def get_estimator(level,
num_augment,
epochs=24,
batch_size=512,
max_train_steps_per_epoch=None,
max_eval_steps_per_epoch=None):
assert 0 <= level <= 10, "the level should be between 0 and 10"
train_data, test_data = load_data()
aug_ops = [
OneOf(
Rotate(level=level, inputs="x", outputs="x", mode="train"),
Identity(level=level, inputs="x", outputs="x", mode="train"),
AutoContrast(level=level, inputs="x", outputs="x", mode="train"),
Equalize(level=level, inputs="x", outputs="x", mode="train"),
Posterize(level=level, inputs="x", outputs="x", mode="train"),
Solarize(level=level, inputs="x", outputs="x", mode="train"),
Sharpness(level=level, inputs="x", outputs="x", mode="train"),
Contrast(level=level, inputs="x", outputs="x", mode="train"),
Color(level=level, inputs="x", outputs="x", mode="train"),
Brightness(level=level, inputs="x", outputs="x", mode="train"),
ShearX(level=level, inputs="x", outputs="x", mode="train"),
ShearY(level=level, inputs="x", outputs="x", mode="train"),
TranslateX(level=level, inputs="x", outputs="x", mode="train"),
TranslateY(level=level, inputs="x", outputs="x", mode="train"),
) for _ in range(num_augment)
]
pipeline = fe.Pipeline(train_data=train_data,
test_data=test_data,
batch_size=batch_size,
ops=aug_ops + [
Normalize(inputs="x",
outputs="x",
mean=(0.4914, 0.4822, 0.4465),
std=(0.2471, 0.2435, 0.2616)),
ChannelTranspose(inputs="x", outputs="x"),
])
model = fe.build(model_fn=MyModel, optimizer_fn="adam")
network = fe.Network(ops=[
ModelOp(model=model, inputs="x", outputs="y_pred"),
CrossEntropy(inputs=("y_pred", "y"), outputs="ce"),
UpdateOp(model=model, loss_name="ce")
])
estimator = fe.Estimator(
pipeline=pipeline,
network=network,
epochs=epochs,
traces=Accuracy(true_key="y", pred_key="y_pred"),
max_train_steps_per_epoch=max_train_steps_per_epoch,
max_eval_steps_per_epoch=max_eval_steps_per_epoch)
return estimator
