[relay][frontend] TensorFlow saved model support (#2586)

* [relay][frontend] TensorFlow saved model support

* Add Examples section

* keep one copy of tensorflow_parser in relay

python/tvm/relay/frontend/tensorflow.py

@@ -4,6 +4,7 @@ from __future__ import absolute_import as _abs
 from __future__ import print_function
 
 import logging
+import warnings
 # Numpy support
 import numpy as np
 
@@ -410,7 +411,7 @@ def _conv(opname):
 def _decode_image():
     def _impl(inputs, attr, params):
         # Image decode wrapper: Expecting user to feed decoded input to next layer drop this layer.
-        print("DecodeJpeg: It's a pass through, please handle preprocessing before input")
+        warnings.warn("DecodeJpeg: It's a pass through, please handle preprocessing before input")
         return inputs[0]
     return _impl
 
@@ -1178,6 +1179,7 @@ class GraphProto(object):
     def __init__(self):
         self._nodes = {}
         self._params = {}
+        self._input_shapes = {}
         self._output_shapes = {}
         self._num_param = 0
         self._num_rnn_layer = False
@@ -1229,36 +1231,55 @@ class GraphProto(object):
             raise NotImplementedError( \
                 "The following operators are not implemented: {}".format(missing_operators))
 
+        for node in graph.node:
+            if node.op == 'Placeholder':
+                if shape and node.name in shape:
+                    self._input_shapes[node.name] = list(shape[node.name])
+                    continue
+                self._input_shapes[node.name] = \
+                    tensor_util.TensorShapeProtoToList(node.attr['shape'].shape)
+                for idx, dim in enumerate(self._input_shapes[node.name]):
+                    if dim < 0:
+                        self._input_shapes[node.name][idx] = 1
+                        warnings.warn("Use 1 instead of -1 in shape of operator %s."
+                                      % node.name)
+
+                # Ignore user's input shape for Non placeholder
+            elif node.op == 'Const':
+                tensor_value = node.attr['value'].tensor
+                self._input_shapes[node.name] = \
+                    tensor_util.TensorShapeProtoToList(tensor_value.tensor_shape)
+                if shape and node.name in shape:
+                    warnings.warn("Ignore the passed shape. Shape in graphdef "
+                                  "will be used for operator %s." % node.name)
+
         # Parse the nodes to re-create TF graph using Relay operators.
         for node in graph.node:
-            # Tensorflow doesn't have seperate list for params extraction.
+            # Tensorflow doesn't have separate list for params extraction.
             # Operator name 'Const' is treated as a parameter to build params dict.
 
             input_shapes = {}
             attr = self._parse_attr(node.attr)
 
-            #Variable converted to Const will not have only value attr
+            # Variable converted to Const will not have only value attr
             if 'value' in attr and node.op == 'Const':
-                tensor_value = attr['value']
-                self._output_shapes[node.name] = \
-                    [tensor_util.TensorShapeProtoToList( \
-                        tensor_value.tensor_shape)]
+                self._output_shapes[node.name] = [self._input_shapes[node.name]]
+            elif shape and node.name in shape:
+                # Give priority to user argument.
+                self._output_shapes[node.name] = [shape[node.name]]
             elif '_output_shapes' in attr:
                 self._output_shapes[node.name] = \
                     [tensor_util.TensorShapeProtoToList(tshape) \
                     for tshape in attr['_output_shapes']]
-            elif shape:
+            else:
                 # Keep the list indexable to avoid key error.
                 # Actual value will be filled after node creation.
                 self._output_shapes[node.name] = [None]
-            else:
-                raise NotImplementedError( \
-                    "Please freeze the graph with add_shapes=True")
 
             if node.op == "Placeholder":
-                self._output_shapes[node.name] = [shape[node.name]]
+                self._output_shapes[node.name] = [self._input_shapes[node.name]]
                 self._nodes[node.name] = [_expr.var(node.name,
-                                                    shape=self._output_shapes[node.name][0],
+                                                    shape=self._input_shapes[node.name],
                                                     dtype=attr['dtype'].name)]
 
             elif node.op == "Const":
@@ -1274,7 +1295,7 @@ class GraphProto(object):
 
             else:
                 # Pass the parsed shapes instead
-                attr["_output_shapes"] = self._output_shapes[node.name]
+                attr["_output_shapes"] = output_shapes = self._output_shapes[node.name]
 
                 # Pass the node name too in attr
                 attr["_node_name"] = node.name
@@ -1301,7 +1322,7 @@ class GraphProto(object):
 
                 op = self._convert_operator(node.op, inputs, attr, graph)
 
-                # Check is op is converted to param
+                # Check if op is converted to param
                 if isinstance(op, np.ndarray):
                     self._params[node.name] = tvm.nd.array(op)
                     op = [_expr.var(node.name,
@@ -1317,6 +1338,14 @@ class GraphProto(object):
 
                 self._nodes[node.name] = op
 
+                # Infer shapes even without specifying "add_shapes=True"
+                if output_shapes == [None]:
+                    out_type = ir_pass.infer_type(self._nodes[node.name][0])
+                    self._output_shapes[node.name] = [get_const_tuple(out_type.checked_type.shape)]
+
+                if self._output_shapes[node.name] and shape and node.name in shape:
+                    assert self._output_shapes[node.name] == list(shape[node.name])
+
             # Infer shapes if passed explicitely
             node_output = self._nodes[node.name]
             out_type = ir_pass.infer_type(node_output[0])

nnvm/python/nnvm/frontend/util/tensorflow_parser.py → python/tvm/relay/frontend/tensorflow_parser.py

@@ -7,16 +7,21 @@ from tvm.contrib import util
 
 
 class TFParser(object):
-    """A Wrapper to handle tensorflow models parsing
-       TensorFlow is needed
-    ```
-    parser = TfParser(model_dir)
-    graph = parser.parse()
-    ```
+    """
+    A Wrapper to handle tensorflow models parsing, TensorFlow is needed
+
     Parameters
     ----------
     model_dir : tensorflow frozen pb file or a directory that contains saved
     model or checkpoints.
+
+    Examples
+    --------
+    .. code-block:: python
+
+        parser = TfParser(model_dir)
+        graph = parser.parse()
+        # graph is related graphdef of the model
     """
 
     def __init__(self, model_dir):
@@ -115,13 +120,16 @@ class TFParser(object):
         """TODO: Load checkpoint model."""
         raise RuntimeError("InputConfiguration: Loading tf checkpoint model is "
                            "not supported yet.")
-        # pylint: disable=unreachable
-        return 0
 
     def parse(self):
-        """Parse tensorflow models: checkpoints, saved models, and single pb
-        file.
         """
+        Parse tensorflow models: checkpoints, saved models, and single frozen pb file.
+
+        Returns
+        -------
+        GraphDef of the passed model
+        """
+
         graph = None
 
         if os.path.isdir(self._model_dir):