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model.py
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model.py
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# coding=utf-8
# for better understanding about yolov3 architecture, refer to this website (in Chinese):
# https://blog.csdn.net/leviopku/article/details/82660381
from __future__ import division, print_function
import tensorflow as tf
slim = tf.contrib.slim
from utils.layer_utils import conv2d, darknet53_body, yolo_block, upsample_layer
class yolov3(object):
def __init__(self, class_num, anchors, batch_norm_decay=0.9):
# self.anchors = [[10, 13], [16, 30], [33, 23],
# [30, 61], [62, 45], [59, 119],
# [116, 90], [156, 198], [373,326]]
self.class_num = class_num
self.anchors = anchors
self.batch_norm_decay = batch_norm_decay
def forward(self, inputs, is_training=False, reuse=False):
# the input img_size, form: [height, weight]
# it will be used later
self.img_size = tf.shape(inputs)[1:3]
# set batch norm params
batch_norm_params = {
'decay': self.batch_norm_decay,
'epsilon': 1e-05,
'scale': True,
'is_training': is_training,
'fused': None, # Use fused batch norm if possible.
}
with slim.arg_scope([slim.conv2d, slim.batch_norm],reuse=reuse):
with slim.arg_scope([slim.conv2d], normalizer_fn=slim.batch_norm,
normalizer_params=batch_norm_params,
biases_initializer=None,
activation_fn=lambda x: tf.nn.leaky_relu(x, alpha=0.1)):
with tf.variable_scope('darknet53_body'):
route_1, route_2, route_3 = darknet53_body(inputs)
with tf.variable_scope('yolov3_head'):
inter1, net = yolo_block(route_3, 512)
feature_map_1 = slim.conv2d(net, 3 * (5 + self.class_num), 1,
stride=1, normalizer_fn=None,
activation_fn=None, biases_initializer=tf.zeros_initializer())
feature_map_1 = tf.identity(feature_map_1, name='feature_map_1')
inter1 = conv2d(inter1, 256, 1)
inter1 = upsample_layer(inter1, route_2.get_shape().as_list())
concat1 = tf.concat([inter1, route_2], axis=3)
inter2, net = yolo_block(concat1, 256)
feature_map_2 = slim.conv2d(net, 3 * (5 + self.class_num), 1,
stride=1, normalizer_fn=None,
activation_fn=None, biases_initializer=tf.zeros_initializer())
feature_map_2 = tf.identity(feature_map_2, name='feature_map_2')
inter2 = conv2d(inter2, 128, 1)
inter2 = upsample_layer(inter2, route_1.get_shape().as_list())
concat2 = tf.concat([inter2, route_1], axis=3)
_, feature_map_3 = yolo_block(concat2, 128)
feature_map_3 = slim.conv2d(feature_map_3, 3 * (5 + self.class_num), 1,
stride=1, normalizer_fn=None,
activation_fn=None, biases_initializer=tf.zeros_initializer())
feature_map_3 = tf.identity(feature_map_3, name='feature_map_3')
return feature_map_1, feature_map_2, feature_map_3
def reorg_layer(self, feature_map, anchors):
'''
feature_map: a feature_map from [feature_map_1, feature_map_2, feature_map_3] returned
from `forward` function
anchors: shape: [3, 2]
'''
# NOTE: size in [h, w] format! don't get messed up!
grid_size = feature_map.shape.as_list()[1:3] # [13, 13]
# the downscale ratio in height and weight
ratio = tf.cast(self.img_size / grid_size, tf.float32)
# rescale the anchors to the feature_map
# NOTE: the anchor is in [w, h] format!
rescaled_anchors = [(anchor[0] / ratio[1], anchor[1] / ratio[0]) for anchor in anchors]
feature_map = tf.reshape(feature_map, [-1, grid_size[0], grid_size[1], 3, 5 + self.class_num])
# split the feature_map along the last dimension
# shape info: take 416x416 input image and the 13*13 feature_map for example:
# box_centers: [N, 13, 13, 3, 2] last_dimension: [center_x, center_y]
# box_sizes: [N, 13, 13, 3, 2] last_dimension: [width, height]
# conf_logits: [N, 13, 13, 3, 1]
# prob_logits: [N, 13, 13, 3, class_num]
box_centers, box_sizes, conf_logits, prob_logits = tf.split(feature_map, [2, 2, 1, self.class_num], axis=-1)
box_centers = tf.nn.sigmoid(box_centers)
# use some broadcast tricks to get the mesh coordinates
grid_x = tf.range(grid_size[1], dtype=tf.int32)
grid_y = tf.range(grid_size[0], dtype=tf.int32)
grid_x, grid_y = tf.meshgrid(grid_x, grid_y)
x_offset = tf.reshape(grid_x, (-1, 1))
y_offset = tf.reshape(grid_y, (-1, 1))
x_y_offset = tf.concat([x_offset, y_offset], axis=-1)
# shape: [13, 13, 1, 2]
x_y_offset = tf.cast(tf.reshape(x_y_offset, [grid_size[0], grid_size[1], 1, 2]), tf.float32)
# get the absolute box coordinates on the feature_map
box_centers = box_centers + x_y_offset
# rescale to the original image scale
box_centers = box_centers * ratio[::-1]
# avoid getting possible nan value with tf.clip_by_value
box_sizes = tf.clip_by_value(tf.exp(box_sizes), 1e-9, 50) * rescaled_anchors
# rescale to the original image scale
box_sizes = box_sizes * ratio[::-1]
# shape: [N, 13, 13, 3, 4]
# last dimension: (center_x, center_y, w, h)
boxes = tf.concat([box_centers, box_sizes], axis=-1)
# shape:
# x_y_offset: [13, 13, 1, 2]
# boxes: [N, 13, 13, 3, 4], rescaled to the original image scale
# conf_logits: [N, 13, 13, 3, 1]
# prob_logits: [N, 13, 13, 3, class_num]
return x_y_offset, boxes, conf_logits, prob_logits
def predict(self, feature_maps):
'''
Receive the returned feature_maps from `forward` function,
the produce the output predictions at the test stage.
'''
feature_map_1, feature_map_2, feature_map_3 = feature_maps
feature_map_anchors = [(feature_map_1, self.anchors[6:9]),
(feature_map_2, self.anchors[3:6]),
(feature_map_3, self.anchors[0:3])]
reorg_results = [self.reorg_layer(feature_map, anchors) for (feature_map, anchors) in feature_map_anchors]
def _reshape(result):
x_y_offset, boxes, conf_logits, prob_logits = result
grid_size = x_y_offset.shape.as_list()[:2]
boxes = tf.reshape(boxes, [-1, grid_size[0] * grid_size[1] * 3, 4])
conf_logits = tf.reshape(conf_logits, [-1, grid_size[0] * grid_size[1] * 3, 1])
prob_logits = tf.reshape(prob_logits, [-1, grid_size[0] * grid_size[1] * 3, self.class_num])
# shape: (take 416*416 input image and feature_map_1 for example)
# boxes: [N, 13*13*3, 4]
# conf_logits: [N, 13*13*3, 1]
# prob_logits: [N, 13*13*3, class_num]
return boxes, conf_logits, prob_logits
boxes_list, confs_list, probs_list = [], [], []
for result in reorg_results:
boxes, conf_logits, prob_logits = _reshape(result)
confs = tf.sigmoid(conf_logits)
probs = tf.sigmoid(prob_logits)
boxes_list.append(boxes)
confs_list.append(confs)
probs_list.append(probs)
# collect results on three scales
# take 416*416 input image for example:
# shape: [N, (13*13+26*26+52*52)*3, 4]
boxes = tf.concat(boxes_list, axis=1)
# shape: [N, (13*13+26*26+52*52)*3, 1]
confs = tf.concat(confs_list, axis=1)
# shape: [N, (13*13+26*26+52*52)*3, class_num]
probs = tf.concat(probs_list, axis=1)
center_x, center_y, width, height = tf.split(boxes, [1, 1, 1, 1], axis=-1)
x_min = center_x - width / 2
y_min = center_y - height / 2
x_max = center_x + width / 2
y_max = center_y + height / 2
boxes = tf.concat([x_min, y_min, x_max, y_max], axis=-1)
return boxes, confs, probs
def loss_layer(self, feature_map_i, y_true, anchors):
'''
calc loss function from a certain scale
'''
# size in [h, w] format! don't get messed up!
grid_size = tf.shape(feature_map_i)[1:3]
# the downscale ratio in height and weight
ratio = tf.cast(self.img_size / grid_size, tf.float32)
# N: batch_size
N = tf.cast(tf.shape(feature_map_i)[0], tf.float32)
x_y_offset, pred_boxes, pred_conf_logits, pred_prob_logits = self.reorg_layer(feature_map_i, anchors)
###########
# get mask
###########
# shape: take 416x416 input image and 13*13 feature_map for example:
# [N, 13, 13, 3, 1]
object_mask = y_true[..., 4:5]
# shape: [N, 13, 13, 3, 4] & [N, 13, 13, 3] ==> [V, 4]
# V: num of true gt box
valid_true_boxes = tf.boolean_mask(y_true[..., 0:4], tf.cast(object_mask[..., 0], 'bool'))
# shape: [V, 2]
valid_true_box_xy = valid_true_boxes[:, 0:2]
valid_true_box_wh = valid_true_boxes[:, 2:4]
# shape: [N, 13, 13, 3, 2]
pred_box_xy = pred_boxes[..., 0:2]
pred_box_wh = pred_boxes[..., 2:4]
# calc iou
# shape: [N, 13, 13, 3, V]
iou = self.broadcast_iou(valid_true_box_xy, valid_true_box_wh, pred_box_xy, pred_box_wh)
# shape: [N, 13, 13, 3]
best_iou = tf.reduce_max(iou, axis=-1)
# get_ignore_mask
ignore_mask = tf.cast(best_iou < 0.5, tf.float32)
# shape: [N, 13, 13, 3, 1]
ignore_mask = tf.expand_dims(ignore_mask, -1)
# get xy coordinates in one cell from the feature_map
# numerical range: 0 ~ 1
# shape: [N, 13, 13, 3, 2]
true_xy = y_true[..., 0:2] / ratio[::-1] - x_y_offset
pred_xy = pred_box_xy / ratio[::-1] - x_y_offset
# get_tw_th
# numerical range: 0 ~ 1
# shape: [N, 13, 13, 3, 2]
true_tw_th = y_true[..., 2:4] / anchors
pred_tw_th = pred_box_wh / anchors
# for numerical stability
true_tw_th = tf.where(condition=tf.equal(true_tw_th, 0),
x=tf.ones_like(true_tw_th), y=true_tw_th)
pred_tw_th = tf.where(condition=tf.equal(pred_tw_th, 0),
x=tf.ones_like(pred_tw_th), y=pred_tw_th)
true_tw_th = tf.log(tf.clip_by_value(true_tw_th, 1e-9, 1e9))
pred_tw_th = tf.log(tf.clip_by_value(pred_tw_th, 1e-9, 1e9))
# box size punishment:
# box with smaller area has bigger weight. This is taken from the yolo darknet C source code.
# shape: [N, 13, 13, 3, 1]
box_loss_scale = 2. - (y_true[..., 2:3] / tf.cast(self.img_size[1], tf.float32)) * (y_true[..., 3:4] / tf.cast(self.img_size[0], tf.float32))
############
# loss_part
############
# shape: [N, 13, 13, 3, 1]
xy_loss = tf.reduce_sum(tf.square(true_xy - pred_xy) * object_mask * box_loss_scale) / N
wh_loss = tf.reduce_sum(tf.square(true_tw_th - pred_tw_th) * object_mask * box_loss_scale) / N
# shape: [N, 13, 13, 3, 1]
conf_pos_mask = object_mask
conf_neg_mask = (1 - object_mask) * ignore_mask
conf_loss_pos = conf_pos_mask * tf.nn.sigmoid_cross_entropy_with_logits(labels=object_mask, logits=pred_conf_logits)
conf_loss_neg = conf_neg_mask * tf.nn.sigmoid_cross_entropy_with_logits(labels=object_mask, logits=pred_conf_logits)
conf_loss = tf.reduce_sum(conf_loss_pos + conf_loss_neg) / N
# shape: [N, 13, 13, 3, 1]
class_loss = object_mask * tf.nn.sigmoid_cross_entropy_with_logits(labels=y_true[..., 5:], logits=pred_prob_logits)
class_loss = tf.reduce_sum(class_loss) / N
return xy_loss, wh_loss, conf_loss, class_loss
def compute_loss(self, y_pred, y_true):
'''
param:
y_pred: returned feature_map list by `forward` function: [feature_map_1, feature_map_2, feature_map_3]
y_true: input y_true by the tf.data pipeline
'''
loss_xy, loss_wh, loss_conf, loss_class = 0., 0., 0., 0.
anchor_group = [self.anchors[6:9], self.anchors[3:6], self.anchors[0:3]]
# calc loss in 3 scales
for i in range(len(y_pred)):
result = self.loss_layer(y_pred[i], y_true[i], anchor_group[i])
loss_xy += result[0]
loss_wh += result[1]
loss_conf += result[2]
loss_class += result[3]
total_loss = loss_xy + loss_wh + loss_conf + loss_class
return [total_loss, loss_xy, loss_wh, loss_conf, loss_class]
def broadcast_iou(self, true_box_xy, true_box_wh, pred_box_xy, pred_box_wh):
'''
maintain an efficient way to calculate the ios matrix between ground truth true boxes and the predicted boxes
note: here we only care about the size match
'''
# shape:
# true_box_??: [V, 2]
# pred_box_??: [N, 13, 13, 3, 2]
# shape: [N, 13, 13, 3, 1, 2]
pred_box_xy = tf.expand_dims(pred_box_xy, -2)
pred_box_wh = tf.expand_dims(pred_box_wh, -2)
# shape: [1, V, 2]
true_box_xy = tf.expand_dims(true_box_xy, 0)
true_box_wh = tf.expand_dims(true_box_wh, 0)
# [N, 13, 13, 3, 1, 2] & [1, V, 2] ==> [N, 13, 13, 3, V, 2]
intersect_mins = tf.maximum(pred_box_xy - pred_box_wh / 2.,
true_box_xy - true_box_wh / 2.)
intersect_maxs = tf.minimum(pred_box_xy + pred_box_wh / 2.,
true_box_xy + true_box_wh / 2.)
intersect_wh = tf.maximum(intersect_maxs - intersect_mins, 0.)
# shape: [N, 13, 13, 3, V]
intersect_area = intersect_wh[..., 0] * intersect_wh[..., 1]
# shape: [N, 13, 13, 3, 1]
pred_box_area = pred_box_wh[..., 0] * pred_box_wh[..., 1]
# shape: [1, V]
true_box_area = true_box_wh[..., 0] * true_box_wh[..., 1]
# [N, 13, 13, 3, V]
iou = intersect_area / (pred_box_area + true_box_area - intersect_area + 1e-10)
return iou