example-apps/PDD/pcb-defect-detection/libs/networks/build_whole_network.py

   1 # -*-coding: utf-8 -*-
   2
   3 from __future__ import absolute_import, division, print_function
   4
   5 import os
   6 import tensorflow as tf
   7 import tensorflow.contrib.slim as slim
   8 import numpy as np
   9
  10 from libs.networks import resnet
  11 from libs.networks import mobilenet_v2
  12 from libs.box_utils import encode_and_decode
  13 from libs.box_utils import boxes_utils
  14 from libs.box_utils import anchor_utils
  15 from libs.configs import cfgs
  16 from libs.losses import losses
  17 from libs.box_utils import show_box_in_tensor
  18 from libs.detection_oprations.proposal_opr import postprocess_rpn_proposals
  19 from libs.detection_oprations.anchor_target_layer_without_boxweight import anchor_target_layer
  20 from libs.detection_oprations.proposal_target_layer import proposal_target_layer
  21
  22
  23 class DetectionNetwork(object):
  24
  25     def __init__(self, base_network_name, is_training):
  26
  27         self.base_network_name = base_network_name
  28         self.is_training = is_training
  29         self.num_anchors_per_location = len(cfgs.ANCHOR_SCALES) * len(cfgs.ANCHOR_RATIOS)
  30
  31     def build_base_network(self, input_img_batch):
  32
  33         if self.base_network_name.startswith('resnet_v1'):
  34             return resnet.resnet_base(input_img_batch, scope_name=self.base_network_name, is_training=self.is_training)
  35
  36         elif self.base_network_name.startswith('MobilenetV2'):
  37             return mobilenet_v2.mobilenetv2_base(input_img_batch, is_training=self.is_training)
  38
  39         else:
  40             raise ValueError('Sry, we only support resnet or mobilenet_v2')
  41
  42     def postprocess_fastrcnn(self, rois, bbox_ppred, scores, img_shape):
  43         '''
  44
  45         :param rois:[-1, 4]
  46         :param bbox_ppred: [-1, (cfgs.Class_num+1) * 4]
  47         :param scores: [-1, cfgs.Class_num + 1]
  48         :return:
  49         '''
  50
  51         with tf.name_scope('postprocess_fastrcnn'):
  52             rois = tf.stop_gradient(rois)
  53             scores = tf.stop_gradient(scores)
  54             bbox_ppred = tf.reshape(bbox_ppred, [-1, cfgs.CLASS_NUM + 1, 4])
  55             bbox_ppred = tf.stop_gradient(bbox_ppred)
  56
  57             bbox_pred_list = tf.unstack(bbox_ppred, axis=1)
  58             score_list = tf.unstack(scores, axis=1)
  59
  60             allclasses_boxes = []
  61             allclasses_scores = []
  62             categories = []
  63             for i in range(1, cfgs.CLASS_NUM+1):
  64
  65                 # 1. decode boxes in each class
  66                 tmp_encoded_box = bbox_pred_list[i]
  67                 tmp_score = score_list[i]
  68                 tmp_decoded_boxes = encode_and_decode.decode_boxes(encoded_boxes=tmp_encoded_box,
  69                                                                    reference_boxes=rois,
  70                                                                    scale_factors=cfgs.ROI_SCALE_FACTORS)
  71                 # tmp_decoded_boxes = encode_and_decode.decode_boxes(boxes=rois,
  72                 #                                                    deltas=tmp_encoded_box,
  73                 #                                                    scale_factor=cfgs.ROI_SCALE_FACTORS)
  74
  75                 # 2. clip to img boundaries
  76                 tmp_decoded_boxes = boxes_utils.clip_boxes_to_img_boundaries(decode_boxes=tmp_decoded_boxes,
  77                                                                              img_shape=img_shape)
  78
  79                 # 3. NMS
  80                 keep = tf.image.non_max_suppression(
  81                     boxes=tmp_decoded_boxes,
  82                     scores=tmp_score,
  83                     max_output_size=cfgs.FAST_RCNN_NMS_MAX_BOXES_PER_CLASS,
  84                     iou_threshold=cfgs.FAST_RCNN_NMS_IOU_THRESHOLD)
  85
  86                 perclass_boxes = tf.gather(tmp_decoded_boxes, keep)
  87                 perclass_scores = tf.gather(tmp_score, keep)
  88
  89                 allclasses_boxes.append(perclass_boxes)
  90                 allclasses_scores.append(perclass_scores)
  91                 categories.append(tf.ones_like(perclass_scores) * i)
  92
  93             final_boxes = tf.concat(allclasses_boxes, axis=0)
  94             final_scores = tf.concat(allclasses_scores, axis=0)
  95             final_category = tf.concat(categories, axis=0)
  96
  97             if self.is_training:
  98                 '''
  99                 in training. We should show the detecitons in the tensorboard. So we add this.
 100                 '''
 101                 kept_indices = tf.reshape(tf.where(tf.greater_equal(final_scores, cfgs.SHOW_SCORE_THRSHOLD)), [-1])
 102
 103                 final_boxes = tf.gather(final_boxes, kept_indices)
 104                 final_scores = tf.gather(final_scores, kept_indices)
 105                 final_category = tf.gather(final_category, kept_indices)
 106
 107         return final_boxes, final_scores, final_category
 108
 109     def roi_pooling(self, feature_maps, rois, img_shape, scope):
 110         '''
 111         Here use roi warping as roi_pooling
 112
 113         :param featuremaps_dict: feature map to crop
 114         :param rois: shape is [-1, 4]. [x1, y1, x2, y2]
 115         :return:
 116         '''
 117
 118         with tf.variable_scope('ROI_Warping_'+scope):
 119             img_h, img_w = tf.cast(img_shape[1], tf.float32), tf.cast(img_shape[2], tf.float32)
 120             N = tf.shape(rois)[0]
 121             x1, y1, x2, y2 = tf.unstack(rois, axis=1)
 122
 123             normalized_x1 = x1 / img_w
 124             normalized_x2 = x2 / img_w
 125             normalized_y1 = y1 / img_h
 126             normalized_y2 = y2 / img_h
 127
 128             normalized_rois = tf.transpose(
 129                 tf.stack([normalized_y1, normalized_x1, normalized_y2, normalized_x2]), name='get_normalized_rois')
 130
 131             normalized_rois = tf.stop_gradient(normalized_rois)
 132
 133             cropped_roi_features = tf.image.crop_and_resize(feature_maps, normalized_rois,
 134                                                             box_ind=tf.zeros(shape=[N, ],
 135                                                                              dtype=tf.int32),
 136                                                             crop_size=[cfgs.ROI_SIZE, cfgs.ROI_SIZE],
 137                                                             name='CROP_AND_RESIZE'
 138                                                             )
 139             roi_features = slim.max_pool2d(cropped_roi_features,
 140                                            [cfgs.ROI_POOL_KERNEL_SIZE, cfgs.ROI_POOL_KERNEL_SIZE],
 141                                            stride=cfgs.ROI_POOL_KERNEL_SIZE)
 142
 143         return roi_features
 144
 145     def build_fastrcnn(self, P_list, rois_list, img_shape):
 146
 147         with tf.variable_scope('Fast-RCNN'):
 148             # 5. ROI Pooling
 149             with tf.variable_scope('rois_pooling'):
 150                 pooled_features_list = []
 151                 for level_name, p, rois in zip(cfgs.LEVLES, P_list, rois_list):  # exclude P6_rois
 152                     # p = tf.Print(p, [tf.shape(p)], summarize=10, message=level_name+'SHPAE***')
 153                     pooled_features = self.roi_pooling(feature_maps=p, rois=rois, img_shape=img_shape,
 154                                                        scope=level_name)
 155                     pooled_features_list.append(pooled_features)
 156
 157                 pooled_features = tf.concat(pooled_features_list, axis=0) # [minibatch_size, H, W, C]
 158
 159             # 6. inferecne rois in Fast-RCNN to obtain fc_flatten features
 160             if self.base_network_name.startswith('resnet'):
 161                 fc_flatten = resnet.restnet_head(inputs=pooled_features,
 162                                                  is_training=self.is_training,
 163                                                  scope_name=self.base_network_name)
 164             elif self.base_network_name.startswith('Mobile'):
 165                 fc_flatten = mobilenet_v2.mobilenetv2_head(inputs=pooled_features,
 166                                                            is_training=self.is_training)
 167             else:
 168                 raise NotImplementedError('only support resnet and mobilenet')
 169
 170             # 7. cls and reg in Fast-RCNN
 171             with slim.arg_scope([slim.fully_connected], weights_regularizer=slim.l2_regularizer(cfgs.WEIGHT_DECAY)):
 172
 173                 cls_score = slim.fully_connected(fc_flatten,
 174                                                  num_outputs=cfgs.CLASS_NUM+1,
 175                                                  weights_initializer=cfgs.INITIALIZER,
 176                                                  activation_fn=None, trainable=self.is_training,
 177                                                  scope='cls_fc')
 178
 179                 bbox_pred = slim.fully_connected(fc_flatten,
 180                                                  num_outputs=(cfgs.CLASS_NUM+1)*4,
 181                                                  weights_initializer=cfgs.BBOX_INITIALIZER,
 182                                                  activation_fn=None, trainable=self.is_training,
 183                                                  scope='reg_fc')
 184                 # for convient. It also produce (cls_num +1) bboxes
 185
 186                 cls_score = tf.reshape(cls_score, [-1, cfgs.CLASS_NUM+1])
 187                 bbox_pred = tf.reshape(bbox_pred, [-1, 4*(cfgs.CLASS_NUM+1)])
 188
 189                 return bbox_pred, cls_score
 190
 191     def assign_levels(self, all_rois, labels=None, bbox_targets=None):
 192         '''
 193
 194         :param all_rois:
 195         :param labels:
 196         :param bbox_targets:
 197         :return:
 198         '''
 199         with tf.name_scope('assign_levels'):
 200             # all_rois = tf.Print(all_rois, [tf.shape(all_rois)], summarize=10, message='ALL_ROIS_SHAPE*****')
 201             xmin, ymin, xmax, ymax = tf.unstack(all_rois, axis=1)
 202
 203             h = tf.maximum(0., ymax - ymin)
 204             w = tf.maximum(0., xmax - xmin)
 205
 206             levels = tf.floor(4. + tf.log(tf.sqrt(w * h + 1e-8) / 224.0) / tf.log(2.))  # 4 + log_2(***)
 207             # use floor instead of round
 208
 209             min_level = int(cfgs.LEVLES[0][-1])
 210             max_level = min(5, int(cfgs.LEVLES[-1][-1]))
 211             levels = tf.maximum(levels, tf.ones_like(levels) * min_level)  # level minimum is 2
 212             levels = tf.minimum(levels, tf.ones_like(levels) * max_level)  # level maximum is 5
 213
 214             levels = tf.stop_gradient(tf.reshape(levels, [-1]))
 215
 216             def get_rois(levels, level_i, rois, labels, bbox_targets):
 217
 218                 level_i_indices = tf.reshape(tf.where(tf.equal(levels, level_i)), [-1])
 219                 # level_i_indices = tf.Print(level_i_indices, [tf.shape(tf.where(tf.equal(levels, level_i)))[0]], message="SHAPE%d***"%level_i,
 220                 #                            summarize=10)
 221                 tf.summary.scalar('LEVEL/LEVEL_%d_rois_NUM'%level_i, tf.shape(level_i_indices)[0])
 222                 level_i_rois = tf.gather(rois, level_i_indices)
 223
 224                 if self.is_training:
 225                     # If you use low version tensorflow, you may uncomment these code.
 226                     level_i_rois = tf.stop_gradient(tf.concat([level_i_rois, [[0, 0, 0., 0.]]], axis=0))
 227                     # # to avoid the num of level i rois is 0.0, which will broken the BP in tf
 228                     #
 229                     level_i_labels = tf.gather(labels, level_i_indices)
 230                     level_i_labels = tf.stop_gradient(tf.concat([level_i_labels, [0]], axis=0))
 231
 232                     level_i_targets = tf.gather(bbox_targets, level_i_indices)
 233                     level_i_targets = tf.stop_gradient(tf.concat([level_i_targets,
 234                                                                     tf.zeros(shape=(1, 4*(cfgs.CLASS_NUM+1)), dtype=tf.float32)],
 235                                                                     axis=0))
 236                     #level_i_rois = tf.stop_gradient(level_i_rois)
 237                     #level_i_labels = tf.gather(labels, level_i_indices)
 238
 239                     #level_i_targets = tf.gather(bbox_targets, level_i_indices)
 240
 241                     return level_i_rois, level_i_labels, level_i_targets
 242                 else:
 243                     return level_i_rois, None, None
 244
 245             rois_list = []
 246             labels_list = []
 247             targets_list = []
 248             for i in range(min_level, max_level+1):
 249                 P_i_rois, P_i_labels, P_i_targets = get_rois(levels, level_i=i, rois=all_rois,
 250                                                              labels=labels,
 251                                                              bbox_targets=bbox_targets)
 252                 rois_list.append(P_i_rois)
 253                 labels_list.append(P_i_labels)
 254                 targets_list.append(P_i_targets)
 255
 256             if self.is_training:
 257                 all_labels = tf.concat(labels_list, axis=0)
 258                 all_targets = tf.concat(targets_list, axis=0)
 259                 return rois_list, all_labels, all_targets
 260             else:
 261                 return rois_list  # [P2_rois, P3_rois, P4_rois, P5_rois] Note: P6 do not assign rois
 262
 263     def add_anchor_img_smry(self, img, anchors, labels):
 264
 265         positive_anchor_indices = tf.reshape(tf.where(tf.greater_equal(labels, 1)), [-1])
 266         negative_anchor_indices = tf.reshape(tf.where(tf.equal(labels, 0)), [-1])
 267
 268         positive_anchor = tf.gather(anchors, positive_anchor_indices)
 269         negative_anchor = tf.gather(anchors, negative_anchor_indices)
 270
 271         pos_in_img = show_box_in_tensor.only_draw_boxes(img_batch=img,
 272                                                         boxes=positive_anchor)
 273         neg_in_img = show_box_in_tensor.only_draw_boxes(img_batch=img,
 274                                                         boxes=negative_anchor)
 275
 276         tf.summary.image('positive_anchor', pos_in_img)
 277         tf.summary.image('negative_anchors', neg_in_img)
 278
 279     def add_roi_batch_img_smry(self, img, rois, labels):
 280         positive_roi_indices = tf.reshape(tf.where(tf.greater_equal(labels, 1)), [-1])
 281
 282         negative_roi_indices = tf.reshape(tf.where(tf.equal(labels, 0)), [-1])
 283
 284         pos_roi = tf.gather(rois, positive_roi_indices)
 285         neg_roi = tf.gather(rois, negative_roi_indices)
 286
 287
 288         pos_in_img = show_box_in_tensor.only_draw_boxes(img_batch=img,
 289                                                                boxes=pos_roi)
 290         neg_in_img = show_box_in_tensor.only_draw_boxes(img_batch=img,
 291                                                                boxes=neg_roi)
 292         tf.summary.image('pos_rois', pos_in_img)
 293         tf.summary.image('neg_rois', neg_in_img)
 294
 295     def build_loss(self, rpn_box_pred, rpn_bbox_targets, rpn_cls_score, rpn_labels,
 296                    bbox_pred, bbox_targets, cls_score, labels):
 297         '''
 298
 299         :param rpn_box_pred: [-1, 4]
 300         :param rpn_bbox_targets: [-1, 4]
 301         :param rpn_cls_score: [-1]
 302         :param rpn_labels: [-1]
 303         :param bbox_pred: [-1, 4*(cls_num+1)]
 304         :param bbox_targets: [-1, 4*(cls_num+1)]
 305         :param cls_score: [-1, cls_num+1]
 306         :param labels: [-1]
 307         :return:
 308         '''
 309         with tf.variable_scope('build_loss') as sc:
 310             with tf.variable_scope('rpn_loss'):
 311
 312                 rpn_bbox_loss = losses.smooth_l1_loss_rpn(bbox_pred=rpn_box_pred,
 313                                                           bbox_targets=rpn_bbox_targets,
 314                                                           label=rpn_labels,
 315                                                           sigma=cfgs.RPN_SIGMA)
 316                 # rpn_cls_loss:
 317                 # rpn_cls_score = tf.reshape(rpn_cls_score, [-1, 2])
 318                 # rpn_labels = tf.reshape(rpn_labels, [-1])
 319                 # ensure rpn_labels shape is [-1]
 320                 rpn_select = tf.reshape(tf.where(tf.not_equal(rpn_labels, -1)), [-1])
 321                 rpn_cls_score = tf.reshape(tf.gather(rpn_cls_score, rpn_select), [-1, 2])
 322                 rpn_labels = tf.reshape(tf.gather(rpn_labels, rpn_select), [-1])
 323                 rpn_cls_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=rpn_cls_score,
 324                                                                                              labels=rpn_labels))
 325
 326                 rpn_cls_loss = rpn_cls_loss * cfgs.RPN_CLASSIFICATION_LOSS_WEIGHT
 327                 rpn_bbox_loss = rpn_bbox_loss * cfgs.RPN_LOCATION_LOSS_WEIGHT
 328
 329             with tf.variable_scope('FastRCNN_loss'):
 330                 if not cfgs.FAST_RCNN_MINIBATCH_SIZE == -1:
 331                     bbox_loss = losses.smooth_l1_loss_rcnn(bbox_pred=bbox_pred,
 332                                                            bbox_targets=bbox_targets,
 333                                                            label=labels,
 334                                                            num_classes=cfgs.CLASS_NUM + 1,
 335                                                            sigma=cfgs.FASTRCNN_SIGMA)
 336
 337                     # cls_score = tf.reshape(cls_score, [-1, cfgs.CLASS_NUM + 1])
 338                     # labels = tf.reshape(labels, [-1])
 339                     cls_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(
 340                         logits=cls_score,
 341                         labels=labels))  # beacause already sample before
 342                 else:
 343                     '''
 344                     applying OHEM here
 345                     '''
 346                     print(20 * "@@")
 347                     print("@@" + 10 * " " + "TRAIN WITH OHEM ...")
 348                     print(20 * "@@")
 349                     cls_loss, bbox_loss = losses.sum_ohem_loss(
 350                         cls_score=cls_score,
 351                         label=labels,
 352                         bbox_targets=bbox_targets,
 353                         bbox_pred=bbox_pred,
 354                         num_ohem_samples=128,
 355                         num_classes=cfgs.CLASS_NUM + 1)
 356                 cls_loss = cls_loss * cfgs.FAST_RCNN_CLASSIFICATION_LOSS_WEIGHT
 357                 bbox_loss = bbox_loss * cfgs.FAST_RCNN_LOCATION_LOSS_WEIGHT
 358             loss_dict = {
 359                 'rpn_cls_loss': rpn_cls_loss,
 360                 'rpn_loc_loss': rpn_bbox_loss,
 361                 'fastrcnn_cls_loss': cls_loss,
 362                 'fastrcnn_loc_loss': bbox_loss
 363             }
 364         return loss_dict
 365
 366     def build_whole_detection_network(self, input_img_batch, gtboxes_batch):
 367
 368         if self.is_training:
 369             # ensure shape is [M, 5]
 370             gtboxes_batch = tf.reshape(gtboxes_batch, [-1, 5])
 371             gtboxes_batch = tf.cast(gtboxes_batch, tf.float32)
 372
 373         img_shape = tf.shape(input_img_batch)
 374
 375         # 1. build base network
 376         P_list = self.build_base_network(input_img_batch)  # [P2, P3, P4, P5, P6]
 377
 378         # 2. build rpn
 379         with tf.variable_scope('build_rpn',
 380                                regularizer=slim.l2_regularizer(cfgs.WEIGHT_DECAY)):
 381
 382             fpn_cls_score =[]
 383             fpn_box_pred = []
 384             for level_name, p in zip(cfgs.LEVLES, P_list):
 385                 if cfgs.SHARE_HEADS:
 386                     reuse_flag = None if level_name==cfgs.LEVLES[0] else True
 387                     scope_list=['rpn_conv/3x3', 'rpn_cls_score', 'rpn_bbox_pred']
 388                 else:
 389                     reuse_flag = None
 390                     scope_list= ['rpn_conv/3x3_%s' % level_name, 'rpn_cls_score_%s' % level_name, 'rpn_bbox_pred_%s' % level_name]
 391                 rpn_conv3x3 = slim.conv2d(
 392                     p, 512, [3, 3],
 393                     trainable=self.is_training, weights_initializer=cfgs.INITIALIZER, padding="SAME",
 394                     activation_fn=tf.nn.relu,
 395                     scope=scope_list[0],
 396                     reuse=reuse_flag)
 397                 rpn_cls_score = slim.conv2d(rpn_conv3x3, self.num_anchors_per_location*2, [1, 1], stride=1,
 398                                             trainable=self.is_training, weights_initializer=cfgs.INITIALIZER,
 399                                             activation_fn=None, padding="VALID",
 400                                             scope=scope_list[1],
 401                                             reuse=reuse_flag)
 402                 rpn_box_pred = slim.conv2d(rpn_conv3x3, self.num_anchors_per_location*4, [1, 1], stride=1,
 403                                            trainable=self.is_training, weights_initializer=cfgs.BBOX_INITIALIZER,
 404                                            activation_fn=None, padding="VALID",
 405                                            scope=scope_list[2],
 406                                            reuse=reuse_flag)
 407                 rpn_box_pred = tf.reshape(rpn_box_pred, [-1, 4])
 408                 rpn_cls_score = tf.reshape(rpn_cls_score, [-1, 2])
 409
 410                 fpn_cls_score.append(rpn_cls_score)
 411                 fpn_box_pred.append(rpn_box_pred)
 412
 413             fpn_cls_score = tf.concat(fpn_cls_score, axis=0, name='fpn_cls_score')
 414             fpn_box_pred = tf.concat(fpn_box_pred, axis=0, name='fpn_box_pred')
 415             fpn_cls_prob = slim.softmax(fpn_cls_score, scope='fpn_cls_prob')
 416
 417         # 3. generate_anchors
 418         all_anchors = []
 419         for i in range(len(cfgs.LEVLES)):
 420             level_name, p = cfgs.LEVLES[i], P_list[i]
 421
 422             p_h, p_w = tf.shape(p)[1], tf.shape(p)[2]
 423             featuremap_height = tf.cast(p_h, tf.float32)
 424             featuremap_width = tf.cast(p_w, tf.float32)
 425             anchors = anchor_utils.make_anchors(base_anchor_size=cfgs.BASE_ANCHOR_SIZE_LIST[i],
 426                                                 anchor_scales=cfgs.ANCHOR_SCALES,
 427                                                 anchor_ratios=cfgs.ANCHOR_RATIOS,
 428                                                 featuremap_height=featuremap_height,
 429                                                 featuremap_width=featuremap_width,
 430                                                 stride=cfgs.ANCHOR_STRIDE_LIST[i],
 431                                                 name="make_anchors_for%s" % level_name)
 432             all_anchors.append(anchors)
 433         all_anchors = tf.concat(all_anchors, axis=0, name='all_anchors_of_FPN')
 434
 435         # 4. postprocess rpn proposals. such as: decode, clip, NMS
 436         with tf.variable_scope('postprocess_FPN'):
 437             rois, roi_scores = postprocess_rpn_proposals(rpn_bbox_pred=fpn_box_pred,
 438                                                          rpn_cls_prob=fpn_cls_prob,
 439                                                          img_shape=img_shape,
 440                                                          anchors=all_anchors,
 441                                                          is_training=self.is_training)
 442             # rois shape [-1, 4]
 443             # +++++++++++++++++++++++++++++++++++++add img smry+++++++++++++++++++++++++++++++++++++++++++++++++++++++
 444
 445             if self.is_training:
 446                 score_gre_05 = tf.reshape(tf.where(tf.greater_equal(roi_scores, 0.5)), [-1])
 447                 score_gre_05_rois = tf.gather(rois, score_gre_05)
 448                 score_gre_05_score = tf.gather(roi_scores, score_gre_05)
 449                 score_gre_05_in_img = show_box_in_tensor.draw_boxes_with_scores(img_batch=input_img_batch,
 450                                                                                 boxes=score_gre_05_rois,
 451                                                                                 scores=score_gre_05_score)
 452                 tf.summary.image('score_greater_05_rois', score_gre_05_in_img)
 453             # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 454
 455         if self.is_training:
 456             with tf.variable_scope('sample_anchors_minibatch'):
 457                 fpn_labels, fpn_bbox_targets = \
 458                     tf.py_func(
 459                         anchor_target_layer,
 460                         [gtboxes_batch, img_shape, all_anchors],
 461                         [tf.float32, tf.float32])
 462                 fpn_bbox_targets = tf.reshape(fpn_bbox_targets, [-1, 4])
 463                 fpn_labels = tf.to_int32(fpn_labels, name="to_int32")
 464                 fpn_labels = tf.reshape(fpn_labels, [-1])
 465                 self.add_anchor_img_smry(input_img_batch, all_anchors, fpn_labels)
 466
 467             # --------------------------------------add smry-----------------------------------------------------------
 468
 469             fpn_cls_category = tf.argmax(fpn_cls_prob, axis=1)
 470             kept_rpppn = tf.reshape(tf.where(tf.not_equal(fpn_labels, -1)), [-1])
 471             fpn_cls_category = tf.gather(fpn_cls_category, kept_rpppn)
 472             acc = tf.reduce_mean(tf.to_float(tf.equal(fpn_cls_category,
 473                                                       tf.to_int64(tf.gather(fpn_labels, kept_rpppn)))))
 474             tf.summary.scalar('ACC/fpn_accuracy', acc)
 475
 476             with tf.control_dependencies([fpn_labels]):
 477                 with tf.variable_scope('sample_RCNN_minibatch'):
 478                     rois, labels, bbox_targets = \
 479                     tf.py_func(proposal_target_layer,
 480                                [rois, gtboxes_batch],
 481                                [tf.float32, tf.float32, tf.float32])
 482                     rois = tf.reshape(rois, [-1, 4])
 483                     labels = tf.to_int32(labels)
 484                     labels = tf.reshape(labels, [-1])
 485                     bbox_targets = tf.reshape(bbox_targets, [-1, 4*(cfgs.CLASS_NUM+1)])
 486                     self.add_roi_batch_img_smry(input_img_batch, rois, labels)
 487         if self.is_training:
 488             rois_list, labels, bbox_targets = self.assign_levels(all_rois=rois,
 489                                                                  labels=labels,
 490                                                                  bbox_targets=bbox_targets)
 491         else:
 492             rois_list = self.assign_levels(all_rois=rois)  # rois_list: [P2_rois, P3_rois, P4_rois, P5_rois]
 493
 494         # -------------------------------------------------------------------------------------------------------------#
 495         #                                            Fast-RCNN                                                         #
 496         # -------------------------------------------------------------------------------------------------------------#
 497
 498         # 5. build Fast-RCNN
 499         # rois = tf.Print(rois, [tf.shape(rois)], 'rois shape', summarize=10)
 500         bbox_pred, cls_score = self.build_fastrcnn(P_list=P_list, rois_list=rois_list,
 501                                                    img_shape=img_shape)
 502         # bbox_pred shape: [-1, 4*(cls_num+1)].
 503         # cls_score shapeï¼?[-1, cls_num+1]
 504
 505         cls_prob = slim.softmax(cls_score, 'cls_prob')
 506
 507
 508         # ----------------------------------------------add smry-------------------------------------------------------
 509         if self.is_training:
 510             cls_category = tf.argmax(cls_prob, axis=1)
 511             fast_acc = tf.reduce_mean(tf.to_float(tf.equal(cls_category, tf.to_int64(labels))))
 512             tf.summary.scalar('ACC/fast_acc', fast_acc)
 513
 514         rois = tf.concat(rois_list, axis=0, name='concat_rois')
 515         #  6. postprocess_fastrcnn
 516         if not self.is_training:
 517             return self.postprocess_fastrcnn(rois=rois, bbox_ppred=bbox_pred, scores=cls_prob, img_shape=img_shape)
 518         else:
 519             '''
 520             when trian. We need build Loss
 521             '''
 522             loss_dict = self.build_loss(rpn_box_pred=fpn_box_pred,
 523                                         rpn_bbox_targets=fpn_bbox_targets,
 524                                         rpn_cls_score=fpn_cls_score,
 525                                         rpn_labels=fpn_labels,
 526                                         bbox_pred=bbox_pred,
 527                                         bbox_targets=bbox_targets,
 528                                         cls_score=cls_score,
 529                                         labels=labels)
 530
 531             final_bbox, final_scores, final_category = self.postprocess_fastrcnn(rois=rois,
 532                                                                                  bbox_ppred=bbox_pred,
 533                                                                                  scores=cls_prob,
 534                                                                                  img_shape=img_shape)
 535             return final_bbox, final_scores, final_category, loss_dict
 536
 537     def get_restorer(self):
 538         checkpoint_path = tf.train.latest_checkpoint(os.path.join(cfgs.TRAINED_CKPT, cfgs.VERSION))
 539
 540         if checkpoint_path != None:
 541             restorer = tf.train.Saver()
 542             print("model restore from :", checkpoint_path)
 543         else:
 544             checkpoint_path = cfgs.PRETRAINED_CKPT
 545             print("model restore from pretrained mode, path is :", checkpoint_path)
 546
 547             model_variables = slim.get_model_variables()
 548             # for var in model_variables:
 549             #     print(var.name)
 550             # print(20*"__++__++__")
 551
 552             def name_in_ckpt_rpn(var):
 553                 return var.op.name
 554
 555             def name_in_ckpt_fastrcnn_head(var):
 556                 '''
 557                 Fast-RCNN/resnet_v1_50/block4 -->resnet_v1_50/block4
 558                 Fast-RCNN/MobilenetV2/** -- > MobilenetV2 **
 559                 :param var:
 560                 :return:
 561                 '''
 562                 return '/'.join(var.op.name.split('/')[1:])
 563             nameInCkpt_Var_dict = {}
 564             for var in model_variables:
 565                 if var.name.startswith(self.base_network_name):
 566                     var_name_in_ckpt = name_in_ckpt_rpn(var)
 567                     nameInCkpt_Var_dict[var_name_in_ckpt] = var
 568             restore_variables = nameInCkpt_Var_dict
 569             for key, item in restore_variables.items():
 570                 print("var_in_graph: ", item.name)
 571                 print("var_in_ckpt: ", key)
 572                 print(20*"___")
 573             restorer = tf.train.Saver(restore_variables)
 574             print(20 * "****")
 575             print("restore from pretrained_weighs in IMAGE_NET")
 576         return restorer, checkpoint_path
 577
 578     def get_gradients(self, optimizer, loss):
 579         '''
 580
 581         :param optimizer:
 582         :param loss:
 583         :return:
 584
 585         return vars and grads that not be fixed
 586         '''
 587
 588         # if cfgs.FIXED_BLOCKS > 0:
 589         #     trainable_vars = tf.trainable_variables()
 590         #     # trained_vars = slim.get_trainable_variables()
 591         #     start_names = [cfgs.NET_NAME + '/block%d'%i for i in range(1, cfgs.FIXED_BLOCKS+1)] + \
 592         #                   [cfgs.NET_NAME + '/conv1']
 593         #     start_names = tuple(start_names)
 594         #     trained_var_list = []
 595         #     for var in trainable_vars:
 596         #         if not var.name.startswith(start_names):
 597         #             trained_var_list.append(var)
 598         #     # slim.learning.train()
 599         #     grads = optimizer.compute_gradients(loss, var_list=trained_var_list)
 600         #     return grads
 601         # else:
 602         #     return optimizer.compute_gradients(loss)
 603         return optimizer.compute_gradients(loss)
 604
 605     def enlarge_gradients_for_bias(self, gradients):
 606
 607         final_gradients = []
 608         with tf.variable_scope("Gradient_Mult") as scope:
 609             for grad, var in gradients:
 610                 scale = 1.0
 611                 if cfgs.MUTILPY_BIAS_GRADIENT and './biases' in var.name:
 612                     scale = scale * cfgs.MUTILPY_BIAS_GRADIENT
 613                 if not np.allclose(scale, 1.0):
 614                     grad = tf.multiply(grad, scale)
 615                 final_gradients.append((grad, var))
 616         return final_gradients
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