Load pretrained VGG model for face detection. (#925)

* load pretrained vgg model

* load pretrained vgg model

* delete irrelevant tools

* delete unused lines

fluid/face_detction/train.py

+import os
 import numpy as np
 import argparse
 import functools
@@ -13,11 +14,13 @@ add_arg = functools.partial(add_arguments, argparser=parser)
 add_arg('batch_size',       int,   32,        "Minibatch size.")
 add_arg('use_gpu',          bool,  True,      "Whether use GPU.")
 add_arg('parallel',         bool,  True,      "Parallel.")
+add_arg('pretrained_model', str,   "./vgg_model/", "The init model path.")
 #yapf: enable
 
 def train(args,
           learning_rate,
-          batch_size):
+          batch_size,
+          pretrained_model):
 
     network = PyramidBox([3, 640, 640])
     face_loss, head_loss = network.train()
@@ -37,9 +40,16 @@ def train(args,
     place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
     exe = fluid.Executor(place)
     exe.run(fluid.default_startup_program())
-    #print(fluid.default_main_program())
-    #print(test_program)
-    #fluid.io.save_persistables(exe, "model")
+
+    # fluid.io.save_inference_model('./vgg_model/', ['image'], [loss], exe)
+    if pretrained_model:
+        def if_exist(var):
+            return os.path.exists(os.path.join(pretrained_model, var.name))
+        fluid.io.load_vars(exe, pretrained_model, predicate=if_exist)
+
+    # print(fluid.default_main_program())
+    # print(test_program)
+    # fluid.io.save_persistables(exe, "model")
 
 
 if __name__ == '__main__':
@@ -48,4 +58,5 @@ if __name__ == '__main__':
 
     train(args,
           learning_rate=0.01,
-          batch_size=args.batch_size)
+          batch_size=args.batch_size,
+          pretrained_model=args.pretrained_model)