Delete clip_chat.py

clip_chat.py

@@ -1,81 +0,0 @@
-import torch
-import clip
-from PIL import Image
-import glob
-import os
-from random import choice
-
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-# model, preprocess = clip.load("ViT-L/14@336px", device=device)
-model, preprocess = clip.load(clip.available_models()[-3], device=device)
-COCO = glob.glob(os.path.join(os.getcwd(), "images", "*"))
-
-
-def load_random_image():
-    image_path = choice(COCO)
-    image = Image.open(image_path)
-    return image
-
-
-def next_image():
-    global image_org, image
-    image_org = load_random_image()
-    image = preprocess(Image.fromarray(image_org)).unsqueeze(0).to(device)
-
-
-def calculate_logits(image_features, text_features):
-    image_features = image_features / image_features.norm(dim=1, keepdim=True)
-    text_features = text_features / text_features.norm(dim=1, keepdim=True)
-
-    logit_scale = model.logit_scale.exp()
-    return logit_scale * image_features @ text_features.t()
-
-
-last = -1
-best = -1
-
-goal = 30
-
-image_org = load_random_image()
-image = preprocess(image_org).unsqueeze(0).to(device)
-with torch.no_grad():
-    image_features = model.encode_image(image)
-
-
-def answer(message):
-    global last, best
-
-    text = clip.tokenize([message]).to(device)
-
-    with torch.no_grad():
-        text_features = model.encode_text(text)
-        logits_per_image, _ = model(image, text)
-        logits = calculate_logits(image_features, text_features).cpu().numpy().flatten()[0]
-
-    if last == -1:
-        is_better = -1
-    elif last > logits:
-        is_better = 0
-    elif last < logits:
-        is_better = 1
-    elif logits > goal:
-        is_better = 2
-    else:
-        is_better = -1
-
-    last = logits
-    if logits > best:
-        best = logits
-        is_better = 3
-
-    return logits, is_better
-
-
-def reset_everything():
-    global last, best, goal, image, image_org
-    last = -1
-    best = -1
-    goal = 21
-    image_org = load_random_image()
-    image = preprocess(image_org).unsqueeze(0).to(device)