rgb_language_cap_onnx

ONNX format of voxerality/rgb_language_cap model

Model inference example:

import onnxruntime as ort
from transformers import AutoTokenizer,AutoImageProcessor
from PIL import Image
import numpy as np

# load the ONNX models (encoder and decoder) 
encoder_onnx_path = 'models/rgb_language_cap_onnx/encoder_model.onnx'    # load from local path
decoder_onnx_path = 'models/rgb_language_cap_onnx/decoder_model.onnx'    # load from local path
encoder_session = ort.InferenceSession(encoder_onnx_path, providers=["CPUExecutionProvider"])  
decoder_session = ort.InferenceSession(decoder_onnx_path, providers=["CPUExecutionProvider"])  

# load the tokenizer and image processor
model_id = "models/rgb_language_cap_onnx"
processor = AutoImageProcessor.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)

# load image
image_path = "img2.jpg"
image = Image.open(image_path)
inputs = processor(images=image, return_tensors="np").pixel_values  

# run encoder model
encoder_outputs = encoder_session.run(
    None, 
    {"pixel_values": inputs}  
)

# extract the encoder hidden states (encoder outputs)
encoder_hidden_states = encoder_outputs[0]

# prepare decoder inputs 
decoder_input_ids = np.array([[tokenizer.bos_token_id]], dtype=np.int64)  

# run decoder model
max_length = 200  # define maximum length of the sequence

for _ in range(max_length):
    decoder_outputs = decoder_session.run(
        None,  
        {
            "input_ids": decoder_input_ids,  # input for the decoder
            "encoder_hidden_states": encoder_hidden_states  # outputs from the encoder
        }
    )

    # extract logits and predict next token
    logits = decoder_outputs[0]
    predicted_token_id = np.argmax(logits[0, -1, :])  # get the predicted token ID from the logits

    # if the predicted token is the EOS token, stop the generation
    if predicted_token_id == tokenizer.eos_token_id:
        break

    # append predicted token ID to the decoder inputs for the next step
    decoder_input_ids = np.concatenate([decoder_input_ids, np.array([[predicted_token_id]])], axis=-1)

# decode the predicted token IDs into text
predicted_text = tokenizer.decode(decoder_input_ids[0], skip_special_tokens=True)

# print the generated caption
print(predicted_text)