TL;DR: How can I use model.whatever_function(input) instead of model.forward(input) for the onnxruntime?
I use CLIP embedding to create embedding for my Image and texts as:
Code is from the official git merge
! pip install ftfy regex tqdm
! pip install git+https://github.com/openai/CLIP.git
import clip
import torch
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model, preprocess = clip.load("RN50", device=device) # Load any model
model = model.eval() # Inference Only
img_size = model.visual.input_resolution
dummy_image = torch.randn(10, 3, img_size, img_size).to(device)
image_embedding = model.encode_image(dummy_image).to(device))
dummy_texts = clip.tokenize(["quick brown fox", "lorem ipsum"]).to(device)
model.encode_text(dummy_texts)
and it works fine giving me [Batch, 1024] tensors for both for the loaded model.
Now I have quantized my model in Onnx as:
model.forward(dummy_image,dummy_texts) # Original CLIP result (1)
torch.onnx.export(model, (dummy_image, dummy_texts), "model.onnx", export_params=True,
input_names=["IMAGE", "TEXT"],
output_names=["LOGITS_PER_IMAGE", "LOGITS_PER_TEXT"],
opset_version=14,
dynamic_axes={
"IMAGE": {
0: "image_batch_size",
},
"TEXT": {
0: "text_batch_size",
},
"LOGITS_PER_IMAGE": {
0: "image_batch_size",
1: "text_batch_size",
},
"LOGITS_PER_TEXT": {
0: "text_batch_size",
1: "image_batch_size",
},
}
)
and the model is saved.
When I test the model as :
# Now run onnxruntime to verify
import onnxruntime as ort
ort_sess = ort.InferenceSession("model.onnx")
result=ort_sess.run(["LOGITS_PER_IMAGE", "LOGITS_PER_TEXT"],
{"IMAGE": dummy_image.numpy(), "TEXT": dummy_texts.numpy()})
It gives me a list of length 2, one for each image and text and the result[0] has shape of [Batch,2].