def engineered_policies(images, logits2d, position_channels, glimpse_shape,
num_times, policy):
"""Engineered policies.
Args:
images: A Tensor of type float32. A 4-D float tensor of shape
[batch_size, height, width, channels].
logits2d: 2D logits tensor of type float32 of shape
[batch_size, height, width, classes].
position_channels: A Tensor of type float32 containing the output of
`utils.position_channels` called on `images`.
glimpse_shape: (Tuple of integer) Glimpse shape.
num_times: (Integer) Number of glimpses.
policy: (String) 'ordered logits', 'sobel_mean', or 'sobel_var'.
Returns:
locations_t: List of 2D Tensors containing policy locations.
"""
if policy == "ordered_logits":
pred_labels = tf.argmax(tf.reduce_mean(logits2d, axis=[1, 2]), axis=-1)
metric2d = utils.batch_gather_nd(logits2d, pred_labels, axis=-1)
elif "sobel" in policy:
edges = sobel_edges(images)
edges = edges[:, :, :, tf.newaxis]
_, orig_h, orig_w, _ = edges.shape.as_list()
_, h, w, _ = logits2d.shape.as_list()
ksize = [1, glimpse_shape[0], glimpse_shape[1], 1]
strides = [
1,
int(np.ceil((orig_h - glimpse_shape[0] + 1) / h)),
int(np.ceil((orig_w - glimpse_shape[1] + 1) / w)), 1
]
mean_per_glimpse = tf.nn.avg_pool(edges,
ksize=ksize,
strides=strides,
padding="VALID")
if "mean" in policy:
metric2d = mean_per_glimpse
elif "var" in policy:
n = np.prod(glimpse_shape)
var_per_glimpse = (n /
(n - 1.)) * (tf.nn.avg_pool(tf.square(edges),
ksize=ksize,
strides=strides,
padding="VALID") -
tf.square(mean_per_glimpse))
metric2d = var_per_glimpse
metric2d = tf.squeeze(metric2d, axis=3)
_, locations_t = utils.sort2d(metric2d,
position_channels,
first_k=num_times,
direction="DESCENDING")
locations_t = tf.unstack(locations_t, axis=0)
return locations_t
def test_sort2d(self, direction):
elements = range(0, 9)
x = tf.convert_to_tensor(np.array(
[np.reshape(elements, (3, 3)),
np.reshape(elements[::-1], (3, 3))]),
dtype=tf.float32)
ref_indices = utils.position_channels(x)
sorted_x, argsorted_x = utils.sort2d(x,
ref_indices,
direction=direction)
sorted_x = self.evaluate(sorted_x)
argsorted_x = self.evaluate(argsorted_x)
# Examples include same elements. So sorted examples should be equal.
self.assertAllEqual(sorted_x[:, 0], sorted_x[:, 1])
# Examples are in reverse order. So, indices should be reversed.
ndims = 2
for i in range(ndims):
self.assertAllEqual(argsorted_x[:, 0, i], argsorted_x[:, 1,
i][::-1])