[add] added mobilenet model use case to quickly check for leaks on modelruns with large tensors (dagrun and modelrun variations)

Author: filipecosta90

test/includes.py

@@ -16,6 +16,7 @@
 except:
     pass
 
+MAX_ITERATIONS = 2 if os.environ.get("MAX_ITERATIONS") == None else os.environ.get("MAX_ITERATIONS")
 TEST_TF = os.environ.get("TEST_TF") != "0" and os.environ.get("WITH_TF") != "0"
 TEST_TFLITE = os.environ.get("TEST_TFLITE") != "0" and os.environ.get("WITH_TFLITE") != "0"
 TEST_PT = os.environ.get("TEST_PT") != "0" and os.environ.get("WITH_PT") != "0"
@@ -24,7 +25,7 @@
 DEVICE = os.environ.get('DEVICE', 'CPU').upper().encode('utf-8', 'ignore').decode('utf-8')
 VALGRIND = os.environ.get("VALGRIND") == "1"
 print(f"Running tests on {DEVICE}\n")
-
+print(f"Using a max of {MAX_ITERATIONS} iterations per test\n")
 # change this to make inference tests longer
 MAX_TRANSACTIONS=100
 
@@ -91,12 +92,35 @@ def load_resnet_test_data():
 
     return model_pb, script, labels, img
 
+def load_mobilenet_v1_test_data():
+    test_data_path = os.path.join(os.path.dirname(__file__), 'test_data')
+    labels_filename = os.path.join(test_data_path, 'imagenet_class_index.json')
+    image_filename = os.path.join(test_data_path, 'panda.jpg')
+    model_filename = os.path.join(test_data_path, 'mobilenet/mobilenet_v1_100_224_cpu_NxHxWxC.pb')
+    input_var = 'input'
+    output_var = 'MobilenetV1/Predictions/Reshape_1'
+
+    with open(model_filename, 'rb') as f:
+        model_pb = f.read()
+
+    with open(labels_filename, 'r') as f:
+        labels = json.load(f)
+
+    img_height, img_width = 224, 224
+
+    img = imread(image_filename)
+    img = resize(img, (img_height, img_width), mode='constant', anti_aliasing=True)
+    img = img.astype(np.float32)
+
+    return model_pb, input_var, output_var, labels, img
 
-def load_mobilenet_test_data():
+def load_mobilenet_v2_test_data():
     test_data_path = os.path.join(os.path.dirname(__file__), 'test_data')
     labels_filename = os.path.join(test_data_path, 'imagenet_class_index.json')
     image_filename = os.path.join(test_data_path, 'panda.jpg')
-    model_filename = os.path.join(test_data_path, 'mobilenet_v2_1.4_224_frozen.pb')
+    model_filename = os.path.join(test_data_path, 'mobilenet/mobilenet_v2_1.4_224_frozen.pb')
+    input_var = 'input'
+    output_var = 'MobilenetV2/Predictions/Reshape_1'
 
     with open(model_filename, 'rb') as f:
         model_pb = f.read()
@@ -110,7 +134,7 @@ def load_mobilenet_test_data():
     img = resize(img, (img_height, img_width), mode='constant', anti_aliasing=True)
     img = img.astype(np.float32)
 
-    return model_pb, labels, img
+    return model_pb, input_var, output_var, labels, img
 
 def load_creditcardfraud_data(env,max_tensors=10000):
     test_data_path = os.path.join(os.path.dirname(__file__), 'test_data')

test/test_data/mobilenet/mobilenet_v1_100_224_cpu_NxHxWxC.pb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bbb2752038ff1749d2b55988bb5f6e999a799c19413a0691b82d29f7aec0bab3
+size 17198345

test/test_data/mobilenet/mobilenet_v1_100_224_gpu_NxHxWxC.pb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f1fe206dfd3cff261cf403b5757abec886da445a80056e55310ddac0b2805a3b
+size 17198345

test/test_data/mobilenet/mobilenet_v1_100_224_gpu_NxHxWxC_fp16_trt.pb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fd925f4b59d8d5035ccb2ecdfbf9b0f47a5ba3acfa81bd5a18536f69021df74a
+size 34277746

test/test_data/mobilenet/mobilenet_v2_1.4_224_frozen.pb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:111479258f3841c93d0a7a377c976c24e8281077818991931429d2277dd88590
+size 24508794

test/test_data/mobilenet/model_saver.py

@@ -0,0 +1,49 @@
+import tensorflow as tf
+import tensorflow_hub as hub
+import ml2rt
+import argparse
+import sys
+
+url = 'https://tfhub.dev/google/imagenet/mobilenet_v1_100_224/quantops/classification/3'
+model_name = 'mobilenet_v1_100_224'
+module = hub.Module(url)
+batch_size = 1
+number_channels = 3
+height, width = hub.get_expected_image_size(module)
+input_var = 'input'
+output_var = 'MobilenetV1/Predictions/Reshape_1'
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--gpu', action="store_true", default=False)
+parser.add_argument('--input-shape', default="NxHxWxC", type=str)
+args = parser.parse_args()
+device = 'gpu' if args.gpu else 'cpu'
+
+gpu_available = tf.test.is_gpu_available(
+    cuda_only=True, min_cuda_compute_capability=None
+)
+
+if gpu_available is False and args.gpu:
+    print("No CUDA GPUs found. Exiting...")
+    sys.exit(1)
+
+var_converter = tf.compat.v1.graph_util.convert_variables_to_constants
+
+if args.input_shape == "NxHxWxC":
+    print("Saving N x H x W x C (1, 224, 224, 3) (with channels_last data format)")
+    images = tf.compat.v1.placeholder(tf.float32, shape=(
+        batch_size, height, width, number_channels), name=input_var)
+elif args.input_shape == "NxHxWxC":
+    print("Saving N x C x H x W (1, 3, 224, 224)")
+    images = tf.placeholder(tf.float32, shape=(
+        batch_size, number_channels, height, width), name=input_var)
+else:
+    print("inputs shape is either NxHxWxC or NxCxHxW. Exiting...")
+    sys.exit(1)
+
+logits = module(images)
+logits = tf.identity(logits, output_var)
+with tf.compat.v1.Session() as sess:
+    sess.run([tf.compat.v1.global_variables_initializer()])
+    ml2rt.save_tensorflow(sess, '{model_name}_{device}_{input_shape}.pb'.format(
+        model_name=model_name, device=device, input_shape=args.input_shape), output=[output_var])

test/tests_sanitizer.py

@@ -0,0 +1,87 @@
+import redis
+from functools import wraps
+import multiprocessing as mp
+from includes import *
+
+'''
+python -m RLTest --test tests_sanitizer.py --module path/to/redisai.so
+'''
+
+
+def test_sanitizer_dagrun_mobilenet_v1(env):
+    if (not TEST_TF or not TEST_PT):
+        return
+    con = env.getConnection()
+    mem_allocator = con.execute_command('info', 'memory')['mem_allocator']
+    if 'jemalloc' in mem_allocator:
+        print("exiting sanitizer test given we're not using stdlib allocator")
+        return
+
+    model_name = 'mobilenet_v1'
+    model_pb, input_var, output_var, labels, img = load_mobilenet_v1_test_data()
+
+    ret = con.execute_command('AI.MODELSET', model_name, 'TF', DEVICE,
+                              'INPUTS', input_var,
+                              'OUTPUTS', output_var,
+                              'BLOB', model_pb)
+    env.assertEqual(ret, b'OK')
+
+    for opnumber in range(1, MAX_ITERATIONS):
+        image_key = 'image'
+        temp_key1 = 'temp_key1'
+        temp_key2 = 'temp_key2'
+        class_key = 'output'
+
+        ret = con.execute_command(
+            'AI.DAGRUN', '|>',
+            'AI.TENSORSET', image_key, 'FLOAT', 1, 224, 224, 3, 'BLOB', img.tobytes(), '|>',
+            'AI.MODELRUN', model_name,
+                         'INPUTS', image_key,
+                         'OUTPUTS', class_key,  '|>',
+            'AI.TENSORGET',  class_key, 'blob'
+        )
+        env.assertEqual([b'OK', b'OK'], ret[:2])
+        env.assertEqual(1001.0, len(ret[2])/4)
+
+
+def test_sanitizer_modelrun_mobilenet_v1(env):
+    if (not TEST_TF or not TEST_PT):
+        return
+    con = env.getConnection()
+    mem_allocator = con.execute_command('info', 'memory')['mem_allocator']
+    if 'jemalloc' in mem_allocator:
+        print("exiting sanitizer test given we're not using stdlib allocator")
+        return
+
+    model_name = 'mobilenet_v1'
+    model_pb, input_var, output_var, labels, img = load_mobilenet_v1_test_data()
+
+    ret = con.execute_command('AI.MODELSET', model_name, 'TF', DEVICE,
+                              'INPUTS', input_var,
+                              'OUTPUTS', output_var,
+                              'BLOB', model_pb)
+    env.assertEqual(ret, b'OK')
+
+    for opnumber in range(1, MAX_ITERATIONS):
+        image_key = 'image'
+        temp_key1 = 'temp_key1'
+        temp_key2 = 'temp_key2'
+        class_key = 'output'
+        ret = con.execute_command(
+            'AI.TENSORSET', image_key, 'FLOAT', 1, 224, 224, 3, 'BLOB', img.tobytes()
+        )
+        env.assertEqual(b'OK', ret)
+
+        ret = con.execute_command(
+            'AI.MODELRUN', model_name,
+            'INPUTS', image_key,
+            'OUTPUTS', class_key
+        )
+
+        env.assertEqual(b'OK', ret)
+
+        ret = con.execute_command(
+            'AI.TENSORGET',  class_key, 'blob'
+        )
+
+        env.assertEqual(1001.0, len(ret)/4)

test/tests_tensorflow.py

@@ -24,10 +24,7 @@ def wrapper(env, *args, **kwargs):
 def test_run_mobilenet(env):
     con = env.getConnection()
 
-    input_var = 'input'
-    output_var = 'MobilenetV2/Predictions/Reshape_1'
-
-    model_pb, labels, img = load_mobilenet_test_data()
+    model_pb, input_var, output_var, labels, img = load_mobilenet_v2_test_data()
 
     con.execute_command('AI.MODELSET', 'mobilenet', 'TF', DEVICE,
                         'INPUTS', input_var, 'OUTPUTS', output_var, 'BLOB', model_pb)
@@ -94,10 +91,7 @@ def test_run_mobilenet_multiproc(env):
 
     con = env.getConnection()
 
-    input_var = 'input'
-    output_var = 'MobilenetV2/Predictions/Reshape_1'
-
-    model_pb, labels, img = load_mobilenet_test_data()
+    model_pb, input_var, output_var, labels, img = load_mobilenet_v2_test_data()
     con.execute_command('AI.MODELSET', 'mobilenet', 'TF', DEVICE,
                         'INPUTS', input_var, 'OUTPUTS', output_var, 'BLOB', model_pb)
     ensureSlaveSynced(con, env)
@@ -627,10 +621,7 @@ def test_tensorflow_modelrun_with_batch_and_minbatch(env):
     minbatch_size = 2
     model_name = 'model'
     another_model_name = 'another_model'
-    inputvar = 'input'
-    outputvar = 'MobilenetV2/Predictions/Reshape_1'
-
-    model_pb, labels, img = load_mobilenet_test_data()
+    model_pb, input_var, output_var, labels, img = load_mobilenet_v2_test_data()
 
     con.execute_command('AI.MODELSET', model_name, 'TF', DEVICE,
                         'BATCHSIZE', batch_size, 'MINBATCHSIZE', minbatch_size,

test/unit/.gitignore

@@ -0,0 +1,8 @@
+# Unit test binaries
+*.run
+
+# Object files
+*.o
+*.ko
+*.obj
+*.elf

test/unit/CMakeLists.txt

@@ -0,0 +1,20 @@
+
+    add_subdirectory("${PROJECT_SOURCE_DIR}/deps/googletest" "deps/googletest")
+
+    macro(package_add_test TESTNAME)
+    # create an exectuable in which the tests will be stored
+    add_executable(${TESTNAME} ${ARGN})
+    # link the Google test infrastructure, mocking library, and a default main fuction to
+    # the test executable.  Remove g_test_main if writing your own main function.
+    target_link_libraries(${TESTNAME} gtest gmock gtest_main)
+    # gtest_discover_tests replaces gtest_add_tests,
+    # see https://cmake.org/cmake/help/v3.10/module/GoogleTest.html for more options to pass to it
+    gtest_discover_tests(${TESTNAME}
+        # set a working directory so your project root so that you can find test data via paths relative to the project root
+        WORKING_DIRECTORY ${PROJECT_DIR}
+        PROPERTIES VS_DEBUGGER_WORKING_DIRECTORY "${PROJECT_DIR}"
+    )
+    set_target_properties(${TESTNAME} PROPERTIES FOLDER tests)
+endmacro()
+
+package_add_test(test1 test1.cpp)