File size: 8,346 Bytes
fbd9366 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 | from pathlib import Path
from types import ModuleType, SimpleNamespace
import sys
import numpy as np
import torch
from groot.vla.experiment import trex_wandb_video_callback as callback_module
from groot.vla.experiment import trex_eval_utils
from groot.vla.experiment.trex_track_force_eval_utils import (
TrexTrackForceVisualization,
_decode_future_video,
overlay_track_motion,
)
from groot.vla.experiment.trex_wandb_video_callback import TrexWandbVideoCallback
def test_track_motion_overlay_outputs_only_three_view_panels():
frames = np.zeros((8, 100, 200, 3), dtype=np.uint8)
tracks = np.full((1, 16, 250, 2), 0.5, dtype=np.float32)
tracks[0, :, :, 0] += np.linspace(-0.2, 0.2, 16)[:, None]
visualization = TrexTrackForceVisualization(
video_frames=frames,
predicted_track_xy=tracks,
target_track_xy=tracks.copy(),
target_track_visibility=np.ones((1, 16, 250), dtype=np.float32),
anchor_timestamps=np.array([0.0], dtype=np.float64),
frame_track_indices=np.array(
[2, 4, 6, 8, 10, 12, 14, 15], dtype=np.int64
),
)
overlaid = overlay_track_motion(visualization, trail_steps=8)
assert overlaid.shape == (8, 50, 300, 3)
assert np.count_nonzero(overlaid[:, :, :100]) > 0
assert np.count_nonzero(overlaid[:, :, 100:200]) > 0
assert np.count_nonzero(overlaid[:, :, 200:]) > 0
def test_track_motion_overlay_rejects_mismatched_video_indices():
visualization = TrexTrackForceVisualization(
video_frames=np.zeros((2, 100, 200, 3), dtype=np.uint8),
predicted_track_xy=np.zeros((1, 16, 250, 2), dtype=np.float32),
target_track_xy=np.zeros((1, 16, 250, 2), dtype=np.float32),
target_track_visibility=np.ones((1, 16, 250), dtype=np.float32),
anchor_timestamps=np.array([0.0], dtype=np.float64),
frame_track_indices=np.array([1], dtype=np.int64),
)
try:
overlay_track_motion(visualization)
except ValueError as exc:
assert "video and frame-track index counts" in str(exc)
else:
raise AssertionError("mismatched frame-track indices must fail")
def test_track_video_starts_with_model_gt_anchor():
conditioning = np.full((250, 2), 0.5, dtype=np.float32)
anchored = np.repeat(conditioning[None], 16, axis=0)
anchored[:, :, 0] += np.linspace(0.0, 0.1, 16)[:, None]
np.testing.assert_allclose(anchored[0], conditioning)
visualization = TrexTrackForceVisualization(
video_frames=np.zeros((9, 100, 200, 3), dtype=np.uint8),
predicted_track_xy=anchored[None],
target_track_xy=anchored[None],
target_track_visibility=np.ones((1, 16, 250), dtype=np.float32),
anchor_timestamps=np.array([0.0], dtype=np.float64),
frame_track_indices=np.array(
[-1, 2, 4, 6, 8, 10, 12, 14, 15], dtype=np.int64
),
conditioning_frame=np.zeros((100, 200, 3), dtype=np.uint8),
conditioning_track_xy=conditioning[None],
conditioning_track_visibility=np.ones(
(1, 250), dtype=np.float32
),
)
overlaid = overlay_track_motion(visualization)
assert overlaid.shape == (9, 50, 300, 3)
assert np.count_nonzero(overlaid[0]) > 0
def test_callback_saves_plain_and_track_overlay_videos_separately(
tmp_path, monkeypatch
):
visualization = TrexTrackForceVisualization(
video_frames=np.zeros((8, 100, 200, 3), dtype=np.uint8),
predicted_track_xy=np.zeros((1, 16, 250, 2), dtype=np.float32),
target_track_xy=np.zeros((1, 16, 250, 2), dtype=np.float32),
target_track_visibility=np.ones((1, 16, 250), dtype=np.float32),
anchor_timestamps=np.array([0.0], dtype=np.float64),
frame_track_indices=np.array(
[2, 4, 6, 8, 10, 12, 14, 15], dtype=np.int64
),
)
monkeypatch.setattr(
callback_module,
"run_trex_track_force_prediction",
lambda *args, **kwargs: visualization,
)
monkeypatch.setattr(
callback_module,
"overlay_track_motion",
lambda *args, **kwargs: np.full_like(
visualization.video_frames, 255
),
)
written: dict[Path, np.ndarray] = {}
def fake_mimsave(path, frames, **kwargs):
written[Path(path)] = np.stack(frames)
monkeypatch.setattr(callback_module.imageio, "mimsave", fake_mimsave)
logged: list[dict] = []
fake_wandb = ModuleType("wandb")
fake_wandb.run = object()
fake_wandb.define_metric = lambda *args, **kwargs: None
fake_wandb.Video = lambda path, **kwargs: {"path": path, **kwargs}
fake_wandb.log = logged.append
monkeypatch.setitem(sys.modules, "wandb", fake_wandb)
callback = TrexWandbVideoCallback.__new__(TrexWandbVideoCallback)
callback.episode_index = 0
callback.num_chunks = 1
callback.every_n_steps = 500
callback.fps = 10
callback.eval_bf16 = False
callback.use_dataset_prompt = False
callback.prompt = "test task"
callback.overlay_tracks = True
callback.save_tracks = True
callback.track_trail_steps = 8
callback.reconstruction_inference_steps = 1
callback.eval_video_dir = tmp_path / "eval_videos"
callback.eval_track_video_dir = tmp_path / "eval_track_videos"
callback.eval_track_dir = tmp_path / "eval_tracks"
callback._episode = SimpleNamespace(
episode_index=0,
get_task=lambda row: "dataset task",
)
callback._eval_transform = object()
class FakeModel:
training = True
def eval(self):
self.training = False
def train(self):
self.training = True
model = FakeModel()
state = SimpleNamespace(is_world_process_zero=True, global_step=500)
callback.on_train_begin(None, state, None)
callback.on_step_end(None, state, None, model=model)
plain_path = callback.eval_video_dir / "train_step_000500.mp4"
track_path = callback.eval_track_video_dir / "train_step_000500.mp4"
assert np.count_nonzero(written[plain_path]) == 0
assert np.all(written[track_path] == 255)
assert (callback.eval_track_dir / "train_step_000500.npz").is_file()
assert TrexWandbVideoCallback.VIDEO_METRIC in logged[0]
assert TrexWandbVideoCallback.TRACK_VIDEO_METRIC in logged[0]
assert model.training
def test_episode_frame_reads_are_cached_across_visualization_chunks(monkeypatch):
opens = 0
class FakeCapture:
def __init__(self, path):
nonlocal opens
opens += 1
self.position = 0
def isOpened(self):
return True
def set(self, prop, value):
self.position = int(value)
def read(self):
value = self.position
self.position += 1
return True, np.full((2, 3, 3), value, dtype=np.uint8)
def release(self):
pass
monkeypatch.setattr(trex_eval_utils.cv2, "VideoCapture", FakeCapture)
episode = trex_eval_utils.TrexEpisode.__new__(
trex_eval_utils.TrexEpisode
)
episode.length = 10
episode.video_dirs = {"video.head_left": "fake.mp4"}
episode._frame_cache = {"video.head_left": {}}
first = episode.get_frames([0, 1, 1, 2], "video.head_left")
second = episode.get_frames([1, 2], "video.head_left")
assert opens == 1
assert first[:, 0, 0, 0].tolist() == [0, 1, 1, 2]
assert second[:, 0, 0, 0].tolist() == [1, 2]
def test_track_force_video_decode_reads_tiling_from_policy_config():
seen = {}
class FakeVAE:
def decode(self, latents, **kwargs):
seen["shape"] = tuple(latents.shape)
seen.update(kwargs)
return torch.zeros(1, 3, 9, 4, 8)
action_head = SimpleNamespace(
config=SimpleNamespace(
tiled=False,
tile_size_height=20,
tile_size_width=21,
tile_stride_height=10,
tile_stride_width=11,
),
vae=FakeVAE(),
)
model = SimpleNamespace(action_head=action_head)
frames = _decode_future_video(
model,
torch.zeros(1, 4, 3, 2, 2),
torch.zeros(1, 4, 2, 2, 2),
)
assert frames.shape == (8, 4, 8, 3)
assert seen == {
"shape": (1, 4, 3, 2, 2),
"tiled": False,
"tile_size": (20, 21),
"tile_stride": (10, 11),
}
|