skeleton: Skeleton, new_skeleton: Skeleton ) -> Tuple[List[str], List[str], List[str]]: - delete_nodes = [] add_nodes = [] if skeleton.node_names != new_skeleton.node_names: @@ -2076,7 +2072,7 @@ def try_and_skip_if_error(func, *args, **kwargs): func(*args, **kwargs) except Exception as e: tb_str = traceback.format_exception( - etype=type(e), value=e, tb=e.__traceback__ + type(e), value=e, tb=e.__traceback__ ) logger.warning( f"Recieved the following error while replacing skeleton:\n" @@ -2307,6 +2303,8 @@ def _do_deletion(context: CommandContext, lf_inst_list: List[int]): lfs_to_remove = [] for lf, inst in lf_inst_list: context.labels.remove_instance(lf, inst, in_transaction=True) + if context.state["instance"] == inst: + context.state["instance"] = None if len(lf.instances) == 0: lfs_to_remove.append(lf) @@ -2449,7 +2447,7 @@ def ask(cls, context: CommandContext, params: dict) -> bool: return super().ask(context, params) -class DeleteFrameLimitPredictions(InstanceDeleteCommand): +class DeleteInstanceLimitPredictions(InstanceDeleteCommand): @staticmethod def get_frame_instance_list(context: CommandContext, params: dict): count_thresh = params["count_threshold"] @@ -2479,6 +2477,36 @@ def ask(cls, context: CommandContext, params: dict) -> bool: return super().ask(context, params) +class DeleteFrameLimitPredictions(InstanceDeleteCommand): + @staticmethod + def get_frame_instance_list(context: CommandContext, params: Dict): + """Called from the parent `InstanceDeleteCommand.ask` method. + + Returns: + List of instances to be deleted. + """ + instances = [] + # Select the instances to be deleted + for lf in context.labels.labeled_frames: + if lf.frame_idx < (params["min_frame_idx"] - 1) or lf.frame_idx > ( + params["max_frame_idx"] - 1 + ): + instances.extend([(lf, inst) for inst in lf.instances]) + return instances + + @classmethod + def ask(cls, context: CommandContext, params: Dict) -> bool: + current_video = context.state["video"] + dialog = FrameRangeDialog( + title="Delete Instances in Frame Range...", max_frame_idx=len(current_video) + ) + results = dialog.get_results() + if results: + params["min_frame_idx"] = results["min_frame_idx"] + params["max_frame_idx"] = results["max_frame_idx"] + return super().ask(context, params) + + class TransposeInstances(EditCommand): topics = [UpdateTopic.project_instances, UpdateTopic.tracks] @@ -2492,7 +2520,16 @@ def do_action(cls, context: CommandContext, params: dict): # Swap tracks for current and subsequent frames when we have tracks old_track, new_track = instances[0].track, instances[1].track if old_track is not None and new_track is not None: - frame_range = (context.state["frame_idx"], context.state["video"].frames) + if context.state["propagate track labels"]: + frame_range = ( + context.state["frame_idx"], + context.state["video"].frames, + ) + else: + frame_range = ( + context.state["frame_idx"], + context.state["frame_idx"] + 1, + ) context.labels.track_swap( context.state["video"], new_track, old_track, frame_range ) @@ -2535,6 +2572,7 @@ def do_action(context: CommandContext, params: dict): return context.labels.remove_instance(context.state["labeled_frame"], selected_inst) + context.state["instance"] = None class DeleteSelectedInstanceTrack(EditCommand): @@ -2552,6 +2590,7 @@ def do_action(context: CommandContext, params: dict): track = selected_inst.track context.labels.remove_instance(context.state["labeled_frame"], selected_inst) + context.state["instance"] = None if track is not None: # remove any instance on this track @@ -2723,7 +2762,6 @@ class GenerateSuggestions(EditCommand): @classmethod def do_action(cls, context: CommandContext, params: dict): - if len(context.labels.videos) == 0: print("Error: no videos to generate suggestions for") return @@ -2851,27 +2889,13 @@ def ask_and_do(cls, context: CommandContext, params: dict): class AddInstance(EditCommand): topics = [UpdateTopic.frame, UpdateTopic.project_instances, UpdateTopic.suggestions] - @staticmethod - def get_previous_frame_index(context: CommandContext) -> Optional[int]: - frames = context.labels.frames( - context.state["video"], - from_frame_idx=context.state["frame_idx"], - reverse=True, - ) - - try: - next_idx = next(frames).frame_idx - except: - return - - return next_idx - @classmethod def do_action(cls, context: CommandContext, params: dict): copy_instance = params.get("copy_instance", None) init_method = params.get("init_method", "best") location = params.get("location", None) mark_complete = params.get("mark_complete", False) + offset = params.get("offset", 0) if context.state["labeled_frame"] is None: return @@ -2879,6 +2903,250 @@ def do_action(cls, context: CommandContext, params: dict): if len(context.state["skeleton"]) == 0: return + ( + copy_instance, + from_predicted, + from_prev_frame, + ) = AddInstance.find_instance_to_copy_from( + context, copy_instance=copy_instance, init_method=init_method + ) + + new_instance = AddInstance.create_new_instance( + context=context, + from_predicted=from_predicted, + copy_instance=copy_instance, + mark_complete=mark_complete, + init_method=init_method, + location=location, + from_prev_frame=from_prev_frame, + offset=offset, + ) + + # Add the instance + context.labels.add_instance(context.state["labeled_frame"], new_instance) + + if context.state["labeled_frame"] not in context.labels.labels: + context.labels.append(context.state["labeled_frame"]) + + @staticmethod + def create_new_instance( + context: CommandContext, + from_predicted: Optional[PredictedInstance], + copy_instance: Optional[Union[Instance, PredictedInstance]], + mark_complete: bool, + init_method: str, + location: Optional[QtCore.QPoint], + from_prev_frame: bool, + offset: int = 0, + ) -> Instance: + """Create new instance.""" + + # Now create the new instance + new_instance = Instance( + skeleton=context.state["skeleton"], + from_predicted=from_predicted, + frame=context.state["labeled_frame"], + ) + + has_missing_nodes = AddInstance.set_visible_nodes( + context=context, + copy_instance=copy_instance, + new_instance=new_instance, + mark_complete=mark_complete, + init_method=init_method, + location=location, + offset=offset, + ) + + if has_missing_nodes: + AddInstance.fill_missing_nodes( + context=context, + copy_instance=copy_instance, + init_method=init_method, + new_instance=new_instance, + location=location, + ) + + # If we're copying a predicted instance or from another frame, copy the track + if hasattr(copy_instance, "score") or from_prev_frame: + copy_instance = cast(Union[PredictedInstance, Instance], copy_instance) + new_instance.track = copy_instance.track + + return new_instance + + @staticmethod + def fill_missing_nodes( + context: CommandContext, + copy_instance: Optional[Union[Instance, PredictedInstance]], + init_method: str, + new_instance: Instance, + location: Optional[QtCore.QPoint], + ): + """Fill in missing nodes for new instance. + + Args: + context: The command context. + copy_instance: The instance to copy from. + init_method: The initialization method. + new_instance: The new instance. + location: The location of the instance. + + Returns: + None + """ + + # mark the node as not "visible" if we're copying from a predicted instance without this node + is_visible = copy_instance is None or (not hasattr(copy_instance, "score")) + + if init_method == "force_directed": + AddMissingInstanceNodes.add_force_directed_nodes( + context=context, + instance=new_instance, + visible=is_visible, + center_point=location, + ) + elif init_method == "random": + AddMissingInstanceNodes.add_random_nodes( + context=context, instance=new_instance, visible=is_visible + ) + elif init_method == "template": + AddMissingInstanceNodes.add_nodes_from_template( + context=context, + instance=new_instance, + visible=is_visible, + center_point=location, + ) + else: + AddMissingInstanceNodes.add_best_nodes( + context=context, instance=new_instance, visible=is_visible + ) + + @staticmethod + def set_visible_nodes( + context: CommandContext, + copy_instance: Optional[Union[Instance, PredictedInstance]], + new_instance: Instance, + mark_complete: bool, + init_method: str, + location: Optional[QtCore.QPoint] = None, + offset: int = 0, + ) -> bool: + """Sets visible nodes for new instance. + + Args: + context: The command context. + copy_instance: The instance to copy from. + new_instance: The new instance. + mark_complete: Whether to mark the instance as complete. + init_method: The initialization method. + location: The location of the mouse click if any. + offset: The offset to apply to all nodes. + + Returns: + Whether the new instance has missing nodes. + """ + + if copy_instance is None: + return True + + has_missing_nodes = False + + # Calculate scale factor for getting new x and y values. + old_size_width = copy_instance.frame.video.shape[2] + old_size_height = copy_instance.frame.video.shape[1] + new_size_width = new_instance.frame.video.shape[2] + new_size_height = new_instance.frame.video.shape[1] + scale_width = new_size_width / old_size_width + scale_height = new_size_height / old_size_height + + # The offset is 0, except when using Ctrl + I or Add Instance button. + offset_x = offset + offset_y = offset + + # Using right click and context menu with option "best" + if (init_method == "best") and (location is not None): + reference_node = next( + (node for node in copy_instance if not node.isnan()), None + ) + reference_x = reference_node.x + reference_y = reference_node.y + offset_x = location.x() - (reference_x * scale_width) + offset_y = location.y() - (reference_y * scale_height) + + # Go through each node in skeleton. + for node in context.state["skeleton"].node_names: + # If we're copying from a skeleton that has this node. + if node in copy_instance and not copy_instance[node].isnan(): + # Ensure x, y inside current frame, then copy x, y, and visible. + # We don't want to copy a PredictedPoint or score attribute. + x_old = copy_instance[node].x + y_old = copy_instance[node].y + + # Copy the instance without scale or offset if predicted + if isinstance(copy_instance, PredictedInstance): + x_new = x_old + y_new = y_old + else: + x_new = x_old * scale_width + y_new = y_old * scale_height + + # Apply offset if in bounds + x_new_offset = x_new + offset_x + y_new_offset = y_new + offset_y + + # Default visibility is same as copied instance. + visible = copy_instance[node].visible + + # If the node is offset to outside the frame, mark as not visible. + if x_new_offset < 0: + x_new = 0 + visible = False + elif x_new_offset > new_size_width: + x_new = new_size_width + visible = False + else: + x_new = x_new_offset + if y_new_offset < 0: + y_new = 0 + visible = False + elif y_new_offset > new_size_height: + y_new = new_size_height + visible = False + else: + y_new = y_new_offset + + # Update the new instance with the new x, y, and visibility. + new_instance[node] = Point( + x=x_new, + y=y_new, + visible=visible, + complete=mark_complete, + ) + else: + has_missing_nodes = True + + return has_missing_nodes + + @staticmethod + def find_instance_to_copy_from( + context: CommandContext, + copy_instance: Optional[Union[Instance, PredictedInstance]], + init_method: bool, + ) -> Tuple[ + Optional[Union[Instance, PredictedInstance]], Optional[PredictedInstance], bool + ]: + """Find instance to copy from. + + Args: + context: The command context. + copy_instance: The `Instance` to copy from. + init_method: The initialization method. + + Returns: + The instance to copy from, the predicted instance (if it is from a predicted + instance, else None), and whether it's from a previous frame. + """ + from_predicted = copy_instance from_prev_frame = False @@ -2904,7 +3172,7 @@ def do_action(cls, context: CommandContext, params: dict): ) or init_method == "prior_frame": # Otherwise, if there are instances in previous frames, # copy the points from one of those instances. - prev_idx = cls.get_previous_frame_index(context) + prev_idx = AddInstance.get_previous_frame_index(context) if prev_idx is not None: prev_instances = context.labels.find( @@ -2929,71 +3197,26 @@ def do_action(cls, context: CommandContext, params: dict): from_prev_frame = True from_predicted = from_predicted if hasattr(from_predicted, "score") else None + from_predicted = cast(Optional[PredictedInstance], from_predicted) - # Now create the new instance - new_instance = Instance( - skeleton=context.state["skeleton"], - from_predicted=from_predicted, - frame=context.state["labeled_frame"], - ) + return copy_instance, from_predicted, from_prev_frame - has_missing_nodes = False - - # go through each node in skeleton - for node in context.state["skeleton"].node_names: - # if we're copying from a skeleton that has this node - if ( - copy_instance is not None - and node in copy_instance - and not copy_instance[node].isnan() - ): - # just copy x, y, and visible - # we don't want to copy a PredictedPoint or score attribute - new_instance[node] = Point( - x=copy_instance[node].x, - y=copy_instance[node].y, - visible=copy_instance[node].visible, - complete=mark_complete, - ) - else: - has_missing_nodes = True - - if has_missing_nodes: - # mark the node as not "visible" if we're copying from a predicted instance without this node - is_visible = copy_instance is None or (not hasattr(copy_instance, "score")) - - if init_method == "force_directed": - AddMissingInstanceNodes.add_force_directed_nodes( - context=context, - instance=new_instance, - visible=is_visible, - center_point=location, - ) - elif init_method == "random": - AddMissingInstanceNodes.add_random_nodes( - context=context, instance=new_instance, visible=is_visible - ) - elif init_method == "template": - AddMissingInstanceNodes.add_nodes_from_template( - context=context, - instance=new_instance, - visible=is_visible, - center_point=location, - ) - else: - AddMissingInstanceNodes.add_best_nodes( - context=context, instance=new_instance, visible=is_visible - ) + @staticmethod + def get_previous_frame_index(context: CommandContext) -> Optional[int]: + """Returns index of previous frame.""" - # If we're copying a predicted instance or from another frame, copy the track - if hasattr(copy_instance, "score") or from_prev_frame: - new_instance.track = copy_instance.track + frames = context.labels.frames( + context.state["video"], + from_frame_idx=context.state["frame_idx"], + reverse=True, + ) - # Add the instance - context.labels.add_instance(context.state["labeled_frame"], new_instance) + try: + next_idx = next(frames).frame_idx + except: + return - if context.state["labeled_frame"] not in context.labels.labels: - context.labels.append(context.state["labeled_frame"]) + return next_idx class SetInstancePointLocations(EditCommand): diff --git a/sleap/gui/dataviews.py b/sleap/gui/dataviews.py index 0a008bea7..721bdc321 100644 --- a/sleap/gui/dataviews.py +++ b/sleap/gui/dataviews.py @@ -15,20 +15,17 @@ """ -from qtpy import QtCore, QtWidgets, QtGui - -import numpy as np import os - from operator import itemgetter +from pathlib import Path +from typing import Any, Callable, List, Optional -from typing import Any, Callable, Dict, List, Optional, Type +import numpy as np +from qtpy import QtCore, QtGui, QtWidgets -from sleap.gui.state import GuiState from sleap.gui.commands import CommandContext -from sleap.gui.color import ColorManager -from sleap.io.dataset import Labels -from sleap.instance import LabeledFrame, Instance +from sleap.gui.state import GuiState +from sleap.instance import LabeledFrame from sleap.skeleton import Skeleton @@ -386,10 +383,25 @@ def getSelectedRowItem(self) -> Any: class VideosTableModel(GenericTableModel): - properties = ("filename", "frames", "height", "width", "channels") - - def item_to_data(self, obj, item): - return {key: getattr(item, key) for key in self.properties} + properties = ( + "name", + "filepath", + "frames", + "height", + "width", + "channels", + ) + + def item_to_data(self, obj, item: "Video"): + data = {} + for property in self.properties: + if property == "name": + data[property] = Path(item.filename).name + elif property == "filepath": + data[property] = str(Path(item.filename).parent) + else: + data[property] = getattr(item, property) + return data class SkeletonNodesTableModel(GenericTableModel): @@ -413,13 +425,6 @@ def set_item(self, item, key, value): elif key == "symmetry": self.context.setNodeSymmetry(skeleton=self.obj, node=item, symmetry=value) - def get_item_color(self, item: Any, key: str): - if self.skeleton: - color = self.context.app.color_manager.get_item_color( - item, parent_skeleton=self.skeleton - ) - return QtGui.QColor(*color) - class SkeletonEdgesTableModel(GenericTableModel): """Table model for skeleton edges.""" @@ -436,14 +441,6 @@ def object_to_items(self, skeleton: Skeleton): ] return items - def get_item_color(self, item: Any, key: str): - if self.skeleton: - edge_pair = (item["source"], item["destination"]) - color = self.context.app.color_manager.get_item_color( - edge_pair, parent_skeleton=self.skeleton - ) - return QtGui.QColor(*color) - class LabeledFrameTableModel(GenericTableModel): """Table model for listing instances in labeled frame. diff --git a/sleap/gui/dialogs/export_clip.py b/sleap/gui/dialogs/export_clip.py index 312f9a807..f84766d18 100644 --- a/sleap/gui/dialogs/export_clip.py +++ b/sleap/gui/dialogs/export_clip.py @@ -11,16 +11,16 @@ def __init__(self): super().__init__(form_name="labeled_clip_form") - can_use_skvideo = VideoWriter.can_use_skvideo() + can_use_ffmpeg = VideoWriter.can_use_ffmpeg() - if can_use_skvideo: + if can_use_ffmpeg: message = ( "MP4 file will be encoded using " - "system ffmpeg via scikit-video (preferred option)." + "system ffmpeg via imageio (preferred option)." ) else: message = ( - "Unable to use ffpmeg via scikit-video. " + "Unable to use ffpmeg via imageio. " "AVI file will be encoding using OpenCV." ) diff --git a/sleap/gui/dialogs/filedialog.py b/sleap/gui/dialogs/filedialog.py index 930c71b0d..ff394d191 100644 --- a/sleap/gui/dialogs/filedialog.py +++ b/sleap/gui/dialogs/filedialog.py @@ -29,7 +29,8 @@ def set_dialog_type(cls, *args, **kwargs): if cls.is_non_native: kwargs["options"] = kwargs.get("options", 0) - kwargs["options"] |= QtWidgets.QFileDialog.DontUseNativeDialog + if not kwargs["options"]: + kwargs["options"] = QtWidgets.QFileDialog.DontUseNativeDialog # Make sure we don't send empty options argument if "options" in kwargs and not kwargs["options"]: diff --git a/sleap/gui/dialogs/frame_range.py b/sleap/gui/dialogs/frame_range.py new file mode 100644 index 000000000..7165dd939 --- /dev/null +++ b/sleap/gui/dialogs/frame_range.py @@ -0,0 +1,42 @@ +"""Frame range dialog.""" +from qtpy import QtWidgets +from sleap.gui.dialogs.formbuilder import FormBuilderModalDialog +from typing import Optional + + +class FrameRangeDialog(FormBuilderModalDialog): + def __init__(self, max_frame_idx: Optional[int] = None, title: str = "Frame Range"): + + super().__init__(form_name="frame_range_form") + min_frame_idx_field = self.form_widget.fields["min_frame_idx"] + max_frame_idx_field = self.form_widget.fields["max_frame_idx"] + + if max_frame_idx is not None: + min_frame_idx_field.setRange(1, max_frame_idx) + min_frame_idx_field.setValue(1) + + max_frame_idx_field.setRange(1, max_frame_idx) + max_frame_idx_field.setValue(max_frame_idx) + + min_frame_idx_field.valueChanged.connect(self._update_max_frame_range) + max_frame_idx_field.valueChanged.connect(self._update_min_frame_range) + + self.setWindowTitle(title) + + def _update_max_frame_range(self, value): + min_frame_idx_field = self.form_widget.fields["min_frame_idx"] + max_frame_idx_field = self.form_widget.fields["max_frame_idx"] + + max_frame_idx_field.setRange(value, max_frame_idx_field.maximum()) + + def _update_min_frame_range(self, value): + min_frame_idx_field = self.form_widget.fields["min_frame_idx"] + max_frame_idx_field = self.form_widget.fields["max_frame_idx"] + + min_frame_idx_field.setRange(min_frame_idx_field.minimum(), value) + + +if __name__ == "__main__": + app = QtWidgets.QApplication([]) + dialog = FrameRangeDialog(max_frame_idx=100) + print(dialog.get_results()) diff --git a/sleap/gui/dialogs/metrics.py b/sleap/gui/dialogs/metrics.py index 864a6adf0..884b373a9 100644 --- a/sleap/gui/dialogs/metrics.py +++ b/sleap/gui/dialogs/metrics.py @@ -120,10 +120,11 @@ def _show_model_params( if cfg_info is None: cfg_info = self.table_view.getSelectedRowItem() + cfg_getter = self._cfg_getter key = cfg_info.path if key not in model_detail_widgets: model_detail_widgets[key] = TrainingEditorWidget.from_trained_config( - cfg_info + cfg_info, cfg_getter ) model_detail_widgets[key].show() diff --git a/sleap/gui/learning/dialog.py b/sleap/gui/learning/dialog.py index d9f872fda..bc26d826c 100644 --- a/sleap/gui/learning/dialog.py +++ b/sleap/gui/learning/dialog.py @@ -1,24 +1,20 @@ """ Dialogs for running training and/or inference in GUI. """ -import cattr -import os +import json import shutil -import atexit import tempfile from pathlib import Path +from typing import Dict, List, Optional, Text, cast + +import cattr +from qtpy import QtCore, QtGui, QtWidgets import sleap from sleap import Labels, Video from sleap.gui.dialogs.filedialog import FileDialog from sleap.gui.dialogs.formbuilder import YamlFormWidget -from sleap.gui.learning import runners, scopedkeydict, configs, datagen, receptivefield - -from typing import Dict, List, Optional, Text, Optional, cast - -from qtpy import QtWidgets, QtCore - -import json +from sleap.gui.learning import configs, datagen, receptivefield, runners, scopedkeydict # List of fields which should show list of skeleton nodes NODE_LIST_FIELDS = [ @@ -128,12 +124,25 @@ def __init__( self.message_widget = QtWidgets.QLabel("") # Layout for entire dialog - layout = QtWidgets.QVBoxLayout() - layout.addWidget(self.tab_widget) - layout.addWidget(self.message_widget) - layout.addWidget(buttons_layout_widget) + content_widget = QtWidgets.QWidget() + content_layout = QtWidgets.QVBoxLayout(content_widget) - self.setLayout(layout) + content_layout.addWidget(self.tab_widget) + content_layout.addWidget(self.message_widget) + content_layout.addWidget(buttons_layout_widget) + + # Create the QScrollArea. + scroll_area = QtWidgets.QScrollArea() + scroll_area.setWidgetResizable(True) + scroll_area.setWidget(content_widget) + + scroll_area.setVerticalScrollBarPolicy(QtCore.Qt.ScrollBarAsNeeded) + scroll_area.setHorizontalScrollBarPolicy(QtCore.Qt.ScrollBarAsNeeded) + + layout = QtWidgets.QVBoxLayout(self) + layout.addWidget(scroll_area) + + self.adjust_initial_size() # Default to most recently trained pipeline (if there is one) self.set_default_pipeline_tab() @@ -157,6 +166,20 @@ def __init__( self.view_datagen ) + def adjust_initial_size(self): + # Get screen size + screen = QtGui.QGuiApplication.primaryScreen().availableGeometry() + + max_width = 1860 + max_height = 1150 + margin = 0.10 + + # Calculate target width and height + target_width = min(screen.width() - screen.width() * margin, max_width) + target_height = min(screen.height() - screen.height() * margin, max_height) + # Set the dialog's dimensions + self.resize(target_width, target_height) + def update_file_lists(self): self._cfg_getter.update() for tab in self.tabs.values(): @@ -579,6 +602,7 @@ def get_selected_frames_to_predict( def get_items_for_inference(self, pipeline_form_data) -> runners.ItemsForInference: predict_frames_choice = pipeline_form_data.get("_predict_frames", "") + batch_size = pipeline_form_data.get("batch_size") frame_selection = self.get_selected_frames_to_predict(pipeline_form_data) frame_count = self.count_total_frames_for_selection_option(frame_selection) @@ -591,6 +615,7 @@ def get_items_for_inference(self, pipeline_form_data) -> runners.ItemsForInferen ) ], total_frame_count=frame_count, + batch_size=batch_size, ) elif predict_frames_choice.startswith("suggested"): items_for_inference = runners.ItemsForInference( @@ -600,6 +625,7 @@ def get_items_for_inference(self, pipeline_form_data) -> runners.ItemsForInferen ) ], total_frame_count=frame_count, + batch_size=batch_size, ) else: items_for_inference = runners.ItemsForInference.from_video_frames_dict( @@ -607,9 +633,24 @@ def get_items_for_inference(self, pipeline_form_data) -> runners.ItemsForInferen total_frame_count=frame_count, labels_path=self.labels_filename, labels=self.labels, + batch_size=batch_size, ) return items_for_inference + def _validate_id_model(self) -> bool: + """Make sure we have instances with tracks set for ID models.""" + if not self.labels.tracks: + message = "Cannot run ID model training without tracks." + return False + + found_tracks = False + for inst in self.labels.instances(): + if type(inst) == sleap.Instance and inst.track is not None: + found_tracks = True + break + + return found_tracks + def _validate_pipeline(self): can_run = True message = "" @@ -628,6 +669,15 @@ def _validate_pipeline(self): f"({', '.join(untrained)})." ) + # Make sure we have instances with tracks set for ID models. + if self.mode == "training" and self.current_pipeline in ( + "top-down-id", + "bottom-up-id", + ): + can_run = self.validate_id_model() + if not can_run: + message = "Cannot run ID model training without tracks." + # Make sure skeleton will be valid for bottom-up inference. if self.mode == "training" and self.current_pipeline == "bottom-up": skeleton = self.labels.skeletons[0] @@ -1088,8 +1138,12 @@ def __init__( self.setLayout(layout) @classmethod - def from_trained_config(cls, cfg_info: configs.ConfigFileInfo): - widget = cls(require_trained=True, head=cfg_info.head_name) + def from_trained_config( + cls, cfg_info: configs.ConfigFileInfo, cfg_getter: configs.TrainingConfigsGetter + ): + widget = cls( + require_trained=True, head=cfg_info.head_name, cfg_getter=cfg_getter + ) widget.acceptSelectedConfigInfo(cfg_info) widget.setWindowTitle(cfg_info.path_dir) return widget diff --git a/sleap/gui/learning/runners.py b/sleap/gui/learning/runners.py index ca60c4127..d0bb1f3ba 100644 --- a/sleap/gui/learning/runners.py +++ b/sleap/gui/learning/runners.py @@ -1,4 +1,5 @@ """Run training/inference in background process via CLI.""" + import abc import attr import os @@ -151,6 +152,7 @@ class ItemsForInference: items: List[ItemForInference] total_frame_count: int + batch_size: int def __len__(self): return len(self.items) @@ -160,6 +162,7 @@ def from_video_frames_dict( cls, video_frames_dict: Dict[Video, List[int]], total_frame_count: int, + batch_size: int, labels: Labels, labels_path: Optional[str] = None, ): @@ -174,7 +177,9 @@ def from_video_frames_dict( video_idx=labels.videos.index(video), ) ) - return cls(items=items, total_frame_count=total_frame_count) + return cls( + items=items, total_frame_count=total_frame_count, batch_size=batch_size + ) @attr.s(auto_attribs=True) @@ -255,12 +260,20 @@ def make_predict_cli_call( "tracking.max_tracking", "tracking.post_connect_single_breaks", "tracking.save_shifted_instances", + "tracking.oks_score_weighting", ) for key in bool_items_as_ints: if key in self.inference_params: self.inference_params[key] = int(self.inference_params[key]) + remove_spaces_items = ("tracking.similarity",) + + for key in remove_spaces_items: + if key in self.inference_params: + value = self.inference_params[key] + self.inference_params[key] = value.replace(" ", "_") + for key, val in self.inference_params.items(): if not key.startswith(("_", "outputs.", "model.", "data.")): cli_args.extend((f"--{key}", str(val))) @@ -496,9 +509,11 @@ def write_pipeline_files( "data_path": os.path.basename(data_path), "models": [Path(p).as_posix() for p in new_cfg_filenames], "output_path": prediction_output_path, - "type": "labels" - if type(item_for_inference) == DatasetItemForInference - else "video", + "type": ( + "labels" + if type(item_for_inference) == DatasetItemForInference + else "video" + ), "only_suggested_frames": only_suggested_frames, "tracking": tracking_args, } @@ -540,6 +555,7 @@ def run_learning_pipeline( """ save_viz = inference_params.get("_save_viz", False) + keep_viz = inference_params.get("_keep_viz", False) if "movenet" in inference_params["_pipeline"]: trained_job_paths = [inference_params["_pipeline"]] @@ -550,8 +566,10 @@ def run_learning_pipeline( labels_filename=labels_filename, labels=labels, config_info_list=config_info_list, + inference_params=inference_params, gui=True, save_viz=save_viz, + keep_viz=keep_viz, ) # Check that all the models were trained @@ -577,8 +595,10 @@ def run_gui_training( labels_filename: str, labels: Labels, config_info_list: List[ConfigFileInfo], + inference_params: Dict[str, Any], gui: bool = True, save_viz: bool = False, + keep_viz: bool = False, ) -> Dict[Text, Text]: """ Runs training for each training job. @@ -588,19 +608,28 @@ def run_gui_training( config_info_list: List of ConfigFileInfo with configs for training. gui: Whether to show gui windows and process gui events. save_viz: Whether to save visualizations from training. + keep_viz: Whether to keep prediction visualization images after training. Returns: Dictionary, keys are head name, values are path to trained config. """ trained_job_paths = dict() - + zmq_ports = None if gui: from sleap.gui.widgets.monitor import LossViewer from sleap.gui.widgets.imagedir import QtImageDirectoryWidget - # open training monitor window - win = LossViewer() + zmq_ports = dict() + zmq_ports["controller_port"] = inference_params.get("controller_port", 9000) + zmq_ports["publish_port"] = inference_params.get("publish_port", 9001) + + # Open training monitor window + win = LossViewer(zmq_ports=zmq_ports) + + # Reassign the values in the inference parameters in case the ports were changed + inference_params["controller_port"] = win.zmq_ports["controller_port"] + inference_params["publish_port"] = win.zmq_ports["publish_port"] win.resize(600, 400) win.show() @@ -664,10 +693,12 @@ def waiting(): # Run training trained_job_path, ret = train_subprocess( job_config=job, + inference_params=inference_params, labels_filename=labels_filename, video_paths=video_path_list, waiting_callback=waiting, save_viz=save_viz, + keep_viz=keep_viz, ) if ret == "success": @@ -806,9 +837,11 @@ def waiting_item(**kwargs): def train_subprocess( job_config: TrainingJobConfig, labels_filename: str, + inference_params: Dict[str, Any], video_paths: Optional[List[Text]] = None, waiting_callback: Optional[Callable] = None, save_viz: bool = False, + keep_viz: bool = False, ): """Runs training inside subprocess.""" run_path = job_config.outputs.run_path @@ -829,10 +862,16 @@ def train_subprocess( training_job_path, labels_filename, "--zmq", + "--controller_port", + str(inference_params["controller_port"]), + "--publish_port", + str(inference_params["publish_port"]), ] if save_viz: cli_args.append("--save_viz") + if keep_viz: + cli_args.append("--keep_viz") # Use cli arg since cli ignores setting in config if job_config.outputs.tensorboard.write_logs: diff --git a/sleap/gui/overlays/base.py b/sleap/gui/overlays/base.py index d27b069ac..879d12810 100644 --- a/sleap/gui/overlays/base.py +++ b/sleap/gui/overlays/base.py @@ -71,7 +71,6 @@ def remove_from_scene(self): except RuntimeError as e: # Internal C++ object (PySide2.QtWidgets.QGraphicsPathItem) already deleted. logger.debug(e) - pass # Stop tracking the items after they been removed from the scene self.items = [] diff --git a/sleap/gui/overlays/tracks.py b/sleap/gui/overlays/tracks.py index 361585719..c5f091658 100644 --- a/sleap/gui/overlays/tracks.py +++ b/sleap/gui/overlays/tracks.py @@ -1,17 +1,16 @@ """Track trail and track list overlays.""" +from typing import Dict, Iterable, List, Optional, Tuple + +import attr +from qtpy import QtCore, QtGui + from sleap.gui.overlays.base import BaseOverlay +from sleap.gui.widgets.video import QtTextWithBackground from sleap.instance import Track from sleap.io.dataset import Labels from sleap.io.video import Video from sleap.prefs import prefs -from sleap.gui.widgets.video import QtTextWithBackground - -import attr - -from typing import Iterable, List, Optional, Dict - -from qtpy import QtCore, QtGui @attr.s(auto_attribs=True) @@ -48,7 +47,9 @@ def __attrs_post_init__(self): @classmethod def get_length_options(cls): - return (0, 10, 50, 100, 250) + if prefs["trail length"] != 0: + return (0, 10, 50, 100, 250, 500, prefs["trail length"]) + return (0, 10, 50, 100, 250, 500) @classmethod def get_shade_options(cls): @@ -56,7 +57,9 @@ def get_shade_options(cls): return {"Dark": 0.6, "Normal": 1.0, "Light": 1.25} - def get_track_trails(self, frame_selection: Iterable["LabeledFrame"]): + def get_track_trails( + self, frame_selection: Iterable["LabeledFrame"] + ) -> Optional[Dict[Track, List[List[Tuple[float, float]]]]]: """Get data needed to draw track trail. Args: @@ -152,6 +155,8 @@ def add_to_scene(self, video: Video, frame_idx: int): frame_selection = self.get_frame_selection(video, frame_idx) all_track_trails = self.get_track_trails(frame_selection) + if all_track_trails is None: + return for track, trails in all_track_trails.items(): trail_color = tuple( diff --git a/sleap/gui/suggestions.py b/sleap/gui/suggestions.py index 48b916437..b85d6ac32 100644 --- a/sleap/gui/suggestions.py +++ b/sleap/gui/suggestions.py @@ -61,6 +61,7 @@ def suggest(cls, params: dict, labels: "Labels" = None) -> List[SuggestionFrame] prediction_score=cls.prediction_score, velocity=cls.velocity, frame_chunk=cls.frame_chunk, + max_point_displacement=cls.max_point_displacement, ) method = str.replace(params["method"], " ", "_") @@ -213,6 +214,7 @@ def _prediction_score_video( ): lfs = labels.find(video) frames = len(lfs) + # initiate an array filled with -1 to store frame index (starting from 0). idxs = np.full((frames), -1, dtype="int") @@ -291,6 +293,56 @@ def _velocity_video( return cls.idx_list_to_frame_list(frame_idxs, video) + @classmethod + def max_point_displacement( + cls, + labels: "Labels", + videos: List[Video], + displacement_threshold: float, + **kwargs, + ): + """Finds frames with maximum point displacement above a threshold.""" + + proposed_suggestions = [] + for video in videos: + proposed_suggestions.extend( + cls._max_point_displacement_video(video, labels, displacement_threshold) + ) + + suggestions = VideoFrameSuggestions.filter_unique_suggestions( + labels, videos, proposed_suggestions + ) + + return suggestions + + @classmethod + def _max_point_displacement_video( + cls, video: Video, labels: "Labels", displacement_threshold: float + ): + # Get numpy of shape (frames, tracks, nodes, x, y) + labels_numpy = labels.numpy(video=video, all_frames=True, untracked=False) + + # Return empty list if not enough frames + n_frames, n_tracks, n_nodes, _ = labels_numpy.shape + + if n_frames < 2: + return [] + + # Calculate displacements + diff = labels_numpy[1:] - labels_numpy[:-1] # (frames - 1, tracks, nodes, x, y) + euc_norm = np.linalg.norm(diff, axis=-1) # (frames - 1, tracks, nodes) + mean_euc_norm = np.nanmean(euc_norm, axis=-1) # (frames - 1, tracks) + + # Find frames where mean displacement is above threshold + threshold_mask = np.any( + mean_euc_norm > displacement_threshold, axis=-1 + ) # (frames - 1,) + frame_idxs = list( + np.argwhere(threshold_mask).flatten() + 1 + ) # [0, len(frames - 1)] + + return cls.idx_list_to_frame_list(frame_idxs, video) + @classmethod def frame_chunk( cls, diff --git a/sleap/gui/utils.py b/sleap/gui/utils.py new file mode 100644 index 000000000..4f8215706 --- /dev/null +++ b/sleap/gui/utils.py @@ -0,0 +1,28 @@ +"""Generic module containing utilities used for the GUI.""" + +import zmq +from typing import Optional + + +def is_port_free(port: int, zmq_context: Optional[zmq.Context] = None) -> bool: + """Checks if a port is free.""" + ctx = zmq.Context.instance() if zmq_context is None else zmq_context + socket = ctx.socket(zmq.REP) + address = f"tcp://127.0.0.1:{port}" + try: + socket.bind(address) + socket.unbind(address) + return True + except zmq.error.ZMQError: + return False + finally: + socket.close() + + +def select_zmq_port(zmq_context: Optional[zmq.Context] = None) -> int: + """Select a port that is free to connect within the given context.""" + ctx = zmq.Context.instance() if zmq_context is None else zmq_context + socket = ctx.socket(zmq.REP) + port = socket.bind_to_random_port("tcp://127.0.0.1") + socket.close() + return port diff --git a/sleap/gui/widgets/docks.py b/sleap/gui/widgets/docks.py index 43e218adb..bd20bf79a 100644 --- a/sleap/gui/widgets/docks.py +++ b/sleap/gui/widgets/docks.py @@ -30,10 +30,8 @@ ) from sleap.gui.dialogs.formbuilder import YamlFormWidget from sleap.gui.widgets.views import CollapsibleWidget -from sleap.skeleton import Skeleton -from sleap.util import decode_preview_image, find_files_by_suffix, get_package_file - -# from sleap.gui.app import MainWindow +from sleap.skeleton import Skeleton, SkeletonDecoder +from sleap.util import find_files_by_suffix, get_package_file class DockWidget(QDockWidget): @@ -365,7 +363,7 @@ def create_templates_groupbox(self) -> QGroupBox: def updatePreviewImage(preview_image_bytes: bytes): # Decode the preview image - preview_image = decode_preview_image(preview_image_bytes) + preview_image = SkeletonDecoder.decode_preview_image(preview_image_bytes) # Create a QImage from the Image preview_image = QtGui.QImage( @@ -557,7 +555,7 @@ def create_table_edit_buttons(self) -> QWidget: hb = QHBoxLayout() self.add_button( - hb, "New Instance", lambda x: main_window.commands.newInstance() + hb, "New Instance", lambda x: main_window.commands.newInstance(offset=10) ) self.add_button( hb, "Delete Instance", main_window.commands.deleteSelectedInstance diff --git a/sleap/gui/widgets/monitor.py b/sleap/gui/widgets/monitor.py index 93bc483e9..fff8a0327 100644 --- a/sleap/gui/widgets/monitor.py +++ b/sleap/gui/widgets/monitor.py @@ -1,20 +1,590 @@ """GUI for monitoring training progress interactively.""" -import numpy as np -from time import perf_counter -from sleap.nn.config.training_job import TrainingJobConfig -import zmq -import jsonpickle import logging -from typing import Optional -from qtpy import QtCore, QtWidgets, QtGui -from qtpy.QtCharts import QtCharts +from time import perf_counter +from typing import Dict, Optional, Tuple + import attr +import jsonpickle +import numpy as np +import zmq +from matplotlib.collections import PathCollection +import matplotlib.transforms as mtransforms +from qtpy import QtCore, QtWidgets +from sleap.gui.utils import is_port_free, select_zmq_port +from sleap.gui.widgets.mpl import MplCanvas +from sleap.nn.config.training_job import TrainingJobConfig logger = logging.getLogger(__name__) +class LossPlot(MplCanvas): + """Matplotlib canvas for diplaying training and validation loss curves.""" + + def __init__( + self, + width: int = 5, + height: int = 4, + dpi: int = 100, + log_scale: bool = True, + ignore_outliers: bool = False, + ): + super().__init__(width=width, height=height, dpi=dpi) + + self._log_scale: bool = log_scale + + self.ignore_outliers = ignore_outliers + + # Initialize the series for the plot + self.series: dict = {} + COLOR_TRAIN = (18, 158, 220) + COLOR_VAL = (248, 167, 52) + COLOR_BEST_VAL = (151, 204, 89) + + # Initialize scatter series for batch training loss + self.series["batch"] = self._init_series( + series_type=self.axes.scatter, + name="Batch Training Loss", + color=COLOR_TRAIN + (48,), + border_color=(255, 255, 255, 25), + ) + + # Initialize line series for epoch training loss + self.series["epoch_loss"] = self._init_series( + series_type=self.axes.plot, + name="Epoch Training Loss", + color=COLOR_TRAIN + (255,), + line_width=3.0, + ) + + # Initialize line series for epoch validation loss + self.series["val_loss"] = self._init_series( + series_type=self.axes.plot, + name="Epoch Validation Loss", + color=COLOR_VAL + (255,), + line_width=3.0, + zorder=4, # Below best validation loss series + ) + + # Initialize scatter series for best epoch validation loss + self.series["val_loss_best"] = self._init_series( + series_type=self.axes.scatter, + name="Best Validation Loss", + color=COLOR_BEST_VAL + (255,), + border_color=(255, 255, 255, 25), + zorder=5, # Above epoch validation loss series + ) + + # Set the x and y positions for the xy labels (as fraction of figure size) + self.ypos_xlabel = 0.1 + self.xpos_ylabel = 0.05 + + # Padding between the axes and the xy labels + self.xpos_padding = 0.2 + self.ypos_padding = 0.1 + + # Set up the major gridlines + self._setup_major_gridlines() + + # Set up the x-axis + self._setup_x_axis() + + # Set up the y-axis + self._set_up_y_axis() + + # Set up the legend + self.legend_width, legend_height = self._setup_legend() + + # Set up the title space + self.ypos_title = None + title_height = self._set_title_space() + self.ypos_title = 1 - title_height - self.ypos_padding + + # Determine the top height of the plot + top_height = max(title_height, legend_height) + + # Adjust the figure layout + self.xpos_left_plot = self.xpos_ylabel + self.xpos_padding + self.xpos_right_plot = 0.97 + self.ypos_bottom_plot = self.ypos_xlabel + self.ypos_padding + self.ypos_top_plot = 1 - top_height - self.ypos_padding + + # Adjust the top parameters as needed + self.fig.subplots_adjust( + left=self.xpos_left_plot, + right=self.xpos_right_plot, + top=self.ypos_top_plot, + bottom=self.ypos_bottom_plot, + ) + + @property + def log_scale(self): + """Returns True if the plot has a log scale for y-axis.""" + + return self._log_scale + + @log_scale.setter + def log_scale(self, val): + """Sets the scale of the y axis to log if True else linear.""" + + if isinstance(val, bool): + self._log_scale = val + + y_scale = "log" if self._log_scale else "linear" + self.axes.set_yscale(y_scale) + self.redraw_plot() + + def set_data_on_scatter(self, xs, ys, which): + """Set data on a scatter plot. + + Not to be used with line plots. + + Args: + xs: The x-coordinates of the data points. + ys: The y-coordinates of the data points. + which: The type of data point. Possible values are: + * "batch" + * "val_loss_best" + """ + + offsets = np.column_stack((xs, ys)) + self.series[which].set_offsets(offsets) + + def add_data_to_plot(self, x, y, which): + """Add data to a line plot. + + Not to be used with scatter plots. + + Args: + x: The x-coordinate of the data point. + y: The y-coordinate of the data point. + which: The type of data point. Possible values are: + * "epoch_loss" + * "val_loss" + """ + + x_data, y_data = self.series[which].get_data() + self.series[which].set_data(np.append(x_data, x), np.append(y_data, y)) + + def resize_axes(self, x, y): + """Resize axes to fit data. + + This is only called when plotting batches. + + Args: + x: The x-coordinates of the data points. + y: The y-coordinates of the data points. + """ + + # Set X scale to show all points + x_min, x_max = self._calculate_xlim(x) + self.axes.set_xlim(x_min, x_max) + + # Set Y scale, ensuring that y_min and y_max do not lead to sngular transform + y_min, y_max = self._calculate_ylim(y) + y_min, y_max = self.axes.yaxis.get_major_locator().nonsingular(y_min, y_max) + self.axes.set_ylim(y_min, y_max) + + # Add gridlines at midpoint between major ticks (major gridlines are automatic) + self._add_midpoint_gridlines() + + # Redraw the plot + self.redraw_plot() + + def redraw_plot(self): + """Redraw the plot.""" + + self.fig.canvas.draw_idle() + + def set_title(self, title, color=None): + """Set the title of the plot. + + Args: + title: The title text to display. + """ + + if color is None: + color = "black" + + self.axes.set_title( + title, fontweight="light", fontsize="small", color=color, x=0.55, y=1.03 + ) + + def update_runtime_title( + self, + epoch: int, + dt_min: int, + dt_sec: int, + last_epoch_val_loss: float = None, + penultimate_epoch_val_loss: float = None, + mean_epoch_time_min: int = None, + mean_epoch_time_sec: int = None, + eta_ten_epochs_min: int = None, + epochs_in_plateau: int = None, + plateau_patience: int = None, + epoch_in_plateau_flag: bool = False, + best_val_x: int = None, + best_val_y: float = None, + epoch_size: int = None, + ): + + # Add training epoch and runtime info + title = self._get_training_epoch_and_runtime_text(epoch, dt_min, dt_sec) + + if last_epoch_val_loss is not None: + + if penultimate_epoch_val_loss is not None: + # Add mean epoch time and ETA for next 10 epochs + eta_text = self._get_eta_text( + mean_epoch_time_min, mean_epoch_time_sec, eta_ten_epochs_min + ) + title = self._add_with_newline(title, eta_text) + + # Add epochs in plateau if flag is set + if epoch_in_plateau_flag: + plateau_text = self._get_epochs_in_plateau_text( + epochs_in_plateau, plateau_patience + ) + title = self._add_with_newline(title, plateau_text) + + # Add last epoch validation loss + last_val_text = self._get_last_validation_loss_text(last_epoch_val_loss) + title = self._add_with_newline(title, last_val_text) + + # Add best epoch validation loss if available + if best_val_x is not None: + best_epoch = (best_val_x // epoch_size) + 1 + best_val_text = self._get_best_validation_loss_text( + best_val_y, best_epoch + ) + title = self._add_with_newline(title, best_val_text) + + self.set_title(title) + + @staticmethod + def _get_training_epoch_and_runtime_text(epoch: int, dt_min: int, dt_sec: int): + """Get the training epoch and runtime text to display in the plot. + + Args: + epoch: The current epoch. + dt_min: The number of minutes since training started. + dt_sec: The number of seconds since training started. + """ + + runtime_text = ( + r"Training Epoch $\mathbf{" + str(epoch + 1) + r"}$ / " + r"Runtime: $\mathbf{" + f"{int(dt_min):02}:{int(dt_sec):02}" + r"}$" + ) + + return runtime_text + + @staticmethod + def _get_eta_text(mean_epoch_time_min, mean_epoch_time_sec, eta_ten_epochs_min): + """Get the mean time and ETA text to display in the plot. + + Args: + mean_epoch_time_min: The mean time per epoch in minutes. + mean_epoch_time_sec: The mean time per epoch in seconds. + eta_ten_epochs_min: The estimated time for the next ten epochs in minutes. + """ + + runtime_text = ( + r"Mean Time per Epoch: $\mathbf{" + + f"{int(mean_epoch_time_min):02}:{int(mean_epoch_time_sec):02}" + + r"}$ / " + r"ETA Next 10 Epochs: $\mathbf{" + f"{int(eta_ten_epochs_min)}" + r"}$ min" + ) + + return runtime_text + + @staticmethod + def _get_epochs_in_plateau_text(epochs_in_plateau, plateau_patience): + """Get the epochs in plateau text to display in the plot. + + Args: + epochs_in_plateau: The number of epochs in plateau. + plateau_patience: The number of epochs to wait before stopping training. + """ + + plateau_text = ( + r"Epochs in Plateau: $\mathbf{" + f"{epochs_in_plateau}" + r"}$ / " + r"$\mathbf{" + f"{plateau_patience}" + r"}$" + ) + + return plateau_text + + @staticmethod + def _get_last_validation_loss_text(last_epoch_val_loss): + """Get the last epoch validation loss text to display in the plot. + + Args: + last_epoch_val_loss: The validation loss from the last epoch. + """ + + last_val_loss_text = ( + "Last Epoch Validation Loss: " + r"$\mathbf{" + f"{last_epoch_val_loss:.3e}" + r"}$" + ) + + return last_val_loss_text + + @staticmethod + def _get_best_validation_loss_text(best_val_y, best_epoch): + """Get the best epoch validation loss text to display in the plot. + + Args: + best_val_x: The epoch number of the best validation loss. + best_val_y: The best validation loss. + """ + + best_val_loss_text = ( + r"Best Epoch Validation Loss: $\mathbf{" + + f"{best_val_y:.3e}" + + r"}$ (epoch $\mathbf{" + + str(best_epoch) + + r"}$)" + ) + + return best_val_loss_text + + @staticmethod + def _add_with_newline(old_text: str, new_text: str): + """Add a new line to the text. + + Args: + old_text: The existing text. + new_text: The text to add on a new line. + """ + + return old_text + "\n" + new_text + + @staticmethod + def _calculate_xlim(x: np.ndarray, dx: float = 0.5): + """Calculates x-axis limits. + + Args: + x: Array of x data to fit the limits to. + dx: The padding to add to the limits. + + Returns: + Tuple of the minimum and maximum x-axis limits. + """ + + x_min = min(x) - dx + x_min = x_min if x_min > 0 else 0 + x_max = max(x) + dx + + return x_min, x_max + + def _calculate_ylim(self, y: np.ndarray, dy: float = 0.02): + """Calculates y-axis limits. + + Args: + y: Array of y data to fit the limits to. + dy: The padding to add to the limits. + + Returns: + Tuple of the minimum and maximum y-axis limits. + """ + + if self.ignore_outliers: + dy = np.ptp(y) * 0.02 + # Set Y scale to exclude outliers + q1, q3 = np.quantile(y, (0.25, 0.75)) + iqr = q3 - q1 # Interquartile range + y_min = q1 - iqr * 1.5 + y_max = q3 + iqr * 1.5 + + # Keep within range of data + y_min = max(y_min, min(y) - dy) + y_max = min(y_max, max(y) + dy) + else: + # Set Y scale to show all points + dy = np.ptp(y) * 0.02 + y_min = min(y) - dy + y_max = max(y) + dy + + # For log scale, low cannot be 0 + if self.log_scale: + y_min = max(y_min, 1e-8) + + return y_min, y_max + + def _set_title_space(self): + """Set up the title space. + + Returns: + The height of the title space as a decimal fraction of the total figure height. + """ + + # Set a dummy title of the plot + n_lines = 5 # Number of lines in the title + title_str = "\n".join( + [r"Number: $\mathbf{" + str(n) + r"}$" for n in range(n_lines + 1)] + ) + self.set_title( + title_str, color="white" + ) # Set the title color to white so it's not visible + + # Draw the canvas to ensure the title is created + self.fig.canvas.draw() + + # Get the title Text object + title = self.axes.title + + # Get the bounding box of the title in display coordinates + bbox = title.get_window_extent() + + # Transform the bounding box to figure coordinates + bbox = bbox.transformed(self.fig.transFigure.inverted()) + + # Calculate the height of the title as a percentage of the total figure height + title_height = bbox.height + + return title_height + + def _setup_x_axis(self): + """Set up the x axis. + + This includes setting the label, limits, and bottom/right adjustment. + """ + + self.axes.set_xlim(0, 1) + self.axes.set_xlabel("Batches", fontweight="bold", fontsize="small") + + # Set the x-label in the center of the axes and some amount above the bottom of the figure + blended_transform = mtransforms.blended_transform_factory( + self.axes.transAxes, self.fig.transFigure + ) + self.axes.xaxis.set_label_coords( + 0.5, self.ypos_xlabel, transform=blended_transform + ) + + def _set_up_y_axis(self): + """Set up the y axis. + + This includes setting the label, limits, scaling, and left adjustment. + """ + + # Set the minimum value of the y-axis depending on scaling + if self.log_scale: + yscale = "log" + y_min = 0.001 + else: + yscale = "linear" + y_min = 0 + self.axes.set_ylim(bottom=y_min) + self.axes.set_yscale(yscale) + + # Set the y-label name, size, wight, and position + self.axes.set_ylabel("Loss", fontweight="bold", fontsize="small") + self.axes.yaxis.set_label_coords( + self.xpos_ylabel, 0.5, transform=self.fig.transFigure + ) + + def _setup_legend(self): + """Set up the legend. + + Returns: + Tuple of the width and height of the legend as a decimal fraction of the total figure width and height. + """ + + # Move the legend outside the plot on the upper left + legend = self.axes.legend( + loc="upper left", + fontsize="small", + bbox_to_anchor=(0, 1), + bbox_transform=self.fig.transFigure, + ) + + # Draw the canvas to ensure the legend is created + self.fig.canvas.draw() + + # Get the bounding box of the legend in display coordinates + bbox = legend.get_window_extent() + + # Transform the bounding box to figure coordinates + bbox = bbox.transformed(self.fig.transFigure.inverted()) + + # Calculate the width and height of the legend as a percentage of the total figure width and height + return bbox.width, bbox.height + + def _setup_major_gridlines(self): + + # Set the outline color of the plot to gray + for spine in self.axes.spines.values(): + spine.set_edgecolor("#d3d3d3") # Light gray color + + # Remove the top and right axis spines + self.axes.spines["top"].set_visible(False) + self.axes.spines["right"].set_visible(False) + + # Set the tick markers color to light gray, but not the tick labels + self.axes.tick_params( + axis="both", which="both", color="#d3d3d3", labelsize="small" + ) + + # Add gridlines at the tick labels + self.axes.grid(True, which="major", linewidth=0.5, color="#d3d3d3") + + def _add_midpoint_gridlines(self): + # Clear existing minor vertical lines + for line in self.axes.get_lines(): + if line.get_linestyle() == ":": + line.remove() + + # Add gridlines at midpoint between major ticks + major_ticks = self.axes.yaxis.get_majorticklocs() + if len(major_ticks) > 1: + prev_major_tick = major_ticks[0] + for major_tick in major_ticks[:-1]: + midpoint = (major_tick + prev_major_tick) / 2 + self.axes.axhline( + midpoint, linestyle=":", linewidth=0.5, color="#d3d3d3" + ) + prev_major_tick = major_tick + + def _init_series( + self, + series_type, + color, + name: Optional[str] = None, + line_width: Optional[float] = None, + border_color: Optional[Tuple[int, int, int]] = None, + zorder: Optional[int] = None, + ): + + # Set the color + color = [c / 255.0 for c in color] # Normalize color values to [0, 1] + + # Create the series + series = series_type( + [], + [], + color=color, + label=name, + marker="o", + zorder=zorder, + ) + + # ax.plot returns a list of PathCollections, so we need to get the first one + if not isinstance(series, PathCollection): + series = series[0] + + if line_width is not None: + series.set_linewidth(line_width) + + # Set the border color (edge color) + if border_color is not None: + border_color = [ + c / 255.0 for c in border_color + ] # Normalize color values to [0, 1] + series.set_edgecolor(border_color) + + return series + + class LossViewer(QtWidgets.QMainWindow): """Qt window for showing in-progress training metrics sent over ZMQ.""" @@ -22,6 +592,7 @@ class LossViewer(QtWidgets.QMainWindow): def __init__( self, + zmq_ports: Dict = None, zmq_context: Optional[zmq.Context] = None, show_controller=True, parent=None, @@ -33,41 +604,62 @@ def __init__( self.cancel_button = None self.canceled = False + # Set up ZMQ ports for communication. + zmq_ports = zmq_ports or dict() + zmq_ports["publish_port"] = zmq_ports.get("publish_port", 9001) + zmq_ports["controller_port"] = zmq_ports.get("controller_port", 9000) + self.zmq_ports = zmq_ports + self.batches_to_show = -1 # -1 to show all - self.ignore_outliers = False - self.log_scale = True + self._ignore_outliers = False + self._log_scale = True self.message_poll_time_ms = 20 # ms self.redraw_batch_time_ms = 500 # ms self.last_redraw_batch = None + self.canvas = None self.reset() - self.setup_zmq(zmq_context) + self._setup_zmq(zmq_context) def __del__(self): - self.unbind() + self._unbind() - def close(self): - """Disconnect from ZMQ ports and close the window.""" - self.unbind() - super().close() + @property + def is_timer_running(self) -> bool: + """Return True if the timer has started.""" + return self.t0 is not None and self.is_running - def unbind(self): - """Disconnect from all ZMQ sockets.""" - if self.sub is not None: - self.sub.unbind(self.sub.LAST_ENDPOINT) - self.sub.close() - self.sub = None + @property + def log_scale(self): + """Returns True if the plot has a log scale for y-axis.""" - if self.zmq_ctrl is not None: - url = self.zmq_ctrl.LAST_ENDPOINT - self.zmq_ctrl.unbind(url) - self.zmq_ctrl.close() - self.zmq_ctrl = None + return self._log_scale - # If we started out own zmq context, terminate it. - if not self.ctx_given and self.ctx is not None: - self.ctx.term() - self.ctx = None + @log_scale.setter + def log_scale(self, val): + """Sets the scale of the y axis to log if True else linear.""" + + if isinstance(val, bool): + self._log_scale = val + + # Set the log scale on the canvas + self.canvas.log_scale = self._log_scale + + @property + def ignore_outliers(self): + """Returns True if the plot ignores outliers.""" + + return self._ignore_outliers + + @ignore_outliers.setter + def ignore_outliers(self, val): + """Sets whether to ignore outliers in the plot.""" + + if isinstance(val, bool): + self._ignore_outliers = val + + # Set the ignore_outliers on the canvas + self.canvas.ignore_outliers = self._ignore_outliers def reset( self, @@ -80,112 +672,34 @@ def reset( what: String identifier indicating which job type the current run corresponds to. """ - self.chart = QtCharts.QChart() - - self.series = dict() + self.canvas = LossPlot( + width=5, + height=4, + dpi=100, + log_scale=self.log_scale, + ignore_outliers=self.ignore_outliers, + ) - COLOR_TRAIN = (18, 158, 220) - COLOR_VAL = (248, 167, 52) - COLOR_BEST_VAL = (151, 204, 89) + self.mp_series = dict() + self.mp_series["batch"] = self.canvas.series["batch"] + self.mp_series["epoch_loss"] = self.canvas.series["epoch_loss"] + self.mp_series["val_loss"] = self.canvas.series["val_loss"] + self.mp_series["val_loss_best"] = self.canvas.series["val_loss_best"] - self.series["batch"] = QtCharts.QScatterSeries() - self.series["batch"].setName("Batch Training Loss") - self.series["batch"].setColor(QtGui.QColor(*COLOR_TRAIN, 48)) - self.series["batch"].setMarkerSize(8.0) - self.series["batch"].setBorderColor(QtGui.QColor(255, 255, 255, 25)) - self.chart.addSeries(self.series["batch"]) - - self.series["epoch_loss"] = QtCharts.QLineSeries() - self.series["epoch_loss"].setName("Epoch Training Loss") - self.series["epoch_loss"].setColor(QtGui.QColor(*COLOR_TRAIN, 255)) - pen = self.series["epoch_loss"].pen() - pen.setWidth(4) - self.series["epoch_loss"].setPen(pen) - self.chart.addSeries(self.series["epoch_loss"]) - - self.series["epoch_loss_scatter"] = QtCharts.QScatterSeries() - self.series["epoch_loss_scatter"].setColor(QtGui.QColor(*COLOR_TRAIN, 255)) - self.series["epoch_loss_scatter"].setMarkerSize(12.0) - self.series["epoch_loss_scatter"].setBorderColor( - QtGui.QColor(255, 255, 255, 25) - ) - self.chart.addSeries(self.series["epoch_loss_scatter"]) - - self.series["val_loss"] = QtCharts.QLineSeries() - self.series["val_loss"].setName("Epoch Validation Loss") - self.series["val_loss"].setColor(QtGui.QColor(*COLOR_VAL, 255)) - pen = self.series["val_loss"].pen() - pen.setWidth(4) - self.series["val_loss"].setPen(pen) - self.chart.addSeries(self.series["val_loss"]) - - self.series["val_loss_scatter"] = QtCharts.QScatterSeries() - self.series["val_loss_scatter"].setColor(QtGui.QColor(*COLOR_VAL, 255)) - self.series["val_loss_scatter"].setMarkerSize(12.0) - self.series["val_loss_scatter"].setBorderColor(QtGui.QColor(255, 255, 255, 25)) - self.chart.addSeries(self.series["val_loss_scatter"]) - - self.series["val_loss_best"] = QtCharts.QScatterSeries() - self.series["val_loss_best"].setName("Best Validation Loss") - self.series["val_loss_best"].setColor(QtGui.QColor(*COLOR_BEST_VAL, 255)) - self.series["val_loss_best"].setMarkerSize(12.0) - self.series["val_loss_best"].setBorderColor(QtGui.QColor(32, 32, 32, 25)) - self.chart.addSeries(self.series["val_loss_best"]) - - axisX = QtCharts.QValueAxis() - axisX.setLabelFormat("%d") - axisX.setTitleText("Batches") - self.chart.addAxis(axisX, QtCore.Qt.AlignBottom) - - # Create the different Y axes that can be used. - self.axisY = dict() - - self.axisY["log"] = QtCharts.QLogValueAxis() - self.axisY["log"].setBase(10) - - self.axisY["linear"] = QtCharts.QValueAxis() - - # Apply settings that apply to all Y axes. - for axisY in self.axisY.values(): - axisY.setLabelFormat("%f") - axisY.setLabelsVisible(True) - axisY.setMinorTickCount(1) - axisY.setTitleText("Loss") - - # Use the default Y axis. - axisY = self.axisY["log"] if self.log_scale else self.axisY["linear"] - - # Add axes to chart and series. - self.chart.addAxis(axisY, QtCore.Qt.AlignLeft) - for series in self.chart.series(): - series.attachAxis(axisX) - series.attachAxis(axisY) - - # Setup legend. - self.chart.legend().setVisible(True) - self.chart.legend().setAlignment(QtCore.Qt.AlignTop) - self.chart.legend().setMarkerShape(QtCharts.QLegend.MarkerShapeCircle) - - # Hide scatters for epoch and val loss from legend. - for s in ("epoch_loss_scatter", "val_loss_scatter"): - self.chart.legend().markers(self.series[s])[0].setVisible(False) - - self.chartView = QtCharts.QChartView(self.chart) - self.chartView.setRenderHint(QtGui.QPainter.Antialiasing) layout = QtWidgets.QVBoxLayout() - layout.addWidget(self.chartView) + layout.addWidget(self.canvas) if self.show_controller: control_layout = QtWidgets.QHBoxLayout() field = QtWidgets.QCheckBox("Log Scale") field.setChecked(self.log_scale) - field.stateChanged.connect(self.toggle_log_scale) + field.stateChanged.connect(self._toggle_log_scale) control_layout.addWidget(field) field = QtWidgets.QCheckBox("Ignore Outliers") field.setChecked(self.ignore_outliers) - field.stateChanged.connect(self.toggle_ignore_outliers) + field.stateChanged.connect(self._toggle_ignore_outliers) control_layout.addWidget(field) control_layout.addWidget(QtWidgets.QLabel("Batches to Show:")) @@ -203,7 +717,7 @@ def reset( # Set connection action for when user selects another option. field.currentIndexChanged.connect( - lambda x: self.set_batches_to_show(self.batch_options[x]) + lambda x: self._set_batches_to_show(self.batch_options[x]) ) # Store field as property and add to layout. @@ -213,10 +727,10 @@ def reset( control_layout.addStretch(1) self.stop_button = QtWidgets.QPushButton("Stop Early") - self.stop_button.clicked.connect(self.stop) + self.stop_button.clicked.connect(self._stop) control_layout.addWidget(self.stop_button) self.cancel_button = QtWidgets.QPushButton("Cancel Training") - self.cancel_button.clicked.connect(self.cancel) + self.cancel_button.clicked.connect(self._cancel) control_layout.addWidget(self.cancel_button) widget = QtWidgets.QWidget() @@ -248,48 +762,16 @@ def reset( self.last_batch_number = 0 self.is_running = False - def toggle_ignore_outliers(self): - """Toggles whether to ignore outliers in chart scaling.""" - self.ignore_outliers = not self.ignore_outliers - - def toggle_log_scale(self): - """Toggle whether to use log-scaled y-axis.""" - self.log_scale = not self.log_scale - self.update_y_axis() - - def set_batches_to_show(self, batches: str): - """Set the number of batches to show on the x-axis. + def set_message(self, text: str): + """Set the chart title text.""" + self.canvas.set_title(text) - Args: - batches: Number of batches as a string. If numeric, this will be converted - to an integer. If non-numeric string (e.g., "All"), then all batches - will be shown. - """ - if batches.isdigit(): - self.batches_to_show = int(batches) - else: - self.batches_to_show = -1 + def close(self): + """Disconnect from ZMQ ports and close the window.""" + self._unbind() + super().close() - def update_y_axis(self): - """Update the y-axis when scale changes.""" - to = "log" if self.log_scale else "linear" - - # Remove other axes. - for name, axisY in self.axisY.items(): - if name != to: - if axisY in self.chart.axes(): - self.chart.removeAxis(axisY) - for series in self.chart.series(): - if axisY in series.attachedAxes(): - series.detachAxis(axisY) - - # Add axis. - axisY = self.axisY[to] - self.chart.addAxis(axisY, QtCore.Qt.AlignLeft) - for series in self.chart.series(): - series.attachAxis(axisY) - - def setup_zmq(self, zmq_context: Optional[zmq.Context] = None): + def _setup_zmq(self, zmq_context: Optional[zmq.Context] = None): """Connect to ZMQ ports that listen to commands and updates. Args: @@ -305,112 +787,69 @@ def setup_zmq(self, zmq_context: Optional[zmq.Context] = None): # Progress monitoring, SUBSCRIBER self.sub = self.ctx.socket(zmq.SUB) self.sub.subscribe("") - self.sub.bind("tcp://127.0.0.1:9001") + + def find_free_port(port: int, zmq_context: zmq.Context): + """Find free port to bind to. + + Args: + port: The port to start searching from. + zmq_context: The ZMQ context to use. + + Returns: + The free port. + """ + attempts = 0 + max_attempts = 10 + while not is_port_free(port=port, zmq_context=zmq_context): + if attempts >= max_attempts: + raise RuntimeError( + f"Could not find free port to display training progress after " + f"{max_attempts} attempts. Please check your network settings " + "or use the CLI `sleap-train` command." + ) + port = select_zmq_port(zmq_context=self.ctx) + attempts += 1 + + return port + + # Find a free port and bind to it. + self.zmq_ports["publish_port"] = find_free_port( + port=self.zmq_ports["publish_port"], zmq_context=self.ctx + ) + publish_address = f"tcp://127.0.0.1:{self.zmq_ports['publish_port']}" + self.sub.bind(publish_address) # Controller, PUBLISHER self.zmq_ctrl = None if self.show_controller: self.zmq_ctrl = self.ctx.socket(zmq.PUB) - self.zmq_ctrl.bind("tcp://127.0.0.1:9000") + + # Find a free port and bind to it. + self.zmq_ports["controller_port"] = find_free_port( + port=self.zmq_ports["controller_port"], zmq_context=self.ctx + ) + controller_address = f"tcp://127.0.0.1:{self.zmq_ports['controller_port']}" + self.zmq_ctrl.bind(controller_address) # Set timer to poll for messages. self.timer = QtCore.QTimer() - self.timer.timeout.connect(self.check_messages) + self.timer.timeout.connect(self._check_messages) self.timer.start(self.message_poll_time_ms) - def cancel(self): - """Set the cancel flag.""" - self.canceled = True - if self.cancel_button is not None: - self.cancel_button.setText("Canceling...") - self.cancel_button.setEnabled(False) - - def stop(self): - """Send command to stop training.""" - if self.zmq_ctrl is not None: - # Send command to stop training. - logger.info("Sending command to stop training.") - self.zmq_ctrl.send_string(jsonpickle.encode(dict(command="stop"))) - - # Disable the button to prevent double messages. - if self.stop_button is not None: - self.stop_button.setText("Stopping...") - self.stop_button.setEnabled(False) - - def add_datapoint(self, x: int, y: float, which: str): - """Add a data point to graph. + def _set_batches_to_show(self, batches: str): + """Set the number of batches to show on the x-axis. Args: - x: The batch number (out of all epochs, not just current), or epoch. - y: The loss value. - which: Type of data point we're adding. Possible values are: - * "batch" (loss for the batch) - * "epoch_loss" (loss for the entire epoch) - * "val_loss" (validation loss for the epoch) + batches: Number of batches as a string. If numeric, this will be converted + to an integer. If non-numeric string (e.g., "All"), then all batches + will be shown. """ - if which == "batch": - self.X.append(x) - self.Y.append(y) - - # Redraw batch at intervals (faster than plotting every batch). - draw_batch = False - if self.last_redraw_batch is None: - draw_batch = True - else: - dt = perf_counter() - self.last_redraw_batch - draw_batch = (dt * 1000) >= self.redraw_batch_time_ms - - if draw_batch: - self.last_redraw_batch = perf_counter() - if self.batches_to_show < 0 or len(self.X) < self.batches_to_show: - xs, ys = self.X, self.Y - else: - xs, ys = ( - self.X[-self.batches_to_show :], - self.Y[-self.batches_to_show :], - ) - - points = [QtCore.QPointF(x, y) for x, y in zip(xs, ys) if y > 0] - self.series["batch"].replace(points) - - # Set X scale to show all points - dx = 0.5 - self.chart.axisX().setRange(min(xs) - dx, max(xs) + dx) - - if self.ignore_outliers: - dy = np.ptp(ys) * 0.02 - # Set Y scale to exclude outliers - q1, q3 = np.quantile(ys, (0.25, 0.75)) - iqr = q3 - q1 # interquartile range - low = q1 - iqr * 1.5 - high = q3 + iqr * 1.5 - - low = max(low, min(ys) - dy) # keep within range of data - high = min(high, max(ys) + dy) - else: - # Set Y scale to show all points - dy = np.ptp(ys) * 0.02 - low = min(ys) - dy - high = max(ys) + dy - - if self.log_scale: - low = max(low, 1e-8) # for log scale, low cannot be 0 - - self.chart.axisY().setRange(low, high) - + if batches.isdigit(): + self.batches_to_show = int(batches) else: - if which == "epoch_loss": - self.series["epoch_loss"].append(x, y) - self.series["epoch_loss_scatter"].append(x, y) - elif which == "val_loss": - self.series["val_loss"].append(x, y) - self.series["val_loss_scatter"].append(x, y) - if self.best_val_y is None or y < self.best_val_y: - self.best_val_x = x - self.best_val_y = y - self.series["val_loss_best"].replace([QtCore.QPointF(x, y)]) + self.batches_to_show = -1 - def set_start_time(self, t0: float): + def _set_start_time(self, t0: float): """Mark the start flag and time of the run. Args: @@ -419,52 +858,31 @@ def set_start_time(self, t0: float): self.t0 = t0 self.is_running = True - def set_end(self): - """Mark the end of the run.""" - self.is_running = False - - def update_runtime(self): + def _update_runtime(self): """Update the title text with the current running time.""" + if self.is_timer_running: dt = perf_counter() - self.t0 dt_min, dt_sec = divmod(dt, 60) - title = f"Training Epoch {self.epoch + 1} / " - title += f"Runtime: {int(dt_min):02}:{int(dt_sec):02}" - if self.last_epoch_val_loss is not None: - if self.penultimate_epoch_val_loss is not None: - title += ( - f"
Mean Time per Epoch: " - f"{int(self.mean_epoch_time_min):02}:{int(self.mean_epoch_time_sec):02} / " - f"ETA Next 10 Epochs: {int(self.eta_ten_epochs_min)} min" - ) - if self.epoch_in_plateau_flag: - title += ( - f"
Epochs in Plateau: " - f"{self.epochs_in_plateau} / " - f"{self.config.optimization.early_stopping.plateau_patience}" - ) - title += ( - f"
Last Epoch Validation Loss: " - f"{self.last_epoch_val_loss:.3e}" - ) - if self.best_val_x is not None: - best_epoch = (self.best_val_x // self.epoch_size) + 1 - title += ( - f"
Best Epoch Validation Loss: " - f"{self.best_val_y:.3e} (epoch {best_epoch})" - ) - self.set_message(title) - - @property - def is_timer_running(self) -> bool: - """Return True if the timer has started.""" - return self.t0 is not None and self.is_running - def set_message(self, text: str): - """Set the chart title text.""" - self.chart.setTitle(text) + self.canvas.update_runtime_title( + epoch=self.epoch, + dt_min=dt_min, + dt_sec=dt_sec, + last_epoch_val_loss=self.last_epoch_val_loss, + penultimate_epoch_val_loss=self.penultimate_epoch_val_loss, + mean_epoch_time_min=self.mean_epoch_time_min, + mean_epoch_time_sec=self.mean_epoch_time_sec, + eta_ten_epochs_min=self.eta_ten_epochs_min, + epochs_in_plateau=self.epochs_in_plateau, + plateau_patience=self.config.optimization.early_stopping.plateau_patience, + epoch_in_plateau_flag=self.epoch_in_plateau_flag, + best_val_x=self.best_val_x, + best_val_y=self.best_val_y, + epoch_size=self.epoch_size, + ) - def check_messages( + def _check_messages( self, timeout: int = 10, times_to_check: int = 10, do_update: bool = True ): """Poll for ZMQ messages and adds any received data to graph. @@ -496,7 +914,7 @@ def check_messages( msg = jsonpickle.decode(self.sub.recv_string()) if msg["event"] == "train_begin": - self.set_start_time(perf_counter()) + self._set_start_time(perf_counter()) self.current_job_output_type = msg["what"] # Make sure message matches current training job. @@ -504,15 +922,15 @@ def check_messages( if not self.is_timer_running: # We must have missed the train_begin message, so start timer now. - self.set_start_time(perf_counter()) + self._set_start_time(perf_counter()) if msg["event"] == "train_end": - self.set_end() + self._set_end() elif msg["event"] == "epoch_begin": self.epoch = msg["epoch"] elif msg["event"] == "epoch_end": self.epoch_size = max(self.epoch_size, self.last_batch_number + 1) - self.add_datapoint( + self._add_datapoint( (self.epoch + 1) * self.epoch_size, msg["logs"]["loss"], "epoch_loss", @@ -521,7 +939,7 @@ def check_messages( # update variables and add points to plot self.penultimate_epoch_val_loss = self.last_epoch_val_loss self.last_epoch_val_loss = msg["logs"]["val_loss"] - self.add_datapoint( + self._add_datapoint( (self.epoch + 1) * self.epoch_size, msg["logs"]["val_loss"], "val_loss", @@ -552,7 +970,7 @@ def check_messages( self.on_epoch.emit() elif msg["event"] == "batch_end": self.last_batch_number = msg["batch"] - self.add_datapoint( + self._add_datapoint( (self.epoch * self.epoch_size) + msg["batch"], msg["logs"]["loss"], "batch", @@ -560,9 +978,155 @@ def check_messages( # Check for messages again (up to times_to_check times). if times_to_check > 0: - self.check_messages( + self._check_messages( timeout=timeout, times_to_check=times_to_check - 1, do_update=False ) if do_update: - self.update_runtime() + self._update_runtime() + + def _add_datapoint(self, x: int, y: float, which: str): + """Add a data point to graph. + + Args: + x: The batch number (out of all epochs, not just current), or epoch. + y: The loss value. + which: Type of data point we're adding. Possible values are: + * "batch" (loss for the batch) + * "epoch_loss" (loss for the entire epoch) + * "val_loss" (validation loss for the epoch) + """ + if which == "batch": + self.X.append(x) + self.Y.append(y) + + # Redraw batch at intervals (faster than plotting every batch). + draw_batch = False + if self.last_redraw_batch is None: + draw_batch = True + else: + dt = perf_counter() - self.last_redraw_batch + draw_batch = (dt * 1000) >= self.redraw_batch_time_ms + + if draw_batch: + self.last_redraw_batch = perf_counter() + if self.batches_to_show < 0 or len(self.X) < self.batches_to_show: + xs, ys = self.X, self.Y + else: + xs, ys = ( + self.X[-self.batches_to_show :], + self.Y[-self.batches_to_show :], + ) + + # Set data, resize and redraw the plot + self._set_data_on_scatter(xs, ys, which) + self._resize_axes(xs, ys) + + else: + + if which == "val_loss": + if self.best_val_y is None or y < self.best_val_y: + self.best_val_x = x + self.best_val_y = y + self._set_data_on_scatter([x], [y], "val_loss_best") + + # Add data and redraw the plot + self._add_data_to_plot(x, y, which) + self._redraw_plot() + + def _set_data_on_scatter(self, xs, ys, which): + """Add data to a scatter plot. + + Not to be used with line plots. + + Args: + xs: The x-coordinates of the data points. + ys: The y-coordinates of the data points. + which: The type of data point. Possible values are: + * "batch" + * "val_loss_best" + """ + + self.canvas.set_data_on_scatter(xs, ys, which) + + def _add_data_to_plot(self, x, y, which): + """Add data to a line plot. + + Not to be used with scatter plots. + + Args: + x: The x-coordinate of the data point. + y: The y-coordinate of the data point. + which: The type of data point. Possible values are: + * "epoch_loss" + * "val_loss" + """ + + self.canvas.add_data_to_plot(x, y, which) + + def _redraw_plot(self): + """Redraw the plot.""" + + self.canvas.redraw_plot() + + def _resize_axes(self, x, y): + """Resize axes to fit data. + + This is only called when plotting batches. + + Args: + x: The x-coordinates of the data points. + y: The y-coordinates of the data points. + """ + self.canvas.resize_axes(x, y) + + def _toggle_ignore_outliers(self): + """Toggles whether to ignore outliers in chart scaling.""" + + self.ignore_outliers = not self.ignore_outliers + + def _toggle_log_scale(self): + """Toggle whether to use log-scaled y-axis.""" + + self.log_scale = not self.log_scale + + def _stop(self): + """Send command to stop training.""" + if self.zmq_ctrl is not None: + # Send command to stop training. + logger.info("Sending command to stop training.") + self.zmq_ctrl.send_string(jsonpickle.encode(dict(command="stop"))) + + # Disable the button to prevent double messages. + if self.stop_button is not None: + self.stop_button.setText("Stopping...") + self.stop_button.setEnabled(False) + + def _cancel(self): + """Set the cancel flag.""" + self.canceled = True + if self.cancel_button is not None: + self.cancel_button.setText("Canceling...") + self.cancel_button.setEnabled(False) + + def _unbind(self): + """Disconnect from all ZMQ sockets.""" + if self.sub is not None: + self.sub.unbind(self.sub.LAST_ENDPOINT) + self.sub.close() + self.sub = None + + if self.zmq_ctrl is not None: + url = self.zmq_ctrl.LAST_ENDPOINT + self.zmq_ctrl.unbind(url) + self.zmq_ctrl.close() + self.zmq_ctrl = None + + # If we started out own zmq context, terminate it. + if not self.ctx_given and self.ctx is not None: + self.ctx.term() + self.ctx = None + + def _set_end(self): + """Mark the end of the run.""" + self.is_running = False diff --git a/sleap/gui/widgets/mpl.py b/sleap/gui/widgets/mpl.py index a9b7fc838..890c1a67a 100644 --- a/sleap/gui/widgets/mpl.py +++ b/sleap/gui/widgets/mpl.py @@ -6,11 +6,10 @@ from qtpy import QtWidgets from matplotlib.figure import Figure -from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as Canvas +from matplotlib.backends.backend_qtagg import FigureCanvasQTAgg as Canvas import matplotlib -# Ensure using PyQt5 backend -matplotlib.use("QT5Agg") +matplotlib.use("QtAgg") class MplCanvas(Canvas): diff --git a/sleap/gui/widgets/training_monitor.py b/sleap/gui/widgets/training_monitor.py deleted file mode 100644 index ed405a747..000000000 --- a/sleap/gui/widgets/training_monitor.py +++ /dev/null @@ -1,566 +0,0 @@ -"""GUI for monitoring training progress interactively.""" - -import numpy as np -from time import perf_counter -from sleap.nn.config.training_job import TrainingJobConfig -import zmq -import jsonpickle -import logging -from typing import Optional -from qtpy import QtCore, QtWidgets, QtGui, QtCharts -import attr - -logger = logging.getLogger(__name__) - - -class LossViewer(QtWidgets.QMainWindow): - """Qt window for showing in-progress training metrics sent over ZMQ.""" - - on_epoch = QtCore.Signal() - - def __init__( - self, - zmq_context: Optional[zmq.Context] = None, - show_controller=True, - parent=None, - ): - super().__init__(parent) - - self.show_controller = show_controller - self.stop_button = None - self.cancel_button = None - self.canceled = False - - self.batches_to_show = -1 # -1 to show all - self.ignore_outliers = False - self.log_scale = True - self.message_poll_time_ms = 20 # ms - self.redraw_batch_time_ms = 500 # ms - self.last_redraw_batch = None - - self.reset() - self.setup_zmq(zmq_context) - - def __del__(self): - self.unbind() - - def close(self): - """Disconnect from ZMQ ports and close the window.""" - self.unbind() - super().close() - - def unbind(self): - """Disconnect from all ZMQ sockets.""" - if self.sub is not None: - self.sub.unbind(self.sub.LAST_ENDPOINT) - self.sub.close() - self.sub = None - - if self.zmq_ctrl is not None: - url = self.zmq_ctrl.LAST_ENDPOINT - self.zmq_ctrl.unbind(url) - self.zmq_ctrl.close() - self.zmq_ctrl = None - - # If we started out own zmq context, terminate it. - if not self.ctx_given and self.ctx is not None: - self.ctx.term() - self.ctx = None - - def reset( - self, - what: str = "", - config: TrainingJobConfig = attr.ib(factory=TrainingJobConfig), - ): - """Reset all chart series. - - Args: - what: String identifier indicating which job type the current run - corresponds to. - """ - self.chart = QtCharts.QChart() - - self.series = dict() - - COLOR_TRAIN = (18, 158, 220) - COLOR_VAL = (248, 167, 52) - COLOR_BEST_VAL = (151, 204, 89) - - self.series["batch"] = QtCharts.QScatterSeries() - self.series["batch"].setName("Batch Training Loss") - self.series["batch"].setColor(QtGui.QColor(*COLOR_TRAIN, 48)) - self.series["batch"].setMarkerSize(8.0) - self.series["batch"].setBorderColor(QtGui.QColor(255, 255, 255, 25)) - self.chart.addSeries(self.series["batch"]) - - self.series["epoch_loss"] = QtCharts.QLineSeries() - self.series["epoch_loss"].setName("Epoch Training Loss") - self.series["epoch_loss"].setColor(QtGui.QColor(*COLOR_TRAIN, 255)) - pen = self.series["epoch_loss"].pen() - pen.setWidth(4) - self.series["epoch_loss"].setPen(pen) - self.chart.addSeries(self.series["epoch_loss"]) - - self.series["epoch_loss_scatter"] = QtCharts.QScatterSeries() - self.series["epoch_loss_scatter"].setColor(QtGui.QColor(*COLOR_TRAIN, 255)) - self.series["epoch_loss_scatter"].setMarkerSize(12.0) - self.series["epoch_loss_scatter"].setBorderColor( - QtGui.QColor(255, 255, 255, 25) - ) - self.chart.addSeries(self.series["epoch_loss_scatter"]) - - self.series["val_loss"] = QtCharts.QLineSeries() - self.series["val_loss"].setName("Epoch Validation Loss") - self.series["val_loss"].setColor(QtGui.QColor(*COLOR_VAL, 255)) - pen = self.series["val_loss"].pen() - pen.setWidth(4) - self.series["val_loss"].setPen(pen) - self.chart.addSeries(self.series["val_loss"]) - - self.series["val_loss_scatter"] = QtCharts.QScatterSeries() - self.series["val_loss_scatter"].setColor(QtGui.QColor(*COLOR_VAL, 255)) - self.series["val_loss_scatter"].setMarkerSize(12.0) - self.series["val_loss_scatter"].setBorderColor(QtGui.QColor(255, 255, 255, 25)) - self.chart.addSeries(self.series["val_loss_scatter"]) - - self.series["val_loss_best"] = QtCharts.QScatterSeries() - self.series["val_loss_best"].setName("Best Validation Loss") - self.series["val_loss_best"].setColor(QtGui.QColor(*COLOR_BEST_VAL, 255)) - self.series["val_loss_best"].setMarkerSize(12.0) - self.series["val_loss_best"].setBorderColor(QtGui.QColor(32, 32, 32, 25)) - self.chart.addSeries(self.series["val_loss_best"]) - - axisX = QtCharts.QValueAxis() - axisX.setLabelFormat("%d") - axisX.setTitleText("Batches") - self.chart.addAxis(axisX, QtCore.Qt.AlignBottom) - - # Create the different Y axes that can be used. - self.axisY = dict() - - self.axisY["log"] = QtCharts.QLogValueAxis() - self.axisY["log"].setBase(10) - - self.axisY["linear"] = QtCharts.QValueAxis() - - # Apply settings that apply to all Y axes. - for axisY in self.axisY.values(): - axisY.setLabelFormat("%f") - axisY.setLabelsVisible(True) - axisY.setMinorTickCount(1) - axisY.setTitleText("Loss") - - # Use the default Y axis. - axisY = self.axisY["log"] if self.log_scale else self.axisY["linear"] - - # Add axes to chart and series. - self.chart.addAxis(axisY, QtCore.Qt.AlignLeft) - for series in self.chart.series(): - series.attachAxis(axisX) - series.attachAxis(axisY) - - # Setup legend. - self.chart.legend().setVisible(True) - self.chart.legend().setAlignment(QtCore.Qt.AlignTop) - self.chart.legend().setMarkerShape(QtCharts.QLegend.MarkerShapeCircle) - - # Hide scatters for epoch and val loss from legend. - for s in ("epoch_loss_scatter", "val_loss_scatter"): - self.chart.legend().markers(self.series[s])[0].setVisible(False) - - self.chartView = QtCharts.QChartView(self.chart) - self.chartView.setRenderHint(QtGui.QPainter.Antialiasing) - layout = QtWidgets.QVBoxLayout() - layout.addWidget(self.chartView) - - if self.show_controller: - control_layout = QtWidgets.QHBoxLayout() - - field = QtWidgets.QCheckBox("Log Scale") - field.setChecked(self.log_scale) - field.stateChanged.connect(self.toggle_log_scale) - control_layout.addWidget(field) - - field = QtWidgets.QCheckBox("Ignore Outliers") - field.setChecked(self.ignore_outliers) - field.stateChanged.connect(self.toggle_ignore_outliers) - control_layout.addWidget(field) - - control_layout.addWidget(QtWidgets.QLabel("Batches to Show:")) - - # Add field for how many batches to show in chart. - field = QtWidgets.QComboBox() - self.batch_options = "200,1000,5000,All".split(",") - for opt in self.batch_options: - field.addItem(opt) - cur_opt_str = ( - "All" if self.batches_to_show < 0 else str(self.batches_to_show) - ) - if cur_opt_str in self.batch_options: - field.setCurrentText(cur_opt_str) - - # Set connection action for when user selects another option. - field.currentIndexChanged.connect( - lambda x: self.set_batches_to_show(self.batch_options[x]) - ) - - # Store field as property and add to layout. - self.batches_to_show_field = field - control_layout.addWidget(self.batches_to_show_field) - - control_layout.addStretch(1) - - self.stop_button = QtWidgets.QPushButton("Stop Early") - self.stop_button.clicked.connect(self.stop) - control_layout.addWidget(self.stop_button) - self.cancel_button = QtWidgets.QPushButton("Cancel Training") - self.cancel_button.clicked.connect(self.cancel) - control_layout.addWidget(self.cancel_button) - - widget = QtWidgets.QWidget() - widget.setLayout(control_layout) - layout.addWidget(widget) - - wid = QtWidgets.QWidget() - wid.setLayout(layout) - self.setCentralWidget(wid) - - self.config = config - self.X = [] - self.Y = [] - self.best_val_x = None - self.best_val_y = None - - self.t0 = None - self.mean_epoch_time_min = None - self.mean_epoch_time_sec = None - self.eta_ten_epochs_min = None - - self.current_job_output_type = what - self.epoch = 0 - self.epoch_size = 1 - self.epochs_in_plateau = 0 - self.last_epoch_val_loss = None - self.penultimate_epoch_val_loss = None - self.epoch_in_plateau_flag = False - self.last_batch_number = 0 - self.is_running = False - - def toggle_ignore_outliers(self): - """Toggles whether to ignore outliers in chart scaling.""" - self.ignore_outliers = not self.ignore_outliers - - def toggle_log_scale(self): - """Toggle whether to use log-scaled y-axis.""" - self.log_scale = not self.log_scale - self.update_y_axis() - - def set_batches_to_show(self, batches: str): - """Set the number of batches to show on the x-axis. - - Args: - batches: Number of batches as a string. If numeric, this will be converted - to an integer. If non-numeric string (e.g., "All"), then all batches - will be shown. - """ - if batches.isdigit(): - self.batches_to_show = int(batches) - else: - self.batches_to_show = -1 - - def update_y_axis(self): - """Update the y-axis when scale changes.""" - to = "log" if self.log_scale else "linear" - - # Remove other axes. - for name, axisY in self.axisY.items(): - if name != to: - if axisY in self.chart.axes(): - self.chart.removeAxis(axisY) - for series in self.chart.series(): - if axisY in series.attachedAxes(): - series.detachAxis(axisY) - - # Add axis. - axisY = self.axisY[to] - self.chart.addAxis(axisY, QtCore.Qt.AlignLeft) - for series in self.chart.series(): - series.attachAxis(axisY) - - def setup_zmq(self, zmq_context: Optional[zmq.Context] = None): - """Connect to ZMQ ports that listen to commands and updates. - - Args: - zmq_context: The `zmq.Context` object to use for connections. A new one is - created if not specified and will be closed when the monitor exits. If - an existing one is provided, it will NOT be closed. - """ - # Keep track of whether we're using an existing context (which we won't close - # when done) or are creating our own (which we should close). - self.ctx_given = zmq_context is not None - self.ctx = zmq.Context() if zmq_context is None else zmq_context - - # Progress monitoring, SUBSCRIBER - self.sub = self.ctx.socket(zmq.SUB) - self.sub.subscribe("") - self.sub.bind("tcp://127.0.0.1:9001") - - # Controller, PUBLISHER - self.zmq_ctrl = None - if self.show_controller: - self.zmq_ctrl = self.ctx.socket(zmq.PUB) - self.zmq_ctrl.bind("tcp://127.0.0.1:9000") - - # Set timer to poll for messages. - self.timer = QtCore.QTimer() - self.timer.timeout.connect(self.check_messages) - self.timer.start(self.message_poll_time_ms) - - def cancel(self): - """Set the cancel flag.""" - self.canceled = True - if self.cancel_button is not None: - self.cancel_button.setText("Canceling...") - self.cancel_button.setEnabled(False) - - def stop(self): - """Send command to stop training.""" - if self.zmq_ctrl is not None: - # Send command to stop training. - logger.info("Sending command to stop training.") - self.zmq_ctrl.send_string(jsonpickle.encode(dict(command="stop"))) - - # Disable the button to prevent double messages. - if self.stop_button is not None: - self.stop_button.setText("Stopping...") - self.stop_button.setEnabled(False) - - def add_datapoint(self, x: int, y: float, which: str): - """Add a data point to graph. - - Args: - x: The batch number (out of all epochs, not just current), or epoch. - y: The loss value. - which: Type of data point we're adding. Possible values are: - * "batch" (loss for the batch) - * "epoch_loss" (loss for the entire epoch) - * "val_loss" (validation loss for the epoch) - """ - if which == "batch": - self.X.append(x) - self.Y.append(y) - - # Redraw batch at intervals (faster than plotting every batch). - draw_batch = False - if self.last_redraw_batch is None: - draw_batch = True - else: - dt = perf_counter() - self.last_redraw_batch - draw_batch = (dt * 1000) >= self.redraw_batch_time_ms - - if draw_batch: - self.last_redraw_batch = perf_counter() - if self.batches_to_show < 0 or len(self.X) < self.batches_to_show: - xs, ys = self.X, self.Y - else: - xs, ys = ( - self.X[-self.batches_to_show :], - self.Y[-self.batches_to_show :], - ) - - points = [QtCore.QPointF(x, y) for x, y in zip(xs, ys) if y > 0] - self.series["batch"].replace(points) - - # Set X scale to show all points - dx = 0.5 - self.chart.axisX().setRange(min(xs) - dx, max(xs) + dx) - - if self.ignore_outliers: - dy = np.ptp(ys) * 0.02 - # Set Y scale to exclude outliers - q1, q3 = np.quantile(ys, (0.25, 0.75)) - iqr = q3 - q1 # interquartile range - low = q1 - iqr * 1.5 - high = q3 + iqr * 1.5 - - low = max(low, min(ys) - dy) # keep within range of data - high = min(high, max(ys) + dy) - else: - # Set Y scale to show all points - dy = np.ptp(ys) * 0.02 - low = min(ys) - dy - high = max(ys) + dy - - if self.log_scale: - low = max(low, 1e-8) # for log scale, low cannot be 0 - - self.chart.axisY().setRange(low, high) - - else: - if which == "epoch_loss": - self.series["epoch_loss"].append(x, y) - self.series["epoch_loss_scatter"].append(x, y) - elif which == "val_loss": - self.series["val_loss"].append(x, y) - self.series["val_loss_scatter"].append(x, y) - if self.best_val_y is None or y < self.best_val_y: - self.best_val_x = x - self.best_val_y = y - self.series["val_loss_best"].replace([QtCore.QPointF(x, y)]) - - def set_start_time(self, t0: float): - """Mark the start flag and time of the run. - - Args: - t0: Start time in seconds. - """ - self.t0 = t0 - self.is_running = True - - def set_end(self): - """Mark the end of the run.""" - self.is_running = False - - def update_runtime(self): - """Update the title text with the current running time.""" - if self.is_timer_running: - dt = perf_counter() - self.t0 - dt_min, dt_sec = divmod(dt, 60) - title = f"Training Epoch {self.epoch + 1} / " - title += f"Runtime: {int(dt_min):02}:{int(dt_sec):02}" - if self.last_epoch_val_loss is not None: - if self.penultimate_epoch_val_loss is not None: - title += ( - f"
Mean Time per Epoch: " - f"{int(self.mean_epoch_time_min):02}:{int(self.mean_epoch_time_sec):02} / " - f"ETA Next 10 Epochs: {int(self.eta_ten_epochs_min)} min" - ) - if self.epoch_in_plateau_flag: - title += ( - f"
Epochs in Plateau: " - f"{self.epochs_in_plateau} / " - f"{self.config.optimization.early_stopping.plateau_patience}" - ) - title += ( - f"
Last Epoch Validation Loss: " - f"{self.last_epoch_val_loss:.3e}" - ) - if self.best_val_x is not None: - best_epoch = (self.best_val_x // self.epoch_size) + 1 - title += ( - f"
Best Epoch Validation Loss: " - f"{self.best_val_y:.3e} (epoch {best_epoch})" - ) - self.set_message(title) - - @property - def is_timer_running(self) -> bool: - """Return True if the timer has started.""" - return self.t0 is not None and self.is_running - - def set_message(self, text: str): - """Set the chart title text.""" - self.chart.setTitle(text) - - def check_messages( - self, timeout: int = 10, times_to_check: int = 10, do_update: bool = True - ): - """Poll for ZMQ messages and adds any received data to graph. - - The message is a dictionary encoded as JSON: - * event - options include - * train_begin - * train_end - * epoch_begin - * epoch_end - * batch_end - * what - this should match the type of model we're training and - ensures that we ignore old messages when we start monitoring - a new training session (when we're training multiple types - of models in a sequence, as for the top-down pipeline). - * logs - dictionary with data relevant for plotting, can include - * loss - * val_loss - - Args: - timeout: Message polling timeout in milliseconds. This is how often we will - check for new command messages. - times_to_check: How many times to check for new messages in the queue before - going back to polling with a timeout. Helps to clear backlogs of - messages if necessary. - do_update: If True (the default), update the GUI text. - """ - if self.sub and self.sub.poll(timeout, zmq.POLLIN): - msg = jsonpickle.decode(self.sub.recv_string()) - - if msg["event"] == "train_begin": - self.set_start_time(perf_counter()) - self.current_job_output_type = msg["what"] - - # Make sure message matches current training job. - if msg.get("what", "") == self.current_job_output_type: - - if not self.is_timer_running: - # We must have missed the train_begin message, so start timer now. - self.set_start_time(perf_counter()) - - if msg["event"] == "train_end": - self.set_end() - elif msg["event"] == "epoch_begin": - self.epoch = msg["epoch"] - elif msg["event"] == "epoch_end": - self.epoch_size = max(self.epoch_size, self.last_batch_number + 1) - self.add_datapoint( - (self.epoch + 1) * self.epoch_size, - msg["logs"]["loss"], - "epoch_loss", - ) - if "val_loss" in msg["logs"].keys(): - # update variables and add points to plot - self.penultimate_epoch_val_loss = self.last_epoch_val_loss - self.last_epoch_val_loss = msg["logs"]["val_loss"] - self.add_datapoint( - (self.epoch + 1) * self.epoch_size, - msg["logs"]["val_loss"], - "val_loss", - ) - # calculate timing and flags at new epoch - if self.penultimate_epoch_val_loss is not None: - mean_epoch_time = (perf_counter() - self.t0) / ( - self.epoch + 1 - ) - self.mean_epoch_time_min, self.mean_epoch_time_sec = divmod( - mean_epoch_time, 60 - ) - self.eta_ten_epochs_min = (mean_epoch_time * 10) // 60 - - val_loss_delta = ( - self.penultimate_epoch_val_loss - - self.last_epoch_val_loss - ) - self.epoch_in_plateau_flag = ( - val_loss_delta - < self.config.optimization.early_stopping.plateau_min_delta - ) or (self.best_val_y < self.last_epoch_val_loss) - self.epochs_in_plateau = ( - self.epochs_in_plateau + 1 - if self.epoch_in_plateau_flag - else 0 - ) - self.on_epoch.emit() - elif msg["event"] == "batch_end": - self.last_batch_number = msg["batch"] - self.add_datapoint( - (self.epoch * self.epoch_size) + msg["batch"], - msg["logs"]["loss"], - "batch", - ) - - # Check for messages again (up to times_to_check times). - if times_to_check > 0: - self.check_messages( - timeout=timeout, times_to_check=times_to_check - 1, do_update=False - ) - - if do_update: - self.update_runtime() diff --git a/sleap/gui/widgets/video.py b/sleap/gui/widgets/video.py index 502ea388e..08ee5bf36 100644 --- a/sleap/gui/widgets/video.py +++ b/sleap/gui/widgets/video.py @@ -62,6 +62,7 @@ QShortcut, QVBoxLayout, QWidget, + QPinchGesture, ) import sleap @@ -240,6 +241,8 @@ def __init__( self._register_shortcuts() + self.context_menu = None + self._menu_actions = dict() if self.context: self.setContextMenuPolicy(QtCore.Qt.CustomContextMenu) self.customContextMenuRequested.connect(self.show_contextual_menu) @@ -358,41 +361,54 @@ def add_shortcut(key, step): def setSeekbarSelection(self, a: int, b: int): self.seekbar.setSelection(a, b) - def show_contextual_menu(self, where: QtCore.QPoint): - if not self.is_menu_enabled: - return + def create_contextual_menu(self, scene_pos: QtCore.QPointF) -> QtWidgets.QMenu: + """Create the context menu for the viewer. - scene_pos = self.view.mapToScene(where) - menu = QtWidgets.QMenu() + This is called when the user right-clicks in the viewer. This function also + stores the menu actions in the `_menu_actions` attribute so that they can be + accessed later and stores the context menu in the `context_menu` attribute. - menu.addAction("Add Instance:").setEnabled(False) + Args: + scene_pos: The position in the scene where the menu was requested. - menu.addAction("Default", lambda: self.context.newInstance(init_method="best")) + Returns: + The created context menu. + """ - menu.addAction( - "Average", - lambda: self.context.newInstance( - init_method="template", location=scene_pos - ), - ) + self.context_menu = QtWidgets.QMenu() + self.context_menu.addAction("Add Instance:").setEnabled(False) + + self._menu_actions = dict() + params_by_action_name = { + "Default": {"init_method": "best", "location": scene_pos}, + "Average": {"init_method": "template", "location": scene_pos}, + "Force Directed": {"init_method": "force_directed", "location": scene_pos}, + "Copy Prior Frame": {"init_method": "prior_frame"}, + "Random": {"init_method": "random", "location": scene_pos}, + } + for action_name, params in params_by_action_name.items(): + self._menu_actions[action_name] = self.context_menu.addAction( + action_name, lambda params=params: self.context.newInstance(**params) + ) - menu.addAction( - "Force Directed", - lambda: self.context.newInstance( - init_method="force_directed", location=scene_pos - ), - ) + return self.context_menu - menu.addAction( - "Copy Prior Frame", - lambda: self.context.newInstance(init_method="prior_frame"), - ) + def show_contextual_menu(self, where: QtCore.QPoint): + """Show the context menu at the given position in the viewer. - menu.addAction( - "Random", - lambda: self.context.newInstance(init_method="random", location=scene_pos), - ) + This is called when the user right-clicks in the viewer. This function calls + `create_contextual_menu` to create the menu and then shows the menu at the + given position. + Args: + where: The position in the viewer where the menu was requested. + """ + + if not self.is_menu_enabled: + return + + scene_pos = self.view.mapToScene(where) + menu = self.create_contextual_menu(scene_pos) menu.exec_(self.mapToGlobal(where)) def load_video(self, video: Video, plot=True): @@ -808,6 +824,8 @@ def __init__(self, state=None, player=None, *args, **kwargs): # Set icon as default background. self.setImage(QImage(sleap.util.get_package_file("gui/background.png"))) + self.grabGesture(Qt.GestureType.PinchGesture) + def dragEnterEvent(self, event): if self.parentWidget(): self.parentWidget().dragEnterEvent(event) @@ -1147,8 +1165,13 @@ def mouseDoubleClickEvent(self, event: QMouseEvent): QGraphicsView.mouseDoubleClickEvent(self, event) def wheelEvent(self, event): - """Custom event handler. Zoom in/out based on scroll wheel change.""" - # zoom on wheel when no mouse buttons are pressed + """Custom event handler to zoom in/out based on scroll wheel change. + + We cannot use the default QGraphicsView.wheelEvent behavior since that will + scroll the view. + """ + + # Zoom on wheel when no mouse buttons are pressed if event.buttons() == Qt.NoButton: angle = event.angleDelta().y() factor = 1.1 if angle > 0 else 0.9 @@ -1156,20 +1179,10 @@ def wheelEvent(self, event): self.zoomFactor = max(factor * self.zoomFactor, 1) self.updateViewer() - # Trigger wheelEvent for all child elements. This is a bit of a hack. - # We can't use QGraphicsView.wheelEvent(self, event) since that will scroll - # view. - # We want to trigger for all children, since wheelEvent should continue rotating - # an skeleton even if the skeleton node/node label is no longer under the - # cursor. - # Note that children expect a QGraphicsSceneWheelEvent event, which is why we're - # explicitly ignoring TypeErrors. Everything seems to work fine since we don't - # care about the mouse position; if we did, we'd need to map pos to scene. + # Trigger only for rotation-relevant children (otherwise GUI crashes) for child in self.items(): - try: + if isinstance(child, (QtNode, QtNodeLabel)): child.wheelEvent(event) - except TypeError: - pass def keyPressEvent(self, event): """Custom event hander, disables default QGraphicsView behavior.""" @@ -1179,6 +1192,23 @@ def keyReleaseEvent(self, event): """Custom event hander, disables default QGraphicsView behavior.""" event.ignore() # Kicks the event up to parent + def event(self, event): + if event.type() == QtCore.QEvent.Gesture: + return self.handleGestureEvent(event) + return super().event(event) + + def handleGestureEvent(self, event): + gesture = event.gesture(Qt.GestureType.PinchGesture) + if gesture: + self.handlePinchGesture(gesture) + return True + + def handlePinchGesture(self, gesture: QPinchGesture): + if gesture.state() == Qt.GestureState.GestureUpdated: + factor = gesture.scaleFactor() + self.zoomFactor = max(factor * self.zoomFactor, 1) + self.updateViewer() + class QtNodeLabel(QGraphicsTextItem): """ @@ -1560,7 +1590,6 @@ def mousePressEvent(self, event): def mouseMoveEvent(self, event): """Custom event handler for mouse move.""" - # print(event) if self.dragParent: self.parentObject().mouseMoveEvent(event) else: @@ -1571,7 +1600,6 @@ def mouseMoveEvent(self, event): def mouseReleaseEvent(self, event): """Custom event handler for mouse release.""" - # print(event) self.unsetCursor() if self.dragParent: self.parentObject().mouseReleaseEvent(event) @@ -1587,7 +1615,9 @@ def mouseReleaseEvent(self, event): def wheelEvent(self, event): """Custom event handler for mouse scroll wheel.""" if self.dragParent: - angle = event.delta() / 20 + self.parentObject().rotation() + angle = ( + event.angleDelta().x() + event.angleDelta().y() + ) / 20 + self.parentObject().rotation() self.parentObject().setRotation(angle) event.accept() @@ -1598,6 +1628,10 @@ def mouseDoubleClickEvent(self, event: QMouseEvent): view = scene.views()[0] view.instanceDoubleClicked.emit(self.parentObject().instance, event) + def hoverEnterEvent(self, event): + """Custom event handler for mouse hover enter.""" + return super().hoverEnterEvent(event) + class QtEdge(QGraphicsPolygonItem): """ @@ -1797,6 +1831,7 @@ def __init__( self.labels = {} self.labels_shown = True self._selected = False + self._is_hovering = False self._bounding_rect = QRectF() # Show predicted instances behind non-predicted ones @@ -1818,6 +1853,7 @@ def __init__( box_pen.setStyle(Qt.DashLine) box_pen.setCosmetic(True) self.box.setPen(box_pen) + self.setAcceptHoverEvents(True) # Add label for highlighted instance self.highlight_label = QtTextWithBackground(parent=self) @@ -1979,7 +2015,12 @@ def updateBox(self, *args, **kwargs): select this instance. """ # Only show box if instance is selected - op = 0.7 if self._selected else 0 + op = 0 + if self._selected: + op = 0.8 + elif self._is_hovering: + op = 0.4 + self.box.setOpacity(op) # Update the position for the box rect = self.getPointsBoundingRect() @@ -2073,6 +2114,16 @@ def paint(self, painter, option, widget=None): """Method required by Qt.""" pass + def hoverEnterEvent(self, event): + self._is_hovering = True + self.updateBox() + return super().hoverEnterEvent(event) + + def hoverLeaveEvent(self, event): + self._is_hovering = False + self.updateBox() + return super().hoverLeaveEvent(event) + class VisibleBoundingBox(QtWidgets.QGraphicsRectItem): """QGraphicsRectItem for user instance bounding boxes. @@ -2263,7 +2314,7 @@ def mouseReleaseEvent(self, event): self.parent.nodes[node_key].setPos(new_x, new_y) # Update the instance - self.parent.updatePoints(complete=True, user_change=True) + self.parent.updatePoints(complete=False, user_change=True) self.resizing = None diff --git a/sleap/info/feature_suggestions.py b/sleap/info/feature_suggestions.py index 51f9038a5..a5f773fa7 100644 --- a/sleap/info/feature_suggestions.py +++ b/sleap/info/feature_suggestions.py @@ -644,7 +644,7 @@ class ParallelFeaturePipeline(object): def get(self, video_idx): """Apply pipeline to single video by idx. Can be called in process.""" video_dict = self.videos_as_dicts[video_idx] - video = cattr.structure(video_dict, Video) + video = Video.cattr().structure(video_dict, Video) group_offset = video_idx * self.pipeline.n_clusters # t0 = time() diff --git a/sleap/info/metrics.py b/sleap/info/metrics.py index 2ac61d339..5bec077e4 100644 --- a/sleap/info/metrics.py +++ b/sleap/info/metrics.py @@ -10,75 +10,6 @@ from sleap.io.dataset import Labels -def matched_instance_distances( - labels_gt: Labels, - labels_pr: Labels, - match_lists_function: Callable, - frame_range: Optional[range] = None, -) -> Tuple[List[int], np.ndarray, np.ndarray, np.ndarray]: - - """ - Distances between ground truth and predicted nodes over a set of frames. - - Args: - labels_gt: the `Labels` object with ground truth data - labels_pr: the `Labels` object with predicted data - match_lists_function: function for determining corresponding instances - Takes two lists of instances and returns "sorted" lists. - frame_range (optional): range of frames for which to compare data - If None, we compare every frame in labels_gt with corresponding - frame in labels_pr. - Returns: - Tuple: - * frame indices map: instance idx (for other matrices) -> frame idx - * distance matrix: (instances * nodes) - * ground truth points matrix: (instances * nodes * 2) - * predicted points matrix: (instances * nodes * 2) - """ - - frame_idxs = [] - points_gt = [] - points_pr = [] - for lf_gt in labels_gt.find(labels_gt.videos[0]): - frame_idx = lf_gt.frame_idx - - # Get instances from ground truth/predicted labels - instances_gt = lf_gt.instances - lfs_pr = labels_pr.find(labels_pr.videos[0], frame_idx=frame_idx) - if len(lfs_pr): - instances_pr = lfs_pr[0].instances - else: - instances_pr = [] - - # Sort ground truth and predicted instances. - # We'll then compare points between corresponding items in lists. - # We can use different "match" functions depending on what we want. - sorted_gt, sorted_pr = match_lists_function(instances_gt, instances_pr) - - # Convert lists of instances to (instances, nodes, 2) matrices. - # This allows match_lists_function to return data as either - # a list of Instances or a (instances, nodes, 2) matrix. - if type(sorted_gt[0]) != np.ndarray: - sorted_gt = list_points_array(sorted_gt) - if type(sorted_pr[0]) != np.ndarray: - sorted_pr = list_points_array(sorted_pr) - - points_gt.append(sorted_gt) - points_pr.append(sorted_pr) - frame_idxs.extend([frame_idx] * len(sorted_gt)) - - # Convert arrays to numpy matrixes - # instances * nodes * (x,y) - points_gt = np.concatenate(points_gt) - points_pr = np.concatenate(points_pr) - - # Calculate distances between corresponding nodes for all corresponding - # ground truth and predicted instances. - D = np.linalg.norm(points_gt - points_pr, axis=2) - - return frame_idxs, D, points_gt, points_pr - - def match_instance_lists( instances_a: List[Union[Instance, PredictedInstance]], instances_b: List[Union[Instance, PredictedInstance]], @@ -165,6 +96,75 @@ def match_instance_lists_nodewise( return instances_a, best_points_array +def matched_instance_distances( + labels_gt: Labels, + labels_pr: Labels, + match_lists_function: Callable = match_instance_lists_nodewise, + frame_range: Optional[range] = None, +) -> Tuple[List[int], np.ndarray, np.ndarray, np.ndarray]: + + """ + Distances between ground truth and predicted nodes over a set of frames. + + Args: + labels_gt: the `Labels` object with ground truth data + labels_pr: the `Labels` object with predicted data + match_lists_function: function for determining corresponding instances + Takes two lists of instances and returns "sorted" lists. + frame_range (optional): range of frames for which to compare data + If None, we compare every frame in labels_gt with corresponding + frame in labels_pr. + Returns: + Tuple: + * frame indices map: instance idx (for other matrices) -> frame idx + * distance matrix: (instances * nodes) + * ground truth points matrix: (instances * nodes * 2) + * predicted points matrix: (instances * nodes * 2) + """ + + frame_idxs = [] + points_gt = [] + points_pr = [] + for lf_gt in labels_gt.find(labels_gt.videos[0]): + frame_idx = lf_gt.frame_idx + + # Get instances from ground truth/predicted labels + instances_gt = lf_gt.instances + lfs_pr = labels_pr.find(labels_pr.videos[0], frame_idx=frame_idx) + if len(lfs_pr): + instances_pr = lfs_pr[0].instances + else: + instances_pr = [] + + # Sort ground truth and predicted instances. + # We'll then compare points between corresponding items in lists. + # We can use different "match" functions depending on what we want. + sorted_gt, sorted_pr = match_lists_function(instances_gt, instances_pr) + + # Convert lists of instances to (instances, nodes, 2) matrices. + # This allows match_lists_function to return data as either + # a list of Instances or a (instances, nodes, 2) matrix. + if type(sorted_gt[0]) != np.ndarray: + sorted_gt = list_points_array(sorted_gt) + if type(sorted_pr[0]) != np.ndarray: + sorted_pr = list_points_array(sorted_pr) + + points_gt.append(sorted_gt) + points_pr.append(sorted_pr) + frame_idxs.extend([frame_idx] * len(sorted_gt)) + + # Convert arrays to numpy matrixes + # instances * nodes * (x,y) + points_gt = np.concatenate(points_gt) + points_pr = np.concatenate(points_pr) + + # Calculate distances between corresponding nodes for all corresponding + # ground truth and predicted instances. + D = np.linalg.norm(points_gt - points_pr, axis=2) + + return frame_idxs, D, points_gt, points_pr + + def point_dist( inst_a: Union[Instance, PredictedInstance], inst_b: Union[Instance, PredictedInstance], @@ -238,46 +238,3 @@ def point_match_count(dist_array: np.ndarray, thresh: float = 5) -> int: def point_nonmatch_count(dist_array: np.ndarray, thresh: float = 5) -> int: """Given an array of distances, returns number which are not <= threshold.""" return dist_array.shape[0] - point_match_count(dist_array, thresh) - - -if __name__ == "__main__": - - labels_gt = Labels.load_json("tests/data/json_format_v1/centered_pair.json") - labels_pr = Labels.load_json( - "tests/data/json_format_v2/centered_pair_predictions.json" - ) - - # OPTION 1 - - # Match each ground truth instance node to the closest corresponding node - # from any predicted instance in the same frame. - - nodewise_matching_func = match_instance_lists_nodewise - - # OPTION 2 - - # Match each ground truth instance to a distinct predicted instance: - # We want to maximize the number of "matching" points between instances, - # where "match" means the points are within some threshold distance. - # Note that each sorted list will be as long as the shorted input list. - - instwise_matching_func = lambda gt_list, pr_list: match_instance_lists( - gt_list, pr_list, point_nonmatch_count - ) - - # PICK THE FUNCTION - - inst_matching_func = nodewise_matching_func - # inst_matching_func = instwise_matching_func - - # Calculate distances - frame_idxs, D, points_gt, points_pr = matched_instance_distances( - labels_gt, labels_pr, inst_matching_func - ) - - # Show mean difference for each node - node_names = labels_gt.skeletons[0].node_names - - for node_idx, node_name in enumerate(node_names): - mean_d = np.nanmean(D[..., node_idx]) - print(f"{node_name}\t\t{mean_d}") diff --git a/sleap/info/summary.py b/sleap/info/summary.py index c6a6af60e..0cad1617e 100644 --- a/sleap/info/summary.py +++ b/sleap/info/summary.py @@ -21,7 +21,7 @@ class StatisticSeries: are frame index and value are some numerical value for the frame. Args: - labels: The :class:`Labels` for which to calculate series. + labels: The `Labels` for which to calculate series. """ labels: Labels @@ -41,7 +41,7 @@ def get_point_score_series( """Get series with statistic of point scores in each frame. Args: - video: The :class:`Video` for which to calculate statistic. + video: The `Video` for which to calculate statistic. reduction: name of function applied to scores: * sum * min @@ -67,7 +67,7 @@ def get_instance_score_series(self, video, reduction="sum") -> Dict[int, float]: """Get series with statistic of instance scores in each frame. Args: - video: The :class:`Video` for which to calculate statistic. + video: The `Video` for which to calculate statistic. reduction: name of function applied to scores: * sum * min @@ -93,7 +93,7 @@ def get_point_displacement_series(self, video, reduction="sum") -> Dict[int, flo same track) from the closest earlier labeled frame. Args: - video: The :class:`Video` for which to calculate statistic. + video: The `Video` for which to calculate statistic. reduction: name of function applied to point scores: * sum * mean @@ -121,7 +121,7 @@ def get_primary_point_displacement_series( Get sum of displacement for single node of each instance per frame. Args: - video: The :class:`Video` for which to calculate statistic. + video: The `Video` for which to calculate statistic. reduction: name of function applied to point scores: * sum * mean @@ -226,7 +226,7 @@ def _calculate_frame_velocity( Calculate total point displacement between two given frames. Args: - lf: The :class:`LabeledFrame` for which we want velocity + lf: The `LabeledFrame` for which we want velocity last_lf: The frame from which to calculate displacement. reduce_function: Numpy function (e.g., np.sum, np.nanmean) is applied to *point* displacement, and then those @@ -246,3 +246,35 @@ def _calculate_frame_velocity( inst_dist = reduce_function(point_dist) val += inst_dist if not np.isnan(inst_dist) else 0 return val + + def get_tracking_score_series( + self, video: Video, reduction: str = "min" + ) -> Dict[int, float]: + """Get series with statistic of tracking scores in each frame. + + Args: + video: The `Video` for which to calculate statistic. + reduction: name of function applied to scores: + * mean + * min + + Returns: + The series dictionary (see class docs for details) + """ + reduce_fn = { + "min": np.nanmin, + "mean": np.nanmean, + }[reduction] + + series = dict() + + for lf in self.labels.find(video): + vals = [ + inst.tracking_score for inst in lf if hasattr(inst, "tracking_score") + ] + if vals: + val = reduce_fn(vals) + if not np.isnan(val): + series[lf.frame_idx] = val + + return series diff --git a/sleap/instance.py b/sleap/instance.py index c14038552..382ececf2 100644 --- a/sleap/instance.py +++ b/sleap/instance.py @@ -364,7 +364,7 @@ class Instance: from_predicted: Optional["PredictedInstance"] = attr.ib(default=None) _points: PointArray = attr.ib(default=None) _nodes: List = attr.ib(default=None) - frame: Union["LabeledFrame", None] = attr.ib(default=None) + frame: Union["LabeledFrame", None] = attr.ib(default=None) # TODO(LM): Make private # The underlying Point array type that this instances point array should be. _point_array_type = PointArray @@ -1049,7 +1049,9 @@ def scores(self) -> np.ndarray: return self.points_and_scores_array[:, 2] @classmethod - def from_instance(cls, instance: Instance, score: float) -> "PredictedInstance": + def from_instance( + cls, instance: Instance, score: float, tracking_score: float = 0.0 + ) -> "PredictedInstance": """Create a `PredictedInstance` from an `Instance`. The fields are copied in a shallow manner with the exception of points. For each @@ -1059,6 +1061,7 @@ def from_instance(cls, instance: Instance, score: float) -> "PredictedInstance": Args: instance: The `Instance` object to shallow copy data from. score: The score for this instance. + tracking_score: The tracking score for this instance. Returns: A `PredictedInstance` for the given `Instance`. @@ -1070,6 +1073,7 @@ def from_instance(cls, instance: Instance, score: float) -> "PredictedInstance": ) kw_args["points"] = PredictedPointArray.from_array(instance._points) kw_args["score"] = score + kw_args["tracking_score"] = tracking_score return cls(**kw_args) @classmethod @@ -1080,6 +1084,7 @@ def from_arrays( instance_score: float, skeleton: Skeleton, track: Optional[Track] = None, + tracking_score: float = 0.0, ) -> "PredictedInstance": """Create a predicted instance from data arrays. @@ -1094,6 +1099,7 @@ def from_arrays( skeleton: A sleap.Skeleton instance with n_nodes nodes to associate with the predicted instance. track: Optional `sleap.Track` to associate with the instance. + tracking_score: Optional float representing the track matching score. Returns: A new `PredictedInstance`. @@ -1114,6 +1120,7 @@ def from_arrays( skeleton=skeleton, score=instance_score, track=track, + tracking_score=tracking_score, ) @classmethod @@ -1124,6 +1131,7 @@ def from_pointsarray( instance_score: float, skeleton: Skeleton, track: Optional[Track] = None, + tracking_score: float = 0.0, ) -> "PredictedInstance": """Create a predicted instance from data arrays. @@ -1138,12 +1146,18 @@ def from_pointsarray( skeleton: A sleap.Skeleton instance with n_nodes nodes to associate with the predicted instance. track: Optional `sleap.Track` to associate with the instance. + tracking_score: Optional float representing the track matching score. Returns: A new `PredictedInstance`. """ return cls.from_arrays( - points, point_confidences, instance_score, skeleton, track=track + points, + point_confidences, + instance_score, + skeleton, + track=track, + tracking_score=tracking_score, ) @classmethod @@ -1154,6 +1168,7 @@ def from_numpy( instance_score: float, skeleton: Skeleton, track: Optional[Track] = None, + tracking_score: float = 0.0, ) -> "PredictedInstance": """Create a predicted instance from data arrays. @@ -1168,12 +1183,18 @@ def from_numpy( skeleton: A sleap.Skeleton instance with n_nodes nodes to associate with the predicted instance. track: Optional `sleap.Track` to associate with the instance. + tracking_score: Optional float representing the track matching score. Returns: A new `PredictedInstance`. """ return cls.from_arrays( - points, point_confidences, instance_score, skeleton, track=track + points, + point_confidences, + instance_score, + skeleton, + track=track, + tracking_score=tracking_score, ) @@ -1214,6 +1235,9 @@ def unstructure_instance(x: Instance): converter.register_unstructure_hook(Instance, unstructure_instance) converter.register_unstructure_hook(PredictedInstance, unstructure_instance) + converter.register_unstructure_hook( + InstancesList, lambda x: [converter.unstructure(inst) for inst in x] + ) ## STRUCTURE HOOKS @@ -1229,35 +1253,37 @@ def structure_points(x, type): def structure_instances_list(x, type): inst_list = [] for inst_data in x: - if "score" in inst_data.keys(): - inst = converter.structure(inst_data, PredictedInstance) - else: - if ( - "from_predicted" in inst_data - and inst_data["from_predicted"] is not None - ): - inst_data["from_predicted"] = converter.structure( - inst_data["from_predicted"], PredictedInstance - ) - inst = converter.structure(inst_data, Instance) + inst = structure_instance(inst_data, type) inst_list.append(inst) return inst_list + def structure_instance(inst_data, type): + """Structure hook for Instance and PredictedInstance objects.""" + from_predicted = None + + if "score" in inst_data.keys(): + inst = converter.structure(inst_data, PredictedInstance) + else: + if ( + "from_predicted" in inst_data + and inst_data["from_predicted"] is not None + ): + from_predicted = converter.structure( + inst_data["from_predicted"], PredictedInstance + ) + # Remove the from_predicted key. We'll add it back afterwards. + inst_data["from_predicted"] = None + + # Structure the instance data, then add the from_predicted attribute. + inst = converter.structure(inst_data, Instance) + inst.from_predicted = from_predicted + return inst + converter.register_structure_hook( Union[List[Instance], List[PredictedInstance]], structure_instances_list ) - - # Structure forward reference for PredictedInstance for the Instance.from_predicted - # attribute. - converter.register_structure_hook_func( - lambda t: t.__class__ is ForwardRef, - lambda v, t: converter.structure(v, t.__forward_value__), - ) - # converter.register_structure_hook( - # ForwardRef("PredictedInstance"), - # lambda x, _: converter.structure(x, PredictedInstance), - # ) + converter.register_structure_hook(InstancesList, structure_instances_list) # We can register structure hooks for point arrays that do nothing # because Instance can have a dict of points passed to it in place of @@ -1278,6 +1304,127 @@ def structure_point_array(x, t): return converter +class InstancesList(list): + """A list of `Instance`s associated with a `LabeledFrame`. + + This class should only be used for the `LabeledFrame.instances` attribute. + """ + + def __init__(self, *args, labeled_frame: Optional["LabeledFrame"] = None): + super(InstancesList, self).__init__(*args) + + # Set the labeled frame for each instance + self.labeled_frame = labeled_frame + + @property + def labeled_frame(self) -> "LabeledFrame": + """Return the `LabeledFrame` associated with this list of instances.""" + + return self._labeled_frame + + @labeled_frame.setter + def labeled_frame(self, labeled_frame: "LabeledFrame"): + """Set the `LabeledFrame` associated with this list of instances. + + This updates the `frame` attribute on each instance. + + Args: + labeled_frame: The `LabeledFrame` to associate with this list of instances. + """ + + try: + # If the labeled frame is the same as the one we're setting, then skip + if self._labeled_frame == labeled_frame: + return + except AttributeError: + # Only happens on init and updates each instance.frame (even if None) + pass + + # Otherwise, update the frame for each instance + self._labeled_frame = labeled_frame + for instance in self: + instance.frame = labeled_frame + + def append(self, instance: Union[Instance, PredictedInstance]): + """Append an `Instance` or `PredictedInstance` to the list, setting the frame. + + Args: + item: The `Instance` or `PredictedInstance` to append to the list. + """ + + if not isinstance(instance, (Instance, PredictedInstance)): + raise ValueError( + f"InstancesList can only contain Instance or PredictedInstance objects," + f" but got {type(instance)}." + ) + instance.frame = self.labeled_frame + super().append(instance) + + def extend(self, instances: List[Union[PredictedInstance, Instance]]): + """Extend the list with a list of `Instance`s or `PredictedInstance`s. + + Args: + instances: A list of `Instance` or `PredictedInstance` objects to add to the + list. + + Returns: + None + """ + for instance in instances: + self.append(instance) + + def __delitem__(self, index): + """Remove instance (by index), and set instance.frame to None.""" + + instance: Instance = self.__getitem__(index) + super().__delitem__(index) + + # Modify the instance to remove reference to the frame + instance.frame = None + + def insert(self, index: int, instance: Union[Instance, PredictedInstance]) -> None: + super().insert(index, instance) + instance.frame = self.labeled_frame + + def __setitem__(self, index, instance: Union[Instance, PredictedInstance]): + """Set nth instance in frame to the given instance. + + Args: + index: The index of instance to replace with new instance. + value: The new instance to associate with frame. + + Returns: + None. + """ + super().__setitem__(index, instance) + instance.frame = self.labeled_frame + + def pop(self, index: int) -> Union[Instance, PredictedInstance]: + """Remove and return instance at index, setting instance.frame to None.""" + + instance = super().pop(index) + instance.frame = None + return instance + + def remove(self, instance: Union[Instance, PredictedInstance]) -> None: + """Remove instance from list, setting instance.frame to None.""" + super().remove(instance) + instance.frame = None + + def clear(self) -> None: + """Remove all instances from list, setting instance.frame to None.""" + for instance in self: + instance.frame = None + super().clear() + + def copy(self) -> list: + """Return a shallow copy of the list of instances as a list. + + Note: This will not return an `InstancesList` object, but a normal list. + """ + return list(self) + + @attr.s(auto_attribs=True, eq=False, repr=False, str=False) class LabeledFrame: """Holds labeled data for a single frame of a video. @@ -1290,9 +1437,7 @@ class LabeledFrame: video: Video = attr.ib() frame_idx: int = attr.ib(converter=int) - _instances: Union[List[Instance], List[PredictedInstance]] = attr.ib( - default=attr.Factory(list) - ) + _instances: InstancesList = attr.ib(default=attr.Factory(InstancesList)) def __attrs_post_init__(self): """Called by attrs. @@ -1302,8 +1447,7 @@ def __attrs_post_init__(self): """ # Make sure all instances have a reference to this frame - for instance in self.instances: - instance.frame = self + self.instances = self._instances def __len__(self) -> int: """Return number of instances associated with frame.""" @@ -1319,13 +1463,8 @@ def index(self, value: Instance) -> int: def __delitem__(self, index): """Remove instance (by index) from frame.""" - value = self.instances.__getitem__(index) - self.instances.__delitem__(index) - # Modify the instance to remove reference to this frame - value.frame = None - def __repr__(self) -> str: """Return a readable representation of the LabeledFrame.""" return ( @@ -1348,9 +1487,6 @@ def insert(self, index: int, value: Instance): """ self.instances.insert(index, value) - # Modify the instance to have a reference back to this frame - value.frame = self - def __setitem__(self, index, value: Instance): """Set nth instance in frame to the given instance. @@ -1363,9 +1499,6 @@ def __setitem__(self, index, value: Instance): """ self.instances.__setitem__(index, value) - # Modify the instance to have a reference back to this frame - value.frame = self - def find( self, track: Optional[Union[Track, int]] = -1, user: bool = False ) -> List[Instance]: @@ -1393,7 +1526,7 @@ def instances(self) -> List[Instance]: return self._instances @instances.setter - def instances(self, instances: List[Instance]): + def instances(self, instances: Union[InstancesList, List[Instance]]): """Set the list of instances associated with this frame. Updates the `frame` attribute on each instance to the @@ -1408,9 +1541,11 @@ def instances(self, instances: List[Instance]): None """ - # Make sure to set the frame for each instance to this LabeledFrame - for instance in instances: - instance.frame = self + # Make sure to set the LabeledFrame for each instance to this frame + if isinstance(instances, InstancesList): + instances.labeled_frame = self + else: + instances = InstancesList(instances, labeled_frame=self) self._instances = instances @@ -1685,22 +1820,20 @@ def complex_frame_merge( * list of conflicting instances from base * list of conflicting instances from new """ - merged_instances = [] - redundant_instances = [] - extra_base_instances = copy(base_frame.instances) - extra_new_instances = [] + merged_instances: List[Instance] = [] # Only used for informing user + redundant_instances: List[Instance] = [] + extra_base_instances: List[Instance] = list(base_frame.instances) + extra_new_instances: List[Instance] = [] for new_inst in new_frame: redundant = False for base_inst in base_frame.instances: if new_inst.matches(base_inst): - base_inst.frame = None extra_base_instances.remove(base_inst) redundant_instances.append(base_inst) redundant = True continue if not redundant: - new_inst.frame = None extra_new_instances.append(new_inst) conflict = False @@ -1732,7 +1865,7 @@ def complex_frame_merge( else: # No conflict, so include all instances in base base_frame.instances.extend(extra_new_instances) - merged_instances = copy(extra_new_instances) + merged_instances: List[Instance] = copy(extra_new_instances) extra_base_instances = [] extra_new_instances = [] diff --git a/sleap/io/asyncvideo.py b/sleap/io/asyncvideo.py deleted file mode 100644 index c48d21a8b..000000000 --- a/sleap/io/asyncvideo.py +++ /dev/null @@ -1,218 +0,0 @@ -""" -Support for loading video frames (by chunk) in background process. -""" - -from sleap import Video -from sleap.message import PairedSender, PairedReceiver - -import cattr -import logging -import time -import numpy as np -from math import ceil -from multiprocessing import Process -from typing import Iterable, Iterator, List, Optional, Tuple - - -logger = logging.getLogger(__name__) - - -class AsyncVideo: - """Supports fetching chunks from video in background process.""" - - def __init__(self, base_port: int = 9010): - self.base_port = base_port - - # Spawn the server as a background process - self.server = AsyncVideoServer(self.base_port) - self.server.start() - - # Create sender/receiver for sending requests and receiving data via ZMQ - sender = PairedSender.from_tcp_ports(self.base_port, self.base_port + 1) - result_receiver = PairedReceiver.from_tcp_ports( - send_port=self.base_port + 2, rec_port=self.base_port + 3 - ) - - sender.setup() - result_receiver.setup() - - self.sender = sender - self.receiver = result_receiver - - # Use "handshake" to ensure that initial messages aren't dropped - self.handshake_success = sender.send_handshake() - - def close(self): - """Close the async video server and communication ports.""" - if self.sender and self.server: - self.sender.send_dict(dict(stop=True)) - self.server.join() - - self.server = None - - if self.sender: - self.sender.close() - self.sender = None - - if self.receiver: - self.receiver.close() - self.receiver = None - - def __del__(self): - self.close() - - @classmethod - def from_video( - cls, - video: Video, - frame_idxs: Optional[Iterable[int]] = None, - frames_per_chunk: int = 64, - ) -> "AsyncVideo": - """Create object and start loading frames in background process.""" - obj = cls() - obj.load_by_chunk( - video=video, frame_idxs=frame_idxs, frames_per_chunk=frames_per_chunk - ) - return obj - - def load_by_chunk( - self, - video: Video, - frame_idxs: Optional[Iterable[int]] = None, - frames_per_chunk: int = 64, - ): - """ - Sends request for loading video in background process. - - Args: - video: The :py:class:`Video` to load - frame_idxs: Frame indices we want to load; if None, then full video - is loaded. - frames_per_chunk: How many frames to load per chunk. - - Returns: - None, data should be accessed via :py:method:`chunks`. - """ - # prime the video since this seems to make frames load faster (!?) - video.test_frame - - request_dict = dict( - video=cattr.unstructure(video), frames_per_chunk=frames_per_chunk - ) - # if no frames are specified, whole video will be loaded - if frame_idxs is not None: - request_dict["frame_idxs"] = list(frame_idxs) - - # send the request - self.sender.send_dict(request_dict) - - @property - def chunks(self) -> Iterator[Tuple[List[int], np.ndarray]]: - """ - Generator for fetching chunks of frames. - - When all chunks are loaded, closes the server and communication ports. - - Yields: - Tuple with (list of frame indices, ndarray of frames) - """ - done = False - while not done: - results = self.receiver.check_messages() - if results: - for result in results: - yield result["frame_idxs"], result["ndarray"] - - if result["chunk"] == result["last_chunk"]: - done = True - - # automatically close when all chunks have been received - self.close() - - -class AsyncVideoServer(Process): - """ - Class which loads video frames in background on request. - - All interactions with video server should go through :py:class:`AsyncVideo` - which runs in local thread. - """ - - def __init__(self, base_port: int): - super(AsyncVideoServer, self).__init__() - - self.video = None - self.base_port = base_port - - def run(self): - receiver = PairedReceiver.from_tcp_ports(self.base_port + 1, self.base_port) - receiver.setup() - - result_sender = PairedSender.from_tcp_ports( - send_port=self.base_port + 3, rec_port=self.base_port + 2 - ) - result_sender.setup() - - running = True - while running: - requests = receiver.check_messages() - if requests: - - for request in requests: - - if "stop" in request: - running = False - logger.debug("stopping async video server") - break - - if "video" in request: - self.video = cattr.structure(request["video"], Video) - logger.debug(f"loaded video: {self.video.filename}") - - if self.video is not None: - if "frames_per_chunk" in request: - - load_time = 0 - send_time = 0 - - per_chunk = request["frames_per_chunk"] - - frame_idxs = request.get( - "frame_idxs", list(range(self.video.frames)) - ) - - frame_count = len(frame_idxs) - chunks = ceil(frame_count / per_chunk) - - for chunk_idx in range(chunks): - start = per_chunk * chunk_idx - end = min(per_chunk * (chunk_idx + 1), frame_count) - chunk_frame_idxs = frame_idxs[start:end] - - # load the frames - t0 = time.time() - frames = self.video[chunk_frame_idxs] - t1 = time.time() - load_time += t1 - t0 - - metadata = dict( - chunk=chunk_idx, - last_chunk=chunks - 1, - frame_idxs=chunk_frame_idxs, - ) - - # send back results - t0 = time.time() - result_sender.send_array(metadata, frames) - t1 = time.time() - send_time += t1 - t0 - - logger.debug(f"returned chunk: {chunk_idx+1}/{chunks}") - - logger.debug(f"total load time: {load_time}") - logger.debug(f"total send time: {send_time}") - else: - logger.warning( - "unable to process message since no video loaded" - ) - logger.warning(request) diff --git a/sleap/io/convert.py b/sleap/io/convert.py index 3353a169b..7045ed71f 100644 --- a/sleap/io/convert.py +++ b/sleap/io/convert.py @@ -70,6 +70,7 @@ def create_parser(): help="Output format. Default ('slp') is SLEAP dataset; " "'analysis' results in analysis.h5 file; " "'analysis.nix' results in an analysis nix file;" + "'analysis.csv' results in an analysis csv file;" "'h5' or 'json' results in SLEAP dataset " "with specified file format.", ) @@ -135,7 +136,12 @@ def main(args: list = None): outnames = [path for path in args.outputs] if len(outnames) < len(vids): # if there are less outnames provided than videos to convert... - out_suffix = "nix" if "nix" in args.format else "h5" + if "nix" in args.format: + out_suffix = "nix" + elif "csv" in args.format: + out_suffix = "csv" + else: + out_suffix = "h5" fn = args.input_path fn = re.sub("(\.json(\.zip)?|\.h5|\.slp)$", "", fn) fn = PurePath(fn) @@ -158,6 +164,20 @@ def main(args: list = None): NixAdaptor.write(outname, labels, args.input_path, video) except ValueError as e: print(e.args[0]) + + elif "csv" in args.format: + from sleap.info.write_tracking_h5 import main as write_analysis + + for video, output_path in zip(vids, outnames): + write_analysis( + labels, + output_path=output_path, + labels_path=args.input_path, + all_frames=True, + video=video, + csv=True, + ) + else: from sleap.info.write_tracking_h5 import main as write_analysis diff --git a/sleap/io/dataset.py b/sleap/io/dataset.py index 45280cc54..1b894089f 100644 --- a/sleap/io/dataset.py +++ b/sleap/io/dataset.py @@ -2055,6 +2055,19 @@ def export(self, filename: str): SleapAnalysisAdaptor.write(filename, self) + def export_csv(self, filename: str): + """Export labels to CSV format. + + Args: + filename: Output path for the CSV format file. + + Notes: + This will write the contents of the labels out as a CSV file. + """ + from sleap.io.format.csv import CSVAdaptor + + CSVAdaptor.write(filename, self) + def export_nwb( self, filename: str, diff --git a/sleap/io/format/coco.py b/sleap/io/format/coco.py index 25122e4d0..44e7fb84a 100644 --- a/sleap/io/format/coco.py +++ b/sleap/io/format/coco.py @@ -180,6 +180,9 @@ def read( if flag == 0: # node not labeled for this instance + if (x, y) != (0, 0): + # If labeled but invisible, place the node at the coord + points[node] = Point(x, y, False) continue is_visible = flag == 2 diff --git a/sleap/io/format/deeplabcut.py b/sleap/io/format/deeplabcut.py index bb5dc3410..5892dba1a 100644 --- a/sleap/io/format/deeplabcut.py +++ b/sleap/io/format/deeplabcut.py @@ -19,10 +19,10 @@ import numpy as np import pandas as pd -from typing import List, Optional +from typing import List, Optional, Dict from sleap import Labels, Video, Skeleton -from sleap.instance import Instance, LabeledFrame, Point +from sleap.instance import Instance, LabeledFrame, Point, Track from sleap.util import find_files_by_suffix from .adaptor import Adaptor, SleapObjectType @@ -119,11 +119,12 @@ def read_frames( # Pull out animal and node names from the columns. start_col = 3 if is_new_format else 1 - animal_names = [] + tracks: Dict[str, Optional[Track]] = {} node_names = [] for animal_name, node_name, _ in data.columns[start_col:][::2]: - if animal_name not in animal_names: - animal_names.append(animal_name) + # Keep the starting frame index for each individual/track + if animal_name not in tracks.keys(): + tracks[animal_name] = None if node_name not in node_names: node_names.append(node_name) @@ -177,23 +178,33 @@ def read_frames( instances = [] if is_multianimal: - for animal_name in animal_names: + for animal_name in tracks.keys(): any_not_missing = False # Get points for each node. instance_points = dict() for node in node_names: - x, y = ( - data[(animal_name, node, "x")][i], - data[(animal_name, node, "y")][i], - ) + if (animal_name, node) in data.columns: + x, y = ( + data[(animal_name, node, "x")][i], + data[(animal_name, node, "y")][i], + ) + else: + x, y = np.nan, np.nan instance_points[node] = Point(x, y) if ~(np.isnan(x) and np.isnan(y)): any_not_missing = True if any_not_missing: + # Create track + if tracks[animal_name] is None: + tracks[animal_name] = Track(spawned_on=i, name=animal_name) # Create instance with points. instances.append( - Instance(skeleton=skeleton, points=instance_points) + Instance( + skeleton=skeleton, + points=instance_points, + track=tracks[animal_name], + ) ) else: # Get points for each node. @@ -270,6 +281,8 @@ def read( skeleton = Skeleton() if project_data.get("multianimalbodyparts", False): skeleton.add_nodes(project_data["multianimalbodyparts"]) + if "uniquebodyparts" in project_data: + skeleton.add_nodes(project_data["uniquebodyparts"]) else: skeleton.add_nodes(project_data["bodyparts"]) @@ -298,13 +311,24 @@ def read( # If subdirectory is foo, we look for foo.mp4 in videos dir. shortname = os.path.split(data_subdir)[-1] - video_path = os.path.join(videos_dir, f"{shortname}.mp4") - - if os.path.exists(video_path): + video_path = None + if os.path.exists(videos_dir): + with os.scandir(videos_dir) as file_iterator: + for file in file_iterator: + if not file.is_file(): + continue + if os.path.splitext(file.name)[0] != shortname: + continue + video_path = os.path.join(videos_dir, file.name) + break + + if video_path is not None and os.path.exists(video_path): video = Video.from_filename(video_path) else: # When no video is found, the individual frame images # stored in the labeled data subdir will be used. + if video_path is None: + video_path = os.path.join(videos_dir, f"{shortname}.mp4") print( f"Unable to find {video_path} so using individual frame images." ) diff --git a/sleap/io/format/hdf5.py b/sleap/io/format/hdf5.py index 353f88e3a..55a30d74f 100644 --- a/sleap/io/format/hdf5.py +++ b/sleap/io/format/hdf5.py @@ -81,7 +81,10 @@ def read_headers( # Extract the Labels JSON metadata and create Labels object with just this # metadata. - dicts = json_loads(f.require_group("metadata").attrs["json"].tobytes().decode()) + json = f.require_group("metadata").attrs["json"] + if not isinstance(json, str): + json = json.tobytes().decode() + dicts = json_loads(json) # These items are stored in separate lists because the metadata group got to be # too big. @@ -151,6 +154,45 @@ def read( points_dset[:]["x"] -= 0.5 points_dset[:]["y"] -= 0.5 + def cast_as_compound(arr, dtype): + out = np.empty(shape=(len(arr),), dtype=dtype) + if out.size == 0: + return out + for i, (name, _) in enumerate(dtype): + out[name] = arr[:, i] + return out + + # cast points, instances, and frames into complex dtype if not already + dtype_points = [("x", " np.ndarray: def get_frames_safely(self, idxs: Iterable[int]) -> Tuple[List[int], np.ndarray]: """Return list of frame indices and frames which were successfully loaded. + Args: + idxs: An iterable object that contains the indices of frames. - idxs: An iterable object that contains the indices of frames. Returns: A tuple of (frame indices, frames), where * frame indices is a subset of the specified idxs, and @@ -1442,19 +1443,31 @@ def to_hdf5( def encode(img): _, encoded = cv2.imencode("." + format, img) - return np.squeeze(encoded) + return np.squeeze(encoded).astype("int8") + + # pad with zeroes to guarantee int8 type in hdf5 file + frames = [] + for i in range(len(frame_numbers)): + frames.append(encode(frame_data[i])) + + max_frame_size = ( + max([len(x) if len(x) else 0 for x in frames]) if len(frames) else 0 + ) - dtype = h5.special_dtype(vlen=np.dtype("int8")) dset = f.create_dataset( - dataset + "/video", (len(frame_numbers),), dtype=dtype + dataset + "/video", + (len(frame_numbers), max_frame_size), + dtype="int8", + compression="gzip", ) dset.attrs["format"] = format dset.attrs["channels"] = self.channels dset.attrs["height"] = self.height dset.attrs["width"] = self.width - for i in range(len(frame_numbers)): - dset[i] = encode(frame_data[i]) + for i, frame in enumerate(frames): + dset[i, 0 : len(frame)] = frame + else: f.create_dataset( dataset + "/video", @@ -1532,22 +1545,17 @@ def cattr(): A cattr converter. """ - # When we are structuring video backends, try to fixup the video file paths - # in case they are coming from a different computer or the file has been moved. - def fixup_video(x, cl): - if "filename" in x: - x["filename"] = Video.fixup_path(x["filename"]) - if "file" in x: - x["file"] = Video.fixup_path(x["file"]) + # Use from_filename to fixup the video path and determine backend + def fixup_video(x: dict, cl: Video): + backend_dict = x.pop("backend") + filename = backend_dict.pop("filename", None) or backend_dict.pop( + "file", None + ) - return Video.make_specific_backend(cl, x) + return Video.from_filename(filename, **backend_dict) vid_cattr = cattr.Converter() - - # Check the type hint for backend and register the video path - # fixup hook for each type in the Union. - for t in attr.fields(Video).backend.type.__args__: - vid_cattr.register_structure_hook(t, fixup_video) + vid_cattr.register_structure_hook(Video, fixup_video) return vid_cattr diff --git a/sleap/io/videowriter.py b/sleap/io/videowriter.py index 510fad739..cd710c9d5 100644 --- a/sleap/io/videowriter.py +++ b/sleap/io/videowriter.py @@ -12,6 +12,7 @@ from abc import ABC, abstractmethod import cv2 import numpy as np +import imageio.v2 as iio class VideoWriter(ABC): @@ -32,22 +33,26 @@ def close(self): @staticmethod def safe_builder(filename, height, width, fps): """Builds VideoWriter based on available dependencies.""" - if VideoWriter.can_use_skvideo(): - return VideoWriterSkvideo(filename, height, width, fps) + if VideoWriter.can_use_ffmpeg(): + return VideoWriterImageio(filename, height, width, fps) else: return VideoWriterOpenCV(filename, height, width, fps) @staticmethod - def can_use_skvideo(): - # See if we can import skvideo + def can_use_ffmpeg(): + """Check if ffmpeg is available for writing videos.""" try: - import skvideo + import imageio_ffmpeg as ffmpeg except ImportError: return False - # See if skvideo can find FFMPEG - if skvideo.getFFmpegVersion() != "0.0.0": - return True + try: + # Try to get the version of the ffmpeg plugin + ffmpeg_version = ffmpeg.get_ffmpeg_version() + if ffmpeg_version: + return True + except Exception: + return False return False @@ -68,11 +73,11 @@ def close(self): self._writer.release() -class VideoWriterSkvideo(VideoWriter): - """Writes video using scikit-video as wrapper for ffmpeg. +class VideoWriterImageio(VideoWriter): + """Writes video using imageio as a wrapper for ffmpeg. Attributes: - filename: Path to mp4 file to save to. + filename: Path to video file to save to. height: Height of movie frames. width: Width of movie frames. fps: Playback framerate to save at. @@ -85,28 +90,38 @@ class VideoWriterSkvideo(VideoWriter): def __init__( self, filename, height, width, fps, crf: int = 21, preset: str = "superfast" ): - import skvideo.io - - fps = str(fps) - self._writer = skvideo.io.FFmpegWriter( + self.filename = filename + self.height = height + self.width = width + self.fps = fps + self.crf = crf + self.preset = preset + + import imageio_ffmpeg as ffmpeg + + # Imageio's ffmpeg writer parameters + # https://imageio.readthedocs.io/en/stable/examples.html#writing-videos-with-ffmpeg-and-vaapi + # Use `ffmpeg -h encoder=libx264`` to see all options for libx264 output_params + # output_params must be a list of strings + # iio.help(name='FFMPEG') to test + self.writer = iio.get_writer( filename, - inputdict={ - "-r": fps, - }, - outputdict={ - "-c:v": "libx264", - "-preset": preset, - "-vf": "scale=trunc(iw/2)*2:trunc(ih/2)*2", # Need even dims for libx264 - "-framerate": fps, - "-crf": str(crf), - "-pix_fmt": "yuv420p", - }, + fps=fps, + codec="libx264", + format="FFMPEG", + pixelformat="yuv420p", + output_params=[ + "-preset", + preset, + "-crf", + str(crf), + ], ) def add_frame(self, img, bgr: bool = False): if bgr: img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) - self._writer.writeFrame(img) + self.writer.append_data(img) def close(self): - self._writer.close() + self.writer.close() diff --git a/sleap/io/visuals.py b/sleap/io/visuals.py index 2018ce0bf..f2dde0be3 100644 --- a/sleap/io/visuals.py +++ b/sleap/io/visuals.py @@ -27,7 +27,13 @@ _sentinel = object() -def reader(out_q: Queue, video: Video, frames: List[int], scale: float = 1.0): +def reader( + out_q: Queue, + video: Video, + frames: List[int], + scale: float = 1.0, + background: str = "original", +): """Read frame images from video and send them into queue. Args: @@ -36,11 +42,13 @@ def reader(out_q: Queue, video: Video, frames: List[int], scale: float = 1.0): video: The `Video` object to read. frames: Full list frame indexes we want to read. scale: Output scale for frame images. + background: output video background. Either original, black, white, grey Returns: None. """ + background = background.lower() cv2.setNumThreads(usable_cpu_count()) total_count = len(frames) @@ -64,6 +72,16 @@ def reader(out_q: Queue, video: Video, frames: List[int], scale: float = 1.0): loaded_chunk_idxs, video_frame_images = video.get_frames_safely( frames_idx_chunk ) + if background != "original": + # fill the frame with the color + fill_values = {"black": 0, "grey": 127, "white": 255} + try: + fill = fill_values[background] + except KeyError: + raise ValueError( + f"Invalid background color: {background}. Options include: {', '.join(fill_values.keys())}" + ) + video_frame_images = video_frame_images * 0 + fill if not loaded_chunk_idxs: print(f"No frames could be loaded from chunk {chunk_i}") @@ -497,6 +515,7 @@ def save_labeled_video( fps: int = 15, scale: float = 1.0, crop_size_xy: Optional[Tuple[int, int]] = None, + background: str = "original", show_edges: bool = True, edge_is_wedge: bool = False, marker_size: int = 4, @@ -515,6 +534,7 @@ def save_labeled_video( fps: Frames per second for output video. scale: scale of image (so we can scale point locations to match) crop_size_xy: size of crop around instances, or None for full images + background: output video background. Either original, black, white, grey show_edges: whether to draw lines between nodes edge_is_wedge: whether to draw edges as wedges (draw as line if False) marker_size: Size of marker in pixels before scaling by `scale` @@ -537,7 +557,7 @@ def save_labeled_video( q2 = Queue(maxsize=10) progress_queue = Queue() - thread_read = Thread(target=reader, args=(q1, video, frames, scale)) + thread_read = Thread(target=reader, args=(q1, video, frames, scale, background)) thread_mark = VideoMarkerThread( in_q=q1, out_q=q2, @@ -695,6 +715,15 @@ def main(args: list = None): "and 'nodes' (default: 'nodes')" ), ) + parser.add_argument( + "--background", + type=str, + default="original", + help=( + "Specify the type of background to be used to save the videos." + "Options for background: original, black, white and grey" + ), + ) args = parser.parse_args(args=args) labels = Labels.load_file( args.data_path, video_search=[os.path.dirname(args.data_path)] @@ -730,6 +759,7 @@ def main(args: list = None): marker_size=args.marker_size, palette=args.palette, distinctly_color=args.distinctly_color, + background=args.background, ) print(f"Video saved as: {filename}") diff --git a/sleap/nn/config/outputs.py b/sleap/nn/config/outputs.py index ffb0d76e4..ccb6077b1 100644 --- a/sleap/nn/config/outputs.py +++ b/sleap/nn/config/outputs.py @@ -151,8 +151,8 @@ class OutputsConfig: save_visualizations: If True, will render and save visualizations of the model predictions as PNGs to "{run_folder}/viz/{split}.{epoch:04d}.png", where the split is one of "train", "validation", "test". - delete_viz_images: If True, delete the saved visualizations after training - completes. This is useful to reduce the model folder size if you do not need + keep_viz_images: If True, keep the saved visualization images after training + completes. This is useful unchecked to reduce the model folder size if you do not need to keep the visualization images. zip_outputs: If True, compress the run folder to a zip file. This will be named "{run_folder}.zip". @@ -170,7 +170,7 @@ class OutputsConfig: runs_folder: Text = "models" tags: List[Text] = attr.ib(factory=list) save_visualizations: bool = True - delete_viz_images: bool = True + keep_viz_images: bool = False zip_outputs: bool = False log_to_csv: bool = True checkpointing: CheckpointingConfig = attr.ib(factory=CheckpointingConfig) diff --git a/sleap/nn/data/augmentation.py b/sleap/nn/data/augmentation.py index 21dfb29e6..b754c0fe9 100644 --- a/sleap/nn/data/augmentation.py +++ b/sleap/nn/data/augmentation.py @@ -1,19 +1,11 @@ """Transformers for applying data augmentation.""" -# Monkey patch for: https://github.com/aleju/imgaug/issues/537 -# TODO: Fix when PyPI/conda packages are available for version fencing. -import numpy - -if hasattr(numpy.random, "_bit_generator"): - numpy.random.bit_generator = numpy.random._bit_generator - import sleap import numpy as np import tensorflow as tf import attr from typing import List, Text, Optional -import imgaug as ia -import imgaug.augmenters as iaa +import albumentations as A from sleap.nn.config import AugmentationConfig from sleap.nn.data.instance_cropping import crop_bboxes @@ -111,15 +103,15 @@ def flip_instances_ud( @attr.s(auto_attribs=True) -class ImgaugAugmenter: - """Data transformer based on the `imgaug` library. +class AlbumentationsAugmenter: + """Data transformer based on the `albumentations` library. This class can generate a `tf.data.Dataset` from an existing one that generates image and instance data. Element of the output dataset will have a set of augmentation transformations applied. Attributes: - augmenter: An instance of `imgaug.augmenters.Sequential` that will be applied to + augmenter: An instance of `albumentations.Compose` that will be applied to each element of the input dataset. image_key: Name of the example key where the image is stored. Defaults to "image". @@ -127,7 +119,7 @@ class ImgaugAugmenter: Defaults to "instances". """ - augmenter: iaa.Sequential + augmenter: A.Compose image_key: str = "image" instances_key: str = "instances" @@ -137,7 +129,7 @@ def from_config( config: AugmentationConfig, image_key: Text = "image", instances_key: Text = "instances", - ) -> "ImgaugAugmenter": + ) -> "AlbumentationsAugmenter": """Create an augmenter from a set of configuration parameters. Args: @@ -148,52 +140,64 @@ def from_config( Defaults to "instances". Returns: - An instance of `ImgaugAugmenter` with the specified augmentation + An instance of `AlbumentationsAugmenter` with the specified augmentation configuration. """ aug_stack = [] if config.rotate: aug_stack.append( - iaa.Affine( - rotate=(config.rotation_min_angle, config.rotation_max_angle) + A.Rotate( + limit=(config.rotation_min_angle, config.rotation_max_angle), p=1.0 ) ) if config.translate: aug_stack.append( - iaa.Affine( + A.Affine( translate_px={ "x": (config.translate_min, config.translate_max), "y": (config.translate_min, config.translate_max), - } + }, + p=1.0, ) ) if config.scale: - aug_stack.append(iaa.Affine(scale=(config.scale_min, config.scale_max))) - if config.uniform_noise: aug_stack.append( - iaa.AddElementwise( - value=(config.uniform_noise_min_val, config.uniform_noise_max_val) - ) + A.Affine(scale=(config.scale_min, config.scale_max), p=1.0) ) + if config.uniform_noise: + + def uniform_noise(image, **kwargs): + return image + np.random.uniform( + config.uniform_noise_min_val, config.uniform_noise_max_val + ) + + aug_stack.append(A.Lambda(image=uniform_noise)) if config.gaussian_noise: aug_stack.append( - iaa.AdditiveGaussianNoise( - loc=config.gaussian_noise_mean, scale=config.gaussian_noise_stddev + A.GaussNoise( + mean=config.gaussian_noise_mean, + var_limit=config.gaussian_noise_stddev, ) ) if config.contrast: aug_stack.append( - iaa.GammaContrast( - gamma=(config.contrast_min_gamma, config.contrast_max_gamma) + A.RandomGamma( + gamma_limit=(config.contrast_min_gamma, config.contrast_max_gamma), + p=1.0, ) ) if config.brightness: aug_stack.append( - iaa.Add(value=(config.brightness_min_val, config.brightness_max_val)) + A.RandomBrightness( + limit=(config.brightness_min_val, config.brightness_max_val), p=1.0 + ) ) return cls( - augmenter=iaa.Sequential(aug_stack), + augmenter=A.Compose( + aug_stack, + keypoint_params=A.KeypointParams(format="xy", remove_invisible=False), + ), image_key=image_key, instances_key=instances_key, ) @@ -226,22 +230,16 @@ def transform_dataset(self, input_ds: tf.data.Dataset) -> tf.data.Dataset: # Define augmentation function to map over each sample. def py_augment(image, instances): """Local processing function that will not be autographed.""" - # Ensure that the transformations applied to all data within this - # example are kept consistent. - aug_det = self.augmenter.to_deterministic() + # Convert to numpy arrays. + img = image.numpy() + kps = instances.numpy() + original_shape = kps.shape + kps = kps.reshape(-1, 2) - # Augment the image. - aug_img = aug_det.augment_image(image.numpy()) - - # This will get converted to a rank 3 tensor (n_instances, n_nodes, 2). - aug_instances = np.full_like(instances, np.nan) - - # Augment each set of points for each instance. - for i, instance in enumerate(instances): - kps = ia.KeypointsOnImage.from_xy_array( - instance.numpy(), tuple(image.shape) - ) - aug_instances[i] = aug_det.augment_keypoints(kps).to_xy_array() + # Augment. + augmented = self.augmenter(image=img, keypoints=kps) + aug_img = augmented["image"] + aug_instances = np.array(augmented["keypoints"]).reshape(original_shape) return aug_img, aug_instances @@ -258,7 +256,6 @@ def augment(frame_data): return frame_data # Apply the augmentation to each element. - # Note: We map sequentially since imgaug gets slower with tf.data parallelism. output_ds = input_ds.map(augment) return output_ds diff --git a/sleap/nn/data/pipelines.py b/sleap/nn/data/pipelines.py index b0892f8a1..2e334456a 100644 --- a/sleap/nn/data/pipelines.py +++ b/sleap/nn/data/pipelines.py @@ -18,7 +18,7 @@ from sleap.nn.data.providers import LabelsReader, VideoReader from sleap.nn.data.augmentation import ( AugmentationConfig, - ImgaugAugmenter, + AlbumentationsAugmenter, RandomCropper, RandomFlipper, ) @@ -68,7 +68,7 @@ PROVIDERS = (LabelsReader, VideoReader) TRANSFORMERS = ( - ImgaugAugmenter, + AlbumentationsAugmenter, RandomCropper, Normalizer, Resizer, @@ -406,7 +406,7 @@ def make_training_pipeline(self, data_provider: Provider) -> Pipeline: self.data_config.labels.skeletons[0], horizontal=self.optimization_config.augmentation_config.flip_horizontal, ) - pipeline += ImgaugAugmenter.from_config( + pipeline += AlbumentationsAugmenter.from_config( self.optimization_config.augmentation_config ) if self.optimization_config.augmentation_config.random_crop: @@ -550,7 +550,7 @@ def make_training_pipeline(self, data_provider: Provider) -> Pipeline: self.data_config.labels.skeletons[0], horizontal=self.optimization_config.augmentation_config.flip_horizontal, ) - pipeline += ImgaugAugmenter.from_config( + pipeline += AlbumentationsAugmenter.from_config( self.optimization_config.augmentation_config ) if self.optimization_config.augmentation_config.random_crop: @@ -713,7 +713,7 @@ def make_training_pipeline(self, data_provider: Provider) -> Pipeline: self.data_config.labels.skeletons[0], horizontal=self.optimization_config.augmentation_config.flip_horizontal, ) - pipeline += ImgaugAugmenter.from_config( + pipeline += AlbumentationsAugmenter.from_config( self.optimization_config.augmentation_config ) pipeline += Normalizer.from_config(self.data_config.preprocessing) @@ -863,7 +863,7 @@ def make_training_pipeline(self, data_provider: Provider) -> Pipeline: self.data_config.labels.skeletons[0], horizontal=aug_config.flip_horizontal, ) - pipeline += ImgaugAugmenter.from_config(aug_config) + pipeline += AlbumentationsAugmenter.from_config(aug_config) if aug_config.random_crop: pipeline += RandomCropper( crop_height=aug_config.random_crop_height, @@ -1028,7 +1028,7 @@ def make_training_pipeline(self, data_provider: Provider) -> Pipeline: horizontal=aug_config.flip_horizontal, ) - pipeline += ImgaugAugmenter.from_config(aug_config) + pipeline += AlbumentationsAugmenter.from_config(aug_config) if aug_config.random_crop: pipeline += RandomCropper( crop_height=aug_config.random_crop_height, @@ -1186,7 +1186,7 @@ def make_training_pipeline(self, data_provider: Provider) -> Pipeline: config=self.data_config.preprocessing, provider=data_provider, ) - pipeline += ImgaugAugmenter.from_config( + pipeline += AlbumentationsAugmenter.from_config( self.optimization_config.augmentation_config ) pipeline += Normalizer.from_config(self.data_config.preprocessing) diff --git a/sleap/nn/data/providers.py b/sleap/nn/data/providers.py index 16f439d10..9e93d0b18 100644 --- a/sleap/nn/data/providers.py +++ b/sleap/nn/data/providers.py @@ -394,9 +394,7 @@ def make_dataset(self) -> tf.data.Dataset: grid in order to properly map points to image coordinates. """ # Grab an image to test for the dtype. - test_image = tf.convert_to_tensor( - self.video.get_frame(self.video.last_frame_idx) - ) + test_image = tf.convert_to_tensor(self.video.get_frame(0)) image_dtype = test_image.dtype def py_fetch_frame(ind): diff --git a/sleap/nn/inference.py b/sleap/nn/inference.py index 6d7d24f8c..3f01a1c3c 100644 --- a/sleap/nn/inference.py +++ b/sleap/nn/inference.py @@ -33,6 +33,7 @@ import atexit import subprocess import rich.progress +import pandas as pd from rich.pretty import pprint from collections import deque import json @@ -1142,6 +1143,7 @@ def export_model( info["frozen_model_inputs"] = frozen_func.inputs info["frozen_model_outputs"] = frozen_func.outputs + info["unragged_outputs"] = unrag_outputs with (Path(save_path) / "info.json").open("w") as fp: json.dump( @@ -1582,6 +1584,15 @@ def _object_builder(): try: for ex in generator: prediction_queue.put(ex) + + except KeyError as e: + # Gracefully handle seeking errors by early termination. + if "Unable to load frame" in str(e): + pass # TODO: Print warning obeying verbosity? (This code path is also + # called for interactive prediction where we don't want any spam.) + else: + raise + finally: prediction_queue.put(None) object_builder.join() @@ -2611,6 +2622,7 @@ def _object_builder(): # Set tracks for predicted instances in this frame. predicted_instances = self.tracker.track( untracked_instances=predicted_instances, + img_hw=ex["image"].shape[-3:-1], img=image, t=frame_ind, ) @@ -2632,6 +2644,15 @@ def _object_builder(): try: for ex in generator: prediction_queue.put(ex) + + except KeyError as e: + # Gracefully handle seeking errors by early termination. + if "Unable to load frame" in str(e): + pass # TODO: Print warning obeying verbosity? (This code path is also + # called for interactive prediction where we don't want any spam.) + else: + raise + finally: prediction_queue.put(None) object_builder.join() @@ -3244,6 +3265,7 @@ def _object_builder(): # Set tracks for predicted instances in this frame. predicted_instances = self.tracker.track( untracked_instances=predicted_instances, + img_hw=ex["image"].shape[-3:-1], img=image, t=frame_ind, ) @@ -3265,6 +3287,15 @@ def _object_builder(): try: for ex in generator: prediction_queue.put(ex) + + except KeyError as e: + # Gracefully handle seeking errors by early termination. + if "Unable to load frame" in str(e): + pass # TODO: Print warning obeying verbosity? (This code path is also + # called for interactive prediction where we don't want any spam.) + else: + raise + finally: prediction_queue.put(None) object_builder.join() @@ -3747,9 +3778,10 @@ def _object_builder(): PredictedInstance.from_numpy( points=pts, point_confidences=confs, - instance_score=np.nanmean(score), + instance_score=np.nanmean(confs), skeleton=skeleton, track=track, + tracking_score=np.nanmean(score), ) ) @@ -3770,6 +3802,15 @@ def _object_builder(): try: for ex in generator: prediction_queue.put(ex) + + except KeyError as e: + # Gracefully handle seeking errors by early termination. + if "Unable to load frame" in str(e): + pass # TODO: Print warning obeying verbosity? (This code path is also + # called for interactive prediction where we don't want any spam.) + else: + raise + finally: prediction_queue.put(None) object_builder.join() @@ -4412,18 +4453,27 @@ def _object_builder(): break # Loop over frames. - for image, video_ind, frame_ind, points, confidences, scores in zip( + for ( + image, + video_ind, + frame_ind, + centroid_vals, + points, + confidences, + scores, + ) in zip( ex["image"], ex["video_ind"], ex["frame_ind"], + ex["centroid_vals"], ex["instance_peaks"], ex["instance_peak_vals"], ex["instance_scores"], ): # Loop over instances. predicted_instances = [] - for i, (pts, confs, score) in enumerate( - zip(points, confidences, scores) + for i, (pts, centroid_val, confs, score) in enumerate( + zip(points, centroid_vals, confidences, scores) ): if np.isnan(pts).all(): continue @@ -4434,9 +4484,10 @@ def _object_builder(): PredictedInstance.from_numpy( points=pts, point_confidences=confs, - instance_score=np.nanmean(score), + instance_score=centroid_val, skeleton=skeleton, track=track, + tracking_score=score, ) ) @@ -4457,6 +4508,15 @@ def _object_builder(): try: for ex in generator: prediction_queue.put(ex) + + except KeyError as e: + # Gracefully handle seeking errors by early termination. + if "Unable to load frame" in str(e): + pass # TODO: Print warning obeying verbosity? (This code path is also + # called for interactive prediction where we don't want any spam.) + else: + raise + finally: prediction_queue.put(None) object_builder.join() @@ -4734,6 +4794,15 @@ def _object_builder(): try: for ex in generator: prediction_queue.put(ex) + + except KeyError as e: + # Gracefully handle seeking errors by early termination. + if "Unable to load frame" in str(e): + pass # TODO: Print warning obeying verbosity? (This code path is also + # called for interactive prediction where we don't want any spam.) + else: + raise + finally: prediction_queue.put(None) object_builder.join() @@ -4939,7 +5008,7 @@ def export_cli(args: Optional[list] = None): export_model( args.models, args.export_path, - unrag_outputs=args.unrag, + unrag_outputs=(not args.ragged), max_instances=args.max_instances, ) @@ -4971,13 +5040,13 @@ def _make_export_cli_parser() -> argparse.ArgumentParser: ), ) parser.add_argument( - "-u", - "--unrag", + "-r", + "--ragged", action="store_true", - default=True, + default=False, help=( - "Convert ragged tensors into regular tensors with NaN padding. " - "Defaults to True." + "Keep tensors ragged if present. If ommited, convert ragged tensors" + " into regular tensors with NaN padding." ), ) parser.add_argument( @@ -5230,15 +5299,14 @@ def _make_provider_from_cli(args: argparse.Namespace) -> Tuple[Provider, str]: args: Parsed CLI namespace. Returns: - A tuple of `(provider, data_path)` with the data `Provider` and path to the data - that was specified in the args. + `(provider_list, data_path_list, output_path_list)` where `provider_list` contains the data providers, + `data_path_list` contains the paths to the specified data, and the `output_path_list` contains the list + of output paths if a CSV file with a column of output paths was provided; otherwise, `output_path_list` + defaults to None """ + # Figure out which input path to use. - labels_path = getattr(args, "labels", None) - if labels_path is not None: - data_path = labels_path - else: - data_path = args.data_path + data_path = args.data_path if data_path is None or data_path == "": raise ValueError( @@ -5246,33 +5314,117 @@ def _make_provider_from_cli(args: argparse.Namespace) -> Tuple[Provider, str]: "Run 'sleap-track -h' to see full command documentation." ) - if data_path.endswith(".slp"): - labels = sleap.load_file(data_path) - - if args.only_labeled_frames: - provider = LabelsReader.from_user_labeled_frames(labels) - elif args.only_suggested_frames: - provider = LabelsReader.from_unlabeled_suggestions(labels) - elif getattr(args, "video.index") != "": - provider = VideoReader( - video=labels.videos[int(getattr(args, "video.index"))], - example_indices=frame_list(args.frames), - ) + data_path_obj = Path(data_path) + + # Set output_path_list to None as a default to return later + output_path_list = None + + # Check that input value is valid + if not data_path_obj.exists(): + raise ValueError("Path to data_path does not exist") + + elif data_path_obj.is_file(): + # If the file is a CSV file, check for data_paths and output_paths + if data_path_obj.suffix.lower() == ".csv": + try: + data_path_column = None + # Read the CSV file + df = pd.read_csv(data_path) + + # collect data_paths from column + for col_index in range(df.shape[1]): + path_str = df.iloc[0, col_index] + if Path(path_str).exists(): + data_path_column = df.columns[col_index] + break + if data_path_column is None: + raise ValueError( + f"Column containing valid data_paths does not exist in the CSV file: {data_path}" + ) + raw_data_path_list = df[data_path_column].tolist() + + # optional output_path column to specify multiple output_paths + output_path_column_index = df.columns.get_loc(data_path_column) + 1 + if ( + output_path_column_index < df.shape[1] + and df.iloc[:, output_path_column_index].dtype == object + ): + # Ensure the next column exists + output_path_list = df.iloc[:, output_path_column_index].tolist() + else: + output_path_list = None + + except pd.errors.EmptyDataError as e: + raise ValueError(f"CSV file is empty: {data_path}. Error: {e}") from e + + # If the file is a text file, collect data_paths + elif data_path_obj.suffix.lower() == ".txt": + try: + with open(data_path_obj, "r") as file: + raw_data_path_list = [line.strip() for line in file.readlines()] + except Exception as e: + raise ValueError( + f"Error reading text file: {data_path}. Error: {e}" + ) from e else: - provider = LabelsReader(labels) + raw_data_path_list = [data_path_obj.as_posix()] - else: - print(f"Video: {data_path}") - # TODO: Clean this up. - video_kwargs = dict( - dataset=vars(args).get("video.dataset"), - input_format=vars(args).get("video.input_format"), - ) - provider = VideoReader.from_filepath( - filename=data_path, example_indices=frame_list(args.frames), **video_kwargs - ) + raw_data_path_list = [Path(p) for p in raw_data_path_list] - return provider, data_path + # Check for multiple video inputs + # Compile file(s) into a list for later iteration + elif data_path_obj.is_dir(): + raw_data_path_list = [ + file_path for file_path in data_path_obj.iterdir() if file_path.is_file() + ] + + # Provider list to accomodate multiple video inputs + provider_list = [] + data_path_list = [] + for file_path in raw_data_path_list: + # Create a provider for each file + if file_path.as_posix().endswith(".slp") and len(raw_data_path_list) > 1: + print(f"slp file skipped: {file_path.as_posix()}") + + elif file_path.as_posix().endswith(".slp"): + labels = sleap.load_file(file_path.as_posix()) + + if args.only_labeled_frames: + provider_list.append(LabelsReader.from_user_labeled_frames(labels)) + elif args.only_suggested_frames: + provider_list.append(LabelsReader.from_unlabeled_suggestions(labels)) + elif getattr(args, "video.index") != "": + provider_list.append( + VideoReader( + video=labels.videos[int(getattr(args, "video.index"))], + example_indices=frame_list(args.frames), + ) + ) + else: + provider_list.append(LabelsReader(labels)) + + data_path_list.append(file_path) + + else: + try: + video_kwargs = dict( + dataset=vars(args).get("video.dataset"), + input_format=vars(args).get("video.input_format"), + ) + provider_list.append( + VideoReader.from_filepath( + filename=file_path.as_posix(), + example_indices=frame_list(args.frames), + **video_kwargs, + ) + ) + print(f"Video: {file_path.as_posix()}") + data_path_list.append(file_path) + # TODO: Clean this up. + except Exception: + print(f"Error reading file: {file_path.as_posix()}") + + return provider_list, data_path_list, output_path_list def _make_predictor_from_cli(args: argparse.Namespace) -> Predictor: @@ -5367,8 +5519,6 @@ def main(args: Optional[list] = None): pprint(vars(args)) print() - output_path = args.output - # Setup devices. if args.cpu or not sleap.nn.system.is_gpu_system(): sleap.nn.system.use_cpu_only() @@ -5406,7 +5556,20 @@ def main(args: Optional[list] = None): print() # Setup data loader. - provider, data_path = _make_provider_from_cli(args) + provider_list, data_path_list, output_path_list = _make_provider_from_cli(args) + + output_path = None + + # if output_path has not been extracted from a csv file yet + if output_path_list is None and args.output is not None: + output_path = args.output + output_path_obj = Path(output_path) + + # check if output_path is valid before running inference + if Path(output_path).is_file() and len(data_path_list) > 1: + raise ValueError( + "output_path argument must be a directory if multiple video inputs are given" + ) # Setup tracker. tracker = _make_tracker_from_cli(args) @@ -5414,25 +5577,94 @@ def main(args: Optional[list] = None): if args.models is not None and "movenet" in args.models[0]: args.models = args.models[0] - # Either run inference (and tracking) or just run tracking + # Either run inference (and tracking) or just run tracking (if using an existing prediction where inference has already been run) if args.models is not None: - # Setup models. - predictor = _make_predictor_from_cli(args) - predictor.tracker = tracker - # Run inference! - labels_pr = predictor.predict(provider) + # Run inference on all files inputed + for i, (data_path, provider) in enumerate(zip(data_path_list, provider_list)): + # Setup models. + data_path_obj = Path(data_path) + predictor = _make_predictor_from_cli(args) + predictor.tracker = tracker - if output_path is None: - output_path = data_path + ".predictions.slp" + # Run inference! + labels_pr = predictor.predict(provider) - labels_pr.provenance["model_paths"] = predictor.model_paths - labels_pr.provenance["predictor"] = type(predictor).__name__ + # if output path was not provided, create an output path + if output_path is None: + # if output path was not provided, create an output path + if output_path_list: + output_path = output_path_list[i] + + else: + output_path = data_path_obj.with_suffix(".predictions.slp") + output_path_obj = Path(output_path) + + # if output_path was provided and multiple inputs were provided, create a directory to store outputs + elif len(data_path_list) > 1: + output_path_obj = Path(output_path) + output_path = ( + output_path_obj + / (data_path_obj.with_suffix(".predictions.slp")).name + ) + output_path_obj = Path(output_path) + # Create the containing directory if needed. + output_path_obj.parent.mkdir(exist_ok=True, parents=True) + + labels_pr.provenance["model_paths"] = predictor.model_paths + labels_pr.provenance["predictor"] = type(predictor).__name__ + + if args.no_empty_frames: + # Clear empty frames if specified. + labels_pr.remove_empty_frames() + + finish_timestamp = str(datetime.now()) + total_elapsed = time() - t0 + print("Finished inference at:", finish_timestamp) + print(f"Total runtime: {total_elapsed} secs") + print(f"Predicted frames: {len(labels_pr)}/{len(provider)}") + + # Add provenance metadata to predictions. + labels_pr.provenance["sleap_version"] = sleap.__version__ + labels_pr.provenance["platform"] = platform.platform() + labels_pr.provenance["command"] = " ".join(sys.argv) + labels_pr.provenance["data_path"] = data_path_obj.as_posix() + labels_pr.provenance["output_path"] = output_path_obj.as_posix() + labels_pr.provenance["total_elapsed"] = total_elapsed + labels_pr.provenance["start_timestamp"] = start_timestamp + labels_pr.provenance["finish_timestamp"] = finish_timestamp + + print("Provenance:") + pprint(labels_pr.provenance) + print() + + labels_pr.provenance["args"] = vars(args) + + # Save results. + try: + labels_pr.save(output_path) + except Exception: + print("WARNING: Provided output path invalid.") + fallback_path = data_path_obj.with_suffix(".predictions.slp") + labels_pr.save(fallback_path) + print("Saved output:", output_path) + + if args.open_in_gui: + subprocess.call(["sleap-label", output_path]) + + # Reset output_path for next iteration + output_path = args.output + + # running tracking on existing prediction file elif getattr(args, "tracking.tracker") is not None: + provider = provider_list[0] + data_path = data_path_list[0] + # Load predictions + data_path = args.data_path print("Loading predictions...") - labels_pr = sleap.load_file(args.data_path) + labels_pr = sleap.load_file(data_path) frames = sorted(labels_pr.labeled_frames, key=lambda lf: lf.frame_idx) print("Starting tracker...") @@ -5444,6 +5676,40 @@ def main(args: Optional[list] = None): if output_path is None: output_path = f"{data_path}.{tracker.get_name()}.slp" + if args.no_empty_frames: + # Clear empty frames if specified. + labels_pr.remove_empty_frames() + + finish_timestamp = str(datetime.now()) + total_elapsed = time() - t0 + print("Finished inference at:", finish_timestamp) + print(f"Total runtime: {total_elapsed} secs") + print(f"Predicted frames: {len(labels_pr)}/{len(provider)}") + + # Add provenance metadata to predictions. + labels_pr.provenance["sleap_version"] = sleap.__version__ + labels_pr.provenance["platform"] = platform.platform() + labels_pr.provenance["command"] = " ".join(sys.argv) + labels_pr.provenance["data_path"] = data_path + labels_pr.provenance["output_path"] = output_path + labels_pr.provenance["total_elapsed"] = total_elapsed + labels_pr.provenance["start_timestamp"] = start_timestamp + labels_pr.provenance["finish_timestamp"] = finish_timestamp + + print("Provenance:") + pprint(labels_pr.provenance) + print() + + labels_pr.provenance["args"] = vars(args) + + # Save results. + labels_pr.save(output_path) + + print("Saved output:", output_path) + + if args.open_in_gui: + subprocess.call(["sleap-label", output_path]) + else: raise ValueError( "Neither tracker type nor path to trained models specified. " @@ -5451,36 +5717,3 @@ def main(args: Optional[list] = None): "To retrack on predictions, must specify tracker. " "Use \"sleap-track --tracking.tracker ...' to specify tracker to use." ) - - if args.no_empty_frames: - # Clear empty frames if specified. - labels_pr.remove_empty_frames() - - finish_timestamp = str(datetime.now()) - total_elapsed = time() - t0 - print("Finished inference at:", finish_timestamp) - print(f"Total runtime: {total_elapsed} secs") - print(f"Predicted frames: {len(labels_pr)}/{len(provider)}") - - # Add provenance metadata to predictions. - labels_pr.provenance["sleap_version"] = sleap.__version__ - labels_pr.provenance["platform"] = platform.platform() - labels_pr.provenance["command"] = " ".join(sys.argv) - labels_pr.provenance["data_path"] = data_path - labels_pr.provenance["output_path"] = output_path - labels_pr.provenance["total_elapsed"] = total_elapsed - labels_pr.provenance["start_timestamp"] = start_timestamp - labels_pr.provenance["finish_timestamp"] = finish_timestamp - - print("Provenance:") - pprint(labels_pr.provenance) - print() - - labels_pr.provenance["args"] = vars(args) - - # Save results. - labels_pr.save(output_path) - print("Saved output:", output_path) - - if args.open_in_gui: - subprocess.call(["sleap-label", output_path]) diff --git a/sleap/nn/peak_finding.py b/sleap/nn/peak_finding.py index 84dca00ae..e1fb43a6e 100644 --- a/sleap/nn/peak_finding.py +++ b/sleap/nn/peak_finding.py @@ -221,7 +221,7 @@ def find_global_peaks_rough( channels = tf.cast(tf.shape(cms)[-1], tf.int64) total_peaks = tf.cast(tf.shape(argmax_cols)[0], tf.int64) sample_subs = tf.range(total_peaks, dtype=tf.int64) // channels - channel_subs = tf.range(total_peaks, dtype=tf.int64) % channels + channel_subs = tf.math.mod(tf.range(total_peaks, dtype=tf.int64), channels) # Gather subscripts. peak_subs = tf.stack([sample_subs, argmax_rows, argmax_cols, channel_subs], axis=1) diff --git a/sleap/nn/system.py b/sleap/nn/system.py index eeb3f3ca4..4cc3d1804 100644 --- a/sleap/nn/system.py +++ b/sleap/nn/system.py @@ -48,7 +48,17 @@ def get_current_gpu() -> tf.config.PhysicalDevice: def use_cpu_only(): """Hide GPUs from TensorFlow to ensure only the CPU is available.""" - tf.config.set_visible_devices([], "GPU") + try: + tf.config.set_visible_devices([], "GPU") + except RuntimeError as ex: + if ( + len(ex.args) > 0 + and ex.args[0] + == "Visible devices cannot be modified after being initialized" + ): + print( + "Failed to set visible GPU. Visible devices cannot be modified after being initialized." + ) def use_gpu(device_ind: int): @@ -58,7 +68,17 @@ def use_gpu(device_ind: int): device_ind: Index of the GPU within the list of system GPUs. """ gpus = get_all_gpus() - tf.config.set_visible_devices(gpus[device_ind], "GPU") + try: + tf.config.set_visible_devices(gpus[device_ind], "GPU") + except RuntimeError as ex: + if ( + len(ex.args) > 0 + and ex.args[0] + == "Visible devices cannot be modified after being initialized" + ): + print( + "Failed to set visible GPU. Visible devices cannot be modified after being initialized." + ) def use_first_gpu(): @@ -159,7 +179,7 @@ def summary(): for gpu in all_gpus: print(f" Device: {gpu.name}") print(f" Available: {gpu in gpus}") - print(f" Initalized: {is_initialized(gpu)}") + print(f" Initialized: {is_initialized(gpu)}") print( f" Memory growth: {tf.config.experimental.get_memory_growth(gpu)}" ) diff --git a/sleap/nn/tracker/components.py b/sleap/nn/tracker/components.py index 10b2953b7..0b77f4ac9 100644 --- a/sleap/nn/tracker/components.py +++ b/sleap/nn/tracker/components.py @@ -12,9 +12,11 @@ """ + import operator from collections import defaultdict -from typing import List, Tuple, Optional, TypeVar, Callable +import logging +from typing import List, Tuple, Union, Optional, TypeVar, Callable import attr import numpy as np @@ -23,9 +25,26 @@ from sleap import PredictedInstance, Instance, Track from sleap.nn import utils +logger = logging.getLogger(__name__) + InstanceType = TypeVar("InstanceType", Instance, PredictedInstance) +def normalized_instance_similarity( + ref_instance: InstanceType, query_instance: InstanceType, img_hw: Tuple[int] +) -> float: + """Computes similarity between instances with normalized keypoints.""" + + normalize_factors = np.array((img_hw[1], img_hw[0])) + ref_visible = ~(np.isnan(ref_instance.points_array).any(axis=1)) + normalized_query_keypoints = query_instance.points_array / normalize_factors + normalized_ref_keypoints = ref_instance.points_array / normalize_factors + dists = np.sum((normalized_query_keypoints - normalized_ref_keypoints) ** 2, axis=1) + similarity = np.nansum(np.exp(-dists)) / np.sum(ref_visible) + + return similarity + + def instance_similarity( ref_instance: InstanceType, query_instance: InstanceType ) -> float: @@ -40,6 +59,95 @@ def instance_similarity( return similarity +def factory_object_keypoint_similarity( + keypoint_errors: Optional[Union[List, int, float]] = None, + score_weighting: bool = False, + normalization_keypoints: str = "all", +) -> Callable: + """Factory for similarity function based on object keypoints. + + Args: + keypoint_errors: The standard error of the distance between the predicted + keypoint and the true value, in pixels. + If None or empty list, defaults to 1. + If a scalar or singleton list, every keypoint has the same error. + If a list, defines the error for each keypoint, the length should be equal + to the number of keypoints in the skeleton. + score_weighting: If True, use `score` of `PredictedPoint` to weigh + `keypoint_errors`. If False, do not add a weight to `keypoint_errors`. + normalization_keypoints: Determine how to normalize similarity score. One of + ["all", "ref", "union"]. If "all", similarity score is normalized by number + of reference points. If "ref", similarity score is normalized by number of + visible reference points. If "union", similarity score is normalized by + number of points both visible in query and reference instance. + Default is "all". + + Returns: + Callable that returns object keypoint similarity between two `Instance`s. + + """ + keypoint_errors = 1 if keypoint_errors is None else keypoint_errors + with np.errstate(divide="ignore"): + kp_precision = 1 / (2 * np.array(keypoint_errors) ** 2) + + def object_keypoint_similarity( + ref_instance: InstanceType, query_instance: InstanceType + ) -> float: + nonlocal kp_precision + # Keypoints + ref_points = ref_instance.points_array + query_points = query_instance.points_array + # Keypoint scores + if score_weighting: + ref_scores = getattr(ref_instance, "scores", np.ones(len(ref_points))) + query_scores = getattr(query_instance, "scores", np.ones(len(query_points))) + else: + ref_scores = 1 + query_scores = 1 + # Number of keypoint for normalization + if normalization_keypoints in ("ref", "union"): + ref_visible = ~(np.isnan(ref_points).any(axis=1)) + if normalization_keypoints == "ref": + max_n_keypoints = np.sum(ref_visible) + elif normalization_keypoints == "union": + query_visible = ~(np.isnan(query_points).any(axis=1)) + max_n_keypoints = np.sum(np.logical_and(ref_visible, query_visible)) + else: # if normalization_keypoints == "all": + max_n_keypoints = len(ref_points) + if max_n_keypoints == 0: + return 0 + + # Make sure the sizes of kp_precision and n_points match + if kp_precision.size > 1 and 2 * kp_precision.size != ref_points.size: + # Correct kp_precision size to fit number of points + n_points = ref_points.size // 2 + mess = ( + "keypoint_errors array should have the same size as the number of " + f"keypoints in the instance: {kp_precision.size} != {n_points}" + ) + + if kp_precision.size > n_points: + kp_precision = kp_precision[:n_points] + mess += "\nTruncating keypoint_errors array." + + else: # elif kp_precision.size < n_points: + pad = n_points - kp_precision.size + kp_precision = np.pad(kp_precision, (0, pad), "edge") + mess += "\nPadding keypoint_errors array by repeating the last value." + logger.warning(mess) + + # Compute distances + dists = np.sum((query_points - ref_points) ** 2, axis=1) * kp_precision + + similarity = ( + np.nansum(ref_scores * query_scores * np.exp(-dists)) / max_n_keypoints + ) + + return similarity + + return object_keypoint_similarity + + def centroid_distance( ref_instance: InstanceType, query_instance: InstanceType, cache: dict = dict() ) -> float: diff --git a/sleap/nn/tracking.py b/sleap/nn/tracking.py index 9865b7db5..558aa9309 100644 --- a/sleap/nn/tracking.py +++ b/sleap/nn/tracking.py @@ -5,12 +5,15 @@ import attr import numpy as np import cv2 +import functools from typing import Callable, Deque, Dict, Iterable, List, Optional, Tuple from sleap import Track, LabeledFrame, Skeleton from sleap.nn.tracker.components import ( + factory_object_keypoint_similarity, instance_similarity, + normalized_instance_similarity, centroid_distance, instance_iou, hungarian_matching, @@ -391,6 +394,7 @@ def get_ref_instances( def get_candidates( self, track_matching_queue_dict: Dict[Track, Deque[MatchedFrameInstance]], + max_tracking: bool, t: int, img: np.ndarray, *args, @@ -404,7 +408,7 @@ def get_candidates( tracks = [] for track, matched_items in track_matching_queue_dict.items(): - if len(tracks) <= self.max_tracks: + if not max_tracking or len(tracks) < self.max_tracks: tracks.append(track) for matched_item in matched_items: ref_t, ref_img = ( @@ -466,6 +470,7 @@ class SimpleMaxTracksCandidateMaker(SimpleCandidateMaker): def get_candidates( self, track_matching_queue_dict: Dict, + max_tracking: bool, *args, **kwargs, ) -> List[InstanceType]: @@ -473,7 +478,7 @@ def get_candidates( candidate_instances = [] tracks = [] for track, matched_instances in track_matching_queue_dict.items(): - if len(tracks) <= self.max_tracks: + if not max_tracking or len(tracks) < self.max_tracks: tracks.append(track) for ref_instance in matched_instances: if ref_instance.instance_t.n_visible_points >= self.min_points: @@ -492,6 +497,8 @@ def get_candidates( instance=instance_similarity, centroid=centroid_distance, iou=instance_iou, + normalized_instance=normalized_instance_similarity, + object_keypoint=factory_object_keypoint_similarity, ) match_policies = dict( @@ -598,8 +605,15 @@ def _init_matching_queue(self): """Factory for instantiating default matching queue with specified size.""" return deque(maxlen=self.track_window) + @property + def has_max_tracking(self) -> bool: + return isinstance( + self.candidate_maker, + (SimpleMaxTracksCandidateMaker, FlowMaxTracksCandidateMaker), + ) + def reset_candidates(self): - if self.max_tracking: + if self.has_max_tracking: for track in self.track_matching_queue_dict: self.track_matching_queue_dict[track] = deque(maxlen=self.track_window) else: @@ -610,14 +624,15 @@ def unique_tracks_in_queue(self) -> List[Track]: """Returns the unique tracks in the matching queue.""" unique_tracks = set() - for match_item in self.track_matching_queue: - for instance in match_item.instances_t: - unique_tracks.add(instance.track) - - if self.max_tracking: + if self.has_max_tracking: for track in self.track_matching_queue_dict.keys(): unique_tracks.add(track) + else: + for match_item in self.track_matching_queue: + for instance in match_item.instances_t: + unique_tracks.add(instance.track) + return list(unique_tracks) @property @@ -627,6 +642,7 @@ def uses_image(self): def track( self, untracked_instances: List[InstanceType], + img_hw: Tuple[int], img: Optional[np.ndarray] = None, t: int = None, ) -> List[InstanceType]: @@ -634,19 +650,25 @@ def track( Args: untracked_instances: List of instances to assign to tracks. + img_hw: (height, width) of the image used to normalize the keypoints. img: Image data of the current frame for flow shifting. t: Current timestep. If not provided, increments from the internal queue. Returns: A list of the instances that were tracked. """ + if self.similarity_function == normalized_instance_similarity: + factory_normalized_instance = functools.partial( + normalized_instance_similarity, img_hw=img_hw + ) + self.similarity_function = factory_normalized_instance if self.candidate_maker is None: return untracked_instances # Infer timestep if not provided. if t is None: - if self.max_tracking: + if self.has_max_tracking: if len(self.track_matching_queue_dict) > 0: # Default to last timestep + 1 if available. @@ -684,10 +706,10 @@ def track( self.pre_cull_function(untracked_instances) # Build a pool of matchable candidate instances. - if self.max_tracking: + if self.has_max_tracking: candidate_instances = self.candidate_maker.get_candidates( track_matching_queue_dict=self.track_matching_queue_dict, - max_tracks=self.max_tracks, + max_tracking=self.max_tracking, t=t, img=img, ) @@ -721,13 +743,16 @@ def track( ) # Add the tracked instances to the dictionary of matched instances. - if self.max_tracking: + if self.has_max_tracking: for tracked_instance in tracked_instances: if tracked_instance.track in self.track_matching_queue_dict: self.track_matching_queue_dict[tracked_instance.track].append( MatchedFrameInstance(t, tracked_instance, img) ) - elif len(self.track_matching_queue_dict) < self.max_tracks: + elif ( + not self.max_tracking + or len(self.track_matching_queue_dict) < self.max_tracks + ): self.track_matching_queue_dict[tracked_instance.track] = deque( maxlen=self.track_window ) @@ -773,7 +798,8 @@ def spawn_for_untracked_instances( # Skip if we've reached the maximum number of tracks. if ( - self.max_tracking + self.has_max_tracking + and self.max_tracking and len(self.track_matching_queue_dict) >= self.max_tracks ): break @@ -838,8 +864,17 @@ def make_tracker_by_name( # Max tracking options max_tracks: Optional[int] = None, max_tracking: bool = False, + # Object keypoint similarity options + oks_errors: Optional[list] = None, + oks_score_weighting: bool = False, + oks_normalization: str = "all", **kwargs, ) -> BaseTracker: + # Parse max_tracking arguments, only True if max_tracks is not None and > 0 + max_tracking = max_tracking if max_tracks else False + if max_tracking and tracker in ("simple", "flow"): + # Force a candidate maker of 'maxtracks' type + tracker += "maxtracks" if tracker.lower() == "none": candidate_maker = None @@ -858,7 +893,14 @@ def make_tracker_by_name( raise ValueError(f"{match} is not a valid tracker matching function.") candidate_maker = tracker_policies[tracker](min_points=min_match_points) - similarity_function = similarity_policies[similarity] + if similarity == "object_keypoint": + similarity_function = factory_object_keypoint_similarity( + keypoint_errors=oks_errors, + score_weighting=oks_score_weighting, + normalization_keypoints=oks_normalization, + ) + else: + similarity_function = similarity_policies[similarity] matching_function = match_policies[match] if tracker == "flow": @@ -931,7 +973,10 @@ def get_by_name_factory_options(cls): option = dict(name="max_tracking", default=False) option["type"] = bool - option["help"] = "If true then the tracker will cap the max number of tracks." + option["help"] = ( + "If true then the tracker will cap the max number of tracks. " + "Falls back to false if `max_tracks` is not defined or 0." + ) options.append(option) option = dict(name="max_tracks", default=None) @@ -1054,6 +1099,42 @@ def int_list_func(s): ] = "For Kalman filter: Number of frames to track with other tracker. 0 means no Kalman filters will be used." options.append(option) + def float_list_func(s): + return [float(x.strip()) for x in s.split(",")] if s else None + + option = dict(name="oks_errors", default="1") + option["type"] = float_list_func + option["help"] = ( + "For Object Keypoint similarity: the standard error of the distance " + "between the predicted keypoint and the true value, in pixels.\n" + "If None or empty list, defaults to 1. If a scalar or singleton list, " + "every keypoint has the same error. If a list, defines the error for each " + "keypoint, the length should be equal to the number of keypoints in the " + "skeleton." + ) + options.append(option) + + option = dict(name="oks_score_weighting", default="0") + option["type"] = int + option["help"] = ( + "For Object Keypoint similarity: if 0 (default), only the distance between the reference " + "and query keypoint is used to compute the similarity. If 1, each distance is weighted " + "by the prediction scores of the reference and query keypoint." + ) + options.append(option) + + option = dict(name="oks_normalization", default="all") + option["type"] = str + option["options"] = ["all", "ref", "union"] + option["help"] = ( + "For Object Keypoint similarity: Determine how to normalize similarity score. " + "If 'all', similarity score is normalized by number of reference points. " + "If 'ref', similarity score is normalized by number of visible reference points. " + "If 'union', similarity score is normalized by number of points both visible " + "in query and reference instance." + ) + options.append(option) + return options @classmethod @@ -1449,6 +1530,7 @@ def run_tracker(frames: List[LabeledFrame], tracker: BaseTracker) -> List[Labele track_args["img"] = lf.video[lf.frame_idx] else: track_args["img"] = None + track_args["img_hw"] = lf.image.shape[-3:-1] new_lf = LabeledFrame( frame_idx=lf.frame_idx, diff --git a/sleap/nn/training.py b/sleap/nn/training.py index 16f027175..c3692637c 100644 --- a/sleap/nn/training.py +++ b/sleap/nn/training.py @@ -508,7 +508,7 @@ def setup_visualization( callbacks = [] try: - matplotlib.use("Qt5Agg") + matplotlib.use("QtAgg") except ImportError: print( "Unable to use Qt backend for matplotlib. " @@ -946,7 +946,7 @@ def train(self): if self.config.outputs.save_outputs: if ( self.config.outputs.save_visualizations - and self.config.outputs.delete_viz_images + and not self.config.outputs.keep_viz_images ): self.cleanup() @@ -997,7 +997,7 @@ def cleanup(self): def package(self): """Package model folder into a zip file for portability.""" - if self.config.outputs.delete_viz_images: + if not self.config.outputs.keep_viz_images: self.cleanup() logger.info(f"Packaging results to: {self.run_path}.zip") shutil.make_archive( @@ -1864,6 +1864,14 @@ def create_trainer_using_cli(args: Optional[List] = None): "already specified in the training job config." ), ) + parser.add_argument( + "--keep_viz", + action="store_true", + help=( + "Keep prediction visualization images in the run folder after training when " + "--save_viz is enabled." + ), + ) parser.add_argument( "--zmq", action="store_true", @@ -1872,6 +1880,18 @@ def create_trainer_using_cli(args: Optional[List] = None): "job config." ), ) + parser.add_argument( + "--publish_port", + type=int, + default=9001, + help="Port to set up the publish address while using ZMQ, defaults to 9001.", + ) + parser.add_argument( + "--controller_port", + type=int, + default=9000, + help="Port to set up the controller address while using ZMQ, defaults to 9000.", + ) parser.add_argument( "--run_name", default="", @@ -1926,6 +1946,10 @@ def create_trainer_using_cli(args: Optional[List] = None): job_config.outputs.tensorboard.write_logs |= args.tensorboard job_config.outputs.zmq.publish_updates |= args.zmq job_config.outputs.zmq.subscribe_to_controller |= args.zmq + job_config.outputs.zmq.controller_address = "tcp://127.0.0.1:" + str( + args.controller_port + ) + job_config.outputs.zmq.publish_address = "tcp://127.0.0.1:" + str(args.publish_port) if args.run_name != "": job_config.outputs.run_name = args.run_name if args.prefix != "": @@ -1933,6 +1957,7 @@ def create_trainer_using_cli(args: Optional[List] = None): if args.suffix != "": job_config.outputs.run_name_suffix = args.suffix job_config.outputs.save_visualizations |= args.save_viz + job_config.outputs.keep_viz_images = args.keep_viz if args.labels_path == "": args.labels_path = None args.video_paths = args.video_paths.split(",") diff --git a/sleap/prefs.py b/sleap/prefs.py index 3d5a2113e..e043afc44 100644 --- a/sleap/prefs.py +++ b/sleap/prefs.py @@ -28,6 +28,8 @@ class Preferences(object): "node label size": 12, "show non-visible nodes": True, "share usage data": True, + "node marker sizes": (1, 2, 3, 4, 6, 8, 12), + "node label sizes": (6, 9, 12, 18, 24, 36), } _filename = "preferences.yaml" @@ -43,10 +45,14 @@ def load_(self): """Load preferences from file (regardless of whether loaded already).""" try: self._prefs = util.get_config_yaml(self._filename) - if not hasattr(self._prefs, "get"): - self._prefs = self._defaults except FileNotFoundError: - self._prefs = self._defaults + pass + + self._prefs = self._prefs or {} + + for k, v in self._defaults.items(): + if k not in self._prefs: + self._prefs[k] = v def save(self): """Save preferences to file.""" diff --git a/sleap/skeleton.py b/sleap/skeleton.py index eca393b8e..fbd1b909c 100644 --- a/sleap/skeleton.py +++ b/sleap/skeleton.py @@ -6,24 +6,24 @@ their connection to each other, and needed meta-data. """ -import attr -import cattr -import numpy as np -import jsonpickle -import json -import h5py +import base64 import copy - +import json import operator from enum import Enum +from io import BytesIO from itertools import count -from typing import Any, Dict, Iterable, List, Optional, Tuple, Union, Text +from typing import Any, Dict, Iterable, List, Optional, Text, Tuple, Union +import attr +import cattr +import h5py import networkx as nx +import numpy as np from networkx.readwrite import json_graph +from PIL import Image from scipy.io import loadmat - NodeRef = Union[str, "Node"] H5FileRef = Union[str, h5py.File] @@ -85,6 +85,502 @@ def matches(self, other: "Node") -> bool: return other.name == self.name and other.weight == self.weight +class SkeletonDecoder: + """Replace jsonpickle.decode with our own decoder. + + This function will decode the following from jsonpickle's encoded format: + + `Node` objects from + { + "py/object": "sleap.skeleton.Node", + "py/state": { "py/tuple": ["thorax1", 1.0] } + } + to `Node(name="thorax1", weight=1.0)` + + `EdgeType` objects from + { + "py/reduce": [ + { "py/type": "sleap.skeleton.EdgeType" }, + { "py/tuple": [1] } + ] + } + to `EdgeType(1)` + + `bytes` from + { + "py/b64": "aVZC..." + } + to `b"iVBO..."` + + and any repeated objects from + { + "py/id": 1 + } + to the object with the same reconstruction id (from top to bottom). + """ + + def __init__(self): + self.decoded_objects: List[Union[Node, EdgeType]] = [] + + def _decode_id(self, id: int) -> Union[Node, EdgeType]: + """Decode the object with the given `py/id` value of `id`. + + Args: + id: The `py/id` value to decode (1-indexed). + objects: The dictionary of objects that have already been decoded. + + Returns: + The object with the given `py/id` value. + """ + return self.decoded_objects[id - 1] + + @staticmethod + def _decode_state(state: dict) -> Node: + """Reconstruct the `Node` object from 'py/state' key in the serialized nx_graph. + + We support states in either dictionary or tuple format: + { + "py/state": { "py/tuple": ["thorax1", 1.0] } + } + or + { + "py/state": {"name": "thorax1", "weight": 1.0} + } + + Args: + state: The state to decode, i.e. state = dict["py/state"] + + Returns: + The `Node` object reconstructed from the state. + """ + + if "py/tuple" in state: + return Node(*state["py/tuple"]) + + return Node(**state) + + @staticmethod + def _decode_object_dict(object_dict) -> Node: + """Decode dict containing `py/object` key in the serialized nx_graph. + + Args: + object_dict: The dict to decode, i.e. + object_dict = {"py/object": ..., "py/state":...} + + Raises: + ValueError: If object_dict does not have 'py/object' and 'py/state' keys. + ValueError: If object_dict['py/object'] is not 'sleap.skeleton.Node'. + + Returns: + The decoded `Node` object. + """ + + if object_dict["py/object"] != "sleap.skeleton.Node": + raise ValueError("Only 'sleap.skeleton.Node' objects are supported.") + + node: Node = SkeletonDecoder._decode_state(state=object_dict["py/state"]) + return node + + def _decode_node(self, encoded_node: dict) -> Node: + """Decode an item believed to be an encoded `Node` object. + + Also updates the list of decoded objects. + + Args: + encoded_node: The encoded node to decode. + + Returns: + The decoded node and the updated list of decoded objects. + """ + + if isinstance(encoded_node, int): + # Using index mapping to replace the object (load from Labels) + return encoded_node + elif "py/object" in encoded_node: + decoded_node: Node = SkeletonDecoder._decode_object_dict(encoded_node) + self.decoded_objects.append(decoded_node) + elif "py/id" in encoded_node: + decoded_node: Node = self._decode_id(encoded_node["py/id"]) + + return decoded_node + + def _decode_nodes(self, encoded_nodes: List[dict]) -> List[Dict[str, Node]]: + """Decode the 'nodes' key in the serialized nx_graph. + + The encoded_nodes is a list of dictionary of two types: + - A dictionary with 'py/object' and 'py/state' keys. + - A dictionary with 'py/id' key. + + Args: + encoded_nodes: The list of encoded nodes to decode. + + Returns: + The decoded nodes. + """ + + decoded_nodes: List[Dict[str, Node]] = [] + for e_node_dict in encoded_nodes: + e_node = e_node_dict["id"] + d_node = self._decode_node(e_node) + decoded_nodes.append({"id": d_node}) + + return decoded_nodes + + def _decode_reduce_dict(self, reduce_dict: Dict[str, List[dict]]) -> EdgeType: + """Decode the 'reduce' key in the serialized nx_graph. + + The reduce_dict is a dictionary in the following format: + { + "py/reduce": [ + { "py/type": "sleap.skeleton.EdgeType" }, + { "py/tuple": [1] } + ] + } + + Args: + reduce_dict: The dictionary to decode i.e. reduce_dict = {"py/reduce": ...} + + Returns: + The decoded `EdgeType` object. + """ + + reduce_list = reduce_dict["py/reduce"] + has_py_type = has_py_tuple = False + for reduce_item in reduce_list: + if reduce_item is None: + # Sometimes the reduce list has None values, skip them + continue + if ( + "py/type" in reduce_item + and reduce_item["py/type"] == "sleap.skeleton.EdgeType" + ): + has_py_type = True + elif "py/tuple" in reduce_item: + edge_type: int = reduce_item["py/tuple"][0] + has_py_tuple = True + + if not has_py_type or not has_py_tuple: + raise ValueError( + "Only 'sleap.skeleton.EdgeType' objects are supported. " + "The 'py/reduce' list must have dictionaries with 'py/type' and " + "'py/tuple' keys." + f"\n\tHas py/type: {has_py_type}\n\tHas py/tuple: {has_py_tuple}" + ) + + edge = EdgeType(edge_type) + self.decoded_objects.append(edge) + + return edge + + def _decode_edge_type(self, encoded_edge_type: dict) -> EdgeType: + """Decode the 'type' key in the serialized nx_graph. + + Args: + encoded_edge_type: a dictionary with either 'py/id' or 'py/reduce' key. + + Returns: + The decoded `EdgeType` object. + """ + + if "py/reduce" in encoded_edge_type: + edge_type = self._decode_reduce_dict(encoded_edge_type) + else: + # Expect a "py/id" instead of "py/reduce" + edge_type = self._decode_id(encoded_edge_type["py/id"]) + return edge_type + + def _decode_links( + self, links: List[dict] + ) -> List[Dict[str, Union[int, Node, EdgeType]]]: + """Decode the 'links' key in the serialized nx_graph. + + The links are the edges in the graph and will have the following keys: + - source: The source node of the edge. + - target: The destination node of the edge. + - type: The type of the edge (e.g. BODY, SYMMETRY). + and more. + + Args: + encoded_links: The list of encoded links to decode. + """ + + for link in links: + for key, value in link.items(): + if key == "source": + link[key] = self._decode_node(value) + elif key == "target": + link[key] = self._decode_node(value) + elif key == "type": + link[key] = self._decode_edge_type(value) + + return links + + @staticmethod + def decode_preview_image( + img_b64: bytes, return_bytes: bool = False + ) -> Union[Image.Image, bytes]: + """Decode a skeleton preview image byte string representation to a `PIL.Image` + + Args: + img_b64: a byte string representation of a skeleton preview image + return_bytes: whether to return the decoded image as bytes + + Returns: + Either a PIL.Image of the skeleton preview image or the decoded image as bytes + (if `return_bytes` is True). + """ + bytes = base64.b64decode(img_b64) + if return_bytes: + return bytes + + buffer = BytesIO(bytes) + img = Image.open(buffer) + return img + + def _decode(self, json_str: str): + dicts = json.loads(json_str) + + # Enforce same format across template and non-template skeletons + if "nx_graph" not in dicts: + # Non-template skeletons use the dicts as the "nx_graph" + dicts = {"nx_graph": dicts} + + # Decode the graph + nx_graph = dicts["nx_graph"] + + self.decoded_objects = [] # Reset the decoded objects incase reusing decoder + for key, value in nx_graph.items(): + if key == "nodes": + nx_graph[key] = self._decode_nodes(value) + elif key == "links": + nx_graph[key] = self._decode_links(value) + + # Decode the preview image (if it exists) + preview_image = dicts.get("preview_image", None) + if preview_image is not None: + dicts["preview_image"] = SkeletonDecoder.decode_preview_image( + preview_image["py/b64"], return_bytes=True + ) + + return dicts + + @classmethod + def decode(cls, json_str: str) -> Dict: + """Decode the given json string into a dictionary. + + Returns: + A dict with `Node`s, `EdgeType`s, and `bytes` decoded/reconstructed. + """ + decoder = cls() + return decoder._decode(json_str) + + +class SkeletonEncoder: + """Replace jsonpickle.encode with our own encoder. + + The input is a dictionary containing python objects that need to be encoded as + JSON strings. The output is a JSON string that represents the input dictionary. + + `Node(name='neck', weight=1.0)` => + { + "py/object": "sleap.Skeleton.Node", + "py/state": {"py/tuple" ["neck", 1.0]} + } + + `` => + {"py/reduce": [ + {"py/type": "sleap.Skeleton.EdgeType"}, + {"py/tuple": [1] } + ] + }` + + Where `name` and `weight` are the attributes of the `Node` class; weight is always 1.0. + `EdgeType` is an enum with values `BODY = 1` and `SYMMETRY = 2`. + + See sleap.skeleton.Node and sleap.skeleton.EdgeType. + + If the object has been "seen" before, it will not be encoded as the full JSON string + but referenced by its `py/id`, which starts at 1 and indexes the objects in the + order they are seen so that the second time the first object is used, it will be + referenced as `{"py/id": 1}`. + """ + + def __init__(self): + """Initializes a SkeletonEncoder instance.""" + # Maps object id to py/id + self._encoded_objects: Dict[int, int] = {} + + @classmethod + def encode(cls, data: Dict[str, Any]) -> str: + """Encodes the input dictionary as a JSON string. + + Args: + data: The data to encode. + + Returns: + json_str: The JSON string representation of the data. + """ + + # This is required for backwards compatibility with SLEAP <=1.3.4 + sorted_data = cls._recursively_sort_dict(data) + + encoder = cls() + encoded_data = encoder._encode(sorted_data) + json_str = json.dumps(encoded_data) + return json_str + + @staticmethod + def _recursively_sort_dict(dictionary: Dict[str, Any]) -> Dict[str, Any]: + """Recursively sorts the dictionary by keys.""" + sorted_dict = dict(sorted(dictionary.items())) + for key, value in sorted_dict.items(): + if isinstance(value, dict): + sorted_dict[key] = SkeletonEncoder._recursively_sort_dict(value) + elif isinstance(value, list): + for i, item in enumerate(value): + if isinstance(item, dict): + sorted_dict[key][i] = SkeletonEncoder._recursively_sort_dict( + item + ) + return sorted_dict + + def _encode(self, obj: Any) -> Any: + """Recursively encodes the input object. + + Args: + obj: The object to encode. Can be a dictionary, list, Node, EdgeType or + primitive data type. + + Returns: + The encoded object as a dictionary. + """ + if isinstance(obj, dict): + encoded_obj = {} + for key, value in obj.items(): + if key == "links": + encoded_obj[key] = self._encode_links(value) + else: + encoded_obj[key] = self._encode(value) + return encoded_obj + elif isinstance(obj, list): + return [self._encode(v) for v in obj] + elif isinstance(obj, EdgeType): + return self._encode_edge_type(obj) + elif isinstance(obj, Node): + return self._encode_node(obj) + else: + return obj # Primitive data types + + def _encode_links(self, links: List[Dict[str, Any]]) -> List[Dict[str, Any]]: + """Encodes the list of links (edges) in the skeleton graph. + + Args: + links: A list of dictionaries, each representing an edge in the graph. + + Returns: + A list of encoded edge dictionaries with keys ordered as specified. + """ + encoded_links = [] + for link in links: + # Use a regular dict (insertion order preserved in Python 3.7+) + encoded_link = {} + + for key, value in link.items(): + if key in ("source", "target"): + encoded_link[key] = self._encode_node(value) + elif key == "type": + encoded_link[key] = self._encode_edge_type(value) + else: + encoded_link[key] = self._encode(value) + encoded_links.append(encoded_link) + + return encoded_links + + def _encode_node(self, node: Union["Node", int]) -> Dict[str, Any]: + """Encodes a Node object. + + Args: + node: The Node object to encode or integer index. The latter requires that + the class has the `idx_to_node` attribute set. + + Returns: + The encoded `Node` object as a dictionary. + """ + if isinstance(node, int): + # We sometimes have the node object already replaced by its index (when + # `node_to_idx` is provided). In this case, the node is already encoded. + return node + + # Check if object has been encoded before + first_encoding = self._is_first_encoding(node) + py_id = self._get_or_assign_id(node, first_encoding) + if first_encoding: + # Full encoding + return { + "py/object": "sleap.skeleton.Node", + "py/state": {"py/tuple": [node.name, node.weight]}, + } + else: + # Reference by py/id + return {"py/id": py_id} + + def _encode_edge_type(self, edge_type: "EdgeType") -> Dict[str, Any]: + """Encodes an EdgeType object. + + Args: + edge_type: The EdgeType object to encode. Either `EdgeType.BODY` or + `EdgeType.SYMMETRY` enum with values 1 and 2 respectively. + + Returns: + The encoded EdgeType object as a dictionary. + """ + # Check if object has been encoded before + first_encoding = self._is_first_encoding(edge_type) + py_id = self._get_or_assign_id(edge_type, first_encoding) + if first_encoding: + # Full encoding + return { + "py/reduce": [ + {"py/type": "sleap.skeleton.EdgeType"}, + {"py/tuple": [edge_type.value]}, + ] + } + else: + # Reference by py/id + return {"py/id": py_id} + + def _get_or_assign_id(self, obj: Any, first_encoding: bool) -> int: + """Gets or assigns a py/id for the object. + + Args: + The object to get or assign a py/id for. + + Returns: + The py/id assigned to the object. + """ + # Object id is unique for each object in the current session + obj_id = id(obj) + # Assign a py/id to the object if it hasn't been assigned one yet + if first_encoding: + py_id = len(self._encoded_objects) + 1 # py/id starts at 1 + # Assign the py/id to the object and store it in _encoded_objects + self._encoded_objects[obj_id] = py_id + return self._encoded_objects[obj_id] + + def _is_first_encoding(self, obj: Any) -> bool: + """Checks if the object is being encoded for the first time. + + Args: + obj: The object to check. + + Returns: + True if this is the first encoding of the object, False otherwise. + """ + obj_id = id(obj) + first_time = obj_id not in self._encoded_objects + return first_time + + class Skeleton: """The main object for representing animal skeletons. @@ -937,7 +1433,7 @@ def to_dict(obj: "Skeleton", node_to_idx: Optional[Dict[Node, int]] = None) -> D # This is a weird hack to serialize the whole _graph into a dict. # I use the underlying to_json and parse it. - return json.loads(obj.to_json(node_to_idx)) + return json.loads(obj.to_json(node_to_idx=node_to_idx)) @classmethod def from_dict(cls, d: Dict, node_to_idx: Dict[Node, int] = None) -> "Skeleton": @@ -999,12 +1495,12 @@ def to_json(self, node_to_idx: Optional[Dict[Node, int]] = None) -> str: Returns: A string containing the JSON representation of the skeleton. """ - jsonpickle.set_encoder_options("simplejson", sort_keys=True, indent=4) + if node_to_idx is not None: - indexed_node_graph = nx.relabel_nodes( - G=self._graph, mapping=node_to_idx - ) # map nodes to int + # Map Nodes to int + indexed_node_graph = nx.relabel_nodes(G=self._graph, mapping=node_to_idx) else: + # Keep graph nodes as Node objects indexed_node_graph = self._graph # Encode to JSON @@ -1023,7 +1519,7 @@ def to_json(self, node_to_idx: Optional[Dict[Node, int]] = None) -> str: else: data = graph - json_str = jsonpickle.encode(data) + json_str = SkeletonEncoder.encode(data) return json_str @@ -1071,7 +1567,7 @@ def from_json( Returns: An instance of the `Skeleton` object decoded from the JSON. """ - dicts = jsonpickle.decode(json_str) + dicts: dict = SkeletonDecoder.decode(json_str) nx_graph = dicts.get("nx_graph", dicts) graph = json_graph.node_link_graph(nx_graph) diff --git a/sleap/training_profiles/baseline.centroid.json b/sleap/training_profiles/baseline.centroid.json index 933989ecf..3a54db25c 100755 --- a/sleap/training_profiles/baseline.centroid.json +++ b/sleap/training_profiles/baseline.centroid.json @@ -116,6 +116,7 @@ "runs_folder": "models", "tags": [], "save_visualizations": true, + "keep_viz_images": false, "log_to_csv": true, "checkpointing": { "initial_model": false, diff --git a/sleap/training_profiles/baseline_large_rf.bottomup.json b/sleap/training_profiles/baseline_large_rf.bottomup.json index ea45c9b25..18fb3104f 100644 --- a/sleap/training_profiles/baseline_large_rf.bottomup.json +++ b/sleap/training_profiles/baseline_large_rf.bottomup.json @@ -125,6 +125,7 @@ "runs_folder": "models", "tags": [], "save_visualizations": true, + "keep_viz_images": false, "log_to_csv": true, "checkpointing": { "initial_model": false, diff --git a/sleap/training_profiles/baseline_large_rf.single.json b/sleap/training_profiles/baseline_large_rf.single.json index 75e97b1a6..3feeccd69 100644 --- a/sleap/training_profiles/baseline_large_rf.single.json +++ b/sleap/training_profiles/baseline_large_rf.single.json @@ -116,6 +116,7 @@ "runs_folder": "models", "tags": [], "save_visualizations": true, + "keep_viz_images": false, "log_to_csv": true, "checkpointing": { "initial_model": false, diff --git a/sleap/training_profiles/baseline_large_rf.topdown.json b/sleap/training_profiles/baseline_large_rf.topdown.json index 9b17f6832..38e96594b 100644 --- a/sleap/training_profiles/baseline_large_rf.topdown.json +++ b/sleap/training_profiles/baseline_large_rf.topdown.json @@ -117,6 +117,7 @@ "runs_folder": "models", "tags": [], "save_visualizations": true, + "keep_viz_images": false, "log_to_csv": true, "checkpointing": { "initial_model": false, diff --git a/sleap/training_profiles/baseline_medium_rf.bottomup.json b/sleap/training_profiles/baseline_medium_rf.bottomup.json index 1cc35330a..61b08515c 100644 --- a/sleap/training_profiles/baseline_medium_rf.bottomup.json +++ b/sleap/training_profiles/baseline_medium_rf.bottomup.json @@ -125,6 +125,7 @@ "runs_folder": "models", "tags": [], "save_visualizations": true, + "keep_viz_images": false, "log_to_csv": true, "checkpointing": { "initial_model": false, diff --git a/sleap/training_profiles/baseline_medium_rf.single.json b/sleap/training_profiles/baseline_medium_rf.single.json index 579f6c8c3..0951bc761 100644 --- a/sleap/training_profiles/baseline_medium_rf.single.json +++ b/sleap/training_profiles/baseline_medium_rf.single.json @@ -116,6 +116,7 @@ "runs_folder": "models", "tags": [], "save_visualizations": true, + "keep_viz_images": false, "log_to_csv": true, "checkpointing": { "initial_model": false, diff --git a/sleap/training_profiles/baseline_medium_rf.topdown.json b/sleap/training_profiles/baseline_medium_rf.topdown.json index 9e3a0bde5..9eccb76c1 100755 --- a/sleap/training_profiles/baseline_medium_rf.topdown.json +++ b/sleap/training_profiles/baseline_medium_rf.topdown.json @@ -117,6 +117,7 @@ "runs_folder": "models", "tags": [], "save_visualizations": true, + "keep_viz_images": false, "log_to_csv": true, "checkpointing": { "initial_model": false, diff --git a/sleap/training_profiles/pretrained.bottomup.json b/sleap/training_profiles/pretrained.bottomup.json index 3e4f3935f..57b7398b5 100644 --- a/sleap/training_profiles/pretrained.bottomup.json +++ b/sleap/training_profiles/pretrained.bottomup.json @@ -122,6 +122,7 @@ "runs_folder": "models", "tags": [], "save_visualizations": true, + "keep_viz_images": false, "log_to_csv": true, "checkpointing": { "initial_model": false, diff --git a/sleap/training_profiles/pretrained.centroid.json b/sleap/training_profiles/pretrained.centroid.json index a5df5e48a..74c43d3e2 100644 --- a/sleap/training_profiles/pretrained.centroid.json +++ b/sleap/training_profiles/pretrained.centroid.json @@ -113,6 +113,7 @@ "runs_folder": "models", "tags": [], "save_visualizations": true, + "keep_viz_images": false, "log_to_csv": true, "checkpointing": { "initial_model": false, diff --git a/sleap/training_profiles/pretrained.single.json b/sleap/training_profiles/pretrained.single.json index 7ca907007..615f0de4d 100644 --- a/sleap/training_profiles/pretrained.single.json +++ b/sleap/training_profiles/pretrained.single.json @@ -113,6 +113,7 @@ "runs_folder": "models", "tags": [], "save_visualizations": true, + "keep_viz_images": false, "log_to_csv": true, "checkpointing": { "initial_model": false, diff --git a/sleap/training_profiles/pretrained.topdown.json b/sleap/training_profiles/pretrained.topdown.json index aeeaebbd8..be0d97de8 100644 --- a/sleap/training_profiles/pretrained.topdown.json +++ b/sleap/training_profiles/pretrained.topdown.json @@ -114,6 +114,7 @@ "runs_folder": "models", "tags": [], "save_visualizations": true, + "keep_viz_images": false, "log_to_csv": true, "checkpointing": { "initial_model": false, diff --git a/sleap/util.py b/sleap/util.py index 5edbf164b..bc3389b7d 100644 --- a/sleap/util.py +++ b/sleap/util.py @@ -3,13 +3,11 @@ Try not to put things in here unless they really have no other place. """ -import base64 import json import os import re import shutil from collections import defaultdict -from io import BytesIO from pathlib import Path from typing import Any, Dict, Hashable, Iterable, List, Optional from urllib.parse import unquote, urlparse @@ -26,7 +24,6 @@ from importlib.resources import files # New in 3.9+ except ImportError: from importlib_resources import files # TODO(LM): Upgrade to importlib.resources. -from PIL import Image import sleap.version as sleap_version @@ -270,30 +267,20 @@ def get_config_file( The full path to the specified config file. """ - desired_path = None # Handle case where get_defaults, but cannot find package_path + desired_path = Path.home() / f".sleap/{sleap_version.__version__}/{shortname}" - if not get_defaults: - desired_path = os.path.expanduser( - f"~/.sleap/{sleap_version.__version__}/{shortname}" - ) + # Make sure there's a ~/.sleap// directory to store user version of the config file. + desired_path.parent.mkdir(parents=True, exist_ok=True) - # Make sure there's a ~/.sleap// directory to store user version of the - # config file. - try: - os.makedirs(os.path.expanduser(f"~/.sleap/{sleap_version.__version__}")) - except FileExistsError: - pass - - # If we don't care whether the file exists, just return the path - if ignore_file_not_found: - return desired_path - - # If we do care whether the file exists, check the package version of the - # config file if we can't find the user version. + # If we don't care whether the file exists, just return the path + if ignore_file_not_found: + return desired_path - if get_defaults or not os.path.exists(desired_path): + # If we do care whether the file exists, check the package version of the config file if we can't find the user version. + if get_defaults or not desired_path.exists(): package_path = get_package_file(f"config/{shortname}") - if not os.path.exists(package_path): + package_path = Path(package_path) + if not package_path.exists(): raise FileNotFoundError( f"Cannot locate {shortname} config file at {desired_path} or {package_path}." ) @@ -384,18 +371,3 @@ def find_files_by_suffix( def parse_uri_path(uri: str) -> str: """Parse a URI starting with 'file:///' to a posix path.""" return Path(url2pathname(urlparse(unquote(uri)).path)).as_posix() - - -def decode_preview_image(img_b64: bytes) -> Image: - """Decode a skeleton preview image byte string representation to a `PIL.Image` - - Args: - img_b64: a byte string representation of a skeleton preview image - - Returns: - A PIL.Image of the skeleton preview - """ - bytes = base64.b64decode(img_b64) - buffer = BytesIO(bytes) - img = Image.open(buffer) - return img diff --git a/sleap/version.py b/sleap/version.py index 437e17fba..698710132 100644 --- a/sleap/version.py +++ b/sleap/version.py @@ -11,8 +11,7 @@ Must be a semver string, "aN" should be appended for alpha releases. """ - -__version__ = "1.3.3" +__version__ = "1.4.1" def versions(): diff --git a/tests/data/dlc/labeled-data/video/CollectedData_LM.csv b/tests/data/dlc/labeled-data/video/CollectedData_LM.csv index f57b667f4..27c86f8af 100644 --- a/tests/data/dlc/labeled-data/video/CollectedData_LM.csv +++ b/tests/data/dlc/labeled-data/video/CollectedData_LM.csv @@ -1,8 +1,8 @@ -scorer,,,LM,LM,LM,LM,LM,LM,LM,LM,LM,LM,LM,LM -individuals,,,individual1,individual1,individual1,individual1,individual1,individual1,individual2,individual2,individual2,individual2,individual2,individual2 -bodyparts,,,A,A,B,B,C,C,A,A,B,B,C,C -coords,,,x,y,x,y,x,y,x,y,x,y,x,y -labeled-data,video,img000.png,0,1,2,3,4,5,6,7,8,9,10,11 -labeled-data,video,img001.png,12,13,,,15,16,17,18,,,20,21 +scorer,,,Scorer,Scorer,Scorer,Scorer,Scorer,Scorer,Scorer,Scorer,Scorer,Scorer,Scorer,Scorer,Scorer,Scorer,Scorer,Scorer +individuals,,,Animal1,Animal1,Animal1,Animal1,Animal1,Animal1,Animal2,Animal2,Animal2,Animal2,Animal2,Animal2,single,single,single,single +bodyparts,,,A,A,B,B,C,C,A,A,B,B,C,C,D,D,E,E +coords,,,x,y,x,y,x,y,x,y,x,y,x,y,x,y,x,y +labeled-data,video,img000.png,0,1,2,3,4,5,6,7,8,9,10,11,,,, +labeled-data,video,img001.png,12,13,,,15,16,17,18,,,20,21,22,23,24,25 labeled-data,video,img002.png,,,,,,,,,,,, -labeled-data,video,img003.png,22,23,24,25,26,27,,,,,, +labeled-data,video,img003.png,26,27,28,29,30,31,,,,,,,32,33,34,35 diff --git a/tests/data/dlc/labeled-data/video/maudlc_testdata.csv b/tests/data/dlc/labeled-data/video/maudlc_testdata.csv new file mode 100644 index 000000000..4e3e3c28c --- /dev/null +++ b/tests/data/dlc/labeled-data/video/maudlc_testdata.csv @@ -0,0 +1,8 @@ +scorer,Scorer,Scorer,Scorer,Scorer,Scorer,Scorer,Scorer,Scorer,Scorer,Scorer,Scorer,Scorer +individuals,Animal1,Animal1,Animal1,Animal1,Animal1,Animal1,Animal2,Animal2,Animal2,Animal2,Animal2,Animal2,single,single,single,single +bodyparts,A,A,B,B,C,C,A,A,B,B,C,C,D,D,E,E +coords,x,y,x,y,x,y,x,y,x,y,x,y,x,y,x,y +labeled-data/video/img000.png,0,1,2,3,4,5,6,7,8,9,10,11,,,, +labeled-data/video/img001.png,12,13,,,15,16,17,18,,,20,21,22,23,24,25 +labeled-data/video/img002.png,,,,,,,,,,,, +labeled-data/video/img003.png,26,27,28,29,30,31,,,,,,,32,33,34,35 diff --git a/tests/data/dlc/labeled-data/video/maudlc_testdata_v2.csv b/tests/data/dlc/labeled-data/video/maudlc_testdata_v2.csv new file mode 100644 index 000000000..27c86f8af --- /dev/null +++ b/tests/data/dlc/labeled-data/video/maudlc_testdata_v2.csv @@ -0,0 +1,8 @@ +scorer,,,Scorer,Scorer,Scorer,Scorer,Scorer,Scorer,Scorer,Scorer,Scorer,Scorer,Scorer,Scorer,Scorer,Scorer,Scorer,Scorer +individuals,,,Animal1,Animal1,Animal1,Animal1,Animal1,Animal1,Animal2,Animal2,Animal2,Animal2,Animal2,Animal2,single,single,single,single +bodyparts,,,A,A,B,B,C,C,A,A,B,B,C,C,D,D,E,E +coords,,,x,y,x,y,x,y,x,y,x,y,x,y,x,y,x,y +labeled-data,video,img000.png,0,1,2,3,4,5,6,7,8,9,10,11,,,, +labeled-data,video,img001.png,12,13,,,15,16,17,18,,,20,21,22,23,24,25 +labeled-data,video,img002.png,,,,,,,,,,,, +labeled-data,video,img003.png,26,27,28,29,30,31,,,,,,,32,33,34,35 diff --git a/tests/data/dlc/madlc_230_config.yaml b/tests/data/dlc/madlc_230_config.yaml index ae2cbb44b..01e1d32c1 100644 --- a/tests/data/dlc/madlc_230_config.yaml +++ b/tests/data/dlc/madlc_230_config.yaml @@ -1,12 +1,12 @@ # Project definitions (do not edit) -Task: madlc_2.3.0 +Task: maudlc_2.3.0 scorer: LM date: Mar1 multianimalproject: true identity: false # Project path (change when moving around) -project_path: D:\social-leap-estimates-animal-poses\pull-requests\sleap\tests\data\dlc\madlc_testdata_v3 +project_path: D:\social-leap-estimates-animal-poses\pull-requests\sleap\tests\data\dlc\maudlc_testdata_v3 # Annotation data set configuration (and individual video cropping parameters) video_sets: @@ -16,7 +16,9 @@ individuals: - individual1 - individual2 - individual3 -uniquebodyparts: [] +uniquebodyparts: +- D +- E multianimalbodyparts: - A - B diff --git a/tests/data/hdf5_format_v1/small_robot.000_small_robot_3_frame.analysis.h5 b/tests/data/hdf5_format_v1/small_robot.000_small_robot_3_frame.analysis.h5 new file mode 100644 index 0000000000000000000000000000000000000000..d2cec1d1b35d682f2eca7c0088c203145ccfd339 GIT binary patch literal 26408 zcmeI4UuYaf7{F(5ljck+NlvV_ial$Qd$5hrSkm|}+N6n4Y+8f(QZJjky)^W4yWXy8 zq-YvSApsu_5zIrY6?`a&w7%3l_Msq1m4FYGL{SP7pQ=*Syv3RM=G*J-X73Vhn!EB% zxt*Es`(|$Tx3ib;o9)hYU-yCg8@Dwwk)lyn$690&56dbghCzaL)DNnHrt&G3Ux+|K zW8xoUTa~<3_3yVEIMUO@ScGB!n5$;&={hK3KhXnFKaU2K-_r#8dS2=mCpf4k+MGJu z+4TCJw#GKe`3nceGnj#zhBLnO`*p4tn%4M>EGku9ti6e~FY91e&!s{ii9}=vQPrQW z`DZ-X$5b!ExassQld*A7Y|!zwR}Xb-te$n?_m}=$pw!C~sx-C96RlXVmGsiS71f@w zph{DRJ<*B<{GAxDUs1i`!bO&37I*MCnqOAH5w4Y7&%+_{Fu<%QqAoLIQo 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b/tests/data/models/min_tracks_2node.UNet.bottomup_multiclass/initial_config.json index 7e52d1703..2ae0e925c 100644 --- a/tests/data/models/min_tracks_2node.UNet.bottomup_multiclass/initial_config.json +++ b/tests/data/models/min_tracks_2node.UNet.bottomup_multiclass/initial_config.json @@ -128,6 +128,7 @@ "runs_folder": "models", "tags": [], "save_visualizations": false, + "keep_viz_images": false, "log_to_csv": true, "checkpointing": { "initial_model": false, diff --git a/tests/data/models/min_tracks_2node.UNet.bottomup_multiclass/training_config.json b/tests/data/models/min_tracks_2node.UNet.bottomup_multiclass/training_config.json index bcb2f26d5..7b6f817aa 100644 --- a/tests/data/models/min_tracks_2node.UNet.bottomup_multiclass/training_config.json +++ b/tests/data/models/min_tracks_2node.UNet.bottomup_multiclass/training_config.json @@ -191,6 +191,7 @@ "runs_folder": "models", "tags": [], "save_visualizations": false, + "keep_viz_images": false, "log_to_csv": true, "checkpointing": { "initial_model": false, diff --git a/tests/data/models/min_tracks_2node.UNet.topdown_multiclass/initial_config.json b/tests/data/models/min_tracks_2node.UNet.topdown_multiclass/initial_config.json index 045890b21..5d8081628 100644 --- a/tests/data/models/min_tracks_2node.UNet.topdown_multiclass/initial_config.json +++ b/tests/data/models/min_tracks_2node.UNet.topdown_multiclass/initial_config.json @@ -141,7 +141,7 @@ "runs_folder": "models", "tags": [], "save_visualizations": false, - "delete_viz_images": true, + "keep_viz_images": false, "zip_outputs": false, "log_to_csv": true, "checkpointing": { diff --git a/tests/data/models/min_tracks_2node.UNet.topdown_multiclass/training_config.json b/tests/data/models/min_tracks_2node.UNet.topdown_multiclass/training_config.json index 070e9d3c0..9591e5b52 100644 --- a/tests/data/models/min_tracks_2node.UNet.topdown_multiclass/training_config.json +++ b/tests/data/models/min_tracks_2node.UNet.topdown_multiclass/training_config.json @@ -208,7 +208,7 @@ "runs_folder": "models", "tags": [], "save_visualizations": false, - "delete_viz_images": true, + "keep_viz_images": false, "zip_outputs": false, "log_to_csv": true, "checkpointing": { diff --git a/tests/data/models/minimal_instance.UNet.bottomup/initial_config.json b/tests/data/models/minimal_instance.UNet.bottomup/initial_config.json index 8e39fea3f..68e4f894e 100644 --- a/tests/data/models/minimal_instance.UNet.bottomup/initial_config.json +++ b/tests/data/models/minimal_instance.UNet.bottomup/initial_config.json @@ -127,6 +127,7 @@ "runs_folder": "models", "tags": [], "save_visualizations": false, + "keep_viz_images": false, "log_to_csv": true, "checkpointing": { "initial_model": false, diff --git a/tests/data/models/minimal_instance.UNet.bottomup/training_config.json b/tests/data/models/minimal_instance.UNet.bottomup/training_config.json index d1fb718ba..e3bfbc5f8 100644 --- a/tests/data/models/minimal_instance.UNet.bottomup/training_config.json +++ b/tests/data/models/minimal_instance.UNet.bottomup/training_config.json @@ -192,6 +192,7 @@ "runs_folder": "models", "tags": [], "save_visualizations": false, + "keep_viz_images": false, "log_to_csv": true, "checkpointing": { "initial_model": false, diff --git a/tests/data/models/minimal_instance.UNet.centered_instance/initial_config.json b/tests/data/models/minimal_instance.UNet.centered_instance/initial_config.json index 739d8e3e7..f4914aae4 100644 --- a/tests/data/models/minimal_instance.UNet.centered_instance/initial_config.json +++ b/tests/data/models/minimal_instance.UNet.centered_instance/initial_config.json @@ -119,6 +119,7 @@ "runs_folder": "models", "tags": [], "save_visualizations": false, + "keep_viz_images": false, "log_to_csv": true, "checkpointing": { "initial_model": false, diff --git a/tests/data/models/minimal_instance.UNet.centered_instance/training_config.json b/tests/data/models/minimal_instance.UNet.centered_instance/training_config.json index 7b6782a68..e747f6862 100644 --- a/tests/data/models/minimal_instance.UNet.centered_instance/training_config.json +++ b/tests/data/models/minimal_instance.UNet.centered_instance/training_config.json @@ -179,6 +179,7 @@ "runs_folder": "models", "tags": [], "save_visualizations": false, + "keep_viz_images": false, "log_to_csv": true, "checkpointing": { "initial_model": false, diff --git a/tests/data/models/minimal_instance.UNet.centroid/initial_config.json b/tests/data/models/minimal_instance.UNet.centroid/initial_config.json index 41d8ac8c3..977654b2e 100644 --- a/tests/data/models/minimal_instance.UNet.centroid/initial_config.json +++ b/tests/data/models/minimal_instance.UNet.centroid/initial_config.json @@ -118,6 +118,7 @@ "runs_folder": "models", "tags": [], "save_visualizations": false, + "keep_viz_images": false, "log_to_csv": true, "checkpointing": { "initial_model": false, diff --git a/tests/data/models/minimal_instance.UNet.centroid/training_config.json b/tests/data/models/minimal_instance.UNet.centroid/training_config.json index 2d2280a31..02e9683e1 100644 --- a/tests/data/models/minimal_instance.UNet.centroid/training_config.json +++ b/tests/data/models/minimal_instance.UNet.centroid/training_config.json @@ -175,6 +175,7 @@ "runs_folder": "models", "tags": [], "save_visualizations": false, + "keep_viz_images": false, "log_to_csv": true, "checkpointing": { "initial_model": false, diff --git a/tests/data/models/minimal_robot.UNet.single_instance/initial_config.json b/tests/data/models/minimal_robot.UNet.single_instance/initial_config.json index cb2e4f353..f2bb907fa 100644 --- a/tests/data/models/minimal_robot.UNet.single_instance/initial_config.json +++ b/tests/data/models/minimal_robot.UNet.single_instance/initial_config.json @@ -120,6 +120,7 @@ "" ], "save_visualizations": false, + "keep_viz_images": true, "log_to_csv": true, "checkpointing": { "initial_model": false, diff --git a/tests/data/models/minimal_robot.UNet.single_instance/training_config.json b/tests/data/models/minimal_robot.UNet.single_instance/training_config.json index 66901c9f0..dffecc1d9 100644 --- a/tests/data/models/minimal_robot.UNet.single_instance/training_config.json +++ b/tests/data/models/minimal_robot.UNet.single_instance/training_config.json @@ -180,6 +180,7 @@ "" ], "save_visualizations": false, + "keep_viz_images": true, "log_to_csv": true, "checkpointing": { "initial_model": false, diff --git a/tests/data/skeleton/fly_skeleton_legs_pystate_dict.json b/tests/data/skeleton/fly_skeleton_legs_pystate_dict.json new file mode 100644 index 000000000..eae83d6bc --- /dev/null +++ b/tests/data/skeleton/fly_skeleton_legs_pystate_dict.json @@ -0,0 +1 @@ +{"directed": true, "graph": {"name": "skeleton_legs.mat", "num_edges_inserted": 23}, "links": [{"edge_insert_idx": 1, "key": 0, "source": {"py/object": "sleap.skeleton.Node", "py/state": {"name": "neck", "weight": 1.0}}, 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{"name": "forelegL1", "weight": 1.0}}, "type": {"py/id": 3}}, {"edge_insert_idx": 8, "key": 0, "source": {"py/id": 4}, "target": {"py/object": "sleap.skeleton.Node", "py/state": {"name": "forelegR1", "weight": 1.0}}, "type": {"py/id": 3}}, {"edge_insert_idx": 11, "key": 0, "source": {"py/id": 4}, "target": {"py/object": "sleap.skeleton.Node", "py/state": {"name": "midlegL1", "weight": 1.0}}, "type": {"py/id": 3}}, {"edge_insert_idx": 14, "key": 0, "source": {"py/id": 4}, "target": {"py/object": "sleap.skeleton.Node", "py/state": {"name": "midlegR1", "weight": 1.0}}, "type": {"py/id": 3}}, {"edge_insert_idx": 17, "key": 0, "source": {"py/id": 4}, "target": {"py/object": "sleap.skeleton.Node", "py/state": {"name": "hindlegL1", "weight": 1.0}}, "type": {"py/id": 3}}, {"edge_insert_idx": 20, "key": 0, "source": {"py/id": 4}, "target": {"py/object": "sleap.skeleton.Node", "py/state": {"name": "hindlegR1", "weight": 1.0}}, "type": {"py/id": 3}}, {"edge_insert_idx": 6, "key": 0, "source": {"py/id": 8}, "target": {"py/object": "sleap.skeleton.Node", "py/state": {"name": "forelegL2", "weight": 1.0}}, "type": {"py/id": 3}}, {"edge_insert_idx": 7, "key": 0, "source": {"py/id": 14}, "target": {"py/object": "sleap.skeleton.Node", "py/state": {"name": "forelegL3", "weight": 1.0}}, "type": {"py/id": 3}}, {"edge_insert_idx": 9, "key": 0, "source": {"py/id": 9}, "target": {"py/object": "sleap.skeleton.Node", "py/state": {"name": "forelegR2", "weight": 1.0}}, "type": {"py/id": 3}}, {"edge_insert_idx": 10, "key": 0, "source": {"py/id": 16}, "target": {"py/object": "sleap.skeleton.Node", "py/state": {"name": "forelegR3", "weight": 1.0}}, "type": {"py/id": 3}}, {"edge_insert_idx": 12, "key": 0, "source": {"py/id": 10}, "target": {"py/object": "sleap.skeleton.Node", "py/state": {"name": "midlegL2", "weight": 1.0}}, "type": {"py/id": 3}}, {"edge_insert_idx": 13, "key": 0, "source": {"py/id": 18}, "target": {"py/object": "sleap.skeleton.Node", "py/state": {"name": "midlegL3", "weight": 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YV;Hw>DCW0e+_qtuBOguu|MTPj0o-wCPyhe` literal 0 HcmV?d00001 diff --git a/tests/fixtures/datasets.py b/tests/fixtures/datasets.py index 801fcc092..c6507caec 100644 --- a/tests/fixtures/datasets.py +++ b/tests/fixtures/datasets.py @@ -41,6 +41,13 @@ def centered_pair_predictions(): return Labels.load_file(TEST_JSON_PREDICTIONS) +@pytest.fixture +def centered_pair_predictions_sorted(centered_pair_predictions): + labels: Labels = centered_pair_predictions + labels.labeled_frames.sort(key=lambda lf: lf.frame_idx) + return labels + + @pytest.fixture def min_labels(): return Labels.load_file(TEST_JSON_MIN_LABELS) @@ -90,6 +97,20 @@ def min_tracks_2node_labels(): ) +@pytest.fixture +def min_tracks_2node_predictions(): + """ + Generated with: + ``` + sleap-track -m "tests/data/models/min_tracks_2node.UNet.bottomup_multiclass" "tests/data/tracks/clip.mp4" + ``` + """ + return Labels.load_file( + "tests/data/tracks/clip.predictions.slp", + video_search=["tests/data/tracks/clip.mp4"], + ) + + @pytest.fixture def min_tracks_13node_labels(): return Labels.load_file( diff --git a/tests/fixtures/skeletons.py b/tests/fixtures/skeletons.py index 311510e6a..b432ca2c7 100644 --- a/tests/fixtures/skeletons.py +++ b/tests/fixtures/skeletons.py @@ -3,14 +3,27 @@ from sleap.skeleton import Skeleton TEST_FLY_LEGS_SKELETON = "tests/data/skeleton/fly_skeleton_legs.json" +TEST_FLY_LEGS_SKELETON_DICT = "tests/data/skeleton/fly_skeleton_legs_pystate_dict.json" @pytest.fixture def fly_legs_skeleton_json(): - """Path to fly_skeleton_legs.json""" + """Path to fly_skeleton_legs.json + + This skeleton json has py/state in tuple format. + """ return TEST_FLY_LEGS_SKELETON +@pytest.fixture +def fly_legs_skeleton_dict_json(): + """Path to fly_skeleton_legs_pystate_dict.json + + This skeleton json has py/state dict format. + """ + return TEST_FLY_LEGS_SKELETON_DICT + + @pytest.fixture def stickman(): diff --git a/tests/fixtures/videos.py b/tests/fixtures/videos.py index b160caedd..08974b3de 100644 --- a/tests/fixtures/videos.py +++ b/tests/fixtures/videos.py @@ -1,12 +1,21 @@ import pytest from sleap.io.video import Video +from sleap.io.format.filehandle import FileHandle TEST_H5_FILE = "tests/data/hdf5_format_v1/training.scale=0.50,sigma=10.h5" TEST_H5_DSET = "/box" TEST_H5_CONFMAPS = "/confmaps" TEST_H5_AFFINITY = "/pafs" TEST_H5_INPUT_FORMAT = "channels_first" +TEST_SMALL_ROBOT3_FRAME_H5 = ( + "tests/data/hdf5_format_v1/small_robot.000_small_robot_3_frame.analysis.h5" +) + + +@pytest.fixture +def small_robot_3_frame_hdf5(): + return FileHandle(filename=TEST_SMALL_ROBOT3_FRAME_H5) @pytest.fixture diff --git a/tests/gui/learning/test_dialog.py b/tests/gui/learning/test_dialog.py index 3d77c891f..389bb48a3 100644 --- a/tests/gui/learning/test_dialog.py +++ b/tests/gui/learning/test_dialog.py @@ -7,6 +7,7 @@ import pytest from qtpy import QtWidgets +import sleap from sleap.gui.learning.dialog import LearningDialog, TrainingEditorWidget from sleap.gui.learning.configs import ( TrainingConfigFilesWidget, @@ -429,3 +430,22 @@ def test_immutablilty_of_trained_config_info( # saving multiple configs from one config info. ld.save(output_dir=tmpdir) ld.save(output_dir=tmpdir) + + +def test_validate_id_model(qtbot, min_labels_slp, min_labels_slp_path): + app = MainWindow(no_usage_data=True) + ld = LearningDialog( + mode="training", + labels_filename=Path(min_labels_slp_path), + labels=min_labels_slp, + ) + assert not ld._validate_id_model() + + # Add track but don't assign it to instances + new_track = sleap.Track(name="new_track") + min_labels_slp.tracks.append(new_track) + assert not ld._validate_id_model() + + # Assign track to instances + min_labels_slp[0][0].track = new_track + assert ld._validate_id_model() diff --git a/tests/gui/test_app.py b/tests/gui/test_app.py index bacda4ae3..def835b6e 100644 --- a/tests/gui/test_app.py +++ b/tests/gui/test_app.py @@ -142,6 +142,7 @@ def assert_frame_chunk_suggestion_ui_updated( # Select and delete instance app.state["instance"] = inst_27_1 app.commands.deleteSelectedInstance() + assert app.state["instance"] is None assert len(app.state["labeled_frame"].instances) == 1 assert app.state["labeled_frame"].instances == [inst_27_0] @@ -179,6 +180,7 @@ def assert_frame_chunk_suggestion_ui_updated( # Delete all instances in track app.commands.deleteSelectedInstanceTrack() + assert app.state["instance"] is None assert len(app.state["labeled_frame"].instances) == 0 app.state["frame_idx"] = 29 @@ -412,6 +414,12 @@ def toggle_and_verify_visibility(expected_visibility: bool = True): window.showNormal() vp = window.player + # Change state and ensure menu-item check updates + color_predicted = window.state["color predicted"] + assert window._menu_actions["color predicted"].isChecked() == color_predicted + window.state["color predicted"] = not color_predicted + assert window._menu_actions["color predicted"].isChecked() == (not color_predicted) + # Enable distinct colors window.state["color predicted"] = True diff --git a/tests/gui/test_commands.py b/tests/gui/test_commands.py index 13aa60e6b..e19e00236 100644 --- a/tests/gui/test_commands.py +++ b/tests/gui/test_commands.py @@ -3,11 +3,15 @@ import sys import time +import numpy as np from pathlib import PurePath, Path +from qtpy import QtCore from typing import List from sleap import Skeleton, Track, PredictedInstance +from sleap.gui.app import MainWindow from sleap.gui.commands import ( + AddInstance, CommandContext, ExportAnalysisFile, ExportDatasetWithImages, @@ -16,6 +20,7 @@ ReplaceVideo, OpenSkeleton, SaveProjectAs, + DeleteFrameLimitPredictions, get_new_version_filename, ) from sleap.instance import Instance, LabeledFrame @@ -65,7 +70,7 @@ def test_import_labels_from_dlc_folder(): assert len(labels.videos) == 2 assert len(labels.skeletons) == 1 assert len(labels.nodes) == 3 - assert len(labels.tracks) == 0 + assert len(labels.tracks) == 3 assert set( [fix_path_separator(l.video.backend.filename) for l in labels.labeled_frames] @@ -847,6 +852,26 @@ def load_and_assert_changes(new_video_path: Path): shutil.move(new_video_path, expected_video_path) +def test_DeleteFrameLimitPredictions( + centered_pair_predictions: Labels, centered_pair_vid: Video +): + """Test deleting instances beyond a certain frame limit.""" + labels = centered_pair_predictions + + # Set-up command context + context = CommandContext.from_labels(labels) + context.state["video"] = centered_pair_vid + + # Set-up params for the command + params = {"min_frame_idx": 900, "max_frame_idx": 1000} + + instances_to_delete = DeleteFrameLimitPredictions.get_frame_instance_list( + context, params + ) + + assert len(instances_to_delete) == 2070 + + @pytest.mark.parametrize("export_extension", [".json.zip", ".slp"]) def test_exportLabelsPackage(export_extension, centered_pair_labels: Labels, tmpdir): def assert_loaded_package_similar(path_to_pkg: Path, sugg=False, pred=False): @@ -922,3 +947,102 @@ def no_gui_ask(cls, context, params): # Case 3: Export all frames and suggested frames with image data. context.exportFullPackage() assert_loaded_package_similar(path_to_pkg, sugg=True, pred=True) + + +def test_newInstance(qtbot, centered_pair_predictions: Labels): + + # Get the data + labels = centered_pair_predictions + lf = labels[0] + pred_inst = lf.instances[0] + video = labels.video + + # Set-up command context + main_window = MainWindow(labels=labels) + context = main_window.commands + context.state["labeled_frame"] = lf + context.state["frame_idx"] = lf.frame_idx + context.state["skeleton"] = labels.skeleton + context.state["video"] = labels.videos[0] + + # Case 1: Double clicking a prediction results in no offset for new instance + + # Double click on prediction + assert len(lf.instances) == 2 + main_window._handle_instance_double_click(instance=pred_inst) + + # Check new instance + assert len(lf.instances) == 3 + new_inst = lf.instances[-1] + assert new_inst.from_predicted is pred_inst + assert np.array_equal(new_inst.numpy(), pred_inst.numpy()) # No offset + + # Case 2: Using Ctrl + I (or menu "Add Instance" button) + + # Connect the action to a slot + add_instance_menu_action = main_window._menu_actions["add instance"] + triggered = False + + def on_triggered(): + nonlocal triggered + triggered = True + + add_instance_menu_action.triggered.connect(on_triggered) + + # Find which instance we are going to copy from + ( + copy_instance, + from_predicted, + from_prev_frame, + ) = AddInstance.find_instance_to_copy_from( + context, copy_instance=None, init_method="best" + ) + + # Click on the menu action + assert len(lf.instances) == 3 + add_instance_menu_action.trigger() + assert triggered, "Action not triggered" + + # Check new instance + assert len(lf.instances) == 4 + new_inst = lf.instances[-1] + offset = 10 + np.nan_to_num(new_inst.numpy() - copy_instance.numpy(), nan=offset) + assert np.all( + np.nan_to_num(new_inst.numpy() - copy_instance.numpy(), nan=offset) == offset + ) + + # Case 3: Using right click and "Default" option + + # Find which instance we are going to copy from + ( + copy_instance, + from_predicted, + from_prev_frame, + ) = AddInstance.find_instance_to_copy_from( + context, copy_instance=None, init_method="best" + ) + + video_player = main_window.player + right_click_location_x = video.shape[2] / 2 + right_click_location_y = video.shape[1] / 2 + right_click_location = QtCore.QPointF( + right_click_location_x, right_click_location_y + ) + video_player.create_contextual_menu(scene_pos=right_click_location) + default_action = video_player._menu_actions["Default"] + default_action.trigger() + + # Check new instance + assert len(lf.instances) == 5 + new_inst = lf.instances[-1] + reference_node_idx = np.where( + np.all( + new_inst.numpy() == [right_click_location_x, right_click_location_y], axis=1 + ) + )[0][0] + offset = ( + new_inst.numpy()[reference_node_idx] - copy_instance.numpy()[reference_node_idx] + ) + diff = np.nan_to_num(new_inst.numpy() - copy_instance.numpy(), nan=offset) + assert np.all(diff == offset) diff --git a/tests/gui/test_dialogs.py b/tests/gui/test_dialogs.py index 4455550fb..611a73c85 100644 --- a/tests/gui/test_dialogs.py +++ b/tests/gui/test_dialogs.py @@ -1,6 +1,5 @@ """Module to test the dialogs of the GUI (contained in sleap/gui/dialogs).""" - import os from pathlib import Path diff --git a/tests/gui/test_monitor.py b/tests/gui/test_monitor.py index 51af0ca92..e0abea692 100644 --- a/tests/gui/test_monitor.py +++ b/tests/gui/test_monitor.py @@ -1,4 +1,3 @@ -from turtle import title from sleap.gui.widgets.monitor import LossViewer from sleap import TrainingJobConfig @@ -12,6 +11,9 @@ def test_monitor_release(qtbot, min_centroid_model_path): win.reset(what="Model Type", config=config) assert win.config.optimization.early_stopping.plateau_patience == 10 + # Ensure zmq port is set correctly + assert win.zmq_ports["controller_port"] == 9000 + assert win.zmq_ports["publish_port"] == 9001 # Ensure all lines of update_runtime() are run error-free win.is_running = True win.t0 = 0 @@ -28,13 +30,17 @@ def test_monitor_release(qtbot, min_centroid_model_path): # Enter "bes_val_x" conditional win.best_val_x = 0 win.best_val_y = win.last_epoch_val_loss - win.update_runtime() + win._update_runtime() win.close() # Make sure the first monitor released its zmq socket - win2 = LossViewer() + controller_port = 9191 + zmq_ports = dict(controller_port=controller_port) + win2 = LossViewer(zmq_ports=zmq_ports) win2.show() + assert win2.zmq_ports["controller_port"] == controller_port + assert win2.zmq_ports["publish_port"] == 9001 # Make sure batches to show field is working correction @@ -47,3 +53,14 @@ def test_monitor_release(qtbot, min_centroid_model_path): assert win2.batches_to_show == 200 win2.close() + + # Ensure zmq port is set correctly + controller_port = 9191 + publish_port = 9101 + zmq_ports = dict(controller_port=controller_port, publish_port=publish_port) + win3 = LossViewer(zmq_ports=zmq_ports) + win3.show() + assert win3.zmq_ports["controller_port"] == controller_port + assert win3.zmq_ports["publish_port"] == publish_port + + win3.close() diff --git a/tests/gui/test_suggestions.py b/tests/gui/test_suggestions.py index bbad73179..196ff2d35 100644 --- a/tests/gui/test_suggestions.py +++ b/tests/gui/test_suggestions.py @@ -24,6 +24,20 @@ def test_velocity_suggestions(centered_pair_predictions): assert suggestions[1].frame_idx == 45 +def test_max_point_displacement_suggestions(centered_pair_predictions): + suggestions = VideoFrameSuggestions.suggest( + labels=centered_pair_predictions, + params=dict( + videos=centered_pair_predictions.videos, + method="max_point_displacement", + displacement_threshold=6, + ), + ) + assert len(suggestions) == 19 + assert suggestions[0].frame_idx == 28 + assert suggestions[1].frame_idx == 82 + + def test_frame_increment(centered_pair_predictions: Labels): # Testing videos that have less frames than desired Samples per Video (stride) # Expected result is there should be n suggestions where n is equal to the frames diff --git a/tests/gui/test_video_player.py b/tests/gui/test_video_player.py index b0661a4e1..c246f0489 100644 --- a/tests/gui/test_video_player.py +++ b/tests/gui/test_video_player.py @@ -3,14 +3,13 @@ from sleap.gui.widgets.video import ( QtVideoPlayer, GraphicsView, - QtInstance, QtVideoPlayer, QtTextWithBackground, VisibleBoundingBox, ) from qtpy import QtCore, QtWidgets -from qtpy.QtGui import QColor +from qtpy.QtGui import QColor, QWheelEvent def test_gui_video(qtbot): @@ -20,10 +19,6 @@ def test_gui_video(qtbot): assert vp.close() - # Click the button 20 times - # for i in range(20): - # qtbot.mouseClick(vp.btn, QtCore.Qt.LeftButton) - def test_gui_video_instances(qtbot, small_robot_mp4_vid, centered_pair_labels): vp = QtVideoPlayer(small_robot_mp4_vid) @@ -144,3 +139,40 @@ def test_VisibleBoundingBox(qtbot, centered_pair_labels): # Check if bounding box scaled appropriately assert inst.box.rect().width() - initial_width == 2 * dx assert inst.box.rect().height() - initial_height == 2 * dy + + +def test_wheelEvent(qtbot): + """Test the wheelEvent method of the GraphicsView class.""" + graphics_view = GraphicsView() + + # Create a QWheelEvent + position = QtCore.QPointF(100, 100) # The position of the wheel event + global_position = QtCore.QPointF(100, 100) # The global position of the wheel event + pixel_delta = QtCore.QPoint(0, 120) # The distance in pixels the wheel is rotated + angle_delta = QtCore.QPoint(0, 120) # The distance in degrees the wheel is rotated + buttons = QtCore.Qt.NoButton # No mouse button is pressed + modifiers = QtCore.Qt.NoModifier # No keyboard modifier is pressed + phase = QtCore.Qt.ScrollUpdate # The phase of the scroll event + inverted = False # The scroll direction is not inverted + source = ( + QtCore.Qt.MouseEventNotSynthesized + ) # The event is not synthesized from a touch or tablet event + + event = QWheelEvent( + position, + global_position, + pixel_delta, + angle_delta, + buttons, + modifiers, + phase, + inverted, + source, + ) + + # Call the wheelEvent method + print( + "Testing GraphicsView.wheelEvent which will result in exit code 127 " + "originating from a segmentation fault if it fails." + ) + graphics_view.wheelEvent(event) diff --git a/tests/gui/widgets/test_docks.py b/tests/gui/widgets/test_docks.py index 69fe56a56..d5c16a763 100644 --- a/tests/gui/widgets/test_docks.py +++ b/tests/gui/widgets/test_docks.py @@ -1,15 +1,17 @@ """Module for testing dock widgets for the `MainWindow`.""" from pathlib import Path -import pytest + +import numpy as np + from sleap import Labels, Video from sleap.gui.app import MainWindow -from sleap.gui.commands import OpenSkeleton +from sleap.gui.commands import AddInstance, OpenSkeleton from sleap.gui.widgets.docks import ( InstancesDock, + SkeletonDock, SuggestionsDock, VideosDock, - SkeletonDock, ) @@ -99,11 +101,35 @@ def test_suggestions_dock(qtbot): assert dock.wgt_layout is dock.widget().layout() -def test_instances_dock(qtbot): +def test_instances_dock(qtbot, centered_pair_predictions: Labels): """Test the `DockWidget` class.""" - main_window = MainWindow() + main_window = MainWindow(labels=centered_pair_predictions) + labels = main_window.labels + context = main_window.commands + lf = context.state["labeled_frame"] dock = InstancesDock(main_window) assert dock.name == "Instances" assert dock.main_window is main_window assert dock.wgt_layout is dock.widget().layout() + + # Test new instance button + + offset = 10 + + # Find instance that we will copy from + ( + copy_instance, + from_predicted, + from_prev_frame, + ) = AddInstance.find_instance_to_copy_from( + context, copy_instance=None, init_method="best" + ) + n_instance = len(lf.instances) + dock.main_window._buttons["new instance"].click() + + # Check that new instance was added with offset + assert len(lf.instances) == n_instance + 1 + new_inst = lf.instances[-1] + diff = np.nan_to_num(new_inst.numpy() - copy_instance.numpy(), nan=offset) + assert np.all(diff == offset) diff --git a/tests/info/test_metrics.py b/tests/info/test_metrics.py new file mode 100644 index 000000000..0d2e097e6 --- /dev/null +++ b/tests/info/test_metrics.py @@ -0,0 +1,55 @@ +import numpy as np + +from sleap import Labels +from sleap.info.metrics import ( + match_instance_lists_nodewise, + matched_instance_distances, +) + + +def test_matched_instance_distances(centered_pair_labels, centered_pair_predictions): + labels_gt = centered_pair_labels + labels_pr = centered_pair_predictions + + # Match each ground truth instance node to the closest corresponding node + # from any predicted instance in the same frame. + + inst_matching_func = match_instance_lists_nodewise + + # Calculate distances + frame_idxs, D, points_gt, points_pr = matched_instance_distances( + labels_gt, labels_pr, inst_matching_func + ) + + # Show mean difference for each node + node_names = labels_gt.skeletons[0].node_names + expected_values = { + "head": 0.872426920709296, + "neck": 0.8016280746914615, + "thorax": 0.8602021363390538, + "abdomen": 1.01012200038258, + "wingL": 1.1297727023475939, + "wingR": 1.0869857897008424, + "forelegL1": 0.780584225081443, + "forelegL2": 1.170805798894702, + "forelegL3": 1.1020486509389473, + "forelegR1": 0.9014698776116817, + "forelegR2": 0.9448001033112047, + "forelegR3": 1.308385214215777, + "midlegL1": 0.9095691623265347, + "midlegL2": 1.2203595627907582, + "midlegL3": 0.9813843358470163, + "midlegR1": 0.9871017182813739, + "midlegR2": 1.0209829335569256, + "midlegR3": 1.0990681234096988, + "hindlegL1": 1.0005335192834348, + "hindlegL2": 1.273539518539708, + "hindlegL3": 1.1752245985832817, + "hindlegR1": 1.1402833959265248, + "hindlegR2": 1.3143221301212737, + "hindlegR3": 1.0441458592503365, + } + + for node_idx, node_name in enumerate(node_names): + mean_d = np.nanmean(D[..., node_idx]) + assert np.isclose(mean_d, expected_values[node_name], atol=1e-6) diff --git a/tests/info/test_summary.py b/tests/info/test_summary.py index 2cf76c166..672d97e63 100644 --- a/tests/info/test_summary.py +++ b/tests/info/test_summary.py @@ -37,6 +37,19 @@ def test_frame_statistics(simple_predictions): x = stats.get_point_displacement_series(video, "max") assert len(x) == 2 - assert len(x) == 2 assert x[0] == 0 assert x[1] == 18.0 + + +def test_get_tracking_score_series(min_tracks_2node_predictions): + + stats = StatisticSeries(min_tracks_2node_predictions) + x = stats.get_tracking_score_series(min_tracks_2node_predictions.video, "min") + assert len(x) == 1500 + assert x[0] == 0.9999966621398926 + assert x[1000] == 0.9998022317886353 + + x = stats.get_tracking_score_series(min_tracks_2node_predictions.video, "mean") + assert len(x) == 1500 + assert x[0] == 0.9999983310699463 + assert x[1000] == 0.9999011158943176 diff --git a/tests/io/test_asyncvideo.py b/tests/io/test_asyncvideo.py deleted file mode 100644 index 1bc3f19c8..000000000 --- a/tests/io/test_asyncvideo.py +++ /dev/null @@ -1,31 +0,0 @@ -import pytest -import sys -from sleap import Video -from sleap.io.asyncvideo import AsyncVideo - - -@pytest.mark.skipif( - sys.platform.startswith("win"), reason="ZMQ testing breaks locally on Windows" -) -def test_async_video(centered_pair_vid, small_robot_mp4_vid): - async_video = AsyncVideo.from_video(centered_pair_vid, frames_per_chunk=23) - - all_idxs = [] - for idxs, frames in async_video.chunks: - assert len(idxs) in (23, 19) # 19 for last chunk - all_idxs.extend(idxs) - - assert frames.shape[0] == len(idxs) - assert frames.shape[1:] == centered_pair_vid.shape[1:] - - assert len(all_idxs) == centered_pair_vid.num_frames - - # make sure we can load another video (i.e., previous video closed) - - async_video = AsyncVideo.from_video( - small_robot_mp4_vid, frame_idxs=range(0, 10, 2), frames_per_chunk=10 - ) - - for idxs, frames in async_video.chunks: - # there should only be single chunk - assert idxs == list(range(0, 10, 2)) diff --git a/tests/io/test_convert.py b/tests/io/test_convert.py index da1971c11..738c3d625 100644 --- a/tests/io/test_convert.py +++ b/tests/io/test_convert.py @@ -8,7 +8,7 @@ import pytest -@pytest.mark.parametrize("format", ["analysis", "analysis.nix"]) +@pytest.mark.parametrize("format", ["analysis", "analysis.nix", "analysis.csv"]) def test_analysis_format( min_labels_slp: Labels, min_labels_slp_path: Labels, @@ -27,7 +27,7 @@ def generate_filenames(paths, format="analysis"): labels_path = str(slp_path) fn = re.sub("(\\.json(\\.zip)?|\\.h5|\\.slp)$", "", labels_path) fn = PurePath(fn) - out_suffix = "nix" if "nix" in format else "h5" + out_suffix = "nix" if "nix" in format else "csv" if "csv" in format else "h5" default_names = [ default_analysis_filename( labels=labels, diff --git a/tests/io/test_dataset.py b/tests/io/test_dataset.py index 5592ae437..d71d4cc83 100644 --- a/tests/io/test_dataset.py +++ b/tests/io/test_dataset.py @@ -1,9 +1,11 @@ import os +import pandas as pd import pytest import numpy as np from pathlib import Path, PurePath import sleap +from sleap.info.write_tracking_h5 import get_nodes_as_np_strings from sleap.skeleton import Skeleton from sleap.instance import Instance, Point, LabeledFrame, PredictedInstance, Track from sleap.io.video import Video, MediaVideo @@ -1234,7 +1236,7 @@ def test_has_frame(): @pytest.fixture def removal_test_labels(): skeleton = Skeleton() - video = Video(backend=MediaVideo(filename="test")) + video = Video(backend=MediaVideo(filename="test.mp4")) lf_user_only = LabeledFrame( video=video, frame_idx=0, instances=[Instance(skeleton=skeleton)] ) @@ -1559,3 +1561,45 @@ def test_export_nwb(centered_pair_predictions: Labels, tmpdir): # Read from NWB file read_labels = NDXPoseAdaptor.read(NDXPoseAdaptor, filehandle.FileHandle(filename)) assert_read_labels_match(centered_pair_predictions, read_labels) + + +@pytest.mark.parametrize( + "labels_fixture_name", + [ + "centered_pair_labels", + "centered_pair_predictions", + "min_labels", + "min_labels_slp", + "min_labels_robot", + ], +) +def test_export_csv(labels_fixture_name, tmpdir, request): + # Retrieve Labels fixture by name + labels_fixture = request.getfixturevalue(labels_fixture_name) + + # Generate the filename for the CSV file + csv_filename = Path(tmpdir) / (labels_fixture_name + "_export.csv") + + # Export to CSV file + labels_fixture.export_csv(str(csv_filename)) + + # Assert that the CSV file was created + assert csv_filename.is_file(), f"CSV file '{csv_filename}' was not created" + + +def test_exported_csv(tmpdir, min_labels_slp, minimal_instance_predictions_csv_path): + # Construct the filename for the CSV file + filename_csv = Path(tmpdir) / "minimal_instance_predictions_export.csv" + labels = min_labels_slp + # Export to CSV file + labels.export_csv(filename_csv) + # Read the CSV file + labels_csv = pd.read_csv(filename_csv) + + # Read the csv file fixture + csv_predictions = pd.read_csv(minimal_instance_predictions_csv_path) + + assert labels_csv.equals(csv_predictions) + + # check number of cols + assert len(labels_csv.columns) - 3 == len(get_nodes_as_np_strings(labels)) * 3 diff --git a/tests/io/test_formats.py b/tests/io/test_formats.py index a89bf60d7..cee754b7c 100644 --- a/tests/io/test_formats.py +++ b/tests/io/test_formats.py @@ -19,6 +19,19 @@ from sleap.gui.app import MainWindow from sleap.gui.state import GuiState from sleap.info.write_tracking_h5 import get_nodes_as_np_strings +from sleap.io.format.sleap_analysis import SleapAnalysisAdaptor + + +def test_sleap_analysis_read(small_robot_3_frame_vid, small_robot_3_frame_hdf5): + + # Single instance hdf5 analysis file test + read_labels = SleapAnalysisAdaptor.read( + file=small_robot_3_frame_hdf5, video=small_robot_3_frame_vid + ) + + assert len(read_labels.videos) == 1 + assert len(read_labels.tracks) == 1 + assert len(read_labels.skeletons) == 1 def test_text_adaptor(tmpdir): @@ -198,7 +211,6 @@ def test_matching_adaptor(centered_pair_predictions_hdf5_path): [ "tests/data/dlc/labeled-data/video/madlc_testdata.csv", "tests/data/dlc/labeled-data/video/madlc_testdata_v2.csv", - "tests/data/dlc/madlc_230_config.yaml", ], ) def test_madlc(test_data): @@ -232,6 +244,78 @@ def test_madlc(test_data): assert labels[2].frame_idx == 3 +@pytest.mark.parametrize( + "test_data", + [ + "tests/data/dlc/labeled-data/video/maudlc_testdata.csv", + "tests/data/dlc/labeled-data/video/maudlc_testdata_v2.csv", + "tests/data/dlc/madlc_230_config.yaml", + ], +) +def test_maudlc(test_data): + labels = read( + test_data, + for_object="labels", + as_format="deeplabcut", + ) + + assert labels.skeleton.node_names == ["A", "B", "C", "D", "E"] + assert len(labels.videos) == 1 + assert len(labels.video.filenames) == 4 + assert labels.videos[0].filenames[0].endswith("img000.png") + assert labels.videos[0].filenames[1].endswith("img001.png") + assert labels.videos[0].filenames[2].endswith("img002.png") + assert labels.videos[0].filenames[3].endswith("img003.png") + + # Assert frames without any coor are not labeled + assert len(labels) == 3 + + # Assert number of instances per frame is correct + assert len(labels[0]) == 2 + assert len(labels[1]) == 3 + assert len(labels[2]) == 2 + + assert_array_equal( + labels[0][0].numpy(), + [[0, 1], [2, 3], [4, 5], [np.nan, np.nan], [np.nan, np.nan]], + ) + assert_array_equal( + labels[0][1].numpy(), + [[6, 7], [8, 9], [10, 11], [np.nan, np.nan], [np.nan, np.nan]], + ) + assert_array_equal( + labels[1][0].numpy(), + [[12, 13], [np.nan, np.nan], [15, 16], [np.nan, np.nan], [np.nan, np.nan]], + ) + assert_array_equal( + labels[1][1].numpy(), + [[17, 18], [np.nan, np.nan], [20, 21], [np.nan, np.nan], [np.nan, np.nan]], + ) + assert_array_equal( + labels[1][2].numpy(), + [[np.nan, np.nan], [np.nan, np.nan], [np.nan, np.nan], [22, 23], [24, 25]], + ) + assert_array_equal( + labels[2][0].numpy(), + [[26, 27], [28, 29], [30, 31], [np.nan, np.nan], [np.nan, np.nan]], + ) + assert_array_equal( + labels[2][1].numpy(), + [[np.nan, np.nan], [np.nan, np.nan], [np.nan, np.nan], [32, 33], [34, 35]], + ) + assert labels[2].frame_idx == 3 + + # Assert tracks are correct + assert len(labels.tracks) == 3 + sorted_animals = sorted(["Animal1", "Animal2", "single"]) + assert sorted([t.name for t in labels.tracks]) == sorted_animals + for t in labels.tracks: + if t.name == "single": + assert t.spawned_on == 1 + else: + assert t.spawned_on == 0 + + @pytest.mark.parametrize( "test_data", [ diff --git a/tests/io/test_videowriter.py b/tests/io/test_videowriter.py index dea193117..35d9bc6df 100644 --- a/tests/io/test_videowriter.py +++ b/tests/io/test_videowriter.py @@ -1,5 +1,7 @@ import os -from sleap.io.videowriter import VideoWriter, VideoWriterOpenCV +import cv2 +from pathlib import Path +from sleap.io.videowriter import VideoWriter, VideoWriterOpenCV, VideoWriterImageio def test_video_writer(tmpdir, small_robot_mp4_vid): @@ -38,3 +40,62 @@ def test_cv_video_writer(tmpdir, small_robot_mp4_vid): writer.close() assert os.path.exists(out_path) + + +def test_imageio_video_writer_avi(tmpdir, small_robot_mp4_vid): + out_path = Path(tmpdir) / "clip.avi" + + # Make sure imageio video writer works + writer = VideoWriterImageio( + out_path, + height=small_robot_mp4_vid.height, + width=small_robot_mp4_vid.width, + fps=small_robot_mp4_vid.fps, + ) + + writer.add_frame(small_robot_mp4_vid[0][0]) + writer.add_frame(small_robot_mp4_vid[1][0]) + + writer.close() + + assert os.path.exists(out_path) + # Check attributes + assert writer.height == small_robot_mp4_vid.height + assert writer.width == small_robot_mp4_vid.width + assert writer.fps == small_robot_mp4_vid.fps + assert writer.filename == out_path + assert writer.crf == 21 + assert writer.preset == "superfast" + + +def test_imageio_video_writer_odd_size(tmpdir, movenet_video): + out_path = Path(tmpdir) / "clip.mp4" + + # Reduce the size of the video frames by 1 pixel in each dimension + reduced_height = movenet_video.height - 1 + reduced_width = movenet_video.width - 1 + + # Initialize the writer with the reduced dimensions + writer = VideoWriterImageio( + out_path, + height=reduced_height, + width=reduced_width, + fps=movenet_video.fps, + ) + + # Resize frames and add them to the video + for i in range(len(movenet_video) - 1): + frame = movenet_video[i][0] # Access the actual frame object + reduced_frame = cv2.resize(frame, (reduced_width, reduced_height)) + writer.add_frame(reduced_frame) + + writer.close() + + # Assertions to validate the test + assert os.path.exists(out_path) + assert writer.height == reduced_height + assert writer.width == reduced_width + assert writer.fps == movenet_video.fps + assert writer.filename == out_path + assert writer.crf == 21 + assert writer.preset == "superfast" diff --git a/tests/io/test_visuals.py b/tests/io/test_visuals.py index d6144e2c1..a1223bfdf 100644 --- a/tests/io/test_visuals.py +++ b/tests/io/test_visuals.py @@ -1,6 +1,7 @@ import numpy as np import os import pytest +import cv2 from sleap.io.dataset import Labels from sleap.io.visuals import ( save_labeled_video, @@ -63,6 +64,46 @@ def test_serial_pipeline(centered_pair_predictions, tmpdir): ) +@pytest.mark.parametrize("background", ["original", "black", "white", "grey"]) +def test_sleap_render_with_different_backgrounds(background): + args = ( + f"-o test_{background}.avi -f 2 --scale 1.2 --frames 1,2 --video-index 0 " + f"--background {background} " + "tests/data/json_format_v2/centered_pair_predictions.json".split() + ) + sleap_render(args) + assert ( + os.path.exists(f"test_{background}.avi") + and os.path.getsize(f"test_{background}.avi") > 0 + ) + + # Check if the background is set correctly if not original background + if background != "original": + saved_video_path = f"test_{background}.avi" + cap = cv2.VideoCapture(saved_video_path) + ret, frame = cap.read() + + # Calculate mean color of the channels + b, g, r = cv2.split(frame) + mean_b = np.mean(b) + mean_g = np.mean(g) + mean_r = np.mean(r) + + # Set threshold values. Color is white if greater than white threshold, black + # if less than grey threshold and grey if in between both threshold values. + white_threshold = 240 + grey_threshold = 40 + + # Check if the average color is white, grey, or black + if all(val > white_threshold for val in [mean_b, mean_g, mean_r]): + background_color = "white" + elif all(val < grey_threshold for val in [mean_b, mean_g, mean_r]): + background_color = "black" + else: + background_color = "grey" + assert background_color == background + + def test_sleap_render(centered_pair_predictions): args = ( "-o testvis.avi -f 2 --scale 1.2 --frames 1,2 --video-index 0 " diff --git a/tests/nn/architectures/test_common.py b/tests/nn/architectures/test_common.py index a40d621ef..96db870ea 100644 --- a/tests/nn/architectures/test_common.py +++ b/tests/nn/architectures/test_common.py @@ -2,7 +2,7 @@ import tensorflow as tf from sleap.nn.system import use_cpu_only -use_cpu_only() # hide GPUs for test +# use_cpu_only() # hide GPUs for test from sleap.nn.architectures import common diff --git a/tests/nn/architectures/test_encoder_decoder.py b/tests/nn/architectures/test_encoder_decoder.py index 3ce019371..8b8f51f0a 100644 --- a/tests/nn/architectures/test_encoder_decoder.py +++ b/tests/nn/architectures/test_encoder_decoder.py @@ -2,7 +2,7 @@ import tensorflow as tf from sleap.nn.system import use_cpu_only -use_cpu_only() # hide GPUs for test +# use_cpu_only() # hide GPUs for test from sleap.nn.architectures import encoder_decoder diff --git a/tests/nn/architectures/test_hourglass.py b/tests/nn/architectures/test_hourglass.py index 4efe79a1c..c45ff1b91 100644 --- a/tests/nn/architectures/test_hourglass.py +++ b/tests/nn/architectures/test_hourglass.py @@ -2,7 +2,7 @@ import tensorflow as tf from sleap.nn.system import use_cpu_only -use_cpu_only() # hide GPUs for test +# use_cpu_only() # hide GPUs for test from sleap.nn.architectures import hourglass from sleap.nn.config import HourglassConfig diff --git a/tests/nn/architectures/test_leap.py b/tests/nn/architectures/test_leap.py index edf07396b..9a73c80d5 100644 --- a/tests/nn/architectures/test_leap.py +++ b/tests/nn/architectures/test_leap.py @@ -2,7 +2,7 @@ import tensorflow as tf from sleap.nn.system import use_cpu_only -use_cpu_only() # hide GPUs for test +# use_cpu_only() # hide GPUs for test from sleap.nn.architectures import leap from sleap.nn.config import LEAPConfig diff --git a/tests/nn/architectures/test_pretrained_encoders.py b/tests/nn/architectures/test_pretrained_encoders.py index f318754ac..b1f7e0af8 100644 --- a/tests/nn/architectures/test_pretrained_encoders.py +++ b/tests/nn/architectures/test_pretrained_encoders.py @@ -3,7 +3,7 @@ import pytest from sleap.nn.system import use_cpu_only -use_cpu_only() # hide GPUs for test +# use_cpu_only() # hide GPUs for test from sleap.nn.architectures import UnetPretrainedEncoder from sleap.nn.config import PretrainedEncoderConfig diff --git a/tests/nn/architectures/test_resnet.py b/tests/nn/architectures/test_resnet.py index 965ea3b72..b0d9d26eb 100644 --- a/tests/nn/architectures/test_resnet.py +++ b/tests/nn/architectures/test_resnet.py @@ -2,7 +2,7 @@ import tensorflow as tf from sleap.nn.system import use_cpu_only -use_cpu_only() # hide GPUs for test +# use_cpu_only() # hide GPUs for test from sleap.nn.architectures import upsampling from sleap.nn.architectures import resnet diff --git a/tests/nn/architectures/test_unet.py b/tests/nn/architectures/test_unet.py index 98b6d7768..1dad7ea05 100644 --- a/tests/nn/architectures/test_unet.py +++ b/tests/nn/architectures/test_unet.py @@ -2,7 +2,7 @@ import tensorflow as tf from sleap.nn.system import use_cpu_only -use_cpu_only() # hide GPUs for test +# use_cpu_only() # hide GPUs for test from sleap.nn.architectures import unet from sleap.nn.config import UNetConfig diff --git a/tests/nn/config/test_config_utils.py b/tests/nn/config/test_config_utils.py index 69e8ddec8..64d83a141 100644 --- a/tests/nn/config/test_config_utils.py +++ b/tests/nn/config/test_config_utils.py @@ -4,7 +4,7 @@ from sleap.nn.system import use_cpu_only -use_cpu_only() # hide GPUs for test +# use_cpu_only() # hide GPUs for test from sleap.nn.config import utils diff --git a/tests/nn/data/test_augmentation.py b/tests/nn/data/test_augmentation.py index d2b468522..2b95a01a3 100644 --- a/tests/nn/data/test_augmentation.py +++ b/tests/nn/data/test_augmentation.py @@ -1,3 +1,4 @@ +import pytest import numpy as np import tensorflow as tf import sleap @@ -9,14 +10,95 @@ from sleap.nn.data import augmentation +@pytest.fixture +def dummy_instances_data_nans(): + return np.full((2, 2), np.nan, dtype=np.float32) + + +@pytest.fixture +def dummy_instances_data_mixed(): + return np.array([[0.1, np.nan], [0.0, 0.8]], dtype=np.float32) + + +@pytest.fixture +def dummy_image_data(): + return np.zeros((100, 100, 3), dtype=np.uint8) + + +@pytest.fixture +def dummy_instances_data_zeros(): + return np.zeros((2, 2), dtype=np.float32) + + +@pytest.fixture +def rotation_min_angle(): + return 90 + + +@pytest.fixture +def rotation_max_angle(): + return 90 + + +@pytest.fixture +def augmentation_config(rotation_min_angle, rotation_max_angle): + return augmentation.AugmentationConfig( + rotate=True, + rotation_min_angle=rotation_min_angle, + rotation_max_angle=rotation_max_angle, + ) + + +@pytest.fixture +def dummy_dataset(dummy_image_data, dummy_instances_data_zeros): + dataset = tf.data.Dataset.from_tensor_slices( + {"image": [dummy_image_data], "instances": [dummy_instances_data_zeros]} + ) + return dataset + + +@pytest.fixture +def augmenter(augmentation_config): + return augmentation.AlbumentationsAugmenter.from_config(augmentation_config) + + +# Test class instantiation and augmentation +@pytest.mark.parametrize( + "dummy_instances_data", + [ + pytest.param("dummy_instances_data_zeros", id="zeros"), + pytest.param("dummy_instances_data_nans", id="nans"), + pytest.param("dummy_instances_data_mixed", id="mixed"), + ], +) +def test_albumentations_augmenter( + dummy_image_data, dummy_instances_data, augmenter, dummy_dataset +): + # Apply augmentation + augmented_dataset = augmenter.transform_dataset(dummy_dataset) + + # Check if augmentation is applied + augmented_example = next(iter(augmented_dataset)) + assert augmented_example["image"].shape == (100, 100, 3) + assert augmented_example["instances"].shape == (2, 2) + + +# Test class method from_config +def test_albumentations_augmenter_from_config(augmentation_config): + augmenter = augmentation.AlbumentationsAugmenter.from_config(augmentation_config) + assert isinstance(augmenter, augmentation.AlbumentationsAugmenter) + assert augmenter.image_key == "image" + assert augmenter.instances_key == "instances" + + def test_augmentation(min_labels): labels_reader = providers.LabelsReader.from_user_instances(min_labels) ds = labels_reader.make_dataset() example_preaug = next(iter(ds)) - augmenter = augmentation.ImgaugAugmenter.from_config( + augmenter = augmentation.AlbumentationsAugmenter.from_config( augmentation.AugmentationConfig( - rotate=True, rotation_min_angle=-90, rotation_max_angle=-90 + rotate=True, rotation_min_angle=90, rotation_max_angle=90 ) ) ds = augmenter.transform_dataset(ds) @@ -52,13 +134,39 @@ def test_augmentation_with_no_instances(min_labels): ) p = min_labels.to_pipeline(user_labeled_only=False) - p += augmentation.ImgaugAugmenter.from_config( + p += augmentation.AlbumentationsAugmenter.from_config( augmentation.AugmentationConfig(rotate=True) ) exs = p.run() assert exs[-1]["instances"].shape[0] == 0 +def test_augmentation_edges(min_labels): + # Tests 1722 + height, width = min_labels[0].video.shape[1:3] + min_labels[0].instances.append( + sleap.Instance.from_numpy( + [[0, 0], [width, height]], + skeleton=min_labels.skeleton, + ) + ) + + labels_reader = providers.LabelsReader.from_user_instances(min_labels) + ds = labels_reader.make_dataset() + example_preaug = next(iter(ds)) + + augmenter = augmentation.AlbumentationsAugmenter.from_config( + augmentation.AugmentationConfig( + rotate=True, rotation_min_angle=90, rotation_max_angle=90 + ) + ) + ds = augmenter.transform_dataset(ds) + + example = next(iter(ds)) + # TODO: check for correctness + assert example["instances"].shape == (3, 2, 2) + + def test_random_cropper(min_labels): cropper = augmentation.RandomCropper(crop_height=64, crop_width=32) assert "image" in cropper.input_keys diff --git a/tests/nn/data/test_data_training.py b/tests/nn/data/test_data_training.py index eb79464e0..c90a29365 100644 --- a/tests/nn/data/test_data_training.py +++ b/tests/nn/data/test_data_training.py @@ -3,7 +3,7 @@ from sleap.nn.data.training import split_labels_train_val -sleap.use_cpu_only() # hide GPUs for test +# sleap.use_cpu_only() # hide GPUs for test def test_split_labels_train_val(): diff --git a/tests/nn/data/test_edge_maps.py b/tests/nn/data/test_edge_maps.py index 295360538..5eb13f9b8 100644 --- a/tests/nn/data/test_edge_maps.py +++ b/tests/nn/data/test_edge_maps.py @@ -2,7 +2,7 @@ import tensorflow as tf from sleap.nn.system import use_cpu_only -use_cpu_only() # hide GPUs for test +# use_cpu_only() # hide GPUs for test from sleap.nn.data import providers from sleap.nn.data import edge_maps diff --git a/tests/nn/data/test_identity.py b/tests/nn/data/test_identity.py index 52d25dd1b..224eff0ba 100644 --- a/tests/nn/data/test_identity.py +++ b/tests/nn/data/test_identity.py @@ -10,7 +10,7 @@ ) -sleap.use_cpu_only() +# sleap.use_cpu_only() def test_make_class_vectors(): diff --git a/tests/nn/data/test_instance_centroids.py b/tests/nn/data/test_instance_centroids.py index 78dee251c..2d8f57627 100644 --- a/tests/nn/data/test_instance_centroids.py +++ b/tests/nn/data/test_instance_centroids.py @@ -3,7 +3,7 @@ import tensorflow as tf from sleap.nn.system import use_cpu_only -use_cpu_only() # hide GPUs for test +# use_cpu_only() # hide GPUs for test import sleap from sleap.nn.data import providers diff --git a/tests/nn/data/test_instance_cropping.py b/tests/nn/data/test_instance_cropping.py index b54fb0e99..688f50dbd 100644 --- a/tests/nn/data/test_instance_cropping.py +++ b/tests/nn/data/test_instance_cropping.py @@ -3,7 +3,7 @@ import tensorflow as tf from sleap.nn.system import use_cpu_only -use_cpu_only() # hide GPUs for test +# use_cpu_only() # hide GPUs for test from sleap.nn.data import providers from sleap.nn.data import instance_centroids diff --git a/tests/nn/data/test_normalization.py b/tests/nn/data/test_normalization.py index 20a1df4ec..d2eb7c290 100644 --- a/tests/nn/data/test_normalization.py +++ b/tests/nn/data/test_normalization.py @@ -3,7 +3,7 @@ import tensorflow as tf from sleap.nn.system import use_cpu_only -use_cpu_only() # hide GPUs for test +# use_cpu_only() # hide GPUs for test from sleap.nn.data import normalization from sleap.nn.data import providers diff --git a/tests/nn/data/test_offset_regression.py b/tests/nn/data/test_offset_regression.py index 31e688839..ce63894d6 100644 --- a/tests/nn/data/test_offset_regression.py +++ b/tests/nn/data/test_offset_regression.py @@ -4,7 +4,7 @@ from sleap.nn.data import offset_regression -sleap.use_cpu_only() # hide GPUs for test +# sleap.use_cpu_only() # hide GPUs for test def test_make_offsets(): diff --git a/tests/nn/data/test_pipelines.py b/tests/nn/data/test_pipelines.py index 30b67e13c..7d442c32d 100644 --- a/tests/nn/data/test_pipelines.py +++ b/tests/nn/data/test_pipelines.py @@ -3,7 +3,7 @@ import tensorflow as tf from sleap.nn.system import use_cpu_only -use_cpu_only() # hide GPUs for test +# use_cpu_only() # hide GPUs for test import sleap from sleap.nn.data import pipelines diff --git a/tests/nn/data/test_providers.py b/tests/nn/data/test_providers.py index 279244ea1..f30216e6a 100644 --- a/tests/nn/data/test_providers.py +++ b/tests/nn/data/test_providers.py @@ -2,7 +2,7 @@ import tensorflow as tf from sleap.nn.system import use_cpu_only -use_cpu_only() # hide GPUs for test +# use_cpu_only() # hide GPUs for test import sleap from sleap.nn.data import providers diff --git a/tests/nn/data/test_resizing.py b/tests/nn/data/test_resizing.py index 440ca66d0..6ef15c2f1 100644 --- a/tests/nn/data/test_resizing.py +++ b/tests/nn/data/test_resizing.py @@ -1,14 +1,10 @@ import pytest import numpy as np import tensorflow as tf -from sleap.nn.system import use_cpu_only - -use_cpu_only() # hide GPUs for test - import sleap from sleap.nn.system import use_cpu_only -use_cpu_only() # hide GPUs for test +# use_cpu_only() # hide GPUs for test from sleap.nn.data import resizing from sleap.nn.data import providers from sleap.nn.data.resizing import SizeMatcher diff --git a/tests/nn/data/test_utils.py b/tests/nn/data/test_utils.py index 213e357e8..7fa98a57a 100644 --- a/tests/nn/data/test_utils.py +++ b/tests/nn/data/test_utils.py @@ -2,7 +2,7 @@ import tensorflow as tf from sleap.nn.system import use_cpu_only -use_cpu_only() # hide GPUs for test +# use_cpu_only() # hide GPUs for test from sleap.nn.data import utils diff --git a/tests/nn/test_evals.py b/tests/nn/test_evals.py index 265994056..48f0d69f8 100644 --- a/tests/nn/test_evals.py +++ b/tests/nn/test_evals.py @@ -20,7 +20,7 @@ from sleap.nn.model import Model -sleap.use_cpu_only() +# sleap.use_cpu_only() def test_compute_oks(): diff --git a/tests/nn/test_heads.py b/tests/nn/test_heads.py index 02fbc2737..a4acbb15f 100644 --- a/tests/nn/test_heads.py +++ b/tests/nn/test_heads.py @@ -21,7 +21,7 @@ ) -sleap.use_cpu_only() +# sleap.use_cpu_only() def test_single_instance_confmaps_head(): diff --git a/tests/nn/test_inference.py b/tests/nn/test_inference.py index fe848bb1c..0a978de0a 100644 --- a/tests/nn/test_inference.py +++ b/tests/nn/test_inference.py @@ -3,12 +3,15 @@ import zipfile from pathlib import Path from typing import cast +import shutil +import csv import numpy as np import pytest +import pandas as pd import tensorflow as tf -import tensorflow_hub as hub from numpy.testing import assert_array_equal, assert_allclose +from sleap.io.video import available_video_exts import sleap from sleap.gui.learning import runners @@ -50,6 +53,7 @@ _make_tracker_from_cli, main as sleap_track, export_cli as sleap_export, + _make_export_cli_parser, ) from sleap.nn.tracking import ( MatchedFrameInstance, @@ -60,7 +64,7 @@ from sleap.instance import Track -sleap.nn.system.use_cpu_only() +# sleap.nn.system.use_cpu_only() @pytest.fixture @@ -925,7 +929,7 @@ def test_load_model(resize_input_shape, model_fixture_name, request): predictor = load_model(model_path, resize_input_layer=resize_input_shape) # Determine predictor type - for (fname, mname, ptype, ishape) in fname_mname_ptype_ishape: + for fname, mname, ptype, ishape in fname_mname_ptype_ishape: if fname in model_fixture_name: expected_model_name = mname expected_predictor_type = ptype @@ -966,7 +970,6 @@ def test_topdown_multi_size_inference( def test_ensure_numpy( min_centroid_model_path, min_centered_instance_model_path, min_labels_slp ): - model = load_model([min_centroid_model_path, min_centered_instance_model_path]) # each frame has same number of instances @@ -1037,7 +1040,6 @@ def test_ensure_numpy( def test_centroid_inference(): - xv, yv = make_grid_vectors(image_height=12, image_width=12, output_stride=1) points = tf.cast([[[1.75, 2.75]], [[3.75, 4.75]], [[5.75, 6.75]]], tf.float32) cms = tf.expand_dims(make_multi_confmaps(points, xv, yv, sigma=1.5), axis=0) @@ -1093,7 +1095,6 @@ def test_centroid_inference(): def export_frozen_graph(model, preds, output_path): - tensors = {} for key, val in preds.items(): @@ -1120,7 +1121,6 @@ def export_frozen_graph(model, preds, output_path): info = json.load(json_file) for tensor_info in info["frozen_model_inputs"] + info["frozen_model_outputs"]: - saved_name = ( tensor_info.split("Tensor(")[1].split(", shape")[0].replace('"', "") ) @@ -1137,7 +1137,6 @@ def export_frozen_graph(model, preds, output_path): def test_single_instance_save(min_single_instance_robot_model_path, tmp_path): - single_instance_model = tf.keras.models.load_model( min_single_instance_robot_model_path + "/best_model.h5", compile=False ) @@ -1152,7 +1151,6 @@ def test_single_instance_save(min_single_instance_robot_model_path, tmp_path): def test_centroid_save(min_centroid_model_path, tmp_path): - centroid_model = tf.keras.models.load_model( min_centroid_model_path + "/best_model.h5", compile=False ) @@ -1171,7 +1169,6 @@ def test_centroid_save(min_centroid_model_path, tmp_path): def test_topdown_save( min_centroid_model_path, min_centered_instance_model_path, min_labels_slp, tmp_path ): - centroid_model = tf.keras.models.load_model( min_centroid_model_path + "/best_model.h5", compile=False ) @@ -1195,7 +1192,6 @@ def test_topdown_save( def test_topdown_id_save( min_centroid_model_path, min_topdown_multiclass_model_path, min_labels_slp, tmp_path ): - centroid_model = tf.keras.models.load_model( min_centroid_model_path + "/best_model.h5", compile=False ) @@ -1217,7 +1213,6 @@ def test_topdown_id_save( def test_single_instance_predictor_save(min_single_instance_robot_model_path, tmp_path): - # directly initialize predictor predictor = SingleInstancePredictor.from_trained_models( min_single_instance_robot_model_path, resize_input_layer=False @@ -1254,10 +1249,33 @@ def test_single_instance_predictor_save(min_single_instance_robot_model_path, tm ) +def test_make_export_cli(): + models_path = r"psuedo/models/path" + export_path = r"psuedo/test/path" + max_instances = 5 + + parser = _make_export_cli_parser() + + # Test default values + args = None + args, _ = parser.parse_known_args(args=args) + assert args.models is None + assert args.export_path == "exported_model" + assert not args.ragged + assert args.max_instances is None + + # Test all arguments + cmd = f"-m {models_path} -e {export_path} -r -n {max_instances}" + args, _ = parser.parse_known_args(args=cmd.split()) + assert args.models == [models_path] + assert args.export_path == export_path + assert args.ragged + assert args.max_instances == max_instances + + def test_topdown_predictor_save( min_centroid_model_path, min_centered_instance_model_path, tmp_path ): - # directly initialize predictor predictor = TopDownPredictor.from_trained_models( centroid_model_path=min_centroid_model_path, @@ -1300,7 +1318,6 @@ def test_topdown_predictor_save( def test_topdown_id_predictor_save( min_centroid_model_path, min_topdown_multiclass_model_path, tmp_path ): - # directly initialize predictor predictor = TopDownMultiClassPredictor.from_trained_models( centroid_model_path=min_centroid_model_path, @@ -1358,7 +1375,7 @@ def test_retracking( # Create sleap-track command cmd = ( f"{slp_path} --tracking.tracker {tracker_method} --video.index 0 --frames 1-3 " - "--cpu" + "--tracking.similarity object_keypoint --cpu" ) if tracker_method == "flow": cmd += " --tracking.save_shifted_instances 1" @@ -1378,6 +1395,8 @@ def test_retracking( parser = _make_cli_parser() args, _ = parser.parse_known_args(args=args) tracker = _make_tracker_from_cli(args) + # Additional check for similarity method + assert tracker.similarity_function.__name__ == "object_keypoint_similarity" output_path = f"{slp_path}.{tracker.get_name()}.slp" # Assert tracked predictions file exists @@ -1433,7 +1452,49 @@ def test_make_predictor_from_cli( assert predictor.max_instances == 5 -def test_sleap_track( +def test_make_predictor_from_cli_mult_input( + centered_pair_predictions: Labels, + min_centroid_model_path: str, + min_centered_instance_model_path: str, + min_bottomup_model_path: str, + tmpdir, +): + slp_path = tmpdir.mkdir("slp_directory") + + slp_file = slp_path / "old_slp.slp" + Labels.save(centered_pair_predictions, slp_file) + + # Copy and paste the video into the temp dir multiple times + num_copies = 3 + for i in range(num_copies): + # Construct the destination path with a unique name for the video + + # Construct the destination path with a unique name for the SLP file + slp_dest_path = slp_path / f"old_slp_copy_{i}.slp" + shutil.copy(slp_file, slp_dest_path) + + # Create sleap-track command + model_args = [ + f"--model {min_centroid_model_path} --model {min_centered_instance_model_path}", + f"--model {min_bottomup_model_path}", + ] + for model_arg in model_args: + args = ( + f"{slp_path} {model_arg} --video.index 0 --frames 1-3 " + "--cpu --max_instances 5" + ).split() + parser = _make_cli_parser() + args, _ = parser.parse_known_args(args=args) + + # Create predictor + predictor = _make_predictor_from_cli(args=args) + if isinstance(predictor, TopDownPredictor): + assert predictor.inference_model.centroid_crop.max_instances == 5 + elif isinstance(predictor, BottomUpPredictor): + assert predictor.max_instances == 5 + + +def test_sleap_track_single_input( centered_pair_predictions: Labels, min_centroid_model_path: str, min_centered_instance_model_path: str, @@ -1452,7 +1513,7 @@ def test_sleap_track( sleap_track(args=args) # Assert predictions file exists - output_path = f"{slp_path}.predictions.slp" + output_path = Path(slp_path).with_suffix(".predictions.slp") assert Path(output_path).exists() # Create invalid sleap-track command @@ -1461,9 +1522,398 @@ def test_sleap_track( sleap_track(args=args) -def test_flow_tracker(centered_pair_predictions: Labels, tmpdir): +@pytest.mark.parametrize("tracking", ["simple", "flow", "None"]) +def test_sleap_track_mult_input_slp( + min_centroid_model_path: str, + min_centered_instance_model_path: str, + tmpdir, + centered_pair_predictions: Labels, + tracking, +): + # Create temporary directory with the structured video files + slp_path = tmpdir.mkdir("slp_directory") + + slp_file = slp_path / "old_slp.slp" + Labels.save(centered_pair_predictions, slp_file) + + slp_path_obj = Path(slp_path) + + # Copy and paste the video into the temp dir multiple times + num_copies = 3 + for i in range(num_copies): + # Construct the destination path with a unique name for the SLP file + slp_dest_path = slp_path / f"old_slp_copy_{i}.slp" + shutil.copy(slp_file, slp_dest_path) + + # Create sleap-track command + args = ( + f"{slp_path} --model {min_centroid_model_path} " + f"--tracking.tracker {tracking} " + f"--model {min_centered_instance_model_path} --video.index 0 --frames 1-3 --cpu" + ).split() + + slp_path_list = [file for file in slp_path_obj.iterdir() if file.is_file()] + + # Run inference + sleap_track(args=args) + + # Assert predictions file exists + expected_extensions = available_video_exts() + + for file_path in slp_path_list: + if file_path.suffix in expected_extensions: + expected_output_file = Path(file_path).with_suffix(".predictions.slp") + assert Path(expected_output_file).exists() + + +@pytest.mark.parametrize("tracking", ["simple", "flow", "None"]) +def test_sleap_track_mult_input_slp_mp4( + min_centroid_model_path: str, + min_centered_instance_model_path: str, + centered_pair_vid_path, + tracking, + tmpdir, + centered_pair_predictions: Labels, +): + # Create temporary directory with the structured video files + slp_path = tmpdir.mkdir("slp_mp4_directory") + + slp_file = slp_path / "old_slp.slp" + Labels.save(centered_pair_predictions, slp_file) + + # Copy and paste the video into temp dir multiple times + num_copies = 3 + for i in range(num_copies): + # Construct the destination path with a unique name + dest_path = slp_path / f"centered_pair_vid_copy_{i}.mp4" + shutil.copy(centered_pair_vid_path, dest_path) + + slp_path_obj = Path(slp_path) + + # Create sleap-track command + args = ( + f"{slp_path} --model {min_centroid_model_path} " + f"--tracking.tracker {tracking} " + f"--model {min_centered_instance_model_path} --video.index 0 --frames 1-3 --cpu" + ).split() + + slp_path_list = [file for file in slp_path_obj.iterdir() if file.is_file()] + + # Run inference + sleap_track(args=args) + + expected_extensions = available_video_exts() + + for file_path in slp_path_list: + if file_path.suffix in expected_extensions: + expected_output_file = Path(file_path).with_suffix(".predictions.slp") + assert Path(expected_output_file).exists() + + +@pytest.mark.parametrize("tracking", ["simple", "flow", "None"]) +def test_sleap_track_mult_input_mp4( + min_centroid_model_path: str, + min_centered_instance_model_path: str, + centered_pair_vid_path, + tracking, + tmpdir, +): + + # Create temporary directory with the structured video files + slp_path = tmpdir.mkdir("mp4_directory") + + # Copy and paste the video into the temp dir multiple times + num_copies = 3 + for i in range(num_copies): + # Construct the destination path with a unique name + dest_path = slp_path / f"centered_pair_vid_copy_{i}.mp4" + shutil.copy(centered_pair_vid_path, dest_path) + + slp_path_obj = Path(slp_path) + + # Create sleap-track command + args = ( + f"{slp_path} --model {min_centroid_model_path} " + f"--tracking.tracker {tracking} " + f"--model {min_centered_instance_model_path} --video.index 0 --frames 1-3 --cpu" + ).split() + + slp_path_list = [file for file in slp_path_obj.iterdir() if file.is_file()] + + # Run inference + sleap_track(args=args) + + # Assert predictions file exists + expected_extensions = available_video_exts() + + for file_path in slp_path_list: + if file_path.suffix in expected_extensions: + expected_output_file = Path(file_path).with_suffix(".predictions.slp") + assert Path(expected_output_file).exists() + + +def test_sleap_track_output_mult( + min_centroid_model_path: str, + min_centered_instance_model_path: str, + centered_pair_vid_path, + tmpdir, +): + + output_path = tmpdir.mkdir("output_directory") + output_path_obj = Path(output_path) + + # Create temporary directory with the structured video files + slp_path = tmpdir.mkdir("mp4_directory") + + # Copy and paste the video into the temp dir multiple times + num_copies = 3 + for i in range(num_copies): + # Construct the destination path with a unique name + dest_path = slp_path / f"centered_pair_vid_copy_{i}.mp4" + shutil.copy(centered_pair_vid_path, dest_path) + + slp_path_obj = Path(slp_path) + + # Create sleap-track command + args = ( + f"{slp_path} --model {min_centroid_model_path} " + f"--tracking.tracker simple " + f"-o {output_path} " + f"--model {min_centered_instance_model_path} --video.index 0 --frames 1-3 --cpu" + ).split() + + slp_path_list = [file for file in slp_path_obj.iterdir() if file.is_file()] + + # Run inference + sleap_track(args=args) + slp_path = Path(slp_path) + + # Check if there are any files in the directory + expected_extensions = available_video_exts() + + for file_path in slp_path_list: + if file_path.suffix in expected_extensions: + expected_output_file = output_path_obj / ( + file_path.stem + ".predictions.slp" + ) + assert Path(expected_output_file).exists() + + +def test_sleap_track_invalid_output( + min_centroid_model_path: str, + min_centered_instance_model_path: str, + centered_pair_vid_path, + centered_pair_predictions: Labels, + tmpdir, +): + + output_path = Path(tmpdir, "output_file.slp").as_posix() + Labels.save(centered_pair_predictions, output_path) + + # Create temporary directory with the structured video files + slp_path = tmpdir.mkdir("mp4_directory") + + # Copy and paste the video into the temp dir multiple times + num_copies = 3 + for i in range(num_copies): + # Construct the destination path with a unique name + dest_path = slp_path / f"centered_pair_vid_copy_{i}.mp4" + shutil.copy(centered_pair_vid_path, dest_path) + + # Create sleap-track command + args = ( + f"{slp_path} --model {min_centroid_model_path} " + f"--tracking.tracker simple " + f"-o {output_path} " + f"--model {min_centered_instance_model_path} --video.index 0 --frames 1-3 --cpu" + ).split() + + # Run inference + with pytest.raises(ValueError): + sleap_track(args=args) + + +def test_sleap_track_invalid_input( + min_centroid_model_path: str, + min_centered_instance_model_path: str, +): + + slp_path = "" + + # Create sleap-track command + args = ( + f"{slp_path} --model {min_centroid_model_path} " + f"--tracking.tracker simple " + f"--model {min_centered_instance_model_path} --video.index 0 --frames 1-3 --cpu" + ).split() + + # Run inference + with pytest.raises(ValueError): + sleap_track(args=args) + + # Test with a non-existent path + slp_path = "/path/to/nonexistent/file.mp4" + + # Create sleap-track command for non-existent path + args = ( + f"{slp_path} --model {min_centroid_model_path} " + f"--tracking.tracker simple " + f"--model {min_centered_instance_model_path} --video.index 0 --frames 1-3 --cpu" + ).split() + + # Run inference and expect a ValueError for non-existent path + with pytest.raises(ValueError): + sleap_track(args=args) + + +def test_sleap_track_csv_input( + min_centroid_model_path: str, + min_centered_instance_model_path: str, + centered_pair_vid_path, + tmpdir, +): + + # Create temporary directory with the structured video files + slp_path = Path(tmpdir.mkdir("mp4_directory")) + + # Copy and paste the video into the temp dir multiple times + num_copies = 3 + file_paths = [] + for i in range(num_copies): + # Construct the destination path with a unique name + dest_path = slp_path / f"centered_pair_vid_copy_{i}.mp4" + shutil.copy(centered_pair_vid_path, dest_path) + file_paths.append(dest_path) + + # Generate output paths for each data_path + output_paths = [ + file_path.with_suffix(".TESTpredictions.slp") for file_path in file_paths + ] + + # Create a CSV file with the file paths + csv_file_path = slp_path / "file_paths.csv" + with open(csv_file_path, mode="w", newline="") as csv_file: + csv_writer = csv.writer(csv_file) + csv_writer.writerow(["data_path", "output_path"]) + for data_path, output_path in zip(file_paths, output_paths): + csv_writer.writerow([data_path, output_path]) + + slp_path_obj = Path(slp_path) + + # Create sleap-track command + args = ( + f"{csv_file_path} --model {min_centroid_model_path} " + f"--tracking.tracker simple " + f"--model {min_centered_instance_model_path} --video.index 0 --frames 1-3 --cpu" + ).split() + + slp_path_list = [file for file in slp_path_obj.iterdir() if file.is_file()] + + # Run inference + sleap_track(args=args) + + # Assert predictions file exists + expected_extensions = available_video_exts() + + for file_path in slp_path_list: + if file_path.suffix in expected_extensions: + expected_output_file = file_path.with_suffix(".TESTpredictions.slp") + assert Path(expected_output_file).exists() + + +def test_sleap_track_invalid_csv( + min_centroid_model_path: str, + min_centered_instance_model_path: str, + tmpdir, +): + + # Create a CSV file with nonexistant data files + csv_nonexistant_files_path = tmpdir / "nonexistant_files.csv" + df_nonexistant_files = pd.DataFrame( + {"data_path": ["video1.mp4", "video2.mp4", "video3.mp4"]} + ) + df_nonexistant_files.to_csv(csv_nonexistant_files_path, index=False) + + # Create an empty CSV file + csv_empty_path = tmpdir / "empty.csv" + open(csv_empty_path, "w").close() + + # Create sleap-track command for missing 'data_path' column + args_missing_column = ( + f"{csv_nonexistant_files_path} --model {min_centroid_model_path} " + f"--tracking.tracker simple " + f"--model {min_centered_instance_model_path} --video.index 0 --frames 1-3 --cpu" + ).split() + + # Run inference and expect ValueError for missing 'data_path' column + with pytest.raises( + ValueError, + ): + sleap_track(args=args_missing_column) + + # Create sleap-track command for empty CSV file + args_empty = ( + f"{csv_empty_path} --model {min_centroid_model_path} " + f"--tracking.tracker simple " + f"--model {min_centered_instance_model_path} --video.index 0 --frames 1-3 --cpu" + ).split() + + # Run inference and expect ValueError for empty CSV file + with pytest.raises(ValueError): + sleap_track(args=args_empty) + + +def test_sleap_track_text_file_input( + min_centroid_model_path: str, + min_centered_instance_model_path: str, + centered_pair_vid_path, + tmpdir, +): + + # Create temporary directory with the structured video files + slp_path = Path(tmpdir.mkdir("mp4_directory")) + + # Copy and paste the video into the temp dir multiple times + num_copies = 3 + file_paths = [] + for i in range(num_copies): + # Construct the destination path with a unique name + dest_path = slp_path / f"centered_pair_vid_copy_{i}.mp4" + shutil.copy(centered_pair_vid_path, dest_path) + file_paths.append(dest_path) + + # Create a text file with the file paths + txt_file_path = slp_path / "file_paths.txt" + with open(txt_file_path, mode="w") as txt_file: + for file_path in file_paths: + txt_file.write(f"{file_path}\n") + + slp_path_obj = Path(slp_path) + + # Create sleap-track command + args = ( + f"{txt_file_path} --model {min_centroid_model_path} " + f"--tracking.tracker simple " + f"--model {min_centered_instance_model_path} --video.index 0 --frames 1-3 --cpu" + ).split() + + slp_path_list = [file for file in slp_path_obj.iterdir() if file.is_file()] + + # Run inference + sleap_track(args=args) + + # Assert predictions file exists + expected_extensions = available_video_exts() + + for file_path in slp_path_list: + if file_path.suffix in expected_extensions: + expected_output_file = Path(file_path).with_suffix(".predictions.slp") + assert Path(expected_output_file).exists() + + +def test_flow_tracker(centered_pair_predictions_sorted: Labels, tmpdir): """Test flow tracker instances are pruned.""" - labels: Labels = centered_pair_predictions + labels: Labels = centered_pair_predictions_sorted track_window = 5 # Setup tracker @@ -1473,17 +1923,20 @@ def test_flow_tracker(centered_pair_predictions: Labels, tmpdir): tracker.candidate_maker = cast(FlowCandidateMaker, tracker.candidate_maker) # Run tracking - frames = sorted(labels.labeled_frames, key=lambda lf: lf.frame_idx) + frames = labels.labeled_frames # Run tracking on subset of frames using psuedo-implementation of # sleap.nn.tracking.run_tracker for lf in frames[:20]: - # Clear the tracks for inst in lf.instances: inst.track = None - track_args = dict(untracked_instances=lf.instances, img=lf.video[lf.frame_idx]) + track_args = dict( + untracked_instances=lf.instances, + img=lf.video[lf.frame_idx], + img_hw=lf.image.shape[-3:-1], + ) tracker.track(**track_args) # Check that saved instances are pruned to track window @@ -1522,12 +1975,15 @@ def test_max_tracks_matching_queue( frames = sorted(labels.labeled_frames, key=lambda lf: lf.frame_idx) for lf in frames[:20]: - # Clear the tracks for inst in lf.instances: inst.track = None - track_args = dict(untracked_instances=lf.instances, img=lf.video[lf.frame_idx]) + track_args = dict( + untracked_instances=lf.instances, + img=lf.video[lf.frame_idx], + img_hw=lf.image.shape[-3:-1], + ) tracker.track(**track_args) if trackername == "flowmaxtracks": @@ -1583,8 +2039,7 @@ def test_movenet_predictor(min_dance_labels, movenet_video): [labels_pr[0][0].numpy(), labels_pr[1][0].numpy()], axis=0 ) - max_diff = np.nanmax(np.abs(points_gt - points_pr)) - assert max_diff < 0.1 + np.testing.assert_allclose(points_gt, points_pr, atol=0.75) @pytest.mark.parametrize( diff --git a/tests/nn/test_inference_identity.py b/tests/nn/test_inference_identity.py index 22be152ea..aaacfef61 100644 --- a/tests/nn/test_inference_identity.py +++ b/tests/nn/test_inference_identity.py @@ -9,7 +9,7 @@ ) -sleap.use_cpu_only() +# sleap.use_cpu_only() def test_group_class_peaks(): diff --git a/tests/nn/test_model.py b/tests/nn/test_model.py index 329e5528f..6c60cb354 100644 --- a/tests/nn/test_model.py +++ b/tests/nn/test_model.py @@ -15,7 +15,7 @@ ModelConfig, ) -sleap.use_cpu_only() +# sleap.use_cpu_only() def test_model_from_config(): diff --git a/tests/nn/test_nn_utils.py b/tests/nn/test_nn_utils.py index 15b9d4bf3..4e8703c05 100644 --- a/tests/nn/test_nn_utils.py +++ b/tests/nn/test_nn_utils.py @@ -6,7 +6,7 @@ from sleap.nn.inference import TopDownPredictor from sleap.nn.utils import tf_linear_sum_assignment, match_points, reset_input_layer -sleap.use_cpu_only() +# sleap.use_cpu_only() def test_tf_linear_sum_assignment(): diff --git a/tests/nn/test_paf_grouping.py b/tests/nn/test_paf_grouping.py index 4856c1fed..d9578bfa9 100644 --- a/tests/nn/test_paf_grouping.py +++ b/tests/nn/test_paf_grouping.py @@ -22,7 +22,7 @@ assign_connections_to_instances, ) -sleap.nn.system.use_cpu_only() +# sleap.nn.system.use_cpu_only() def test_get_connection_candidates(): diff --git a/tests/nn/test_peak_finding.py b/tests/nn/test_peak_finding.py index 93beaa193..243653202 100644 --- a/tests/nn/test_peak_finding.py +++ b/tests/nn/test_peak_finding.py @@ -22,7 +22,7 @@ ) -sleap.nn.system.use_cpu_only() +# sleap.nn.system.use_cpu_only() def test_find_local_offsets(): diff --git a/tests/nn/test_system.py b/tests/nn/test_system.py index fc95bb0ea..7b16f1219 100644 --- a/tests/nn/test_system.py +++ b/tests/nn/test_system.py @@ -4,13 +4,19 @@ be available. """ -from sleap.nn.system import get_gpu_memory -from sleap.nn.system import get_all_gpus +from sleap.nn.system import ( + get_gpu_memory, + get_all_gpus, + use_cpu_only, + use_gpu, + is_gpu_system, +) import os import pytest import subprocess import tensorflow as tf import shutil +import platform def test_get_gpu_memory(): @@ -93,3 +99,17 @@ def test_gpu_device_order(): """Indirectly tests GPU device order by ensuring environment variable is set.""" assert os.environ["CUDA_DEVICE_ORDER"] == "PCI_BUS_ID" + + +@pytest.mark.skipif( + not ("arm64" in platform.platform()), + reason="Only test on macosx-arm64", +) +def test_reinitialize(): + """This test tries to change the devices after they have been initialized.""" + assert is_gpu_system() + use_gpu(0) + tf.zeros((1,)) + tf.ones((1,)) + # The following would normally throw: + # RuntimeError: Visible devices cannot be modified after being initialized + use_cpu_only() diff --git a/tests/nn/test_tracker_components.py b/tests/nn/test_tracker_components.py index f861241ee..0c7ba2b0a 100644 --- a/tests/nn/test_tracker_components.py +++ b/tests/nn/test_tracker_components.py @@ -9,23 +9,82 @@ FrameMatches, greedy_matching, ) +from sleap.io.dataset import Labels from sleap.instance import PredictedInstance from sleap.skeleton import Skeleton +def tracker_by_name(frames=None, **kwargs): + t = Tracker.make_tracker_by_name(**kwargs) + print(kwargs) + print(t.candidate_maker) + if frames is None: + t.track([]) + t.final_pass([]) + return + + for lf in frames: + # Clear the tracks + for inst in lf.instances: + inst.track = None + + track_args = dict(untracked_instances=lf.instances, img=lf.video[lf.frame_idx]) + t.track(**track_args, img_hw=(1, 1)) + t.final_pass(frames) + + @pytest.mark.parametrize( "tracker", ["simple", "flow", "simplemaxtracks", "flowmaxtracks"] ) -@pytest.mark.parametrize("similarity", ["instance", "iou", "centroid"]) +@pytest.mark.parametrize( + "similarity", + ["instance", "normalized_instance", "iou", "centroid", "object_keypoint"], +) @pytest.mark.parametrize("match", ["greedy", "hungarian"]) @pytest.mark.parametrize("count", [0, 2]) -def test_tracker_by_name(tracker, similarity, match, count): - t = Tracker.make_tracker_by_name( - "flow", "instance", "greedy", clean_instance_count=2 +def test_tracker_by_name( + centered_pair_predictions_sorted, + tracker, + similarity, + match, + count, +): + # This is slow, so limit to 5 time points + frames = centered_pair_predictions_sorted[:5] + + tracker_by_name( + frames=frames, + tracker=tracker, + similarity=similarity, + match=match, + max_tracks=count, + ) + + +@pytest.mark.parametrize( + "tracker", ["simple", "flow", "simplemaxtracks", "flowmaxtracks"] +) +@pytest.mark.parametrize("oks_score_weighting", ["True", "False"]) +@pytest.mark.parametrize("oks_normalization", ["all", "ref", "union"]) +def test_oks_tracker_by_name( + centered_pair_predictions_sorted, + tracker, + oks_score_weighting, + oks_normalization, +): + # This is slow, so limit to 5 time points + frames = centered_pair_predictions_sorted[:5] + + tracker_by_name( + frames=frames, + tracker=tracker, + similarity="object_keypoint", + matching="greedy", + oks_score_weighting=oks_score_weighting, + oks_normalization=oks_normalization, + max_tracks=2, ) - t.track([]) - t.final_pass([]) def test_cull_instances(centered_pair_predictions): @@ -232,7 +291,7 @@ def test_max_tracking_large_gap_single_track(): tracked = [] for insts in preds: - tracked_insts = tracker.track(insts) + tracked_insts = tracker.track(insts, img_hw=(1, 1)) tracked.append(tracked_insts) all_tracks = list(set([inst.track for frame in tracked for inst in frame])) @@ -249,7 +308,7 @@ def test_max_tracking_large_gap_single_track(): tracked = [] for insts in preds: - tracked_insts = tracker.track(insts) + tracked_insts = tracker.track(insts, img_hw=(1, 1)) tracked.append(tracked_insts) all_tracks = list(set([inst.track for frame in tracked for inst in frame])) @@ -296,7 +355,7 @@ def test_max_tracking_small_gap_on_both_tracks(): tracked = [] for insts in preds: - tracked_insts = tracker.track(insts) + tracked_insts = tracker.track(insts, img_hw=(1, 1)) tracked.append(tracked_insts) all_tracks = list(set([inst.track for frame in tracked for inst in frame])) @@ -313,7 +372,7 @@ def test_max_tracking_small_gap_on_both_tracks(): tracked = [] for insts in preds: - tracked_insts = tracker.track(insts) + tracked_insts = tracker.track(insts, img_hw=(1, 1)) tracked.append(tracked_insts) all_tracks = list(set([inst.track for frame in tracked for inst in frame])) @@ -365,7 +424,7 @@ def test_max_tracking_extra_detections(): tracked = [] for insts in preds: - tracked_insts = tracker.track(insts) + tracked_insts = tracker.track(insts, img_hw=(1, 1)) tracked.append(tracked_insts) all_tracks = list(set([inst.track for frame in tracked for inst in frame])) @@ -382,7 +441,7 @@ def test_max_tracking_extra_detections(): tracked = [] for insts in preds: - tracked_insts = tracker.track(insts) + tracked_insts = tracker.track(insts, img_hw=(1, 1)) tracked.append(tracked_insts) all_tracks = list(set([inst.track for frame in tracked for inst in frame])) diff --git a/tests/nn/test_tracking_integration.py b/tests/nn/test_tracking_integration.py index a6592dc4d..625302fd0 100644 --- a/tests/nn/test_tracking_integration.py +++ b/tests/nn/test_tracking_integration.py @@ -102,7 +102,7 @@ def run_tracker(frames, tracker): new_lf = LabeledFrame( frame_idx=lf.frame_idx, video=lf.video, - instances=tracker.track(**track_args), + instances=tracker.track(**track_args, img_hw=lf.image.shape[-3:-1]), ) new_lfs.append(new_lf) @@ -138,6 +138,8 @@ def main(f, dir): instance=sleap.nn.tracker.components.instance_similarity, centroid=sleap.nn.tracker.components.centroid_distance, iou=sleap.nn.tracker.components.instance_iou, + normalized_instance=sleap.nn.tracker.components.normalized_instance_similarity, + object_keypoint=sleap.nn.tracker.components.factory_object_keypoint_similarity(), ) scales = ( 1, diff --git a/tests/nn/test_training.py b/tests/nn/test_training.py index 55f404929..72db17bb5 100644 --- a/tests/nn/test_training.py +++ b/tests/nn/test_training.py @@ -25,7 +25,7 @@ create_trainer_using_cli as sleap_train, ) -sleap.use_cpu_only() +# sleap.use_cpu_only() @pytest.fixture @@ -44,7 +44,7 @@ def cfg(): cfg = TrainingJobConfig() cfg.data.instance_cropping.center_on_part = "A" cfg.model.backbone.unet = UNetConfig( - max_stride=8, output_stride=1, filters=8, filters_rate=1.0 + max_stride=8, output_stride=1, filters=2, filters_rate=1.0 ) cfg.optimization.preload_data = False cfg.optimization.batch_size = 1 @@ -123,34 +123,61 @@ def test_train_load_single_instance( assert (w == w2).all() -def test_train_single_instance(min_labels_robot, cfg): +def test_train_single_instance(min_labels_robot, cfg, tmp_path): cfg.model.heads.single_instance = SingleInstanceConfmapsHeadConfig( sigma=1.5, output_stride=1, offset_refinement=False ) + + # Set save directory + cfg.outputs.run_name = "test_run" + cfg.outputs.runs_folder = str(tmp_path / "training_runs") # ensure it's a string + cfg.outputs.save_visualizations = True + cfg.outputs.keep_viz_images = True + cfg.outputs.save_outputs = True # enable saving + trainer = SingleInstanceModelTrainer.from_config( cfg, training_labels=min_labels_robot ) trainer.setup() trainer.train() + + run_path = Path(cfg.outputs.runs_folder, cfg.outputs.run_name) + viz_path = run_path / "viz" + assert trainer.keras_model.output_names[0] == "SingleInstanceConfmapsHead" assert tuple(trainer.keras_model.outputs[0].shape) == (None, 320, 560, 2) + assert viz_path.exists() -def test_train_single_instance_with_offset(min_labels_robot, cfg): +def test_train_single_instance_with_offset(min_labels_robot, cfg, tmp_path): cfg.model.heads.single_instance = SingleInstanceConfmapsHeadConfig( sigma=1.5, output_stride=1, offset_refinement=True ) + + # Set save directory + cfg.outputs.run_name = "test_run" + cfg.outputs.runs_folder = str(tmp_path / "training_runs") # ensure it's a string + cfg.outputs.save_visualizations = False + cfg.outputs.keep_viz_images = False + cfg.outputs.save_outputs = True # enable saving + trainer = SingleInstanceModelTrainer.from_config( cfg, training_labels=min_labels_robot ) trainer.setup() trainer.train() + + run_path = Path(cfg.outputs.runs_folder, cfg.outputs.run_name) + viz_path = run_path / "viz" + assert trainer.keras_model.output_names[0] == "SingleInstanceConfmapsHead" assert tuple(trainer.keras_model.outputs[0].shape) == (None, 320, 560, 2) assert trainer.keras_model.output_names[1] == "OffsetRefinementHead" assert tuple(trainer.keras_model.outputs[1].shape) == (None, 320, 560, 4) + assert not viz_path.exists() + def test_train_centroids(training_labels, cfg): cfg.model.heads.centroid = CentroidsHeadConfig( @@ -251,12 +278,12 @@ def test_train_bottomup_with_offset(training_labels, cfg): def test_train_bottomup_multiclass(min_tracks_2node_labels, cfg): labels = min_tracks_2node_labels - cfg.data.preprocessing.input_scaling = 0.5 + cfg.data.preprocessing.input_scaling = 0.25 cfg.model.heads.multi_class_bottomup = sleap.nn.config.MultiClassBottomUpConfig( confmaps=sleap.nn.config.MultiInstanceConfmapsHeadConfig( - output_stride=2, offset_refinement=False + output_stride=4, offset_refinement=False ), - class_maps=sleap.nn.config.ClassMapsHeadConfig(output_stride=2), + class_maps=sleap.nn.config.ClassMapsHeadConfig(output_stride=4), ) trainer = sleap.nn.training.BottomUpMultiClassModelTrainer.from_config( cfg, training_labels=labels @@ -266,8 +293,8 @@ def test_train_bottomup_multiclass(min_tracks_2node_labels, cfg): assert trainer.keras_model.output_names[0] == "MultiInstanceConfmapsHead" assert trainer.keras_model.output_names[1] == "ClassMapsHead" - assert tuple(trainer.keras_model.outputs[0].shape) == (None, 256, 256, 2) - assert tuple(trainer.keras_model.outputs[1].shape) == (None, 256, 256, 2) + assert tuple(trainer.keras_model.outputs[0].shape) == (None, 64, 64, 2) + assert tuple(trainer.keras_model.outputs[1].shape) == (None, 64, 64, 2) def test_train_topdown_multiclass(min_tracks_2node_labels, cfg): @@ -360,3 +387,26 @@ def test_resume_training_cli( trainer = sleap_train(cli_args) assert trainer.config.model.base_checkpoint == base_checkpoint_path + + +@pytest.mark.parametrize("keep_viz_cli", ["", "--keep_viz"]) +def test_keep_viz_cli( + keep_viz_cli, + min_single_instance_robot_model_path: str, + tmp_path: str, +): + """Test training CLI for --keep_viz option.""" + cfg_dir = min_single_instance_robot_model_path + cfg = TrainingJobConfig.load_json(str(Path(cfg_dir, "training_config.json"))) + + # Save training config to tmp folder + cfg_path = str(Path(tmp_path, "training_config.json")) + cfg.save_json(cfg_path) + + cli_args = [cfg_path, keep_viz_cli] + trainer = sleap_train(cli_args) + + # Check that --keep_viz is set correctly + assert trainer.config.outputs.keep_viz_images == ( + True if keep_viz_cli == "--keep_viz" else False + ) diff --git a/tests/test_instance.py b/tests/test_instance.py index 74a8b192e..58a630a8b 100644 --- a/tests/test_instance.py +++ b/tests/test_instance.py @@ -1,19 +1,21 @@ -import os -import math import copy +import math +import os +from typing import List -import pytest import numpy as np +import pytest -from sleap.skeleton import Skeleton +from sleap import Labels from sleap.instance import ( Instance, - PredictedInstance, + InstancesList, + LabeledFrame, Point, + PredictedInstance, PredictedPoint, - LabeledFrame, ) -from sleap import Labels +from sleap.skeleton import Skeleton def test_instance_node_get_set_item(skeleton): @@ -310,6 +312,8 @@ def test_frame_merge_predicted_and_user(skeleton, centered_pair_vid): # and we want to retain both even though they perfectly match. assert user_inst in user_frame.instances assert pred_inst in user_frame.instances + assert user_inst.frame == user_frame + assert pred_inst.frame == user_frame assert len(user_frame.instances) == 2 @@ -529,3 +533,216 @@ def test_instance_structuring_from_predicted(centered_pair_predictions): # Unstructure -> structure labels_copy = labels.copy() + + +def test_instances_list(centered_pair_predictions): + + labels = centered_pair_predictions + + def test_extend(instances: InstancesList, list_of_instances: List[Instance]): + instances.extend(list_of_instances) + assert len(instances) == len(list_of_instances) + for instance in instances: + assert isinstance(instance, PredictedInstance) + if instances.labeled_frame is None: + assert instance.frame is None + else: + assert instance.frame == instances.labeled_frame + + def test_append(instances: InstancesList, instance: Instance): + prev_len = len(instances) + instances.append(instance) + assert len(instances) == prev_len + 1 + assert instances[-1] == instance + assert instance.frame == instances.labeled_frame + + def test_labeled_frame_setter( + instances: InstancesList, labeled_frame: LabeledFrame + ): + instances.labeled_frame = labeled_frame + for instance in instances: + assert instance.frame == labeled_frame + + # Case 1: Create an empty instances list + labeled_frame = labels.labeled_frames[0] + list_of_instances = list(labeled_frame.instances) + instances = InstancesList() + assert len(instances) == 0 + assert instances._labeled_frame is None + assert instances.labeled_frame is None + + # Extend instances list + assert not isinstance(list_of_instances, InstancesList) + assert isinstance(list_of_instances, list) + test_extend(instances, list_of_instances) + + # Set the labeled frame + test_labeled_frame_setter(instances, labeled_frame) + + # Case 2: Create an empy instances list but initialize the labeled frame + instances = InstancesList(labeled_frame=labeled_frame) + assert len(instances) == 0 + assert instances._labeled_frame == labeled_frame + assert instances.labeled_frame == labeled_frame + + # Extend instances to the list from a different labeled frame + labeled_frame = labels.labeled_frames[1] + list_of_instances = list(labeled_frame.instances) + test_extend(instances, list_of_instances) + + # Add instance to the list + instance = list_of_instances[0] + instance.frame = None + test_append(instances, instance) + + # Set the labeled frame + test_labeled_frame_setter(instances, labeled_frame) + + # Test InstancesList.copy + instances_copy = instances.copy() + assert len(instances_copy) == len(instances) + assert not isinstance(instances_copy, InstancesList) + assert isinstance(instances_copy, list) + + # Test InstancesList.clear + instances_in_instances = list(instances) + instances.clear() + assert len(instances) == 0 + for instance in instances_in_instances: + assert instance.frame is None + + # Case 3: Create an instances list with a list of instances + labeled_frame = labels.labeled_frames[0] + list_of_instances = list(labeled_frame.instances) + instances = InstancesList(list_of_instances) + assert len(instances) == len(list_of_instances) + assert instances._labeled_frame is None + assert instances.labeled_frame is None + for instance in instances: + assert instance.frame is None + + # Add instance to the list + instance = list_of_instances[0] + test_append(instances, instance) + + # Case 4: Create an instances list with a list of instances and initialize the frame + labeled_frame_1 = labels.labeled_frames[0] + labeled_frame_2 = labels.labeled_frames[1] + list_of_instances = list(labeled_frame_2.instances) + instances = InstancesList(list_of_instances, labeled_frame=labeled_frame_1) + assert len(instances) == len(list_of_instances) + assert instances._labeled_frame == labeled_frame + assert instances.labeled_frame == labeled_frame + for instance in instances: + assert instance.frame == labeled_frame + + # Test InstancesList.__delitem__ + instance_to_remove = instances[0] + del instances[0] + assert instance_to_remove not in instances + assert instance_to_remove.frame is None + + # Test InstancesList.insert + instances.insert(0, instance_to_remove) + assert instances[0] == instance_to_remove + assert instance_to_remove.frame == instances.labeled_frame + + # Test InstancesList.__setitem__ + new_instance = labeled_frame_1.instances[0] + new_instance.frame = None + instances[0] = new_instance + assert instances[0] == new_instance + assert new_instance.frame == instances.labeled_frame + + # Test InstancesList.pop + popped_instance = instances.pop(0) + assert popped_instance.frame is None + + # Test InstancesList.remove + instance_to_remove = instances[0] + instances.remove(instance_to_remove) + assert instance_to_remove.frame is None + assert instance_to_remove not in instances + + # Case 5: Create an instances list from an instances list + instances_1 = InstancesList(list_of_instances, labeled_frame=labeled_frame_1) + instances = InstancesList(instances_1) + assert len(instances) == len(instances_1) + assert instances._labeled_frame is None + assert instances.labeled_frame is None + for instance in instances: + assert instance.frame is None + + +def test_instances_list_with_labeled_frame(centered_pair_predictions): + labels: Labels = centered_pair_predictions + labels_lf_0: LabeledFrame = labels.labeled_frames[0] + video = labels_lf_0.video + frame_idx = labels_lf_0.frame_idx + + def test_post_init(labeled_frame: LabeledFrame): + for instance in labeled_frame.instances: + assert instance.frame == labeled_frame + + # Create labeled frame from list of instances + instances = list(labels_lf_0.instances) + for instance in instances: + instance.frame = None # Change frame to None to test if it is set correctly + labeled_frame = LabeledFrame(video=video, frame_idx=frame_idx, instances=instances) + assert isinstance(labeled_frame.instances, InstancesList) + assert len(labeled_frame.instances) == len(instances) + test_post_init(labeled_frame) + + # Create labeled frame from instances list + instances = InstancesList(labels_lf_0.instances) + labeled_frame = LabeledFrame(video=video, frame_idx=frame_idx, instances=instances) + assert isinstance(labeled_frame.instances, InstancesList) + assert len(labeled_frame.instances) == len(instances) + test_post_init(labeled_frame) + + # Test LabeledFrame.__len__ + assert len(labeled_frame.instances) == len(instances) + + # Test LabeledFrame.__getitem__ + assert labeled_frame[0] == instances[0] + + # Test LabeledFrame.index + assert labeled_frame.index(instances[0]) == instances.index(instances[0]) == 0 + + # Test LabeledFrame.__delitem__ + instance_to_remove = labeled_frame[0] + del labeled_frame[0] + assert instance_to_remove not in labeled_frame.instances + assert instance_to_remove.frame is None + + # Test LabeledFrame.__repr__ + print(labeled_frame) + + # Test LabeledFrame.insert + labeled_frame.insert(0, instance_to_remove) + assert labeled_frame[0] == instance_to_remove + assert instance_to_remove.frame == labeled_frame + + # Test LabeledFrame.__setitem__ + new_instance = instances[1] + new_instance.frame = None + labeled_frame[0] = new_instance + assert labeled_frame[0] == new_instance + assert new_instance.frame == labeled_frame + + # Test instances.setter (empty list) + labeled_frame.instances = [] + assert len(labeled_frame.instances) == 0 + assert labeled_frame.instances.labeled_frame == labeled_frame + # Test instances.setter (InstancesList) + labeled_frame.instances = labels.labeled_frames[1].instances + assert len(labeled_frame.instances) == len(labels.labeled_frames[1].instances) + assert labeled_frame.instances.labeled_frame == labeled_frame + for instance in labeled_frame.instances: + assert instance.frame == labeled_frame + # Test instances.setter (populated list) + labeled_frame.instances = list(labels.labeled_frames[1].instances) + assert len(labeled_frame.instances) == len(labels.labeled_frames[1].instances) + assert labeled_frame.instances.labeled_frame == labeled_frame + for instance in labeled_frame.instances: + assert instance.frame == labeled_frame diff --git a/tests/test_skeleton.py b/tests/test_skeleton.py index 1f7c3a853..2320342f6 100644 --- a/tests/test_skeleton.py +++ b/tests/test_skeleton.py @@ -1,10 +1,74 @@ -import os import copy - -import jsonpickle +import os import pytest +import json + +from networkx.readwrite import json_graph +from sleap.skeleton import Skeleton, SkeletonDecoder +from sleap.skeleton import SkeletonEncoder + + +def test_decoded_encoded_Skeleton_from_load_json(fly_legs_skeleton_json): + """ + Test Skeleton decoded from SkeletonEncoder.encode matches the original Skeleton. + """ + # Get the skeleton from the fixture + skeleton = Skeleton.load_json(fly_legs_skeleton_json) + # Get the graph from the skeleton + indexed_node_graph = skeleton._graph + graph = json_graph.node_link_data(indexed_node_graph) + + # Encode the graph as a json string to test .encode method + encoded_json_str = SkeletonEncoder.encode(graph) + + # Get the skeleton from the encoded json string + decoded_skeleton = Skeleton.from_json(encoded_json_str) + + # Check that the decoded skeleton is the same as the original skeleton + assert skeleton.matches(decoded_skeleton) + + +@pytest.mark.parametrize( + "skeleton_fixture_name", ["flies13_skeleton", "skeleton", "stickman"] +) +def test_decoded_encoded_Skeleton(skeleton_fixture_name, request): + """ + Test Skeleton decoded from SkeletonEncoder.encode matches the original Skeleton. + """ + # Use request.getfixturevalue to get the actual fixture value by name + skeleton = request.getfixturevalue(skeleton_fixture_name) + + # Get the graph from the skeleton + indexed_node_graph = skeleton._graph + graph = json_graph.node_link_data(indexed_node_graph) + + # Encode the graph as a json string to test .encode method + encoded_json_str = SkeletonEncoder.encode(graph) + + # Assert that the encoded json has keys in sorted order (backwards compatibility) + encoded_dict = json.loads(encoded_json_str) + sorted_keys = sorted(encoded_dict.keys()) + assert list(encoded_dict.keys()) == sorted_keys + for key, value in encoded_dict.items(): + if isinstance(value, dict): + assert list(value.keys()) == sorted(value.keys()) + elif isinstance(value, list): + for item in value: + if isinstance(item, dict): + assert list(item.keys()) == sorted(item.keys()) + + # Get the skeleton from the encoded json string + decoded_skeleton = Skeleton.from_json(encoded_json_str) + + # Check that the decoded skeleton is the same as the original skeleton + assert skeleton.matches(decoded_skeleton) + + # Now make everything into a JSON string + skeleton_json_str = skeleton.to_json() + decoded_skeleton_json_str = decoded_skeleton.to_json() -from sleap.skeleton import Skeleton + # Check that the JSON strings are the same + assert json.loads(skeleton_json_str) == json.loads(decoded_skeleton_json_str) def test_add_dupe_node(skeleton): @@ -194,9 +258,9 @@ def test_json(skeleton: Skeleton, tmpdir): ) assert skeleton.is_template == False json_str = skeleton.to_json() - json_dict = jsonpickle.decode(json_str) + json_dict = SkeletonDecoder.decode(json_str) json_dict_keys = list(json_dict.keys()) - assert "nx_graph" not in json_dict_keys + assert "nx_graph" in json_dict_keys # SkeletonDecoder adds this key assert "preview_image" not in json_dict_keys assert "description" not in json_dict_keys @@ -208,7 +272,7 @@ def test_json(skeleton: Skeleton, tmpdir): skeleton._is_template = True json_str = skeleton.to_json() - json_dict = jsonpickle.decode(json_str) + json_dict = SkeletonDecoder.decode(json_str) json_dict_keys = list(json_dict.keys()) assert "nx_graph" in json_dict_keys assert "preview_image" in json_dict_keys @@ -224,6 +288,26 @@ def test_json(skeleton: Skeleton, tmpdir): assert skeleton.matches(skeleton_copy) +def test_decode_preview_image(flies13_skeleton: Skeleton): + skeleton = flies13_skeleton + img_b64 = skeleton.preview_image + img = SkeletonDecoder.decode_preview_image(img_b64) + assert img.mode == "RGBA" + + +def test_skeleton_decoder(fly_legs_skeleton_json, fly_legs_skeleton_dict_json): + """Test that SkeletonDecoder can decode both tuple and dict py/state formats.""" + + skeleton_tuple_pystate = Skeleton.load_json(fly_legs_skeleton_json) + assert isinstance(skeleton_tuple_pystate, Skeleton) + + skeleton_dict_pystate = Skeleton.load_json(fly_legs_skeleton_dict_json) + assert isinstance(skeleton_dict_pystate, Skeleton) + + # These are the same skeleton, so they should match + assert skeleton_dict_pystate.matches(skeleton_tuple_pystate) + + def test_hdf5(skeleton, stickman, tmpdir): filename = os.path.join(tmpdir, "skeleton.h5") diff --git a/tests/test_util.py b/tests/test_util.py index a7916d47f..35b41afa8 100644 --- a/tests/test_util.py +++ b/tests/test_util.py @@ -1,5 +1,4 @@ import pytest -from sleap.skeleton import Skeleton from sleap.util import * @@ -147,10 +146,3 @@ def test_save_dict_to_hdf5(tmpdir): assert f["bar"][-1].decode() == "zop" assert f["cab"]["a"][()] == 2 - - -def test_decode_preview_image(flies13_skeleton: Skeleton): - skeleton = flies13_skeleton - img_b64 = skeleton.preview_image - img = decode_preview_image(img_b64) - assert img.mode == "RGBA"