def testLossCostDecorated(self):
params = {
'trainable': True,
'normalizer_fn': slim.batch_norm,
'normalizer_params': {
'scale': True
}
}
with slim.arg_scope([slim.layers.conv2d], **params):
image = tf.constant(0.0, shape=[1, 1, 1, NUM_CHANNELS])
conv1 = slim.layers.conv2d(image,
2, [1, 1],
padding='SAME',
scope='conv1')
with self.cached_session():
tf.global_variables_initializer().run()
name_to_var = {v.op.name: v for v in tf.global_variables()}
gamma1 = name_to_var['conv1/BatchNorm/gamma']
gamma1.assign([1] * 2).eval()
self.gamma_flop_reg = model_size_regularizer.GammaModelSizeRegularizer(
[conv1.op],
gamma_threshold=0.1,
regularizer_decorator=dummy_decorator.DummyDecorator,
decorator_parameters={'scale': 0.5})
conv = self.get_conv('conv1')
self.assertEqual(_coeff(conv) * 3 * 1, self.loss([conv]))
self.assertEqual(_coeff(conv) * 2 * NUM_CHANNELS, self.cost([conv]))
def testInceptionV2_TotalCost(self):
conv_params = {
'activation_fn': tf.nn.relu6,
'weights_regularizer': contrib_layers.l2_regularizer(0.00004),
'weights_initializer': tf.random_normal_initializer(stddev=0.03),
'trainable': True,
'biases_initializer': tf.constant_initializer(0.0),
'normalizer_fn': contrib_layers.batch_norm,
'normalizer_params': {
'is_training': False,
'decay': 0.9997,
'scale': True,
'epsilon': 0.001,
}
}
tf.reset_default_graph()
with slim.arg_scope([slim.layers.conv2d, slim.layers.separable_conv2d],
**conv_params):
# Build model.
image = tf.zeros([1, 224, 224, 3])
net, _ = inception.inception_v2_base(image)
logits = slim.layers.fully_connected(
net,
1001,
activation_fn=None,
scope='logits',
weights_initializer=tf.random_normal_initializer(stddev=1e-3),
biases_initializer=tf.constant_initializer(0.0))
# Instantiate regularizers.
flop_reg = flop_regularizer.GammaFlopsRegularizer(
[logits.op], gamma_threshold=0.5)
p100_reg = latency_regularizer.GammaLatencyRegularizer(
[logits.op], gamma_threshold=0.5, hardware='P100')
v100_reg = latency_regularizer.GammaLatencyRegularizer(
[logits.op], gamma_threshold=0.5, hardware='V100')
model_size_reg = model_size_regularizer.GammaModelSizeRegularizer(
[logits.op], gamma_threshold=0.5)
with self.cached_session():
tf.global_variables_initializer().run()
# Verify costs are expected.
self.assertAllClose(3.86972e+09, flop_reg.get_cost())
self.assertAllClose(517536.0, p100_reg.get_cost())
self.assertAllClose(173330.453125, v100_reg.get_cost())
self.assertAllClose(1.11684e+07, model_size_reg.get_cost())