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)