diff --git a/predict.py b/predict.py
index b74c4608dd..18192015fe 100755
--- a/predict.py
+++ b/predict.py
@@ -12,6 +12,8 @@
 from unet import UNet
 from utils.utils import plot_img_and_mask
 
+
+# for predicting single PIL image
 def predict_img(net,
                 full_img,
                 device,
@@ -33,6 +35,27 @@ def predict_img(net,
     return mask[0].long().squeeze().numpy()
 
 
+# for predicting batch of nd.array images
+def predict_imgs(net,
+                 image_arrays,
+                 device,
+                 scale_factor=1,
+                 out_threshold=0.5):
+    net.eval()
+    img = torch.from_numpy(image_arrays)
+    img = img.to(device=device, dtype=torch.float32)
+
+    with torch.no_grad():
+        output = net(img).cpu()
+        if net.n_classes > 1:
+            mask = output.argmax(dim=1)
+        else:
+            mask = torch.sigmoid(output) > out_threshold
+
+    return mask.long().squeeze().numpy()
+
+
+
 def get_args():
     parser = argparse.ArgumentParser(description='Predict masks from input images')
     parser.add_argument('--model', '-m', default='MODEL.pth', metavar='FILE',
@@ -80,38 +103,56 @@ def mask_to_image(mask: np.ndarray, mask_values):
     args = get_args()
     logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(message)s')
 
-    in_files = args.input
-    out_files = get_output_filenames(args)
+    image_in_file = False
+    if image_in_file:
+        in_files = args.input
+        out_files = get_output_filenames(args)
 
-    net = UNet(n_channels=3, n_classes=args.classes, bilinear=args.bilinear)
+        net = UNet(n_channels=3, n_classes=args.classes, bilinear=args.bilinear)
 
-    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-    logging.info(f'Loading model {args.model}')
-    logging.info(f'Using device {device}')
+        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        logging.info(f'Loading model {args.model}')
+        logging.info(f'Using device {device}')
 
-    net.to(device=device)
-    state_dict = torch.load(args.model, map_location=device)
-    mask_values = state_dict.pop('mask_values', [0, 1])
-    net.load_state_dict(state_dict)
+        net.to(device=device)
+        state_dict = torch.load(args.model, map_location=device)
+        mask_values = state_dict.pop('mask_values', [0, 1])
+        net.load_state_dict(state_dict)
 
-    logging.info('Model loaded!')
+        logging.info('Model loaded!')
 
-    for i, filename in enumerate(in_files):
-        logging.info(f'Predicting image {filename} ...')
-        img = Image.open(filename)
+        for i, filename in enumerate(in_files):
+            logging.info(f'Predicting image {filename} ...')
+            img = Image.open(filename)
 
-        mask = predict_img(net=net,
-                           full_img=img,
-                           scale_factor=args.scale,
-                           out_threshold=args.mask_threshold,
-                           device=device)
+            mask = predict_img(net=net,
+                               full_img=img,
+                               scale_factor=args.scale,
+                               out_threshold=args.mask_threshold,
+                               device=device)
 
-        if not args.no_save:
-            out_filename = out_files[i]
-            result = mask_to_image(mask, mask_values)
-            result.save(out_filename)
-            logging.info(f'Mask saved to {out_filename}')
+            if not args.no_save:
+                out_filename = out_files[i]
+                result = mask_to_image(mask, mask_values)
+                result.save(out_filename)
+                logging.info(f'Mask saved to {out_filename}')
 
-        if args.viz:
-            logging.info(f'Visualizing results for image {filename}, close to continue...')
-            plot_img_and_mask(img, mask)
+            if args.viz:
+                logging.info(f'Visualizing results for image {filename}, close to continue...')
+                plot_img_and_mask(img, mask)
+    else:
+        batch_size = 128
+
+        net = torch.hub.load('milesial/Pytorch-UNet', 'unet_carvana', pretrained=True, scale=1.0)
+        device = torch.device('cpu' if torch.cuda.is_available() else 'cpu')
+        logging.info(f'Using device {device}')
+        net.to(device=device)
+        mask_values = [0, 1]
+
+        image_arrays = np.random.rand(batch_size, 3, 112, 112)
+        mask_arrays = predict_imgs(net=net,
+                                   image_arrays=image_arrays,
+                                   scale_factor=args.scale,
+                                   out_threshold=args.mask_threshold,
+                                   device=device)
+        print(mask_arrays.shape)