mirror of
https://github.com/jantic/DeOldify.git
synced 2026-08-31 02:11:49 +08:00
Jason Antic
906 lines
18 KiB
Plaintext
906 lines
18 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Pretrained GAN"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"os.environ['CUDA_VISIBLE_DEVICES']='0' "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import fastai\n",
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"from fastai import *\n",
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"from fastai.vision import *\n",
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"from fastai.callbacks.tensorboard import *\n",
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"from fastai.vision.gan import *\n",
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"from fasterai.generators import *\n",
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"from fasterai.critics import *\n",
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"from fasterai.dataset import *\n",
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"from fasterai.loss import *\n",
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"from PIL import Image, ImageDraw, ImageFont\n",
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"from PIL import ImageFile"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Setup"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"path = Path('data/imagenet/ILSVRC/Data/CLS-LOC')\n",
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"path_hr = path\n",
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"path_lr = path/'bandw'\n",
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"\n",
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"proj_id = 'ColorizeNew72'\n",
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"gen_name = proj_id + '_gen'\n",
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"crit_name = proj_id + '_crit'\n",
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"\n",
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"name_gen = proj_id + '_image_gen'\n",
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"path_gen = path/name_gen\n",
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"\n",
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"TENSORBOARD_PATH = Path('data/tensorboard/' + proj_id)\n",
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"\n",
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"nf_factor = 1.25"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def save_all(suffix=''):\n",
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" learn_gen.save(gen_name + str(sz) + suffix)\n",
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" learn_crit.save(crit_name + str(sz) + suffix)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def load_all(suffix=''):\n",
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" learn_gen.load(gen_name + str(sz) + suffix, with_opt=False)\n",
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" learn_crit.load(crit_name + str(sz) + suffix, with_opt=False)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_data(bs:int, sz:int, keep_pct:float):\n",
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" return get_colorize_data(sz=sz, bs=bs, crappy_path=path_lr, good_path=path_hr, \n",
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" random_seed=None, keep_pct=keep_pct)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_crit_data(classes, bs, sz):\n",
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" src = ImageList.from_folder(path, include=classes, recurse=True).random_split_by_pct(0.1, seed=42)\n",
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" ll = src.label_from_folder(classes=classes)\n",
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" data = (ll.transform(get_transforms(max_zoom=2.), size=sz)\n",
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" .databunch(bs=bs).normalize(imagenet_stats))\n",
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" return data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def crappify(fn,i):\n",
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" dest = path_lr/fn.relative_to(path_hr)\n",
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" dest.parent.mkdir(parents=True, exist_ok=True)\n",
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" img = PIL.Image.open(fn).convert('LA').convert('RGB')\n",
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" img.save(dest) "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def save_preds(dl):\n",
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" i=0\n",
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" names = dl.dataset.items\n",
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" \n",
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" for b in dl:\n",
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" preds = learn_gen.pred_batch(batch=b, reconstruct=True)\n",
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" for o in preds:\n",
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" o.save(path_gen/names[i].name)\n",
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" i += 1"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def save_gen_images(learn_gen):\n",
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" if path_gen.exists(): shutil.rmtree(path_gen)\n",
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" path_gen.mkdir(exist_ok=True)\n",
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" data_gen = get_data(bs=bs, sz=sz, keep_pct=0.085)\n",
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" save_preds(data_gen.fix_dl)\n",
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" PIL.Image.open(path_gen.ls()[0])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Crappified data"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Prepare the input data by crappifying images."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Uncomment the first time you run this notebook."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#il = ImageItemList.from_folder(path_hr)\n",
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"#parallel(crappify, il.items)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Pre-training"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Pre-train generator"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Now let's pretrain the generator."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"bs=128\n",
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"sz=64\n",
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"keep_pct=1.0"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"data_gen = get_data(bs=bs, sz=sz, keep_pct=keep_pct)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"learn_gen = gen_learner_deep(data=data_gen, gen_loss=FeatureLoss(), nf_factor=nf_factor)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"learn_gen.callback_fns.append(partial(ImageGenTensorboardWriter, base_dir=TENSORBOARD_PATH, name='GenPre'))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"learn_gen.fit_one_cycle(2, pct_start=0.8, max_lr=slice(1e-3))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"learn_gen.save(gen_name)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"learn_gen.load(gen_name, with_opt=False)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"learn_gen.unfreeze()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"learn_gen.fit_one_cycle(2, pct_start=0.01, max_lr=slice(3e-7, 3e-4))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"learn_gen.save(gen_name)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"bs=32\n",
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"sz=128\n",
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"keep_pct=1.0"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"learn_gen.data = get_data(sz=sz, bs=bs, keep_pct=keep_pct)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"learn_gen.unfreeze()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"learn_gen.fit_one_cycle(2, pct_start=0.01, max_lr=slice(1e-7,1e-4))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"learn_gen.save(gen_name)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"bs=16\n",
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"sz=192\n",
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"keep_pct=0.50"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"learn_gen.data = get_data(sz=sz, bs=bs, keep_pct=keep_pct)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"learn_gen.unfreeze()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"learn_gen.fit_one_cycle(1, pct_start=0.01, max_lr=slice(5e-8,5e-5))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"learn_gen.save(gen_name)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Save generated images"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"save_gen_images(gen_name)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Train critic"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Pretrain the critic on crappy vs not crappy."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"bs=64\n",
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"sz=128"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"learn_gen=None\n",
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"gc.collect()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"loss_critic = AdaptiveLoss(nn.BCEWithLogitsLoss())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"data_crit = get_crit_data([name_gen, 'test'], bs=bs, sz=sz)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"data_crit.show_batch(rows=3, ds_type=DatasetType.Train, imgsize=3)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"learn_critic = colorize_crit_learner(data=data_crit, nf=256)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"learn_critic.callback_fns.append(partial(LearnerTensorboardWriter, base_dir=TENSORBOARD_PATH, name='CriticPre'))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"learn_critic.fit_one_cycle(6, 1e-3)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"learn_critic.save(crit_name)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"bs=16\n",
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"sz=192"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"learn_critic.data=get_crit_data([name_gen, 'test'], bs=bs, sz=sz)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"learn_critic.fit_one_cycle(4, 1e-4)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"learn_critic.save(crit_name)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## GAN"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
|
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"Now we'll combine those pretrained model in a GAN."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"learn_crit=None\n",
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"learn_gen=None\n",
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"gc.collect()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"lr=2e-5\n",
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"sz=192\n",
|
|
"bs=10"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#placeholder- not actually used\n",
|
|
"data_crit = get_crit_data([name_gen, 'test'], bs=bs, sz=sz)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"learn_crit = colorize_crit_learner(data=data_crit, nf=256).load(crit_name, with_opt=False)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"learn_gen = gen_learner_deep(data=data_gen, gen_loss=FeatureLoss(), nf_factor=nf_factor).load(gen_name, with_opt=False)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"switcher = partial(AdaptiveGANSwitcher, critic_thresh=0.65)\n",
|
|
"learn = GANLearner.from_learners(learn_gen, learn_crit, weights_gen=(1.0,2.0), show_img=False, switcher=switcher,\n",
|
|
" opt_func=partial(optim.Adam, betas=(0.,0.9)), wd=1e-3)\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))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"for i in range(1,21):\n",
|
|
" learn.data = get_data(sz=sz, bs=bs, keep_pct=0.001)\n",
|
|
" learn_gen.freeze_to(-1)\n",
|
|
" learn.fit(1,lr)\n",
|
|
" save_all('_01_' + str(i))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"save_all('_01')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"learn.show_results(rows=bs)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Save Generated Images Again"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"bs=16\n",
|
|
"sz=192"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"learn_gen = gen_learner_deep(data=data_gen, gen_loss=FeatureLoss(), nf_factor=nf_factor).load('ColorizeNew72_gen192_04_12', with_opt=False)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"save_gen_images(gen_name)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Train Critic Again"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"bs=16\n",
|
|
"sz=192"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"learn_gen=None\n",
|
|
"gc.collect()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"loss_critic = AdaptiveLoss(nn.BCEWithLogitsLoss())"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"data_crit = get_crit_data([name_gen, 'test'], bs=bs, sz=sz)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"data_crit.show_batch(rows=3, ds_type=DatasetType.Train, imgsize=3)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"learn_critic = colorize_crit_learner(data=data_crit, nf=256).load(crit_name+'4', with_opt=False)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"learn_critic.callback_fns.append(partial(LearnerTensorboardWriter, base_dir=TENSORBOARD_PATH, name='CriticPre'))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"learn_critic.fit_one_cycle(4, 1e-4)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"learn_critic.save(crit_name + '5')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### GAN Again"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"learn_crit=None\n",
|
|
"learn_gen=None\n",
|
|
"gc.collect()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"lr=5e-6\n",
|
|
"sz=192\n",
|
|
"bs=10"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"data_crit = get_crit_data([name_gen, 'test'], bs=bs, sz=sz)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"learn_crit = colorize_crit_learner(data=data_crit, nf=256).load(crit_name + '5', with_opt=False)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"learn_gen = gen_learner_deep(data=data_gen, gen_loss=FeatureLoss(), nf_factor=nf_factor).load('ColorizeNew72_gen192_04_12', with_opt=False)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"switcher = partial(AdaptiveGANSwitcher, critic_thresh=0.65)\n",
|
|
"learn = GANLearner.from_learners(learn_gen, learn_crit, weights_gen=(1.0,2.0), show_img=False, switcher=switcher,\n",
|
|
" opt_func=partial(optim.Adam, betas=(0.,0.9)), wd=1e-3)\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))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"for i in range(1,101):\n",
|
|
" learn.data = get_data(sz=sz, bs=bs, keep_pct=0.001)\n",
|
|
" learn_gen.freeze_to(-1)\n",
|
|
" learn.fit(1,lr)\n",
|
|
" save_all('_05_' + str(i))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## fin"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"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": "3.7.0"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|