nbs/24_imgnet_tiny-widish.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tiny Imagenet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['CUDA_VISIBLE_DEVICES']='2'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import shutil,timm,os,torch,random,datasets,math,warnings\n",
    "import fastcore.all as fc, numpy as np, matplotlib as mpl, matplotlib.pyplot as plt\n",
    "import k_diffusion as K, torchvision.transforms as T\n",
    "import torchvision.transforms.functional as TF,torch.nn.functional as F\n",
    "\n",
    "from torch.utils.data import DataLoader,default_collate\n",
    "from pathlib import Path\n",
    "from torch.nn import init\n",
    "from fastcore.foundation import L\n",
    "from torch import nn,tensor\n",
    "from operator import itemgetter\n",
    "from torcheval.metrics import MulticlassAccuracy\n",
    "from functools import partial\n",
    "from torch.optim import lr_scheduler\n",
    "from torch import optim\n",
    "from torchvision.io import read_image,ImageReadMode\n",
    "from glob import glob\n",
    "\n",
    "from miniai.datasets import *\n",
    "from miniai.conv import *\n",
    "from miniai.learner import *\n",
    "from miniai.activations import *\n",
    "from miniai.init import *\n",
    "from miniai.sgd import *\n",
    "from miniai.resnet import *\n",
    "from miniai.augment import *\n",
    "from miniai.accel import *\n",
    "from miniai.training import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastprogress import progress_bar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.set_printoptions(precision=5, linewidth=140, sci_mode=False)\n",
    "torch.manual_seed(1)\n",
    "mpl.rcParams['figure.dpi'] = 70\n",
    "\n",
    "set_seed(42)\n",
    "if fc.defaults.cpus>8: fc.defaults.cpus=8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_data = Path('data')\n",
    "path_data.mkdir(exist_ok=True)\n",
    "path = path_data/'tiny-imagenet-200'\n",
    "\n",
    "url = 'http://cs231n.stanford.edu/tiny-imagenet-200.zip'\n",
    "if not path.exists():\n",
    "    path_zip = fc.urlsave(url, path_data)\n",
    "    shutil.unpack_archive('data/tiny-imagenet-200.zip', 'data')\n",
    "\n",
    "bs = 512\n",
    "\n",
    "class TinyDS:\n",
    "    def __init__(self, path):\n",
    "        self.path = Path(path)\n",
    "        self.files = glob(str(path/'**/*.JPEG'), recursive=True)\n",
    "    def __len__(self): return len(self.files)\n",
    "    def __getitem__(self, i): return self.files[i],Path(self.files[i]).parent.parent.name\n",
    "\n",
    "tds = TinyDS(path/'train')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_anno = path/'val'/'val_annotations.txt'\n",
    "anno = dict(o.split('\\t')[:2] for o in path_anno.read_text().splitlines())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class TinyValDS(TinyDS):\n",
    "    def __getitem__(self, i): return self.files[i],anno[os.path.basename(self.files[i])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vds = TinyValDS(path/'val')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class TfmDS:\n",
    "    def __init__(self, ds, tfmx=fc.noop, tfmy=fc.noop): self.ds,self.tfmx,self.tfmy = ds,tfmx,tfmy\n",
    "    def __len__(self): return len(self.ds)\n",
    "    def __getitem__(self, i):\n",
    "        x,y = self.ds[i]\n",
    "        return self.tfmx(x),self.tfmy(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "id2str = (path/'wnids.txt').read_text().splitlines()\n",
    "str2id = {v:k for k,v in enumerate(id2str)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "xmean,xstd = (tensor([0.47565, 0.40303, 0.31555]), tensor([0.28858, 0.24402, 0.26615]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tfmx(x):\n",
    "    img = read_image(x, mode=ImageReadMode.RGB)/255\n",
    "    return (img-xmean[:,None,None])/xstd[:,None,None]\n",
    "\n",
    "def tfmy(y): return tensor(str2id[y])\n",
    "\n",
    "tfm_tds = TfmDS(tds, tfmx, tfmy)\n",
    "tfm_vds = TfmDS(vds, tfmx, tfmy)\n",
    "\n",
    "def denorm(x): return (x*xstd[:,None,None]+xmean[:,None,None]).clip(0,1)\n",
    "\n",
    "all_synsets = [o.split('\\t') for o in (path/'words.txt').read_text().splitlines()]\n",
    "synsets = {k:v.split(',', maxsplit=1)[0] for k,v in all_synsets if k in id2str}\n",
    "\n",
    "dls = DataLoaders(*get_dls(tfm_tds, tfm_vds, bs=bs, num_workers=8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tfm_batch(b, tfm_x=fc.noop, tfm_y = fc.noop): return tfm_x(b[0]),tfm_y(b[1])\n",
    "\n",
    "tfms = nn.Sequential(T.Pad(4), T.RandomCrop(64),\n",
    "                     T.RandomHorizontalFlip(),\n",
    "                     RandErase())\n",
    "augcb = BatchTransformCB(partial(tfm_batch, tfm_x=tfms), on_val=False)\n",
    "\n",
    "act_gr = partial(GeneralRelu, leak=0.1, sub=0.4)\n",
    "iw = partial(init_weights, leaky=0.1)\n",
    "\n",
    "nfs = (32,64,128,256,512,1024)\n",
    "\n",
    "def get_dropmodel(act=act_gr, nfs=nfs, norm=nn.BatchNorm2d, drop=0.1):\n",
    "    layers = [nn.Conv2d(3, nfs[0], 5, padding=2)]\n",
    "#     layers += [ResBlock(nfs[0], nfs[0], ks=3, stride=1, act=act, norm=norm)]\n",
    "    layers += [ResBlock(nfs[i], nfs[i+1], act=act, norm=norm, stride=2)\n",
    "               for i in range(len(nfs)-1)]\n",
    "    layers += [nn.AdaptiveAvgPool2d(1), nn.Flatten(), nn.Dropout(drop)]\n",
    "    layers += [nn.Linear(nfs[-1], 200, bias=False), nn.BatchNorm1d(200)]\n",
    "    return nn.Sequential(*layers).apply(iw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def res_blocks(n_bk, ni, nf, stride=1, ks=3, act=act_gr, norm=None):\n",
    "    return nn.Sequential(*[\n",
    "        ResBlock(ni if i==0 else nf, nf, stride=stride if i==n_bk-1 else 1, ks=ks, act=act, norm=norm)\n",
    "        for i in range(n_bk)])\n",
    "\n",
    "nbks = (3,2,2,1,1)\n",
    "\n",
    "def get_dropmodel(act=act_gr, nfs=nfs, nbks=nbks, norm=nn.BatchNorm2d, drop=0.2):\n",
    "    layers = [ResBlock(3, nfs[0], ks=5, stride=1, act=act, norm=norm)]\n",
    "    layers += [res_blocks(nbks[i], nfs[i], nfs[i+1], act=act, norm=norm, stride=2)\n",
    "               for i in range(len(nfs)-1)]\n",
    "    layers += [nn.AdaptiveAvgPool2d(1), nn.Flatten(), nn.Dropout(drop)]\n",
    "    layers += [nn.Linear(nfs[-1], 200, bias=False), nn.BatchNorm1d(200)]\n",
    "    return nn.Sequential(*layers).apply(iw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt_func = partial(optim.AdamW, eps=1e-5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics = MetricsCB(accuracy=MulticlassAccuracy())\n",
    "cbs = [DeviceCB(), metrics, ProgressCB(plot=True), MixedPrecision()]\n",
    "\n",
    "epochs = 25\n",
    "lr = 3e-2\n",
    "tmax = epochs * len(dls.train)\n",
    "sched = partial(lr_scheduler.OneCycleLR, max_lr=lr, total_steps=tmax)\n",
    "xtra = [BatchSchedCB(sched), augcb]\n",
    "learn = Learner(get_dropmodel(), dls, F.cross_entropy, lr=lr, cbs=cbs+xtra, opt_func=opt_func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "aug_tfms = nn.Sequential(T.Pad(4), T.RandomCrop(64),\n",
    "                     T.RandomHorizontalFlip(),\n",
    "                     T.TrivialAugmentWide())\n",
    "\n",
    "norm_tfm = T.Normalize(xmean, xstd)\n",
    "erase_tfm = RandErase()\n",
    "\n",
    "from PIL import Image\n",
    "\n",
    "def tfmx(x, aug=False):\n",
    "    x = Image.open(x).convert('RGB')\n",
    "    if aug: x = aug_tfms(x)\n",
    "    x = TF.to_tensor(x)\n",
    "    x = norm_tfm(x)\n",
    "    if aug: x = erase_tfm(x[None])[0]\n",
    "    return x\n",
    "\n",
    "tfm_tds = TfmDS(tds, partial(tfmx, aug=True), tfmy)\n",
    "tfm_vds = TfmDS(vds, tfmx, tfmy)\n",
    "\n",
    "dls = DataLoaders(*get_dls(tfm_tds, tfm_vds, bs=bs, num_workers=8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def conv(ni, nf, ks=3, stride=1, act=nn.ReLU, norm=None, bias=True):\n",
    "    layers = []\n",
    "    if norm: layers.append(norm(ni))\n",
    "    if act : layers.append(act())\n",
    "    layers.append(nn.Conv2d(ni, nf, stride=stride, kernel_size=ks, padding=ks//2, bias=bias))\n",
    "    return nn.Sequential(*layers)\n",
    "\n",
    "def _conv_block(ni, nf, stride, act=act_gr, norm=None, ks=3):\n",
    "    return nn.Sequential(conv(ni, nf, stride=1     , act=act, norm=norm, ks=ks),\n",
    "                         conv(nf, nf, stride=stride, act=act, norm=norm, ks=ks))\n",
    "\n",
    "class ResBlock(nn.Module):\n",
    "    def __init__(self, ni, nf, stride=1, ks=3, act=act_gr, norm=None):\n",
    "        super().__init__()\n",
    "        self.convs = _conv_block(ni, nf, stride, act=act, ks=ks, norm=norm)\n",
    "        self.idconv = fc.noop if ni==nf else conv(ni, nf, ks=1, stride=1, act=None, norm=norm)\n",
    "        self.pool = fc.noop if stride==1 else nn.AvgPool2d(2, ceil_mode=True)\n",
    "\n",
    "    def forward(self, x): return self.convs(x) + self.idconv(self.pool(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_dropmodel(act=act_gr, nfs=nfs, nbks=nbks, norm=nn.BatchNorm2d, drop=0.2):\n",
    "    layers = [nn.Conv2d(3, nfs[0], 5, padding=2)]\n",
    "    layers += [res_blocks(nbks[i], nfs[i], nfs[i+1], act=act, norm=norm, stride=2)\n",
    "               for i in range(len(nfs)-1)]\n",
    "    layers += [act_gr(), norm(nfs[-1]), nn.AdaptiveAvgPool2d(1), nn.Flatten(), nn.Dropout(drop)]\n",
    "    layers += [nn.Linear(nfs[-1], 200, bias=False), nn.BatchNorm1d(200)]\n",
    "    return nn.Sequential(*layers).apply(iw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 50\n",
    "lr = 0.1\n",
    "tmax = epochs * len(dls.train)\n",
    "sched = partial(lr_scheduler.OneCycleLR, max_lr=lr, total_steps=tmax)\n",
    "xtra = [BatchSchedCB(sched)]\n",
    "model = get_dropmodel(nbks=(1,2,4,2,2), nfs=(32, 64, 128, 512, 768, 1024), drop=0.1)\n",
    "learn = Learner(model, dls, F.cross_entropy, lr=lr, cbs=cbs+xtra, opt_func=opt_func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
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       "        /* gets rid of default border in Firefox and Opera. */\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>accuracy</th>\n",
       "      <th>loss</th>\n",
       "      <th>epoch</th>\n",
       "      <th>train</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0.022</td>\n",
       "      <td>5.068</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0.037</td>\n",
       "      <td>4.833</td>\n",
       "      <td>0</td>\n",
       "      <td>eval</td>\n",
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       "    <tr>\n",
       "      <td>0.046</td>\n",
       "      <td>4.766</td>\n",
       "      <td>1</td>\n",
       "      <td>train</td>\n",
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       "    <tr>\n",
       "      <td>0.065</td>\n",
       "      <td>4.545</td>\n",
       "      <td>1</td>\n",
       "      <td>eval</td>\n",
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       "    <tr>\n",
       "      <td>0.072</td>\n",
       "      <td>4.501</td>\n",
       "      <td>2</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0.078</td>\n",
       "      <td>4.342</td>\n",
       "      <td>2</td>\n",
       "      <td>eval</td>\n",
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       "    <tr>\n",
       "      <td>0.099</td>\n",
       "      <td>4.268</td>\n",
       "      <td>3</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0.135</td>\n",
       "      <td>3.958</td>\n",
       "      <td>3</td>\n",
       "      <td>eval</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0.137</td>\n",
       "      <td>4.010</td>\n",
       "      <td>4</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0.134</td>\n",
       "      <td>4.026</td>\n",
       "      <td>4</td>\n",
       "      <td>eval</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0.166</td>\n",
       "      <td>3.801</td>\n",
       "      <td>5</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0.162</td>\n",
       "      <td>3.899</td>\n",
       "      <td>5</td>\n",
       "      <td>eval</td>\n",
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       "    <tr>\n",
       "      <td>0.195</td>\n",
       "      <td>3.635</td>\n",
       "      <td>6</td>\n",
       "      <td>train</td>\n",
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       "    <tr>\n",
       "      <td>0.212</td>\n",
       "      <td>3.536</td>\n",
       "      <td>6</td>\n",
       "      <td>eval</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0.214</td>\n",
       "      <td>3.503</td>\n",
       "      <td>7</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0.242</td>\n",
       "      <td>3.391</td>\n",
       "      <td>7</td>\n",
       "      <td>eval</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0.237</td>\n",
       "      <td>3.382</td>\n",
       "      <td>8</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0.260</td>\n",
       "      <td>3.325</td>\n",
       "      <td>8</td>\n",
       "      <td>eval</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0.252</td>\n",
       "      <td>3.293</td>\n",
       "      <td>9</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0.300</td>\n",
       "      <td>3.074</td>\n",
       "      <td>9</td>\n",
       "      <td>eval</td>\n",
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       "    <tr>\n",
       "      <td>0.269</td>\n",
       "      <td>3.202</td>\n",
       "      <td>10</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0.287</td>\n",
       "      <td>3.198</td>\n",
       "      <td>10</td>\n",
       "      <td>eval</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0.286</td>\n",
       "      <td>3.118</td>\n",
       "      <td>11</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0.295</td>\n",
       "      <td>3.080</td>\n",
       "      <td>11</td>\n",
       "      <td>eval</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0.296</td>\n",
       "      <td>3.055</td>\n",
       "      <td>12</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0.307</td>\n",
       "      <td>3.070</td>\n",
       "      <td>12</td>\n",
       "      <td>eval</td>\n",
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       "    <tr>\n",
       "      <td>0.309</td>\n",
       "      <td>2.984</td>\n",
       "      <td>13</td>\n",
       "      <td>train</td>\n",
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       "    <tr>\n",
       "      <td>0.323</td>\n",
       "      <td>3.021</td>\n",
       "      <td>13</td>\n",
       "      <td>eval</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0.319</td>\n",
       "      <td>2.931</td>\n",
       "      <td>14</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0.334</td>\n",
       "      <td>2.866</td>\n",
       "      <td>14</td>\n",
       "      <td>eval</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0.333</td>\n",
       "      <td>2.868</td>\n",
       "      <td>15</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0.312</td>\n",
       "      <td>2.970</td>\n",
       "      <td>15</td>\n",
       "      <td>eval</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0.343</td>\n",
       "      <td>2.813</td>\n",
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",
      "text/plain": [
       "<Figure size 420x280 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.fit(epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(learn.model, 'models/inettiny-widish-50')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### fin -"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python3",
   "language": "python",
   "name": "python3"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "0abad3eee1904c2bb8a2f963fed5fba6": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "13c327d321444b32aa4349ec81e98cde": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "184b1cfa56be41c7845062a4e7c5fa59": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
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