""" This code is adapted from: https://github.com/wielandbrendel/bag-of-local-features-models """ import torch.nn as nn import math import torch from collections import OrderedDict from torch.utils import model_zoo from .normalizer import Normalizer import os dir_path = os.path.dirname(os.path.realpath(__file__)) __all__ = ['bagnet9', 'bagnet17', 'bagnet33'] model_urls = { 'bagnet9': 'https://bitbucket.org/wielandbrendel/bag-of-feature-pretrained-models/raw/249e8fa82c0913623a807d9d35eeab9da7dcc2a8/bagnet8-34f4ccd2.pth.tar', 'bagnet17': 'https://bitbucket.org/wielandbrendel/bag-of-feature-pretrained-models/raw/249e8fa82c0913623a807d9d35eeab9da7dcc2a8/bagnet16-105524de.pth.tar', 'bagnet33': 'https://bitbucket.org/wielandbrendel/bag-of-feature-pretrained-models/raw/249e8fa82c0913623a807d9d35eeab9da7dcc2a8/bagnet32-2ddd53ed.pth.tar', } class Bottleneck(nn.Module): expansion = 4 def __init__(self, inplanes, planes, stride=1, downsample=None, kernel_size=1): super(Bottleneck, self).__init__() # print('Creating bottleneck with kernel size {} and stride {} with padding {}'.format(kernel_size, stride, (kernel_size - 1) // 2)) self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm2d(planes) self.conv2 = nn.Conv2d(planes, planes, kernel_size=kernel_size, stride=stride, padding=0, bias=False) # changed padding from (kernel_size - 1) // 2 self.bn2 = nn.BatchNorm2d(planes) self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) self.bn3 = nn.BatchNorm2d(planes * 4) self.relu = nn.ReLU(inplace=True) self.downsample = downsample self.stride = stride def forward(self, x, **kwargs): 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) if residual.size(-1) != out.size(-1): diff = residual.size(-1) - out.size(-1) residual = residual[:,:,:-diff,:-diff] out += residual out = self.relu(out) return out class BagNet(nn.Module): def __init__(self, block, layers, strides=[1, 2, 2, 2], kernel3=[0, 0, 0, 0], num_classes=1000, avg_pool=True): self.inplanes = 64 super(BagNet, self).__init__() self.conv1 = nn.Conv2d(3, 64, kernel_size=1, stride=1, padding=0, bias=False) self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=0, bias=False) self.bn1 = nn.BatchNorm2d(64, momentum=0.001) self.relu = nn.ReLU(inplace=True) self.layer1 = self._make_layer(block, 64, layers[0], stride=strides[0], kernel3=kernel3[0], prefix='layer1') self.layer2 = self._make_layer(block, 128, layers[1], stride=strides[1], kernel3=kernel3[1], prefix='layer2') self.layer3 = self._make_layer(block, 256, layers[2], stride=strides[2], kernel3=kernel3[2], prefix='layer3') self.layer4 = self._make_layer(block, 512, layers[3], stride=strides[3], kernel3=kernel3[3], prefix='layer4') self.avgpool = nn.AvgPool2d(1, stride=1) self.fc = nn.Linear(512 * block.expansion, num_classes) self.avg_pool = avg_pool self.block = block 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, math.sqrt(2. / n)) elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() def _make_layer(self, block, planes, blocks, stride=1, kernel3=0, prefix=''): downsample = None if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(planes * block.expansion), ) layers = [] kernel = 1 if kernel3 == 0 else 3 layers.append(block(self.inplanes, planes, stride, downsample, kernel_size=kernel)) self.inplanes = planes * block.expansion for i in range(1, blocks): kernel = 1 if kernel3 <= i else 3 layers.append(block(self.inplanes, planes, kernel_size=kernel)) return nn.Sequential(*layers) def forward(self, x): x = self.conv1(x) x = self.conv2(x) x = self.bn1(x) x = self.relu(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) if self.avg_pool: x = nn.AvgPool2d(x.size()[2], stride=1)(x) x = x.view(x.size(0), -1) x = self.fc(x) else: x = x.permute(0,2,3,1) x = self.fc(x) return x def bagnet33(pretrained=False, strides=[2, 2, 2, 1], **kwargs): """Constructs a Bagnet-33 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = BagNet(Bottleneck, [3, 4, 6, 3], strides=strides, kernel3=[1,1,1,1], **kwargs) if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['bagnet33'])) return model def bagnet17(pretrained=False, strides=[2, 2, 2, 1], **kwargs): """Constructs a Bagnet-17 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = BagNet(Bottleneck, [3, 4, 6, 3], strides=strides, kernel3=[1,1,1,0], **kwargs) if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['bagnet17'])) return model def bagnet9(pretrained=False, strides=[2, 2, 2, 1], **kwargs): """Constructs a Bagnet-9 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = BagNet(Bottleneck, [3, 4, 6, 3], strides=strides, kernel3=[1,1,0,0], **kwargs) if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['bagnet9'])) return model # --- DeepGaze Adaptation ---- class RGBBagNet17(nn.Sequential): def __init__(self): super(RGBBagNet17, self).__init__() self.bagnet = bagnet17(pretrained=True, avg_pool=False) self.normalizer = Normalizer() super(RGBBagNet17, self).__init__(self.normalizer, self.bagnet) class RGBBagNet33(nn.Sequential): def __init__(self): super(RGBBagNet33, self).__init__() self.bagnet = bagnet33(pretrained=True, avg_pool=False) self.normalizer = Normalizer() super(RGBBagNet33, self).__init__(self.normalizer, self.bagnet)