Add UpSampling2D circuit and test files

circuits/UpSampling2D.circom

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+pragma circom 2.0.0;
+
+template UpSampling2D(nRows, nCols, nChannels, size) {
+    signal input in[nRows][nCols][nChannels];
+    signal input out[nRows * size][nCols * size][nChannels];
+
+    // nearest neighbor interpolation
+    for (var i = 0; i < nRows; i++) {
+        for (var j = 0; j < nCols; j++) {
+            for (var c = 0; c < nChannels; c++) {
+                for (var k = 0; k < size; k++) {
+                    for (var l = 0; l < size; l++) {
+                        out[i * size + k][j * size + l][c] === in[i][j][c];
+                    }
+                }
+            }
+        }
+    }
+}

models/upSampling2D_input.json

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+{"in": [[[842674, 497907, 66624], [875287, 832625, 34934]]], "out": [[[842674, 497907, 66624], [842674, 497907, 66624], [875287, 832625, 34934], [875287, 832625, 34934]], [[842674, 497907, 66624], [842674, 497907, 66624], [875287, 832625, 34934], [875287, 832625, 34934]]]}

models/upSampling2d.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.layers import Input, UpSampling2D\n",
+    "from tensorflow.keras import Model\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputs = Input(shape=(1,2,3))\n",
+    "x = UpSampling2D(size=2)(inputs)\n",
+    "model = Model(inputs, x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"model_1\"\n",
+      "_________________________________________________________________\n",
+      " Layer (type)                Output Shape              Param #   \n",
+      "=================================================================\n",
+      " input_2 (InputLayer)        [(None, 1, 2, 3)]         0         \n",
+      "                                                                 \n",
+      " up_sampling2d_1 (UpSampling  (None, 2, 4, 3)          0         \n",
+      " 2D)                                                             \n",
+      "                                                                 \n",
+      "=================================================================\n",
+      "Total params: 0\n",
+      "Trainable params: 0\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[[842674, 497907,  66624],\n",
+       "         [875287, 832625,  34934]]]])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X = (np.random.rand(1,1,2,3)*1e6).astype(int)\n",
+    "X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1/1 [==============================] - 0s 27ms/step\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([[[[842674., 497907.,  66624.],\n",
+       "         [842674., 497907.,  66624.],\n",
+       "         [875287., 832625.,  34934.],\n",
+       "         [875287., 832625.,  34934.]],\n",
+       "\n",
+       "        [[842674., 497907.,  66624.],\n",
+       "         [842674., 497907.,  66624.],\n",
+       "         [875287., 832625.,  34934.],\n",
+       "         [875287., 832625.,  34934.]]]], dtype=float32)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y = model.predict(X)\n",
+    "y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def UpSampling2DInt(nRows, nCols, nChannels, size, input):\n",
+    "    out = [[[None for _ in range(nChannels)] for _ in range(nCols*size)] for _ in range(nRows*size)]\n",
+    "    for i in range(nRows):\n",
+    "        for j in range(nCols):\n",
+    "            for c in range(nChannels):\n",
+    "                for k in range(size):\n",
+    "                    for l in range(size):\n",
+    "                        out[i*size+k][j*size+l][c] = input[i][j][c]\n",
+    "    return out\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_in = [[[int(X[0][i][j][k]) for k in range(3)] for j in range(2)] for i in range(1)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[[842674, 497907, 66624],\n",
+       "  [842674, 497907, 66624],\n",
+       "  [875287, 832625, 34934],\n",
+       "  [875287, 832625, 34934]],\n",
+       " [[842674, 497907, 66624],\n",
+       "  [842674, 497907, 66624],\n",
+       "  [875287, 832625, 34934],\n",
+       "  [875287, 832625, 34934]]]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "out = UpSampling2DInt(1, 2, 3, 2, X_in)\n",
+    "out"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert np.all(y[0].astype(int) == np.array(out))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "in_json = {\n",
+    "    \"in\": X_in,\n",
+    "    \"out\": out\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "with open(\"upSampling2D_input.json\", \"w\") as f:\n",
+    "    json.dump(in_json, f)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "keras2circom",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

test/UpSampling2D.js

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+const chai = require("chai");
+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("UpSampling2D layer test", function () {
+    this.timeout(100000000);
+
+    // UpSampling with strides==poolSize
+    it("(1,2,3) -> (2,4,3)", async () => {
+        const INPUT = require("../models/upSampling2D_input.json");
+
+        const circuit = await wasm_tester(path.join(__dirname, "circuits", "UpSampling2D_test.circom"));
+
+        const witness = await circuit.calculateWitness(INPUT, true);
+
+        assert(Fr.eq(Fr.e(witness[0]),Fr.e(1)));
+    });
+});

test/circuits/UpSampling2D_test.circom

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+pragma circom 2.0.0;
+
+include "../../circuits/UpSampling2D.circom";
+
+component main = UpSampling2D(1, 2, 3, 2);