def __init__(self, in_planes, planes, stride=1): super(Bottleneck, self).__init__() self.conv1 = Quantize(nn.Conv2d)(in_planes, planes, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm2d(planes) self.conv2 = Quantize(nn.Conv2d)(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(planes) self.conv3 = Quantize(nn.Conv2d)(planes, self.expansion * planes, kernel_size=1, bias=False) self.bn3 = nn.BatchNorm2d(self.expansion * planes) self.shortcut = nn.Sequential() if stride != 1 or in_planes != self.expansion * planes: self.shortcut = nn.Sequential( Quantize(nn.Conv2d)(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(self.expansion * planes))
def __init__(self, block, layers, num_classes=1000): self.inplanes = 64 super(ResNet, self).__init__() self.conv1 = Quantize(nn.Conv2d)(3, 64, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(64) self.relu = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) 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=2) self.layer4 = self._make_layer(block, 512, layers[3], stride=2) self.avgpool = nn.AvgPool2d(7) self.fc = Quantize(nn.Linear)(512 * block.expansion, num_classes) 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 __init__(self, features, num_classes=1000): super(VGG, self).__init__() self.features = features self.classifier = nn.Sequential( Quantize(nn.Linear)(512 * 7 * 7, 4096), nn.ReLU(True), nn.Dropout(), Quantize(nn.Linear)(4096, 4096), nn.ReLU(True), nn.Dropout(), Quantize(nn.Linear)(4096, num_classes), ) self._initialize_weights()
def __init__(self, block, num_blocks, num_classes=10): super(ResNet, self).__init__() self.in_planes = 64 self.conv1 = Quantize(nn.Conv2d)(3, 64, kernel_size=3, stride=1, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(64) self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1) self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2) self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2) self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2) self.linear = Quantize(nn.Linear)(512 * block.expansion, num_classes)
def conv3x3(in_planes, out_planes, stride=1): "3x3 convolution with padding" return Quantize(nn.Conv2d)(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
def __init__(self, num_classes=1000, small_input=False, use_ttq=False): super(AlexNet, self).__init__() self.feature_output_size = 256 if small_input else 256 * 6 * 6 if not use_ttq: Conv2d = nn.Conv2d Linear = nn.Linear else: Conv2d = Quantize(nn.Conv2d) Linear = Quantize(nn.Linear) feature_layers = [ Conv2d(3, 64, kernel_size=11, stride=4, padding=2), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=3, stride=2), Conv2d(64, 192, kernel_size=5, padding=2), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=3, stride=2), Conv2d(192, 384, kernel_size=3, padding=1), nn.ReLU(inplace=True), Conv2d(384, 256, kernel_size=3, padding=1), nn.ReLU(inplace=True), Conv2d(256, 256, kernel_size=3, padding=1), nn.ReLU(inplace=True), ] if not small_input: feature_layers.append(nn.MaxPool2d(kernel_size=3, stride=2)) self.features = nn.Sequential(*feature_layers) fc_size = min(self.feature_output_size, 4096) self.classifier = nn.Sequential( nn.Dropout(0.5), Linear(self.feature_output_size, fc_size), nn.ReLU(inplace=True), nn.Dropout(0.5), Linear(fc_size, fc_size), nn.ReLU(inplace=True), Linear(fc_size, num_classes), ) for m in self.modules(): if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear): init.kaiming_uniform(m.weight.data) if m.bias is not None: m.bias.data.zero_() if isinstance(m, TTQ): init.uniform(m.W_p.data, 0.05, 0.1) init.uniform(m.W_n.data, -0.1, -0.05)
def __init__(self, inplanes, planes, stride=1, downsample=None): super(Bottleneck, self).__init__() self.conv1 = Quantize(nn.Conv2d)(inplanes, planes, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm2d(planes) self.conv2 = Quantize(nn.Conv2d)(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(planes) self.conv3 = Quantize(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 make_layers(cfg, batch_norm=False): layers = [] in_channels = 3 for v in cfg: if v == 'M': layers += [nn.MaxPool2d(kernel_size=2, stride=2)] else: conv2d = Quantize(nn.Conv2d)(in_channels, v, kernel_size=3, padding=1) if batch_norm: layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)] else: layers += [conv2d, nn.ReLU(inplace=True)] in_channels = v return nn.Sequential(*layers)
def _make_layer(self, block, planes, blocks, stride=1): downsample = None if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( Quantize(nn.Conv2d)(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(planes * block.expansion), ) layers = [] layers.append(block(self.inplanes, planes, stride, downsample)) self.inplanes = planes * block.expansion for i in range(1, blocks): layers.append(block(self.inplanes, planes)) return nn.Sequential(*layers)
def __init__(self): super().__init__() self.fc1 = Quantize(nn.Linear)(784, 512) self.fc2 = Quantize(nn.Linear)(512, 512) self.fc3 = Quantize(nn.Linear)(512, 512) self.fc4 = Quantize(nn.Linear)(512, 10)
def __init__(self): super().__init__() self.conv1 = Quantize(nn.Conv2d)(1, 10, kernel_size=5) self.conv2 = Quantize(nn.Conv2d)(10, 20, kernel_size=5) self.fc1 = Quantize(nn.Linear)(320, 128) self.fc2 = Quantize(nn.Linear)(128, 10)