def test_static_parameterizer(self):
shape = (1, 2, 3, 4)
var = self._test_parameterizer(
parameterizers.StaticParameterizer(tf.initializers.zeros()),
tf.initializers.random_uniform(), shape)
self.assertEqual(var.shape, shape)
self.assertAllClose(var, np.zeros(shape), rtol=0, atol=1e-7)
def run_same(self, batch, input_support, channels, filters, kernel_support,
corr, strides_down, strides_up, padding, extra_pad_end,
channel_separable, data_format, activation, use_bias):
assert channels == filters == 1
# Create input array.
input_shape = (batch, 1) + input_support
inputs = np.arange(np.prod(input_shape))
inputs = inputs.reshape(input_shape).astype(np.float32)
if data_format != "channels_first":
tf_inputs = tf.constant(np.moveaxis(inputs, 1, -1))
else:
tf_inputs = tf.constant(inputs)
# Create kernel array. This is an identity kernel, so the outputs should
# be equal to the inputs except for up- and downsampling.
tf_kernel = parameterizers.StaticParameterizer(
initializers.IdentityInitializer())
# Run SignalConv* layer.
layer_class = {
3: signal_conv.SignalConv1D,
4: signal_conv.SignalConv2D,
5: signal_conv.SignalConv3D,
}[inputs.ndim]
layer = layer_class(1,
kernel_support,
corr=corr,
strides_down=strides_down,
strides_up=strides_up,
padding=padding,
extra_pad_end=extra_pad_end,
channel_separable=channel_separable,
data_format=data_format,
activation=activation,
use_bias=use_bias,
kernel_parameterizer=tf_kernel)
tf_outputs = layer(tf_inputs)
with self.cached_session() as sess:
sess.run(tf.global_variables_initializer())
outputs = sess.run(tf_outputs)
# Check that SignalConv* computes the correct output size.
predicted_shape = layer.compute_output_shape(tf_inputs.shape)
self.assertEqual(outputs.shape, tuple(predicted_shape.as_list()))
# If not using channels_first, convert back to it to compare to input.
if data_format != "channels_first":
outputs = np.moveaxis(outputs, -1, 1)
# Upsample and then downsample inputs.
expected = inputs
if not all(s == 1 for s in strides_up):
expected = self.numpy_upsample(expected, strides_up, extra_pad_end)
slices = (slice(None), slice(None))
slices += tuple(slice(None, None, s) for s in strides_down)
expected = expected[slices]
self.assertAllClose(expected, outputs, rtol=0, atol=1e-3)
def run_valid(self, batch, input_support, channels, filters,
kernel_support, corr, strides_down, strides_up, padding,
extra_pad_end, channel_separable, data_format, activation,
use_bias):
assert padding == "valid"
# Create input array.
inputs = np.random.randint(32, size=(batch, channels) + input_support)
inputs = inputs.astype(np.float32)
if data_format != "channels_first":
tf_inputs = tf.constant(np.moveaxis(inputs, 1, -1))
else:
tf_inputs = tf.constant(inputs)
# Create kernel array.
kernel = np.random.randint(16,
size=kernel_support + (channels, filters))
kernel = kernel.astype(np.float32)
tf_kernel = parameterizers.StaticParameterizer(
tf.constant_initializer(kernel))
# Run SignalConv* layer.
layer_class = {
3: signal_conv.SignalConv1D,
4: signal_conv.SignalConv2D,
5: signal_conv.SignalConv3D,
}[inputs.ndim]
layer = layer_class(filters,
kernel_support,
corr=corr,
strides_down=strides_down,
strides_up=strides_up,
padding="valid",
extra_pad_end=extra_pad_end,
channel_separable=channel_separable,
data_format=data_format,
activation=activation,
use_bias=use_bias,
kernel_parameterizer=tf_kernel)
tf_outputs = layer(tf_inputs)
with self.cached_session() as sess:
sess.run(tf.global_variables_initializer())
outputs = sess.run(tf_outputs)
# Check that SignalConv* computes the correct output size.
predicted_shape = layer.compute_output_shape(tf_inputs.shape)
self.assertEqual(outputs.shape, tuple(predicted_shape.as_list()))
# If not using channels_first, convert back to it to compare to SciPy.
if data_format != "channels_first":
outputs = np.moveaxis(outputs, -1, 1)
# Compute the equivalent result using SciPy and compare.
expected = self.scipy_convolve_valid(corr, inputs, kernel,
strides_down, strides_up,
extra_pad_end, channel_separable)
self.assertAllClose(expected, outputs, rtol=0, atol=1e-3)
