"""resnet in pytorch [1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. Deep Residual Learning for Image Recognition https://arxiv.org/abs/1512.03385v1 """ import torch import torch.nn as nn import torch.distributed as dist from models.switched_conv.switched_conv_hard_routing import SwitchNorm, RouteTop1 from trainer.networks import register_model class BasicBlock(nn.Module): """Basic Block for resnet 18 and resnet 34 """ #BasicBlock and BottleNeck block #have different output size #we use class attribute expansion #to distinct expansion = 1 def __init__(self, in_channels, out_channels, stride=1): super().__init__() #residual function self.residual_function = nn.Sequential( nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False), nn.BatchNorm2d(out_channels), nn.ReLU(inplace=True), nn.Conv2d(out_channels, out_channels * BasicBlock.expansion, kernel_size=3, padding=1, bias=False), nn.BatchNorm2d(out_channels * BasicBlock.expansion) ) #shortcut self.shortcut = nn.Sequential() #the shortcut output dimension is not the same with residual function #use 1*1 convolution to match the dimension if stride != 1 or in_channels != BasicBlock.expansion * out_channels: self.shortcut = nn.Sequential( nn.Conv2d(in_channels, out_channels * BasicBlock.expansion, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(out_channels * BasicBlock.expansion) ) def forward(self, x): return nn.ReLU(inplace=True)(self.residual_function(x) + self.shortcut(x)) class BottleNeck(nn.Module): """Residual block for resnet over 50 layers """ expansion = 4 def __init__(self, in_channels, out_channels, stride=1): super().__init__() self.residual_function = nn.Sequential( nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False), nn.BatchNorm2d(out_channels), nn.ReLU(inplace=True), nn.Conv2d(out_channels, out_channels, stride=stride, kernel_size=3, padding=1, bias=False), nn.BatchNorm2d(out_channels), nn.ReLU(inplace=True), nn.Conv2d(out_channels, out_channels * BottleNeck.expansion, kernel_size=1, bias=False), nn.BatchNorm2d(out_channels * BottleNeck.expansion), ) self.shortcut = nn.Sequential() if stride != 1 or in_channels != out_channels * BottleNeck.expansion: self.shortcut = nn.Sequential( nn.Conv2d(in_channels, out_channels * BottleNeck.expansion, stride=stride, kernel_size=1, bias=False), nn.BatchNorm2d(out_channels * BottleNeck.expansion) ) def forward(self, x): return nn.ReLU(inplace=True)(self.residual_function(x) + self.shortcut(x)) class ResNetTail(nn.Module): def __init__(self, block, num_block, num_classes=100): super().__init__() self.in_channels = 64 self.conv4_x = self._make_layer(block, 128, num_block[2], 2) self.conv5_x = self._make_layer(block, 256, num_block[3], 2) self.avg_pool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(256 * block.expansion, num_classes) def _make_layer(self, block, out_channels, num_blocks, stride): strides = [stride] + [1] * (num_blocks - 1) layers = [] for stride in strides: layers.append(block(self.in_channels, out_channels, stride)) self.in_channels = out_channels * block.expansion return nn.Sequential(*layers) def forward(self, x): output = self.conv4_x(x) output = self.conv5_x(output) output = self.avg_pool(output) output = output.view(output.size(0), -1) output = self.fc(output) return output class DropoutNorm(SwitchNorm): def __init__(self, group_size, dropout_rate, accumulator_size=256): super().__init__(group_size, accumulator_size) self.accumulator_desired_size = accumulator_size self.group_size = group_size self.dropout_rate = dropout_rate self.register_buffer("accumulator_index", torch.zeros(1, dtype=torch.long, device='cpu')) self.register_buffer("accumulator_filled", torch.zeros(1, dtype=torch.long, device='cpu')) self.register_buffer("accumulator", torch.zeros(accumulator_size, group_size)) def add_norm_to_buffer(self, x): flatten_dims = [0] + [k+2 for k in range(len(x.shape)-2)] flat = x.mean(dim=flatten_dims) self.accumulator[self.accumulator_index] = flat.detach().clone() self.accumulator_index += 1 if self.accumulator_index >= self.accumulator_desired_size: self.accumulator_index *= 0 if self.accumulator_filled <= 0: self.accumulator_filled += 1 # Input into forward is a switching tensor of shape (batch,groups,) def forward(self, x: torch.Tensor): assert len(x.shape) >= 2 if not self.training: return x # Only accumulate the "winning" switch slots. mask = torch.nn.functional.one_hot(x.argmax(dim=1), num_classes=x.shape[1]) if len(x.shape) > 2: mask = mask.permute(0, 3, 1, 2) # TODO: Make this more extensible. xtop = torch.ones_like(x) xtop[mask != 1] = 0 # Push the accumulator to the right device on the first iteration. if self.accumulator.device != xtop.device: self.accumulator = self.accumulator.to(xtop.device) self.add_norm_to_buffer(xtop) # Reduce across all distributed entities, if needed if dist.is_available() and dist.is_initialized(): dist.all_reduce(self.accumulator, op=dist.ReduceOp.SUM) self.accumulator /= dist.get_world_size() # Compute the dropout probabilities. This module is a no-op before the accumulator is initialized. if self.accumulator_filled > 0: with torch.no_grad(): probs = torch.mean(self.accumulator, dim=0) * self.dropout_rate bs, br = x.shape[:2] drop = torch.rand((bs, br), device=x.device) > probs.unsqueeze(0) # Ensure that there is always at least one switch left un-dropped out fix_blank = (drop.sum(dim=1, keepdim=True) == 0).repeat(1, br) drop = drop.logical_or(fix_blank) x = drop * x return x class HardRoutingGate(nn.Module): def __init__(self, breadth, dropout_rate=.8): super().__init__() self.norm = DropoutNorm(breadth, dropout_rate, accumulator_size=128) def forward(self, x): soft = self.norm(nn.functional.softmax(x, dim=1)) return RouteTop1.apply(soft) return soft class ResNet(nn.Module): def __init__(self, block, num_block, num_classes=100, num_tails=8): super().__init__() self.in_channels = 32 self.conv1 = nn.Sequential( nn.Conv2d(3, 32, kernel_size=3, padding=1, bias=False), nn.BatchNorm2d(32), nn.ReLU(inplace=True)) self.conv2_x = self._make_layer(block, 32, num_block[0], 1) self.conv3_x = self._make_layer(block, 64, num_block[1], 2) self.tails = nn.ModuleList([ResNetTail(block, num_block, 256) for _ in range(num_tails)]) self.selector = ResNetTail(block, num_block, num_tails) self.selector_gate = nn.Linear(256, 1) self.gate = HardRoutingGate(num_tails) self.final_linear = nn.Linear(256, num_classes) def _make_layer(self, block, out_channels, num_blocks, stride): strides = [stride] + [1] * (num_blocks - 1) layers = [] for stride in strides: layers.append(block(self.in_channels, out_channels, stride)) self.in_channels = out_channels * block.expansion return nn.Sequential(*layers) def forward(self, x, coarse_label, return_selector=False): output = self.conv1(x) output = self.conv2_x(output) output = self.conv3_x(output) keys = [] for t in self.tails: keys.append(t(output)) keys = torch.stack(keys, dim=1) query = self.selector(output).unsqueeze(2) selector = self.selector_gate(query * keys).squeeze(-1) selector = self.gate(selector) values = self.final_linear(selector.unsqueeze(-1) * keys) if return_selector: return values.sum(dim=1), selector else: return values.sum(dim=1) #bs = output.shape[0] #return (tailouts[coarse_label] * torch.eye(n=bs, device=x.device).view(bs,bs,1)).sum(dim=1) @register_model def register_cifar_resnet18_branched(opt_net, opt): """ return a ResNet 18 object """ return ResNet(BasicBlock, [2, 2, 2, 2]) def resnet34(): """ return a ResNet 34 object """ return ResNet(BasicBlock, [3, 4, 6, 3]) def resnet50(): """ return a ResNet 50 object """ return ResNet(BottleNeck, [3, 4, 6, 3]) def resnet101(): """ return a ResNet 101 object """ return ResNet(BottleNeck, [3, 4, 23, 3]) def resnet152(): """ return a ResNet 152 object """ return ResNet(BottleNeck, [3, 8, 36, 3]) if __name__ == '__main__': model = ResNet(BasicBlock, [2,2,2,2]) for j in range(10): v = model(torch.randn(256,3,32,32), None) print(v.shape) l = nn.MSELoss()(v, torch.randn_like(v)) l.backward()