Add MiddleBury

README.md

@@ -90,11 +90,13 @@ First you should download [RIFE model reported by our paper](https://drive.googl
 
 We will release our training and benchmark validation code soon.
 
-**Vimeo90K**
+**Vimeo90K**: Download [Vimeo90K dataset](http://toflow.csail.mit.edu/) at ./vimeo_interp_test
-Download [Vimeo90K dataset](http://toflow.csail.mit.edu/) at ./vimeo_interp_test
+**MiddleBury**: Download [MiddleBury OTHER dataset](https://vision.middlebury.edu/flow/data/) at ./other-data and ./other-gt-interp
 ```
-$ python3 benchmark/Vimeo90K_benchmark.py
+$ python3 benchmark/Vimeo90K.py
 (Final result: "Avg PSNR: 35.695 SSIM: 0.9788")
+$ python3 benchmark/MiddelBury_Other.py
+(Final result: "2.058")
 ```
 
 ## Citation

benchmark/MiddleBury_Other.py

@@ -0,0 +1,37 @@
+import os
+import sys
+sys.path.append('.')
+import cv2
+import math
+import torch
+import argparse
+import numpy as np
+from torch.nn import functional as F
+from pytorch_msssim import ssim_matlab
+from model.RIFE import Model
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+model = Model()
+model.load_model('./train_log')
+model.eval()
+model.device()
+
+name = ['Beanbags', 'Dimetrodon', 'DogDance', 'Grove2', 'Grove3', 'Hydrangea', 'MiniCooper', 'RubberWhale', 'Urban2', 'Urban3', 'Venus', 'Walking']
+IE_list = []
+for i in name:
+    i0 = cv2.imread('other-data/{}/frame10.png'.format(i)).transpose(2, 0, 1) / 255.
+    i1 = cv2.imread('other-data/{}/frame11.png'.format(i)).transpose(2, 0, 1) / 255.
+    gt = cv2.imread('other-gt-interp/{}/frame10i11.png'.format(i)) 
+    h, w = i0.shape[1], i0.shape[2]
+    imgs = torch.zeros([1, 6, 480, 640])
+    ph = (480 - h) // 2
+    pw = (640 - w) // 2
+    imgs[:, :3, :h, :w] = torch.from_numpy(i0).unsqueeze(0).float()
+    imgs[:, 3:, :h, :w] = torch.from_numpy(i1).unsqueeze(0).float()
+    I0 = imgs[:, :3]
+    I2 = imgs[:, 3:]
+    pred = model.inference(I0, I2)
+    out = pred[0].cpu().numpy().transpose(1, 2, 0)
+    out = np.round(out[:h, :w] * 255)
+    IE_list.append(np.abs((out - gt * 1.0)).mean())
+    print(np.mean(IE_list))