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clip.py
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clip.py
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import argparse
from PIL import Image
from scipy.special import softmax
from tokenizer import tokenize
import openvino as ov
import numpy as np
import openvino.properties.hint as hints
def load_model(image_encoder_path, text_encoder_path, device, throughputmode=False):
core = ov.Core()
#core.set_property(device, {hints.inference_precision: ov.Type.f32})
ie = core.read_model(image_encoder_path)
te = core.read_model(text_encoder_path)
config = {}
if throughputmode:
config["PERFORMANCE_HINT"] = "THROUGHPUT"
ienc = core.compile_model(ie, device.upper(), config)
tenc = core.compile_model(te, device.upper(), config)
#model.max_text_len = 256
return ienc, tenc
def normalize(arr, mean=(0,0,0), std=(1,1,1)):
arr = arr.astype(np.float32)
arr /= 255.0
for i in range(3):
arr[...,i] = (arr[...,i] - mean[i]) / std[i]
return arr
def preprocess_image(input_image, shape=[224,224]):
img = input_image.resize(shape, Image.Resampling.NEAREST)
img = np.asarray(img)
img = normalize(img, mean=[0.485,0.456,0.406], std=[0.229,0.224,0.225])
return img.transpose(2,0,1)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Run OpenCLIP with OpenVINO')
parser.add_argument('-v', '--visual_model_path', help='Path to OpenCLIP visual encoder models')
parser.add_argument('-t', '--text_model_path', help='Path to OpenCLIP text encoder model')
parser.add_argument('-i', '--image_path', help='Path to image')
parser.add_argument('-d', '--device', help='Select device to execute')
parser.add_argument('-p', '--prompt', help='Text prompt')
args = parser.parse_args()
image = preprocess_image(Image.open(args.image_path))
tokens = tokenize(args.prompt.split(','))
print("Loading Model...")
ienc, tenc = load_model(args.visual_model_path, args.text_model_path, args.device)
print("Inferencing...")
image_feature = ienc.infer_new_request({"x": image[None]})
text_feature = tenc.infer_new_request(tokens)
tfeat = text_feature.to_tuple()[0]
ifeat = image_feature.to_tuple()[0]
probs = softmax(100.0 * ifeat @ tfeat.T)
print([x for x in probs[0]])