134 lines
4.7 KiB
Python
134 lines
4.7 KiB
Python
# 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
|
|
|
|
import numpy as np
|
|
import pytest
|
|
import torch
|
|
from skimage.metrics import peak_signal_noise_ratio
|
|
|
|
from pytorch_lightning.metrics.functional import psnr
|
|
from pytorch_lightning.metrics.regression import PSNR
|
|
from tests.metrics.utils import BATCH_SIZE, MetricTester, NUM_BATCHES
|
|
|
|
torch.manual_seed(42)
|
|
|
|
Input = namedtuple('Input', ["preds", "target"])
|
|
|
|
_input_size = (NUM_BATCHES, BATCH_SIZE, 32, 32)
|
|
_inputs = [
|
|
Input(
|
|
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 _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_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, reduction, dim",
|
|
[
|
|
(_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_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, 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, 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, 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, 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)
|