add demo & doc

tensorflow2fluid/convert.py

+from src.paddle_emitter import PaddleEmitter
+from src.tensorflow_parser import TensorflowCkptParser
+from src.tensorflow_parser import TensorflowPbParser
+from six import text_type as _text_type
+import argparse
+import sys
+import os
+
+def _get_parser():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--meta_file", "-m", type=_text_type, default=None, help="meta file path for checkpoint format")
+    parser.add_argument("--ckpt_dir", "-c", type=_text_type, default=None, help="checkpoint directory")
+    parser.add_argument("--pb_file", "-p", type=_text_type, default=None, help="pb model file path")
+    parser.add_argument("--in_nodes", "-i", type=_text_type, nargs="+", default=None, help="input nodes name")
+    parser.add_argument("--input_shape", "-is", type=_text_type, nargs="+", default=None, help="input tensor shape")
+    parser.add_argument("--output_nodes", "-o", type=_text_type, nargs="+", default=None, help="output nodes name")
+    parser.add_argument("--save_dir", "-s", type=_text_type, default=None, help="path to save transformed paddle model")
+    return parser
+
+def _convert(args):
+    if args.meta_file is None and args.pb_file is None:
+        raise Exception("Need to define --meta_file or --pb_file")
+    assert args.in_nodes is not None
+    assert args.output_nodes is not None
+    assert args.input_shape is not None
+    assert args.save_dir is not None
+
+    if os.path.exists(args.save_dir):
+        sys.stderr.write("save_dir already exists, change to a new path\n")
+        return
+    
+    os.makedirs(args.save_dir)
+
+    input_shape = list()
+    for shape_str in args.input_shape:
+        items = shape_str.split(',')
+        for i in range(len(items)):
+            if items[i] != "None":
+                items[i] = int(items[i])
+        input_shape.append(items)
+
+    if args.meta_file is not None:
+        parser = TensorflowCkptParser(args.meta_file, args.ckpt_dir, args.output_nodes, input_shape, args.in_nodes)
+    else:
+        parser = TensorflowPbParser(args.pb_file, args.output_nodes, input_shape, args.in_nodes)
+    emitter = PaddleEmitter(parser, args.save_dir)
+    emitter.run()
+
+if __name__ == "__main__":
+    parser = _get_parser()
+    args = parser.parse_args()
+    _convert(args)

tensorflow2fluid/demo/inception_v3/export_to_ckpt.py

+from tensorflow.contrib.slim.nets import inception
+from tensorflow.contrib.framework.python.ops import arg_scope
+import tensorflow.contrib.slim as slim
+import tensorflow as tf
+import numpy
+import sys
+numpy.random.seed(13)
+
+ckpt_file = sys.argv[1]
+checkpoint_dir = sys.argv[2]
+
+def get_tuned_variables():
+    CHECKPOINT_EXCLUDE_SCOPES = 'InceptionV3/Logits,InceptionV3/AuxLogits'
+    exclusions = [scope.strip() for scope in CHECKPOINT_EXCLUDE_SCOPES.split(',')]
+    variables_to_restore = []
+
+    for var in slim.get_model_variables():
+        excluded = False
+        for exclusion in exclusions:
+            if var.op.name.startswith(exclusion):
+                excluded = True
+                break
+        if not excluded:
+            variables_to_restore.append(var)
+    return variables_to_restore
+
+def load_model():
+    img_size = inception.inception_v3.default_image_size
+    img = tf.placeholder(tf.float32, shape=[None, img_size, img_size, 3], name='inputs')
+    with slim.arg_scope(inception.inception_v3_arg_scope()):
+        logits, _ = inception.inception_v3(img, num_classes=1000, is_training=False)
+    sess = tf.Session()
+    init = tf.global_variables_initializer()
+    sess.run(init)
+    load_model = tf.contrib.slim.assign_from_checkpoint_fn(ckpt_file, get_tuned_variables(), ignore_missing_vars=True)
+    load_model(sess)
+    return sess
+
+def save_checkpoint(sess):
+    saver = tf.train.Saver()
+    saver.save(sess, checkpoint_dir+"/model")
+
+if __name__ == "__main__":
+    sess = load_model()
+    save_checkpoint(sess)

tensorflow2fluid/src/paddle_emitter.py

@@ -124,8 +124,8 @@ class PaddleEmitter(object):
 
         ref_name_recorder = open(self.save_dir + "/ref_name.txt", 'w')
         total_nodes_num = len(self.graph.topological_sort)
-        translated_nodes_count = 0
-        sys.stderr.write("Start to translate all the nodes(Total_num:{})\n".
+        translated_nodes_count = 1
+        sys.stderr.write("\nStart to translate all the nodes(Total_num:{})\n".
                          format(total_nodes_num))
         for node in self.graph.topological_sort:
             sys.stderr.write(
@@ -168,6 +168,9 @@ class PaddleEmitter(object):
         filew.write(self.body_code)
         filew.close()
 
+        sys.stderr.write("Model translated!\n\n")
+        sys.stderr.flush()
+
         return self.body_code
 
     def emit_placeholder(self, node):