upd

src/common/validation.py

@@ -29,7 +29,8 @@ def val_image_pair(model, hr_image, lr_image, output_image_path=None, device='cu
         # metrics
         hr_image = modcrop(hr_image, model.scale)
         left, right = _rgb2ycbcr(pred_lr_image)[:, :, 0], _rgb2ycbcr(hr_image)[:, :, 0]
-        return PSNR(left, right, model.scale), cal_ssim(left, right), run_time_ns
+        lr_area = np.prod(lr_image.shape[-2:])
+        return PSNR(left, right, model.scale), cal_ssim(left, right), run_time_ns, lr_area
 
 def valid_steps(model, datasets, config, log_prefix=""):
     # ray.init(num_cpus=16, num_gpus=1, ignore_reinit_error=True, log_to_driver=False, runtime_env={"working_dir": "../"})
@@ -39,7 +40,10 @@ def valid_steps(model, datasets, config, log_prefix=""):
     for i in range(len(dataset_names)):
         dataset_name = dataset_names[i]
         psnrs, ssims = [], []  
-        run_times_ns = []          
+        run_times_ns = []
+        lr_areas = []
+        total_area = 0   
+        start_time = time.time() 
 
         predictions_path = config.valout_dir / dataset_name
         if not predictions_path.exists():
@@ -51,6 +55,8 @@ def valid_steps(model, datasets, config, log_prefix=""):
             output_image_path = predictions_path / f'{Path(hr_image_path).stem}_rcnet.png' if config.save_predictions else None
             task = val_image_pair(model, hr_image, lr_image, output_image_path, device=config.device)
             tasks.append(task)
+
+        total_time = time.time() - start_time 
         # ready_refs, remaining_refs = ray.wait(tasks, num_returns=1, timeout=None)
         # while len(remaining_refs) > 0:
         #     print(f"\rReady {len(ready_refs)+1}/{len(test_dataset)}",  end="     ")
@@ -58,22 +64,42 @@ def valid_steps(model, datasets, config, log_prefix=""):
         # print("\r", end="                                             ")
         # tasks = [ray.get(task) for task in tasks]
 
-        for psnr, ssim, run_time_ns in tasks:
+        for psnr, ssim, run_time_ns, lr_area in tasks:
             psnrs.append(psnr)
             ssims.append(ssim)
             run_times_ns.append(run_time_ns)
+            lr_areas.append(lr_area)
+            total_area += lr_area
 
-        config.logger.info(
+        row = [
-            '\r{} | Dataset {} | AVG Val PSNR: {:02f}, AVG: SSIM: {:04f}'.format(log_prefix, dataset_name, np.mean(np.asarray(psnrs)), np.mean(np.asarray(ssims))))
-        config.writer.flush()
-
-        results.append([
             dataset_name, 
             np.mean(psnrs), 
             np.mean(ssims), 
             np.mean(run_times_ns)*1e-9, 
-            np.percentile(run_times_ns, q=95)*1e-9])
+            np.percentile(run_times_ns, q=95)*1e-9,
+            len(test_dataset),
+            np.mean(lr_areas),
+            total_area,
+            total_time
+        ]
+        results.append(row)
+        column_names = [
+            'Dataset', 
+            'AVG PSNR', 
+            'AVG SSIM', 
+            f'AVG {config.device} Time, s', 
+            f'P95 {config.device} Time, s',
+            'Image count',
+            'AVG image area',
+            'Total area',
+            'Total time, s'
+        ]
+        config.logger.info(pd.DataFrame(
+            [row], 
+            columns=column_names
+        ).set_index('Dataset'))
+        config.writer.flush()
 
-    results = pd.DataFrame(results, columns=['Dataset', 'PSNR', 'SSIM', f'AVG {config.device} Time, s', f'P95 {config.device} Time, s']).set_index('Dataset')
+    results = pd.DataFrame(results, columns=column_names).set_index('Dataset')
 
     return results

src/validate.py

@@ -40,7 +40,7 @@ class ValOptions():
         if not args.valout_dir.exists():
             args.valout_dir.mkdir()
         args.current_iter = args.model_name.split('_')[-1]
-        args.results_path = os.path.join(args.valout_dir, f'results_{args.device}.csv')
+        args.results_path = os.path.join(args.valout_dir, f'results_{args.model_name}_{args.device}.csv')
         # Tensorboard for monitoring
         writer = SummaryWriter(log_dir=args.valout_dir)
         logger_name = f'val_{args.model_path.stem}'