updates

test.py

@@ -78,6 +78,9 @@ def test(cfg,
         if batch_i == 0 and not os.path.exists('test_batch0.jpg'):
             plot_images(imgs=imgs, targets=targets, paths=paths, fname='test_batch0.jpg')
 
+        # Disable gradients
+        with torch.no_grad():
+
             # Run model
             inf_out, train_out = model(imgs)  # inference and training outputs
 
@@ -220,7 +223,7 @@ if __name__ == '__main__':
     opt = parser.parse_args()
     print(opt)
 
-    with torch.no_grad():
+    # Test
     test(opt.cfg,
          opt.data,
          opt.weights,
@@ -229,3 +232,17 @@ if __name__ == '__main__':
          opt.conf_thres,
          opt.nms_thres,
          opt.save_json or any([x in opt.data for x in ['coco.data', 'coco2014.data', 'coco2017.data']]))
+
+    # # Parameter study
+    # y = []
+    # x = np.arange(0.4, 0.81, 0.1)
+    # for v in x:
+    #     y.append(test(opt.cfg, opt.data, opt.weights, opt.batch_size, opt.img_size, 0.1, v, True)[0])
+    # y = np.stack(y, 0)
+    #
+    # fig, ax = plt.subplots(1, 1, figsize=(12, 6))
+    # ax.plot(x, y[:, 2], marker='.', label='mAP@0.5')
+    # ax.plot(x, y[:, 3], marker='.', label='mAP@0.5:0.95')
+    # ax.legend()
+    # fig.tight_layout()
+    # plt.savefig('parameters.jpg', dpi=200)

train.py

@@ -323,7 +323,6 @@ def train():
         if opt.prebias:
             print_model_biases(model)
         elif not opt.notest or final_epoch:  # Calculate mAP
-            with torch.no_grad():
             is_coco = any([x in data for x in ['coco.data', 'coco2014.data', 'coco2017.data']]) and model.nc == 80
             results, maps = test.test(cfg,
                                       data,