Add Keras model test with TF2 model (#315)

* Add MNIST test with tf2 model

* Clean up model generating script for TF2

* Update README with information on Keras and TF2

* Improve TF 2 support description

Author: lantiga

README.md

@@ -130,6 +130,8 @@ RedisAI currently supports PyTorch (libtorch), Tensorflow (libtensorflow), Tenso
 | 0.9.0   | 1.3.1   | 1.14.0     | 2.0.0  | 1.0.0         |
 | master  | 1.4.0   | 1.15.0     | 2.0.0  | 1.0.0         |
 
+Note: Keras and TensorFlow 2.x are supported through graph freezing. See [this script](https://github.com/RedisAI/RedisAI/blob/master/test/test_data/tf2-minimal.py) to see how to export a frozen graph from Keras and TensorFlow 2.x. Note that a frozen graph will be executed using the TensorFlow 1.15 backend. Should any 2.0 ops be not supported on the 1.15 after freezing, please open an Issue.
+
 ## Documentation
 
 Read the docs at [redisai.io](http://redisai.io). Checkout our [showcase repo](https://github.com/RedisAI/redisai-examples) for a lot of examples written using different client libraries.

test/test_data/graph_v2.pb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9970a47f2818a1137c1e39e96a8c43938abced0eb44f78f5beabe0add9449539
+size 408865

test/test_data/tf2-minimal.py

@@ -0,0 +1,32 @@
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
+import numpy as np
+
+# From https://github.com/leimao/Frozen_Graph_TensorFlow
+
+tf.random.set_seed(seed=0)
+
+model = keras.Sequential(layers=[
+                             keras.layers.InputLayer(input_shape=(28, 28), name="input"),
+                             keras.layers.Flatten(input_shape=(28, 28), name="flatten"),
+                             keras.layers.Dense(128, activation="relu", name="dense"),
+                             keras.layers.Dense(10, activation="softmax", name="output")
+                         ], name="FCN")
+
+model.compile(optimizer="adam",
+              loss="sparse_categorical_crossentropy",
+              metrics=["accuracy"])
+
+full_model = tf.function(lambda x: model(x))
+full_model = full_model.get_concrete_function(
+                 tf.TensorSpec(model.inputs[0].shape, model.inputs[0].dtype))
+
+frozen_func = convert_variables_to_constants_v2(full_model)
+frozen_func.graph.as_graph_def()
+
+tf.io.write_graph(graph_or_graph_def=frozen_func.graph,
+                  logdir=".",
+                  name="graph_v2.pb",
+                  as_text=False)
+

test/tests_tensorflow.py

@@ -242,6 +242,73 @@ def test_run_tf_model(env):
         env.assertFalse(con2.execute_command('EXISTS', 'm'))
 
 
+@skip_if_no_TF
+def test_run_tf2_model(env):
+    con = env.getConnection()
+
+    test_data_path = os.path.join(os.path.dirname(__file__), 'test_data')
+    model_filename = os.path.join(test_data_path, 'graph_v2.pb')
+
+    with open(model_filename, 'rb') as f:
+        model_pb = f.read()
+
+    ret = con.execute_command('AI.MODELSET', 'm', 'TF', DEVICE,
+                              'INPUTS', 'x', 'OUTPUTS', 'Identity', model_pb)
+    env.assertEqual(ret, b'OK')
+
+    ensureSlaveSynced(con, env)
+
+    ret = con.execute_command('AI.MODELGET', 'm')
+    env.assertEqual(len(ret), 6)
+    env.assertEqual(ret[-1], b'')
+
+    ret = con.execute_command('AI.MODELSET', 'm', 'TF', DEVICE, 'TAG', 'asdf',
+                              'INPUTS', 'x', 'OUTPUTS', 'Identity', model_pb)
+    env.assertEqual(ret, b'OK')
+
+    ensureSlaveSynced(con, env)
+
+    ret = con.execute_command('AI.MODELGET', 'm')
+    env.assertEqual(len(ret), 6)
+    env.assertEqual(ret[-1], b'asdf')
+
+    zero_values = [0] * (28 * 28)
+
+    con.execute_command('AI.TENSORSET', 'x', 'FLOAT',
+                        1, 1, 28, 28, 'VALUES', *zero_values)
+
+    ensureSlaveSynced(con, env)
+
+    con.execute_command('AI.MODELRUN', 'm', 'INPUTS', 'x', 'OUTPUTS', 'y')
+
+    ensureSlaveSynced(con, env)
+
+    tensor = con.execute_command('AI.TENSORGET', 'y', 'VALUES')
+    values = tensor[-1]
+    for value in values:
+        env.assertAlmostEqual(float(value), 0.1, 1E-4)
+
+    if env.useSlaves:
+        con2 = env.getSlaveConnection()
+        tensor2 = con2.execute_command('AI.TENSORGET', 'y', 'VALUES')
+        env.assertEqual(tensor2, tensor)
+
+    for _ in env.reloadingIterator():
+        env.assertExists('m')
+        env.assertExists('x')
+        env.assertExists('y')
+
+    con.execute_command('AI.MODELDEL', 'm')
+    ensureSlaveSynced(con, env)
+
+    env.assertFalse(env.execute_command('EXISTS', 'm'))
+
+    ensureSlaveSynced(con, env)
+    if env.useSlaves:
+        con2 = env.getSlaveConnection()
+        env.assertFalse(con2.execute_command('EXISTS', 'm'))
+
+
 @skip_if_no_TF
 def test_run_tf_model_errors(env):
     con = env.getConnection()