def train():
"""
Example:
>>> train()
"""
import random
np.random.seed(1031726816 % 4294967295)
torch.manual_seed(137852547 % 4294967295)
random.seed(2497950049 % 4294967295)
xpu = xpu_device.XPU.from_argv()
print('Chosen xpu = {!r}'.format(xpu))
cifar_num = 10
if ub.argflag('--lab'):
datasets = cifar_training_datasets(output_colorspace='LAB',
norm_mode='independent',
cifar_num=cifar_num)
elif ub.argflag('--rgb'):
datasets = cifar_training_datasets(output_colorspace='RGB',
norm_mode='independent',
cifar_num=cifar_num)
elif ub.argflag('--rgb-dep'):
datasets = cifar_training_datasets(output_colorspace='RGB',
norm_mode='dependant',
cifar_num=cifar_num)
else:
raise AssertionError('specify --rgb / --lab')
import netharn.models.densenet
# batch_size = (128 // 3) * 3
batch_size = 64
# initializer_ = (initializers.KaimingNormal, {
# 'nonlinearity': 'relu',
# })
lr = 0.1
initializer_ = (initializers.LSUV, {})
hyper = hyperparams.HyperParams(
workdir=ub.ensuredir('train_cifar_work'),
model=(
netharn.models.densenet.DenseNet,
{
'cifar': True,
'block_config': (32, 32, 32), # 100 layer depth
'num_classes': datasets['train'].n_classes,
'drop_rate': float(ub.argval('--drop_rate', default=.2)),
'groups': 1,
}),
optimizer=(
torch.optim.SGD,
{
# 'weight_decay': .0005,
'weight_decay':
float(ub.argval('--weight_decay', default=.0005)),
'momentum': 0.9,
'nesterov': True,
'lr': 0.1,
}),
scheduler=(nh.schedulers.ListedLR, {
'points': {
0: lr,
150: lr * 0.1,
250: lr * 0.01,
},
'interpolate': False
}),
monitor=(nh.Monitor, {
'minimize': ['loss'],
'maximize': ['mAP'],
'patience': 314,
'max_epoch': 314,
}),
initializer=initializer_,
criterion=(torch.nn.CrossEntropyLoss, {}),
# Specify anything else that is special about your hyperparams here
# Especially if you make a custom_batch_runner
augment=str(datasets['train'].augmenter),
other=ub.dict_union(
{
# TODO: type of augmentation as a parameter dependency
# 'augmenter': str(datasets['train'].augmenter),
# 'augment': datasets['train'].augment,
'batch_size': batch_size,
'colorspace': datasets['train'].output_colorspace,
'n_classes': datasets['train'].n_classes,
# 'center_inputs': datasets['train'].center_inputs,
},
datasets['train'].center_inputs.__dict__),
)
# if ub.argflag('--rgb-indie'):
# hyper.other['norm'] = 'dependant'
hyper.input_ids['train'] = datasets['train'].input_id
xpu = xpu_device.XPU.cast('auto')
print('xpu = {}'.format(xpu))
data_kw = {'batch_size': batch_size}
if xpu.is_gpu():
data_kw.update({'num_workers': 8, 'pin_memory': True})
tags = ['train', 'vali', 'test']
loaders = ub.odict()
for tag in tags:
dset = datasets[tag]
shuffle = tag == 'train'
data_kw_ = data_kw.copy()
if tag != 'train':
data_kw_['batch_size'] = max(batch_size // 4, 1)
loader = torch.utils.data.DataLoader(dset, shuffle=shuffle, **data_kw_)
loaders[tag] = loader
harn = fit_harness.FitHarness(
hyper=hyper,
datasets=datasets,
xpu=xpu,
loaders=loaders,
)
# harn.monitor = early_stop.EarlyStop(patience=40)
harn.monitor = monitor.Monitor(min_keys=['loss'],
max_keys=['global_acc', 'class_acc'],
patience=40)
harn.initialize()
harn.run()
def __init__(harn, hyper=None, train_dpath=None):
if isinstance(hyper, dict):
hyper = hyperparams.HyperParams(**hyper)
harn.hyper = hyper
harn.main_prog = None
harn.datasets = None
harn.loaders = None
harn.model = None
harn.optimizer = None
harn.scheduler = None
harn.monitor = None
harn.criterion = None
harn.dynamics = {'batch_step': 1}
harn.paths = None
harn.train_dpath = train_dpath
harn.nice_dpath = None
harn._initialized = False
harn.flog = None
harn.tlog = None
harn.dry = None
# Track current epoch number
harn.epoch = 0
# Track current iteration within an epoch
harn.bxs = {
'train': 0,
'vali': 0,
'test': 0,
}
harn.intervals = {
'display_train': 1,
'display_vali': 1,
'display_test': 1,
'log_iter_train': None,
'log_iter_test': None,
'log_iter_vali': None,
'vali': 1,
'test': 1,
# how often to take a snapshot
'snapshot': 1,
# how often to remove old snapshots
'cleanup': 10,
}
harn.config = {
'show_prog': True,
'use_tqdm': None,
'prog_backend': 'tqdm',
# A loss that would be considered large
'large_loss': 100,
# number of recent / best snapshots to keep
'num_keep': 10,
'keep_freq': 10,
}
harn.current_tag = None