Merge branch 'unit-tests-for-channel-sparse-convolution' into 'dev'

Added shape tests for channel sparse convolution

See merge request CMIC/NiftyNet!211

niftynet/layer/channel_sparse_convolution.py

@@ -150,10 +150,17 @@ def layer_op(self,input_tensor,input_mask,output_mask):
         'w', shape=w_full_size,
         initializer=self.initializers['w'],
         regularizer=self.regularizers['w'])
+    assert(spatial_rank in [2,3])
+    if spatial_rank==2:
+        transpositions = [[3,2,1,0],[1,0,2,3],[3,2,0,1]]
+    else:
+        transpositions = [[4,3,2,1,0],[1,0,2,3,4],[4,3,2,0,1]]
+
     sparse_kernel = tf.transpose(tf.boolean_mask(
                        tf.transpose(tf.boolean_mask(
-                         tf.transpose(conv_kernel,[4,3,2,1,0]),
-                         _output_mask),[1,0,2,3,4]),_input_mask),[4,3,2,0,1])
+                         tf.transpose(conv_kernel,transpositions[0]),
+                         _output_mask),transpositions[1]),_input_mask),
+                         transpositions[2])
     output_tensor = tf.nn.convolution(input=input_tensor,
                                       filter=sparse_kernel,
                                       strides=full_stride,

tests/channel_sparse_convolution_test.py

@@ -0,0 +1,73 @@
+from __future__ import absolute_import, print_function
+
+import tensorflow as tf
+import numpy as np
+
+from niftynet.layer.channel_sparse_convolution import ChannelSparseConvolutionalLayer
+
+class ChannelSparseConvolutionalLayerTest(tf.test.TestCase):
+    def test_3d_shape(self):
+        x = tf.random_normal(shape=[2,4,5,6,4])
+        conv1 = ChannelSparseConvolutionalLayer(4)
+        conv2 = ChannelSparseConvolutionalLayer(8,kernel_size=[1,1,3])
+        conv3 = ChannelSparseConvolutionalLayer(4, acti_func='relu')
+        conv4 = ChannelSparseConvolutionalLayer(8, with_bn=False)
+        conv5 = ChannelSparseConvolutionalLayer(4, with_bias=True)
+        x1, mask1=conv1(x, None, True, 1.)
+        x2, mask2=conv2(x1, mask1, True, 1.)
+        x3, mask3=conv3(x2, mask2, True, .5)
+        x4, mask4=conv4(x3, mask3, True, .75)
+        x5, mask5=conv5(x4, mask4, True, 1.)
+
+        with self.test_session() as sess:
+            sess.run(tf.global_variables_initializer())
+            out1, out2, out3, out4, out5 = sess.run([x1,x2,x3,x4,x5])
+        self.assertAllClose([2,4,5,6,4], out1.shape)
+        self.assertAllClose([2,4,5,6,8], out2.shape)
+        self.assertAllClose([2,4,5,6,2], out3.shape)
+        self.assertAllClose([2,4,5,6,6], out4.shape)
+        self.assertAllClose([2,4,5,6,4], out5.shape)
+
+    def test_2d_shape(self):
+        x = tf.random_normal(shape=[2,4,5,4])
+        conv1 = ChannelSparseConvolutionalLayer(4)
+        conv2 = ChannelSparseConvolutionalLayer(8,kernel_size=[1,1,3])
+        conv3 = ChannelSparseConvolutionalLayer(4, acti_func='relu')
+        conv4 = ChannelSparseConvolutionalLayer(8, with_bn=False)
+        conv5 = ChannelSparseConvolutionalLayer(4, with_bias=True)
+        x1, mask1=conv1(x, None, True, 1.)
+        x2, mask2=conv2(x1, mask1, True, 1.)
+        x3, mask3=conv3(x2, mask2, True, .5)
+        x4, mask4=conv4(x3, mask3, True, .75)
+        x5, mask5=conv5(x4, mask4, True, 1.)
+
+        with self.test_session() as sess:
+            sess.run(tf.global_variables_initializer())
+            out1, out2, out3, out4, out5 = sess.run([x1,x2,x3,x4,x5])
+        self.assertAllClose([2,4,5,4], out1.shape)
+        self.assertAllClose([2,4,5,8], out2.shape)
+        self.assertAllClose([2,4,5,2], out3.shape)
+        self.assertAllClose([2,4,5,6], out4.shape)
+        self.assertAllClose([2,4,5,4], out5.shape)
+
+    def test_masks(self):
+        x = tf.random_normal(shape=[2,4,5,4])
+        conv1 = ChannelSparseConvolutionalLayer(10)
+        conv2 = ChannelSparseConvolutionalLayer(10)
+        conv3 = ChannelSparseConvolutionalLayer(10)
+        conv4 = ChannelSparseConvolutionalLayer(10)
+        x1, mask1=conv1(x, None, True, 1.)
+        x2, mask2=conv2(x1, mask1, True, .5)
+        x3, mask3=conv3(x2, mask2, True, .2)
+        x4, mask4=conv4(x3, mask3, True, 1.)
+
+        with self.test_session() as sess:
+            sess.run(tf.global_variables_initializer())
+            out1, out2, out3, out4 = sess.run([mask1, mask2, mask3, mask4])
+        self.assertAllClose([10, 5, 2, 10], [np.sum(out1),
+                                             np.sum(out2),
+                                             np.sum(out3),
+                                             np.sum(out4)])
+
+if __name__ == "__main__":
+    tf.test.main()