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Cleaning up the training notebooks and clarifying with comments

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Jason Antic
2019-04-27 13:04:05 -07:00
parent ebed64be4c
commit 3e3207fe92
5 changed files with 287 additions and 891 deletions
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ColorizeTrainingArtistic.ipynb
+127 -393
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@@ -4,7 +4,16 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## Pretrained GAN"
"## Artistic Model Training"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### NOTES: \n",
"* This is \"NoGAN\" based training, described in the DeOldify readme.\n",
"* This model prioritizes colorful renderings. It has higher variation in renderings at different resolutions compared to the \"stable\" model"
]
},
{
@@ -14,7 +23,7 @@
"outputs": [],
"source": [
"import os\n",
"os.environ['CUDA_VISIBLE_DEVICES']='1' "
"os.environ['CUDA_VISIBLE_DEVICES']='0' "
]
},
{
@@ -32,6 +41,7 @@
"from fasterai.critics import *\n",
"from fasterai.dataset import *\n",
"from fasterai.loss import *\n",
"from fasterai.save import *\n",
"from PIL import Image, ImageDraw, ImageFont\n",
"from PIL import ImageFile"
]
@@ -53,8 +63,10 @@
"path_hr = path\n",
"path_lr = path/'bandw'\n",
"\n",
"proj_id = 'Artistic2'\n",
"proj_id = 'ArtisticModel'\n",
"\n",
"gen_name = proj_id + '_gen'\n",
"pre_gen_name = gen_name + '_0'\n",
"crit_name = proj_id + '_crit'\n",
"\n",
"name_gen = proj_id + '_image_gen'\n",
@@ -62,30 +74,8 @@
"\n",
"TENSORBOARD_PATH = Path('data/tensorboard/' + proj_id)\n",
"\n",
"nf_factor = 1.50\n",
"pct_start = 1e-4"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def save_all(suffix=''):\n",
" learn_gen.save(gen_name + str(sz) + suffix)\n",
" learn_crit.save(crit_name + str(sz) + suffix)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def load_all(suffix=''):\n",
" learn_gen.load(gen_name + str(sz) + suffix, with_opt=False)\n",
" learn_crit.load(crit_name + str(sz) + suffix, with_opt=False)"
"nf_factor = 1.5\n",
"pct_start = 1e-8"
]
},
{
@@ -96,42 +86,21 @@
"source": [
"def get_data(bs:int, sz:int, keep_pct:float):\n",
" return get_colorize_data(sz=sz, bs=bs, crappy_path=path_lr, good_path=path_hr, \n",
" random_seed=None, keep_pct=keep_pct)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
" random_seed=None, keep_pct=keep_pct)\n",
"\n",
"def get_crit_data(classes, bs, sz):\n",
" src = ImageList.from_folder(path, include=classes, recurse=True).random_split_by_pct(0.1, seed=42)\n",
" ll = src.label_from_folder(classes=classes)\n",
" data = (ll.transform(get_transforms(max_zoom=2.), size=sz)\n",
" .databunch(bs=bs).normalize(imagenet_stats))\n",
" return data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def crappify(fn,i):\n",
" return data\n",
"\n",
"def create_training_images(fn,i):\n",
" dest = path_lr/fn.relative_to(path_hr)\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": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
" img.save(dest) \n",
" \n",
"def save_preds(dl):\n",
" i=0\n",
" names = dl.dataset.items\n",
@@ -140,15 +109,8 @@
" preds = learn_gen.pred_batch(batch=b, reconstruct=True)\n",
" for o in preds:\n",
" o.save(path_gen/names[i].name)\n",
" i += 1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
" i += 1\n",
" \n",
"def save_gen_images(learn_gen):\n",
" if path_gen.exists(): shutil.rmtree(path_gen)\n",
" path_gen.mkdir(exist_ok=True)\n",
@@ -161,21 +123,14 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## Crappified data"
"## Create black and white training images"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Prepare the input data by crappifying images."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Uncomment the first time you run this notebook."
"Only runs if the directory isn't already created."
]
},
{
@@ -184,29 +139,31 @@
"metadata": {},
"outputs": [],
"source": [
"#il = ImageItemList.from_folder(path_hr)\n",
"#parallel(crappify, il.items)"
"if not path_lr.exists():\n",
" il = ImageItemList.from_folder(path_hr)\n",
" parallel(create_training_images, il.items)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Pre-training"
"## Pre-train generator"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Pre-train generator"
"#### NOTE\n",
"Most of the training takes place here in pretraining for NoGAN. The goal here is to take the generator as far as possible with conventional training, as that is much easier to control and obtain glitch-free results compared to GAN training."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's pretrain the generator."
"### 64px"
]
},
{
@@ -262,16 +219,7 @@
"metadata": {},
"outputs": [],
"source": [
"learn_gen.save(gen_name)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"learn_gen.load(gen_name, with_opt=False)"
"learn_gen.save(pre_gen_name)"
]
},
{
@@ -298,16 +246,14 @@
"metadata": {},
"outputs": [],
"source": [
"learn_gen.save(gen_name)"
"learn_gen.save(pre_gen_name)"
]
},
{
"cell_type": "code",
"execution_count": null,
"cell_type": "markdown",
"metadata": {},
"outputs": [],
"source": [
"learn_gen.load(gen_name)"
"### 128px"
]
},
{
@@ -354,7 +300,14 @@
"metadata": {},
"outputs": [],
"source": [
"learn_gen.save(gen_name)"
"learn_gen.save(pre_gen_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 192px"
]
},
{
@@ -401,14 +354,62 @@
"metadata": {},
"outputs": [],
"source": [
"learn_gen.save(gen_name)"
"learn_gen.save(pre_gen_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Save generated images"
"## Repeatable GAN Cycle"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### NOTE\n",
"Best results so far have been based on repeating the cycle below a few times (about 5-8?), until diminishing returns are hit (no improvement in image quality). Each time you repeat the cycle, you want to increment that old_checkpoint_num by 1 so that new check points don't overwrite the old. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"old_checkpoint_num = 0\n",
"checkpoint_num = old_checkpoint_num + 1\n",
"gen_old_checkpoint_name = gen_name + '_' + str(old_checkpoint_num)\n",
"gen_new_checkpoint_name = gen_name + '_' + str(checkpoint_num)\n",
"crit_old_checkpoint_name = crit_name + '_' + str(old_checkpoint_num)\n",
"crit_new_checkpoint_name= crit_name + '_' + str(checkpoint_num)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Save Generated Images"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"bs=8\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(gen_old_checkpoint_name, with_opt=False)"
]
},
{
@@ -424,14 +425,14 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Train critic"
"### Pretrain Critic"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pretrain the critic on crappy vs not crappy."
"##### Only need full pretraining of critic when starting from scratch. Otherwise, just finetune!"
]
},
{
@@ -440,8 +441,17 @@
"metadata": {},
"outputs": [],
"source": [
"bs=64\n",
"sz=128"
"if old_checkpoint_num == 0:\n",
" bs=64\n",
" sz=128\n",
" learn_gen=None\n",
" gc.collect()\n",
" data_crit = get_crit_data([name_gen, 'test'], bs=bs, sz=sz)\n",
" data_crit.show_batch(rows=3, ds_type=DatasetType.Train, imgsize=3)\n",
" learn_critic = colorize_crit_learner(data=data_crit, nf=256)\n",
" learn_critic.callback_fns.append(partial(LearnerTensorboardWriter, base_dir=TENSORBOARD_PATH, name='CriticPre'))\n",
" learn_critic.fit_one_cycle(6, 1e-3)\n",
" learn_critic.save(crit_old_checkpoint_name)"
]
},
{
@@ -450,8 +460,8 @@
"metadata": {},
"outputs": [],
"source": [
"learn_gen=None\n",
"gc.collect()"
"bs=16\n",
"sz=192"
]
},
{
@@ -478,268 +488,7 @@
"metadata": {},
"outputs": [],
"source": [
"learn_critic = colorize_crit_learner(data=data_crit, nf=256)"
]
},
{
"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(6, 1e-3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"learn_critic.save(crit_name)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"bs=16\n",
"sz=192"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"learn_critic.data=get_crit_data([name_gen, 'test'], bs=bs, sz=sz)"
]
},
{
"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)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## GAN"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we'll combine those pretrained model in a GAN."
]
},
{
"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=1e-5\n",
"sz=192\n",
"bs=9"
]
},
{
"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(1,101):\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('_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=12\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('ColorizeNew79_gen192_06_40', 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": [
"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 + '6', with_opt=False)"
"learn_critic = colorize_crit_learner(data=data_crit, nf=256).load(crit_old_checkpoint_name, with_opt=False)"
]
},
{
@@ -766,14 +515,14 @@
"metadata": {},
"outputs": [],
"source": [
"learn_critic.save(crit_name + '7')"
"learn_critic.save(crit_new_checkpoint_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### GAN Again"
"### GAN"
]
},
{
@@ -813,8 +562,7 @@
"metadata": {},
"outputs": [],
"source": [
"#learn_crit = colorize_crit_learner(data=data_crit, nf=256).load(crit_name + '7', with_opt=False)\n",
"learn_crit = colorize_crit_learner(data=data_crit, nf=256).load('ColorizeNew79_crit192_07_100', with_opt=False)"
"learn_crit = colorize_crit_learner(data=data_crit, nf=256).load(crit_new_checkpoint_name, with_opt=False)"
]
},
{
@@ -823,7 +571,7 @@
"metadata": {},
"outputs": [],
"source": [
"learn_gen = gen_learner_deep(data=data_gen, gen_loss=FeatureLoss(), nf_factor=nf_factor).load('ColorizeNew79_gen192_07_100', with_opt=False)"
"learn_gen = gen_learner_deep(data=data_gen, gen_loss=FeatureLoss(), nf_factor=nf_factor).load(gen_old_checkpoint_name, with_opt=False)"
]
},
{
@@ -836,27 +584,16 @@
"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('_07_' + str(i))"
"learn.callback_fns.append(partial(GANTensorboardWriter, base_dir=TENSORBOARD_PATH, name='GanLearner', visual_iters=100))\n",
"learn.callback_fns.append(partial(GANSaveCallback, learn_gen=learn_gen, filename=gen_new_checkpoint_name, save_iters=100))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## fin"
"#### Instructions: \n",
"Find the checkpoint just before where glitches start to be introduced. This is all very new so you may need to play around with just how far you go here with keep_pct."
]
},
{
@@ -864,14 +601,11 @@
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
"source": [
"learn.data = get_data(sz=sz, bs=bs, keep_pct=0.03)\n",
"learn_gen.freeze_to(-1)\n",
"learn.fit(1,lr)"
]
}
],
"metadata": {
@@ -890,7 +624,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
"version": "3.7.0"
}
},
"nbformat": 4,