def __init__(self, num_classes=10, num_channels=1): super(AlexNet, self).__init__() self.num_classes = num_classes self.input_size = 224 self.dimension = 4 self.conv1 = layer.Conv2d(num_channels, 64, 11, stride=4, padding=2) self.conv2 = layer.Conv2d(64, 192, 5, padding=2) self.conv3 = layer.Conv2d(192, 384, 3, padding=1) self.conv4 = layer.Conv2d(384, 256, 3, padding=1) self.conv5 = layer.Conv2d(256, 256, 3, padding=1) self.linear1 = layer.Linear(4096) self.linear2 = layer.Linear(4096) self.linear3 = layer.Linear(num_classes) self.pooling1 = layer.MaxPool2d(2, 2, padding=0) self.pooling2 = layer.MaxPool2d(2, 2, padding=0) self.pooling3 = layer.MaxPool2d(2, 2, padding=0) self.avg_pooling1 = layer.AvgPool2d(3, 2, padding=0) self.relu1 = layer.ReLU() self.relu2 = layer.ReLU() self.relu3 = layer.ReLU() self.relu4 = layer.ReLU() self.relu5 = layer.ReLU() self.relu6 = layer.ReLU() self.relu7 = layer.ReLU() self.flatten = layer.Flatten() self.dropout1 = layer.Dropout() self.dropout2 = layer.Dropout() self.softmax_cross_entropy = layer.SoftMaxCrossEntropy()
def __init__(self): self.conv1 = layer.Conv2d(1, 20, 5, padding=0) self.conv2 = layer.Conv2d(20, 50, 5, padding=0) self.linear1 = layer.Linear(4 * 4 * 50, 500) self.linear2 = layer.Linear(500, 10) self.pooling1 = layer.MaxPool2d(2, 2, padding=0) self.pooling2 = layer.MaxPool2d(2, 2, padding=0) self.relu1 = layer.ReLU() self.relu2 = layer.ReLU() self.relu3 = layer.ReLU() self.flatten = layer.Flatten()
def __init__(self, num_classes=10, num_channels=1): super(CNN, self).__init__() self.num_classes = num_classes self.input_size = 28 self.dimension = 4 self.conv1 = layer.Conv2d(num_channels, 20, 5, padding=0, activation="RELU") self.conv2 = layer.Conv2d(20, 50, 5, padding=0, activation="RELU") self.linear1 = layer.Linear(500) self.linear2 = layer.Linear(num_classes) self.pooling1 = layer.MaxPool2d(2, 2, padding=0) self.pooling2 = layer.MaxPool2d(2, 2, padding=0) self.relu = layer.ReLU() self.flatten = layer.Flatten() self.softmax_cross_entropy = layer.SoftMaxCrossEntropy()
def _make_layer(self, block, planes, blocks, stride=1): downsample = None if stride != 1 or self.inplanes != planes * block.expansion: conv = layer.Conv2d( self.inplanes, planes * block.expansion, 1, stride=stride, bias=False, ) bn = layer.BatchNorm2d(planes * block.expansion) def _downsample(x): return bn(conv(x)) downsample = _downsample 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)) def forward(x): for layer in layers: x = layer(x) return x return forward, layers
def __init__(self, block, layers, num_classes=10, num_channels=3): self.inplanes = 64 super(ResNet, self).__init__() self.num_classes = num_classes self.input_size = 224 self.dimension = 4 self.conv1 = layer.Conv2d(num_channels, 64, 7, stride=2, padding=3, bias=False) self.bn1 = layer.BatchNorm2d(64) self.relu = layer.ReLU() self.maxpool = layer.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1, layers1 = self._make_layer(block, 64, layers[0]) self.layer2, layers2 = self._make_layer(block, 128, layers[1], stride=2) self.layer3, layers3 = self._make_layer(block, 256, layers[2], stride=2) self.layer4, layers4 = self._make_layer(block, 512, layers[3], stride=2) self.avgpool = layer.AvgPool2d(7, stride=1) self.flatten = layer.Flatten() self.fc = layer.Linear(num_classes) self.softmax_cross_entropy = layer.SoftMaxCrossEntropy() self.register_layers(*layers1, *layers2, *layers3, *layers4)
def conv3x3(in_planes, out_planes, stride=1): """3x3 convolution with padding""" return layer.Conv2d( in_planes, out_planes, 3, stride=stride, padding=1, bias=False, )
def __init__(self, onnx_model, num_classes=10, image_size=224, num_channels=3): super(MyModel, self).__init__(onnx_model) self.dimension = 4 self.num_classes = num_classes self.input_size = image_size self.num_channels = num_channels self.conv = layer.Conv2d(1280, 10, 1, padding=0)
def __init__(self, inplanes, planes, stride=1, downsample=None): super(Bottleneck, self).__init__() self.conv1 = layer.Conv2d(inplanes, planes, 1, bias=False) self.bn1 = layer.BatchNorm2d(planes) self.relu1 = layer.ReLU() self.conv2 = layer.Conv2d(planes, planes, 3, stride=stride, padding=1, bias=False) self.bn2 = layer.BatchNorm2d(planes) self.relu2 = layer.ReLU() self.conv3 = layer.Conv2d(planes, planes * self.expansion, 1, bias=False) self.bn3 = layer.BatchNorm2d(planes * self.expansion) self.add = layer.Add() self.relu3 = layer.ReLU() self.downsample = downsample self.stride = stride
def __init__(self): super(MyModel, self).__init__() self.conv1 = layer.Conv2d(2, 2) self.bn1 = layer.BatchNorm2d(2) self.doublelinear1 = DoubleLinear(2, 4, 2) self.optimizer = opt.SGD()
def __init__(self, in_filters, out_filters, reps, strides=1, padding=0, start_with_relu=True, grow_first=True): super(Block, self).__init__() if out_filters != in_filters or strides != 1: self.skip = layer.Conv2d(in_filters, out_filters, 1, stride=strides, padding=padding, bias=False) self.skipbn = layer.BatchNorm2d(out_filters) else: self.skip = None self.layers = [] filters = in_filters if grow_first: self.layers.append(layer.ReLU()) self.layers.append( layer.SeparableConv2d(in_filters, out_filters, 3, stride=1, padding=1, bias=False)) self.layers.append(layer.BatchNorm2d(out_filters)) filters = out_filters for i in range(reps - 1): self.layers.append(layer.ReLU()) self.layers.append( layer.SeparableConv2d(filters, filters, 3, stride=1, padding=1, bias=False)) self.layers.append(layer.BatchNorm2d(filters)) if not grow_first: self.layers.append(layer.ReLU()) self.layers.append( layer.SeparableConv2d(in_filters, out_filters, 3, stride=1, padding=1, bias=False)) self.layers.append(layer.BatchNorm2d(out_filters)) if not start_with_relu: self.layers = self.layers[1:] else: self.layers[0] = layer.ReLU() if strides != 1: self.layers.append(layer.MaxPool2d(3, strides, padding + 1)) self.register_layers(*self.layers) self.add = layer.Add()
def __init__(self, num_classes=10, num_channels=3): """ Constructor Args: num_classes: number of classes """ super(Xception, self).__init__() self.num_classes = num_classes self.input_size = 299 self.dimension = 4 self.conv1 = layer.Conv2d(num_channels, 32, 3, 2, 0, bias=False) self.bn1 = layer.BatchNorm2d(32) self.relu1 = layer.ReLU() self.conv2 = layer.Conv2d(32, 64, 3, 1, 1, bias=False) self.bn2 = layer.BatchNorm2d(64) self.relu2 = layer.ReLU() # do relu here self.block1 = Block(64, 128, 2, 2, padding=0, start_with_relu=False, grow_first=True) self.block2 = Block(128, 256, 2, 2, padding=0, start_with_relu=True, grow_first=True) self.block3 = Block(256, 728, 2, 2, padding=0, start_with_relu=True, grow_first=True) self.block4 = Block(728, 728, 3, 1, start_with_relu=True, grow_first=True) self.block5 = Block(728, 728, 3, 1, start_with_relu=True, grow_first=True) self.block6 = Block(728, 728, 3, 1, start_with_relu=True, grow_first=True) self.block7 = Block(728, 728, 3, 1, start_with_relu=True, grow_first=True) self.block8 = Block(728, 728, 3, 1, start_with_relu=True, grow_first=True) self.block9 = Block(728, 728, 3, 1, start_with_relu=True, grow_first=True) self.block10 = Block(728, 728, 3, 1, start_with_relu=True, grow_first=True) self.block11 = Block(728, 728, 3, 1, start_with_relu=True, grow_first=True) self.block12 = Block(728, 1024, 2, 2, start_with_relu=True, grow_first=False) self.conv3 = layer.SeparableConv2d(1024, 1536, 3, 1, 1) self.bn3 = layer.BatchNorm2d(1536) self.relu3 = layer.ReLU() # do relu here self.conv4 = layer.SeparableConv2d(1536, 2048, 3, 1, 1) self.bn4 = layer.BatchNorm2d(2048) self.relu4 = layer.ReLU() self.globalpooling = layer.MaxPool2d(10, 1) self.flatten = layer.Flatten() self.fc = layer.Linear(num_classes) self.softmax_cross_entropy = layer.SoftMaxCrossEntropy()