70 lines
2.2 KiB
Python
70 lines
2.2 KiB
Python
"""Tests for TrajectoryFlowModel architecture."""
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from __future__ import annotations
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import torch
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import pytest
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from tools.models import TrajectoryFlowModel
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class TestTrajectoryFlowModel:
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"""Test the Conditional Flow Matching model."""
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@pytest.fixture
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def model(self):
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return TrajectoryFlowModel(
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seq_len=64, d_model=128, nhead=4, num_layers=4,
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dim_feedforward=256, dropout=0.1, cond_dim=3,
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)
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def test_output_shape(self, model):
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"""(4, 64, 3) input → (4, 64, 3) output."""
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batch = 4
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x_t = torch.randn(batch, 64, 3)
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t = torch.rand(batch)
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cond = torch.randn(batch, 3)
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out = model(x_t, t, cond)
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assert out.shape == (batch, 64, 3)
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def test_single_sample(self, model):
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"""(1, 64, 3) works."""
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x_t = torch.randn(1, 64, 3)
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t = torch.rand(1)
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cond = torch.randn(1, 3)
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out = model(x_t, t, cond)
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assert out.shape == (1, 64, 3)
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def test_deterministic(self, model):
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"""Eval mode, same input → same output."""
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model.eval()
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x_t = torch.randn(2, 64, 3)
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t = torch.tensor([0.3, 0.7])
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cond = torch.randn(2, 3)
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with torch.no_grad():
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out1 = model(x_t, t, cond)
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out2 = model(x_t, t, cond)
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torch.testing.assert_close(out1, out2)
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def test_different_timesteps(self, model):
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"""t=0.1 vs t=0.9 gives different output."""
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model.eval()
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x_t = torch.randn(1, 64, 3)
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cond = torch.randn(1, 3)
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with torch.no_grad():
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out_early = model(x_t, torch.tensor([0.1]), cond)
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out_late = model(x_t, torch.tensor([0.9]), cond)
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assert not torch.allclose(out_early, out_late, atol=1e-5)
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def test_gradient_flows(self, model):
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"""Backward works, grad on x_t exists."""
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model.train()
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x_t = torch.randn(2, 64, 3, requires_grad=True)
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t = torch.rand(2)
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cond = torch.randn(2, 3)
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out = model(x_t, t, cond)
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loss = out.sum()
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loss.backward()
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assert x_t.grad is not None
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assert x_t.grad.shape == (2, 64, 3)
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assert x_t.grad.abs().sum() > 0
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