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3ebdee57b8
Coverage audit (pytest --cov) found real, non-model logic at 0%/low cover. Add unit tests that need no model download: - img2img_runner.py 0% -> 100%: the MPS->CPU fallback orchestration, mocked via injected load_pipeline/reload_on_cpu callables. Guards the production behavior hit this session (native-res SDXL OOMs on MPS, must retry on CPU; non-MPS errors must propagate; "mps"-worded error on a cpu device must not reload). - ctrlregen/tiling.py 0% -> 40%: the pure tile math (tile_positions, make_blend_weight, resize_center_crop) that decides how large images are split and blended. (run_tiled stays model-bound, untested.) - isobmff.py 93% -> 100%: size==0 (box-to-EOF) and truncated 64-bit largesize parsing branches for AVIF/HEIF/JXL C2PA stripping. - c2pa.py: non-PNG-signed .png reads as clean (has_c2pa_metadata / extract_c2pa_chunk) instead of mis-parsing. 309 tests pass (+23). Document in CLAUDE.md that these pure helpers are unit-tested without downloads so future sessions don't skip them as "ML". No src/ change, no release. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
77 lines
2.6 KiB
Python
77 lines
2.6 KiB
Python
"""Unit tests for the pure tiling helpers (no GPU/model required).
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``tiling.py`` imports torch at module top, so skip cleanly when torch is
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absent. The helpers themselves are pure numpy/PIL/math -- they decide how a
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large image is split into overlapping tiles and blended back, so a regression
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here would seam or crop the CtrlRegen output wrongly.
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"""
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from __future__ import annotations
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import numpy as np
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import pytest
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pytest.importorskip("torch")
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from PIL import Image
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from remove_ai_watermarks.noai.ctrlregen.tiling import (
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make_blend_weight,
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resize_center_crop,
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tile_positions,
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)
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class TestTilePositions:
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def test_image_smaller_than_tile_single_position(self):
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assert tile_positions(500, 512, 64) == [0]
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def test_image_equal_to_tile_single_position(self):
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assert tile_positions(512, 512, 64) == [0]
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def test_first_is_zero_last_is_total_minus_tile(self):
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# The tiles must fully cover the span: first starts at 0, last ends at
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# the far edge (start == total - tile), or the image's edge is missed.
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pos = tile_positions(2000, 512, 64)
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assert pos[0] == 0
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assert pos[-1] == 2000 - 512
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def test_overlap_positions_are_monotonic_and_exact(self):
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assert tile_positions(1000, 512, 64) == [0, 244, 488]
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def test_zero_overlap_tiles_are_contiguous(self):
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# 1024 wide, 512 tile, no overlap -> two tiles butting at 512.
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assert tile_positions(1024, 512, 0) == [0, 512]
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class TestMakeBlendWeight:
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def test_zero_overlap_is_all_ones(self):
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w = make_blend_weight(8, 8, 0)
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assert w.shape == (8, 8)
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assert w.dtype == np.float64
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assert np.all(w == 1.0)
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def test_overlap_ramps_corners_to_zero_center_to_one(self):
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w = make_blend_weight(16, 16, 4)
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assert w[0, 0] == 0.0 # cosine ramp starts at 0
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assert w[8, 8] == 1.0 # center is unweighted
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assert w.max() == 1.0
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assert w.min() == 0.0
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def test_weight_is_point_symmetric(self):
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# Symmetric ramps on both edges -> mask equals its 180-degree rotation,
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# so opposite tile seams blend identically.
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w = make_blend_weight(16, 16, 4)
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assert np.allclose(w, w[::-1, ::-1])
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class TestResizeCenterCrop:
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@pytest.mark.parametrize(("width", "height"), [(400, 800), (800, 400), (300, 300), (1000, 1001)])
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def test_output_is_always_square_of_requested_size(self, width: int, height: int):
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out = resize_center_crop(Image.new("RGB", (width, height)), 256)
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assert out.size == (256, 256)
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def test_default_size_is_512(self):
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out = resize_center_crop(Image.new("RGB", (640, 480)))
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assert out.size == (512, 512)
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