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b/webknossos/Changelog.md @@ -17,6 +17,7 @@ For upgrade instructions, please check the respective _Breaking Changes_ section ### Added ### Changed +- Performance improvements for reading from and writing to sharded zarr3 datasets, also speeding up the automated tests [#963](https://github.com/scalableminds/webknossos-libs/pull/963) ### Fixed diff --git a/webknossos/poetry.lock b/webknossos/poetry.lock index dc0115c9f..dac24d19b 100644 --- a/webknossos/poetry.lock +++ b/webknossos/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. 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optional = true } pylibCZIrw = { version = "3.5.1", source = "scm", optional = true } +pytest-timestamper = "^0.0.9" [tool.poetry.extras] pims = ["pims"] diff --git a/webknossos/tests/dataset/test_dataset.py b/webknossos/tests/dataset/test_dataset.py index e4c586ad9..6da4b534c 100644 --- a/webknossos/tests/dataset/test_dataset.py +++ b/webknossos/tests/dataset/test_dataset.py @@ -2571,7 +2571,14 @@ def test_aligned_downsampling(data_format: DataFormat, output_path: Path) -> Non num_channels=3, data_format=input_layer.data_format, ) - test_mag = test_layer.add_mag("1") + + chunks_per_shard = None + if data_format == DataFormat.Zarr3: + # Writing compressed zarr with large shard shape is slow + # compare https://github.com/scalableminds/webknossos-libs/issues/964 + chunks_per_shard = (4, 4, 4) + + test_mag = test_layer.add_mag("1", chunks_per_shard=chunks_per_shard) test_mag.write( absolute_offset=(0, 0, 0), # assuming the layer has 3 channels: @@ -2601,8 +2608,15 @@ def test_guided_downsampling(data_format: DataFormat, output_path: Path) -> None input_dataset = Dataset.open(ds_path) input_layer = input_dataset.get_layer("color") + + chunks_per_shard = None + if data_format == DataFormat.Zarr3: + # Writing compressed zarr with large shard shape is slow + # compare https://github.com/scalableminds/webknossos-libs/issues/964 + chunks_per_shard = (4, 4, 4) + # Adding additional mags to the input dataset for testing - input_layer.get_or_add_mag("2-2-1") + input_layer.add_mag("2-2-1", chunks_per_shard=chunks_per_shard) input_layer.redownsample() assert len(input_layer.mags) == 2 # Use the mag with the best resolution @@ -2619,7 +2633,9 @@ def test_guided_downsampling(data_format: DataFormat, output_path: Path) -> None data_format=input_layer.data_format, ) # Create the same mag in the new output dataset - output_mag = output_layer.add_mag(finest_input_mag.mag) + output_mag = output_layer.add_mag( + finest_input_mag.mag, chunks_per_shard=chunks_per_shard + ) # Copying some data into the output dataset input_data = finest_input_mag.read(absolute_offset=(0, 0, 0), size=(24, 24, 24)) output_mag.write(absolute_offset=(0, 0, 0), data=input_data) diff --git a/webknossos/webknossos/dataset/_array.py b/webknossos/webknossos/dataset/_array.py index a3c3c64eb..97eaa940a 100644 --- a/webknossos/webknossos/dataset/_array.py +++ b/webknossos/webknossos/dataset/_array.py @@ -346,10 +346,9 @@ def read(self, offset: Vec3IntLike, shape: Vec3IntLike) -> np.ndarray: offset.y : (offset.y + shape.y), offset.z : (offset.z + shape.z), ] - if data.shape != shape: - padded_data = np.zeros( - (self.info.num_channels,) + shape.to_tuple(), dtype=data.dtype - ) + shape_with_channels = (self.info.num_channels,) + shape.to_tuple() + if data.shape != shape and data.shape != shape_with_channels: + padded_data = np.zeros(shape_with_channels, dtype=data.dtype) padded_data[ :, 0 : data.shape[1], @@ -591,10 +590,9 @@ def read(self, offset: Vec3IntLike, shape: Vec3IntLike) -> np.ndarray: offset.y : (offset.y + shape.y), offset.z : (offset.z + shape.z), ] - if data.shape != shape: - padded_data = np.zeros( - (self.info.num_channels,) + shape.to_tuple(), dtype=data.dtype - ) + shape_with_channels = (self.info.num_channels,) + shape.to_tuple() + if data.shape != shape and data.shape != shape_with_channels: + padded_data = np.zeros(shape_with_channels, dtype=data.dtype) padded_data[ :, 0 : data.shape[1],