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txtai/test/python/testmodels.py
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Python

"""
Models module tests
"""
import unittest
from unittest.mock import patch
import torch
from txtai.models import Models, ClsPooling, MeanPooling, PoolingFactory
class TestModels(unittest.TestCase):
"""
Models tests.
"""
@patch("torch.cuda.is_available")
def testDeviceid(self, cuda):
"""
Test the deviceid method
"""
cuda.return_value = True
self.assertEqual(Models.deviceid(True), 0)
self.assertEqual(Models.deviceid(False), -1)
self.assertEqual(Models.deviceid(0), 0)
self.assertEqual(Models.deviceid(1), 1)
# Test direct torch device
# pylint: disable=E1101
self.assertEqual(Models.deviceid(torch.device("cpu")), torch.device("cpu"))
cuda.return_value = False
self.assertEqual(Models.deviceid(True), -1)
self.assertEqual(Models.deviceid(False), -1)
self.assertEqual(Models.deviceid(0), -1)
self.assertEqual(Models.deviceid(1), -1)
def testDevice(self):
"""
Tests the device method
"""
# pylint: disable=E1101
self.assertEqual(Models.device("cpu"), torch.device("cpu"))
self.assertEqual(Models.device(torch.device("cpu")), torch.device("cpu"))
def testPooling(self):
"""
Tests pooling methods
"""
# Device id
device = Models.deviceid(True)
# Test mean pooling
pooling = PoolingFactory.create({"path": "sentence-transformers/nli-mpnet-base-v2", "device": device})
self.assertEqual(type(pooling), MeanPooling)
pooling = PoolingFactory.create({"method": "meanpooling", "path": "flax-sentence-embeddings/multi-qa_v1-MiniLM-L6-cls_dot", "device": device})
self.assertEqual(type(pooling), MeanPooling)
# Test CLS pooling
pooling = PoolingFactory.create({"path": "flax-sentence-embeddings/multi-qa_v1-MiniLM-L6-cls_dot", "device": device})
self.assertEqual(type(pooling), ClsPooling)
pooling = PoolingFactory.create({"method": "clspooling", "path": "sentence-transformers/nli-mpnet-base-v2", "device": device})
self.assertEqual(type(pooling), ClsPooling)
# Test CLS pooling encoding
self.assertEqual(pooling.encode(["test"])[0].shape, (768,))