import numpy as np
import keras.backend as K
from keras.layers import Input
from keras_fcn.blocks import (
vgg_conv,
vgg_fc,
vgg_deconv,
vgg_score
)
def test_vgg_conv():
if K.image_data_format() == 'channels_first':
x = Input(shape=(3, 224, 224))
y1_shape = (None, 64, 112, 112)
y2_shape = (None, 128, 56, 56)
else:
x = Input(shape=(224, 224, 3))
y1_shape = (None, 112, 112, 64)
y2_shape = (None, 56, 56, 128)
block1 = vgg_conv(filters=64, convs=2, block_name='block1')
y = block1(x)
assert K.int_shape(y) == y1_shape
block2 = vgg_conv(filters=128, convs=2, block_name='block2')
y = block2(y)
assert K.int_shape(y) == y2_shape
def test_vgg_fc():
if K.image_data_format() == 'channels_first':
x1 = K.variable(np.random.random((1, 512, 14, 14)))
y1_shape = (1, 512, 8, 8)
else:
x1 = K.variable(np.random.random((1, 14, 14, 512)))
y1_shape = (1, 8, 8, 512)
def test_vgg_deconv():
if K.image_data_format() == 'channels_first':
x1 = K.variable(np.random.random((1, 512, 8, 8)))
y1_shape = (1, 21, 18, 18)
x2 = K.variable(np.random.random((1, 512, 27, 27)))
y2_shape = (1, 21, 38, 38)
x3 = K.variable(np.random.random((1, 256, 53, 53)))
y3_shape = (1, 21, 312, 312)
else:
x1 = K.variable(np.random.random((1, 8, 8, 512)))
y1_shape = (1, 18, 18, 21)
x2 = K.variable(np.random.random((1, 27, 27, 512)))
y2_shape = (1, 38, 38, 21)
x3 = K.variable(np.random.random((1, 53, 53, 256)))
y3_shape = (1, 312, 312, 21)
upscore1 = vgg_deconv(classes=21)(x1, None)
assert K.int_shape(upscore1) == y1_shape
assert not np.any(np.isnan(K.eval(upscore1)))
upscore2 = vgg_deconv(classes=21)(x2, upscore1)
assert K.int_shape(upscore2) == y2_shape
assert not np.any(np.isnan(K.eval(upscore2)))
upscore3 = vgg_deconv(classes=21, kernel_size=(16, 16),
strides=(8, 8))(x3, upscore2)
assert K.int_shape(upscore3) == y3_shape
assert not np.any(np.isnan(K.eval(upscore3)))
def test_vgg_score():
if K.image_data_format() == 'channels_first':
x1 = K.variable(np.random.random((1, 3, 224, 224)))
x2 = K.variable(np.random.random((1, 21, 312, 312)))
y_shape = (1, 21, 224, 224)
else:
x1 = K.variable(np.random.random((1, 224, 224, 3)))
x2 = K.variable(np.random.random((1, 312, 312, 21)))
y_shape = (1, 224, 224, 21)
score = vgg_score(crop_offset='centered')(x1, x2)
assert K.int_shape(score) == y_shape
