def __init__(self, pooled_height, pooled_width, **kwargs):self.pooled_height = pooled_heightself.pooled_width = pooled_widthsuper(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_shapeassert 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)

@staticmethod (http://twitter.com/staticmethod)
def _pool_roi(feature_map, roi, pooled_height, pooled_width):""" Applies ROI Pooling to a single image and a single ROI"""
# 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*h_step, j*w_step, (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上面几行代码十分巧妙。我们定义了一个辅助函数「pool_area」，其输入为我们刚刚创建的元组指定的边界框，输出为该区域中每个通道的最大值。

@staticmethod (http://twitter.com/staticmethod)
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 call(self, x):""" Maps the input tensor of the ROI layer to its output"""def curried_pool_rois(x): return ROIPoolingLayer._pool_rois(x[0], x[1], self.pooled_height, self.pooled_width)pooled_areas = tf.map_fn(curried_pool_rois, x, dtype=tf.float32)return pooled_areas

import tensorflow as tf
from tensorflow.keras.layers import Layerclass ROIPoolingLayer(Layer):""" Implements Region Of Interest Max Pooling for channel-first images and relative bounding box coordinates# Constructor parameterspooled_height, pooled_width (int) -- specify height and width of layer outputsShape 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_heightself.pooled_width = pooled_widthsuper(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_shapeassert 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# Parametersx[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# Outputpooled_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], self.pooled_height, self.pooled_width)pooled_areas = tf.map_fn(curried_pool_rois, x, dtype=tf.float32)return pooled_areas@staticmethoddef _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@staticmethoddef _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 areasregion_height = h_end - h_startregion_width  = w_end - w_starth_step = tf.cast( region_height / pooled_height, 'int32')w_step = tf.cast( region_width  / pooled_width , 'int32')areas = [[(i*h_step, j*w_step, (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 resultdef 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

import numpy as np
# Define parameters
batch_size = 1
img_height = 200
img_width = 100
n_channels = 1
n_rois = 2
pooled_height = 3
pooled_width = 7
# Create feature map input
feature_maps_shape = (batch_size, img_height, img_width, n_channels)
feature_maps_tf = tf.placeholder(tf.float32, shape=feature_maps_shape)
feature_maps_np = np.ones(feature_maps_tf.shape, dtype='float32')
feature_maps_np[0, img_height-1, img_width-3, 0] = 50
print(f"feature_maps_np.shape = {feature_maps_np.shape}")
# Create batch size
roiss_tf = tf.placeholder(tf.float32, shape=(batch_size, n_rois, 4))
roiss_np = np.asarray([[[0.5,0.2,0.7,0.4], [0.0,0.0,1.0,1.0]]], dtype='float32')
print(f"roiss_np.shape = {roiss_np.shape}")
# Create layer
roi_layer = ROIPoolingLayer(pooled_height, pooled_width)
pooled_features = roi_layer([feature_maps_tf, roiss_tf])
print(f"output shape of layer call = {pooled_features.shape}")
# Run tensorflow session
with tf.Session() as session:result = session.run(pooled_features, feed_dict={feature_maps_tf:feature_maps_np,  roiss_tf:roiss_np})print(f"result.shape = {result.shape}")
print(f"first  roi embedding=n{result[0,0,:,:,0]}")ooled_features.shape}")

feature_maps_np.shape = (1, 200, 100, 1)
roiss_np.shape = (1, 2, 4)
output shape of layer call = (1, 2, 3, 7, 1)
result.shape = (1, 2, 3, 7, 1)
first  roi embedding=
[[1. 1. 1. 1. 1. 1. 1.][1. 1. 1. 1. 1. 1. 1.][1. 1. 1. 1. 1. 1. 1.]]
second roi embedding=
[[ 1.  1.  1.  1.  1.  1.  1.][ 1.  1.  1.  1.  1.  1.  1.][ 1.  1.  1.  1.  1.  1. 50.]]