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l2o_eval.py
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l2o_eval.py
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# Copyright (c) 2023. Samsung Electronics Co., Ltd. All Rights Reserved.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""
Evaluate a trained MLP-based optimizer on a given task (see tasks.py for the list of tasks):
python l2o_eval.py --ckpt path_to_checkpoint -t task_index
"""
import numpy as np
import time
import argparse
import random
from datetime import datetime
import torch
import torch.nn.functional as F
import torchvision.models
import models.vision
from functools import partial
from tasks import trainloader_mapping, testloader_mapping, TASKS, TEST_SEEDS
from config import init_config, process
from models.metaopt import MetaOpt
def seed_everything(seed):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
def test_model(model, device, test_loader):
"""
Tests the model on a test set.
:param model: nn.Module based neural net
:param device: cpu or cuda
:param test_loader: DataLoader
:return: test accuracy and loss
"""
model.eval().to(device)
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
data, target = data.to(device), target.to(device)
output = model(data)
test_loss += F.cross_entropy(
output, target, reduction='sum'
).item() # sum up batch loss
pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
test_acc = 100.0 * correct / len(test_loader.dataset)
return test_acc, test_loss
def eval_opt(opt_cls, test_cfg, device, seed, print_interval=100, max_iters=1000,
metaopt_cfg=None, metaopt_state=None):
"""
Evaluates an SGD based optimizer or a learned optimizer on a given task.
:param opt_cls: optimizer class such as torch.optim.SGD/Adam/AdamW or MetaOpt
:param test_cfg: task config from tasks.py
:param device: cpu or cuda
:param seed: test seed from tasks.py
:param print_interval: print interval of loss/acc
:param max_iters: max number of training iterations
:param metaopt_cfg: config of the learned optimizer (from the checkpoint)
:param metaopt_state: state of the learned optimizer (from the checkpoint)
:return: test accuracy
"""
seed_everything(seed)
if torch.cuda.is_available():
torch.cuda.empty_cache()
net = eval(test_cfg['net_cls'])(**test_cfg['net_args']).to(device).train()
print('Training %s net with %d parameters' % (test_cfg['name'], sum([p.numel() for p in net.parameters()])))
if metaopt_state is not None:
opt = opt_cls(**metaopt_cfg, parameters=net.parameters()).to(device).eval()
opt.load_state_dict(metaopt_state)
else:
opt = opt_cls(net.parameters())
t = time.time()
train_loader = trainloader_mapping[test_cfg['dataset']](batch_size=args.batch_size)
test_loader = testloader_mapping[test_cfg['dataset']]()
epochs = int(np.ceil(max_iters / len(train_loader)))
step = 0
for epoch in range(epochs):
for _, (x, y) in enumerate(train_loader):
output = net(x.to(device))
y = y.to(device)
loss = F.cross_entropy(output, y)
loss.backward()
pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability
acc = 100.0 * pred.eq(y.view_as(pred)).sum() / len(y)
opt.step()
opt.zero_grad()
if (step + 1) % print_interval == 0 or step == max_iters - 1:
test_acc_, test_loss_ = test_model(net, device, test_loader)
print('Training {} net: seed={}, step={:05d}/{:05d}, train loss={:.3f}, train acc={:.3f}, '
'test loss={:.3f}, test acc={:.3f}, '
'speed: {:.2f} s/b, mem ram/gpu: {:.2f}/{:.2f}G'.format(test_cfg['name'],
seed,
step + 1,
max_iters,
loss.item(),
acc.item(),
test_loss_,
test_acc_,
(time.time() - t) / (step + 1),
process.memory_info().rss / 10 ** 9,
-1 if device == 'cpu' else (
torch.cuda.memory_reserved(0) /
10 ** 9)))
step += 1
if step >= max_iters:
break
if step >= max_iters:
break
test_acc_, test_loss_ = test_model(net, device, test_loader)
print('seed: {}, test loss: {:.3f}, test accuracy: {:.3f}\n'.format(seed,
test_loss_,
test_acc_))
return test_acc_
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='l2o evaluation')
parser.add_argument('-c', '--ckpt', type=str, default=None, help='path to the trained l2o checkpoint')
args = init_config(parser, steps=1000, inner_steps=None) # during eval, steps should equal inner_steps
seed_everything(args.seed)
if args.ckpt in [None, 'none']:
opt_args = {}
if args.opt == 'adam':
opt_fn = torch.optim.Adam
elif args.opt == 'adamw':
opt_fn = torch.optim.AdamW
elif args.opt == 'sgd':
opt_fn = torch.optim.SGD
opt_args = {'momentum': 0.9}
else:
raise NotImplementedError(f'unknown optimizer {args.opt}')
metaopt = partial(opt_fn, lr=args.lr, weight_decay=args.wd, **opt_args)
print(f'Using {args.opt}')
metaopt_cfg = None
metaopt_state = None
else:
state_dict = torch.load(args.ckpt, map_location=args.device)
metaopt = MetaOpt
metaopt_cfg = state_dict['metaopt_cfg']
metaopt_state = state_dict['model_state_dict']
print('MetaOpt with config %s' % str(metaopt_cfg),
'loaded from step %d' % state_dict['step'])
for task in args.train_tasks:
cgf = TASKS[task]
print('\nEval %s, task:' % str(metaopt), cgf)
acc = []
for seed in TEST_SEEDS:
acc.append(eval_opt(metaopt, cgf, args.device, seed,
print_interval=1,
max_iters=args.steps,
metaopt_cfg=metaopt_cfg,
metaopt_state=metaopt_state))
print('test acc for %d seeds: %.3f +- %.3f' % (len(acc), np.mean(acc), np.std(acc)))
print('done!', datetime.today())