def testCalculateReceptiveBoxes(self):
boxes = feature_extractor.CalculateReceptiveBoxes(
height=1, width=2, rf=291, stride=32, padding=145)
exp_boxes = [[-145., -145., 145., 145.], [-145., -113., 145., 177.]]
with self.test_session() as sess:
boxes_out = sess.run(boxes)
self.assertAllEqual(exp_boxes, boxes_out)
def testCalculateReceptiveBoxes(self):
boxes = feature_extractor.CalculateReceptiveBoxes(height=1,
width=2,
rf=291,
stride=32,
padding=145)
exp_boxes = [[-145., -145., 145., 145.], [-145., -113., 145., 177.]]
self.assertAllEqual(exp_boxes, boxes)
def _ProcessSingleScale(scale_index, boxes, features, scales, scores):
"""Resizes the image and run feature extraction and keypoint selection.
This function will be passed into tf.while_loop() and be called
repeatedly. The input boxes are collected from the previous iteration
[0: scale_index -1]. We get the current scale by
image_scales[scale_index], and run resize image, feature extraction and
keypoint selection. Then we will get a new set of selected_boxes for
current scale. In the end, we concat the previous boxes with current
selected_boxes as the output.
Args:
scale_index: A valid index in the image_scales.
boxes: Box tensor with the shape of [N, 4].
features: Feature tensor with the shape of [N, depth].
scales: Scale tensor with the shape of [N].
scores: Attention score tensor with the shape of [N].
Returns:
scale_index: The next scale index for processing.
boxes: Concatenated box tensor with the shape of [K, 4]. K >= N.
features: Concatenated feature tensor with the shape of [K, depth].
scales: Concatenated scale tensor with the shape of [K].
scores: Concatenated score tensor with the shape of [K].
"""
scale = tf.gather(image_scales, scale_index)
new_image_size = tf.dtypes.cast(
tf.round(original_image_shape_float * scale), tf.int32)
resized_image = tf.image.resize(image_tensor, new_image_size)
attention_prob, feature_map = attention_model_fn(resized_image)
attention_prob = tf.squeeze(attention_prob, axis=[0])
feature_map = tf.squeeze(feature_map, axis=[0])
rf_boxes = feature_extractor.CalculateReceptiveBoxes(
tf.shape(feature_map)[0],
tf.shape(feature_map)[1], rf, stride, padding)
# Re-project back to the original image space.
rf_boxes = tf.divide(rf_boxes, scale)
attention_prob = tf.reshape(attention_prob, [-1])
feature_map = tf.reshape(feature_map, [-1, feature_depth])
# Use attention score to select feature vectors.
indices = tf.reshape(tf.where(attention_prob >= abs_thres), [-1])
selected_boxes = tf.gather(rf_boxes, indices)
selected_features = tf.gather(feature_map, indices)
selected_scores = tf.gather(attention_prob, indices)
selected_scales = tf.ones_like(selected_scores, tf.float32) / scale
# Concat with the previous result from different scales.
boxes = tf.concat([boxes, selected_boxes], 0)
features = tf.concat([features, selected_features], 0)
scales = tf.concat([scales, selected_scales], 0)
scores = tf.concat([scores, selected_scores], 0)
return scale_index + 1, boxes, features, scales, scores
def _ResizeAndExtract(scale_index):
"""Helper function to resize image then extract features.
Args:
scale_index: A valid index in image_scales.
Returns:
global_descriptor: [1,D] tensor denoting the extracted global descriptor.
boxes: Box tensor with the shape of [K, 4].
local_descriptors: Local descriptor tensor with the shape of [K, depth].
scales: Scale tensor with the shape of [K].
scores: Score tensor with the shape of [K].
"""
scale = tf.gather(image_scales, scale_index)
new_image_size = tf.dtypes.cast(
tf.round(original_image_shape_float * scale), tf.int32)
resized_image = tf.image.resize(image_tensor, new_image_size)
global_descriptor, attention_prob, feature_map = model_fn(
resized_image)
attention_prob = tf.squeeze(attention_prob, axis=[0])
feature_map = tf.squeeze(feature_map, axis=[0])
# Compute RF boxes and re-project them to the original image space.
rf_boxes = feature_extractor.CalculateReceptiveBoxes(
tf.shape(feature_map)[0],
tf.shape(feature_map)[1], rf, stride, padding)
rf_boxes = tf.divide(rf_boxes, scale)
attention_prob = tf.reshape(attention_prob, [-1])
feature_map = tf.reshape(feature_map, [-1, tf.shape(feature_map)[2]])
# Use attention score to select local features.
indices = tf.reshape(tf.where(attention_prob >= abs_thres), [-1])
boxes = tf.gather(rf_boxes, indices)
local_descriptors = tf.gather(feature_map, indices)
scores = tf.gather(attention_prob, indices)
scales = tf.ones_like(scores, tf.float32) / scale
return global_descriptor, boxes, local_descriptors, scales, scores
