Metadata-Version: 2.4
Name: frame-analytics
Version: 0.3.0
Summary: Fast MSE, PSNR and SSIM for PyTorch (CPU + CUDA) at float64-reference accuracy
Author: Nilas
License-Expression: Apache-2.0
Project-URL: Homepage, https://github.com/NevermindNilas/frame_analytics
Project-URL: Source, https://github.com/NevermindNilas/frame_analytics
Project-URL: Issues, https://github.com/NevermindNilas/frame_analytics/issues
Keywords: ssim,psnr,mse,image-quality,video-quality,metrics,pytorch,cuda,perceptual-loss
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: C++
Classifier: Environment :: GPU :: NVIDIA CUDA
Classifier: Topic :: Scientific/Engineering :: Image Processing
Classifier: Topic :: Multimedia :: Video
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: torch>=2.0
Requires-Dist: numpy>=1.20
Provides-Extra: bench
Requires-Dist: scikit-image; extra == "bench"
Requires-Dist: opencv-python; extra == "bench"
Requires-Dist: pytorch-msssim; extra == "bench"
Requires-Dist: piq; extra == "bench"
Requires-Dist: torchmetrics; extra == "bench"
Requires-Dist: kornia; extra == "bench"
Provides-Extra: test
Requires-Dist: pytest; extra == "test"
Requires-Dist: scikit-image; extra == "test"
Dynamic: license-file

# frame_analytics

Image and video quality metrics for PyTorch — CPU and CUDA — at float64-reference
accuracy. Fused kernels for forward and backward, so the same code works as an
evaluation metric and as a training loss.

```bash
pip install frame-analytics
```

```python
import torch, frame_analytics as fa

a = torch.randint(0, 256, (8, 3, 1080, 1920), dtype=torch.uint8, device="cuda")
b = torch.randint(0, 256, (8, 3, 1080, 1920), dtype=torch.uint8, device="cuda")

fa.mse(a, b)
fa.psnr(a, b, data_range=255.0)
fa.ssim(a, b, data_range=255.0)      # Wang et al. 2004, exactly
fa.ms_ssim(a, b, data_range=255.0)   # Wang et al. 2003
fa.gmsd(a, b, data_range=255.0)      # Xue et al. 2014
fa.lpips(a, b, data_range=255.0)     # Zhang et al. 2018, weights included
fa.l1(a, b); fa.charbonnier(a, b); fa.huber(a, b)

fa.ssim(a, b, reduction="none")      # per-image, (8,)
fa.ssim(a, b, return_map=True)       # (8, 3, 1070, 1910)

# the convention the super-resolution literature reports
fa.psnr(a, b, data_range=255.0, luma="matlab", crop_border=4)
```

Accepts `(H,W)` / `(C,H,W)` / `(N,C,H,W)`, uint8 through float64. uint8 stays
uint8 into the kernel — 2 bytes/pixel of bandwidth instead of 8.

## Speed

Speedup over each library, across 512²→4K and batch 1→8, RTX 3090 / 16-thread CPU:

| | SSIM | MS-SSIM | GMSD | PSNR / MSE |
|---|---|---|---|---|
| pytorch-msssim | 16–22× | 7.5–14× | — | — |
| kornia | 18–32× | — | — | 2.0–8.5× |
| torchmetrics | 24–28× | — | — | 4.9–14× |
| piq | 25–30× | 6.7–20× | 40–50× | 12–37× |
| scikit-image (CPU) | 21–29× | — | — | 170–920× |
| OpenCV recipe (CPU) | 6.0–7.6× | — | — | 0.7–7.3× (`cv2.PSNR`) |
| [fused-ssim](https://github.com/rahul-goel/fused-ssim) (CUDA) | 1.2–1.9× | — | — | — |
| torch built-ins (L1 / Huber) | — | — | — | 2.4–100× |

Selected absolute numbers, RTX 3090, ms/call (lower is better):

| CUDA, uint8 in | 512² | 1080p | 1080p ×8 | 4K ×8 |
|---|---:|---:|---:|---:|
| SSIM | 0.024 | 0.085 | 0.651 | 2.642 |
| PSNR | 0.020 | 0.028 | 0.045 | 0.151 |
| MS-SSIM | 0.331 | 0.512 | 3.088 | — |
| GMSD | 0.023 | 0.055 | 0.313 | — |
| L1 | 0.021 | 0.026 | 0.115 | — |

SSIM sustains 25.6 Gpixel/s (~11 700 fps at 1080p). PSNR at 4K ×8 hits 880 GB/s
— 94% of the card's theoretical bandwidth, the ceiling for anything that must
read both frames.

| CPU, uint8 in, ms/call | 512² | 1080p | 1080p ×8 | 4K ×8 |
|---|---:|---:|---:|---:|
| **PSNR**, 1 channel | **0.008** | **0.015** | **0.102** | **1.41** |
| `cv2.PSNR`, per frame | 0.006 | 0.049 | 0.740 | 4.24 |
| kornia | 0.046 | 0.114 | 3.29 | 14.4 |
| torchmetrics | 0.081 | 0.195 | 5.52 | 22.7 |
| scikit-image | 1.43 | 11.7 | 93.5 | 381 |
| | | | | |
| **L1**, RGB | **0.011** | **0.039** | **1.03** | — |
| `F.l1_loss` | 0.193 | 3.05 | 30.1 | — |

Streaming 1080p RGB via CUDA graphs, host uint8 in, python float out: **931 fps**
(2 941 fps for resident tensors).

Full per-size CPU and CUDA tables: `python bench/bench.py`.

### Where it doesn't win

- `cv2.PSNR` on one sub-megapixel single-channel frame: 0.006 ms against our
  0.008. Not the kernel — that runs the same 262 144 pixels in 3.8 µs, less than
  `cv2.PSNR` takes for the whole call — but the ~4 µs of Python in front of it,
  which is a fixed cost and so only visible when there is nothing else to pay
  for. One frame bigger, or one channel wider, and it inverts: 3.3× at 1080p,
  7.3× at 1080p ×8.
- **Without the compiled extension** the portable PyTorch fallback is only ~1.1×
  faster than `pytorch-msssim` and uses *more* memory. The speed claims are
  claims about the kernels; the fallback exists to be correct, not to win.

## Memory

Peak allocation *above the two input frames*, RTX 3090, 3-channel, MiB per
call. Nothing here is the frames themselves — those you already have:

| CUDA, forward | 1080p | 1080p ×8 | 4K ×8 |
|---|---:|---:|---:|
| **SSIM** | **0.02** | **0.19** | **0.75** |
| pytorch-msssim | 288 | 2253 | 9048 |
| piq | 336 | 2633 | 10568 |
| kornia | 360 | 2850 | 11397 |
| torchmetrics | 550 | 4388 | 17509 |
| | | | |
| **MS-SSIM** | **15.5** | **120** | **476** |
| pytorch-msssim | 288 | 2253 | 9048 |
| piq | 336 | 2633 | 10568 |
| | | | |
| **GMSD** | **0.02** | **0.19** | **0.75** |
| piq | 72 | 760 | 3040 |
| | | | |
| **PSNR / MSE / L1 / Charbonnier** | **0.007** | **0.007** | **0.007** |
| torchmetrics, kornia, `F.l1_loss` | 48 | 380 | 1520 |

A fused kernel has nowhere to put an intermediate. What is left for SSIM and
GMSD is the per-block partial sums — sized by the tile grid, so 4K ×8 costs
0.75 MiB where the alternatives cost 9–17 GiB — and for the pixel metrics, the
output scalar. The libraries above are not doing anything wrong; five or six
frame-shaped temporaries is what the textbook formulation asks for, and 2253
MiB at 1080p ×8 is almost exactly 6× the 380 MiB input.

MS-SSIM is the one metric that must materialise something: the four
downsampled copies of both frames. That geometric series sums to a third of a
frame each, and nothing full-resolution survives — 120 MiB at 1080p ×8, where
the frame pair itself is 380.

The CPU story is the same one. There is no `max_memory_allocated` for host
memory, so these are peak process footprints, each measured in its own
interpreter — how much more memory the OS had to hand over, which is the
number that decides whether the box swaps:

| CPU, forward | 1080p | 1080p ×8 |
|---|---:|---:|
| **SSIM** | **2.0** | **2.4** |
| pytorch-msssim | 433 | 2796 |
| piq | 446 | 3256 |
| kornia | 457 | 3305 |
| torchmetrics | 915 | 7026 |
| | | |
| **MS-SSIM**, uint8 in | **8.9** | **10.6** |
| **MS-SSIM**, fp32 in | 16.8 | 127 |
| pytorch-msssim | 462 | 2898 |
| piq | 471 | 3256 |
| | | |
| **GMSD** | **0.11** | **0.69** |
| piq | 84 | 793 |
| | | |
| **PSNR / MSE / L1** | **<0.01** | **<0.01** |
| kornia (PSNR) | 3.0 | 190 |
| torchmetrics, `F.l1_loss` | 27–28 | 380 |

The pixel metrics reduce two buffers to a scalar and allocate nothing at all.
SSIM's 2 MiB does not move when the pixel count goes up 8×, so it is fixed
working set rather than anything per-pixel. MS-SSIM is cheaper from uint8 than
from float because the cast is fused into the first pooling step: fed uint8 it
never materialises a full-resolution float copy of either frame, and only the
pyramid survives.

| CUDA, loss step (fwd + bwd, fp32) | 1080p | 1080p ×8 | 4K ×8 |
|---|---:|---:|---:|
| **SSIM** | **94** | **752** | **3022** |
| pytorch-msssim | 480 | 3763 | 15098 |
| piq | 456 | 3579 | 14350 |
| | | | |
| **MS-SSIM** | **118** | **942** | **3782** |
| pytorch-msssim | 480 | 3763 | 15098 |
| piq | 456 | 3579 | 14350 |
| | | | |
| **GMSD** | 78 | **616** | **2465** |
| piq | 72 | 760 | 3040 |
| | | | |
| **L1** | **24** | **190** | **760** |
| `F.l1_loss` | 96 | 760 | 3040 |

Every row includes the gradient with respect to the prediction — 24 MiB at
1080p, 760 at 4K ×8 — which is unavoidable and which every implementation
pays. For `fa.l1` it is the *entire* figure: the backward writes the gradient
and allocates nothing else. SSIM costs three more planes on top of it and
MS-SSIM four, at every size — the fused backward recomputes the local moments
instead of saving them, while the autograd implementations keep the forward's
activations, which is where the 15 GiB comes from. GMSD on a single 1080p
frame is the one row that loses, by 6 MiB.

`StreamingMetrics` at 1080p RGB holds **18 MiB** — the two device frames plus
the CUDA graph's private pool — and allocates **nothing** per frame, whether
it is scoring one metric or nine.

Without the compiled extension the portable path allocates like the rest of
the field, and slightly worse than the best of it: 2830 MiB for SSIM at
1080p ×8, and 4.5–4.7 GiB as a loss step depending on what `torch.compile`
managed to fuse.

Full tables: `python bench/bench_memory.py`.

## Accuracy

Defaults reproduce `ssim_index.m`: 11×11 Gaussian, σ=1.5, K=(0.01, 0.03),
`valid` support. Every kernel is gated against a float64 transcription of the
paper (`python tests/validate.py`).

| implementation | SSIM abs. error |
|---|---:|
| **frame_analytics** (CUDA / CPU / portable) | **2.4e-09 / 3.1e-09 / 6.9e-09** |
| pytorch-msssim | 1.0e-07 |
| fused-ssim (`padding="valid"`) | 3.3e-06 |
| kornia | 2.1e-05 |
| torchmetrics | 2.2e-05 |
| scikit-image (defaults) | 1.1e-03 |
| fused-ssim (`padding="same"`, its default) | 3.0e-03 |
| piq (defaults) | 8.8e-03 |

The bottom entries aren't bugs — different windows, MATLAB-style downsampling,
zero padding. Defensible defaults, different metric.

MSE and PSNR sum in float64 whatever the input is typed as — a 4K frame has
8.3M residuals, and summing those in float32 would lose ~4 significant digits
straight into the dB figure. Squaring the residual is a separate question from
summing it, and the two backends answer it differently:

- uint8, and float64 input, are **exact** on both backends — an integer
  accumulator for the first, a float64 residual for the second.
- float16/float32 input is exact on CPU, and squared in float32 on CUDA. That
  costs at most one float32 ulp on the term, ~6e-08 relative worst case and
  ~1e-10 on real frames, so CPU and CUDA MSE agree to about 1e-08 rather than
  bit-for-bit. It buys up to 2x: a GA102 retires float64 at 1/64 the float
  rate, and a float64 residual left the kernel 2-3x over its DRAM floor with
  fp16 input no faster than fp32.

Worst abs. error vs float64 reference, over CPU and CUDA, native and portable:

| metric | error |
|---|---:|
| MS-SSIM | 3.7e-08 |
| GMSD | 2.8e-09 |
| L1, Huber (uint8) | 8.9e-16 — exact, integer accumulator |
| MSE, PSNR (uint8, float64) | exact |
| MSE, PSNR (float16/32, CUDA) | 2.4e-08 relative — 1.0e-07 dB |
| Charbonnier | 1.3e-07 |
| LPIPS | 2.1e-08 (float32) / 3.1e-10 (float64) |

GMSD's similarity map is float32, so deviations below ~1e-7 measure rounding,
not the images (`gmsd(x, x)` ≈ 3e-08, not 0). A typical GMSD is ~0.03.

## LPIPS

PSNR, SSIM and MS-SSIM all reward blur, so a model trained on them alone
converges to something smooth. LPIPS (Zhang et al. 2018) is the term that
punishes that, and the third number every super-resolution and restoration
paper reports.

```python
fa.lpips(a, b, data_range=255.0)          # lower is better, 0 is identical
crit = fa.LPIPS(data_range=1.0)
loss = fa.L1()(pred, target) + 0.1 * crit.loss(pred, target)
```

**The weights ship in the wheel.** 9.4 MiB, float32, no download, no
`torchvision` dependency, works with no network at all. Every other library
pulls the trunk from `torchvision.models` at first call, which means a
**233 MiB** download for AlexNet (528 MiB for VGG) into the torch hub cache.
That checkpoint is ~94% classifier head; LPIPS taps five post-ReLU activations
out of the conv trunk and discards the rest, so what actually gets used is
2.47M parameters. Only those ship. See
[`frame_analytics/weights/`](frame_analytics/weights/) for provenance and the
upstream BSD notices, and `tools/build_lpips_weights.py` for the extraction.

The `alex` trunk is the one the literature reports and the only one bundled.
VGG is deliberately absent — 56 MiB for the training-loss variant. Point
`FA_LPIPS_WEIGHTS` at a blob of the same layout to supply your own.

### It is also more accurate than the alternatives, which is why it is faster

`torch.backends.cudnn.allow_tf32` defaults to **True**, so on Ampere and later
every LPIPS implementation that leaves it alone runs its convolutions with a
10-bit mantissa. Measured against a float64 transcription of the forward:

| | worst abs. error |
|---|---:|
| **frame_analytics** | **1.5e-08** |
| frame_analytics, `allow_tf32=True` | 1.5e-05 |
| `lpips` (the reference package) | 1.5e-05 |
| torchmetrics | 1.5e-05 |

1.5e-05 is the fourth decimal — the decimal LPIPS is printed to. We turn TF32
off by default; `allow_tf32=True` is there if you measure that you need it.

On this card you do not: RTX 3090, ms/call, fp32 in, and the `tf32` column is
our own with the default flipped.

| forward | 1×256² | 1×512² | 1×1080p | 8×256² | 8×512² |
|---|---:|---:|---:|---:|---:|
| **frame_analytics** | **1.31** | **1.50** | **10.4** | **2.40** | **8.34** |
| frame_analytics (tf32) | 1.39 | 2.82 | 16.2 | 4.54 | 15.0 |
| `lpips` package | 1.91 | 3.25 | 15.8 | 4.84 | 16.2 |
| torchmetrics | 2.58 | 4.10 | 17.2 | 5.77 | 17.6 |
| piq (VGG trunk — a different metric) | 6.32 | 21.6 | 146 | 37.1 | 138 |

**1.5–2.2× faster than the reference package while being 1000× closer to the
reference forward.** The convolutions are the same cuDNN calls everyone makes;
the win is the tail — unit-normalise, square the difference, apply the channel
weights, reduce — fused into one region per layer instead of ~10 separate
elementwise kernels. That is also where the memory goes:

| peak MiB above the input pair | 1×512² | 1×1080p | 8×256² | 8×512² |
|---|---:|---:|---:|---:|
| **frame_analytics** | **35.5** | **249** | **62.4** | **245** |
| `lpips` package, torchmetrics | 65.1 | 520 | 127 | 514 |
| piq (VGG trunk) | 430 | 3403 | 860 | 3440 |

As a loss step (forward + backward), 1080p: **17.9 ms / 445 MiB** against the
reference package's 25.0 ms / 616 MiB.

Full tables: `python bench/bench_lpips.py`.

Caveats:

- LPIPS has no paper formula to be correct against — it *is* its weights. It is
  gated two independent ways: against `richzhang/PerceptualSimilarity` itself
  (agreement to 3e-08, i.e. float32 rounding), and against a float64 numpy
  transcription of the forward in `reference.py` that shares no code with the
  torch path.
- `allow_tf32` covers the forward only. The backward's convolutions run under
  whatever cuDNN setting is live when `.backward()` is called — deliberate, in
  that the reported number is what has to survive to four decimals and a loss
  gradient does not.
- 3-channel RGB is the calibrated case. 1-channel input is replicated to three,
  which is what the field does with grayscale but is not something the human
  judgements behind the weights ever covered.
- Minimum 32×32 after cropping; the stride-4 conv and two max-pools leave
  nothing behind below that.

## Training

Every metric is differentiable, and the SSIM family has a fused CUDA backward
rather than autograd over the portable path — which is where the memory goes:

```python
crit_ms = fa.MSSSIM(data_range=1.0)
crit_l1 = fa.L1()
loss = 0.84 * crit_ms.loss(pred, target) + 0.16 * crit_l1(pred, target)
loss.backward()
```

That mix is Zhao et al., *Loss Functions for Image Restoration with Neural
Networks* (2016) — the reason MS-SSIM is here at all. Both terms still reward
blur; add [LPIPS](#lpips) if that matters for what you are training.

```python
loss = 0.84 * crit_ms.loss(pred, target) + 0.16 * crit_l1(pred, target) \
     + 0.10 * fa.LPIPS(data_range=1.0).loss(pred, target)
```

`fa.LPIPS` holds its 2.47M trunk weights as buffers, not parameters, so
`.parameters()` and `.state_dict()` are both empty: it cannot leak frozen
ImageNet weights into your optimiser or your checkpoint. The weights are also
shared process-wide per (trunk, device, dtype), so constructing several costs
nothing after the first.

MS-SSIM loss step, 1080p ×8, float32 RGB: **12.7 ms / 942 MiB** against
pytorch-msssim's 81.3 ms / 3763 MiB — **6.4× faster on 4.0× less memory**. The
fused backward recomputes the local moments instead of storing them, so nothing
full-resolution survives the forward pass at any of the five scales. Both
figures include the 190 MiB gradient neither implementation can avoid; see
[Memory](#memory).

Gradients are verified two independent ways — against autograd over the portable
path, and against central differences on the float64 reference forward — both to
~1e-6 relative. The scalar-reduction backward issues no device→host sync, so it
does not stall the training pipeline.

Caveats:

- `GMSD.loss()` returns `1 − mean(GMS)`, not the deviation. `d√var/dvar` is
  unbounded as the variance goes to zero, which is exactly where a converging
  model lives; the mean is the well-behaved objective from the same map.
  `fa.gmsd()` still gives you the published metric.
- MS-SSIM's per-scale factors are clamped at zero, so on anti-correlated content
  the value **and its gradient** are exactly zero. Standard formulation, every
  implementation clamps — but MS-SSIM alone cannot pull a diverged model back.
  The L1 term above removes the dead zone.
- The native backward is CUDA + float32 only; CPU, float64, uint8 and
  `downsample=True` fall back to autograd over the portable path.
- The fused backward is a kernel, not a graph, so `create_graph=True` (gradient
  penalties, HVPs, `torch.func.hessian`) raises rather than silently returning
  zeros. For a second derivative use `backend_hint="torch"` with
  `fa.set_compile_enabled(False)`.

## Reporting conventions

`luma=` and `crop_border=` are on every metric. Nearly every super-resolution
and restoration paper reports PSNR/SSIM on the luma plane of a border-cropped
frame, so a library without them produces right-looking numbers that quietly
disagree with the literature.

```python
fa.psnr(a, b, data_range=255.0, luma="matlab", crop_border=scale)   # Y-PSNR
fa.ssim(a, b, data_range=255.0, luma="matlab", crop_border=scale)   # Y-SSIM
```

`"matlab"` is the studio-range Y′ of MATLAB's `rgb2ycbcr`, i.e. what BasicSR's
`test_y_channel=True` computes. `"bt601"` and `"bt709"` are the full-range
definitions. The crop is a view, so it costs nothing.

## Install

```bash
pip install frame-analytics
```

torch ≥ 2.0, numpy — and nothing else. No `torchvision`, and nothing is fetched
at first call: the LPIPS weights are inside the wheel. It is 9.7 MiB, of which
8.8 is those weights and 0.8 is the kernels.

The kernels arrive **precompiled**, one wheel per platform and no version
matrix — the same wheel works on every Python and every torch build.

That is not the usual arrangement, because the usual arrangement cannot be
published. A torch C++ extension is ABI-locked to the exact {python} × {torch} ×
{CUDA} it was compiled against, and a wheel filename has nowhere to record
"torch 2.6": pip would match on Python and platform alone and hand you an
`undefined symbol` ImportError. So the kernels sit behind a plain C ABI instead
— raw pointers, an int dtype code, a stream handle, int return codes — and the
binaries link neither libtorch nor libpython. On Windows they import exactly one
library, `KERNEL32.dll`; on Linux, glibc. An NVIDIA driver at runtime is the
only external requirement, and only for the CUDA half.

Install on a platform with no wheel and the sources — which ship too — compile
on first call (~1 min, cached thereafter). No compiler at all and every native
kernel still has a portable PyTorch fallback returning the same numbers.
`fa.backend_status()` reports which tier is live and where its binary came from;
`backend_hint="torch"` / `"native"` forces either. On Windows the MSVC build
environment is located automatically.

To compile from source at install time instead:

```bash
FA_BUILD_EXT=1 FA_CUDA_ARCHS="8.6 9.0+PTX" pip install frame-analytics
```

From a checkout:

```bash
pip install -e .
python tests/validate.py           # accuracy gate
python bench/bench.py              # speed + accuracy tables
python bench/bench_training.py     # MS-SSIM / GMSD / pixel losses
python bench/bench_memory.py       # peak memory, forward and loss step
python bench/bench_fused_ssim.py   # head-to-head vs fused-ssim
python bench/bench_lpips.py        # LPIPS vs lpips / torchmetrics / piq
```

The LPIPS weight blob is checked in, not generated at install time — building
it needs `torchvision` and `lpips`, which no CI runner has. To rebuild it:

```bash
pip install torchvision lpips
python tools/build_lpips_weights.py
```

## API

Every metric takes `reduction` (`"mean"` → scalar, `"none"` → per-image `(N,)`),
`dtype`, `luma`, `crop_border` and — where there is a kernel — `backend_hint`.

```python
mse        (x, y, *, reduction="mean", dtype=None, out_dtype=torch.float64,
            luma=None, crop_border=0)
psnr       (x, y, *, data_range=None, eps=0.0, ...)
ssim       (x, y, *, data_range=None, win_size=11, sigma=1.5, K=(0.01, 0.03),
            return_map=False, downsample=False, backend_hint="auto", ...)
ms_ssim    (x, y, *, data_range=None, win_size=11, sigma=1.5, K=(0.01, 0.03),
            weights=MS_SSIM_WEIGHTS, backend_hint="auto", ...)
gmsd       (x, y, *, data_range=None, T=None, eps=None, downsample=True,
            return_map=False, backend_hint="auto", ...)
gms        (x, y, ...)                      # mean of the same map
lpips      (x, y, *, net="alex", data_range=None, return_map=False,
            allow_tf32=False, ...)
l1         (x, y, ...)
charbonnier(x, y, *, eps=1e-3, ...)         # mean sqrt(d^2 + eps^2)
huber      (x, y, *, delta=1.0, ...)        # matches torch.nn.HuberLoss
rgb_to_luma(t, mode="bt601", *, data_range=None, dtype=None)
```

`lpips` has no `backend_hint`: it is cuDNN convolutions plus a `torch.compile`d
tail, with no C-ABI kernel behind it. `fa.available_nets()` lists the trunks
present in the install and `fa.lpips_weights_path()` says where they came from.

`data_range` defaults to 255 for integer input, 1.0 for float. `downsample=True`
on `ssim` applies MATLAB `ssim.m`'s automatic box-downsample (off by default, as
in `ssim_index.m` and every PyTorch library); on `gmsd` it is the paper's 2×
prefilter and is *on* by default.

Module forms `MSE`, `PSNR`, `SSIM`, `MSSSIM`, `GMSD`, `L1`, `Charbonnier`,
`Huber`, `LPIPS` cache what they can and expose `.loss()`:

```python
crit = fa.SSIM(data_range=1.0)
loss = crit.loss(pred, target)     # 1 - SSIM, fused backward on CUDA float32
loss.backward()

sm = fa.StreamingMetrics((1, 3, 1080, 1920), device="cuda", dtype=torch.uint8,
                         metrics=("psnr", "ssim", "ms_ssim", "gmsd"))
for ref, dist in frames:
    out = sm.update(ref, dist)     # one graph replay, all four metrics
```

`StreamingMetrics` accepts any of `mse`, `psnr`, `ssim`, `ms_ssim`, `gmsd`,
`gms`, `l1`, `charbonnier`, `huber`, `lpips`; they all capture into the same
CUDA graph, so scoring a frame on ten metrics is still one replay. LPIPS is
capturable because the trunk is loaded and moved to the device during the
pre-capture warm-up, leaving convolutions and nothing else inside the captured
region.

## License

Apache 2.0 — except the bundled LPIPS weights, which carry their upstream BSD
terms; see [`frame_analytics/weights/`](frame_analytics/weights/).

Wang, Bovik, Sheikh, Simoncelli. *Image Quality Assessment: From Error
Visibility to Structural Similarity.* IEEE TIP 13(4), 2004.

Zhang, Isola, Efros, Shechtman, Wang. *The Unreasonable Effectiveness of Deep
Features as a Perceptual Metric.* CVPR 2018.
