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faster_rcnn_wrapper.py
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faster_rcnn_wrapper.py
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from _tf_compat_import import compat_tensorflow as tf
from tf_contrib.resnet_v1 import resnet_v1_block, resnet_v1
import tf_contrib.slim as slim
from tf_contrib.resnet_utils import arg_scope, conv2d_same
import numpy as np
class FasterRCNNSlim:
def __init__(self):
self._blocks = [resnet_v1_block('block1', base_depth=64, num_units=3, stride=2),
resnet_v1_block('block2', base_depth=128, num_units=4, stride=2),
resnet_v1_block('block3', base_depth=256, num_units=23, stride=1),
resnet_v1_block('block4', base_depth=512, num_units=3, stride=1)]
self._image = tf.placeholder(tf.float32, shape=[1, None, None, 3])
self._im_info = tf.placeholder(tf.float32, shape=[3])
self._anchor_scales = [4, 8, 16, 32]
self._num_scales = len(self._anchor_scales)
self._anchor_ratios = [1]
self._num_ratios = len(self._anchor_ratios)
self._num_anchors = self._num_scales * self._num_ratios
self._scope = 'resnet_v1_101'
with arg_scope([slim.conv2d, slim.conv2d_in_plane, slim.conv2d_transpose, slim.separable_conv2d,
slim.fully_connected],
weights_regularizer=slim.l2_regularizer(0.0001),
biases_regularizer=tf.no_regularizer,
biases_initializer=tf.constant_initializer(0.0)):
# in _build_network
initializer = tf.random_normal_initializer(stddev=0.01)
initializer_bbox = tf.random_normal_initializer(stddev=0.001)
# in _image_to_head
with slim.arg_scope(self._resnet_arg_scope()):
# in _build_base
with tf.variable_scope(self._scope, self._scope):
net_conv = conv2d_same(self._image, 64, 7, stride=2, scope='conv1')
net_conv = tf.pad(net_conv, [[0, 0], [1, 1], [1, 1], [0, 0]])
net_conv = slim.max_pool2d(net_conv, [3, 3], stride=2, padding='VALID', scope='pool1')
net_conv, _ = resnet_v1(net_conv, self._blocks[:-1], global_pool=False, include_root_block=False,
scope=self._scope)
with tf.variable_scope(self._scope, self._scope):
# in _anchor_component
with tf.variable_scope('ANCHOR-default'):
height = tf.cast(tf.ceil(self._im_info[0] / 16.0), dtype=tf.int32)
width = tf.cast(tf.ceil(self._im_info[1] / 16.0), dtype=tf.int32)
shift_x = tf.range(width) * 16
shift_y = tf.range(height) * 16
shift_x, shift_y = tf.meshgrid(shift_x, shift_y)
sx = tf.reshape(shift_x, [-1])
sy = tf.reshape(shift_y, [-1])
shifts = tf.transpose(tf.stack([sx, sy, sx, sy]))
k = width * height
shifts = tf.transpose(tf.reshape(shifts, [1, k, 4]), perm=[1, 0, 2])
anchors = np.array([[-24, -24, 39, 39], [-56, -56, 71, 71],
[-120, -120, 135, 135], [-248, -248, 263, 263]], dtype=np.int32)
a = anchors.shape[0]
anchor_constant = tf.constant(anchors.reshape([1, a, 4]), dtype=tf.int32)
length = k * a
anchors_tf = tf.reshape(anchor_constant + shifts, shape=[length, 4])
anchors = tf.cast(anchors_tf, dtype=tf.float32)
self._anchors = anchors
self._anchor_length = length
# in _region_proposal
rpn = slim.conv2d(net_conv, 512, [3, 3], trainable=False, weights_initializer=initializer,
scope='rpn_conv/3x3')
rpn_cls_score = slim.conv2d(rpn, self._num_anchors * 2, [1, 1], trainable=False,
weights_initializer=initializer, padding='VALID', activation_fn=None,
scope='rpn_cls_score')
rpn_cls_score_reshape = self._reshape(rpn_cls_score, 2, 'rpn_cls_score_reshape')
rpn_cls_prob_reshape = self._softmax(rpn_cls_score_reshape, 'rpn_cls_prob_reshape')
# rpn_cls_pred = tf.argmax(tf.reshape(rpn_cls_score_reshape, [-1, 2]), axis=1, name='rpn_cls_pred')
rpn_cls_prob = self._reshape(rpn_cls_prob_reshape, self._num_anchors * 2, 'rpn_cls_prob')
rpn_bbox_pred = slim.conv2d(rpn, self._num_anchors * 4, [1, 1], trainable=False,
weights_initializer=initializer, padding='VALID', activation_fn=None,
scope='rpn_bbox_pred')
# in _proposal_layer
with tf.variable_scope('rois'):
post_nms_topn = 300
nms_thresh = 0.7
scores = rpn_cls_prob[:, :, :, self._num_anchors:]
scores = tf.reshape(scores, [-1])
rpn_bbox_pred = tf.reshape(rpn_bbox_pred, [-1, 4])
boxes = tf.cast(self._anchors, rpn_bbox_pred.dtype)
widths = boxes[:, 2] - boxes[:, 0] + 1.0
heights = boxes[:, 3] - boxes[:, 1] + 1.0
ctr_x = boxes[:, 0] + widths * 0.5
ctr_y = boxes[:, 1] + heights * 0.5
dx = rpn_bbox_pred[:, 0]
dy = rpn_bbox_pred[:, 1]
dw = rpn_bbox_pred[:, 2]
dh = rpn_bbox_pred[:, 3]
pred_ctr_x = dx * widths + ctr_x
pred_ctr_y = dy * heights + ctr_y
pred_w = tf.exp(dw) * widths
pred_h = tf.exp(dh) * heights
pred_boxes0 = pred_ctr_x - pred_w * 0.5
pred_boxes1 = pred_ctr_y - pred_h * 0.5
pred_boxes2 = pred_ctr_x + pred_w * 0.5
pred_boxes3 = pred_ctr_y + pred_h * 0.5
b0 = tf.clip_by_value(pred_boxes0, 0, self._im_info[1] - 1)
b1 = tf.clip_by_value(pred_boxes1, 0, self._im_info[0] - 1)
b2 = tf.clip_by_value(pred_boxes2, 0, self._im_info[1] - 1)
b3 = tf.clip_by_value(pred_boxes3, 0, self._im_info[0] - 1)
proposals = tf.stack([b0, b1, b2, b3], axis=1)
indices = tf.image.non_max_suppression(proposals, scores, max_output_size=post_nms_topn,
iou_threshold=nms_thresh)
boxes = tf.cast(tf.gather(proposals, indices), dtype=tf.float32)
# rpn_scores = tf.reshape(tf.gather(scores, indices), [-1, 1])
batch_inds = tf.zeros([tf.shape(indices)[0], 1], dtype=tf.float32)
rois = tf.concat([batch_inds, boxes], 1)
# in _crop_pool_layer
with tf.variable_scope('pool5'):
batch_ids = tf.squeeze(tf.slice(rois, [0, 0], [-1, 1], name='bath_id'), [1])
bottom_shape = tf.shape(net_conv)
height = (tf.cast(bottom_shape[1], dtype=tf.float32) - 1) * 16.0
width = (tf.cast(bottom_shape[2], dtype=tf.float32) - 1) * 16.0
x1 = tf.slice(rois, [0, 1], [-1, 1], name='x1') / width
y1 = tf.slice(rois, [0, 2], [-1, 1], name='y1') / height
x2 = tf.slice(rois, [0, 3], [-1, 1], name='x2') / width
y2 = tf.slice(rois, [0, 4], [-1, 1], name='y2') / height
bboxes = tf.stop_gradient(tf.concat([y1, x1, y2, x2], 1))
pool5 = tf.image.crop_and_resize(net_conv, bboxes, tf.cast(batch_ids, dtype=tf.int32), [7, 7],
name='crops')
# in _head_to_tail
with slim.arg_scope(self._resnet_arg_scope()):
fc7, _ = resnet_v1(pool5, self._blocks[-1:], global_pool=False, include_root_block=False,
scope=self._scope)
fc7 = tf.reduce_mean(fc7, axis=[1, 2])
with tf.variable_scope(self._scope, self._scope):
# in _region_classification
cls_score = slim.fully_connected(fc7, 2, weights_initializer=initializer, trainable=False,
activation_fn=None, scope='cls_score')
cls_prob = self._softmax(cls_score, 'cls_prob')
# cls_pred = tf.argmax(cls_score, 'cls_pred')
bbox_pred = slim.fully_connected(fc7, 2*4, weights_initializer=initializer_bbox, trainable=False,
activation_fn=None, scope='bbox_pred')
self._cls_score = cls_score
self._cls_prob = cls_prob
self._bbox_pred = bbox_pred
self._rois = rois
stds = np.tile(np.array([0.1, 0.1, 0.2, 0.2]), 2)
means = np.tile(np.array([0.0, 0.0, 0.0, 0.0]), 2)
self._bbox_pred *= stds
self._bbox_pred += means
@staticmethod
def _resnet_arg_scope():
batch_norm_params = {
'is_training': False,
'decay': 0.997,
'epsilon': 1e-5,
'scale': True,
'trainable': False,
'updates_collections': tf.GraphKeys.UPDATE_OPS
}
with arg_scope([slim.conv2d],
weights_regularizer=slim.l2_regularizer(0.0001),
weights_initializer=slim.variance_scaling_initializer(),
trainable=False,
activation_fn=tf.nn.relu,
normalizer_fn=slim.batch_norm,
normalizer_params=batch_norm_params):
with arg_scope([slim.batch_norm], **batch_norm_params) as arg_sc:
return arg_sc
@staticmethod
def _reshape(bottom, num_dim, name):
input_shape = tf.shape(bottom)
with tf.variable_scope(name):
to_caffe = tf.transpose(bottom, [0, 3, 1, 2])
reshaped = tf.reshape(to_caffe, [1, num_dim, -1, input_shape[2]])
to_tf = tf.transpose(reshaped, [0, 2, 3, 1])
return to_tf
@staticmethod
def _softmax(bottom, name):
if name.startswith('rpn_cls_prob_reshape'):
input_shape = tf.shape(bottom)
bottom_reshaped = tf.reshape(bottom, [-1, input_shape[-1]])
reshaped_score = tf.nn.softmax(bottom_reshaped, name=name)
return tf.reshape(reshaped_score, input_shape)
return tf.nn.softmax(bottom, name=name)
def test_image(self, sess, image, im_info):
return sess.run([self._cls_score, self._cls_prob, self._bbox_pred, self._rois], feed_dict={
self._image: image,
self._im_info: im_info
})