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91 lines
3.5 KiB
91 lines
3.5 KiB
2 years ago
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import requests
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import threading
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import os
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import json
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import sys
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import requests
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import torch
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from PIL import Image
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from transformers import BlipProcessor, BlipForConditionalGeneration
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thread_list = []
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results = []
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def process(image_url):
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processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
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model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
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# img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
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raw_image = Image.open(requests.get(image_url, stream=True).raw).convert('RGB')
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inputs = processor(raw_image, return_tensors="pt")
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out = model.generate(**inputs)
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result = processor.decode(out[0], skip_special_tokens=True)
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results.append(result)
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def main(params):
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activation_id = os.environ.get('__OW_ACTIVATION_ID')
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# file = open('action/1/bin/exec__.py', 'r')
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# content = file.read()
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# print(content)
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print("Current directory----",os.getcwd())
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print("root----",os.listdir('/'))
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print("inside action----",os.listdir('/action/models/transformers'))
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print("current directory contents------",os.listdir(os.getcwd()))
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# print("/action/model contents------",os.listdir('../models/transformers'))
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# print("action/1/bin------",os.listdir('/action/1/bin'))
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# params = json.loads(sys.argv[1])
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img_urls = params["image_url_links"]
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modelpath = '/action/models/transformers'
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# print("Line 32------",os.listdir(model_path))
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# processor = BlipProcessor.from_pretrained(modelpath)
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# model = BlipForConditionalGeneration.from_pretrained(modelpath)
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processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
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model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
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img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
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raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
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inputs = processor(raw_image, return_tensors="pt")
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out = model.generate(**inputs)
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result = processor.decode(out[0], skip_special_tokens=True)
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print(res)
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# # processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
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# # model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
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# img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
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# raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
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# # raw_image = Image.open('puppy.jpg').convert('RGB')
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# inputs = processor(raw_image, return_tensors="pt")
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# out = model.generate(**inputs)
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# res = processor.decode(out[0], skip_special_tokens=True)
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# print(res)
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# # result = []
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# for image_url in img_urls:
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# thread_list.append(threading.Thread(target=process, args=[image_url]))
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# for thread in thread_list:
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# thread.start()
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# for thread in thread_list:
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# thread.join()
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# text = process(image_url)
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# result.append(text)
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#https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg
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print(json.dumps({"activation_id": str(activation_id),
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"image_url_links": img_urls,
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"result":res
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}))
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return({"activation_id": str(activation_id),
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"image_url_links": img_urls,
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"result":res
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})
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if __name__ == "__main__":
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# print("Line 52.....",sys.argv)
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main(params)
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