def main():
args = parse_args()
n, prefix = args['n_epochs'], args['prefix']
bs, img_sz = args['batch_size'], args['image_size']
prefix += '_' if prefix else ''
bunch = create_data_bunch(bs,
img_sz,
args['train_size'],
args['valid_size'],
use_cache=args['use_cache'])
train_sz, valid_sz = len(bunch.train_dl) / bunch.c, len(
bunch.valid_dl) / bunch.c
learn = create_cnn(bunch, args['network'])
learn.metrics = [accuracy, error_rate]
if args['continue']:
log.info('Continue training using cached data')
log.info('Epochs: %d', args['n_epochs'])
log.info('Model: %s', args['network_name'])
log.info('# of classes: %d', bunch.c)
log.info('Train size (per class): %d', train_sz)
log.info('Valid size (per class): %d', valid_sz)
if args['continue']:
cbs = [SaveModelCallback(learn, name='bestmodel_continue')]
try:
learn.load(f'{prefix}final_224')
except Exception as e:
log.error('Cannot restore model')
log.error(e)
sys.exit(1)
learn.unfreeze()
learn.fit_one_cycle(n, callabacks=cbs, max_lr=slice(3e-5, 3e-5))
learn.save(f'{prefix}continued_224')
else:
cbs = [SaveModelCallback(learn)]
learn.fit_one_cycle(1)
learn.save(f'{prefix}one_224')
learn.unfreeze()
learn.freeze_to(-2)
learn.fit_one_cycle(n - 2, max_lr=slice(1e-4, 1e-3))
learn.save(f'{prefix}unfreeze_224')
learn.unfreeze()
learn.fit_one_cycle(1, callbacks=cbs, max_lr=slice(10e-5, 5e-5))
learn.save(f'{prefix}final_224')
log.info('Done!')
def train_model(model, epochs, lr, wd, module_string, ct, path):
plt.close('all')
learn = basic_train.Learner(data=db,
model=model,
loss_func=loss_func,
wd=wd,
callback_fns=ActivationStats,
bn_wd=bn_wd,
true_wd=true_wd)
start = time.perf_counter()
if ct:
learn.load(path)
print('Model loaded: ', path)
learn.fit_one_cycle(
epochs,
max_lr=lr,
wd=wd,
callbacks=[
SaveModelCallback(learn,
every='improvement',
monitor='valid_loss',
name='best_%s_bs%s_lr%.0e_wd%.0e' %
(module_string, bs, lr, wd))
])
end = time.perf_counter()
delta_t = end - start
return learn, delta_t
def _get_callbacks(self, name):
"""Retrieve callbacks to be used for training.
Args:
name (str): Name of training stage (used to save files).
csv saved in: f'saved/model_csv/{exp_name}_{name}.csv'
model weights saved in: f'saved/model_weights/{exp_name}_{name}.pth'
Returns:
List of Callbacks.
"""
callbacks = []
# Logs metrics for each training stage
callbacks.append(CSVLogger(
learn=self.learn,
append=False,
filename=os.path.join(CSV_FOLDER, f'{self.exp_name}_{name}')
))
# Saves the best model weights
callbacks.append(SaveModelCallback(
learn=self.learn,
# Also loads best model weights at the end of training
every='improvement',
name=f'{self.exp_name}_{name}',
))
return callbacks
def train_lm(path,
filename,
model='AWD_LSTM',
epochs=8,
pretrained_fnames=None,
preds=True):
#get data after running preprocess
print(f'loading data from {path}/{filename};')
data_lm = load_data(path, filename, bs=64, bptt=70)
#change config if XL
if model == 'XL':
config = tfmerXL_lm_config.copy()
config['mem_len'] = 150
config['output_p'] = 0.1
config['embed_p'] = 0.1
config['ff_p'] = 0.1
config['resid_p'] = 0.1
config['d_inner'] = 1024
config['d_model'] = 128
else:
config = None
#load pretrained weights
if pretrained_fnames: pretrained_fnames = pretrained_fnames.split(',')
learn = language_model_learner(data_lm,
models[model],
config=config,
pretrained=False,
pretrained_fnames=pretrained_fnames)
print(f'training lm model {model}; pretrained from {pretrained_fnames};')
#early stopping and saving at every epoch
cb = [SaveModelCallback(learn), EarlyStoppingCallback(learn)]
if pretrained_fnames:
#layered training
print(f'training lm model head;')
learn.fit_one_cycle(1, 3e-3, moms=(0.8, 0.7))
print(f'saving lm model head to {path}/{filename}_head;')
learn.save(filename + '_head')
learn.unfreeze()
print(f'training for {epochs} epochs')
learn.fit_one_cycle(epochs, 3e-4, moms=(0.8, 0.7), callbacks=cb)
print(f'saving model to {path}/{filename}_finetuned')
learn.save(filename + '_finetuned')
#generate outputs from validation set
if preds:
print(
f'generating predictions and saving to {path}/{filename}_preds.txt;'
)
get_valid_preds(learn, data_lm, filename + '_' + model + '_preds.txt')
def multi_train(get_learn, epoch_len, epochs, opts, lrs, checkpoints, tb_log_root,autoSave=True):
'''
可以从checkpoint继续训练，为了保证训练连续性，需要手动设置lr与checkpoint保存时一致。
'''
# 清理tensorboard log dir
if os.path.exists(tb_log_root): shutil.rmtree(tb_log_root)
os.mkdir(tb_log_root)
if not os.path.exists('./run_log/'): os.mkdir('./run_log/')
txtlog = open('./run_log/log.txt',mode='w')
for i,(opt,lr,checkpoint) in enumerate(zip(opts,lrs,checkpoints)):
# create a learner
learn = get_learn()
# set optimizer
learn.opt_func = opt
# load checkpoint
if checkpoint is not None:
with open(checkpoint,'rb') as f:
learn.load(f)
# 在txt log中记录
csv_log_dir = f'csv_log/'
if not os.path.exists(learn.path/csv_log_dir): os.mkdir(learn.path/csv_log_dir)
csv_fname = csv_log_dir+f'run_{i}'
txt_write(txtlog,i,opt,lr,learn.path,csv_fname)
callbacks = []
# get csvlogger callback
csvLog = CSVLogger(learn,filename=csv_fname)
callbacks += [csvLog]
if autoSave:
# savemodel callback
autoSave = SaveModelCallback(learn,monitor='valid_loss',mode='min',every='improvement',name=f'run_{i}')
callbacks += [autoSave]
# get tensorboard callback
tbCb = get_tbCb(learn,tb_log_root+f'run_{i}')
callbacks += [tbCb]
# train
fit(learn=learn, epoch_len=epoch_len, epochs=epochs, lr=lr, callbacks=callbacks)
txtlog.close()
def main2(data_dir, output_dir, epochs):
crate_dir(output_dir)
em_sz, nh, nl = 400, 1150, 3
wd = 1e-7
bptt = 70
opt_fn = partial(optim.Adam, betas=(0.8, 0.99))
bs = 32
lr = 1e-3
last_epoch = get_last_epoch(path.join(args.data_dir, 'models'))
print('\033[1;34m', 'Loading data', '\033[0;0m')
data = load_data(data_dir, 'data_save.pkl')
model = language_model_learner(data,
text.models.AWD_LSTM,
drop_mult=0.5,
metrics=[accuracy])
try:
print('\033[1;34m', 'Loading checkpoint', '\033[0;0m')
model.load("last")
print('\033[0;32m', 'Loaded last checkpoint', '\033[0;0m')
except FileNotFoundError:
print('\033[1;31m', 'No checkpoint founded', '\033[0;0m')
pass
model.fit(epochs,
lr=slice(lr / 2.6, lr),
wd=1e-7,
callbacks=[
SaveModelCallback(model,
every='epoch',
monitor='accuracy',
name=f'check_{last_epoch}')
])
print('\033[0;32m', 'Saving model', '\033[0;0m')
model.save("last")
model.export("model.pkl")
def create_callbacks(learn):
return [
EarlyStoppingCallback(learn, patience=3),
SaveModelCallback(learn),
CSVLogger(learn)]
def train(train_dataset: torch.utils.data.Dataset,
test_dataset: torch.utils.data.Dataset,
training_config: dict = train_config,
global_config: dict = global_config):
"""
Template training routine. Takes a training and a test dataset wrapped
as torch.utils.data.Dataset type and two corresponding generic
configs for both gobal path settings and training settings.
Returns the fitted fastai.train.Learner object which can be
used to assess the resulting metrics and error curves etc.
"""
for path in global_config.values():
create_dirs(path)
# wrap datasets with Dataloader classes
train_loader = torch.utils.data.DataLoader(
train_dataset, **train_config["DATA_LOADER_CONFIG"])
test_loader = torch.utils.data.DataLoader(
test_dataset, **train_config["DATA_LOADER_CONFIG"])
databunch = DataBunch(train_loader, test_loader)
# instantiate model and learner
if training_config["WEIGHTS"] is None:
model = training_config["MODEL"](**training_config["MODEL_CONFIG"])
else:
model = load_model(training_config["MODEL"],
training_config["MODEL_CONFIG"],
training_config["WEIGHTS"],
training_config["DEVICE"])
learner = Learner(databunch,
model,
metrics=train_config["METRICS"],
path=global_config["ROOT_PATH"],
model_dir=global_config["WEIGHT_DIR"],
loss_func=train_config["LOSS"])
# model name & paths
name = "_".join([train_config["DATE"], train_config["SESSION_NAME"]])
modelpath = os.path.join(global_config["WEIGHT_DIR"], name)
if train_config["MIXED_PRECISION"]:
learner.to_fp16()
learner.save(modelpath)
torch.backends.cudnn.benchmark = True
cbs = [
SaveModelCallback(learner),
LearnerTensorboardWriter(
learner,
Path(os.path.join(global_config["LOG_DIR"]), "tensorboardx"),
name),
TerminateOnNaNCallback()
]
# perform training iteration
try:
if train_config["ONE_CYCLE"]:
learner.fit_one_cycle(train_config["EPOCHS"],
max_lr=train_config["LR"],
callbacks=cbs)
else:
learner.fit(train_config["EPOCHS"],
lr=train_config["LR"],
callbacks=cbs)
# save model files
except KeyboardInterrupt:
learner.save(modelpath)
raise KeyboardInterrupt
learner.save(modelpath)
val_loss = min(learner.recorder.val_losses)
val_metrics = learner.recorder.metrics
# log using the logging tool
logger = log.Log(train_config, run_name=train_config['SESSION_NAME'])
logger.log_metric('Validation Loss', val_loss)
logger.log.metrics(val_metrics)
logger.end_run()
#write csv log file
log_content = train_config.copy()
log_content["VAL_LOSS"] = val_loss
log_content["VAL_METRICS"] = val_metrics
log_path = os.path.join(global_config["LOG_DIR"], train_config["LOGFILE"])
write_log(log_path, log_content)
return learner, log_content, name
print(learn.callback_fns)
# --- TRAINING ---
if config['FINETUNE']:
stage0_logger = pd.read_csv(
learn.path / 'logs_{}fold.csv'.format(config['FOLD_NUMBER']))
best_epoch = stage0_logger['metric_tot'].idxmax()
learn.load('{0}{1}/models/{1}_{3}_{2}'.format(config['PATH_WEIGHTS'],
config['MODEL_NAME'],
best_epoch, 'stage0'))
learn.unfreeze()
checkpoint_callback = SaveModelCallback(learn,
name=config['MODEL_NAME'] +
'_stage1',
every='epoch',
monitor='valid_loss')
# reduce lr by factor after patience epochs
reduce_lr_callback = ReduceLROnPlateauCallback(learn,
monitor='metric_tot',
factor=0.5,
patience=5,
min_lr=1e-6)
logger = CSVLogger(learn, 'logs_{}fold'.format(config['FOLD_NUMBER']))
learn.fit(
40,
lr=1e-2 / 10.,
wd=0.,
callbacks=[checkpoint_callback, reduce_lr_callback],
