Create variables on correct devices and with correct types

[zakajd]

Related to #288

Proposed Changes

• Propagate input dtype though whole computation pipeline and cast intermediate kernels / filters to it.
• Add tests to verify that for float32 and float64. float16 is not included, because not all operations are defined for CPU.

[codecov[bot]]

Codecov Report

Merging #292 (27f9cab) into master (19f0d92) will decrease coverage by 2.33%. The diff coverage is 100.00%.

@@            Coverage Diff             @@
##           master     #292      +/-   ##
==========================================
- Coverage   93.35%   91.02%   -2.34%     
==========================================
  Files          33       33              
  Lines        2289     2284       -5     
==========================================
- Hits         2137     2079      -58     
- Misses        152      205      +53     

Flag	Coverage Δ
unittests	91.02% <100.00%> (-2.34%)	:arrow_down:

Flags with carried forward coverage won't be shown. Click here to find out more.

Impacted Files	Coverage Δ
piq/brisque.py	99.01% <100.00%> (ø)
piq/dss.py	100.00% <100.00%> (+1.03%)	:arrow_up:
piq/fsim.py	100.00% <100.00%> (ø)
piq/functional/filters.py	100.00% <100.00%> (ø)
piq/ms_ssim.py	100.00% <100.00%> (ø)
piq/ssim.py	100.00% <100.00%> (ø)
piq/vsi.py	100.00% <100.00%> (ø)
piq/gs.py	32.96% <0.00%> (-59.35%)	:arrow_down:

[sonarcloud[bot]]

Kudos, SonarCloud Quality Gate passed!   

0 Bugs
0 Vulnerabilities
0 Security Hotspots
0 Code Smells

No Coverage information
0.0% Duplication

[zakajd]

As discussed with Denis and Sergei, we won't create kernels as torch.DoubleTensor by default.

Reasoning: It's much more common to have single precision float tensors as inputs. So having kernels in float64 will create a lot of casting back to float32 every time metrics are computed.

This PR adds support for float64 input inference and slight loss of precision due to kernel's dtype is not considered important.

@denproc please review again and merge.