import sys
import os
sys.path.append(os.path.dirname(os.path.dirname(os.getcwd())))
from training_structures.MFM import train_MFM,test_MFM
from fusions.common_fusions import Concat
from unimodals.MVAE import LeNetEncoder,DeLeNet
from unimodals.common_models import MLP
from torch import nn
import torch
from objective_functions.recon import recon_weighted_sum,sigmloss1dcentercrop
from datasets.avmnist.get_data_robust import get_dataloader


filename='avmnist_MFM_robust_best.pt'
traindata, validdata, testdata, robustdata = get_dataloader('../../../../yiwei/avmnist/_MFAS/avmnist')
channels=6

classes=10
n_latent=200
fuse=Concat()

encoders=[LeNetEncoder(1,channels,3,n_latent,twooutput=False).cuda(),LeNetEncoder(1,channels,5,n_latent,twooutput=False).cuda()]
decoders=[DeLeNet(1,channels,3,n_latent).cuda(),DeLeNet(1,channels,5,n_latent).cuda()]

intermediates=[MLP(n_latent,n_latent//2,n_latent//2).cuda(),MLP(n_latent,n_latent//2,n_latent//2).cuda(),MLP(2*n_latent,n_latent,n_latent//2).cuda()]
head=MLP(n_latent//2,40,classes).cuda()
recon_loss=recon_weighted_sum([sigmloss1dcentercrop(28,34),sigmloss1dcentercrop(112,130)],[1.0,1.0])

train_MFM(encoders,decoders,head,intermediates,fuse,recon_loss,traindata,validdata,25,savedir=filename)

model=torch.load(filename)
print("Testing:")
test_MFM(model,testdata)

print("Robustness testing:")
test(model,testdata)

import sys
import os
sys.path.append(os.path.dirname(os.path.dirname(os.getcwd())))
from training_structures.MFM import train_MFM,test_MFM
from fusions.common_fusions import Concat
from unimodals.MVAE import LeNetEncoder,DeLeNet
from unimodals.common_models import MLP
from torch import nn
import torch
from objective_functions.recon import recon_weighted_sum,sigmloss1dcentercrop
from datasets.avmnist.get_data_robust import get_dataloader


filename='avmnist_MFM_robust_best.pt'
traindata, validdata, testdata, robustdata = get_dataloader('../../../../yiwei/avmnist/_MFAS/avmnist')
channels=6

classes=10
n_latent=200
fuse=Concat()

encoders=[LeNetEncoder(1,channels,3,n_latent,twooutput=False).cuda(),LeNetEncoder(1,channels,5,n_latent,twooutput=False).cuda()]
decoders=[DeLeNet(1,channels,3,n_latent).cuda(),DeLeNet(1,channels,5,n_latent).cuda()]

intermediates=[MLP(n_latent,n_latent//2,n_latent//2).cuda(),MLP(n_latent,n_latent//2,n_latent//2).cuda(),MLP(2*n_latent,n_latent,n_latent//2).cuda()]
head=MLP(n_latent//2,40,classes).cuda()
recon_loss=recon_weighted_sum([sigmloss1dcentercrop(28,34),sigmloss1dcentercrop(112,130)],[1.0,1.0])

train_MFM(encoders,decoders,head,intermediates,fuse,recon_loss,traindata,validdata,25,savedir=filename)

model=torch.load(filename)
print("Testing:")
test_MFM(model,testdata)

print("Robustness testing:")
test(model,testdata)
