feature_extractor.py

import torch
from torch.autograd import Variable
import torch.nn as nn
import math
import numpy as np
import torch.nn.functional as F

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 Flatten(nn.Module):
    def __init__(self):
        super(Flatten, self).__init__()
        
    def forward(self, x):        
        return x.view(x.size(0), -1)


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