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Last active July 3, 2024 10:06
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ROI Pooling Layer
import tensorflow as tf
from tensorflow.keras.layers import Layer
class ROIPoolingLayer(Layer):
""" Implements Region Of Interest Max Pooling
for channel-first images and relative bounding box coordinates
# Constructor parameters
pooled_height, pooled_width (int) --
specify height and width of layer outputs
Shape of inputs
[(batch_size, pooled_height, pooled_width, n_channels),
(batch_size, num_rois, 4)]
Shape of output
(batch_size, num_rois, pooled_height, pooled_width, n_channels)
def __init__(self, pooled_height, pooled_width, **kwargs):
self.pooled_height = pooled_height
self.pooled_width = pooled_width
super(ROIPoolingLayer, self).__init__(**kwargs)
def compute_output_shape(self, input_shape):
""" Returns the shape of the ROI Layer output
feature_map_shape, rois_shape = input_shape
assert feature_map_shape[0] == rois_shape[0]
batch_size = feature_map_shape[0]
n_rois = rois_shape[1]
n_channels = feature_map_shape[3]
return (batch_size, n_rois, self.pooled_height,
self.pooled_width, n_channels)
def call(self, x):
""" Maps the input tensor of the ROI layer to its output
# Parameters
x[0] -- Convolutional feature map tensor,
shape (batch_size, pooled_height, pooled_width, n_channels)
x[1] -- Tensor of region of interests from candidate bounding boxes,
shape (batch_size, num_rois, 4)
Each region of interest is defined by four relative
coordinates (x_min, y_min, x_max, y_max) between 0 and 1
# Output
pooled_areas -- Tensor with the pooled region of interest, shape
(batch_size, num_rois, pooled_height, pooled_width, n_channels)
def curried_pool_rois(x):
return ROIPoolingLayer._pool_rois(x[0], x[1],
pooled_areas = tf.map_fn(curried_pool_rois, x, dtype=tf.float32)
return pooled_areas
def _pool_rois(feature_map, rois, pooled_height, pooled_width):
""" Applies ROI pooling for a single image and varios ROIs
def curried_pool_roi(roi):
return ROIPoolingLayer._pool_roi(feature_map, roi,
pooled_height, pooled_width)
pooled_areas = tf.map_fn(curried_pool_roi, rois, dtype=tf.float32)
return pooled_areas
def _pool_roi(feature_map, roi, pooled_height, pooled_width):
""" Applies ROI pooling to a single image and a single region of interest
# Compute the region of interest
feature_map_height = int(feature_map.shape[0])
feature_map_width = int(feature_map.shape[1])
h_start = tf.cast(feature_map_height * roi[0], 'int32')
w_start = tf.cast(feature_map_width * roi[1], 'int32')
h_end = tf.cast(feature_map_height * roi[2], 'int32')
w_end = tf.cast(feature_map_width * roi[3], 'int32')
region = feature_map[h_start:h_end, w_start:w_end, :]
# Divide the region into non overlapping areas
region_height = h_end - h_start
region_width = w_end - w_start
h_step = tf.cast( region_height / pooled_height, 'int32')
w_step = tf.cast( region_width / pooled_width , 'int32')
areas = [[(
(i+1)*h_step if i+1 < pooled_height else region_height,
(j+1)*w_step if j+1 < pooled_width else region_width
for j in range(pooled_width)]
for i in range(pooled_height)]
# take the maximum of each area and stack the result
def pool_area(x):
return tf.math.reduce_max(region[x[0]:x[2], x[1]:x[3], :], axis=[0,1])
pooled_features = tf.stack([[pool_area(x) for x in row] for row in areas])
return pooled_features
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