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rps_net.py
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rps_net.py
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'''
RPS network script with resnet-18
Copyright (c) Jathushan Rajasegaran, 2019
'''
from __future__ import print_function
# import argparse
# import os
# import shutil
# import torch
# import pdb
# import torch.nn as nn
# import torch.nn.parallel
# import torch.backends.cudnn as cudnn
# import torch.optim as optim
# import torch.utils.data as data
# import torchvision.transforms as transforms
# import torchvision.datasets as datasets
# import copy
# import torch
import torch.nn as nn
import torch.nn.functional as F
# import torch.optim as optim
# from torch.autograd import Variable
# from torch.autograd import gradcheck
class RPS_net(nn.Module):
def __init__(self, args):
super(RPS_net, self).__init__()
self.args = args
self.final_layers = []
self.init(None)
def init(self, best_path):
"""Initialize all parameters"""
self.conv1 = []
self.conv2 = []
self.conv3 = []
self.conv4 = []
self.conv5 = []
self.conv6 = []
self.conv7 = []
self.conv8 = []
self.conv9 = []
self.fc1 = []
# conv1
for i in range(self.args.M):
exec("self.m1" + str(i) + " = nn.Sequential(nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3),nn.BatchNorm2d(64),nn.ReLU())")
exec("self.conv1.append(self.m1" + str(i) + ")")
# conv2
for i in range(self.args.M):
exec("self.m2" + str(i) + " = nn.Sequential(nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),nn.BatchNorm2d(64),nn.ReLU(), nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),nn.BatchNorm2d(64))")
exec("self.conv2.append(self.m2" + str(i) + ")")
# conv3
for i in range(self.args.M):
exec("self.m3" + str(i) + " = nn.Sequential(nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),nn.BatchNorm2d(64),nn.ReLU(), nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),nn.BatchNorm2d(64))")
exec("self.conv3.append(self.m3" + str(i) + ")")
self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
# conv4
for i in range(self.args.M):
exec("self.m4" + str(i) + " = nn.Sequential(nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),nn.BatchNorm2d(128),nn.ReLU(), nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1),nn.BatchNorm2d(128))")
exec("self.conv4.append(self.m4" + str(i) + ")")
exec("self.m4" + str("x") + " = nn.Sequential(nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),nn.BatchNorm2d(128),nn.ReLU())")
exec("self.conv4.append(self.m4" + str("x") + ")")
# conv5
for i in range(self.args.M):
exec("self.m5" + str(i) + " = nn.Sequential(nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1),nn.BatchNorm2d(128),nn.ReLU(), nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1),nn.BatchNorm2d(128))")
exec("self.conv5.append(self.m5" + str(i) + ")")
self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
# conv6
for i in range(self.args.M):
exec("self.m6" + str(i) + " = nn.Sequential(nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),nn.BatchNorm2d(256),nn.ReLU(), nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),nn.BatchNorm2d(256))")
exec("self.conv6.append(self.m6" + str(i) + ")")
exec("self.m6" + str("x") + " = nn.Sequential(nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),nn.BatchNorm2d(256),nn.ReLU())")
exec("self.conv6.append(self.m6" + str("x") + ")")
# conv7
for i in range(self.args.M):
exec("self.m7" + str(i) + " = nn.Sequential(nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),nn.BatchNorm2d(256),nn.ReLU(), nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),nn.BatchNorm2d(256))")
exec("self.conv7.append(self.m7" + str(i) + ")")
self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)
# conv8
for i in range(self.args.M):
exec("self.m8" + str(i) + " = nn.Sequential(nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1),nn.BatchNorm2d(512),nn.ReLU(), nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),nn.BatchNorm2d(512))")
exec("self.conv8.append(self.m8" + str(i) + ")")
exec("self.m8" + str("x") + " = nn.Sequential(nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1),nn.BatchNorm2d(512),nn.ReLU())")
exec("self.conv8.append(self.m8" + str("x") + ")")
# conv9
for i in range(self.args.M):
exec("self.m9" + str(i) + " = nn.Sequential(nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),nn.BatchNorm2d(512),nn.ReLU(), nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),nn.BatchNorm2d(512))")
exec("self.conv9.append(self.m9" + str(i) + ")")
if len(self.final_layers) < 1:
self.final_layer1 = nn.Linear(512, 1000)
self.final_layers.append(self.final_layer1)
self.cuda()
def forward(self, x, path, last):
M = self.args.M
div =1
p=0.5
y = self.conv1[0](x)
for j in range(1,self.args.M):
if(path[0][j]==1):
y += self.conv1[j](x)
x = F.relu(y)
y = self.conv2[0](x)
for j in range(1,self.args.M):
if(path[1][j]==1):
y += self.conv2[j](x)
x = y + x
x = F.relu(x)
y = self.conv3[0](x)
for j in range(1,self.args.M):
if(path[2][j]==1):
y += self.conv3[j](x)
x = y + x
x = F.relu(x)
x = self.pool1(x)
y = self.conv4[-1](x)
for j in range(self.args.M):
if(path[3][j]==1):
y += self.conv4[j](x)
x = y
x = F.relu(x)
y = self.conv5[0](x)
for j in range(1, self.args.M):
if(path[4][j]==1):
y += self.conv5[j](x)
x = y + x
x = F.relu(x)
x = self.pool2(x)
y = self.conv6[-1](x)
for j in range(self.args.M):
if(path[5][j]==1):
y += self.conv6[j](x)
x = y
x = F.relu(x)
y = self.conv7[0](x)
for j in range(1, self.args.M):
if(path[6][j]==1):
y += self.conv7[j](x)
x = y
x = F.relu(x)
x = self.pool3(x)
y = self.conv8[-1](x)
for j in range(self.args.M):
if(path[7][j]==1):
y += self.conv8[j](x)
x = y
x = F.relu(x)
y = self.conv9[0](x)
for j in range(1, self.args.M):
if(path[8][j]==1):
y += self.conv9[j](x)
x = y + x
x = F.relu(x)
x = self.pool4(x)
x = F.avg_pool2d(x, (7,7), stride=(1,1))
x = x.view(-1, 512)
x = self.final_layers[last](x)
return x
class RPS_net_cifar(nn.Module):
def __init__(self, args):
super(RPS_net_cifar, self).__init__()
self.args = args
self.final_layers = []
self.init(None)
def init(self, best_path):
"""Initialize all parameters"""
self.conv1 = []
self.conv2 = []
self.conv3 = []
self.conv4 = []
self.conv5 = []
self.conv6 = []
self.conv7 = []
self.conv8 = []
self.conv9 = []
self.fc1 = []
div = 1
a1 = 64//div
a2 = 64//div
a3 = 128//div
a4 = 256//div
a5 = 512//div
self.a5 =a5
# conv1
for i in range(self.args.M):
exec("self.m1" + str(i) + " = nn.Sequential(nn.Conv2d(3, "+str(a1)+", kernel_size=3, stride=1, padding=1),nn.BatchNorm2d("+str(a1)+"),nn.ReLU())")
exec("self.conv1.append(self.m1" + str(i) + ")")
# conv2
for i in range(self.args.M):
exec("self.m2" + str(i) + " = nn.Sequential(nn.Conv2d("+str(a1)+", "+str(a2)+", kernel_size=3, stride=1, padding=1),nn.BatchNorm2d("+str(a2)+"),nn.ReLU(), nn.Conv2d("+str(a2)+", "+str(a2)+", kernel_size=3, stride=1, padding=1),nn.BatchNorm2d("+str(a1)+"))")
exec("self.conv2.append(self.m2" + str(i) + ")")
# conv3
for i in range(self.args.M):
exec("self.m3" + str(i) + " = nn.Sequential(nn.Conv2d("+str(a2)+", "+str(a2)+", kernel_size=3, stride=1, padding=1),nn.BatchNorm2d("+str(a2)+"),nn.ReLU(), nn.Conv2d("+str(a2)+", "+str(a2)+", kernel_size=3, stride=1, padding=1),nn.BatchNorm2d("+str(a2)+"))")
exec("self.conv3.append(self.m3" + str(i) + ")")
# conv4
for i in range(self.args.M):
exec("self.m4" + str(i) + " = nn.Sequential(nn.Conv2d("+str(a2)+", "+str(a3)+", kernel_size=3, stride=1, padding=1),nn.BatchNorm2d("+str(a3)+"),nn.ReLU(), nn.Conv2d("+str(a3)+", "+str(a3)+", kernel_size=3, stride=1, padding=1),nn.BatchNorm2d("+str(a3)+"))")
exec("self.conv4.append(self.m4" + str(i) + ")")
exec("self.m4" + str("x") + " = nn.Sequential(nn.Conv2d("+str(a2)+", "+str(a3)+", kernel_size=3, stride=1, padding=1),nn.BatchNorm2d("+str(a3)+"),nn.ReLU())")
exec("self.conv4.append(self.m4" + str("x") + ")")
# conv5
for i in range(self.args.M):
exec("self.m5" + str(i) + " = nn.Sequential(nn.Conv2d("+str(a3)+", "+str(a3)+", kernel_size=3, stride=1, padding=1),nn.BatchNorm2d("+str(a3)+"),nn.ReLU(), nn.Conv2d("+str(a3)+", "+str(a3)+", kernel_size=3, stride=1, padding=1),nn.BatchNorm2d("+str(a3)+"))")
exec("self.conv5.append(self.m5" + str(i) + ")")
self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
# conv6
for i in range(self.args.M):
exec("self.m6" + str(i) + " = nn.Sequential(nn.Conv2d("+str(a3)+", "+str(a4)+", kernel_size=3, stride=1, padding=1),nn.BatchNorm2d("+str(a4)+"),nn.ReLU(), nn.Conv2d("+str(a4)+", "+str(a4)+", kernel_size=3, stride=1, padding=1),nn.BatchNorm2d("+str(a4)+"))")
exec("self.conv6.append(self.m6" + str(i) + ")")
exec("self.m6" + str("x") + " = nn.Sequential(nn.Conv2d("+str(a3)+", "+str(a4)+", kernel_size=3, stride=1, padding=1),nn.BatchNorm2d("+str(a4)+"),nn.ReLU())")
exec("self.conv6.append(self.m6" + str("x") + ")")
# conv7
for i in range(self.args.M):
exec("self.m7" + str(i) + " = nn.Sequential(nn.Conv2d("+str(a4)+", "+str(a4)+", kernel_size=3, stride=1, padding=1),nn.BatchNorm2d("+str(a4)+"),nn.ReLU(), nn.Conv2d("+str(a4)+", "+str(a4)+", kernel_size=3, stride=1, padding=1),nn.BatchNorm2d("+str(a4)+"))")
exec("self.conv7.append(self.m7" + str(i) + ")")
self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
# conv8
for i in range(self.args.M):
exec("self.m8" + str(i) + " = nn.Sequential(nn.Conv2d("+str(a4)+", "+str(a5)+", kernel_size=3, stride=1, padding=1),nn.BatchNorm2d("+str(a5)+"),nn.ReLU(), nn.Conv2d("+str(a5)+", "+str(a5)+", kernel_size=3, stride=1, padding=1),nn.BatchNorm2d("+str(a5)+"))")
exec("self.conv8.append(self.m8" + str(i) + ")")
exec("self.m8" + str("x") + " = nn.Sequential(nn.Conv2d("+str(a4)+", "+str(a5)+", kernel_size=3, stride=1, padding=1),nn.BatchNorm2d("+str(a5)+"),nn.ReLU())")
exec("self.conv8.append(self.m8" + str("x") + ")")
# conv9
for i in range(self.args.M):
exec("self.m9" + str(i) + " = nn.Sequential(nn.Conv2d("+str(a5)+", "+str(a5)+", kernel_size=3, stride=1, padding=1),nn.BatchNorm2d("+str(a5)+"),nn.ReLU(), nn.Conv2d("+str(a5)+", "+str(a5)+", kernel_size=3, stride=1, padding=1),nn.BatchNorm2d("+str(a5)+"))")
exec("self.conv9.append(self.m9" + str(i) + ")")
self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
if len(self.final_layers) < 1:
self.final_layer1 = nn.Linear(a5, 100)
self.final_layers.append(self.final_layer1)
self.cuda()
def forward(self, x, path, last):
M = self.args.M
div =1
p=0.5
y = self.conv1[0](x)
for j in range(1,self.args.M):
if(path[0][j]==1):
y += self.conv1[j](x)
x = F.relu(y)
y = self.conv2[0](x)
for j in range(1,self.args.M):
if(path[1][j]==1):
y += self.conv2[j](x)
x = y + x
x = F.relu(x)
y = self.conv3[0](x)
for j in range(1,self.args.M):
if(path[2][j]==1):
y += self.conv3[j](x)
x = y + x
x = F.relu(x)
y = self.conv4[-1](x)
for j in range(self.args.M):
if(path[3][j]==1):
y += self.conv4[j](x)
x = y
x = F.relu(x)
y = self.conv5[0](x)
for j in range(1, self.args.M):
if(path[4][j]==1):
y += self.conv5[j](x)
x = y + x
x = F.relu(x)
x = self.pool1(x)
y = self.conv6[-1](x)
for j in range(self.args.M):
if(path[5][j]==1):
y += self.conv6[j](x)
x = y
x = F.relu(x)
y = self.conv7[0](x)
for j in range(1, self.args.M):
if(path[6][j]==1):
y += self.conv7[j](x)
x = y
x = F.relu(x)
x = self.pool2(x)
y = self.conv8[-1](x)
for j in range(self.args.M):
if(path[7][j]==1):
y += self.conv8[j](x)
x = y
x = F.relu(x)
y = self.conv9[0](x)
for j in range(1, self.args.M):
if(path[8][j]==1):
y += self.conv9[j](x)
x = y + x
x = F.relu(x)
# x = self.pool3(x)
x = F.avg_pool2d(x, (8, 8), stride=(1,1))
x = x.view(-1, self.a5)
x = self.final_layers[last](x)
return x
class RPS_net_mlp(nn.Module):
def __init__(self, args):
super(RPS_net_mlp, self).__init__()
self.args = args
self.final_layers = []
self.init(None)
def init(self, best_path):
"""Initialize all parameters"""
self.mlp1 = []
self.mlp2 = []
self.fc1 = []
#mlp1
for i in range(self.args.M):
# exec("self.m1" + str(i) + " = nn.Sequential(nn.Linear(784, 256),nn.BatchNorm2d(256),nn.ReLU())")
exec("self.m1" + str(i) + " = nn.Linear(784, 400)")
exec("self.mlp1.append(self.m1" + str(i) + ")")
#mlp2
for i in range(self.args.M):
exec("self.m2" + str(i) + " = nn.Linear(400, 128)")
exec("self.mlp2.append(self.m2" + str(i) + ")")
if len(self.final_layers) < 1:
self.final_layer1 = nn.Linear(128, 10)
self.final_layers.append(self.final_layer1)
self.cuda()
def forward(self, x, path, last):
M = self.args.M
y = self.mlp1[0](x)
for j in range(1,self.args.M):
if(path[0][j]==1):
y += self.mlp1[j](x)
x = F.relu(y)
y = self.mlp2[0](x)
for j in range(1,self.args.M):
if(path[1][j]==1):
y += self.mlp2[j](x)
x = F.relu(y)
x = self.final_layers[last](x)
return x