def test_tf_batch_map_offsets():
np.random.seed(42)
input = np.random.random((4, 100, 100))
offsets = np.random.random((4, 100, 100, 2)) * 2
sp_mapped_vals = sp_batch_map_offsets(input, offsets)
tf_mapped_vals = tf_batch_map_offsets(K.variable(input),
K.variable(offsets))
assert np.allclose(sp_mapped_vals, K.eval(tf_mapped_vals), atol=1e-5)
def call(self, x):
inputs_shape = tf.shape(input=x)
# super(ConvOffset2D, self).build(inputs_shape)
offset = super(ConvOffset2D, self).call(x)
# offset = slim.conv2d(x,self.filters*2,(3,3),padding='SAME')
offset = self._to_bc_h_w_2(offset, inputs_shape)
x = self._to_bc_h_w(x, inputs_shape)
x_offset = tf_batch_map_offsets(x, offset)
x_offset = self._to_b_h_w_c(x_offset, inputs_shape)
return x_offset
def test_tf_batch_map_offsets_grad():
np.random.seed(42)
input = np.random.random((4, 100, 100))
offsets = np.random.random((4, 100, 100, 2)) * 2
input = K.variable(input)
offsets = K.variable(offsets)
tf_mapped_vals = tf_batch_map_offsets(input, offsets)
grad = K.gradients(tf_mapped_vals, input)[0]
grad = K.eval(grad)
assert not np.allclose(grad, 0)