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Diffstat (limited to 'anime-face-detector/faster_rcnn_wrapper.py')
| -rw-r--r-- | anime-face-detector/faster_rcnn_wrapper.py | 204 |
1 files changed, 0 insertions, 204 deletions
diff --git a/anime-face-detector/faster_rcnn_wrapper.py b/anime-face-detector/faster_rcnn_wrapper.py deleted file mode 100644 index 3ea09ff..0000000 --- a/anime-face-detector/faster_rcnn_wrapper.py +++ /dev/null @@ -1,204 +0,0 @@ -import tensorflow as tf -from tensorflow.contrib.slim.python.slim.nets.resnet_v1 import resnet_v1_block, resnet_v1 -import tensorflow.contrib.slim as slim -from tensorflow.contrib.slim.python.slim.nets.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=tf.contrib.layers.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.to_int32(tf.ceil(self._im_info[0] / 16.0)) - width = tf.to_int32(tf.ceil(self._im_info[1] / 16.0)) - - 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.to_float(tf.gather(proposals, indices)) - # 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.to_float(bottom_shape[1]) - 1) * 16.0 - width = (tf.to_float(bottom_shape[2]) - 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.to_int32(batch_ids), [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 - }) |