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train.py
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train.py
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import os
import random
import time
import torch
import torch.backends.cudnn as cudnn
import models
from utils.logger import Logger
import yaml
import argparse
from utils import utils
import sys
def add_learner_params():
parser = argparse.ArgumentParser(description='ML')
parser.add_argument('--problem', default='finetune',
help='The problem to train',
choices=models.REGISTERED_MODELS,
)
parser.add_argument('--name', default='demo',
help='Name for the experiment')
parser.add_argument('--nodes', default='',
help='slurm nodes for the experiment')
parser.add_argument('--slurm_partition', default='',
help='slurm partitions for the experiment')
# optimizer params
parser.add_argument('--lr_schedule', default='warmup-const')
parser.add_argument('--loss', default='ce')
parser.add_argument('--opt', default='sgd',
help='Optimizer to use', choices=['sgd', 'adam', 'lars'])
parser.add_argument('--iters', default=-1, type=int,
help='The number of optimizer updates')
parser.add_argument('--warmup', default=0, type=float,
help='The number of warmup iterations in proportion to \'iters\'')
parser.add_argument('--lr', default=0.1, type=float,
help='Base learning rate')
parser.add_argument('--inner_lr', default=0.1, type=float,
help='inner learning rate')
parser.add_argument('--q', default=0.66, type=float,
help='generalized ce loss q value')
parser.add_argument('--wd', '--weight_decay',
default=1e-4, type=float, dest='weight_decay')
# noise params
parser.add_argument('--corruption_prob', type=float, default=0.9,
help='label noise')
parser.add_argument('--corruption_type', '-ctype', type=str, default='unif',
help='Type of corruption ("unif" or "flip" or "flip2" or "asym").')
# trainer params
parser.add_argument('--save_freq', default=1000000000000,
type=int, help='Frequency to save the model')
parser.add_argument('--log_freq', default=100,
type=int, help='Logging frequency')
parser.add_argument('--eval_freq', default=100000000000,
type=int, help='Evaluation frequency')
parser.add_argument('-j', '--workers', default=4, type=int,
help='The number of data loader workers')
parser.add_argument('--seed', default=222, type=int, help='Random seed')
# parallelizm params:
parser.add_argument('--test_bs', default=256, type=int)
parser.add_argument('--encoder_ckpt', default='',
help='Path to the encoder checkpoint')
parser.add_argument('--encoder_type', default='simclr',
help='pretrained "imagenet" or "simclr" for simclr')
parser.add_argument('--config', default='configs/cifar10.yaml', type=str,
help='the number of nodes (scripts launched)',
)
#
parser.add_argument('--cuda', action='store_true')
parser.add_argument('--neptune', action='store_true')
params = parser.parse_args()
d = vars(params)
if params.config:
with open(params.config, 'r') as stream:
try:
params_2 = yaml.safe_load(stream)
except yaml.YAMLError as exc:
print(exc)
for k, v in params_2.items():
d[k] = v
return params
def main():
args = add_learner_params()
if args.seed != -1:
random.seed(args.seed)
torch.manual_seed(args.seed)
cudnn.deterministic = True
args.root = 'logs/'+args.name+'/'
if args.neptune:
import neptune
project = "arighosh/pretrain_noisy_label"
neptune.init(project_qualified_name=project,
api_token=os.environ["NEPTUNE_API_TOKEN"])
neptune.create_experiment(
name=args.name, send_hardware_metrics=False, params=vars(args))
fmt = {
'train_time': '.3f',
'val_time': '.3f',
'train_epoch': '.1f',
'lr': '.1e',
}
logger = Logger('logs', base=args.root, fmt=fmt)
if torch.cuda.is_available():
device = torch.device('cuda')
else:
device = torch.device('cpu')
if args.cuda:
assert device.type == 'cuda', 'no gpu found!'
with open(args.root+'config.yml', 'w') as outfile:
yaml.dump(vars(args), outfile, default_flow_style=False)
# create model
model = models.REGISTERED_MODELS[args.problem](args, device=device)
cur_iter = 0
# Data loading code
model.prepare_data()
continue_training = cur_iter < args.iters
data_time, it_time = 0, 0
best_acc = 0.
best_valid_acc, best_acc_with_valid = 0, 0
while continue_training:
train_loader, test_loader, valid_loader, meta_loader = model.dataloaders(
iters=args.iters)
train_logs = []
model.train()
start_time = time.time()
for _, batch in enumerate(train_loader):
cur_iter += 1
batch = [x.to(device) for x in batch]
data_time += time.time() - start_time
logs = {}
if args.problem not in {'finetune'}:
meta_batch = next(iter(meta_loader))
meta_batch = [x.to(device) for x in meta_batch]
logs = model.train_step(batch, meta_batch, cur_iter)
else:
logs = model.train_step(batch, cur_iter)
# save logs for the batch
train_logs.append({k: utils.tonp(v) for k, v in logs.items()})
if cur_iter % args.eval_freq == 0 or cur_iter >= args.iters:
test_start_time = time.time()
test_logs, valid_logs = [], []
model.eval()
with torch.no_grad():
for batch in test_loader:
batch = [x.to(device) for x in batch]
logs = model.test_step(batch)
test_logs.append(logs)
for batch in valid_loader:
batch = [x.to(device) for x in batch]
logs = model.test_step(batch)
valid_logs.append(logs)
model.train()
test_logs = utils.agg_all_metrics(test_logs)
valid_logs = utils.agg_all_metrics(valid_logs)
best_acc = max(best_acc, float(test_logs['acc']))
test_logs['best_acc'] = best_acc
if float(valid_logs['acc']) > best_valid_acc:
best_valid_acc = float(valid_logs['acc'])
best_acc_with_valid = float(test_logs['acc'])
test_logs['best_acc_with_valid'] = best_acc_with_valid
#
if args.neptune:
for k, v in test_logs.items():
neptune.log_metric('test_'+k, float(v))
for k, v in valid_logs.items():
neptune.log_metric('valid_'+k, float(v))
test_it_time = time.time()-test_start_time
neptune.log_metric('test_it_time', test_it_time)
neptune.log_metric('test_cur_iter', cur_iter)
logger.add_logs(cur_iter, test_logs, pref='test_')
it_time += time.time() - start_time
if (cur_iter % args.log_freq == 0 or cur_iter >= args.iters):
train_logs = utils.agg_all_metrics(train_logs)
if args.neptune:
for k, v in train_logs.items():
neptune.log_metric('train_'+k, float(v))
neptune.log_metric('train_it_time', it_time)
neptune.log_metric('train_data_time', data_time)
neptune.log_metric(
'train_lr', model.optimizer.param_groups[0]['lr'])
neptune.log_metric('train_cur_iter', cur_iter)
logger.add_logs(cur_iter, train_logs, pref='train_')
logger.add_scalar(
cur_iter, 'lr', model.optimizer.param_groups[0]['lr'])
logger.add_scalar(cur_iter, 'data_time', data_time)
logger.add_scalar(cur_iter, 'it_time', it_time)
logger.iter_info()
logger.save()
data_time, it_time = 0, 0
train_logs = []
if cur_iter >= args.iters:
continue_training = False
break
start_time = time.time()
if __name__ == '__main__':
main()