Update README.md

README.md

@@ -3,7 +3,7 @@ license: apache-2.0
 ---
 ONNX format of voxerality/rgb_language_cap model
 
-Run on CPU
+Model inference example:
 
 ```python
 import onnxruntime as ort
@@ -64,71 +64,7 @@ for _ in range(max_length):
 
 # 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)
-```
-
-
-Run on GPU
-
-```python
-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=["CUDAExecutionProvider"])  
-decoder_session = ort.InferenceSession(decoder_onnx_path, providers=["CUDAExecutionProvider"])  
-
-# 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)
+print(predicted_text)
+```