mirror of
https://github.com/jantic/DeOldify.git
synced 2026-08-30 18:02:24 +08:00
Getting rid of unused code and generally cleaning up
This commit is contained in:
Jason Antic
@@ -18,3 +18,9 @@ fasterai/fastai
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fastai
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*.pth
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video
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test_images/James1.jpg
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test_images/James2.jpg
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test_images/James3.jpg
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test_images/James4.jpg
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test_images/James5.jpg
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test_images/James6.jpg
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@@ -14,7 +14,7 @@
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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']='2' "
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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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@@ -26,11 +26,10 @@
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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 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.tensorboard 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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@@ -54,14 +53,14 @@
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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 = 'ColorizeNew11'\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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"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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@@ -72,7 +71,9 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"loss_gen = FeatureLoss()"
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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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@@ -81,20 +82,9 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"def save_all():\n",
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" learn_gen.save(gen_name + str(sz))\n",
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" learn_crit.save(crit_name + str(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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"def load_all():\n",
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" learn_gen.load(gen_name + str(sz))\n",
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" learn_crit.load(crit_name + str(sz))"
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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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@@ -115,7 +105,7 @@
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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 = ImageItemList.from_folder(path, include=classes, recurse=True).random_split_by_pct(0.1, seed=42)\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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@@ -152,6 +142,20 @@
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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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@@ -187,7 +191,14 @@
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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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"# 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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@@ -197,22 +208,15 @@
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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": "markdown",
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"metadata": {},
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"source": [
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"## 128px"
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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=0.1"
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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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@@ -230,7 +234,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"learn_gen = colorize_gen_learner(data=data_gen, gen_loss=loss_gen, nf_factor=nf_factor)"
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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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@@ -248,7 +252,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"learn_gen.fit_one_cycle(8, pct_start=0.8)"
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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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@@ -260,6 +264,15 @@
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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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@@ -275,7 +288,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"learn_gen.load(gen_name)"
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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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@@ -284,7 +297,92 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"learn_gen.fit_one_cycle(8, slice(1e-6,1e-3))"
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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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@@ -300,7 +398,7 @@
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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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"### Save generated images"
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]
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},
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{
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@@ -309,50 +407,14 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"learn_gen.load(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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"# shutil.rmtree(path_gen)"
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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_gen.mkdir(exist_ok=True)"
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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_preds(data_gen.fix_dl)"
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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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"PIL.Image.open(path_gen.ls()[0])"
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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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"### Train critic"
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]
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},
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{
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@@ -362,6 +424,16 @@
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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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@@ -423,7 +495,44 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"learn_critic.fit_one_cycle(10, 1e-3)"
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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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@@ -466,9 +575,9 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"bs=24\n",
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"sz=128\n",
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"lr=8e-5"
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"lr=2e-5\n",
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"sz=192\n",
|
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"bs=10"
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||||
]
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||||
},
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||||
{
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@@ -487,7 +596,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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||||
"learn_crit = colorize_crit_learner(data=data_crit, nf=256).load(crit_name)"
|
||||
"learn_crit = colorize_crit_learner(data=data_crit, nf=256).load(crit_name, with_opt=False)"
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||||
]
|
||||
},
|
||||
{
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||||
@@ -496,7 +605,7 @@
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||||
"metadata": {},
|
||||
"outputs": [],
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||||
"source": [
|
||||
"learn_gen = colorize_gen_learner(data=data_gen, gen_loss=loss_gen, nf_factor=nf_factor).load(gen_name)"
|
||||
"learn_gen = gen_learner_deep(data=data_gen, gen_loss=FeatureLoss(), nf_factor=nf_factor).load(gen_name, with_opt=False)"
|
||||
]
|
||||
},
|
||||
{
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||||
@@ -506,7 +615,7 @@
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||||
"outputs": [],
|
||||
"source": [
|
||||
"switcher = partial(AdaptiveGANSwitcher, critic_thresh=0.65)\n",
|
||||
"learn = GANLearner.from_learners(learn_gen, learn_crit, weights_gen=(1.0,0.75), show_img=False, switcher=switcher,\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))"
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@@ -518,7 +627,11 @@
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||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"learn.data=get_data(sz=sz, bs=bs, keep_pct=0.25)"
|
||||
"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))"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -527,158 +640,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"learn_gen.freeze_to(-1)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"learn.fit(1,lr)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"save_all()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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": [
|
||||
"learn.data = get_data(sz=sz, bs=bs, keep_pct=0.05)\n",
|
||||
"learn_gen.freeze_to(-1)\n",
|
||||
"learn.fit(1,lr)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"save_all()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"load_all()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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=0.05)\n",
|
||||
"learn_gen.freeze_to(-1)\n",
|
||||
"learn.fit(1,lr)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"save_all()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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=0.05)\n",
|
||||
"learn_gen.freeze_to(-1)\n",
|
||||
"learn.fit(1,lr)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"save_all()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"lr=lr/1.75\n",
|
||||
"sz=256\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=0.05)\n",
|
||||
"learn_gen.freeze_to(-1)\n",
|
||||
"learn.fit(1,lr)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"save_all()"
|
||||
"save_all('_01')"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -690,6 +652,213 @@
|
||||
"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": {},
|
||||
File diff suppressed because it is too large
Load Diff
+49
-23
@@ -9,7 +9,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -19,7 +19,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -45,7 +45,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -67,7 +67,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -78,7 +78,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -89,7 +89,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -100,7 +100,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -114,7 +114,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -127,7 +127,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -144,7 +144,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -210,7 +210,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -221,7 +221,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -230,11 +230,11 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"learn_gen = gen_learner_deep(arch=models.resnet101, data=data_gen, gen_loss=FeatureLoss2(), nf_factor=nf_factor)"
|
||||
"learn_gen = gen_learner_wide(data=data_gen, gen_loss=FeatureLoss(), nf_factor=nf_factor)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -623,7 +623,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"learn_gen = gen_learner_wide(arch=models.resnet101, data=data_gen, gen_loss=FeatureLoss2(), nf_factor=nf_factor).load(gen_name, with_opt=False)"
|
||||
"learn_gen = gen_learner_wide(data=data_gen, gen_loss=FeatureLoss(), nf_factor=nf_factor).load(gen_name, with_opt=False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -670,7 +670,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -684,7 +684,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"learn_gen = gen_learner_wide(arch=models.resnet101, data=data_gen, gen_loss=FeatureLoss2(), nf_factor=nf_factor).load('ColorizeNew73_gen192_05_7', with_opt=False)"
|
||||
"learn_gen = gen_learner_wide(data=data_gen, gen_loss=FeatureLoss(), nf_factor=nf_factor).load('ColorizeNew73_gen192_08_211', with_opt=False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -756,7 +756,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"learn_critic = colorize_crit_learner(data=data_crit, nf=256).load(crit_name + '5', with_opt=False)"
|
||||
"learn_critic = colorize_crit_learner(data=data_crit, nf=256).load(crit_name + '8', with_opt=False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -783,7 +783,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"learn_critic.save(crit_name + '6')"
|
||||
"learn_critic.save(crit_name + '9')"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -810,7 +810,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"lr=1e-6\n",
|
||||
"lr=2e-5\n",
|
||||
"sz=192\n",
|
||||
"bs=5"
|
||||
]
|
||||
@@ -830,7 +830,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"learn_crit = colorize_crit_learner(data=data_crit, nf=256).load(crit_name + '6', with_opt=False)"
|
||||
"learn_crit = colorize_crit_learner(data=data_crit, nf=256).load(crit_name + '9', with_opt=False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -839,7 +839,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"learn_gen = gen_learner_wide(arch=models.resnet101, data=data_gen, gen_loss=FeatureLoss2(), nf_factor=nf_factor).load('ColorizeNew73_gen192_05_7', with_opt=False)"
|
||||
"learn_gen = gen_learner_wide(data=data_gen, gen_loss=FeatureLoss(), nf_factor=nf_factor).load('ColorizeNew73_gen192_08_211', with_opt=False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -865,7 +865,33 @@
|
||||
" 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('_06_' + str(i))"
|
||||
" save_all('_09_' + str(i))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"for i in range(101,201):\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('_09_' + str(i))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"for i in range(201,301):\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('_09_' + str(i))"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
+206
-155
File diff suppressed because it is too large
Load Diff
+18
-45
@@ -16,20 +16,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import fastai\n",
|
||||
"import ffmpeg\n",
|
||||
"from fastai import *\n",
|
||||
"from fastai.vision import *\n",
|
||||
"from fastai.callbacks.tensorboard import *\n",
|
||||
"from fastai.vision.gan import *\n",
|
||||
"from fasterai.dataset import *\n",
|
||||
"from fasterai.visualize import *\n",
|
||||
"from fasterai.loss import *\n",
|
||||
"from fasterai.filters import *\n",
|
||||
"from fasterai.generators import *\n",
|
||||
"from pathlib import Path\n",
|
||||
"from itertools import repeat\n",
|
||||
"from IPython.display import HTML, display\n",
|
||||
"plt.style.use('dark_background')\n",
|
||||
"torch.backends.cudnn.benchmark=True"
|
||||
]
|
||||
@@ -44,7 +31,7 @@
|
||||
"#It literally just is a number multiplied by 16 to get the square render resolution. \n",
|
||||
"#Note that this doesn't affect the resolution of the final output- the output is the same resolution as the input.\n",
|
||||
"#Example: render_factor=21 => color is rendered at 16x21 = 336x336 px. \n",
|
||||
"render_factor=43\n",
|
||||
"render_factor=36\n",
|
||||
"\n",
|
||||
"#Specify media_url. Many sources will work (YouTube, Imgur, Twitter, Reddit, etc). \n",
|
||||
"#Complete list here: https://rg3.github.io/youtube-dl/supportedsites.html . \n",
|
||||
@@ -53,7 +40,7 @@
|
||||
"#source_url = 'https://www.youtube.com/watch?v=gZShc8oshtU'\n",
|
||||
"#source_url = 'https://www.youtube.com/watch?v=fk6qiJjEEBo'\n",
|
||||
"#source_url = 'https://twitter.com/silentmoviegifs/status/1088830101863759872'\n",
|
||||
"source_url = None\n",
|
||||
"source_url = 'https://i.imgur.com/Ob9pZad.gifv'\n",
|
||||
"file_name = 'video14.mp4'"
|
||||
]
|
||||
},
|
||||
@@ -75,8 +62,7 @@
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"#colorizer = get_video_colorizer(render_factor=render_factor)\n",
|
||||
"colorizer = get_video_colorizer2(render_factor=render_factor)"
|
||||
"colorizer = get_video_colorizer(render_factor=render_factor)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -84,6 +70,15 @@
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[Imgur] Ob9pZad: Downloading webpage\n",
|
||||
"[download] Destination: video/source/video14.mp4\n",
|
||||
"[download] 100% of 10.08MiB in 00:02\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
@@ -101,8 +96,8 @@
|
||||
" background: #F44336;\n",
|
||||
" }\n",
|
||||
" </style>\n",
|
||||
" <progress value='0' class='progress-bar-interrupted' max='1511', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
|
||||
" Interrupted\n",
|
||||
" <progress value='755' class='' max='755', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
|
||||
" 100.00% [755/755 02:57<00:00]\n",
|
||||
" </div>\n",
|
||||
" "
|
||||
],
|
||||
@@ -114,32 +109,10 @@
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"ename": "KeyboardInterrupt",
|
||||
"evalue": "",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[0;32m<ipython-input-5-ad80579766ad>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mcolorizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolorize_from_url\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource_url\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfile_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mcolorizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolorize_from_file_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
|
||||
"\u001b[0;32m/media/jason/Projects/Deep Learning/DeOldifyV2/DeOldify/fasterai/visualize.py\u001b[0m in \u001b[0;36mcolorize_from_file_name\u001b[0;34m(self, file_name)\u001b[0m\n\u001b[1;32m 128\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcolorize_from_file_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfile_name\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 129\u001b[0m \u001b[0msource_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msource_folder\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mfile_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 130\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_colorize_from_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 131\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 132\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_colorize_from_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msource_path\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mPath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/media/jason/Projects/Deep Learning/DeOldifyV2/DeOldify/fasterai/visualize.py\u001b[0m in \u001b[0;36m_colorize_from_path\u001b[0;34m(self, source_path)\u001b[0m\n\u001b[1;32m 132\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_colorize_from_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msource_path\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mPath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 133\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_extract_raw_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 134\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_colorize_raw_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 135\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_build_video\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 136\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/media/jason/Projects/Deep Learning/DeOldifyV2/DeOldify/fasterai/visualize.py\u001b[0m in \u001b[0;36m_colorize_raw_frames\u001b[0;34m(self, source_path)\u001b[0m\n\u001b[1;32m 104\u001b[0m \u001b[0mimg_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbwframes_folder\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 105\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 106\u001b[0;31m \u001b[0mcolor_image\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_transformed_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 107\u001b[0m \u001b[0mcolor_image\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolorframes_folder\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 108\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/media/jason/Projects/Deep Learning/DeOldifyV2/DeOldify/fasterai/visualize.py\u001b[0m in \u001b[0;36mget_transformed_image\u001b[0;34m(self, path, render_factor)\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_transformed_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mPath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrender_factor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m->\u001b[0m\u001b[0mImage\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[0morig_image\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open_pil_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m \u001b[0mfiltered_image\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morig_image\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morig_image\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrender_factor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrender_factor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 42\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mfiltered_image\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/media/jason/Projects/Deep Learning/DeOldifyV2/DeOldify/fasterai/filters.py\u001b[0m in \u001b[0;36mfilter\u001b[0;34m(self, orig_image, filtered_image, render_factor)\u001b[0m\n\u001b[1;32m 101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 102\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfilter\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilters\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 103\u001b[0;31m \u001b[0mfiltered_image\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfilter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morig_image\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfiltered_image\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrender_factor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 104\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 105\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mfiltered_image\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/media/jason/Projects/Deep Learning/DeOldifyV2/DeOldify/fasterai/filters.py\u001b[0m in \u001b[0;36mfilter\u001b[0;34m(self, orig_image, filtered_image, render_factor)\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mfilter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morig_image\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mPilImage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfiltered_image\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mPilImage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrender_factor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m->\u001b[0m\u001b[0mPilImage\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 65\u001b[0m \u001b[0mrender_sz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrender_factor\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrender_base\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m \u001b[0mmodel_image\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_model_process\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morig\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfiltered_image\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msz\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrender_sz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_to_orig\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
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"\u001b[0;32m/media/jason/Projects/Deep Learning/DeOldifyV2/DeOldify/fasterai/filters.py\u001b[0m in \u001b[0;36m_model_process\u001b[0;34m(self, orig, sz)\u001b[0m\n\u001b[1;32m 44\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdo_x\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 45\u001b[0m result = self.learn.pred_batch(ds_type=DatasetType.Valid, \n\u001b[0;32m---> 46\u001b[0;31m batch=(x[None].cuda(),y[None]), reconstruct=True)\n\u001b[0m\u001b[1;32m 47\u001b[0m \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdenorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdo_x\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
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"\u001b[0;32m/media/jason/Projects/Deep Learning/DeOldifyV2/DeOldify/fastai/basic_train.py\u001b[0m in \u001b[0;36mpred_batch\u001b[0;34m(self, ds_type, batch, reconstruct)\u001b[0m\n\u001b[1;32m 315\u001b[0m \u001b[0mcb_handler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCallbackHandler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 316\u001b[0m \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0myb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0myb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 317\u001b[0;31m \u001b[0mpreds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloss_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcb_handler\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 318\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_loss_func2activ\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss_func\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpreds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 319\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mreconstruct\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
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"\u001b[0;32m/media/jason/Projects/Deep Learning/DeOldifyV2/DeOldify/fastai/basic_train.py\u001b[0m in \u001b[0;36mloss_batch\u001b[0;34m(model, xb, yb, loss_func, opt, cb_handler)\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_listy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mxb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mxb\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_listy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0myb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0myb\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mxb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 25\u001b[0m \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_loss_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
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"\u001b[0;32m~/anaconda3/envs/fastaiv1/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 487\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 488\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 489\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 490\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 491\u001b[0m \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
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"\u001b[0;32m/media/jason/Projects/Deep Learning/DeOldifyV2/DeOldify/fastai/layers.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 131\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 132\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0morig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 133\u001b[0;31m \u001b[0mnres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 134\u001b[0m \u001b[0;31m# We have to remove res.orig to avoid hanging refs and therefore memory leaks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 135\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0morig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m~/anaconda3/envs/fastaiv1/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 487\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 488\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 489\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 490\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 491\u001b[0m \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m~/anaconda3/envs/fastaiv1/lib/python3.7/site-packages/torch/nn/modules/container.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 90\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 91\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_modules\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 92\u001b[0;31m \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 93\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 94\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m~/anaconda3/envs/fastaiv1/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 487\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 488\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 489\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 490\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 491\u001b[0m \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m~/anaconda3/envs/fastaiv1/lib/python3.7/site-packages/torch/nn/modules/container.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 90\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 91\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_modules\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 92\u001b[0;31m \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 93\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 94\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m~/anaconda3/envs/fastaiv1/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 483\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 484\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_pre_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 485\u001b[0;31m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 486\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tracing_state\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 487\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m~/anaconda3/envs/fastaiv1/lib/python3.7/site-packages/torch/nn/utils/spectral_norm.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, module, inputs)\u001b[0m\n\u001b[1;32m 98\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 99\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 100\u001b[0;31m \u001b[0msetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_weight\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdo_power_iteration\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 102\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_solve_v_and_rescale\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight_mat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_sigma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m~/anaconda3/envs/fastaiv1/lib/python3.7/site-packages/torch/nn/utils/spectral_norm.py\u001b[0m in \u001b[0;36mcompute_weight\u001b[0;34m(self, module, do_power_iteration)\u001b[0m\n\u001b[1;32m 84\u001b[0m \u001b[0mv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 85\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 86\u001b[0;31m \u001b[0msigma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweight_mat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 87\u001b[0m \u001b[0mweight\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mweight\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0msigma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 88\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Video created here: video/result/video14.mp4\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
||||
@@ -271,7 +271,7 @@
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"colorizer = get_video_colorizer2(render_factor=render_factor)"
|
||||
"colorizer = get_video_colorizer(render_factor=render_factor)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -27,24 +27,3 @@ def custom_gan_critic(n_channels:int=3, nf:int=256, n_blocks:int=3, p:int=0.15):
|
||||
|
||||
def colorize_crit_learner(data:ImageDataBunch, loss_critic=AdaptiveLoss(nn.BCEWithLogitsLoss()), nf:int=256)->Learner:
|
||||
return Learner(data, custom_gan_critic(nf=nf), metrics=accuracy_thresh_expand, loss_func=loss_critic, wd=1e-3)
|
||||
|
||||
|
||||
|
||||
def custom_gan_critic2(n_channels:int=3, nf:int=256, n_blocks:int=3, p:int=0.15):
|
||||
"Critic to train a `GAN`."
|
||||
layers = [
|
||||
_conv(n_channels, nf, ks=4, stride=2),
|
||||
nn.Dropout2d(p/2),
|
||||
_conv(nf, nf, ks=3, stride=1)]
|
||||
for i in range(n_blocks):
|
||||
layers += [
|
||||
nn.Dropout2d(p),
|
||||
_conv(nf, nf*2, ks=4, stride=2, self_attention=(i==0))]
|
||||
nf *= 2
|
||||
layers += [
|
||||
_conv(nf, 1, ks=4, bias=False, padding=0, use_activ=False),
|
||||
Flatten()]
|
||||
return nn.Sequential(*layers)
|
||||
|
||||
def colorize_crit_learner2(data:ImageDataBunch, loss_critic=AdaptiveLoss(nn.BCEWithLogitsLoss()), nf:int=256)->Learner:
|
||||
return Learner(data, custom_gan_critic2(nf=nf), metrics=accuracy_thresh_expand, loss_func=loss_critic, wd=1e-3)
|
||||
+33
-31
@@ -4,6 +4,38 @@ from .unet import DynamicUnetWide, DynamicUnetDeep
|
||||
from .loss import FeatureLoss
|
||||
from .dataset import *
|
||||
|
||||
#Weights are implicitly read from ./models/ folder
|
||||
def gen_inference_wide(root_folder:Path, weights_name:str, nf_factor:int=2, arch=models.resnet101)->Learner:
|
||||
data = get_dummy_databunch()
|
||||
learn = gen_learner_wide(data=data, gen_loss=F.l1_loss, nf_factor=nf_factor, arch=arch)
|
||||
learn.path = root_folder
|
||||
learn.load(weights_name)
|
||||
learn.model.eval()
|
||||
return learn
|
||||
|
||||
def gen_learner_wide(data:ImageDataBunch, gen_loss=FeatureLoss(), arch=models.resnet101, nf_factor:int=2)->Learner:
|
||||
return unet_learner_wide(data, arch=arch, wd=1e-3, blur=True, norm_type=NormType.Spectral,
|
||||
self_attention=True, y_range=(-3.,3.), loss_func=gen_loss, nf_factor=nf_factor)
|
||||
|
||||
#The code below is meant to be merged into fastaiv1 ideally
|
||||
def unet_learner_wide(data:DataBunch, arch:Callable, pretrained:bool=True, blur_final:bool=True,
|
||||
norm_type:Optional[NormType]=NormType, split_on:Optional[SplitFuncOrIdxList]=None,
|
||||
blur:bool=False, self_attention:bool=False, y_range:Optional[Tuple[float,float]]=None, last_cross:bool=True,
|
||||
bottle:bool=False, nf_factor:int=1, **kwargs:Any)->Learner:
|
||||
"Build Unet learner from `data` and `arch`."
|
||||
meta = cnn_config(arch)
|
||||
body = create_body(arch, pretrained)
|
||||
model = to_device(DynamicUnetWide(body, n_classes=data.c, blur=blur, blur_final=blur_final,
|
||||
self_attention=self_attention, y_range=y_range, norm_type=norm_type, last_cross=last_cross,
|
||||
bottle=bottle, nf_factor=nf_factor), data.device)
|
||||
learn = Learner(data, model, **kwargs)
|
||||
learn.split(ifnone(split_on,meta['split']))
|
||||
if pretrained: learn.freeze()
|
||||
apply_init(model[2], nn.init.kaiming_normal_)
|
||||
return learn
|
||||
|
||||
#----------------------------------------------------------------------
|
||||
|
||||
#Weights are implicitly read from ./models/ folder
|
||||
def gen_inference_deep(root_folder:Path, weights_name:str, arch=models.resnet34, nf_factor:float=1.25)->Learner:
|
||||
data = get_dummy_databunch()
|
||||
@@ -34,34 +66,4 @@ def unet_learner_deep(data:DataBunch, arch:Callable, pretrained:bool=True, blur_
|
||||
apply_init(model[2], nn.init.kaiming_normal_)
|
||||
return learn
|
||||
|
||||
#-----------------------------
|
||||
|
||||
#Weights are implicitly read from ./models/ folder
|
||||
def gen_inference_wide(root_folder:Path, weights_name:str, nf_factor:int=2, arch=models.resnet34)->Learner:
|
||||
data = get_dummy_databunch()
|
||||
learn = gen_learner_wide(data=data, gen_loss=F.l1_loss, nf_factor=nf_factor, arch=arch)
|
||||
learn.path = root_folder
|
||||
learn.load(weights_name)
|
||||
learn.model.eval()
|
||||
return learn
|
||||
|
||||
def gen_learner_wide(data:ImageDataBunch, gen_loss=FeatureLoss(), arch=models.resnet34, nf_factor:int=2)->Learner:
|
||||
return unet_learner_wide(data, arch=arch, wd=1e-3, blur=True, norm_type=NormType.Spectral,
|
||||
self_attention=True, y_range=(-3.,3.), loss_func=gen_loss, nf_factor=nf_factor)
|
||||
|
||||
#The code below is meant to be merged into fastaiv1 ideally
|
||||
def unet_learner_wide(data:DataBunch, arch:Callable, pretrained:bool=True, blur_final:bool=True,
|
||||
norm_type:Optional[NormType]=NormType, split_on:Optional[SplitFuncOrIdxList]=None,
|
||||
blur:bool=False, self_attention:bool=False, y_range:Optional[Tuple[float,float]]=None, last_cross:bool=True,
|
||||
bottle:bool=False, nf_factor:int=1, **kwargs:Any)->Learner:
|
||||
"Build Unet learner from `data` and `arch`."
|
||||
meta = cnn_config(arch)
|
||||
body = create_body(arch, pretrained)
|
||||
model = to_device(DynamicUnetWide(body, n_classes=data.c, blur=blur, blur_final=blur_final,
|
||||
self_attention=self_attention, y_range=y_range, norm_type=norm_type, last_cross=last_cross,
|
||||
bottle=bottle, nf_factor=nf_factor), data.device)
|
||||
learn = Learner(data, model, **kwargs)
|
||||
learn.split(ifnone(split_on,meta['split']))
|
||||
if pretrained: learn.freeze()
|
||||
apply_init(model[2], nn.init.kaiming_normal_)
|
||||
return learn
|
||||
#-----------------------------
|
||||
+1
-107
@@ -4,39 +4,8 @@ from fastai.torch_core import *
|
||||
from fastai.callbacks import hook_outputs
|
||||
import torchvision.models as models
|
||||
|
||||
class FeatureLoss3(nn.Module):
|
||||
def __init__(self, layer_wgts=[5,15,2]):
|
||||
super().__init__()
|
||||
|
||||
self.m_feat = models.vgg16_bn(True).features.cuda().eval()
|
||||
requires_grad(self.m_feat, False)
|
||||
blocks = [i-1 for i,o in enumerate(children(self.m_feat)) if isinstance(o,nn.MaxPool2d)]
|
||||
layer_ids = blocks[2:5]
|
||||
self.loss_features = [self.m_feat[i] for i in layer_ids]
|
||||
self.hooks = hook_outputs(self.loss_features, detach=False)
|
||||
self.wgts = layer_wgts
|
||||
self.metric_names = ['pixel',] + [f'feat_{i}' for i in range(len(layer_ids))]
|
||||
self.base_loss = F.l1_loss
|
||||
|
||||
def _make_features(self, x, clone=False):
|
||||
self.m_feat(x)
|
||||
return [(o.clone() if clone else o) for o in self.hooks.stored]
|
||||
|
||||
def forward(self, input, target):
|
||||
out_feat = self._make_features(target, clone=True)
|
||||
in_feat = self._make_features(input)
|
||||
self.feat_losses = [self.base_loss(input,target)]
|
||||
self.feat_losses += [self.base_loss(f_in, f_out)*w
|
||||
for f_in, f_out, w in zip(in_feat, out_feat, self.wgts)]
|
||||
|
||||
self.metrics = dict(zip(self.metric_names, self.feat_losses))
|
||||
return sum(self.feat_losses)
|
||||
|
||||
|
||||
|
||||
def __del__(self): self.hooks.remove()
|
||||
|
||||
class FeatureLoss2(nn.Module):
|
||||
class FeatureLoss(nn.Module):
|
||||
def __init__(self, layer_wgts=[20,70,10]):
|
||||
super().__init__()
|
||||
|
||||
@@ -67,81 +36,6 @@ class FeatureLoss2(nn.Module):
|
||||
def __del__(self): self.hooks.remove()
|
||||
|
||||
|
||||
#"Before activations" in ESRGAN paper
|
||||
class FeatureLoss(nn.Module):
|
||||
def __init__(self, layer_wgts=[5,15,2]):
|
||||
super().__init__()
|
||||
|
||||
self.m_feat = models.vgg16_bn(True).features.cuda().eval()
|
||||
requires_grad(self.m_feat, False)
|
||||
blocks = [i-2 for i,o in enumerate(children(self.m_feat)) if isinstance(o,nn.MaxPool2d)]
|
||||
layer_ids = blocks[2:5]
|
||||
self.loss_features = [self.m_feat[i] for i in layer_ids]
|
||||
self.hooks = hook_outputs(self.loss_features, detach=False)
|
||||
self.wgts = layer_wgts
|
||||
self.metric_names = ['pixel',] + [f'feat_{i}' for i in range(len(layer_ids))]
|
||||
self.base_loss = F.l1_loss
|
||||
|
||||
def _make_features(self, x, clone=False):
|
||||
self.m_feat(x)
|
||||
return [(o.clone() if clone else o) for o in self.hooks.stored]
|
||||
|
||||
def forward(self, input, target):
|
||||
out_feat = self._make_features(target, clone=True)
|
||||
in_feat = self._make_features(input)
|
||||
self.feat_losses = [self.base_loss(input,target)]
|
||||
self.feat_losses += [self.base_loss(f_in, f_out)*w
|
||||
for f_in, f_out, w in zip(in_feat, out_feat, self.wgts)]
|
||||
|
||||
self.metrics = dict(zip(self.metric_names, self.feat_losses))
|
||||
return sum(self.feat_losses)
|
||||
|
||||
|
||||
|
||||
def __del__(self): self.hooks.remove()
|
||||
|
||||
|
||||
|
||||
class PretrainFeatureLoss(nn.Module):
|
||||
def __init__(self, layer_wgts=[5,15,2], gram_wgt:float=5e3):
|
||||
super().__init__()
|
||||
self.gram_wgt = gram_wgt
|
||||
self.m_feat = models.vgg16_bn(True).features.cuda().eval()
|
||||
requires_grad(self.m_feat, False)
|
||||
blocks = [i-2 for i,o in enumerate(children(self.m_feat)) if isinstance(o,nn.MaxPool2d)]
|
||||
layer_ids = blocks[2:5]
|
||||
self.loss_features = [self.m_feat[i] for i in layer_ids]
|
||||
self.hooks = hook_outputs(self.loss_features, detach=False)
|
||||
self.wgts = layer_wgts
|
||||
self.metric_names = ['pixel',] + [f'feat_{i}' for i in range(len(layer_ids))]
|
||||
self.base_loss = F.l1_loss
|
||||
|
||||
def _gram_matrix(self, x:torch.Tensor):
|
||||
n,c,h,w = x.size()
|
||||
x = x.view(n, c, -1)
|
||||
return (x @ x.transpose(1,2))/(c*h*w)
|
||||
|
||||
def _make_features(self, x, clone=False):
|
||||
self.m_feat(x)
|
||||
return [(o.clone() if clone else o) for o in self.hooks.stored]
|
||||
|
||||
def forward(self, input, target):
|
||||
out_feat = self._make_features(target, clone=True)
|
||||
in_feat = self._make_features(input)
|
||||
self.feat_losses = [self.base_loss(input,target)]
|
||||
|
||||
self.feat_losses += [self.base_loss(self._gram_matrix(f_in), self._gram_matrix(f_out))*w**2 * self.gram_wgt
|
||||
for f_in, f_out, w in zip(in_feat, out_feat, self.wgts)]
|
||||
|
||||
self.feat_losses += [self.base_loss(f_in, f_out)*w
|
||||
for f_in, f_out, w in zip(in_feat, out_feat, self.wgts)]
|
||||
|
||||
self.metrics = dict(zip(self.metric_names, self.feat_losses))
|
||||
return sum(self.feat_losses)
|
||||
|
||||
def __del__(self): self.hooks.remove()
|
||||
|
||||
|
||||
#Includes wasserstein loss
|
||||
class WassFeatureLoss(nn.Module):
|
||||
def __init__(self, layer_wgts=[5,15,2], wass_wgts=[3.0,0.7,0.01]):
|
||||
|
||||
+3
-12
@@ -134,25 +134,16 @@ class VideoColorizer():
|
||||
self._colorize_raw_frames(source_path)
|
||||
self._build_video(source_path)
|
||||
|
||||
|
||||
def get_video_colorizer2(root_folder:Path=Path('./'), weights_name:str='ColorizeVideos_gen2',
|
||||
def get_video_colorizer(root_folder:Path=Path('./'), weights_name:str='ColorizeVideos_gen',
|
||||
results_dir = 'result_images', render_factor:int=36)->VideoColorizer:
|
||||
learn = gen_inference_wide(root_folder=root_folder, weights_name=weights_name, arch=models.resnet101)
|
||||
filtr = MasterFilter([ColorizerFilter(learn=learn)], render_factor=render_factor)
|
||||
vis = ModelImageVisualizer(filtr, results_dir=results_dir)
|
||||
return VideoColorizer(vis)
|
||||
|
||||
|
||||
def get_video_colorizer(root_folder:Path=Path('./'), weights_name:str='ColorizeVideos_gen',
|
||||
results_dir = 'result_images', render_factor:int=21, nf_factor:float=1.25)->VideoColorizer:
|
||||
learn = gen_inference_deep(root_folder=root_folder, weights_name=weights_name, nf_factor=nf_factor)
|
||||
filtr = MasterFilter([ColorizerFilter(learn=learn)], render_factor=render_factor)
|
||||
vis = ModelImageVisualizer(filtr, results_dir=results_dir)
|
||||
return VideoColorizer(vis)
|
||||
|
||||
def get_image_colorizer(root_folder:Path=Path('./'), weights_name:str='ColorizeImages_gen',
|
||||
results_dir = 'result_images', render_factor:int=21, arch=models.resnet34)->ModelImageVisualizer:
|
||||
learn = gen_inference_wide(root_folder=root_folder, weights_name=weights_name, arch=arch)
|
||||
results_dir = 'result_images', render_factor:int=21)->ModelImageVisualizer:
|
||||
learn = gen_inference_wide(root_folder=root_folder, weights_name=weights_name, arch=models.resnet101)
|
||||
filtr = MasterFilter([ColorizerFilter(learn=learn)], render_factor=render_factor)
|
||||
vis = ModelImageVisualizer(filtr, results_dir=results_dir)
|
||||
return vis
|
||||
|
||||
@@ -1,3 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:b06c958ff878b962410047379219d73f56e9202a50d36b9930360ef1a5a63618
|
||||
size 758956
|
||||
oid sha256:aa2c33c044e68f1821e82221960f535927f81f92c02bde7e1d1148dbfc89fe0c
|
||||
size 865210
|
||||
|
||||
@@ -1,3 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:16cbf5c4eda1c272fba854e0bd4864dd5f4ac72aab7f9b496a70320f3cbd65f2
|
||||
size 78956
|
||||
oid sha256:f3bca916f97811587da59dd240ec4b497497ed52a94f93a81a0c77b23cbe6cb3
|
||||
size 61827
|
||||
|
||||
Reference in New Issue
Block a user