def test_conv2D_block_vanilla():
v = K.variable(np.ones([1, 16, 16, 1]), dtype=np.float32)
out = conv2d_block(v, use_batch_norm=True)
assert out.shape.as_list() == [1, 16, 16, 16]
def test_conv2d_block_unsupported_dropout():
v = K.variable(np.ones([1, 16, 16, 1]), dtype=np.float32)
with pytest.raises(ValueError):
out = conv2d_block(v, use_batch_norm=True, dropout_type="foo")
def test_conv2D_block_elu():
v = K.variable(np.ones([1, 32, 32, 1]), dtype=np.float32)
out = conv2d_block(v, activation="elu")
assert out.shape.as_list() == [1, 32, 32, 16]
def test_conv2D_block_standard_dropout():
v = K.variable(np.ones([1, 16, 16, 1]), dtype=np.float32)
out = conv2d_block(v, use_batch_norm=True, dropout_type="standard")
assert out.shape.as_list() == [1, 16, 16, 16]
def test_conv2D_block_filters():
v = K.variable(np.ones([1, 32, 32, 1]), dtype=np.float32)
out = conv2d_block(v, use_batch_norm=False, filters=32)
assert out.shape.as_list() == [1, 32, 32, 32]