Fix the instlalation requirements'

clip/clip.py

@@ -9,8 +9,8 @@ from PIL import Image
 from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
 from tqdm import tqdm
 
-from model import build_model
+from .model import build_model
-from simple_tokenizer import SimpleTokenizer as _Tokenizer
+from .simple_tokenizer import SimpleTokenizer as _Tokenizer
 
 __all__ = ["available_models", "load", "tokenize"]
 _tokenizer = _Tokenizer()
@@ -24,7 +24,7 @@ _MODELS = {
 def _download(url: str, root: str = os.path.expanduser("~/.cache/clip")):
     os.makedirs(root, exist_ok=True)
     filename = os.path.basename(url)
-    
+
     expected_sha256 = url.split("/")[-2]
     download_target = os.path.join(root, filename)
 
@@ -38,7 +38,7 @@ def _download(url: str, root: str = os.path.expanduser("~/.cache/clip")):
             warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file")
 
     with urllib.request.urlopen(url) as source, open(download_target, "wb") as output:
-        with tqdm(total=int(source.info().get("Content-Length")), ncols=80) as loop:        
+        with tqdm(total=int(source.info().get("Content-Length")), ncols=80) as loop:
             while True:
                 buffer = source.read(8192)
                 if not buffer:

setup.py

@@ -5,7 +5,8 @@ import os
 from setuptools import setup, find_packages
 
 core_req = ['ftfy', 'regex', 'tqdm', 'torch==1.7.1', 'torchvision']
-extra_requires={'cuda': ['cudatoolkit==11.0']}
+extras_require={'cuda': ['cudatoolkit==11.0'],
+                'dev': ['pytest']}
 
 setup(
     name='clip_by_openai',
@@ -20,7 +21,7 @@ setup(
     packages=find_packages(exclude=["tests*"]),
     python_requires=">=3",
     install_requires=core_req,
-    extra_requires=extra_requires,
+    extras_require=extras_require,
     classifiers=[
         "Development Status :: 5 - Production/Stable",
         "Intended Audience :: Developers",

tests/test_encode.py

@@ -1,4 +1,16 @@
 import clip
 import torch
-device = 'cpu'
+import torch
-model, preprocess = clip.load("ViT-B/32", device=device)
+from PIL import Image
+def test_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 torhc.no_grad():
+        assert model.encode_image(image), "Encoding an image does not work"
+        assert model.encode_text(text), "Encoding text does not work"
+        logits_per_image, logits_per_text = model(image, text)
+        probs = logits_per_image.softmax(dim=-1).cpu().numpy()
+    print("Label probs:", probs)
+