TTModel_exps.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "from functools import partial\n",
    "\n",
    "from collections import OrderedDict\n",
    "from typing import Sequence, Any, Iterable, Optional, List\n",
    "import numpy as np\n",
    "# import click\n",
    "# import click_log\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as tnnf\n",
    "from torchvision.datasets.mnist import MNIST\n",
    "import torchvision.transforms as transforms\n",
    "from torch.utils.data import DataLoader, random_split\n",
    "from torch.optim.lr_scheduler import StepLR\n",
    "from tt_model import *\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = {\n",
    "    'resize_shape': (32, 32),\n",
    "    \n",
    "    'in_factors': (4, 4, 4, 4, 4),\n",
    "    'l1_ranks': (8, 8, 8, 8),\n",
    "    'hidd_out_factors': (2, 2, 2, 2, 2),\n",
    "    'ein_string1': \"nabcde,aoiv,bijw,cjkx,dkly,elpz\",\n",
    "    \n",
    "    'hidd_in_factors': (4, 8),\n",
    "    'l2_ranks': (16,),\n",
    "    'out_factors': (5, 2),\n",
    "    'ein_string2': 'nab,aoix,bipy',\n",
    "}\n",
    "\n",
    "parameters_config = {\n",
    "    'batch_size': 10000,\n",
    "    'train_dataset_size': 40000,\n",
    "    'device': torch.device('cuda'),\n",
    "    'learning_rate': 1e-3,\n",
    "    'n_epochs': 100\n",
    "}\n",
    "\n",
    "class AttrDict(dict):\n",
    "    def __init__(self, *args, **kwargs):\n",
    "        super(AttrDict, self).__init__(*args, **kwargs)\n",
    "        self.__dict__ = self\n",
    "        \n",
    "model_cfg = AttrDict(config)\n",
    "params_cfg = AttrDict(parameters_config)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = TTModel(model_cfg)\n",
    "model = model.to(params_cfg.device)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "MNIST_DATASET_SIZE = 60000\n",
    "NUM_LABELS = 10\n",
    "\n",
    "MNIST_TRANSFORM = transforms.Compose((\n",
    "    transforms.Pad(2),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.1,), (0.2752,))\n",
    "))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "dataset = MNIST('mnist', train=True, download=True, transform=MNIST_TRANSFORM)\n",
    "assert len(dataset) == MNIST_DATASET_SIZE\n",
    "train_dataset, val_dataset = random_split(\n",
    "    dataset, (params_cfg.train_dataset_size, MNIST_DATASET_SIZE - params_cfg.train_dataset_size)\n",
    ")\n",
    "\n",
    "train_loader, val_loader = (\n",
    "    DataLoader(\n",
    "        dataset_, batch_size=params_cfg.batch_size, shuffle=True, pin_memory=(params_cfg.device.type == \"cuda\")\n",
    "    )\n",
    "    for dataset_ in (train_dataset, val_dataset)\n",
    ")\n",
    "\n",
    "\n"
   ]
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   "source": [
    "def acc(model, loader):\n",
    "    accs = []\n",
    "    with torch.no_grad():\n",
    "        for b, gt in tqdm(loader):\n",
    "            out = model(b.to(params_cfg.device)).argmax(1).cpu().numpy()\n",
    "            gt = gt.numpy()\n",
    "            accs.append(sum(out == gt) / len(out))\n",
    "    return sum(accs) / len(accs) \n",
    "\n",
    "\n",
    "\n",
    "lf = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=params_cfg.learning_rate)\n",
    "for ep in range(params_cfg.n_epochs):\n",
    "    for b, gt in tqdm(train_loader):\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        out = model(b.to(params_cfg.device))\n",
    "        loss = lf(out, gt.to(params_cfg.device))\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "    \n",
    "    print(acc(model, val_loader))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
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   ]
  },
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   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
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