Improved utilites, adds examples, tests

examples/python/README.md

@@ -4,6 +4,8 @@
 [Examples of Tensor Images transformations](https://github.com/pytorch/vision/blob/master/examples/python/tensor_transforms.ipynb)
 - [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pytorch/vision/blob/master/examples/python/video_api.ipynb)
 [Example of VideoAPI](https://github.com/pytorch/vision/blob/master/examples/python/video_api.ipynb)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pytorch/vision/blob/master/examples/python/visualization_utils.ipynb)
+[Example of Visualization Utils](https://github.com/pytorch/vision/blob/master/examples/python/visualization_utils.ipynb)
 
 
 Prior to v0.8.0, transforms in torchvision have traditionally been PIL-centric and presented multiple limitations due to 
@@ -16,3 +18,5 @@ features:
 - read and decode data directly as torch tensor with torchscript support (for PNG and JPEG image formats)
 
 Furthermore, previously we used to provide a very high-level API for video decoding which left little control to the user. We're now expanding that API (and replacing it in the future) with a lower-level API that allows the user a frame-based access to a video.
+
+Torchvision also provides utilities to visualize results. You can make grid of images, plot bounding boxes as well as segmentation masks. Thse utilities work standalone as well as with torchvision models for detection and segmentation.

test/test_utils.py

@@ -9,6 +9,9 @@
 import torchvision.transforms.functional as F
 from PIL import Image
 
+boxes = torch.tensor([[0, 0, 20, 20], [0, 0, 0, 0],
+                     [10, 15, 30, 35], [23, 35, 93, 95]], dtype=torch.float)
+
 masks = torch.tensor([
     [
         [-2.2799, -2.2799, -2.2799, -2.2799, -2.2799],
@@ -106,8 +109,8 @@ def test_save_image_single_pixel_file_object(self):
 
     def test_draw_boxes(self):
         img = torch.full((3, 100, 100), 255, dtype=torch.uint8)
-        boxes = torch.tensor([[0, 0, 20, 20], [0, 0, 0, 0],
-                             [10, 15, 30, 35], [23, 35, 93, 95]], dtype=torch.float)
+        img_cp = img.clone()
+        boxes_cp = boxes.clone()
         labels = ["a", "b", "c", "d"]
         colors = ["green", "#FF00FF", (0, 255, 0), "red"]
         result = utils.draw_bounding_boxes(img, boxes, labels=labels, colors=colors, fill=True)
@@ -119,9 +122,41 @@ def test_draw_boxes(self):
 
         expected = torch.as_tensor(np.array(Image.open(path))).permute(2, 0, 1)
         self.assertTrue(torch.equal(result, expected))
+        # Check if modification is not in place
+        self.assertTrue(torch.all(torch.eq(boxes, boxes_cp)).item())
+        self.assertTrue(torch.all(torch.eq(img, img_cp)).item())
+
+    def test_draw_boxes_vanilla(self):
+        img = torch.full((3, 100, 100), 0, dtype=torch.uint8)
+        img_cp = img.clone()
+        boxes_cp = boxes.clone()
+        result = utils.draw_bounding_boxes(img, boxes, fill=False, width=7)
+
+        path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets", "fakedata", "draw_boxes_vanilla.png")
+        if not os.path.exists(path):
+            res = Image.fromarray(result.permute(1, 2, 0).contiguous().numpy())
+            res.save(path)
+
+        expected = torch.as_tensor(np.array(Image.open(path))).permute(2, 0, 1)
+        self.assertTrue(torch.equal(result, expected))
+        # Check if modification is not in place
+        self.assertTrue(torch.all(torch.eq(boxes, boxes_cp)).item())
+        self.assertTrue(torch.all(torch.eq(img, img_cp)).item())
+
+    def test_draw_invalid_boxes(self):
+        img_tp = ((1, 1, 1), (1, 2, 3))
+        img_wrong1 = torch.full((3, 5, 5), 255, dtype=torch.float)
+        img_wrong2 = torch.full((1, 3, 5, 5), 255, dtype=torch.uint8)
+        boxes = torch.tensor([[0, 0, 20, 20], [0, 0, 0, 0],
+                             [10, 15, 30, 35], [23, 35, 93, 95]], dtype=torch.float)
+        self.assertRaises(TypeError, utils.draw_bounding_boxes, img_tp, boxes)
+        self.assertRaises(ValueError, utils.draw_bounding_boxes, img_wrong1, boxes)
+        self.assertRaises(ValueError, utils.draw_bounding_boxes, img_wrong2, boxes)
 
     def test_draw_segmentation_masks_colors(self):
         img = torch.full((3, 5, 5), 255, dtype=torch.uint8)
+        img_cp = img.clone()
+        masks_cp = masks.clone()
         colors = ["#FF00FF", (0, 255, 0), "red"]
         result = utils.draw_segmentation_masks(img, masks, colors=colors)
 
@@ -134,9 +169,14 @@ def test_draw_segmentation_masks_colors(self):
 
         expected = torch.as_tensor(np.array(Image.open(path))).permute(2, 0, 1)
         self.assertTrue(torch.equal(result, expected))
+        # Check if modification is not in place
+        self.assertTrue(torch.all(torch.eq(img, img_cp)).item())
+        self.assertTrue(torch.all(torch.eq(masks, masks_cp)).item())
 
     def test_draw_segmentation_masks_no_colors(self):
         img = torch.full((3, 20, 20), 255, dtype=torch.uint8)
+        img_cp = img.clone()
+        masks_cp = masks.clone()
         result = utils.draw_segmentation_masks(img, masks, colors=None)
 
         path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets",
@@ -148,6 +188,20 @@ def test_draw_segmentation_masks_no_colors(self):
 
         expected = torch.as_tensor(np.array(Image.open(path))).permute(2, 0, 1)
         self.assertTrue(torch.equal(result, expected))
+        # Check if modification is not in place
+        self.assertTrue(torch.all(torch.eq(img, img_cp)).item())
+        self.assertTrue(torch.all(torch.eq(masks, masks_cp)).item())
+
+    def test_draw_invalid_masks(self):
+        img_tp = ((1, 1, 1), (1, 2, 3))
+        img_wrong1 = torch.full((3, 5, 5), 255, dtype=torch.float)
+        img_wrong2 = torch.full((1, 3, 5, 5), 255, dtype=torch.uint8)
+        img_wrong3 = torch.full((4, 5, 5), 255, dtype=torch.uint8)
+
+        self.assertRaises(TypeError, utils.draw_segmentation_masks, img_tp, masks)
+        self.assertRaises(ValueError, utils.draw_segmentation_masks, img_wrong1, masks)
+        self.assertRaises(ValueError, utils.draw_segmentation_masks, img_wrong2, masks)
+        self.assertRaises(ValueError, utils.draw_segmentation_masks, img_wrong3, masks)
 
 
 if __name__ == '__main__':

torchvision/utils.py

@@ -20,7 +20,8 @@ def make_grid(
     pad_value: int = 0,
     **kwargs
 ) -> torch.Tensor:
-    """Make a grid of images.
+    """
+    Make a grid of images.
 
     Args:
         tensor (Tensor or list): 4D mini-batch Tensor of shape (B x C x H x W)
@@ -37,9 +38,12 @@ def make_grid(
             images separately rather than the (min, max) over all images. Default: ``False``.
         pad_value (float, optional): Value for the padded pixels. Default: ``0``.
 
-    Example:
-        See this notebook `here <https://gist.github.com/anonymous/bf16430f7750c023141c562f3e9f2a91>`_
+    Returns:
+        grid (Tensor): the tensor containing grid of images.
 
+    Example:
+        See this notebook
+        `here <https://github.com/pytorch/vision/blob/master/examples/python/visualization_utils.ipynb>`_
     """
     if not (torch.is_tensor(tensor) or
             (isinstance(tensor, list) and all(torch.is_tensor(t) for t in tensor))):
@@ -117,7 +121,8 @@ def save_image(
     format: Optional[str] = None,
     **kwargs
 ) -> None:
-    """Save a given Tensor into an image file.
+    """
+    Save a given Tensor into an image file.
 
     Args:
         tensor (Tensor or list): Image to be saved. If given a mini-batch tensor,
@@ -150,7 +155,7 @@ def draw_bounding_boxes(
     """
     Draws bounding boxes on given image.
     The values of the input image should be uint8 between 0 and 255.
-    If filled, Resulting Tensor should be saved as PNG image.
+    If fill is True, Resulting Tensor should be saved as PNG image.
 
     Args:
         image (Tensor): Tensor of shape (C x H x W) and dtype uint8.
@@ -166,6 +171,13 @@ def draw_bounding_boxes(
             also search in other directories, such as the `fonts/` directory on Windows or `/Library/Fonts/`,
             `/System/Library/Fonts/` and `~/Library/Fonts/` on macOS.
         font_size (int): The requested font size in points.
+
+    Returns:
+        img (Tensor[C, H, W]): Image Tensor of dtype uint8 with bounding boxes plotted.
+
+    Example:
+        See this notebook
+        `linked <https://github.com/pytorch/vision/blob/master/examples/python/visualization_utils.ipynb>`_
     """
 
     if not isinstance(image, torch.Tensor):
@@ -209,7 +221,7 @@ def draw_bounding_boxes(
         if labels is not None:
             draw.text((bbox[0], bbox[1]), labels[i], fill=color, font=txt_font)
 
-    return torch.from_numpy(np.array(img_to_draw)).permute(2, 0, 1)
+    return torch.from_numpy(np.array(img_to_draw)).permute(2, 0, 1).to(dtype=torch.uint8)
 
 
 @torch.no_grad()
@@ -230,6 +242,13 @@ def draw_segmentation_masks(
         alpha (float): Float number between 0 and 1 denoting factor of transpaerency of masks.
         colors (List[Union[str, Tuple[int, int, int]]]): List containing the colors of masks. The colors can
             be represented as `str` or `Tuple[int, int, int]`.
+
+    Returns:
+        img (Tensor[C, H, W]): Image Tensor of dtype uint8 with segmentation masks plotted.
+
+    Example:
+        See this notebook
+        `attached <https://github.com/pytorch/vision/blob/master/examples/python/visualization_utils.ipynb>`_
     """
 
     if not isinstance(image, torch.Tensor):