Making artistic model mode parameterized, defaults to false. Various other thangs

ImageColorizerColab.ipynb

@@ -172,22 +172,33 @@
     "render_factor=36"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#◢ Artistic vs Stable Model"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "ny3egicahAB4"
-   },
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "artistic = False #@param {type:\"boolean\"}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "# Now download the pretrained weights, which I have saved to my google drive\n",
-    "# note that the id is the ending part of the shareable link url (after open?id=)\n",
-    "# The pretrained weights can be downloaded from https://www.dropbox.com/s/3e4dqky91h824ik/colorize_gen.pth\n",
-    "!mkdir \"models\"\n",
-    "download = drive.CreateFile({'id': '1ReNu8QCgi4vfyDovgYGMs7tlUGrnz72V'})\n",
-    "download.GetContentFile('./models/colorize_gen.pth')"
+    "!mkdir 'models'\n",
+    "if artistic:\n",
+    "    !wget https://www.dropbox.com/s/9ne9su2mc5t0m38/ColorizeImagesArtistic_gen.pth?dl=0 -O ./models/ColorizeImagesArtistic_gen.pth\n",
+    "else:\n",
+    "    !wget https://www.dropbox.com/s/ztgygpaz1z3jkjg/ColorizeImagesStable_gen.pth?dl=0 -O ./models/ColorizeImagesStable_gen.pth"
    ]
   },
   {
@@ -224,7 +235,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "vis = get_artistic_image_colorizer(results_dir=results_dir, render_factor=render_factor)"
+    "vis = get_image_colorizer(render_factor=render_factor, artistic=artistic)"
    ]
   },
   {

VideoColorizer.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -31,7 +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=36\n",
+    "render_factor=28\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",
@@ -40,29 +40,18 @@
     "#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 = 'https://i.imgur.com/Ob9pZad.gifv'\n",
-    "file_name = 'video14.mp4'"
+    "#source_url = 'https://i.imgur.com/Ob9pZad.gifv'\n",
+    "source_url = None\n",
+    "file_name = 'video15.mp4'"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/media/jason/Projects/Deep Learning/DeOldifyV2/DeOldify/fastai/data_block.py:426: UserWarning: Your training set is empty. If this is by design, pass `ignore_empty=True` to remove this warning.\n",
-      "  warn(\"Your training set is empty. If this is by design, pass `ignore_empty=True` to remove this warning.\")\n",
-      "/media/jason/Projects/Deep Learning/DeOldifyV2/DeOldify/fastai/data_block.py:429: UserWarning: Your validation set is empty. If this is by design, use `no_split()`\n",
-      "                 or pass `ignore_empty=True` when labelling to remove this warning.\n",
-      "  or pass `ignore_empty=True` when labelling to remove this warning.\"\"\")\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "colorizer = get_video_colorizer(render_factor=render_factor)"
+    "colorizer = get_video_colorizer(render_factor=render_factor, artistic=True)"
    ]
   },
   {
@@ -74,45 +63,7 @@
      "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": [
-       "\n",
-       "    <div>\n",
-       "        <style>\n",
-       "            /* Turns off some styling */\n",
-       "            progress {\n",
-       "                /* gets rid of default border in Firefox and Opera. */\n",
-       "                border: none;\n",
-       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
-       "                background-size: auto;\n",
-       "            }\n",
-       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
-       "                background: #F44336;\n",
-       "            }\n",
-       "        </style>\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",
-       "    "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Video created here: video/result/video14.mp4\n"
+      "Video created here: video/result/video15.mp4\n"
      ]
     }
    ],

VideoColorizerColab.ipynb

@@ -153,6 +153,22 @@
     "torch.backends.cudnn.benchmark=True"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#◢ Artistic vs Stable Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "artistic = False #@param {type:\"boolean\"}"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -160,7 +176,10 @@
    "outputs": [],
    "source": [
     "!mkdir 'models'\n",
-    "!wget https://www.dropbox.com/s/zqt6pzcmoztda0l/ColorizeVideos_gen2.pth?dl=0 -O ./models/ColorizeImagesStable_gen.pth"
+    "if artistic:\n",
+    "    !wget https://www.dropbox.com/s/9ne9su2mc5t0m38/ColorizeImagesArtistic_gen.pth?dl=0 -O ./models/ColorizeImagesArtistic_gen.pth\n",
+    "else:\n",
+    "    !wget https://www.dropbox.com/s/ztgygpaz1z3jkjg/ColorizeImagesStable_gen.pth?dl=0 -O ./models/ColorizeImagesStable_gen.pth"
    ]
   },
   {
@@ -271,7 +290,7 @@
    },
    "outputs": [],
    "source": [
-    "colorizer = get_video_colorizer(render_factor=render_factor)"
+    "colorizer = get_video_colorizer(render_factor=render_factor, artistic=artistic)"
    ]
   },
   {

fasterai/visualize.py

@@ -141,13 +141,33 @@ class VideoColorizer():
         self._colorize_raw_frames(source_path)
         self._build_video(source_path)
 
-def get_video_colorizer(root_folder:Path=Path('./'), weights_name:str='ColorizeImagesStable_gen', 
+
+def get_video_colorizer(render_factor:int=36, artistic:bool=False)->VideoColorizer:
+    if artistic:
+        return get_artistic_video_colorizer(render_factor=render_factor)
+    else:
+        return get_stable_video_colorizer(render_factor=render_factor)
+
+def get_stable_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_artistic_video_colorizer(root_folder:Path=Path('./'), weights_name:str='ColorizeImagesArtistic_gen', 
+        results_dir='result_images', render_factor:int=36)->VideoColorizer:
+    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 VideoColorizer(vis)
+
+def get_image_colorizer(render_factor:int=36, artistic:bool=False)->ModelImageVisualizer:
+    if artistic:
+        return get_artistic_image_colorizer(render_factor=render_factor)
+    else:
+        return get_stable_image_colorizer(render_factor=render_factor)
+
 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)
@@ -162,6 +182,13 @@ def get_artistic_image_colorizer(root_folder:Path=Path('./'), weights_name:str='
     vis = ModelImageVisualizer(filtr, results_dir=results_dir)
     return vis
 
+def get_artistic_image_colorizer2(root_folder:Path=Path('./'), weights_name:str='ColorizeImagesArtistic2_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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