def test_tf_static_superloss_categorical_ce(self):
sl = SuperLoss(CrossEntropy(inputs=['y_pred', 'y'], outputs='ce'))
sl.build(framework="tf", device=None)
output = self.do_forward(sl,
data=[self.tf_pred_cat, self.tf_true_cat],
state=self.state)
self.assertTrue(np.allclose(output.numpy(), -0.0026386082))
def test_tf_static_superloss_hinge(self):
true = tf.constant([[-1, 1, 1, -1], [1, 1, 1, 1], [-1, -1, 1, -1], [1, -1, -1, -1]])
pred = tf.constant([[0.1, 0.9, 0.05, 0.05], [0.1, -0.2, 0.0, -0.7], [0.0, 0.15, 0.8, 0.05],
[1.0, -1.0, -1.0, -1.0]])
sl = SuperLoss(Hinge(inputs=('x1', 'x2'), outputs='x'))
sl.build('tf', None)
output = self.do_forward(sl, data=[pred, true], state=self.state)
self.assertTrue(np.allclose(output.numpy(), -0.072016776))
def test_torch_superloss_hinge(self):
true = torch.tensor([[-1, 1, 1, -1], [1, 1, 1, 1], [-1, -1, 1, -1],
[1, -1, -1, -1]]).to("cuda:0" if torch.cuda.is_available() else "cpu")
pred = torch.tensor([[0.1, 0.9, 0.05, 0.05], [0.1, -0.2, 0.0, -0.7], [0.0, 0.15, 0.8, 0.05],
[1.0, -1.0, -1.0, -1.0]]).to("cuda:0" if torch.cuda.is_available() else "cpu")
sl = SuperLoss(Hinge(inputs=('x1', 'x2'), outputs='x'))
sl.build('torch', "cuda:0" if torch.cuda.is_available() else "cpu")
output = sl.forward(data=[pred, true], state=self.state)
self.assertTrue(np.allclose(output.to("cpu").numpy(), -0.072016776))
def test_torch_superloss_binary_ce(self):
sl = SuperLoss(CrossEntropy(inputs=['y_pred', 'y'], outputs='ce'))
sl.build(framework="torch",
device="cuda:0" if torch.cuda.is_available() else "cpu")
output = sl.forward(
data=[self.torch_pred_binary, self.torch_true_binary],
state=self.state)
self.assertTrue(
np.allclose(output.detach().to("cpu").numpy(), -0.0026238672))
def instantiate_system():
system = sample_system_object()
model = fe.build(model_fn=fe.architecture.pytorch.LeNet, optimizer_fn='adam', model_name='tf')
system.network = fe.Network(ops=[
ModelOp(model=model, inputs="x_out", outputs="y_pred"),
SuperLoss(CrossEntropy(inputs=['y_pred', 'y'], outputs='ce'))
])
return system
def get_estimator(epochs=50,
batch_size=128,
max_train_steps_per_epoch=None,
max_eval_steps_per_epoch=None,
save_dir=tempfile.mkdtemp()):
# step 1
train_data, eval_data = cifair100.load_data()
# Add label noise to simulate real-world labeling problems
corrupt_dataset(train_data)
test_data = eval_data.split(range(len(eval_data) // 2))
pipeline = fe.Pipeline(
train_data=train_data,
eval_data=eval_data,
test_data=test_data,
batch_size=batch_size,
ops=[
Normalize(inputs="x", outputs="x", mean=(0.4914, 0.4822, 0.4465), std=(0.2471, 0.2435, 0.2616)),
PadIfNeeded(min_height=40, min_width=40, image_in="x", image_out="x", mode="train"),
RandomCrop(32, 32, image_in="x", image_out="x", mode="train"),
Sometimes(HorizontalFlip(image_in="x", image_out="x", mode="train")),
CoarseDropout(inputs="x", outputs="x", mode="train", max_holes=1),
ChannelTranspose(inputs="x", outputs="x")
])
# step 2
model = fe.build(model_fn=big_lenet, optimizer_fn='adam')
network = fe.Network(ops=[
ModelOp(model=model, inputs="x", outputs="y_pred"),
SuperLoss(CrossEntropy(inputs=("y_pred", "y"), outputs="ce"), output_confidence="confidence"),
UpdateOp(model=model, loss_name="ce")
])
# step 3
traces = [
MCC(true_key="y", pred_key="y_pred"),
BestModelSaver(model=model, save_dir=save_dir, metric="mcc", save_best_mode="max", load_best_final=True),
LabelTracker(metric="confidence",
label="data_labels",
label_mapping={
"Normal": 0, "Corrupted": 1
},
mode="train",
outputs="label_confidence"),
ImageSaver(inputs="label_confidence", save_dir=save_dir, mode="train"),
]
estimator = fe.Estimator(pipeline=pipeline,
network=network,
epochs=epochs,
traces=traces,
max_train_steps_per_epoch=max_train_steps_per_epoch,
max_eval_steps_per_epoch=max_eval_steps_per_epoch)
return estimator
def test_tf_superloss_sparse_categorical_ce(self):
sl = SuperLoss(CrossEntropy(inputs=['y_pred', 'y'], outputs='ce'))
sl.build(framework="tf", device=None)
output = sl.forward(data=[self.tf_pred_sparse, self.tf_true_sparse], state=self.state)
self.assertTrue(np.allclose(output.numpy(), -0.024740249))