paraug 0.7.0

Parity Augmentation — bit-exact CPU/GPU parity for image augmentation

paraug

Bit-exact CPU/GPU parity for image augmentation.

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paraug is a general-purpose PyTorch-native augmentation library. 35 primitives (7 geometric + 28 photometric, including CutMix / MixUp / GridMask / RandomErasing), GPU-batch-native, with bit-exact CPU/GPU parity: the same seed produces the same output on CPU and CUDA. Per-primitive RNG is sampled on CPU regardless of tensor device, so a training run that randomly switches between CPU and GPU stages — or a unit test that swaps backends — stays deterministic.

It's a drop-in replacement for kornia.augmentation / torchvision.v2 when you need reproducibility across heterogeneous hardware, and a batch-native alternative to albumentations when you want GPU acceleration.

Why parity matters

Most augmentation libraries (albumentations, kornia, torchvision) use device- local RNG. Same seed, different output across CPU/CUDA. This bites in three places:

1. Reproducibility: paper-to-code lineage breaks when a reviewer can't match published numbers.
2. Debugging: CPU-side unit tests don't catch GPU-only bugs and vice versa.
3. Distributed training: workers on heterogeneous hardware drift apart.

paraug fixes this by isolating RNG to CPU (torch.Generator(device="cpu")) and routing only the deterministic torch ops through device. Tolerance:

• Elementwise ops (gamma, noise, color jitter, …): atol 1e-6
• grid_sample-class ops (affine, perspective, tps, …): atol 2e-4 (bilinear ulp drift across ATen vs cuDNN)

Installation

pip install paraug

Or from source:

pip install git+https://github.com/alieuidsh/paraug.git

Quickstart

import torch
from paraug import AugPipeline

# Build your own config from the 31 primitives. Per-op `p` is independent
# (each op fires with its own probability per sample).
aug = AugPipeline({
    "geometric": {
        "affine": {"p": 1.0, "rot_deg": 15.0, "scale_range": (0.9, 1.1)},
        "tps":    {"p": 0.5, "max_disp": 12.0, "n_ctrl": 5},
    },
    "photometric": {
        "gamma":         {"p": 0.5},
        "color_jitter":  {"p": 0.5},
        "gaussian_blur": {"p": 0.3},
    },
})

# Input expectations: float tensor in [0, 1], shape (B, C, H, W).
# (Numpy HWC uint8 is also accepted; paraug normalises internally.)
img  = torch.rand(2, 3, 256, 256)         # (B, C, H, W) in [0, 1]
mask = torch.ones(2, 1, 256, 256)         # optional segmentation mask

# aug always returns a (img, mask) tuple — discard with `_` if no mask:
img_out, mask_out = aug(img, mask=mask, seed_base=42, epoch=0, step=0)
img_only, _      = aug(img,             seed_base=42, epoch=0, step=0)

Same call on GPU is bit-exact within tolerance:

img_cuda, mask_cuda = aug(img.cuda(), mask=mask.cuda(),
                           seed_base=42, epoch=0, step=0)
assert (img_out - img_cuda.cpu()).abs().max() < 2e-4

seed_base, epoch, step

These three integers compose into the per-item RNG seed (along with the item's batch position). Same triple → same output for that item.

• seed_base — run-level seed. Pin this in your config; reuse across the whole training run.
• epoch — change across epochs so the same dataset sample gets different augmentation each pass.
• step — change within an epoch so successive batches of the same underlying dataset position (rare; usually step = global_step) don't collide.

For inference / one-shot use, all three may be 0 (aug(img, seed_base=0)). The split exists so training-time augmentation is reproducible and varies along the right axes; you don't have to use all three.

Where to put paraug in your training code

The first instinct, transferred from torchvision.transforms, is to put augmentation inside Dataset.__getitem__ so each worker processes one sample at a time. Don't do this with paraug — it's a batch-native GPU library, and per-sample CPU placement throws away the GPU acceleration.

# ❌ DON'T — per-sample CPU augmentation in worker processes
class MyDataset(torch.utils.data.Dataset):
    def __init__(self):
        self.aug = AugPipeline(cfg)
    def __getitem__(self, idx):
        img = load_image(idx)                    # (C, H, W), CPU
        img, _ = self.aug(img.unsqueeze(0), seed_base=idx)   # CPU aug
        return img.squeeze(0)

# ✅ DO — Dataset loads only, train loop augments the GPU batch
class MyDataset(torch.utils.data.Dataset):
    def __getitem__(self, idx):
        return load_image(idx)                   # just I/O + resize

aug = AugPipeline(cfg, canvas_size=(224, 224))
for step, (images, labels) in enumerate(loader):
    images = images.to(device, non_blocking=True)
    images, _ = aug(images, seed_base=42, epoch=epoch, step=step)
    logits = model(images)
    ...

Measured on a 5060 Ti at bs=32 canvas=224×224 (rough config of 5 ops):

Placement	Wall time / batch	Throughput
Per-sample CPU in Dataset	219 ms	146 samples/s
Batch GPU in train loop	75 ms	429 samples/s

→ 2.9× speedup just from moving augmentation to the right place. The gap grows with batch size, canvas size, and op count (paraug's per-op launch overhead is amortised across the batch).

For larger-batch / larger-canvas setups, also see set_fast_noise(True) and chunk_size below.

Compositing: compose(foreground, background, mask)

compose blends a foreground onto a background through a mask, then runs the configured aug:

from paraug import AugPipeline

aug = AugPipeline({
    "geometric":   {"affine": {"p": 1.0, "rot_deg": 10.0}},
    "photometric": {"gamma": {"p": 0.5}},
})

# numpy (H, W, 3) uint8 in → numpy out  (also accepts torch tensors)
img, mask = aug.compose(
    foreground = paper_image,   # the sheet to paste
    background = scene_image,   # the static backdrop
    mask       = paper_mask,    # 255 = foreground, 0 = background
)

Data flow:

1. geometric primitives warp (foreground, mask) together — the foreground sheet rotates / scales / warps while the background stays put.
2. blend — composite = fg_w * mask_w + background * (1 - mask_w).
3. photometric primitives perturb the composite.
4. optional canvas_size stretch (see below).

Layered synthesis is just two compose calls — pass-1 output becomes pass-2's foreground:

# "content printed on paper, then paper photographed in a scene"
img1, m1 = aug.compose(content, paper_tone, content_mask)   # printing
img2, m2 = aug.compose(img1,    scene_bg,   paper_mask)      # photographing

Use two AugPipeline instances if the two passes need different aug.

Fixed output size: canvas_size

aug = AugPipeline(config, canvas_size=(512, 512))

Every __call__ / compose output is stretched to (512, 512) with a non-uniform F.interpolate — input aspect ratio is not preserved. This is the right choice when downstream batching needs uniform shapes and the task is consistent under stretch (train and inference both stretch to the same canvas, so the model learns in canvas space). Default None keeps the output size equal to the input.

Ground truth carried inside the tensor — the mask, or channels stacked via n_image_channels — is stretched alongside the image for free. For GT stored as coordinates outside the tensor, pass return_transform=True to compose and rescale with the returned scale_x / scale_y:

img, mask, t = aug.compose(fg, bg, m, return_transform=True)
line_x = [x * t["scale_x"] for x in line_x]
line_y = [y * t["scale_y"] for y in line_y]

Nested-frame layout: place_into_canvas

When the segmentation target is a sub-region of a larger frame — and the outer frame is itself rectangular — the model can latch onto the outer rectangle as a shortcut. Random layout at training time forces it to learn that the wider surrounding frame is a distractor.

place_into_canvas embeds a foreground (and its mask) at a random position inside a larger constant-colour canvas, with random per-axis margins:

from paraug import place_into_canvas

# content: (H, W, 3) uint8 — the inner region you actually want to segment
# content_mask: (H, W) uint8 — segmentation target
padded, padded_mask = place_into_canvas(
    content, content_mask,
    canvas_size=(800, 1000),
    fill=(245, 245, 245),               # background colour
    margin_frac_range=(0.05, 0.30),     # 5-30% margin per side, randomised
    seed_base=epoch_step_seed,
)

The deterministic CPU-side per-item RNG (same seed_base / epoch / step convention as the primitives) makes every batch position bit-exactly reproducible across CPU and CUDA.

Performance tuning: fast_noise and chunk_size

Two opt-in knobs trade a small contract for a large speed / VRAM win on GPU; both default to off so behaviour matches the docs above for callers that don't set them.

paraug.set_fast_noise(True) — speed

Switches the three CPU-sample noise primitives (gaussian_noise, jpeg_approx, salt_pepper_noise) to GPU-side torch.randn / torch.rand. The CPU-path noise tensor is the per-call wall-time hot spot at large canvases (~350 ms at bs=20 canvas=1024) because it's filled in a Python per-item loop and copied to GPU; the GPU path takes ~6 ms. Measured ~1.85× end-to-end speedup on a 5060 Ti at bs=20 canvas=1024 with a 14-op pipeline.

Contract: cuRAND ≠ MT19937, so fast_noise=True produces different output than fast_noise=False for the same seed. Determinism per (seed_base, epoch, step) is preserved within either mode. Leave off when running the parity tests; turn on for production training.

AugPipeline(cfg, ..., chunk_size=N) — VRAM

Splits the batch into sub-batches of size N internally, runs the full pipeline on each, concatenates outputs. Per-call peak alloc scales with N instead of batch size. 30-40% peak alloc reduction at bs=20 → chunk_size=5, with no wall-clock penalty (cache hits between primitives offset the per-chunk launch overhead).

Contract: chunked output is deterministic per (seed_base, epoch, step, chunk_size) but the per-item seed namespace shifts when you change chunk_size, so don't expect bit-equality if you toggle it mid-run.

Optional presets

paraug.presets ships hand-tuned configs for common deployment scenarios. Each preset returns a deep-copyable dict you can adjust:

from paraug import AugPipeline, presets
cfg = presets.OOD_PRINTED_PAPER()         # one current preset; more may follow
aug = AugPipeline(cfg, canvas_size=(512, 512))

Presets are not the primary API — build your own config from the 31 primitives (Quickstart above) for any task that doesn't match a preset exactly. See paraug/presets.py for what each preset contains and examples/05_ood_printed_paper.py for a full layered-synthesis example.

Stacking extra spatial channels (GT-as-channel)

n_image_channels=N declares that the first N input channels are the "image" (geometric + photometric) and any remaining channels follow geometric warp only. Photometric primitives skip the extra channels, so stacked ground-truth fields stay numerically intact while sharing the exact back-warp grid as the image:

import torch
from paraug import AugPipeline

# (B, 3, H, W) RGB + (B, 2, H, W) full-image heatmap GT = 5 channels.
img_rgb  = torch.rand(2, 3, 256, 256)
gt_h     = render_h_line_heatmap(...)   # your renderer; (B, 1, H, W)
gt_v     = render_v_line_heatmap(...)   # (B, 1, H, W)
img_5ch  = torch.cat([img_rgb, gt_h, gt_v], dim=1)   # (B, 5, H, W)

aug = AugPipeline({
    "geometric":   {"affine": {"p": 1.0, "rot_deg": 10.0},
                      "tps":    {"p": 0.5, "max_disp": 8.0, "n_ctrl": 5}},
    "photometric": {"gamma": {"p": 0.5, "gamma_range": (0.8, 1.2)}},
}, n_image_channels=3)

out, _ = aug(img_5ch, seed_base=42)
# out[:, :3] = warped + gamma-corrected RGB
# out[:, 3:] = warped (only) heatmap — gamma did NOT touch it

This eliminates a common pain point in tasks where GT is a 2-D field (line heatmaps, segmentation masks with continuous labels, distance transforms, tangent fields): instead of solving a separate forward-warp problem for GT, render GT as image channels, stack, and let grid_sample warp everything in one pass. The default n_image_channels=None preserves the prior behaviour for callers that don't need the split.

random_shadow is geometric in dispatch but multiplicative in effect; the split correctly treats it as photometric so extra channels are not dimmed by the shadow factor.

Sampling-mode note (mask vs extra channels)

Extra channels stacked onto img are sampled with bilinear interpolation — same as the image. If you need nearest interpolation (e.g. integer class labels or segmentation IDs that must not be interpolated), pass that tensor as the mask= argument instead of stacking it onto img:

Path	Interp	Photometric applied?	Channel count
img[:, :n_image_channels] (RGB / image)	bilinear	yes	any
img[:, n_image_channels:] (extra)	bilinear	no	any
mask argument	nearest	no	1 (single-channel)

paraug warps img and mask with the same back-warp grid in every geometric primitive — only the interpolation mode differs. Photometric primitives never modify mask.

Primitives

Geometric (7)

Name	Description
affine	Rotation + scale + translation via F.affine_grid
perspective	4-point homography from corner jitter
random_crop_pad	Scale-then-pad crop, area-preserving
elastic_transform	Bilinear-upsampled random displacement field
optical_distortion	Radial barrel / pincushion (k·r²)
random_shadow	Soft-blurred triangle multiplicative shadow
tps	Thin-plate-spline-like warp from low-res control grid

Photometric (28)

Intensity / color: gamma, color_jitter, hue_shift, random_grayscale, lighting, clahe, local_contrast, sharpness.

Noise: gaussian_noise, salt_pepper_noise, salt_patches.

Blur / artifacts: gaussian_blur, motion_blur, jpeg_approx, defocus_blur.

Lighting / glare: vignette, specular_highlight, specular_streaks, paper_glare.

Colour cast / WB: spatial_color_cast, white_balance_shift.

Content overlays: cutout, paper_texture_overlay, watermark, random_text_overlay, background_compose, stains, creases.

Region drop / pair mixing (v0.7.0): random_erasing, grid_mask, cutmix, mixup. See CutMix / MixUp label mixing below for the paraug.mix_info helper that recovers the per-item (λ, partner_idx) for classification training.

CutMix / MixUp label mixing

cutmix and mixup mix pairs of items in a batch. For classification training the labels need to be mixed by the same λ paraug used internally. paraug doesn't track labels — paraug.mix_info() recovers the (λ, partner_idx, gate) tensors using the same seed:

import paraug

aug = paraug.AugPipeline({"photometric": {
    "cutmix": {"p": 1.0, "alpha": 1.0},
}})

for step, (images, labels) in enumerate(loader):
    images = images.to(device)
    images, _ = aug(images, seed_base=step, epoch=epoch, step=step)
    # Recover λ + partner using the same triple paraug used:
    info = paraug.mix_info("cutmix", seed_base=step, epoch=epoch,
                            step=step, B=images.shape[0],
                            p=1.0, alpha=1.0)
    labels_a = labels                       # original labels
    labels_b = labels[info["perm"]]         # partner labels
    lam = info["lam"].to(device)            # (B,) sampled λ
    # mix labels per-item; only items where info["gate"] is True
    # actually got cutmix-augmented.
    # ...your loss code...

For exact area-ratio λ after rounding (cutmix only), pass primitive="cutmix_actual_lam" and img_shape=(B, C, H, W).

Inspecting spec keys: paraug.describe(name)

Every primitive accepts a {"p": ..., ...primitive-specific keys...} spec dict. To find the spec keys (and their defaults) for any primitive without grep-ing the source, call paraug.describe:

>>> import paraug
>>> paraug.describe("affine")
affine (geometric)
==================
Random rotation / scale / translation.

    spec = {"p": prob, "rot_deg": float, "scale_range": (lo, hi),
            "translate_frac": float (fraction of H/W)}

spec keys (with defaults):
  scale_range            = (0.85, 1.15)
  p                      = 1.0
  rot_deg                = 30.0
  translate_frac         = 0.05

>>> paraug.describe()            # one-line summary of every primitive
>>> info = paraug.describe("affine", return_dict=True)   # programmatic
>>> info["spec_keys"]
{'scale_range': (0.85, 1.15), 'p': 1.0, 'rot_deg': 30.0, 'translate_frac': 0.05}

Defaults are extracted by AST walk of each primitive function's spec.get(...) calls, so they stay in sync with the implementation.

Parity comparison

Library	Bit-exact CPU↔GPU	Per-item RNG	GPU native	Mask-aware	Batch-native	# Geometric¹	# Photometric¹	License
paraug	✓ (1e-6 / 2e-4)²	✓	✓ (torch)	✓	✓	7	28	Apache 2.0
albumentations	✗ (numpy-only)	✓	✗	✓	partial	~20	~50+	MIT
kornia	✗ (device-local RNG)	✓	✓ (torch)	✓	✓	~10	~45	Apache 2.0
torchvision.v2	✗ (device-local RNG)	✓	✓ (torch)	partial	✓	~18	~12	BSD-3
imgaug	✗ (numpy-only)	✓	✗	✓	partial	~20	~40	MIT
augly	✗ (PIL-only)	✓	✗	✗	✗	~5	~20	MIT

_{¹ External counts are approximate as of 2026-05 (sampled from each project's __init__.py / docs index). Versions move fast — consult each project's authoritative API reference for current numbers. paraug counts are code-exact (len(GEOMETRIC_PRIMITIVES) / len(PHOTOMETRIC_PRIMITIVES)).}

_{² Tolerance verified by tests/test_parity.py on NVIDIA 5060 Ti + 4080 at v0.1.0; exact bounds: 1e-6 for the 6 elementwise photometric ops listed in PHOTO_ELEMENTWISE (gamma / gaussian_noise / color_jitter / vignette / cutout / hue_shift), 2e-4 for grid_sample-class (geometric) and conv-class (blur) ops. GitHub free CI runners are CPU-only, so the 13 CUDA parity tests skip on CI — community verification on additional GPU SKUs is welcome (open a PR with the result, or run pytest tests/test_parity.py -k cpu_vs_cuda locally and post the output).}

When to use paraug

• Cross-device reproducibility (paper-grade ablation where CPU↔GPU drift breaks a baseline)
• Distributed training on heterogeneous hardware
• Unit-test-friendly augmentation pipelines (CPU-side RNG means a test on a free CI runner reproduces a developer's GPU result)

When NOT to use paraug

• You need 50+ primitive options out of the box → try albumentations or imgaug
• You need PIL-style per-image API → try augly
• You need built-in compositional ops like OneOf / SomeOf → try albumentations

Migration from torchvision / albumentations / kornia

paraug uses a dict-based config instead of the Compose([...]) flat list, but the underlying ops are the same. Direct equivalents:

torchvision.transforms.v2	albumentations	kornia.augmentation	paraug
RandomAffine	Affine	RandomAffine	affine
RandomPerspective	Perspective	RandomPerspective	perspective
ElasticTransform	ElasticTransform	RandomElasticTransform	elastic_transform
RandomResizedCrop	RandomResizedCrop	RandomResizedCrop	random_crop_pad + canvas_size
ColorJitter	ColorJitter	ColorJitter	color_jitter
RandomGrayscale	ToGray	RandomGrayscale	random_grayscale
GaussianBlur	GaussianBlur	RandomGaussianBlur	gaussian_blur
GaussianNoise	GaussNoise	RandomGaussianNoise	gaussian_noise
RandomErasing	CoarseDropout	RandomErasing	random_erasing
(none)	GridDropout	RandomGridShuffle (no equiv)	grid_mask
CutMix	(none)	RandomCutMixV2	cutmix (+ paraug.mix_info)
MixUp	(none)	RandomMixUpV2	mixup (+ paraug.mix_info)
HorizontalFlip / VerticalFlip	HorizontalFlip	RandomHorizontalFlip	use affine with rot_deg=0 (paraug doesn't have a separate flip yet — open an issue if you need one)

Example: a typical classification pipeline rewritten

# torchvision.transforms.v2 style:
#     transforms.Compose([
#         transforms.RandomResizedCrop(224),
#         transforms.RandomHorizontalFlip(),
#         transforms.ColorJitter(0.4, 0.4, 0.4),
#         transforms.RandomErasing(p=0.25),
#         transforms.ToTensor(),
#     ])

# paraug equivalent (canvas_size handles resize; flip TBD — affine is the closest match):
import paraug
aug = paraug.AugPipeline({
    "geometric":   {"affine":         {"p": 1.0, "rot_deg": 5.0, "scale_range": (0.8, 1.0)}},
    "photometric": {"color_jitter":   {"p": 1.0, "brightness": 0.4, "contrast": 0.4, "saturation": 0.4},
                    "random_erasing": {"p": 0.25, "size_frac_range": (0.02, 0.2)}},
}, canvas_size=(224, 224))

# Train loop — apply on the GPU batch, NOT inside Dataset.__getitem__:
for step, (images, labels) in enumerate(loader):
    images = images.to(device, non_blocking=True)
    images, _ = aug(images, seed_base=42, epoch=epoch, step=step)
    logits = model(images)
    # ...

See Where to put paraug for why the train-loop placement (vs Dataset.__getitem__) matters — paraug is GPU-batch-native, and per-sample CPU placement throws away 2-3× speedup.

Examples

See examples/:

• 01_quickstart.py — minimal load → augment → save
• 02_classification.py — Dataset + DataLoader + train loop with batch-GPU augmentation (the "Where to put paraug" pattern, end-to-end)
• 02_mask_aware.py — image + segmentation mask warped together
• 03_cpu_gpu_parity.py — same seed on CPU and CUDA, assert max_abs_diff < 2e-4
• 04_compose_layered.py — two-pass compose for layered synthesis
• 05_ood_printed_paper.py — OOD_PRINTED_PAPER preset, full pipeline

Citation

@software{paraug2026,
  author = {alieuidsh},
  title  = {paraug: Bit-exact CPU/GPU parity for image augmentation},
  year   = {2026},
  url    = {https://github.com/alieuidsh/paraug},
}

License

Apache 2.0 — see LICENSE.