App_main.py

import os
os.system('pip install -U openmim')
os.system('mim install mmcv')
import glob
import mmcv
import mmengine
import numpy as np
from mmengine import Config, get
from mmengine.dataset import Compose
from mmpl.registry import MODELS, VISUALIZERS
from mmpl.utils import register_all_modules
register_all_modules()
# os.system('nvidia-smi')
# os.system('ls /usr/local')
# 

import gradio as gr
import torch

device = 'cuda:0' if torch.cuda.is_available() else 'cpu'


def construct_sample(img, pipeline):
    img = np.array(img)[:, :, ::-1]
    inputs = {
        'ori_shape': img.shape[:2],
        'img': img,
    }
    pipeline = Compose(pipeline)
    sample = pipeline(inputs)
    return sample

def build_model(cp, model_cfg):
    model_cpkt = torch.load(cp, map_location='cpu')
    model = MODELS.build(model_cfg)
    model.load_state_dict(model_cpkt, strict=True)
    model.to(device=device)
    model.eval()
    return model


# Function for building extraction
def inference_func(ori_img, cp):
    checkpoint = f'pretrain/{cp}_anchor.pth'
    cfg = f'configs/huggingface/rsprompter_anchor_{cp}_config.py'
    cfg = Config.fromfile(cfg)
    sample = construct_sample(ori_img, cfg.predict_pipeline)
    sample['inputs'] = [sample['inputs']]
    sample['data_samples'] = [sample['data_samples']]

    print('Use: ', device)
    model = build_model(checkpoint, cfg.model_cfg)

    with torch.no_grad():
        pred_results = model.predict_step(sample, batch_idx=0)

    cfg.visualizer.setdefault('save_dir', 'visualizer')
    visualizer = VISUALIZERS.build(cfg.visualizer)

    data_sample = pred_results[0]
    img = np.array(ori_img).copy()
    out_file = 'visualizer/test_img.jpg'
    mmengine.mkdir_or_exist(os.path.dirname(out_file))
    visualizer.add_datasample(
        'test_img',
        img,
        draw_gt=False,
        data_sample=data_sample,
        show=False,
        wait_time=0.01,
        pred_score_thr=0.4,
        out_file=out_file
    )
    img_bytes = get(out_file)
    img = mmcv.imfrombytes(img_bytes, channel_order='rgb')
    return img

title = "RSPrompter"
description = "Gradio demo for RSPrompter. Upload image from WHU building dataset, NWPU dataset, or SSDD Dataset or click any one of the examples, " \
              "Then select the prompt model, and click \"Submit\" and wait for the result. \n \n" \
              "Paper: RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model"

article = "<p style='text-align: center'><a href='https://kyanchen.github.io/RSPrompter/' target='_blank'>RSPrompter Project " \
          "Page</a></p> "

files = glob.glob('examples/*')
examples = [[f, f.split('/')[-1].split('_')[0]] for f in files]

with gr.Blocks() as demo:
    image_input = gr.Image(type='pil', label='Input Img')
    # with gr.Row().style(equal_height=True):
    # image_LR_output = gr.outputs.Image(label='LR Img', type='numpy')
    image_output = gr.Image(label='Segment Result', type='numpy')
    with gr.Row():
        checkpoint = gr.Radio(['WHU', 'NWPU', 'SSDD'], label='Checkpoint')

io = gr.Interface(fn=inference_func,
                  inputs=[image_input, checkpoint],
                  outputs=[image_output],
                  title=title,
                  description=description,
                  article=article,
                  allow_flagging='auto',
                  examples=examples,
                  cache_examples=True,
                  layout="grid"
                  )
io.launch()