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main_distrib.py
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import os
import torch
import torch.nn as nn
from watchdog import WatchDog
import torch.distributed as dist
from dataloader import DataLoader
import torch.multiprocessing as mp
from arguments import ArgumentParser
from modelfactory import ModelFactory
from torch.nn.parallel import DistributedDataParallel as DDP
from utils import *
def gather_all_test(gpu_ind, args, train_net, testloader):
c, t = test(gpu_ind, args, train_net, testloader)
total = torch.tensor([c, t], dtype=torch.float32, device=gpu_ind)
dist.all_reduce(total, dist.ReduceOp.SUM, async_op=False)
cnt, tot = total.tolist()
return (cnt / tot) * 100
def main_worker( gpu_ind, args, shared_alpha):
rank = args.global_rank + gpu_ind
dist.init_process_group(
backend='nccl',
init_method='env://',
world_size=args.world_size,
rank=rank
)
criteria = loss_func(args).to(gpu_ind)
test_watchdog = WatchDog()
train_net = ModelFactory(args).to(gpu_ind)
train_net = init_net(gpu_ind, args, train_net).to(gpu_ind)
trainloader, testloader = DataLoader(args)
max_acc = 0
torch.cuda.set_device(gpu_ind)
train_net = DDP(train_net, device_ids=[gpu_ind])
if args.method == 'normal':
if args.resume is not None:
train_net.load_state_dict(load_model(gpu_ind, args))
max_acc = gather_all_test(gpu_ind, args, train_net, testloader)
optimizer = get_optimizer(args, train_net.parameters())
scheduler = get_scheduler(args, optimizer)
for e in range(args.epoch):
normal_train_single_epoch(gpu_ind, args, e, train_net, trainloader, criteria, optimizer)
if e % 1 == 0:
test_watchdog.start()
acc = gather_all_test(gpu_ind, args, train_net, testloader)
test_watchdog.stop()
if gpu_ind == 0:
print("TEST RESULT:\tAcc:", round(acc, 3), "\tBest Acc:", round(max_acc,3), "\tTime:", test_watchdog.get_time_in_sec(), 'seconds')
if acc > max_acc:
max_acc = acc
save_model(gpu_ind, args, train_net)
scheduler.step()
if gpu_ind == 0:
print("FINAL TEST RESULT:\tAcc:", round(max_acc, 3))
if args.method == 'pranc':
alpha, basis_mat, train_net, train_net_shape_vec = pranc_init(gpu_ind, args, train_net)
if args.lr > 0:
alpha_optimizer = get_optimizer(args, [alpha], 'pranc')
net_optimizer = get_optimizer(args, train_net.parameters(), 'network')
batchnorms = []
for m in train_net.modules():
if isinstance(m, nn.BatchNorm2d):
for p in m.parameters():
batchnorms.append(p)
if len(batchnorms) > 0:
batchnorm_optimizer = get_optimizer(args, batchnorms, 'batchnorm')
else:
batchnorm_optimizer = None
alpha_scheduler = get_scheduler(args, alpha_optimizer)
if batchnorm_optimizer is not None:
batchnorm_scheduler = get_scheduler(args, batchnorm_optimizer)
else:
batchnorm_scheduler = None
else:
alpha_scheduler = None
batchnorm_scheduler = None
max_acc = gather_all_test(gpu_ind, args, train_net, testloader)
for e in range(args.epoch):
pranc_train_single_epoch(gpu_ind, args, e, basis_mat, train_net, train_net_shape_vec, alpha, trainloader, criteria, alpha_optimizer, net_optimizer, batchnorm_optimizer)
if e % 1 == 0 :
test_watchdog.start()
acc = gather_all_test(gpu_ind, args, train_net, testloader)
test_watchdog.stop()
if gpu_ind == 0:
print("TEST RESULT:\tAcc:", round(acc, 3), "\tBest Acc:", round(max_acc,3), "\tTime:", test_watchdog.get_time_in_sec(), 'seconds')
if acc > max_acc:
save_model(gpu_ind, args, train_net)
save_signature(gpu_ind, args, alpha, train_net, shared_alpha)
max_acc = acc
alpha_scheduler.step()
if batchnorm_scheduler is not None:
batchnorm_scheduler.step()
print("FINAL TEST RESULT:\tAcc:", round(max_acc, 3))
if args.method == 'pranc_bin':
alpha, train_net, train_net_shape_vec, perm, perm_inverse = pranc_bin_init(gpu_ind, args, train_net)
if args.lr > 0:
alpha_optimizer = get_optimizer(args, [alpha], 'pranc')
net_optimizer = get_optimizer(args, train_net.parameters(), 'network')
batchnorms = []
for m in train_net.modules():
if isinstance(m, nn.BatchNorm2d):
for p in m.parameters():
batchnorms.append(p)
if len(batchnorms) > 0:
batchnorm_optimizer = get_optimizer(args, batchnorms, 'batchnorm')
else:
batchnorm_optimizer = None
alpha_scheduler = get_scheduler(args, alpha_optimizer)
if batchnorm_optimizer is not None:
batchnorm_scheduler = get_scheduler(args, batchnorm_optimizer)
else:
batchnorm_scheduler = None
else:
alpha_scheduler = None
batchnorm_scheduler = None
max_acc = gather_all_test(gpu_ind, args, train_net, testloader)
for e in range(args.epoch):
pranc_bin_train_single_epoch(gpu_ind, args, e, train_net, train_net_shape_vec, alpha, trainloader, criteria, alpha_optimizer, net_optimizer, perm, perm_inverse, batchnorm_optimizer)
if e % 1 == 0 :
test_watchdog.start()
acc = gather_all_test(gpu_ind, args, train_net, testloader)
test_watchdog.stop()
if gpu_ind == 0:
print("TEST RESULT:\tAcc:", round(acc, 3), "\tBest Acc:", round(max_acc,3), "\tTime:", test_watchdog.get_time_in_sec(), 'seconds')
if acc > max_acc:
save_model(gpu_ind, args, train_net)
save_signature(gpu_ind, args, alpha, train_net, shared_alpha)
max_acc = acc
alpha_scheduler.step()
if batchnorm_scheduler is not None:
batchnorm_scheduler.step()
print("FINAL TEST RESULT:\tAcc:", round(max_acc, 3))
if args.method == 'ppb':
alpha, train_net, train_net_shape_vec, perm, perm_inverse = ppb_init(gpu_ind, args, train_net)
if args.lr > 0:
alpha_optimizer = get_optimizer(args, [alpha], 'pranc')
net_optimizer = get_optimizer(args, train_net.parameters(), 'network')
batchnorms = []
for m in train_net.modules():
if isinstance(m, nn.BatchNorm2d):
for p in m.parameters():
batchnorms.append(p)
if len(batchnorms) > 0:
batchnorm_optimizer = get_optimizer(args, batchnorms, 'batchnorm')
else:
batchnorm_optimizer = None
alpha_scheduler = get_scheduler(args, alpha_optimizer)
if batchnorm_optimizer is not None:
batchnorm_scheduler = get_scheduler(args, batchnorm_optimizer)
else:
batchnorm_scheduler = None
else:
alpha_scheduler = None
batchnorm_scheduler = None
max_acc = gather_all_test(gpu_ind, args, train_net, testloader)
for e in range(args.epoch):
ppb_train_single_epoch(gpu_ind, args, e, train_net, train_net_shape_vec, alpha, trainloader, criteria, alpha_optimizer, net_optimizer, perm, perm_inverse, batchnorm_optimizer)
if e % 1 == 0 :
test_watchdog.start()
acc = gather_all_test(gpu_ind, args, train_net, testloader)
test_watchdog.stop()
if gpu_ind == 0:
print("TEST RESULT:\tAcc:", round(acc, 3), "\tBest Acc:", round(max_acc,3), "\tTime:", test_watchdog.get_time_in_sec(), 'seconds')
if acc > max_acc:
save_model(gpu_ind, args, train_net)
save_signature(gpu_ind, args, alpha, train_net, shared_alpha)
max_acc = acc
alpha_scheduler.step()
if batchnorm_scheduler is not None:
batchnorm_scheduler.step()
print("FINAL TEST RESULT:\tAcc:", round(max_acc, 3))
if args.method == 'pranc_otf':
alpha_encoder, alpha_classifier, train_net = pranc_otf_init(gpu_ind, args, train_net)
if args.lr > 0:
alpha_optimizer = get_optimizer(args, [alpha_encoder, alpha_classifier], 'pranc')
net_optimizer = get_optimizer(args, train_net.parameters(), 'network')
batchnorms = []
for m in train_net.modules():
if isinstance(m, nn.BatchNorm2d):
for p in m.parameters():
batchnorms.append(p)
if len(batchnorms) > 0:
batchnorm_optimizer = get_optimizer(args, batchnorms, 'batchnorm')
else:
batchnorm_optimizer = None
alpha_scheduler = get_scheduler(args, alpha_optimizer)
if batchnorm_optimizer is not None:
batchnorm_scheduler = get_scheduler(args, batchnorm_optimizer)
else:
batchnorm_scheduler = None
else:
alpha_scheduler = None
batchnorm_scheduler = None
max_acc = gather_all_test(gpu_ind, args, train_net, testloader)
for e in range(args.epoch):
pranc_otf_train_single_epoch(gpu_ind, args, e, train_net, alpha_encoder, alpha_classifier, trainloader, criteria, alpha_optimizer, net_optimizer, batchnorm_optimizer)
if e % 1 == 0 :
test_watchdog.start()
acc = gather_all_test(gpu_ind, args, train_net, testloader)
test_watchdog.stop()
if gpu_ind == 0:
print("TEST RESULT:\tAcc:", round(acc, 3), "\tBest Acc:", round(max_acc,3), "\tTime:", test_watchdog.get_time_in_sec(), 'seconds')
if acc > max_acc:
save_model(gpu_ind, args, train_net)
save_signature_otf(gpu_ind, args, alpha_encoder, alpha_classifier, train_net)
max_acc = acc
alpha_scheduler.step()
if batchnorm_scheduler is not None:
batchnorm_scheduler.step()
print("FINAL TEST RESULT:\tAcc:", round(max_acc, 3))
if __name__ == '__main__':
number_of_gpus = torch.cuda.device_count()
max_acc = 0
args = ArgumentParser()
os.environ['MASTER_ADDR'] = args.dist_addr
os.environ['MASTER_PORT'] = str(args.dist_port)
if args.method == 'pranc':
assert args.num_alpha % args.world_size == 0
shared_alpha = torch.zeros(args.num_alpha)
shared_alpha.share_memory_()
else:
shared_alpha = None
mp.spawn(main_worker, nprocs = number_of_gpus, args=(args, shared_alpha))
pass