backbone.py

# This code is modified from https://github.com/facebookresearch/low-shot-shrink-hallucinate

import torch
from torch.autograd import Variable
import torch.nn as nn
import math
import numpy as np
import torch.nn.functional as F
from torch.nn.utils.weight_norm import WeightNorm
# Basic ResNet model

def init_layer(L):
    # Initialization using fan-in
    if isinstance(L, nn.Conv2d):
        n = L.kernel_size[0]*L.kernel_size[1]*L.out_channels
        L.weight.data.normal_(0,math.sqrt(2.0/float(n)))
    elif isinstance(L, nn.BatchNorm2d):
        L.weight.data.fill_(1)
        L.bias.data.fill_(0)

class distLinear(nn.Module):
    def __init__(self, indim, outdim):
        super(distLinear, self).__init__()
        self.L = nn.Linear( indim, outdim, bias = False)
        self.class_wise_learnable_norm = True  #See the issue#4&8 in the github
        if self.class_wise_learnable_norm:
            WeightNorm.apply(self.L, 'weight', dim=0) #split the weight update component to direction and norm

        if outdim <=200:
            self.scale_factor = 2; #a fixed scale factor to scale the output of cos value into a reasonably large input for softmax
        else:
            self.scale_factor = 10; #in omniglot, a larger scale factor is required to handle >1000 output classes.

    def forward(self, x):
        x_norm = torch.norm(x, p=2, dim =1).unsqueeze(1).expand_as(x)
        x_normalized = x.div(x_norm+ 0.00001)
        if not self.class_wise_learnable_norm:
            L_norm = torch.norm(self.L.weight.data, p=2, dim =1).unsqueeze(1).expand_as(self.L.weight.data)
            self.L.weight.data = self.L.weight.data.div(L_norm + 0.00001)
        cos_dist = self.L(x_normalized) #matrix product by forward function, but when using WeightNorm, this also multiply the cosine distance by a class-wise learnable norm, see the issue#4&8 in the github
        scores = self.scale_factor* (cos_dist)

        return scores

class Flatten(nn.Module):
    def __init__(self):
        super(Flatten, self).__init__()

    def forward(self, x):
        return x.view(x.size(0), -1)


class Linear_fw(nn.Linear): #used in MAML to forward input with fast weight
    def __init__(self, in_features, out_features):
        super(Linear_fw, self).__init__(in_features, out_features)
        self.weight.fast = None #Lazy hack to add fast weight link
        self.weight_mu = nn.Parameter(torch.zeros((out_features, in_features), device='cuda'))
        self.weight.mu = self.weight_mu
        self.weight.mu_fast = None
        self.weight.logvar = None
        self.bias.fast = None
        self.bias_mu = nn.Parameter(torch.zeros(out_features, device='cuda'))
        self.bias.mu = self.bias_mu
        self.bias.mu_fast = None
        self.bias.logvar = None

    def forward(self, x):
        if self.weight.fast is not None and self.bias.fast is not None:
            preds = []
            for w, b in zip(self.weight.fast, self.bias.fast):
                preds.append(F.linear(x, w, b))

            out = sum(preds) / len(preds)
        else:
            out = super(Linear_fw, self).forward(x)
        return out

class Conv2d_fw(nn.Conv2d): #used in MAML to forward input with fast weight
    def __init__(self, in_channels, out_channels, kernel_size, stride=1,padding=0, bias = True):
        super(Conv2d_fw, self).__init__(in_channels, out_channels, kernel_size, stride=stride, padding=padding, bias=bias)
        self.weight.fast = None
        if not self.bias is None:
            self.bias.fast = None

    def forward(self, x):
        if self.bias is None:
            if self.weight.fast is not None:
                out = F.conv2d(x, self.weight.fast, None, stride= self.stride, padding=self.padding)
            else:
                out = super(Conv2d_fw, self).forward(x)
        else:
            if self.weight.fast is not None and self.bias.fast is not None:
                out = F.conv2d(x, self.weight.fast, self.bias.fast, stride= self.stride, padding=self.padding)
            else:
                out = super(Conv2d_fw, self).forward(x)

        return out

class BatchNorm2d_fw(nn.BatchNorm2d): #used in MAML to forward input with fast weight
    def __init__(self, num_features):
        super(BatchNorm2d_fw, self).__init__(num_features)
        self.weight.fast = None
        self.bias.fast = None

    def forward(self, x):
        running_mean = torch.zeros(x.data.size()[1]).cuda()
        running_var = torch.ones(x.data.size()[1]).cuda()
        if self.weight.fast is not None and self.bias.fast is not None:
            out = F.batch_norm(x, running_mean, running_var, self.weight.fast, self.bias.fast, training = True, momentum = 1)
            #batch_norm momentum hack: follow hack of Kate Rakelly in pytorch-maml/src/layers.py
        else:
            out = F.batch_norm(x, running_mean, running_var, self.weight, self.bias, training = True, momentum = 1)
        return out

# Simple Conv Block
class ConvBlock(nn.Module):
    maml = False #Default
    def __init__(self, indim, outdim, pool = True, padding = 1):
        super(ConvBlock, self).__init__()
        self.indim  = indim
        self.outdim = outdim
        if self.maml:
            self.C      = Conv2d_fw(indim, outdim, 3, padding = padding)
            self.BN     = BatchNorm2d_fw(outdim)
        else:
            self.C      = nn.Conv2d(indim, outdim, 3, padding= padding)
            self.BN     = nn.BatchNorm2d(outdim)
        self.relu   = nn.ReLU(inplace=True)

        self.parametrized_layers = [self.C, self.BN, self.relu]
        if pool:
            self.pool   = nn.MaxPool2d(2)
            self.parametrized_layers.append(self.pool)

        for layer in self.parametrized_layers:
            init_layer(layer)

        self.trunk = nn.Sequential(*self.parametrized_layers)


    def forward(self,x):
        out = self.trunk(x)
        return out

# Simple ResNet Block
class SimpleBlock(nn.Module):
    maml = False #Default
    def __init__(self, indim, outdim, half_res):
        super(SimpleBlock, self).__init__()
        self.indim = indim
        self.outdim = outdim
        if self.maml:
            self.C1 = Conv2d_fw(indim, outdim, kernel_size=3, stride=2 if half_res else 1, padding=1, bias=False)
            self.BN1 = BatchNorm2d_fw(outdim)
            self.C2 = Conv2d_fw(outdim, outdim,kernel_size=3, padding=1,bias=False)
            self.BN2 = BatchNorm2d_fw(outdim)
        else:
            self.C1 = nn.Conv2d(indim, outdim, kernel_size=3, stride=2 if half_res else 1, padding=1, bias=False)
            self.BN1 = nn.BatchNorm2d(outdim)
            self.C2 = nn.Conv2d(outdim, outdim,kernel_size=3, padding=1,bias=False)
            self.BN2 = nn.BatchNorm2d(outdim)
        self.relu1 = nn.ReLU(inplace=True)
        self.relu2 = nn.ReLU(inplace=True)

        self.parametrized_layers = [self.C1, self.C2, self.BN1, self.BN2]

        self.half_res = half_res

        # if the input number of channels is not equal to the output, then need a 1x1 convolution
        if indim!=outdim:
            if self.maml:
                self.shortcut = Conv2d_fw(indim, outdim, 1, 2 if half_res else 1, bias=False)
                self.BNshortcut = BatchNorm2d_fw(outdim)
            else:
                self.shortcut = nn.Conv2d(indim, outdim, 1, 2 if half_res else 1, bias=False)
                self.BNshortcut = nn.BatchNorm2d(outdim)

            self.parametrized_layers.append(self.shortcut)
            self.parametrized_layers.append(self.BNshortcut)
            self.shortcut_type = '1x1'
        else:
            self.shortcut_type = 'identity'

        for layer in self.parametrized_layers:
            init_layer(layer)

    def forward(self, x):
        out = self.C1(x)
        out = self.BN1(out)
        out = self.relu1(out)
        out = self.C2(out)
        out = self.BN2(out)
        short_out = x if self.shortcut_type == 'identity' else self.BNshortcut(self.shortcut(x))
        out = out + short_out
        out = self.relu2(out)
        return out



# Bottleneck block
class BottleneckBlock(nn.Module):
    maml = False #Default
    def __init__(self, indim, outdim, half_res):
        super(BottleneckBlock, self).__init__()
        bottleneckdim = int(outdim/4)
        self.indim = indim
        self.outdim = outdim
        if self.maml:
            self.C1 = Conv2d_fw(indim, bottleneckdim, kernel_size=1,  bias=False)
            self.BN1 = BatchNorm2d_fw(bottleneckdim)
            self.C2 = Conv2d_fw(bottleneckdim, bottleneckdim, kernel_size=3, stride=2 if half_res else 1,padding=1)
            self.BN2 = BatchNorm2d_fw(bottleneckdim)
            self.C3 = Conv2d_fw(bottleneckdim, outdim, kernel_size=1, bias=False)
            self.BN3 = BatchNorm2d_fw(outdim)
        else:
            self.C1 = nn.Conv2d(indim, bottleneckdim, kernel_size=1,  bias=False)
            self.BN1 = nn.BatchNorm2d(bottleneckdim)
            self.C2 = nn.Conv2d(bottleneckdim, bottleneckdim, kernel_size=3, stride=2 if half_res else 1,padding=1)
            self.BN2 = nn.BatchNorm2d(bottleneckdim)
            self.C3 = nn.Conv2d(bottleneckdim, outdim, kernel_size=1, bias=False)
            self.BN3 = nn.BatchNorm2d(outdim)

        self.relu = nn.ReLU()
        self.parametrized_layers = [self.C1, self.BN1, self.C2, self.BN2, self.C3, self.BN3]
        self.half_res = half_res


        # if the input number of channels is not equal to the output, then need a 1x1 convolution
        if indim!=outdim:
            if self.maml:
                self.shortcut = Conv2d_fw(indim, outdim, 1, stride=2 if half_res else 1, bias=False)
            else:
                self.shortcut = nn.Conv2d(indim, outdim, 1, stride=2 if half_res else 1, bias=False)

            self.parametrized_layers.append(self.shortcut)
            self.shortcut_type = '1x1'
        else:
            self.shortcut_type = 'identity'

        for layer in self.parametrized_layers:
            init_layer(layer)


    def forward(self, x):

        short_out = x if self.shortcut_type == 'identity' else self.shortcut(x)
        out = self.C1(x)
        out = self.BN1(out)
        out = self.relu(out)
        out = self.C2(out)
        out = self.BN2(out)
        out = self.relu(out)
        out = self.C3(out)
        out = self.BN3(out)
        out = out + short_out

        out = self.relu(out)
        return out


class ConvNet(nn.Module):
    def __init__(self, depth, flatten = True, pool=False):
        super(ConvNet,self).__init__()
        trunk = []
        for i in range(depth):
            indim = 3 if i == 0 else 64
            outdim = 64
            B = ConvBlock(indim, outdim, pool = ( i <4 ) ) #only pooling for fist 4 layers
            trunk.append(B)

        if pool:
            trunk.append(nn.AdaptiveAvgPool2d((1,1)))

        if flatten:
            trunk.append(Flatten())

        self.trunk = nn.Sequential(*trunk)
        self.final_feat_dim: int = outdim

    def forward(self,x):
        out = self.trunk(x)
        return out

class ConvNetNopool(nn.Module): #Relation net use a 4 layer conv with pooling in only first two layers, else no pooling
    def __init__(self, depth):
        super(ConvNetNopool,self).__init__()
        trunk = []
        for i in range(depth):
            indim = 3 if i == 0 else 64
            outdim = 64
            B = ConvBlock(indim, outdim, pool = ( i in [0,1] ), padding = 0 if i in[0,1] else 1  ) #only first two layer has pooling and no padding
            trunk.append(B)

        self.trunk = nn.Sequential(*trunk)
        self.final_feat_dim = [64,19,19]

    def forward(self,x):
        out = self.trunk(x)
        return out

class ConvNetS(nn.Module): #For omniglot, only 1 input channel, output dim is 64
    def __init__(self, depth, flatten = True):
        super(ConvNetS,self).__init__()
        trunk = []
        for i in range(depth):
            indim = 1 if i == 0 else 64
            outdim = 64
            B = ConvBlock(indim, outdim, pool = ( i <4 ) ) #only pooling for fist 4 layers
            trunk.append(B)

        if flatten:
            trunk.append(Flatten())

        #trunk.append(nn.BatchNorm1d(64))    #TODO remove
        #trunk.append(nn.ReLU(inplace=True)) #TODO remove
        #trunk.append(nn.Linear(64, 64))     #TODO remove
        self.trunk = nn.Sequential(*trunk)
        self.final_feat_dim = 64

    def forward(self,x):
        out = x[:,0:1,:,:] #only use the first dimension
        out = self.trunk(out)
        #out = torch.tanh(out) #TODO remove
        return out

class ConvNetSNopool(nn.Module): #Relation net use a 4 layer conv with pooling in only first two layers, else no pooling. For omniglot, only 1 input channel, output dim is [64,5,5]
    def __init__(self, depth):
        super(ConvNetSNopool,self).__init__()
        trunk = []
        for i in range(depth):
            indim = 1 if i == 0 else 64
            outdim = 64
            B = ConvBlock(indim, outdim, pool = ( i in [0,1] ), padding = 0 if i in[0,1] else 1  ) #only first two layer has pooling and no padding
            trunk.append(B)

        self.trunk = nn.Sequential(*trunk)
        self.final_feat_dim = [64,5,5]

    def forward(self,x):
        out = x[:,0:1,:,:] #only use the first dimension
        out = self.trunk(out)
        return out

class ResNet(nn.Module):
    maml = False #Default
    def __init__(self,block,list_of_num_layers, list_of_out_dims, flatten = True):
        # list_of_num_layers specifies number of layers in each stage
        # list_of_out_dims specifies number of output channel for each stage
        super(ResNet,self).__init__()
        assert len(list_of_num_layers)==4, 'Can have only four stages'
        if self.maml:
            conv1 = Conv2d_fw(3, 64, kernel_size=7, stride=2, padding=3,
                                               bias=False)
            bn1 = BatchNorm2d_fw(64)
        else:
            conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                                               bias=False)
            bn1 = nn.BatchNorm2d(64)

        relu = nn.ReLU()
        pool1 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        init_layer(conv1)
        init_layer(bn1)


        trunk = [conv1, bn1, relu, pool1]

        indim = 64
        for i in range(4):

            for j in range(list_of_num_layers[i]):
                half_res = (i>=1) and (j==0)
                B = block(indim, list_of_out_dims[i], half_res)
                trunk.append(B)
                indim = list_of_out_dims[i]

        if flatten:
            avgpool = nn.AvgPool2d(7)
            trunk.append(avgpool)
            trunk.append(Flatten())
            self.final_feat_dim = indim
        else:
            self.final_feat_dim = [ indim, 7, 7]

        self.trunk = nn.Sequential(*trunk)

    def forward(self,x):
        out = self.trunk(x)
        return out

# Backbone for QMUL regression
class Conv3(nn.Module):
    def __init__(self):
        super(Conv3, self).__init__()
        self.layer1 = nn.Conv2d(3, 36, 3,stride=2,dilation=2)
        self.layer2 = nn.Conv2d(36,36, 3,stride=2,dilation=2)
        self.layer3 = nn.Conv2d(36,36, 3,stride=2,dilation=2)

    def return_clones(self):
        layer1_w = self.layer1.weight.data.clone().detach()
        layer2_w = self.layer2.weight.data.clone().detach()
        layer3_w = self.layer3.weight.data.clone().detach()
        return [layer1_w, layer2_w, layer3_w]

    def assign_clones(self, weights_list):
        self.layer1.weight.data.copy_(weights_list[0])
        self.layer2.weight.data.copy_(weights_list[1])
        self.layer3.weight.data.copy_(weights_list[2])

    def forward(self, x):
        out = F.relu(self.layer1(x))
        out = F.relu(self.layer2(out))
        out = F.relu(self.layer3(out))
        out = out.view(out.size(0), -1)
        return out


# just to test the kernel hypothesis
class BackboneKernel(nn.Module):
    def __init__(self, input_dim: int, output_dim: int, num_layers: int, hidden_dim: int, flatten: bool =False, **kwargs):
        super().__init__()
        self.input_dim = input_dim
        self.output_dim = output_dim
        self.num_layers = num_layers
        self.hidden_dim = hidden_dim
        self.flatten = flatten
        self.model = self.create_model()

    def create_model(self):

        assert self.num_layers >= 1, "Number of hidden layers must be at least 1"
        modules = [nn.Linear(self.input_dim, self.hidden_dim), nn.ReLU()]
        if self.flatten:
            modules = [nn.Flatten()] + modules
        for i in range(self.num_layers - 1):
            modules.append(nn.Linear(self.hidden_dim, self.hidden_dim))
            modules.append(nn.ReLU())
        modules.append(nn.Linear(self.hidden_dim, self.output_dim))

        model = nn.Sequential(*modules)
        return model

    def forward(self, x, **params):
        r"""
        Computes the covariance between x1 and x2.
        This method should be imlemented by all Kernel subclasses.

        Args:
            :attr:`x1` (Tensor `n x d` or `b x n x d`):
                First set of data
            :attr:`x2` (Tensor `m x d` or `b x m x d`):
                Second set of data
            :attr:`diag` (bool):
                Should the Kernel compute the whole kernel, or just the diag?
            :attr:`last_dim_is_batch` (tuple, optional):
                If this is true, it treats the last dimension of the data as another batch dimension.
                (Useful for additive structure over the dimensions). Default: False

        Returns:
            :class:`Tensor` or :class:`gpytorch.lazy.LazyTensor`.
                The exact size depends on the kernel's evaluation mode:

                * `full_covar`: `n x m` or `b x n x m`
                * `full_covar` with `last_dim_is_batch=True`: `k x n x m` or `b x k x n x m`
                * `diag`: `n` or `b x n`
                * `diag` with `last_dim_is_batch=True`: `k x n` or `b x k x n`
        """
        out = self.model(x)

        return out


class ConvNet4WithKernel(nn.Module):
    def __init__(self):
        super(ConvNet4WithKernel, self).__init__()
        conv_out_size = 1600
        hn_kernel_layers_no = 4
        hn_kernel_hidden_dim = 64
        self.input_dim = conv_out_size
        self.output_dim = conv_out_size
        self.num_layers = hn_kernel_layers_no
        self.hidden_dim = hn_kernel_hidden_dim
        self.Conv4 = ConvNet(4)
        self.nn_kernel = BackboneKernel(self.input_dim, self.output_dim,
                                        self.num_layers, self.hidden_dim)
        self.final_feat_dim = self.output_dim
    def forward(self, x):
        x = self.Conv4(x)
        out = self.nn_kernel(x)
        return out



class ResNet10WithKernel(nn.Module):
    def __init__(self):
        super(ResNet10WithKernel, self).__init__()
        conv_out_size = None
        hn_kernel_layers_no = None
        hn_kernel_hidden_dim = None
        self.input_dim = conv_out_size
        self.output_dim = conv_out_size
        self.num_layers = hn_kernel_layers_no
        self.hidden_dim = hn_kernel_hidden_dim
        self.Conv4 = ConvNet(4)
        self.nn_kernel = BackboneKernel(self.input_dim, self.output_dim,
                                        self.num_layers, self.hidden_dim)

    def forward(self, x):
        x = self.Conv4(x)
        x = torch.unsqueeze(torch.flatten(x), 0)
        out = self.nn_kernel(x)
        return out


def Conv4():
    return ConvNet(4)

def Conv4Pool():
    return ConvNet(4, pool=True)
def Conv6():
    return ConvNet(6)

def Conv4NP():
    return ConvNetNopool(4)

def Conv6NP():
    return ConvNetNopool(6)

def Conv4S():
    return ConvNetS(4)

def Conv4SNP():
    return ConvNetSNopool(4)

def ResNet10( flatten = True):
    return ResNet(SimpleBlock, [1,1,1,1],[64,128,256,512], flatten)

def ResNet18( flatten = True):
    return ResNet(SimpleBlock, [2,2,2,2],[64,128,256,512], flatten)

def ResNet34( flatten = True):
    return ResNet(SimpleBlock, [3,4,6,3],[64,128,256,512], flatten)

def ResNet50( flatten = True):
    return ResNet(BottleneckBlock, [3,4,6,3], [256,512,1024,2048], flatten)

def ResNet101( flatten = True):
    return ResNet(BottleneckBlock, [3,4,23,3],[256,512,1024,2048], flatten)

def Conv4WithKernel():
    return ConvNet4WithKernel()

def ResNetWithKernel():
    return ResNet10WithKernel()