EGNet/resnet.py

import torch.nn as nn
import math
# import torch.utils.model_zoo as model_zoo
import torch
import numpy as np
import torch.nn.functional as F
affine_par = True


# def outS(i):
#     i = int(i)
#     i = (i+1)/2
#     i = int(np.ceil((i+1)/2.0))
#     i = (i+1)/2
#     return i
def conv3x3(in_planes, out_planes, stride=1):
    "3x3 convolution with padding"
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=1, bias=False)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes, affine = affine_par)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes, affine = affine_par)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1,  dilation_ = 1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, stride=stride, bias=False) # change
        self.bn1 = nn.BatchNorm2d(planes,affine = affine_par)
        for i in self.bn1.parameters():
            i.requires_grad = False
        padding = 1
        if dilation_ == 2:
            padding = 2
        elif dilation_ == 4:
            padding = 4
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, # change
                               padding=padding, bias=False, dilation = dilation_)
        self.bn2 = nn.BatchNorm2d(planes,affine = affine_par)
        for i in self.bn2.parameters():
            i.requires_grad = False
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4, affine = affine_par)
        for i in self.bn3.parameters():
            i.requires_grad = False
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride



    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out





class ResNet(nn.Module):
    def __init__(self, block, layers):
        self.inplanes = 64
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(64,affine = affine_par)
        for i in self.bn1.parameters():
            i.requires_grad = False
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, ceil_mode=True) # change
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        # self.layer3 = self._make_layer(block, 256, layers[2], stride=1, dilation__ = 2)
        # self.layer4 = self._make_layer(block, 512, layers[3], stride=1, dilation__ = 4)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=1, dilation__ = 2)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, 0.01)
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
        #        for i in m.parameters():
        #            i.requires_grad = False

    def _make_layer(self, block, planes, blocks, stride=1,dilation__ = 1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion or dilation__ == 2 or dilation__ == 4:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion,affine = affine_par),
            )
        for i in downsample._modules['1'].parameters():
            i.requires_grad = False
        layers = []
        layers.append(block(self.inplanes, planes, stride,dilation_=dilation__, downsample = downsample ))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes,dilation_=dilation__))

        return nn.Sequential(*layers)
    # def _make_pred_layer(self,block, dilation_series, padding_series,NoLabels):
    # return block(dilation_series,padding_series,NoLabels)

    def forward(self, x):
        tmp_x = []
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        tmp_x.append(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        tmp_x.append(x)
        x = self.layer2(x)
        tmp_x.append(x)
        x = self.layer3(x)
        tmp_x.append(x)
        x = self.layer4(x)
        tmp_x.append(x)

        return tmp_x



class ResNet_locate(nn.Module):
    def __init__(self, block, layers):
        super(ResNet_locate,self).__init__()
        self.resnet = ResNet(block, layers)
        self.in_planes = 512
        self.out_planes = [512, 256, 256, 128]

        self.ppms_pre = nn.Conv2d(2048, self.in_planes, 1, 1, bias=False)
        ppms, infos = [], []
        for ii in [1, 3, 5]:
            ppms.append(nn.Sequential(nn.AdaptiveAvgPool2d(ii), nn.Conv2d(self.in_planes, self.in_planes, 1, 1, bias=False), nn.ReLU(inplace=True)))
        self.ppms = nn.ModuleList(ppms)

        self.ppm_cat = nn.Sequential(nn.Conv2d(self.in_planes * 4, self.in_planes, 3, 1, 1, bias=False), nn.ReLU(inplace=True))
        # self.ppm_score = nn.Conv2d(self.in_planes, 1, 1, 1)
        for ii in self.out_planes:
            infos.append(nn.Sequential(nn.Conv2d(self.in_planes, ii, 3, 1, 1, bias=False), nn.ReLU(inplace=True)))
        self.infos = nn.ModuleList(infos)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, 0.01)
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def load_pretrained_model(self, model):
        self.resnet.load_state_dict(model)

    def forward(self, x):
        x_size = x.size()[2:]
        xs = self.resnet(x)

        xs_1 = self.ppms_pre(xs[-1])
        xls = [xs_1]
        for k in range(len(self.ppms)):
            xls.append(F.interpolate(self.ppms[k](xs_1), xs_1.size()[2:], mode='bilinear', align_corners=True))
        xls = self.ppm_cat(torch.cat(xls, dim=1))
        top_score = None
        # top_score = F.interpolate(self.ppm_score(xls), x_size, mode='bilinear', align_corners=True)

        infos = []
        for k in range(len(self.infos)):
            infos.append(self.infos[k](F.interpolate(xls, xs[len(self.infos) - 1 - k].size()[2:], mode='bilinear', align_corners=True)))

        return xs, top_score, infos

class BottleneckEZ(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1,  dilation_ = 1, downsample=None):
        super(BottleneckEZ, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, stride=stride, bias=False) # change
        # self.bn1 = nn.BatchNorm2d(planes,affine = affine_par)
        # for i in self.bn1.parameters():
        #     i.requires_grad = False
        padding = 1
        if dilation_ == 2:
            padding = 2
        elif dilation_ == 4:
            padding = 4
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, # change
                               padding=padding, bias=False, dilation = dilation_)
        # self.bn2 = nn.BatchNorm2d(planes,affine = affine_par)
        # for i in self.bn2.parameters():
        #     i.requires_grad = False
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        # self.bn3 = nn.BatchNorm2d(planes * 4, affine = affine_par)
        # for i in self.bn3.parameters():
        #     i.requires_grad = False
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride



    def forward(self, x):
        residual = x

        out = self.conv1(x)
        # out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        # out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        # out = self.bn3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out



def resnet50(pretrained=False):
    """Constructs a ResNet-50 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on Places
    """
    # model = ResNet(Bottleneck, [3, 4, 6, 3])
    model = ResNet(Bottleneck, [3, 4, 6, 3])
    if pretrained:
        model.load_state_dict(load_url(model_urls['resnet50']), strict=False)
    return model

def resnet101(pretrained=False):
    """Constructs a ResNet-101 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    # model = ResNet(Bottleneck, [3, 4, 23, 3])
    model = ResNet_locate(Bottleneck, [3, 4, 23, 3])
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))
    return model