From a17cc6f145e6ffa0af46e224308ddee28fe523f0 Mon Sep 17 00:00:00 2001
From: Cathie So <socathie@gmail.com>
Date: Fri, 18 Nov 2022 16:51:32 +0800
Subject: [PATCH] AveragePooling2D layer

---
 circuits/AveragePooling2D.circom              |  28 ++
 models/averagePooling2D_input.json            |   1 +
 models/averagePooling2D_output.json           |   1 +
 models/averagePooling2D_stride_input.json     |   1 +
 models/averagePooling2D_stride_output.json    |   1 +
 models/averagePooling2d.ipynb                 | 457 ++++++++++++++++++
 test/AveragePooling2D.js                      |  63 +++
 .../AveragePooling2D_stride_test.circom       |   6 +
 test/circuits/AveragePooling2D_test.circom    |   6 +
 9 files changed, 564 insertions(+)
 create mode 100644 circuits/AveragePooling2D.circom
 create mode 100644 models/averagePooling2D_input.json
 create mode 100644 models/averagePooling2D_output.json
 create mode 100644 models/averagePooling2D_stride_input.json
 create mode 100644 models/averagePooling2D_stride_output.json
 create mode 100644 models/averagePooling2d.ipynb
 create mode 100644 test/AveragePooling2D.js
 create mode 100644 test/circuits/AveragePooling2D_stride_test.circom
 create mode 100644 test/circuits/AveragePooling2D_test.circom

diff --git a/circuits/AveragePooling2D.circom b/circuits/AveragePooling2D.circom
new file mode 100644
index 0000000..36a2458
--- /dev/null
+++ b/circuits/AveragePooling2D.circom
@@ -0,0 +1,28 @@
+pragma circom 2.0.3;
+
+include "./SumPooling2D.circom";
+
+// AveragePooling2D layer, poolSize is required to be equal for both dimensions, might lose precision compared to SumPooling2D
+// scaledInvPoolSize is required to perform fixed point division, it is calculated as 1000/poolSize
+template AveragePooling2D (nRows, nCols, nChannels, poolSize, strides, scaledInvPoolSize) {
+    signal input in[nRows][nCols][nChannels];
+    signal output out[(nRows-poolSize)\strides+1][(nCols-poolSize)\strides+1][nChannels];
+
+    component sumPooling2D = SumPooling2D (nRows, nCols, nChannels, poolSize, strides);
+
+    for (var i=0; i<nRows; i++) {
+        for (var j=0; j<nCols; j++) {
+            for (var k=0; k<nChannels; k++) {
+                sumPooling2D.in[i][j][k] <== in[i][j][k];
+            }
+        }
+    }
+
+    for (var i=0; i<(nRows-poolSize)\strides+1; i++) {
+        for (var j=0; j<(nCols-poolSize)\strides+1; j++) {
+            for (var k=0; k<nChannels; k++) {
+                out[i][j][k] <== sumPooling2D.out[i][j][k]*scaledInvPoolSize;
+            }
+        }
+    }
+}
\ No newline at end of file
diff --git a/models/averagePooling2D_input.json b/models/averagePooling2D_input.json
new file mode 100644
index 0000000..cf4a9ad
--- /dev/null
+++ b/models/averagePooling2D_input.json
@@ -0,0 +1 @@
+{"in": [799, 748, 592, 257, 431, 673, 106, 851, 280, 236, 390, 798, 836, 980, 727, 514, 812, 243, 251, 963, 618, 119, 47, 814, 638, 548, 787, 902, 39, 912, 353, 88, 852, 263, 468, 683, 403, 656, 790, 19, 721, 713, 811, 273, 41, 478, 33, 658, 919, 362, 676, 337, 991, 477, 726, 570, 131, 224, 248, 306, 564, 420, 123, 303, 359, 278, 162, 857, 822, 265, 489, 792, 290, 673, 67]}
\ No newline at end of file
diff --git a/models/averagePooling2D_output.json b/models/averagePooling2D_output.json
new file mode 100644
index 0000000..61bb7df
--- /dev/null
+++ b/models/averagePooling2D_output.json
@@ -0,0 +1 @@
+{"out": [455469, 738453, 531556, 274700, 459195, 669521, 503281, 237557, 717073, 371134, 734342, 527759]}
\ No newline at end of file
diff --git a/models/averagePooling2D_stride_input.json b/models/averagePooling2D_stride_input.json
new file mode 100644
index 0000000..a6af0a6
--- /dev/null
+++ b/models/averagePooling2D_stride_input.json
@@ -0,0 +1 @@
+{"in": [229, 850, 68, 486, 23, 406, 444, 143, 528, 978, 772, 385, 506, 818, 556, 529, 992, 942, 433, 613, 14, 412, 661, 503, 432, 363, 755, 795, 545, 505, 902, 248, 472, 894, 300, 83, 14, 423, 255, 966, 613, 612, 252, 86, 216, 925, 1000, 968, 543, 895, 425, 213, 313, 765, 276, 515, 637, 73, 484, 336, 706, 832, 724, 387, 658, 984, 272, 193, 56, 713, 836, 147, 807, 302, 894, 364, 195, 391, 559, 870, 323, 113, 50, 687, 10, 520, 186, 427, 879, 582, 624, 201, 600, 294, 449, 39, 969, 709, 964, 810, 306, 180, 218, 184, 345, 161, 712, 229, 285, 333, 834, 469, 549, 840, 683, 227, 494, 34, 544, 416, 319, 129, 237, 124, 808, 190, 87, 770, 527, 736, 58, 752, 811, 435, 107, 418, 684, 555, 422, 209, 231, 225, 929, 63, 683, 452, 744, 98, 940, 298, 407, 217, 296, 929, 817, 510, 469, 264, 100, 906, 776, 649, 733, 154, 211, 803, 852, 600, 826, 494, 88, 686, 311, 943, 972, 182, 625, 939, 639, 71, 721, 549, 827, 323, 820, 474, 583, 283, 143, 473, 602, 7, 969, 180, 543, 364, 958, 573, 322, 463, 215, 836, 143, 79, 286, 375, 600, 55, 352, 526, 225, 105, 54, 516, 128, 952, 496, 491, 194, 42, 471, 542, 430, 347, 392, 590, 955, 48, 560, 553, 603, 900, 255, 6, 815, 835, 250, 39, 499, 373, 447, 809, 30, 125, 765, 10, 732, 953, 54, 197, 458, 46, 864, 508, 451, 314, 912, 870, 9, 63, 670, 834, 606, 445, 260, 311, 718, 23, 816, 9, 834, 912, 43, 336, 907, 126, 113, 360, 792, 271, 579, 296, 550, 34, 487, 31, 277, 556, 932, 59, 991, 879, 104, 606, 795, 400, 229, 338, 585, 164]}
\ No newline at end of file
diff --git a/models/averagePooling2D_stride_output.json b/models/averagePooling2D_stride_output.json
new file mode 100644
index 0000000..11e4c4c
--- /dev/null
+++ b/models/averagePooling2D_stride_output.json
@@ -0,0 +1 @@
+{"out": [481652, 407720, 397296, 550302, 465132, 405480, 546492, 641142, 525509, 332400, 533350, 477367, 420135, 527670, 480126, 602435, 421352, 441346, 449364, 435906, 434331, 383111, 460026, 489111, 440164, 517465, 367168, 640665, 391247, 337821, 629925, 491980, 346866, 584274, 395374, 398564, 463114, 544752, 304197, 531883, 476978, 263684, 491554, 548672, 485007, 535823, 400439, 398486]}
\ No newline at end of file
diff --git a/models/averagePooling2d.ipynb b/models/averagePooling2d.ipynb
new file mode 100644
index 0000000..a776e10
--- /dev/null
+++ b/models/averagePooling2d.ipynb
@@ -0,0 +1,457 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.layers import Input, AveragePooling2D\n",
+    "from tensorflow.keras import Model\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputs = Input(shape=(5,5,3))\n",
+    "x = AveragePooling2D(pool_size=2)(inputs)\n",
+    "model = Model(inputs, x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"model\"\n",
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "input_1 (InputLayer)         [(None, 5, 5, 3)]         0         \n",
+      "_________________________________________________________________\n",
+      "average_pooling2d (AveragePo (None, 2, 2, 3)           0         \n",
+      "=================================================================\n",
+      "Total params: 0\n",
+      "Trainable params: 0\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[[0.79941942, 0.74789538, 0.59164868],\n",
+       "         [0.25731687, 0.43079569, 0.67277259],\n",
+       "         [0.10620233, 0.85056582, 0.27951353],\n",
+       "         [0.23562352, 0.39038001, 0.79778411],\n",
+       "         [0.8357717 , 0.97955005, 0.7266173 ]],\n",
+       "\n",
+       "        [[0.51417529, 0.8118348 , 0.24336546],\n",
+       "         [0.25096464, 0.96328609, 0.61843696],\n",
+       "         [0.119168  , 0.04735649, 0.81364784],\n",
+       "         [0.63780521, 0.54847675, 0.78713755],\n",
+       "         [0.90202837, 0.03890637, 0.91213939]],\n",
+       "\n",
+       "        [[0.35266164, 0.08792946, 0.85172066],\n",
+       "         [0.26321742, 0.46759433, 0.68288282],\n",
+       "         [0.40252278, 0.65572821, 0.78984132],\n",
+       "         [0.01883342, 0.72079971, 0.71303053],\n",
+       "         [0.81122497, 0.27290105, 0.04123257]],\n",
+       "\n",
+       "        [[0.47782615, 0.03275952, 0.658033  ],\n",
+       "         [0.91941939, 0.36194587, 0.67565511],\n",
+       "         [0.33704936, 0.99096846, 0.47735187],\n",
+       "         [0.72613102, 0.56987187, 0.13081093],\n",
+       "         [0.22415976, 0.24756073, 0.3059867 ]],\n",
+       "\n",
+       "        [[0.56391481, 0.4201842 , 0.12265913],\n",
+       "         [0.30285527, 0.35852079, 0.27757368],\n",
+       "         [0.16175994, 0.85718027, 0.82230896],\n",
+       "         [0.26478634, 0.48851923, 0.7921817 ],\n",
+       "         [0.28952273, 0.67304093, 0.06748879]]]])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X = np.random.rand(1,5,5,3)\n",
+    "X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[[0.45546904, 0.73845303, 0.5315559 ],\n",
+       "         [0.27469975, 0.45919478, 0.6695208 ]],\n",
+       "\n",
+       "        [[0.5032811 , 0.23755729, 0.71707296],\n",
+       "         [0.37113416, 0.7343421 , 0.52775866]]]], dtype=float32)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y = model.predict(X)\n",
+    "y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "in_json = {\n",
+    "    \"in\": (X*1000).round().astype(int).flatten().tolist()\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "out_json = {\n",
+    "    \"out\": (y*1000000).round().astype(int).flatten().tolist()\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"averagePooling2D_input.json\", \"w\") as f:\n",
+    "    json.dump(in_json, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"averagePooling2D_output.json\", \"w\") as f:\n",
+    "    json.dump(out_json, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputs = Input(shape=(10,10,3))\n",
+    "x = AveragePooling2D(pool_size=3, strides=2)(inputs)\n",
+    "model = Model(inputs, x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"model_1\"\n",
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "input_2 (InputLayer)         [(None, 10, 10, 3)]       0         \n",
+      "_________________________________________________________________\n",
+      "average_pooling2d_1 (Average (None, 4, 4, 3)           0         \n",
+      "=================================================================\n",
+      "Total params: 0\n",
+      "Trainable params: 0\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[[0.22890555, 0.85041378, 0.06779732],\n",
+       "         [0.48589264, 0.02266948, 0.40622616],\n",
+       "         [0.44445561, 0.14270991, 0.52828695],\n",
+       "         [0.97840965, 0.77211542, 0.38526407],\n",
+       "         [0.50635591, 0.8181549 , 0.55638397],\n",
+       "         [0.5286157 , 0.99152978, 0.94160218],\n",
+       "         [0.43308486, 0.61300801, 0.01441346],\n",
+       "         [0.41196092, 0.66083358, 0.50274479],\n",
+       "         [0.43239334, 0.36299657, 0.75523639],\n",
+       "         [0.79494342, 0.54508873, 0.50525316]],\n",
+       "\n",
+       "        [[0.9020999 , 0.24772335, 0.47150931],\n",
+       "         [0.89449072, 0.3000441 , 0.08307242],\n",
+       "         [0.01358907, 0.42335199, 0.25529873],\n",
+       "         [0.96579533, 0.61313033, 0.61151241],\n",
+       "         [0.25208166, 0.08639203, 0.21649145],\n",
+       "         [0.92453334, 0.99969662, 0.967767  ],\n",
+       "         [0.5433799 , 0.8949675 , 0.42470272],\n",
+       "         [0.21281325, 0.31257917, 0.76526748],\n",
+       "         [0.27621526, 0.51521548, 0.63729558],\n",
+       "         [0.07299284, 0.48444809, 0.33561529]],\n",
+       "\n",
+       "        [[0.70601855, 0.8317636 , 0.72355013],\n",
+       "         [0.38743008, 0.65819   , 0.98399934],\n",
+       "         [0.27198234, 0.19261583, 0.05592798],\n",
+       "         [0.71332382, 0.83599739, 0.146508  ],\n",
+       "         [0.80672464, 0.30171949, 0.89364836],\n",
+       "         [0.36422076, 0.19461557, 0.39124177],\n",
+       "         [0.55943354, 0.8701974 , 0.32333383],\n",
+       "         [0.112641  , 0.05007738, 0.68745648],\n",
+       "         [0.00967434, 0.52027848, 0.18585381],\n",
+       "         [0.42687039, 0.87908493, 0.58204461]],\n",
+       "\n",
+       "        [[0.62397307, 0.20070912, 0.59996998],\n",
+       "         [0.29361894, 0.44911537, 0.03897977],\n",
+       "         [0.96865747, 0.70915783, 0.96419869],\n",
+       "         [0.81024922, 0.30562819, 0.1801592 ],\n",
+       "         [0.21768026, 0.18380171, 0.34534391],\n",
+       "         [0.16082187, 0.7118644 , 0.22879558],\n",
+       "         [0.28455653, 0.33329776, 0.83359956],\n",
+       "         [0.4687193 , 0.54920126, 0.84046875],\n",
+       "         [0.68282059, 0.22739523, 0.49361669],\n",
+       "         [0.03411186, 0.54415078, 0.41603531]],\n",
+       "\n",
+       "        [[0.31892508, 0.12912804, 0.23726577],\n",
+       "         [0.12371354, 0.80789297, 0.1900606 ],\n",
+       "         [0.08689361, 0.77045651, 0.52718228],\n",
+       "         [0.73578308, 0.05798768, 0.75215889],\n",
+       "         [0.81062456, 0.4348042 , 0.10698637],\n",
+       "         [0.41818395, 0.68385989, 0.55543978],\n",
+       "         [0.42203409, 0.20898943, 0.2305859 ],\n",
+       "         [0.2254489 , 0.92917129, 0.06261501],\n",
+       "         [0.68267239, 0.45162828, 0.74446966],\n",
+       "         [0.09766314, 0.93992282, 0.29803756]],\n",
+       "\n",
+       "        [[0.40713836, 0.21731745, 0.29569526],\n",
+       "         [0.92857264, 0.81727493, 0.50989932],\n",
+       "         [0.46922035, 0.26405319, 0.10015353],\n",
+       "         [0.90563048, 0.77569636, 0.64890088],\n",
+       "         [0.73278409, 0.15416215, 0.21097848],\n",
+       "         [0.80344191, 0.85181936, 0.59977194],\n",
+       "         [0.82625412, 0.4936839 , 0.08758774],\n",
+       "         [0.68558023, 0.31131136, 0.94278468],\n",
+       "         [0.9719367 , 0.18216101, 0.62541528],\n",
+       "         [0.93857558, 0.63881935, 0.07076875]],\n",
+       "\n",
+       "        [[0.72111024, 0.54873648, 0.82728856],\n",
+       "         [0.32253487, 0.8195936 , 0.47359238],\n",
+       "         [0.58336718, 0.28273074, 0.14337095],\n",
+       "         [0.47253913, 0.60175625, 0.00741499],\n",
+       "         [0.96913856, 0.17957238, 0.54324548],\n",
+       "         [0.36436872, 0.95819875, 0.57252854],\n",
+       "         [0.32249913, 0.46273069, 0.21466613],\n",
+       "         [0.83643273, 0.14336048, 0.07904751],\n",
+       "         [0.28560862, 0.37533039, 0.5999072 ],\n",
+       "         [0.054667  , 0.35150756, 0.52614206]],\n",
+       "\n",
+       "        [[0.22527483, 0.10493393, 0.05412538],\n",
+       "         [0.51578913, 0.1276487 , 0.95206018],\n",
+       "         [0.49637907, 0.49117785, 0.19388209],\n",
+       "         [0.0419323 , 0.47122981, 0.54210318],\n",
+       "         [0.42988827, 0.34715218, 0.39231958],\n",
+       "         [0.59039996, 0.95516798, 0.04812591],\n",
+       "         [0.55996745, 0.5526294 , 0.60321441],\n",
+       "         [0.90023733, 0.25475278, 0.00611862],\n",
+       "         [0.81462751, 0.83519043, 0.25030184],\n",
+       "         [0.03942264, 0.4993068 , 0.37277123]],\n",
+       "\n",
+       "        [[0.44700766, 0.80933802, 0.02973002],\n",
+       "         [0.12466789, 0.76516878, 0.00993801],\n",
+       "         [0.73189799, 0.95344014, 0.05378748],\n",
+       "         [0.19737565, 0.45815115, 0.04647837],\n",
+       "         [0.86443086, 0.50759114, 0.45055146],\n",
+       "         [0.31402517, 0.91191013, 0.87016792],\n",
+       "         [0.00926922, 0.06309485, 0.67024439],\n",
+       "         [0.83425368, 0.60553292, 0.44488278],\n",
+       "         [0.25951232, 0.31132679, 0.71798798],\n",
+       "         [0.02348344, 0.81559946, 0.00943836]],\n",
+       "\n",
+       "        [[0.83417892, 0.91241579, 0.04269609],\n",
+       "         [0.33633382, 0.90686503, 0.12641927],\n",
+       "         [0.11315277, 0.35970268, 0.79233967],\n",
+       "         [0.27147799, 0.57898939, 0.29555894],\n",
+       "         [0.55042621, 0.03351812, 0.48741676],\n",
+       "         [0.03064493, 0.27668794, 0.55592554],\n",
+       "         [0.93152056, 0.05873603, 0.99069304],\n",
+       "         [0.87857339, 0.10417172, 0.60625062],\n",
+       "         [0.79475439, 0.39983446, 0.22882984],\n",
+       "         [0.3379021 , 0.58498553, 0.16360706]]]])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X = np.random.rand(1,10,10,3)\n",
+    "X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[[0.48165163, 0.40772024, 0.3972965 ],\n",
+       "         [0.55030197, 0.4651319 , 0.40548024],\n",
+       "         [0.5464923 , 0.64114237, 0.5255094 ],\n",
+       "         [0.3323996 , 0.5333504 , 0.4773672 ]],\n",
+       "\n",
+       "        [[0.42013475, 0.5276699 , 0.48012605],\n",
+       "         [0.6024354 , 0.4213521 , 0.441346  ],\n",
+       "         [0.44936445, 0.43590552, 0.43433058],\n",
+       "         [0.38311118, 0.46002626, 0.4891111 ]],\n",
+       "\n",
+       "        [[0.440164  , 0.5174649 , 0.36716762],\n",
+       "         [0.6406646 , 0.39124662, 0.33782133],\n",
+       "         [0.6299255 , 0.49198008, 0.3468656 ],\n",
+       "         [0.5842741 , 0.3953741 , 0.39856434]],\n",
+       "\n",
+       "        [[0.46311432, 0.544752  , 0.30419722],\n",
+       "         [0.53188324, 0.47697794, 0.26368374],\n",
+       "         [0.49155417, 0.54867196, 0.48500708],\n",
+       "         [0.5358231 , 0.40043873, 0.39848566]]]], dtype=float32)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y = model.predict(X)\n",
+    "y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "in_json = {\n",
+    "    \"in\": (X*1000).round().astype(int).flatten().tolist()\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "out_json = {\n",
+    "    \"out\": (y*1000000).round().astype(int).flatten().tolist()\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"averagePooling2D_stride_input.json\", \"w\") as f:\n",
+    "    json.dump(in_json, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"averagePooling2D_stride_output.json\", \"w\") as f:\n",
+    "    json.dump(out_json, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "tf24",
+   "language": "python",
+   "name": "tf24"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/test/AveragePooling2D.js b/test/AveragePooling2D.js
new file mode 100644
index 0000000..eee7c79
--- /dev/null
+++ b/test/AveragePooling2D.js
@@ -0,0 +1,63 @@
+const chai = require("chai");
+const { Console } = require("console");
+const path = require("path");
+
+const wasm_tester = require("circom_tester").wasm;
+
+const F1Field = require("ffjavascript").F1Field;
+const Scalar = require("ffjavascript").Scalar;
+exports.p = Scalar.fromString("21888242871839275222246405745257275088548364400416034343698204186575808495617");
+const Fr = new F1Field(exports.p);
+
+const assert = chai.assert;
+
+
+
+describe("AveragePooling2D layer test", function () {
+    this.timeout(100000000);
+
+    // AveragePooling with strides==poolSize
+    it("(5,5,3) -> (2,2,3)", async () => {
+        const json = require("../models/averagePooling2D_input.json");
+        const OUTPUT = require("../models/averagePooling2D_output.json");
+
+        const circuit = await wasm_tester(path.join(__dirname, "circuits", "AveragePooling2D_test.circom"));
+        //await circuit.loadConstraints();
+        //assert.equal(circuit.nVars, 76);
+        //assert.equal(circuit.constraints.length, 0);
+
+        const INPUT = {
+            "in": json.in
+        }
+
+        const witness = await circuit.calculateWitness(INPUT, true);
+
+        assert(Fr.eq(Fr.e(witness[0]),Fr.e(1)));
+
+        for (var i=0; i<2*2*3; i++) {
+            assert((witness[i+1]-Fr.e(OUTPUT.out[i]))<Fr.e(500));
+            assert((Fr.e(OUTPUT.out[i])-witness[i+1])<Fr.e(500));
+        }
+    });
+
+    // AveragePooling with strides!=poolSize
+    it("(10,10,3) -> (4,4,3)", async () => {
+        const json = require("../models/averagePooling2D_stride_input.json");
+        const OUTPUT = require("../models/averagePooling2D_stride_output.json");
+
+        const circuit = await wasm_tester(path.join(__dirname, "circuits", "AveragePooling2D_stride_test.circom"));
+
+        const INPUT = {
+            "in": json.in
+        }
+
+        const witness = await circuit.calculateWitness(INPUT, true);
+
+        assert(Fr.eq(Fr.e(witness[0]),Fr.e(1)));
+
+        for (var i=0; i<4*4*3; i++) {
+            assert((witness[i+1]-Fr.e(OUTPUT.out[i]))<Fr.e(1000));
+            assert((Fr.e(OUTPUT.out[i])-witness[i+1])<Fr.e(1000));
+        }
+    });
+});
\ No newline at end of file
diff --git a/test/circuits/AveragePooling2D_stride_test.circom b/test/circuits/AveragePooling2D_stride_test.circom
new file mode 100644
index 0000000..d023fbc
--- /dev/null
+++ b/test/circuits/AveragePooling2D_stride_test.circom
@@ -0,0 +1,6 @@
+pragma circom 2.0.3;
+
+include "../../circuits/AveragePooling2D.circom";
+
+// poolSize!=strides
+component main = AveragePooling2D(10, 10, 3, 3, 2, 111);
\ No newline at end of file
diff --git a/test/circuits/AveragePooling2D_test.circom b/test/circuits/AveragePooling2D_test.circom
new file mode 100644
index 0000000..66e0ec6
--- /dev/null
+++ b/test/circuits/AveragePooling2D_test.circom
@@ -0,0 +1,6 @@
+pragma circom 2.0.3;
+
+include "../../circuits/AveragePooling2D.circom";
+
+// poolSize=strides - default Keras settings
+component main = AveragePooling2D(5, 5, 3, 2, 2, 250);
\ No newline at end of file