def testCreatePooling2DWithStrides(self):
height, width = 6, 8
# Test strides tuple
images = tf.random.uniform((5, height, width, 3), seed=1)
layer = pooling_layers.MaxPooling2D(
[2, 2], strides=(2, 2), padding="same"
)
output = layer(images)
self.assertListEqual(
output.get_shape().as_list(), [5, height / 2, width / 2, 3]
)
# Test strides integer
layer = pooling_layers.MaxPooling2D([2, 2], strides=2, padding="same")
output = layer(images)
self.assertListEqual(
output.get_shape().as_list(), [5, height / 2, width / 2, 3]
)
# Test unequal strides
layer = pooling_layers.MaxPooling2D(
[2, 2], strides=(2, 1), padding="same"
)
output = layer(images)
self.assertListEqual(
output.get_shape().as_list(), [5, height / 2, width, 3]
)
def testMaxPooling2DPaddingSame(self):
height, width = 7, 9
images = tf.random.uniform((5, height, width, 4), seed=1)
layer = pooling_layers.MaxPooling2D(
images.get_shape()[1:3], strides=2, padding='same')
output = layer(images)
self.assertListEqual(output.get_shape().as_list(), [5, 4, 5, 4])
def testCreateMaxPooling2DChannelsFirst(self):
height, width = 7, 9
images = tf.random.uniform((5, 2, height, width))
layer = pooling_layers.MaxPooling2D([2, 2],
strides=1,
data_format="channels_first")
output = layer(images)
self.assertListEqual(output.get_shape().as_list(), [5, 2, 6, 8])
def testCreateMaxPooling2D(self):
height, width = 7, 9
images = tf.random.uniform((5, height, width, 4))
layer = pooling_layers.MaxPooling2D([2, 2], strides=2)
output = layer(images)
self.assertListEqual(output.get_shape().as_list(), [5, 3, 4, 4])
