Add dim to pytorch_lightning.metrics.PSNR (#5957)

* Add dim to PSNR * Update CHANGELOG.md * Update CHANGELOG.md Co-authored-by: Jirka Borovec <[email protected]> * Add reduction tests * Recover warnings on reduction and add tests * Add copyright texts * Refactor PSNR * Change warnings * Update pytorch_lightning/metrics/functional/psnr.py Change functional.psnr dim doc Co-authored-by: Nicki Skafte <[email protected]> * Change PSNR dim docs * Apply suggestions from code review * tests Co-authored-by: Jirka Borovec <[email protected]> Co-authored-by: Nicki Skafte <[email protected]> Co-authored-by: Jirka Borovec <[email protected]>
Lightning-AI · Feb 17, 2021 · 6a9cec4 · 6a9cec4
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diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -87,6 +87,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added `LightningModule.configure_callbacks` to enable the definition of model-specific callbacks ([#5621](https://github.com/PyTorchLightning/pytorch-lightning/pull/5621))
 
 
+- Added `dim` to `PSNR` metric for mean-squared-error reduction ([#5957](https://github.com/PyTorchLightning/pytorch-lightning/pull/5957))
+
+
 - Added promxial policy optimization template to pl_examples ([#5394](https://github.com/PyTorchLightning/pytorch-lightning/pull/5394))
 
 

diff --git a/pytorch_lightning/metrics/functional/psnr.py b/pytorch_lightning/metrics/functional/psnr.py
@@ -1,27 +1,56 @@
-from typing import Optional, Tuple
+# 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 Optional, Tuple, Union
 
 import torch
 
-from pytorch_lightning.utilities import rank_zero_warn
+from pytorch_lightning import utilities
+from pytorch_lightning.metrics import utils
 
 
 def _psnr_compute(
     sum_squared_error: torch.Tensor,
-    n_obs: int,
-    data_range: float,
+    n_obs: torch.Tensor,
+    data_range: torch.Tensor,
     base: float = 10.0,
     reduction: str = 'elementwise_mean',
 ) -> torch.Tensor:
-    if reduction != 'elementwise_mean':
-        rank_zero_warn(f'The `reduction={reduction}` parameter is unused and will not have any effect.')
     psnr_base_e = 2 * torch.log(data_range) - torch.log(sum_squared_error / n_obs)
     psnr = psnr_base_e * (10 / torch.log(torch.tensor(base)))
-    return psnr
+    return utils.reduce(psnr, reduction=reduction)
 
 
-def _psnr_update(preds: torch.Tensor, target: torch.Tensor) -> Tuple[torch.Tensor, int]:
-    sum_squared_error = torch.sum(torch.pow(preds - target, 2))
-    n_obs = target.numel()
+def _psnr_update(preds: torch.Tensor,
+                 target: torch.Tensor,
+                 dim: Optional[Union[int, Tuple[int, ...]]] = None) -> Tuple[torch.Tensor, torch.Tensor]:
+    if dim is None:
+        sum_squared_error = torch.sum(torch.pow(preds - target, 2))
+        n_obs = torch.tensor(target.numel(), device=target.device)
+        return sum_squared_error, n_obs
+
+    sum_squared_error = torch.sum(torch.pow(preds - target, 2), dim=dim)
+
+    if isinstance(dim, int):
+        dim_list = [dim]
+    else:
+        dim_list = list(dim)
+    if not dim_list:
+        n_obs = torch.tensor(target.numel(), device=target.device)
+    else:
+        n_obs = torch.tensor(target.size(), device=target.device)[dim_list].prod()
+        n_obs = n_obs.expand_as(sum_squared_error)
+
     return sum_squared_error, n_obs
 
 
@@ -31,21 +60,27 @@ def psnr(
     data_range: Optional[float] = None,
     base: float = 10.0,
     reduction: str = 'elementwise_mean',
+    dim: Optional[Union[int, Tuple[int, ...]]] = None,
 ) -> torch.Tensor:
     """
     Computes the peak signal-to-noise ratio
 
     Args:
         preds: estimated signal
         target: groun truth signal
-        data_range: the range of the data. If None, it is determined from the data (max - min)
+        data_range:
+            the range of the data. If None, it is determined from the data (max - min). ``data_range`` must be given
+            when ``dim`` is not None.
         base: a base of a logarithm to use (default: 10)
         reduction: a method to reduce metric score over labels.
 
             - ``'elementwise_mean'``: takes the mean (default)
             - ``'sum'``: takes the sum
             - ``'none'``: no reduction will be applied
 
+        dim:
+            Dimensions to reduce PSNR scores over provided as either an integer or a list of integers. Default is
+            None meaning scores will be reduced across all dimensions.
     Return:
         Tensor with PSNR score
 
@@ -57,9 +92,17 @@ def psnr(
         tensor(2.5527)
 
     """
+    if dim is None and reduction != 'elementwise_mean':
+        utilities.rank_zero_warn(f'The `reduction={reduction}` will not have any effect when `dim` is None.')
+
     if data_range is None:
+        if dim is not None:
+            # Maybe we could use `torch.amax(target, dim=dim) - torch.amin(target, dim=dim)` in PyTorch 1.7 to calculate
+            # `data_range` in the future.
+            raise ValueError("The `data_range` must be given when `dim` is not None.")
+
         data_range = target.max() - target.min()
     else:
         data_range = torch.tensor(float(data_range))
-    sum_squared_error, n_obs = _psnr_update(preds, target)
-    return _psnr_compute(sum_squared_error, n_obs, data_range, base, reduction)
+    sum_squared_error, n_obs = _psnr_update(preds, target, dim=dim)
+    return _psnr_compute(sum_squared_error, n_obs, data_range, base=base, reduction=reduction)
diff --git a/pytorch_lightning/metrics/regression/psnr.py b/pytorch_lightning/metrics/regression/psnr.py
@@ -11,10 +11,11 @@
 # 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, Optional
+from typing import Any, Optional, Sequence, Tuple, Union
 
 import torch
 
+from pytorch_lightning import utilities
 from pytorch_lightning.metrics.functional.psnr import _psnr_compute, _psnr_update
 from pytorch_lightning.metrics.metric import Metric
 
@@ -29,14 +30,19 @@ class PSNR(Metric):
     <https://en.wikipedia.org/wiki/Mean_squared_error>`_ function.
 
     Args:
-        data_range: the range of the data. If None, it is determined from the data (max - min)
+        data_range:
+            the range of the data. If None, it is determined from the data (max - min).
+            The ``data_range`` must be given when ``dim`` is not None.
         base: a base of a logarithm to use (default: 10)
         reduction: a method to reduce metric score over labels.
 
             - ``'elementwise_mean'``: takes the mean (default)
             - ``'sum'``: takes the sum
             - ``'none'``: no reduction will be applied
 
+        dim:
+            Dimensions to reduce PSNR scores over, provided as either an integer or a list of integers. Default is
+            None meaning scores will be reduced across all dimensions and all batches.
         compute_on_step:
             Forward only calls ``update()`` and return None if this is set to False. default: True
         dist_sync_on_step:
@@ -61,6 +67,7 @@ def __init__(
         data_range: Optional[float] = None,
         base: float = 10.0,
         reduction: str = 'elementwise_mean',
+        dim: Optional[Union[int, Tuple[int, ...]]] = None,
         compute_on_step: bool = True,
         dist_sync_on_step: bool = False,
         process_group: Optional[Any] = None,
@@ -71,16 +78,30 @@ def __init__(
             process_group=process_group,
         )
 
-        self.add_state("sum_squared_error", default=torch.tensor(0.0), dist_reduce_fx="sum")
-        self.add_state("total", default=torch.tensor(0), dist_reduce_fx="sum")
+        if dim is None and reduction != 'elementwise_mean':
+            utilities.rank_zero_warn(f'The `reduction={reduction}` will not have any effect when `dim` is None.')
+
+        if dim is None:
+            self.add_state("sum_squared_error", default=torch.tensor(0.0), dist_reduce_fx="sum")
+            self.add_state("total", default=torch.tensor(0), dist_reduce_fx="sum")
+        else:
+            self.add_state("sum_squared_error", default=[])
+            self.add_state("total", default=[])
+
         if data_range is None:
+            if dim is not None:
+                # Maybe we could use `torch.amax(target, dim=dim) - torch.amin(target, dim=dim)` in PyTorch 1.7 to
+                # calculate `data_range` in the future.
+                raise ValueError("The `data_range` must be given when `dim` is not None.")
+
             self.data_range = None
             self.add_state("min_target", default=torch.tensor(0.0), dist_reduce_fx=torch.min)
             self.add_state("max_target", default=torch.tensor(0.0), dist_reduce_fx=torch.max)
         else:
             self.register_buffer("data_range", torch.tensor(float(data_range)))
         self.base = base
         self.reduction = reduction
+        self.dim = tuple(dim) if isinstance(dim, Sequence) else dim
 
     def update(self, preds: torch.Tensor, target: torch.Tensor):
         """
@@ -90,14 +111,18 @@ def update(self, preds: torch.Tensor, target: torch.Tensor):
             preds: Predictions from model
             target: Ground truth values
         """
-        if self.data_range is None:
-            # keep track of min and max target values
-            self.min_target = min(target.min(), self.min_target)
-            self.max_target = max(target.max(), self.max_target)
-
-        sum_squared_error, n_obs = _psnr_update(preds, target)
-        self.sum_squared_error += sum_squared_error
-        self.total += n_obs
+        sum_squared_error, n_obs = _psnr_update(preds, target, dim=self.dim)
+        if self.dim is None:
+            if self.data_range is None:
+                # keep track of min and max target values
+                self.min_target = min(target.min(), self.min_target)
+                self.max_target = max(target.max(), self.max_target)
+
+            self.sum_squared_error += sum_squared_error
+            self.total += n_obs
+        else:
+            self.sum_squared_error.append(sum_squared_error)
+            self.total.append(n_obs)
 
     def compute(self):
         """
@@ -107,4 +132,11 @@ def compute(self):
             data_range = self.data_range
         else:
             data_range = self.max_target - self.min_target
-        return _psnr_compute(self.sum_squared_error, self.total, data_range, self.base, self.reduction)
+
+        if self.dim is None:
+            sum_squared_error = self.sum_squared_error
+            total = self.total
+        else:
+            sum_squared_error = torch.cat([values.flatten() for values in self.sum_squared_error])
+            total = torch.cat([values.flatten() for values in self.total])
+        return _psnr_compute(sum_squared_error, total, data_range, base=self.base, reduction=self.reduction)
diff --git a/tests/metrics/regression/test_psnr.py b/tests/metrics/regression/test_psnr.py
@@ -1,3 +1,17 @@
+# 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 collections import namedtuple
 from functools import partial
 
@@ -14,67 +28,106 @@
 
 Input = namedtuple('Input', ["preds", "target"])
 
+_input_size = (NUM_BATCHES, BATCH_SIZE, 32, 32)
 _inputs = [
     Input(
-        preds=torch.randint(n_cls_pred, (NUM_BATCHES, BATCH_SIZE), dtype=torch.float),
-        target=torch.randint(n_cls_target, (NUM_BATCHES, BATCH_SIZE), dtype=torch.float),
+        preds=torch.randint(n_cls_pred, _input_size, dtype=torch.float),
+        target=torch.randint(n_cls_target, _input_size, dtype=torch.float),
     ) for n_cls_pred, n_cls_target in [(10, 10), (5, 10), (10, 5)]
 ]
 
 
-def _sk_metric(preds, target, data_range):
-    sk_preds = preds.view(-1).numpy()
-    sk_target = target.view(-1).numpy()
-    return peak_signal_noise_ratio(sk_target, sk_preds, data_range=data_range)
+def _to_sk_peak_signal_noise_ratio_inputs(value, dim):
+    value = value.numpy()
+    batches = value[None] if value.ndim == len(_input_size) - 1 else value
+
+    if dim is None:
+        return [batches]
+
+    num_dims = np.size(dim)
+    if not num_dims:
+        return batches
+
+    inputs = []
+    for batch in batches:
+        batch = np.moveaxis(batch, dim, np.arange(-num_dims, 0))
+        psnr_input_shape = batch.shape[-num_dims:]
+        inputs.extend(batch.reshape(-1, *psnr_input_shape))
+    return inputs
+
+
+def _sk_psnr(preds, target, data_range, reduction, dim):
+    sk_preds_lists = _to_sk_peak_signal_noise_ratio_inputs(preds, dim=dim)
+    sk_target_lists = _to_sk_peak_signal_noise_ratio_inputs(target, dim=dim)
+    np_reduce_map = {"elementwise_mean": np.mean, "none": np.array, "sum": np.sum}
+    return np_reduce_map[reduction]([
+        peak_signal_noise_ratio(sk_target, sk_preds, data_range=data_range)
+        for sk_target, sk_preds in zip(sk_target_lists, sk_preds_lists)
+    ])
 
 
-def _base_e_sk_metric(preds, target, data_range):
-    sk_preds = preds.view(-1).numpy()
-    sk_target = target.view(-1).numpy()
-    return peak_signal_noise_ratio(sk_target, sk_preds, data_range=data_range) * np.log(10)
+def _base_e_sk_psnr(preds, target, data_range, reduction, dim):
+    return _sk_psnr(preds, target, data_range, reduction, dim) * np.log(10)
 
 
 @pytest.mark.parametrize(
-    "preds, target, data_range",
+    "preds, target, data_range, reduction, dim",
     [
-        (_inputs[0].preds, _inputs[0].target, 10),
-        (_inputs[1].preds, _inputs[1].target, 10),
-        (_inputs[2].preds, _inputs[2].target, 5),
+        (_inputs[0].preds, _inputs[0].target, 10, "elementwise_mean", None),
+        (_inputs[1].preds, _inputs[1].target, 10, "elementwise_mean", None),
+        (_inputs[2].preds, _inputs[2].target, 5, "elementwise_mean", None),
+        (_inputs[2].preds, _inputs[2].target, 5, "elementwise_mean", 1),
+        (_inputs[2].preds, _inputs[2].target, 5, "elementwise_mean", (1, 2)),
+        (_inputs[2].preds, _inputs[2].target, 5, "sum", (1, 2)),
     ],
 )
 @pytest.mark.parametrize(
     "base, sk_metric",
     [
-        (10.0, _sk_metric),
-        (2.718281828459045, _base_e_sk_metric),
+        (10.0, _sk_psnr),
+        (2.718281828459045, _base_e_sk_psnr),
     ],
 )
 class TestPSNR(MetricTester):
 
     @pytest.mark.parametrize("ddp", [True, False])
     @pytest.mark.parametrize("dist_sync_on_step", [True, False])
-    def test_psnr(self, preds, target, data_range, base, sk_metric, ddp, dist_sync_on_step):
+    def test_psnr(self, preds, target, data_range, base, reduction, dim, sk_metric, ddp, dist_sync_on_step):
+        _args = {"data_range": data_range, "base": base, "reduction": reduction, "dim": dim}
         self.run_class_metric_test(
             ddp,
             preds,
             target,
             PSNR,
-            partial(sk_metric, data_range=data_range),
-            metric_args={
-                "data_range": data_range,
-                "base": base
-            },
+            partial(sk_metric, data_range=data_range, reduction=reduction, dim=dim),
+            metric_args=_args,
             dist_sync_on_step=dist_sync_on_step,
         )
 
-    def test_psnr_functional(self, preds, target, sk_metric, data_range, base):
+    def test_psnr_functional(self, preds, target, sk_metric, data_range, base, reduction, dim):
+        _args = {"data_range": data_range, "base": base, "reduction": reduction, "dim": dim}
         self.run_functional_metric_test(
             preds,
             target,
             psnr,
-            partial(sk_metric, data_range=data_range),
-            metric_args={
-                "data_range": data_range,
-                "base": base
-            },
+            partial(sk_metric, data_range=data_range, reduction=reduction, dim=dim),
+            metric_args=_args,
         )
+
+
+@pytest.mark.parametrize("reduction", ["none", "sum"])
+def test_reduction_for_dim_none(reduction):
+    match = f"The `reduction={reduction}` will not have any effect when `dim` is None."
+    with pytest.warns(UserWarning, match=match):
+        PSNR(reduction=reduction, dim=None)
+
+    with pytest.warns(UserWarning, match=match):
+        psnr(_inputs[0].preds, _inputs[0].target, reduction=reduction, dim=None)
+
+
+def test_missing_data_range():
+    with pytest.raises(ValueError):
+        PSNR(data_range=None, dim=0)
+
+    with pytest.raises(ValueError):
+        psnr(_inputs[0].preds, _inputs[0].target, data_range=None, dim=0)
Original file line number	Diff line number	Diff line change
Expand Up		@@ -87,6 +87,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
		- Added `LightningModule.configure_callbacks` to enable the definition of model-specific callbacks ([#5621](https://github.com/PyTorchLightning/pytorch-lightning/pull/5621))


		- Added `dim` to `PSNR` metric for mean-squared-error reduction ([#5957](https://github.com/PyTorchLightning/pytorch-lightning/pull/5957))


		- Added promxial policy optimization template to pl_examples ([#5394](https://github.com/PyTorchLightning/pytorch-lightning/pull/5394))


Expand Down