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KaiyangZhou
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linting and formatting code
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‎.style.yapf

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[style]
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BASED_ON_STYLE = pep8
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BLANK_LINE_BEFORE_NESTED_CLASS_OR_DEF = true
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BLANK_LINE_BEFORE_MODULE_DOCSTRING = true
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BLANK_LINE_BEFORE_CLASS_DOCSTRING = true
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SPLIT_BEFORE_EXPRESSION_AFTER_OPENING_PAREN = true
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DEDENT_CLOSING_BRACKETS = true
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SPACES_BEFORE_COMMENT = 1

‎projects/OSNet_AIN/osnet_child.py

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# Basic layers
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##########
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class ConvLayer(nn.Module):
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"""Convolution layer (conv + bn + relu)."""
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def __init__(
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class Conv1x1(nn.Module):
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"""1x1 convolution + bn + relu."""
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def __init__(self, in_channels, out_channels, stride=1, groups=1):
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class Conv1x1Linear(nn.Module):
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"""1x1 convolution + bn (w/o non-linearity)."""
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def __init__(self, in_channels, out_channels, stride=1, bn=True):
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class Conv3x3(nn.Module):
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"""3x3 convolution + bn + relu."""
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def __init__(self, in_channels, out_channels, stride=1, groups=1):
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class LightConv3x3(nn.Module):
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"""Lightweight 3x3 convolution.
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1x1 (linear) + dw 3x3 (nonlinear).
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class LightConvStream(nn.Module):
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"""Lightweight convolution stream."""
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def __init__(self, in_channels, out_channels, depth):
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# Building blocks for omni-scale feature learning
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##########
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class ChannelGate(nn.Module):
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"""A mini-network that generates channel-wise gates conditioned on input tensor."""
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def __init__(
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class OSBlock(nn.Module):
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"""Omni-scale feature learning block."""
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def __init__(self, in_channels, out_channels, reduction=4, T=4, **kwargs):
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class OSBlockINv1(nn.Module):
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"""Omni-scale feature learning block with instance normalization."""
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def __init__(self, in_channels, out_channels, reduction=4, T=4, **kwargs):
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class OSBlockINv2(nn.Module):
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"""Omni-scale feature learning block with instance normalization."""
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def __init__(self, in_channels, out_channels, reduction=4, T=4, **kwargs):
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class OSBlockINv3(nn.Module):
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"""Omni-scale feature learning block with instance normalization."""
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def __init__(self, in_channels, out_channels, reduction=4, T=4, **kwargs):
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# Network architecture
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##########
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class OSNet(nn.Module):
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"""Omni-Scale Network.
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Reference:

‎projects/OSNet_AIN/osnet_search.py

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# Basic layers
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##########
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class ConvLayer(nn.Module):
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"""Convolution layer (conv + bn + relu)."""
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def __init__(
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class Conv1x1(nn.Module):
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"""1x1 convolution + bn + relu."""
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def __init__(
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class Conv1x1Linear(nn.Module):
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"""1x1 convolution + bn (w/o non-linearity)."""
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def __init__(self, in_channels, out_channels, stride=1, bn=True):
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class Conv3x3(nn.Module):
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"""3x3 convolution + bn + relu."""
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def __init__(self, in_channels, out_channels, stride=1, groups=1):
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class LightConv3x3(nn.Module):
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"""Lightweight 3x3 convolution.
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1x1 (linear) + dw 3x3 (nonlinear).
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class LightConvStream(nn.Module):
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"""Lightweight convolution stream."""
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def __init__(self, in_channels, out_channels, depth):
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# Building blocks for omni-scale feature learning
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##########
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class ChannelGate(nn.Module):
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"""A mini-network that generates channel-wise gates conditioned on input tensor."""
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def __init__(
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class OSBlock(nn.Module):
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"""Omni-scale feature learning block."""
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def __init__(self, in_channels, out_channels, reduction=4, T=4, **kwargs):
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class OSBlockINv1(nn.Module):
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"""Omni-scale feature learning block with instance normalization."""
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def __init__(self, in_channels, out_channels, reduction=4, T=4, **kwargs):
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class OSBlockINv2(nn.Module):
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"""Omni-scale feature learning block with instance normalization."""
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def __init__(self, in_channels, out_channels, reduction=4, T=4, **kwargs):
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class OSBlockINv3(nn.Module):
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"""Omni-scale feature learning block with instance normalization."""
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def __init__(self, in_channels, out_channels, reduction=4, T=4, **kwargs):
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class NASBlock(nn.Module):
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"""Neural architecture search layer."""
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def __init__(self, in_channels, out_channels, search_space=None):
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# Network architecture
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class OSNet(nn.Module):
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"""Omni-Scale Network.
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‎torchreid/data/datamanager.py

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class DataManager(object):
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r"""Base data manager.
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class ImageDataManager(DataManager):
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r"""Image data manager.
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class VideoDataManager(DataManager):
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r"""Video data manager.
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‎torchreid/data/datasets/dataset.py

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class Dataset(object):
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"""An abstract class representing a Dataset.
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This is the base class for ``ImageDataset`` and ``VideoDataset``.
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class ImageDataset(Dataset):
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class VideoDataset(Dataset):
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"""A base class representing VideoDataset.
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All other video datasets should subclass it.

‎torchreid/data/datasets/image/cuhk01.py

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class CUHK01(ImageDataset):
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"""CUHK01.
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‎torchreid/data/datasets/image/cuhk02.py

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class CUHK02(ImageDataset):
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"""CUHK02.
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‎torchreid/data/datasets/image/cuhk03.py

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class CUHK03(ImageDataset):
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"""CUHK03.
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‎torchreid/data/datasets/image/dukemtmcreid.py

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class DukeMTMCreID(ImageDataset):
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"""DukeMTMC-reID.
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‎torchreid/data/datasets/image/grid.py

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class GRID(ImageDataset):
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"""GRID.
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‎torchreid/data/datasets/image/ilids.py

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class iLIDS(ImageDataset):
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"""QMUL-iLIDS.
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‎torchreid/data/datasets/image/market1501.py

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class Market1501(ImageDataset):
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"""Market1501.
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‎torchreid/data/datasets/image/msmt17.py

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class MSMT17(ImageDataset):
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"""MSMT17.
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‎torchreid/data/datasets/image/prid.py

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class PRID(ImageDataset):
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"""PRID (single-shot version of prid-2011)
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‎torchreid/data/datasets/image/sensereid.py

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class SenseReID(ImageDataset):
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"""SenseReID.
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‎torchreid/data/datasets/image/viper.py

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class VIPeR(ImageDataset):
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"""VIPeR.
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‎torchreid/data/datasets/video/dukemtmcvidreid.py

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class DukeMTMCVidReID(VideoDataset):
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"""DukeMTMCVidReID.
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‎torchreid/data/datasets/video/ilidsvid.py

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class iLIDSVID(VideoDataset):
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"""iLIDS-VID.
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‎torchreid/data/datasets/video/mars.py

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class Mars(VideoDataset):
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"""MARS.
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‎torchreid/data/datasets/video/prid2011.py

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class PRID2011(VideoDataset):
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"""PRID2011.
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‎torchreid/data/sampler.py

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class RandomIdentitySampler(Sampler):
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"""Randomly samples N identities each with K instances.
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