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layer_decode_detections.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Jul 4 20:35:55 2018
@author: yy
"""
from __future__ import division
import tensorflow as tf
import keras.backend as K
from keras.engine.topology import InputSpec
from keras.engine.topology import Layer
class DecodeDetections(Layer):
def __init__(self,
confidence_thresh=0.01,
iou_threshold=0.45,
top_k=200,
nms_max_output_size=400,
coords='centroids',
normalize_coords=True,
img_height=None,
img_width=None,
**kwargs):
if K.backend() != 'tensorflow':
raise TypeError("This layer only supports TensorFlow at the moment, but you are using the {} backend.".format(K.backend()))
if normalize_coords and ((img_height is None) or (img_width is None)):
raise ValueError("If relative box coordinates are supposed to be converted to absolute coordinates, the decoder needs the image size in order to decode the predictions, but `img_height == {}` and `img_width == {}`".format(img_height, img_width))
if coords != 'centroids':
raise ValueError("The DetectionOutput layer currently only supports the 'centroids' coordinate format.")
self.confidence_thresh = confidence_thresh
self.iou_threshold = iou_threshold
self.top_k = top_k
self.normalize_coords = normalize_coords
self.img_height = img_height
self.img_width = img_width
self.coords = coords
self.nms_max_output_size = nms_max_output_size
self.tf_confidence_thresh = tf.constant(self.confidence_thresh, name='confidence_thresh')
self.tf_iou_threshold = tf.constant(self.iou_threshold, name='iou_threshold')
self.tf_top_k = tf.constant(self.top_k, name='top_k')
self.tf_normalize_coords = tf.constant(self.normalize_coords, name='normalize_coords')
self.tf_img_height = tf.constant(self.img_height, dtype=tf.float32, name='img_height')
self.tf_img_width = tf.constant(self.img_width, dtype=tf.float32, name='img_width')
self.tf_nms_max_output_size = tf.constant(self.nms_max_output_size, name='nms_max_output_size')
super(DecodeDetections, self).__init__(**kwargs)
def build(self, input_shape):
self.input_spec = [InputSpec(shape=input_shape)]
super(DecodeDetections, self).build(input_shape)
def call(self, y_pred, mask=None):
# 转换： anchor box offsets -》 image offsets.
cx = y_pred[...,-12] * y_pred[...,-4] * y_pred[...,-6] + y_pred[...,-8] # cx = cx_pred * cx_variance * w_anchor + cx_anchor
cy = y_pred[...,-11] * y_pred[...,-3] * y_pred[...,-5] + y_pred[...,-7] # cy = cy_pred * cy_variance * h_anchor + cy_anchor
w = tf.exp(y_pred[...,-10] * y_pred[...,-2]) * y_pred[...,-6] # w = exp(w_pred * variance_w) * w_anchor
h = tf.exp(y_pred[...,-9] * y_pred[...,-1]) * y_pred[...,-5] # h = exp(h_pred * variance_h) * h_anchor
# 转换： centroids -》 corners
xmin = cx - 0.5 * w
ymin = cy - 0.5 * h
xmax = cx + 0.5 * w
ymax = cy + 0.5 * h
# 归一化
def normalized_coords():
xmin1 = tf.expand_dims(xmin * self.tf_img_width, axis=-1)
ymin1 = tf.expand_dims(ymin * self.tf_img_height, axis=-1)
xmax1 = tf.expand_dims(xmax * self.tf_img_width, axis=-1)
ymax1 = tf.expand_dims(ymax * self.tf_img_height, axis=-1)
return xmin1, ymin1, xmax1, ymax1
def non_normalized_coords():
return tf.expand_dims(xmin, axis=-1), tf.expand_dims(ymin, axis=-1), tf.expand_dims(xmax, axis=-1), tf.expand_dims(ymax, axis=-1)
xmin, ymin, xmax, ymax = tf.cond(self.tf_normalize_coords, normalized_coords, non_normalized_coords)
# 连接 the one-hot class confidences 和 结果张量
y_pred = tf.concat(values=[y_pred[...,:-12], xmin, ymin, xmax, ymax], axis=-1)
##过滤结果
n_classes = y_pred.shape[2] - 4
# 三方面过滤：
# -- confidence thresholding
# -- non-maximum suppression (NMS)
# -- top-k filtering
def filter_predictions(batch_item):
# class
def filter_single_class(index):
confidences = tf.expand_dims(batch_item[..., index], axis=-1)
class_id = tf.fill(dims=tf.shape(confidences), value=tf.to_float(index))
box_coordinates = batch_item[...,-4:]
single_class = tf.concat([class_id, confidences, box_coordinates], axis=-1)
# confidence
threshold_met = single_class[:,1] > self.tf_confidence_thresh
single_class = tf.boolean_mask(tensor=single_class,
mask=threshold_met)
# NMS
def perform_nms():
scores = single_class[...,1]
xmin = tf.expand_dims(single_class[...,-4], axis=-1)
ymin = tf.expand_dims(single_class[...,-3], axis=-1)
xmax = tf.expand_dims(single_class[...,-2], axis=-1)
ymax = tf.expand_dims(single_class[...,-1], axis=-1)
boxes = tf.concat(values=[ymin, xmin, ymax, xmax], axis=-1)
maxima_indices = tf.image.non_max_suppression(boxes=boxes,
scores=scores,
max_output_size=self.tf_nms_max_output_size,
iou_threshold=self.iou_threshold,
name='non_maximum_suppresion')
maxima = tf.gather(params=single_class,
indices=maxima_indices,
axis=0)
return maxima
def no_confident_predictions():
return tf.constant(value=0.0, shape=(1,6))
single_class_nms = tf.cond(tf.equal(tf.size(single_class), 0), no_confident_predictions, perform_nms)
padded_single_class = tf.pad(tensor=single_class_nms,
paddings=[[0, self.tf_nms_max_output_size - tf.shape(single_class_nms)[0]], [0, 0]],
mode='CONSTANT',
constant_values=0.0)
return padded_single_class
filtered_single_classes = tf.map_fn(fn=lambda i: filter_single_class(i),
elems=tf.range(1,n_classes),
dtype=tf.float32,
parallel_iterations=128,
back_prop=False,
swap_memory=False,
infer_shape=True,
name='loop_over_classes')
filtered_predictions = tf.reshape(tensor=filtered_single_classes, shape=(-1,6))
def top_k():
return tf.gather(params=filtered_predictions,
indices=tf.nn.top_k(filtered_predictions[:, 1], k=self.tf_top_k, sorted=True).indices,
axis=0)
def pad_and_top_k():
padded_predictions = tf.pad(tensor=filtered_predictions,
paddings=[[0, self.tf_top_k - tf.shape(filtered_predictions)[0]], [0, 0]],
mode='CONSTANT',
constant_values=0.0)
return tf.gather(params=padded_predictions,
indices=tf.nn.top_k(padded_predictions[:, 1], k=self.tf_top_k, sorted=True).indices,
axis=0)
top_k_boxes = tf.cond(tf.greater_equal(tf.shape(filtered_predictions)[0], self.tf_top_k), top_k, pad_and_top_k)
return top_k_boxes
output_tensor = tf.map_fn(fn=lambda x: filter_predictions(x),
elems=y_pred,
dtype=None,
parallel_iterations=128,
back_prop=False,
swap_memory=False,
infer_shape=True,
name='loop_over_batch')
return output_tensor
def compute_output_shape(self, input_shape):
batch_size, n_boxes, last_axis = input_shape
return (batch_size, self.tf_top_k, 6)
def get_config(self):
config = {
'confidence_thresh': self.confidence_thresh,
'iou_threshold': self.iou_threshold,
'top_k': self.top_k,
'nms_max_output_size': self.nms_max_output_size,
'coords': self.coords,
'normalize_coords': self.normalize_coords,
'img_height': self.img_height,
'img_width': self.img_width,
}
base_config = super(DecodeDetections, self).get_config()
return dict(list(base_config.items()) + list(config.items()))