optimize clip tests (#15)

tests/func/test_pytorch.py

@@ -1,3 +1,5 @@
+from pathlib import Path
+
 import open_clip
 import pytest
 from torch import Size, Tensor
@@ -8,10 +10,10 @@
 from datachain.lib.pytorch import PytorchDataset
 
 
-@pytest.fixture
-def fake_dataset(tmp_path, catalog):
+@pytest.fixture(scope="module")
+def fake_dataset(tmpdir_factory):
     # Create fake images in labeled dirs
-    data_path = tmp_path / "data" / ""
+    data_path = Path(tmpdir_factory.mktemp("data"))
     for i, (img, label) in enumerate(FakeData()):
         label = str(label)
         (data_path / label).mkdir(parents=True, exist_ok=True)
@@ -37,11 +39,11 @@ def test_pytorch_dataset(fake_dataset):
         transform=transform,
         tokenizer=tokenizer,
     )
-    for img, text, label in pt_dataset:
-        assert isinstance(img, Tensor)
-        assert isinstance(text, Tensor)
-        assert isinstance(label, int)
-        assert img.size() == Size([3, 64, 64])
+    img, text, label = next(iter(pt_dataset))
+    assert isinstance(img, Tensor)
+    assert isinstance(text, Tensor)
+    assert isinstance(label, int)
+    assert img.size() == Size([3, 64, 64])
 
 
 def test_pytorch_dataset_sample(fake_dataset):
@@ -62,8 +64,8 @@ def test_to_pytorch(fake_dataset):
     tokenizer = open_clip.get_tokenizer("ViT-B-32")
     pt_dataset = fake_dataset.to_pytorch(transform=transform, tokenizer=tokenizer)
     assert isinstance(pt_dataset, IterableDataset)
-    for img, text, label in pt_dataset:
-        assert isinstance(img, Tensor)
-        assert isinstance(text, Tensor)
-        assert isinstance(label, int)
-        assert img.size() == Size([3, 64, 64])
+    img, text, label = next(iter(pt_dataset))
+    assert isinstance(img, Tensor)
+    assert isinstance(text, Tensor)
+    assert isinstance(label, int)
+    assert img.size() == Size([3, 64, 64])

tests/unit/lib/conftest.py

@@ -0,0 +1,21 @@
+import pytest
+import torch
+from torch import float32
+from torchvision.transforms import v2
+
+
+@pytest.fixture(scope="session")
+def fake_clip_model():
+    class Model:
+        def encode_image(self, tensor):
+            return torch.randn(len(tensor), 512)
+
+        def encode_text(self, tensor):
+            return torch.randn(len(tensor), 512)
+
+    def tokenizer(tensor, context_length=77):
+        return torch.randn(len(tensor), context_length)
+
+    model = Model()
+    preprocess = v2.ToDtype(float32, scale=True)
+    return model, preprocess, tokenizer

tests/unit/lib/test_clip.py

@@ -1,4 +1,3 @@
-import open_clip
 import pytest
 from PIL import Image
 from transformers import CLIPModel, CLIPProcessor
@@ -7,10 +6,6 @@
 
 IMAGES = [Image.new(mode="RGB", size=(64, 64)), Image.new(mode="RGB", size=(32, 32))]
 TEXTS = ["text1", "text2"]
-MODEL, _, PREPROCESS = open_clip.create_model_and_transforms(
-    "ViT-B-32", pretrained="laion2b_s34b_b79k"
-)
-TOKENIZER = open_clip.get_tokenizer("ViT-B-32")
 
 
 @pytest.mark.parametrize(
@@ -20,15 +15,16 @@
 @pytest.mark.parametrize("text", [None, "text", TEXTS])
 @pytest.mark.parametrize("prob", [True, False])
 @pytest.mark.parametrize("image_to_text", [True, False])
-def test_similarity_scores(images, text, prob, image_to_text):
+def test_similarity_scores(fake_clip_model, images, text, prob, image_to_text):
+    model, preprocess, tokenizer = fake_clip_model
     if not (images or text):
         with pytest.raises(ValueError):
             scores = similarity_scores(
-                images, text, MODEL, PREPROCESS, TOKENIZER, prob, image_to_text
+                images, text, model, preprocess, tokenizer, prob, image_to_text
             )
     else:
         scores = similarity_scores(
-            images, text, MODEL, PREPROCESS, TOKENIZER, prob, image_to_text
+            images, text, model, preprocess, tokenizer, prob, image_to_text
         )
         assert isinstance(scores, list)
         if not images:

tests/unit/lib/test_text.py

@@ -1,15 +1,13 @@
-import open_clip
 import torch
 from transformers import CLIPModel, CLIPProcessor
 
 from datachain.lib.file import TextFile
 from datachain.lib.text import convert_text
 
 
-def test_convert_text():
+def test_convert_text(fake_clip_model):
     text = "thisismytext"
-    tokenizer_model = "ViT-B-32"
-    tokenizer = open_clip.get_tokenizer(tokenizer_model)
+    model, _, tokenizer = fake_clip_model
     converted_text = convert_text(text, tokenizer=tokenizer)
     assert isinstance(converted_text, torch.Tensor)
 
@@ -22,7 +20,6 @@ def test_convert_text():
     converted_text = convert_text(
         text, tokenizer=tokenizer, tokenizer_kwargs=tokenizer_kwargs
     )
-    model, _, _ = open_clip.create_model_and_transforms(tokenizer_model)
     converted_text = convert_text(text, tokenizer=tokenizer, encoder=model.encode_text)
     assert converted_text.dtype == torch.float32