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| """ | |
| 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) | |