gen.py

import open_clip
import torch
from PIL import Image
device = "cuda" if torch.cuda.is_available() else "cpu"
model, _, transform = open_clip.create_model_and_transforms(
    model_name="coca_biogpt_vitb16",
    pretrained="coca_biogpt_vitb16.pt",
)
model.to(device)
model.eval()
nb = 1
path = "example.png"
im = Image.open(path).convert("RGB")
im = transform(im).unsqueeze(0)
im = im.to(device)
im = im.repeat(nb,1,1,1)
print(im.shape)
tokenizer = open_clip.get_tokenizer("coca_biogpt_vitb16")
print(tokenizer.tokenizer)
with torch.no_grad():
    generated = model.generate(
        im, 
        pad_token_id=1, eos_token_id=2, sot_token_id=0, max_seq_len=256, seq_len=60,
        # generation_type='top_p',
        #generation_type='top_k',
        generation_type='beam_search',
        #repetition_penalty=1.,
        #top_k=100,
        #top_p=0.1,
        # text=text,
        #temperature=1.0,
        #min_seq_len=40,
    )
    print(generated)
    for i in range(nb):
        if hasattr(tokenizer, "tokenizer"):
            print(tokenizer.tokenizer.decode(generated[i]))
        else:
            print(open_clip.decode(generated[i]))