Merge pull request #418 from rapidsai/branch-22.10

Author: raydouglass

CHANGELOG.md

@@ -1,3 +1,26 @@
+# cuCIM 22.10.00 (12 Oct 2022)
+
+## 🐛 Bug Fixes
+
+- Correctly use dtype when computing shared memory requirements of separable convolution ([#409](https://github.com/rapidsai/cucim/pull/409)) [@grlee77](https://github.com/grlee77)
+- Forward-merge branch-22.08 to branch-22.10 ([#403](https://github.com/rapidsai/cucim/pull/403)) [@jakirkham](https://github.com/jakirkham)
+- Add missing imports of euler_number and perimeter_crofton ([#386](https://github.com/rapidsai/cucim/pull/386)) [@grlee77](https://github.com/grlee77)
+
+## 📖 Documentation
+
+- update pypi CHANGELOG.md for releases 22.08.00 and 22.08.01 ([#404](https://github.com/rapidsai/cucim/pull/404)) [@grlee77](https://github.com/grlee77)
+- Update README.md ([#396](https://github.com/rapidsai/cucim/pull/396)) [@HesAnEasyCoder](https://github.com/HesAnEasyCoder)
+
+## 🚀 New Features
+
+- Allow cupy 11 ([#399](https://github.com/rapidsai/cucim/pull/399)) [@galipremsagar](https://github.com/galipremsagar)
+- Add cucim.skimage.feature.match_descriptors ([#338](https://github.com/rapidsai/cucim/pull/338)) [@grlee77](https://github.com/grlee77)
+
+## 🛠️ Improvements
+
+- Merge docs and add links ([#415](https://github.com/rapidsai/cucim/pull/415)) [@jakirkham](https://github.com/jakirkham)
+- Add benchmarks for scikit-image functions introduced in 22.08 ([#378](https://github.com/rapidsai/cucim/pull/378)) [@grlee77](https://github.com/grlee77)
+
 # cuCIM 22.08.00 (17 Aug 2022)
 
 ## 🚨 Breaking Changes

README.md

@@ -28,6 +28,8 @@ cuCIM supports the following formats:
 - [GTC 2021 cuCIM: A GPU Image I/O and Processing Toolkit [S32194]](https://www.nvidia.com/en-us/on-demand/search/?facet.mimetype[]=event%20session&layout=list&page=1&q=cucim&sort=date)
   - [video](https://www.nvidia.com/en-us/on-demand/session/gtcspring21-s32194/)
 
+**[Developer Page](https://developer.nvidia.com/multidimensional-image-processing)**
+
 **Blogs**
 - [Enhanced Image Analysis with Multidimensional Image Processing](https://developer.nvidia.com/blog/enhanced-image-analysis-with-multidimensional-image-processing/)
 - [Accelerating Scikit-Image API with cuCIM: n-Dimensional Image Processing and IO on GPUs](https://developer.nvidia.com/blog/cucim-rapid-n-dimensional-image-processing-and-i-o-on-gpus/)
@@ -68,7 +70,7 @@ pip install scipy scikit-image cupy-cuda110
 
 ### Notebooks
 
-Please check out our [Welcome](notebooks/Welcome.ipynb) notebook ([NBViewer](https://nbviewer.jupyter.org/github/rapidsai/cucim/blob/branch-22.08/notebooks/Welcome.ipynb))
+Please check out our [Welcome](notebooks/Welcome.ipynb) notebook ([NBViewer](https://nbviewer.jupyter.org/github/rapidsai/cucim/blob/branch-22.10/notebooks/Welcome.ipynb))
 
 #### Downloading sample images
 
@@ -108,4 +110,4 @@ is used in this project.
 
 Apache-2.0 License (see [LICENSE](LICENSE) file).
 
-Copyright (c) 2020-2021, NVIDIA CORPORATION.
+Copyright (c) 2020-2022, NVIDIA CORPORATION.

VERSION

@@ -1 +1 @@
-22.08.01
+22.10.00

benchmarks/skimage/cucim_morphology_bench.py

@@ -138,6 +138,7 @@ def main(args):
         ("white_tophat", dict(), dict(), False, True),
         ("black_tophat", dict(), dict(), False, True),
         # _skeletonize.py
+        ("medial_axis", dict(random_state=123), dict(return_distance=[False, True]), False, False),
         ("thin", dict(), dict(), False, True),
         # grayreconstruct.py
         ("reconstruction", dict(), dict(), False, True),
@@ -149,7 +150,7 @@ def main(args):
             continue
 
         ndim = len(shape)
-        if function_name == 'thin':
+        if function_name in ['thin', 'medial_axis']:
             if ndim != 2:
                 raise ValueError("only 2d benchmark data has been implemented")
 
@@ -263,7 +264,7 @@ def main(args):
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser(description='Benchmarking cuCIM morphology functions')
-    func_name_choices = ['binary_erosion', 'binary_dilation', 'binary_opening', 'binary_closing', 'remove_small_objects', 'remove_small_holes', 'erosion', 'dilation', 'opening', 'closing', 'white_tophat', 'black_tophat', 'thin', 'reconstruction']
+    func_name_choices = ['binary_erosion', 'binary_dilation', 'binary_opening', 'binary_closing', 'remove_small_objects', 'remove_small_holes', 'erosion', 'dilation', 'opening', 'closing', 'white_tophat', 'black_tophat', 'thin', 'medial_axis', 'reconstruction']
     dtype_choices = ['float16', 'float32', 'float64', 'int8', 'int16', 'int32', 'int64', 'uint8', 'uint16', 'uint32', 'uint64']
     parser.add_argument('-i','--img_size', type=str, help='Size of input image (omit color channel, it will be appended as needed)', required=True)
     parser.add_argument('-d','--dtype', type=str, help='Dtype of input image', choices = dtype_choices, required=True)

benchmarks/skimage/cucim_segmentation_bench.py

@@ -4,9 +4,7 @@
 import pickle
 
 import cucim.skimage
-import cucim.skimage.data
-import cucim.skimage.exposure
-import cucim.skimage.segmentation
+from cucim.skimage import data, exposure, measure, segmentation
 import cupy as cp
 import numpy as np
 import pandas as pd
@@ -21,7 +19,6 @@ def __init__(
         self,
         function_name,
         shape,
-        contiguous_labels=True,
         dtypes=np.float32,
         fixed_kwargs={},
         var_kwargs={},
@@ -31,8 +28,6 @@ def __init__(
         run_cpu=True,
     ):
 
-        self.contiguous_labels = contiguous_labels
-
         super().__init__(
             function_name=function_name,
             shape=shape,
@@ -45,44 +40,33 @@ def __init__(
             run_cpu=run_cpu,
         )
 
-    def set_args(self, dtype):
-        a = np.array(
-            [
-                [0, 0, 1, 1, 0, 0, 0, 0],
-                [0, 0, 0, 1, 0, 0, 4, 0],
-                [2, 2, 0, 0, 3, 0, 4, 4],
-                [0, 0, 0, 0, 0, 5, 0, 0],
-            ],
-            dtype=dtype,
+    def _generate_labels(self, dtype):
+        ndim = len(self.shape)
+        blobs_kwargs = dict(blob_size_fraction=0.05,
+                            volume_fraction=0.35,
+                            seed=5)
+        # binary blobs only creates square outputs
+        labels = measure.label(
+            data.binary_blobs(max(self.shape), n_dim=ndim, **blobs_kwargs)
         )
-        tiling = tuple(s // a_s for s, a_s in zip(self.shape, a.shape))
-        if self.contiguous_labels:
-            labels = np.kron(a, np.ones(tiling, dtype=a.dtype))
-        else:
-            labels = np.tile(a, tiling)
-        labels_d = cp.asarray(labels)
+        print(f"# labels generated = {labels.max()}")
+
+        # crop to rectangular
+        labels = labels[tuple(slice(s) for s in self.shape)]
+        return labels.astype(dtype, copy=False)
+
+    def set_args(self, dtype):
+        labels_d = self._genereate_labels(dtype)
+        labels = cp.asnumpy(labels_d)
         self.args_cpu = (labels,)
         self.args_gpu = (labels_d,)
 
 
 class LabelAndImageBench(LabelBench):
 
     def set_args(self, dtype):
-        a = np.array(
-            [
-                [0, 0, 1, 1, 0, 0, 0, 0],
-                [0, 0, 0, 1, 0, 0, 4, 0],
-                [2, 2, 0, 0, 3, 0, 4, 4],
-                [0, 0, 0, 0, 0, 5, 0, 0],
-            ],
-            dtype=dtype,
-        )
-        tiling = tuple(s // a_s for s, a_s in zip(self.shape, a.shape))
-        if self.contiguous_labels:
-            labels = np.kron(a, np.ones(tiling, dtype=a.dtype))
-        else:
-            labels = np.tile(a, tiling)
-        labels_d = cp.asarray(labels)
+        labels_d = self._genereate_labels(dtype)
+        labels = cp.asnumpy(labels_d)
         image_d = cp.random.standard_normal(labels.shape).astype(np.float32)
         image = cp.asnumpy(image_d)
         self.args_cpu = (image, labels)
@@ -150,7 +134,6 @@ def set_args(self, dtype):
         self.args_gpu = (data, markers)
 
 
-
 def main(args):
 
     pfile = "cucim_segmentation_results.pickle"
@@ -175,6 +158,14 @@ def main(args):
             False,
             True,
         ),
+        # _expand_labels.py
+        (
+            "expand_labels",
+            dict(distance=1),
+            dict(),
+            False,
+            True,
+        ),
         # _join.py
         (
             "relabel_sequential",
@@ -221,6 +212,16 @@ def main(args):
             False,
             False,
         ),
+        (
+            "chan_vese",
+            dict(),
+            # Reduced number of iterations so scikit-image comparison will not
+            # take minutes to complete. Empirically, approximately the same
+            # acceleration was measured for 10 or 100 iterations.
+            dict(max_num_iter=[10], init_level_set=["checkerboard", "disk"]),
+            False,
+            False,
+        ),
         # omit: disk_level_set (simple array generation function)
         # omit: checkerboard_level_set (simple array generation function)
     ]:
@@ -239,7 +240,7 @@ def main(args):
         if shape[-1] == 3 and not allow_color:
             continue
 
-        if function_name in ["clear_border", "relabel_sequential", "find_boundaries", "mark_boundaries", "random_walker"]:
+        if function_name in ["clear_border", "expand_labels", "relabel_sequential", "find_boundaries", "mark_boundaries", "random_walker"]:
             if function_name == 'random_walker':
                 fixed_kwargs['channel_axis'] = -1 if shape[-1] == 3 else None
 
@@ -263,7 +264,7 @@ def main(args):
             all_results = pd.concat([all_results, results["full"]])
 
 
-        elif function_name in ["inverse_gaussian_gradient", "morphological_geodesic_active_contour", "morphological_chan_vese"]:
+        elif function_name in ["inverse_gaussian_gradient", "morphological_geodesic_active_contour", "morphological_chan_vese", "chan_vese"]:
 
             if function_name == "morphological_geodesic_active_contour":
                 bench_class = MorphGeodesicBench
@@ -296,7 +297,7 @@ def main(args):
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser(description='Benchmarking cuCIM segmentation functions')
-    func_name_choices = ["clear_border", "relabel_sequential", "find_boundaries", "mark_boundaries", "random_walker", "inverse_gaussian_gradient", "morphological_geodesic_active_contour", "morphological_chan_vese"]
+    func_name_choices = ["clear_border", "expand_labels", "relabel_sequential", "find_boundaries", "mark_boundaries", "random_walker", "inverse_gaussian_gradient", "morphological_geodesic_active_contour", "morphological_chan_vese", "chan_vese"]
     label_dtype_choices = ['int8', 'int16', 'int32', 'int64', 'uint8', 'uint16', 'uint32', 'uint64']
     dtype_choices = ['float16', 'float32', 'float64', 'int8', 'int16', 'int32', 'int64', 'uint8', 'uint16', 'uint32', 'uint64']
     parser.add_argument('-i','--img_size', type=str, help='Size of input image (omit color channel, it will be appended as needed)', required=True)

benchmarks/skimage/run-nv-bench-morphology.sh

@@ -1,8 +1,20 @@
 #!/bin/bash
 param_shape=(512,512 3840,2160 3840,2160,3 192,192,192)
-param_filt=(binary_erosion binary_dilation binary_opening binary_closing remove_small_objects remove_small_holes erosion dilation opening closing white_tophat black_tophat thin reconstruction)
-# Note: user-specified dtype ignored for binary_* functions and thin (these only accept binary input)
-param_dt=(float32 uint8)
+param_filt=(binary_erosion binary_dilation binary_opening binary_closing remove_small_objects remove_small_holes erosion dilation opening closing white_tophat black_tophat medial_axis thin reconstruction)
+
+param_dt=(uint8)
+for shape in "${param_shape[@]}"; do
+    for filt in "${param_filt[@]}"; do
+        for dt in "${param_dt[@]}"; do
+            python cucim_morphology_bench.py -f $filt -i $shape -d $dt -t 10
+        done
+    done
+done
+
+# Note: Omit binary_*, medial_axis and thin from floating point benchmarks.
+#       (these functions only take binary input).
+param_filt_float=(remove_small_objects remove_small_holes erosion dilation opening closing white_tophat black_tophat reconstruction)
+param_dt=(float32)
 for shape in "${param_shape[@]}"; do
     for filt in "${param_filt[@]}"; do
         for dt in "${param_dt[@]}"; do

benchmarks/skimage/run-nv-bench-segmentation.sh

@@ -2,7 +2,7 @@
 param_shape=(512,512 3840,2160 3840,2160,3 192,192,192)
 
 # these require an integer-valued label image
-param_filt=(clear_border relabel_sequential find_boundaries mark_boundaries random_walker)
+param_filt=(clear_border expand_labels relabel_sequential find_boundaries mark_boundaries random_walker)
 param_dt=(float32)
 param_dt_label=(uint8 uint32)
 for shape in "${param_shape[@]}"; do
@@ -16,7 +16,7 @@ for shape in "${param_shape[@]}"; do
 done
 
 # these do not require an integer-valued input image
-param_filt=(inverse_gaussian_gradient morphological_geodesic_active_contour morphological_chan_vese)
+param_filt=(inverse_gaussian_gradient morphological_geodesic_active_contour morphological_chan_vese chan_vese)
 param_dt=(float32)
 for shape in "${param_shape[@]}"; do
     for filt in "${param_filt[@]}"; do

conda/recipes/cucim/meta.yaml

@@ -29,7 +29,7 @@ requirements:
     - python {{ python_version }}.*
     - libcucim {{ version }}.*
     - click
-    - cupy >=9,<11.0.0a0
+    - cupy >=9,<12.0.0a0
     - numpy 1.19
     - scipy
     - scikit-image >=0.19.0,<0.20.0a0
@@ -38,7 +38,7 @@ requirements:
     - python {{ python_version }}.*
     - libcucim {{ version }}.*
     - click
-    - cupy >=9,<11.0.0a0
+    - cupy >=9,<12.0.0a0
     - {{ pin_compatible('numpy') }}
     - scipy
     - scikit-image >=0.19.0,<0.20.0a0
@@ -51,8 +51,10 @@ tests:                                 # [linux64]
     - cucim                            # [linux64]
 
 about:
-  home: http://rapids.ai/
+  home: https://developer.nvidia.com/multidimensional-image-processing
+  summary: cucim Python package
   license: Apache-2.0
   license_family: Apache
   license_file: LICENSE
-  summary: cucim Python package
+  doc_url: https://docs.rapids.ai/api/cucim/stable/
+  dev_url: https://github.com/rapidsai/cucim

conda/recipes/libcucim/meta.yaml

@@ -44,8 +44,10 @@ requirements:
     - libwebp-base  # [linux or osx]
 
 about:
-  home: http://rapids.ai/
+  home: https://developer.nvidia.com/multidimensional-image-processing
+  summary: libcucim C++ library
   license: Apache-2.0
   license_family: Apache
   license_file: LICENSE
-  summary: libcucim C++ library
+  doc_url: https://docs.rapids.ai/api/cucim/stable/
+  dev_url: https://github.com/rapidsai/cucim

cpp/plugins/cucim.kit.cumed/VERSION

@@ -1 +1 @@
-22.08.01
+22.10.00