def compare_unary_op(self, kk_func, z_layer, shape, rtol=1e-5, atol=1e-5):
x = klayers.Input(shape=shape[1:])
batch = shape[0]
kkresult = kk_func(x)
x_value = np.random.uniform(0, 1, shape)
k_grads = KK.get_session().run(KK.gradients(kkresult, x),
feed_dict={x: x_value})
k_output = KK.get_session().run(kkresult, feed_dict={x: x_value})
model = Sequential()
model.add(InputLayer(shape[1:]))
model.add(z_layer)
z_output = model.forward(x_value)
grad_output = np.array(z_output)
grad_output.fill(1.0)
z_grad = model.backward(x_value, grad_output)
z_output2 = model.forward(x_value)
z_grad2 = model.backward(x_value, grad_output)
self.assert_allclose(z_output, z_output2, rtol, atol)
self.assert_allclose(z_grad, z_grad2, rtol, atol)
self.assert_allclose(z_output, k_output, rtol, atol)
self.assert_allclose(z_grad, k_grads[0], rtol, atol)
def compare_layer(self,
klayer,
zlayer,
input_data,
weight_converter=None,
is_training=False,
rtol=1e-6,
atol=1e-6):
"""
Compare forward results for Keras layer against Zoo Keras API layer.
"""
from keras.models import Sequential as KSequential
from zoo.pipeline.api.keras.models import Sequential as ZSequential
zmodel = ZSequential()
zmodel.add(zlayer)
kmodel = KSequential()
kmodel.add(klayer)
koutput = kmodel.predict(input_data)
from zoo.pipeline.api.keras.layers import BatchNormalization
if isinstance(zlayer, BatchNormalization):
k_running_mean = K.eval(klayer.running_mean)
k_running_std = K.eval(klayer.running_std)
zlayer.set_running_mean(k_running_mean)
zlayer.set_running_std(k_running_std)
if kmodel.get_weights():
zmodel.set_weights(weight_converter(klayer, kmodel.get_weights()))
zmodel.training(is_training)
zoutput = zmodel.forward(input_data)
self.assert_allclose(zoutput, koutput, rtol=rtol, atol=atol)
def test_square_as_first_layer(self):
def z_func(x):
return square(x)
ll = Lambda(function=z_func, input_shape=[2, 3])
seq = Sequential()
seq.add(ll)
result = seq.forward(np.ones([2, 3]))
assert (result == np.ones([2, 3])).all()
def compare_layer(self, klayer, zlayer, input_data, weight_converter=None,
is_training=False, rtol=1e-6, atol=1e-6):
"""
Compare forward results for Keras layer against Zoo Keras API layer.
"""
from keras.models import Sequential as KSequential
from zoo.pipeline.api.keras.models import Sequential as ZSequential
zmodel = ZSequential()
zmodel.add(zlayer)
kmodel = KSequential()
kmodel.add(klayer)
koutput = kmodel.predict(input_data)
from zoo.pipeline.api.keras.layers import BatchNormalization
if isinstance(zlayer, BatchNormalization):
k_running_mean = K.eval(klayer.running_mean)
k_running_std = K.eval(klayer.running_std)
zlayer.set_running_mean(k_running_mean)
zlayer.set_running_std(k_running_std)
if kmodel.get_weights():
zmodel.set_weights(weight_converter(klayer, kmodel.get_weights()))
zmodel.training(is_training)
zoutput = zmodel.forward(input_data)
self.assert_allclose(zoutput, koutput, rtol=rtol, atol=atol)
def predict(model_path, image_path, top_n):
sc = init_nncontext(
"Image classification inference example using int8 quantized model")
images = ImageSet.read(image_path, sc, image_codec=1)
model = ImageClassifier.load_model(model_path)
output = model.predict_image_set(images)
label_map = model.get_config().label_map()
# list of images composing uri and results in tuple format
predicts = output.get_predict().collect()
sequential = Sequential()
sequential.add(Activation("softmax", input_shape=predicts[0][1][0].shape))
for pre in predicts:
(uri, probs) = pre
out = sequential.forward(probs[0])
sortedProbs = [(prob, index) for index, prob in enumerate(out)]
sortedProbs.sort()
print("Image : %s, top %d prediction result" % (uri, top_n))
for i in range(top_n):
print(
"\t%s, %f" %
(label_map[sortedProbs[999 - i][1]], sortedProbs[999 - i][0]))
