lec_9_v7.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: datasets in /Applications/anaconda3/envs/py_main_v2/lib/python3.9/site-packages (2.13.1)\n",
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      "Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /Applications/anaconda3/envs/py_main_v2/lib/python3.9/site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard->torchtnt>=0.0.5->torcheval) (0.5.0)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in /Applications/anaconda3/envs/py_main_v2/lib/python3.9/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard->torchtnt>=0.0.5->torcheval) (3.2.2)\n"
     ]
    }
   ],
   "source": [
    "!pip install datasets torcheval fastprogress fastcore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Applications/anaconda3/envs/py_main_v2/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "import torchvision.transforms.functional as TF\n",
    "import torch\n",
    "from torch import nn,tensor\n",
    "import torch.nn.functional as F\n",
    "from operator import itemgetter\n",
    "from torch.utils.data import default_collate, DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-0.0214,  0.1406, -0.1751,  0.0648, -0.1705,  0.0588, -0.1111, -0.0332,\n",
       "          0.3879, -0.3144],\n",
       "        [ 0.2268,  0.1256, -0.0925,  0.0632, -0.2830,  0.0299,  0.2020,  0.1026,\n",
       "          0.1944, -0.3026]], grad_fn=<AddmmBackward0>)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = 784\n",
    "m = nn.Sequential(nn.Linear(g, 50), nn.ReLU(), nn.Linear(50, 10))\n",
    "t = torch.randn(2, g)\n",
    "m(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset fashion_mnist (/Users/arun/.cache/huggingface/datasets/fashion_mnist/fashion_mnist/1.0.0/0a671f063342996f19779d38c0ab4abef9c64f757b35af8134b331c294d7ba48)\n",
      "100%|██████████| 2/2 [00:00<00:00,  5.90it/s]\n"
     ]
    }
   ],
   "source": [
    "x,y = 'image','label'\n",
    "name = 'fashion_mnist'\n",
    "dsr = load_dataset(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def inplace(f):\n",
    "  def _f(b):\n",
    "    f(b)\n",
    "    return b\n",
    "  return _f\n",
    "\n",
    "@inplace\n",
    "def transform(b):\n",
    "  b[x] = [torch.flatten(TF.to_tensor(i)) for i in b[x]]\n",
    "\n",
    "dst = dsr.with_transform(transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['image', 'label'],\n",
       "        num_rows: 60000\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['image', 'label'],\n",
       "        num_rows: 10000\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_dls(train, valid, batch_size, **kwargs):\n",
    "  return (\n",
    "      DataLoader(train, batch_size=batch_size, shuffle=True, **kwargs),\n",
    "      DataLoader(valid, batch_size=batch_size*2, shuffle=False, **kwargs),\n",
    "  )\n",
    "\n",
    "def collate_dict(trds):\n",
    "  g = itemgetter(*trds.features)\n",
    "  def _f(b):\n",
    "    return g(default_collate(b))\n",
    "  return _f\n",
    "\n",
    "class DataLoaders:\n",
    "  def __init__(self, *ds):\n",
    "    self.train,self.valid = ds[:2]\n",
    "\n",
    "  @classmethod\n",
    "  def from_dd(cls, ds, batch_size, **kwargs):\n",
    "    f = collate_dict(ds['train'])\n",
    "    return cls(*get_dls(*ds.values(), batch_size, collate_fn=f, **kwargs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([tensor([1, 1])], [tensor([2, 2])])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = [{'a': [1], 'b': [2]}, {'a': [1], 'b': [2]}]\n",
    "k = default_collate(t)\n",
    "g = itemgetter('a', 'b')\n",
    "g(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([1024, 784]), torch.Size([1024]))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bs = 1024\n",
    "dls = DataLoaders.from_dd(dst, batch_size=bs)\n",
    "xb,yb = next(iter(dls.train))\n",
    "xb.shape,yb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Basic Learner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Applications/anaconda3/envs/py_main_v2/lib/python3.9/site-packages/fastprogress/fastprogress.py:107: UserWarning: Couldn't import ipywidgets properly, progress bar will use console behavior\n",
      "  warn(\"Couldn't import ipywidgets properly, progress bar will use console behavior\")\n"
     ]
    }
   ],
   "source": [
    "import fastcore.all as fc\n",
    "from torch import optim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Learner:\n",
    "  def __init__(self, model, dls, lr, loss_func=F.cross_entropy, opt_func=optim.SGD):\n",
    "    fc.store_attr()\n",
    "\n",
    "  @property\n",
    "  def training(self):\n",
    "    return self.model.training\n",
    "\n",
    "  def calc_stats(self):\n",
    "    n = len(self.xb)\n",
    "    self.accs.append((self.preds.argmax(dim=1)==self.yb).float().sum())\n",
    "    self.losses.append(self.loss*n)\n",
    "    self.ns.append(n)\n",
    "\n",
    "  def one_batch(self):\n",
    "    self.xb,self.yb = self.batch\n",
    "    self.preds = self.model(self.xb)\n",
    "    self.loss = self.loss_func(self.preds, self.yb)\n",
    "    if self.training:\n",
    "      self.loss.backward()\n",
    "      self.opt.step()\n",
    "      self.opt.zero_grad()\n",
    "    self.calc_stats()\n",
    "\n",
    "  def one_epoch(self, train):\n",
    "    self.model.training = train\n",
    "    self.dl = self.dls.train if train else self.dls.valid\n",
    "    for self.batch in self.dl:\n",
    "      self.one_batch()\n",
    "    n = sum(self.ns)\n",
    "    avgacc = sum(self.accs).item()/n\n",
    "    avgloss = sum(self.losses).item()/n\n",
    "    print(f'train:{train}, acc:{avgacc:.2}, loss:{avgloss:.2}')\n",
    "\n",
    "  def fit(self, n_epochs):\n",
    "    self.n_epochs = n_epochs\n",
    "    self.ns,self.accs,self.losses = [],[],[]\n",
    "    self.opt = self.opt_func(self.model.parameters(), lr=self.lr)\n",
    "    for self.epoch in range(n_epochs):\n",
    "      self.one_epoch(True)\n",
    "      with torch.no_grad():\n",
    "        self.one_epoch(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "m,nh,nout = 784,50,10\n",
    "model = nn.Sequential(nn.Linear(m,nh), nn.ReLU(), nn.Linear(nh,nout))\n",
    "learner = Learner(model, dls, lr=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train:True, acc:0.61, loss:1.2\n",
      "train:False, acc:0.62, loss:1.1\n"
     ]
    }
   ],
   "source": [
    "learner.fit(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Learner with Callbacks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CancelFitException(Exception):\n",
    "  pass\n",
    "\n",
    "class CancelEpochException(Exception):\n",
    "  pass\n",
    "\n",
    "class CancelBatchException(Exception):\n",
    "  pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total batch process: 3\n"
     ]
    }
   ],
   "source": [
    "class Callback:\n",
    "  order = 0\n",
    "\n",
    "def run_cbs(cbs, method_nm, learn=None):\n",
    "  for cb in sorted(cbs, key=lambda x: x.order):\n",
    "    method = getattr(cb, method_nm, None)\n",
    "    if method is not None:\n",
    "      method(learn)\n",
    "\n",
    "class CompletionCB(Callback):\n",
    "  def before_epoch(self, learn):\n",
    "    self.n = 0\n",
    "\n",
    "  def after_batch(self, learn):\n",
    "    self.n += 1\n",
    "\n",
    "  def after_epoch(self, learn):\n",
    "    print(f'total batch process: {self.n}')\n",
    "\n",
    "cbs = [CompletionCB()]\n",
    "run_cbs(cbs, 'before_epoch')\n",
    "run_cbs(cbs, 'after_batch')\n",
    "run_cbs(cbs, 'after_batch')\n",
    "run_cbs(cbs, 'after_batch')\n",
    "run_cbs(cbs, 'after_epoch')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Metric:\n",
    "  def __init__(self, nm):\n",
    "    self.nm = nm\n",
    "    self.reset()\n",
    "\n",
    "  def reset(self):\n",
    "    self.ns,self.vals = [],[]\n",
    "\n",
    "  def add(self, out, targ=None, n=1):\n",
    "    val = self.calc(out, targ)\n",
    "    self.ns.append(n)\n",
    "    self.vals.append(val)\n",
    "\n",
    "  def compute(self):\n",
    "    ns = sum(self.ns) or 1\n",
    "    avgval = sum(tensor(self.vals)*tensor(self.ns)).item()/ns\n",
    "    print(f'{self.nm}: {avgval:.2}')\n",
    "\n",
    "  def calc(self, out, targ=None):\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: 0.62\n"
     ]
    }
   ],
   "source": [
    "loss_metric = Metric('loss')\n",
    "loss_metric.add(0.6, n=32)\n",
    "loss_metric.add(0.9, n=2)\n",
    "loss_metric.compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Accuracy(Metric):\n",
    "  def calc(self, out, targ):\n",
    "    return (out==targ).float().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.45\n"
     ]
    }
   ],
   "source": [
    "acc_metric = Accuracy('accuracy')\n",
    "acc_metric.add(tensor([0, 1, 2, 0, 1, 2]), tensor([0, 1, 1, 2, 1, 0]))\n",
    "acc_metric.add(tensor([1, 1, 2, 0, 1]), tensor([0, 1, 1, 2, 1]))\n",
    "acc_metric.compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torcheval.metrics import Mean, MulticlassAccuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0.4545)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_metric_v2 = MulticlassAccuracy()\n",
    "acc_metric_v2.update(tensor([0, 1, 2, 0, 1, 2]), tensor([0, 1, 1, 2, 1, 0]))\n",
    "acc_metric_v2.update(tensor([1, 1, 2, 0, 1]), tensor([0, 1, 1, 2, 1]))\n",
    "acc_metric_v2.compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0.6176, dtype=torch.float64)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss_metric_v2 = Mean()\n",
    "loss_metric_v2.update(tensor(0.6), weight=32)\n",
    "loss_metric_v2.update(tensor(0.9), weight=2)\n",
    "loss_metric_v2.compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torcheval.metrics.aggregation.mean.Mean"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(Mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections.abc import Mapping\n",
    "from copy import copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# move all data to cpu before performing any metric calculation.\n",
    "# this is to avoid any gradient accumulation + freeup gpu.\n",
    "def to_cpu(x):\n",
    "  if isinstance(x, Mapping):\n",
    "    return {k:to_cpu(i) for k,i in x.items()}\n",
    "  if isinstance(x, list):\n",
    "    return [to_cpu(i) for i in x]\n",
    "  if isinstance(x, tuple):\n",
    "    return tuple(to_cpu(list(x)))\n",
    "  res = x.detach().cpu()\n",
    "  return res.float() if res.dtype==torch.float16 else res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MetricsCB(Callback):\n",
    "  def __init__(self, *ms, **metrics):\n",
    "    for i in ms:\n",
    "      metrics[type(i).__name__] = i\n",
    "    self.metrics = metrics\n",
    "    self.all_metrics = copy(self.metrics)\n",
    "    self.all_metrics['loss'] = self.loss = Mean()\n",
    "\n",
    "  def before_fit(self, learn):\n",
    "    learn.metrics = self\n",
    "\n",
    "  def before_epoch(self, learn):\n",
    "    [i.reset() for i  in self.all_metrics.values()]\n",
    "\n",
    "  def after_batch(self, learn):\n",
    "    x,y,*_ = to_cpu(learn.batch)\n",
    "    for i in list(self.metrics.values()):\n",
    "      i.update(to_cpu(learn.preds), y)\n",
    "    self.loss.update(to_cpu(learn.loss), weight=len(x))\n",
    "\n",
    "  def after_epoch(self, learn):\n",
    "    log = {k:f'{v.compute():.3f}' for k,v in self.all_metrics.items()}\n",
    "    log['epoch'] = f'{learn.epoch}'\n",
    "    log['train'] = 'train' if learn.model.training else 'eval'\n",
    "    self._log(log)\n",
    "\n",
    "  def _log(self, d):\n",
    "    print(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'a': [1]}\n",
      "-----\n",
      "[1, 2]\n",
      "[1, 2]\n"
     ]
    }
   ],
   "source": [
    "t = {'a': [1]}\n",
    "b = copy(t)\n",
    "print(b)\n",
    "print('-----')\n",
    "b['a'].append(2)\n",
    "print(b['a'])\n",
    "print(t['a'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (0): Linear(in_features=784, out_features=50, bias=True)\n",
       "  (1): ReLU()\n",
       "  (2): Linear(in_features=50, out_features=10, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_model():\n",
    "  return nn.Sequential(nn.Linear(m, nh), nn.ReLU(), nn.Linear(nh, nout))\n",
    "get_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Flxible Learner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "class with_cbs:\n",
    "  def __init__(self, nm):\n",
    "    self.nm = nm\n",
    "\n",
    "  def __call__(self, f):\n",
    "    def _f(o, *args, **kwargs):\n",
    "      try:\n",
    "        o.callback(f'before_{self.nm}')\n",
    "        f(o, *args, **kwargs)\n",
    "        o.callback(f'after_{self.nm}')\n",
    "      except globals()[f'Cancel{self.nm.title()}Exception']:\n",
    "        pass\n",
    "      finally:\n",
    "        o.callback(f'cleanup_{self.nm}')\n",
    "    return _f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn.functional as F\n",
    "from torch import optim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Learner:\n",
    "  def __init__(self, model, dls, lr=0.2, loss_func=F.cross_entropy, opt_func=optim.SGD, cbs=None):\n",
    "     cbs = fc.L(cbs)\n",
    "     fc.store_attr()\n",
    "\n",
    "  def callback(self, method_nm):\n",
    "    run_cbs(self.cbs, method_nm, self)\n",
    "\n",
    "  @property\n",
    "  def training(self):\n",
    "    return self.model.training\n",
    "\n",
    "  @with_cbs('batch')\n",
    "  def _one_batch(self):\n",
    "    self.xb,self.yb = self.batch\n",
    "    self.preds = self.model(self.xb)\n",
    "    self.loss = self.loss_func(self.preds, self.yb)\n",
    "    if self.training:\n",
    "      self.loss.backward()\n",
    "      self.opt.step()\n",
    "      self.opt.zero_grad()\n",
    "\n",
    "  @with_cbs('epoch')\n",
    "  def _one_epoch(self):\n",
    "    for self.batch in self.dl:\n",
    "      self._one_batch()\n",
    "\n",
    "  def one_epoch(self, training):\n",
    "    self.model.train(training)\n",
    "    self.dl = self.dls.train if training else self.dls.valid\n",
    "    self._one_epoch()\n",
    "\n",
    "  @with_cbs('fit')\n",
    "  def _fit(self, train, valid):\n",
    "    for self.epoch in self.epochs:\n",
    "      if train:\n",
    "        self.one_epoch(True)\n",
    "      if valid:\n",
    "        self.one_epoch(False)\n",
    "\n",
    "  def fit(self, n_epochs, lr=None, cbs=None, train=True, valid=True):\n",
    "    cbs = fc.L(cbs)\n",
    "    for cb in cbs:\n",
    "      self.cbs.add(cb)\n",
    "    lr = self.lr if lr is None else lr\n",
    "    self.opt = self.opt_func(self.model.parameters(), lr=lr)\n",
    "    self.n_epochs = n_epochs\n",
    "    self.epochs = range(n_epochs)\n",
    "    try:\n",
    "      self._fit(train, valid)\n",
    "    finally:\n",
    "      for cb in cbs:\n",
    "        self.cbs.remove(cb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'accuracy': '0.607', 'loss': '1.176', 'epoch': 0, 'train': 'train'}\n",
      "{'accuracy': '0.701', 'loss': '0.831', 'epoch': 0, 'train': 'eval'}\n"
     ]
    }
   ],
   "source": [
    "model = get_model()\n",
    "cbs = [MetricsCB(accuracy=MulticlassAccuracy())]\n",
    "learner = Learner(model, dls=dls, cbs=cbs)\n",
    "learner.fit(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Progress Bar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastprogress import progress_bar, master_bar\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0poch 1/2 : |----------------------------------------| 0.00% [0/2 00:00<?]\n",
      "1poch 1/2 : |████████████████████--------------------| 50.00% [1/2 00:01<00:01]\n",
      "0poch 2/2 : |----------------------------------------| 0.00% [0/2 00:00<?]      \n",
      "1poch 2/2 : |████████████████████--------------------| 50.00% [1/2 00:01<00:01]\n",
      "Epoch 2/2 :                                                                     \r"
     ]
    }
   ],
   "source": [
    "t = master_bar(range(2))\n",
    "for i in t:\n",
    "  for j in progress_bar(range(2), parent=t, leave=False):\n",
    "    print(j)\n",
    "    time.sleep(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1 :                                                                           \r"
     ]
    }
   ],
   "source": [
    "mb = master_bar(range(1))\n",
    "mb.names = ['cos', 'sin']\n",
    "for i in mb:\n",
    "  for k in progress_bar(range(1000), parent=mb):\n",
    "    if k%300 ==0 or k+1==1000:\n",
    "      pcnt = (1/1000)*k\n",
    "      x1 = torch.linspace(0, pcnt*2*torch.pi, 10000)\n",
    "      y1 = torch.cos(x1)\n",
    "      y2 = torch.sin(x1)\n",
    "      graphs = [[x1,y1], [x1, y2]]\n",
    "      x_bounds,y_bounds = [0, 2*torch.pi], [-1, 1]\n",
    "      mb.update_graph(graphs, x_bounds, y_bounds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ProgressCB(Callback):\n",
    "  order = MetricsCB.order+1\n",
    "\n",
    "  def _log(self, d):\n",
    "    if self.first:\n",
    "      self.mbar.write(list(d.keys()), table=True)\n",
    "      self.first = False\n",
    "    self.mbar.write(list(d.values()), table=True)\n",
    "\n",
    "  def before_fit(self, learn):\n",
    "    self.losses = []\n",
    "    self.val_losses = []\n",
    "    self.first = True\n",
    "    learn.epochs = self.mbar = master_bar(learn.epochs)\n",
    "    if hasattr(learn, 'metrics'):\n",
    "      learn.metrics._log = self._log\n",
    "\n",
    "  def before_epoch(self, learn):\n",
    "    learn.dl = progress_bar(learn.dl, parent=self.mbar, leave=False)\n",
    "\n",
    "  def after_batch(self, learn):\n",
    "    if learn.training:\n",
    "      self.losses.append(learn.loss.item())\n",
    "    else:\n",
    "      self.val_losses.append(learn.loss.item())\n",
    "    self.mbar.update_graph([[fc.L.range(self.losses), self.losses], [fc.L.range(self.val_losses), self.val_losses]])\n",
    "\n",
    "  def after_epoch(self, learn):\n",
    "    if not learn.training:\n",
    "     pass\n",
    "     self.mbar.update_graph([[fc.L.range(self.losses), self.losses], [fc.L.range(self.val_losses), self.val_losses]])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1 : |----------------------------------------| 0.00% [0/59 00:00<?]\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy  loss      epoch     train                                               \n",
      "0.615     1.149     0         train     \n",
      "0.704     0.826     0         eval                                              \n"
     ]
    }
   ],
   "source": [
    "model = get_model()\n",
    "cbs = [MetricsCB(accuracy=MulticlassAccuracy()), ProgressCB()]\n",
    "learner = Learner(model, dls, cbs=cbs)\n",
    "learner.fit(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Learner with Custom Batch Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "from functools import partial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_cbs(cbs, method_nm, learn=None):\n",
    "  for cb in sorted(cbs, key=lambda x: x.order):\n",
    "    method = getattr(cb, method_nm, None)\n",
    "    if method is not None:\n",
    "      method(learn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Learner:\n",
    "  def __init__(self, model, dls, lr=0.2, loss_func=F.cross_entropy, opt_func=optim.SGD, cbs=None):\n",
    "    cbs = fc.L(cbs)\n",
    "    fc.store_attr()\n",
    "\n",
    "  def __getattr__(self, name):\n",
    "    if name in ('predict', 'get_loss', 'backward', 'step', 'zero_grad'):\n",
    "      return partial(self.callback, name)\n",
    "    raise AttributeError(name)\n",
    "\n",
    "  def callback(self, method_nm):\n",
    "    run_cbs(self.cbs, method_nm, self)\n",
    "\n",
    "  @property\n",
    "  def training(self):\n",
    "    return self.model.training\n",
    "\n",
    "  @with_cbs('batch')\n",
    "  def _one_batch(self):\n",
    "    self.xb,self.yb = self.batch\n",
    "    self.predict()\n",
    "    self.callback('after_predict')\n",
    "    self.get_loss()\n",
    "    self.callback('after_loss')\n",
    "    if self.training:\n",
    "      self.backward()\n",
    "      self.callback('after_backward')\n",
    "      self.step()\n",
    "      self.callback('after_step')\n",
    "      self.zero_grad()\n",
    "\n",
    "  @with_cbs('epoch')\n",
    "  def _one_epoch(self):\n",
    "    for self.batch in self.dl:\n",
    "      self._one_batch()\n",
    "\n",
    "  def one_epoch(self, train):\n",
    "    self.model.train(train)\n",
    "    self.dl = self.dls.train if train else self.dls.valid\n",
    "    self._one_epoch()\n",
    "\n",
    "  @with_cbs('fit')\n",
    "  def _fit(self, train, valid):\n",
    "    for self.epoch in self.epochs:\n",
    "      if train:\n",
    "        self.one_epoch(True)\n",
    "      if valid:\n",
    "        self.one_epoch(False)\n",
    "\n",
    "  def fit(self, n_epochs, lr=None, cbs=None, train=True, valid=True):\n",
    "    cbs = fc.L(cbs)\n",
    "    lr = lr if lr else self.lr\n",
    "    self.opt = self.opt_func(self.model.parameters(), lr=lr)\n",
    "    self.n_epochs = n_epochs\n",
    "    self.epochs = range(n_epochs)\n",
    "    try:\n",
    "       self._fit(train, valid)\n",
    "    finally:\n",
    "      for cb in cbs:\n",
    "        self.cbs.remove(cb)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "class TrainCB(Callback):\n",
    "  def predict(self, learn):\n",
    "    learn.preds = learn.model(learn.xb)\n",
    "\n",
    "  def get_loss(self, learn):\n",
    "    learn.loss = learn.loss_func(learn.preds, learn.yb)\n",
    "\n",
    "  def backward(self, learn):\n",
    "    learn.loss.backward()\n",
    "\n",
    "  def step(self, learn):\n",
    "    learn.opt.step()\n",
    "\n",
    "  def zero_grad(self, learn):\n",
    "    learn.opt.zero_grad()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy  loss      epoch     train                                               \n",
      "0.617     1.140     0         train     \n",
      "0.660     0.826     0         eval                                              \n"
     ]
    }
   ],
   "source": [
    "model = get_model()\n",
    "metrics = MetricsCB(accuracy=MulticlassAccuracy())\n",
    "cbs = [metrics, TrainCB(), ProgressCB()]\n",
    "learner = Learner(model, dls, cbs=cbs)\n",
    "learner.fit(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "class TrainLearner(Learner):\n",
    "  def predict(self):\n",
    "    self.preds = self.model(self.xb)\n",
    "\n",
    "  def get_loss(self):\n",
    "    self.loss = self.loss_func(self.preds, self.yb)\n",
    "\n",
    "  def backward(self):\n",
    "    self.loss.backward()\n",
    "\n",
    "  def step(self):\n",
    "    self.opt.step()\n",
    "\n",
    "  def zero_grad(self):\n",
    "    self.opt.zero_grad()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MomentumLearner(TrainLearner):\n",
    "  def __init__(self, model, dls, lr=0.2, loss_func=F.cross_entropy, opt_func=optim.SGD, mom=0.85, cbs=None):\n",
    "    self.mom = mom\n",
    "    super().__init__(model, dls, lr=lr, loss_func=loss_func, opt_func=opt_func, cbs=cbs)\n",
    "\n",
    "  def zero_grad(self):\n",
    "    with torch.no_grad():\n",
    "      for p in self.model.parameters():\n",
    "        p.grad *= self.mom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy  loss      epoch     train                                               \n",
      "0.672     0.955     0         train     \n",
      "0.798     0.581     0         eval                                              \n",
      "0.820     0.515     1         train                                               \n",
      "0.807     0.535     1         eval                                              \n",
      "0.839     0.460     2         train                                               \n",
      "0.833     0.476     2         eval                                              \n",
      "0.844     0.438     3         train                                               \n",
      "0.841     0.446     3         eval                                              \n",
      "0.855     0.412     4         train                                               \n",
      "0.839     0.443     4         eval                                              \n"
     ]
    }
   ],
   "source": [
    "model = get_model()\n",
    "metrics = MetricsCB(accuracy=MulticlassAccuracy())\n",
    "cbs = [metrics, ProgressCB()]\n",
    "learner = MomentumLearner(model, dls, lr=0.1, cbs=cbs, mom=0.85)\n",
    "learner.fit(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LRFinderCB(Callback):\n",
    "  def __init__(self, lr_mult=1.3):\n",
    "    fc.store_attr()\n",
    "\n",
    "  def before_fit(self, learn):\n",
    "    self.min = math.inf\n",
    "    self.lrs,self.losses = [],[]\n",
    "\n",
    "  def after_batch(self, learn):\n",
    "    if not learn.training:\n",
    "      raise CancelEpochException()\n",
    "    self.lrs.append(learn.opt.param_groups[0]['lr'])\n",
    "    loss = to_cpu(learn.loss)\n",
    "    self.losses.append(loss)\n",
    "    if loss < self.min:\n",
    "      self.min = loss\n",
    "    if loss > 3*self.min:\n",
    "      raise CancelFitException()\n",
    "    for g in learn.opt.param_groups:\n",
    "      g['lr'] *= self.lr_mult"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lrfind = LRFinderCB()\n",
    "cbs = [lrfind]\n",
    "model = get_model()\n",
    "learner = MomentumLearner(model, dls, cbs=cbs, lr=1e-4)\n",
    "learner.fit(1)\n",
    "plt.plot(lrfind.lrs, lrfind.losses)\n",
    "plt.xscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.optim.lr_scheduler import ExponentialLR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LRFinderCB(Callback):\n",
    "  def __init__(self, gamma=1.3, max_mult=3):\n",
    "    fc.store_attr()\n",
    "\n",
    "  def before_fit(self, learn):\n",
    "    self.sched = ExponentialLR(learn.opt, gamma=self.gamma)\n",
    "    self.min = math.inf\n",
    "    self.losses,self.lrs = [],[]\n",
    "\n",
    "  def after_batch(self, learn):\n",
    "    if not learn.training:\n",
    "      raise CancelEpochException()\n",
    "    loss = to_cpu(learn.loss)\n",
    "    self.losses.append(loss)\n",
    "    self.lrs.append(learn.opt.param_groups[0]['lr'])\n",
    "    if loss < self.min:\n",
    "      self.min = loss\n",
    "    if math.isnan(loss) or loss>self.max_mult*self.min:\n",
    "      raise CancelFitException()\n",
    "    self.sched.step()\n",
    "\n",
    "  def cleanup_fit(self, learn):\n",
    "    plt.plot(self.lrs, self.losses)\n",
    "    plt.xscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = get_model()\n",
    "cbs = [LRFinderCB()]\n",
    "learner = MomentumLearner(model, dls, lr=1e-4, cbs=cbs)\n",
    "learner.fit(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first 0\n",
      "fit exception\n"
     ]
    }
   ],
   "source": [
    "for i in range(5):\n",
    "  try:\n",
    "    print('first', i)\n",
    "    try:\n",
    "      # raise CancelEpochException()\n",
    "      raise CancelFitException()\n",
    "      print('inside one')\n",
    "    except CancelEpochException:\n",
    "      print('epoch exception')\n",
    "  except CancelFitException:\n",
    "    print('fit exception')\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py_main_v2",
   "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": "3.9.16"
  },
  "widgets": {
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      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_e624cd9e894f46cdba54d615862633c7",
      "placeholder": "​",
      "style": "IPY_MODEL_1241cf76b0804a99b3f2411f1f2bcd9a",
      "value": " 8.85k/8.85k [00:00&lt;00:00, 427kB/s]"
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    "042c0da5880f48199af9840c6b86a11f": {
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      "value": "Downloading readme: 100%"
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