def test_base_model_inheritance_ver1(clean_config):
x = tf.ones([32, 64, 64, 3], dtype=tf.float32)
m1 = Conv2D('conv', 32, 3)
m2 = Dense('dense', 160)
m3 = Stack([m1, m2])
res1 = m3(x)
assert shape(res1) == [32, 64, 64, 160]
def test_base_model_inheritance_ver2(clean_config):
x = tf.ones([32, 64, 64, 3], dtype=tf.float32)
m4 = Conv2D('conv', 160, 3)
m9 = Dense('dense', 160)
x = m9(x)
m5 = Residual('res', m4)
res2 = m5(x)
assert shape(res2) == [32, 64, 64, 160]
def test_base_model_inheritance_ver3(clean_config):
x = tf.ones([32, 64, 64, 160], dtype=tf.float32)
m1 = Conv2D('conv1', 32, 3)
m7 = Conv2D('conv2', 3, 3)
m8 = Dense('dense', 128)
m6 = Inception('inception', m7, m7, [m1, m8])
res3 = m6(x)
assert shape(res3) == [32, 64, 64, 3]
def test_Inception_basic(clean_config):
x = tf.constant(np.ones([32, 64, 64, 3], np.float32))
m1 = Conv2D('conv3', 64, 3)
m2 = Dense('dense1', 128)
paths = [Conv2D('conv_{}'.format(i), 32 + 32 * i, 3) for i in range(3)]
m = Inception('inception1', m1, m2, paths)
y = m(x)
assert shape(y) == [32, 64, 64, 128]
def test_stack_basic(clean_config):
models = [
Conv2D('conv1', 64, 3),
Conv2D('conv2', 128, 3),
Conv2D('conv3', 256, 3)
]
x = tf.ones([32, 64, 64, 3], dtype=tf.float32)
st = Stack(models)
res = st(x)
assert shape(res) == [32, 64, 64, 256]
def test_merge_basic(clean_config):
m1 = Dense('d1', 256)
m2 = Conv2D('conv1', 64, 3)
m3 = Conv2D('conv2', 128, 3)
concate = partial(concat, axis=3)
m = Merge(merger=concate, models=[m2, m3])
x = tf.ones([32, 64, 64, 3], dtype=tf.float32)
res = m(x)
res = m1(res)
assert shape(res) == [32, 64, 64, 256]
def test_residual_basic(clean_config):
m1 = Conv2D('conv1', 3, 3)
r = Residual('res1', m1)
x = tf.ones([32, 64, 64, 3], dtype=tf.float32)
res1 = r(x)
res2 = m1(x)
assert shape(res1) == [32, 64, 64, 3]
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
a, b = sess.run([res1, res2])
assert all_close(a, b) is False
def shape(self):
return shape(self.data)
def _(t):
return shape(t.unbox())
def _(t, nb_partition, id_partition, axis=0):
with tf.variable_scope('split_with_index'):
return tf.slice(
t, split_start(shape(t), nb_partition, id_partition, axis),
split_shape(shape(t), nb_partition, id_partition, axis))
def _(raw_data):
return ListModeDataWithoutTOF(const[backends.TensorFlowBackend](raw_data.astype(np.float32), name='lors'),
const[backends.TensorFlowBackend](np.ones([shape(raw_data)[0], 1], dtype=np.float32),
name='values'))
def test_conv2d_basic(tensorflow_test):
m = Conv2D('conv1', 64, 3)
x = tf.constant(np.ones([32, 64, 64, 3], np.float32))
y = m(x)
assert shape(y) == [32, 64, 64, 64]
def test_upsampling_basic(clean_config):
m1 = UpSampling2D('ups1', (2, 2), method=2)
x = tf.ones([32, 64, 64, 3], dtype=tf.float32)
res = m1(x)
assert shape(res) == [32, 128, 128, 3]
def test_downsampling_basic(clean_config):
m1 = DownSampling2D('dsp1', (3, 3), (1, 1), 'valid')
x = tf.ones([32, 64, 64, 3], dtype=tf.float32)
res = m1(x)
assert shape(res) == [32, 62, 62, 3]
