[Frontend] [Tensorflow2] Added test infrastructure for TF2 frozen models

python/tvm/relay/testing/tf.py

@@ -28,6 +28,8 @@
 # Tensorflow imports
 import tensorflow as tf
 from tensorflow.core.framework import graph_pb2
+
+import tvm
 from tvm.contrib.download import download_testdata
 
 try:
@@ -73,6 +75,46 @@ def convert_to_list(x):
     return x
 
 
+def vmobj_to_list(o):
+    """Converts TVM objects returned by VM execution to Python List.
+
+    Parameters
+    ----------
+    o : Obj
+        VM Object as output from VM runtime executor.
+
+    Returns
+    -------
+    result : list
+        Numpy objects as list with equivalent values to the input object.
+
+    """
+
+    if isinstance(o, tvm.nd.NDArray):
+        result = [o.asnumpy()]
+    elif isinstance(o, tvm.runtime.container.ADT):
+        result = []
+        for f in o:
+            result.extend(vmobj_to_list(f))
+    elif isinstance(o, tvm.relay.backend.interpreter.ConstructorValue):
+        if o.constructor.name_hint == "Cons":
+            tl = vmobj_to_list(o.fields[1])
+            hd = vmobj_to_list(o.fields[0])
+            hd.extend(tl)
+            result = hd
+        elif o.constructor.name_hint == "Nil":
+            result = []
+        elif "tensor_nil" in o.constructor.name_hint:
+            result = [0]
+        elif "tensor" in o.constructor.name_hint:
+            result = [o.fields[0].asnumpy()]
+        else:
+            raise RuntimeError("Unknown object type: %s" % o.constructor.name_hint)
+    else:
+        raise RuntimeError("Unknown object type: %s" % type(o))
+    return result
+
+
 def AddShapesToGraphDef(session, out_node):
     """Add shapes attribute to nodes of the graph.
         Input graph here is the default graph in context.

tests/python/frontend/tensorflow2/common.py

@@ -0,0 +1,105 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# pylint: disable=import-self, invalid-name, unused-argument, too-many-lines, len-as-condition, broad-except
+# pylint: disable=import-outside-toplevel, redefined-builtin
+"""TF2 to relay converter test utilities"""
+
+import tvm
+from tvm import relay
+
+from tvm.runtime.vm import VirtualMachine
+import tvm.contrib.graph_executor as runtime
+from tvm.relay.frontend.tensorflow import from_tensorflow
+
+import tvm.testing
+from tvm.relay.testing.tf import vmobj_to_list as vmobj_to_list
+
+import tensorflow as tf
+from tensorflow.python.eager.def_function import Function
+
+
+def run_tf_code(func, input_):
+    if type(func) is Function:
+        out = func(input_)
+        if isinstance(out, list):
+            a = [x.numpy() for x in out]
+        else:
+            a = [out.numpy()]
+    else:
+        a = func(tf.constant(input_))
+        if type(a) is dict:
+            a = [x.numpy() for x in a.values()]
+        elif type(a) is list:
+            a = [x.numpy() for x in a]
+        else:
+            a = a.numpy()
+    return a
+
+
+def compile_graph_executor(mod, params, target="llvm", target_host="llvm", opt_level=3):
+    with tvm.transform.PassContext(opt_level):
+        lib = relay.build(mod, target=target, target_host=target_host, params=params)
+    return lib
+
+
+def compile_vm(mod, params, target="llvm", target_host="llvm", opt_level=3, disabled_pass=None):
+    with tvm.transform.PassContext(opt_level, disabled_pass=disabled_pass):
+        vm_exec = relay.vm.compile(mod, target, target_host, params=params)
+    return vm_exec
+
+
+def run_vm(vm_exec, input_, ctx=tvm.cpu(0)):
+    vm = VirtualMachine(vm_exec, ctx)
+    _out = vm.invoke("main", input_)
+    return vmobj_to_list(_out)
+
+
+def run_graph_executor(lib, input_, ctx=tvm.cpu(0)):
+    mod = runtime.GraphModule(lib["default"](ctx))
+    mod.set_input(0, input_)
+    mod.run()
+    return [mod.get_output(0).asnumpy()]
+
+
+def compare_tf_tvm(gdef, input_, output_, runtime="vm", output_tensors=None):
+    """compare tf and tvm execution for the same input.
+
+    Parameters
+    ----------
+    gdef: TF2 graph def extracted to be fed into from_tensorflow parser.
+    (https://www.tensorflow.org/code/tensorflow/core/framework/graph.proto)
+
+    input_: a single numpy array object
+
+    output_: the expected output from TF to match TVM output with
+
+    runtime: choose TVM runtime; either "vm" for VirtualMachine or "graph" for GraphExecutor
+
+    output_tensors : List of output tensor names (Optional)
+            if not specified then the last node is assumed as graph output.
+    """
+    mod, params = from_tensorflow(gdef, outputs=output_tensors)
+    if runtime == "vm":
+        exec_ = compile_vm(mod, params)
+        tvm_out = run_vm(exec_, input_)
+    elif runtime == "graph":
+        lib = compile_graph_executor(mod, params)
+        tvm_out = run_graph_executor(lib, input_)
+    else:
+        raise RuntimeError("Runtime input not supported: %s" % runtime)
+
+    tvm.testing.assert_allclose(output_, tvm_out, atol=1e-5)