3 from __future__ import absolute_import
4 from __future__ import print_function
5 from __future__ import division
7 import tensorflow as tf
8 import tensorflow.contrib.slim as slim
10 sys.path.append("../")
11 sys.path.append("../data/lib_coco")
12 sys.path.append('../data/lib_coco/PythonAPI/')
17 from libs.configs import cfgs
18 from libs.networks import build_whole_network
19 from data.io import image_preprocess
20 from libs.box_utils import show_box_in_tensor
21 from help_utils import tools
22 from data.lib_coco.get_coco_next_batch import next_img
25 os.environ["CUDA_VISIBLE_DEVICES"] = cfgs.GPU_GROUP
28 def preprocess_img(img_plac, gtbox_plac):
31 :param img_plac: [H, W, 3] uint 8 img. In RGB.
32 :param gtbox_plac: shape of [-1, 5]. [xmin, ymin, xmax, ymax, label]
36 img = tf.cast(img_plac, tf.float32)
38 # gtboxes_and_label = tf.cast(gtbox_plac, tf.float32)
39 img, gtboxes_and_label = image_preprocess.short_side_resize(img_tensor=img,
40 gtboxes_and_label=gtbox_plac,
41 target_shortside_len=cfgs.IMG_SHORT_SIDE_LEN,
42 length_limitation=cfgs.IMG_MAX_LENGTH)
43 img, gtboxes_and_label = image_preprocess.random_flip_left_right(img_tensor=img,
44 gtboxes_and_label=gtboxes_and_label)
45 img = img - tf.constant([[cfgs.PIXEL_MEAN]])
46 img_batch = tf.expand_dims(img, axis=0)
48 # gtboxes_and_label = tf.Print(gtboxes_and_label, [tf.shape(gtboxes_and_label)], message='gtbox shape')
49 return img_batch, gtboxes_and_label
53 faster_rcnn = build_whole_network.DetectionNetwork(base_network_name=cfgs.NET_NAME,
56 with tf.name_scope('get_batch'):
57 img_plac = tf.placeholder(dtype=tf.uint8, shape=[None, None, 3])
58 gtbox_plac = tf.placeholder(dtype=tf.int32, shape=[None, 5])
60 img_batch, gtboxes_and_label = preprocess_img(img_plac, gtbox_plac)
61 # gtboxes_and_label = tf.reshape(gtboxes_and_label_batch, [-1, 5])
63 biases_regularizer = tf.no_regularizer
64 weights_regularizer = tf.contrib.layers.l2_regularizer(cfgs.WEIGHT_DECAY)
66 # list as many types of layers as possible, even if they are not used now
67 with slim.arg_scope([slim.conv2d, slim.conv2d_in_plane, \
68 slim.conv2d_transpose, slim.separable_conv2d, slim.fully_connected],
69 weights_regularizer=weights_regularizer,
70 biases_regularizer=biases_regularizer,
71 biases_initializer=tf.constant_initializer(0.0)):
72 final_bbox, final_scores, final_category, loss_dict = faster_rcnn.build_whole_detection_network(
73 input_img_batch=img_batch,
74 gtboxes_batch=gtboxes_and_label)
76 # ----------------------------------------------------------------------------------------------------build loss
77 weight_decay_loss = 0 # tf.add_n(slim.losses.get_regularization_losses())
78 rpn_location_loss = loss_dict['rpn_loc_loss']
79 rpn_cls_loss = loss_dict['rpn_cls_loss']
80 rpn_total_loss = rpn_location_loss + rpn_cls_loss
82 fastrcnn_cls_loss = loss_dict['fastrcnn_cls_loss']
83 fastrcnn_loc_loss = loss_dict['fastrcnn_loc_loss']
84 fastrcnn_total_loss = fastrcnn_cls_loss + fastrcnn_loc_loss
86 total_loss = rpn_total_loss + fastrcnn_total_loss + weight_decay_loss
87 # ____________________________________________________________________________________________________build loss
91 # ---------------------------------------------------------------------------------------------------add summary
92 tf.summary.scalar('RPN_LOSS/cls_loss', rpn_cls_loss)
93 tf.summary.scalar('RPN_LOSS/location_loss', rpn_location_loss)
94 tf.summary.scalar('RPN_LOSS/rpn_total_loss', rpn_total_loss)
96 tf.summary.scalar('FAST_LOSS/fastrcnn_cls_loss', fastrcnn_cls_loss)
97 tf.summary.scalar('FAST_LOSS/fastrcnn_location_loss', fastrcnn_loc_loss)
98 tf.summary.scalar('FAST_LOSS/fastrcnn_total_loss', fastrcnn_total_loss)
100 tf.summary.scalar('LOSS/total_loss', total_loss)
101 tf.summary.scalar('LOSS/regular_weights', weight_decay_loss)
103 gtboxes_in_img = show_box_in_tensor.draw_boxes_with_categories(img_batch=img_batch,
104 boxes=gtboxes_and_label[:, :-1],
105 labels=gtboxes_and_label[:, -1])
106 if cfgs.ADD_BOX_IN_TENSORBOARD:
107 detections_in_img = show_box_in_tensor.draw_boxes_with_categories_and_scores(img_batch=img_batch,
109 labels=final_category,
111 tf.summary.image('Compare/final_detection', detections_in_img)
112 tf.summary.image('Compare/gtboxes', gtboxes_in_img)
114 # ___________________________________________________________________________________________________add summary
116 global_step = slim.get_or_create_global_step()
117 lr = tf.train.piecewise_constant(global_step,
118 boundaries=[np.int64(cfgs.DECAY_STEP[0]), np.int64(cfgs.DECAY_STEP[1])],
119 values=[cfgs.LR, cfgs.LR / 10., cfgs.LR / 100.])
120 tf.summary.scalar('lr', lr)
121 optimizer = tf.train.MomentumOptimizer(lr, momentum=cfgs.MOMENTUM)
123 # ---------------------------------------------------------------------------------------------compute gradients
124 gradients = faster_rcnn.get_gradients(optimizer, total_loss)
126 # enlarge_gradients for bias
127 if cfgs.MUTILPY_BIAS_GRADIENT:
128 gradients = faster_rcnn.enlarge_gradients_for_bias(gradients)
130 if cfgs.GRADIENT_CLIPPING_BY_NORM:
131 with tf.name_scope('clip_gradients_YJR'):
132 gradients = slim.learning.clip_gradient_norms(gradients,
133 cfgs.GRADIENT_CLIPPING_BY_NORM)
134 # _____________________________________________________________________________________________compute gradients
139 train_op = optimizer.apply_gradients(grads_and_vars=gradients,
140 global_step=global_step)
141 summary_op = tf.summary.merge_all()
143 tf.global_variables_initializer(),
144 tf.local_variables_initializer()
147 restorer, restore_ckpt = faster_rcnn.get_restorer()
148 saver = tf.train.Saver(max_to_keep=30)
150 config = tf.ConfigProto()
151 config.gpu_options.allow_growth = True
153 with tf.Session(config=config) as sess:
155 if not restorer is None:
156 restorer.restore(sess, restore_ckpt)
157 print('restore model')
159 summary_path = os.path.join(cfgs.SUMMARY_PATH, cfgs.VERSION)
160 tools.mkdir(summary_path)
161 summary_writer = tf.summary.FileWriter(summary_path, graph=sess.graph)
163 for step in range(cfgs.MAX_ITERATION):
165 img_id, img, gt_info = next_img(step=step)
166 training_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time()))
168 if step % cfgs.SHOW_TRAIN_INFO_INTE != 0 and step % cfgs.SMRY_ITER != 0:
169 _, global_stepnp = sess.run([train_op, global_step],
170 feed_dict={img_plac: img,
175 if step % cfgs.SHOW_TRAIN_INFO_INTE == 0 and step % cfgs.SMRY_ITER != 0:
178 _, global_stepnp, rpnLocLoss, rpnClsLoss, rpnTotalLoss, \
179 fastrcnnLocLoss, fastrcnnClsLoss, fastrcnnTotalLoss, totalLoss = \
181 [train_op, global_step, rpn_location_loss, rpn_cls_loss, rpn_total_loss,
182 fastrcnn_loc_loss, fastrcnn_cls_loss, fastrcnn_total_loss, total_loss],
183 feed_dict={img_plac: img,
184 gtbox_plac: gt_info})
186 print(""" {}: step{} image_name:{} |\t
187 rpn_loc_loss:{} |\t rpn_cla_loss:{} |\t rpn_total_loss:{} |
188 fast_rcnn_loc_loss:{} |\t fast_rcnn_cla_loss:{} |\t fast_rcnn_total_loss:{} |
189 total_loss:{} |\t per_cost_time:{}s""" \
190 .format(training_time, global_stepnp, str(img_id), rpnLocLoss, rpnClsLoss,
191 rpnTotalLoss, fastrcnnLocLoss, fastrcnnClsLoss, fastrcnnTotalLoss, totalLoss,
194 if step % cfgs.SMRY_ITER == 0:
195 _, global_stepnp, summary_str = sess.run([train_op, global_step, summary_op],
196 feed_dict={img_plac: img,
199 summary_writer.add_summary(summary_str, global_stepnp)
200 summary_writer.flush()
202 if (step > 0 and step % cfgs.SAVE_WEIGHTS_INTE == 0) or (step == cfgs.MAX_ITERATION - 1):
204 save_dir = os.path.join(cfgs.TRAINED_CKPT, cfgs.VERSION)
205 if not os.path.exists(save_dir):
208 save_ckpt = os.path.join(save_dir, 'voc_' + str(global_stepnp) + 'model.ckpt')
209 saver.save(sess, save_ckpt)
210 print(' weights had been saved')
213 if __name__ == '__main__':