eval/curricularface/model_irse.py

# The implementation is adopted from TFace,made pubicly available under the Apache-2.0 license at
# https://github.com/Tencent/TFace/blob/master/recognition/torchkit/backbone/model_irse.py
from collections import namedtuple

from torch.nn import BatchNorm1d, BatchNorm2d, Conv2d, Dropout, Linear, MaxPool2d, Module, PReLU, Sequential

from .common import Flatten, SEModule, initialize_weights


class BasicBlockIR(Module):
    """ BasicBlock for IRNet
    """

    def __init__(self, in_channel, depth, stride):
        super(BasicBlockIR, self).__init__()
        if in_channel == depth:
            self.shortcut_layer = MaxPool2d(1, stride)
        else:
            self.shortcut_layer = Sequential(
                Conv2d(in_channel, depth, (1, 1), stride, bias=False),
                BatchNorm2d(depth))
        self.res_layer = Sequential(
            BatchNorm2d(in_channel),
            Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False),
            BatchNorm2d(depth), PReLU(depth),
            Conv2d(depth, depth, (3, 3), stride, 1, bias=False),
            BatchNorm2d(depth))

    def forward(self, x):
        shortcut = self.shortcut_layer(x)
        res = self.res_layer(x)

        return res + shortcut


class BottleneckIR(Module):
    """ BasicBlock with bottleneck for IRNet
    """

    def __init__(self, in_channel, depth, stride):
        super(BottleneckIR, self).__init__()
        reduction_channel = depth // 4
        if in_channel == depth:
            self.shortcut_layer = MaxPool2d(1, stride)
        else:
            self.shortcut_layer = Sequential(
                Conv2d(in_channel, depth, (1, 1), stride, bias=False),
                BatchNorm2d(depth))
        self.res_layer = Sequential(
            BatchNorm2d(in_channel),
            Conv2d(
                in_channel, reduction_channel, (1, 1), (1, 1), 0, bias=False),
            BatchNorm2d(reduction_channel), PReLU(reduction_channel),
            Conv2d(
                reduction_channel,
                reduction_channel, (3, 3), (1, 1),
                1,
                bias=False), BatchNorm2d(reduction_channel),
            PReLU(reduction_channel),
            Conv2d(reduction_channel, depth, (1, 1), stride, 0, bias=False),
            BatchNorm2d(depth))

    def forward(self, x):
        shortcut = self.shortcut_layer(x)
        res = self.res_layer(x)

        return res + shortcut


class BasicBlockIRSE(BasicBlockIR):

    def __init__(self, in_channel, depth, stride):
        super(BasicBlockIRSE, self).__init__(in_channel, depth, stride)
        self.res_layer.add_module('se_block', SEModule(depth, 16))


class BottleneckIRSE(BottleneckIR):

    def __init__(self, in_channel, depth, stride):
        super(BottleneckIRSE, self).__init__(in_channel, depth, stride)
        self.res_layer.add_module('se_block', SEModule(depth, 16))


class Bottleneck(namedtuple('Block', ['in_channel', 'depth', 'stride'])):
    '''A named tuple describing a ResNet block.'''


def get_block(in_channel, depth, num_units, stride=2):
    return [Bottleneck(in_channel, depth, stride)] + \
           [Bottleneck(depth, depth, 1) for i in range(num_units - 1)]


def get_blocks(num_layers):
    if num_layers == 18:
        blocks = [
            get_block(in_channel=64, depth=64, num_units=2),
            get_block(in_channel=64, depth=128, num_units=2),
            get_block(in_channel=128, depth=256, num_units=2),
            get_block(in_channel=256, depth=512, num_units=2)
        ]
    elif num_layers == 34:
        blocks = [
            get_block(in_channel=64, depth=64, num_units=3),
            get_block(in_channel=64, depth=128, num_units=4),
            get_block(in_channel=128, depth=256, num_units=6),
            get_block(in_channel=256, depth=512, num_units=3)
        ]
    elif num_layers == 50:
        blocks = [
            get_block(in_channel=64, depth=64, num_units=3),
            get_block(in_channel=64, depth=128, num_units=4),
            get_block(in_channel=128, depth=256, num_units=14),
            get_block(in_channel=256, depth=512, num_units=3)
        ]
    elif num_layers == 100:
        blocks = [
            get_block(in_channel=64, depth=64, num_units=3),
            get_block(in_channel=64, depth=128, num_units=13),
            get_block(in_channel=128, depth=256, num_units=30),
            get_block(in_channel=256, depth=512, num_units=3)
        ]
    elif num_layers == 152:
        blocks = [
            get_block(in_channel=64, depth=256, num_units=3),
            get_block(in_channel=256, depth=512, num_units=8),
            get_block(in_channel=512, depth=1024, num_units=36),
            get_block(in_channel=1024, depth=2048, num_units=3)
        ]
    elif num_layers == 200:
        blocks = [
            get_block(in_channel=64, depth=256, num_units=3),
            get_block(in_channel=256, depth=512, num_units=24),
            get_block(in_channel=512, depth=1024, num_units=36),
            get_block(in_channel=1024, depth=2048, num_units=3)
        ]

    return blocks


class Backbone(Module):

    def __init__(self, input_size, num_layers, mode='ir'):
        """ Args:
            input_size: input_size of backbone
            num_layers: num_layers of backbone
            mode: support ir or irse
        """
        super(Backbone, self).__init__()
        assert input_size[0] in [112, 224], \
            'input_size should be [112, 112] or [224, 224]'
        assert num_layers in [18, 34, 50, 100, 152, 200], \
            'num_layers should be 18, 34, 50, 100 or 152'
        assert mode in ['ir', 'ir_se'], \
            'mode should be ir or ir_se'
        self.input_layer = Sequential(
            Conv2d(3, 64, (3, 3), 1, 1, bias=False), BatchNorm2d(64),
            PReLU(64))
        blocks = get_blocks(num_layers)
        if num_layers <= 100:
            if mode == 'ir':
                unit_module = BasicBlockIR
            elif mode == 'ir_se':
                unit_module = BasicBlockIRSE
            output_channel = 512
        else:
            if mode == 'ir':
                unit_module = BottleneckIR
            elif mode == 'ir_se':
                unit_module = BottleneckIRSE
            output_channel = 2048

        if input_size[0] == 112:
            self.output_layer = Sequential(
                BatchNorm2d(output_channel), Dropout(0.4), Flatten(),
                Linear(output_channel * 7 * 7, 512),
                BatchNorm1d(512, affine=False))
        else:
            self.output_layer = Sequential(
                BatchNorm2d(output_channel), Dropout(0.4), Flatten(),
                Linear(output_channel * 14 * 14, 512),
                BatchNorm1d(512, affine=False))

        modules = []
        mid_layer_indices = []  # [2, 15, 45, 48], total 49 layers for IR101
        for block in blocks:
            if len(mid_layer_indices) == 0:
                mid_layer_indices.append(len(block) - 1)
            else:
                mid_layer_indices.append(len(block) + mid_layer_indices[-1])
            for bottleneck in block:
                modules.append(
                    unit_module(bottleneck.in_channel, bottleneck.depth,
                                bottleneck.stride))
        self.body = Sequential(*modules)
        self.mid_layer_indices = mid_layer_indices[-4:]

        # self.dtype = next(self.parameters()).dtype
        initialize_weights(self.modules())

    def device(self):
        return next(self.parameters()).device

    def dtype(self):
        return next(self.parameters()).dtype

    def forward(self, x, return_mid_feats=False):
        x = self.input_layer(x)
        if not return_mid_feats:
            x = self.body(x)
            x = self.output_layer(x)
            return x
        else:
            out_feats = []
            for idx, module in enumerate(self.body):
                x = module(x)
                if idx in self.mid_layer_indices:
                    out_feats.append(x)
            x = self.output_layer(x)
            return x, out_feats


def IR_18(input_size):
    """ Constructs a ir-18 model.
    """
    model = Backbone(input_size, 18, 'ir')

    return model


def IR_34(input_size):
    """ Constructs a ir-34 model.
    """
    model = Backbone(input_size, 34, 'ir')

    return model


def IR_50(input_size):
    """ Constructs a ir-50 model.
    """
    model = Backbone(input_size, 50, 'ir')

    return model


def IR_101(input_size):
    """ Constructs a ir-101 model.
    """
    model = Backbone(input_size, 100, 'ir')

    return model


def IR_152(input_size):
    """ Constructs a ir-152 model.
    """
    model = Backbone(input_size, 152, 'ir')

    return model


def IR_200(input_size):
    """ Constructs a ir-200 model.
    """
    model = Backbone(input_size, 200, 'ir')

    return model


def IR_SE_50(input_size):
    """ Constructs a ir_se-50 model.
    """
    model = Backbone(input_size, 50, 'ir_se')

    return model


def IR_SE_101(input_size):
    """ Constructs a ir_se-101 model.
    """
    model = Backbone(input_size, 100, 'ir_se')

    return model


def IR_SE_152(input_size):
    """ Constructs a ir_se-152 model.
    """
    model = Backbone(input_size, 152, 'ir_se')

    return model


def IR_SE_200(input_size):
    """ Constructs a ir_se-200 model.
    """
    model = Backbone(input_size, 200, 'ir_se')

    return model