set 12 threads to prepare datasets

IGEV-Stereo/core/extractor.py

@@ -325,24 +325,21 @@ class SubModule(nn.Module):
 
 
 class Feature(SubModule):
-    def __init__(self, freeze):
+    def __init__(self):
         super(Feature, self).__init__()
         pretrained =  True
-        self.model = timm.create_model('mobilenetv2_100', pretrained=pretrained, features_only=True)
-        if freeze:
-            for p in self.model.parameters():
-                p.requires_grad = False
+        model = timm.create_model('mobilenetv2_100', pretrained=pretrained, features_only=True)
         layers = [1,2,3,5,6]
         chans = [16, 24, 32, 96, 160]
-        self.conv_stem = self.model.conv_stem
-        self.bn1 = self.model.bn1
-        self.act1 = self.model.act1
+        self.conv_stem = model.conv_stem
+        self.bn1 = model.bn1
+        self.act1 = model.act1
 
-        self.block0 = torch.nn.Sequential(*self.model.blocks[0:layers[0]])
-        self.block1 = torch.nn.Sequential(*self.model.blocks[layers[0]:layers[1]])
-        self.block2 = torch.nn.Sequential(*self.model.blocks[layers[1]:layers[2]])
-        self.block3 = torch.nn.Sequential(*self.model.blocks[layers[2]:layers[3]])
-        self.block4 = torch.nn.Sequential(*self.model.blocks[layers[3]:layers[4]])
+        self.block0 = torch.nn.Sequential(*model.blocks[0:layers[0]])
+        self.block1 = torch.nn.Sequential(*model.blocks[layers[0]:layers[1]])
+        self.block2 = torch.nn.Sequential(*model.blocks[layers[1]:layers[2]])
+        self.block3 = torch.nn.Sequential(*model.blocks[layers[2]:layers[3]])
+        self.block4 = torch.nn.Sequential(*model.blocks[layers[3]:layers[4]])
 
         self.deconv32_16 = Conv2x_IN(chans[4], chans[3], deconv=True, concat=True)
         self.deconv16_8 = Conv2x_IN(chans[3]*2, chans[2], deconv=True, concat=True)

IGEV-Stereo/core/geometry.py

@@ -60,7 +60,6 @@ class Combined_Geo_Encoding_Volume:
     
     @staticmethod
     def corr(fmap1, fmap2):
-        # batch, dim, ht, wd
         B, D, H, W1 = fmap1.shape
         _, _, _, W2 = fmap2.shape
         fmap1 = fmap1.view(B, D, H, W1)

IGEV-Stereo/core/igev_stereo.py

@@ -100,7 +100,7 @@ class IGEVStereo(nn.Module):
 
         self.context_zqr_convs = nn.ModuleList([nn.Conv2d(context_dims[i], args.hidden_dims[i]*3, 3, padding=3//2) for i in range(self.args.n_gru_layers)])
 
-        self.feature = Feature(args.freeze_backbone_params)
+        self.feature = Feature()
 
         self.stem_2 = nn.Sequential(
             BasicConv_IN(3, 32, kernel_size=3, stride=2, padding=1),
@@ -167,7 +167,6 @@ class IGEVStereo(nn.Module):
             match_right = self.desc(self.conv(features_right[0]))
             gwc_volume = build_gwc_volume(match_left, match_right, self.args.max_disp//4, 8)
             gwc_volume = self.corr_stem(gwc_volume)
-            # 3d unet
             gwc_volume = self.corr_feature_att(gwc_volume, features_left[0])
             geo_encoding_volume = self.cost_agg(gwc_volume, features_left)
 
@@ -183,7 +182,6 @@ class IGEVStereo(nn.Module):
                 spx_pred = self.spx(xspx)
                 spx_pred = F.softmax(spx_pred, 1)
 
-            # Content Network
             cnet_list = self.cnet(image1, num_layers=self.args.n_gru_layers)
             net_list = [torch.tanh(x[0]) for x in cnet_list]
             inp_list = [torch.relu(x[1]) for x in cnet_list]

IGEV-Stereo/core/stereo_datasets.py

@@ -293,7 +293,7 @@ class CREStereo(StereoDataset):
 
 
 class Middlebury(StereoDataset):
-    def __init__(self, aug_params=None, root='/data/Middlebury', split='H'):
+    def __init__(self, aug_params=None, root='/data/Middlebury', split='F'):
         super(Middlebury, self).__init__(aug_params, sparse=True, reader=frame_utils.readDispMiddlebury)
         assert os.path.exists(root)
         assert split in "FHQ"

IGEV-Stereo/core/submodule.py

@@ -157,7 +157,6 @@ def groupwise_correlation(fea1, fea2, num_groups):
     return cost
 
 def build_gwc_volume(refimg_fea, targetimg_fea, maxdisp, num_groups):
-    # batch, groups, max_disp, height, width
     B, C, H, W = refimg_fea.shape
     volume = refimg_fea.new_zeros([B, num_groups, maxdisp, H, W])
     for i in range(maxdisp):

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, ImageEnhance
+from PIL import Image
 
 import cv2
 cv2.setNumThreads(0)
@@ -198,40 +198,21 @@ 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):
-        img1 = self.chromatic_augmentation(img1)
-        img2 = self.chromatic_augmentation(img2)
+        image_stack = np.concatenate([img1, img2], axis=0)
+        image_stack = np.array(self.photo_aug(Image.fromarray(image_stack)), dtype=np.uint8)
+        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(1):
+            for _ in range(np.random.randint(1, 3)):
                 x0 = np.random.randint(0, wd)
                 y0 = np.random.randint(0, ht)
                 dx = np.random.randint(50, 100)

IGEV-Stereo/dataset_utils/create_crestereo_subsets.py

@@ -1,112 +0,0 @@
-import os
-import sys
-
-sys.path.append("..")
-
-import numpy as np
-import logging
-import os
-import shutil
-import random
-from pathlib import Path
-from glob import glob
-import matplotlib.pyplot as plt
-
-from core.utils import frame_utils
-
-
-def unique(lst):
-    return dict(zip(*np.unique(lst, return_counts=True)))
-
-
-def ensure_path_exists(path):
-    if not os.path.exists(path):
-        os.makedirs(path)
-
-
-class CREStereo():
-    def __init__(self, aug_params=None, root='/data/CREStereo'):
-        self.root = root
-        assert os.path.exists(root)
-
-        # disp_list = self.selector('_left.disp.png')
-        # image1_list = self.selector('_left.jpg')
-        # image2_list = self.selector('_right.jpg')
-
-        # assert len(image1_list) == len(image2_list) == len(disp_list) > 0
-        # for img1, img2, disp in zip(image1_list, image2_list, disp_list):
-        #     # if random.randint(1, 20000) != 1:
-        #     #     continue
-        #     self.image_list += [[img1, img2]]
-        #     self.disparity_list += [disp]
-
-    def get_path_info(self, path):
-        position, filename = os.path.split(path)
-        root, sub_folder = os.path.split(position)
-        return root, sub_folder, filename
-
-    def get_new_file(self, path):
-        root, sub_folder, filename = self.get_path_info(path)
-        return os.path.join(root, 'subset', sub_folder, filename)
-
-    def divide(self, num):
-        ensure_path_exists(os.path.join(self.root, 'subset'))
-        for sub_folder in ['tree', 'shapenet', 'reflective', 'hole']:
-            ensure_path_exists(os.path.join(self.root, 'subset', sub_folder))
-            disp1_list = self.single_folder_selector(sub_folder, '_left.disp.png')
-            disp2_list = self.single_folder_selector(sub_folder, '_right.disp.png')
-            image1_list = self.single_folder_selector(sub_folder, '_left.jpg')
-            image2_list = self.single_folder_selector(sub_folder, '_right.jpg')
-
-            assert len(image1_list) == len(image2_list) == len(disp1_list) == len(disp2_list) > 0
-            lists = []
-            for img1, img2, disp1, disp2 in zip(image1_list, image2_list, disp1_list, disp2_list):
-                lists += [[img1, img2, disp1, disp2]]
-            subset = random.sample(lists, num)
-
-            for s in subset:
-                for element in s:
-                    print(element)
-                    print(self.get_new_file(element))
-                    shutil.copy(element, self.get_new_file(element))
-
-
-
-    def selector(self, suffix):
-        files = list(glob(os.path.join(self.root, f"hole/*{suffix}")))
-        files += list(glob(os.path.join(self.root, f"shapenet/*{suffix}")))
-        files += list(glob(os.path.join(self.root, f"tree/*{suffix}")))
-        files += list(glob(os.path.join(self.root, f"reflective/*{suffix}")))
-        return sorted(files)
-
-    def single_folder_selector(self, sub_folder, suffix):
-        return sorted(list(glob(os.path.join(self.root, f"{sub_folder}/*{suffix}"))))
-
-    def disparity_distribution(self):
-        disp_lists = self.selector('_left.disp.png')
-        disparities = {}
-        for filename in disp_lists:
-            print(filename)
-            disp_gt, _ = frame_utils.readDispCREStereo(filename)
-            [rows, cols] = disp_gt.shape
-            disp_gt = (disp_gt * 32).astype(int)
-
-            cnt = unique(disp_gt)
-            for i in cnt:
-                if i in disparities:
-                    disparities[i] += cnt[i]
-                else:
-                    disparities[i] = cnt[i]
-
-        x = []
-        y = []
-        for key in disparities.keys():
-            x.append(key / 32)
-            y.append(disparities[key])
-
-        plt.scatter(x, y)
-        plt.show()
-
-
-c = CREStereo()
-c.divide(10000)

IGEV-Stereo/evaluate-history.sh

@@ -1,8 +0,0 @@
-#!/bin/bash
-for iter in {1..100}
-do
-  iter=$((iter * 1000))
-  echo "These are the results of ${iter} iterations."
-  python evaluate_stereo.py --dataset middlebury_H --max_disp 384 --freeze_backbone_params --restore_ckpt ~/checkpoints/igev_stereo/${iter}_fix-validation.pth
-done
-

IGEV-Stereo/evaluate_stereo.py

@@ -20,7 +20,7 @@ def count_parameters(model):
     return sum(p.numel() for p in model.parameters() if p.requires_grad)
 
 @torch.no_grad()
-def validate_eth3d(model, iters=32, mixed_prec=False, max_disp=192):
+def validate_eth3d(model, iters=32, mixed_prec=False):
     """ Peform validation using the ETH3D (train) split """
     model.eval()
     aug_params = {}
@@ -67,7 +67,7 @@ def validate_eth3d(model, iters=32, mixed_prec=False, max_disp=192):
 
 
 @torch.no_grad()
-def validate_kitti(model, iters=32, mixed_prec=False, max_disp=192):
+def validate_kitti(model, iters=32, mixed_prec=False):
     """ Peform validation using the KITTI-2015 (train) split """
     model.eval()
     aug_params = {}
@@ -96,7 +96,7 @@ def validate_kitti(model, iters=32, mixed_prec=False, max_disp=192):
         epe = torch.sum((flow_pr - flow_gt)**2, dim=0).sqrt()
 
         epe_flattened = epe.flatten()
-        val = (valid_gt.flatten() >= 0.5) & (flow_gt.abs().flatten() < max_disp)
+        val = (valid_gt.flatten() >= 0.5) & (flow_gt.abs().flatten() < 192)
         # val = valid_gt.flatten() >= 0.5
 
         out = (epe_flattened > 3.0)
@@ -120,7 +120,7 @@ def validate_kitti(model, iters=32, mixed_prec=False, max_disp=192):
 
 
 @torch.no_grad()
-def validate_sceneflow(model, iters=32, mixed_prec=False, max_disp=192):
+def validate_sceneflow(model, iters=32, mixed_prec=False):
     """ Peform validation using the Scene Flow (TEST) split """
     model.eval()
     val_dataset = datasets.SceneFlowDatasets(dstype='frames_finalpass', things_test=True)
@@ -144,7 +144,7 @@ def validate_sceneflow(model, iters=32, mixed_prec=False, max_disp=192):
         epe = torch.abs(flow_pr - flow_gt)
 
         epe = epe.flatten()
-        val = (valid_gt.flatten() >= 0.5) & (flow_gt.abs().flatten() < max_disp)
+        val = (valid_gt.flatten() >= 0.5) & (flow_gt.abs().flatten() < 192)
 
         if(np.isnan(epe[val].mean().item())):
             continue
@@ -169,7 +169,7 @@ def validate_sceneflow(model, iters=32, mixed_prec=False, max_disp=192):
 
 
 @torch.no_grad()
-def validate_middlebury(model, iters=32, split='H', mixed_prec=False, max_disp=192):
+def validate_middlebury(model, iters=32, split='H', mixed_prec=False):
     """ Peform validation using the Middlebury-V3 dataset """
     model.eval()
     aug_params = {}
@@ -196,11 +196,11 @@ def validate_middlebury(model, iters=32, split='H', mixed_prec=False, max_disp=1
         occ_mask = Image.open(imageL_file.replace('im0.png', 'mask0nocc.png')).convert('L')
         occ_mask = np.ascontiguousarray(occ_mask, dtype=np.float32).flatten()
 
-        val = (valid_gt.reshape(-1) >= 0.5) & (flow_gt[0].reshape(-1) < max_disp) & (occ_mask==255)
+        val = (valid_gt.reshape(-1) >= 0.5) & (flow_gt[0].reshape(-1) < 192) & (occ_mask==255)
         out = (epe_flattened > 2.0)
         image_out = out[val].float().mean().item()
         image_epe = epe_flattened[val].mean().item()
-        logging.info(f"Middlebury Iter {val_id+1} out of {len(val_dataset)}. EPE {round(image_epe,4)} Err2.0 {round(image_out,4)}")
+        logging.info(f"Middlebury Iter {val_id+1} out of {len(val_dataset)}. EPE {round(image_epe,4)} D1 {round(image_out,4)}")
         epe_list.append(image_epe)
         out_list.append(image_out)
 
@@ -208,10 +208,10 @@ def validate_middlebury(model, iters=32, split='H', mixed_prec=False, max_disp=1
     out_list = np.array(out_list)
 
     epe = np.mean(epe_list)
-    err2 = 100 * np.mean(out_list)
+    d1 = 100 * np.mean(out_list)
 
-    print(f"Validation Middlebury{split}: EPE {epe}, Err2.0 {err2}")
-    return {f'middlebury{split}-epe': epe, f'middlebury{split}-err2.0': err2}
+    print(f"Validation Middlebury{split}: EPE {epe}, D1 {d1}")
+    return {f'middlebury{split}-epe': epe, f'middlebury{split}-d1': d1}
 
 
 if __name__ == '__main__':
@@ -231,7 +231,6 @@ if __name__ == '__main__':
     parser.add_argument('--slow_fast_gru', action='store_true', help="iterate the low-res GRUs more frequently")
     parser.add_argument('--n_gru_layers', type=int, default=3, help="number of hidden GRU levels")
     parser.add_argument('--max_disp', type=int, default=192, help="max disp of geometry encoding volume")
-    parser.add_argument('--freeze_backbone_params', action="store_true", help="freeze backbone parameters")
     args = parser.parse_args()
 
     model = torch.nn.DataParallel(IGEVStereo(args), device_ids=[0])
@@ -243,12 +242,6 @@ if __name__ == '__main__':
         assert args.restore_ckpt.endswith(".pth")
         logging.info("Loading checkpoint...")
         checkpoint = torch.load(args.restore_ckpt)
-
-        unwanted_prefix = '_orig_mod.'
-        for k, v in list(checkpoint.items()):
-            if k.startswith(unwanted_prefix):
-                checkpoint[k[len(unwanted_prefix):]] = checkpoint.pop(k)
-
         model.load_state_dict(checkpoint, strict=True)
         logging.info(f"Done loading checkpoint")
 
@@ -259,13 +252,13 @@ if __name__ == '__main__':
     use_mixed_precision = args.corr_implementation.endswith("_cuda")
 
     if args.dataset == 'eth3d':
-        validate_eth3d(model, iters=args.valid_iters, mixed_prec=use_mixed_precision, max_disp=args.max_disp)
+        validate_eth3d(model, iters=args.valid_iters, mixed_prec=use_mixed_precision)
 
     elif args.dataset == 'kitti':
-        validate_kitti(model, iters=args.valid_iters, mixed_prec=use_mixed_precision, max_disp=args.max_disp)
+        validate_kitti(model, iters=args.valid_iters, mixed_prec=use_mixed_precision)
 
     elif args.dataset in [f"middlebury_{s}" for s in 'FHQ']:
-        validate_middlebury(model, iters=args.valid_iters, split=args.dataset[-1], mixed_prec=use_mixed_precision, max_disp=args.max_disp)
+        validate_middlebury(model, iters=args.valid_iters, split=args.dataset[-1], mixed_prec=use_mixed_precision)
 
     elif args.dataset == 'sceneflow':
-        validate_sceneflow(model, iters=args.valid_iters, mixed_prec=use_mixed_precision, max_disp=args.max_disp)
+        validate_sceneflow(model, iters=args.valid_iters, mixed_prec=use_mixed_precision)

IGEV-Stereo/train_stereo.py

@@ -67,10 +67,9 @@ def sequence_loss(disp_preds, disp_init_pred, disp_gt, valid, loss_gamma=0.9, ma
 
     metrics = {
         'epe': epe.mean().item(),
-        '0.5px': (epe < 0.5).float().mean().item(),
-        '1.0px': (epe < 1.0).float().mean().item(),
-        '2.0px': (epe < 2.0).float().mean().item(),
-        '4.0px': (epe < 4.0).float().mean().item(),
+        '1px': (epe < 1).float().mean().item(),
+        '3px': (epe < 3).float().mean().item(),
+        '5px': (epe < 5).float().mean().item(),
     }
 
     return disp_loss, metrics
@@ -78,7 +77,7 @@ def sequence_loss(disp_preds, disp_init_pred, disp_gt, valid, loss_gamma=0.9, ma
 
 def fetch_optimizer(args, model):
     """ Create the optimizer and learning rate scheduler """
-    optimizer = optim.AdamW(filter(lambda p: p.requires_grad, model.parameters()), lr=args.lr, weight_decay=args.wdecay, eps=1e-8)
+    optimizer = optim.AdamW(model.parameters(), lr=args.lr, weight_decay=args.wdecay, eps=1e-8)
 
     # todo: cosine scheduler, warm-up
     scheduler = optim.lr_scheduler.OneCycleLR(optimizer, args.lr, args.num_steps+100,
@@ -187,7 +186,7 @@ def train(args):
                 save_path = Path(ckpt_path + '/%d_%s.pth' % (total_steps + 1, args.name))
                 logging.info(f"Saving file {save_path.absolute()}")
                 torch.save(model.state_dict(), save_path)
-                results = validate_middlebury(model.module, iters=args.valid_iters, max_disp=args.max_disp)
+                results = validate_middlebury(model.module, iters=args.valid_iters)
                 logger.write_dict(results)
                 model.train()
                 model.module.freeze_bn()
@@ -222,7 +221,6 @@ if __name__ == '__main__':
     parser.add_argument('--train_datasets', nargs='+', default=['sceneflow'], help="training datasets.")
     parser.add_argument('--lr', type=float, default=0.0002, help="max learning rate.")
     parser.add_argument('--num_steps', type=int, default=200000, help="length of training schedule.")
-    parser.add_argument('--freeze_backbone_params', action="store_true", help="freeze backbone parameters")
     parser.add_argument('--image_size', type=int, nargs='+', default=[320, 736], help="size of the random image crops used during training.")
     parser.add_argument('--train_iters', type=int, default=22, help="number of updates to the disparity field in each forward pass.")
     parser.add_argument('--wdecay', type=float, default=.00001, help="Weight decay in optimizer.")
@@ -242,8 +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('--saturation_range', type=float, nargs='+', default=[0, 1.4], help='color saturation')
+    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('--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')