diff --git a/CHANGELOG.md b/CHANGELOG.md
index edc319511b195..2a46f49211268 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -9,6 +9,11 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Added
 
+- `Accuracy` metric now generalizes to Top-k accuracy for (multi-dimensional) multi-class inputs using the `top_k` parameter ([#4838](https://github.com/PyTorchLightning/pytorch-lightning/pull/4838))
+
+- `Accuracy` metric now enables the computation of subset accuracy for multi-label or multi-dimensional multi-class inputs with the `subset_accuracy` parameter ([#4838](https://github.com/PyTorchLightning/pytorch-lightning/pull/4838))
+
+- `HammingDistance` metric to compute the hamming distance (loss) ([#4838](https://github.com/PyTorchLightning/pytorch-lightning/pull/4838))
 
 ### Changed
 
diff --git a/docs/source/metrics.rst b/docs/source/metrics.rst
index 387cbc3bd7482..5396403402072 100644
--- a/docs/source/metrics.rst
+++ b/docs/source/metrics.rst
@@ -292,6 +292,12 @@ FBeta
 .. autoclass:: pytorch_lightning.metrics.classification.FBeta
     :noindex:
 
+Hamming Distance
+~~~~~~~~~~~~~~~~
+
+.. autoclass:: pytorch_lightning.metrics.classification.HammingDistance
+    :noindex:
+
 Precision
 ~~~~~~~~~
 
@@ -323,10 +329,9 @@ Functional Metrics (Classification)
 accuracy [func]
 ~~~~~~~~~~~~~~~
 
-.. autofunction:: pytorch_lightning.metrics.functional.classification.accuracy
+.. autofunction:: pytorch_lightning.metrics.functional.accuracy
     :noindex:
 
-
 auc [func]
 ~~~~~~~~~~
 
@@ -382,6 +387,11 @@ fbeta [func]
 .. autofunction:: pytorch_lightning.metrics.functional.fbeta
     :noindex:
 
+hamming_distance [func]
+~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autofunction:: pytorch_lightning.metrics.functional.hamming_distance
+    :noindex:
 
 iou [func]
 ~~~~~~~~~~
diff --git a/pytorch_lightning/metrics/__init__.py b/pytorch_lightning/metrics/__init__.py
index 2f28ad9abb0b7..c792fc5e71b03 100644
--- a/pytorch_lightning/metrics/__init__.py
+++ b/pytorch_lightning/metrics/__init__.py
@@ -15,6 +15,7 @@
 
 from pytorch_lightning.metrics.classification import (  # noqa: F401
     Accuracy,
+    HammingDistance,
     Precision,
     Recall,
     ConfusionMatrix,
diff --git a/pytorch_lightning/metrics/classification/__init__.py b/pytorch_lightning/metrics/classification/__init__.py
index 79579035f6726..78163a9673887 100644
--- a/pytorch_lightning/metrics/classification/__init__.py
+++ b/pytorch_lightning/metrics/classification/__init__.py
@@ -15,6 +15,7 @@
 from pytorch_lightning.metrics.classification.average_precision import AveragePrecision  # noqa: F401
 from pytorch_lightning.metrics.classification.confusion_matrix import ConfusionMatrix  # noqa: F401
 from pytorch_lightning.metrics.classification.f_beta import FBeta, Fbeta, F1  # noqa: F401
+from pytorch_lightning.metrics.classification.hamming_distance import HammingDistance  # noqa: F401
 from pytorch_lightning.metrics.classification.precision_recall import Precision, Recall  # noqa: F401
 from pytorch_lightning.metrics.classification.precision_recall_curve import PrecisionRecallCurve  # noqa: F401
 from pytorch_lightning.metrics.classification.roc import ROC  # noqa: F401
diff --git a/pytorch_lightning/metrics/classification/accuracy.py b/pytorch_lightning/metrics/classification/accuracy.py
index 330691a379574..e248c132026a4 100644
--- a/pytorch_lightning/metrics/classification/accuracy.py
+++ b/pytorch_lightning/metrics/classification/accuracy.py
@@ -16,35 +16,57 @@
 import torch
 
 from pytorch_lightning.metrics.metric import Metric
-from pytorch_lightning.metrics.utils import _input_format_classification
+from pytorch_lightning.metrics.functional.accuracy import _accuracy_update, _accuracy_compute
 
 
 class Accuracy(Metric):
     r"""
     Computes `Accuracy <https://en.wikipedia.org/wiki/Accuracy_and_precision>`_:
 
-    .. math:: \text{Accuracy} = \frac{1}{N}\sum_i^N 1(y_i = \hat{y_i})
+    .. math::
+        \text{Accuracy} = \frac{1}{N}\sum_i^N 1(y_i = \hat{y}_i)
 
     Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a
-    tensor of predictions.  Works with binary, multiclass, and multilabel
-    data.  Accepts logits from a model output or integer class values in
-    prediction.  Works with multi-dimensional preds and target.
+    tensor of predictions.
 
-    Forward accepts
+    For multi-class and multi-dimensional multi-class data with probability predictions, the
+    parameter ``top_k`` generalizes this metric to a Top-K accuracy metric: for each sample the
+    top-K highest probability items are considered to find the correct label.
 
-    - ``preds`` (float or long tensor): ``(N, ...)`` or ``(N, C, ...)`` where C is the number of classes
-    - ``target`` (long tensor): ``(N, ...)``
+    For multi-label and multi-dimensional multi-class inputs, this metric computes the "global"
+    accuracy by default, which counts all labels or sub-samples separately. This can be
+    changed to subset accuracy (which requires all labels or sub-samples in the sample to
+    be correctly predicted) by setting ``subset_accuracy=True``.
 
-    If preds and target are the same shape and preds is a float tensor, we use the ``self.threshold`` argument.
-    This is the case for binary and multi-label logits.
-
-    If preds has an extra dimension as in the case of multi-class scores we perform an argmax on ``dim=1``.
+    Accepts all input types listed in :ref:`metrics:Input types`.
 
     Args:
         threshold:
-            Threshold value for binary or multi-label logits. default: 0.5
+            Threshold probability value for transforming probability predictions to binary
+            `(0,1)` predictions, in the case of binary or multi-label inputs.
+        top_k:
+            Number of highest probability predictions considered to find the correct label, relevant
+            only for (multi-dimensional) multi-class inputs with probability predictions. The
+            default value (``None``) will be interpreted as 1 for these inputs.
+
+            Should be left at default (``None``) for all other types of inputs.
+        subset_accuracy:
+            Whether to compute subset accuracy for multi-label and multi-dimensional
+            multi-class inputs (has no effect for other input types).
+
+            For multi-label inputs, if the parameter is set to `True`, then all labels for
+            each sample must be correctly predicted for the sample to count as correct. If it
+            is set to `False`, then all labels are counted separately - this is equivalent to
+            flattening inputs beforehand (i.e. ``preds = preds.flatten()`` and same for ``target``).
+
+            For multi-dimensional multi-class inputs, if the parameter is set to `True`, then all
+            sub-sample (on the extra axis) must be correct for the sample to be counted as correct.
+            If it is set to `False`, then all sub-samples are counter separately - this is equivalent,
+            in the case of label predictions, to flattening the inputs beforehand (i.e.
+            ``preds = preds.flatten()`` and same for ``target``). Note that the ``top_k`` parameter
+            still applies in both cases, if set.
         compute_on_step:
-            Forward only calls ``update()`` and return None if this is set to False. default: True
+            Forward only calls ``update()`` and return None if this is set to False.
         dist_sync_on_step:
             Synchronize metric state across processes at each ``forward()``
             before returning the value at the step. default: False
@@ -63,10 +85,19 @@ class Accuracy(Metric):
         >>> accuracy(preds, target)
         tensor(0.5000)
 
+        >>> target = torch.tensor([0, 1, 2])
+        >>> preds = torch.tensor([[0.1, 0.9, 0], [0.3, 0.1, 0.6], [0.2, 0.5, 0.3]])
+        >>> accuracy = Accuracy(top_k=2)
+        >>> accuracy(preds, target)
+        tensor(0.6667)
+
     """
+
     def __init__(
         self,
         threshold: float = 0.5,
+        top_k: Optional[int] = None,
+        subset_accuracy: bool = False,
         compute_on_step: bool = True,
         dist_sync_on_step: bool = False,
         process_group: Optional[Any] = None,
@@ -82,24 +113,35 @@ def __init__(
         self.add_state("correct", default=torch.tensor(0), dist_reduce_fx="sum")
         self.add_state("total", default=torch.tensor(0), dist_reduce_fx="sum")
 
+        if not 0 <= threshold <= 1:
+            raise ValueError("The `threshold` should lie in the [0,1] interval.")
+
+        if top_k is not None and top_k <= 0:
+            raise ValueError("The `top_k` should be an integer larger than 1.")
+
         self.threshold = threshold
+        self.top_k = top_k
+        self.subset_accuracy = subset_accuracy
 
     def update(self, preds: torch.Tensor, target: torch.Tensor):
         """
-        Update state with predictions and targets.
+        Update state with predictions and targets. See :ref:`metrics:Input types` for more information
+        on input types.
 
         Args:
-            preds: Predictions from model
-            target: Ground truth values
+            preds: Predictions from model (probabilities, or labels)
+            target: Ground truth labels
         """
-        preds, target = _input_format_classification(preds, target, self.threshold)
-        assert preds.shape == target.shape
 
-        self.correct += torch.sum(preds == target)
-        self.total += target.numel()
+        correct, total = _accuracy_update(
+            preds, target, threshold=self.threshold, top_k=self.top_k, subset_accuracy=self.subset_accuracy
+        )
+
+        self.correct += correct
+        self.total += total
 
-    def compute(self):
+    def compute(self) -> torch.Tensor:
         """
-        Computes accuracy over state.
+        Computes accuracy based on inputs passed in to ``update`` previously.
         """
-        return self.correct.float() / self.total
+        return _accuracy_compute(self.correct, self.total)
diff --git a/pytorch_lightning/metrics/classification/hamming_distance.py b/pytorch_lightning/metrics/classification/hamming_distance.py
new file mode 100644
index 0000000000000..b3281cd60987c
--- /dev/null
+++ b/pytorch_lightning/metrics/classification/hamming_distance.py
@@ -0,0 +1,105 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import Any, Callable, Optional
+
+import torch
+from pytorch_lightning.metrics.metric import Metric
+from pytorch_lightning.metrics.functional.hamming_distance import _hamming_distance_update, _hamming_distance_compute
+
+
+class HammingDistance(Metric):
+    r"""
+    Computes the average `Hamming distance <https://en.wikipedia.org/wiki/Hamming_distance>`_ (also
+    known as Hamming loss) between targets and predictions:
+
+    .. math::
+        \text{Hamming distance} = \frac{1}{N \cdot L}\sum_i^N \sum_l^L 1(y_{il} \neq \hat{y_{il}})
+
+    Where :math:`y` is a tensor of target values, :math:`\hat{y}` is a tensor of predictions,
+    and :math:`\bullet_{il}` refers to the :math:`l`-th label of the :math:`i`-th sample of that
+    tensor.
+
+    This is the same as ``1-accuracy`` for binary data, while for all other types of inputs it
+    treats each possible label separately - meaning that, for example, multi-class data is
+    treated as if it were multi-label.
+
+    Accepts all input types listed in :ref:`metrics:Input types`.
+
+    Args:
+        threshold:
+            Threshold probability value for transforming probability predictions to binary
+            `(0,1)` predictions, in the case of binary or multi-label inputs.
+        compute_on_step:
+            Forward only calls ``update()`` and return None if this is set to False.
+        dist_sync_on_step:
+            Synchronize metric state across processes at each ``forward()``
+            before returning the value at the step.
+        process_group:
+            Specify the process group on which synchronization is called. default: None (which selects the entire world)
+        dist_sync_fn:
+            Callback that performs the allgather operation on the metric state. When ``None``, DDP
+            will be used to perform the all gather.
+
+    Example:
+
+        >>> from pytorch_lightning.metrics import HammingDistance
+        >>> target = torch.tensor([[0, 1], [1, 1]])
+        >>> preds = torch.tensor([[0, 1], [0, 1]])
+        >>> hamming_distance = HammingDistance()
+        >>> hamming_distance(preds, target)
+        tensor(0.2500)
+
+    """
+
+    def __init__(
+        self,
+        threshold: float = 0.5,
+        compute_on_step: bool = True,
+        dist_sync_on_step: bool = False,
+        process_group: Optional[Any] = None,
+        dist_sync_fn: Callable = None,
+    ):
+        super().__init__(
+            compute_on_step=compute_on_step,
+            dist_sync_on_step=dist_sync_on_step,
+            process_group=process_group,
+            dist_sync_fn=dist_sync_fn,
+        )
+
+        self.add_state("correct", default=torch.tensor(0), dist_reduce_fx="sum")
+        self.add_state("total", default=torch.tensor(0), dist_reduce_fx="sum")
+
+        if not 0 <= threshold <= 1:
+            raise ValueError("The `threshold` should lie in the [0,1] interval.")
+        self.threshold = threshold
+
+    def update(self, preds: torch.Tensor, target: torch.Tensor):
+        """
+        Update state with predictions and targets. See :ref:`metrics:Input types` for more information
+        on input types.
+
+        Args:
+            preds: Predictions from model (probabilities, or labels)
+            target: Ground truth labels
+        """
+        correct, total = _hamming_distance_update(preds, target, self.threshold)
+
+        self.correct += correct
+        self.total += total
+
+    def compute(self) -> torch.Tensor:
+        """
+        Computes hamming distance based on inputs passed in to ``update`` previously.
+        """
+        return _hamming_distance_compute(self.correct, self.total)
diff --git a/pytorch_lightning/metrics/classification/helpers.py b/pytorch_lightning/metrics/classification/helpers.py
index f52fb743b6979..bac9be59b1c9f 100644
--- a/pytorch_lightning/metrics/classification/helpers.py
+++ b/pytorch_lightning/metrics/classification/helpers.py
@@ -405,6 +405,11 @@ def _input_format_classification(
     else:
         preds, target = preds.squeeze(), target.squeeze()
 
+    # Convert half precision tensors to full precision, as not all ops are supported
+    # for example, min() is not supported
+    if preds.dtype == torch.float16:
+        preds = preds.float()
+
     case = _check_classification_inputs(
         preds,
         target,
diff --git a/pytorch_lightning/metrics/functional/__init__.py b/pytorch_lightning/metrics/functional/__init__.py
index 12812b75f8663..1b28f534f80e7 100644
--- a/pytorch_lightning/metrics/functional/__init__.py
+++ b/pytorch_lightning/metrics/functional/__init__.py
@@ -13,7 +13,6 @@
 # limitations under the License.
 from pytorch_lightning.metrics.functional.average_precision import average_precision  # noqa: F401
 from pytorch_lightning.metrics.functional.classification import (  # noqa: F401
-    accuracy,
     auc,
     auroc,
     dice_score,
@@ -32,8 +31,10 @@
 )
 from pytorch_lightning.metrics.functional.confusion_matrix import confusion_matrix  # noqa: F401
 # TODO: unify metrics between class and functional, add below
+from pytorch_lightning.metrics.functional.accuracy import accuracy  # noqa: F401
 from pytorch_lightning.metrics.functional.explained_variance import explained_variance  # noqa: F401
 from pytorch_lightning.metrics.functional.f_beta import fbeta, f1  # noqa: F401
+from pytorch_lightning.metrics.functional.hamming_distance import hamming_distance  # noqa: F401
 from pytorch_lightning.metrics.functional.mean_absolute_error import mean_absolute_error  # noqa: F401
 from pytorch_lightning.metrics.functional.mean_squared_error import mean_squared_error  # noqa: F401
 from pytorch_lightning.metrics.functional.mean_squared_log_error import mean_squared_log_error  # noqa: F401
diff --git a/pytorch_lightning/metrics/functional/accuracy.py b/pytorch_lightning/metrics/functional/accuracy.py
new file mode 100644
index 0000000000000..8ba0e49b881b8
--- /dev/null
+++ b/pytorch_lightning/metrics/functional/accuracy.py
@@ -0,0 +1,120 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import Tuple, Optional
+
+import torch
+from pytorch_lightning.metrics.classification.helpers import _input_format_classification
+
+
+def _accuracy_update(
+    preds: torch.Tensor, target: torch.Tensor, threshold: float, top_k: Optional[int], subset_accuracy: bool
+) -> Tuple[torch.Tensor, torch.Tensor]:
+
+    preds, target, mode = _input_format_classification(preds, target, threshold=threshold, top_k=top_k)
+
+    if mode == "binary" or (mode == "multi-label" and subset_accuracy):
+        correct = (preds == target).all(dim=1).sum()
+        total = torch.tensor(target.shape[0], device=target.device)
+    elif mode == "multi-label" and not subset_accuracy:
+        correct = (preds == target).sum()
+        total = torch.tensor(target.numel(), device=target.device)
+    elif mode == "multi-class" or (mode == "multi-dim multi-class" and not subset_accuracy):
+        correct = (preds * target).sum()
+        total = target.sum()
+    elif mode == "multi-dim multi-class" and subset_accuracy:
+        sample_correct = (preds * target).sum(dim=(1, 2))
+        correct = (sample_correct == target.shape[2]).sum()
+        total = torch.tensor(target.shape[0], device=target.device)
+
+    return correct, total
+
+
+def _accuracy_compute(correct: torch.Tensor, total: torch.Tensor) -> torch.Tensor:
+    return correct.float() / total
+
+
+def accuracy(
+    preds: torch.Tensor,
+    target: torch.Tensor,
+    threshold: float = 0.5,
+    top_k: Optional[int] = None,
+    subset_accuracy: bool = False,
+) -> torch.Tensor:
+    r"""
+    Computes `Accuracy <https://en.wikipedia.org/wiki/Accuracy_and_precision>`_:
+
+    .. math::
+        \text{Accuracy} = \frac{1}{N}\sum_i^N 1(y_i = \hat{y}_i)
+
+    Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a
+    tensor of predictions.
+
+    For multi-class and multi-dimensional multi-class data with probability predictions, the
+    parameter ``top_k`` generalizes this metric to a Top-K accuracy metric: for each sample the
+    top-K highest probability items are considered to find the correct label.
+
+    For multi-label and multi-dimensional multi-class inputs, this metric computes the "global"
+    accuracy by default, which counts all labels or sub-samples separately. This can be
+    changed to subset accuracy (which requires all labels or sub-samples in the sample to
+    be correctly predicted) by setting ``subset_accuracy=True``.
+
+    Accepts all input types listed in :ref:`metrics:Input types`.
+
+    Args:
+        preds: Predictions from model (probabilities, or labels)
+        target: Ground truth labels
+        threshold:
+            Threshold probability value for transforming probability predictions to binary
+            `(0,1)` predictions, in the case of binary or multi-label inputs.
+        top_k:
+            Number of highest probability predictions considered to find the correct label, relevant
+            only for (multi-dimensional) multi-class inputs with probability predictions. The
+            default value (``None``) will be interpreted as 1 for these inputs.
+
+            Should be left at default (``None``) for all other types of inputs.
+        subset_accuracy:
+            Whether to compute subset accuracy for multi-label and multi-dimensional
+            multi-class inputs (has no effect for other input types).
+
+            For multi-label inputs, if the parameter is set to `True`, then all labels for
+            each sample must be correctly predicted for the sample to count as correct. If it
+            is set to `False`, then all labels are counted separately - this is equivalent to
+            flattening inputs beforehand (i.e. ``preds = preds.flatten()`` and same for ``target``).
+
+            For multi-dimensional multi-class inputs, if the parameter is set to `True`, then all
+            sub-sample (on the extra axis) must be correct for the sample to be counted as correct.
+            If it is set to `False`, then all sub-samples are counter separately - this is equivalent,
+            in the case of label predictions, to flattening the inputs beforehand (i.e.
+            ``preds = preds.flatten()`` and same for ``target``). Note that the ``top_k`` parameter
+            still applies in both cases, if set.
+
+    Example:
+
+        >>> from pytorch_lightning.metrics.functional import accuracy
+        >>> target = torch.tensor([0, 1, 2, 3])
+        >>> preds = torch.tensor([0, 2, 1, 3])
+        >>> accuracy(preds, target)
+        tensor(0.5000)
+
+        >>> target = torch.tensor([0, 1, 2])
+        >>> preds = torch.tensor([[0.1, 0.9, 0], [0.3, 0.1, 0.6], [0.2, 0.5, 0.3]])
+        >>> accuracy(preds, target, top_k=2)
+        tensor(0.6667)
+    """
+
+    if top_k is not None and top_k <= 0:
+        raise ValueError("The `top_k` should be an integer larger than 1.")
+
+    correct, total = _accuracy_update(preds, target, threshold, top_k, subset_accuracy)
+    return _accuracy_compute(correct, total)
diff --git a/pytorch_lightning/metrics/functional/classification.py b/pytorch_lightning/metrics/functional/classification.py
index 140dff7159da8..f4e66217c3466 100644
--- a/pytorch_lightning/metrics/functional/classification.py
+++ b/pytorch_lightning/metrics/functional/classification.py
@@ -229,47 +229,6 @@ def stat_scores_multiple_classes(
     return tps.float(), fps.float(), tns.float(), fns.float(), sups.float()
 
 
-def accuracy(
-        pred: torch.Tensor,
-        target: torch.Tensor,
-        num_classes: Optional[int] = None,
-        class_reduction: str = 'micro',
-        return_state: bool = False
-) -> torch.Tensor:
-    """
-    Computes the accuracy classification score
-
-    Args:
-        pred: predicted labels
-        target: ground truth labels
-        num_classes: number of classes
-        class_reduction: method to reduce metric score over labels
-
-            - ``'micro'``: calculate metrics globally (default)
-            - ``'macro'``: calculate metrics for each label, and find their unweighted mean.
-            - ``'weighted'``: calculate metrics for each label, and find their weighted mean.
-            - ``'none'``: returns calculated metric per class
-        return_state: returns a internal state that can be ddp reduced
-            before doing the final calculation
-
-    Return:
-         A Tensor with the accuracy score.
-
-    Example:
-
-        >>> x = torch.tensor([0, 1, 2, 3])
-        >>> y = torch.tensor([0, 1, 2, 2])
-        >>> accuracy(x, y)
-        tensor(0.7500)
-
-    """
-    tps, fps, tns, fns, sups = stat_scores_multiple_classes(
-        pred=pred, target=target, num_classes=num_classes)
-    if return_state:
-        return {'tps': tps, 'sups': sups}
-    return class_reduce(tps, sups, sups, class_reduction=class_reduction)
-
-
 def _confmat_normalize(cm):
     """ Normalization function for confusion matrix """
     cm = cm / cm.sum(-1, keepdim=True)
diff --git a/pytorch_lightning/metrics/functional/hamming_distance.py b/pytorch_lightning/metrics/functional/hamming_distance.py
new file mode 100644
index 0000000000000..7d8ecafd08b00
--- /dev/null
+++ b/pytorch_lightning/metrics/functional/hamming_distance.py
@@ -0,0 +1,71 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import Tuple, Union
+
+import torch
+from pytorch_lightning.metrics.classification.helpers import _input_format_classification
+
+
+def _hamming_distance_update(
+    preds: torch.Tensor, target: torch.Tensor, threshold: float = 0.5
+) -> Tuple[torch.Tensor, int]:
+    preds, target, _ = _input_format_classification(preds, target, threshold=threshold)
+
+    correct = (preds == target).sum()
+    total = preds.numel()
+
+    return correct, total
+
+
+def _hamming_distance_compute(correct: torch.Tensor, total: Union[int, torch.Tensor]) -> torch.Tensor:
+    return 1 - correct.float() / total
+
+
+def hamming_distance(preds: torch.Tensor, target: torch.Tensor, threshold: float = 0.5) -> torch.Tensor:
+    r"""
+    Computes the average `Hamming distance <https://en.wikipedia.org/wiki/Hamming_distance>`_ (also
+    known as Hamming loss) between targets and predictions:
+
+    .. math::
+        \text{Hamming distance} = \frac{1}{N \cdot L} \sum_i^N \sum_l^L 1(y_{il} \neq \hat{y}_{il})
+
+    Where :math:`y` is a tensor of target values, :math:`\hat{y}` is a tensor of predictions,
+    and :math:`\bullet_{il}` refers to the :math:`l`-th label of the :math:`i`-th sample of that
+    tensor.
+
+    This is the same as ``1-accuracy`` for binary data, while for all other types of inputs it
+    treats each possible label separately - meaning that, for example, multi-class data is
+    treated as if it were multi-label.
+
+    Accepts all input types listed in :ref:`metrics:Input types`.
+
+    Args:
+        preds: Predictions from model
+        target: Ground truth
+        threshold:
+            Threshold probability value for transforming probability predictions to binary
+            (0,1) predictions, in the case of binary or multi-label inputs.
+
+    Example:
+
+        >>> from pytorch_lightning.metrics.functional import hamming_distance
+        >>> target = torch.tensor([[0, 1], [1, 1]])
+        >>> preds = torch.tensor([[0, 1], [0, 1]])
+        >>> hamming_distance(preds, target)
+        tensor(0.2500)
+
+    """
+
+    correct, total = _hamming_distance_update(preds, target, threshold)
+    return _hamming_distance_compute(correct, total)
diff --git a/pytorch_lightning/metrics/utils.py b/pytorch_lightning/metrics/utils.py
index 92faca200d0aa..8c59bb4991cab 100644
--- a/pytorch_lightning/metrics/utils.py
+++ b/pytorch_lightning/metrics/utils.py
@@ -169,8 +169,7 @@ def select_topk(prob_tensor: torch.Tensor, topk: int = 1, dim: int = 1) -> torch
                 [1, 1, 0]], dtype=torch.int32)
     """
     zeros = torch.zeros_like(prob_tensor)
-    topk_tensor = zeros.scatter(1, prob_tensor.topk(k=topk, dim=dim).indices, 1.0)
-
+    topk_tensor = zeros.scatter(dim, prob_tensor.topk(k=topk, dim=dim).indices, 1.0)
     return topk_tensor.int()
 
 
diff --git a/tests/metrics/classification/test_accuracy.py b/tests/metrics/classification/test_accuracy.py
index 017438269bdbf..7b28e07c894dd 100644
--- a/tests/metrics/classification/test_accuracy.py
+++ b/tests/metrics/classification/test_accuracy.py
@@ -1,9 +1,13 @@
+from functools import partial
+
 import numpy as np
 import pytest
 import torch
-from sklearn.metrics import accuracy_score
+from sklearn.metrics import accuracy_score as sk_accuracy
 
-from pytorch_lightning.metrics.classification.accuracy import Accuracy
+from pytorch_lightning.metrics import Accuracy
+from pytorch_lightning.metrics.functional import accuracy
+from pytorch_lightning.metrics.classification.helpers import _input_format_classification
 from tests.metrics.classification.inputs import (
     _binary_inputs,
     _binary_prob_inputs,
@@ -13,101 +17,155 @@
     _multidim_multiclass_prob_inputs,
     _multilabel_inputs,
     _multilabel_prob_inputs,
+    _multilabel_multidim_prob_inputs,
+    _multilabel_multidim_inputs,
 )
 from tests.metrics.utils import THRESHOLD, MetricTester
 
 torch.manual_seed(42)
 
 
-def _sk_accuracy_binary_prob(preds, target):
-    sk_preds = (preds.view(-1).numpy() >= THRESHOLD).astype(np.uint8)
-    sk_target = target.view(-1).numpy()
-
-    return accuracy_score(y_true=sk_target, y_pred=sk_preds)
+def _sk_accuracy(preds, target, subset_accuracy):
+    sk_preds, sk_target, mode = _input_format_classification(preds, target, threshold=THRESHOLD)
+    sk_preds, sk_target = sk_preds.numpy(), sk_target.numpy()
 
+    if mode == "multi-dim multi-class" and not subset_accuracy:
+        sk_preds, sk_target = np.transpose(sk_preds, (0, 2, 1)), np.transpose(sk_target, (0, 2, 1))
+        sk_preds, sk_target = sk_preds.reshape(-1, sk_preds.shape[2]), sk_target.reshape(-1, sk_target.shape[2])
+    elif mode == mode == "multi-dim multi-class" and subset_accuracy:
+        return np.all(sk_preds == sk_target, axis=(1, 2)).mean()
+    elif mode == "multi-label" and not subset_accuracy:
+        sk_preds, sk_target = sk_preds.reshape(-1), sk_target.reshape(-1)
 
-def _sk_accuracy_binary(preds, target):
-    sk_preds = preds.view(-1).numpy()
-    sk_target = target.view(-1).numpy()
+    return sk_accuracy(y_true=sk_target, y_pred=sk_preds)
 
-    return accuracy_score(y_true=sk_target, y_pred=sk_preds)
 
+@pytest.mark.parametrize(
+    "preds, target, subset_accuracy",
+    [
+        (_binary_prob_inputs.preds, _binary_prob_inputs.target, False),
+        (_binary_inputs.preds, _binary_inputs.target, False),
+        (_multilabel_prob_inputs.preds, _multilabel_prob_inputs.target, True),
+        (_multilabel_prob_inputs.preds, _multilabel_prob_inputs.target, False),
+        (_multilabel_inputs.preds, _multilabel_inputs.target, True),
+        (_multilabel_inputs.preds, _multilabel_inputs.target, False),
+        (_multiclass_prob_inputs.preds, _multiclass_prob_inputs.target, False),
+        (_multiclass_inputs.preds, _multiclass_inputs.target, False),
+        (_multidim_multiclass_prob_inputs.preds, _multidim_multiclass_prob_inputs.target, False),
+        (_multidim_multiclass_prob_inputs.preds, _multidim_multiclass_prob_inputs.target, True),
+        (_multidim_multiclass_inputs.preds, _multidim_multiclass_inputs.target, False),
+        (_multidim_multiclass_inputs.preds, _multidim_multiclass_inputs.target, True),
+        (_multilabel_multidim_prob_inputs.preds, _multilabel_multidim_prob_inputs.target, True),
+        (_multilabel_multidim_prob_inputs.preds, _multilabel_multidim_prob_inputs.target, False),
+        (_multilabel_multidim_inputs.preds, _multilabel_multidim_inputs.target, True),
+        (_multilabel_multidim_inputs.preds, _multilabel_multidim_inputs.target, False),
+    ],
+)
+class TestAccuracies(MetricTester):
+    @pytest.mark.parametrize("ddp", [False, True])
+    @pytest.mark.parametrize("dist_sync_on_step", [False, True])
+    def test_accuracy_class(self, ddp, dist_sync_on_step, preds, target, subset_accuracy):
+        self.run_class_metric_test(
+            ddp=ddp,
+            preds=preds,
+            target=target,
+            metric_class=Accuracy,
+            sk_metric=partial(_sk_accuracy, subset_accuracy=subset_accuracy),
+            dist_sync_on_step=dist_sync_on_step,
+            metric_args={"threshold": THRESHOLD, "subset_accuracy": subset_accuracy},
+        )
 
-def _sk_accuracy_multilabel_prob(preds, target):
-    sk_preds = (preds.view(-1).numpy() >= THRESHOLD).astype(np.uint8)
-    sk_target = target.view(-1).numpy()
+    def test_accuracy_fn(self, preds, target, subset_accuracy):
+        self.run_functional_metric_test(
+            preds,
+            target,
+            metric_functional=accuracy,
+            sk_metric=partial(_sk_accuracy, subset_accuracy=subset_accuracy),
+            metric_args={"threshold": THRESHOLD, "subset_accuracy": subset_accuracy},
+        )
 
-    return accuracy_score(y_true=sk_target, y_pred=sk_preds)
 
+_l1to4 = [0.1, 0.2, 0.3, 0.4]
+_l1to4t3 = np.array([_l1to4, _l1to4, _l1to4])
+_l1to4t3_mc = [_l1to4t3.T, _l1to4t3.T, _l1to4t3.T]
 
-def _sk_accuracy_multilabel(preds, target):
-    sk_preds = preds.view(-1).numpy()
-    sk_target = target.view(-1).numpy()
+# The preds in these examples always put highest probability on class 3, second highest on class 2,
+# third highest on class 1, and lowest on class 0
+_topk_preds_mc = torch.tensor([_l1to4t3, _l1to4t3]).float()
+_topk_target_mc = torch.tensor([[1, 2, 3], [2, 1, 0]])
 
-    return accuracy_score(y_true=sk_target, y_pred=sk_preds)
+# This is like for MC case, but one sample in each batch is sabotaged with 0 class prediction :)
+_topk_preds_mdmc = torch.tensor([_l1to4t3_mc, _l1to4t3_mc]).float()
+_topk_target_mdmc = torch.tensor([[[1, 1, 0], [2, 2, 2], [3, 3, 3]], [[2, 2, 0], [1, 1, 1], [0, 0, 0]]])
 
 
-def _sk_accuracy_multiclass_prob(preds, target):
-    sk_preds = torch.argmax(preds, dim=len(preds.shape) - 1).view(-1).numpy()
-    sk_target = target.view(-1).numpy()
+# Replace with a proper sk_metric test once sklearn 0.24 hits :)
+@pytest.mark.parametrize(
+    "preds, target, exp_result, k, subset_accuracy",
+    [
+        (_topk_preds_mc, _topk_target_mc, 1 / 6, 1, False),
+        (_topk_preds_mc, _topk_target_mc, 3 / 6, 2, False),
+        (_topk_preds_mc, _topk_target_mc, 5 / 6, 3, False),
+        (_topk_preds_mc, _topk_target_mc, 1 / 6, 1, True),
+        (_topk_preds_mc, _topk_target_mc, 3 / 6, 2, True),
+        (_topk_preds_mc, _topk_target_mc, 5 / 6, 3, True),
+        (_topk_preds_mdmc, _topk_target_mdmc, 1 / 6, 1, False),
+        (_topk_preds_mdmc, _topk_target_mdmc, 8 / 18, 2, False),
+        (_topk_preds_mdmc, _topk_target_mdmc, 13 / 18, 3, False),
+        (_topk_preds_mdmc, _topk_target_mdmc, 1 / 6, 1, True),
+        (_topk_preds_mdmc, _topk_target_mdmc, 2 / 6, 2, True),
+        (_topk_preds_mdmc, _topk_target_mdmc, 3 / 6, 3, True),
+    ],
+)
+def test_topk_accuracy(preds, target, exp_result, k, subset_accuracy):
+    topk = Accuracy(top_k=k, subset_accuracy=subset_accuracy)
 
-    return accuracy_score(y_true=sk_target, y_pred=sk_preds)
+    for batch in range(preds.shape[0]):
+        topk(preds[batch], target[batch])
 
+    assert topk.compute() == exp_result
 
-def _sk_accuracy_multiclass(preds, target):
-    sk_preds = preds.view(-1).numpy()
-    sk_target = target.view(-1).numpy()
+    # Test functional
+    total_samples = target.shape[0] * target.shape[1]
 
-    return accuracy_score(y_true=sk_target, y_pred=sk_preds)
+    preds = preds.view(total_samples, 4, -1)
+    target = target.view(total_samples, -1)
 
+    assert accuracy(preds, target, top_k=k, subset_accuracy=subset_accuracy) == exp_result
 
-def _sk_accuracy_multidim_multiclass_prob(preds, target):
-    sk_preds = torch.argmax(preds, dim=len(preds.shape) - 2).view(-1).numpy()
-    sk_target = target.view(-1).numpy()
 
-    return accuracy_score(y_true=sk_target, y_pred=sk_preds)
+# Only MC and MDMC with probs input type should be accepted for top_k
+@pytest.mark.parametrize(
+    "preds, target",
+    [
+        (_binary_prob_inputs.preds, _binary_prob_inputs.target),
+        (_binary_inputs.preds, _binary_inputs.target),
+        (_multilabel_prob_inputs.preds, _multilabel_prob_inputs.target),
+        (_multilabel_inputs.preds, _multilabel_inputs.target),
+        (_multiclass_inputs.preds, _multiclass_inputs.target),
+        (_multidim_multiclass_inputs.preds, _multidim_multiclass_inputs.target),
+        (_multilabel_multidim_prob_inputs.preds, _multilabel_multidim_prob_inputs.target),
+        (_multilabel_multidim_inputs.preds, _multilabel_multidim_inputs.target),
+    ],
+)
+def test_topk_accuracy_wrong_input_types(preds, target):
+    topk = Accuracy(top_k=1)
 
+    with pytest.raises(ValueError):
+        topk(preds[0], target[0])
 
-def _sk_accuracy_multidim_multiclass(preds, target):
-    sk_preds = preds.view(-1).numpy()
-    sk_target = target.view(-1).numpy()
+    with pytest.raises(ValueError):
+        accuracy(preds[0], target[0], top_k=1)
 
-    return accuracy_score(y_true=sk_target, y_pred=sk_preds)
 
+@pytest.mark.parametrize("top_k, threshold", [(0, 0.5), (None, 1.5)])
+def test_wrong_params(top_k, threshold):
+    preds, target = _multiclass_prob_inputs.preds, _multiclass_prob_inputs.target
 
-def test_accuracy_invalid_shape():
     with pytest.raises(ValueError):
-        acc = Accuracy()
-        acc.update(preds=torch.rand(1), target=torch.rand(1, 2, 3))
+        acc = Accuracy(threshold=threshold, top_k=top_k)
+        acc(preds, target)
+        acc.compute()
 
-
-@pytest.mark.parametrize("ddp", [True, False])
-@pytest.mark.parametrize("dist_sync_on_step", [True, False])
-@pytest.mark.parametrize(
-    "preds, target, sk_metric",
-    [
-        (_binary_prob_inputs.preds, _binary_prob_inputs.target, _sk_accuracy_binary_prob),
-        (_binary_inputs.preds, _binary_inputs.target, _sk_accuracy_binary),
-        (_multilabel_prob_inputs.preds, _multilabel_prob_inputs.target, _sk_accuracy_multilabel_prob),
-        (_multilabel_inputs.preds, _multilabel_inputs.target, _sk_accuracy_multilabel),
-        (_multiclass_prob_inputs.preds, _multiclass_prob_inputs.target, _sk_accuracy_multiclass_prob),
-        (_multiclass_inputs.preds, _multiclass_inputs.target, _sk_accuracy_multiclass),
-        (
-            _multidim_multiclass_prob_inputs.preds,
-            _multidim_multiclass_prob_inputs.target,
-            _sk_accuracy_multidim_multiclass_prob,
-        ),
-        (_multidim_multiclass_inputs.preds, _multidim_multiclass_inputs.target, _sk_accuracy_multidim_multiclass),
-    ],
-)
-class TestAccuracy(MetricTester):
-    def test_accuracy(self, ddp, dist_sync_on_step, preds, target, sk_metric):
-        self.run_class_metric_test(
-            ddp=ddp,
-            preds=preds,
-            target=target,
-            metric_class=Accuracy,
-            sk_metric=sk_metric,
-            dist_sync_on_step=dist_sync_on_step,
-            metric_args={"threshold": THRESHOLD},
-        )
+    with pytest.raises(ValueError):
+        accuracy(preds, target, threshold=threshold, top_k=top_k)
diff --git a/tests/metrics/classification/test_hamming_distance.py b/tests/metrics/classification/test_hamming_distance.py
new file mode 100644
index 0000000000000..73c2abe771fae
--- /dev/null
+++ b/tests/metrics/classification/test_hamming_distance.py
@@ -0,0 +1,82 @@
+import pytest
+import torch
+from sklearn.metrics import hamming_loss as sk_hamming_loss
+
+from pytorch_lightning.metrics import HammingDistance
+from pytorch_lightning.metrics.functional import hamming_distance
+from pytorch_lightning.metrics.classification.helpers import _input_format_classification
+from tests.metrics.classification.inputs import (
+    _binary_inputs,
+    _binary_prob_inputs,
+    _multiclass_inputs,
+    _multiclass_prob_inputs,
+    _multidim_multiclass_inputs,
+    _multidim_multiclass_prob_inputs,
+    _multilabel_inputs,
+    _multilabel_prob_inputs,
+    _multilabel_multidim_prob_inputs,
+    _multilabel_multidim_inputs,
+)
+from tests.metrics.utils import THRESHOLD, MetricTester
+
+torch.manual_seed(42)
+
+
+def _sk_hamming_loss(preds, target):
+    sk_preds, sk_target, _ = _input_format_classification(preds, target, threshold=THRESHOLD)
+    sk_preds, sk_target = sk_preds.numpy(), sk_target.numpy()
+    sk_preds, sk_target = sk_preds.reshape(sk_preds.shape[0], -1), sk_target.reshape(sk_target.shape[0], -1)
+
+    return sk_hamming_loss(y_true=sk_target, y_pred=sk_preds)
+
+
+@pytest.mark.parametrize(
+    "preds, target",
+    [
+        (_binary_prob_inputs.preds, _binary_prob_inputs.target),
+        (_binary_inputs.preds, _binary_inputs.target),
+        (_multilabel_prob_inputs.preds, _multilabel_prob_inputs.target),
+        (_multilabel_inputs.preds, _multilabel_inputs.target),
+        (_multiclass_prob_inputs.preds, _multiclass_prob_inputs.target),
+        (_multiclass_inputs.preds, _multiclass_inputs.target),
+        (_multidim_multiclass_prob_inputs.preds, _multidim_multiclass_prob_inputs.target),
+        (_multidim_multiclass_inputs.preds, _multidim_multiclass_inputs.target),
+        (_multilabel_multidim_prob_inputs.preds, _multilabel_multidim_prob_inputs.target),
+        (_multilabel_multidim_inputs.preds, _multilabel_multidim_inputs.target),
+    ],
+)
+class TestHammingDistance(MetricTester):
+    @pytest.mark.parametrize("ddp", [True, False])
+    @pytest.mark.parametrize("dist_sync_on_step", [False, True])
+    def test_hamming_distance_class(self, ddp, dist_sync_on_step, preds, target):
+        self.run_class_metric_test(
+            ddp=ddp,
+            preds=preds,
+            target=target,
+            metric_class=HammingDistance,
+            sk_metric=_sk_hamming_loss,
+            dist_sync_on_step=dist_sync_on_step,
+            metric_args={"threshold": THRESHOLD},
+        )
+
+    def test_hamming_distance_fn(self, preds, target):
+        self.run_functional_metric_test(
+            preds,
+            target,
+            metric_functional=hamming_distance,
+            sk_metric=_sk_hamming_loss,
+            metric_args={"threshold": THRESHOLD},
+        )
+
+
+@pytest.mark.parametrize("threshold", [1.5])
+def test_wrong_params(threshold):
+    preds, target = _multiclass_prob_inputs.preds, _multiclass_prob_inputs.target
+
+    with pytest.raises(ValueError):
+        ham_dist = HammingDistance(threshold=threshold)
+        ham_dist(preds, target)
+        ham_dist.compute()
+
+    with pytest.raises(ValueError):
+        hamming_distance(preds, target, threshold=threshold)
diff --git a/tests/metrics/classification/test_inputs.py b/tests/metrics/classification/test_inputs.py
index c4d01d282fa57..30d3f06707301 100644
--- a/tests/metrics/classification/test_inputs.py
+++ b/tests/metrics/classification/test_inputs.py
@@ -22,6 +22,8 @@
 torch.manual_seed(42)
 
 # Some additional inputs to test on
+_ml_prob_half = Input(_ml_prob.preds.half(), _ml_prob.target)
+
 _mc_prob_2cls_preds = rand(NUM_BATCHES, BATCH_SIZE, 2)
 _mc_prob_2cls_preds /= _mc_prob_2cls_preds.sum(dim=2, keepdim=True)
 _mc_prob_2cls = Input(_mc_prob_2cls_preds, randint(high=2, size=(NUM_BATCHES, BATCH_SIZE)))
@@ -133,6 +135,8 @@ def _mlmd_prob_to_mc_preds_tr(x):
         (_mdmc_prob_many_dims, None, None, 2, "multi-dim multi-class", _top2_rshp2, _onehot_rshp1),
         ###########################
         # Test some special cases
+        # Make sure that half precision works, i.e. is converted to full precision
+        (_ml_prob_half, None, None, None, "multi-label", lambda x: _ml_preds_tr(x.float()), _rshp1),
         # Binary as multiclass
         (_bin, None, None, None, "multi-class", _onehot2, _onehot2),
         # Binary probs as multiclass
diff --git a/tests/metrics/functional/test_classification.py b/tests/metrics/functional/test_classification.py
index 4a2d690ec08a3..ec2c8bf387005 100644
--- a/tests/metrics/functional/test_classification.py
+++ b/tests/metrics/functional/test_classification.py
@@ -3,7 +3,6 @@
 import pytest
 import torch
 from sklearn.metrics import (
-    accuracy_score as sk_accuracy,
     jaccard_score as sk_jaccard_score,
     precision_score as sk_precision,
     recall_score as sk_recall,
@@ -14,7 +13,6 @@
 from pytorch_lightning.metrics.functional.classification import (
     stat_scores,
     stat_scores_multiple_classes,
-    accuracy,
     precision,
     recall,
     dice_score,
@@ -28,7 +26,6 @@
 
 
 @pytest.mark.parametrize(['sklearn_metric', 'torch_metric', 'only_binary'], [
-    pytest.param(sk_accuracy, accuracy, False, id='accuracy'),
     pytest.param(partial(sk_jaccard_score, average='macro'), iou, False, id='iou'),
     pytest.param(partial(sk_precision, average='micro'), precision, False, id='precision'),
     pytest.param(partial(sk_recall, average='micro'), recall, False, id='recall'),
@@ -164,39 +161,6 @@ def test_stat_scores_multiclass(pred, target, reduction,
     assert torch.allclose(torch.tensor(expected_support).to(sup), sup)
 
 
-def test_multilabel_accuracy():
-    # Dense label indicator matrix format
-    y1 = torch.tensor([[0, 1, 1], [1, 0, 1]])
-    y2 = torch.tensor([[0, 0, 1], [1, 0, 1]])
-
-    assert torch.allclose(accuracy(y1, y2, class_reduction='none'), torch.tensor([2 / 3, 1.]))
-    assert torch.allclose(accuracy(y1, y1, class_reduction='none'), torch.tensor([1., 1.]))
-    assert torch.allclose(accuracy(y2, y2, class_reduction='none'), torch.tensor([1., 1.]))
-    assert torch.allclose(accuracy(y2, torch.logical_not(y2), class_reduction='none'), torch.tensor([0., 0.]))
-    assert torch.allclose(accuracy(y1, torch.logical_not(y1), class_reduction='none'), torch.tensor([0., 0.]))
-
-    # num_classes does not match extracted number from input we expect a warning
-    with pytest.warns(RuntimeWarning,
-                      match=r'You have set .* number of classes which is'
-                            r' different from predicted (.*) and'
-                            r' target (.*) number of classes'):
-        _ = accuracy(y2, torch.zeros_like(y2), num_classes=3)
-
-
-def test_accuracy():
-    pred = torch.tensor([0, 1, 2, 3])
-    target = torch.tensor([0, 1, 2, 2])
-    acc = accuracy(pred, target)
-
-    assert acc.item() == 0.75
-
-    pred = torch.tensor([0, 1, 2, 2])
-    target = torch.tensor([0, 1, 1, 3])
-    acc = accuracy(pred, target)
-
-    assert acc.item() == 0.50
-
-
 @pytest.mark.parametrize(['pred', 'target', 'expected_prec', 'expected_rec'], [
     pytest.param(torch.tensor([1., 0., 1., 0.]), torch.tensor([0., 1., 1., 0.]), [0.5, 0.5], [0.5, 0.5]),
     pytest.param(to_onehot(torch.tensor([1., 0., 1., 0.])), torch.tensor([0., 1., 1., 0.]), [0.5, 0.5], [0.5, 0.5])
diff --git a/tests/metrics/regression/test_ssim.py b/tests/metrics/regression/test_ssim.py
index f581188e89fce..8bb304850e3f2 100644
--- a/tests/metrics/regression/test_ssim.py
+++ b/tests/metrics/regression/test_ssim.py
@@ -53,9 +53,7 @@ def _sk_metric(preds, target, data_range, multichannel):
 class TestSSIM(MetricTester):
     atol = 6e-5
 
-    # TODO: for some reason this test hangs with ddp=True
-    # @pytest.mark.parametrize("ddp", [True, False])
-    @pytest.mark.parametrize("ddp", [False])
+    @pytest.mark.parametrize("ddp", [True, False])
     @pytest.mark.parametrize("dist_sync_on_step", [True, False])
     def test_ssim(self, preds, target, multichannel, ddp, dist_sync_on_step):
         self.run_class_metric_test(
diff --git a/tests/metrics/utils.py b/tests/metrics/utils.py
index c607a466b2068..4bd6608ce3fcf 100644
--- a/tests/metrics/utils.py
+++ b/tests/metrics/utils.py
@@ -11,6 +11,11 @@
 
 from pytorch_lightning.metrics import Metric
 
+try:
+    set_start_method("spawn")
+except RuntimeError:
+    pass
+
 NUM_PROCESSES = 2
 NUM_BATCHES = 10
 BATCH_SIZE = 32
@@ -165,10 +170,7 @@ def setup_class(self):
         """Setup the metric class. This will spawn the pool of workers that are
         used for metric testing and setup_ddp
         """
-        try:
-            set_start_method("spawn")
-        except RuntimeError:
-            pass
+
         self.poolSize = NUM_PROCESSES
         self.pool = Pool(processes=self.poolSize)
         self.pool.starmap(setup_ddp, [(rank, self.poolSize) for rank in range(self.poolSize)])