Update requirements, add image_patch_classifier tool

codes/requirements.txt

@@ -14,4 +14,5 @@ tensorboard
 pytorch_fid
 kornia
 linear_attention_transformer
-vector_quantize_pytorch
+vector_quantize_pytorch
+orjson

codes/test_image_patch_classifier.py

@@ -0,0 +1,74 @@
+import os.path as osp
+import logging
+import time
+import argparse
+from collections import OrderedDict
+
+import os
+
+import utils
+import utils.options as option
+import utils.util as util
+from data.util import bgr2ycbcr
+import models.archs.SwitchedResidualGenerator_arch as srg
+from models.ExtensibleTrainer import ExtensibleTrainer
+from switched_conv.switched_conv_util import save_attention_to_image, save_attention_to_image_rgb
+from switched_conv.switched_conv import compute_attention_specificity
+from data import create_dataset, create_dataloader
+from tqdm import tqdm
+import torch
+import models.networks as networks
+import torchvision
+
+
+if __name__ == "__main__":
+    #### options
+    torch.backends.cudnn.benchmark = True
+    want_metrics = False
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-opt', type=str, help='Path to options YAML file.', default='../options/train_imgset_structural_classifier.yml')
+    opt = option.parse(parser.parse_args().opt, is_train=False)
+    opt = option.dict_to_nonedict(opt)
+    utils.util.loaded_options = opt
+
+    util.mkdirs(
+        (path for key, path in opt['path'].items()
+         if not key == 'experiments_root' and 'pretrain_model' not in key and 'resume' not in key))
+    util.setup_logger('base', opt['path']['log'], 'test_' + opt['name'], level=logging.INFO,
+                      screen=True, tofile=True)
+    logger = logging.getLogger('base')
+    logger.info(option.dict2str(opt))
+
+    #### Create test dataset and dataloader
+    test_loaders = []
+    for phase, dataset_opt in sorted(opt['datasets'].items()):
+        dataset_opt['dataset']['includes_labels'] = False
+        del dataset_opt['dataset']['labeler']
+        test_set = create_dataset(dataset_opt)
+        if hasattr(test_set, 'wrapped_dataset'):
+            test_set = test_set.wrapped_dataset
+        test_loader = create_dataloader(test_set, dataset_opt, opt)
+        logger.info('Number of test images: {:d}'.format(len(test_set)))
+        test_loaders.append(test_loader)
+
+    model = ExtensibleTrainer(opt)
+    gen = model.netsG['generator']
+    label_to_search_for = 4
+
+    for test_loader in test_loaders:
+        test_set_name = test_loader.dataset.opt['name']
+        test_start_time = time.time()
+        dataset_dir = osp.join(opt['path']['results_root'], opt['name'])
+        util.mkdir(dataset_dir)
+
+        tq = tqdm(test_loader)
+        step = 1
+        for data in tq:
+            hq = data['hq'].to('cuda')
+            res = gen(hq)
+            res = torch.nn.functional.interpolate(res, size=hq.shape[2:], mode="nearest")
+            res_lbl = res[:, label_to_search_for, :, :].unsqueeze(1)
+            res_lbl_mask = (1.0 * (res_lbl > .5))*.5 + .5
+            hq = hq * res_lbl_mask
+            torchvision.utils.save_image(hq, os.path.join(dataset_dir, "%i.png" % (step,)))
+            step += 1

codes/train.py

@@ -293,7 +293,7 @@ class Trainer:
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
-    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_exd_imgsetext_rrdb_bigboi_psnr_4x.yml')
+    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_imgset_structural_classifier.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()