DeFadeTraining.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 multiprocessing\n",
    "from torch import autograd\n",
    "from fastai.conv_learner import *\n",
    "from fastai.transforms import TfmType\n",
    "from fasterai.transforms import *\n",
    "from fasterai.images import *\n",
    "from fasterai.dataset import *\n",
    "from fasterai.visualize import *\n",
    "from fasterai.callbacks import *\n",
    "from fasterai.loss import *\n",
    "from fasterai.modules import *\n",
    "from fasterai.training import *\n",
    "from fasterai.generators import *\n",
    "from fastai.torch_imports import *\n",
    "from pathlib import Path\n",
    "from itertools import repeat\n",
    "import tensorboardX\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/train')\n",
    "\n",
    "proj_id = 'defade'\n",
    "TENSORBOARD_PATH = Path('data/tensorboard/' + proj_id + '_cont2')\n",
    "\n",
    "gpath = IMAGENET.parent/(proj_id + '_gen_192.h5')\n",
    "dpath = IMAGENET.parent/(proj_id + '_critic_192.h5')\n",
    "\n",
    "c_lr=5e-4\n",
    "c_lrs = np.array([c_lr,c_lr,c_lr])\n",
    "\n",
    "g_lr=c_lr/5\n",
    "g_lrs = np.array([g_lr/1000,g_lr/100,g_lr])\n",
    "\n",
    "keep_pcts=[0.25,0.25]\n",
    "gen_freeze_tos=[-1,0]\n",
    "\n",
    "lrs_unfreeze_factor=1.0\n",
    "\n",
    "x_tfms = [RandomLighting(0.5, 0.5)]\n",
    "extra_aug_tfms = []\n",
    "torch.backends.cudnn.benchmark=True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "netG = Unet34(nf_factor=2).cuda()\n",
    "#netGVis = ModelVisualizationHook(TENSORBOARD_PATH, netG, 'netG')\n",
    "#load_model(netG, gpath)\n",
    "\n",
    "netD = DCCritic(ni=3, nf=512).cuda()\n",
    "#netDVis = ModelVisualizationHook(TENSORBOARD_PATH, netD, 'netD')\n",
    "#load_model(netD, dpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainer = GANTrainer(netD=netD, netG=netG, genloss_fns=[FeatureLoss(multiplier=1e2)])\n",
    "trainerVis = GANVisualizationHook(TENSORBOARD_PATH, trainer, 'trainer', jupyter=False, visual_iters=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scheds=[]\n",
    "\n",
    "scheds.extend(GANTrainSchedule.generate_schedules(szs=[64,64], bss=[32,32], path=IMAGENET, x_tfms=x_tfms, extra_aug_tfms=extra_aug_tfms,keep_pcts=keep_pcts, \n",
    "    save_base_name=proj_id, c_lrs=c_lrs, g_lrs=g_lrs, lrs_unfreeze_factor=lrs_unfreeze_factor, gen_freeze_tos=gen_freeze_tos))\n",
    "\n",
    "#unshock\n",
    "scheds.extend(GANTrainSchedule.generate_schedules(szs=[96], bss=[16], path=IMAGENET, x_tfms=x_tfms, extra_aug_tfms=extra_aug_tfms, keep_pcts=[0.1], \n",
    "    save_base_name=proj_id, c_lrs=c_lrs/20, g_lrs=g_lrs/20, lrs_unfreeze_factor=lrs_unfreeze_factor, gen_freeze_tos=[-1]))\n",
    "\n",
    "scheds.extend(GANTrainSchedule.generate_schedules(szs=[96,96], bss=[16,16], path=IMAGENET, x_tfms=x_tfms, extra_aug_tfms=extra_aug_tfms, keep_pcts=keep_pcts, \n",
    "    save_base_name=proj_id, c_lrs=c_lrs/2, g_lrs=g_lrs/2, lrs_unfreeze_factor=lrs_unfreeze_factor, gen_freeze_tos=gen_freeze_tos))\n",
    "\n",
    "\n",
    "#unshock\n",
    "scheds.extend(GANTrainSchedule.generate_schedules(szs=[128], bss=[8], path=IMAGENET, x_tfms=x_tfms, extra_aug_tfms=extra_aug_tfms, keep_pcts=[0.1], \n",
    "    save_base_name=proj_id, c_lrs=c_lrs/30, g_lrs=g_lrs/30, lrs_unfreeze_factor=lrs_unfreeze_factor, gen_freeze_tos=[-1]))\n",
    "\n",
    "scheds.extend(GANTrainSchedule.generate_schedules(szs=[128,128], bss=[8,8], path=IMAGENET, x_tfms=x_tfms, extra_aug_tfms=extra_aug_tfms, keep_pcts=keep_pcts, \n",
    "    save_base_name=proj_id, c_lrs=c_lrs/3, g_lrs=g_lrs/3, lrs_unfreeze_factor=lrs_unfreeze_factor, gen_freeze_tos=gen_freeze_tos))\n",
    "\n",
    "\n",
    "#unshock\n",
    "scheds.extend(GANTrainSchedule.generate_schedules(szs=[192], bss=[4], path=IMAGENET, x_tfms=x_tfms, extra_aug_tfms=extra_aug_tfms, keep_pcts=[0.1], \n",
    "    save_base_name=proj_id, c_lrs=c_lrs/80, g_lrs=g_lrs/80, lrs_unfreeze_factor=lrs_unfreeze_factor, gen_freeze_tos=[-1]))\n",
    "\n",
    "scheds.extend(GANTrainSchedule.generate_schedules(szs=[192], bss=[4], path=IMAGENET, x_tfms=x_tfms, extra_aug_tfms=extra_aug_tfms, keep_pcts=[0.25], \n",
    "    save_base_name=proj_id, c_lrs=c_lrs/8, g_lrs=g_lrs/8, lrs_unfreeze_factor=lrs_unfreeze_factor, gen_freeze_tos=[-1]))\n",
    "\n",
    "scheds.extend(GANTrainSchedule.generate_schedules(szs=[192], bss=[4], path=IMAGENET, x_tfms=x_tfms, extra_aug_tfms=extra_aug_tfms, keep_pcts=[0.25], \n",
    "    save_base_name=proj_id, c_lrs=c_lrs/16, g_lrs=g_lrs/16, lrs_unfreeze_factor=lrs_unfreeze_factor, gen_freeze_tos=[0]))\n",
    "\n",
    "\n",
    "\n",
    "#unshock\n",
    "scheds.extend(GANTrainSchedule.generate_schedules(szs=[256], bss=[2], path=IMAGENET, x_tfms=x_tfms, extra_aug_tfms=extra_aug_tfms, keep_pcts=[0.1], \n",
    "    save_base_name=proj_id, c_lrs=c_lrs/160, g_lrs=g_lrs/160, lrs_unfreeze_factor=lrs_unfreeze_factor, gen_freeze_tos=[-1]))\n",
    "\n",
    "scheds.extend(GANTrainSchedule.generate_schedules(szs=[256], bss=[2], path=IMAGENET, x_tfms=x_tfms, extra_aug_tfms=extra_aug_tfms, keep_pcts=[0.25], \n",
    "    save_base_name=proj_id, c_lrs=c_lrs/16, g_lrs=g_lrs/16, lrs_unfreeze_factor=lrs_unfreeze_factor, gen_freeze_tos=[-1]))\n",
    "\n",
    "scheds.extend(GANTrainSchedule.generate_schedules(szs=[256], bss=[2], path=IMAGENET, x_tfms=x_tfms, extra_aug_tfms=extra_aug_tfms, keep_pcts=[0.25], \n",
    "    save_base_name=proj_id, c_lrs=c_lrs/32, g_lrs=g_lrs/32, lrs_unfreeze_factor=lrs_unfreeze_factor, gen_freeze_tos=[0]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainer.train(scheds=scheds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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