--- /dev/null
+# -*- 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()
+
+#
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Code Review / ealt-edge.git / blobdiff