test_model.py: cleanup

test_model.py

@@ -8,7 +8,6 @@ from nets import Model
 
 import wandb
 
-import random
 from torch.utils.data import DataLoader
 from dataset import CTDDataset
 from train import normalize_and_colormap, parse_yaml, inference as ctd_inference
@@ -79,45 +78,16 @@ if __name__ == '__main__':
     model.to(device)
     model.eval()
 
-    CTD = True
-    if not CTD:
-        left_img = cv2.imread("../test_imgs/left.png")
-        right_img = cv2.imread("../test_imgs/right.png")
-        in_h, in_w = left_img.shape[:2]
-
-        # Resize image in case the GPU memory overflows
-        eval_h, eval_w = (in_h, in_w)
-
-        # FIXME borked for some reason, hopefully not very important
-        imgL = cv2.resize(left_img, (eval_w, eval_h), interpolation=cv2.INTER_LINEAR)
-        imgR = cv2.resize(right_img, (eval_w, eval_h), interpolation=cv2.INTER_LINEAR)
-
-        pred = inference(imgL, imgR, model, n_iter=20)
-
-        t = float(in_w) / float(eval_w)
-        disp = cv2.resize(pred, (in_w, in_h), interpolation=cv2.INTER_LINEAR) * t
-
-        disp_vis = (disp - disp.min()) / (disp.max() - disp.min()) * 255.0
-        disp_vis = disp_vis.astype("uint8")
-        disp_vis = cv2.applyColorMap(disp_vis, cv2.COLORMAP_INFERNO)
-
-        combined_img = np.hstack((left_img, disp_vis))
-        # cv2.namedWindow("output", cv2.WINDOW_NORMAL)
-        # cv2.imshow("output", combined_img)
-        cv2.imwrite("output.jpg", disp_vis)
-        # cv2.waitKey(0)
-
-    else:
-        dataset = CTDDataset('/media/Data1/connecting_the_dots_data/ctd_data/', data_type=data_type,
-                             pattern_path=reference_pattern_path, augment=augment)
-        dataloader = DataLoader(dataset, args.batch_size, shuffle=True,
-                                num_workers=0, drop_last=False, persistent_workers=False, pin_memory=True)
-        for batch in dataloader:
-            for left, right, disparity in zip(batch['left'], batch['right'], batch['disparity']):
-                right = right.transpose(0, 2).transpose(0, 1)
-                left_img = left
-                imgL = left.cpu().detach().numpy()
-                right_img = right
-                imgR = right.cpu().detach().numpy()
-                gt_disp = disparity
-                do_infer(left_img, right_img, gt_disp, model)
+    dataset = CTDDataset('/media/Data1/connecting_the_dots_data/ctd_data/', data_type=data_type,
+                         pattern_path=reference_pattern_path, augment=augment)
+    dataloader = DataLoader(dataset, args.batch_size, shuffle=True,
+                            num_workers=0, drop_last=False, persistent_workers=False, pin_memory=True)
+    for batch in dataloader:
+        for left, right, disparity in zip(batch['left'], batch['right'], batch['disparity']):
+            right = right.transpose(0, 2).transpose(0, 1)
+            left_img = left
+            imgL = left.cpu().detach().numpy()
+            right_img = right
+            imgR = right.cpu().detach().numpy()
+            gt_disp = disparity
+            do_infer(left_img, right_img, gt_disp, model)