def _anchor_component(self):
"""
# 为特征图生产anchors
:return:
"""
with tf.variable_scope('ANCHOR_' + self._tag) as scope:
# just to get the shape right
height = tf.to_int32(
tf.ceil(self._im_info[0] / np.float32(self._feat_stride[0])))
width = tf.to_int32(
tf.ceil(self._im_info[1] / np.float32(self._feat_stride[0])))
if cfg.USE_E2E_TF:
anchors, anchor_length = generate_anchors_pre_tf(
height, width, self._feat_stride, self._anchor_scales,
self._anchor_ratios)
else:
anchors, anchor_length = tf.py_func(generate_anchors_pre, [
height, width, self._feat_stride, self._anchor_scales,
self._anchor_ratios
], [tf.float32, tf.int32],
name="generate_anchors")
anchors.set_shape([None, 4])
anchor_length.set_shape([])
self._anchors = anchors
self._anchor_length = anchor_length
def network(is_training=True):
global anchors
with tf.variable_scope('network') as scope:
net = vgg16_head(image_placeholder,1.0)
# just to get the shape right
height = tf.to_int32(tf.ceil(imgsize_placeholder[0] / np.float32(16)))
width = tf.to_int32(tf.ceil(imgsize_placeholder[1] / np.float32(16)))
anchors, anchor_length = generate_anchors_pre_tf(height,width)
anchors.set_shape([None, 4])
anchor_length.set_shape([])
rois = _region_proposal(net,is_training)
if cfg.POOLING_MODE == 'crop':
pool5 = _crop_pool_layer(net, rois, "pool5")#128x7x7x512
else:
raise NotImplementedError
fc7 = vgg16_tail(pool5, is_training)
cls_prob, bbox_pred = _region_classification(fc7, is_training, initializer=tf.truncated_normal_initializer(mean=0.0, stddev=0.01), initializer_bbox=tf.truncated_normal_initializer(mean=0.0, stddev=0.001))
_score_summaries.update(_predictions)
return rois, cls_prob, bbox_pred
def _anchor_component(self):
with tf.variable_scope('ANCHOR_' + self._tag) as scope:
# self._im_info np.array(): stores image dimensions. Format:
# 0: height
# 1: width
# 2: layers
# number of horizontal and vertical anchors.
height = tf.to_int32(
tf.ceil(self._im_info[0] / np.float32(self._feat_stride[0])))
width = tf.to_int32(
tf.ceil(self._im_info[1] / np.float32(self._feat_stride[0])))
# Use an end-to-end tensorflow model.
# Note: models in E2E tensorflow mode have only been tested in feed-forward mode,
# but these models are exportable to other tensorflow instances as GraphDef files.
if cfg.USE_E2E_TF:
# Generates TF anchors
anchors, anchor_length = generate_anchors_pre_tf(
height, width, self._feat_stride, self._anchor_scales,
self._anchor_ratios)
else:
# Generates numpy anchors
anchors, anchor_length = tf.py_func(generate_anchors_pre, [
height, width, self._feat_stride, self._anchor_scales,
self._anchor_ratios
], [tf.float32, tf.int32],
name="generate_anchors")
anchors.set_shape([None, 4])
anchor_length.set_shape([])
self._anchors = anchors
self._anchor_length = anchor_length
def _anchor_component(self, selection):
with tf.variable_scope('ANCHOR_' + self._tag) as scope:
# just to get the shape right
height = tf.to_int32(
tf.ceil(self._im_info[0] / np.float32(self._feat_stride[0])))
width = tf.to_int32(
tf.ceil(self._im_info[1] / np.float32(self._feat_stride[0])))
if cfg.USE_E2E_TF:
anchors, anchor_length = generate_anchors_pre_tf(
height, width, self._feat_stride, self._anchor_scales,
self._anchor_ratios)
else:
anchors, anchor_length = tf.py_func(generate_anchors_pre, [
height, width, self._feat_stride, self._anchor_scales,
self._anchor_ratios
], [tf.float32, tf.int32],
name="generate_anchors")
anchors.set_shape([1, None, None, self._num_anchors * 4])
self._anchors = tf.reshape(anchors, [-1, 4])
anchor_length.set_shape([])
anchors_as = self.select_scale(anchors, selection, 4)
anchors_as = tf.reshape(anchors_as, [-1, 4])
self._anchors_as = anchors_as
self._anchor_length = anchor_length
def _anchor_component(self):
with tf.variable_scope('ANCHOR_' + self._tag) as scope:
# 1. 计算特征图尺寸
# 利用原图尺寸及stride数值，推算出经过特征提取后输出的特征图尺寸：height & width
# just to get the shape right
height = tf.to_int32(
tf.ceil(self._im_info[0] / np.float32(self._feat_stride[0])))
width = tf.to_int32(
tf.ceil(self._im_info[1] / np.float32(self._feat_stride[0])))
# 2. 获得所有anchor值，anchor包括H, W, X, Y四个参数
# anchor: [H*W*9, 4]
# todo: 修改anchor数量
if cfg.USE_E2E_TF:
anchors, anchor_length = generate_anchors_pre_tf(
height, width, self._feat_stride, self._anchor_scales,
self._anchor_ratios)
else:
anchors, anchor_length = tf.py_func(generate_anchors_pre, [
height, width, self._feat_stride, self._anchor_scales,
self._anchor_ratios
], [tf.float32, tf.int32],
name="generate_anchors")
anchors.set_shape([None, 4])
anchor_length.set_shape([])
self._anchors = anchors
self._anchor_length = anchor_length
def _anchor_component(self):
# 生成每张图的anchor
with tf.variable_scope('ANCHOR_' + self._tag) as scope:
# just to get the shape right
# 卷积特征图的尺寸 self._feat_stride[0]=16 经过4次pooling
height = tf.to_int32(tf.ceil(self._im_info[0] / np.float32(self._feat_stride[0])))
width = tf.to_int32(tf.ceil(self._im_info[1] / np.float32(self._feat_stride[0])))
# 默认使用tensorflow端到端，不使用numpy
if cfg.USE_E2E_TF:
anchors, anchor_length = generate_anchors_pre_tf(
height,
width,
self._feat_stride,
self._anchor_scales,
self._anchor_ratios
)
else:
anchors, anchor_length = tf.py_func(generate_anchors_pre,
[height, width,
self._feat_stride, self._anchor_scales, self._anchor_ratios],
[tf.float32, tf.int32], name="generate_anchors")
anchors.set_shape([None, 4])
anchor_length.set_shape([])
self._anchors = anchors
self._anchor_length = anchor_length
def _anchor_component(self):
with tf.variable_scope('ANCHOR_') as scope:
# just to get the shape right
height = tf.to_int32(tf.ceil(self._im_info[0] / np.float32(self._feat_stride[0])))
width = tf.to_int32(tf.ceil(self._im_info[1] / np.float32(self._feat_stride[0])))
anchors, anchor_length = generate_anchors_pre_tf(height, width)
anchors.set_shape([None, 5])
anchor_length.set_shape([])
self._anchors = anchors
self._anchor_length = anchor_length
def _anchor_component(self):
with tf.variable_scope('ANCHOR_' + self._tag) as scope:
# just to get the shape right
height = tf.to_int32(tf.ceil(self._im_info[0] / np.float32(self._feat_stride[0])))
width = tf.to_int32(tf.ceil(self._im_info[1] / np.float32(self._feat_stride[0])))
if cfg.USE_E2E_TF:
anchors, anchor_length = generate_anchors_pre_tf(
height,
width,
self._feat_stride,
self._anchor_scales,
self._anchor_ratios
)
else:
anchors, anchor_length = tf.py_func(generate_anchors_pre,
[height, width,
self._feat_stride, self._anchor_scales, self._anchor_ratios],
[tf.float32, tf.int32], name="generate_anchors")
anchors.set_shape([None, 4])
anchor_length.set_shape([])
self._anchors = anchors
self._anchor_length = anchor_length
def _anchor_component(self, net_conv):
"""
all_anchors: (-1, 4)
"""
with tf.variable_scope('ANCHOR_' + self._tag) as scope:
# just to get the shape right
print('anchor_strides: ', self._anchor_strides)
height, width = tf.shape(net_conv)[1], tf.shape(net_conv)[2]
if cfg.USE_E2E_TF:
anchors, anchor_length = generate_anchors_pre_tf(
height, width, self._anchor_strides[0], self._anchor_sizes,
self._anchor_ratios)
else:
anchors, anchor_length = tf.py_func(generate_anchors_pre, [
height, width, self._anchor_strides[0], self._anchor_sizes,
self._anchor_ratios
], [tf.float32, tf.int32],
name="generate_anchors")
anchors.set_shape([None, 4])
anchor_length.set_shape([])
self._anchors = anchors
self._anchor_length = anchor_length
