AntoineH/smart_augmentation
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Cross Validation splits + New mesure process time (train utils)

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Harle, Antoine (Contracteur) 2020-02-03 15:08:22 -05:00
parent bce882de38
commit 385bc9977c
3 changed files with 51 additions and 30 deletions
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20
higher/smart_aug/train_utils.py
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@ -150,7 +150,7 @@ def train_classic(model, opt_param, epochs=1, print_freq=1):
log = []
for epoch in range(epochs):
#print_torch_mem("Start epoch")
t0 = time.process_time()
t0 = time.perf_counter()
for i, (features, labels) in enumerate(dl_train):
#viz_sample_data(imgs=features, labels=labels, fig_name='../samples/data_sample_epoch{}_noTF'.format(epoch))
#print_torch_mem("Start iter")
@ -164,7 +164,7 @@ def train_classic(model, opt_param, epochs=1, print_freq=1):
optim.step()
#### Tests ####
tf = time.process_time()
tf = time.perf_counter()
val_loss = compute_vaLoss(model=model, dl_it=dl_val_it, dl=dl_val)
accuracy, f1 =test(model)
@ -176,8 +176,8 @@ def train_classic(model, opt_param, epochs=1, print_freq=1):
print('Epoch : %d/%d'%(epoch,epochs))
print('Time : %.00f'%(tf - t0))
print('Train loss :',loss.item(), '/ val loss', val_loss.item())
print('Accuracy :', accuracy)
print('F1 :', f1.data)
print('Accuracy max:', accuracy)
print('F1 :', f1)
#### Log ####
data={
@ -219,7 +219,7 @@ def run_dist_dataugV3(model, opt_param, epochs=1, inner_it=1, dataug_epoch_start
"""
device = next(model.parameters()).device
log = []
dl_val_it = iter(dl_val)
#dl_val_it = iter(dl_val)
val_loss=None
high_grad_track = True
@ -251,8 +251,11 @@ def run_dist_dataugV3(model, opt_param, epochs=1, inner_it=1, dataug_epoch_start
meta_opt.zero_grad()
for epoch in range(1, epochs+1):
t0 = time.process_time()
t0 = time.perf_counter()
dl_train, dl_val = next_CVSplit()
dl_val_it = iter(dl_val)
for i, (xs, ys) in enumerate(dl_train):
xs, ys = xs.to(device), ys.to(device)
@ -303,7 +306,7 @@ def run_dist_dataugV3(model, opt_param, epochs=1, inner_it=1, dataug_epoch_start
#diffopt.detach_()
model['model'].detach_()
tf = time.process_time()
tf = time.perf_counter()
if (save_sample_freq and epoch%save_sample_freq==0): #Data sample saving
try:
@ -345,7 +348,8 @@ def run_dist_dataugV3(model, opt_param, epochs=1, inner_it=1, dataug_epoch_start
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('Accuracy max:', max([x["acc"] for x in log]))
print('F1 :', f1)
print('Data Augmention : {} (Epoch {})'.format(model._data_augmentation, dataug_epoch_start))
if not model['data_aug']._fixed_prob: print('TF Proba :', model['data_aug']['prob'].data)
#print('proba grad',model['data_aug']['prob'].grad)