add setup.py

tensorflow2fluid/demo/export_to_checkpoint.py

+from tensorflow.contrib.slim.nets import inception
+from tensorflow.contrib.slim.nets import vgg as vgg
+from tensorflow.contrib.slim.nets import resnet_v1 as resnet_v1
+from tensorflow.contrib.framework.python.ops import arg_scope
+import tensorflow.contrib.slim as slim
+import tensorflow as tf
+import numpy
+from six import text_type as _text_type
+
+def inception_v3(ckpt_file):
+    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 
+
+    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 resnet_v1_50(ckpt_file):
+    img_size = resnet_v1.resnet_v1.default_image_size
+    img = tf.placeholder(tf.float32, shape=[None, img_size, img_size, 3], name='inputs')
+    with slim.arg_scope(resnet_v1.resnet_arg_scope()):
+        net, endpoint = resnet_v1.resnet_v1_50(img, num_classes=None, is_training=False)
+
+    sess = tf.Session()
+    load_model = tf.contrib.slim.assign_from_checkpoint_fn(ckpt_file, tf.contrib.slim.get_model_variables("resnet_v1_50"))
+    load_model(sess)
+    return sess
+
+def resnet_v1_101(ckpt_file):
+    img_size = resnet_v1.resnet_v1.default_image_size
+    img = tf.placeholder(tf.float32, shape=[None, img_size, img_size, 3], name='inputs')
+    with slim.arg_scope(resnet_v1.resnet_arg_scope()):
+        net, endpoint = resnet_v1.resnet_v1_101(img, num_classes=None, is_training=False)
+
+    sess = tf.Session()
+    sess.run(tf.global_variables_initializer())
+    load_model = tf.contrib.slim.assign_from_checkpoint_fn(ckpt_file, tf.contrib.slim.get_model_variables("resnet_v1_101"))
+    load_model(sess)
+    return sess
+
+def vgg_16(ckpt_file):
+    img_size = vgg.vgg_16.default_image_size
+    inputs = tf.placeholder(tf.float32, shape=[None, img_size, img_size, 3],
+            name="inputs")
+    logits, endpoint = vgg.vgg_16(inputs, num_classes=1000, is_training=False)
+    sess = tf.Session()
+
+    load_model = tf.contrib.slim.assign_from_checkpoint_fn(ckpt_file,
+            tf.contrib.slim.get_model_variables("vgg_16"))
+    load_model(sess)
+    return sess
+
+def vgg_19(ckpt_file):
+    img_size = vgg.vgg_19.default_image_size
+    inputs = tf.placeholder(tf.float32, shape=[None, img_size, img_size, 3],
+            name="inputs")
+    logits, endpoint = vgg.vgg_19(inputs, num_classes=1000, is_training=False)
+    sess = tf.Session()
+    load_model = tf.contrib.slim.assign_from_checkpoint_fn(ckpt_file,
+            tf.contrib.slim.get_model_variables("vgg_19"))
+    load_model(sess)
+    return sess
+
+def save_checkpoint(sess, save_dir):
+    saver = tf.train.Saver()
+    saver.save(sess, save_dir+"/model")
+
+def get_parser():
+    import argparse
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model", "-m", type=_text_type, default=None, help="inception_v3/resnet_v1_50/resnet_v1_101/vgg_16/vgg_19")
+    parser.add_argument("--ckpt_file", "-c", type=_text_type, default=None, help="parameters ckpt file")
+    parser.add_argument("--save_dir", "-s", type=_text_type, default=None, help="model path")
+    return parser
+
+if __name__ == "__main__":
+    parser = get_parser()
+    args = parser.parse_args()
+    sess = None
+    if args.model is None or args.save_dir is None or args.ckpt_file is None:
+        raise Exception("--model, --ckpt_file and --save_dir are needed")
+    if args.model == "inception_v3":
+        sess = inception_v3(args.ckpt_file)
+    elif args.model == "resnet_v1_50":
+        sess = resnet_v1_50(args.ckpt_file)
+    elif args.model == "resnet_v1_101":
+        sess = resnet_v1_101(args.ckpt_file)
+    elif args.model == "vgg_16":
+        sess = vgg_16(args.ckpt_file)
+    elif args.model == "vgg_19":
+        sess = vgg_19(args.ckpt_file)
+    else:
+        raise Exception("Only support inception_v3/resnet_v1_50/resnet_v1_101/vgg_16/vgg_19")
+    save_checkpoint(sess, args.save_dir)

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/convert.py → tensorflow2fluid/src/convert.py

-from src.paddle_emitter import PaddleEmitter
-from src.tensorflow_parser import TensorflowCkptParser
-from src.tensorflow_parser import TensorflowPbParser
+from paddle_emitter import PaddleEmitter
+from tensorflow_parser import TensorflowCkptParser
+from tensorflow_parser import TensorflowPbParser
 from six import text_type as _text_type
 import argparse
 import sys
@@ -15,6 +15,7 @@ def _get_parser():
     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")
+    parser.add_argument("--version", "-v", action="version", version="tensorflow2fluid version=0.0.1 Release @2019.01.28")
     return parser
 
 def _convert(args):
@@ -39,14 +40,22 @@ def _convert(args):
                 items[i] = int(items[i])
         input_shape.append(items)
 
+    sys.stderr.write("\nLoading tensorflow model......\n")
     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)
+    sys.stderr.write("Tensorflow model loaded!\n")
     emitter = PaddleEmitter(parser, args.save_dir)
     emitter.run()
 
-if __name__ == "__main__":
+    open(args.save_dir+"/__init__.py", "w").close()
+
+def _main():
+    os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
     parser = _get_parser()
     args = parser.parse_args()
     _convert(args)
+
+if __name__ == "__main__":
+    _main()

tensorflow2fluid/src/paddle_emitter.py

@@ -66,6 +66,7 @@ class PaddleEmitter(object):
         return axis
 
     def export_weights(self, weight, paddle_var_name, dir):
+        self.save_var_set.add(paddle_var_name)
         numpy_dtype_map = {
             "int16": framework.VarType.INT16,
             "int32": framework.VarType.INT32,
@@ -119,13 +120,17 @@ class PaddleEmitter(object):
             self.body_code += (self.tab * indent) + code + "\n"
 
     def run(self):
+        print("new version")
         node = self.graph.tf_graph.node[0]
         self.add_codes(0, self.header_code)
 
+        self.save_var_set = set()
+
         ref_name_recorder = open(self.save_dir + "/ref_name.txt", 'w')
         total_nodes_num = len(self.graph.topological_sort)
         translated_nodes_count = 1
-        sys.stderr.write("\nStart to translate all the nodes(Total_num:{})\n".
+        sys.stderr.write("\nModel Translating......\n")
+        sys.stderr.write("Start to translate all the nodes(Total_num:{})\n".
                          format(total_nodes_num))
         for node in self.graph.topological_sort:
             sys.stderr.write(
@@ -167,6 +172,10 @@ class PaddleEmitter(object):
         filew = open(self.save_dir + "/mymodel.py", 'w')
         filew.write(self.body_code)
         filew.close()
+        filew = open(self.save_dir + "/save_var.list", 'w')
+        for var in self.save_var_set:
+            filew.write(var + '\n')
+        filew.close()
 
         sys.stderr.write("Model translated!\n\n")
         sys.stderr.flush()

tensorflow2fluid/src/tensorflow_parser.py

@@ -18,7 +18,6 @@ from tensorflow.python.framework import tensor_util
 from tensorflow.python.tools import strip_unused_lib
 from tensorflow.python.framework import dtypes
 
-
 class TensorflowCkptParser(object):
     def __init__(self,
                  meta_file,