utils/torch_utils/clip_practice.py

import torch
import clip
from PIL import Image

from pdb import set_trace as st

device = "cuda" if torch.cuda.is_available() else "cpu"
model, preprocess = clip.load("ViT-B/16", device=device)

image = preprocess(Image.open("utils.torch_utils/CLIP.png")).unsqueeze(0).to(device)
text = clip.tokenize(["a diagram", "a dog", "a cat"]).to(device)

# with torch.no_grad():
#     image_features = model.encode_image(image)
#     text_features = model.encode_text(text)
    
#     logits_per_image, logits_per_text = model(image, text)
#     probs = logits_per_image.softmax(dim=-1).cpu().numpy()

with torch.no_grad():
    x = image.type(model.dtype) # 1 3 224 224
    self = model.visual
    x = self.conv1(x)  # shape = [*, width, grid, grid]
    x = x.reshape(x.shape[0], x.shape[1], -1)  # shape = [*, width, grid ** 2]
    x = x.permute(0, 2, 1)  # shape = [*, grid ** 2, width]
    x = torch.cat([self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1)  # shape = [*, grid ** 2 + 1, width]
    x = x + self.positional_embedding.to(x.dtype)
    x = self.ln_pre(x)

    x = x.permute(1, 0, 2)  # NLD -> LND
    x = self.transformer(x)
    x = x.permute(1, 0, 2)  # LND -> NLD , 1, 50, 768
    st()

    pass


print("Label probs:", probs)  # prints: [[0.9927937  0.00421068 0.00299572]]