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import statistics
import unittest
import numpy as np
import torch
from sentence_transformers import SentenceTransformer
from encoder_models import SBertEncoder, get_encoder
from utils import get_gpu, slice_embeddings, is_nested_list_of_type, flatten_list, compute_f1, Scores
class TestUtils(unittest.TestCase):
def test_get_gpu(self):
gpu_count = torch.cuda.device_count()
gpu_available = torch.cuda.is_available()
# Test single boolean input
self.assertEqual(get_gpu(True), 0 if gpu_available else "cpu")
self.assertEqual(get_gpu(False), "cpu")
# Test single string input
self.assertEqual(get_gpu("cpu"), "cpu")
self.assertEqual(get_gpu("gpu"), 0 if gpu_available else "cpu")
self.assertEqual(get_gpu("cuda"), 0 if gpu_available else "cpu")
# Test single integer input
self.assertEqual(get_gpu(0), 0 if gpu_available else "cpu")
self.assertEqual(get_gpu(1), 1 if gpu_available else "cpu")
# Test list input with unique elements
self.assertEqual(get_gpu([True, "cpu", 0]), [0, "cpu"] if gpu_available else ["cpu", "cpu", "cpu"])
# Test list input with duplicate elements
self.assertEqual(get_gpu([0, 0, "gpu"]), [0] if gpu_available else ["cpu", "cpu", "cpu"])
# Test list input with duplicate elements of different types
self.assertEqual(get_gpu([True, 0, "gpu"]), [0] if gpu_available else ["cpu", "cpu", "cpu"])
# Test list input with all integers
self.assertEqual(get_gpu(list(range(gpu_count))),
list(range(gpu_count)) if gpu_available else gpu_count * ["cpu"])
with self.assertRaises(ValueError):
get_gpu("invalid")
with self.assertRaises(ValueError):
get_gpu(torch.cuda.device_count())
def test_slice_embeddings(self):
embeddings = np.random.rand(10, 5)
num_sentences = [3, 2, 5]
expected_output = [embeddings[:3], embeddings[3:5], embeddings[5:]]
self.assertTrue(
all(np.array_equal(a, b) for a, b in zip(slice_embeddings(embeddings, num_sentences),
expected_output))
)
num_sentences_nested = [[2, 1], [3, 4]]
expected_output_nested = [[embeddings[:2], embeddings[2:3]], [embeddings[3:6], embeddings[6:]]]
self.assertTrue(
slice_embeddings(embeddings, num_sentences_nested), expected_output_nested
)
with self.assertRaises(TypeError):
slice_embeddings(embeddings, "invalid")
def test_is_nested_list_of_type(self):
# Test case: Depth 0, single element matching element_type
self.assertTrue(is_nested_list_of_type("test", str, 0))
# Test case: Depth 0, single element not matching element_type
self.assertFalse(is_nested_list_of_type("test", int, 0))
# Test case: Depth 1, list of elements matching element_type
self.assertTrue(is_nested_list_of_type(["apple", "banana"], str, 1))
# Test case: Depth 1, list of elements not matching element_type
self.assertFalse(is_nested_list_of_type([1, 2, 3], str, 1))
# Test case: Depth 0 (Wrong), list of elements matching element_type
self.assertFalse(is_nested_list_of_type([1, 2, 3], str, 0))
# Depth 2
self.assertTrue(is_nested_list_of_type([[1, 2], [3, 4]], int, 2))
self.assertTrue(is_nested_list_of_type([['1', '2'], ['3', '4']], str, 2))
self.assertFalse(is_nested_list_of_type([[1, 2], ["a", "b"]], int, 2))
# Depth 3
self.assertFalse(is_nested_list_of_type([[[1], [2]], [[3], [4]]], list, 3))
self.assertTrue(is_nested_list_of_type([[[1], [2]], [[3], [4]]], int, 3))
with self.assertRaises(ValueError):
is_nested_list_of_type([1, 2], int, -1)
def test_flatten_list(self):
self.assertEqual(flatten_list([1, [2, 3], [[4], 5]]), [1, 2, 3, 4, 5])
self.assertEqual(flatten_list([]), [])
self.assertEqual(flatten_list([1, 2, 3]), [1, 2, 3])
self.assertEqual(flatten_list([[[[1]]]]), [1])
def test_compute_f1(self):
self.assertAlmostEqual(compute_f1(0.5, 0.5), 0.5)
self.assertAlmostEqual(compute_f1(1, 0), 0.0)
self.assertAlmostEqual(compute_f1(0, 1), 0.0)
self.assertAlmostEqual(compute_f1(1, 1), 1.0)
def test_scores(self):
scores = Scores(precision=0.8, recall=[0.7, 0.9])
self.assertAlmostEqual(scores.f1, compute_f1(0.8, statistics.fmean([0.7, 0.9])))
class TestSBertEncoder(unittest.TestCase):
def setUp(self, device=None):
if device is None:
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
else:
self.device = device
self.model_name = "stsb-roberta-large"
self.batch_size = 8
self.verbose = False
self.encoder = SBertEncoder(self.model_name, self.device, self.batch_size, self.verbose)
def test_initialization(self):
self.assertIsInstance(self.encoder.model, SentenceTransformer)
self.assertEqual(self.encoder.device, self.device)
self.assertEqual(self.encoder.batch_size, self.batch_size)
self.assertEqual(self.encoder.verbose, self.verbose)
def test_encode_single_device(self):
sentences = ["This is a test sentence.", "Here is another sentence."]
embeddings = self.encoder.encode(sentences)
self.assertIsInstance(embeddings, np.ndarray)
self.assertEqual(embeddings.shape[0], len(sentences))
self.assertEqual(embeddings.shape[1], self.encoder.model.get_sentence_embedding_dimension())
def test_encode_multi_device(self):
if torch.cuda.device_count() < 2:
self.skipTest("Multi-GPU test requires at least 2 GPUs.")
else:
devices = ["cuda:0", "cuda:1"]
self.setUp(devices)
sentences = ["This is a test sentence.", "Here is another sentence.", "This is a test sentence."]
embeddings = self.encoder.encode(sentences)
self.assertIsInstance(embeddings, np.ndarray)
self.assertEqual(embeddings.shape[0], 3)
self.assertEqual(embeddings.shape[1], self.encoder.model.get_sentence_embedding_dimension())
class TestGetEncoder(unittest.TestCase):
def test_get_sbert_encoder(self):
model_name = "stsb-roberta-large"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
batch_size = 8
verbose = False
encoder = get_encoder(model_name, device, batch_size, verbose)
self.assertIsInstance(encoder, SBertEncoder)
self.assertEqual(encoder.device, device)
self.assertEqual(encoder.batch_size, batch_size)
self.assertEqual(encoder.verbose, verbose)
def test_get_use_encoder(self):
model_name = "use"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
batch_size = 8
verbose = False
encoder = get_encoder(model_name, device, batch_size, verbose)
self.assertIsInstance(encoder, SBertEncoder) # SBertEncoder is returned for "use" for now
# Uncomment below when implementing USE class
# self.assertIsInstance(encoder, USE)
# self.assertEqual(encoder.model_name, model_name)
# self.assertEqual(encoder.device, device)
# self.assertEqual(encoder.batch_size, batch_size)
# self.assertEqual(encoder.verbose, verbose)
if __name__ == '__main__':
unittest.main()
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