added asymmetric chromatic augmentation & adjusted augmentor param

IGEV-Stereo/core/stereo_datasets.py

@@ -352,7 +352,7 @@ def fetch_dataloader(args):
         train_dataset = new_dataset if train_dataset is None else train_dataset + new_dataset
 
     train_loader = data.DataLoader(train_dataset, batch_size=args.batch_size, 
-        pin_memory=True, shuffle=True, num_workers=int(os.environ.get('SLURM_CPUS_PER_TASK', 6))-2, drop_last=True)
+        pin_memory=True, shuffle=True, num_workers=12, drop_last=True)
 
     logging.info('Training with %d image pairs' % len(train_dataset))
     return train_loader

IGEV-Stereo/core/utils/augmentor.py

@@ -5,7 +5,7 @@ import os
 import time
 from glob import glob
 from skimage import color, io
-from PIL import Image
+from PIL import Image, ImageEnhance
 
 import cv2
 cv2.setNumThreads(0)
@@ -198,21 +198,40 @@ class SparseFlowAugmentor:
         self.v_flip_prob = 0.1
 
         # photometric augmentation params
-        self.photo_aug = Compose([ColorJitter(brightness=0.3, contrast=0.3, saturation=saturation_range, hue=0.3/3.14), AdjustGamma(*gamma)])
+        # self.photo_aug = Compose([ColorJitter(brightness=0.3, contrast=0.3, saturation=saturation_range, hue=0.3/3.14), AdjustGamma(*gamma)])
-        self.asymmetric_color_aug_prob = 0.2
         self.eraser_aug_prob = 0.5
-        
+
+    def chromatic_augmentation(self, img):
+        random_brightness = np.random.uniform(0.8, 1.2)
+        random_contrast = np.random.uniform(0.8, 1.2)
+        random_gamma = np.random.uniform(0.8, 1.2)
+
+        img = Image.fromarray(img)
+
+        enhancer = ImageEnhance.Brightness(img)
+        img = enhancer.enhance(random_brightness)
+        enhancer = ImageEnhance.Contrast(img)
+        img = enhancer.enhance(random_contrast)
+
+        gamma_map = [
+            255 * 1.0 * pow(ele / 255.0, random_gamma) for ele in range(256)
+        ] * 3
+        img = img.point(gamma_map)  # use PIL's point-function to accelerate this part
+
+        img_ = np.array(img)
+
+        return img_
+
     def color_transform(self, img1, img2):
-        image_stack = np.concatenate([img1, img2], axis=0)
+        img1 = self.chromatic_augmentation(img1)
-        image_stack = np.array(self.photo_aug(Image.fromarray(image_stack)), dtype=np.uint8)
+        img2 = self.chromatic_augmentation(img2)
-        img1, img2 = np.split(image_stack, 2, axis=0)
         return img1, img2
 
     def eraser_transform(self, img1, img2):
         ht, wd = img1.shape[:2]
         if np.random.rand() < self.eraser_aug_prob:
             mean_color = np.mean(img2.reshape(-1, 3), axis=0)
-            for _ in range(np.random.randint(1, 3)):
+            for _ in range(1):
                 x0 = np.random.randint(0, wd)
                 y0 = np.random.randint(0, ht)
                 dx = np.random.randint(50, 100)

IGEV-Stereo/train_stereo.py

@@ -22,7 +22,6 @@ from evaluate_stereo import *
 import core.stereo_datasets as datasets
 import torch.nn.functional as F
 
-torch.backends.cudnn.benchmark = True
 
 ckpt_path = './checkpoints/igev_stereo'
 log_path = './checkpoints/igev_stereo'
@@ -241,8 +240,8 @@ if __name__ == '__main__':
     parser.add_argument('--max_disp', type=int, default=192, help="max disp of geometry encoding volume")
 
     # Data augmentation
-    parser.add_argument('--img_gamma', type=float, nargs='+', default=None, help="gamma range")
+    # parser.add_argument('--img_gamma', type=float, nargs='+', default=None, help="gamma range")
-    parser.add_argument('--saturation_range', type=float, nargs='+', default=[0, 1.4], help='color saturation')
+    # parser.add_argument('--saturation_range', type=float, nargs='+', default=[0, 1.4], help='color saturation')
     parser.add_argument('--do_flip', default=False, choices=['h', 'v'], help='flip the images horizontally or vertically')
     parser.add_argument('--spatial_scale', type=float, nargs='+', default=[-0.2, 0.4], help='re-scale the images randomly')
     parser.add_argument('--noyjitter', action='store_true', help='don\'t simulate imperfect rectification')