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https://github.com/socathie/circomlib-ml.git
synced 2026-01-10 06:28:08 -05:00
Add Reshape2D circuit and test files
This commit is contained in:
drCathieSo.eth
15
circuits/Reshape2D.circom
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15
circuits/Reshape2D.circom
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pragma circom 2.0.0;
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// Reshape layer with that accepts a 1D input
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template Reshape2D (nRows, nCols, nChannels) {
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signal input in[nRows*nCols*nChannels];
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signal input out[nRows][nCols][nChannels];
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for (var i=0; i<nRows; i++) {
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for (var j=0; j<nCols; j++) {
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for (var k=0; k<nChannels; k++) {
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out[i][j][k] === in[i*nCols*nChannels + j*nChannels + k];
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}
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}
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}
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}
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1
models/reshape2D_input.json
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1
models/reshape2D_input.json
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{"in": [9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807], "out": [9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807]}
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205
models/reshape2d.ipynb
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205
models/reshape2d.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"from tensorflow.keras.layers import Input, Reshape\n",
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"from tensorflow.keras import Model\n",
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"inputs = Input(shape=(75,))\n",
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"x = Reshape((5, 5, 3))(inputs)\n",
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"model = Model(inputs, x)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Model: \"model\"\n",
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"_________________________________________________________________\n",
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" Layer (type) Output Shape Param # \n",
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"=================================================================\n",
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" input_1 (InputLayer) [(None, 75)] 0 \n",
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" \n",
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" reshape (Reshape) (None, 5, 5, 3) 0 \n",
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" \n",
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"=================================================================\n",
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"Total params: 0\n",
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"Trainable params: 0\n",
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"Non-trainable params: 0\n",
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"_________________________________________________________________\n"
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]
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}
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],
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"source": [
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"model.summary()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[0.72327659, 0.97464849, 0.2592479 , 0.63799774, 0.89013732,\n",
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" 0.95867971, 0.06431743, 0.55685192, 0.77031965, 0.09982323,\n",
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" 0.10704737, 0.40713332, 0.57294341, 0.21883552, 0.22967276,\n",
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" 0.6221842 , 0.64159904, 0.684413 , 0.59126341, 0.88438877,\n",
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" 0.56715972, 0.93006015, 0.85704814, 0.79864291, 0.39604054,\n",
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" 0.30495253, 0.38333952, 0.69453548, 0.59207958, 0.30889659,\n",
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" 0.17302571, 0.41351124, 0.37527957, 0.43118255, 0.31526054,\n",
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" 0.12925303, 0.30129156, 0.73921834, 0.98336451, 0.03352392,\n",
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" 0.27839826, 0.6811155 , 0.96320785, 0.16882282, 0.68572833,\n",
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" 0.20924115, 0.30604142, 0.09080768, 0.63244108, 0.55914947,\n",
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" 0.60870048, 0.49377892, 0.9710362 , 0.12959508, 0.62162852,\n",
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" 0.26827067, 0.84771621, 0.40895646, 0.52476578, 0.48532215,\n",
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" 0.27144489, 0.19194784, 0.85410267, 0.11912042, 0.37034274,\n",
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" 0.25759208, 0.88306728, 0.98917787, 0.61814043, 0.49141046,\n",
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" 0.74162286, 0.81722887, 0.4728493 , 0.19955073, 0.42201694]])"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"X = np.random.rand(1,75)\n",
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"X"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"1/1 [==============================] - 0s 44ms/step\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2024-02-04 00:14:24.910151: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"array([[[[0.7232766 , 0.9746485 , 0.2592479 ],\n",
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" [0.63799775, 0.8901373 , 0.9586797 ],\n",
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" [0.06431743, 0.5568519 , 0.77031964],\n",
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" [0.09982323, 0.10704737, 0.4071333 ],\n",
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" [0.5729434 , 0.21883552, 0.22967276]],\n",
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"\n",
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" [[0.6221842 , 0.64159906, 0.684413 ],\n",
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" [0.5912634 , 0.88438874, 0.5671597 ],\n",
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" [0.93006015, 0.85704815, 0.79864293],\n",
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" [0.39604053, 0.30495253, 0.38333952],\n",
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" [0.6945355 , 0.5920796 , 0.3088966 ]],\n",
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"\n",
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" [[0.17302571, 0.41351125, 0.37527958],\n",
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" [0.43118253, 0.31526053, 0.12925303],\n",
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" [0.30129156, 0.73921835, 0.9833645 ],\n",
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" [0.03352392, 0.27839825, 0.6811155 ],\n",
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" [0.96320784, 0.16882282, 0.6857283 ]],\n",
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"\n",
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" [[0.20924115, 0.30604142, 0.09080768],\n",
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" [0.6324411 , 0.55914944, 0.60870045],\n",
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" [0.4937789 , 0.9710362 , 0.12959507],\n",
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" [0.6216285 , 0.26827067, 0.8477162 ],\n",
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" [0.40895647, 0.5247658 , 0.48532215]],\n",
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"\n",
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" [[0.2714449 , 0.19194785, 0.8541027 ],\n",
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" [0.11912042, 0.37034273, 0.25759208],\n",
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" [0.8830673 , 0.9891779 , 0.6181404 ],\n",
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" [0.49141046, 0.74162287, 0.81722885],\n",
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" [0.4728493 , 0.19955073, 0.42201695]]]], dtype=float32)"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"y = model.predict(X)\n",
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"y"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"in_json = {\n",
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" \"in\": (X*1e36).round().astype(int).flatten().tolist(),\n",
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" \"out\": (X*1e36).round().astype(int).flatten().tolist()\n",
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"}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"import json"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"with open(\"reshape2D_input.json\", \"w\") as f:\n",
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" json.dump(in_json, f)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "keras2circom",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.13"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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25
test/Reshape2D.js
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25
test/Reshape2D.js
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const chai = require("chai");
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const path = require("path");
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const wasm_tester = require("circom_tester").wasm;
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const F1Field = require("ffjavascript").F1Field;
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const Scalar = require("ffjavascript").Scalar;
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exports.p = Scalar.fromString("21888242871839275222246405745257275088548364400416034343698204186575808495617");
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const Fr = new F1Field(exports.p);
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const assert = chai.assert;
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describe("Reshape2D layer test", function () {
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this.timeout(100000000);
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it("75 -> (5,5,3)", async () => {
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const INPUT = require("../models/reshape2D_input.json");
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const circuit = await wasm_tester(path.join(__dirname, "circuits", "Reshape2D_test.circom"));
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const witness = await circuit.calculateWitness(INPUT, true);
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assert(Fr.eq(Fr.e(witness[0]),Fr.e(1)));
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});
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});
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5
test/circuits/Reshape2D_test.circom
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5
test/circuits/Reshape2D_test.circom
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pragma circom 2.0.0;
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include "../../circuits/Reshape2D.circom";
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component main = Reshape2D(5, 5, 3);
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