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train_ori.py
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train_ori.py
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# modify v2
"""Train and val."""
import logging
import os
import time
import torch
import subprocess
from utils.config import FLAGS, _ENV_EXPAND
from utils.common import get_params_by_name
from utils.common import set_random_seed
from utils.common import create_exp_dir
from utils.common import setup_logging
from utils.common import save_status
from utils.common import get_device
from utils.common import extract_item
from utils.common import get_data_queue_size
from utils.common import bn_calibration
from utils import dataflow
from utils import optim
from utils import distributed as udist
from utils import prune
from mmseg import seg_dataflow
from mmseg.loss import CrossEntropyLoss, JointsMSELoss, accuracy_keypoint
import models.mobilenet_base as mb
import common as mc
from mmseg.validation import SegVal, keypoint_val
from pruners import *
from prune_utils import load_pruner, masked_parameters, prune_loop
def shrink_model(model_wrapper,
ema,
optimizer,
prune_info,
threshold=1e-3,
ema_only=False):
r"""Dynamic network shrinkage to discard dead atomic blocks.
Args:
model_wrapper: model to be shrinked.
ema: An instance of `ExponentialMovingAverage`, could be None.
optimizer: Global optimizer.
prune_info: An instance of `PruneInfo`, could be None.
threshold: A small enough constant.
ema_only: If `True`, regard an atomic block as dead only when
`$$\hat{alpha} \le threshold$$`. Otherwise use both current value
and momentum version.
"""
model = mc.unwrap_model(model_wrapper)
for block_name, block in model.get_named_block_list().items(): # inverted residual blocks
assert isinstance(block, mb.InvertedResidualChannels)
masks = [
bn.weight.detach().abs() > threshold
for bn in block.get_depthwise_bn()
]
if ema is not None:
masks_ema = [
ema.average('{}.{}.weight'.format(
block_name, name)).detach().abs() > threshold
for name in block.get_named_depthwise_bn().keys()
]
if not ema_only:
masks = [
mask0 | mask1 for mask0, mask1 in zip(masks, masks_ema)
]
else:
masks = masks_ema
block.compress_by_mask(masks,
ema=ema,
optimizer=optimizer,
prune_info=prune_info,
prefix=block_name,
verbose=False,
reuse_mask=FLAGS.reuse_mask)
if optimizer is not None:
assert set(optimizer.param_groups[0]['params']) == set(
model.parameters())
mc.model_profiling(model,
FLAGS.image_size,
FLAGS.image_size,
num_forwards=0,
verbose=False)
if udist.is_master():
logging.info('Model Shrink to FLOPS: {}'.format(model.n_macs))
logging.info('Current model: {}'.format(mb.output_network(model)))
def get_prune_weights(model, use_transformer=False):
"""Get variables for pruning."""
# ['features.2.ops.0.1.1.weight', 'features.2.ops.1.1.1.weight', 'features.2.ops.2.1.1.weight'...]
if use_transformer:
return get_params_by_name(mc.unwrap_model(model), FLAGS._bn_to_prune_transformer.weight)
return get_params_by_name(mc.unwrap_model(model), FLAGS._bn_to_prune.weight)
@udist.master_only
def summary_bn(model, prefix):
"""Summary BN's weights."""
weights = get_prune_weights(model)
for name, param in zip(FLAGS._bn_to_prune.weight, weights):
mc.summary_writer.add_histogram(
'{}/{}/{}'.format(prefix, 'bn_scale', name), param.detach(),
FLAGS._global_step)
if len(FLAGS._bn_to_prune.weight) > 0:
mc.summary_writer.add_histogram(
'{}/bn_scale/all'.format(prefix),
torch.cat([weight.detach() for weight in weights]),
FLAGS._global_step)
@udist.master_only
def log_pruned_info(model, flops_pruned, infos, prune_threshold):
"""Log pruning-related information."""
if udist.is_master():
logging.info('Flops threshold: {}'.format(prune_threshold))
for info in infos:
if FLAGS.prune_params['logging_verbose']:
logging.info(
'layer {}, total channel: {}, pruned channel: {}, flops'
' total: {}, flops pruned: {}, pruned rate: {:.3f}'.format(
*info))
mc.summary_writer.add_scalar(
'prune_ratio/{}/{}'.format(prune_threshold, info[0]), info[-1],
FLAGS._global_step)
logging.info('Pruned model: {}'.format(
prune.output_searched_network(model, infos, FLAGS.prune_params)))
flops_remain = model.n_macs - flops_pruned
if udist.is_master():
logging.info(
'Prune threshold: {}, flops pruned: {}, flops remain: {}'.format(
prune_threshold, flops_pruned, flops_remain))
mc.summary_writer.add_scalar('prune/flops/{}'.format(prune_threshold),
flops_remain, FLAGS._global_step)
def run_one_epoch(epoch,
loader,
model,
criterion,
optimizer,
lr_scheduler,
ema,
rho_scheduler,
meters,
max_iter=None,
phase='train'):
"""Run one epoch."""
assert phase in [
'train', 'val', 'test', 'bn_calibration'
] or phase.startswith(
'prune'), "phase not be in train/val/test/bn_calibration/prune."
train = phase == 'train'
if train:
model.train()
else:
model.eval()
if phase == 'bn_calibration':
model.apply(bn_calibration)
if not FLAGS.use_hdfs:
if FLAGS.use_distributed:
loader.sampler.set_epoch(epoch)
results = None
data_iterator = iter(loader)
if not FLAGS.use_hdfs:
if FLAGS.use_distributed:
if FLAGS.dataset == 'coco':
data_fetcher = dataflow.DataPrefetcherKeypoint(data_iterator)
else:
data_fetcher = dataflow.DataPrefetcher(data_iterator)
else:
logging.warning('Not use prefetcher')
data_fetcher = data_iterator
for batch_idx, data in enumerate(data_fetcher):
if FLAGS.dataset == 'coco':
input, target, target_weight, meta = data
# print(input.shape, target.shape, target_weight.shape, meta)
# (4, 3, 384, 288), (4, 17, 96, 72), (4, 17, 1),
else:
input, target = data
# for debug only
# if batch_idx > 100:
# break
# used for bn calibration
if max_iter is not None:
assert phase == 'bn_calibration'
if batch_idx >= max_iter:
break
target = target.cuda(non_blocking=True)
if train:
optimizer.zero_grad()
rho = rho_scheduler(FLAGS._global_step)
if FLAGS.dataset == 'coco':
outputs = model(input)
if isinstance(outputs, list):
loss = criterion(outputs[0], target, target_weight)
for output in outputs[1:]:
loss += criterion(output, target, target_weight)
else:
output = outputs
loss = criterion(output, target, target_weight)
_, avg_acc, cnt, pred = accuracy_keypoint(output.detach().cpu().numpy(),
target.detach().cpu().numpy()) # cnt=17
meters['acc'].cache(avg_acc)
meters['loss'].cache(loss)
else:
loss = mc.forward_loss(model, criterion, input, target, meters, task=FLAGS.model_kwparams.task, distill=FLAGS.distill)
if FLAGS.prune_params['method'] is not None:
loss_l2 = optim.cal_l2_loss(model, FLAGS.weight_decay,
FLAGS.weight_decay_method) # manual weight decay
loss_bn_l1 = prune.cal_bn_l1_loss(get_prune_weights(model),
FLAGS._bn_to_prune.penalty, rho)
if FLAGS.prune_params.use_transformer:
transformer_weights = get_prune_weights(model, True)
loss_bn_l1 += prune.cal_bn_l1_loss(transformer_weights,
FLAGS._bn_to_prune_transformer.penalty, rho)
transformer_dict = []
for name, weight in zip(FLAGS._bn_to_prune_transformer.weight, transformer_weights):
transformer_dict.append(sum(weight > FLAGS.model_shrink_threshold).item())
FLAGS._bn_to_prune_transformer.add_info_list('channels', transformer_dict)
FLAGS._bn_to_prune_transformer.update_penalty()
if udist.is_master() and FLAGS._global_step % FLAGS.log_interval == 0:
logging.info(transformer_dict)
# logging.info(FLAGS._bn_to_prune_transformer.penalty)
meters['loss_l2'].cache(loss_l2)
meters['loss_bn_l1'].cache(loss_bn_l1)
loss = loss + loss_l2 + loss_bn_l1
loss.backward()
if FLAGS.use_distributed:
udist.allreduce_grads(model)
if FLAGS._global_step % FLAGS.log_interval == 0:
results = mc.reduce_and_flush_meters(meters)
if udist.is_master():
logging.info('Epoch {}/{} Iter {}/{} Lr: {} {}: '.format(
epoch, FLAGS.num_epochs, batch_idx, len(loader), optimizer.param_groups[0]["lr"], phase)
+ ', '.join('{}: {:.4f}'.format(k, v)
for k, v in results.items()))
for k, v in results.items():
mc.summary_writer.add_scalar('{}/{}'.format(phase, k),
v, FLAGS._global_step)
if udist.is_master(
) and FLAGS._global_step % FLAGS.log_interval == 0:
mc.summary_writer.add_scalar('train/learning_rate',
optimizer.param_groups[0]['lr'],
FLAGS._global_step)
if FLAGS.prune_params['method'] is not None:
mc.summary_writer.add_scalar('train/l2_regularize_loss',
extract_item(loss_l2),
FLAGS._global_step)
mc.summary_writer.add_scalar('train/bn_l1_loss',
extract_item(loss_bn_l1),
FLAGS._global_step)
mc.summary_writer.add_scalar('prune/rho', rho,
FLAGS._global_step)
mc.summary_writer.add_scalar(
'train/current_epoch',
FLAGS._global_step / FLAGS._steps_per_epoch,
FLAGS._global_step)
if FLAGS.data_loader_workers > 0:
mc.summary_writer.add_scalar(
'data/train/prefetch_size',
get_data_queue_size(data_iterator), FLAGS._global_step)
if udist.is_master(
) and FLAGS._global_step % FLAGS.log_interval_detail == 0:
summary_bn(model, 'train')
optimizer.step()
if FLAGS.lr_scheduler == 'poly':
optim.poly_learning_rate(optimizer,
FLAGS.lr,
epoch * FLAGS._steps_per_epoch + batch_idx + 1,
FLAGS.num_epochs * FLAGS._steps_per_epoch)
else:
lr_scheduler.step()
if FLAGS.use_distributed and FLAGS.allreduce_bn:
udist.allreduce_bn(model)
FLAGS._global_step += 1
# NOTE: after steps count update
if ema is not None:
model_unwrap = mc.unwrap_model(model)
ema_names = ema.average_names()
params = get_params_by_name(model_unwrap, ema_names)
for name, param in zip(ema_names, params):
ema(name, param, FLAGS._global_step)
else:
if FLAGS.dataset == 'coco':
outputs = model(input)
if isinstance(outputs, list):
loss = criterion(outputs[0], target, target_weight)
for output in outputs[1:]:
loss += criterion(output, target, target_weight)
else:
output = outputs
loss = criterion(output, target, target_weight)
_, avg_acc, cnt, pred = accuracy_keypoint(output.detach().cpu().numpy(),
target.detach().cpu().numpy()) # cnt=17
meters['acc'].cache(avg_acc)
meters['loss'].cache(loss)
else:
mc.forward_loss(model, criterion, input, target, meters, task=FLAGS.model_kwparams.task, distill=False)
if not train:
results = mc.reduce_and_flush_meters(meters)
if udist.is_master():
logging.info(
'Epoch {}/{} {}: '.format(epoch, FLAGS.num_epochs, phase)
+ ', '.join(
'{}: {:.4f}'.format(k, v) for k, v in results.items()))
for k, v in results.items():
mc.summary_writer.add_scalar('{}/{}'.format(phase, k), v,
FLAGS._global_step)
return results
def train_val_test():
"""Train and val."""
torch.backends.cudnn.benchmark = True # For acceleration
# model
model, model_wrapper = mc.get_model()
ema = mc.setup_ema(model)
criterion = torch.nn.CrossEntropyLoss(reduction='mean').cuda()
criterion_smooth = optim.CrossEntropyLabelSmooth(
FLAGS.model_kwparams['num_classes'],
FLAGS['label_smoothing'],
reduction='mean').cuda()
if model.task == 'segmentation':
criterion = CrossEntropyLoss().cuda()
criterion_smooth = CrossEntropyLoss().cuda()
if FLAGS.dataset == 'coco':
criterion = JointsMSELoss(use_target_weight=True).cuda()
criterion_smooth = JointsMSELoss(use_target_weight=True).cuda()
if FLAGS.get('log_graph_only', False):
if udist.is_master():
_input = torch.zeros(1, 3, FLAGS.image_size,
FLAGS.image_size).cuda()
_input = _input.requires_grad_(True)
if isinstance(model_wrapper, (torch.nn.DataParallel, udist.AllReduceDistributedDataParallel)):
mc.summary_writer.add_graph(model_wrapper.module, (_input,), verbose=True)
else:
mc.summary_writer.add_graph(model_wrapper, (_input,), verbose=True)
return
# save init
if FLAGS.resume == '':
optimizer = optim.get_optimizer(model_wrapper, FLAGS)
logging.info('save init at : {}'.format(FLAGS.log_dir))
save_status(model_wrapper, None, optimizer, ema, 0,
1, (None, None),
os.path.join(FLAGS.log_dir, 'init_checkpoint'))
# check pretrained
if FLAGS.pretrained:
checkpoint = torch.load(FLAGS.pretrained,
map_location=lambda storage, loc: storage)
if ema:
ema.load_state_dict(checkpoint['ema'])
ema.to(get_device(model))
# update keys from external models
if isinstance(checkpoint, dict) and 'model' in checkpoint:
checkpoint = checkpoint['model']
if (hasattr(FLAGS, 'pretrained_model_remap_keys')
and FLAGS.pretrained_model_remap_keys):
new_checkpoint = {}
new_keys = list(model_wrapper.state_dict().keys())
old_keys = list(checkpoint.keys())
for key_new, key_old in zip(new_keys, old_keys):
new_checkpoint[key_new] = checkpoint[key_old]
if udist.is_master():
logging.info('remap {} to {}'.format(key_new, key_old))
checkpoint = new_checkpoint
model_wrapper.load_state_dict(checkpoint)
if udist.is_master():
logging.info('Loaded model {}.'.format(FLAGS.pretrained))
# check resume training
if FLAGS.resume:
checkpoint = torch.load(os.path.join(FLAGS.resume,
'latest_checkpoint.pt'),
map_location=lambda storage, loc: storage)
model_wrapper = checkpoint['model'].cuda()
model = model_wrapper.module
# model = checkpoint['model'].module
optimizer = checkpoint['optimizer']
for state in optimizer.state.values():
for k, v in state.items():
if isinstance(v, torch.Tensor):
state[k] = v.cuda()
# model_wrapper.load_state_dict(checkpoint['model'])
# optimizer.load_state_dict(checkpoint['optimizer'])
if ema:
# ema.load_state_dict(checkpoint['ema'])
ema = checkpoint['ema'].cuda()
ema.to(get_device(model))
last_epoch = checkpoint['last_epoch']
lr_scheduler = optim.get_lr_scheduler(optimizer, FLAGS, last_epoch=(last_epoch + 1) * FLAGS._steps_per_epoch)
lr_scheduler.last_epoch = (last_epoch + 1) * FLAGS._steps_per_epoch
best_val = extract_item(checkpoint['best_val'])
train_meters, val_meters = checkpoint['meters']
FLAGS._global_step = (last_epoch + 1) * FLAGS._steps_per_epoch
if udist.is_master():
logging.info('Loaded checkpoint {} at epoch {}.'.format(
FLAGS.resume, last_epoch))
else:
lr_scheduler = optim.get_lr_scheduler(optimizer, FLAGS)
# last_epoch = lr_scheduler.last_epoch
last_epoch = -1
best_val = 1.
if not FLAGS.distill:
train_meters = mc.get_meters('train', FLAGS.prune_params['method'])
val_meters = mc.get_meters('val')
else:
train_meters = mc.get_distill_meters('train', FLAGS.prune_params['method'])
val_meters = mc.get_distill_meters('val')
if FLAGS.model_kwparams.task == 'segmentation':
best_val = 0.
if not FLAGS.distill:
train_meters = mc.get_seg_meters('train', FLAGS.prune_params['method'])
val_meters = mc.get_seg_meters('val')
else:
train_meters = mc.get_seg_distill_meters('train', FLAGS.prune_params['method'])
val_meters = mc.get_seg_distill_meters('val')
FLAGS._global_step = 0
if not FLAGS.resume and udist.is_master():
logging.info(model_wrapper)
assert FLAGS.profiling, '`m.macs` is used for calculating penalty'
# if udist.is_master():
# model.apply(lambda m: print(m))
if FLAGS.profiling:
if 'gpu' in FLAGS.profiling:
mc.profiling(model, use_cuda=True)
if 'cpu' in FLAGS.profiling:
mc.profiling(model, use_cuda=False)
if FLAGS.dataset == 'cityscapes':
(train_set, val_set, test_set) = seg_dataflow.cityscapes_datasets(FLAGS)
segval = SegVal(num_classes=19)
elif FLAGS.dataset == 'ade20k':
(train_set, val_set, test_set) = seg_dataflow.ade20k_datasets(FLAGS)
segval = SegVal(num_classes=150)
elif FLAGS.dataset == 'coco':
(train_set, val_set, test_set) = seg_dataflow.coco_datasets(FLAGS)
# print(len(train_set), len(val_set)) # 149813 104125
segval = None
else:
# data
(train_transforms, val_transforms,
test_transforms) = dataflow.data_transforms(FLAGS)
(train_set, val_set, test_set) = dataflow.dataset(train_transforms,
val_transforms,
test_transforms, FLAGS)
segval = None
(train_loader, calib_loader, val_loader,
test_loader) = dataflow.data_loader(train_set, val_set, test_set, FLAGS)
# get bn's weights
if FLAGS.prune_params.use_transformer:
FLAGS._bn_to_prune, FLAGS._bn_to_prune_transformer = prune.get_bn_to_prune(model, FLAGS.prune_params)
else:
FLAGS._bn_to_prune = prune.get_bn_to_prune(model, FLAGS.prune_params)
rho_scheduler = prune.get_rho_scheduler(FLAGS.prune_params,
FLAGS._steps_per_epoch)
if FLAGS.test_only and (test_loader is not None):
if udist.is_master():
logging.info('Start testing.')
test_meters = mc.get_meters('test')
validate(last_epoch, calib_loader, test_loader, criterion, test_meters,
model_wrapper, ema, 'test')
return
# already broadcast by AllReduceDistributedDataParallel
# optimizer load same checkpoint/same initialization
if udist.is_master():
logging.info('Start training.')
for epoch in range(last_epoch + 1, FLAGS.num_epochs):
# train
results = run_one_epoch(epoch,
train_loader,
model_wrapper,
criterion_smooth,
optimizer,
lr_scheduler,
ema,
rho_scheduler,
train_meters,
phase='train')
if (epoch + 1) % FLAGS.eval_interval == 0:
# val
results, model_eval_wrapper = validate(epoch, calib_loader, val_loader,
criterion, val_meters,
model_wrapper, ema, 'val', segval, val_set)
if FLAGS.prune_params['method'] is not None and FLAGS.prune_params['bn_prune_filter'] is not None:
prune_threshold = FLAGS.model_shrink_threshold # 1e-3
masks = prune.cal_mask_network_slimming_by_threshold(
get_prune_weights(model_eval_wrapper), prune_threshold) # get mask for all bn weights (depth-wise)
FLAGS._bn_to_prune.add_info_list('mask', masks)
flops_pruned, infos = prune.cal_pruned_flops(FLAGS._bn_to_prune)
log_pruned_info(mc.unwrap_model(model_eval_wrapper), flops_pruned,
infos, prune_threshold)
if not FLAGS.distill:
if flops_pruned >= FLAGS.model_shrink_delta_flops \
or epoch == FLAGS.num_epochs - 1:
ema_only = (epoch == FLAGS.num_epochs - 1)
shrink_model(model_wrapper, ema, optimizer, FLAGS._bn_to_prune,
prune_threshold, ema_only)
model_kwparams = mb.output_network(mc.unwrap_model(model_wrapper))
if udist.is_master():
if FLAGS.model_kwparams.task == 'classification' and results['top1_error'] < best_val:
best_val = results['top1_error']
logging.info('New best validation top1 error: {:.4f}'.format(best_val))
save_status(model_wrapper, model_kwparams, optimizer, ema,
epoch, best_val, (train_meters, val_meters),
os.path.join(FLAGS.log_dir, 'best_model'))
elif FLAGS.model_kwparams.task == 'segmentation' and FLAGS.dataset != 'coco' and results[
'mIoU'] > best_val:
best_val = results['mIoU']
logging.info('New seg mIoU: {:.4f}'.format(best_val))
save_status(model_wrapper, model_kwparams, optimizer, ema,
epoch, best_val, (train_meters, val_meters),
os.path.join(FLAGS.log_dir, 'best_model'))
elif FLAGS.dataset == 'coco' and results > best_val:
best_val = results
logging.info('New Result: {:.4f}'.format(best_val))
save_status(model_wrapper, model_kwparams, optimizer, ema,
epoch, best_val, (train_meters, val_meters),
os.path.join(FLAGS.log_dir, 'best_model'))
# save latest checkpoint
save_status(model_wrapper, model_kwparams, optimizer, ema, epoch,
best_val, (train_meters, val_meters),
os.path.join(FLAGS.log_dir, 'latest_checkpoint'))
# save ckpt more often
if (epoch + 1) % 5 == 0:
model_kwparams = mb.output_network(mc.unwrap_model(model_wrapper))
save_status(model_wrapper, model_kwparams, optimizer, ema, epoch,
best_val, (train_meters, val_meters),
os.path.join(FLAGS.log_dir, 'latest_checkpoint'))
return
def validate(epoch, calib_loader, val_loader, criterion, val_meters,
model_wrapper, ema, phase, segval=None, val_set=None):
"""Calibrate and validate."""
assert phase in ['test', 'val']
model_eval_wrapper = mc.get_ema_model(ema, model_wrapper)
# bn_calibration
if FLAGS.prune_params['method'] is not None:
if FLAGS.get('bn_calibration', False):
if not FLAGS.use_distributed:
logging.warning(
'Only GPU0 is used when calibration when use DataParallel')
with torch.no_grad():
_ = run_one_epoch(epoch,
calib_loader,
model_eval_wrapper,
criterion,
None,
None,
None,
None,
val_meters,
max_iter=FLAGS.bn_calibration_steps,
phase='bn_calibration')
if FLAGS.use_distributed:
udist.allreduce_bn(model_eval_wrapper)
# val
with torch.no_grad():
if FLAGS.model_kwparams.task == 'segmentation':
if FLAGS.dataset == 'coco':
results = 0
if udist.is_master():
results = keypoint_val(val_set, val_loader, model_eval_wrapper.module, criterion)
else:
assert segval is not None
results = segval.run(epoch,
val_loader,
model_eval_wrapper.module if FLAGS.single_gpu_test else model_eval_wrapper,
FLAGS)
else:
results = run_one_epoch(epoch,
val_loader,
model_eval_wrapper,
criterion,
None,
None,
None,
None,
val_meters,
phase=phase)
summary_bn(model_eval_wrapper, phase)
return results, model_eval_wrapper
def main():
"""Entry."""
NUM_IMAGENET_TRAIN = 1281167
if FLAGS.dataset == 'cityscapes':
NUM_IMAGENET_TRAIN = 2975
elif FLAGS.dataset == 'ade20k':
NUM_IMAGENET_TRAIN = 20210
elif FLAGS.dataset == 'coco':
NUM_IMAGENET_TRAIN = 149813
mc.setup_distributed(NUM_IMAGENET_TRAIN)
if FLAGS.net_params and FLAGS.model_kwparams.task == 'segmentation':
tag, input_channels, block1, block2, block3, block4, last_channel = FLAGS.net_params.split('-')
input_channels = [int(item) for item in input_channels.split('_')]
block1 = [int(item) for item in block1.split('_')]
block2 = [int(item) for item in block2.split('_')]
block3 = [int(item) for item in block3.split('_')]
block4 = [int(item) for item in block4.split('_')]
last_channel = int(last_channel)
inverted_residual_setting = []
for item in [block1, block2, block3, block4]:
for _ in range(item[0]):
inverted_residual_setting.append(
[item[1], item[2:-int(len(item) / 2 - 1)], item[-int(len(item) / 2 - 1):]])
FLAGS.model_kwparams.input_channel = input_channels
FLAGS.model_kwparams.inverted_residual_setting = inverted_residual_setting
FLAGS.model_kwparams.last_channel = last_channel
if udist.is_master():
FLAGS.log_dir = '{}/{}'.format(FLAGS.log_dir,
time.strftime("%Y%m%d-%H%M%S"))
# yapf: disable
create_exp_dir(FLAGS.log_dir, FLAGS.config_path, blacklist_dirs=[
'exp', '.git', 'pretrained', 'tmp', 'deprecated', 'bak', 'output'])
# yapf: enable
setup_logging(FLAGS.log_dir)
for k, v in _ENV_EXPAND.items():
logging.info('Env var expand: {} to {}'.format(k, v))
logging.info(FLAGS)
set_random_seed(FLAGS.get('random_seed', 0))
with mc.SummaryWriterManager():
train_val_test()
if __name__ == "__main__":
main()