mirror of
https://github.com/open-mmlab/mmdeploy.git
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ea0a9e5d43
* feat(tools): add onnx2ncnn_quant_table * feat(tools): add quantization image dataset * feat(tools): add image dataset * feat(tools/deploy.py): support quant * fix(CI): lint * fix(CI): format * docs(zh_cn): add quantization usage * docs(zh_cn): add benchmark * feat(tools): add onnx2ncnn_quant_table * docs(zh_cn): add more test result * CI(github): add quant script * CI(.github/scripts): add test quant * fix(CI): remove pushd and popd * feat(CI): debug * fix(CI): path error * fix(CI): fix path * fix(CI): install wget * fix(CI): review advices * improvement(mmdeploy): review advice * fix(tools): rename to onnx2ncnn_quant_table.py * improvement(tools): rename file * improvement(test): remove useless * fix(tools/quant_image_dataset): remove loadFile in test.pipeline * docs(quantization.md): update description * fix(CI): protobuf version * fix(CI): pip install * docs(quantization): review advice * fix(CI): revert mmcv version * fix(CI): udpate pb version * fix(CI): update
90 lines
3.3 KiB
Python
90 lines
3.3 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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from typing import Optional, Sequence
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import mmcv
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from mmcv import FileClient
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from torch.utils.data import Dataset
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from mmdeploy.utils import Codebase, get_codebase
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class QuantizationImageDataset(Dataset):
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def __init__(
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self,
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path: str,
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deploy_cfg: mmcv.Config,
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model_cfg: mmcv.Config,
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file_client_args: Optional[dict] = None,
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extensions: Sequence[str] = ('.jpg', '.jpeg', '.png', '.ppm', '.bmp',
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'.pgm', '.tif'),
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):
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super().__init__()
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codebase_type = get_codebase(deploy_cfg)
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self.exclude_pipe = ['LoadImageFromFile']
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if codebase_type == Codebase.MMCLS:
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from mmcls.datasets.pipelines import Compose
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elif codebase_type == Codebase.MMDET:
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from mmdet.datasets.pipelines import Compose
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elif codebase_type == Codebase.MMDET3D:
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from mmdet3d.datasets.pipelines import Compose
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self.exclude_pipe.extend([
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'LoadMultiViewImageFromFiles', 'LoadImageFromFileMono3D',
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'LoadPointsFromMultiSweeps', 'LoadPointsFromDict'
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])
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elif codebase_type == Codebase.MMEDIT:
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from mmedit.datasets.pipelines import Compose
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self.exclude_pipe.extend(
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['LoadImageFromFileList', 'LoadPairedImageFromFile'])
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elif codebase_type == Codebase.MMOCR:
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from mmocr.datasets.pipelines import Compose
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self.exclude_pipe.extend(
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['LoadImageFromNdarray', 'LoadImageFromLMDB'])
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elif codebase_type == Codebase.MMPOSE:
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from mmpose.datasets.pipelines import Compose
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elif codebase_type == Codebase.MMROTATE:
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from mmrotate.datasets.pipelines import Compose
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elif codebase_type == Codebase.MMSEG:
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from mmseg.datasets.pipelines import Compose
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else:
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raise Exception(
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'Not supported codebase_type {}'.format(codebase_type))
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pipeline = filter(lambda val: val['type'] not in self.exclude_pipe,
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model_cfg.data.test.pipeline)
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self.preprocess = Compose(list(pipeline))
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self.samples = []
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self.extensions = tuple(set([i.lower() for i in extensions]))
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self.file_client = FileClient.infer_client(file_client_args, path)
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self.path = path
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assert self.file_client.isdir(path)
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files = list(
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self.file_client.list_dir_or_file(
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path,
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list_dir=False,
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list_file=True,
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recursive=False,
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))
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for file in files:
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if self.is_valid_file(self.file_client.join_path(file)):
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path = self.file_client.join_path(self.path, file)
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self.samples.append(path)
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def __len__(self):
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return len(self.samples)
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def __getitem__(self, index):
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sample = self.samples[index]
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image = mmcv.imread(sample)
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data = dict(img=image)
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data = self.preprocess(data)
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from mmcv.parallel import collate
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data = collate([data], samples_per_gpu=1)
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return {'img': data['img'].squeeze()}
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def is_valid_file(self, filename: str) -> bool:
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"""Check if a file is a valid sample."""
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return filename.lower().endswith(self.extensions)
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