--- /dev/null
+# -*-coding: utf-8 -*-
+
+from __future__ import absolute_import, division, print_function
+
+import os
+import tensorflow as tf
+import tensorflow.contrib.slim as slim
+import numpy as np
+
+from libs.networks import resnet
+from libs.networks import mobilenet_v2
+from libs.box_utils import encode_and_decode
+from libs.box_utils import boxes_utils
+from libs.box_utils import anchor_utils
+from libs.configs import cfgs
+from libs.losses import losses
+from libs.box_utils import show_box_in_tensor
+from libs.detection_oprations.proposal_opr import postprocess_rpn_proposals
+from libs.detection_oprations.anchor_target_layer_without_boxweight import anchor_target_layer
+from libs.detection_oprations.proposal_target_layer import proposal_target_layer
+
+
+class DetectionNetwork(object):
+
+ def __init__(self, base_network_name, is_training):
+
+ self.base_network_name = base_network_name
+ self.is_training = is_training
+ self.num_anchors_per_location = len(cfgs.ANCHOR_SCALES) * len(cfgs.ANCHOR_RATIOS)
+
+ def build_base_network(self, input_img_batch):
+
+ if self.base_network_name.startswith('resnet_v1'):
+ return resnet.resnet_base(input_img_batch, scope_name=self.base_network_name, is_training=self.is_training)
+
+ elif self.base_network_name.startswith('MobilenetV2'):
+ return mobilenet_v2.mobilenetv2_base(input_img_batch, is_training=self.is_training)
+
+ else:
+ raise ValueError('Sry, we only support resnet or mobilenet_v2')
+
+ def postprocess_fastrcnn(self, rois, bbox_ppred, scores, img_shape):
+ '''
+
+ :param rois:[-1, 4]
+ :param bbox_ppred: [-1, (cfgs.Class_num+1) * 4]
+ :param scores: [-1, cfgs.Class_num + 1]
+ :return:
+ '''
+
+ with tf.name_scope('postprocess_fastrcnn'):
+ rois = tf.stop_gradient(rois)
+ scores = tf.stop_gradient(scores)
+ bbox_ppred = tf.reshape(bbox_ppred, [-1, cfgs.CLASS_NUM + 1, 4])
+ bbox_ppred = tf.stop_gradient(bbox_ppred)
+
+ bbox_pred_list = tf.unstack(bbox_ppred, axis=1)
+ score_list = tf.unstack(scores, axis=1)
+
+ allclasses_boxes = []
+ allclasses_scores = []
+ categories = []
+ for i in range(1, cfgs.CLASS_NUM+1):
+
+ # 1. decode boxes in each class
+ tmp_encoded_box = bbox_pred_list[i]
+ tmp_score = score_list[i]
+ tmp_decoded_boxes = encode_and_decode.decode_boxes(encoded_boxes=tmp_encoded_box,
+ reference_boxes=rois,
+ scale_factors=cfgs.ROI_SCALE_FACTORS)
+ # tmp_decoded_boxes = encode_and_decode.decode_boxes(boxes=rois,
+ # deltas=tmp_encoded_box,
+ # scale_factor=cfgs.ROI_SCALE_FACTORS)
+
+ # 2. clip to img boundaries
+ tmp_decoded_boxes = boxes_utils.clip_boxes_to_img_boundaries(decode_boxes=tmp_decoded_boxes,
+ img_shape=img_shape)
+
+ # 3. NMS
+ keep = tf.image.non_max_suppression(
+ boxes=tmp_decoded_boxes,
+ scores=tmp_score,
+ max_output_size=cfgs.FAST_RCNN_NMS_MAX_BOXES_PER_CLASS,
+ iou_threshold=cfgs.FAST_RCNN_NMS_IOU_THRESHOLD)
+
+ perclass_boxes = tf.gather(tmp_decoded_boxes, keep)
+ perclass_scores = tf.gather(tmp_score, keep)
+
+ allclasses_boxes.append(perclass_boxes)
+ allclasses_scores.append(perclass_scores)
+ categories.append(tf.ones_like(perclass_scores) * i)
+
+ final_boxes = tf.concat(allclasses_boxes, axis=0)
+ final_scores = tf.concat(allclasses_scores, axis=0)
+ final_category = tf.concat(categories, axis=0)
+
+ if self.is_training:
+ '''
+ in training. We should show the detecitons in the tensorboard. So we add this.
+ '''
+ kept_indices = tf.reshape(tf.where(tf.greater_equal(final_scores, cfgs.SHOW_SCORE_THRSHOLD)), [-1])
+
+ final_boxes = tf.gather(final_boxes, kept_indices)
+ final_scores = tf.gather(final_scores, kept_indices)
+ final_category = tf.gather(final_category, kept_indices)
+
+ return final_boxes, final_scores, final_category
+
+ def roi_pooling(self, feature_maps, rois, img_shape, scope):
+ '''
+ Here use roi warping as roi_pooling
+
+ :param featuremaps_dict: feature map to crop
+ :param rois: shape is [-1, 4]. [x1, y1, x2, y2]
+ :return:
+ '''
+
+ with tf.variable_scope('ROI_Warping_'+scope):
+ img_h, img_w = tf.cast(img_shape[1], tf.float32), tf.cast(img_shape[2], tf.float32)
+ N = tf.shape(rois)[0]
+ x1, y1, x2, y2 = tf.unstack(rois, axis=1)
+
+ normalized_x1 = x1 / img_w
+ normalized_x2 = x2 / img_w
+ normalized_y1 = y1 / img_h
+ normalized_y2 = y2 / img_h
+
+ normalized_rois = tf.transpose(
+ tf.stack([normalized_y1, normalized_x1, normalized_y2, normalized_x2]), name='get_normalized_rois')
+
+ normalized_rois = tf.stop_gradient(normalized_rois)
+
+ cropped_roi_features = tf.image.crop_and_resize(feature_maps, normalized_rois,
+ box_ind=tf.zeros(shape=[N],
+ dtype=tf.int32),
+ crop_size=[cfgs.ROI_SIZE, cfgs.ROI_SIZE],
+ name='CROP_AND_RESIZE'
+ )
+ roi_features = slim.max_pool2d(cropped_roi_features,
+ [cfgs.ROI_POOL_KERNEL_SIZE, cfgs.ROI_POOL_KERNEL_SIZE],
+ stride=cfgs.ROI_POOL_KERNEL_SIZE)
+
+ return roi_features
+
+ def build_fastrcnn(self, P_list, rois_list, img_shape):
+
+ with tf.variable_scope('Fast-RCNN'):
+ # 5. ROI Pooling
+ with tf.variable_scope('rois_pooling'):
+ pooled_features_list = []
+ for level_name, p, rois in zip(cfgs.LEVLES, P_list, rois_list): # exclude P6_rois
+ # p = tf.Print(p, [tf.shape(p)], summarize=10, message=level_name+'SHPAE***')
+ pooled_features = self.roi_pooling(feature_maps=p, rois=rois, img_shape=img_shape,
+ scope=level_name)
+ pooled_features_list.append(pooled_features)
+
+ pooled_features = tf.concat(pooled_features_list, axis=0) # [minibatch_size, H, W, C]
+
+ # 6. inferecne rois in Fast-RCNN to obtain fc_flatten features
+ if self.base_network_name.startswith('resnet'):
+ fc_flatten = resnet.restnet_head(inputs=pooled_features,
+ is_training=self.is_training,
+ scope_name=self.base_network_name)
+ elif self.base_network_name.startswith('Mobile'):
+ fc_flatten = mobilenet_v2.mobilenetv2_head(inputs=pooled_features,
+ is_training=self.is_training)
+ else:
+ raise NotImplementedError('only support resnet and mobilenet')
+
+ # 7. cls and reg in Fast-RCNN
+ with slim.arg_scope([slim.fully_connected], weights_regularizer=slim.l2_regularizer(cfgs.WEIGHT_DECAY)):
+
+ cls_score = slim.fully_connected(fc_flatten,
+ num_outputs=cfgs.CLASS_NUM+1,
+ weights_initializer=cfgs.INITIALIZER,
+ activation_fn=None, trainable=self.is_training,
+ scope='cls_fc')
+
+ bbox_pred = slim.fully_connected(fc_flatten,
+ num_outputs=(cfgs.CLASS_NUM+1)*4,
+ weights_initializer=cfgs.BBOX_INITIALIZER,
+ activation_fn=None, trainable=self.is_training,
+ scope='reg_fc')
+ # for convient. It also produce (cls_num +1) bboxes
+
+ cls_score = tf.reshape(cls_score, [-1, cfgs.CLASS_NUM+1])
+ bbox_pred = tf.reshape(bbox_pred, [-1, 4*(cfgs.CLASS_NUM+1)])
+
+ return bbox_pred, cls_score
+
+ def assign_levels(self, all_rois, labels=None, bbox_targets=None):
+ '''
+
+ :param all_rois:
+ :param labels:
+ :param bbox_targets:
+ :return:
+ '''
+ with tf.name_scope('assign_levels'):
+ # all_rois = tf.Print(all_rois, [tf.shape(all_rois)], summarize=10, message='ALL_ROIS_SHAPE*****')
+ xmin, ymin, xmax, ymax = tf.unstack(all_rois, axis=1)
+
+ h = tf.maximum(0., ymax - ymin)
+ w = tf.maximum(0., xmax - xmin)
+
+ levels = tf.floor(4. + tf.log(tf.sqrt(w * h + 1e-8) / 224.0) / tf.log(2.)) # 4 + log_2(***)
+ # use floor instead of round
+
+ min_level = int(cfgs.LEVLES[0][-1])
+ max_level = min(5, int(cfgs.LEVLES[-1][-1]))
+ levels = tf.maximum(levels, tf.ones_like(levels) * min_level) # level minimum is 2
+ levels = tf.minimum(levels, tf.ones_like(levels) * max_level) # level maximum is 5
+
+ levels = tf.stop_gradient(tf.reshape(levels, [-1]))
+
+ def get_rois(levels, level_i, rois, labels, bbox_targets):
+
+ level_i_indices = tf.reshape(tf.where(tf.equal(levels, level_i)), [-1])
+ tf.summary.scalar('LEVEL/LEVEL_%d_rois_NUM'%level_i, tf.shape(level_i_indices)[0])
+ level_i_rois = tf.gather(rois, level_i_indices)
+
+ # if self.is_training:
+ # level_i_rois = tf.stop_gradient(tf.concat([level_i_rois, [[0, 0, 0., 0.]]], axis=0))
+ # # to avoid the num of level i rois is 0.0, which will broken the BP in tf
+ #
+ # level_i_labels = tf.gather(labels, level_i_indices)
+ # level_i_labels = tf.stop_gradient(tf.concat([level_i_labels, [0]], axis=0))
+ #
+ # level_i_targets = tf.gather(bbox_targets, level_i_indices)
+ # level_i_targets = tf.stop_gradient(tf.concat([level_i_targets,
+ # tf.zeros(shape=(1, 4*(cfgs.CLASS_NUM+1)), dtype=tf.float32)],
+ # axis=0))
+ #
+ # return level_i_rois, level_i_labels, level_i_targets
+ if self.is_training:
+ level_i_labels = tf.gather(labels, level_i_indices)
+ level_i_targets = tf.gather(bbox_targets, level_i_indices)
+ return level_i_rois, level_i_labels, level_i_targets
+ else:
+ return level_i_rois, None, None
+
+ rois_list = []
+ labels_list = []
+ targets_list = []
+ for i in range(min_level, max_level+1):
+ P_i_rois, P_i_labels, P_i_targets = get_rois(levels, level_i=i, rois=all_rois,
+ labels=labels,
+ bbox_targets=bbox_targets)
+ rois_list.append(P_i_rois)
+ labels_list.append(P_i_labels)
+ targets_list.append(P_i_targets)
+
+ if self.is_training:
+ all_labels = tf.concat(labels_list, axis=0)
+ all_targets = tf.concat(targets_list, axis=0)
+
+ return rois_list, \
+ tf.stop_gradient(all_labels), \
+ tf.stop_gradient(all_targets)
+ else:
+ return rois_list # [P2_rois, P3_rois, P4_rois, P5_rois] Note: P6 do not assign rois
+ # ---------------------------------------------------------------------------------------
+
+ def add_anchor_img_smry(self, img, anchors, labels):
+
+ positive_anchor_indices = tf.reshape(tf.where(tf.greater_equal(labels, 1)), [-1])
+ negative_anchor_indices = tf.reshape(tf.where(tf.equal(labels, 0)), [-1])
+
+ positive_anchor = tf.gather(anchors, positive_anchor_indices)
+ negative_anchor = tf.gather(anchors, negative_anchor_indices)
+
+ pos_in_img = show_box_in_tensor.only_draw_boxes(img_batch=img,
+ boxes=positive_anchor)
+ neg_in_img = show_box_in_tensor.only_draw_boxes(img_batch=img,
+ boxes=negative_anchor)
+
+ tf.summary.image('positive_anchor', pos_in_img)
+ tf.summary.image('negative_anchors', neg_in_img)
+
+ def add_roi_batch_img_smry(self, img, rois, labels):
+ positive_roi_indices = tf.reshape(tf.where(tf.greater_equal(labels, 1)), [-1])
+
+ negative_roi_indices = tf.reshape(tf.where(tf.equal(labels, 0)), [-1])
+
+ pos_roi = tf.gather(rois, positive_roi_indices)
+ neg_roi = tf.gather(rois, negative_roi_indices)
+
+
+ pos_in_img = show_box_in_tensor.only_draw_boxes(img_batch=img,
+ boxes=pos_roi)
+ neg_in_img = show_box_in_tensor.only_draw_boxes(img_batch=img,
+ boxes=neg_roi)
+ tf.summary.image('pos_rois', pos_in_img)
+ tf.summary.image('neg_rois', neg_in_img)
+
+ def build_loss(self, rpn_box_pred, rpn_bbox_targets, rpn_cls_score, rpn_labels,
+ bbox_pred, bbox_targets, cls_score, labels):
+ '''
+
+ :param rpn_box_pred: [-1, 4]
+ :param rpn_bbox_targets: [-1, 4]
+ :param rpn_cls_score: [-1]
+ :param rpn_labels: [-1]
+ :param bbox_pred: [-1, 4*(cls_num+1)]
+ :param bbox_targets: [-1, 4*(cls_num+1)]
+ :param cls_score: [-1, cls_num+1]
+ :param labels: [-1]
+ :return:
+ '''
+ with tf.variable_scope('build_loss') as sc:
+ with tf.variable_scope('rpn_loss'):
+
+ rpn_bbox_loss = losses.smooth_l1_loss_rpn(bbox_pred=rpn_box_pred,
+ bbox_targets=rpn_bbox_targets,
+ label=rpn_labels,
+ sigma=cfgs.RPN_SIGMA)
+ # rpn_cls_loss:
+ # rpn_cls_score = tf.reshape(rpn_cls_score, [-1, 2])
+ # rpn_labels = tf.reshape(rpn_labels, [-1])
+ # ensure rpn_labels shape is [-1]
+ rpn_select = tf.reshape(tf.where(tf.not_equal(rpn_labels, -1)), [-1])
+ rpn_cls_score = tf.reshape(tf.gather(rpn_cls_score, rpn_select), [-1, 2])
+ rpn_labels = tf.reshape(tf.gather(rpn_labels, rpn_select), [-1])
+ rpn_cls_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=rpn_cls_score,
+ labels=rpn_labels))
+
+ rpn_cls_loss = rpn_cls_loss * cfgs.RPN_CLASSIFICATION_LOSS_WEIGHT
+ rpn_bbox_loss = rpn_bbox_loss * cfgs.RPN_LOCATION_LOSS_WEIGHT
+
+ with tf.variable_scope('FastRCNN_loss'):
+ if not cfgs.FAST_RCNN_MINIBATCH_SIZE == -1:
+ bbox_loss = losses.smooth_l1_loss_rcnn(bbox_pred=bbox_pred,
+ bbox_targets=bbox_targets,
+ label=labels,
+ num_classes=cfgs.CLASS_NUM + 1,
+ sigma=cfgs.FASTRCNN_SIGMA)
+
+ # cls_score = tf.reshape(cls_score, [-1, cfgs.CLASS_NUM + 1])
+ # labels = tf.reshape(labels, [-1])
+ cls_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(
+ logits=cls_score,
+ labels=labels)) # beacause already sample before
+ else:
+ '''
+ applying OHEM here
+ '''
+ print(20 * "@@")
+ print("@@" + 10 * " " + "TRAIN WITH OHEM ...")
+ print(20 * "@@")
+ cls_loss = bbox_loss = losses.sum_ohem_loss(
+ cls_score=cls_score,
+ label=labels,
+ bbox_targets=bbox_targets,
+ nr_ohem_sampling=128,
+ nr_classes=cfgs.CLASS_NUM + 1)
+ cls_loss = cls_loss * cfgs.FAST_RCNN_CLASSIFICATION_LOSS_WEIGHT
+ bbox_loss = bbox_loss * cfgs.FAST_RCNN_LOCATION_LOSS_WEIGHT
+ loss_dict = {
+ 'rpn_cls_loss': rpn_cls_loss,
+ 'rpn_loc_loss': rpn_bbox_loss,
+ 'fastrcnn_cls_loss': cls_loss,
+ 'fastrcnn_loc_loss': bbox_loss
+ }
+ return loss_dict
+
+
+ def build_whole_detection_network(self, input_img_batch, gtboxes_batch):
+
+ if self.is_training:
+ # ensure shape is [M, 5]
+ gtboxes_batch = tf.reshape(gtboxes_batch, [-1, 5])
+ gtboxes_batch = tf.cast(gtboxes_batch, tf.float32)
+
+ img_shape = tf.shape(input_img_batch)
+
+ # 1. build base network
+ P_dict = self.build_base_network(input_img_batch) # [P2, P3, P4, P5, P6]
+
+ # 2. build fpn by build rpn for each level
+ with tf.variable_scope("build_FPN", regularizer=slim.l2_regularizer(cfgs.WEIGHT_DECAY)):
+ fpn_box_delta = {}
+ fpn_cls_score = {}
+ fpn_cls_prob = {}
+ for key in cfgs.LEVLES:
+ if cfgs.SHARE_HEADS:
+ reuse_flag = None if key == cfgs.LEVLES[0] else True
+ scope_list = ['fpn_conv/3x3', 'fpn_cls_score', 'fpn_bbox_pred']
+ else:
+ reuse_flag = None
+ scope_list= ['fpn_conv/3x3_%s' % key, 'fpn_cls_score_%s' % key, 'fpn_bbox_pred_%s' % key]
+ rpn_conv3x3 = slim.conv2d(
+ P_dict[key], 512, [3, 3],
+ trainable=self.is_training, weights_initializer=cfgs.INITIALIZER, padding="SAME",
+ activation_fn=tf.nn.relu,
+ scope=scope_list[0],
+ reuse=reuse_flag)
+ rpn_cls_score = slim.conv2d(rpn_conv3x3, self.num_anchors_per_location*2, [1, 1], stride=1,
+ trainable=self.is_training, weights_initializer=cfgs.INITIALIZER,
+ activation_fn=None, padding="VALID",
+ scope=scope_list[1],
+ reuse=reuse_flag)
+ rpn_box_delta = slim.conv2d(rpn_conv3x3, self.num_anchors_per_location*4, [1, 1], stride=1,
+ trainable=self.is_training, weights_initializer=cfgs.BBOX_INITIALIZER,
+ activation_fn=None, padding="VALID",
+ scope=scope_list[2],
+ reuse=reuse_flag)
+ fpn_box_delta[key] = tf.reshape(rpn_box_delta, [-1, 4])
+ fpn_cls_score[key] = tf.reshape(rpn_cls_score, [-1, 2])
+ fpn_cls_prob[key] = slim.softmax(fpn_cls_score[key])
+
+ # 3. generate anchors for fpn. (by generate for each level)
+ anchors_dict = {}
+ anchor_list = []
+ with tf.name_scope("generate_FPN_anchors"):
+ for key in cfgs.LEVLES:
+ p_h, p_w = tf.to_float(tf.shape(P_dict[key])[1]), tf.to_float(tf.shape(P_dict[key])[2])
+ id_ = int(key[-1]) - int(cfgs.LEVLES[0][-1]) # such as : 2-2, 3-3
+ tmp_anchors = anchor_utils.make_anchors(base_anchor_size=cfgs.BASE_ANCHOR_SIZE_LIST[id_],
+ anchor_scales=cfgs.ANCHOR_SCALES,
+ anchor_ratios=cfgs.ANCHOR_RATIOS,
+ stride=cfgs.ANCHOR_STRIDE_LIST[id_],
+ featuremap_height=p_h,
+ featuremap_width=p_w,
+ name='%s_make_anchors' % key)
+ anchors_dict[key] = tmp_anchors
+ anchor_list.append(tmp_anchors)
+ all_anchors = tf.concat(anchor_list, axis=0)
+
+ # 4. postprocess fpn proposals. such as: decode, clip, NMS
+ # Need to Note: Here we NMS for each level instead of NMS for all anchors.
+ rois_list = []
+ rois_scores_list = []
+ with tf.name_scope("postproces_fpn"):
+ for key in cfgs.LEVLES:
+ tmp_rois, tmp_roi_scores = postprocess_rpn_proposals(rpn_bbox_pred=fpn_box_delta[key],
+ rpn_cls_prob=fpn_cls_prob[key],
+ img_shape=img_shape,
+ anchors=anchors_dict[key],
+ is_training=self.is_training)
+ rois_list.append(tmp_rois)
+ rois_scores_list.append(tmp_roi_scores)
+ allrois = tf.concat(rois_list, axis=0)
+ allrois_scores = tf.concat(rois_scores_list, axis=0)
+ fpn_topk = cfgs.FPN_TOP_K_PER_LEVEL_TRAIN if self.is_training else cfgs.FPN_TOP_K_PER_LEVEL_TEST
+ topk = tf.minimum(fpn_topk, tf.shape(allrois)[0])
+
+ rois_scores, topk_indices = tf.nn.top_k(allrois_scores, k=topk)
+
+ rois = tf.stop_gradient(tf.gather(allrois, topk_indices))
+ rois_scores = tf.stop_gradient(rois_scores)
+
+ # +++++++++++++++++++++++++++++++++++++add img smry+++++++++++++++++++++++++++++++++++++++++++++++++++++++
+ if self.is_training:
+ score_gre_05 = tf.reshape(tf.where(tf.greater_equal(rois_scores, 0.5)), [-1])
+ score_gre_05_rois = tf.gather(rois, score_gre_05)
+ score_gre_05_score = tf.gather(rois_scores, score_gre_05)
+ score_gre_05_in_img = show_box_in_tensor.draw_boxes_with_scores(img_batch=input_img_batch,
+ boxes=score_gre_05_rois,
+ scores=score_gre_05_score)
+ tf.summary.image('score_greater_05_rois', score_gre_05_in_img)
+ # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
+ # sample for fpn. We should concat all the anchors
+ if self.is_training:
+ with tf.variable_scope('sample_anchors_minibatch'):
+ fpn_labels, fpn_bbox_targets = \
+ tf.py_func(
+ anchor_target_layer,
+ [gtboxes_batch, img_shape, all_anchors],
+ [tf.float32, tf.float32])
+ fpn_bbox_targets = tf.reshape(fpn_bbox_targets, [-1, 4])
+ fpn_labels = tf.to_int32(fpn_labels, name="to_int32")
+ fpn_labels = tf.reshape(fpn_labels, [-1])
+ self.add_anchor_img_smry(input_img_batch, all_anchors, fpn_labels)
+
+ with tf.control_dependencies([fpn_labels]):
+ with tf.variable_scope('sample_RCNN_minibatch'):
+ rois, labels, bbox_targets = \
+ tf.py_func(proposal_target_layer,
+ [rois, gtboxes_batch],
+ [tf.float32, tf.float32, tf.float32])
+ rois = tf.reshape(rois, [-1, 4])
+ labels = tf.to_int32(labels)
+ labels = tf.reshape(labels, [-1])
+ bbox_targets = tf.reshape(bbox_targets, [-1, 4*(cfgs.CLASS_NUM+1)])
+ self.add_roi_batch_img_smry(input_img_batch, rois, labels)
+ if self.is_training:
+ rois_list, labels, bbox_targets = self.assign_levels(all_rois=rois,
+ labels=labels,
+ bbox_targets=bbox_targets)
+ else:
+ rois_list = self.assign_levels(all_rois=rois) # rois_list: [P2_rois, P3_rois, P4_rois, P5_rois]
+
+ # -------------------------------------------------------------------------------------------------------------#
+ # Fast-RCNN #
+ # -------------------------------------------------------------------------------------------------------------#
+
+ # 5. build Fast-RCNN
+ # rois = tf.Print(rois, [tf.shape(rois)], 'rois shape', summarize=10)
+ bbox_pred, cls_score = self.build_fastrcnn(P_list=[P_dict[key] for key in cfgs.LEVLES],
+ rois_list=rois_list,
+ img_shape=img_shape)
+ # bbox_pred shape: [-1, 4*(cls_num+1)].
+ # cls_score shape： [-1, cls_num+1]
+
+ cls_prob = slim.softmax(cls_score, 'cls_prob')
+
+
+ # ----------------------------------------------add smry-------------------------------------------------------
+ if self.is_training:
+ cls_category = tf.argmax(cls_prob, axis=1)
+ fast_acc = tf.reduce_mean(tf.to_float(tf.equal(cls_category, tf.to_int64(labels))))
+ tf.summary.scalar('ACC/fast_acc', fast_acc)
+
+ rois = tf.concat(rois_list, axis=0, name='concat_rois')
+ # 6. postprocess_fastrcnn
+ if not self.is_training:
+ return self.postprocess_fastrcnn(rois=rois, bbox_ppred=bbox_pred, scores=cls_prob, img_shape=img_shape)
+ else:
+ '''
+ when trian. We need build Loss
+ '''
+ loss_dict = self.build_loss(rpn_box_pred=tf.concat([fpn_box_delta[key] for key in cfgs.LEVLES], axis=0),
+ rpn_bbox_targets=fpn_bbox_targets,
+ rpn_cls_score=tf.concat([fpn_cls_score[key] for key in cfgs.LEVLES], axis=0),
+ rpn_labels=fpn_labels,
+ bbox_pred=bbox_pred,
+ bbox_targets=bbox_targets,
+ cls_score=cls_score,
+ labels=labels)
+
+ final_bbox, final_scores, final_category = self.postprocess_fastrcnn(rois=rois,
+ bbox_ppred=bbox_pred,
+ scores=cls_prob,
+ img_shape=img_shape)
+ return final_bbox, final_scores, final_category, loss_dict
+
+ def get_restorer(self):
+ checkpoint_path = tf.train.latest_checkpoint(os.path.join(cfgs.TRAINED_CKPT, cfgs.VERSION))
+
+ if checkpoint_path != None:
+ restorer = tf.train.Saver()
+ print("model restore from :", checkpoint_path)
+ else:
+ checkpoint_path = cfgs.PRETRAINED_CKPT
+ print("model restore from pretrained mode, path is :", checkpoint_path)
+
+ model_variables = slim.get_model_variables()
+ # for var in model_variables:
+ # print(var.name)
+ # print(20*"__++__++__")
+
+ def name_in_ckpt_rpn(var):
+ return var.op.name
+
+ def name_in_ckpt_fastrcnn_head(var):
+ '''
+ Fast-RCNN/resnet_v1_50/block4 -->resnet_v1_50/block4
+ Fast-RCNN/MobilenetV2/** -- > MobilenetV2 **
+ :param var:
+ :return:
+ '''
+ return '/'.join(var.op.name.split('/')[1:])
+ nameInCkpt_Var_dict = {}
+ for var in model_variables:
+ if var.name.startswith(self.base_network_name):
+ var_name_in_ckpt = name_in_ckpt_rpn(var)
+ nameInCkpt_Var_dict[var_name_in_ckpt] = var
+ restore_variables = nameInCkpt_Var_dict
+ for key, item in restore_variables.items():
+ print("var_in_graph: ", item.name)
+ print("var_in_ckpt: ", key)
+ print(20*"___")
+ restorer = tf.train.Saver(restore_variables)
+ print(20 * "****")
+ print("restore from pretrained_weighs in IMAGE_NET")
+ return restorer, checkpoint_path
+
+ def get_gradients(self, optimizer, loss):
+ '''
+
+ :param optimizer:
+ :param loss:
+ :return:
+
+ return vars and grads that not be fixed
+ '''
+
+ # if cfgs.FIXED_BLOCKS > 0:
+ # trainable_vars = tf.trainable_variables()
+ # # trained_vars = slim.get_trainable_variables()
+ # start_names = [cfgs.NET_NAME + '/block%d'%i for i in range(1, cfgs.FIXED_BLOCKS+1)] + \
+ # [cfgs.NET_NAME + '/conv1']
+ # start_names = tuple(start_names)
+ # trained_var_list = []
+ # for var in trainable_vars:
+ # if not var.name.startswith(start_names):
+ # trained_var_list.append(var)
+ # # slim.learning.train()
+ # grads = optimizer.compute_gradients(loss, var_list=trained_var_list)
+ # return grads
+ # else:
+ # return optimizer.compute_gradients(loss)
+ return optimizer.compute_gradients(loss)
+
+ def enlarge_gradients_for_bias(self, gradients):
+
+ final_gradients = []
+ with tf.variable_scope("Gradient_Mult") as scope:
+ for grad, var in gradients:
+ scale = 1.0
+ if cfgs.MUTILPY_BIAS_GRADIENT and './biases' in var.name:
+ scale = scale * cfgs.MUTILPY_BIAS_GRADIENT
+ if not np.allclose(scale, 1.0):
+ grad = tf.multiply(grad, scale)
+ final_gradients.append((grad, var))
+ return final_gradients
+
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Code Review / ealt-edge.git / blobdiff