def testSeparableConv2DSameWithInputOddSize(self):
n, n2 = 5, 3
# Input image.
x = create_test_input(1, n, n, 1)
# Convolution kernel.
dw = create_test_input(1, 3, 3, 1)
dw = tf.reshape(dw, [3, 3, 1, 1])
tf.get_variable('Conv/depthwise_weights', initializer=dw)
tf.get_variable('Conv/pointwise_weights',
initializer=tf.ones([1, 1, 1, 1]))
tf.get_variable('Conv/biases', initializer=tf.zeros([1]))
tf.get_variable_scope().reuse_variables()
y1 = slim.separable_conv2d(x,
1, [3, 3],
depth_multiplier=1,
stride=1,
scope='Conv')
y1_expected = tf.to_float([[14, 28, 43, 58, 34], [28, 48, 66, 84, 46],
[43, 66, 84, 102,
55], [58, 84, 102, 120, 64],
[34, 46, 55, 64, 30]])
y1_expected = tf.reshape(y1_expected, [1, n, n, 1])
y2 = resnet_utils.subsample(y1, 2)
y2_expected = tf.to_float([[14, 43, 34], [43, 84, 55], [34, 55, 30]])
y2_expected = tf.reshape(y2_expected, [1, n2, n2, 1])
y3 = xception.separable_conv2d_same(x,
1,
3,
depth_multiplier=1,
regularize_depthwise=True,
stride=2,
scope='Conv')
y3_expected = y2_expected
y4 = slim.separable_conv2d(x,
1, [3, 3],
depth_multiplier=1,
stride=2,
scope='Conv')
y4_expected = y2_expected
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
self.assertAllClose(y1.eval(), y1_expected.eval())
self.assertAllClose(y2.eval(), y2_expected.eval())
self.assertAllClose(y3.eval(), y3_expected.eval())
self.assertAllClose(y4.eval(), y4_expected.eval())
def testSeparableConv2DSameWithInputEvenSize(self):
n, n2 = 4, 2
# Input image.
x = create_test_input(1, n, n, 1)
# Convolution kernel =======================================
dw = create_test_input(1, 3, 3, 1)
dw = tf.reshape(dw, [3, 3, 1, 1])
# tf variables for kernel in slim.separable_conv2d()
# enable reuse of tf variables in slim.separable_conv2d()
tf.get_variable('Conv/depthwise_weights', initializer=dw)
tf.get_variable('Conv/pointwise_weights',
initializer=tf.ones([1, 1, 1, 1]))
tf.get_variable('Conv/biases', initializer=tf.zeros([1]))
tf.get_variable_scope().reuse_variables()
#-------------------------------------------------------
# test 1: separable conv 2d of 4x4 input
y1 = slim.separable_conv2d(x,
1, [3, 3],
depth_multiplier=1,
stride=1,
scope='Conv')
# an expected result when n=4 from separable_conv2d()
y1_expected = tf.to_float([[14, 28, 43, 26], [28, 48, 66, 37],
[43, 66, 84, 46], [26, 37, 46, 22]])
# reshape to [n,n] to [1,n,n,1]
y1_expected = tf.reshape(y1_expected, [1, n, n, 1])
#-------------------------------------------------------
# test 2: subsampling to 4x4 to 2x2
# subsampling of test result y from 4x4 to 2x2
y2 = resnet_utils.subsample(y1, 2)
y2_expected = tf.to_float([[14, 43], [43, 84]])
y2_expected = tf.reshape(y2_expected, [1, n2, n2, 1])
#-------------------------------------------------------
# test 3: separable conv 2d of 4x4 input with stride2 and use_explicit_padding= True
y3 = xception.separable_conv2d_same(x,
1,
3,
depth_multiplier=1,
regularize_depthwise=True,
stride=2,
scope='Conv')
y3_expected = y2_expected
#-------------------------------------------------------
# test 4: separable conv 2d of 4x4 input with stride2
y4 = slim.separable_conv2d(x,
1, [3, 3],
depth_multiplier=1,
stride=2,
scope='Conv')
y4_expected = tf.to_float([[48, 37], [37, 22]])
y4_expected = tf.reshape(y4_expected, [1, n2, n2, 1])
# run tests by tf.sess
with self.test_session() as sess:
print('[tf.Test] run testSeparableConv2DSameWithInputEvenSize()')
sess.run(tf.global_variables_initializer())
self.assertAllClose(y1.eval(), y1_expected.eval())
self.assertAllClose(y2.eval(), y2_expected.eval())
self.assertAllClose(y3.eval(), y3_expected.eval())
self.assertAllClose(y4.eval(), y4_expected.eval())