[Frontend][TF] Fix Placeholder issue (apache#2834)

* [Frontend][TF] Fix Placeholder issue

* Add test cases

nnvm/python/nnvm/frontend/tensorflow.py

@@ -127,7 +127,7 @@ def _impl(inputs, attr, params):
 
 def _elemwise(name):
     def _impl(inputs, attr, *args):
-        assert len(inputs) == 2, "Math op take 2 inputs, {} given".format(len(inputs))
+        assert len(inputs) == 2, "{} take 2 inputs, {} given".format(name, len(inputs))
         op_name = _math_name_picker(name)(attr)
         return get_nnvm_op(op_name)(*inputs)
     return _impl
@@ -1237,16 +1237,24 @@ def from_tensorflow(self, graph, layout="NHWC", shape=None, outputs=None):
 
         for node in graph.node:
             if node.op == 'Placeholder' or node.op == 'PlaceholderWithDefault':
+                # Give priority to user argument.
                 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)
+                else:
+                    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)
+
+                self._nodes[node.name] = _sym.Variable(name=node.name,
+                                                       shape=self._input_shapes[node.name])
+                self._output_shapes[node.name] = [self._input_shapes[node.name]]
+                self._outputs_are_0d[node.name] = [ \
+                    not tshape if isinstance(tshape, list) else False \
+                    for tshape in self._output_shapes[node.name]]
 
             # Ignore user's input shape for Non placeholder
             elif node.op == 'Const':
@@ -1304,7 +1312,7 @@ def from_tensorflow(self, graph, layout="NHWC", shape=None, outputs=None):
 
                 attr = self._parse_attr(node.attr)
 
-            else:
+            elif node.op != "Placeholder":
                 # Pass the parsed shapes instead
                 attr["_output_shapes"] = output_shapes = self._output_shapes[node.name]
 

nnvm/tests/python/frontend/tensorflow/test_forward.py

@@ -941,6 +941,29 @@ def test_forward_resnetv2():
                     tvm_output = run_tvm_graph(graph_def, data, 'input_tensor', len(tf_output), target=device)
                     tvm.testing.assert_allclose(np.squeeze(tvm_output[0]), np.squeeze(tf_output[0]), rtol=1e-5, atol=1e-5)
 
+#######################################################################
+# Placeholder
+# -----------
+def test_forward_placeholder():
+    '''test a simple pb with Placeholder node in the end of GraphDef'''
+    with tf.Graph().as_default():
+        graph_def = tf_testing.get_workload("Custom/placeholder.pb")
+
+        # Call the utility to import the graph definition into default graph.
+        graph_def = tf_testing.ProcessGraphDefParam(graph_def)
+
+
+        data = np.random.uniform(size=(1, 224, 224, 3)).astype('float32')
+        out_node = 'mul'
+
+        with tf.Session() as sess:
+            # Add shapes to the graph.
+            graph_def = tf_testing.AddShapesToGraphDef(sess, out_node)
+            tf_output = run_tf_graph(sess, data, 'Placeholder:0', out_node + ':0')
+            tvm_output = run_tvm_graph(graph_def, data, 'Placeholder')
+            print("tf_output is {}\ntvm_output is {}".format(tf_output, tvm_output))
+            tvm.testing.assert_allclose(np.squeeze(tvm_output[0]), np.squeeze(tf_output[0]), rtol=1e-5, atol=1e-5)
+
 #######################################################################
 # PTB
 # ---
@@ -1262,6 +1285,7 @@ def test_forward_rel_ops():
     test_forward_inception_v1()
     test_forward_mobilenet()
     test_forward_resnetv2()
+    test_forward_placeholder()
     test_forward_ptb()
 
     # RNN

python/tvm/relay/frontend/tensorflow.py

@@ -239,7 +239,7 @@ def _impl(inputs, attr, params):
 
 def _elemwise(name):
     def _impl(inputs, attr, *args):
-        assert len(inputs) == 2, "Math op take 2 inputs, {} given".format(len(inputs))
+        assert len(inputs) == 2, "{} take 2 inputs, {} given".format(name, len(inputs))
         return _get_relay_op(name)(*inputs)
     return _impl
 
@@ -1704,16 +1704,23 @@ def from_tensorflow(self, graph, layout="NHWC", shape=None, outputs=None):
             node_name_prefix = node.name.rsplit('/', 1)[0]
             control_flow_node_map[node_name_prefix].add(node.op)
             if node.op == 'Placeholder':
+                # Give priority to user argument.
                 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)
+                else:
+                    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)
+
+                self._output_shapes[node.name] = [self._input_shapes[node.name]]
+                attr = self._parse_attr(node.attr)
+                self._nodes[node.name] = [_expr.var(node.name,
+                                                    shape=self._input_shapes[node.name],
+                                                    dtype=attr['dtype'].name)]
 
                 # Ignore user's input shape for Non placeholder
             elif node.op == 'Const':
@@ -1736,11 +1743,6 @@ def from_tensorflow(self, graph, layout="NHWC", shape=None, outputs=None):
             # Variable converted to Const will not have only value attr
             if 'value' in attr and node.op == 'Const':
                 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 node.op == 'Placeholder':
-                self._output_shapes[node.name] = [self._input_shapes[node.name]]
             elif '_output_shapes' in attr:
                 self._output_shapes[node.name] = \
                     [tensor_util.TensorShapeProtoToList(tshape) \
@@ -1755,13 +1757,7 @@ def from_tensorflow(self, graph, layout="NHWC", shape=None, outputs=None):
                 not shape if isinstance(tshape, list) else False \
                 for tshape in self._output_shapes[node.name]]
 
-            if node.op == "Placeholder":
-                self._output_shapes[node.name] = [self._input_shapes[node.name]]
-                self._nodes[node.name] = [_expr.var(node.name,
-                                                    shape=self._input_shapes[node.name],
-                                                    dtype=attr['dtype'].name)]
-
-            elif node.op == "Const":
+            if node.op == "Const":
                 # All Const nodes are Param nodes, lets parse
                 self._num_param += 1
                 for key, value in node.attr.items():
@@ -1772,7 +1768,7 @@ def from_tensorflow(self, graph, layout="NHWC", shape=None, outputs=None):
 
                 attr = self._parse_attr(node.attr)
 
-            else:
+            elif node.op != "Placeholder":
                 # Pass the parsed shapes instead
                 attr["_output_shapes"] = output_shapes = self._output_shapes[node.name]
 
@@ -1816,7 +1812,8 @@ def from_tensorflow(self, graph, layout="NHWC", shape=None, outputs=None):
                         input_shapes[in_sym[0]] = input_shape
                         # This means the node is 1d in Relay and 0d in TF.
                         # See `_expand_dims_0d_aware`.
-                        if self._outputs_are_0d[node_name][tensor_slot] and input_shape:
+                        if node_name in self._outputs_are_0d \
+                                and self._outputs_are_0d[node_name][tensor_slot] and input_shape:
                             input_0d_mismatch.add(in_sym[0])
 
                 attr['_input_shapes'] = input_shapes

tests/python/frontend/tensorflow/test_forward.py

@@ -1133,6 +1133,27 @@ def test_forward_resnetv2():
                     tvm_output = run_tvm_graph(graph_def, data, 'input_tensor', len(tf_output), target=device)
                     tvm.testing.assert_allclose(np.squeeze(tvm_output[0]), np.squeeze(tf_output[0]), rtol=1e-5, atol=1e-5)
 
+#######################################################################
+# Placeholder
+# -----------
+def test_forward_placeholder():
+    '''test a simple pb with Placeholder node in the end of GraphDef'''
+    with tf.Graph().as_default():
+        graph_def = tf_testing.get_workload("Custom/placeholder.pb")
+        # Call the utility to import the graph definition into default graph.
+        graph_def = tf_testing.ProcessGraphDefParam(graph_def)
+
+        data = np.random.uniform(size=(1, 224, 224, 3)).astype('float32')
+        out_node = 'mul'
+
+        with tf.Session() as sess:
+            # Add shapes to the graph.
+            graph_def = tf_testing.AddShapesToGraphDef(sess, out_node)
+            tf_output = run_tf_graph(sess, data, 'Placeholder:0', out_node + ':0')
+            tvm_output = run_tvm_graph(graph_def, data, 'Placeholder')
+            print("tf_output is {}\ntvm_output is {}".format(tf_output, tvm_output))
+            tvm.testing.assert_allclose(np.squeeze(tvm_output[0]), np.squeeze(tf_output[0]), rtol=1e-5, atol=1e-5)
+
 #######################################################################
 # PTB
 # ---
@@ -1514,6 +1535,7 @@ def test_forward_rel_ops():
     test_forward_inception_v1()
     test_forward_mobilenet()
     test_forward_resnetv2()
+    test_forward_placeholder()
     test_forward_ptb()
 
     # RNN