def testSeparableConv1DPaddingSame(self):
length = 9
data = random_ops.random_uniform((5, length, 32), seed=1)
layer = conv_layers.SeparableConv1D(
64, length, padding='same')
output = layer.apply(data)
self.assertEqual(output.get_shape().as_list(), [5, length, 64])
def testSeparableConv1DNoBias(self):
length = 9
data = random_ops.random_uniform((5, length, 4))
layer = conv_layers.SeparableConv1D(
32, 3, activation=nn_ops.relu, use_bias=False)
output = layer.apply(data)
self.assertEqual(output.op.name, 'separable_conv1d/Relu')
self.assertEqual(layer.bias, None)
def testCreateSeparableConv1DWithStridesChannelsFirst(self):
data_format = 'channels_first'
length = 10
data = random_ops.random_uniform((5, 3, length), seed=1)
layer = conv_layers.SeparableConv1D(
32, 3, strides=2, padding='same', data_format=data_format)
output = layer.apply(data)
self.assertEqual(output.get_shape().as_list(), [5, 32, length // 2])
def testSeparableConv1DBiasRegularizer(self): length = 9 data = random_ops.random_uniform((5, length, 4)) reg = lambda x: 0.1 * math_ops.reduce_sum(x) layer = conv_layers.SeparableConv1D(32, 3, bias_regularizer=reg) layer.apply(data) loss_keys = ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES) self.assertEqual(len(loss_keys), 1) self.assertEqual(layer.losses, loss_keys)
def testCreateSeparableConv1DChannelsFirst(self): length = 9 data = random_ops.random_uniform((5, 4, length)) layer = conv_layers.SeparableConv1D(32, 3, data_format='channels_first') output = layer.apply(data) self.assertEqual(output.get_shape().as_list(), [5, 32, length - 2]) self.assertEqual(layer.depthwise_kernel.get_shape().as_list(), [3, 4, 1]) self.assertEqual(layer.pointwise_kernel.get_shape().as_list(), [1, 4, 32]) self.assertEqual(layer.bias.get_shape().as_list(), [32])
def testCreateSeparableConv1DDepthMultiplier(self): length = 9 data = random_ops.random_uniform((5, length, 4)) layer = conv_layers.SeparableConv1D(32, 3, depth_multiplier=2) output = layer.apply(data) self.assertEqual(output.get_shape().as_list(), [5, length - 2, 32]) self.assertEqual(layer.depthwise_kernel.get_shape().as_list(), [3, 4, 2]) self.assertEqual(layer.pointwise_kernel.get_shape().as_list(), [1, 8, 32]) self.assertEqual(layer.bias.get_shape().as_list(), [32])
def testCreateSeparableConv1D(self): length = 9 data = random_ops.random_uniform((5, length, 4)) layer = conv_layers.SeparableConv1D(32, 3, activation=nn_ops.relu) output = layer.apply(data) self.assertEqual(output.op.name, 'separable_conv1d/Relu') self.assertEqual(output.get_shape().as_list(), [5, length - 2, 32]) self.assertEqual(layer.depthwise_kernel.get_shape().as_list(), [3, 4, 1]) self.assertEqual(layer.pointwise_kernel.get_shape().as_list(), [1, 4, 32]) self.assertEqual(layer.bias.get_shape().as_list(), [32])
def testConstraints(self):
d_constraint = lambda x: x / math_ops.reduce_sum(x)
p_constraint = lambda x: x / math_ops.reduce_sum(x)
b_constraint = lambda x: x / math_ops.reduce_max(x)
layer = conv_layers.SeparableConv1D(2, 3,
depthwise_constraint=d_constraint,
pointwise_constraint=p_constraint,
bias_constraint=b_constraint)
inputs = random_ops.random_uniform((5, 3, 5), seed=1)
layer(inputs)
self.assertEqual(layer.depthwise_constraint, d_constraint)
self.assertEqual(layer.pointwise_constraint, p_constraint)
self.assertEqual(layer.bias_constraint, b_constraint)
def testCreateSeparableConv1DWithStrides(self): length = 10 data = random_ops.random_uniform((5, length, 3), seed=1) layer = conv_layers.SeparableConv1D(32, 3, strides=2, padding='same') output = layer.apply(data) self.assertEqual(output.get_shape().as_list(), [5, length // 2, 32])
