simplified setup.py

tests/test_consistency.py

@@ -0,0 +1,25 @@
+import numpy as np
+import pytest
+import torch
+from PIL import Image
+
+import clip
+
+
+@pytest.mark.parametrize('model_name', clip.available_models())
+def test_consistency(model_name):
+    device = "cpu"
+    jit_model, transform = clip.load(model_name, device=device)
+    py_model, _ = clip.load(model_name, device=device, jit=False)
+
+    image = transform(Image.open("CLIP.png")).unsqueeze(0).to(device)
+    text = clip.tokenize(["a diagram", "a dog", "a cat"]).to(device)
+
+    with torch.no_grad():
+        logits_per_image, _ = jit_model(image, text)
+        jit_probs = logits_per_image.softmax(dim=-1).cpu().numpy()
+
+        logits_per_image, _ = py_model(image, text)
+        py_probs = logits_per_image.softmax(dim=-1).cpu().numpy()
+
+    assert np.allclose(jit_probs, py_probs, atol=0.01, rtol=0.1)

tests/test_encode.py

@@ -1,16 +0,0 @@
-import clip
-import torch
-import torch
-from PIL import Image
-def test_smoke_simple_cpu():
-    device = 'cpu'
-    model, preprocess = clip.load("ViT-B/32", device=device)
-    image = preprocess(Image.open('CLIP.png')).unsqueeze(0).to(device)
-    text = clip.tokenize(["a diagram", "a dog", "a cat"]).to(device)
-    with torch.no_grad():
-        model.encode_image(image)
-        model.encode_text(text)
-        logits_per_image, logits_per_text = model(image, text)
-        probs = logits_per_image.softmax(dim=-1).cpu().numpy()
-    assert True
-