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Running
on
Zero
| 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 | |