Misc

.idea/vcs.xml

@@ -2,5 +2,7 @@
 <project version="4">
   <component name="VcsDirectoryMappings">
     <mapping directory="" vcs="Git" />
+    <mapping directory="$PROJECT_DIR$/codes/models/flownet2" vcs="Git" />
+    <mapping directory="$PROJECT_DIR$/codes/switched_conv" vcs="Git" />
   </component>
 </project>

codes/scripts/extract_subimages_with_ref.py

@@ -242,7 +242,7 @@ class TiledDataset(data.Dataset):
 
         h, w, c = img.shape
         # Uncomment to filter any image that doesnt meet a threshold size.
-        if min(h,w) < 512:
+        if min(h,w) < 1024:
             return None
         left = 0
         right = w

codes/train2.py

@@ -8,9 +8,9 @@ from tqdm import tqdm
 import torch
 from data.data_sampler import DistIterSampler
 
-from models.ExtensibleTrainer import ExtensibleTrainer
 from utils import util, options as option
 from data import create_dataloader, create_dataset
+from models.ExtensibleTrainer import ExtensibleTrainer
 from time import time
 
 
@@ -19,7 +19,7 @@ def init_dist(backend='nccl', **kwargs):
     import torch.distributed as dist
     import torch.multiprocessing as mp
 
-    """initialization for distributneed training"""
+    """initialization for distributed training"""
     if mp.get_start_method(allow_none=True) != 'spawn':
         mp.set_start_method('spawn')
     rank = int(os.environ['RANK'])
@@ -122,7 +122,7 @@ def main():
 
     torch.backends.cudnn.benchmark = True
     # torch.backends.cudnn.deterministic = True
-    #torch.autograd.set_detect_anomaly(True)
+    # torch.autograd.set_detect_anomaly(True)
 
     # Save the compiled opt dict to the global loaded_options variable.
     util.loaded_options = opt
@@ -170,6 +170,8 @@ def main():
     else:
         current_step = -1 if 'start_step' not in opt.keys() else opt['start_step']
         start_epoch = 0
+    if 'force_start_step' in opt.keys():
+        current_step = opt['force_start_step']
 
     #### training
     logger.info('Start training from epoch: {:d}, iter: {:d}'.format(start_epoch, current_step))
@@ -185,10 +187,6 @@ def main():
                 print("Data fetch: %f" % (time() - _t))
                 _t = time()
 
-            #tb_logger.add_graph(model.netsG['generator'].module, [train_data['LQ'].to('cuda'),
-            #                                                      train_data['lq_fullsize_ref'].float().to('cuda'),
-            #                                                      train_data['lq_center'].to('cuda')])
-
             current_step += 1
             if current_step > total_iters:
                 break
@@ -241,9 +239,6 @@ def main():
             #### validation
             if opt['datasets'].get('val', None) and current_step % opt['train']['val_freq'] == 0:
                 if opt['model'] in ['sr', 'srgan', 'corruptgan', 'spsrgan', 'extensibletrainer'] and rank <= 0:  # image restoration validation
-                    model.force_restore_swapout()
-                    val_batch_sz = 1 if 'batch_size' not in opt['datasets']['val'].keys() else opt['datasets']['val']['batch_size']
-                    # does not support multi-GPU validation
                     avg_psnr = 0.
                     avg_fea_loss = 0.
                     idx = 0
@@ -263,23 +258,22 @@ def main():
                             if visuals is None:
                                 continue
 
+                            if colab_mode:
+                                colab_imgs_to_copy.append(save_img_path)
+
+                            # calculate PSNR
                             sr_img = util.tensor2img(visuals['rlt'][b])  # uint8
-                            #gt_img = util.tensor2img(visuals['GT'][b])  # uint8
+                            gt_img = util.tensor2img(visuals['GT'][b])  # uint8
+                            sr_img, gt_img = util.crop_border([sr_img, gt_img], opt['scale'])
+                            avg_psnr += util.calculate_psnr(sr_img, gt_img)
+
+                            # calculate fea loss
+                            avg_fea_loss += model.compute_fea_loss(visuals['rlt'][b], visuals['GT'][b])
 
                             # Save SR images for reference
                             img_base_name = '{:s}_{:d}.png'.format(img_name, current_step)
                             save_img_path = os.path.join(img_dir, img_base_name)
                             util.save_img(sr_img, save_img_path)
-                            if colab_mode:
-                                colab_imgs_to_copy.append(save_img_path)
-
-                            # calculate PSNR (Naw - don't do that. PSNR sucks)
-                            #sr_img, gt_img = util.crop_border([sr_img, gt_img], opt['scale'])
-                            #avg_psnr += util.calculate_psnr(sr_img, gt_img)
-                            #pbar.update('Test {}'.format(img_name))
-
-                            # calculate fea loss
-                            avg_fea_loss += model.compute_fea_loss(visuals['rlt'][b], visuals['GT'][b])
 
                     if colab_mode:
                         util.copy_files_to_server(opt['ssh_server'], opt['ssh_username'], opt['ssh_password'],
@@ -293,7 +287,7 @@ def main():
                     logger.info('# Validation # PSNR: {:.4e} Fea: {:.4e}'.format(avg_psnr, avg_fea_loss))
                     # tensorboard logger
                     if opt['use_tb_logger'] and 'debug' not in opt['name'] and rank <= 0:
-                        #tb_logger.add_scalar('val_psnr', avg_psnr, current_step)
+                        tb_logger.add_scalar('val_psnr', avg_psnr, current_step)
                         tb_logger.add_scalar('val_fea', avg_fea_loss, current_step)
 
     if rank <= 0: