mmselfsup/openselfsup/models/necks.py

import torch.nn as nn
from mmcv.cnn import kaiming_init, normal_init

from .registry import NECKS


@NECKS.register_module
class LinearNeck(nn.Module):

    def __init__(self, in_channels, out_channels, with_avg_pool=True):
        super(LinearNeck, self).__init__()
        self.with_avg_pool = with_avg_pool
        if with_avg_pool:
            self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(in_channels, out_channels)

    def init_weights(self, init_linear='normal'):
        assert init_linear in ['normal', 'kaiming'], \
            "Undefined init_linear: {}".format(init_linear)
        for m in self.modules():
            if isinstance(m, nn.Linear):
                if init_linear == 'normal':
                    normal_init(m, std=0.01)
                else:
                    kaiming_init(m, mode='fan_in', nonlinearity='relu')
            elif isinstance(m,
                            (nn.BatchNorm2d, nn.GroupNorm, nn.SyncBatchNorm)):
                if m.weight is not None:
                    nn.init.constant_(m.weight, 1)
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)

    def forward(self, x):
        assert len(x) == 1
        if self.with_avg_pool:
            x = self.avgpool(x[0])
        return [self.fc(x.view(x.size(0), -1))]


@NECKS.register_module
class NonLinearNeckV0(nn.Module):

    def __init__(self,
                 in_channels,
                 hid_channels,
                 out_channels,
                 with_avg_pool=True):
        super(NonLinearNeckV0, self).__init__()
        self.with_avg_pool = with_avg_pool
        if with_avg_pool:
            self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.mlp = nn.Sequential(
            nn.Linear(in_channels, hid_channels),
            nn.BatchNorm1d(hid_channels, momentum=0.001, affine=False),
            nn.ReLU(inplace=True), nn.Dropout(),
            nn.Linear(hid_channels, out_channels), nn.ReLU(inplace=True))

    def init_weights(self, init_linear='normal'):
        assert init_linear in ['normal', 'kaiming'], \
            "Undefined init_linear: {}".format(init_linear)
        for m in self.modules():
            if isinstance(m, nn.Linear):
                if init_linear == 'normal':
                    normal_init(m, std=0.01)
                else:
                    kaiming_init(m, mode='fan_in', nonlinearity='relu')
            elif isinstance(m,
                            (nn.BatchNorm1d, nn.BatchNorm2d, nn.GroupNorm, nn.SyncBatchNorm)):
                if m.weight is not None:
                    nn.init.constant_(m.weight, 1)
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)

    def forward(self, x):
        assert len(x) == 1
        if self.with_avg_pool:
            x = self.avgpool(x[0])
        return [self.mlp(x.view(x.size(0), -1))]


@NECKS.register_module
class NonLinearNeckV1(nn.Module):

    def __init__(self,
                 in_channels,
                 hid_channels,
                 out_channels,
                 with_avg_pool=True):
        super(NonLinearNeckV1, self).__init__()
        self.with_avg_pool = with_avg_pool
        if with_avg_pool:
            self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.mlp = nn.Sequential(
            nn.Linear(in_channels, hid_channels), nn.ReLU(inplace=True),
            nn.Linear(hid_channels, out_channels))

    def init_weights(self, init_linear='normal'):
        assert init_linear in ['normal', 'kaiming'], \
            "Undefined init_linear: {}".format(init_linear)
        for m in self.modules():
            if isinstance(m, nn.Linear):
                if init_linear == 'normal':
                    normal_init(m, std=0.01)
                else:
                    kaiming_init(m, mode='fan_in', nonlinearity='relu')
            elif isinstance(m,
                            (nn.BatchNorm2d, nn.GroupNorm, nn.SyncBatchNorm)):
                if m.weight is not None:
                    nn.init.constant_(m.weight, 1)
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)

    def forward(self, x):
        assert len(x) == 1
        if self.with_avg_pool:
            x = self.avgpool(x[0])
        return [self.mlp(x.view(x.size(0), -1))]


@NECKS.register_module
class AvgPoolNeck(nn.Module):

    def __init__(self):
        super(AvgPoolNeck, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))

    def init_weights(self, **kwargs):
        pass

    def forward(self, x):
        assert len(x) == 1
        return [self.avg_pool(x[0])]