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9277d79dff
- Data pipeline: loader (parquet→numpy), transforms (log, local-frame Rodrigues rotation, Normalizer), StepsDataset with event-ID-based split - Model: SinusoidalEmbedding, ConditionEncoder, ResBlock, DenoisingMLP - Schedule: cosine DDPM and conditional flow matching loss (Lipman 2022) - Samplers: flow (Euler ODE), DDPM ancestral, DDIM deterministic - Training loop: AdamW + cosine LR, grad clipping, best-val checkpoint - Validation: per-dimension marginal summary (normalised space) - CLI: TOML config support with CLI-overrides; hyperparam-encoded output directory; config.toml with git hash saved into each run's checkpoint dir - 21 unit tests covering transforms, network, flow/DDPM losses, dataset splits Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
43 lines
1.2 KiB
Python
43 lines
1.2 KiB
Python
import torch
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import pytest
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from giant.model.network import DenoisingMLP, SinusoidalEmbedding
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def test_sinusoidal_embedding_shape():
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emb = SinusoidalEmbedding(64)
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t = torch.rand(16)
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assert emb(t).shape == (16, 64)
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def test_sinusoidal_embedding_batch_1():
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emb = SinusoidalEmbedding(32)
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t = torch.tensor([0.5])
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assert emb(t).shape == (1, 32)
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def test_denoising_mlp_output_shape():
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B = 8
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model = DenoisingMLP(pdg_vocab=5, mat_vocab=3)
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x_t = torch.randn(B, 6)
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t = torch.rand(B)
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cond_cont = torch.randn(B, 9)
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cond_cat = torch.stack([
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torch.randint(0, 5, (B,)),
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torch.randint(0, 3, (B,)),
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], dim=1)
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out = model(x_t, t, cond_cont, cond_cat)
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assert out.shape == (B, 6)
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def test_denoising_mlp_gradients_flow():
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B = 4
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model = DenoisingMLP(pdg_vocab=3, mat_vocab=2, hidden_dim=32, n_blocks=2)
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x_t = torch.randn(B, 6)
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t = torch.rand(B)
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cond_cont = torch.randn(B, 9)
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cond_cat = torch.zeros(B, 2, dtype=torch.long)
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loss = model(x_t, t, cond_cont, cond_cat).sum()
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loss.backward()
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for name, p in model.named_parameters():
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assert p.grad is not None, f"no grad for {name}"
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