Datasets:

ChangChrisLiu
/

GWAM_Data

@@ -1,156 +0,0 @@
-from __future__ import annotations
-import sys
-from pathlib import Path
-import numpy as np
-sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "realtime"))
-from gwam_realtime_env_graph import (
-    build_online_visual_features,
-    encode_rle,
-    to_gnn_graph,
-    visibility_change_edges,
-)
-class FakeVisualBackend:
-    def image_embedding(self, rgb_frame):
-        return np.asarray(rgb_frame, dtype=np.float32)
-    def pool_mask(self, image_embedding, mask):
-        if not mask.any():
-            return None
-        seed = float(image_embedding[mask].mean()) if image_embedding.ndim == 3 else float(mask.mean())
-        vec = np.linspace(0.0, 1.0, 256, dtype=np.float32) + seed / 255.0
-        vec /= np.linalg.norm(vec).clip(min=1e-6)
-        return vec.astype(np.float16)
-    def type_clip32(self, nodes):
-        arr = np.zeros((256, 32), dtype=np.float32)
-        for i in range(min(len(nodes), 256)):
-            arr[i, i % 32] = 1.0
-        return arr, {"model": "fake", "projection_seed": 20260702}
-def _decode_for_test(rle, shape):
-    vals = []
-    cur = 0
-    for count in rle:
-        vals.extend([cur] * int(count))
-        cur = 1 - cur
-    return np.array(vals, dtype=bool).reshape(shape)
-def test_encode_rle_starts_with_zero_run_and_roundtrips_simple_mask():
-    mask = np.array([[False, True, True], [False, False, True]], dtype=bool)
-    rle = encode_rle(mask)
-    assert rle == [1, 2, 2, 1]
-    assert _decode_for_test(rle, mask.shape).tolist() == mask.tolist()
-def test_visibility_change_edges_are_self_edges_by_view():
-    prev = np.zeros((4, 3), dtype=bool)
-    cur = prev.copy()
-    cur[2, 1] = True
-    rows = visibility_change_edges(cur, prev)
-    assert rows == [{"src": 2, "dst": 2, "rel": "visibility_change", "view": 1}]
-def test_to_gnn_graph_active_masks_family_onehot_and_edges():
-    node_state = np.zeros((5, 32), dtype=np.float32)
-    node_state[[0, 2, 4], 0] = 1
-    node_state[[0, 2, 4], 1] = [9, 0, 5]
-    node_state[[0, 2, 4], 2:22] = np.arange(60, dtype=np.float32).reshape(3, 20)
-    view_visible = np.zeros((5, 3), dtype=bool)
-    view_visible[0, 0] = True
-    view_visible[2, 2] = True
-    graph = to_gnn_graph(
-        node_state=node_state,
-        static_edges=[{"src": 2, "dst": 0, "rel": "part_of"}],
-        dynamic_edges=[{"src": 0, "dst": 4, "rel": "contact"}, {"src": 1, "dst": 2, "rel": "contact"}],
-        view_visible=view_visible,
-        family_encoding="onehot",
-    )
-    assert graph["slot_ids"].tolist() == [0, 2, 4]
-    assert graph["x"].shape == (3, 33)  # 20 state + 10 family + 3 visibility flags
-    assert graph["edge_index"].tolist() == [[1, 0], [0, 2]]
-    assert graph["edge_attr"].shape == (2, 8)
-    assert "family_idx" not in graph["feature_schema"]
-    assert "family_onehot_9" in graph["feature_schema"]
-def test_online_visual_features_sparse_block_matches_phase2_policy_with_fake_backend():
-    mask = np.zeros((256, 256), dtype=bool)
-    mask[20:40, 30:50] = True
-    rgb_frames = {0: np.full((256, 256, 3), 42, dtype=np.uint8)}
-    rle_rows = [{"n": 1, "v": 0, "rle": encode_rle(mask)}]
-    nodes = [{"category_text": "robot"}, {"category_text": "drawer handle"}]
-    visual = build_online_visual_features(
-        rgb_frames=rgb_frames,
-        rle_rows=rle_rows,
-        nodes=nodes,
-        visual_backend=FakeVisualBackend(),
-        t=0,
-    )
-    assert visual["t"].tolist() == [0]
-    assert visual["n"].tolist() == [1]
-    assert visual["v"].tolist() == [0]
-    assert visual["feat"].shape == (1, 256)
-    assert visual["feat"].dtype == np.float16
-    assert visual["type_clip32"].shape == (256, 32)
-    assert visual["visual_computed"].tolist() == [True]
-    assert visual["summary"]["features_written"] == 1
-    assert visual["summary"]["invalid_visible_pairs"] == 0
-    assert visual["summary"]["invisible_features_injected"] == 0
-def test_final_fused_graph_has_packaged_loader_342d_feature_layout():
-    node_state = np.zeros((5, 32), dtype=np.float32)
-    node_state[[0, 1], 0] = 1
-    node_state[[0, 1], 1] = [9, 6]
-    node_state[[0, 1], 2:22] = np.arange(40, dtype=np.float32).reshape(2, 20)
-    view_visible = np.zeros((5, 3), dtype=bool)
-    view_visible[1, 0] = True
-    view_centroid = np.zeros((5, 3, 2), dtype=np.float16)
-    view_area = np.zeros((5, 3, 1), dtype=np.float16)
-    view_bbox = np.zeros((5, 3, 4), dtype=np.float16)
-    view_centroid[1, 0] = [0.25, 0.5]
-    view_area[1, 0, 0] = 0.1
-    view_bbox[1, 0] = [0.1, 0.2, 0.3, 0.4]
-    mask = np.zeros((256, 256), dtype=bool)
-    mask[10:20, 10:20] = True
-    visual = build_online_visual_features(
-        rgb_frames={0: np.full((256, 256, 3), 7, dtype=np.uint8)},
-        rle_rows=[{"n": 1, "v": 0, "rle": encode_rle(mask)}],
-        nodes=[{"category_text": "robot"}, {"category_text": "handle"}],
-        visual_backend=FakeVisualBackend(),
-        t=0,
-    )
-    graph = to_gnn_graph(
-        node_state=node_state,
-        static_edges=[{"src": 1, "dst": 0, "rel": "part_of"}],
-        dynamic_edges=[{"src": 0, "dst": 1, "rel": "contact"}],
-        view_visible=view_visible,
-        view_centroid=view_centroid,
-        view_area=view_area,
-        view_bbox=view_bbox,
-        visual_features=visual,
-        include_visual=True,
-        include_type_clip32=True,
-        include_full_view_evidence=True,
-    )
-    assert graph["slot_ids"].tolist() == [0, 1]
-    assert graph["x"].shape == (2, 342)
-    assert len(graph["feature_schema"]) == 342
-    assert graph["visual_feature_valid"].tolist() == [False, True]
-    assert graph["metadata"]["include_visual"] is True
-    assert graph["metadata"]["include_type_clip32"] is True
-    assert graph["metadata"]["include_full_view_evidence"] is True
-    assert graph["edge_attr"].shape == (2, 8)
-    assert graph["feature_schema"][30] == "visual_sam2_mean_000"
-    assert graph["feature_schema"][286] == "type_clip32_000"
-    assert graph["feature_schema"][318] == "view_right_visible"