spottune_models.py

import torch
import torch.nn as nn
import torch.nn.functional as F

from torch.autograd import Variable
from torch.nn.parameter import Parameter
import math

class DownsampleB(nn.Module):
    def __init__(self, nIn, nOut, stride=2):
        super(DownsampleB, self).__init__()
        self.avg = nn.AvgPool2d(stride)

    def forward(self, x):
        residual = self.avg(x)
        return torch.cat((residual, residual*0),1)

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

# No projection: identity shortcut
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 = nn.Sequential(nn.ReLU(True), conv3x3(planes, planes))
        self.bn2 = nn.BatchNorm2d(planes)

    def forward(self, x):
        out = self.conv1(x)
        out = self.bn1(out)
        out = F.relu(out)

        out = self.conv2(out)
        y = self.bn2(out)

        return y

class ResNet(nn.Module):
    def __init__(self, block, layers, num_class = 10):
        super(ResNet, self).__init__()

        factor = 1
        self.in_planes = int(32*factor)
        self.conv1 = conv3x3(3, int(32*factor))
        self.bn1 = nn.BatchNorm2d(int(32*factor))
        self.relu = nn.ReLU(inplace=True)

        strides = [2, 2, 2]
        filt_sizes = [64, 128, 256]
        self.blocks, self.ds = [], []
        self.parallel_blocks, self.parallel_ds = [], []

        for idx, (filt_size, num_blocks, stride) in enumerate(zip(filt_sizes, layers, strides)):
            blocks, ds = self._make_layer(block, filt_size, num_blocks, stride=stride)
            self.blocks.append(nn.ModuleList(blocks))
            self.ds.append(ds)
        self.blocks = nn.ModuleList(self.blocks)
        self.ds = nn.ModuleList(self.ds)
    
        self.in_planes = int(32*factor)
        for idx, (filt_size, num_blocks, stride) in enumerate(zip(filt_sizes, layers, strides)):
            blocks, ds = self._make_layer(block, filt_size, num_blocks, stride=stride)
            self.parallel_blocks.append(nn.ModuleList(blocks))
            self.parallel_ds.append(ds)
        self.parallel_blocks = nn.ModuleList(self.parallel_blocks)
        self.parallel_ds = nn.ModuleList(self.parallel_ds)

        self.bn2 = nn.Sequential(nn.BatchNorm2d(int(256*factor)), nn.ReLU(True)) 
        self.avgpool = nn.AdaptiveAvgPool2d(1)        
        self.linear = nn.Linear(int(256*factor), num_class)

        self.layer_config = layers
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def seed(self, x):
        x = self.bn1(self.conv1(x))
        return x

    def _make_layer(self, block, planes, blocks, stride=1):

        downsample = nn.Sequential()
        if stride != 1 or self.in_planes != planes * block.expansion:
            downsample = DownsampleB(self.in_planes, planes * block.expansion, 2)

        layers = [block(self.in_planes, planes, stride)]
        self.in_planes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.in_planes, planes))

        return layers, downsample

    def forward(self, x, policy=None):
        t = 0
        x = self.seed(x)
    
        if policy is not None:
            for segment, num_blocks in enumerate(self.layer_config):
                    for b in range(num_blocks):
                        action = policy[:,t].contiguous()
                        action_mask = action.float().view(-1,1,1,1)

                        residual = self.ds[segment](x) if b==0 else x
                        output = self.blocks[segment][b](x)
			
                        residual_ = self.parallel_ds[segment](x) if b==0 else x
                        output_ = self.parallel_blocks[segment][b](x)
                        f1 = F.relu(residual + output)
                        f2 = F.relu(residual_ + output_)
                        x = f1*(1-action_mask) + f2*action_mask
                        t += 1   
        else:
            for segment, num_blocks in enumerate(self.layer_config):
                for b in range(num_blocks):
                    residual = self.ds[segment](x) if b==0 else x
                    output = self.blocks[segment][b](x)
                    x = F.relu(residual + output)
                    t += 1
	x = self.bn2(x)
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.linear(x)
        return x

def resnet26(num_class=10, blocks=BasicBlock):
    return  ResNet(blocks, [4,4,4], num_class)