# -*- coding:utf-8 -*- from __future__ import absolute_import from __future__ import print_function from __future__ import division import tensorflow as tf import tensorflow.contrib.slim as slim import os, sys sys.path.append("../") sys.path.append("../data/lib_coco") sys.path.append('../data/lib_coco/PythonAPI/') import numpy as np import time from libs.configs import cfgs from libs.networks import build_whole_network from data.io import image_preprocess from libs.box_utils import show_box_in_tensor from help_utils import tools from data.lib_coco.get_coco_next_batch import next_img os.environ["CUDA_VISIBLE_DEVICES"] = cfgs.GPU_GROUP def preprocess_img(img_plac, gtbox_plac): ''' :param img_plac: [H, W, 3] uint 8 img. In RGB. :param gtbox_plac: shape of [-1, 5]. [xmin, ymin, xmax, ymax, label] :return: ''' img = tf.cast(img_plac, tf.float32) # gtboxes_and_label = tf.cast(gtbox_plac, tf.float32) img, gtboxes_and_label = image_preprocess.short_side_resize(img_tensor=img, gtboxes_and_label=gtbox_plac, target_shortside_len=cfgs.IMG_SHORT_SIDE_LEN, length_limitation=cfgs.IMG_MAX_LENGTH) img, gtboxes_and_label = image_preprocess.random_flip_left_right(img_tensor=img, gtboxes_and_label=gtboxes_and_label) img = img - tf.constant([[cfgs.PIXEL_MEAN]]) img_batch = tf.expand_dims(img, axis=0) # gtboxes_and_label = tf.Print(gtboxes_and_label, [tf.shape(gtboxes_and_label)], message='gtbox shape') return img_batch, gtboxes_and_label def train(): faster_rcnn = build_whole_network.DetectionNetwork(base_network_name=cfgs.NET_NAME, is_training=True) with tf.name_scope('get_batch'): img_plac = tf.placeholder(dtype=tf.uint8, shape=[None, None, 3]) gtbox_plac = tf.placeholder(dtype=tf.int32, shape=[None, 5]) img_batch, gtboxes_and_label = preprocess_img(img_plac, gtbox_plac) # gtboxes_and_label = tf.reshape(gtboxes_and_label_batch, [-1, 5]) biases_regularizer = tf.no_regularizer weights_regularizer = tf.contrib.layers.l2_regularizer(cfgs.WEIGHT_DECAY) # list as many types of layers as possible, even if they are not used now with slim.arg_scope([slim.conv2d, slim.conv2d_in_plane, \ slim.conv2d_transpose, slim.separable_conv2d, slim.fully_connected], weights_regularizer=weights_regularizer, biases_regularizer=biases_regularizer, biases_initializer=tf.constant_initializer(0.0)): final_bbox, final_scores, final_category, loss_dict = faster_rcnn.build_whole_detection_network( input_img_batch=img_batch, gtboxes_batch=gtboxes_and_label) # ----------------------------------------------------------------------------------------------------build loss weight_decay_loss = 0 # tf.add_n(slim.losses.get_regularization_losses()) rpn_location_loss = loss_dict['rpn_loc_loss'] rpn_cls_loss = loss_dict['rpn_cls_loss'] rpn_total_loss = rpn_location_loss + rpn_cls_loss fastrcnn_cls_loss = loss_dict['fastrcnn_cls_loss'] fastrcnn_loc_loss = loss_dict['fastrcnn_loc_loss'] fastrcnn_total_loss = fastrcnn_cls_loss + fastrcnn_loc_loss total_loss = rpn_total_loss + fastrcnn_total_loss + weight_decay_loss # ____________________________________________________________________________________________________build loss # ---------------------------------------------------------------------------------------------------add summary tf.summary.scalar('RPN_LOSS/cls_loss', rpn_cls_loss) tf.summary.scalar('RPN_LOSS/location_loss', rpn_location_loss) tf.summary.scalar('RPN_LOSS/rpn_total_loss', rpn_total_loss) tf.summary.scalar('FAST_LOSS/fastrcnn_cls_loss', fastrcnn_cls_loss) tf.summary.scalar('FAST_LOSS/fastrcnn_location_loss', fastrcnn_loc_loss) tf.summary.scalar('FAST_LOSS/fastrcnn_total_loss', fastrcnn_total_loss) tf.summary.scalar('LOSS/total_loss', total_loss) tf.summary.scalar('LOSS/regular_weights', weight_decay_loss) gtboxes_in_img = show_box_in_tensor.draw_boxes_with_categories(img_batch=img_batch, boxes=gtboxes_and_label[:, :-1], labels=gtboxes_and_label[:, -1]) if cfgs.ADD_BOX_IN_TENSORBOARD: detections_in_img = show_box_in_tensor.draw_boxes_with_categories_and_scores(img_batch=img_batch, boxes=final_bbox, labels=final_category, scores=final_scores) tf.summary.image('Compare/final_detection', detections_in_img) tf.summary.image('Compare/gtboxes', gtboxes_in_img) # ___________________________________________________________________________________________________add summary global_step = slim.get_or_create_global_step() lr = tf.train.piecewise_constant(global_step, boundaries=[np.int64(cfgs.DECAY_STEP[0]), np.int64(cfgs.DECAY_STEP[1])], values=[cfgs.LR, cfgs.LR / 10., cfgs.LR / 100.]) tf.summary.scalar('lr', lr) optimizer = tf.train.MomentumOptimizer(lr, momentum=cfgs.MOMENTUM) # ---------------------------------------------------------------------------------------------compute gradients gradients = faster_rcnn.get_gradients(optimizer, total_loss) # enlarge_gradients for bias if cfgs.MUTILPY_BIAS_GRADIENT: gradients = faster_rcnn.enlarge_gradients_for_bias(gradients) if cfgs.GRADIENT_CLIPPING_BY_NORM: with tf.name_scope('clip_gradients_YJR'): gradients = slim.learning.clip_gradient_norms(gradients, cfgs.GRADIENT_CLIPPING_BY_NORM) # _____________________________________________________________________________________________compute gradients # train_op train_op = optimizer.apply_gradients(grads_and_vars=gradients, global_step=global_step) summary_op = tf.summary.merge_all() init_op = tf.group( tf.global_variables_initializer(), tf.local_variables_initializer() ) restorer, restore_ckpt = faster_rcnn.get_restorer() saver = tf.train.Saver(max_to_keep=30) config = tf.ConfigProto() config.gpu_options.allow_growth = True with tf.Session(config=config) as sess: sess.run(init_op) if not restorer is None: restorer.restore(sess, restore_ckpt) print('restore model') summary_path = os.path.join(cfgs.SUMMARY_PATH, cfgs.VERSION) tools.mkdir(summary_path) summary_writer = tf.summary.FileWriter(summary_path, graph=sess.graph) for step in range(cfgs.MAX_ITERATION): img_id, img, gt_info = next_img(step=step) training_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())) if step % cfgs.SHOW_TRAIN_INFO_INTE != 0 and step % cfgs.SMRY_ITER != 0: _, global_stepnp = sess.run([train_op, global_step], feed_dict={img_plac: img, gtbox_plac: gt_info} ) else: if step % cfgs.SHOW_TRAIN_INFO_INTE == 0 and step % cfgs.SMRY_ITER != 0: start = time.time() _, global_stepnp, rpnLocLoss, rpnClsLoss, rpnTotalLoss, \ fastrcnnLocLoss, fastrcnnClsLoss, fastrcnnTotalLoss, totalLoss = \ sess.run( [train_op, global_step, rpn_location_loss, rpn_cls_loss, rpn_total_loss, fastrcnn_loc_loss, fastrcnn_cls_loss, fastrcnn_total_loss, total_loss], feed_dict={img_plac: img, gtbox_plac: gt_info}) end = time.time() print(""" {}: step{} image_name:{} |\t rpn_loc_loss:{} |\t rpn_cla_loss:{} |\t rpn_total_loss:{} | fast_rcnn_loc_loss:{} |\t fast_rcnn_cla_loss:{} |\t fast_rcnn_total_loss:{} | total_loss:{} |\t per_cost_time:{}s""" \ .format(training_time, global_stepnp, str(img_id), rpnLocLoss, rpnClsLoss, rpnTotalLoss, fastrcnnLocLoss, fastrcnnClsLoss, fastrcnnTotalLoss, totalLoss, (end - start))) else: if step % cfgs.SMRY_ITER == 0: _, global_stepnp, summary_str = sess.run([train_op, global_step, summary_op], feed_dict={img_plac: img, gtbox_plac: gt_info} ) summary_writer.add_summary(summary_str, global_stepnp) summary_writer.flush() if (step > 0 and step % cfgs.SAVE_WEIGHTS_INTE == 0) or (step == cfgs.MAX_ITERATION - 1): save_dir = os.path.join(cfgs.TRAINED_CKPT, cfgs.VERSION) if not os.path.exists(save_dir): os.mkdir(save_dir) save_ckpt = os.path.join(save_dir, 'voc_' + str(global_stepnp) + 'model.ckpt') saver.save(sess, save_ckpt) print(' weights had been saved') if __name__ == '__main__': train() #