mirror of
https://github.com/open-mmlab/mmsegmentation.git
synced 2025-06-03 22:03:48 +08:00
310ec4afc7
## Motivation Make MMSeginferencer easier to be used ## Modification 1. Add `_load_weights_to_model` to MMSeginferencer, it is for get `dataset_meta` from ckpt 2. Modify and remove some parameters of `__call__`, `visualization` and `postprocess` 3. Add function of save seg mask, remove dump pkl. 4. Refine docstring of MMSeginferencer and SegLocalVisualizer 5. Add the user documentation of MMSeginferencer ## BC-breaking (Optional) yes, remove some parameters, we need to discuss whether keep them with deprecated waring or just remove them as the MMSeginferencer just merged in mmseg a few days. Co-authored-by: xiexinch <xiexinch@outlook.com>
114 lines
2.9 KiB
Python
114 lines
2.9 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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import torch.nn as nn
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from mmengine import ConfigDict
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from torch.utils.data import DataLoader, Dataset
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from mmseg.apis import MMSegInferencer
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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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from mmseg.utils import register_all_modules
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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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class ExampleDataset(Dataset):
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def __init__(self) -> None:
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super().__init__()
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self.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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def __getitem__(self, idx):
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return dict(img=torch.tensor([1]), img_metas=dict())
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def __len__(self):
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return 1
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def test_inferencer():
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register_all_modules()
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test_dataset = ExampleDataset()
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data_loader = DataLoader(
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test_dataset,
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batch_size=1,
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sampler=None,
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num_workers=0,
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shuffle=False,
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)
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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=data_loader)
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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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