Shaun ZhouandClaude Opus 5
d6adea5b05
88 个测试覆盖配置继承、注册表、GPU 守卫、YOLO 格式解析、数据集、模型形状、 标签模式、聚类原语与 SimCLR 损失，以及 train.py / eval.py / 聚类脚本的入口接线。 含两条回归测试： - PCA 白化放大噪声方向导致簇塌陷（默认 whiten=False 的依据） - classes 写成 data.yaml 路径时 num_classes 必须是类别数而非路径字符串长度 Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
149 lines
5.7 KiB
Python
149 lines
5.7 KiB
Python
"""SimCLR 自监督：损失的数学性质 + 聚类脚本的 simclr 分支端到端。"""
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import sys
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import pytest
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torch = pytest.importorskip("torch")
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pytest.importorskip("PIL")
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import numpy as np # noqa: E402
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from PIL import Image # noqa: E402
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from engine.simclr import SimCLRTrainer, nt_xent_loss # noqa: E402
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from models.heads.projection import ProjectionHead # noqa: E402
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from models.registry import BACKBONES # noqa: E402
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from utils.device import NoNvidiaGpuError # noqa: E402
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def test_nt_xent_is_minimal_when_views_agree():
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"""两个视图完全一致且各样本互相正交时，损失应接近理论下界 log(2B−1) 的反面——
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即正样本对独占概率质量，损失趋近 0。"""
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z = torch.eye(4) # 4 个互相正交的样本
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aligned = nt_xent_loss(z, z.clone(), temperature=0.05)
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misaligned = nt_xent_loss(z, z.roll(1, dims=0), temperature=0.05)
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assert aligned < 0.1
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assert misaligned > aligned
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def test_nt_xent_upper_bound_at_chance_level():
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"""所有表征相同时无法区分正负样本，损失 = log(2B−1)。"""
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z = torch.ones(8, 16)
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loss = float(nt_xent_loss(z, z.clone(), temperature=0.2))
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assert loss == pytest.approx(float(torch.log(torch.tensor(2 * 8 - 1))), abs=1e-4)
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def test_nt_xent_is_symmetric_and_temperature_sensitive():
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torch.manual_seed(0)
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z1, z2 = torch.randn(6, 8), torch.randn(6, 8)
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assert float(nt_xent_loss(z1, z2, 0.2)) == pytest.approx(float(nt_xent_loss(z2, z1, 0.2)), abs=1e-5)
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# 温度越低，随机表征的损失越大（softmax 更尖，惩罚更重）
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assert nt_xent_loss(z1, z2, 0.05) > nt_xent_loss(z1, z2, 0.5)
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def test_nt_xent_gradient_pulls_positive_pair_together():
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torch.manual_seed(0)
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z1 = torch.randn(4, 8, requires_grad=True)
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z2 = torch.randn(4, 8, requires_grad=True)
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before = float(torch.nn.functional.cosine_similarity(z1, z2).mean().detach())
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optimizer = torch.optim.SGD([z1, z2], lr=1.0)
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for _ in range(20):
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optimizer.zero_grad()
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nt_xent_loss(z1, z2, 0.2).backward()
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optimizer.step()
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after = float(torch.nn.functional.cosine_similarity(z1, z2).mean().detach())
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assert after > before
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def test_projection_head_accepts_feature_map_and_vector():
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head = ProjectionHead(16, hidden_channels=32, out_channels=8)
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assert head(torch.randn(4, 16, 5, 5)).shape == (4, 8)
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assert head(torch.randn(4, 16)).shape == (4, 8)
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@pytest.fixture
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def tiny_image_dir(tmp_path):
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root = tmp_path / "imgs"
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root.mkdir()
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rng = np.random.default_rng(0)
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for i in range(8):
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Image.fromarray(rng.integers(0, 255, (40, 60, 3), dtype=np.uint8)).save(root / f"{i}.png")
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return root
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def test_simclr_trainer_runs_and_saves_backbone_only(tiny_image_dir, tmp_path):
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from torch.utils.data import DataLoader
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from datasets.unlabeled import UnlabeledImageDataset, unlabeled_collate
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dataset = UnlabeledImageDataset(
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tiny_image_dir,
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transforms=[{"name": "Resize", "size": [32, 32]}, {"name": "ToTensor"}],
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views=2,
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view_transforms=[{"name": "RandomResizedCrop", "size": [32, 32], "scale": [0.5, 1.0]}],
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)
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loader = DataLoader(dataset, batch_size=4, shuffle=True, drop_last=True, collate_fn=unlabeled_collate)
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backbone = BACKBONES.build({"name": "tiny_conv", "widths": [8, 16], "depths": [1, 1]})
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trainer = SimCLRTrainer(
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backbone,
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loader,
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{"epochs": 2, "amp": False, "log_interval": 100, "projection": {"hidden_channels": 16,
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"out_channels": 8}},
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torch.device("cpu"),
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tmp_path / "run",
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)
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path = trainer.fit()
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assert path.exists()
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state = torch.load(path, map_location="cpu", weights_only=False)
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# 只存骨架，不含投影头
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assert all("projector" not in key and "mlp" not in key for key in state["model"])
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backbone.load_state_dict(state["model"]) # 能被干净地加载回去
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def test_cluster_script_simclr_branch_end_to_end(tiny_image_dir, tmp_path, monkeypatch):
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"""method=simclr 全流程：训练 -> 存权重 -> 抽特征 -> 聚类 -> 产出。"""
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sys.path.insert(0, "scripts")
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import cluster_unlabeled as cu
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monkeypatch.setattr(cu, "require_cuda", lambda: ["FAKE-GPU"])
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work_dir = tmp_path / "run"
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monkeypatch.setattr(
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sys, "argv",
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["cluster_unlabeled.py", "--config", "configs/cluster/film.yaml",
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"--work-dir", str(work_dir), "--root", str(tiny_image_dir), "--set",
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"device=cpu", "method=simclr", "loader.num_workers=0", "loader.batch_size=4",
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"simclr.epochs=1", "simclr.batch_size=4", "simclr.amp=False",
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"simclr.backbone.widths=[8,16]", "simclr.backbone.depths=[1,1]",
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"simclr.projection.hidden_channels=16", "simclr.projection.out_channels=8",
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"num_clusters=2", "n_init=2"],
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)
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assert cu.main() == 0
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assert (work_dir / "simclr_backbone.pth").exists()
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assert (work_dir / "assignments.csv").exists()
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assert (work_dir / "summary.json").exists()
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assert (work_dir / "clusters" / "cluster_00").is_dir()
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def test_simclr_branch_is_blocked_without_gpu(tiny_image_dir, tmp_path, monkeypatch):
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"""无 Nvidia GPU 时 simclr 分支必须被守卫拦下（pretrained 分支不受影响）。"""
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sys.path.insert(0, "scripts")
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import cluster_unlabeled as cu
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def refuse():
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raise NoNvidiaGpuError("没有 Nvidia GPU")
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monkeypatch.setattr(cu, "require_cuda", refuse)
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monkeypatch.setattr(
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sys, "argv",
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["cluster_unlabeled.py", "--config", "configs/cluster/film.yaml",
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"--work-dir", str(tmp_path / "run"), "--root", str(tiny_image_dir),
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"--set", "device=cpu", "method=simclr", "loader.num_workers=0"],
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)
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assert cu.main() == 1
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