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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>
61 lines
1.7 KiB
Python
61 lines
1.7 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import tempfile
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import numpy as np
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import torch
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from mmengine import ConfigDict
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from utils import * # noqa: F401, F403
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from mmseg.apis import MMSegInferencer
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from mmseg.registry import MODELS
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from mmseg.utils import register_all_modules
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def test_inferencer():
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register_all_modules()
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visualizer = dict(
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type='SegLocalVisualizer',
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vis_backends=[dict(type='LocalVisBackend')],
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name='visualizer')
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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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visualizer=visualizer,
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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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cfg = ConfigDict(cfg_dict)
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model = MODELS.build(cfg.model)
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ckpt = model.state_dict()
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ckpt_filename = tempfile.mktemp()
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torch.save(ckpt, ckpt_filename)
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# test initialization
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infer = MMSegInferencer(cfg, ckpt_filename)
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# test forward
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img = np.random.randint(0, 256, (4, 4, 3))
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infer(img)
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imgs = [img, img]
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infer(imgs)
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results = infer(imgs, out_dir=tempfile.gettempdir())
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# test results
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assert 'predictions' in results
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assert 'visualization' in results
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assert len(results['predictions']) == 2
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assert results['predictions'][0].shape == (4, 4)
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