AntoineH/smart_augmentation
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Confmat / F1 + Minor fix

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Harle, Antoine (Contracteur) 2020-01-31 16:43:10 -05:00
parent 250ce2c3cf
commit 3ccacd0366
5 changed files with 120 additions and 32 deletions
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30
higher/smart_aug/train_utils.py
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@ -10,6 +10,8 @@ import higher
from datasets import *
from utils import *
confmat = ConfusionMatrix(num_classes=len(dl_test.dataset.classes))
def test(model):
"""Evaluate a model on test data.
@ -17,7 +19,7 @@ def test(model):
model (nn.Module): Model to test.
Returns:
(float, Tensor) Returns the accuracy and test loss of the model.
(float, Tensor) Returns the accuracy and F1 score of the model.
"""
device = next(model.parameters()).device
model.eval()
@ -30,7 +32,8 @@ def test(model):
correct = 0
total = 0
loss = []
#loss = []
confmat.reset()
with torch.no_grad():
for features, labels in dl_test:
features,labels = features.to(device), labels.to(device)
@ -40,11 +43,16 @@ def test(model):
total += labels.size(0)
correct += (predicted == labels).sum().item()
loss.append(F.cross_entropy(outputs, labels).item())
#loss.append(F.cross_entropy(outputs, labels).item())
confmat.update(labels, predicted)
accuracy = 100 * correct / total
return accuracy, np.mean(loss)
#print(confmat)
#from sklearn.metrics import f1_score
#f1 = f1_score(labels.data.to('cpu'), predicted.data.to('cpu'), average="macro")
return accuracy, confmat.f1_metric(average="macro")
def compute_vaLoss(model, dl_it, dl):
"""Evaluate a model on a batch of data.
@ -202,7 +210,7 @@ def run_dist_dataugV3(model, opt_param, epochs=1, inner_it=1, dataug_epoch_start
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)
hp_opt (bool): Wether to learn inner optimizer parameters. (default: False)
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)
save_sample_freq (int): Number of epochs between saves of samples of data. If set to None, no sample will be saved. (default: None)
Returns:
(list) Logs of training. Each items is a dict containing results of an epoch.
@ -310,7 +318,7 @@ def run_dist_dataugV3(model, opt_param, epochs=1, inner_it=1, dataug_epoch_start
val_loss = compute_vaLoss(model=model, dl_it=dl_val_it, dl=dl_val)
# Test model
accuracy, test_loss =test(model)
accuracy, f1 =test(model)
model.train()
#### Log ####
@ -320,6 +328,7 @@ def run_dist_dataugV3(model, opt_param, epochs=1, inner_it=1, dataug_epoch_start
"train_loss": loss.item(),
"val_loss": val_loss.item(),
"acc": accuracy,
"f1": f1.cpu().numpy().tolist(),
"time": tf - t0,
"param": param,
@ -360,15 +369,6 @@ def run_dist_dataugV3(model, opt_param, epochs=1, inner_it=1, dataug_epoch_start
grad_callback=(lambda grads: clip_norm(grads, max_norm=10)),
track_higher_grads=high_grad_track)
#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
def run_simple_smartaug(model, opt_param, epochs=1, inner_it=1, print_freq=1, unsup_loss=1):