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test_on_official_model.py
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test_on_official_model.py
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from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
from util.vision_util import process_vision_info
from pprint import pprint
from datasets import load_dataset
import torch
# data = load_dataset("Trelis/chess_pieces")
# train_shape = len(data['train'])
# test_shape = len(data['test'])
#
# for idx in range(test_shape):
# image = data['test'][idx]['image']
# image.save(f'test_data/{idx}.png')
# print(f"Train Dataset Shape: {train_shape} examples")
# print(f"Test Dataset Shape: {test_shape} examples")
model = Qwen2VLForConditionalGeneration.from_pretrained(
"Qwen/Qwen2-VL-2B-Instruct", torch_dtype="auto", device_map="auto"
)
# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
model = Qwen2VLForConditionalGeneration.from_pretrained(
"Qwen/Qwen2-VL-2B-Instruct",
torch_dtype=torch.bfloat16,
attn_implementation="flash_attention_2",
device_map="auto",
)
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", padding_side="left")
# The default range for the number of visual tokens per image in the model is 4-16384. You can set min_pixels and max_pixels according to your needs, such as a token count range of 256-1280, to balance speed and memory usage.
# min_pixels = 256*28*28
# max_pixels = 1280*28*28
# processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels)
messages1 = [
{
"role": "user",
"content": [
{
"type": "image",
"image": "/home/admin/finetune-Qwen2-VL/VLM/Eri_whitebkgrd/Eri_1.jpg",
},
{"type": "text", "text": "What would you caption the character in this picture?"},
],
}
]
messages2 = [
{
"role": "user",
"content": [
{
"type": "image",
"image": "train_data/46.png",
},
{"type": "text", "text": "What kind of chess pieces in this picture?"},
],
}
]
# messages3 = [
# {
# "role": "user",
# "content": [
# {
# "type": "video",
# "video": "test_data/1.mp4",
# "max_pixels": 360 * 420,
# "fps": 1.0,
# },
# {"type": "text", "text": "描述一下这个视频"},
# ],
# }
# ]
messages = [messages1]
texts = [
processor.apply_chat_template(msg, tokenize=False, add_generation_prompt=True)
for msg in messages
]
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
text=texts,
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
inputs = inputs.to("cuda")
# Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
pprint(output_text)