AntoineH/smart_augmentation
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Harle, Antoine (Contracteur) 2020-01-29 06:36:12 -05:00
parent 96ed9fe2ae
commit 5cd50ca9f3
4 changed files with 198 additions and 103 deletions
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higher/smart_aug/train_utils.py
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@ -364,7 +364,7 @@ def run_dist_dataugV3(model, opt_param, epochs=1, inner_it=1, dataug_epoch_start
return log
def run_simple_smartaug(model, opt_param, epochs=1, inner_it=1, print_freq=1, unsup_loss=1, save_sample_freq=None):
def run_simple_smartaug(model, opt_param, epochs=1, inner_it=1, print_freq=1, unsup_loss=1):
"""Simple training of an augmented model with higher.
This function is intended to be used with Augmented_model containing an Higher_model (see dataug.py).
@ -380,13 +380,11 @@ def run_simple_smartaug(model, opt_param, epochs=1, inner_it=1, print_freq=1, un
inner_it (int): Number of inner iteration before a meta-step. 0 inner iteration means there's no meta-step. (default: 1)
print_freq (int): Number of epoch between display of the state of training. If set to None, no display will be done. (default:1)
unsup_loss (float): Proportion of the unsup_loss loss added to the supervised loss. If set to 0, the loss is only computed on augmented inputs. (default: 1)
save_sample_freq (int): Number of epochs between saves of samples of data. If set to None, only one save would be done at the end of the training. (default: None)
Returns:
(list) Logs of training. Each items is a dict containing results of an epoch.
(dict) A dictionary containing a whole state of the trained network.
"""
device = next(model.parameters()).device
log = []
## Optimizers ##
hyper_param = list(model['data_aug'].parameters())
@ -407,55 +405,15 @@ def run_simple_smartaug(model, opt_param, epochs=1, inner_it=1, print_freq=1, un
tf = time.process_time()
if (save_sample_freq and epoch%save_sample_freq==0): #Data sample saving
try:
viz_sample_data(imgs=xs, labels=ys, fig_name='../samples/data_sample_epoch{}_noTF'.format(epoch))
viz_sample_data(imgs=model['data_aug'](xs), labels=ys, fig_name='../samples/data_sample_epoch{}'.format(epoch))
except:
print("Couldn't save samples epoch"+epoch)
pass
val_loss = model._val_loss
# Test model
accuracy, test_loss =test(model)
model.train()
#### Log ####
param = [{'p': p.item(), 'm':model['data_aug']['mag'].item()} for p in model['data_aug']['prob']] if model['data_aug']._shared_mag else [{'p': p.item(), 'm': m.item()} for p, m in zip(model['data_aug']['prob'], model['data_aug']['mag'])]
data={
"epoch": epoch,
"train_loss": loss.item(),
"val_loss": val_loss.item(),
"acc": accuracy,
"time": tf - t0,
"mix_dist": model['data_aug']['mix_dist'].item(),
"param": param,
}
log.append(data)
#############
#### Print ####
if(print_freq and epoch%print_freq==0):
print('-'*9)
print('Epoch : %d/%d'%(epoch,epochs))
print('Time : %.00f'%(tf - t0))
print('Train loss :',loss.item(), '/ val loss', val_loss.item())
print('Accuracy :', max([x["acc"] for x in log]))
print('Data Augmention : {} (Epoch {})'.format(model._data_augmentation, 0))
print('Train loss :',loss.item(), '/ val loss', model.val_loss().item())
if not model['data_aug']._fixed_prob: print('TF Proba :', model['data_aug']['prob'].data)
#print('proba grad',model['data_aug']['prob'].grad)
if not model['data_aug']._fixed_mag: print('TF Mag :', model['data_aug']['mag'].data)
#print('Mag grad',model['data_aug']['mag'].grad)
if not model['data_aug']._fixed_mix: print('Mix:', model['data_aug']['mix_dist'].item())
#print('Reg loss:', model['data_aug'].reg_loss().item())
#############
#Data sample saving
try:
viz_sample_data(imgs=xs, labels=ys, fig_name='../samples/data_sample_epoch{}_noTF'.format(epoch))
viz_sample_data(imgs=model['data_aug'](xs), labels=ys, fig_name='../samples/data_sample_epoch{}'.format(epoch))
except:
print("Couldn't save finals samples")
pass
return log
return model['model'].state_dict()