| """Pose cascade + guided report. Pure-logic tests need no models; endpoint tests use tiny stub models so every |
| branch of analyse_frame is exercised exactly (flag off / gate rejects / gate agrees / gate unavailable / guided / legacy request).""" |
| import numpy as np |
| import pytest |
| import torch |
| import torch.nn as nn |
| from fastapi import FastAPI |
| from fastapi.testclient import TestClient |
|
|
| from app.config import settings |
| from app.routers import pose as pose_router |
| from app.services import hf_loader |
| from app.services.cascade import UNKNOWN, cascade_decide, collapse, guided_report, top_k |
| from app.utils.geometry import FEATURE_NAMES |
| from app.utils.rules_classifier import _POSE_FEATURE_BANDS |
|
|
| BANDED = next(p for p, b in _POSE_FEATURE_BANDS.items() if b) |
| BANDLESS = next(p for p, b in _POSE_FEATURE_BANDS.items() if not b) |
| ANG = {n: 90.0 for n in FEATURE_NAMES} |
| DEVS = {n: 12.0 for n in FEATURE_NAMES} |
|
|
|
|
| def test_collapse_sums_imperfect_onto_base(): |
| assert collapse({"warrior_2": 0.5, "imperfect_warrior_2": 0.25, UNKNOWN: 0.25}) == {"warrior_2": 0.75, UNKNOWN: 0.25} |
|
|
|
|
| def test_gate_rejects_even_when_namer_is_confident(): |
| r = cascade_decide("warrior_2", {"warrior_2": 0.99, UNKNOWN: 0.01}, {UNKNOWN: 0.7, "warrior_2": 0.3}, ANG, 0.9, DEVS) |
| assert r.pose_id == UNKNOWN and r.gated and r.correctness == 0.0 and r.correctness_source == "none" |
|
|
|
|
| def test_gate_pass_returns_namer_pose_and_gate_correctness(): |
| r = cascade_decide(BANDED, {BANDED: 0.8, "plank": 0.2}, {BANDED: 0.6, UNKNOWN: 0.4}, ANG, 0.83, DEVS) |
| assert r.pose_id == BANDED and not r.gated and r.correctness == pytest.approx(0.83) and r.correctness_source == "gate_head" |
| assert r.deviations_source == "rule_bands" |
| assert r.candidates[0][0] == BANDED |
|
|
|
|
| def test_bandless_pose_falls_back_to_gate_deviations(): |
| r = cascade_decide(BANDLESS, {BANDLESS: 0.9}, {BANDLESS: 0.8, UNKNOWN: 0.2}, ANG, 0.7, DEVS) |
| assert r.pose_id == BANDLESS and r.deviations_source == "gate_head" and r.deviations["knee_l"] == 12.0 |
|
|
|
|
| def test_namer_unknown_stays_unknown_without_being_counted_as_gated(): |
| r = cascade_decide(UNKNOWN, {UNKNOWN: 0.9, BANDED: 0.1}, {BANDED: 0.9, UNKNOWN: 0.1}, ANG, 0.8, DEVS) |
| assert r.pose_id == UNKNOWN and not r.gated |
|
|
|
|
| def test_gate_disagreeing_on_which_pose_does_not_veto(): |
| |
| r = cascade_decide(BANDED, {BANDED: 0.7}, {"plank": 0.8, UNKNOWN: 0.2}, ANG, 0.6, DEVS) |
| assert r.pose_id == BANDED and r.gate_pose == "plank" |
|
|
|
|
| def test_top_k_orders_descending(): |
| assert [n for n, _ in top_k({"a": .1, "b": .6, "c": .3}, 2)] == ["b", "c"] |
|
|
|
|
| def test_guided_report_scores_against_the_chosen_pose_not_the_detected_one(): |
| g = guided_report(BANDED, "plank", ANG) |
| assert g["target_pose"] == BANDED and g["matches"] is False and g["target_has_bands"] is True |
| assert guided_report(BANDED, BANDED, ANG)["matches"] is True |
| assert guided_report(BANDLESS, BANDLESS, ANG)["target_has_bands"] is False |
|
|
|
|
| |
| class Stub(nn.Module): |
| def __init__(self, classes, top, corr=0.5): |
| super().__init__() |
| self.logits = torch.full((1, len(classes)), -5.0) |
| self.logits[0, classes.index(top)] = 5.0 |
| self.corr = torch.tensor([float(np.log(corr / (1 - corr)))]) |
| self.dev = torch.full((1, 15), 0.1) |
|
|
| def forward(self, x): |
| return self.logits, self.corr, self.dev |
|
|
|
|
| CLASSES = [UNKNOWN, "mountain_pose", "warrior_2", "plank"] |
|
|
|
|
| @pytest.fixture |
| def client(monkeypatch): |
| app = FastAPI(); app.include_router(pose_router.router, prefix="/api") |
| return TestClient(app), monkeypatch |
|
|
|
|
| def payload(**kw): |
| p = {"angles": [170.0] * 15} |
| p.update(kw); return p |
|
|
|
|
| def install(monkeypatch, namer_top, gate_top=None, cascade_on=True, gate_corr=0.9): |
| monkeypatch.setattr(pose_router, "get_mlp_model", lambda: (Stub(CLASSES, namer_top, 0.5), CLASSES)) |
| monkeypatch.setattr(settings, "ENABLE_POSE_CASCADE", cascade_on) |
| monkeypatch.setattr(pose_router, "get_gate_model", |
| lambda: (Stub(CLASSES, gate_top, gate_corr), CLASSES) if gate_top else (None, [])) |
|
|
|
|
| def test_flag_off_is_the_original_response_shape(client): |
| c, mp = client; install(mp, "mountain_pose", gate_top=None, cascade_on=False) |
| r = c.post("/api/analyse_frame", json=payload()).json() |
| assert r["cascade"] is None and r["candidates"] is None and r["guided"] is None |
| assert {"pose_id", "correctness_score", "deviations", "motion_state"} <= set(r) |
|
|
|
|
| def test_cascade_gate_rejects(client): |
| c, mp = client; install(mp, "mountain_pose", gate_top=UNKNOWN) |
| r = c.post("/api/analyse_frame", json=payload()).json() |
| assert r["pose_id"] == UNKNOWN and r["cascade"]["active"] and r["cascade"]["gated"] |
|
|
|
|
| def test_cascade_gate_agrees_uses_gate_correctness_and_returns_candidates(client): |
| c, mp = client; install(mp, "mountain_pose", gate_top="mountain_pose", gate_corr=0.9) |
| r = c.post("/api/analyse_frame", json=payload()).json() |
| assert r["pose_id"] == "mountain_pose" and r["correctness_score"] == pytest.approx(0.9, abs=1e-3) |
| assert r["cascade"]["correctness_source"] == "gate_head" and len(r["candidates"]) == 3 |
|
|
|
|
| def test_cascade_requested_but_gate_unavailable_falls_back_and_says_so(client): |
| c, mp = client; install(mp, "mountain_pose", gate_top=None, cascade_on=True) |
| mp.setattr(hf_loader, "gate_load_error", "FileNotFoundError: no gate checkpoint") |
| r = c.post("/api/analyse_frame", json=payload()).json() |
| assert r["cascade"]["active"] is False and "no gate checkpoint" in r["cascade"]["reason"] |
| assert r["pose_id"] |
|
|
|
|
| def test_guided_block_present_only_with_target(client): |
| c, mp = client; install(mp, "mountain_pose", gate_top="mountain_pose") |
| assert c.post("/api/analyse_frame", json=payload()).json()["guided"] is None |
| g = c.post("/api/analyse_frame", json=payload(target_pose="warrior_2")).json()["guided"] |
| assert g["target_pose"] == "warrior_2" and g["matches"] is False and set(g["target_deviations"]) == set(FEATURE_NAMES) |
|
|
|
|
| def test_bad_input_still_rejected(client): |
| c, mp = client; install(mp, "mountain_pose", gate_top="mountain_pose") |
| assert c.post("/api/analyse_frame", json={"angles": [1.0] * 14}).status_code == 400 |
|
|
