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main_wsn_omniglot.py
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main_wsn_omniglot.py
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import torch
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torchvision import datasets, transforms
import os
import os.path
from collections import OrderedDict
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sn
import pandas as pd
import random
import argparse,time
import math
from copy import deepcopy
from utils import *
from networks.utils import *
from networks.subnet import SubnetLinear, SubnetConv2d
import wandb
def train(args, model, device, x,y, optimizer,criterion, task_id_nominal, consolidated_masks):
model.train()
r=np.arange(x.size(0))
np.random.shuffle(r)
r=torch.LongTensor(r).to(device)
# Loop batches
for i in range(0,len(r),args.batch_size_train):
if ((i + args.batch_size_train) <= len(r)):
b=r[i:i+args.batch_size_train]
else:
b=r[i:]
data = x[b]
data, target = data.to(device), y[b].to(device)
optimizer.zero_grad()
output = model(data, task_id_nominal, mask=None, mode="train")
loss = criterion(output, target)
loss.backward()
# Continual Subnet no backprop
curr_head_keys = ["last.{}.weight".format(task_id_nominal), "last.{}.bias".format(task_id_nominal)]
if consolidated_masks is not None and consolidated_masks != {}: # Only do this for tasks 1 and beyond
# if args.use_continual_masks:
for key in consolidated_masks.keys():
# Skip if not task head is not for curent task
if 'last' in key:
if key not in curr_head_keys:
continue
# Determine whether it's an output head or not
if (len(key.split('.')) == 3): # e.g. last.1.weight
module_name, task_num, module_attr = key.split('.')
# curr_module = getattr(model, module_name)[int(task_num)]
else: # e.g. fc1.weight
module_name, module_attr = key.split('.')
# curr_module = getattr(model, module_name)
# Zero-out gradients
if (hasattr(getattr(model, module_name), module_attr)):
if (getattr(getattr(model, module_name), module_attr) is not None):
getattr(getattr(model, module_name), module_attr).grad[consolidated_masks[key] == 1] = 0
optimizer.step()
def test(args, model, device, x, y, criterion, task_id_nominal, curr_task_masks=None, mode="test"):
model.eval()
total_loss = 0
total_num = 0
correct = 0
r=np.arange(x.size(0))
r=torch.LongTensor(r).to(device)
with torch.no_grad():
# Loop batches
for i in range(0,len(r),args.batch_size_test):
if ((i + args.batch_size_test) <= len(r)):
b=r[i:i+args.batch_size_test]
else: b=r[i:]
data = x[b]
data, target = data.to(device), y[b].to(device)
output = model(data, task_id_nominal, mask=curr_task_masks, mode=mode)
loss = criterion(output, target)
pred = output.argmax(dim=1, keepdim=True)
correct += pred.eq(target.view_as(pred)).sum().item()
total_loss += loss.data.cpu().numpy().item()*len(b)
total_num += len(b)
acc = 100. * correct / total_num
final_loss = total_loss / total_num
return final_loss, acc
def main(args):
tstart=time.time()
## Device Setting
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
exp_dir = "results_{}".format(args.dataset)
## Load PermutedMNIST
from dataloader import omniglot_rotation
data, taskcla, inputsize = omniglot_rotation.get(seed=args.seed, pc_valid=args.pc_valid)
acc_matrix=np.zeros((100,100))
sparsity_matrix = []
criterion = torch.nn.CrossEntropyLoss()
# Primes and Mod table of product of primes
print ('Task primes ---')
num_tasks = len(taskcla)
num_primes = num_tasks // 10
# Model Definition
if args.model == "large":
from networks.lenet import SubnetLeNet_Large as MLPNet
elif args.model == "default":
from networks.lenet import SubnetLeNet_Default as MLPNet
else:
raise Exception("[ERROR] The model " + str(args.model) + " is not supported!")
# Model Instantiation
model = MLPNet(taskcla, args.sparsity).to(device)
print ('Model parameters ---')
for k_t, (m, param) in enumerate(model.named_parameters()):
print (k_t,m,param.shape)
print ('-'*40)
task_id = 0
task_list = []
sparsity_per_task = {}
per_task_masks, consolidated_masks = {}, {}
for k, ncla in taskcla:
print('*'*100)
print('Task {:2d} ({:s})'.format(k,data[k]['name']))
print('*'*100)
xtrain=data[k]['train']['x']
ytrain=data[k]['train']['y']
xvalid=data[k]['valid']['x']
yvalid=data[k]['valid']['y']
xtest =data[k]['test']['x']
ytest =data[k]['test']['y']
task_list.append(k)
lr = args.lr
best_loss=np.inf
print ('-'*40)
print ('Task ID :{} | Learning Rate : {}'.format(task_id, lr))
print ('-'*40)
best_model=get_model(model)
if args.optim == "sgd":
optimizer = optim.SGD(model.parameters(), lr=lr)
elif args.optim == "adam":
optimizer = optim.Adam(model.parameters(), lr=lr)
else:
raise Exception("[ERROR] The optimizer " + str(args.optim) + " is not supported!")
for epoch in range(1, args.n_epochs+1):
# Train
clock0 = time.time()
train(args, model, device, xtrain, ytrain, optimizer, criterion, task_id, consolidated_masks)
clock1 = time.time()
tr_loss,tr_acc = test(args, model, device, xtrain, ytrain, criterion, task_id, curr_task_masks=None, mode="valid")
clock2=time.time()
print('Epoch {:3d} | Train: loss={:.3f}, acc={:5.1f}% | time={:5.1f}ms | test time={:5.1f}ms'.format(epoch,\
tr_loss,tr_acc, 1000*(clock1-clock0), (clock2 - clock1)*1000 ), end='')
# Validate
valid_loss,valid_acc = test(args, model, device, xvalid, yvalid, criterion, task_id, curr_task_masks=None, mode="valid")
print(' Valid: loss={:.3f}, acc={:5.1f}% |'.format(valid_loss, valid_acc),end='')
# Adapt lr
if valid_loss<best_loss:
best_loss=valid_loss
best_model=get_model(model)
patience=args.lr_patience
print(' *',end='')
else:
patience-=1
if patience<=0:
lr/=args.lr_factor
print(' lr={:.1e}'.format(lr),end='')
if lr<args.lr_min:
print()
break
patience=args.lr_patience
adjust_learning_rate(optimizer, epoch, args)
print()
# Restore best model
set_model_(model,best_model)
# Save the per-task-dependent masks
per_task_masks[task_id] = model.get_masks(task_id)
# Consolidate task masks to keep track of parameters to-update or not
curr_head_keys = ["last.{}.weight".format(task_id), "last.{}.bias".format(task_id)]
if task_id == 0:
consolidated_masks = deepcopy(per_task_masks[task_id])
else:
for key in per_task_masks[task_id].keys():
# Skip output head from other tasks
# Also don't consolidate output head mask after training on new tasks; continue
if "last" in key:
if key in curr_head_keys:
consolidated_masks[key] = deepcopy(per_task_masks[task_id][key])
continue
# Or operation on sparsity
if consolidated_masks[key] is not None and per_task_masks[task_id][key] is not None:
consolidated_masks[key] = 1 - ((1 - consolidated_masks[key]) * (1 - per_task_masks[task_id][key]))
# Print Sparsity
sparsity_per_layer = print_sparsity(consolidated_masks)
all_sparsity = global_sparsity(consolidated_masks)
print("Global Sparsity: {}".format(all_sparsity))
sparsity_matrix.append(all_sparsity)
sparsity_per_task[task_id] = sparsity_per_layer
# Test
print ('-'*40)
test_loss, test_acc = test(args, model, device, xtest, ytest, criterion, task_id, curr_task_masks=per_task_masks[task_id], mode="test")
print('Test: loss={:.3f} , acc={:5.1f}%'.format(test_loss,test_acc))
log_dict = {
'test_loss': test_loss,
'test_acc': test_acc,
'task': task_id,
}
wandb.log(log_dict)
# save accuracy
for jj in np.array(task_list):
xtest = data[jj]['test']['x']
ytest = data[jj]['test']['y']
if jj <= task_id:
_, acc_matrix[task_id,jj] = test(args, model, device, xtest, ytest,criterion, jj, curr_task_masks=per_task_masks[jj], mode="test")
else:
_, acc_matrix[task_id,jj] = test(args, model, device, xtest, ytest,criterion, jj, curr_task_masks=per_task_masks[task_id], mode="test")
print('Accuracies =')
for i_a in range(task_id+1):
print('\t',end='')
for j_a in range(args.n_tasks):
print('{:5.1f} '.format(acc_matrix[i_a,j_a]),end='')
print()
# update task id
task_id +=1
# Save
safe_save(exp_dir + "/omniglot/" + args.name + ".test_acc", acc_matrix)
safe_save(exp_dir + "/omniglot/" + args.name + ".cap", sparsity_matrix)
safe_save(exp_dir + "/omniglot/" + args.name + ".spar", sparsity_per_task)
safe_save(exp_dir + "/omniglot/" + args.name + ".pertask", per_task_masks)
safe_save(exp_dir + "/omniglot/" + args.name + ".fullmask", consolidated_masks)
torch.save(model.to("cpu"), exp_dir + "/omniglot/" + args.name + ".ptmodel")
print('-'*50)
# Simulation Results
print ('Task Order : {}'.format(np.array(task_list)))
print ('Diagonal Final Avg Accuracy: {:5.2f}%'.format( np.mean([acc_matrix[i,i] for i in range(len(taskcla))] )))
print ('Final Avg accuracy: {:5.2f}%'.format( np.mean(acc_matrix[len(taskcla) - 1])))
bwt=np.mean((acc_matrix[-1]-np.diag(acc_matrix))[:-1])
print ('Backward transfer: {:5.2f}%'.format(bwt))
print('[Elapsed time = {:.1f} ms]'.format((time.time()-tstart)*1000))
print('-'*50)
if __name__ == "__main__":
# Training parameters
parser = argparse.ArgumentParser(description='Sequential PMNIST with GPM')
parser.add_argument('--batch_size_train', type=int, default=10, metavar='N',
help='input batch size for training (default: 10)')
parser.add_argument('--batch_size_test', type=int, default=10, metavar='N',
help='input batch size for testing (default: 64)')
parser.add_argument('--n_epochs', type=int, default=5, metavar='N',
help='number of training epochs/task (default: 5)')
parser.add_argument('--seed', type=int, default=2, metavar='S',
help='random seed (default: 2)')
parser.add_argument('--pc_valid',default=0.1,type=float,
help='fraction of training data used for validation')
# Optimizer parameters
parser.add_argument('--optim', type=str, default="adam", metavar='OPTIM',
help='optimizer choice')
parser.add_argument('--lr', type=float, default=1e-3, metavar='LR',
help='learning rate (default: 0.01)')
parser.add_argument('--momentum', type=float, default=0.9, metavar='M',
help='SGD momentum (default: 0.9)')
parser.add_argument('--lr_min', type=float, default=1e-5, metavar='LRM',
help='minimum lr rate (default: 1e-5)')
parser.add_argument('--lr_patience', type=int, default=6, metavar='LRP',
help='hold before decaying lr (default: 6)')
parser.add_argument('--lr_factor', type=int, default=2, metavar='LRF',
help='lr decay factor (default: 2)')
# Architecture
parser.add_argument('--n_hidden', type=int, default=256, metavar='NH',
help='number of hidden units in MLP (default: 100)')
parser.add_argument('--n_outputs', type=int, default=10, metavar='NO',
help='number of output units in MLP (default: 10)')
parser.add_argument('--n_tasks', type=int, default=100, metavar='NT',
help='number of tasks (default: 10)')
# CUDA parameters
parser.add_argument('--gpu', type=str, default="0", metavar='GPU',
help="GPU ID for single GPU training")
# CSNB parameters
parser.add_argument('--sparsity', type=float, default=0.5, metavar='SPARSITY',
help="Target current sparsity for each layer")
# OMNIGLOT parameters
parser.add_argument('--model', type=str, default='large', metavar='MODEL',
help='Default or Large')
parser.add_argument("--dataset",
default='omniglot',
type=str,
help="Dataset to train and test on.")
parser.add_argument('--name', type=str, default='hard')
parser.add_argument('--soft', type=float, default=0.0)
parser.add_argument('--soft_grad', type=float, default=1.0)
args = parser.parse_args()
args.sparsity = 1 - args.sparsity
print('='*100)
print('Arguments =')
for arg in vars(args):
print('\t'+arg+':',getattr(args,arg))
print('='*100)
# update name
name = "{}_SEED_{}_LR_{}_SPARSITY_{:0.2f}_{}_soft{}_grad{}".format(
args.dataset,
args.seed,
args.lr,
1 - args.sparsity,
args.name, args.soft, args.soft_grad)
args.name = name
wandb.init(project='wsn_{}'.format(args.dataset),
entity='haeyong',
name=name,
config=args)
main(args)