AML/model.py at main · naderAsadi/AML · GitHub

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import math
import logging
import numpy as np

import torch
from torch import nn
import torch.utils.data
from torch.nn import functional as F
from torch.autograd import Variable


def normalize(x):
    x_norm = torch.norm(x, p=2, dim=1).unsqueeze(1).expand_as(x)
    x_normalized = x.div(x_norm + 0.00001)
    return x_normalized

class distLinear(nn.Module):
    def __init__(self, indim, outdim, weight=None):
        super(distLinear, self).__init__()
        self.L = nn.Linear( indim, outdim, bias = False)
        if weight is not None:
            self.L.weight.data = Variable(weight)

        self.scale_factor = 10

    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)

        L_norm = torch.norm(self.L.weight, p=2, dim =1).unsqueeze(1).expand_as(self.L.weight.data)
        cos_dist = torch.mm(x_normalized,self.L.weight.div(L_norm + 0.00001).transpose(0,1))

        scores = self.scale_factor * (cos_dist)

        return scores

# Classifiers
# -----------------------------------------------------------------------------------

def conv3x3(in_planes, out_planes, stride=1):
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=1, bias=False)

class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_planes, planes, stride=1):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(in_planes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion * planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1,
                          stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion * planes)
            )

        # self.nin = nn.Conv2d(planes, planes, 1)
        # self.activation = topkrelu()
        self.activation = nn.ReLU()

    def forward(self, x):
        out = self.activation(self.bn1(self.conv1(x)))
        # out = self.activation(self.nin(self.bn1(self.conv1(x))))
        out = self.bn2(self.conv2(out))
        out += self.shortcut(x)
        out = self.activation(out)
        # out = self.activation(self.nin(out))
        return out

class ResNet(nn.Module):
    def __init__(self, block, num_blocks, num_classes, nf, input_size, dist_linear=False):
        super(ResNet, self).__init__()
        self.in_planes = nf
        self.input_size = input_size

        self.conv1 = conv3x3(input_size[0], nf * 1)
        self.bn1 = nn.BatchNorm2d(nf * 1)
        #self.bn1  = CategoricalConditionalBatchNorm(nf, 2)
        self.layer1 = self._make_layer(block, nf * 1, num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, nf * 2, num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, nf * 4, num_blocks[2], stride=2)
        self.layer4 = self._make_layer(block, nf * 8, num_blocks[3], stride=2)

        # hardcoded for now
        last_hid = nf * 8 * block.expansion if input_size[1] in [8,16,21,32,42] else 640

        if dist_linear:
            self.linear = distLinear(last_hid,num_classes)
        else:
            self.linear = nn.Linear(last_hid, num_classes)

        self.activation = nn.ReLU()

    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1] * (num_blocks - 1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_planes, planes, stride))
            self.in_planes = planes * block.expansion
        return nn.Sequential(*layers)

    def return_hidden(self, x):
        bsz = x.size(0)
        #pre_bn = self.conv1(x.view(bsz, 3, 32, 32))
        #post_bn = self.bn1(pre_bn, 1 if is_real else 0)
        #out = F.relu(post_bn)
        out = self.activation(self.bn1(self.conv1(x.view(bsz, *self.input_size))))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = F.avg_pool2d(out, 4)
        out = out.view(out.size(0), -1)
        return out

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

def ResNet18(nclasses, nf=20, input_size=(3, 32, 32), *args, **kwargs):
    return ResNet(BasicBlock, [2, 2, 2, 2], nclasses, nf, input_size, *args, **kwargs)

if __name__ == '__main__':
    x = torch.randn(10, 3, 100, 100)
    mask = torch.FloatTensor(size=(10,)).uniform_(0,1)
    mask = mask > 0.5
    my_bn = MyBatchNorm2d(3, affine=True)
    out = my_bn(x, stat_mask=mask)