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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)
COCO = glob.glob(os.path.join(os.getcwd(), "images", "*"))
# with open('coco_paths.txt', 'r') as file:
# COCO = file.read().split('\n')[:-1]
# COCO = [c.replace("/media/bonilla/My Book/coco/train2017/", "D:\\coco\\train2017\\") for c in COCO]
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)
last = -1
best = -1
goal = 21
image_org = load_random_image()
image = preprocess(image_org).unsqueeze(0).to(device)
def answer(message):
global last, best
text = clip.tokenize([message]).to(device)
with torch.no_grad():
logits_per_image, _ = model(image, text)
logits = logits_per_image.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 # logit2sentence(logits) + " " + is_better + " " + f"({logits})"
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)
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