File size: 8,515 Bytes
9c6594c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
from collections.abc import Sequence
from typing import Any, Optional, Union

from typing_extensions import Literal

from torchmetrics.image.d_lambda import SpectralDistortionIndex
from torchmetrics.image.ergas import ErrorRelativeGlobalDimensionlessSynthesis
from torchmetrics.image.psnr import PeakSignalNoiseRatio
from torchmetrics.image.rase import RelativeAverageSpectralError
from torchmetrics.image.rmse_sw import RootMeanSquaredErrorUsingSlidingWindow
from torchmetrics.image.sam import SpectralAngleMapper
from torchmetrics.image.ssim import MultiScaleStructuralSimilarityIndexMeasure, StructuralSimilarityIndexMeasure
from torchmetrics.image.tv import TotalVariation
from torchmetrics.image.uqi import UniversalImageQualityIndex
from torchmetrics.utilities.prints import _deprecated_root_import_class


class _ErrorRelativeGlobalDimensionlessSynthesis(ErrorRelativeGlobalDimensionlessSynthesis):
    """Wrapper for deprecated import.

    >>> from torch import rand
    >>> preds = rand([16, 1, 16, 16])
    >>> target = preds * 0.75
    >>> ergas = _ErrorRelativeGlobalDimensionlessSynthesis()
    >>> ergas(preds, target).round()
    tensor(10.)

    """

    def __init__(
        self,
        ratio: float = 4,
        reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
        **kwargs: Any,
    ) -> None:
        _deprecated_root_import_class("ErrorRelativeGlobalDimensionlessSynthesis", "image")
        super().__init__(ratio=ratio, reduction=reduction, **kwargs)


class _MultiScaleStructuralSimilarityIndexMeasure(MultiScaleStructuralSimilarityIndexMeasure):
    """Wrapper for deprecated import.

    >>> from torch import rand
    >>> preds = rand([3, 3, 256, 256])
    >>> target = preds * 0.75
    >>> ms_ssim = _MultiScaleStructuralSimilarityIndexMeasure(data_range=1.0)
    >>> ms_ssim(preds, target)
    tensor(0.9628)

    """

    def __init__(
        self,
        gaussian_kernel: bool = True,
        kernel_size: Union[int, Sequence[int]] = 11,
        sigma: Union[float, Sequence[float]] = 1.5,
        reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
        data_range: Optional[Union[float, tuple[float, float]]] = None,
        k1: float = 0.01,
        k2: float = 0.03,
        betas: tuple[float, ...] = (0.0448, 0.2856, 0.3001, 0.2363, 0.1333),
        normalize: Literal["relu", "simple", None] = "relu",
        **kwargs: Any,
    ) -> None:
        _deprecated_root_import_class("MultiScaleStructuralSimilarityIndexMeasure", "image")
        super().__init__(
            gaussian_kernel=gaussian_kernel,
            kernel_size=kernel_size,
            sigma=sigma,
            reduction=reduction,
            data_range=data_range,
            k1=k1,
            k2=k2,
            betas=betas,
            normalize=normalize,
            **kwargs,
        )


class _PeakSignalNoiseRatio(PeakSignalNoiseRatio):
    """Wrapper for deprecated import.

    >>> from torch import tensor
    >>> psnr = _PeakSignalNoiseRatio()
    >>> preds = tensor([[0.0, 1.0], [2.0, 3.0]])
    >>> target = tensor([[3.0, 2.0], [1.0, 0.0]])
    >>> psnr(preds, target)
    tensor(2.5527)

    """

    def __init__(
        self,
        data_range: Optional[Union[float, tuple[float, float]]] = None,
        base: float = 10.0,
        reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
        dim: Optional[Union[int, tuple[int, ...]]] = None,
        **kwargs: Any,
    ) -> None:
        _deprecated_root_import_class("PeakSignalNoiseRatio", "image")
        super().__init__(data_range=data_range, base=base, reduction=reduction, dim=dim, **kwargs)


class _RelativeAverageSpectralError(RelativeAverageSpectralError):
    """Wrapper for deprecated import.

    >>> from torch import rand
    >>> preds = rand(4, 3, 16, 16)
    >>> target = rand(4, 3, 16, 16)
    >>> rase = _RelativeAverageSpectralError()
    >>> rase(preds, target)
    tensor(5326.40...)

    """

    def __init__(
        self,
        window_size: int = 8,
        **kwargs: dict[str, Any],
    ) -> None:
        _deprecated_root_import_class("RelativeAverageSpectralError", "image")
        super().__init__(window_size=window_size, **kwargs)


class _RootMeanSquaredErrorUsingSlidingWindow(RootMeanSquaredErrorUsingSlidingWindow):
    """Wrapper for deprecated import.

    >>> from torch import rand
    >>> preds = rand(4, 3, 16, 16)
    >>> target = rand(4, 3, 16, 16)
    >>> rmse_sw = RootMeanSquaredErrorUsingSlidingWindow()
    >>> rmse_sw(preds, target)
    tensor(0.4158)

    """

    def __init__(
        self,
        window_size: int = 8,
        **kwargs: dict[str, Any],
    ) -> None:
        _deprecated_root_import_class("RootMeanSquaredErrorUsingSlidingWindow", "image")
        super().__init__(window_size=window_size, **kwargs)


class _SpectralAngleMapper(SpectralAngleMapper):
    """Wrapper for deprecated import.

    >>> from torch import rand
    >>> preds = rand([16, 3, 16, 16])
    >>> target = rand([16, 3, 16, 16])
    >>> sam = _SpectralAngleMapper()
    >>> sam(preds, target)
    tensor(0.5914)

    """

    def __init__(
        self,
        reduction: Literal["elementwise_mean", "sum", "none"] = "elementwise_mean",
        **kwargs: Any,
    ) -> None:
        _deprecated_root_import_class("SpectralAngleMapper", "image")
        super().__init__(reduction=reduction, **kwargs)


class _SpectralDistortionIndex(SpectralDistortionIndex):
    """Wrapper for deprecated import.

    >>> from torch import rand
    >>> preds = rand([16, 3, 16, 16])
    >>> target = rand([16, 3, 16, 16])
    >>> sdi = _SpectralDistortionIndex()
    >>> sdi(preds, target)
    tensor(0.0234)

    """

    def __init__(
        self, p: int = 1, reduction: Literal["elementwise_mean", "sum", "none"] = "elementwise_mean", **kwargs: Any
    ) -> None:
        _deprecated_root_import_class("SpectralDistortionIndex", "image")
        super().__init__(p=p, reduction=reduction, **kwargs)


class _StructuralSimilarityIndexMeasure(StructuralSimilarityIndexMeasure):
    """Wrapper for deprecated import.

    >>> import torch
    >>> preds = torch.rand([3, 3, 256, 256])
    >>> target = preds * 0.75
    >>> ssim = _StructuralSimilarityIndexMeasure(data_range=1.0)
    >>> ssim(preds, target)
    tensor(0.9219)

    """

    def __init__(
        self,
        gaussian_kernel: bool = True,
        sigma: Union[float, Sequence[float]] = 1.5,
        kernel_size: Union[int, Sequence[int]] = 11,
        reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
        data_range: Optional[Union[float, tuple[float, float]]] = None,
        k1: float = 0.01,
        k2: float = 0.03,
        return_full_image: bool = False,
        return_contrast_sensitivity: bool = False,
        **kwargs: Any,
    ) -> None:
        _deprecated_root_import_class("StructuralSimilarityIndexMeasure", "image")
        super().__init__(
            gaussian_kernel=gaussian_kernel,
            sigma=sigma,
            kernel_size=kernel_size,
            reduction=reduction,
            data_range=data_range,
            k1=k1,
            k2=k2,
            return_full_image=return_full_image,
            return_contrast_sensitivity=return_contrast_sensitivity,
            **kwargs,
        )


class _TotalVariation(TotalVariation):
    """Wrapper for deprecated import.

    >>> from torch import rand
    >>> tv = _TotalVariation()
    >>> img = rand(5, 3, 28, 28)
    >>> tv(img)
    tensor(7546.8018)

    """

    def __init__(self, reduction: Literal["mean", "sum", "none", None] = "sum", **kwargs: Any) -> None:
        _deprecated_root_import_class("TotalVariation", "image")
        super().__init__(reduction=reduction, **kwargs)


class _UniversalImageQualityIndex(UniversalImageQualityIndex):
    """Wrapper for deprecated import.

    >>> import torch
    >>> preds = torch.rand([16, 1, 16, 16])
    >>> target = preds * 0.75
    >>> uqi = _UniversalImageQualityIndex()
    >>> uqi(preds, target)
    tensor(0.9216)

    """

    def __init__(
        self,
        kernel_size: Sequence[int] = (11, 11),
        sigma: Sequence[float] = (1.5, 1.5),
        reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
        **kwargs: Any,
    ) -> None:
        _deprecated_root_import_class("UniversalImageQualityIndex", "image")
        super().__init__(kernel_size=kernel_size, sigma=sigma, reduction=reduction, **kwargs)