upgrade ndslice api (#397)

* upgrade ndslice api * fix dub * fix hoffman and appveyor * fix example * fix circle ci * fix circle ci * ditto * ditto
libmir · Sep 9, 2018 · 427e0e9 · 427e0e9
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commit 427e0e9
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Showing 19 changed files with 182 additions and 167 deletions.
diff --git a/.gitignore b/.gitignore
@@ -48,3 +48,7 @@ benchmarks/glas/gemm_report
 *.dylib
 
 mir-test-unittest
+examples/lda_hoffman_sparse
+benchmarks/ndslice/binarization
+benchmarks/ndslice/convolution
+benchmarks/ndslice/euclidean_distance
diff --git a/.travis.yml b/.travis.yml
@@ -22,8 +22,8 @@ script:
  - echo "$ARCH"
  - dub test --arch "$ARCH" --build=unittest-cov
  - dub build --single --arch "$ARCH" examples/lda_hoffman_sparse.d
- - dub fetch imageformats --version 6.1.0 && dub build --arch "$ARCH" imageformats
- # - dub build --single --arch "$ARCH" examples/median_filter.d
+ - dub fetch imageformats --version 7.0.0 && dub build --arch "$ARCH" imageformats
+ - dub build --single --arch "$ARCH" examples/median_filter.d
  - dub build --single --arch "$ARCH" examples/means_of_columns.d
 after_success:
  - bash <(curl -s https://codecov.io/bash)

diff --git a/appveyor.yml b/appveyor.yml
@@ -2,7 +2,7 @@ platform: x64
 environment:
  matrix:
   - DC: ldc
-    DVersion: 1.7.0
+    DVersion: '1.10.0'
     arch: x64
 
 skip_tags: true
@@ -14,23 +14,41 @@ branches:
 install:
   - ps: function SetUpDCompiler
         {
-          echo "downloading ...";
-          if($env:arch -eq "x86"){
-            $env:DConf = "m32";
-          }
-          elseif($env:arch -eq "x64"){
-            $env:DConf = "m64";
-          }
-          $env:toolchain = "msvc";
-          $version = $env:DVersion;
-          Invoke-WebRequest "http://downloads.dlang.org/releases/2.x/$($version)/dmd.$($version).windows.7z" -OutFile "c:\dmd.7z";
-          echo "finished.";
-          pushd c:\\;
-          7z x ldc.zip > $null;
-          popd;
+            if($env:DC -eq "dmd"){
+              if($env:arch -eq "x86"){
+                $env:DConf = "m32";
+              }
+              elseif($env:arch -eq "x64"){
+                $env:DConf = "m64";
+              }
+              echo "downloading ...";
+              $env:toolchain = "msvc";
+              $version = $env:DVersion;
+              Invoke-WebRequest "http://downloads.dlang.org/releases/2.x/$($version)/dmd.$($version).windows.7z" -OutFile "c:\dmd.7z";
+              echo "finished.";
+              pushd c:\\;
+              7z x dmd.7z > $null;
+              popd;
+            }
+            elseif($env:DC -eq "ldc"){
+              echo "downloading ...";
+              if($env:arch -eq "x86"){
+                $env:DConf = "m32";
+              }
+              elseif($env:arch -eq "x64"){
+                $env:DConf = "m64";
+              }
+              $env:toolchain = "msvc";
+              $version = $env:DVersion;
+              Invoke-WebRequest "https://github.com/ldc-developers/ldc/releases/download/v$($version)/ldc2-$($version)-windows-x64.7z" -OutFile "c:\ldc.7z";
+              echo "finished.";
+              pushd c:\\;
+              7z x ldc.7z > $null;
+              popd;
+            }
         }
   - ps: SetUpDCompiler
-  - powershell -Command Invoke-WebRequest https://code.dlang.org/files/dub-1.7.2-windows-x86.zip -OutFile dub.zip
+  - powershell -Command Invoke-WebRequest https://code.dlang.org/files/dub-1.9.0-windows-x86.zip -OutFile dub.zip
   - 7z x dub.zip -odub > nul
   - set PATH=%CD%\%binpath%;%CD%\dub;%PATH%
   - dub --version
@@ -44,10 +62,14 @@ before_build:
             $env:compilersetupargs = "amd64";
             $env:Darch = "x86_64";
         }
-  - ps:
-        $version = $env:DVersion;
-        $env:PATH += ";C:\ldc2-$($version)-win64-msvc\bin";
-        $env:DC = "ldmd2";
+  - ps : if($env:DC -eq "dmd"){
+           $env:PATH += ";C:\dmd2\windows\bin;";
+         }
+         elseif($env:DC -eq "ldc"){
+           $version = $env:DVersion;
+           $env:PATH += ";C:\ldc2-$($version)-windows-x64\bin";
+           $env:DC = "ldc2";
+         }
   - ps: $env:compilersetup = "C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\vcvarsall";
   - '"%compilersetup%" %compilersetupargs%'
 

diff --git a/benchmarks/ndslice/binarization.d b/benchmarks/ndslice/binarization.d
@@ -20,17 +20,17 @@ LDC - the LLVM D compiler (918073):
 
 $ dub run --build=release-nobounds --compiler=ldmd2 --single binarization.d
 +/
-import std.datetime : benchmark, Duration;
+import std.datetime.stopwatch : benchmark, Duration;
 import std.stdio : writefln;
 import std.conv : to;
-import std.algorithm.comparison : min;
+import mir.utility : min;
 
 import mir.ndslice;
-import mir.ndslice.internal : fastmath;
+import mir.math.common : fastmath;
 
 alias F = double;
 
-void binarizationLockstep(Slice!(Contiguous, [2], F*) input, F threshold, Slice!(Contiguous, [2], F*) output)
+void binarizationLockstep(Slice!(F*, 2) input, F threshold, Slice!(F*, 2) output)
 {
     import std.range : lockstep;
     foreach(i, ref o; lockstep(input.flattened, output.flattened))
@@ -39,9 +39,9 @@ void binarizationLockstep(Slice!(Contiguous, [2], F*) input, F threshold, Slice!
     }
 }
 
-void binarizationAssumeSameStructure(Slice!(Contiguous, [2], F*) input, F threshold, Slice!(Contiguous, [2], F*) output)
+void binarizationAssumeSameStructure(Slice!(F*, 2) input, F threshold, Slice!(F*, 2) output)
 {
-    import mir.ndslice.algorithm : each;
+    import mir.algorithm.iteration : each;
     import mir.ndslice.topology : zip;
 
     zip(input, output).each!( (p) {
@@ -51,8 +51,8 @@ void binarizationAssumeSameStructure(Slice!(Contiguous, [2], F*) input, F thresh
 
 // __gshared is used to prevent specialized optimization for input data
 __gshared n = 256; // image size
-__gshared Slice!(Contiguous, [2], F*) a;
-__gshared Slice!(Contiguous, [2], F*) b;
+__gshared Slice!(F*, 2) a;
+__gshared Slice!(F*, 2) b;
 __gshared F t; // threshold
 
 void main()

diff --git a/benchmarks/ndslice/convolution.d b/benchmarks/ndslice/convolution.d
@@ -7,7 +7,7 @@
 }
 +/
 /+
-Benchmark demonstrates performance superiority of using mir.ndslice.algorithm over looped code, for
+Benchmark demonstrates performance superiority of using mir.algorithm.iteration over looped code, for
 multidimensional processing with ndslice package.
 
 $ ldc2 --version
@@ -20,25 +20,25 @@ LDC - the LLVM D compiler (918073):
 
 $ dub run --build=release-nobounds --compiler=ldmd2 --single convolution.d
 +/
-import std.datetime : benchmark, Duration;
+import std.datetime.stopwatch : benchmark, Duration;
 import std.stdio : writefln;
 import std.conv : to;
-import std.algorithm.comparison : min;
+import mir.utility : min;
 
 import mir.ndslice;
-import mir.ndslice.internal : fastmath;
+import mir.math.common : fastmath;
 
 alias F = double;
 
-@fastmath void convLoop(Slice!(Contiguous, [2], F*) input, Slice!(Contiguous, [2], F*) output, Slice!(Contiguous, [2], F*) kernel)
+@fastmath void convLoop(Slice!(F*, 2) input, Slice!(F*, 2) output, Slice!(F*, 2) kernel)
 {
     auto kr = kernel.length!0; // kernel row size
     auto kc = kernel.length!1; // kernel column size
     foreach (r; 0 .. output.length!0)
         foreach (c; 0 .. output.length!1)
         {
             // take window to input at given pixel coordinate
-            Slice!(Canonical, [2], F*) window = input[r .. r + kr, c .. c + kc];
+            Slice!(F*, 2, Canonical) window = input[r .. r + kr, c .. c + kc];
 
             // calculate result for current pixel
             F v = 0.0f;
@@ -54,9 +54,9 @@ static @fastmath F kapply(F v, F e, F k) @safe @nogc nothrow pure
     return v + (e * k);
 }
 
-void convAlgorithm(Slice!(Contiguous, [2], F*) input, Slice!(Contiguous, [2], F*) output, Slice!(Contiguous, [2], F*) kernel)
+void convAlgorithm(Slice!(F*, 2) input, Slice!(F*, 2) output, Slice!(F*, 2) kernel)
 {
-    import mir.ndslice.algorithm : reduce;
+    import mir.algorithm.iteration : reduce;
     import mir.ndslice.topology : windows, map;
 
     auto mapping = input
@@ -72,9 +72,9 @@ void convAlgorithm(Slice!(Contiguous, [2], F*) input, Slice!(Contiguous, [2], F*
 // __gshared is used to prevent specialized optimization for input data
 __gshared n = 256; // image size
 __gshared m = 5; // kernel size
-__gshared Slice!(Contiguous, [2], F*) a;
-__gshared Slice!(Contiguous, [2], F*) b;
-__gshared Slice!(Contiguous, [2], F*) k;
+__gshared Slice!(F*, 2) a;
+__gshared Slice!(F*, 2) b;
+__gshared Slice!(F*, 2) k;
 
 void main()
 {
@@ -95,5 +95,5 @@ void main()
     }
 
     writefln("%26s = %s", "loops", bestBench[0]);
-    writefln("%26s = %s", "mir.ndslice.algorithm", bestBench[1]);
+    writefln("%26s = %s", "mir.algorithm.iteration", bestBench[1]);
 }
diff --git a/benchmarks/ndslice/dot_product.d b/benchmarks/ndslice/dot_product.d
@@ -20,7 +20,7 @@ $ dub run --build=release-nobounds --compiler=ldmd2 --single dot_product.d
 import std.numeric : dotProduct;
 import std.array;
 import std.typecons;
-import std.datetime;
+import std.datetime.stopwatch : benchmark, Duration;
 import std.stdio;
 import std.conv;
 import std.range: std_zip = zip;
@@ -36,8 +36,8 @@ __gshared F result;
 __gshared n = 8000;
 __gshared F[] a;
 __gshared F[] b;
-__gshared Slice!(Contiguous, [1], F*) asl;
-__gshared Slice!(Contiguous, [1], F*) bsl;
+__gshared Slice!(F*) asl;
+__gshared Slice!(F*) bsl;
 
 void main()
 {

diff --git a/benchmarks/ndslice/euclidean_distance.d b/benchmarks/ndslice/euclidean_distance.d
@@ -18,18 +18,16 @@ LDC - the LLVM D compiler (918073):
 $ dub run --build=release-nobounds --compiler=ldmd2 --single dot_product.d
 +/
 import std.numeric : euclideanDistance;
-import std.array;
-import std.typecons;
-import std.datetime;
+import std.datetime.stopwatch : benchmark, Duration;
 import std.stdio;
-import std.conv;
+import std.conv: to;
 import std.range: std_zip = zip;
 import std.algorithm: std_reduce = reduce;
-import std.math : sqrt;
 
+import mir.array.allocation;
 import mir.ndslice;
 import mir.utility;
-import mir.ndslice.internal : fastmath;
+import mir.math.common : sqrt, fastmath;
 
 alias F = double;
 
@@ -44,8 +42,8 @@ __gshared F result;
 __gshared n = 8000;
 __gshared F[] a;
 __gshared F[] b;
-__gshared Slice!(Contiguous, [1], F*) asl;
-__gshared Slice!(Contiguous, [1], F*) bsl;
+__gshared Slice!(F*) asl;
+__gshared Slice!(F*) bsl;
 
 void main()
 {

diff --git a/circle.yml b/circle.yml
@@ -1,8 +1,8 @@
 machine:
   environment:
-    DMD: 2.073.2 # for rdmd
-    LDC: 1.4.0
-    DUB: 1.1.0
+    DMD: 2.082.0 # for rdmd
+    LDC: 1.10.0
+    DUB: 1.9.0
     PATH: "${HOME}/dmd2/linux/bin64:${HOME}/ldc2-$LDC-linux-x86_64/bin:${PATH}"
     LD_LIBRARY_PATH: "${HOME}/dmd2/linux/lib64:${HOME}/ldc2-$LDC-linux-x86_64/lib:${LD_LIBRARY_PATH}"
 checkout:

diff --git a/dub.json b/dub.json
@@ -29,7 +29,7 @@
         }
     ],
     "dependencies": {
-        "mir-random": ">0.3.0 <1.0.0",
-        "mir-algorithm": ">0.8.0 <2.0.0"
+        "mir-random": ">=1.0.0 <2.0.0",
+        "mir-algorithm": ">=2.0.0 <2.5.0"
     }
 }
diff --git a/examples/means_of_columns.d b/examples/means_of_columns.d
@@ -28,14 +28,14 @@
 * faster than the numpy version.
 */
 
-import std.datetime;
+import std.datetime.stopwatch;
 import std.conv : to;
 import std.stdio;
 import mir.ndslice;
 
 enum testCount = 10_000;
-__gshared Slice!(Contiguous, [1], double*) means;
-Slice!(Contiguous, [2], int*) sl;
+__gshared Slice!(double*) means;
+Slice!(int*, 2) sl;
 
 void main() {
     sl = iota!int(100, 1000).slice;

diff --git a/examples/median_filter.d b/examples/median_filter.d
@@ -2,7 +2,7 @@
 /+ dub.json:
 {
     "name": "median_filter",
-    "dependencies": {"mir": {"path": ".."}, "imageformats": "==6.1.0"},
+    "dependencies": {"mir": {"path": ".."}, "imageformats": "==7.0.0"},
 }
 +/
 
@@ -40,8 +40,8 @@ Returns:
         Dense data layout is guaranteed.
 +/
 
-Slice!(Contiguous, [3], C*) movingWindowByChannel(alias filter, C)
-(Slice!(Universal, [3], C*) image, size_t nr, size_t nc)
+Slice!(C*, 3) movingWindowByChannel(alias filter, C)
+(Slice!(C*, 3, Universal) image, size_t nr, size_t nc)
 {
         // 0. 3D
         // The last dimension represents the color channel.
@@ -54,7 +54,7 @@ Slice!(Contiguous, [3], C*) movingWindowByChannel(alias filter, C)
         .windows(nr, nc)
         // 3. 5D
         // Unpacks the windows.
-        .unpack
+        .unpack.unpack
         // 4. 5D
         // Brings the color channel dimension to the third position.
         .transposed!(0, 1, 4)
@@ -74,7 +74,7 @@ Params:
 Returns:
     median value over the range `r`
 +/
-T median(SliceKind kind, Iterator, T)(Slice!(kind, [2], Iterator) sl, T[] buf)
+T median(Iterator, SliceKind kind, T)(Slice!(Iterator, 2, kind) sl, T[] buf)
 {
     import std.algorithm.sorting : topN;
     // copy sl to the buffer