DeOldify/ColorizeTraining.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%reload_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import fastai\n",
    "from fastai import *\n",
    "from fastai.vision import *\n",
    "from fastai.callbacks import *\n",
    "from fastai.vision.gan import *\n",
    "from fasterai.dataset import *\n",
    "from fasterai.visualize import *\n",
    "from fasterai.tensorboard import *\n",
    "from fasterai.loss import *\n",
    "from fasterai.critics import *\n",
    "from fasterai.generators import *\n",
    "from pathlib import Path\n",
    "from itertools import repeat\n",
    "torch.cuda.set_device(2)\n",
    "plt.style.use('dark_background')\n",
    "torch.backends.cudnn.benchmark=True\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "IMAGENET = Path('data/imagenet/ILSVRC/Data/CLS-LOC')\n",
    "BWIMAGENET = Path('data/imagenet/ILSVRC/Data/CLS-LOC/bandw')\n",
    "\n",
    "proj_id = 'colorizeV5o'\n",
    "TENSORBOARD_PATH = Path('data/tensorboard/' + proj_id)\n",
    "\n",
    "gpath = IMAGENET.parent/(proj_id + '_gen_64.h5')\n",
    "dpath = IMAGENET.parent/(proj_id + '_critic_64.h5')\n",
    "\n",
    "torch.backends.cudnn.benchmark=True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def decolorize(fn:str, i:int):\n",
    "    dest = BWIMAGENET/fn.relative_to(IMAGENET)\n",
    "    dest.parent.mkdir(parents=True, exist_ok=True)\n",
    "    img = PIL.Image.open(fn).convert('LA').convert('RGB')\n",
    "    img.save(dest)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uncomment the first time you run this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#il = ImageItemList.from_folder(IMAGENET/'val')\n",
    "#parallel(decolorize, il.items, max_workers=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#il = ImageItemList.from_folder(IMAGENET/'train')\n",
    "#parallel(decolorize, il.items, max_workers=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data(sz:int, bs:int, keep_pct:float):\n",
    "    return get_colorize_data(sz=sz, bs=bs, crappy_path=BWIMAGENET, good_path=IMAGENET, \n",
    "                             random_seed=None, keep_pct=keep_pct,num_workers=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save():\n",
    "    learn_gen.save(proj_id + '_gen_' + str(sz))\n",
    "    learn_crit.save(proj_id + '_crit_' + str(sz))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load():\n",
    "    learn_gen.load(proj_id + '_gen_' + str(sz))\n",
    "    learn_crit.load(proj_id + '_crit_' + str(sz))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def colorize_gen_learner_exp(data:ImageDataBunch, gen_loss=FeatureLoss(), arch=models.resnet34):\n",
    "    return unet_learner2(data, arch, wd=1e-3, blur=True, norm_type=NormType.Spectral,\n",
    "                        self_attention=True, y_range=(-3.,3.), loss_func=gen_loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Needed to instantiate critic but not actually used\n",
    "sz=64\n",
    "bs=32\n",
    "\n",
    "data = get_data(sz=sz, bs=bs, keep_pct=1.0)\n",
    "learn_crit = colorize_crit_learner(data=data, nf=256)\n",
    "learn_crit.unfreeze()\n",
    "\n",
    "gen_loss = FeatureLoss2(gram_wgt=5e3)\n",
    "learn_gen = colorize_gen_learner_exp(data=data)\n",
    "\n",
    "switcher = partial(AdaptiveGANSwitcher, critic_thresh=0.65)\n",
    "learn = GANLearner.from_learners(learn_gen, learn_crit, weights_gen=(1.0,1.0), show_img=False, switcher=switcher,\n",
    "                                 opt_func=partial(optim.Adam, betas=(0.,0.99)), wd=1e-3)\n",
    "\n",
    "learn.callback_fns.append(partial(GANDiscriminativeLR, mult_lr=5.))\n",
    "learn.callback_fns.append(partial(GANTensorboardWriter, base_dir=TENSORBOARD_PATH, name='GanLearner', visual_iters=100))\n",
    "\n",
    "lr=1e-4\n",
    "unfreeze_fctr=0.05"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 64px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn_gen.freeze_to(-1)\n",
    "learn.fit(1,lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn_gen.unfreeze()\n",
    "learn.fit(1,lr*unfreeze_fctr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "save()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 96px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "load()\n",
    "lr=lr/2\n",
    "sz=96\n",
    "#bs=bs//2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.data = get_data(sz=sz, bs=bs, keep_pct=0.1)\n",
    "learn_gen.freeze_to(-1)\n",
    "learn.fit(1,lr/10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.data = get_data(sz=sz, bs=bs, keep_pct=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn_gen.freeze_to(-1)\n",
    "learn.fit(1,lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn_gen.unfreeze()\n",
    "learn.fit(1,lr*unfreeze_fctr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "save()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 128px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr=lr/2\n",
    "sz=128\n",
    "bs=bs//2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.data = get_data(sz=sz, bs=bs, keep_pct=0.1)\n",
    "learn_gen.freeze_to(-1)\n",
    "learn.fit(1,lr/10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.data = get_data(sz=sz, bs=bs, keep_pct=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn_gen.freeze_to(-1)\n",
    "learn.fit(1,lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn_gen.unfreeze()\n",
    "learn.fit(1,lr*unfreeze_fctr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "save()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 160px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr=lr/1.5\n",
    "sz=160\n",
    "bs=int(bs//1.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bs=10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.data = get_data(sz=sz, bs=bs, keep_pct=0.1)\n",
    "learn_gen.freeze_to(-1)\n",
    "learn.fit(1,lr/10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.data = get_data(sz=sz, bs=bs, keep_pct=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn_gen.freeze_to(-1)\n",
    "learn.fit(1,lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn_gen.unfreeze()\n",
    "learn.fit(1,lr*unfreeze_fctr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "save()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 192px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr=lr/1.5\n",
    "sz=192\n",
    "bs=int(bs//1.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.data = get_data(sz=sz, bs=bs, keep_pct=0.1)\n",
    "learn_gen.freeze_to(-1)\n",
    "learn.fit(1,lr/10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.data = get_data(sz=sz, bs=bs, keep_pct=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn_gen.freeze_to(-1)\n",
    "learn.fit(1,lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn_gen.unfreeze()\n",
    "learn.fit(1,lr*unfreeze_fctr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "save()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 224px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr=lr/1.5\n",
    "sz=224\n",
    "bs=int(bs//1.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.data = get_data(sz=sz, bs=bs, keep_pct=0.1)\n",
    "learn_gen.freeze_to(-1)\n",
    "learn.fit(1,lr/10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.data = get_data(sz=sz, bs=bs, keep_pct=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn_gen.freeze_to(-1)\n",
    "learn.fit(1,lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn_gen.unfreeze()\n",
    "learn.fit(1,lr*unfreeze_fctr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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