Merge branch 'main' into fix_ssl_issue

gallery/v2_transforms/plot_transforms_v2.py

@@ -99,10 +99,9 @@
     format="XYXY", canvas_size=img.shape[-2:])
 
 transforms = v2.Compose([
-    v2.RandomPhotometricDistort(),
-    v2.RandomIoUCrop(),
-    v2.RandomHorizontalFlip(p=0.5),
-    v2.SanitizeBoundingBoxes(),
+    v2.RandomResizedCrop(size=(224, 224), antialias=True),
+    v2.RandomPhotometricDistort(p=1),
+    v2.RandomHorizontalFlip(p=1),
 ])
 out_img, out_bboxes = transforms(img, bboxes)
 

references/classification/presets.py

@@ -61,7 +61,7 @@ def __init__(
 
         transforms.extend(
             [
-                T.ConvertImageDtype(torch.float),
+                T.ToDtype(torch.float, scale=True) if use_v2 else T.ConvertImageDtype(torch.float),
                 T.Normalize(mean=mean, std=std),
             ]
         )
@@ -106,7 +106,7 @@ def __init__(
             transforms.append(T.PILToTensor())
 
         transforms += [
-            T.ConvertImageDtype(torch.float),
+            T.ToDtype(torch.float, scale=True) if use_v2 else T.ConvertImageDtype(torch.float),
             T.Normalize(mean=mean, std=std),
         ]
 

references/detection/presets.py

@@ -73,7 +73,7 @@ def __init__(
             # Note: we could just convert to pure tensors even in v2.
             transforms += [T.ToImage() if use_v2 else T.PILToTensor()]
 
-        transforms += [T.ConvertImageDtype(torch.float)]
+        transforms += [T.ToDtype(torch.float, scale=True)]
 
         if use_v2:
             transforms += [
@@ -103,7 +103,7 @@ def __init__(self, backend="pil", use_v2=False):
         else:
             raise ValueError(f"backend can be 'datapoint', 'tensor' or 'pil', but got {backend}")
 
-        transforms += [T.ConvertImageDtype(torch.float)]
+        transforms += [T.ToDtype(torch.float, scale=True)]
 
         if use_v2:
             transforms += [T.ToPureTensor()]

references/detection/transforms.py

@@ -53,14 +53,17 @@ def forward(
         return image, target
 
 
-class ConvertImageDtype(nn.Module):
-    def __init__(self, dtype: torch.dtype) -> None:
+class ToDtype(nn.Module):
+    def __init__(self, dtype: torch.dtype, scale: bool = False) -> None:
         super().__init__()
         self.dtype = dtype
+        self.scale = scale
 
     def forward(
         self, image: Tensor, target: Optional[Dict[str, Tensor]] = None
     ) -> Tuple[Tensor, Optional[Dict[str, Tensor]]]:
+        if not self.scale:
+            return image.to(dtype=self.dtype), target
         image = F.convert_image_dtype(image, self.dtype)
         return image, target
 

references/segmentation/presets.py

@@ -60,7 +60,7 @@ def __init__(
             ]
         else:
             # No need to explicitly convert masks as they're magically int64 already
-            transforms += [T.ConvertImageDtype(torch.float)]
+            transforms += [T.ToDtype(torch.float, scale=True)]
 
         transforms += [T.Normalize(mean=mean, std=std)]
         if use_v2:
@@ -97,7 +97,7 @@ def __init__(
             transforms += [T.ToImage() if use_v2 else T.PILToTensor()]
 
         transforms += [
-            T.ConvertImageDtype(torch.float),
+            T.ToDtype(torch.float, scale=True),
             T.Normalize(mean=mean, std=std),
         ]
         if use_v2:

references/segmentation/transforms.py

@@ -81,11 +81,14 @@ def __call__(self, image, target):
         return image, target
 
 
-class ConvertImageDtype:
-    def __init__(self, dtype):
+class ToDtype:
+    def __init__(self, dtype, scale=False):
         self.dtype = dtype
+        self.scale = scale
 
     def __call__(self, image, target):
+        if not self.scale:
+            return image.to(dtype=self.dtype), target
         image = F.convert_image_dtype(image, self.dtype)
         return image, target
 

references/segmentation/v2_extras.py

@@ -78,6 +78,6 @@ def _coco_detection_masks_to_voc_segmentation_mask(self, target):
     def forward(self, image, target):
         segmentation_mask = self._coco_detection_masks_to_voc_segmentation_mask(target)
         if segmentation_mask is None:
-            segmentation_mask = torch.zeros(v2.functional.get_spatial_size(image), dtype=torch.uint8)
+            segmentation_mask = torch.zeros(v2.functional.get_size(image), dtype=torch.uint8)
 
         return image, datapoints.Mask(segmentation_mask)

test/datasets_utils.py

@@ -662,27 +662,39 @@ class VideoDatasetTestCase(DatasetTestCase):
     FEATURE_TYPES = (torch.Tensor, torch.Tensor, int)
     REQUIRED_PACKAGES = ("av",)
 
-    DEFAULT_FRAMES_PER_CLIP = 1
+    FRAMES_PER_CLIP = 1
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
         self.dataset_args = self._set_default_frames_per_clip(self.dataset_args)
 
-    def _set_default_frames_per_clip(self, inject_fake_data):
+    def _set_default_frames_per_clip(self, dataset_args):
         argspec = inspect.getfullargspec(self.DATASET_CLASS.__init__)
         args_without_default = argspec.args[1 : (-len(argspec.defaults) if argspec.defaults else None)]
         frames_per_clip_last = args_without_default[-1] == "frames_per_clip"
 
-        @functools.wraps(inject_fake_data)
+        @functools.wraps(dataset_args)
         def wrapper(tmpdir, config):
-            args = inject_fake_data(tmpdir, config)
+            args = dataset_args(tmpdir, config)
             if frames_per_clip_last and len(args) == len(args_without_default) - 1:
-                args = (*args, self.DEFAULT_FRAMES_PER_CLIP)
+                args = (*args, self.FRAMES_PER_CLIP)
 
             return args
 
         return wrapper
 
+    def test_output_format(self):
+        for output_format in ["TCHW", "THWC"]:
+            with self.create_dataset(output_format=output_format) as (dataset, _):
+                for video, *_ in dataset:
+                    if output_format == "TCHW":
+                        num_frames, num_channels, *_ = video.shape
+                    else:  # output_format == "THWC":
+                        num_frames, *_, num_channels = video.shape
+
+                assert num_frames == self.FRAMES_PER_CLIP
+                assert num_channels == 3
+
     @test_all_configs
     def test_transforms_v2_wrapper(self, config):
         # `output_format == "THWC"` is not supported by the wrapper. Thus, we skip the `config` if it is set explicitly

test/test_image.py

@@ -83,12 +83,9 @@ def test_decode_jpeg(img_path, pil_mode, mode):
     with Image.open(img_path) as img:
         is_cmyk = img.mode == "CMYK"
         if pil_mode is not None:
-            if is_cmyk:
-                # libjpeg does not support the conversion
-                pytest.xfail("Decoding a CMYK jpeg isn't supported")
             img = img.convert(pil_mode)
         img_pil = torch.from_numpy(np.array(img))
-        if is_cmyk:
+        if is_cmyk and mode == ImageReadMode.UNCHANGED:
             # flip the colors to match libjpeg
             img_pil = 255 - img_pil
 

test/test_transforms_v2.py

@@ -1256,6 +1256,20 @@ def test_sanitize_bounding_boxes(min_size, labels_getter, sample_type):
         assert out_labels.tolist() == valid_indices
 
 
+def test_sanitize_bounding_boxes_no_label():
+    # Non-regression test for https://github.com/pytorch/vision/issues/7878
+
+    img = make_image()
+    boxes = make_bounding_boxes()
+
+    with pytest.raises(ValueError, match="or a two-tuple whose second item is a dict"):
+        transforms.SanitizeBoundingBoxes()(img, boxes)
+
+    out_img, out_boxes = transforms.SanitizeBoundingBoxes(labels_getter=None)(img, boxes)
+    assert isinstance(out_img, datapoints.Image)
+    assert isinstance(out_boxes, datapoints.BoundingBoxes)
+
+
 def test_sanitize_bounding_boxes_errors():
 
     good_bbox = datapoints.BoundingBoxes(

torchvision/csrc/io/image/cpu/decode_jpeg.cpp

@@ -67,6 +67,58 @@ static void torch_jpeg_set_source_mgr(
   src->pub.next_input_byte = src->data;
 }
 
+inline unsigned char clamped_cmyk_rgb_convert(
+    unsigned char k,
+    unsigned char cmy) {
+  // Inspired from Pillow:
+  // https://github.com/python-pillow/Pillow/blob/07623d1a7cc65206a5355fba2ae256550bfcaba6/src/libImaging/Convert.c#L568-L569
+  int v = k * cmy + 128;
+  v = ((v >> 8) + v) >> 8;
+  return std::clamp(k - v, 0, 255);
+}
+
+void convert_line_cmyk_to_rgb(
+    j_decompress_ptr cinfo,
+    const unsigned char* cmyk_line,
+    unsigned char* rgb_line) {
+  int width = cinfo->output_width;
+  for (int i = 0; i < width; ++i) {
+    int c = cmyk_line[i * 4 + 0];
+    int m = cmyk_line[i * 4 + 1];
+    int y = cmyk_line[i * 4 + 2];
+    int k = cmyk_line[i * 4 + 3];
+
+    rgb_line[i * 3 + 0] = clamped_cmyk_rgb_convert(k, 255 - c);
+    rgb_line[i * 3 + 1] = clamped_cmyk_rgb_convert(k, 255 - m);
+    rgb_line[i * 3 + 2] = clamped_cmyk_rgb_convert(k, 255 - y);
+  }
+}
+
+inline unsigned char rgb_to_gray(int r, int g, int b) {
+  // Inspired from Pillow:
+  // https://github.com/python-pillow/Pillow/blob/07623d1a7cc65206a5355fba2ae256550bfcaba6/src/libImaging/Convert.c#L226
+  return (r * 19595 + g * 38470 + b * 7471 + 0x8000) >> 16;
+}
+
+void convert_line_cmyk_to_gray(
+    j_decompress_ptr cinfo,
+    const unsigned char* cmyk_line,
+    unsigned char* gray_line) {
+  int width = cinfo->output_width;
+  for (int i = 0; i < width; ++i) {
+    int c = cmyk_line[i * 4 + 0];
+    int m = cmyk_line[i * 4 + 1];
+    int y = cmyk_line[i * 4 + 2];
+    int k = cmyk_line[i * 4 + 3];
+
+    int r = clamped_cmyk_rgb_convert(k, 255 - c);
+    int g = clamped_cmyk_rgb_convert(k, 255 - m);
+    int b = clamped_cmyk_rgb_convert(k, 255 - y);
+
+    gray_line[i] = rgb_to_gray(r, g, b);
+  }
+}
+
 } // namespace
 
 torch::Tensor decode_jpeg(const torch::Tensor& data, ImageReadMode mode) {
@@ -102,20 +154,29 @@ torch::Tensor decode_jpeg(const torch::Tensor& data, ImageReadMode mode) {
   jpeg_read_header(&cinfo, TRUE);
 
   int channels = cinfo.num_components;
+  bool cmyk_to_rgb_or_gray = false;
 
   if (mode != IMAGE_READ_MODE_UNCHANGED) {
     switch (mode) {
       case IMAGE_READ_MODE_GRAY:
-        if (cinfo.jpeg_color_space != JCS_GRAYSCALE) {
+        if (cinfo.jpeg_color_space == JCS_CMYK ||
+            cinfo.jpeg_color_space == JCS_YCCK) {
+          cinfo.out_color_space = JCS_CMYK;
+          cmyk_to_rgb_or_gray = true;
+        } else {
           cinfo.out_color_space = JCS_GRAYSCALE;
-          channels = 1;
         }
+        channels = 1;
         break;
       case IMAGE_READ_MODE_RGB:
-        if (cinfo.jpeg_color_space != JCS_RGB) {
+        if (cinfo.jpeg_color_space == JCS_CMYK ||
+            cinfo.jpeg_color_space == JCS_YCCK) {
+          cinfo.out_color_space = JCS_CMYK;
+          cmyk_to_rgb_or_gray = true;
+        } else {
           cinfo.out_color_space = JCS_RGB;
-          channels = 3;
         }
+        channels = 3;
         break;
       /*
        * Libjpeg does not support converting from CMYK to grayscale etc. There
@@ -139,12 +200,28 @@ torch::Tensor decode_jpeg(const torch::Tensor& data, ImageReadMode mode) {
   auto tensor =
       torch::empty({int64_t(height), int64_t(width), channels}, torch::kU8);
   auto ptr = tensor.data_ptr<uint8_t>();
+  torch::Tensor cmyk_line_tensor;
+  if (cmyk_to_rgb_or_gray) {
+    cmyk_line_tensor = torch::empty({int64_t(width), 4}, torch::kU8);
+  }
+
   while (cinfo.output_scanline < cinfo.output_height) {
     /* jpeg_read_scanlines expects an array of pointers to scanlines.
      * Here the array is only one element long, but you could ask for
      * more than one scanline at a time if that's more convenient.
      */
-    jpeg_read_scanlines(&cinfo, &ptr, 1);
+    if (cmyk_to_rgb_or_gray) {
+      auto cmyk_line_ptr = cmyk_line_tensor.data_ptr<uint8_t>();
+      jpeg_read_scanlines(&cinfo, &cmyk_line_ptr, 1);
+
+      if (channels == 3) {
+        convert_line_cmyk_to_rgb(&cinfo, cmyk_line_ptr, ptr);
+      } else if (channels == 1) {
+        convert_line_cmyk_to_gray(&cinfo, cmyk_line_ptr, ptr);
+      }
+    } else {
+      jpeg_read_scanlines(&cinfo, &ptr, 1);
+    }
     ptr += stride;
   }
 

torchvision/datasets/video_utils.py

@@ -187,9 +187,9 @@ def subset(self, indices: List[int]) -> "VideoClips":
         }
         return type(self)(
             video_paths,
-            self.num_frames,
-            self.step,
-            self.frame_rate,
+            clip_length_in_frames=self.num_frames,
+            frames_between_clips=self.step,
+            frame_rate=self.frame_rate,
             _precomputed_metadata=metadata,
             num_workers=self.num_workers,
             _video_width=self._video_width,

torchvision/transforms/v2/_augment.py

@@ -229,7 +229,7 @@ class MixUp(_BaseMixUpCutMix):
         alpha (float, optional): hyperparameter of the Beta distribution used for mixup. Default is 1.
         num_classes (int): number of classes in the batch. Used for one-hot-encoding.
         labels_getter (callable or "default", optional): indicates how to identify the labels in the input.
-            By default, this will pick the second parameter a the labels if it's a tensor. This covers the most
+            By default, this will pick the second parameter as the labels if it's a tensor. This covers the most
             common scenario where this transform is called as ``MixUp()(imgs_batch, labels_batch)``.
             It can also be a callable that takes the same input as the transform, and returns the labels.
     """
@@ -279,7 +279,7 @@ class CutMix(_BaseMixUpCutMix):
         alpha (float, optional): hyperparameter of the Beta distribution used for mixup. Default is 1.
         num_classes (int): number of classes in the batch. Used for one-hot-encoding.
         labels_getter (callable or "default", optional): indicates how to identify the labels in the input.
-            By default, this will pick the second parameter a the labels if it's a tensor. This covers the most
+            By default, this will pick the second parameter as the labels if it's a tensor. This covers the most
             common scenario where this transform is called as ``CutMix()(imgs_batch, labels_batch)``.
             It can also be a callable that takes the same input as the transform, and returns the labels.
     """

torchvision/transforms/v2/_utils.py

@@ -112,7 +112,7 @@ def _find_labels_default_heuristic(inputs: Any) -> torch.Tensor:
         inputs = inputs[1]
 
     # MixUp, CutMix
-    if isinstance(inputs, torch.Tensor):
+    if is_pure_tensor(inputs):
         return inputs
 
     if not isinstance(inputs, collections.abc.Mapping):