vit encoder + transformer decoder model - export onnx example

refer to this code:


# If you want to combine a Vision Transformer (ViT) as an encoder with a Transformer-based decoder,
# you can follow the steps below.
# We will use the Hugging Face Transformers library and PyTorch.

# Install the required libraries:
# pip install torch torchvision transformers onnx

# Define the combined model:
# -----------------------------------------
import torch
import torch.nn as nn
from transformers import ViTModel, ViTConfig, AutoModelForSeq2SeqLM

class ViTTransformer(nn.Module):
def __init__(self, vit_model, transformer_decoder):
super(ViTTransformer, self).__init__()
self.vit = vit_model
self.transformer_decoder = transformer_decoder

def forward(self, x, decoder_input_ids, **kwargs):
encoder_outputs = self.vit(x)
outputs = self.transformer_decoder(decoder_input_ids, encoder_outputs=encoder_outputs, **kwargs)
return outputs
# -----------------------------------------

# Load the ViT and Transformer decoder models:
# Assuming you have a pre-trained ViT model and a pre-trained Transformer decoder model, load them as follows:

# -----------------------------------------
vit_config = ViTConfig()
vit_model = ViTModel(vit_config)
transformer_decoder = AutoModelForSeq2SeqLM.from_pretrained("your-pretrained-transformer-decoder")

# Create the combined model and load the checkpoint if you have one:
# -----------------------------------------
combined_model = ViTTransformer(vit_model, transformer_decoder)
# -----------------------------------------

# # If you have a checkpoint, load it as follows:
# # checkpoint = torch.load('path/to/checkpoint.pth')
# # combined_model.load_state_dict(checkpoint['model_state_dict'])
# Export the combined model to ONNX format:
# The process of exporting the combined model to ONNX is more complicated due to the dynamic nature of the Transformer-based decoder.
# You might need to modify the export code depending on your specific use case.
# However, here is a general example:

# -----------------------------------------
# # Set the combined model to evaluation mode
# Create dummy input tensors with the correct dimensions
# (B x C x H x W) for image input and (B x seq_len) for decoder input
dummy_image_input = torch.randn(1, 3, 224, 224)
dummy_decoder_input = torch.randint(0, transformer_decoder.config.vocab_size, (1, 5))

# Export the combined model to ONNX format
(dummy_image_input, dummy_decoder_input),
input_names=["image_input", "decoder_input"],
"image_input": {0: "batch_size"},
"decoder_input": {0: "batch_size", 1: "sequence_length"},
"output": {0: "batch_size", 1: "sequence_length"},
# -----------------------------------------

# This code will create an ONNX file (vit_transformer.onnx) containing the combined ViT and Transformer decoder model.
# Note that you might need to adjust the code according to the specific needs of your application.


Thank you.🙇ðŸŧ‍♂️

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