Merge NN (tf2.1) #330
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Merge NN (tf2.1) #330
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- Agnostic to centroid calculation method. - Propagates all frame example metadata to individual instance examples. - Tests for coordinate transformations correctness, especially for tricky bounding box and cropping calculations.
- Low level confidence map generation functions - Multi-instance confidence map generator - Multi-instance centroid confidence map generator - Instance-cropped center instance confidence map generator - Instance-cropped all instance confidence map generator - Tests
- Useful for downstream processing that affects shape.
- "Full" to indicate it refers to the input to the instance cropper, which may have been modified relative to the "raw" image, e.g., due to padding/scaling. - Rename attribute `drop_full_image` to `keep_full_image`
- The general expectation is that points in individual examples are already on the image grid scale. - The "scale" key can be used to track scaling operations, including aspect ratio changes. This information is sufficient to map points on example images/confidence maps to the raw images. - Commented scaling behavior across pipeline modules. - Changed behavior of `MultiConfidenceMaps` to compute confidence maps on the current image grid under the assumption that the "instances" key is at the same scale as the "image".
- Documented the normalization modes expected when using different pretrained models from tf.keras.applications. - Still needs tests and a transformer block class.
- Implements edge confidence maps; equivalent of part confidence maps where scalar values are scaled by the distance of the sampling grid points to the line segment formed by edges. - Implement part affinity fields as simple unit vectors weighted by the edge confidence maps for masking.
- Transformers that operate over multiple elements of the data:
- Shuffler
- Batcher (this one is tricky with variable length elements)
- Prefetcher
If the tracking module wants the full image (i.e., flow shift), then we need to save it and pass it along. This wasn't being done correctly for the topdown pipeline. Fixes issue #325.
The dialog allows for deleting user/predicted/all instances in specified frames/videos/tracks. The old "delete from clip" command has been modified to delete all predicted instances in the clip, regardless of whether an instance is selected. Previously it would only delete instances which matched the track of the selected instance if there was one. This was confusiong, so the current change helps address issue #312.
- Allows for evaluation with a single model (e.g., just centroids) when ground truth data is available.
- Allows for exclusion of specific elements in a dataset given a callable that evaluates a conditional on each element. - Fixes #314: bottom-up inference now properly excludes examples that when inference fails to find any local peaks.
- Fixes #322: properly handles case where no centroids are detected.
- Fixes image plotting when doing weird normalization like caffe-mode.
The default backend (Tk) is giving me errors related to multi- processing.
Without a lock we were getting errors when using viz during training. With a non-recursive lock I get a deadlock when starting inference. It appears that everything works with a recursive lock. I don't see any noticable performance cost but we should keep an eye out for this. Fixes issue #321.
- Lots of these will need to be rewritten for the new format, but should be easy enough to port them from the git history later. - There's still a bit of legacy code in nn, but will refactor later.
- Commit 33339cd changed how UNet was built to ensure stride symmetry across encoder vs decoder branches, but this broke the tests. - Inspecting the output shapes closely reveals a discrepancy between the reference and the CARE implementations, so added an attribute to switch off between these. - Fixed pooling when using a stem such that stacked architectures can be correctly instantiated. - Fixed and added more tests.
At least for MacOS X I was getting a "too many files open" error on a dataset with very many videos even if we weren't reading frames from any of the videos.
Types of vertical marks: - thick dark blue line for **suggested** frame with **user** instance, otherwise - thick light blue line for **suggested** frame with **predicted** instance, otherwise - thick black line for frame with **user** instance (whether or not it has track identity), otherwise - *thin* black line for frame with **predicted** instance **without track**, otherwise - unfilled blue rectangle for **suggested** frame without any instances. Plus horizontal bars for instances with track identities. Resolves issue #311.
…pipe-integration
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