train_adv_car.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mPrecompiling RobotZoo [74be38bb-dcc2-4b9e-baf3-d6373cd95f10]\n"
     ]
    }
   ],
   "source": [
    "using Revise\n",
    "using LazySets\n",
    "using DifferentialEquations\n",
    "using LazySets\n",
    "using ProgressBars\n",
    "using JLD2\n",
    "using Flux\n",
    "using LinearAlgebra\n",
    "using Zygote\n",
    "using ReverseDiff\n",
    "using Plots\n",
    "using Statistics\n",
    "using Optimisers, ParameterSchedulers\n",
    "using RobotDynamics\n",
    "using RobotZoo\n",
    "using Random\n",
    "import RobotDynamics as RD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "using CUDA\n",
    "\n",
    "# Check if GPU is available\n",
    "if CUDA.functional()\n",
    "    device!(2)\n",
    "    CUDA.allowscalar(false)  # Disallow scalar operations on the GPU (optional)\n",
    "else\n",
    "    println(\"GPU is not available. Using CPU.\")\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hyperrectangle{Float64, Vector{Float64}, Vector{Float64}}([2.0, 1.0, 1.5707963267948966], [0.5, 1.0, 1.5707963267948966])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = Hyperrectangle(low = [0, 0, 0], high = [4,4, π])\n",
    "U = Hyperrectangle(low = [-1, -1], high = [1,1])\n",
    "X_unsafe = Hyperrectangle(low = [1.5, 0,0], high = [2.5,2, π])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3×10001 Matrix{AbstractVector{Float64}}:\n",
       " [2.1394, 1.91681, 0.801756]  …  [1.66455, 1.99508, 1.00014]\n",
       " [0.290693, -0.214964]           [0.615991, 0.570133]\n",
       " [0.0]                           [0.0]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_training_data = load_object(\"car_seq_training_data.jld2\")\n",
    "raw_test_data = load_object(\"car_seq_test_data.jld2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(sum(raw_training_data[3, :]), size(raw_training_data[3, :])) = ([2.5e6], (4490000,))\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0.0%┣                                              ┫ 0/20 [00:02<00:-43, -2s/it]\n",
      "\u001b[33m\u001b[1m┌ \u001b[22m\u001b[39m\u001b[33m\u001b[1mWarning: \u001b[22m\u001b[39mLayer with Float32 parameters got Float64 input.\n",
      "\u001b[33m\u001b[1m│ \u001b[22m\u001b[39m  The input will be converted, but any earlier layers may be very slow.\n",
      "\u001b[33m\u001b[1m│ \u001b[22m\u001b[39m  layer = Dense(3 => 16, relu)  \u001b[90m# 64 parameters\u001b[39m\n",
      "\u001b[33m\u001b[1m│ \u001b[22m\u001b[39m  summary(x) = \"3×128 Matrix{Float64}\"\n",
      "\u001b[33m\u001b[1m└ \u001b[22m\u001b[39m\u001b[90m@ Flux ~/.julia/packages/Flux/n3cOc/src/layers/stateless.jl:60\u001b[39m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(epoch, loss, test_loss) = (1, 0.004457999215399697, 0.008754111781976038)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "5.0%┣██                                      ┫ 1/20 [03:34:38<Inf:Inf, InfGs/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(epoch, loss, test_loss) = (2, 0.0045352203943739445, 0.0027486134805809865)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "10.0%┣███▋                                ┫ 2/20 [06:16:50<113:02:58, 22.6ks/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(epoch, loss, test_loss) = (3, 0.0005384272028262268, 0.0029274569641504207)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "15.0%┣█████▌                               ┫ 3/20 [09:11:16<78:05:45, 16.5ks/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(epoch, loss, test_loss) = (4, 0.0008145125870184383, 0.00478295030559233)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "20.0%┣███████▍                             ┫ 4/20 [12:02:23<64:12:44, 14.4ks/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(epoch, loss, test_loss) = (5, 0.001290942884855604, 0.004127572520508326)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "25.0%┣█████████▎                           ┫ 5/20 [14:50:26<55:39:09, 13.4ks/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(epoch, loss, test_loss) = (6, 0.0005012323810134213, 0.0019385678998571313)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "30.0%┣███████████                          ┫ 6/20 [17:37:36<49:21:17, 12.7ks/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(epoch, loss, test_loss) = (7, 0.0008879941159856069, 0.0017635500855709228)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "35.0%┣█████████████                        ┫ 7/20 [20:42:03<44:51:06, 12.4ks/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(epoch, loss, test_loss) = (8, 0.001337591362700942, 0.0036801435748395187)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "40.0%┣██████████████▉                      ┫ 8/20 [23:53:00<40:56:34, 12.3ks/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(epoch, loss, test_loss) = (9, 0.0016689274066685146, 0.0019066233792381382)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "45.0%┣████████████████▋                    ┫ 9/20 [26:42:36<36:43:34, 12.0ks/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(epoch, loss, test_loss) = (10, 1.8776555260881834e-5, 0.0032943576377008742)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "50.0%┣██████████████████                  ┫ 10/20 [29:22:53<32:38:45, 11.8ks/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(epoch, loss, test_loss) = (11, 0.0013709432859345724, 0.002497215998212575)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "55.0%┣███████████████████▉                ┫ 11/20 [32:14:55<29:01:26, 11.6ks/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(epoch, loss, test_loss) = (12, 0.0040661475875264355, 8.883145379510089e-5)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "60.0%┣█████████████████████▋              ┫ 12/20 [35:01:07<25:28:05, 11.5ks/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(epoch, loss, test_loss) = (13, 0.001647996045704606, 0.0014808305104058762)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "65.0%┣███████████████████████▍            ┫ 13/20 [37:18:33<21:45:49, 11.2ks/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(epoch, loss, test_loss) = (14, 0.002165996255952175, 0.0029863568953501646)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "70.0%┣█████████████████████████▏          ┫ 14/20 [39:51:49<18:23:55, 11.0ks/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(epoch, loss, test_loss) = (15, 0.0027971612643864353, 0.0008512081164398264)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "75.0%┣███████████████████████████         ┫ 15/20 [42:37:32<15:13:24, 11.0ks/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(epoch, loss, test_loss) = (16, 0.00039403167931867145, 0.0032530453054710725)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "80.0%┣████████████████████████████▉       ┫ 16/20 [45:22:57<12:06:07, 10.9ks/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(epoch, loss, test_loss) = (17, 9.642525791818132e-5, 0.002031198119520181)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "85.0%┣██████████████████████████████▋     ┫ 17/20 [48:18:57<09:03:33, 10.9ks/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(epoch, loss, test_loss) = (18, 7.513949993080406e-5, 0.0016723902212551986)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "90.0%┣████████████████████████████████▍   ┫ 18/20 [51:24:05<06:02:50, 10.9ks/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(epoch, loss, test_loss) = (19, 4.071845682836247e-5, 0.003600745450094169)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "95.0%┣██████████████████████████████████▏ ┫ 19/20 [54:14:19<03:00:48, 10.8ks/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(epoch, loss, test_loss) = (20, 0.0014981105940953502, 0.0009802720561346938)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100.0%┣██████████████████████████████████████┫ 20/20 [57:02:58<00:00, 10.8ks/it]\n",
      "100.0%┣██████████████████████████████████████┫ 20/20 [57:02:58<00:00, 10.8ks/it]\n"
     ]
    }
   ],
   "source": [
    "# TODO: find A B as with a batchsize dim on the last, using jacobian, Delta should be with the same size of x\n",
    "include(\"affine_dynamics.jl\")\n",
    "using Flux\n",
    "using ProgressBars\n",
    "using JLD2\n",
    "\n",
    "batchsize = 128\n",
    "@show sum(raw_training_data[3,:]), size(raw_training_data[3,:])\n",
    "state_dim = size(reduce(hcat,raw_training_data[1,:]))[1]\n",
    "\n",
    "training_data = raw_training_data\n",
    "test_data = raw_test_data\n",
    "\n",
    "model = Chain(\n",
    "    Dense(3 => 16, relu),   # activation function inside layer\n",
    "    Dense(16 => 64, relu),   # activation function inside layer\n",
    "    Dense(64 => 16, relu),   # activation function inside layer\n",
    "    Dense(16 => 1)\n",
    ")\n",
    "\n",
    "# model = Chain(\n",
    "#     Dense(4 => 16, sigmoid),   # activation function inside layer\n",
    "#     Dense(16 => 64, sigmoid),   # activation function inside layer\n",
    "#     Dense(64 => 16, sigmoid),   # activation function inside layer\n",
    "#     Dense(16 => 1)\n",
    "# )\n",
    "\n",
    "# model = Chain(\n",
    "#     Dense(4 => 16, tanh_fast),   # activation function inside layer\n",
    "#     Dense(16 => 64, tanh_fast),   # activation function inside layer\n",
    "#     Dense(64 => 16, tanh_fast),   # activation function inside layer\n",
    "#     Dense(16 => 1)\n",
    "# )\n",
    "\n",
    "train_loader = Flux.DataLoader(training_data, batchsize=batchsize, shuffle=true)\n",
    "test_loader = Flux.DataLoader(test_data, batchsize=batchsize, shuffle=true)\n",
    "\n",
    "λ = 1\n",
    "μ = 0.1\n",
    "α = 0.0\n",
    "use_pgd=true\n",
    "use_adv = true\n",
    "lr_lambda = 0.0001\n",
    "mu = 0.9\n",
    "V_lambda=0\n",
    "\n",
    "lr_decay_rate = 0.2\n",
    "lr_decay_epoch =4\n",
    "total_epoch = 20\n",
    "\n",
    "ini_lr = 0.01\n",
    "optim = Flux.setup(Flux.Optimise.NADAM(ini_lr, (0.9, 0.999), 0.1), model)  # will store optimiser momentum, etc.\n",
    "sched = ParameterSchedulers.Stateful(Step(ini_lr, lr_decay_rate, lr_decay_epoch)) # setup schedule of your choice\n",
    "\n",
    "eps = 1e-3\n",
    "dyn_model = RobotZoo.DubinsCar()\n",
    "n,m = RD.dims(dyn_model)\n",
    "\n",
    "training_losses = []\n",
    "test_losses = []\n",
    "least_loss = 1000\n",
    "test_loss = 0\n",
    "loss = 0\n",
    "for epoch in ProgressBar(1:total_epoch)\n",
    "    training_loss_epcoh = []\n",
    "    test_loss_epcoh = []\n",
    "    ∇l_lambda = 0\n",
    "    ∇l_alpha = 0\n",
    "    for item in train_loader\n",
    "        ∇l_alpha = 0\n",
    "        ∇l_lambda = 0\n",
    "        x_batch = reduce(hcat,item[1,:])\n",
    "        u_batch = reduce(hcat,item[2,:])\n",
    "        y_init_batch = reduce(hcat,item[3,:])\n",
    "        A = []\n",
    "        B = []\n",
    "        Δ = []\n",
    "        for i in 1:size(x_batch, 2)\n",
    "            z = RD.KnotPoint(x_batch[:, i],u_batch[:, i],0.0,1e-3 ) \n",
    "            ∇f = zeros(n, n + m)\n",
    "            RD.jacobian!(RD.StaticReturn(), RD.ForwardAD(), dyn_model, ∇f, zeros(n), z)\n",
    "            A_ = ∇f[:, 1:n]\n",
    "            B_ = ∇f[:, n+1:end]\n",
    "            Δ_ = RobotDynamics.dynamics(dyn_model, x_batch[:, i] .- eps, u_batch[:, i].-eps) - A_ * (x_batch[:, i].-eps) - B_ * (u_batch[:, i] .- eps)\n",
    "            push!(A, A_)\n",
    "            push!(B, B_)\n",
    "            push!(Δ, Δ_)\n",
    "        end\n",
    "        A = cat(A..., dims=3)\n",
    "        B = cat(B..., dims=3)\n",
    "        Δ = cat(Δ..., dims=2)\n",
    "        use_pgd && (u_batch = pgd_find_u_notce(model, A, x_batch, B, u_batch, U; α = α,Δ=Δ))\n",
    "        if use_adv\n",
    "            X_lcoal = [Hyperrectangle(x_batch[:, i], radius_hyperrectangle(X) ./ 20) for i=1:size(x_batch)[2]]\n",
    "            x_batch = pgd_find_x_notce(model, A, x_batch, B, u_batch, X_lcoal; α = α,Δ=Δ)\n",
    "        else\n",
    "            x_batch = x_batch\n",
    "        end\n",
    "\n",
    "        use_pgd && (u_batch = pgd_find_u_notce(model, A, x_batch, B, u_batch, U; α = α,Δ=Δ))\n",
    "        training_loss, grads = Flux.withgradient(model) do m \n",
    "            loss_naive_safeset(m, x_batch, y_init_batch) + λ .* loss_naive_fi(m, A, x_batch, B, u_batch,y_init_batch;use_pgd=false,use_adv = false, α=α,Δ=Δ) + μ .* loss_regularization(m, x_batch, y_init_batch)\n",
    "        end\n",
    "        # Update the parameters so as to reduce the objective,\n",
    "        # according the chosen optimisation rule:\n",
    "        Flux.update!(optim, model, grads[1])\n",
    "\n",
    "        loss = loss_naive_safeset(model, x_batch, y_init_batch) + λ .* loss_naive_fi(model, A, x_batch, B, u_batch,y_init_batch;use_pgd=use_pgd,use_adv = false, α=α,Δ=Δ) + μ .* loss_regularization(model, x_batch, y_init_batch)\n",
    "        push!(training_loss_epcoh, loss)  # logging, outside gradient context\n",
    "        \n",
    "    end\n",
    "    for item in test_loader\n",
    "        x_batch = reduce(hcat,item[1,:])\n",
    "        u_batch = reduce(hcat,item[2,:])\n",
    "        y_init_batch = reduce(hcat,item[3,:])\n",
    "        A = []\n",
    "        B = []\n",
    "        Δ = []\n",
    "        for i in 1:size(x_batch, 2)\n",
    "            z = RD.KnotPoint(x_batch[:, i],u_batch[:, i],0.0,1e-3 ) \n",
    "            ∇f = zeros(n, n + m)\n",
    "            RD.jacobian!(RD.StaticReturn(), RD.ForwardAD(), dyn_model, ∇f, zeros(n), z)\n",
    "            A_ = ∇f[:, 1:n]\n",
    "            B_ = ∇f[:, n+1:end]\n",
    "            Δ_ = RobotDynamics.dynamics(dyn_model, x_batch[:, i] .- eps, u_batch[:, i].-eps) - A_ * (x_batch[:, i].-eps) - B_ * (u_batch[:, i] .- eps)\n",
    "            push!(A, A_)\n",
    "            push!(B, B_)\n",
    "            push!(Δ, Δ_)\n",
    "        end\n",
    "        A = cat(A..., dims=3)\n",
    "        B = cat(B..., dims=3)\n",
    "        Δ = cat(Δ..., dims=2)\n",
    "\n",
    "        test_loss =  loss_naive_safeset(model, x_batch, y_init_batch) + λ .* loss_naive_fi(model, A, x_batch, B, u_batch,y_init_batch;use_pgd=use_pgd,use_adv = false, α=α,Δ=Δ) + μ .* loss_regularization(model, x_batch, y_init_batch)\n",
    "        push!(test_loss_epcoh, test_loss)\n",
    "    end\n",
    "    nextlr = ParameterSchedulers.next!(sched) # advance schedule\n",
    "    Optimisers.adjust!(optim, nextlr) # update optimizer state, by default this changes the learning rate `eta`\n",
    "    lr_lambda = lr_lambda * lr_decay_rate^(floor(epoch / lr_decay_epoch))\n",
    "    @show epoch, loss, test_loss\n",
    "    model_state = Flux.state(model)\n",
    "    jldsave(\"car_adv20_model_1_0_0.1_pgd_relu_$epoch.jld2\"; model_state)\n",
    "    push!(training_losses, sum(training_loss_epcoh)) \n",
    "    push!(test_losses, sum(test_loss_epcoh))\n",
    "\n",
    "end\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using Plots\n",
    "\n",
    "function plot_loss(train_loss::Vector, test_loss::Vector; xlabel=\"Epoch\", ylabel=\"Loss\", title=\"Training and Test Loss\")\n",
    "    plot(train_loss ./ (size(training_data[3,:] / batchsize)), label=\"Training\", xlabel=xlabel, ylabel=ylabel, title=title)\n",
    "    plot!(test_loss ./ (size(test_data[3,:] / batchsize)), label=\"Test\")\n",
    "end\n",
    "\n",
    "plot_loss(training_losses, test_losses)"
   ]
  }
 ],
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   "mimetype": "application/julia",
   "name": "julia",
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}