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9179281420f2e8b77da02d6ce0ceb6ee1bc5d8cb
blackbird/actpred/test_actpred.py
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qing 9179281420 fix(pybot×actpred): v3 审计 P2 清零——验收链 fixed_range + 导出断言 + overrun 节流 
- v3-P2-1: _npu_test/_onnx_validate 按 normalize_mode 分支（fixed_range 免 stats，
  HailoPredictor 透传模式，torch 参考同口径反归一化）
- v3-P2-2: export_deploy/export_onnx 一致性 diff 超 1e-4 即 sys.exit（P2-f 闭环）
- v3-P2-3: run_loop overrun 告警节流（第1次+每100次），降 hz 建议仅 >10% 输出
- P2-a1: default_column_weights 扩展列（action_dim>18）默认 1.0
- P2-c: 补 predictor 异常清理路径测试（异常传播 + finally 落盘）
- P2-d/P2-e: Resize 口径与 resume scheduler T_max 语义文档化
- R2-4: _set.sh 安装后断言 CPU 版 torch，误装 CUDA 即拦截
测试: pybot 89 passed (+1)、actpred 148 passed/18 skipped
2026-08-14 23:01:56 +08:00

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"""actpred 模块单元测试
运行方式：
cd actpred && python -m pytest test_actpred.py -v
# 或
cd actpred && python test_actpred.py
"""
import contextlib
import importlib.util
import io
import os
import sys
import tempfile
import types
import unittest
from pathlib import Path
from unittest import mock
import numpy as np
from PIL import Image
import torch
from config import (Config, DataConfig, ModelConfig, TrainConfig,
build_argparser, apply_overrides, validate_config_values,
parse_checkpoint_config)
from dataset import _natural_key, ActionDataset, DataRecorder
from model import ActionPredictor
from mt_parser import parse_mt_action, parse_mt_directory
from predict import ActionPredictorInference, parse_action_history
from pretrain_mt import MTActionDataset
def _write_mt(dir_path, name, content):
path = Path(dir_path) / name
path.write_text(content, encoding="utf-8")
return path
def _make_seq_mt(n_rows):
"""生成 3 列 @Action 块，第 i 行为 [i, i*10, i*100]"""
lines = ["@Action", "a.A b.A c.A;"]
for i in range(n_rows):
lines.append(f"{i} {i * 10} {i * 100};")
lines.append("@")
return "\n".join(lines) + "\n"
class TestNaturalKey(unittest.TestCase):
"""测试自然排序键函数"""
def test_natural_order_no_padding(self):
"""无前导零的文件名应按数字序排列"""
files = [Path(f"{i}.jpg") for i in [3, 1, 10, 2, 20]]
sorted_files = sorted(files, key=_natural_key)
names = [f.name for f in sorted_files]
self.assertEqual(names, ["1.jpg", "2.jpg", "3.jpg", "10.jpg", "20.jpg"])
def test_natural_order_with_padding(self):
"""有前导零的文件名排序也应正确"""
files = [Path(f"{i:04d}.jpg") for i in [3, 1, 10, 2, 20]]
sorted_files = sorted(files, key=_natural_key)
names = [f.name for f in sorted_files]
self.assertEqual(names, ["0001.jpg", "0002.jpg", "0003.jpg", "0010.jpg", "0020.jpg"])
class TestParseActionHistory(unittest.TestCase):
"""测试动作历史解析函数"""
def test_single_step(self):
history = parse_action_history("1,2,3,4", action_dim=4)
self.assertEqual(history.shape, (1, 4))
np.testing.assert_array_equal(history[0], [1, 2, 3, 4])
def test_multi_step(self):
history = parse_action_history("1,0,0,0:0,1,0,0:0,0,1,0", action_dim=4)
self.assertEqual(history.shape, (3, 4))
def test_wrong_dim_raises(self):
with self.assertRaises(ValueError):
parse_action_history("1,2,3", action_dim=4)
class TestActionPredictor(unittest.TestCase):
"""测试多帧注意力模型前向传播"""
def test_forward_multi_frame(self):
data_cfg = DataConfig(image_width=64, image_height=64, action_dim=18, history_len=5, frame_len=3)
model_cfg = ModelConfig(cnn_backbone="simple")
model = ActionPredictor(model_cfg, data_cfg)
model.eval()
images = torch.randn(2, 3, 3, 64, 64)
history = torch.randn(2, 5, 18)
with torch.no_grad():
out = model(images, history)
self.assertEqual(out.shape, (2, 18))
def test_forward_single_frame_degenerate(self):
"""frame_len=1 退化为单帧输入也应正常前向"""
data_cfg = DataConfig(image_width=32, image_height=32, action_dim=4, history_len=3, frame_len=1)
model_cfg = ModelConfig(cnn_backbone="simple")
model = ActionPredictor(model_cfg, data_cfg)
model.eval()
images = torch.randn(2, 1, 3, 32, 32)
history = torch.randn(2, 3, 4)
with torch.no_grad():
out = model(images, history)
self.assertEqual(out.shape, (2, 4))
def test_forward_dynamic_batch(self):
data_cfg = DataConfig(image_width=32, image_height=32, action_dim=4, history_len=3, frame_len=2)
model_cfg = ModelConfig(cnn_backbone="simple")
model = ActionPredictor(model_cfg, data_cfg)
model.eval()
for batch_size in [1, 4, 8]:
images = torch.randn(batch_size, 2, 3, 32, 32)
history = torch.randn(batch_size, 3, 4)
with torch.no_grad():
out = model(images, history)
self.assertEqual(out.shape, (batch_size, 4))
def test_custom_attn_config(self):
"""自定义 attn_dim/heads/layers 应可构建并前向"""
data_cfg = DataConfig(image_width=32, image_height=32, action_dim=4, history_len=3, frame_len=2)
model_cfg = ModelConfig(attn_dim=64, attn_heads=2, attn_layers=1)
model = ActionPredictor(model_cfg, data_cfg)
model.eval()
with torch.no_grad():
out = model(torch.randn(1, 2, 3, 32, 32), torch.randn(1, 3, 4))
self.assertEqual(out.shape, (1, 4))
class TestDataRecorder(unittest.TestCase):
"""测试数据录制器"""
def test_record_and_flush(self):
with tempfile.TemporaryDirectory() as tmpdir:
recorder = DataRecorder(tmpdir, action_dim=4)
for _ in range(10):
img = np.random.randint(0, 255, (64, 64, 3), dtype=np.uint8)
action = np.random.randn(4).astype(np.float32)
recorder.record(img, action)
episode_path = recorder.flush()
self.assertTrue(os.path.exists(os.path.join(episode_path, "actions.npy")))
images_dir = os.path.join(episode_path, "images")
self.assertTrue(os.path.isdir(images_dir))
self.assertEqual(len(os.listdir(images_dir)), 10)
actions = np.load(os.path.join(episode_path, "actions.npy"))
self.assertEqual(actions.shape, (10, 4))
def test_multiple_episodes_increment_index(self):
with tempfile.TemporaryDirectory() as tmpdir:
r1 = DataRecorder(tmpdir, action_dim=4)
r1.flush()
r2 = DataRecorder(tmpdir, action_dim=4)
r2.flush()
self.assertNotEqual(r1.episode_dir, r2.episode_dir)
self.assertIn("episode_0001", str(r2.episode_dir))
class TestActionDataset(unittest.TestCase):
"""测试数据集加载"""
@staticmethod
def _create_dummy_episode(base_dir, n_frames=10, action_dim=4, image_size=32):
ep_dir = base_dir / "episode_0001"
images_dir = ep_dir / "images"
images_dir.mkdir(parents=True, exist_ok=True)
actions = []
for i in range(n_frames):
img = Image.fromarray(
np.random.randint(0, 255, (image_size, image_size, 3), dtype=np.uint8))
img.save(images_dir / f"{i:04d}.jpg")
actions.append(np.random.randn(action_dim).astype(np.float32))
np.save(ep_dir / "actions.npy", np.array(actions))
def test_dataset_loading(self):
with tempfile.TemporaryDirectory() as tmpdir:
self._create_dummy_episode(Path(tmpdir), n_frames=10, action_dim=4)
ds = ActionDataset(tmpdir, history_len=3, action_dim=4,
image_width=32, image_height=32,
frame_len=3)
# 采样起点 max(history_len, frame_len-1) = 3 → 10 - 3 = 7 samples
self.assertEqual(len(ds), 7)
sample = ds[0]
self.assertIn("images", sample)
self.assertIn("action_history", sample)
self.assertIn("target", sample)
self.assertEqual(sample["images"].shape, (3, 3, 32, 32))
self.assertEqual(sample["action_history"].shape, (3, 4))
self.assertEqual(sample["target"].shape, (4,))
def test_frame_len_gt_history_shifts_start(self):
"""frame_len-1 > history_len 时采样起点由帧窗口决定"""
with tempfile.TemporaryDirectory() as tmpdir:
self._create_dummy_episode(Path(tmpdir), n_frames=10, action_dim=4)
ds = ActionDataset(tmpdir, history_len=3, action_dim=4,
image_width=32, image_height=32,
frame_len=6)
# 起点 max(3, 5) = 5 → 10 - 5 = 5 samples
self.assertEqual(len(ds), 5)
sample = ds[0]
self.assertEqual(sample["images"].shape, (6, 3, 32, 32))
def test_episode_count(self):
with tempfile.TemporaryDirectory() as tmpdir:
self._create_dummy_episode(Path(tmpdir))
count = ActionDataset.get_episode_count(tmpdir)
self.assertEqual(count, 1)
def test_empty_stats_raises(self):
"""空数据集计算归一化统计量应报错"""
with tempfile.TemporaryDirectory() as tmpdir:
with self.assertRaises(ValueError):
ActionDataset(tmpdir, history_len=5, action_dim=4,
normalize_actions=True)
def test_invalid_stats_path_raises(self):
"""指定的 stats 路径不存在应报错"""
with tempfile.TemporaryDirectory() as tmpdir:
with self.assertRaises(FileNotFoundError):
ActionDataset(tmpdir, history_len=3, action_dim=4,
normalize_actions=True,
action_stats_path="/nonexistent/path.npy")
def test_bad_stats_shape_raises(self):
"""stats 文件形状不是 (2, action_dim) 时应报错而非广播出诡异结果"""
with tempfile.TemporaryDirectory() as tmpdir:
base = Path(tmpdir)
self._create_dummy_episode(base, n_frames=10, action_dim=4)
stats_path = base / "bad_stats.npy"
np.save(stats_path, np.zeros((2, 3), dtype=np.float32))
with self.assertRaisesRegex(ValueError, "形状"):
ActionDataset(tmpdir, history_len=3, action_dim=4,
image_width=32, image_height=32,
frame_len=3, normalize_actions=True,
action_stats_path=str(stats_path))
def test_action_dim_mismatch_raises(self):
"""actions.npy 列数与 action_dim 不一致应在加载时抛 ValueError"""
with tempfile.TemporaryDirectory() as tmpdir:
base = Path(tmpdir)
ep_dir = base / "episode_0001"
images_dir = ep_dir / "images"
images_dir.mkdir(parents=True, exist_ok=True)
for i in range(8):
Image.fromarray(np.random.randint(0, 255, (16, 16, 3), dtype=np.uint8)
).save(images_dir / f"{i:04d}.jpg")
np.save(ep_dir / "actions.npy", np.random.randn(8, 6).astype(np.float32))
with self.assertRaisesRegex(ValueError, "actions.npy"):
ActionDataset(tmpdir, history_len=3, action_dim=4)
def test_frame_mismatch_warns(self):
"""图像与动作帧数不一致时应打印 WARNING 并截断"""
with tempfile.TemporaryDirectory() as tmpdir:
base = Path(tmpdir)
ep_dir = base / "episode_0001"
images_dir = ep_dir / "images"
images_dir.mkdir(parents=True, exist_ok=True)
for i in range(8):
Image.fromarray(np.random.randint(0, 255, (16, 16, 3), dtype=np.uint8)
).save(images_dir / f"{i:04d}.jpg")
np.save(ep_dir / "actions.npy", np.random.randn(12, 4).astype(np.float32))
buf = io.StringIO()
with contextlib.redirect_stdout(buf):
ds = ActionDataset(tmpdir, history_len=3, action_dim=4)
self.assertIn("WARNING", buf.getvalue())
self.assertEqual(len(ds), 8 - 3) # 按较少者（8 帧图像）截断
def test_stats_cover_history_values(self):
"""归一化统计量必须覆盖 history 独有取值，否则 z-score 爆炸
col0 仅在 history（首行）出现 100，targets 全为 0：只按 targets
算统计时 std 落到 1e-8 地板值，z=(100-0)/1e-8=1e10。
"""
with tempfile.TemporaryDirectory() as tmpdir:
base = Path(tmpdir)
ep_dir = base / "episode_0001"
images_dir = ep_dir / "images"
images_dir.mkdir(parents=True, exist_ok=True)
for i in range(7):
Image.fromarray(np.random.randint(0, 255, (16, 16, 3), dtype=np.uint8)
).save(images_dir / f"{i:04d}.jpg")
actions = np.zeros((7, 4), dtype=np.float32)
actions[0, 0] = 100.0
np.save(ep_dir / "actions.npy", actions)
ds = ActionDataset(tmpdir, history_len=5, action_dim=4,
normalize_actions=True)
self.assertGreater(ds._action_std[0], 1.0) # 而非 1e-8 地板值
z_max = (np.abs(actions[:, 0] - ds._action_mean[0])
/ ds._action_std[0]).max()
self.assertLess(z_max, 10.0)
class TestConfig(unittest.TestCase):
"""测试配置和 CLI 参数"""
def test_default_config(self):
cfg = Config()
self.assertEqual(cfg.data.image_width, 640)
self.assertEqual(cfg.data.image_height, 480)
self.assertEqual(cfg.data.action_dim, 18)
self.assertEqual(cfg.data.history_len, 5)
self.assertEqual(cfg.data.frame_len, 3)
self.assertEqual(cfg.model.cnn_backbone, "simple")
self.assertEqual(cfg.model.attn_dim, 128)
self.assertEqual(cfg.model.attn_heads, 4)
self.assertEqual(cfg.model.attn_layers, 2)
self.assertEqual(cfg.train.epochs, 100)
def test_apply_overrides(self):
parser = build_argparser("test")
args = parser.parse_args(["--epochs", "50", "--lr", "0.01",
"--backbone", "mobilenet",
"--frame-len", "4", "--attn-dim", "64"])
cfg = apply_overrides(Config(), args)
self.assertEqual(cfg.train.epochs, 50)
self.assertEqual(cfg.train.lr, 0.01)
self.assertEqual(cfg.model.cnn_backbone, "mobilenet")
self.assertEqual(cfg.data.frame_len, 4)
self.assertEqual(cfg.model.attn_dim, 64)
def test_normalize_actions_flag(self):
"""--normalize-actions 和 --no-normalize-actions 都应可用"""
parser = build_argparser("test")
args = parser.parse_args(["--normalize-actions"])
self.assertTrue(args.normalize_actions)
parser = build_argparser("test")
args = parser.parse_args(["--no-normalize-actions"])
self.assertFalse(args.normalize_actions)
parser = build_argparser("test")
args = parser.parse_args([])
self.assertIsNone(args.normalize_actions)
class TestMtParser(unittest.TestCase):
"""测试 .mt 脚本 @Action 解析器"""
def test_parse_basic_and_forward_fill(self):
"""正常解析：跳过表头/注释，`-` 沿用上一行同列值"""
content = (
"@Action\n"
"s1.A s2.A spk.F;\t# column name\n"
" 10 20 100;\n"
" 11 21 101;\n"
" - 22 -;\n"
"@\n"
)
with tempfile.TemporaryDirectory() as tmpdir:
path = _write_mt(tmpdir, "basic.mt", content)
matrix = parse_mt_action(path)
self.assertEqual(matrix.shape, (3, 3))
self.assertEqual(matrix.dtype, np.float32)
np.testing.assert_array_equal(matrix[0], [10, 20, 100])
np.testing.assert_array_equal(matrix[2], [11, 22, 101])
def test_first_row_dash_fills_zero(self):
"""首行 `-` 填 0（与 pybot/bot4cj 运行时语义一致），后续行 `-` 沿用"""
content = (
"@Servo\n"
" name max min;\n"
" s1 90 -;\n"
" s2 180 -;\n"
"@\n"
"\n"
"@Action\n"
"s1.A s2.A spk.F;\n"
" - - -;\n"
" 45 - 10;\n"
"@\n"
)
with tempfile.TemporaryDirectory() as tmpdir:
path = _write_mt(tmpdir, "servo.mt", content)
matrix = parse_mt_action(path)
np.testing.assert_array_equal(matrix[0], [0, 0, 0])
np.testing.assert_array_equal(matrix[1], [45, 0, 10])
def test_action_as_last_block_parses(self):
"""@Action 为文件最后一个块（无后续 @ 段）时也应解析成功。
回归：旧正则 @Action\\s+(.*?)\\s*@ 要求后续必有 @，
末位块会报"未找到 @Action 块"，parse_mt_directory 进而
静默跳过该文件，造成预训练数据丢失。"""
content = (
"@Servo\n"
" name max min;\n"
" s1 90 -;\n"
"@\n"
"\n"
"@Action\n"
"s1.A s2.A spk.F;\n"
" 10 20 100;\n"
" - 22 -;\n"
)
with tempfile.TemporaryDirectory() as tmpdir:
path = _write_mt(tmpdir, "lastblock.mt", content)
matrix = parse_mt_action(path)
np.testing.assert_array_equal(matrix, [[10, 20, 100], [10, 22, 100]])
def test_std_mt_first_row_matches_pybot(self):
"""跨模块回归锚点：std.mt 首行解析结果与 pybot 运行时执行值一致
与 pybot/tests/test_hardvars.py 的 journal 期望值互为对照：
首行 `-`（s2/s4/s6/s8）解析为 0，而非 @Servo max 列。
"""
content = (
"@Servo\n"
" name max min offset controller channel;\n"
" s1 90 - - pca9685-0 0;\n"
" s2 180 - - - 1;\n"
" s3 -90 - - - 2;\n"
" s4 -180 - - - 3;\n"
"@\n"
"\n"
"@Action\n"
"s1.A s2.A s3.A s4.A s5.A s6.A s7.A s8.A s9.A s10.A spk.F spk.delay;\n"
" 45 - 45 - 45 - 45 - 90 90 10 200;\n"
"@\n"
)
with tempfile.TemporaryDirectory() as tmpdir:
path = _write_mt(tmpdir, "std.mt", content)
matrix = parse_mt_action(path)
np.testing.assert_array_equal(
matrix[0], [45, 0, 45, 0, 45, 0, 45, 0, 90, 90, 10, 200])
def test_no_action_block_raises(self):
"""无 @Action 块应抛 ValueError"""
content = "@Servo\nname max;\n s1 90;\n@\n"
with tempfile.TemporaryDirectory() as tmpdir:
path = _write_mt(tmpdir, "noaction.mt", content)
with self.assertRaisesRegex(ValueError, "@Action"):
parse_mt_action(path)
def test_header_only_raises(self):
"""只有表头没有数据行应抛 ValueError"""
content = "@Action\na.A b.A;\n@\n"
with tempfile.TemporaryDirectory() as tmpdir:
path = _write_mt(tmpdir, "empty.mt", content)
with self.assertRaises(ValueError):
parse_mt_action(path)
def test_row_col_mismatch_raises(self):
"""数据行列数与表头不一致应抛带行号的 ValueError"""
content = "@Action\na.A b.A;\n 1 2;\n 1 2 3;\n@\n"
with tempfile.TemporaryDirectory() as tmpdir:
path = _write_mt(tmpdir, "mismatch.mt", content)
with self.assertRaisesRegex(ValueError, "第 3 行"):
parse_mt_action(path)
def test_invalid_value_raises(self):
"""非数值且非 `-` 的单元格应抛带行列号的 ValueError"""
content = "@Action\na.A b.A;\n 1 xyz;\n@\n"
with tempfile.TemporaryDirectory() as tmpdir:
path = _write_mt(tmpdir, "badval.mt", content)
with self.assertRaisesRegex(ValueError, "xyz"):
parse_mt_action(path)
def test_directory_skips_bad_files_with_warning(self):
"""parse_mt_directory 跳过坏文件并打印 WARNING，保留好文件"""
with tempfile.TemporaryDirectory() as tmpdir:
_write_mt(tmpdir, "good.mt", _make_seq_mt(4))
_write_mt(tmpdir, "bad.mt", "@Action\na.A b.A;\n 1 xyz;\n@\n")
buf = io.StringIO()
with contextlib.redirect_stdout(buf):
results = parse_mt_directory(tmpdir)
self.assertEqual(len(results), 1)
self.assertTrue(results[0][0].endswith("good.mt"))
self.assertIn("WARNING", buf.getvalue())
self.assertIn("bad.mt", buf.getvalue())
class TestMTActionDataset(unittest.TestCase):
"""测试预训练滑动窗口数据集"""
@staticmethod
def _build(mt_dir, history_len, action_dim, file_indices=None):
with contextlib.redirect_stdout(io.StringIO()):
return MTActionDataset(mt_dir, history_len, action_dim,
file_indices=file_indices)
def test_sliding_window_count_and_shapes(self):
"""N 行数据、窗口 H → N-H 个样本，形状与 target 值正确"""
with tempfile.TemporaryDirectory() as tmpdir:
_write_mt(tmpdir, "a.mt", _make_seq_mt(7))
ds = self._build(tmpdir, history_len=3, action_dim=3)
self.assertEqual(len(ds), 4) # 7 - 3 = 4 samples
sample = ds[0]
self.assertEqual(sample["action_history"].shape, (3, 3))
self.assertEqual(sample["target"].shape, (3,))
# 样本 0 的 target 是第 4 行数据 [3, 30, 300]
np.testing.assert_array_equal(sample["target"].numpy(), [3, 30, 300])
def test_pad_columns_to_action_dim(self):
"""列数 < action_dim 时右侧补零"""
with tempfile.TemporaryDirectory() as tmpdir:
_write_mt(tmpdir, "a.mt", _make_seq_mt(5))
ds = self._build(tmpdir, history_len=3, action_dim=5)
sample = ds[0]
self.assertEqual(sample["action_history"].shape, (3, 5))
np.testing.assert_array_equal(
sample["action_history"].numpy()[:, 3:], np.zeros((3, 2)))
np.testing.assert_array_equal(sample["target"].numpy()[3:], [0, 0])
def test_truncate_columns_to_action_dim(self):
"""列数 > action_dim 时截断多余列"""
with tempfile.TemporaryDirectory() as tmpdir:
_write_mt(tmpdir, "a.mt", _make_seq_mt(5))
ds = self._build(tmpdir, history_len=3, action_dim=2)
sample = ds[0]
self.assertEqual(sample["action_history"].shape, (3, 2))
np.testing.assert_array_equal(sample["target"].numpy(), [3, 30])
def test_file_indices_filter(self):
"""file_indices 只加载指定文件，实现文件级 train/val 划分"""
with tempfile.TemporaryDirectory() as tmpdir:
_write_mt(tmpdir, "a.mt", _make_seq_mt(7)) # 4 samples
_write_mt(tmpdir, "b.mt", _make_seq_mt(4)) # 1 sample
ds_all = self._build(tmpdir, history_len=3, action_dim=3)
ds_a = self._build(tmpdir, history_len=3, action_dim=3,
file_indices=[0])
ds_b = self._build(tmpdir, history_len=3, action_dim=3,
file_indices=[1])
self.assertEqual(len(ds_all), 5)
self.assertEqual(len(ds_a), 4)
self.assertEqual(len(ds_b), 1)
class TestValidateConfigValues(unittest.TestCase):
"""测试 checkpoint config 值域校验"""
def test_default_config_passes(self):
cfg = Config()
validate_config_values(cfg.data, cfg.model) # 不应抛异常
def test_out_of_range_or_non_int_raises(self):
for field, value in [("image_width", 99999), ("image_width", 0),
("image_height", 0),
("history_len", -1), ("frame_len", 0),
("frame_len", 99999), ("action_dim", "18")]:
cfg = Config()
setattr(cfg.data, field, value)
with self.assertRaises(ValueError, msg=f"{field}={value}"):
validate_config_values(cfg.data, cfg.model)
def test_model_field_out_of_range_raises(self):
"""model 字段越界（fused_hidden_dim 超限、dropout 越界、attn_* 越界）应抛 ValueError"""
for field, value in [("fused_hidden_dim", 10 ** 9),
("fused_hidden_dim", 0),
("cnn_feature_dim", 99999),
("attn_dim", 0),
("attn_dim", 99999),
("attn_heads", 0),
("attn_heads", 128),
("attn_layers", 0),
("dropout", 1.5),
("dropout", -0.1)]:
cfg = Config()
setattr(cfg.model, field, value)
with self.assertRaises(ValueError, msg=f"{field}={value}"):
validate_config_values(cfg.data, cfg.model)
def test_attn_dim_not_divisible_by_heads_raises(self):
"""attn_dim 不能被 attn_heads 整除应抛 ValueError"""
cfg = Config()
cfg.model.attn_dim = 130
cfg.model.attn_heads = 4
with self.assertRaisesRegex(ValueError, "整除"):
validate_config_values(cfg.data, cfg.model)
def test_train_field_out_of_range_raises(self):
"""train 字段越界（lr 为负、val_split>1 等）应抛 ValueError"""
for field, value in [("lr", -1.0), ("lr", 0.0),
("val_split", 2.0), ("val_split", -0.1),
("batch_size", 0), ("epochs", 0),
("patience", -1), ("num_workers", -1),
("max_grad_norm", -1.0), ("weight_decay", -1.0)]:
cfg = Config()
setattr(cfg.train, field, value)
with self.assertRaises(ValueError, msg=f"{field}={value}"):
validate_config_values(cfg.data, cfg.model, cfg.train)
def test_default_train_passes(self):
cfg = Config()
validate_config_values(cfg.data, cfg.model, cfg.train) # 不应抛异常
class TestParseCheckpointConfig(unittest.TestCase):
"""测试 checkpoint config 结构解析（未知键/缺段应给明确错误而非裸 TypeError）"""
def test_valid_config_roundtrip(self):
cfg = Config()
parsed = parse_checkpoint_config(cfg.to_dict())
self.assertEqual(parsed.data.action_dim, cfg.data.action_dim)
self.assertEqual(parsed.model.cnn_backbone, cfg.model.cnn_backbone)
self.assertEqual(parsed.train.epochs, cfg.train.epochs)
def test_unknown_key_raises(self):
cfg = Config()
d = cfg.to_dict()
d["data"]["evil_extra_key"] = 123
with self.assertRaisesRegex(ValueError, "evil_extra_key"):
parse_checkpoint_config(d)
def test_legacy_checkpoint_config_rejected(self):
"""旧架构 checkpoint（含 history_encoder/action_hidden_dim）应拒载"""
cfg = Config()
d = cfg.to_dict()
d["model"]["history_encoder"] = "mlp"
d["model"]["action_hidden_dim"] = 64
with self.assertRaisesRegex(ValueError, "history_encoder"):
parse_checkpoint_config(d)
def test_missing_section_raises(self):
cfg = Config()
d = cfg.to_dict()
del d["data"]
with self.assertRaisesRegex(ValueError, "data"):
parse_checkpoint_config(d)
def test_out_of_range_raises(self):
cfg = Config()
d = cfg.to_dict()
d["model"]["fused_hidden_dim"] = 10 ** 9
with self.assertRaisesRegex(ValueError, "fused_hidden_dim"):
parse_checkpoint_config(d)
def test_non_dict_raises(self):
with self.assertRaises(ValueError):
parse_checkpoint_config("not a dict")
def test_train_section_unknown_key_raises(self):
"""train 段含未知键也应报 ValueError（与 data/model 段一致）"""
cfg = Config()
d = cfg.to_dict()
d["train"]["unknown_field"] = 1
with self.assertRaisesRegex(ValueError, "train.*unknown_field"):
parse_checkpoint_config(d)
def test_train_section_non_dict_raises(self):
"""train 段为非 dict 应报 ValueError，而非裸 TypeError"""
cfg = Config()
d = cfg.to_dict()
d["train"] = "not a dict"
with self.assertRaisesRegex(ValueError, "train"):
parse_checkpoint_config(d)
def test_missing_train_section_still_ok(self):
"""旧 checkpoint 无 train 段（推理 checkpoint 可能被裁剪）应可用默认值"""
cfg = Config()
d = cfg.to_dict()
del d["train"]
parsed = parse_checkpoint_config(d)
self.assertEqual(parsed.train.batch_size, TrainConfig().batch_size)
def test_warn_init_from_mismatch(self):
"""--init-from 兼容性警告：normalize 或 action_dim 不一致时提示"""
from config import warn_init_from_mismatch
cfg = Config()
# 显式设定当前运行的 normalize 值，不依赖默认值（默认已改为 True）
cfg.data.normalize_actions = False
init_cfg = {
"data": {"normalize_actions": True, "action_dim": 12},
}
buf = io.StringIO()
with contextlib.redirect_stdout(buf):
warn_init_from_mismatch(init_cfg, cfg)
out = buf.getvalue()
self.assertIn("normalize_actions=True", out)
self.assertIn("action_dim=12", out)
buf = io.StringIO()
with contextlib.redirect_stdout(buf):
warn_init_from_mismatch(
{"data": {"normalize_actions": False,
"action_dim": cfg.data.action_dim}}, cfg)
self.assertEqual(buf.getvalue(), "")
with contextlib.redirect_stdout(buf):
warn_init_from_mismatch(None, cfg)
self.assertEqual(buf.getvalue(), "")
class TestDataRecorderValidation(unittest.TestCase):
"""测试 DataRecorder 的输入校验与目录原子分配"""
def test_wrong_action_dim_raises(self):
"""record 传入错误维度的 action 应抛 ValueError"""
with tempfile.TemporaryDirectory() as tmpdir:
recorder = DataRecorder(tmpdir, action_dim=4)
img = np.zeros((32, 32, 3), dtype=np.uint8)
with self.assertRaisesRegex(ValueError, "维度"):
recorder.record(img, np.zeros(3, dtype=np.float32))
def test_dir_collision_retries_next_index(self):
"""目标 episode 目录已被占用时应重试递增索引（模拟并发竞态）"""
with tempfile.TemporaryDirectory() as tmpdir:
r1 = DataRecorder(tmpdir, action_dim=4)
self.assertIn("episode_0000", str(r1.episode_dir))
# 让 glob 返回空 → r2 从 0 开始分配，与 r1 冲突后应重试到 0001
with mock.patch.object(Path, "glob", return_value=iter([])):
r2 = DataRecorder(tmpdir, action_dim=4)
self.assertIn("episode_0001", str(r2.episode_dir))
class TestActionPredictorInference(unittest.TestCase):
"""测试推理封装端到端：checkpoint 保存 → 加载 → 推理"""
@staticmethod
def _make_checkpoint(tmpdir, normalize_actions=False, image_width=32,
image_height=32, frame_len=3):
cfg = Config()
cfg.data.image_width = image_width
cfg.data.image_height = image_height
cfg.data.action_dim = 4
cfg.data.history_len = 3
cfg.data.frame_len = frame_len
cfg.data.normalize_actions = normalize_actions
model = ActionPredictor(cfg.model, cfg.data)
model.eval()
path = os.path.join(tmpdir, "best_inference.pt")
torch.save({"model_state_dict": model.state_dict(),
"config": cfg.to_dict()}, path)
return path, model, cfg
def test_end_to_end_predict(self):
"""加载 checkpoint 后多帧推理输出形状正确、数值有限且可复现"""
with tempfile.TemporaryDirectory() as tmpdir:
path, _, _ = self._make_checkpoint(tmpdir)
predictor = ActionPredictorInference(path, device="cpu")
self.assertEqual(predictor.action_dim, 4)
self.assertEqual(predictor.history_len, 3)
self.assertEqual(predictor.frame_len, 3)
images = [np.random.randint(0, 255, (32, 32, 3), dtype=np.uint8)
for _ in range(3)]
history = np.random.randn(3, 4).astype(np.float32)
pred1 = predictor.predict(images, history)
pred2 = predictor.predict(images, history)
self.assertEqual(pred1.shape, (4,))
self.assertTrue(np.all(np.isfinite(pred1)))
np.testing.assert_array_equal(pred1, pred2) # eval 模式确定性
def test_loaded_weights_match_saved(self):
"""加载后的模型权重应与保存时逐张量一致"""
with tempfile.TemporaryDirectory() as tmpdir:
path, model, _ = self._make_checkpoint(tmpdir)
predictor = ActionPredictorInference(path, device="cpu")
loaded_sd = predictor._model.state_dict()
for key, tensor in model.state_dict().items():
self.assertTrue(torch.equal(loaded_sd[key].cpu(), tensor),
msg=f"权重不一致: {key}")
def test_wrong_history_shape_raises(self):
"""action_history 形状错误应抛 ValueError"""
with tempfile.TemporaryDirectory() as tmpdir:
path, _, _ = self._make_checkpoint(tmpdir)
predictor = ActionPredictorInference(path, device="cpu")
images = [np.zeros((32, 32, 3), dtype=np.uint8)] * 3
with self.assertRaisesRegex(ValueError, "action_history"):
predictor.predict(images, np.zeros((2, 4), dtype=np.float32))
def test_wrong_frame_count_raises(self):
"""传入帧数与 frame_len 不一致应抛 ValueError"""
with tempfile.TemporaryDirectory() as tmpdir:
path, _, _ = self._make_checkpoint(tmpdir)
predictor = ActionPredictorInference(path, device="cpu")
images = [np.zeros((32, 32, 3), dtype=np.uint8)] * 2
with self.assertRaisesRegex(ValueError, "帧"):
predictor.predict(images, np.zeros((3, 4), dtype=np.float32))
def test_missing_action_stats_raises(self):
"""启用归一化但缺 action_stats.npy 应抛 FileNotFoundError"""
with tempfile.TemporaryDirectory() as tmpdir:
path, _, _ = self._make_checkpoint(tmpdir, normalize_actions=True)
with self.assertRaises(FileNotFoundError):
ActionPredictorInference(path, device="cpu")
def test_normalize_identity_stats_matches_raw(self):
"""mean=0/std=1 的归一化路径输出应与非归一化一致"""
with tempfile.TemporaryDirectory() as tmpdir:
raw_dir = os.path.join(tmpdir, "raw")
norm_dir = os.path.join(tmpdir, "norm")
os.makedirs(raw_dir)
os.makedirs(norm_dir)
torch.manual_seed(0)
raw_path, model, cfg = self._make_checkpoint(raw_dir)
# 同一份权重另存为启用归一化的 checkpoint
cfg.data.normalize_actions = True
norm_path = os.path.join(norm_dir, "best_inference.pt")
torch.save({"model_state_dict": model.state_dict(),
"config": cfg.to_dict()}, norm_path)
stats = np.stack([np.zeros(4), np.ones(4)]).astype(np.float32)
np.save(os.path.join(norm_dir, "action_stats.npy"), stats)
image = np.random.randint(0, 255, (32, 32, 3), dtype=np.uint8)
history = np.random.randn(3, 4).astype(np.float32)
pred_raw = ActionPredictorInference(
raw_path, device="cpu").predict([image] * 3, history)
pred_norm = ActionPredictorInference(
norm_path, device="cpu").predict([image] * 3, history)
np.testing.assert_allclose(pred_norm, pred_raw, rtol=1e-5, atol=1e-6)
def test_stats_shape_mismatch_raises(self):
"""action_stats.npy 形状与 action_dim 不一致应抛 ValueError"""
with tempfile.TemporaryDirectory() as tmpdir:
path, _, _ = self._make_checkpoint(tmpdir, normalize_actions=True)
np.save(os.path.join(tmpdir, "action_stats.npy"), np.zeros((2, 7)))
with self.assertRaisesRegex(ValueError, "action_stats.npy"):
ActionPredictorInference(path, device="cpu")
def test_invalid_config_in_checkpoint_raises(self):
"""checkpoint 中 config 值域非法应在加载时抛 ValueError"""
with tempfile.TemporaryDirectory() as tmpdir:
path, model, cfg = self._make_checkpoint(tmpdir)
cfg_dict = cfg.to_dict()
cfg_dict["data"]["image_width"] = 99999
bad_path = os.path.join(tmpdir, "bad.pt")
torch.save({"model_state_dict": model.state_dict(),
"config": cfg_dict}, bad_path)
with self.assertRaisesRegex(ValueError, "image_width"):
ActionPredictorInference(bad_path, device="cpu")
def test_unknown_config_key_in_checkpoint_raises(self):
"""checkpoint config 含未知键应在加载时抛带键名的 ValueError 而非裸 TypeError"""
with tempfile.TemporaryDirectory() as tmpdir:
path, model, cfg = self._make_checkpoint(tmpdir)
cfg_dict = cfg.to_dict()
cfg_dict["data"]["evil_extra_key"] = 123
bad_path = os.path.join(tmpdir, "bad.pt")
torch.save({"model_state_dict": model.state_dict(),
"config": cfg_dict}, bad_path)
with self.assertRaisesRegex(ValueError, "evil_extra_key"):
ActionPredictorInference(bad_path, device="cpu")
@unittest.skipUnless(importlib.util.find_spec("onnxscript") is not None,
"torch.onnx.export 需要 onnxscript（当前环境未安装）")
class TestExportOnnx(unittest.TestCase):
"""测试 ONNX 导出（环境缺 onnxscript 时自动跳过）"""
def test_export_creates_onnx_file(self):
"""从 checkpoint 导出 ONNX 文件应成功生成"""
from export_onnx import main as export_onnx_main
with tempfile.TemporaryDirectory() as tmpdir:
path, _, _ = TestActionPredictorInference._make_checkpoint(tmpdir)
out_path = os.path.join(tmpdir, "model.onnx")
argv = ["export_onnx.py", "--model", path, "--output", out_path]
with mock.patch("sys.argv", argv):
export_onnx_main()
self.assertTrue(os.path.exists(out_path))
self.assertGreater(os.path.getsize(out_path), 0)
class TestDeployWrapper(unittest.TestCase):
"""部署版导出（export_deploy.DeployWrapper）的图等价性"""
def test_pool_kernel_matches_conv_output(self):
"""固定 kernel AvgPool 必须覆盖卷积真实输出尺寸。
回归：旧实现用 H//16 取 kernel，1080 高度下卷积输出 68 行、
kernel 取 67 会静默丢弃末行（训练/部署图数值不等价）。"""
from export_deploy import DeployWrapper
cfg = Config()
cfg.data.image_height, cfg.data.image_width = 1080, 1920
model = ActionPredictor(cfg.model, cfg.data, pretrained=False).eval()
w = DeployWrapper(model)
self.assertEqual(w.pool.kernel_size, (68, 120))
cfg.data.image_height, cfg.data.image_width = 224, 224
model = ActionPredictor(cfg.model, cfg.data, pretrained=False).eval()
w = DeployWrapper(model)
self.assertEqual(w.pool.kernel_size, (14, 14))
def test_mobilenet_backbone_rejected(self):
"""MobileNet 的 DFC 改造未实现，应给出明确错误而非 AttributeError"""
from export_deploy import DeployWrapper
cfg = Config()
cfg.model.cnn_backbone = "mobilenet"
model = ActionPredictor(cfg.model, cfg.data, pretrained=False).eval()
with self.assertRaises(ValueError):
DeployWrapper(model)
class TestCompileAndCalibrationHelpers(unittest.TestCase):
"""dfc_compile / gen_calibration 的辅助函数（P3 修复回归）"""
def test_start_nodes_follows_frame_count(self):
"""start_nodes 按 frame_count 生成输入名（旧实现硬编码 3 帧）"""
from dfc_compile import start_nodes
self.assertEqual(start_nodes(3), [
"frame0", "frame1", "frame2", "action_history", "pred_token"])
self.assertEqual(start_nodes(1),
["frame0", "action_history", "pred_token"])
def test_build_image_samples_memmap_chunked(self):
"""分块 memmap 生成的校准图像形状/dtype/值域正确，且可清理"""
from gen_calibration import build_image_samples
rng = np.random.default_rng(0)
images = build_image_samples(
n_samples=6, frame_len=2, height=16, width=16, rng=rng,
chunk_samples=2)
try:
self.assertIsInstance(images, np.memmap)
self.assertEqual(images.shape, (6, 2, 3, 16, 16))
self.assertEqual(images.dtype, np.uint8)
self.assertTrue((images <= 255).all())
self.assertTrue(os.path.exists(images.filename))
finally:
images._mmap.close()
os.unlink(images.filename)
def test_build_image_samples_invalid_chunk(self):
from gen_calibration import build_image_samples
with self.assertRaises(ValueError):
build_image_samples(4, 2, 16, 16, np.random.default_rng(0),
chunk_samples=0)
class TestResumeCheckpoint(unittest.TestCase):
"""测试 --resume 断点续训练：checkpoint 格式与状态恢复"""
def test_last_checkpoint_contains_resume_keys(self):
"""last.pt 必须包含恢复训练所需的全部键"""
required_keys = {
"epoch", "model_state_dict", "optimizer_state_dict",
"scheduler_state_dict", "best_val_loss", "patience_counter",
"config",
}
cfg = Config()
cfg.data.action_dim = 4
model = ActionPredictor(cfg.model, cfg.data)
optimizer = torch.optim.AdamW(model.parameters(), lr=0.001)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, T_max=100)
with tempfile.TemporaryDirectory() as tmpdir:
last_path = os.path.join(tmpdir, "last.pt")
torch.save({
"epoch": 10,
"model_state_dict": model.state_dict(),
"optimizer_state_dict": optimizer.state_dict(),
"scheduler_state_dict": scheduler.state_dict(),
"best_val_loss": 0.5,
"patience_counter": 3,
"config": cfg.to_dict(),
}, last_path)
ckpt = torch.load(last_path, map_location="cpu", weights_only=True)
self.assertTrue(required_keys.issubset(ckpt.keys()),
f"缺少键: {required_keys - set(ckpt.keys())}")
self.assertEqual(ckpt["epoch"], 10)
self.assertEqual(ckpt["best_val_loss"], 0.5)
self.assertEqual(ckpt["patience_counter"], 3)
def test_resume_restores_model_weights(self):
"""resume 加载后模型权重应与保存时一致"""
cfg = Config()
cfg.data.action_dim = 4
torch.manual_seed(0)
model = ActionPredictor(cfg.model, cfg.data)
optimizer = torch.optim.AdamW(model.parameters(), lr=0.001)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, T_max=100)
with tempfile.TemporaryDirectory() as tmpdir:
last_path = os.path.join(tmpdir, "last.pt")
torch.save({
"epoch": 5,
"model_state_dict": model.state_dict(),
"optimizer_state_dict": optimizer.state_dict(),
"scheduler_state_dict": scheduler.state_dict(),
"best_val_loss": 1.0,
"patience_counter": 0,
"config": cfg.to_dict(),
}, last_path)
# 重新创建模型并恢复
model2 = ActionPredictor(cfg.model, cfg.data)
ckpt = torch.load(last_path, map_location="cpu", weights_only=True)
model2.load_state_dict(ckpt["model_state_dict"])
for k, v in model.state_dict().items():
self.assertTrue(torch.equal(model2.state_dict()[k], v),
msg=f"权重不一致: {k}")
@unittest.skipUnless(
importlib.util.find_spec("onnxscript") is not None
and importlib.util.find_spec("onnxruntime") is not None,
"OnnxPredictor 测试需要 onnxscript + onnxruntime")
class TestOnnxPredictor(unittest.TestCase):
"""ONNX Runtime 后端测试：导出小模型后与 torch 前向对齐"""
@staticmethod
def _export_small_onnx(tmpdir):
cfg = Config()
cfg.data.image_width = 32
cfg.data.image_height = 32
cfg.data.action_dim = 4
cfg.data.history_len = 3
cfg.data.frame_len = 3
model = ActionPredictor(cfg.model, cfg.data)
model.eval()
out_path = os.path.join(tmpdir, "model.onnx")
torch.onnx.export(
model,
(torch.randn(1, 3, 3, 32, 32), torch.randn(1, 3, 4)),
out_path,
input_names=["images", "action_history"],
output_names=["predicted_action"],
opset_version=18)
return out_path, model
def _frames(self, n):
return [np.random.randint(0, 255, (48, 64, 3), dtype=np.uint8)
for _ in range(n)]
def test_dims_derived_from_session(self):
from onnx_infer import OnnxPredictor
with tempfile.TemporaryDirectory() as tmpdir:
path, _ = self._export_small_onnx(tmpdir)
with OnnxPredictor(path) as p:
self.assertEqual((p.frame_len, p.history_len, p.action_dim),
(3, 3, 4))
self.assertEqual((p.image_width, p.image_height), (32, 32))
def test_matches_torch_forward(self):
from onnx_infer import OnnxPredictor
with tempfile.TemporaryDirectory() as tmpdir:
path, model = self._export_small_onnx(tmpdir)
frames = self._frames(3)
history = np.random.randn(3, 4).astype(np.float32)
with OnnxPredictor(path) as p:
onnx_out = p.predict(frames, history)
pre = np.stack([p._preprocess(f) for f in frames])
with torch.no_grad():
torch_out = model(
torch.from_numpy(pre[np.newaxis]),
torch.from_numpy(history[np.newaxis])).numpy()
self.assertEqual(onnx_out.shape, (4,))
np.testing.assert_allclose(onnx_out, torch_out.reshape(-1),
atol=1e-4)
def test_frame_count_mismatch_raises(self):
from onnx_infer import OnnxPredictor
with tempfile.TemporaryDirectory() as tmpdir:
path, _ = self._export_small_onnx(tmpdir)
with OnnxPredictor(path) as p:
with self.assertRaises(ValueError):
p.predict(self._frames(1), np.zeros((3, 4), np.float32))
def test_history_shape_mismatch_raises(self):
from onnx_infer import OnnxPredictor
with tempfile.TemporaryDirectory() as tmpdir:
path, _ = self._export_small_onnx(tmpdir)
with OnnxPredictor(path) as p:
with self.assertRaises(ValueError):
p.predict(self._frames(3), np.zeros((2, 4), np.float32))
def test_stats_normalization_roundtrip(self):
from onnx_infer import OnnxPredictor
with tempfile.TemporaryDirectory() as tmpdir:
path, _ = self._export_small_onnx(tmpdir)
stats_path = os.path.join(tmpdir, "action_stats.npy")
mean, std = np.full(4, 0.5), np.full(4, 2.0)
np.save(stats_path, np.stack([mean, std]))
frames = self._frames(3)
history = np.random.randn(3, 4).astype(np.float32)
with OnnxPredictor(path) as plain, \
OnnxPredictor(path, stats_path=stats_path) as normed:
expected = plain.predict(frames, (history - mean) / std)
result = normed.predict(frames, history)
np.testing.assert_allclose(result, expected * std + mean,
atol=1e-5)
def test_bad_stats_shape_raises(self):
from onnx_infer import OnnxPredictor
with tempfile.TemporaryDirectory() as tmpdir:
path, _ = self._export_small_onnx(tmpdir)
stats_path = os.path.join(tmpdir, "action_stats.npy")
np.save(stats_path, np.zeros((2, 5)))
with self.assertRaises(ValueError):
OnnxPredictor(path, stats_path=stats_path)
@unittest.skipUnless(
importlib.util.find_spec("onnxscript") is not None
and importlib.util.find_spec("onnxruntime") is not None,
"ONNX 元数据测试需要 onnxscript + onnxruntime")
class TestOnnxMetadata(unittest.TestCase):
"""v3-P1-2：export_onnx 写入训练配置元数据，onnx_infer 自动识别
覆盖 normalize_mode（zscore/fixed_range/none）与 predict_delta 的
元数据写入、自动识别、显式参数覆盖与残差加回数值语义。
"""
@staticmethod
def _write_meta(path, meta):
import onnx
m = onnx.load(path)
for k, v in meta.items():
p = m.metadata_props.add()
p.key, p.value = k, v
onnx.save(m, path)
@staticmethod
def _read_meta(path):
import onnx
return {p.key: p.value for p in onnx.load(path).metadata_props}
def _frames(self, n):
return [np.random.randint(0, 255, (48, 64, 3), dtype=np.uint8)
for _ in range(n)]
def _export_via_script(self, tmpdir, normalize_actions=False,
normalize_mode="zscore", predict_delta=False):
"""经 export_onnx.main 导出小模型，返回 ONNX 路径"""
import export_onnx
cfg = Config()
cfg.data.image_width = 32
cfg.data.image_height = 32
cfg.data.action_dim = 4
cfg.data.history_len = 3
cfg.data.frame_len = 3
cfg.data.normalize_actions = normalize_actions
cfg.data.normalize_mode = normalize_mode
cfg.train.predict_delta = predict_delta
model = ActionPredictor(cfg.model, cfg.data)
model.eval()
ckpt = os.path.join(tmpdir, "best_inference.pt")
torch.save({"model_state_dict": model.state_dict(),
"config": cfg.to_dict()}, ckpt)
out = os.path.join(tmpdir, "model.onnx")
with mock.patch.object(
sys, "argv",
["export_onnx.py", "--model", ckpt, "--output", out]):
with contextlib.redirect_stdout(io.StringIO()):
export_onnx.main()
return out
def test_export_writes_metadata_unnormalized(self):
with tempfile.TemporaryDirectory() as tmpdir:
meta = self._read_meta(self._export_via_script(tmpdir))
self.assertEqual(meta["normalize_mode"], "none") # 未归一化训练
self.assertEqual(meta["predict_delta"], "0")
self.assertEqual(meta["action_dim"], "4")
self.assertEqual(meta["imu_dim"], "0")
def test_export_writes_metadata_fixed_range_delta(self):
with tempfile.TemporaryDirectory() as tmpdir:
meta = self._read_meta(self._export_via_script(
tmpdir, normalize_actions=True,
normalize_mode="fixed_range", predict_delta=True))
self.assertEqual(meta["normalize_mode"], "fixed_range")
self.assertEqual(meta["predict_delta"], "1")
def test_export_metadata_roundtrip_predictor(self):
"""导出的 ONNX 直接被 OnnxPredictor 自动识别，无需显式参数"""
from onnx_infer import OnnxPredictor
with tempfile.TemporaryDirectory() as tmpdir:
out = self._export_via_script(
tmpdir, normalize_actions=True,
normalize_mode="fixed_range", predict_delta=True)
with OnnxPredictor(out) as p:
self.assertEqual(p.normalize_mode, "fixed_range")
self.assertTrue(p._predict_delta)
def test_predictor_metadata_fallback_old_model(self):
"""旧版无元数据 ONNX：回退 zscore + 非残差"""
from onnx_infer import OnnxPredictor
with tempfile.TemporaryDirectory() as tmpdir:
path, _ = TestOnnxPredictor._export_small_onnx(tmpdir)
with OnnxPredictor(path) as p:
self.assertEqual(p.normalize_mode, "zscore")
self.assertFalse(p._predict_delta)
def test_explicit_param_overrides_metadata(self):
from onnx_infer import OnnxPredictor
with tempfile.TemporaryDirectory() as tmpdir:
path, _ = TestOnnxPredictor._export_small_onnx(tmpdir)
self._write_meta(path, {"normalize_mode": "fixed_range",
"predict_delta": "1"})
with OnnxPredictor(path, normalize_mode="zscore",
predict_delta=False) as p:
self.assertEqual(p.normalize_mode, "zscore")
self.assertFalse(p._predict_delta)
def test_predict_delta_adds_last_history(self):
"""残差输出 = 普通输出 + history 末步（none 模式下口径直接可比）"""
from onnx_infer import OnnxPredictor
with tempfile.TemporaryDirectory() as tmpdir:
path, _ = TestOnnxPredictor._export_small_onnx(tmpdir)
frames = self._frames(3)
history = np.random.randn(3, 4).astype(np.float32)
with OnnxPredictor(path, normalize_mode="none") as plain:
base = plain.predict(frames, history)
with OnnxPredictor(path, normalize_mode="none",
predict_delta=True) as delta:
out = delta.predict(frames, history)
np.testing.assert_allclose(out, base + history[-1], atol=1e-5)
def test_none_mode_ignores_stats(self):
"""none 模式（训练未归一化）：即使传 stats 也不做归一化"""
from onnx_infer import OnnxPredictor
with tempfile.TemporaryDirectory() as tmpdir:
path, _ = TestOnnxPredictor._export_small_onnx(tmpdir)
stats_path = os.path.join(tmpdir, "action_stats.npy")
np.save(stats_path, np.stack([np.full(4, 0.5), np.full(4, 2.0)]))
frames = self._frames(3)
history = np.random.randn(3, 4).astype(np.float32)
with OnnxPredictor(path, stats_path=stats_path,
normalize_mode="none") as p:
out = p.predict(frames, history)
with OnnxPredictor(path, normalize_mode="none") as q:
ref = q.predict(frames, history)
np.testing.assert_allclose(out, ref, atol=1e-6)
# ---------------- HailoPredictor 测试用 fake hailo_platform ----------------
class _FakeFormatType:
UINT8 = "UINT8"
FLOAT32 = "FLOAT32"
class _FakeFormat:
def __init__(self, fmt_type):
self.type = fmt_type
class _FakeQuantInfo:
def __init__(self, qp_zp=0.0, qp_scale=1.0):
self.qp_zp = qp_zp
self.qp_scale = qp_scale
class _FakeVStreamInfo:
def __init__(self, name, shape, fmt_type, quant=None):
self.name = name
self.shape = tuple(shape)
self.format = _FakeFormat(fmt_type)
self.quant_info = quant or _FakeQuantInfo()
class _FakeStream:
def __init__(self):
self._buffer = None
def set_buffer(self, buf):
self._buffer = buf
def get_buffer(self):
return self._buffer
class _FakeBindings:
def __init__(self):
self._streams = {}
def input(self, name=""):
return self._streams.setdefault(("in", name), _FakeStream())
def output(self, name=""):
return self._streams.setdefault(("out", name), _FakeStream())
class _FakeConfigured:
def __init__(self, transform):
self._transform = transform
def create_bindings(self):
return _FakeBindings()
def run(self, bindings_list, timeout=0):
for b in bindings_list:
self._transform(b)
class _FakeInferModel:
def __init__(self, transform):
self._configured = _FakeConfigured(transform)
@contextlib.contextmanager
def configure(self):
yield self._configured
class _FakeVDevice:
def __init__(self, transform):
self._transform = transform
def create_infer_model(self, path):
return _FakeInferModel(self._transform)
def __enter__(self):
return self
def __exit__(self, *exc):
return False
def _install_fake_hailo(test, infos_in, infos_out, transform):
"""向 sys.modules 注入 fake hailo_platform，无需硬件即可测试"""
fake = types.ModuleType("hailo_platform")
fake.FormatType = _FakeFormatType
fake.HEF = lambda path: types.SimpleNamespace(
get_input_vstream_infos=lambda: list(infos_in),
get_output_vstream_infos=lambda: list(infos_out))
fake.VDevice = lambda: _FakeVDevice(transform)
patcher = mock.patch.dict(sys.modules, {"hailo_platform": fake})
test.addCleanup(patcher.stop)
patcher.start()
def _first_row_transform(hist_name, out_name, quant_in=None, quant_out=None):
"""transform：输出 = 历史动作首行（量化边缘时先反量化再量化）"""
def transform(b):
hist = np.asarray(b.input(hist_name).get_buffer(), dtype=np.float32)
if quant_in is not None:
hist = (hist - quant_in.qp_zp) * quant_in.qp_scale
first = hist.reshape(-1, hist.shape[-1])[0]
if quant_out is not None:
first = np.clip(np.round(first / quant_out.qp_scale + quant_out.qp_zp),
0, 255).astype(np.uint8)
b.output(out_name).get_buffer()[...] = first
return transform
class TestHailoPredictor(unittest.TestCase):
"""HEF 推理封装测试（fake hailo_platform 注入，无需 Hailo 硬件）"""
def _frames(self, n, size=8):
return [np.random.randint(0, 255, (size, size, 3), dtype=np.uint8)
for _ in range(n)]
def test_float_ncfhw_layout(self):
from hailo_infer import HailoPredictor
infos_in = [
_FakeVStreamInfo("net/images", (1, 2, 3, 4, 4), _FakeFormatType.FLOAT32),
_FakeVStreamInfo("net/history", (1, 2, 3), _FakeFormatType.FLOAT32),
]
infos_out = [_FakeVStreamInfo("net/action", (1, 3), _FakeFormatType.FLOAT32)]
seen_shapes = {}
def transform(b):
seen_shapes["image"] = np.asarray(
b.input("net/images").get_buffer()).shape
_first_row_transform("net/history", "net/action")(b)
_install_fake_hailo(self, infos_in, infos_out, transform)
history = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.float32)
with HailoPredictor("fake.hef") as p:
self.assertEqual((p.frame_len, p.history_len, p.action_dim), (2, 2, 3))
self.assertEqual((p.image_width, p.image_height), (4, 4))
result = p.predict(self._frames(2), history)
np.testing.assert_allclose(result, [1, 2, 3])
self.assertEqual(seen_shapes["image"], (1, 2, 3, 4, 4))
def test_uint8_quantized_roundtrip(self):
from hailo_infer import HailoPredictor
q_in = _FakeQuantInfo(qp_zp=128.0, qp_scale=0.01)
q_out = _FakeQuantInfo(qp_zp=10.0, qp_scale=0.5)
infos_in = [
_FakeVStreamInfo("net/images", (1, 2, 3, 4, 4), _FakeFormatType.UINT8),
_FakeVStreamInfo("net/history", (1, 2, 3), _FakeFormatType.UINT8, q_in),
]
infos_out = [_FakeVStreamInfo("net/action", (1, 3), _FakeFormatType.UINT8, q_out)]
_install_fake_hailo(
self, infos_in, infos_out,
_first_row_transform("net/history", "net/action", q_in, q_out))
history = np.array([[0.5, -0.25, 0.0], [1, 1, 1]], dtype=np.float32)
with HailoPredictor("fake.hef") as p:
result = p.predict(self._frames(2), history)
np.testing.assert_allclose(result, [0.5, -0.25, 0.0], atol=0.51)
def test_nhwc_fused_layout(self):
from hailo_infer import HailoPredictor
infos_in = [
_FakeVStreamInfo("net/images", (1, 4, 4, 6), _FakeFormatType.UINT8),
_FakeVStreamInfo("net/history", (1, 2, 3), _FakeFormatType.FLOAT32),
]
infos_out = [_FakeVStreamInfo("net/action", (1, 3), _FakeFormatType.FLOAT32)]
seen_shapes = {}
def transform(b):
seen_shapes["image"] = np.asarray(
b.input("net/images").get_buffer()).shape
_first_row_transform("net/history", "net/action")(b)
_install_fake_hailo(self, infos_in, infos_out, transform)
history = np.zeros((2, 3), dtype=np.float32)
with HailoPredictor("fake.hef") as p:
self.assertEqual(p.frame_len, 2) # 6 通道 = 2 帧 × 3
result = p.predict(self._frames(2), history)
np.testing.assert_allclose(result, [0, 0, 0])
self.assertEqual(seen_shapes["image"], (1, 4, 4, 6))
def test_multiframe_inputs_layout(self):
"""新版 5 输入 HEF：frame×3 (H,W,3) + history + pred_token 常量"""
from hailo_infer import HailoPredictor
q_frame = _FakeQuantInfo(qp_zp=114.0, qp_scale=0.018723)
q_pred = _FakeQuantInfo(qp_zp=123.0, qp_scale=18571.0)
infos_in = [
_FakeVStreamInfo("net/input_layer1", (64, 64, 3),
_FakeFormatType.UINT8, q_frame),
_FakeVStreamInfo("net/input_layer2", (64, 64, 3),
_FakeFormatType.UINT8, q_frame),
_FakeVStreamInfo("net/input_layer3", (64, 64, 3),
_FakeFormatType.UINT8, q_frame),
_FakeVStreamInfo("net/input_layer4", (1, 5, 18),
_FakeFormatType.UINT8,
_FakeQuantInfo(qp_zp=0.0, qp_scale=1.960784)),
_FakeVStreamInfo("net/input_layer5", (1, 1, 128),
_FakeFormatType.UINT8, q_pred),
]
infos_out = [_FakeVStreamInfo(
"net/conv49", (1, 1, 18), _FakeFormatType.UINT8,
_FakeQuantInfo(qp_zp=2.0, qp_scale=0.837115))]
seen = {}
def transform(b):
for name in ("net/input_layer1", "net/input_layer2", "net/input_layer3"):
seen[name] = np.asarray(b.input(name).get_buffer())
seen["history"] = np.asarray(b.input("net/input_layer4").get_buffer())
seen["pred"] = np.asarray(b.input("net/input_layer5").get_buffer())
b.output("net/conv49").get_buffer()[...] = 2 # 反量化后为 0
_install_fake_hailo(self, infos_in, infos_out, transform)
with HailoPredictor("fake.hef") as p:
self.assertEqual((p.frame_len, p.history_len, p.action_dim), (3, 5, 18))
self.assertEqual((p.image_width, p.image_height), (64, 64))
result = p.predict(self._frames(3, size=64), np.zeros((5, 18), np.float32))
self.assertEqual(seen["net/input_layer1"].dtype, np.uint8)
self.assertEqual(seen["net/input_layer1"].shape, (64, 64, 3))
self.assertEqual(seen["history"].shape, (1, 5, 18))
np.testing.assert_array_equal(seen["history"], 0)
np.testing.assert_array_equal(seen["pred"], 123) # 退化量化：喂 0 → q=123
np.testing.assert_allclose(result, 0)
def test_multiframe_scrambled_report_order(self):
"""HEF vstream 报告顺序可能被打乱（实测 1,5,4,3,2），
帧输入必须按 input_layerN 排序绑定，否则 frame1/frame2 错位"""
from hailo_infer import HailoPredictor
infos_in = [
_FakeVStreamInfo("net/input_layer1", (8, 8, 3),
_FakeFormatType.FLOAT32),
_FakeVStreamInfo("net/input_layer3", (8, 8, 3),
_FakeFormatType.FLOAT32),
_FakeVStreamInfo("net/input_layer2", (8, 8, 3),
_FakeFormatType.FLOAT32),
_FakeVStreamInfo("net/input_layer4", (1, 2, 3),
_FakeFormatType.FLOAT32),
_FakeVStreamInfo("net/input_layer5", (1, 1, 4),
_FakeFormatType.FLOAT32),
]
infos_out = [_FakeVStreamInfo("net/conv49", (1, 1, 3),
_FakeFormatType.FLOAT32)]
seen = {}
def transform(b):
for name in ("net/input_layer1", "net/input_layer2",
"net/input_layer3"):
seen[name] = np.asarray(b.input(name).get_buffer()).copy()
_first_row_transform("net/input_layer4", "net/conv49")(b)
_install_fake_hailo(self, infos_in, infos_out, transform)
frames = self._frames(3)
frames[0][:] = 10
frames[1][:] = 20
frames[2][:] = 30
with HailoPredictor("fake.hef") as p:
p.predict(frames, np.zeros((2, 3), dtype=np.float32))
np.testing.assert_array_equal(
seen["net/input_layer1"], np.full((8, 8, 3), 10, np.float32))
np.testing.assert_array_equal(
seen["net/input_layer2"], np.full((8, 8, 3), 20, np.float32))
np.testing.assert_array_equal(
seen["net/input_layer3"], np.full((8, 8, 3), 30, np.float32))
def _fake_five_input_hef(self, q_frame):
infos_in = [
_FakeVStreamInfo("net/input_layer1", (8, 8, 3),
_FakeFormatType.UINT8, q_frame),
_FakeVStreamInfo("net/input_layer2", (8, 8, 3),
_FakeFormatType.UINT8, q_frame),
_FakeVStreamInfo("net/input_layer3", (8, 8, 3),
_FakeFormatType.UINT8, q_frame),
_FakeVStreamInfo("net/input_layer4", (1, 5, 18),
_FakeFormatType.UINT8,
_FakeQuantInfo(qp_zp=0.0, qp_scale=1.960784)),
_FakeVStreamInfo("net/input_layer5", (1, 1, 128),
_FakeFormatType.UINT8,
_FakeQuantInfo(qp_zp=123.0, qp_scale=0.016)),
]
infos_out = [_FakeVStreamInfo(
"net/conv49", (1, 1, 18), _FakeFormatType.UINT8,
_FakeQuantInfo(qp_zp=2.0, qp_scale=0.837115))]
seen = {}
def transform(b):
seen["f0"] = np.asarray(
b.input("net/input_layer1").get_buffer()).copy()
b.output("net/conv49").get_buffer()[...] = 2
_install_fake_hailo(self, infos_in, infos_out, transform)
return seen
def test_frame_normalized_when_quant_grid_covers_negatives(self):
"""帧输入归一化判定来自量化网格：dequant(0)≈-2.1（实测 HEF
zp=114, scale=0.0187）⇒ 须先 ImageNet 归一化再量化。
回归：旧启发式 zp*scale<0 将其误判为原始像素直通，图像路径
被饱和破坏（实机 max_diff 2.55 → 修复后 0.37）。"""
from hailo_infer import HailoPredictor
seen = self._fake_five_input_hef(
_FakeQuantInfo(qp_zp=114.0, qp_scale=0.018723))
frame = np.full((8, 8, 3), 128, dtype=np.uint8)
with HailoPredictor("fake.hef") as p:
self.assertTrue(p._normalize_frames)
p.predict([frame] * 3, np.zeros((5, 18), np.float32))
# 中灰 128 → 归一化 (128/255-mean)/std → 量化码 ≈ [118, 125, 137]
np.testing.assert_array_equal(
seen["f0"][:, :, 0], np.full((8, 8), 118, np.uint8))
np.testing.assert_array_equal(
seen["f0"][:, :, 1], np.full((8, 8), 125, np.uint8))
np.testing.assert_array_equal(
seen["f0"][:, :, 2], np.full((8, 8), 137, np.uint8))
def test_frame_raw_when_quant_grid_is_pixel_range(self):
"""原始像素校准（zp=0, scale=1）⇒ 帧直通不归一化"""
from hailo_infer import HailoPredictor
seen = self._fake_five_input_hef(
_FakeQuantInfo(qp_zp=0.0, qp_scale=1.0))
frame = np.full((8, 8, 3), 128, dtype=np.uint8)
with HailoPredictor("fake.hef") as p:
self.assertFalse(p._normalize_frames)
p.predict([frame] * 3, np.zeros((5, 18), np.float32))
np.testing.assert_array_equal(
seen["f0"], np.full((8, 8, 3), 128, np.uint8))
def test_frame_count_mismatch_raises(self):
from hailo_infer import HailoPredictor
infos_in = [
_FakeVStreamInfo("net/images", (1, 2, 3, 4, 4), _FakeFormatType.FLOAT32),
_FakeVStreamInfo("net/history", (1, 2, 3), _FakeFormatType.FLOAT32),
]
infos_out = [_FakeVStreamInfo("net/action", (1, 3), _FakeFormatType.FLOAT32)]
_install_fake_hailo(self, infos_in, infos_out,
_first_row_transform("net/history", "net/action"))
with HailoPredictor("fake.hef") as p:
with self.assertRaises(ValueError):
p.predict(self._frames(1), np.zeros((2, 3), dtype=np.float32))
def test_output_dim_mismatch_raises(self):
from hailo_infer import HailoPredictor
infos_in = [
_FakeVStreamInfo("net/images", (1, 2, 3, 4, 4), _FakeFormatType.FLOAT32),
_FakeVStreamInfo("net/history", (1, 2, 3), _FakeFormatType.FLOAT32),
]
infos_out = [_FakeVStreamInfo("net/action", (1, 4), _FakeFormatType.FLOAT32)]
_install_fake_hailo(self, infos_in, infos_out, lambda b: None)
with self.assertRaises(ValueError):
HailoPredictor("fake.hef")
def test_stats_normalization_roundtrip(self):
from hailo_infer import HailoPredictor
infos_in = [
_FakeVStreamInfo("net/images", (1, 2, 3, 4, 4), _FakeFormatType.FLOAT32),
_FakeVStreamInfo("net/history", (1, 2, 3), _FakeFormatType.FLOAT32),
]
infos_out = [_FakeVStreamInfo("net/action", (1, 3), _FakeFormatType.FLOAT32)]
_install_fake_hailo(self, infos_in, infos_out,
_first_row_transform("net/history", "net/action"))
with tempfile.TemporaryDirectory() as tmpdir:
stats_path = os.path.join(tmpdir, "action_stats.npy")
np.save(stats_path, np.stack([np.full(3, 1.0), np.full(3, 2.0)]))
history = np.array([[3, 3, 3], [1, 1, 1]], dtype=np.float32)
with HailoPredictor("fake.hef", stats_path=stats_path) as p:
result = p.predict(self._frames(2), history)
np.testing.assert_allclose(result, [3, 3, 3], atol=1e-6)
class TestDfcCompile(unittest.TestCase):
"""dfc_compile.py 的 HAR 输入层连通性检查（编译前拦截输入断连）"""
@staticmethod
def _make_har(layers):
import json
import tarfile
data = json.dumps(
{"name": "t", "net_params": {}, "layers": layers}).encode()
path = tempfile.mktemp(suffix=".har")
with tarfile.open(path, "w") as tf:
ti = tarfile.TarInfo("net.hn")
ti.size = len(data)
tf.addfile(ti, io.BytesIO(data))
return path
@staticmethod
def _layers(connect_input2=True):
return {
"n/input_layer1": {"type": "input_layer", "input": [],
"output": ["n/conv1"]},
"n/input_layer2": {"type": "input_layer", "input": [],
"output": ["n/conv2"]},
"n/conv1": {"type": "conv", "input": ["n/input_layer1"],
"output": ["n/out"]},
"n/conv2": {"type": "conv", "input": ["n/input_layer2"],
"output": ["n/out" if connect_input2 else "n/dead"]},
"n/dead": {"type": "conv", "input": ["n/conv2"], "output": []},
"n/out": {"type": "output_layer",
"input": ["n/conv1"] + (["n/conv2"] if connect_input2 else []),
"output": []},
}
def test_input_layer_names_sorted_by_suffix(self):
from dfc_compile import har_input_layer_names
har = self._make_har(self._layers())
try:
names = har_input_layer_names(har)
finally:
os.unlink(har)
self.assertEqual(names, ["n/input_layer1", "n/input_layer2"])
def test_connectivity_pass(self):
from dfc_compile import check_input_connectivity
har = self._make_har(self._layers())
try:
check_input_connectivity(har, ["n/input_layer1", "n/input_layer2"])
finally:
os.unlink(har)
def test_disconnected_input_blocked(self):
from dfc_compile import check_input_connectivity
har = self._make_har(self._layers(connect_input2=False))
try:
with self.assertRaises(SystemExit) as ctx:
check_input_connectivity(har, ["n/input_layer1", "n/input_layer2"])
finally:
os.unlink(har)
self.assertIn("n/input_layer2", str(ctx.exception))
class TestR1PredictDelta(unittest.TestCase):
"""R1-C 残差预测：dataset Δ target 构造与 predict 逆变换"""
@staticmethod
def _create_episode(base_dir, actions, image_size=16):
ep_dir = base_dir / "episode_0001"
images_dir = ep_dir / "images"
images_dir.mkdir(parents=True, exist_ok=True)
n = len(actions)
for i in range(n):
Image.fromarray(np.random.randint(0, 255, (image_size, image_size, 3),
dtype=np.uint8)).save(
images_dir / f"{i:04d}.jpg")
np.save(ep_dir / "actions.npy", np.array(actions, dtype=np.float32))
def test_delta_target_unnormalized(self):
"""不归一化时 target 应为 Δ=actions[t]-actions[t-1]"""
with tempfile.TemporaryDirectory() as tmpdir:
# 线性递增动作，Δ 恒为 [1,1]
actions = [[float(i), float(i)] for i in range(8)]
self._create_episode(Path(tmpdir), actions)
ds = ActionDataset(tmpdir, history_len=2, action_dim=2,
image_width=16, image_height=16, frame_len=2,
predict_delta=True)
sample = ds[0]
# 第 0 样本 t_start=2，target = a[2]-a[1] = [1,1]
np.testing.assert_allclose(sample["target"].numpy(), [1.0, 1.0],
atol=1e-6)
def test_absolute_target_when_disabled(self):
"""predict_delta=False 时 target 应为绝对动作 actions[t]"""
with tempfile.TemporaryDirectory() as tmpdir:
actions = [[float(i), float(i)] for i in range(8)]
self._create_episode(Path(tmpdir), actions)
ds = ActionDataset(tmpdir, history_len=2, action_dim=2,
image_width=16, image_height=16, frame_len=2,
predict_delta=False)
# t_start=2，target = a[2] = [2,2]
np.testing.assert_allclose(ds[0]["target"].numpy(), [2.0, 2.0],
atol=1e-6)
def test_delta_target_normalized_space(self):
"""归一化开启时 Δ 应在归一化空间求差（先 z-score 再相减）"""
with tempfile.TemporaryDirectory() as tmpdir:
rng = np.random.RandomState(0)
actions = rng.randn(12, 3).astype(np.float32)
self._create_episode(Path(tmpdir), actions)
ds = ActionDataset(tmpdir, history_len=2, action_dim=3,
image_width=16, image_height=16, frame_len=2,
normalize_actions=True, predict_delta=True)
mean, std = ds._action_mean, ds._action_std
sample = ds[0]
t = 2 # 第 0 样本 t_start
expected = (actions[t] - mean) / std - (actions[t - 1] - mean) / std
np.testing.assert_allclose(sample["target"].numpy(), expected, atol=1e-5)
class TestR1Rollout(unittest.TestCase):
"""R1-B 多步展开：dataset K 步序列结构与窗口对齐"""
@staticmethod
def _create_episode(base_dir, n, action_dim, image_size=16):
ep_dir = base_dir / "episode_0001"
images_dir = ep_dir / "images"
images_dir.mkdir(parents=True, exist_ok=True)
for i in range(n):
Image.fromarray(np.random.randint(0, 255, (image_size, image_size, 3),
dtype=np.uint8)).save(
images_dir / f"{i:04d}.jpg")
actions = np.arange(n * action_dim, dtype=np.float32).reshape(n, action_dim)
np.save(ep_dir / "actions.npy", actions)
return actions
def test_rollout_sample_count(self):
"""rollout_len=K 时样本数 = n - start - (K-1)"""
with tempfile.TemporaryDirectory() as tmpdir:
self._create_episode(Path(tmpdir), n=10, action_dim=2)
ds = ActionDataset(tmpdir, history_len=2, action_dim=2,
image_width=16, image_height=16, frame_len=2,
rollout_len=3)
# start=max(2,1)=2，n-(K-1)=8，样本数=8-2=6
self.assertEqual(len(ds), 6)
def test_rollout_structure_and_alignment(self):
"""多步样本返回 K 步，每步 history/target/prev_action 与时刻对齐"""
with tempfile.TemporaryDirectory() as tmpdir:
actions = self._create_episode(Path(tmpdir), n=10, action_dim=2)
ds = ActionDataset(tmpdir, history_len=2, action_dim=2,
image_width=16, image_height=16, frame_len=2,
rollout_len=3)
sample = ds[0]
self.assertIn("steps", sample)
steps = sample["steps"]
self.assertEqual(len(steps), 3)
for k, step in enumerate(steps):
t = 2 + k # t_start=2
self.assertEqual(step["images"].shape, (2, 3, 16, 16))
self.assertEqual(step["action_history"].shape, (2, 2))
self.assertEqual(step["target"].shape, (2,))
self.assertEqual(step["prev_action"].shape, (2,))
# 未归一化：history=actions[t-2:t]、target=actions[t]、prev=actions[t-1]
np.testing.assert_allclose(step["action_history"].numpy(),
actions[t - 2:t], atol=1e-6)
np.testing.assert_allclose(step["target"].numpy(),
actions[t], atol=1e-6)
np.testing.assert_allclose(step["prev_action"].numpy(),
actions[t - 1], atol=1e-6)
def test_rollout_delta_prev_action_is_base(self):
"""残差+多步：prev_action 应为 prev+target 的基准（target=Δ 时 prev+Δ=abs）"""
with tempfile.TemporaryDirectory() as tmpdir:
actions = self._create_episode(Path(tmpdir), n=10, action_dim=2)
ds = ActionDataset(tmpdir, history_len=2, action_dim=2,
image_width=16, image_height=16, frame_len=2,
predict_delta=True, rollout_len=3)
step = ds[0]["steps"][1] # t=3
# prev + Δ 应还原绝对动作 actions[t]
recon = step["prev_action"].numpy() + step["target"].numpy()
np.testing.assert_allclose(recon, actions[3], atol=1e-6)
def test_single_step_has_no_steps_key(self):
"""rollout_len=1 时保持旧版扁平结构（无 steps 键）"""
with tempfile.TemporaryDirectory() as tmpdir:
self._create_episode(Path(tmpdir), n=8, action_dim=2)
ds = ActionDataset(tmpdir, history_len=2, action_dim=2,
image_width=16, image_height=16, frame_len=2,
rollout_len=1)
sample = ds[0]
self.assertNotIn("steps", sample)
self.assertIn("target", sample)
class TestR1TrainHelpers(unittest.TestCase):
"""R1 训练辅助：_shift_history 与 _collate"""
def test_shift_history_appends_and_drops(self):
from train import _shift_history
history = torch.tensor([[[1.0], [2.0], [3.0]]]) # (1,3,1)
new_action = torch.tensor([[4.0]]) # (1,1)
out = _shift_history(history, new_action)
self.assertEqual(out.shape, (1, 3, 1))
np.testing.assert_allclose(out[0, :, 0].numpy(), [2.0, 3.0, 4.0])
def test_collate_single_step_passthrough(self):
from train import _collate
b = [{"images": torch.zeros(2, 3, 4, 4),
"action_history": torch.zeros(2, 2),
"target": torch.zeros(2)} for _ in range(3)]
out = _collate(b)
self.assertNotIn("steps", out)
self.assertEqual(out["images"].shape, (3, 2, 3, 4, 4))
def test_collate_multi_step_stacks_per_k(self):
from train import _collate
def mk(k):
return {"images": torch.full((2, 3, 4, 4), float(k)),
"action_history": torch.zeros(2, 2),
"target": torch.zeros(2),
"prev_action": torch.zeros(2)}
# 2 个样本，每个 K=3 步
b = [{"steps": [mk(k) for k in range(3)]} for _ in range(2)]
out = _collate(b)
self.assertEqual(len(out["steps"]), 3)
# 第 k 步 batch 的 images 值应全为 k
for k in range(3):
self.assertEqual(out["steps"][k]["images"].shape, (2, 2, 3, 4, 4))
self.assertTrue(
torch.allclose(out["steps"][k]["images"],
torch.full((2, 2, 3, 4, 4), float(k))))
class TestR4Evaluate(unittest.TestCase):
"""R4 离线评估：滚动口径、平滑度、误差增长、越界、汇总"""
@staticmethod
def _create_episode(base_dir, name, actions, image_size=16):
ep_dir = base_dir / name
images_dir = ep_dir / "images"
images_dir.mkdir(parents=True, exist_ok=True)
for i in range(len(actions)):
Image.fromarray(np.random.randint(0, 255, (image_size, image_size, 3),
dtype=np.uint8)).save(
images_dir / f"{i:04d}.jpg")
np.save(ep_dir / "actions.npy", np.array(actions, dtype=np.float32))
class _ConstPredictor:
"""恒定输出 c 的假推理器（鸭子类型），用于可控验证滚动逻辑"""
frame_len = 2
history_len = 2
action_dim = 2
def __init__(self, c):
self._c = np.asarray(c, dtype=np.float32)
self.calls = 0
def predict(self, images, history):
self.calls += 1
return self._c.copy()
def test_rollout_uses_self_prediction_feedback(self):
"""滚动应把自预测回写进历史（复刻 run_loop），步数 = n - t0"""
import evaluate
with tempfile.TemporaryDirectory() as tmpdir:
actions = np.arange(20, dtype=np.float32).reshape(10, 2)
self._create_episode(Path(tmpdir), "episode_0001", actions)
image_files, acts, _ = evaluate._load_episode(
tmpdir, "episode_0001")
pred = self._ConstPredictor([100.0, 100.0])
steps = evaluate.rollout_episode(pred, image_files, acts)
# t0 = max(H=2, F-1=1) = 2，horizon = 10-2 = 8
self.assertEqual(len(steps), 8)
self.assertEqual(pred.calls, 8)
# 恒预测 100，真值 a[t]，逐步 mse = mean((100-a[t])^2)
self.assertAlmostEqual(steps[0]["mse"],
float(np.mean((100 - acts[2]) ** 2)))
def test_rollout_respects_horizon(self):
import evaluate
with tempfile.TemporaryDirectory() as tmpdir:
actions = np.zeros((10, 2), dtype=np.float32)
self._create_episode(Path(tmpdir), "episode_0001", actions)
image_files, acts, _ = evaluate._load_episode(
tmpdir, "episode_0001")
pred = self._ConstPredictor([0.0, 0.0])
steps = evaluate.rollout_episode(pred, image_files, acts, horizon=3)
self.assertEqual(len(steps), 3)
def test_rollout_short_episode_returns_none(self):
import evaluate
with tempfile.TemporaryDirectory() as tmpdir:
actions = np.zeros((2, 2), dtype=np.float32) # n=2 <= t0=2
self._create_episode(Path(tmpdir), "episode_0001", actions)
image_files, acts, _ = evaluate._load_episode(
tmpdir, "episode_0001")
pred = self._ConstPredictor([0.0, 0.0])
self.assertIsNone(evaluate.rollout_episode(pred, image_files, acts))
def test_smoothness_constant_vs_noisy(self):
import evaluate
const = np.tile([1.0, 2.0], (10, 1))
rng = np.random.RandomState(0)
noisy = const + rng.randn(10, 2).astype(np.float32)
self.assertEqual(evaluate._smoothness(const), 0.0)
self.assertGreater(evaluate._smoothness(noisy), 0.0)
self.assertEqual(evaluate._smoothness(np.zeros((1, 2))), 0.0)
def test_oob_detection(self):
"""越界：预测超出经验范围应标记 oob=True"""
import evaluate
with tempfile.TemporaryDirectory() as tmpdir:
actions = np.zeros((10, 2), dtype=np.float32) # 真值全 0
self._create_episode(Path(tmpdir), "episode_0001", actions)
image_files, acts, _ = evaluate._load_episode(
tmpdir, "episode_0001")
pred = self._ConstPredictor([5.0, 5.0]) # 远超 [0,0] 范围
lo = np.zeros(2, dtype=np.float32)
hi = np.zeros(2, dtype=np.float32)
steps = evaluate.rollout_episode(pred, image_files, acts,
lo=lo, hi=hi)
self.assertTrue(all(s["oob"] for s in steps))
def test_summarize_keys_and_growth(self):
import evaluate
# 构造误差随步数线性增长的滚动记录
steps = [{"t": k, "mse": float(k + 1),
"pred": np.zeros(2), "true": np.zeros(2), "oob": False}
for k in range(40)]
lo = np.zeros(2); hi = np.zeros(2)
rep = evaluate.summarize([steps], lo, hi)
for key in ("n_episodes", "n_steps", "overall_mse", "error_growth",
"error_curve", "pred_smoothness", "true_smoothness",
"smoothness_ratio", "oob_rate"):
self.assertIn(key, rep)
self.assertEqual(rep["n_episodes"], 1)
self.assertEqual(rep["n_steps"], 40)
# 误差递增 → error_growth > 1
self.assertGreater(rep["error_growth"], 1.0)
self.assertEqual(len(rep["error_curve"]), 40)
def test_action_bounds_physical(self):
"""越界判据为固定物理界（与 action_norm 常量表共用），不再依赖数据统计"""
import evaluate
lo, hi = evaluate._action_bounds(12)
np.testing.assert_allclose(lo, np.zeros(12), atol=1e-6)
np.testing.assert_allclose(hi[:10], [180.0] * 10, atol=1e-6)
np.testing.assert_allclose(hi[10:], [5000.0, 2000.0], atol=1e-6)
# action_dim > 18 的超出列按 [0,1] 补足
lo20, hi20 = evaluate._action_bounds(20)
self.assertEqual(len(hi20), 20)
np.testing.assert_allclose(hi20[18:], [1.0, 1.0], atol=1e-6)
def test_error_growth_per_episode_median(self):
"""error_growth 按 episode 归一化取中位数，不被长短混叠主导"""
import evaluate
# ep1: 40 步线性增长 → g1 = mean(31..40)/mean(1..10) = 35.5/5.5
ep1 = [{"t": k, "mse": float(k + 1), "pred": np.zeros(2),
"true": np.zeros(2), "oob": False} for k in range(40)]
# ep2: 8 步末段尖峰 → g2 = 100/1
ep2 = [{"t": k, "mse": (1.0 if k < 6 else 100.0), "pred": np.zeros(2),
"true": np.zeros(2), "oob": False} for k in range(8)]
rep = evaluate.summarize([ep1, ep2], None, None)
g1 = 35.5 / 5.5
g2 = 100.0
self.assertAlmostEqual(rep["error_growth"], float(np.median([g1, g2])),
places=5)
class TestFixedRangeNorm(unittest.TestCase):
"""固定物理范围归一化 + 逐列加权：action_norm 常量表、dataset/predict 链路、
config 校验、加权损失"""
# ---------------- action_norm.py ----------------
def test_normalize_known_values(self):
from action_norm import normalize_action
a = np.zeros(18, dtype=np.float32)
a[0] = 90.0 # 舵机中位
a[10] = 2500.0 # spk.F 中位
a[11] = 1000.0 # spk.delay 中位
out = normalize_action(a)
self.assertAlmostEqual(float(out[0]), 0.5, places=6)
self.assertAlmostEqual(float(out[10]), 0.5, places=6)
self.assertAlmostEqual(float(out[11]), 0.5, places=6)
# 保留位恒 0 → 归一化后仍 0
np.testing.assert_allclose(out[12:], 0.0, atol=1e-7)
def test_normalize_clamps_out_of_range(self):
from action_norm import normalize_action
a = np.array([-10.0, 270.0] + [0.0] * 16, dtype=np.float32)
out = normalize_action(a)
self.assertEqual(float(out[0]), 0.0)
self.assertEqual(float(out[1]), 1.0)
def test_denormalize_roundtrip(self):
from action_norm import normalize_action, denormalize_action
rng = np.random.RandomState(0)
a = np.zeros((7, 18), dtype=np.float32)
a[:, :10] = rng.uniform(0, 180, (7, 10))
a[:, 10] = rng.uniform(0, 5000, 7)
a[:, 11] = rng.uniform(0, 2000, 7)
np.testing.assert_allclose(
denormalize_action(normalize_action(a)), a, rtol=1e-5, atol=1e-4)
def test_action_dim_prefix_and_padding(self):
from action_norm import normalize_action, action_bounds
# action_dim < 18 取前缀
lo4, hi4 = action_bounds(4)
np.testing.assert_allclose(hi4, [180.0] * 4)
out = normalize_action(np.array([90.0, 45.0, 0.0, 180.0],
dtype=np.float32))
np.testing.assert_allclose(out, [0.5, 0.25, 0.0, 1.0], atol=1e-6)
# action_dim > 18 按 [0,1] 补足
lo20, hi20 = action_bounds(20)
self.assertEqual(len(lo20), 20)
np.testing.assert_allclose(hi20[18:], [1.0, 1.0])
def test_default_column_weights(self):
from action_norm import default_column_weights
w18 = default_column_weights(18)
np.testing.assert_allclose(w18[:10], 1.0)
np.testing.assert_allclose(w18[10:12], 0.2)
np.testing.assert_allclose(w18[12:], 0.0)
# 小 action_dim：全部落在舵机段 → 全 1.0
np.testing.assert_allclose(default_column_weights(4), 1.0)
# 12 列：舵机 1.0 + spk 0.2
w12 = default_column_weights(12)
np.testing.assert_allclose(w12[10:], 0.2)
# 20 列：超出 18 列语义布局的扩展列默认 1.0（新执行器须参与训练）
w20 = default_column_weights(20)
np.testing.assert_allclose(w20[12:18], 0.0)
np.testing.assert_allclose(w20[18:], 1.0)
# ---------------- dataset 链路 ----------------
def test_dataset_fixed_range_no_stats_needed(self):
"""fixed_range 模式不需要 action_stats.npy，目标值按物理范围归一化"""
with tempfile.TemporaryDirectory() as tmpdir:
base = Path(tmpdir)
ep_dir = base / "episode_0001"
images_dir = ep_dir / "images"
images_dir.mkdir(parents=True)
for i in range(8):
Image.fromarray(np.random.randint(
0, 255, (16, 16, 3), dtype=np.uint8)).save(
images_dir / f"{i:04d}.jpg")
actions = np.zeros((8, 4), dtype=np.float32)
actions[:, 0] = 90.0 # 舵机中位 → 0.5
actions[:, 1] = 180.0 # 满行程 → 1.0
np.save(ep_dir / "actions.npy", actions)
ds = ActionDataset(tmpdir, history_len=3, action_dim=4,
image_width=16, image_height=16, frame_len=3,
normalize_actions=True,
normalize_mode="fixed_range")
sample = ds[0]
np.testing.assert_allclose(sample["target"].numpy()[:2],
[0.5, 1.0], atol=1e-6)
np.testing.assert_allclose(
sample["action_history"].numpy()[:, 0], 0.5, atol=1e-6)
# fixed_range 不产生统计量
self.assertIsNone(ds.get_action_stats())
def test_dataset_fixed_range_clamps(self):
"""越界值 clamp 到 [0,1]（防御口径）"""
with tempfile.TemporaryDirectory() as tmpdir:
base = Path(tmpdir)
ep_dir = base / "episode_0001"
images_dir = ep_dir / "images"
images_dir.mkdir(parents=True)
for i in range(8):
Image.fromarray(np.random.randint(
0, 255, (16, 16, 3), dtype=np.uint8)).save(
images_dir / f"{i:04d}.jpg")
actions = np.zeros((8, 4), dtype=np.float32)
actions[:, 0] = 999.0 # 远超 180 → clamp 1.0
np.save(ep_dir / "actions.npy", actions)
ds = ActionDataset(tmpdir, history_len=3, action_dim=4,
image_width=16, image_height=16, frame_len=3,
normalize_actions=True,
normalize_mode="fixed_range")
self.assertEqual(float(ds[0]["target"][0]), 1.0)
def test_dataset_zscore_unchanged(self):
"""zscore 默认模式行为不变（仍计算/加载统计量）"""
with tempfile.TemporaryDirectory() as tmpdir:
TestActionDataset._create_dummy_episode(
Path(tmpdir), n_frames=10, action_dim=4)
ds = ActionDataset(tmpdir, history_len=3, action_dim=4,
image_width=32, image_height=32, frame_len=3,
normalize_actions=True)
self.assertIsNotNone(ds._action_mean)
self.assertFalse(ds._fixed_range)
def test_mt_dataset_fixed_range(self):
"""MTActionDataset fixed_range：免统计量，按物理范围归一化"""
with tempfile.TemporaryDirectory() as tmpdir:
_write_mt(tmpdir, "a.mt", _make_seq_mt(6))
with contextlib.redirect_stdout(io.StringIO()):
ds = MTActionDataset(tmpdir, history_len=3, action_dim=3,
normalize_mode="fixed_range")
sample = ds[0]
# _make_seq_mt 第 i 行 [i, 10i, 100i]，target 为第 4 行 [3,30,300]
# 除数 180 → [3/180, 30/180, 300/180(clamp 1.0)]
np.testing.assert_allclose(
sample["target"].numpy(),
[3 / 180, 30 / 180, 1.0], atol=1e-6)
# ---------------- config 校验与 CLI ----------------
def test_cli_normalize_mode_and_column_weights(self):
parser = build_argparser()
args = parser.parse_args(
["--normalize-mode", "fixed_range",
"--column-weights", "1,1,1,0.2"])
cfg = apply_overrides(Config(), args)
cfg.data.action_dim = 4
self.assertEqual(cfg.data.normalize_mode, "fixed_range")
self.assertEqual(cfg.data.column_weights, [1.0, 1.0, 1.0, 0.2])
validate_config_values(cfg.data, cfg.model) # 不应抛异常
def test_bad_normalize_mode_raises(self):
cfg = Config()
cfg.data.normalize_mode = "minmax" # 非法值
with self.assertRaisesRegex(ValueError, "normalize_mode"):
validate_config_values(cfg.data, cfg.model)
def test_column_weights_length_mismatch_raises(self):
cfg = Config()
cfg.data.column_weights = [1.0, 0.2] # action_dim=18 不匹配
with self.assertRaisesRegex(ValueError, "column_weights"):
validate_config_values(cfg.data, cfg.model)
def test_column_weights_all_zero_raises(self):
cfg = Config()
cfg.data.column_weights = [0.0] * 18
with self.assertRaisesRegex(ValueError, "column_weights"):
validate_config_values(cfg.data, cfg.model)
def test_checkpoint_roundtrip_new_keys(self):
"""新键随 checkpoint 序列化/解析往返一致；旧 checkpoint 缺省兼容"""
cfg = Config()
cfg.data.normalize_mode = "fixed_range"
cfg.data.column_weights = [1.0] * 10 + [0.2] * 2 + [0.0] * 6
parsed = parse_checkpoint_config(cfg.to_dict())
self.assertEqual(parsed.data.normalize_mode, "fixed_range")
self.assertEqual(parsed.data.column_weights,
[1.0] * 10 + [0.2] * 2 + [0.0] * 6)
# 旧 checkpoint 无新键 → 默认 zscore / None，行为不变
d = cfg.to_dict()
del d["data"]["normalize_mode"]
del d["data"]["column_weights"]
parsed = parse_checkpoint_config(d)
self.assertEqual(parsed.data.normalize_mode, "zscore")
self.assertIsNone(parsed.data.column_weights)
def test_warn_init_from_normalize_mode_mismatch(self):
from config import warn_init_from_mismatch
cfg = Config()
cfg.data.normalize_mode = "fixed_range"
init_cfg = {"data": {"normalize_actions": True,
"normalize_mode": "zscore"}}
buf = io.StringIO()
with contextlib.redirect_stdout(buf):
warn_init_from_mismatch(init_cfg, cfg)
self.assertIn("normalize_mode", buf.getvalue())
# ---------------- 加权损失 ----------------
def test_weighted_mse(self):
from train import _weighted_mse
pred = torch.tensor([[1.0, 2.0, 3.0]])
target = torch.zeros(1, 3)
# 等权 → 与普通 MSE 一致
w1 = torch.ones(3)
self.assertAlmostEqual(
float(_weighted_mse(pred, target, w1)),
float(torch.nn.functional.mse_loss(pred, target)), places=6)
# 只留第 0 列权重：loss = (1·1²)/(1) = 1
w = torch.tensor([1.0, 0.0, 0.0])
self.assertAlmostEqual(float(_weighted_mse(pred, target, w)), 1.0,
places=6)
# None 退化为普通 MSE
self.assertAlmostEqual(
float(_weighted_mse(pred, target, None)),
float(torch.nn.functional.mse_loss(pred, target)), places=6)
# ---------------- predict 链路 ----------------
def test_predict_fixed_range_no_stats(self):
"""fixed_range checkpoint 无需 action_stats.npy，归一化口径与
action_norm 一致（与 raw 模型 + 手工归一化对照）"""
from action_norm import normalize_action, denormalize_action
with tempfile.TemporaryDirectory() as tmpdir:
raw_dir = os.path.join(tmpdir, "raw")
fixed_dir = os.path.join(tmpdir, "fixed")
os.makedirs(raw_dir)
os.makedirs(fixed_dir)
torch.manual_seed(0)
path, model, cfg = \
TestActionPredictorInference._make_checkpoint(raw_dir)
# 同一份权重另存为 fixed_range 归一化 checkpoint（无 stats 文件）
cfg.data.normalize_actions = True
cfg.data.normalize_mode = "fixed_range"
fixed_path = os.path.join(fixed_dir, "best_inference.pt")
torch.save({"model_state_dict": model.state_dict(),
"config": cfg.to_dict()}, fixed_path)
image = np.random.randint(0, 255, (32, 32, 3), dtype=np.uint8)
history = np.random.uniform(0, 180, (3, 4)).astype(np.float32)
# fixed_range 端：内部 normalize → 模型 → denormalize
pred_fixed = ActionPredictorInference(
fixed_path, device="cpu").predict([image] * 3, history)
# 对照：raw 模型直接吃手工归一化的历史，再手工反归一化
pred_raw = ActionPredictorInference(raw_dir + "/best_inference.pt",
device="cpu").predict(
[image] * 3, normalize_action(history, 4))
np.testing.assert_allclose(
pred_fixed, denormalize_action(pred_raw, 4),
rtol=1e-5, atol=1e-5)
class TestR2ImuNorm(unittest.TestCase):
"""R2 姿态归一化：物理量程除数、pad/truncate、None 兜底"""
def test_scales_applied(self):
from imu_norm import normalize_imu, IMU_SCALES
raw = [180.0, -180.0, 32768.0, -32768.0, 16384.0, 655.36, -655.36, 0.0]
out = normalize_imu(raw, 8)
np.testing.assert_allclose(out, np.asarray(raw) / IMU_SCALES, atol=1e-6)
def test_none_gives_zeros(self):
from imu_norm import normalize_imu
out = normalize_imu(None, 8)
self.assertEqual(out.shape, (8,))
np.testing.assert_array_equal(out, np.zeros(8, np.float32))
def test_short_input_pads_zero(self):
from imu_norm import normalize_imu
out = normalize_imu([90.0, -90.0], 8)
self.assertEqual(out.shape, (8,))
np.testing.assert_allclose(out[:2], [0.5, -0.5], atol=1e-6)
np.testing.assert_array_equal(out[2:], np.zeros(6, np.float32))
def test_long_input_truncates(self):
from imu_norm import normalize_imu
out = normalize_imu([180.0] * 10, 8)
self.assertEqual(out.shape, (8,))
np.testing.assert_allclose(out[:2], [1.0, 1.0], atol=1e-6)
def test_imu_dim_prefix_of_scales(self):
"""imu_dim<8 时取 IMU_SCALES 前缀（roll/pitch 子集可用）"""
from imu_norm import normalize_imu
out = normalize_imu([180.0, -180.0], 2)
self.assertEqual(out.shape, (2,))
np.testing.assert_allclose(out, [1.0, -1.0], atol=1e-6)
def test_multi_sensor_tiled_scales(self):
"""多传感器（8×N，N≤4）：归一化除数按 8 槽模式平铺"""
from imu_norm import normalize_imu, imu_scales, IMU_SCALES
scales = imu_scales(32)
self.assertEqual(scales.shape, (32,))
for k in range(4):
np.testing.assert_array_equal(scales[k * 8:(k + 1) * 8],
IMU_SCALES)
# 单传感器 8 槽模式 [±180, ∓180, 32768×6] 拼接两组 → 归一化重复
raw = ([180.0, -180.0] + [32768.0] * 6) * 2
out = normalize_imu(raw, 16)
np.testing.assert_allclose(
out, ([1.0, -1.0] + [1.0] * 6) * 2, atol=1e-6)
class TestR2ImuModel(unittest.TestCase):
"""R2 方案一模型：独立 IMU token 注入（序列 F+H+2）"""
@staticmethod
def _make_cfg(imu_dim):
cfg = Config()
cfg.data.image_width = 32
cfg.data.image_height = 32
cfg.data.action_dim = 4
cfg.data.history_len = 3
cfg.data.frame_len = 3
cfg.data.imu_dim = imu_dim
return cfg
def test_imu_token_changes_output(self):
"""imu_dim>0：不同姿态输入应产生不同输出（token 真的进了序列）"""
cfg = self._make_cfg(imu_dim=8)
model = ActionPredictor(cfg.model, cfg.data).eval()
imgs = torch.randn(1, 3, 3, 32, 32)
hist = torch.randn(1, 3, 4)
with torch.no_grad():
out_zero = model(imgs, hist, torch.zeros(1, 8)).numpy()
out_imu = model(imgs, hist, torch.randn(1, 8)).numpy()
self.assertGreater(np.abs(out_zero - out_imu).max(), 1e-6)
def test_none_imu_equals_zero_vector(self):
"""imu=None 应与显式零向量一致（无姿态先验语义）"""
cfg = self._make_cfg(imu_dim=8)
model = ActionPredictor(cfg.model, cfg.data).eval()
imgs = torch.randn(1, 3, 3, 32, 32)
hist = torch.randn(1, 3, 4)
with torch.no_grad():
a = model(imgs, hist).numpy()
b = model(imgs, hist, torch.zeros(1, 8)).numpy()
np.testing.assert_allclose(a, b, atol=1e-6)
def test_imu_dim_zero_has_no_imu_proj(self):
"""imu_dim=0（默认）不建 imu_proj，旧 checkpoint 完全兼容"""
cfg = self._make_cfg(imu_dim=0)
model = ActionPredictor(cfg.model, cfg.data)
self.assertFalse(hasattr(model, "imu_proj"))
# 序列长度保持 F+H+1
self.assertEqual(model.pos_embed.shape[1], 3 + 3 + 1)
def test_imu_dim_positive_seq_len(self):
"""imu_dim>0 时序列长度 F+H+2"""
cfg = self._make_cfg(imu_dim=8)
model = ActionPredictor(cfg.model, cfg.data)
self.assertEqual(model.pos_embed.shape[1], 3 + 3 + 2)
def test_dynamic_batch_with_imu(self):
"""batch 维动态：imu 输入随 batch 变化"""
cfg = self._make_cfg(imu_dim=8)
model = ActionPredictor(cfg.model, cfg.data).eval()
with torch.no_grad():
out = model(torch.randn(2, 3, 3, 32, 32),
torch.randn(2, 3, 4), torch.randn(2, 8))
self.assertEqual(out.shape, (2, 4))
class TestR2ImuDataset(unittest.TestCase):
"""R2 数据集：imu.npy 加载、归一化、缺失补零、形状修正"""
@staticmethod
def _create_episode(base_dir, name, n, action_dim, image_size=16,
imus=None):
ep_dir = base_dir / name
images_dir = ep_dir / "images"
images_dir.mkdir(parents=True, exist_ok=True)
for i in range(n):
Image.fromarray(np.random.randint(0, 255, (image_size, image_size, 3),
dtype=np.uint8)).save(
images_dir / f"{i:04d}.jpg")
actions = np.arange(n * action_dim, dtype=np.float32).reshape(
n, action_dim)
np.save(ep_dir / "actions.npy", actions)
if imus is not None:
np.save(ep_dir / "imu.npy", np.asarray(imus, dtype=np.float32))
def test_imu_loaded_and_normalized(self):
"""imu_dim>0 且 imu.npy 存在 → 样本带归一化 imu 键"""
with tempfile.TemporaryDirectory() as tmpdir:
# roll=180 → 归一化 1.0；ax=32768 → 1.0
imus = [[180.0, -180.0, 32768.0, 0, 0, 0, 0, 0]] * 10
self._create_episode(Path(tmpdir), "episode_0001", 10, 2,
imus=imus)
ds = ActionDataset(tmpdir, history_len=2, action_dim=2,
image_width=16, image_height=16, frame_len=2,
imu_dim=8)
sample = ds[0]
self.assertIn("imu", sample)
np.testing.assert_allclose(
sample["imu"].numpy(),
[1.0, -1.0, 1.0, 0, 0, 0, 0, 0], atol=1e-6)
def test_missing_imu_fills_zero(self):
"""无 imu.npy 的 episode → 零向量（混合数据集可训）"""
with tempfile.TemporaryDirectory() as tmpdir:
self._create_episode(Path(tmpdir), "episode_0001", 10, 2)
ds = ActionDataset(tmpdir, history_len=2, action_dim=2,
image_width=16, image_height=16, frame_len=2,
imu_dim=8)
np.testing.assert_array_equal(
ds[0]["imu"].numpy(), np.zeros(8, np.float32))
def test_imu_dim_zero_no_imu_key(self):
"""imu_dim=0 时样本无 imu 键（旧行为不变）"""
with tempfile.TemporaryDirectory() as tmpdir:
self._create_episode(Path(tmpdir), "episode_0001", 10, 2,
imus=[[1.0] * 8] * 10)
ds = ActionDataset(tmpdir, history_len=2, action_dim=2,
image_width=16, image_height=16, frame_len=2)
self.assertNotIn("imu", ds[0])
def test_imu_dim_mismatch_padded(self):
"""imu.npy 列数与 imu_dim 不一致 → pad/truncate 不炸"""
with tempfile.TemporaryDirectory() as tmpdir:
imus = [[180.0, -180.0]] * 10 # 只有 2 列，imu_dim=8
self._create_episode(Path(tmpdir), "episode_0001", 10, 2,
imus=imus)
ds = ActionDataset(tmpdir, history_len=2, action_dim=2,
image_width=16, image_height=16, frame_len=2,
imu_dim=8)
imu = ds[0]["imu"].numpy()
self.assertEqual(imu.shape, (8,))
np.testing.assert_allclose(imu[:2], [1.0, -1.0], atol=1e-6)
np.testing.assert_array_equal(imu[2:], np.zeros(6, np.float32))
def test_multi_sensor_16wide_normalized(self):
"""双传感器 imu.npy（16 列）：两组 8 槽分别按物理量程归一化"""
with tempfile.TemporaryDirectory() as tmpdir:
imus = [[180.0, 0, 0, 0, 0, 0, 0, 0, # sensor0 roll
0, 0, 0, 0, 0, 0, 0, 32768.0]] * 10 # sensor1 gz
self._create_episode(Path(tmpdir), "episode_0001", 10, 2,
imus=imus)
ds = ActionDataset(tmpdir, history_len=2, action_dim=2,
image_width=16, image_height=16, frame_len=2,
imu_dim=16)
imu = ds[0]["imu"].numpy()
self.assertEqual(imu.shape, (16,))
np.testing.assert_allclose(imu[0], 1.0, atol=1e-6)
np.testing.assert_allclose(imu[15], 1.0, atol=1e-6)
np.testing.assert_array_equal(imu[1:15], np.zeros(14, np.float32))
def test_short_imu_aligned_with_zero_pad(self):
"""imu.npy 行数少于帧数 → 尾部补零对齐"""
with tempfile.TemporaryDirectory() as tmpdir:
imus = [[180.0] + [0.0] * 7] * 5 # 5 行 < 10 帧
self._create_episode(Path(tmpdir), "episode_0001", 10, 2,
imus=imus)
ds = ActionDataset(tmpdir, history_len=2, action_dim=2,
image_width=16, image_height=16, frame_len=2,
imu_dim=8)
# 第 0 样本 t_start=2（有 imu），末样本 t_start=8（超出 5 行 → 0）
np.testing.assert_allclose(ds[0]["imu"].numpy()[0], 1.0, atol=1e-6)
np.testing.assert_array_equal(ds[-1]["imu"].numpy(),
np.zeros(8, np.float32))
def test_rollout_steps_carry_imu(self):
"""多步展开时每步带对应时刻的 imu"""
with tempfile.TemporaryDirectory() as tmpdir:
imus = [[float(t), 0, 0, 0, 0, 0, 0, 0] for t in range(10)]
self._create_episode(Path(tmpdir), "episode_0001", 10, 2,
imus=imus)
ds = ActionDataset(tmpdir, history_len=2, action_dim=2,
image_width=16, image_height=16, frame_len=2,
rollout_len=3, imu_dim=8)
steps = ds[0]["steps"]
for k, step in enumerate(steps):
t = 2 + k
self.assertIn("imu", step)
np.testing.assert_allclose(step["imu"].numpy()[0],
t / 180.0, atol=1e-6)
@unittest.skipUnless(
importlib.util.find_spec("onnxscript") is not None
and importlib.util.find_spec("onnxruntime") is not None,
"OnnxPredictor 测试需要 onnxscript + onnxruntime")
class TestR2OnnxPredictorImu(unittest.TestCase):
"""R2 ONNX 后端：含 imu 输入的导出模型推理对齐"""
@staticmethod
def _export_imu_onnx(tmpdir):
cfg = Config()
cfg.data.image_width = 32
cfg.data.image_height = 32
cfg.data.action_dim = 4
cfg.data.history_len = 3
cfg.data.frame_len = 3
cfg.data.imu_dim = 8
model = ActionPredictor(cfg.model, cfg.data)
model.eval()
out_path = os.path.join(tmpdir, "model_imu.onnx")
torch.onnx.export(
model,
(torch.randn(1, 3, 3, 32, 32), torch.randn(1, 3, 4),
torch.randn(1, 8)),
out_path,
input_names=["images", "action_history", "imu"],
output_names=["predicted_action"],
opset_version=18)
return out_path, model
def _frames(self, n):
return [np.random.randint(0, 255, (48, 64, 3), dtype=np.uint8)
for _ in range(n)]
def test_imu_dim_detected(self):
from onnx_infer import OnnxPredictor
with tempfile.TemporaryDirectory() as tmpdir:
path, _ = self._export_imu_onnx(tmpdir)
with OnnxPredictor(path) as p:
self.assertEqual(p.imu_dim, 8)
def test_imu_predict_matches_torch(self):
"""predict(imu=原始姿态) 应与 torch（手动归一化）前向一致"""
from imu_norm import normalize_imu
from onnx_infer import OnnxPredictor
with tempfile.TemporaryDirectory() as tmpdir:
path, model = self._export_imu_onnx(tmpdir)
frames = self._frames(3)
history = np.random.randn(3, 4).astype(np.float32)
imu_raw = np.array([5.0, -3.0, 100, 200, 16384, 10, -20, 30],
dtype=np.float32)
with OnnxPredictor(path) as p:
onnx_out = p.predict(frames, history, imu=imu_raw)
pre = np.stack([p._preprocess(f) for f in frames])
with torch.no_grad():
torch_out = model(
torch.from_numpy(pre[np.newaxis]),
torch.from_numpy(history[np.newaxis]),
torch.from_numpy(
normalize_imu(imu_raw, 8)[np.newaxis])).numpy()
np.testing.assert_allclose(onnx_out, torch_out.reshape(-1),
atol=1e-4)
def test_imu_changes_output(self):
"""姿态输入应影响输出（确认 imu 真的喂进了图）"""
from onnx_infer import OnnxPredictor
with tempfile.TemporaryDirectory() as tmpdir:
path, _ = self._export_imu_onnx(tmpdir)
frames = self._frames(3)
history = np.random.randn(3, 4).astype(np.float32)
with OnnxPredictor(path) as p:
a = p.predict(frames, history, imu=None)
b = p.predict(frames, history,
imu=np.array([180.0] + [0.0] * 7, np.float32))
self.assertGreater(np.abs(a - b).max(), 1e-6)
def test_no_imu_model_imu_dim_none(self):
"""旧两输入 ONNX：imu_dim 为 None（pybot 鸭子类型走旧两参路径）"""
from onnx_infer import OnnxPredictor
with tempfile.TemporaryDirectory() as tmpdir:
path, _ = TestOnnxPredictor._export_small_onnx(tmpdir)
with OnnxPredictor(path) as p:
self.assertIsNone(p.imu_dim)
class TestR2EvaluateImu(unittest.TestCase):
"""R2 评估：滚动时姿态真值按时刻传给 predict"""
class _ImuRecordingPredictor:
"""记录每步收到的 imu 的假推理器"""
frame_len = 2
history_len = 2
action_dim = 2
imu_dim = 8
def __init__(self):
self.imus_received = []
def predict(self, images, history, imu=None):
self.imus_received.append(
None if imu is None else np.asarray(imu).copy())
return np.zeros(2, dtype=np.float32)
def test_rollout_passes_imu_per_step(self):
"""imus 存在时逐步传 imus[t]（t0 起步与帧窗对齐）"""
import evaluate
with tempfile.TemporaryDirectory() as tmpdir:
actions = np.zeros((10, 2), dtype=np.float32)
TestR4Evaluate._create_episode(Path(tmpdir), "episode_0001",
actions)
imus = np.arange(80, dtype=np.float32).reshape(10, 8)
np.save(Path(tmpdir) / "episode_0001" / "imu.npy", imus)
image_files, acts, loaded = evaluate._load_episode(
tmpdir, "episode_0001")
np.testing.assert_array_equal(loaded, imus)
pred = self._ImuRecordingPredictor()
steps = evaluate.rollout_episode(pred, image_files, acts,
imus=loaded)
self.assertEqual(len(steps), 8) # t0=2 → t=2..9
for k, rec in enumerate(steps):
t = 2 + k
np.testing.assert_array_equal(pred.imus_received[k], imus[t])
def test_rollout_without_imu_passes_none(self):
"""imu.npy 缺失 → 每步 imu=None（等价零向量，不炸）"""
import evaluate
with tempfile.TemporaryDirectory() as tmpdir:
actions = np.zeros((10, 2), dtype=np.float32)
TestR4Evaluate._create_episode(Path(tmpdir), "episode_0001",
actions)
image_files, acts, loaded = evaluate._load_episode(
tmpdir, "episode_0001")
self.assertIsNone(loaded)
pred = self._ImuRecordingPredictor()
evaluate.rollout_episode(pred, image_files, acts, imus=loaded)
self.assertTrue(all(v is None for v in pred.imus_received))
def test_legacy_predictor_not_given_imu_kwarg(self):
"""imu_dim=None 的旧推理器不应收到 imu 关键字（鸭子类型兼容）"""
import evaluate
with tempfile.TemporaryDirectory() as tmpdir:
actions = np.zeros((10, 2), dtype=np.float32)
TestR4Evaluate._create_episode(Path(tmpdir), "episode_0001",
actions)
image_files, acts, _ = evaluate._load_episode(
tmpdir, "episode_0001")
pred = TestR4Evaluate._ConstPredictor([0.0, 0.0]) # 无 imu_dim
steps = evaluate.rollout_episode(pred, image_files, acts)
self.assertEqual(len(steps), 8)
self.assertEqual(pred.calls, 8)
class TestR2HailoImu(unittest.TestCase):
"""R2 HEF 封装：6 输入布局（frame×3 + history + pred_token + imu）"""
def _frames(self, n, size=8):
return [np.random.randint(0, 255, (size, size, 3), dtype=np.uint8)
for _ in range(n)]
def _install_six_input_fake(self, seen):
infos_in = [
_FakeVStreamInfo("net/input_layer1", (8, 8, 3),
_FakeFormatType.FLOAT32),
_FakeVStreamInfo("net/input_layer2", (8, 8, 3),
_FakeFormatType.FLOAT32),
_FakeVStreamInfo("net/input_layer3", (8, 8, 3),
_FakeFormatType.FLOAT32),
_FakeVStreamInfo("net/input_layer4", (1, 2, 3),
_FakeFormatType.FLOAT32),
_FakeVStreamInfo("net/input_layer5", (1, 1, 4),
_FakeFormatType.FLOAT32),
_FakeVStreamInfo("net/input_layer6", (1, 1, 1, 8),
_FakeFormatType.FLOAT32),
]
infos_out = [_FakeVStreamInfo("net/conv49", (1, 1, 3),
_FakeFormatType.FLOAT32)]
def transform(b):
seen["imu"] = np.asarray(
b.input("net/input_layer6").get_buffer()).copy()
_first_row_transform("net/input_layer4", "net/conv49")(b)
_install_fake_hailo(self, infos_in, infos_out, transform)
def test_imu_input_classified_and_bound(self):
"""imu (1,1,1,8) 输入应被识别为姿态而非 pred_token，绑定归一化值"""
from hailo_infer import HailoPredictor
seen = {}
self._install_six_input_fake(seen)
with HailoPredictor("fake.hef") as p:
self.assertEqual(p.imu_dim, 8)
# roll=180° → 归一化 1.0
p.predict(self._frames(3), np.zeros((2, 3), np.float32),
imu=np.array([180.0] + [0.0] * 7, np.float32))
self.assertEqual(seen["imu"].shape, (1, 1, 1, 8))
np.testing.assert_allclose(seen["imu"][0, 0, 0, 0], 1.0, atol=1e-6)
np.testing.assert_array_equal(seen["imu"][0, 0, 0, 1:],
np.zeros(7, np.float32))
def test_imu_none_binds_zeros(self):
"""imu=None → 零向量绑定（无姿态先验）"""
from hailo_infer import HailoPredictor
seen = {}
self._install_six_input_fake(seen)
with HailoPredictor("fake.hef") as p:
p.predict(self._frames(3), np.zeros((2, 3), np.float32))
np.testing.assert_array_equal(seen["imu"], np.zeros((1, 1, 1, 8)))
def test_imu_uint8_quantized(self):
"""uint8 量化边缘：归一化值按 quant_info 量化"""
from hailo_infer import HailoPredictor
q_imu = _FakeQuantInfo(qp_zp=128.0, qp_scale=0.01)
infos_in = [
_FakeVStreamInfo("net/input_layer1", (8, 8, 3),
_FakeFormatType.FLOAT32),
_FakeVStreamInfo("net/input_layer2", (8, 8, 3),
_FakeFormatType.FLOAT32),
_FakeVStreamInfo("net/input_layer3", (8, 8, 3),
_FakeFormatType.FLOAT32),
_FakeVStreamInfo("net/input_layer4", (1, 2, 3),
_FakeFormatType.FLOAT32),
_FakeVStreamInfo("net/input_layer5", (1, 1, 4),
_FakeFormatType.FLOAT32),
_FakeVStreamInfo("net/input_layer6", (1, 1, 1, 8),
_FakeFormatType.UINT8, q_imu),
]
infos_out = [_FakeVStreamInfo("net/conv49", (1, 1, 3),
_FakeFormatType.FLOAT32)]
seen = {}
def transform(b):
seen["imu"] = np.asarray(
b.input("net/input_layer6").get_buffer()).copy()
_first_row_transform("net/input_layer4", "net/conv49")(b)
_install_fake_hailo(self, infos_in, infos_out, transform)
with HailoPredictor("fake.hef") as p:
# roll=18° → 归一化 0.1 → 量化 round(0.1/0.01+128)=138
p.predict(self._frames(3), np.zeros((2, 3), np.float32),
imu=np.array([18.0] + [0.0] * 7, np.float32))
self.assertEqual(seen["imu"].dtype, np.uint8)
self.assertEqual(seen["imu"][0, 0, 0, 0], 138)
# 零值 → round(0/0.01+128)=128
np.testing.assert_array_equal(seen["imu"][0, 0, 0, 1:],
np.full(7, 128, np.uint8))
class TestR2DfcCompileImu(unittest.TestCase):
"""R2 dfc_compile：--with-imu 时 start_nodes 末尾追加 imu"""
def test_start_nodes_with_imu(self):
from dfc_compile import start_nodes
self.assertEqual(start_nodes(3, with_imu=True),
["frame0", "frame1", "frame2",
"action_history", "pred_token", "imu"])
def test_start_nodes_without_imu_unchanged(self):
from dfc_compile import start_nodes
self.assertEqual(start_nodes(3),
["frame0", "frame1", "frame2",
"action_history", "pred_token"])
self.assertEqual(start_nodes(3, with_imu=False), start_nodes(3))
class TestR2ConfigImu(unittest.TestCase):
"""R2 配置：imu_dim 校验与覆盖"""
def test_default_imu_dim_zero(self):
self.assertEqual(DataConfig().imu_dim, 0)
def test_imu_dim_out_of_range_raises(self):
data = DataConfig(imu_dim=65)
with self.assertRaises(ValueError):
validate_config_values(data, ModelConfig())
data = DataConfig(imu_dim=-1)
with self.assertRaises(ValueError):
validate_config_values(data, ModelConfig())
def test_imu_dim_cli_override(self):
args = build_argparser().parse_args(["--imu-dim", "8"])
cfg = apply_overrides(Config(), args)
self.assertEqual(cfg.data.imu_dim, 8)
def test_checkpoint_config_roundtrip_with_imu(self):
cfg = Config()
cfg.data.imu_dim = 8
restored = parse_checkpoint_config(cfg.to_dict())
self.assertEqual(restored.data.imu_dim, 8)
if __name__ == "__main__":
unittest.main(verbosity=2)
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