coco
29 lines
881 B
Python
29 lines
881 B
Python
import onnxruntime as ort
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import torch
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from PIL import Image
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from clip import clip
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from numpy import ndarray
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from onnxruntime.transformers import optimizer
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from torch import Tensor
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from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize, InterpolationMode
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model = "clip-text-encoder.onnx"
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def to_numpy(tensor: Tensor, dtype=None):
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r: ndarray = tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()
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r.astype(dtype=dtype)
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return r
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if __name__ == '__main__':
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input_text = "by the sea"
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text_encoder = ort.InferenceSession(model)
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text_input = clip.tokenize(input_text)
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print("Token:", text_input, len(text_input[0]))
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print("InputNames", text_encoder.get_inputs()[0].name)
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arr = text_encoder.run(None, {text_encoder.get_inputs()[0].name: to_numpy(text_input, dtype=int)})[0]
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print(arr)
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