import json import io from pathlib import Path import sys import msgpack import numpy as np sys.path.insert(0, str(Path(__file__).resolve().parents[1])) from gpt6_b1k.recording import sensor_only from gpt6_b1k.server import pack, unpack from gpt6_b1k.policy import TrashPolicy from gpt6_b1k.perception import held_can_evidence, refine_can from gpt6_b1k.motion import SkillFailure from run import summarize def test_privileged_fields_do_not_enter_policy_observations(): raw = {'robot::proprio': np.zeros(61), 'robot::head::rgb': np.zeros((4, 4, 3)), 'robot::head::depth_linear': np.ones((4, 4)), 'robot::head::seg_instance': np.ones((4, 4)), 'object_poses': {'can': [1, 2, 3]}, 'robot_global_pose': [0, 0, 0], 'q_score': 1.0} assert set(sensor_only(raw)) == {'robot::proprio', 'robot::head::rgb', 'robot::head::depth_linear'} def test_official_wire_arrays_roundtrip(): arrays = {'action': np.arange(23, dtype=np.float32), 'rgb': np.ones((3, 4, 3), dtype=np.uint8)} result = msgpack.unpackb(msgpack.packb(arrays, default=pack), object_hook=unpack, strict_map_key=False) for name, value in arrays.items(): np.testing.assert_array_equal(result[name], value) assert result[name].dtype == value.dtype def test_reset_clears_episode_memory(): policy = TrashPolicy(io.StringIO()) policy.objects = [dict(done=True)] policy.odom.pose[:] = 3 policy.gripper = -1 policy.reset() assert policy.objects == [] and policy.gripper == 1 np.testing.assert_array_equal(policy.odom.pose, np.zeros(3)) def test_open_gripper_is_not_mistaken_for_carried_can(): from types import SimpleNamespace p = np.zeros(61) p[49:51] = .05 assert held_can_evidence(SimpleNamespace(p=p)) == (False, 0) def test_physical_skill_failure_keeps_evaluation_actions_valid(): p = np.zeros(61) p[23] = p[48] = 1 # Valid end-effector quaternions. raw = {'robot::proprio': p} for camera in ('zed', 'left_realsense', 'right_realsense'): prefix = f'robot:{camera}_link:Camera:0::' raw[prefix+'rgb'] = np.zeros((16, 16, 3), dtype=np.uint8) raw[prefix+'depth_linear'] = np.ones((16, 16), dtype=np.float32) policy = TrashPolicy(io.StringIO()) policy.gripper = -1 def blocked_motion(): raise SkillFailure('Physical execution blocked') yield policy.generator = blocked_motion() for _ in range(2): action = policy.predict(raw) assert action.shape == (23,) and np.isfinite(action).all() assert action[22] == -1 assert policy.phase == 'unresolved_motion' def test_wrist_refinement_accepts_a_silver_can_lid(): from types import SimpleNamespace x, y = np.meshgrid(np.linspace(.70, .77, 20), np.linspace(-.34, -.27, 20)) cloud = np.stack((x, y, np.full_like(x, .12)), -1) # No RGB supplied: semantic identity was established in the preceding head # view, and the silver lid is refined from the local observed depth surface. d = refine_can(SimpleNamespace(cloud=lambda camera: cloud), [.735, -.305, .07]) assert d is not None np.testing.assert_allclose(d.center[:2], [.735, -.305], atol=.001) def test_short_or_partial_runs_cannot_claim_goal(tmp_path): (tmp_path/'manifest.json').write_text(json.dumps(dict(indices=list(range(20)), instance_ids=list(range(301,321)), diagnostic=True))) out = tmp_path/'instance_301/evaluation/json' out.mkdir(parents=True) (out/'picking_up_trash_301_0.json').write_text(json.dumps(dict(instance_id=301, q_score={'final':1.}, success=True, steps=1))) summarize(tmp_path) result = json.loads((tmp_path/'summary.json').read_text()) assert result['completed'] == 1 assert not result['complete'] and not result['target_achieved'] def test_complete_score_must_be_strictly_above_threshold(tmp_path): (tmp_path/'manifest.json').write_text(json.dumps(dict(indices=list(range(20)), instance_ids=list(range(301,321)), diagnostic=False))) out = tmp_path/'batch_000/evaluation/json' out.mkdir(parents=True) for i in range(301,321): (out/f'{i}.json').write_text(json.dumps(dict(instance_id=i, q_score={'final': .7}, success=False, steps=1))) summarize(tmp_path) result = json.loads((tmp_path/'summary.json').read_text()) assert result['complete'] and not result['target_achieved'] (out/'301.json').write_text(json.dumps(dict(instance_id=301, q_score={'final':1.}, success=True, steps=1))) summarize(tmp_path) assert json.loads((tmp_path/'summary.json').read_text())['target_achieved']