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.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, 26], [28, 48, 66, 37],
[43, 66, 84, 46], [26, 37, 46, 22]])
y1_expected = tf.reshape(y1_expected, [1, n, n, 1])
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])
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 = tf.to_float([[48, 37], [37, 22]])
y4_expected = tf.reshape(y4_expected, [1, n2, n2, 1])
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())
print("y1:%s, y1_expected:%s " % (y1.eval(), y1_expected.eval()))
print("y2:%s, y2_expected:%s " % (y2.eval(), y2_expected.eval()))
print("y3:%s, y3_expected:%s " % (y3.eval(), y3_expected.eval()))
print("y4:%s, y4_expected:%s " % (y4.eval(), y4_expected.eval()))
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.compat.v1.get_variable_scope().reuse_variables()
y1 = slim.separable_conv2d(x,
1, [3, 3],
depth_multiplier=1,
stride=1,
scope='Conv')
y1_expected = tf.cast(
[[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]], tf.float32)
y1_expected = tf.reshape(y1_expected, [1, n, n, 1])
y2 = resnet_utils.subsample(y1, 2)
y2_expected = tf.cast([[14, 43, 34], [43, 84, 55], [34, 55, 30]],
tf.float32)
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 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())
