0.3.1 fix for running on cpu, update readme usage instructions

.gitignore

@@ -3,6 +3,7 @@
 .vscode/
 bench/
 cache/
+ci_env/
 clip-interrogator/
 clip_interrogator.egg-info/
 dist/

README.md

@@ -26,11 +26,16 @@ The **CLIP Interrogator** is a prompt engineering tool that combines OpenAI's [C
 Create and activate a Python virtual environment
 ```bash
 python3 -m venv ci_env
-source ci_env/bin/activate
+(for linux  ) source ci_env/bin/activate
+(for windows) .\ci_env\Scripts\activate
 ```
 
 Install with PIP
 ```
+# install torch with GPU support for example:
+pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu117
+
+# install blip and clip-interrogator
 pip install -e git+https://github.com/pharmapsychotic/BLIP.git@lib#egg=blip
 pip install clip-interrogator
 ```

clip_interrogator/__init__.py

@@ -1,4 +1,4 @@
 from .clip_interrogator import Interrogator, Config
 
-__version__ = '0.2.0'
+__version__ = '0.3.1'
 __author__ = 'pharmapsychotic'

clip_interrogator/clip_interrogator.py

@@ -81,12 +81,12 @@ class Interrogator():
             self.clip_model, _, self.clip_preprocess = open_clip.create_model_and_transforms(
                 clip_model_name, 
                 pretrained=clip_model_pretrained_name, 
-                precision='fp16',
+                precision='fp16' if config.device == 'cuda' else 'fp32',
                 device=config.device,
                 jit=False,
                 cache_dir=config.clip_model_path
             )
-            self.clip_model.half().to(config.device).eval()
+            self.clip_model.to(config.device).eval()
         else:
             self.clip_model = config.clip_model
             self.clip_preprocess = config.clip_preprocess
@@ -256,6 +256,8 @@ class LabelTable():
                         if data.get('hash') == hash:
                             self.labels = data['labels']
                             self.embeds = data['embeds']
+                            if self.device == 'cpu':
+                                self.embeds = [e.astype(np.float32) for e in self.embeds]
                     except Exception as e:
                         print(f"Error loading cached table {desc}: {e}")
 

run_cli.py

@@ -40,8 +40,12 @@ def main():
         print(f"    available models: {models}")
         exit(1)
 
-    # generate a nice prompt
+    # select device
     device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    if not torch.cuda.is_available():
+        print("CUDA is not available, using CPU. Warning: this will be very slow!")
+
+    # generate a nice prompt
     config = Config(device=device, clip_model_name=args.clip)
     ci = Interrogator(config)
 

run_gradio.py

@@ -2,12 +2,16 @@
 import argparse
 import gradio as gr
 import open_clip
+import torch
 from clip_interrogator import Interrogator, Config
 
 parser = argparse.ArgumentParser()
 parser.add_argument('-s', '--share', action='store_true', help='Create a public link')
 args = parser.parse_args()
 
+if not torch.cuda.is_available():
+    print("CUDA is not available, using CPU. Warning: this will be very slow!")
+
 ci = Interrogator(Config(cache_path="cache", clip_model_path="cache"))
 
 def inference(image, mode, clip_model_name, blip_max_length, blip_num_beams):

setup.py

@@ -5,7 +5,7 @@ from setuptools import setup, find_packages
 
 setup(
     name="clip-interrogator",
-    version="0.2.0",
+    version="0.3.1",
     license='MIT',
     author='pharmapsychotic',
     author_email='me@pharmapsychotic.com',