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Refactor tile graph for full CUDA pipeline

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This commit is contained in:
Meta Vens 2026-04-22 20:17:25 +01:00
commit 124d31fa8a
10 changed files with 563 additions and 381 deletions
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20
tests/test_tile_graph.py
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@ -16,7 +16,7 @@ from repixelizer.tile_graph import build_tile_graph_model, optimize_tile_graph
from repixelizer.types import InferenceResult
def test_tile_graph_model_extracts_candidates_and_adjacency_from_component_walk() -> None:
def test_tile_graph_model_extracts_candidates_and_adjacency_from_lattice_proposals() -> None:
lowres = np.zeros((4, 4, 4), dtype=np.float32)
lowres[..., 3] = 1.0
lowres[:, 1] = np.asarray([1.0, 1.0, 1.0, 1.0], dtype=np.float32)
@ -30,11 +30,7 @@ def test_tile_graph_model_extracts_candidates_and_adjacency_from_component_walk(
top_candidates=[],
)
model = build_tile_graph_model(
source,
inference=inference,
analysis=analyze_source(source, seed=7),
)
model = build_tile_graph_model(source, inference=inference, analysis=analyze_source(source, seed=7), device="cpu")
assert model.candidate_rgba.shape[0] > 4
choice_counts = np.diff(model.cell_candidate_offsets)
@ -42,6 +38,7 @@ def test_tile_graph_model_extracts_candidates_and_adjacency_from_component_walk(
assert np.all(choice_counts >= 1)
assert model.average_choices >= 1.0
assert model.edge_density > 0.0
assert model.model_device == "cpu"
def test_tile_graph_candidates_use_literal_source_pixel_colors() -> None:
@ -214,12 +211,13 @@ def test_tile_graph_cuda_matches_cpu_on_small_case() -> None:
confidence=1.0,
top_candidates=[],
)
analysis = analyze_source(fake, seed=7)
cpu_analysis = analyze_source(fake, seed=7, device="cpu")
cuda_analysis = analyze_source(fake, seed=7, device="cuda")
cpu_artifacts, cpu_diag = optimize_tile_graph(
fake,
inference=inference,
analysis=analysis,
analysis=cpu_analysis,
steps=0,
seed=7,
device="cpu",
@ -227,13 +225,15 @@ def test_tile_graph_cuda_matches_cpu_on_small_case() -> None:
cuda_artifacts, cuda_diag = optimize_tile_graph(
fake,
inference=inference,
analysis=analysis,
analysis=cuda_analysis,
steps=0,
seed=7,
device="cuda",
)
assert cpu_diag["tile_graph_model_device"] == "cpu"
assert cpu_diag["tile_graph_solver_device"] == "cpu"
assert cuda_diag["tile_graph_model_device"] == "cuda"
assert cuda_diag["tile_graph_solver_device"] == "cuda"
assert np.allclose(cpu_artifacts.target_rgba, cuda_artifacts.target_rgba, atol=1e-5)
@ -252,6 +252,7 @@ def test_pipeline_tile_graph_mode_writes_reconstruction_diagnostics(tmp_path: Pa
diagnostics_dir=diagnostics_dir,
steps=24,
reconstruction_mode="tile-graph",
device="cpu",
)
import json
@ -260,6 +261,7 @@ def test_pipeline_tile_graph_mode_writes_reconstruction_diagnostics(tmp_path: Pa
assert output_path.exists()
assert run_json["settings"]["reconstruction_mode"] == "tile-graph"
assert run_json["reconstruction"]["mode"] == "tile-graph"
assert run_json["reconstruction"]["tile_graph_model_device"] == "cpu"
assert run_json["reconstruction"]["tile_graph_candidate_count"] > 0
assert set(run_json["source_fidelity"].keys()) == {"snap_initial", "solver_target", "final_output"}
assert result.diagnostics["reconstruction"]["mode"] == "tile-graph"