Enhancement: Added interface for GMSD and Multi-Scale GMSD (#151)

* enh(gmsd): added functions to calculate GMSD and MS-GMSD

* enh(gmsd): hit 100% coverage

* enh(gmsd): Made changes to align the GMSD with MATLAB version
- tests to validate values with MATLAB results
- changed the valid colour transfer of the images before any pooling
- changes proposed by @zalajd

* enh(gmsd): changed the name of the constant to match the paper

* enh(ci): changes proposed by @snk4tr

README.md

@@ -197,6 +197,18 @@ By default input images are normalized with ImageNet statistics before forwardin
  This is port of MATLAB version from the authors of original paper.
  It can be used both as a measure and as a loss function. In any case it should me minimized.
  Usually values of GMSD lie in [0, 0.35] interval.
+
+ To compute GMSD as a measure, use lower case function from the library:
+ ```python
+ import torch
+ from piq import gmsd
+
+ prediction = torch.rand(3, 3, 256, 256)
+ target = torch.rand(3, 3, 256, 256)
+ gmsd: torch.Tensor = gmsd(prediction, target, data_range=1.)
+ ```
+
+ In order to use GMSD as a loss function, use corresponding PyTorch module:
  ```python
  import torch
  from piq import GMSDLoss
@@ -384,11 +396,25 @@ Now LPIPS is supported only for VGG16 model. If you need other models, check [or
 
  <!-- MultiScale GMSD EXAMPLES -->
  <details>
- <summary>MultiScale GMSD (MS-GMSD)</summary>
+ <summary>Multi-Scale GMSD (MS-GMSD)</summary>
  <p>
 
  It can be used both as a measure and as a loss function. In any case it should me minimized.
- By defualt scale weights are initialized with values from the paper. You can change them by passing a list of 4 variables to `scale_weights` argument during initialization. Both GMSD and MS-GMSD computed for greyscale images, but to take contrast changes into account authors propoced to also add chromatic component. Use flag `chromatic` to use MS-GMSDc version of the loss
+ By defualt scale weights are initialized with values from the paper. You can change them by passing a list of 4 variables to `scale_weights` argument during initialization. Both GMSD and MS-GMSD computed for greyscale images, but to take contrast changes into account authors propoced to also add chromatic component. Use flag `chromatic` to use MS-GMSDc version of the loss.
+
+ Note that input tensors should contain images with height and width equal `2 ** number_of_scales + 1` at least.
+
+ To compute Multi-Scale GMSD as a measure, use lower case function from the library:
+ ```python
+ import torch
+ from piq import multi_scale_gmsd
+
+ prediction = torch.rand(3, 3, 256, 256)
+ target = torch.rand(3, 3, 256, 256)
+ multi_scale_gmsd: torch.Tensor = multi_scale_gmsd(prediction, target, data_range=1.)
+ ```
+
+ In order to use Multi-Scale GMSD as a loss function, use corresponding PyTorch module:
  ```python
  import torch
  from piq import MultiScaleGMSDLoss

piq/__init__.py

@@ -5,7 +5,7 @@
 from .fid import FID
 from .kid import KID
 from .tv import TVLoss, total_variation
-from .gmsd import GMSDLoss, MultiScaleGMSDLoss
+from .gmsd import gmsd, multi_scale_gmsd, GMSDLoss, MultiScaleGMSDLoss
 from .gs import GS
 from .isc import IS, inception_score
 from .vif import VIFLoss, vif_p