boxmot/appearance/backbones/osnet.py

# Mikel Broström 🔥 Yolo Tracking 🧾 AGPL-3.0 license

from __future__ import absolute_import, division

import warnings

import torch
from torch import nn
from torch.nn import functional as F

__all__ = ["osnet_x1_0", "osnet_x0_75", "osnet_x0_5", "osnet_x0_25", "osnet_ibn_x1_0"]

pretrained_urls = {
    "osnet_x1_0": "https://drive.google.com/uc?id=1LaG1EJpHrxdAxKnSCJ_i0u-nbxSAeiFY",
    "osnet_x0_75": "https://drive.google.com/uc?id=1uwA9fElHOk3ZogwbeY5GkLI6QPTX70Hq",
    "osnet_x0_5": "https://drive.google.com/uc?id=16DGLbZukvVYgINws8u8deSaOqjybZ83i",
    "osnet_x0_25": "https://drive.google.com/uc?id=1rb8UN5ZzPKRc_xvtHlyDh-cSz88YX9hs",
    "osnet_ibn_x1_0": "https://drive.google.com/uc?id=1sr90V6irlYYDd4_4ISU2iruoRG8J__6l",
}


##########
# Basic layers
##########
class ConvLayer(nn.Module):
    """Convolution layer (conv + bn + relu)."""

    def __init__(
        self,
        in_channels,
        out_channels,
        kernel_size,
        stride=1,
        padding=0,
        groups=1,
        IN=False,
    ):
        super(ConvLayer, self).__init__()
        self.conv = nn.Conv2d(
            in_channels,
            out_channels,
            kernel_size,
            stride=stride,
            padding=padding,
            bias=False,
            groups=groups,
        )
        if IN:
            self.bn = nn.InstanceNorm2d(out_channels, affine=True)
        else:
            self.bn = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        x = self.relu(x)
        return x


class Conv1x1(nn.Module):
    """1x1 convolution + bn + relu."""

    def __init__(self, in_channels, out_channels, stride=1, groups=1):
        super(Conv1x1, self).__init__()
        self.conv = nn.Conv2d(
            in_channels,
            out_channels,
            1,
            stride=stride,
            padding=0,
            bias=False,
            groups=groups,
        )
        self.bn = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        x = self.relu(x)
        return x


class Conv1x1Linear(nn.Module):
    """1x1 convolution + bn (w/o non-linearity)."""

    def __init__(self, in_channels, out_channels, stride=1):
        super(Conv1x1Linear, self).__init__()
        self.conv = nn.Conv2d(
            in_channels, out_channels, 1, stride=stride, padding=0, bias=False
        )
        self.bn = nn.BatchNorm2d(out_channels)

    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        return x


class Conv3x3(nn.Module):
    """3x3 convolution + bn + relu."""

    def __init__(self, in_channels, out_channels, stride=1, groups=1):
        super(Conv3x3, self).__init__()
        self.conv = nn.Conv2d(
            in_channels,
            out_channels,
            3,
            stride=stride,
            padding=1,
            bias=False,
            groups=groups,
        )
        self.bn = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        x = self.relu(x)
        return x


class LightConv3x3(nn.Module):
    """Lightweight 3x3 convolution.

    1x1 (linear) + dw 3x3 (nonlinear).
    """

    def __init__(self, in_channels, out_channels):
        super(LightConv3x3, self).__init__()
        self.conv1 = nn.Conv2d(
            in_channels, out_channels, 1, stride=1, padding=0, bias=False
        )
        self.conv2 = nn.Conv2d(
            out_channels,
            out_channels,
            3,
            stride=1,
            padding=1,
            bias=False,
            groups=out_channels,
        )
        self.bn = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.bn(x)
        x = self.relu(x)
        return x


##########
# Building blocks for omni-scale feature learning
##########
class ChannelGate(nn.Module):
    """A mini-network that generates channel-wise gates conditioned on input tensor."""

    def __init__(
        self,
        in_channels,
        num_gates=None,
        return_gates=False,
        gate_activation="sigmoid",
        reduction=16,
        layer_norm=False,
    ):
        super(ChannelGate, self).__init__()
        if num_gates is None:
            num_gates = in_channels
        self.return_gates = return_gates
        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
        self.fc1 = nn.Conv2d(
            in_channels, in_channels // reduction, kernel_size=1, bias=True, padding=0
        )
        self.norm1 = None
        if layer_norm:
            self.norm1 = nn.LayerNorm((in_channels // reduction, 1, 1))
        self.relu = nn.ReLU(inplace=True)
        self.fc2 = nn.Conv2d(
            in_channels // reduction, num_gates, kernel_size=1, bias=True, padding=0
        )
        if gate_activation == "sigmoid":
            self.gate_activation = nn.Sigmoid()
        elif gate_activation == "relu":
            self.gate_activation = nn.ReLU(inplace=True)
        elif gate_activation == "linear":
            self.gate_activation = None
        else:
            raise RuntimeError("Unknown gate activation: {}".format(gate_activation))

    def forward(self, x):
        input = x
        x = self.global_avgpool(x)
        x = self.fc1(x)
        if self.norm1 is not None:
            x = self.norm1(x)
        x = self.relu(x)
        x = self.fc2(x)
        if self.gate_activation is not None:
            x = self.gate_activation(x)
        if self.return_gates:
            return x
        return input * x


class OSBlock(nn.Module):
    """Omni-scale feature learning block."""

    def __init__(
        self, in_channels, out_channels, IN=False, bottleneck_reduction=4, **kwargs
    ):
        super(OSBlock, self).__init__()
        mid_channels = out_channels // bottleneck_reduction
        self.conv1 = Conv1x1(in_channels, mid_channels)
        self.conv2a = LightConv3x3(mid_channels, mid_channels)
        self.conv2b = nn.Sequential(
            LightConv3x3(mid_channels, mid_channels),
            LightConv3x3(mid_channels, mid_channels),
        )
        self.conv2c = nn.Sequential(
            LightConv3x3(mid_channels, mid_channels),
            LightConv3x3(mid_channels, mid_channels),
            LightConv3x3(mid_channels, mid_channels),
        )
        self.conv2d = nn.Sequential(
            LightConv3x3(mid_channels, mid_channels),
            LightConv3x3(mid_channels, mid_channels),
            LightConv3x3(mid_channels, mid_channels),
            LightConv3x3(mid_channels, mid_channels),
        )
        self.gate = ChannelGate(mid_channels)
        self.conv3 = Conv1x1Linear(mid_channels, out_channels)
        self.downsample = None
        if in_channels != out_channels:
            self.downsample = Conv1x1Linear(in_channels, out_channels)
        self.IN = None
        if IN:
            self.IN = nn.InstanceNorm2d(out_channels, affine=True)

    def forward(self, x):
        identity = x
        x1 = self.conv1(x)
        x2a = self.conv2a(x1)
        x2b = self.conv2b(x1)
        x2c = self.conv2c(x1)
        x2d = self.conv2d(x1)
        x2 = self.gate(x2a) + self.gate(x2b) + self.gate(x2c) + self.gate(x2d)
        x3 = self.conv3(x2)
        if self.downsample is not None:
            identity = self.downsample(identity)
        out = x3 + identity
        if self.IN is not None:
            out = self.IN(out)
        return F.relu(out)


##########
# Network architecture
##########
class OSNet(nn.Module):
    """Omni-Scale Network.

    Reference:
        - Zhou et al. Omni-Scale Feature Learning for Person Re-Identification. ICCV, 2019.
        - Zhou et al. Learning Generalisable Omni-Scale Representations
          for Person Re-Identification. TPAMI, 2021.
    """

    def __init__(
        self,
        num_classes,
        blocks,
        layers,
        channels,
        feature_dim=512,
        loss="softmax",
        IN=False,
        **kwargs
    ):
        super(OSNet, self).__init__()
        num_blocks = len(blocks)
        assert num_blocks == len(layers)
        assert num_blocks == len(channels) - 1
        self.loss = loss
        self.feature_dim = feature_dim

        # convolutional backbone
        self.conv1 = ConvLayer(3, channels[0], 7, stride=2, padding=3, IN=IN)
        self.maxpool = nn.MaxPool2d(3, stride=2, padding=1)
        self.conv2 = self._make_layer(
            blocks[0],
            layers[0],
            channels[0],
            channels[1],
            reduce_spatial_size=True,
            IN=IN,
        )
        self.conv3 = self._make_layer(
            blocks[1], layers[1], channels[1], channels[2], reduce_spatial_size=True
        )
        self.conv4 = self._make_layer(
            blocks[2], layers[2], channels[2], channels[3], reduce_spatial_size=False
        )
        self.conv5 = Conv1x1(channels[3], channels[3])
        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
        # fully connected layer
        self.fc = self._construct_fc_layer(
            self.feature_dim, channels[3], dropout_p=None
        )
        # identity classification layer
        self.classifier = nn.Linear(self.feature_dim, num_classes)

        self._init_params()

    def _make_layer(
        self, block, layer, in_channels, out_channels, reduce_spatial_size, IN=False
    ):
        layers = []

        layers.append(block(in_channels, out_channels, IN=IN))
        for i in range(1, layer):
            layers.append(block(out_channels, out_channels, IN=IN))

        if reduce_spatial_size:
            layers.append(
                nn.Sequential(
                    Conv1x1(out_channels, out_channels), nn.AvgPool2d(2, stride=2)
                )
            )

        return nn.Sequential(*layers)

    def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
        if fc_dims is None or fc_dims < 0:
            self.feature_dim = input_dim
            return None

        if isinstance(fc_dims, int):
            fc_dims = [fc_dims]

        layers = []
        for dim in fc_dims:
            layers.append(nn.Linear(input_dim, dim))
            layers.append(nn.BatchNorm1d(dim))
            layers.append(nn.ReLU(inplace=True))
            if dropout_p is not None:
                layers.append(nn.Dropout(p=dropout_p))
            input_dim = dim

        self.feature_dim = fc_dims[-1]

        return nn.Sequential(*layers)

    def _init_params(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)

            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

            elif isinstance(m, nn.BatchNorm1d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

            elif isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, 0, 0.01)
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)

    def featuremaps(self, x):
        x = self.conv1(x)
        x = self.maxpool(x)
        x = self.conv2(x)
        x = self.conv3(x)
        x = self.conv4(x)
        x = self.conv5(x)
        return x

    def forward(self, x, return_featuremaps=False):
        x = self.featuremaps(x)
        if return_featuremaps:
            return x
        v = self.global_avgpool(x)
        v = v.view(v.size(0), -1)
        if self.fc is not None:
            v = self.fc(v)
        if not self.training:
            return v
        y = self.classifier(v)
        if self.loss == "softmax":
            return y
        elif self.loss == "triplet":
            return y, v
        else:
            raise KeyError("Unsupported loss: {}".format(self.loss))


def init_pretrained_weights(model, key=""):
    """Initializes model with pretrained weights.

    Layers that don't match with pretrained layers in name or size are kept unchanged.
    """
    import errno
    import os
    from collections import OrderedDict

    import gdown

    def _get_torch_home():
        ENV_TORCH_HOME = "TORCH_HOME"
        ENV_XDG_CACHE_HOME = "XDG_CACHE_HOME"
        DEFAULT_CACHE_DIR = "~/.cache"
        torch_home = os.path.expanduser(
            os.getenv(
                ENV_TORCH_HOME,
                os.path.join(os.getenv(ENV_XDG_CACHE_HOME, DEFAULT_CACHE_DIR), "torch"),
            )
        )
        return torch_home

    torch_home = _get_torch_home()
    model_dir = os.path.join(torch_home, "checkpoints")
    try:
        os.makedirs(model_dir)
    except OSError as e:
        if e.errno == errno.EEXIST:
            # Directory already exists, ignore.
            pass
        else:
            # Unexpected OSError, re-raise.
            raise
    filename = key + "_imagenet.pth"
    cached_file = os.path.join(model_dir, filename)

    if not os.path.exists(cached_file):
        gdown.download(pretrained_urls[key], cached_file, quiet=False)

    state_dict = torch.load(cached_file)
    model_dict = model.state_dict()
    new_state_dict = OrderedDict()
    matched_layers, discarded_layers = [], []

    for k, v in state_dict.items():
        if k.startswith("module."):
            k = k[7:]  # discard module.

        if k in model_dict and model_dict[k].size() == v.size():
            new_state_dict[k] = v
            matched_layers.append(k)
        else:
            discarded_layers.append(k)

    model_dict.update(new_state_dict)
    model.load_state_dict(model_dict)

    if len(matched_layers) == 0:
        warnings.warn(
            'The pretrained weights from "{}" cannot be loaded, '
            "please check the key names manually "
            "(** ignored and continue **)".format(cached_file)
        )
    else:
        print(
            'Successfully loaded imagenet pretrained weights from "{}"'.format(
                cached_file
            )
        )
        if len(discarded_layers) > 0:
            print(
                "** The following layers are discarded "
                "due to unmatched keys or layer size: {}".format(discarded_layers)
            )


##########
# Instantiation
##########
def osnet_x1_0(num_classes=1000, pretrained=True, loss="softmax", **kwargs):
    # standard size (width x1.0)
    model = OSNet(
        num_classes,
        blocks=[OSBlock, OSBlock, OSBlock],
        layers=[2, 2, 2],
        channels=[64, 256, 384, 512],
        loss=loss,
        **kwargs
    )
    if pretrained:
        init_pretrained_weights(model, key="osnet_x1_0")
    return model


def osnet_x0_75(num_classes=1000, pretrained=True, loss="softmax", **kwargs):
    # medium size (width x0.75)
    model = OSNet(
        num_classes,
        blocks=[OSBlock, OSBlock, OSBlock],
        layers=[2, 2, 2],
        channels=[48, 192, 288, 384],
        loss=loss,
        **kwargs
    )
    if pretrained:
        init_pretrained_weights(model, key="osnet_x0_75")
    return model


def osnet_x0_5(num_classes=1000, pretrained=True, loss="softmax", **kwargs):
    # tiny size (width x0.5)
    model = OSNet(
        num_classes,
        blocks=[OSBlock, OSBlock, OSBlock],
        layers=[2, 2, 2],
        channels=[32, 128, 192, 256],
        loss=loss,
        **kwargs
    )
    if pretrained:
        init_pretrained_weights(model, key="osnet_x0_5")
    return model


def osnet_x0_25(num_classes=1000, pretrained=True, loss="softmax", **kwargs):
    # very tiny size (width x0.25)
    model = OSNet(
        num_classes,
        blocks=[OSBlock, OSBlock, OSBlock],
        layers=[2, 2, 2],
        channels=[16, 64, 96, 128],
        loss=loss,
        **kwargs
    )
    if pretrained:
        init_pretrained_weights(model, key="osnet_x0_25")
    return model


def osnet_ibn_x1_0(num_classes=1000, pretrained=True, loss="softmax", **kwargs):
    # standard size (width x1.0) + IBN layer
    # Ref: Pan et al. Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net. ECCV, 2018.
    model = OSNet(
        num_classes,
        blocks=[OSBlock, OSBlock, OSBlock],
        layers=[2, 2, 2],
        channels=[64, 256, 384, 512],
        loss=loss,
        IN=True,
        **kwargs
    )
    if pretrained:
        init_pretrained_weights(model, key="osnet_ibn_x1_0")
    return model