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MODEL_NAME="allenai/MolmoE-1B-0924"
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, trust_remote_code=True)
from transformers import AutoModelForCausalLM, AutoProcessor, GenerationConfig
from PIL import Image
import requests
# load the processor
processor = AutoProcessor.from_pretrained(
'allenai/MolmoE-1B-0924',
trust_remote_code=True,
torch_dtype='auto',
device_map='auto'
)
# load the model
model = AutoModelForCausalLM.from_pretrained(
'allenai/MolmoE-1B-0924',
trust_remote_code=True,
torch_dtype='auto',
device_map='auto'
)
# process the image and text
inputs = processor.process(
images=[Image.open(requests.get("https://picsum.photos/id/237/536/354", stream=True).raw)],
text="Describe this image."
)
# move inputs to the correct device and make a batch of size 1
inputs = {k: v.to(model.device).unsqueeze(0) for k, v in inputs.items()}
# generate output; maximum 200 new tokens; stop generation when <|endoftext|> is generated
output = model.generate_from_batch(
inputs,
GenerationConfig(max_new_tokens=200, stop_strings="<|endoftext|>"),
tokenizer=processor.tokenizer
)
# only get generated tokens; decode them to text
generated_tokens = output[0,inputs['input_ids'].size(1):]
generated_text = processor.tokenizer.decode(generated_tokens, skip_special_tokens=True)
# print the generated text
print(generated_text)
# >>> This photograph captures a small black puppy, likely a Labrador or a similar breed,
# sitting attentively on a weathered wooden deck. The deck, composed of three...
# import cv2
# class Solution():
# def __init__(self,prompt):
# self.prompt= prompt
# self.output_dir=None
# # read a mp4 file and getting its frame at a particular interval.
# def read_frame(self,file,interval=1):
# video=cv2.VideoCapture(file)
# fps= video.get(cv2.CAP_PROP_FPS)
# frame_interval= fps*interval# fps= 24 frame/sec and interval = 1 sec so frame interval = 24 frame
# while True:
# success, frame=video.read()
# if not success:
# break
# if frame % frame_interval==0:
# # process this frame
# """
# .. to do
# """
# def find(self,input_message):
# read a .mp4 file
# get a interval N spaced
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