vfdev-5: @jasonkriss @anmolsjoshi please merge if OK
diff --git a/ignite/utils.py b/ignite/utils.py
index d90af2bf..c9a9d3e7 100644
--- a/ignite/utils.py
+++ b/ignite/utils.py
@@ -42,6 +42,8 @@ def apply_to_type(input_, input_type, func):
 
 
 def to_onehot(indices, num_classes):
-    """Convert a tensor of indices to a tensor of one-hot indicators."""
-    onehot = torch.zeros(indices.size(0), num_classes, device=indices.device)
+    """Convert a tensor of indices of any shape `(N, ...)` to a
+    tensor of one-hot indicators of shape `(N, num_classes, ...)`.
+    """
+    onehot = torch.zeros(indices.shape[0], num_classes, *indices.shape[1:], device=indices.device)
     return onehot.scatter_(1, indices.unsqueeze(1), 1)

Improve to_onehot to any shape
Idea is to improve `to_onehot` to accept any input shape: (N, ) and (N, H, W, D1, D2, ...).
Currently supported shape is `(N, )`. Thoughts ?
diff --git a/tests/ignite/test_utils.py b/tests/ignite/test_utils.py
index 5c119d4b..324489f1 100644
--- a/tests/ignite/test_utils.py
+++ b/tests/ignite/test_utils.py
@@ -39,3 +39,18 @@ def test_to_onehot():
     actual = to_onehot(indices, 4)
     expected = torch.eye(4)
     assert actual.equal(expected)
+
+    y = torch.randint(0, 21, size=(1000,))
+    y_ohe = to_onehot(y, num_classes=21)
+    y2 = torch.argmax(y_ohe, dim=1)
+    assert y.equal(y2)
+
+    y = torch.randint(0, 21, size=(4, 250, 255))
+    y_ohe = to_onehot(y, num_classes=21)
+    y2 = torch.argmax(y_ohe, dim=1)
+    assert y.equal(y2)
+
+    y = torch.randint(0, 21, size=(4, 150, 155, 4, 6))
+    y_ohe = to_onehot(y, num_classes=21)
+    y2 = torch.argmax(y_ohe, dim=1)
+    assert y.equal(y2)

pip install -e .
parse_log_pytest
pytest
numpy mock pytest codecov pytest-cov tqdm scikit-learn visdom torchvision tensorboardX gym
apt-get update apt-get install -y gcc
3.6
pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning
attrs @ file:///opt/conda/conda-bld/attrs_1642510447205/work
certifi==2021.5.30
charset-normalizer==2.0.12
cloudpickle==2.2.1
codecov==2.1.13
coverage==6.2
dataclasses==0.8
decorator==4.4.2
gym==0.26.2
gym-notices==0.0.8
idna==3.10
importlib-metadata @ file:///tmp/build/80754af9/importlib-metadata_1631916693255/work
importlib-resources==5.4.0
iniconfig @ file:///home/linux1/recipes/ci/iniconfig_1610983019677/work
joblib==1.1.1
jsonpatch==1.32
jsonpointer==2.3
mock==5.2.0
more-itertools @ file:///tmp/build/80754af9/more-itertools_1637733554872/work
networkx==2.5.1
numpy==1.19.5
packaging @ file:///tmp/build/80754af9/packaging_1637314298585/work
Pillow==8.4.0
pluggy @ file:///tmp/build/80754af9/pluggy_1615976315926/work
protobuf==4.21.0
py @ file:///opt/conda/conda-bld/py_1644396412707/work
pyparsing @ file:///tmp/build/80754af9/pyparsing_1635766073266/work
pytest==6.2.4
pytest-cov==4.0.0
-e git+https://github.com/pytorch/ignite.git@81ba5b36de92453422a43252b604a335059fcedd#egg=pytorch_ignite
requests==2.27.1
scikit-learn==0.24.2
scipy==1.5.4
six==1.17.0
tensorboardX==2.6.2.2
threadpoolctl==3.1.0
toml @ file:///tmp/build/80754af9/toml_1616166611790/work
tomli==1.2.3
torch==1.10.1
torchvision==0.11.2
tornado==6.1
tqdm==4.64.1
typing_extensions @ file:///opt/conda/conda-bld/typing_extensions_1647553014482/work
urllib3==1.26.20
visdom==0.2.4
websocket-client==1.3.1
zipp @ file:///tmp/build/80754af9/zipp_1633618647012/work

name: ignite
channels:
  - defaults
  - https://repo.anaconda.com/pkgs/main
  - https://repo.anaconda.com/pkgs/r
  - conda-forge
dependencies:
  - _libgcc_mutex=0.1=main
  - _openmp_mutex=5.1=1_gnu
  - attrs=21.4.0=pyhd3eb1b0_0
  - ca-certificates=2025.2.25=h06a4308_0
  - certifi=2021.5.30=py36h06a4308_0
  - importlib-metadata=4.8.1=py36h06a4308_0
  - importlib_metadata=4.8.1=hd3eb1b0_0
  - iniconfig=1.1.1=pyhd3eb1b0_0
  - ld_impl_linux-64=2.40=h12ee557_0
  - libffi=3.3=he6710b0_2
  - libgcc-ng=11.2.0=h1234567_1
  - libgomp=11.2.0=h1234567_1
  - libstdcxx-ng=11.2.0=h1234567_1
  - more-itertools=8.12.0=pyhd3eb1b0_0
  - ncurses=6.4=h6a678d5_0
  - openssl=1.1.1w=h7f8727e_0
  - packaging=21.3=pyhd3eb1b0_0
  - pip=21.2.2=py36h06a4308_0
  - pluggy=0.13.1=py36h06a4308_0
  - py=1.11.0=pyhd3eb1b0_0
  - pyparsing=3.0.4=pyhd3eb1b0_0
  - pytest=6.2.4=py36h06a4308_2
  - python=3.6.13=h12debd9_1
  - readline=8.2=h5eee18b_0
  - setuptools=58.0.4=py36h06a4308_0
  - sqlite=3.45.3=h5eee18b_0
  - tk=8.6.14=h39e8969_0
  - toml=0.10.2=pyhd3eb1b0_0
  - typing_extensions=4.1.1=pyh06a4308_0
  - wheel=0.37.1=pyhd3eb1b0_0
  - xz=5.6.4=h5eee18b_1
  - zipp=3.6.0=pyhd3eb1b0_0
  - zlib=1.2.13=h5eee18b_1
  - pip:
      - charset-normalizer==2.0.12
      - cloudpickle==2.2.1
      - codecov==2.1.13
      - coverage==6.2
      - dataclasses==0.8
      - decorator==4.4.2
      - gym==0.26.2
      - gym-notices==0.0.8
      - idna==3.10
      - importlib-resources==5.4.0
      - joblib==1.1.1
      - jsonpatch==1.32
      - jsonpointer==2.3
      - mock==5.2.0
      - networkx==2.5.1
      - numpy==1.19.5
      - pillow==8.4.0
      - protobuf==4.21.0
      - pytest-cov==4.0.0
      - requests==2.27.1
      - scikit-learn==0.24.2
      - scipy==1.5.4
      - six==1.17.0
      - tensorboardx==2.6.2.2
      - threadpoolctl==3.1.0
      - tomli==1.2.3
      - torch==1.10.1
      - torchvision==0.11.2
      - tornado==6.1
      - tqdm==4.64.1
      - urllib3==1.26.20
      - visdom==0.2.4
      - websocket-client==1.3.1
prefix: /opt/conda/envs/ignite

tests/ignite/test_utils.py::test_to_onehot
tests/ignite/test_utils.py::test_convert_tensor
ignite/utils.py
ignite/utils.py