fix/update for pretrained transfer learning

+from __future__ import division, print_function
+
+import os
+import sys
+import tensorflow as tf
+import numpy as np
+
+from model import yolov3
+from misc_utils import parse_anchors, load_weights
+
+img_size = 416
+weight_path = '../../data/darknet_weights/yolov3.weights'
+save_path = '../../data/darknet_weights/yolov3.ckpt'
+anchors = parse_anchors('../../data/yolo_anchors.txt')
+
+model = yolov3(80, anchors)
+with tf.Session() as sess:
+    inputs = tf.placeholder(tf.float32, [1, img_size, img_size, 3])
+
+    with tf.variable_scope('yolov3'):
+        feature_map = model.forward(inputs)
+
+    saver = tf.train.Saver(var_list=tf.global_variables(scope='yolov3'))
+
+    load_ops = load_weights(tf.global_variables(scope='yolov3'), weight_path)
+    sess.run(load_ops)
+    saver.save(sess, save_path=save_path)
+    print('TensorFlow model checkpoint has been saved to {}'.format(save_path))
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@@ -97,9 +97,9 @@ saver_to_restore = tf.train.Saver()
 
 
 with tf.Session() as sess:
 with tf.Session() as sess:
     sess.run([tf.global_variables_initializer()])
     sess.run([tf.global_variables_initializer()])
-    if os.path.exists(args.restore_path):
+    try:
         saver_to_restore.restore(sess, args.restore_path)
         saver_to_restore.restore(sess, args.restore_path)
-    else:
+    except:
         raise ValueError('there is no model to evaluate. You should move/create the checkpoint file to restore path')
         raise ValueError('there is no model to evaluate. You should move/create the checkpoint file to restore path')
 
 
     print('\nStart evaluation...\n')
     print('\nStart evaluation...\n')

@@ -102,8 +102,12 @@ else:
 with tf.Session() as sess:
 with tf.Session() as sess:
     sess.run([tf.global_variables_initializer(), tf.local_variables_initializer()])
     sess.run([tf.global_variables_initializer(), tf.local_variables_initializer()])
 
 
-    if os.path.exists(args.restore_path):
-        saver_to_restore.restore(sess, args.restore_path)
+    try:
+        saver_to_restore.restore(sess, restore_path)
+        print("Restoring parameters...")
+    except:
+        print("*** Failed to restore parameters!!! You would need pretrained weights ***")
+
 
 
     print('\nStart training...: Total epoches =', args.total_epoches, '\n')
     print('\nStart training...: Total epoches =', args.total_epoches, '\n')
 
 
@@ -184,7 +188,6 @@ with tf.Session() as sess:
                 best_mAP = mAP
                 best_mAP = mAP
                 saver_best.save(sess, args.save_dir + 'best_model_Epoch_{}_step_{}_mAP_{:.4f}_loss_{:.4f}_lr_{:.7g}'.format(
                 saver_best.save(sess, args.save_dir + 'best_model_Epoch_{}_step_{}_mAP_{:.4f}_loss_{:.4f}_lr_{:.7g}'.format(
                                    epoch, int(__global_step), best_mAP, val_loss_total.average, __lr))
                                    epoch, int(__global_step), best_mAP, val_loss_total.average, __lr))
-                saver_to_restore.save(sess, restore_path)
                 
                 
       ## all epoches end
       ## all epoches end
       sess.run(val_init_op)
       sess.run(val_init_op)
@@ -226,5 +229,4 @@ with tf.Session() as sess:
       if save_optimizer and mAP > best_mAP:
       if save_optimizer and mAP > best_mAP:
           best_mAP = mAP
           best_mAP = mAP
           saver_best.save(sess, save_dir + 'best_model_Epoch_{}_step_{}_mAP_{:.4f}_loss_{:.4f}_lr_{:.7g}'.format(
           saver_best.save(sess, save_dir + 'best_model_Epoch_{}_step_{}_mAP_{:.4f}_loss_{:.4f}_lr_{:.7g}'.format(
-                            epoch, int(__global_step), best_mAP, val_loss_total.average, __lr))
-          saver_to_restore.save(sess, restore_path)
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+                            epoch, int(__global_step), best_mAP, val_loss_total.average, __lr))
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