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