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72e20a8854
## Motivation Supports inference for ultra-large-scale remote sensing images. ## Modification Add RSImageInference.py in demo. ## Use cases Taking the inference of Vaihingen dataset images using PSPNet as an example, the following settings are required: **img**: Specify the path of the image. **model**: Provide the configuration file for the model. **checkpoint**: Specify the weight file for the model. **out**: Set the output path for the results. **batch_size**: Determine the batch size used during inference. **win_size**: Specify the width and height(512x512) of the sliding window. **stride**: Set the stride(400x400) for sliding the window. **thread(default: 1)**: Specify the number of threads to be used for inference. **Inference device (default: cuda:0)**: Specify the device for inference (e.g., cuda:0 for CPU). ```shell python demo/rs_image_inference.py demo/demo.png projects/pp_mobileseg/configs/pp_mobileseg/pp_mobileseg_mobilenetv3_2x16_80k_ade20k_512x512_tiny.py pp_mobileseg_mobilenetv3_2xb16_3rdparty-tiny_512x512-ade20k-a351ebf5.pth --batch-size 8 --device cpu --thread 2 ``` --------- Co-authored-by: xiexinch <xiexinch@outlook.com>
74 lines
2.4 KiB
Python
74 lines
2.4 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import os.path as osp
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from unittest import TestCase
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import numpy as np
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from mmengine import ConfigDict, init_default_scope
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from utils import * # noqa: F401, F403
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from mmseg.apis import RSImage, RSInferencer
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from mmseg.registry import MODELS
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class TestRSImage(TestCase):
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def test_read_whole_image(self):
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init_default_scope('mmseg')
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img_path = osp.join(
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osp.dirname(__file__),
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'../data/pseudo_loveda_dataset/img_dir/0.png')
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rs_image = RSImage(img_path)
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window_size = (16, 16)
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rs_image.create_grids(window_size)
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image_data = rs_image.read(rs_image.grids[0])
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self.assertIsNotNone(image_data)
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def test_write_image_data(self):
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init_default_scope('mmseg')
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img_path = osp.join(
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osp.dirname(__file__),
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'../data/pseudo_loveda_dataset/img_dir/0.png')
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rs_image = RSImage(img_path)
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window_size = (16, 16)
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rs_image.create_grids(window_size)
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data = np.random.random((16, 16)).astype(np.int8)
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rs_image.write(data, rs_image.grids[0])
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class TestRSInferencer(TestCase):
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def test_read_and_inference(self):
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init_default_scope('mmseg')
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cfg_dict = dict(
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model=dict(
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type='InferExampleModel',
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data_preprocessor=dict(type='SegDataPreProcessor'),
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backbone=dict(type='InferExampleBackbone'),
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decode_head=dict(type='InferExampleHead'),
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test_cfg=dict(mode='whole')),
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test_dataloader=dict(
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dataset=dict(
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type='ExampleDataset',
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pipeline=[
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations'),
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dict(type='PackSegInputs')
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])),
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test_pipeline=[
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations'),
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dict(type='PackSegInputs')
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])
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cfg = ConfigDict(cfg_dict)
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model = MODELS.build(cfg.model)
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model.cfg = cfg
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inferencer = RSInferencer.from_model(model)
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img_path = osp.join(
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osp.dirname(__file__),
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'../data/pseudo_loveda_dataset/img_dir/0.png')
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rs_image = RSImage(img_path)
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window_size = (16, 16)
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stride = (16, 16)
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inferencer.run(rs_image, window_size, stride)
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