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Jason Antic
2019-03-25 17:52:16 -07:00
parent d09d0ea4c2
commit e4a3600608
19 changed files with 3447 additions and 147 deletions
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ColorizeTrainingWide.ipynb
+49 -48
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@@ -9,7 +9,7 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -19,7 +19,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -45,7 +45,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -67,7 +67,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -78,7 +78,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -89,7 +89,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -100,7 +100,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -114,7 +114,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -127,7 +127,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -144,7 +144,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -210,7 +210,7 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -221,7 +221,7 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -230,7 +230,7 @@
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -593,7 +593,7 @@
"metadata": {},
"outputs": [],
"source": [
"lr=2e-5\n",
"lr=1e-5\n",
"sz=192\n",
"bs=5"
]
@@ -670,7 +670,7 @@
},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -684,7 +684,7 @@
"metadata": {},
"outputs": [],
"source": [
"learn_gen = gen_learner_wide(data=data_gen, gen_loss=FeatureLoss(), nf_factor=nf_factor).load('ColorizeNew73_gen192_08_211', with_opt=False)"
"learn_gen = gen_learner_wide(data=data_gen, gen_loss=FeatureLoss(), nf_factor=nf_factor).load('ColorizeNew73_gen192_05_7', with_opt=False)"
]
},
{
@@ -756,7 +756,7 @@
"metadata": {},
"outputs": [],
"source": [
"learn_critic = colorize_crit_learner(data=data_crit, nf=256).load(crit_name + '8', with_opt=False)"
"learn_critic = colorize_crit_learner(data=data_crit, nf=256).load(crit_name + '5', with_opt=False)"
]
},
{
@@ -783,7 +783,34 @@
"metadata": {},
"outputs": [],
"source": [
"learn_critic.save(crit_name + '9')"
"learn_critic.save(crit_name + '6')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"learn_critic.load(crit_name + '6', with_opt=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"learn_critic.fit_one_cycle(4, 1e-5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"learn_critic.save(crit_name + '6')"
]
},
{
@@ -810,7 +837,7 @@
"metadata": {},
"outputs": [],
"source": [
"lr=2e-5\n",
"lr=1e-5\n",
"sz=192\n",
"bs=5"
]
@@ -830,7 +857,7 @@
"metadata": {},
"outputs": [],
"source": [
"learn_crit = colorize_crit_learner(data=data_crit, nf=256).load(crit_name + '9', with_opt=False)"
"learn_crit = colorize_crit_learner(data=data_crit, nf=256).load(crit_name + '6', with_opt=False)"
]
},
{
@@ -839,7 +866,7 @@
"metadata": {},
"outputs": [],
"source": [
"learn_gen = gen_learner_wide(data=data_gen, gen_loss=FeatureLoss(), nf_factor=nf_factor).load('ColorizeNew73_gen192_08_211', with_opt=False)"
"learn_gen = gen_learner_wide(data=data_gen, gen_loss=FeatureLoss(), nf_factor=nf_factor).load('ColorizeNew73_gen192_05_7', with_opt=False)"
]
},
{
@@ -865,33 +892,7 @@
" 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(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))"
" save_all('_06b_' + str(i))"
]
},
{
ImageColorizerArtistic.ipynb
+3302
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File diff suppressed because it is too large Load Diff
ImageColorizerColab.ipynb
+2 -8
View File
@@ -110,13 +110,7 @@
"from IPython.display import Image\n",
"import fastai\n",
"from fastai import *\n",
"from fastai.vision 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 google.colab import drive\n",
@@ -175,7 +169,7 @@
"#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=21"
"render_factor=36"
]
},
{
@@ -230,7 +224,7 @@
"metadata": {},
"outputs": [],
"source": [
"vis = get_colorize_visualizer(results_dir=results_dir, render_factor=render_factor)"
"vis = get_artistic_image_colorizer(results_dir=results_dir, render_factor=render_factor)"
]
},
{
ImageColorizer.ipynb → ImageColorizerStable.ipynb
+45 -56
View File
@@ -7,7 +7,7 @@
"outputs": [],
"source": [
"import os\n",
"os.environ['CUDA_VISIBLE_DEVICES']='1' "
"os.environ['CUDA_VISIBLE_DEVICES']='2' "
]
},
{
@@ -16,20 +16,8 @@
"metadata": {},
"outputs": [],
"source": [
"import fastai\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",
"plt.style.use('dark_background')\n",
"torch.backends.cudnn.benchmark=True"
"plt.style.use('dark_background')"
]
},
{
@@ -51,7 +39,8 @@
"metadata": {},
"outputs": [],
"source": [
"vis = get_image_colorizer(root_folder=Path('data/imagenet/ILSVRC/Data/CLS-LOC/bandw'), weights_name='ColorizeNew73_gen192_08_211', render_factor=render_factor)"
"vis = get_stable_image_colorizer(render_factor=render_factor)\n",
"#vis = get_image_colorizer(root_folder=Path('data/imagenet/ILSVRC/Data/CLS-LOC/bandw'), weights_name='ColorizeNew73_gen192_09_49', render_factor=render_factor)"
]
},
{
@@ -231,7 +220,7 @@
"metadata": {},
"outputs": [],
"source": [
"vis.plot_transformed_image(\"test_images/abe.jpg\", render_factor=20)"
"vis.plot_transformed_image(\"test_images/abe.jpg\", render_factor=13)"
]
},
{
@@ -312,7 +301,7 @@
"metadata": {},
"outputs": [],
"source": [
"vis.plot_transformed_image(\"test_images/pinkerton.jpg\", render_factor=18)"
"vis.plot_transformed_image(\"test_images/pinkerton.jpg\", render_factor=11)"
]
},
{
@@ -411,7 +400,7 @@
"metadata": {},
"outputs": [],
"source": [
"vis.plot_transformed_image(\"test_images/HelenKeller.jpg\")"
"vis.plot_transformed_image(\"test_images/HelenKeller.jpg\", render_factor=35)"
]
},
{
@@ -456,7 +445,7 @@
"metadata": {},
"outputs": [],
"source": [
"vis.plot_transformed_image(\"test_images/unnamed.jpg\")"
"vis.plot_transformed_image(\"test_images/unnamed.jpg\", render_factor=28)"
]
},
{
@@ -492,7 +481,7 @@
"metadata": {},
"outputs": [],
"source": [
"vis.plot_transformed_image(\"test_images/kids_pit.jpg\")"
"vis.plot_transformed_image(\"test_images/kids_pit.jpg\", render_factor=30)"
]
},
{
@@ -501,7 +490,7 @@
"metadata": {},
"outputs": [],
"source": [
"vis.plot_transformed_image(\"test_images/last_samurai.jpg\")"
"vis.plot_transformed_image(\"test_images/last_samurai.jpg\", render_factor=20)"
]
},
{
@@ -510,7 +499,7 @@
"metadata": {},
"outputs": [],
"source": [
"vis.plot_transformed_image(\"test_images/AnselAdamsWhiteChurch.jpg\")"
"vis.plot_transformed_image(\"test_images/AnselAdamsWhiteChurch.jpg\", render_factor=25)"
]
},
{
@@ -519,7 +508,7 @@
"metadata": {},
"outputs": [],
"source": [
"vis.plot_transformed_image(\"test_images/opium.jpg\")"
"vis.plot_transformed_image(\"test_images/opium.jpg\", render_factor=30)"
]
},
{
@@ -528,7 +517,7 @@
"metadata": {},
"outputs": [],
"source": [
"vis.plot_transformed_image(\"test_images/dorothea_lange_2.jpg\")"
"vis.plot_transformed_image(\"test_images/dorothea_lange_2.jpg\", render_factor=42)"
]
},
{
@@ -600,7 +589,7 @@
"metadata": {},
"outputs": [],
"source": [
"vis.plot_transformed_image(\"test_images/Unidentified1855.jpg\", render_factor=19)"
"vis.plot_transformed_image(\"test_images/Unidentified1855.jpg\", render_factor=119)"
]
},
{
@@ -627,7 +616,7 @@
"metadata": {},
"outputs": [],
"source": [
"vis.plot_transformed_image(\"test_images/school_kids.jpg\", render_factor=17)"
"vis.plot_transformed_image(\"test_images/school_kids.jpg\", render_factor=20)"
]
},
{
@@ -690,7 +679,7 @@
"metadata": {},
"outputs": [],
"source": [
"vis.plot_transformed_image(\"test_images/Greece1911.jpg\", render_factor=29)"
"vis.plot_transformed_image(\"test_images/Greece1911.jpg\", render_factor=24)"
]
},
{
@@ -1347,7 +1336,7 @@
"metadata": {},
"outputs": [],
"source": [
"vis.plot_transformed_image(\"test_images/Texas1938Woman.png\", render_factor=45)"
"vis.plot_transformed_image(\"test_images/Texas1938Woman.png\", render_factor=35)"
]
},
{
@@ -1428,7 +1417,7 @@
"metadata": {},
"outputs": [],
"source": [
"vis.plot_transformed_image(\"test_images/NorwegianBride1920s.jpg\", render_factor=40)"
"vis.plot_transformed_image(\"test_images/NorwegianBride1920s.jpg\", render_factor=30)"
]
},
{
@@ -1482,7 +1471,7 @@
"metadata": {},
"outputs": [],
"source": [
"vis.plot_transformed_image(\"test_images/1899SodaFountain.jpg\", render_factor=37)"
"vis.plot_transformed_image(\"test_images/1899SodaFountain.jpg\", render_factor=38)"
]
},
{
@@ -1527,7 +1516,7 @@
"metadata": {},
"outputs": [],
"source": [
"vis.plot_transformed_image(\"test_images/1900sSaloon.jpg\", render_factor=33)"
"vis.plot_transformed_image(\"test_images/1900sSaloon.jpg\", render_factor=43)"
]
},
{
@@ -1572,7 +1561,7 @@
"metadata": {},
"outputs": [],
"source": [
"vis.plot_transformed_image(\"test_images/Apsaroke1908.png\", render_factor=42)"
"vis.plot_transformed_image(\"test_images/Apsaroke1908.png\", render_factor=40)"
]
},
{
@@ -1635,7 +1624,7 @@
"metadata": {},
"outputs": [],
"source": [
"vis.plot_transformed_image(\"test_images/VictorianLivingRoom.jpg\")"
"vis.plot_transformed_image(\"test_images/VictorianLivingRoom.jpg\", render_factor=45)"
]
},
{
@@ -1662,7 +1651,7 @@
"metadata": {},
"outputs": [],
"source": [
"vis.plot_transformed_image(\"test_images/MadisonSquare1900.jpg\", render_factor=44)"
"vis.plot_transformed_image(\"test_images/MadisonSquare1900.jpg\", render_factor=46)"
]
},
{
@@ -1788,7 +1777,7 @@
"metadata": {},
"outputs": [],
"source": [
"vis.plot_transformed_image(\"test_images/FatMensShop.jpg\", render_factor=24)"
"vis.plot_transformed_image(\"test_images/FatMensShop.jpg\", render_factor=21)"
]
},
{
@@ -1824,7 +1813,7 @@
"metadata": {},
"outputs": [],
"source": [
"vis.plot_transformed_image(\"test_images/JerseyShore1905.png\", render_factor=43)"
"vis.plot_transformed_image(\"test_images/JerseyShore1905.png\", render_factor=46)"
]
},
{
@@ -1860,7 +1849,7 @@
"metadata": {},
"outputs": [],
"source": [
"vis.plot_transformed_image(\"test_images/Cork1905.jpg\", render_factor=37)"
"vis.plot_transformed_image(\"test_images/Cork1905.jpg\", render_factor=28)"
]
},
{
@@ -2103,7 +2092,7 @@
"metadata": {},
"outputs": [],
"source": [
"vis.plot_transformed_image(\"test_images/GalwayIreland1902.jpg\", render_factor=42)"
"vis.plot_transformed_image(\"test_images/GalwayIreland1902.jpg\", render_factor=35)"
]
},
{
@@ -2202,7 +2191,7 @@
"metadata": {},
"outputs": [],
"source": [
"vis.plot_transformed_image(\"test_images/CafeTerrace1925Paris.jpg\", render_factor=27)"
"vis.plot_transformed_image(\"test_images/CafeTerrace1925Paris.jpg\", render_factor=25)"
]
},
{
@@ -2337,7 +2326,7 @@
"metadata": {},
"outputs": [],
"source": [
"vis.plot_transformed_image(\"test_images/RoyalUniversityMedStudent1900Ireland.jpg\")"
"vis.plot_transformed_image(\"test_images/RoyalUniversityMedStudent1900Ireland.jpg\", render_factor=45)"
]
},
{
@@ -2346,7 +2335,7 @@
"metadata": {},
"outputs": [],
"source": [
"vis.plot_transformed_image(\"test_images/LewisTomalinLondon1895.png\")"
"vis.plot_transformed_image(\"test_images/LewisTomalinLondon1895.png\", render_factor=25)"
]
},
{
@@ -2355,7 +2344,7 @@
"metadata": {},
"outputs": [],
"source": [
"vis.plot_transformed_image(\"test_images/SunHelmetsLondon1933.jpg\")"
"vis.plot_transformed_image(\"test_images/SunHelmetsLondon1933.jpg\", render_factor=40)"
]
},
{
@@ -2364,7 +2353,7 @@
"metadata": {},
"outputs": [],
"source": [
"vis.plot_transformed_image(\"test_images/Killarney1910.jpg\", render_factor=41)"
"vis.plot_transformed_image(\"test_images/Killarney1910.jpg\", render_factor=45)"
]
},
{
@@ -2400,7 +2389,7 @@
"metadata": {},
"outputs": [],
"source": [
"vis.plot_transformed_image(\"test_images/WaterfordIreland1909.jpg\", render_factor=45)"
"vis.plot_transformed_image(\"test_images/WaterfordIreland1909.jpg\", render_factor=35)"
]
},
{
@@ -2481,7 +2470,7 @@
"metadata": {},
"outputs": [],
"source": [
"vis.plot_transformed_image(\"test_images/ParisLadies1910.jpg\", render_factor=25)"
"vis.plot_transformed_image(\"test_images/ParisLadies1910.jpg\", render_factor=20)"
]
},
{
@@ -2589,7 +2578,7 @@
"metadata": {},
"outputs": [],
"source": [
"vis.plot_transformed_image(\"test_images/MuffinManlLondon1910.jpg\")"
"vis.plot_transformed_image(\"test_images/MuffinManlLondon1910.jpg\", render_factor=45)"
]
},
{
@@ -2670,7 +2659,7 @@
"metadata": {},
"outputs": [],
"source": [
"vis.plot_transformed_image(\"test_images/SecondHandClothesLondonLate1800s.jpg\", render_factor=21)"
"vis.plot_transformed_image(\"test_images/SecondHandClothesLondonLate1800s.jpg\", render_factor=33)"
]
},
{
@@ -2679,7 +2668,7 @@
"metadata": {},
"outputs": [],
"source": [
"vis.plot_transformed_image(\"test_images/SoapBoxRacerParis1920s.jpg\")"
"vis.plot_transformed_image(\"test_images/SoapBoxRacerParis1920s.jpg\", render_factor=40)"
]
},
{
@@ -2706,7 +2695,7 @@
"metadata": {},
"outputs": [],
"source": [
"vis.plot_transformed_image(\"test_images/LondonStreetDoctor1877.png\", render_factor=19)"
"vis.plot_transformed_image(\"test_images/LondonStreetDoctor1877.png\", render_factor=38)"
]
},
{
@@ -2814,7 +2803,7 @@
"metadata": {},
"outputs": [],
"source": [
"vis.plot_transformed_image(\"test_images/TouristsGermany1904.jpg\")"
"vis.plot_transformed_image(\"test_images/TouristsGermany1904.jpg\", render_factor=35)"
]
},
{
@@ -2850,7 +2839,7 @@
"metadata": {},
"outputs": [],
"source": [
"vis.plot_transformed_image(\"test_images/1939GypsyKids.jpg\")"
"vis.plot_transformed_image(\"test_images/1939GypsyKids.jpg\", render_factor=37)"
]
},
{
@@ -2985,7 +2974,7 @@
"metadata": {},
"outputs": [],
"source": [
"vis.plot_transformed_image(\"test_images/1940Connecticut.jpg\", render_factor=45)"
"vis.plot_transformed_image(\"test_images/1940Connecticut.jpg\", render_factor=46)"
]
},
{
@@ -3030,7 +3019,7 @@
"metadata": {},
"outputs": [],
"source": [
"vis.plot_transformed_image(\"test_images/1890sChineseImmigrants.jpg\", render_factor=45)"
"vis.plot_transformed_image(\"test_images/1890sChineseImmigrants.jpg\", render_factor=25)"
]
},
{
@@ -3057,7 +3046,7 @@
"metadata": {},
"outputs": [],
"source": [
"vis.plot_transformed_image(\"test_images/1959ParisFriends.png\", render_factor=45)"
"vis.plot_transformed_image(\"test_images/1959ParisFriends.png\", render_factor=40)"
]
},
{
@@ -3138,7 +3127,7 @@
"metadata": {},
"outputs": [],
"source": [
"vis.plot_transformed_image(\"test_images/1936ParisCafe.jpg\")"
"vis.plot_transformed_image(\"test_images/1936ParisCafe.jpg\", render_factor=46)"
]
},
{
@@ -3156,7 +3145,7 @@
"metadata": {},
"outputs": [],
"source": [
"vis.plot_transformed_image(\"test_images/1940PAFamily.jpg\", render_factor=28)"
"vis.plot_transformed_image(\"test_images/1940PAFamily.jpg\", render_factor=42)"
]
},
{
VideoColorizerColab.ipynb
+2 -2
View File
@@ -160,7 +160,7 @@
"outputs": [],
"source": [
"!mkdir 'models'\n",
"!wget https://www.dropbox.com/s/zqt6pzcmoztda0l/ColorizeVideos_gen2.pth?dl=0 -O ./models/ColorizeVideos_gen2.pth"
"!wget https://www.dropbox.com/s/zqt6pzcmoztda0l/ColorizeVideos_gen2.pth?dl=0 -O ./models/ColorizeImagesStable_gen.pth"
]
},
{
@@ -285,7 +285,7 @@
"else:\n",
" #UPLOAD File Here\n",
" source_media = files.upload()\n",
" os.system('ln -s /content/WORKFOLDER/' + list(source_media.keys())[0] + ' ' + source_path)\n",
" os.system('ln -f -s /content/WORKFOLDER/' + list(source_media.keys())[0] + ' ' + source_path)\n",
" colorizer.colorize_from_file_name(file_name)"
]
},
fasterai/generators.py
+3 -3
View File
@@ -37,7 +37,7 @@ def unet_learner_wide(data:DataBunch, arch:Callable, pretrained:bool=True, blur_
#----------------------------------------------------------------------
#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:
def gen_inference_deep(root_folder:Path, weights_name:str, arch=models.resnet34, nf_factor:float=1.5)->Learner:
data = get_dummy_databunch()
learn = gen_learner_deep(data=data, gen_loss=F.l1_loss, arch=arch, nf_factor=nf_factor)
learn.path = root_folder
@@ -45,7 +45,7 @@ def gen_inference_deep(root_folder:Path, weights_name:str, arch=models.resnet34,
learn.model.eval()
return learn
def gen_learner_deep(data:ImageDataBunch, gen_loss=FeatureLoss(), arch=models.resnet34, nf_factor:float=1.25)->Learner:
def gen_learner_deep(data:ImageDataBunch, gen_loss=FeatureLoss(), arch=models.resnet34, nf_factor:float=1.5)->Learner:
return unet_learner_deep(data, 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)
@@ -53,7 +53,7 @@ def gen_learner_deep(data:ImageDataBunch, gen_loss=FeatureLoss(), arch=models.re
def unet_learner_deep(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:float=1.0, **kwargs:Any)->Learner:
bottle:bool=False, nf_factor:float=1.5, **kwargs:Any)->Learner:
"Build Unet learner from `data` and `arch`."
meta = cnn_config(arch)
body = create_body(arch, pretrained)
fasterai/visualize.py
+20 -6
View File
@@ -11,12 +11,18 @@ from scipy import misc
from PIL import Image
import ffmpeg
import youtube_dl
import gc
class ModelImageVisualizer():
def __init__(self, filter:IFilter, results_dir:str=None):
self.filter = filter
self.results_dir=None if results_dir is None else Path(results_dir)
def _clean_mem(self):
return
#torch.cuda.empty_cache()
#gc.collect()
def _open_pil_image(self, path:Path)->Image:
return PIL.Image.open(path).convert('RGB')
@@ -37,6 +43,7 @@ class ModelImageVisualizer():
image.save(result_path)
def get_transformed_image(self, path:Path, render_factor:int=None)->Image:
self._clean_mem()
orig_image = self._open_pil_image(path)
filtered_image = self.filter.filter(orig_image, orig_image, render_factor=render_factor)
return filtered_image
@@ -134,16 +141,23 @@ class VideoColorizer():
self._colorize_raw_frames(source_path)
self._build_video(source_path)
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)
def get_video_colorizer(root_folder:Path=Path('./'), weights_name:str='ColorizeImagesStable_gen',
results_dir='result_images', render_factor:int=36)->VideoColorizer:
learn = gen_inference_wide(root_folder=root_folder, weights_name=weights_name)
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)->ModelImageVisualizer:
learn = gen_inference_wide(root_folder=root_folder, weights_name=weights_name, arch=models.resnet101)
def get_stable_image_colorizer(root_folder:Path=Path('./'), weights_name:str='ColorizeImagesStable_gen',
results_dir='result_images', render_factor:int=36)->ModelImageVisualizer:
learn = gen_inference_wide(root_folder=root_folder, weights_name=weights_name)
filtr = MasterFilter([ColorizerFilter(learn=learn)], render_factor=render_factor)
vis = ModelImageVisualizer(filtr, results_dir=results_dir)
return vis
def get_artistic_image_colorizer(root_folder:Path=Path('./'), weights_name:str='ColorizeImagesArtistic_gen',
results_dir='result_images', render_factor:int=36)->ModelImageVisualizer:
learn = gen_inference_deep(root_folder=root_folder, weights_name=weights_name)
filtr = MasterFilter([ColorizerFilter(learn=learn)], render_factor=render_factor)
vis = ModelImageVisualizer(filtr, results_dir=results_dir)
return vis
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