def test_model_backward(num_classes):
bounds = (0, 255)
channels = num_classes
def mean_brightness_net(images):
logits = mx.symbol.mean(images, axis=(2, 3))
return logits
images = mx.symbol.Variable('images')
logits = mean_brightness_net(images)
model = MXNetModel(
images,
logits,
{},
ctx=mx.cpu(),
num_classes=num_classes,
bounds=bounds,
channel_axis=1)
test_image = np.random.rand(channels, 5, 5).astype(np.float32)
test_grad_pre = np.random.rand(num_classes).astype(np.float32)
test_grad = model.backward_one(test_grad_pre, test_image)
assert test_grad.shape == test_image.shape
manual_grad = np.repeat(np.repeat(
(test_grad_pre / 25.).reshape((-1, 1, 1)),
5, axis=1), 5, axis=2)
np.testing.assert_almost_equal(
test_grad,
manual_grad)
def test_model_backward(num_classes):
bounds = (0, 255)
channels = num_classes
def mean_brightness_net(images):
logits = mx.symbol.mean(images, axis=(2, 3))
return logits
images = mx.symbol.Variable('images')
logits = mean_brightness_net(images)
model = MXNetModel(
images,
logits,
{},
ctx=mx.cpu(),
num_classes=num_classes,
bounds=bounds,
channel_axis=1)
test_image = np.random.rand(channels, 5, 5).astype(np.float32)
test_grad_pre = np.random.rand(num_classes).astype(np.float32)
test_grad = model.backward(test_grad_pre, test_image)
assert test_grad.shape == test_image.shape
manual_grad = np.repeat(np.repeat(
(test_grad_pre / 25.).reshape((-1, 1, 1)),
5, axis=1), 5, axis=2)
np.testing.assert_almost_equal(
test_grad,
manual_grad)
def test_model_forward_gradient(num_classes):
bounds = (0, 255)
channels = num_classes
def mean_brightness_net(images):
logits = mx.symbol.mean(images, axis=(2, 3))
return logits
images = mx.symbol.Variable("images")
logits = mean_brightness_net(images)
preprocessing = (
np.arange(num_classes)[:, None, None],
np.random.uniform(size=(channels, 5, 5)) + 1,
)
model = MXNetModel(
images,
logits,
{},
ctx=mx.cpu(),
num_classes=num_classes,
bounds=bounds,
preprocessing=preprocessing,
channel_axis=1,
)
test_images = np.random.rand(5, channels, 5, 5).astype(np.float32)
test_labels = [7] * 5
epsilon = 1e-2
_, g1 = model.forward_and_gradient(test_images, test_labels)
l1 = model._loss_fn(test_images - epsilon / 2 * g1, test_labels)
l2 = model._loss_fn(test_images + epsilon / 2 * g1, test_labels)
assert np.all(1e4 * (l2 - l1) > 1)
# make sure that gradient is numerically correct
np.testing.assert_array_almost_equal(
1e4 * (l2 - l1),
1e4 * epsilon *
(np.linalg.norm(g1.reshape(len(g1), -1), axis=(-1))**2).sum(),
decimal=1,
)
def test_model_gradient(num_classes):
bounds = (0, 255)
channels = num_classes
def mean_brightness_net(images):
logits = mx.symbol.mean(images, axis=(2, 3))
return logits
images = mx.symbol.Variable('images')
logits = mean_brightness_net(images)
preprocessing = (np.arange(num_classes)[:, None, None],
np.random.uniform(size=(channels, 5, 5)) + 1)
model = MXNetModel(
images,
logits,
{},
ctx=mx.cpu(),
num_classes=num_classes,
bounds=bounds,
preprocessing=preprocessing,
channel_axis=1)
test_images = np.random.rand(2, channels, 5, 5).astype(np.float32)
test_image = test_images[0]
test_label = 7
epsilon = 1e-2
_, g1 = model.predictions_and_gradient(test_image, test_label)
l1 = model._loss_fn(test_image - epsilon / 2 * g1, test_label)
l2 = model._loss_fn(test_image + epsilon / 2 * g1, test_label)
assert 1e4 * (l2 - l1) > 1
# make sure that gradient is numerically correct
np.testing.assert_array_almost_equal(
1e4 * (l2 - l1),
1e4 * epsilon * np.linalg.norm(g1)**2,
decimal=1)
def test_model_gradient(num_classes):
bounds = (0, 255)
channels = num_classes
def mean_brightness_net(images):
logits = mx.symbol.mean(images, axis=(2, 3))
return logits
images = mx.symbol.Variable('images')
logits = mean_brightness_net(images)
preprocessing = (np.arange(num_classes)[:, None, None],
np.random.uniform(size=(channels, 5, 5)) + 1)
model = MXNetModel(
images,
logits,
{},
ctx=mx.cpu(),
num_classes=num_classes,
bounds=bounds,
preprocessing=preprocessing,
channel_axis=1)
test_images = np.random.rand(2, channels, 5, 5).astype(np.float32)
test_image = test_images[0]
test_label = 7
epsilon = 1e-2
_, g1 = model.forward_and_gradient_one(test_image, test_label)
l1 = model._loss_fn(test_image - epsilon / 2 * g1, test_label)
l2 = model._loss_fn(test_image + epsilon / 2 * g1, test_label)
assert 1e4 * (l2 - l1) > 1
# make sure that gradient is numerically correct
np.testing.assert_array_almost_equal(
1e4 * (l2 - l1),
1e4 * epsilon * np.linalg.norm(g1)**2,
decimal=1)
def test_model(num_classes):
bounds = (0, 255)
channels = num_classes
def mean_brightness_net(images):
logits = mx.symbol.mean(images, axis=(2, 3))
return logits
images = mx.symbol.Variable('images')
logits = mean_brightness_net(images)
model = MXNetModel(
images,
logits,
{},
ctx=mx.cpu(),
num_classes=num_classes,
bounds=bounds,
channel_axis=1)
test_images = np.random.rand(2, channels, 5, 5).astype(np.float32)
test_label = 7
# Tests
assert model.forward(test_images).shape \
== (2, num_classes)
test_logits = model.forward_one(test_images[0])
assert test_logits.shape == (num_classes,)
test_gradient = model.gradient_one(test_images[0], test_label)
assert test_gradient.shape == test_images[0].shape
np.testing.assert_almost_equal(
model.forward_and_gradient_one(test_images[0], test_label)[0],
test_logits)
np.testing.assert_almost_equal(
model.forward_and_gradient_one(test_images[0], test_label)[1],
test_gradient)
assert model.num_classes() == num_classes
def test_model(num_classes):
bounds = (0, 255)
channels = num_classes
def mean_brightness_net(images):
logits = mx.symbol.mean(images, axis=(2, 3))
return logits
images = mx.symbol.Variable('images')
logits = mean_brightness_net(images)
model = MXNetModel(
images,
logits,
{},
ctx=mx.cpu(),
num_classes=num_classes,
bounds=bounds,
channel_axis=1)
test_images = np.random.rand(2, channels, 5, 5).astype(np.float32)
test_label = 7
# Tests
assert model.batch_predictions(test_images).shape \
== (2, num_classes)
test_logits = model.predictions(test_images[0])
assert test_logits.shape == (num_classes,)
test_gradient = model.gradient(test_images[0], test_label)
assert test_gradient.shape == test_images[0].shape
np.testing.assert_almost_equal(
model.predictions_and_gradient(test_images[0], test_label)[0],
test_logits)
np.testing.assert_almost_equal(
model.predictions_and_gradient(test_images[0], test_label)[1],
test_gradient)
assert model.num_classes() == num_classes
