batch_synthesis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch as t\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as f\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "t.set_default_device('mps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/miniconda/base/lib/python3.10/site-packages/torch/autograd/__init__.py:251: UserWarning: The operator 'aten::sgn.out' is not currently supported on the MPS backend and will fall back to run on the CPU. This may have performance implications. (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/mps/MPSFallback.mm:13.)\n",
      "  Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5000028014183044\n",
      "0.3435509204864502\n",
      "0.10862623155117035\n",
      "0.07161346077919006\n",
      "tensor(0.0688, device='mps:0', grad_fn=<MeanBackward0>)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 3200x300 with 32 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([512, 32, 787])\n",
      "torch.Size([512, 32, 2])\n"
     ]
    }
   ],
   "source": [
    "class cnvs(nn.Module):\n",
    "    def __init__(self, bs, ch=4):\n",
    "        super().__init__()\n",
    "        self.c1 = nn.Conv2d(bs, bs*ch, 3, groups=bs)\n",
    "        self.c2 = nn.Conv2d(bs*ch, bs*ch, 3, groups=bs)\n",
    "        self.c3 = nn.Conv2d(bs*ch, bs*ch, 3, groups=bs)\n",
    "        self.a = nn.ReLU()\n",
    "        self.m = nn.MaxPool2d(2)\n",
    "        self.l = nn.Parameter(t.randn(2, bs, 16*ch))\n",
    "        self.s = nn.Sigmoid()\n",
    "        self.bs = bs\n",
    "    \n",
    "    def forward(self, x):\n",
    "        x = self.m(self.a(self.c1(x)))\n",
    "        x = self.m(self.a(self.c2(x)))\n",
    "        x = self.a(self.c3(x))\n",
    "        x = x.view(x.shape[0], self.bs, -1)\n",
    "        x = t.einsum('abc,dbc->bad', x, self.l)\n",
    "        x = self.s(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "sn = 32\n",
    "bs = 512\n",
    "\n",
    "mod = cnvs(bs)\n",
    "dist = t.rand(sn, bs, 28, 28)\n",
    "# pad with zeros\n",
    "dat = t.zeros(sn, bs, 32, 32)\n",
    "dat[:, :, 2:30, 2:30] = dist / 8\n",
    "dat.requires_grad = True\n",
    "\n",
    "oneone1 = t.tensor([1,1], dtype=t.float32).expand(sn//4, 2)\n",
    "zerozero1 = t.tensor([0,0], dtype=t.float32).expand(sn//4, 2)\n",
    "oneone2 = t.tensor([1,1], dtype=t.float32).expand(sn//8, 2)\n",
    "zerozero2 = t.tensor([0,0], dtype=t.float32).expand(sn//8, 2)\n",
    "zeroone = t.tensor([0,1], dtype=t.float32).expand(sn//8, 2)\n",
    "onezero = t.tensor([1,0], dtype=t.float32).expand(sn//8, 2)\n",
    "lbl = t.cat([oneone1, zerozero1, oneone2, zerozero2, zeroone, onezero], dim=0)\n",
    "lbl = lbl.repeat(bs, 1, 1)\n",
    "\n",
    "loss = nn.L1Loss()\n",
    "opt = t.optim.Adam([dat, *mod.parameters()], lr=0.02)\n",
    "\n",
    "l = 0\n",
    "for i in range(20):\n",
    "    opt.zero_grad()\n",
    "    out = mod(dat)\n",
    "    l = loss(out, lbl)\n",
    "    l.backward()\n",
    "    opt.step()\n",
    "    if i % 5 == 0:\n",
    "        print(l.item())\n",
    "print(l)\n",
    "\n",
    "y = mod(dat).detach().view(bs, sn, 2)[0].cpu().numpy()\n",
    "data = dat[:, :, 2:30, 2:30]\n",
    "img = data[:, 0, :, :].detach().cpu().numpy()\n",
    "fig, axs = plt.subplots(2, sn//2, figsize=(sn, 3))\n",
    "for i in range(sn//2):\n",
    "    # cmap for rgb\n",
    "    axs[0, i].imshow(img[i], cmap='gray')\n",
    "    axs[0, i].axis('off')\n",
    "    axs[0, i].set_title(f\"{y[i][0]:.2f}, {y[i][1]:.2f}\")\n",
    "for i in range(sn//2, sn):\n",
    "    axs[1, i-sn//2].imshow(img[i], cmap='gray')\n",
    "    axs[1, i-sn//2].axis('off')\n",
    "    axs[1, i-sn//2].set_title(f\"{y[i][0]:.2f}, {y[i][1]:.2f}\")\n",
    "plt.show()\n",
    "\n",
    "data = t.permute(t.flatten(data, 2), (1, 0, 2))\n",
    "labs = mod(dat).detach().view(bs, sn, 2).detach()\n",
    "\n",
    "lebs = t.cat([labs[:, :sn//2, :], t.ones(bs, sn//2, 1)], dim=2)\n",
    "lebs = t.cat([lebs, t.zeros(bs, sn//2, 3)], dim=1)\n",
    "data = t.cat([data, lebs], dim=2)\n",
    "\n",
    "print(data.shape)\n",
    "print(labs.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a transformer\n",
    "class trans(nn.Module):\n",
    "    def __init__(self, dm=800):\n",
    "        super().__init__()\n",
    "        self.inbed = nn.Linear(787, dm)\n",
    "        self.think = nn.TransformerEncoderLayer(d_model=dm, nhead=1)\n",
    "        self.thinker = nn.TransformerEncoder(self.think, num_layers=4)\n",
    "        self.out = nn.Linear(dm, 2)\n",
    "    def forward(self, x):\n",
    "        x = self.inbed(x)\n",
    "        x = self.thinker(x)\n",
    "        return self.out(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/miniconda/base/lib/python3.10/site-packages/torch/nn/modules/transformer.py:282: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.self_attn.batch_first was not True(use batch_first for better inference performance)\n",
      "  warnings.warn(f\"enable_nested_tensor is True, but self.use_nested_tensor is False because {why_not_sparsity_fast_path}\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32, 100, 2])\n"
     ]
    }
   ],
   "source": [
    "learner = trans()\n",
    "print(learner(t.randn(32, 100, 787)).shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of trainable parameters in the learner model: 29862242\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "sn = 32\n",
    "bs = 512\n",
    "\n",
    "oneone1 = t.tensor([1,1], dtype=t.float32).expand(sn//4, 2)\n",
    "zerozero1 = t.tensor([0,0], dtype=t.float32).expand(sn//4, 2)\n",
    "oneone2 = t.tensor([1,1], dtype=t.float32).expand(sn//8, 2)\n",
    "zerozero2 = t.tensor([0,0], dtype=t.float32).expand(sn//8, 2)\n",
    "zeroone = t.tensor([0,1], dtype=t.float32).expand(sn//8, 2)\n",
    "onezero = t.tensor([1,0], dtype=t.float32).expand(sn//8, 2)\n",
    "lbl = t.cat([oneone1, zerozero1, oneone2, zerozero2, zeroone, onezero], dim=0)\n",
    "lbl = lbl.repeat(bs, 1, 1)\n",
    "\n",
    "total_params = sum(p.numel() for p in learner.parameters() if p.requires_grad)\n",
    "print(f\"Number of trainable parameters in the learner model: {total_params}\")\n",
    "\n",
    "\n",
    "loss = nn.L1Loss()\n",
    "optz = t.optim.Adam(learner.parameters(), lr=0.001)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.2375, device='mps:0')\n"
     ]
    }
   ],
   "source": [
    "def lossr(outputs, labels):\n",
    "    # try both permutations of features A and B\n",
    "    loss1 = t.mean(t.mean(t.abs(outputs - labels), dim=2), dim=1)\n",
    "    loss2 = t.mean(t.mean(t.abs(outputs @ t.tensor([[0, 1], [1, 0]], dtype=t.float32) - labels), dim=2), dim=1)\n",
    "    loss_stack = t.stack([loss1, loss2], dim=1)\n",
    "    losser = t.min(loss_stack, dim=1).values\n",
    "    losser = t.mean(losser)\n",
    "    \n",
    "    return losser\n",
    "testout = t.tensor([[[1, 1], [0, 0], [0, .1], [0, 0]]], dtype=t.float32)\n",
    "testlbl = t.tensor([[[1, 1], [0, 0], [1, 0], [0, 1]]], dtype=t.float32)\n",
    "print(lossr(testout, testlbl))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "learner.load_state_dict(t.load(\"./checkpoints/overnight_19688.pt\")['model_state_dict'])\n",
    "optz.load_state_dict(t.load(\"./checkpoints/overnight_19688.pt\")['optimizer_state_dict'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "for n in range(1, 10):\n",
    "    mod = cnvs(bs)\n",
    "    dist = t.rand(sn, bs, 28, 28)\n",
    "    dat = t.zeros(sn, bs, 32, 32)\n",
    "    dat[:, :, 2:30, 2:30] = dist / 8\n",
    "    dat.requires_grad = True\n",
    "\n",
    "    opt = t.optim.Adam([dat, *mod.parameters()], lr=0.02)\n",
    "\n",
    "    l = 0\n",
    "    for i in range(20):\n",
    "        opt.zero_grad()\n",
    "        out = mod(dat)\n",
    "        l = loss(out, lbl)\n",
    "        l.backward()\n",
    "        opt.step()\n",
    "\n",
    "    data = dat[:, :, 2:30, 2:30]\n",
    "    data = t.permute(t.flatten(data, 2), (1, 0, 2))\n",
    "    labs = mod(dat).detach().view(bs, sn, 2).detach()\n",
    "    lebs = t.cat([labs[:, :sn//2, :], t.ones(bs, sn//2, 1)], dim=2)\n",
    "    lebs = t.cat([lebs, t.zeros(bs, sn//2, 3)], dim=1)\n",
    "    data = t.cat([data, lebs], dim=2)\n",
    "\n",
    "    optz.zero_grad()\n",
    "    outp = learner(data)\n",
    "    l = lossr(outp, labs)\n",
    "    l.backward()\n",
    "    optz.step()\n",
    "\n",
    "    # add learner loss to tensorboard\n",
    "\n",
    "    if n % 107 == 0:\n",
    "        print(l.item())\n",
    "        t.save({\n",
    "            'step': n,\n",
    "            'model_state_dict': learner.state_dict(),\n",
    "            'optimizer_state_dict': optz.state_dict(),\n",
    "            'loss': l,\n",
    "            }, f'overnight_{n}.pt')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5000264644622803\n",
      "0.3452422320842743\n",
      "0.11759375035762787\n",
      "0.08237085491418839\n",
      "tensor(0.0776, device='mps:0', grad_fn=<MeanBackward0>)\n",
      "tensor([[1.0000e+00, 1.0000e+00],\n",
      "        [1.0000e+00, 1.0000e+00],\n",
      "        [1.0000e+00, 1.0000e+00],\n",
      "        [1.0000e+00, 1.0000e+00],\n",
      "        [1.0000e+00, 1.0000e+00],\n",
      "        [1.0000e+00, 1.0000e+00],\n",
      "        [1.0000e+00, 1.0000e+00],\n",
      "        [1.0000e+00, 1.0000e+00],\n",
      "        [1.2274e-17, 4.9882e-15],\n",
      "        [3.8137e-16, 4.3332e-16],\n",
      "        [3.7030e-17, 1.0374e-15],\n",
      "        [5.9790e-18, 1.5662e-14],\n",
      "        [1.5134e-15, 5.8040e-14],\n",
      "        [3.2136e-17, 3.8460e-15],\n",
      "        [9.2549e-16, 1.2311e-14],\n",
      "        [2.6027e-17, 2.8957e-15]], device='mps:0')\n",
      "(32, 28, 28)\n",
      "(32, 2)\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 3200x1600 with 32 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "mod = cnvs(bs)\n",
    "dist = t.rand(sn, bs, 28, 28)\n",
    "# pad with zeros\n",
    "dat = t.zeros(sn, bs, 32, 32)\n",
    "dat[:, :, 2:30, 2:30] = dist / 8\n",
    "dat.requires_grad = True\n",
    "\n",
    "oneone1 = t.tensor([1,1], dtype=t.float32).expand(sn//4, 2)\n",
    "zerozero1 = t.tensor([0,0], dtype=t.float32).expand(sn//4, 2)\n",
    "oneone2 = t.tensor([1,1], dtype=t.float32).expand(sn//8, 2)\n",
    "zerozero2 = t.tensor([0,0], dtype=t.float32).expand(sn//8, 2)\n",
    "zeroone = t.tensor([0,1], dtype=t.float32).expand(sn//8, 2)\n",
    "onezero = t.tensor([1,0], dtype=t.float32).expand(sn//8, 2)\n",
    "lbl = t.cat([oneone1, zerozero1, oneone2, zerozero2, zeroone, onezero], dim=0)\n",
    "lbl = lbl.repeat(bs, 1, 1)\n",
    "\n",
    "lossy = nn.L1Loss()\n",
    "opty = t.optim.Adam([dat, *mod.parameters()], lr=0.02)\n",
    "\n",
    "l = 0\n",
    "for i in range(20):\n",
    "    opty.zero_grad()\n",
    "    out = mod(dat)\n",
    "    l = lossy(out, lbl)\n",
    "    l.backward()\n",
    "    opty.step()\n",
    "    if i % 5 == 0:\n",
    "        print(l.item())\n",
    "print(l)\n",
    "\n",
    "\n",
    "\n",
    "data = dat[:, :, 2:30, 2:30]\n",
    "img = data[:, 0, :, :].detach().cpu().numpy()\n",
    "data = t.permute(t.flatten(data, 2), (1, 0, 2))\n",
    "labs = mod(dat).detach().view(bs, sn, 2).detach()\n",
    "print(labs[0, :sn//2, :])\n",
    "lebs = t.cat([labs[:, :sn//2, :], t.ones(bs, sn//2, 1)], dim=2)\n",
    "lebs = t.cat([lebs, t.zeros(bs, sn//2, 3)], dim=1)\n",
    "data = t.cat([data, lebs], dim=2)\n",
    "\n",
    "y = learner(data)[0].detach().cpu().numpy()\n",
    "\n",
    "print(img.shape)\n",
    "print(y.shape)\n",
    "\n",
    "fig, axs = plt.subplots(4, sn//4, figsize=(sn, 16))\n",
    "for i in range(sn//4):\n",
    "    # cmap for rgb\n",
    "    axs[0, i].imshow(img[i], cmap='gray')\n",
    "    axs[0, i].axis('off')\n",
    "    axs[0, i].set_title(f\"{y[i][0]:.2f}, {y[i][1]:.2f}, {lbl[0][i][0]:.2f}, {lbl[0][i][1]:.2f}\")\n",
    "for i in range(sn//4, sn//2):\n",
    "    axs[1, i-sn//4].imshow(img[i], cmap='gray')\n",
    "    axs[1, i-sn//4].axis('off')\n",
    "    axs[1, i-sn//4].set_title(f\"{y[i][0]:.2f}, {y[i][1]:.2f}, {lbl[0][i][0]:.2f}, {lbl[0][i][1]:.2f}\")\n",
    "for i in range(sn//2, sn//2 + sn//4):\n",
    "    axs[2, i-sn//2].imshow(img[i], cmap='gray')\n",
    "    axs[2, i-sn//2].axis('off')\n",
    "    axs[2, i-sn//2].set_title(f\"{y[i][0]:.2f}, {y[i][1]:.2f}, {lbl[0][i][0]:.2f}, {lbl[0][i][1]:.2f}\")\n",
    "for i in range(sn//2 + sn//4, sn):\n",
    "    axs[3, i-(sn//2 + sn//4)].imshow(img[i], cmap='gray')\n",
    "    axs[3, i-(sn//2 + sn//4)].axis('off')\n",
    "    axs[3, i-(sn//2 + sn//4)].set_title(f\"{y[i][0]:.2f}, {y[i][1]:.2f}, {lbl[0][i][0]:.2f}, {lbl[0][i][1]:.2f}\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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