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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>
39 lines
983 B
Python
39 lines
983 B
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import torch.nn as nn
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from mmseg.models import EncoderDecoder
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from mmseg.models.decode_heads.decode_head import BaseDecodeHead
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from mmseg.registry import MODELS
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@MODELS.register_module(name='InferExampleHead')
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class ExampleDecodeHead(BaseDecodeHead):
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def __init__(self, num_classes=19, out_channels=None):
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super().__init__(
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3, 3, num_classes=num_classes, out_channels=out_channels)
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def forward(self, inputs):
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return self.cls_seg(inputs[0])
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@MODELS.register_module(name='InferExampleBackbone')
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class ExampleBackbone(nn.Module):
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def __init__(self):
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super().__init__()
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self.conv = nn.Conv2d(3, 3, 3)
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def init_weights(self, pretrained=None):
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pass
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def forward(self, x):
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return [self.conv(x)]
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@MODELS.register_module(name='InferExampleModel')
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class ExampleModel(EncoderDecoder):
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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