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[js/webgpu] Fix max pool shape end with 0 (#21698)
Bug: https://github.com/microsoft/onnxruntime/issues/21386 ### Description <!-- Describe your changes. --> ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. -->
This commit is contained in:
Xu Xing
GitHub
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commit
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4 changed files with 106 additions and 28 deletions
67
js/web/test/data/ops/max-pool.jsonc
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67
js/web/test/data/ops/max-pool.jsonc
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@ -0,0 +1,67 @@
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[
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{
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"name": "MaxPool",
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"operator": "MaxPool",
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"attributes": [
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{ "name": "kernel_shape", "data": [3], "type": "ints" },
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{ "name": "dilations", "data": [1], "type": "ints" }
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],
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"cases": [
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{
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"name": "T[3,5,5] T[3,5,3]",
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"inputs": [
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{
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"data": [
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1.764052391052246, 0.40015721321105957, 0.978738009929657, 2.2408931255340576, 1.8675580024719238,
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-0.9772778749465942, 0.9500884413719177, -0.15135720372200012, -0.10321885347366333, 0.4105985164642334,
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0.14404356479644775, 1.4542734622955322, 0.7610377073287964, 0.12167501449584961, 0.44386324286460876,
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0.3336743414402008, 1.4940791130065918, -0.2051582634449005, 0.3130677044391632, -0.8540957570075989,
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-2.5529897212982178, 0.653618574142456, 0.8644362092018127, -0.7421650290489197, 2.269754648208618, 1, 1,
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1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 100, 100, 100, 100, 100, 100, 100,
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100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100
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],
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"dims": [3, 5, 5],
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"type": "float32"
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}
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],
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"outputs": [
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{
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"data": [
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1.764052391052246, 2.2408931255340576, 2.2408931255340576, 0.9500884413719177, 0.9500884413719177,
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0.4105985164642334, 1.4542734622955322, 1.4542734622955322, 0.7610377073287964, 1.4940791130065918,
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1.4940791130065918, 0.3130677044391632, 0.8644362092018127, 0.8644362092018127, 2.269754648208618, 1, 1,
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1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,
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100, 100
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],
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"dims": [3, 5, 3],
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"type": "float32"
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}
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]
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}
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]
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},
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{
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"name": "MaxPool",
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"operator": "MaxPool",
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"attributes": [{ "name": "kernel_shape", "data": [3], "type": "ints" }],
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"cases": [
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{
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"name": "T[1,1,5] T[1,1,3]",
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"inputs": [
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{
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"data": [1.764052391052246, 0.40015721321105957, 0.978738009929657, 2.2408931255340576, 1.8675580024719238],
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"dims": [1, 1, 5],
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"type": "float32"
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}
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],
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"outputs": [
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{
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"data": [1.764052391052246, 2.2408931255340576, 2.2408931255340576],
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"dims": [1, 1, 3],
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"type": "float32"
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}
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]
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}
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]
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}
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]
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@ -1371,6 +1371,7 @@
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"matmul.jsonc",
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"matmulnbits.jsonc",
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"matmul-broadcast.jsonc",
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"max-pool.jsonc",
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"mul.jsonc",
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"mul_int32.jsonc",
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"multihead-attention.jsonc",
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@ -48,7 +48,6 @@ class ConvBase : public JsKernel {
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std::vector<float> activation_params = info.GetAttrsOrDefault<float>("activation_params");
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int64_t channels_last = is_channels_last ? 1 : info.GetAttrOrDefault<int64_t>("channels_last", 0);
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// currently only support Conv 1D/2D. TODO: support Conv3D and other
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JSEP_INIT_KERNEL_ATTRIBUTE(Conv, ({
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"format" : $11 ? "NHWC" : "NCHW",
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"auto_pad" : $1,
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@ -65,8 +64,8 @@ class ConvBase : public JsKernel {
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JSEP_HEAP32_INDEX_START(dilations),
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JSEP_HEAP32_INDEX_END(dilations),
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static_cast<int32_t>(conv_attrs_.group),
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JSEP_HEAP32_INDEX_START(kernel_shape),
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JSEP_HEAP32_INDEX_END(kernel_shape),
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JSEP_HEAP32_INDEX_START(kernel_shapes),
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JSEP_HEAP32_INDEX_END(kernel_shapes),
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JSEP_HEAP32_INDEX_START(local_pads),
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JSEP_HEAP32_INDEX_END(local_pads),
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JSEP_HEAP32_INDEX_START(strides),
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@ -9,38 +9,45 @@
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namespace onnxruntime {
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namespace js {
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#define POOL_ATTRIBUTES_JS_OBJ_MAPPING ({ \
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"format" : $15 ? "NHWC" : "NCHW", \
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"auto_pad" : $1, \
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"ceil_mode" : $2, \
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"count_include_pad" : $3, \
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"storage_order" : $4, \
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"dilations" : [ $5, $6 ], \
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"kernel_shape" : [ $7, $8 ], \
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"pads" : [ $9, $10, $11, $12 ], \
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"strides" : [ $13, $14 ] \
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#define POOL_ATTRIBUTES_JS_OBJ_MAPPING ({ \
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"format" : $13 ? "NHWC" : "NCHW", \
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"auto_pad" : $1, \
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"ceil_mode" : $2, \
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"count_include_pad" : $3, \
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"storage_order" : $4, \
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"dilations" : $5 ? Array.from(HEAP32.subarray($5, $6)) : [], \
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"kernel_shape" : $7 ? Array.from(HEAP32.subarray($7, $8)) : [], \
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"pads" : $9 ? Array.from(HEAP32.subarray($9, $10)) : [], \
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"strides" : $11 ? Array.from(HEAP32.subarray($11, $12)) : [] \
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})
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#define POOL_ATTRIBUTES_PARAM_LIST \
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static_cast<int32_t>(pool_attrs_.auto_pad), \
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static_cast<int32_t>(pool_attrs_.ceil_mode), \
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static_cast<int32_t>(pool_attrs_.count_include_pad), \
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static_cast<int32_t>(pool_attrs_.storage_order), \
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static_cast<int32_t>(pool_attrs_.dilations.size() > 0 ? pool_attrs_.dilations[0] : 0), \
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static_cast<int32_t>(pool_attrs_.dilations.size() > 1 ? pool_attrs_.dilations[1] : 0), \
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static_cast<int32_t>(pool_attrs_.kernel_shape.size() > 0 ? pool_attrs_.kernel_shape[0] : 0), \
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static_cast<int32_t>(pool_attrs_.kernel_shape.size() > 1 ? pool_attrs_.kernel_shape[1] : 0), \
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static_cast<int32_t>(pool_attrs_.pads.size() > 0 ? pool_attrs_.pads[0] : 0), \
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static_cast<int32_t>(pool_attrs_.pads.size() > 1 ? pool_attrs_.pads[1] : 0), \
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static_cast<int32_t>(pool_attrs_.pads.size() > 2 ? pool_attrs_.pads[2] : 0), \
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static_cast<int32_t>(pool_attrs_.pads.size() > 3 ? pool_attrs_.pads[3] : 0), \
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static_cast<int32_t>(pool_attrs_.strides.size() > 0 ? pool_attrs_.strides[0] : 0), \
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static_cast<int32_t>(pool_attrs_.strides.size() > 1 ? pool_attrs_.strides[1] : 0), \
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#define POOL_ATTRIBUTES_PARAM_LIST \
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static_cast<int32_t>(pool_attrs_.auto_pad), \
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static_cast<int32_t>(pool_attrs_.ceil_mode), \
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static_cast<int32_t>(pool_attrs_.count_include_pad), \
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static_cast<int32_t>(pool_attrs_.storage_order), \
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JSEP_HEAP32_INDEX_START(dilations), \
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JSEP_HEAP32_INDEX_END(dilations), \
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JSEP_HEAP32_INDEX_START(kernel_shapes), \
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JSEP_HEAP32_INDEX_END(kernel_shapes), \
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JSEP_HEAP32_INDEX_START(pads), \
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JSEP_HEAP32_INDEX_END(pads), \
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JSEP_HEAP32_INDEX_START(strides), \
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JSEP_HEAP32_INDEX_END(strides), \
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static_cast<int32_t>(is_channels_last)
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#define GLOBAL_POOL_ATTRIBUTES_JS_OBJ_MAPPING ({"format" : $1 ? "NHWC" : "NCHW"})
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#define GLOBAL_POOL_ATTRIBUTES_PARAM_LIST static_cast<int32_t>(is_channels_last)
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template <typename Type>
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inline const std::vector<Type> CastTensorShapeVector(const TensorShapeVector& shape) {
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std::vector<Type> castedShapes(shape.size(), 0);
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for (size_t i = 0; i < shape.size(); ++i) {
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castedShapes[i] = gsl::narrow_cast<Type>(shape[i]);
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}
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return castedShapes;
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}
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template <typename PoolType, bool is_channels_last>
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class Pool : public JsKernel, public PoolBase {
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public:
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@ -54,6 +61,10 @@ class Pool : public JsKernel, public PoolBase {
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// TODO: GlobalLpPool
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}
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} else {
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auto kernel_shapes{CastTensorShapeVector<int32_t>(pool_attrs_.kernel_shape)};
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auto strides{CastTensorShapeVector<int32_t>(pool_attrs_.strides)};
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auto dilations{CastTensorShapeVector<int32_t>(pool_attrs_.dilations)};
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auto pads{CastTensorShapeVector<int32_t>(pool_attrs_.pads)};
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if constexpr (PoolType::type == onnxruntime::PoolType::kAveragePool) {
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JSEP_INIT_KERNEL_ATTRIBUTE(AveragePool, POOL_ATTRIBUTES_JS_OBJ_MAPPING, POOL_ATTRIBUTES_PARAM_LIST);
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} else if constexpr (PoolType::type == onnxruntime::PoolType::kMaxPool) {
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