fast-reid/projects/FastFace/fastface/modeling/iresnet.py

# encoding: utf-8
"""
@author:  xingyu liao
@contact: sherlockliao01@gmail.com
"""

import torch
from torch import nn

from fastreid.layers import get_norm
from fastreid.modeling.backbones import BACKBONE_REGISTRY


def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=dilation, groups=groups, bias=False, dilation=dilation)


def conv1x1(in_planes, out_planes, stride=1):
    """1x1 convolution"""
    return nn.Conv2d(in_planes,
                     out_planes,
                     kernel_size=1,
                     stride=stride,
                     bias=False)


class IBasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, bn_norm, stride=1, downsample=None,
                 groups=1, base_width=64, dilation=1):
        super().__init__()
        if groups != 1 or base_width != 64:
            raise ValueError('BasicBlock only supports groups=1 and base_width=64')
        if dilation > 1:
            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
        self.bn1 = get_norm(bn_norm, inplanes)
        self.conv1 = conv3x3(inplanes, planes)
        self.bn2 = get_norm(bn_norm, planes)
        self.prelu = nn.PReLU(planes)
        self.conv2 = conv3x3(planes, planes, stride)
        self.bn3 = get_norm(bn_norm, planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x
        out = self.bn1(x)
        out = self.conv1(out)
        out = self.bn2(out)
        out = self.prelu(out)
        out = self.conv2(out)
        out = self.bn3(out)
        if self.downsample is not None:
            identity = self.downsample(x)
        out += identity
        return out


class IResNet(nn.Module):
    fc_scale = 7 * 7

    def __init__(self, block, layers, bn_norm, dropout=0, zero_init_residual=False,
                 groups=1, width_per_group=64, replace_stride_with_dilation=None, fp16=False):
        super().__init__()
        self.inplanes = 64
        self.dilation = 1
        self.fp16 = fp16
        if replace_stride_with_dilation is None:
            replace_stride_with_dilation = [False, False, False]
        if len(replace_stride_with_dilation) != 3:
            raise ValueError("replace_stride_with_dilation should be None "
                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
        self.groups = groups
        self.base_width = width_per_group
        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = get_norm(bn_norm, self.inplanes)
        self.prelu = nn.PReLU(self.inplanes)
        self.layer1 = self._make_layer(block, 64, layers[0], bn_norm, stride=2)
        self.layer2 = self._make_layer(block,
                                       128,
                                       layers[1],
                                       bn_norm,
                                       stride=2,
                                       dilate=replace_stride_with_dilation[0])
        self.layer3 = self._make_layer(block,
                                       256,
                                       layers[2],
                                       bn_norm,
                                       stride=2,
                                       dilate=replace_stride_with_dilation[1])
        self.layer4 = self._make_layer(block,
                                       512,
                                       layers[3],
                                       bn_norm,
                                       stride=2,
                                       dilate=replace_stride_with_dilation[2])
        self.bn2 = get_norm(bn_norm, 512 * block.expansion)
        self.dropout = nn.Dropout(p=dropout, inplace=True)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.normal_(m.weight, 0, 0.1)
            elif m.__class__.__name__.find('Norm') != -1:
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

        if zero_init_residual:
            for m in self.modules():
                if isinstance(m, IBasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)

    def _make_layer(self, block, planes, blocks, bn_norm, stride=1, dilate=False):
        downsample = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                get_norm(bn_norm, planes * block.expansion),
            )
        layers = []
        layers.append(
            block(self.inplanes, planes, bn_norm, stride, downsample, self.groups,
                  self.base_width, previous_dilation))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(
                block(self.inplanes,
                      planes,
                      bn_norm,
                      groups=self.groups,
                      base_width=self.base_width,
                      dilation=self.dilation))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.prelu(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.bn2(x)
        x = self.dropout(x)
        return x


@BACKBONE_REGISTRY.register()
def build_iresnet_backbone(cfg):
    """
    Create a IResNet instance from config.
    Returns:
        ResNet: a :class:`ResNet` instance.
    """

    # fmt: off
    bn_norm = cfg.MODEL.BACKBONE.NORM
    depth   = cfg.MODEL.BACKBONE.DEPTH
    dropout = cfg.MODEL.BACKBONE.DROPOUT
    fp16    = cfg.SOLVER.AMP.ENABLED
    # fmt: on

    num_blocks_per_stage = {
        '18x': [2, 2, 2, 2],
        '34x': [3, 4, 6, 3],
        '50x': [3, 4, 14, 3],
        '100x': [3, 13, 30, 3],
        '200x': [6, 26, 60, 6],
    }[depth]

    model = IResNet(IBasicBlock, num_blocks_per_stage, bn_norm, dropout, fp16=fp16)
    return model