webnn: support all axes for resample2d

Allow arbitrary axes as long as they are valid dimensions for resample2d instead of restricting them to point to spacial dimensions. Apply transposes for input & output when the axes is not supported by the backend. See spec pr: webmachinelearning/webnn#752 Change-Id: I5112abf1f43328d901000a4cff8468bd9bfe2dfb Cq-Include-Trybots: luci.chromium.try:win11-blink-rel,mac14.arm64-blink-rel,mac14-blink-rel, linux-blink-rel Bug: 329658123, 334914468
web-platform-tests · Aug 23, 2024 · ff3820b · ff3820b
1 parent 26c8809
commit ff3820b
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Showing 2 changed files with 122 additions and 9 deletions.
diff --git a/webnn/conformance_tests/resample2d.https.any.js b/webnn/conformance_tests/resample2d.https.any.js
@@ -278,6 +278,44 @@ const resample2dTests = [
       }
     }
   },
+  {
+    'name':
+        'resample2d(upsample) float32 4D tensor explicit options.axes=[3, 2]',
+    'graph': {
+      'inputs': {
+        'resample2dInput': {
+          'data': [
+            59.92947006225586, 41.98918914794922, 66.39534759521484,
+            90.7006607055664, 86.95105743408203, 79.10005187988281
+          ],
+          'descriptor': {'dimensions': [1, 1, 2, 3], 'dataType': 'float32'}
+        }
+      },
+      'operators': [{
+        'name': 'resample2d',
+        'arguments': [
+          {'input': 'resample2dInput'},
+          {'options': {'sizes': [6, 4], 'axes': [3, 2]}}
+        ],
+        'outputs': 'resample2dOutput'
+      }],
+      'expectedOutputs': {
+        'resample2dOutput': {
+          'data': [
+            59.92947006225586, 59.92947006225586, 41.98918914794922,
+            41.98918914794922, 66.39534759521484, 66.39534759521484,
+            59.92947006225586, 59.92947006225586, 41.98918914794922,
+            41.98918914794922, 66.39534759521484, 66.39534759521484,
+            90.7006607055664,  90.7006607055664,  86.95105743408203,
+            86.95105743408203, 79.10005187988281, 79.10005187988281,
+            90.7006607055664,  90.7006607055664,  86.95105743408203,
+            86.95105743408203, 79.10005187988281, 79.10005187988281
+          ],
+          'descriptor': {'dimensions': [1, 1, 4, 6], 'dataType': 'float32'}
+        }
+      }
+    }
+  },
   {
     'name':
         'resample2d(upsample) float32 4D tensor explicit options.mode=\'nearest-neighbor\'',
@@ -429,6 +467,80 @@ const resample2dTests = [
         }
       }
     }
+  },
+  {
+    'name': 'resample2d(upsample) float32 4D tensor options.axes=[0, 1]',
+    'graph': {
+      'inputs': {
+        'resample2dInput': {
+          'data': [
+            59.92947006225586, 90.7006607055664, 41.98918914794922,
+            86.95105743408203, 66.39534759521484, 79.10005187988281
+          ],
+          'descriptor': {'dimensions': [3, 2, 1, 1], 'dataType': 'float32'}
+        }
+      },
+      'operators': [{
+        'name': 'resample2d',
+        'arguments': [
+          {'input': 'resample2dInput'},
+          {'options': {'sizes': [6, 4], 'axes': [0, 1]}}
+        ],
+        'outputs': 'resample2dOutput'
+      }],
+      'expectedOutputs': {
+        'resample2dOutput': {
+          'data': [
+            59.92947006225586, 59.92947006225586, 90.7006607055664,
+            90.7006607055664,  59.92947006225586, 59.92947006225586,
+            90.7006607055664,  90.7006607055664,  41.98918914794922,
+            41.98918914794922, 86.95105743408203, 86.95105743408203,
+            41.98918914794922, 41.98918914794922, 86.95105743408203,
+            86.95105743408203, 66.39534759521484, 66.39534759521484,
+            79.10005187988281, 79.10005187988281, 66.39534759521484,
+            66.39534759521484, 79.10005187988281, 79.10005187988281
+          ],
+          'descriptor': {'dimensions': [6, 4, 1, 1], 'dataType': 'float32'}
+        }
+      }
+    }
+  },
+  {
+    'name': 'resample2d(upsample) float32 4D tensor options.axes=[1, 0]',
+    'graph': {
+      'inputs': {
+        'resample2dInput': {
+          'data': [
+            59.92947006225586, 90.7006607055664, 41.98918914794922,
+            86.95105743408203, 66.39534759521484, 79.10005187988281
+          ],
+          'descriptor': {'dimensions': [3, 2, 1, 1], 'dataType': 'float32'}
+        }
+      },
+      'operators': [{
+        'name': 'resample2d',
+        'arguments': [
+          {'input': 'resample2dInput'},
+          {'options': {'sizes': [4, 6], 'axes': [1, 0]}}
+        ],
+        'outputs': 'resample2dOutput'
+      }],
+      'expectedOutputs': {
+        'resample2dOutput': {
+          'data': [
+            59.92947006225586, 59.92947006225586, 90.7006607055664,
+            90.7006607055664,  59.92947006225586, 59.92947006225586,
+            90.7006607055664,  90.7006607055664,  41.98918914794922,
+            41.98918914794922, 86.95105743408203, 86.95105743408203,
+            41.98918914794922, 41.98918914794922, 86.95105743408203,
+            86.95105743408203, 66.39534759521484, 66.39534759521484,
+            79.10005187988281, 79.10005187988281, 66.39534759521484,
+            66.39534759521484, 79.10005187988281, 79.10005187988281
+          ],
+          'descriptor': {'dimensions': [6, 4, 1, 1], 'dataType': 'float32'}
+        }
+      }
+    }
   }
 ];
 

diff --git a/webnn/validation_tests/resample2d.https.any.js b/webnn/validation_tests/resample2d.https.any.js
@@ -55,6 +55,16 @@ const tests = [
     options: {scales: [2.0, 2.0], sizes: [3, 6]},
     output: {dataType: 'float32', dimensions: [1, 1, 3, 6]},
   },
+  {
+    name:
+        '[resample2d] Test building resample2d with non consecutive axes=[0,2]',
+    input: {dataType: 'float32', dimensions: [1, 1, 2, 4]},
+    options: {
+      axes: [0, 2],
+      label: label,
+    },
+    output: {dataType: 'float32', dimensions: [1, 1, 2, 4]},
+  },
   {
     name:
         '[resample2d] Throw if the dataType of input is not float32 or float16',
@@ -177,15 +187,6 @@ const tests = [
       label: label,
     },
   },
-  {
-    // The valid values in the axes sequence are [0, 1], [1, 2] or [2, 3]
-    name: '[resample2d] Throw if the values of axes are inconsecutive',
-    input: {dataType: 'float32', dimensions: [1, 1, 2, 4]},
-    options: {
-      axes: [0, 2],
-      label: label,
-    },
-  },
   {
     name: '[resample2d] Throw if the values of axes are same',
     input: {dataType: 'float32', dimensions: [1, 1, 2, 4]},