Fixes and mods to support training classifiers on imagenet

codes/data/torch_dataset.py

@@ -4,6 +4,9 @@ import torchvision.transforms as T
 from torchvision import datasets
 
 # Wrapper for basic pytorch datasets which re-wraps them into a format usable by ExtensibleTrainer.
+from utils.util import opt_get
+
+
 class TorchDataset(Dataset):
     def __init__(self, opt):
         DATASET_MAP = {
@@ -14,7 +17,7 @@ class TorchDataset(Dataset):
             "imagefolder": datasets.ImageFolder
         }
         normalize = T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-        if opt['train']:
+        if opt_get(opt, ['random_crop'], False):
             transforms = [
                 T.RandomResizedCrop(opt['image_size']),
                 T.RandomHorizontalFlip(),
@@ -23,8 +26,9 @@ class TorchDataset(Dataset):
             ]
         else:
             transforms = [
-                T.Resize(opt['val_resize']),
+                T.Resize(opt['image_size']),
                 T.CenterCrop(opt['image_size']),
+                T.RandomHorizontalFlip(),
                 T.ToTensor(),
                 normalize,
             ]

codes/data/zip_file_dataset.py

@@ -35,6 +35,7 @@ class ZipFileDataset(torch.utils.data.Dataset):
         return tensor
 
     def __getitem__(self, i):
+        try:
             fname = self.all_files[i]
             out = {
                 'hq': self.load_image(fname),
@@ -47,6 +48,9 @@ class ZipFileDataset(torch.utils.data.Dataset):
                 else:
                     aname = fname.replace('1.jpg', '0.jpg')
                 out['alt_hq'] = self.load_image(aname)
+        except:
+            print(f"Error loading {fname} from zipfile. Attempting to recover by loading next element.")
+            return self[i+1]
         return out
 
 if __name__ == '__main__':

codes/scripts/extract_square_images.py

@@ -14,17 +14,19 @@ def main():
     split_img = False
     opt = {}
     opt['n_thread'] = 5
-    opt['compression_level'] = 95  # JPEG compression quality rating.
-    # CV_IMWRITE_PNG_COMPRESSION from 0 to 9. A higher value means a smaller size and longer
-    # compression time. If read raw images during training, use 0 for faster IO speed.
 
     opt['dest'] = 'file'
-    opt['input_folder'] = ['E:\\4k6k\\datasets\\images\\lsun\\lsun\\cats']
-    opt['save_folder'] = 'E:\\4k6k\\datasets\\images\\lsun\\lsun\\cats\\256_4_by_3'
-    opt['imgsize'] = (256,192)
+    opt['input_folder'] = ['E:\\4k6k\datasets\\ns_images\\imagesets\\imageset_256_masked']
+    opt['save_folder'] = 'E:\\4k6k\datasets\\ns_images\\imagesets\\imageset_128_masked'
+    opt['imgsize'] = (128,128)
     opt['bottom_crop'] = 0
     opt['keep_folder'] = False
 
+    #opt['format'] = 'jpg'
+    #opt['cv2_write_options'] = [cv2.IMWRITE_JPEG_QUALITY, 95]
+    opt['format'] = 'png'
+    opt['cv2_write_options'] = [cv2.IMWRITE_PNG_COMPRESSION, 9]
+
     save_folder = opt['save_folder']
     if not osp.exists(save_folder):
         os.makedirs(save_folder)
@@ -93,7 +95,7 @@ class TiledDataset(data.Dataset):
                 pts = os.path.split(pts[0])
             output_folder = osp.join(self.opt['save_folder'], pts[-1])
             os.makedirs(output_folder, exist_ok=True)
-        cv2.imwrite(osp.join(output_folder, basename.replace('.webp', '.jpg')), img, [cv2.IMWRITE_JPEG_QUALITY, self.opt['compression_level']])
+        cv2.imwrite(osp.join(output_folder, basename.replace('.webp', self.opt['format'])), img, self.opt['cv2_write_options'])
         return None
 
     def __len__(self):

codes/train.py

@@ -295,7 +295,7 @@ class Trainer:
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
-    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_byol_resnet_imagenet_yt.yml')
+    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_imagenet_resnet50_yt_pretrained.yml')
     parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
     parser.add_argument('--local_rank', type=int, default=0)
     args = parser.parse_args()

codes/trainer/ExtensibleTrainer.py

@@ -371,7 +371,7 @@ class ExtensibleTrainer(BaseModel):
                 if load_path is not None:
                     if self.rank <= 0:
                         logger.info('Loading model for [%s]' % (load_path,))
-                    self.load_network(load_path, net, self.opt['path']['strict_load'])
+                    self.load_network(load_path, net, self.opt['path']['strict_load'], opt_get(self.opt, ['path', f'pretrain_base_path_{name}']))
                 if hasattr(net.module, 'network_loaded'):
                     net.module.network_loaded()
 

codes/trainer/base_model.py

@@ -97,7 +97,7 @@ class BaseModel():
                                             save_path, os.path.join(self.opt['remote_path'], 'models', save_filename))
         return save_path
 
-    def load_network(self, load_path, network, strict=True):
+    def load_network(self, load_path, network, strict=True, pretrain_base_path=None):
         #if isinstance(network, nn.DataParallel) or isinstance(network, DistributedDataParallel):
         network = network.module
         load_net = torch.load(load_path)
@@ -105,9 +105,15 @@ class BaseModel():
         # Support loading torch.save()s for whole models as well as just state_dicts.
         if 'state_dict' in load_net:
             load_net = load_net['state_dict']
-
-        is_srflow = False
         load_net_clean = OrderedDict()  # remove unnecessary 'module.'
+
+        if pretrain_base_path is not None:
+            t = load_net
+            load_net = {}
+            for k, v in t.items():
+                if k.startswith(pretrain_base_path):
+                    load_net[k[len(pretrain_base_path):]] = v
+
         for k, v in load_net.items():
             if k.startswith('module.'):
                 load_net_clean[k.replace('module.', '')] = v

codes/trainer/eval/categorization_loss_eval.py

@@ -18,7 +18,7 @@ class CategorizationLossEvaluator(evaluator.Evaluator):
         normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                          std=[0.229, 0.224, 0.225])
         self.dataset = torchvision.datasets.ImageFolder(
-            'F:\\4k6k\\datasets\\images\\imagenet_2017\\val',
+            'E:\\4k6k\\datasets\\images\\imagenet_2017\\val',
             transforms.Compose([
                 transforms.Resize(256),
                 transforms.CenterCrop(224),
@@ -27,7 +27,7 @@ class CategorizationLossEvaluator(evaluator.Evaluator):
             ]))
         self.dataloader = DataLoader(self.dataset, self.batch_sz, shuffle=False, num_workers=4)
         self.gen_output_index = opt_eval['gen_index'] if 'gen_index' in opt_eval.keys() else 0
-        self.masking = opt_get(opt_eval, ['masking'], True)
+        self.masking = opt_get(opt_eval, ['masking'], False)
         if self.masking:
             self.mask_producer = UResnetMaskProducer(pretrained_uresnet_path= '../experiments/train_imagenet_pixpro_resnet/models/66500_generator.pth',
                                                      kmeans_centroid_path='../experiments/k_means_uresnet_imagenet_256.pth',