Merge branch 'main' into main

#Colab_demo.ipynb#

@@ -0,0 +1,133 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Untitled0.ipynb",
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/hzwer/arXiv2020-RIFE/blob/main/Colab_demo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "FypCcZkNNt2p"
+      },
+      "source": [
+        "!git clone https://github.com/hzwer/arXiv2020-RIFE"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "1wysVHxoN54f"
+      },
+      "source": [
+        "!gdown --id 1zYc3PEN4t6GOUoVYJjvcXoMmM3kFDNGS\n",
+        "!7z e RIFE_trained_model_new.zip"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "AhbHfRBJRAUt"
+      },
+      "source": [
+        "!mkdir /content/arXiv2020-RIFE/train_log\n",
+        "!mv *.pkl /content/arXiv2020-RIFE/train_log/\n",
+        "%cd /content/arXiv2020-RIFE/\n",
+        "!gdown --id 1i3xlKb7ax7Y70khcTcuePi6E7crO_dFc"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rirngW5uRMdg"
+      },
+      "source": [
+        "Please upload your video to content/arXiv2020-RIFE/video.mp4, or use our demo video."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "dnLn4aHHPzN3"
+      },
+      "source": [
+        "!nvidia-smi\n",
+        "!python3 inference_video.py --Colab_demo.ipynb=2 --video=demo.mp4 --montage --skip"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "77KK6lxHgJhf"
+      },
+      "source": [
+        "Our demo.mp4 is 25FPS. You can adjust the parameters for your own perference.\n",
+        "For example: \n",
+        "--fps=60 --Colab_demo.ipynb=1 --video=mydemo.avi --png"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Qzp53hpF5ynz"
+      },
+      "source": [
+        "!nvidia-smi\n",
+        "!python3 inference_video_parallel.py --Colab_demo.ipynb=1 --video=demo.mp4 --skip"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KjqnCQ_-53I7"
+      },
+      "source": [
+        "Try our 4 road parallel to process your video, expect 100% speedup! (Do not support montage mode)\n",
+        "\n",
+        "Unfortunately, no difference can be seen on Google colab servers."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "0zIBbVE3UfUD"
+      },
+      "source": [
+        "from IPython.display import display, Image\n",
+        "import moviepy.editor as mpy\n",
+        "display(mpy.ipython_display('demo_4X_100fps.mp4', height=256, max_duration=100.))"
+      ],
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

Colab_demo.ipynb

@@ -77,7 +77,7 @@
       },
       "source": [
         "!nvidia-smi\n",
-        "!python3 inference_video.py --times=2 --video=demo.mp4 --montage --skip"
+        "!python3 inference_video.py --exp=2 --video=demo.mp4 --montage --skip"
       ],
       "execution_count": null,
       "outputs": []
@@ -90,7 +90,7 @@
       "source": [
         "Our demo.mp4 is 25FPS. You can adjust the parameters for your own perference.\n",
         "For example: \n",
-        "--fps=60 --times=1 --video=mydemo.avi --png"
+        "--fps=60 --exp=1 --video=mydemo.avi --png"
       ]
     },
     {
@@ -100,7 +100,7 @@
       },
       "source": [
         "!nvidia-smi\n",
-        "!python3 inference_video_parallel.py --times=1 --video=demo.mp4 --skip"
+        "!python3 inference_video_parallel.py --exp=1 --video=demo.mp4 --skip"
       ],
       "execution_count": null,
       "outputs": []

README.md

@@ -38,15 +38,15 @@ The models under different setting is coming soon.
 
 You can use our [demo video](https://drive.google.com/file/d/1i3xlKb7ax7Y70khcTcuePi6E7crO_dFc/view?usp=sharing) or use your own video to process. 
 ```
-$ python3 inference_video.py --times=1 --video=video.mp4 
+$ python3 inference_video.py --exp=1 --video=video.mp4 
 ```
 (generate video_2X_xxfps.mp4, you can use this script repeatly to get 4X, 8X...)
 ```
-$ python3 inference_video.py --times=2 --video=video.mp4
+$ python3 inference_video.py --exp=2 --video=video.mp4
 ```
-(we specificly support times=2 for 4X interpolation)
+(we specificly support exp=2 for 4X interpolation)
 ```
-$ python3 inference_video.py --times=2 --video=video.mp4 --fps=60
+$ python3 inference_video.py --exp=2 --video=video.mp4 --fps=60
 ```
 (add slomo effect)
 ```
@@ -54,7 +54,7 @@ $ python3 inference_video.py --video=video.mp4 --montage --png
 ```
 (if you want to montage the origin video, and save the png format output)
 ```
-$ python3 inference_video_parallel.py --times=2 --video=video.mp4
+$ python3 inference_video_parallel.py --exp=2 --video=video.mp4
 ```
 (Try our parallel process to get 100% speedup!)
 
@@ -63,7 +63,7 @@ The warning info, 'Warning: Your video has *** static frames, it may change the
 **Image Interpolation**
 
 ```
-$ python3 inference_img.py --img img0.png img1.png --times=4
+$ python3 inference_img.py --img img0.png img1.png --exp=4
 ```
 (2^4=16X interpolation results)
 After that, you can use pngs to generate mp4:

inference_img.py

@@ -9,7 +9,7 @@ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 parser = argparse.ArgumentParser(description='Interpolation for a pair of images')
 parser.add_argument('--img', dest='img', nargs=2, required=True)
-parser.add_argument('--times', default=4, type=int)
+parser.add_argument('--exp', default=4, type=int)
 args = parser.parse_args()
 
 model = Model()
@@ -30,7 +30,7 @@ img0 = F.pad(img0, padding)
 img1 = F.pad(img1, padding)
 
 img_list = [img0, img1]
-for i in range(args.times):
+for i in range(args.exp):
     tmp = []
     for j in range(len(img_list) - 1):
         mid = model.inference(img_list[j], img_list[j + 1])

inference_video.py

@@ -19,10 +19,10 @@ parser.add_argument('--skip', dest='skip', action='store_true', help='whether to
 parser.add_argument('--fps', dest='fps', type=int, default=None)
 parser.add_argument('--png', dest='png', action='store_true', help='whether to output png format outputs')
 parser.add_argument('--ext', dest='ext', type=str, default='mp4', help='output video extension')
-parser.add_argument('--times', dest='times', type=int, default=1)
+parser.add_argument('--exp', dest='exp', type=int, default=1)
 args = parser.parse_args()
-assert (args.times == 1 or args.times == 2)
-args.times = 2 ** args.times
+assert (args.exp == 1 or args.exp == 2)
+args.exp = 2 ** args.exp
 
 from model.RIFE import Model
 model = Model()
@@ -33,7 +33,7 @@ model.device()
 videoCapture = cv2.VideoCapture(args.video)
 fps = np.round(videoCapture.get(cv2.CAP_PROP_FPS))
 if args.fps is None:
-    args.fps = fps * args.times
+    args.fps = fps * args.exp
 success, frame = videoCapture.read()
 h, w, _ = frame.shape
 fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')
@@ -42,7 +42,7 @@ if args.png:
         os.mkdir('output')
 else:
     video_path_wo_ext, ext = os.path.splitext(args.video)
-    output = cv2.VideoWriter('{}_{}X_{}fps.{}'.format(video_path_wo_ext, args.times, int(np.round(args.fps)), args.ext), fourcc, args.fps, (w, h))
+    output = cv2.VideoWriter('{}_{}X_{}fps.{}'.format(video_path_wo_ext, args.exp, int(np.round(args.fps)), args.ext), fourcc, args.fps, (w, h))
     
 cnt = 0
 def writeframe(frame):
@@ -88,25 +88,25 @@ while success:
             mid2 = frame
         else:
             mid1 = model.inference(I0, I1)
-            if args.times == 4:
+            if args.exp == 4:
                 mid = model.inference(torch.cat((I0, mid1), 0), torch.cat((mid1, I1), 0))
             mid1 = (((mid1[0] * 255.).cpu().detach().numpy().transpose(1, 2, 0))).astype('uint8')
-            if args.times == 4:
+            if args.exp == 4:
                 mid0 = (((mid[0] * 255.).cpu().detach().numpy().transpose(1, 2, 0))).astype('uint8')
                 mid2 = (((mid[1]* 255.).cpu().detach().numpy().transpose(1, 2, 0))).astype('uint8')
         if args.montage:
             writeframe(np.concatenate((lastframe, lastframe), 1))
-            if args.times == 4:
+            if args.exp == 4:
                 writeframe(np.concatenate((lastframe, mid0[:h, :w]), 1))
             writeframe(np.concatenate((lastframe, mid1[:h, :w]), 1))
-            if args.times == 4:
+            if args.exp == 4:
                 writeframe(np.concatenate((lastframe, mid2[:h, :w]), 1))
         else:
             writeframe(lastframe)
-            if args.times == 4:
+            if args.exp == 4:
                 writeframe(mid0[:h, :w])
             writeframe(mid1[:h, :w])
-            if args.times == 4:
+            if args.exp == 4:
                 writeframe(mid2[:h, :w])
         pbar.update(1)
 if args.montage:

inference_video_parallel.py

@@ -118,4 +118,4 @@ while success:
         pbar.update(4)
         img_list = img_list[-1:]
 pbar.close()
-vid_out.release()
+vid_out.release()