Merge branch 'main' into fix/celeb_a_split

Author: NicolasHug

setup.py

@@ -79,7 +79,7 @@ def get_version():
 
 def write_version_file(version, sha):
     # Exists for BC, probably completely useless.
-    with open(ROOT_DIR / "torchvision/version.py", "w") as f:
+    with open(ROOT_DIR / "torchvision" / "version.py", "w") as f:
         f.write(f"__version__ = '{version}'\n")
         f.write(f"git_version = {repr(sha)}\n")
         f.write("from torchvision.extension import _check_cuda_version\n")
@@ -194,7 +194,7 @@ def make_C_extension():
 
 def find_libpng():
     # Returns (found, include dir, library dir, library name)
-    if sys.platform in ("linux", "darwin"):
+    if sys.platform in ("linux", "darwin", "aix"):
         libpng_config = shutil.which("libpng-config")
         if libpng_config is None:
             warnings.warn("libpng-config not found")

torchvision/datasets/flowers102.py

@@ -112,3 +112,108 @@ def download(self):
         for id in ["label", "setid"]:
             filename, md5 = self._file_dict[id]
             download_url(self._download_url_prefix + filename, str(self._base_folder), md5=md5)
+
+    classes = [
+        "pink primrose",
+        "hard-leaved pocket orchid",
+        "canterbury bells",
+        "sweet pea",
+        "english marigold",
+        "tiger lily",
+        "moon orchid",
+        "bird of paradise",
+        "monkshood",
+        "globe thistle",
+        "snapdragon",
+        "colt's foot",
+        "king protea",
+        "spear thistle",
+        "yellow iris",
+        "globe-flower",
+        "purple coneflower",
+        "peruvian lily",
+        "balloon flower",
+        "giant white arum lily",
+        "fire lily",
+        "pincushion flower",
+        "fritillary",
+        "red ginger",
+        "grape hyacinth",
+        "corn poppy",
+        "prince of wales feathers",
+        "stemless gentian",
+        "artichoke",
+        "sweet william",
+        "carnation",
+        "garden phlox",
+        "love in the mist",
+        "mexican aster",
+        "alpine sea holly",
+        "ruby-lipped cattleya",
+        "cape flower",
+        "great masterwort",
+        "siam tulip",
+        "lenten rose",
+        "barbeton daisy",
+        "daffodil",
+        "sword lily",
+        "poinsettia",
+        "bolero deep blue",
+        "wallflower",
+        "marigold",
+        "buttercup",
+        "oxeye daisy",
+        "common dandelion",
+        "petunia",
+        "wild pansy",
+        "primula",
+        "sunflower",
+        "pelargonium",
+        "bishop of llandaff",
+        "gaura",
+        "geranium",
+        "orange dahlia",
+        "pink-yellow dahlia?",
+        "cautleya spicata",
+        "japanese anemone",
+        "black-eyed susan",
+        "silverbush",
+        "californian poppy",
+        "osteospermum",
+        "spring crocus",
+        "bearded iris",
+        "windflower",
+        "tree poppy",
+        "gazania",
+        "azalea",
+        "water lily",
+        "rose",
+        "thorn apple",
+        "morning glory",
+        "passion flower",
+        "lotus",
+        "toad lily",
+        "anthurium",
+        "frangipani",
+        "clematis",
+        "hibiscus",
+        "columbine",
+        "desert-rose",
+        "tree mallow",
+        "magnolia",
+        "cyclamen",
+        "watercress",
+        "canna lily",
+        "hippeastrum",
+        "bee balm",
+        "ball moss",
+        "foxglove",
+        "bougainvillea",
+        "camellia",
+        "mallow",
+        "mexican petunia",
+        "bromelia",
+        "blanket flower",
+        "trumpet creeper",
+        "blackberry lily",
+    ]

torchvision/datasets/mnist.py

@@ -35,8 +35,8 @@ class MNIST(VisionDataset):
     """
 
     mirrors = [
-        "http://yann.lecun.com/exdb/mnist/",
         "https://ossci-datasets.s3.amazonaws.com/mnist/",
+        "http://yann.lecun.com/exdb/mnist/",
     ]
 
     resources = [
@@ -514,7 +514,7 @@ def read_sn3_pascalvincent_tensor(path: str, strict: bool = True) -> torch.Tenso
         data = f.read()
 
     # parse
-    if sys.byteorder == "little":
+    if sys.byteorder == "little" or sys.platform == "aix":
         magic = get_int(data[0:4])
         nd = magic % 256
         ty = magic // 256
@@ -527,7 +527,7 @@ def read_sn3_pascalvincent_tensor(path: str, strict: bool = True) -> torch.Tenso
     torch_type = SN3_PASCALVINCENT_TYPEMAP[ty]
     s = [get_int(data[4 * (i + 1) : 4 * (i + 2)]) for i in range(nd)]
 
-    if sys.byteorder == "big":
+    if sys.byteorder == "big" and not sys.platform == "aix":
         for i in range(len(s)):
             s[i] = int.from_bytes(s[i].to_bytes(4, byteorder="little"), byteorder="big", signed=False)
 

torchvision/ops/focal_loss.py

@@ -20,7 +20,7 @@ def sigmoid_focal_loss(
         targets (Tensor): A float tensor with the same shape as inputs. Stores the binary
                 classification label for each element in inputs
                 (0 for the negative class and 1 for the positive class).
-        alpha (float): Weighting factor in range (0,1) to balance
+        alpha (float): Weighting factor in range [0, 1] to balance
                 positive vs negative examples or -1 for ignore. Default: ``0.25``.
         gamma (float): Exponent of the modulating factor (1 - p_t) to
                 balance easy vs hard examples. Default: ``2``.
@@ -33,6 +33,9 @@ def sigmoid_focal_loss(
     """
     # Original implementation from https://github.com/facebookresearch/fvcore/blob/master/fvcore/nn/focal_loss.py
 
+    if not (0 <= alpha <= 1) or alpha != -1:
+        raise ValueError(f"Invalid alpha value: {alpha}. alpha must be in the range [0,1] or -1 for ignore.")
+
     if not torch.jit.is_scripting() and not torch.jit.is_tracing():
         _log_api_usage_once(sigmoid_focal_loss)
     p = torch.sigmoid(inputs)