File size: 3,852 Bytes
9635a89 | 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 | """Tests for the streaming predictor."""
from unittest.mock import MagicMock, patch
import numpy as np
import pytest
import torch
def _make_mock_model(n_vertices=100, hidden=64):
"""Create a mock model that returns predictable outputs."""
model = MagicMock()
model.feature_dims = {"text": (2, 32), "audio": (2, 32)}
model.config = MagicMock()
model.config.hidden = hidden
def fake_forward(batch, pool_outputs=True):
return torch.randn(1, n_vertices, 10)
model.__call__ = fake_forward
model.return_value = torch.randn(1, n_vertices, 10)
return model
class TestStreamingPredictor:
def test_buffer_fill(self):
from cortexlab.inference.streaming import StreamingPredictor
model = _make_mock_model()
sp = StreamingPredictor(model, window_trs=5, step_trs=1)
# First 4 frames should return None (buffer not full)
for _ in range(4):
features = {
"text": torch.randn(2, 32),
"audio": torch.randn(2, 32),
}
result = sp.push_frame(features)
assert result is None
def test_prediction_emission(self):
from cortexlab.inference.streaming import StreamingPredictor
model = _make_mock_model()
sp = StreamingPredictor(model, window_trs=3, step_trs=1)
for i in range(3):
features = {"text": torch.randn(2, 32), "audio": torch.randn(2, 32)}
result = sp.push_frame(features)
# 3rd frame should trigger prediction
assert result is not None
assert isinstance(result, np.ndarray)
assert result.shape == (100,)
def test_step_control(self):
from cortexlab.inference.streaming import StreamingPredictor
model = _make_mock_model()
sp = StreamingPredictor(model, window_trs=3, step_trs=2)
results = []
for _ in range(5):
features = {"text": torch.randn(2, 32), "audio": torch.randn(2, 32)}
result = sp.push_frame(features)
results.append(result)
# With window=3, step=2: first prediction at frame 4 (buffer full at 3, then wait 2 more)
# Frame 0: buffer[0], not full -> None
# Frame 1: buffer[0,1], not full -> None
# Frame 2: buffer[0,1,2], full, frames_since=3 >= step=2 -> predict
# Frame 3: frames_since=1 < step=2 -> None
# Frame 4: frames_since=2 >= step=2 -> predict
assert results[0] is None
assert results[1] is None
assert results[2] is not None
assert results[3] is None
assert results[4] is not None
def test_missing_modality(self):
from cortexlab.inference.streaming import StreamingPredictor
model = _make_mock_model()
sp = StreamingPredictor(model, window_trs=2, step_trs=1)
# Only provide text, not audio
for _ in range(2):
features = {"text": torch.randn(2, 32)}
result = sp.push_frame(features)
assert result is not None
def test_flush(self):
from cortexlab.inference.streaming import StreamingPredictor
model = _make_mock_model()
sp = StreamingPredictor(model, window_trs=3, step_trs=1)
for _ in range(3):
features = {"text": torch.randn(2, 32), "audio": torch.randn(2, 32)}
sp.push_frame(features)
results = sp.flush()
assert len(results) >= 1
def test_reset(self):
from cortexlab.inference.streaming import StreamingPredictor
model = _make_mock_model()
sp = StreamingPredictor(model, window_trs=3, step_trs=1)
for _ in range(3):
features = {"text": torch.randn(2, 32)}
sp.push_frame(features)
sp.reset()
assert len(sp._buffer) == 0
assert sp._frames_since_emit == 0
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