def testMultipleConvMaskAdded(self):
number_of_layers = 5
kernel_size = 3
base_depth = 4
depth_step = 7
input_tensor = array_ops.ones((8, self.height, self.width, base_depth))
top_layer = input_tensor
for ix in range(number_of_layers):
top_layer = layers.masked_conv2d(
top_layer, base_depth + (ix + 1) * depth_step, kernel_size)
masks = ops.get_collection(core_layers.MASK_COLLECTION)
self.assertEqual(len(masks), number_of_layers)
for ix in range(number_of_layers):
self.assertListEqual(masks[ix].get_shape().as_list(), [
kernel_size, kernel_size, base_depth + ix * depth_step,
base_depth + (ix + 1) * depth_step
])
masked_weight = ops.get_collection(
core_layers.MASKED_WEIGHT_COLLECTION)
self.assertEqual(len(masked_weight), number_of_layers)
for ix in range(number_of_layers):
self.assertListEqual(masked_weight[ix].get_shape().as_list(), [
kernel_size, kernel_size, base_depth + ix * depth_step,
base_depth + (ix + 1) * depth_step
])
def testSingleConvMaskAdded(self):
kernel_size = 3
input_depth, output_depth = 8, 32
input_tensor = array_ops.ones(
(8, self.height, self.width, input_depth))
layers.masked_conv2d(input_tensor, output_depth, kernel_size)
masks = ops.get_collection(core_layers.MASK_COLLECTION)
self.assertEqual(len(masks), 1)
self.assertListEqual(
masks[0].get_shape().as_list(),
[kernel_size, kernel_size, input_depth, output_depth])
masked_weight = ops.get_collection(
core_layers.MASKED_WEIGHT_COLLECTION)
self.assertEqual(len(masked_weight), 1)
self.assertListEqual(
masked_weight[0].get_shape().as_list(),
[kernel_size, kernel_size, input_depth, output_depth])
def _build_convolutional_model(self, number_of_layers):
# Create a graph with several conv2d layers
kernel_size = 3
base_depth = 4
depth_step = 7
height, width = 7, 9
with variable_scope.variable_scope("conv_model"):
input_tensor = array_ops.ones((8, height, width, base_depth))
top_layer = input_tensor
for ix in range(number_of_layers):
top_layer = layers.masked_conv2d(
top_layer,
base_depth + (ix + 1) * depth_step,
kernel_size,
scope="Conv_" + str(ix))
return top_layer
def testInvalidRank5(self):
input_tensor = array_ops.ones((8, 8, self.height, self.width, 3))
with self.assertRaisesRegexp(ValueError, 'rank'):
layers.masked_conv2d(input_tensor, 32, 3)