MLECO-2493 Add python OD example with TFLite delegate

Signed-off-by: Raviv Shalev <raviv.shalev@arm.com>
Change-Id: I25fcccbf912be0c5bd4fbfd2e97552341958af35

5 files changed, 133 insertions, 10 deletions

diff --git a/python/pyarmnn/examples/tests/conftest.py b/python/pyarmnn/examples/tests/conftest.py
index b7fa73b852..4f1ac5f379 100644
--- a/python/pyarmnn/examples/tests/conftest.py
+++ b/python/pyarmnn/examples/tests/conftest.py
@@ -20,20 +20,38 @@ def test_data_folder():
     data_dir = os.path.join(script_dir, "testdata")
     if not os.path.exists(data_dir):
         os.mkdir(data_dir)
+
+    sys_arch = os.uname().machine
+    if sys_arch == "x86_64":
+        libarmnn_url = "https://github.com/ARM-software/armnn/releases/download/v21.11/ArmNN-linux-x86_64.tar.gz"
+    else:
+        libarmnn_url = "https://github.com/ARM-software/armnn/releases/download/v21.11/ArmNN-linux-aarch64.tar.gz"
+
+
     files_to_download = ["https://raw.githubusercontent.com/opencv/opencv/4.0.0/samples/data/messi5.jpg",
                          "https://raw.githubusercontent.com/opencv/opencv/4.0.0/samples/data/basketball1.png",
                          "https://raw.githubusercontent.com/opencv/opencv/4.0.0/samples/data/Megamind.avi",
                          "https://github.com/ARM-software/ML-zoo/raw/master/models/object_detection/ssd_mobilenet_v1/tflite_uint8/ssd_mobilenet_v1.tflite",
                          "https://git.mlplatform.org/ml/ethos-u/ml-embedded-evaluation-kit.git/plain/resources/kws/samples/yes.wav",
-                         "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-speech-sdk/master/sampledata/audiofiles/myVoiceIsMyPassportVerifyMe04.wav"
+                         "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-speech-sdk/master/sampledata/audiofiles/myVoiceIsMyPassportVerifyMe04.wav",
+                         "https://tfhub.dev/google/lite-model/magenta/arbitrary-image-stylization-v1-256/int8/prediction/1?lite-format=tflite",
+                         "https://tfhub.dev/google/lite-model/magenta/arbitrary-image-stylization-v1-256/int8/transfer/1?lite-format=tflite",
+                         libarmnn_url
                          ]
 
     for file in files_to_download:
         path, filename = ntpath.split(file)
+        if filename == '1?lite-format=tflite' and 'prediction' in file:
+            filename = 'style_predict.tflite'
+        elif filename == '1?lite-format=tflite' and 'transfer' in file:
+            filename = 'style_transfer.tflite'
         file_path = os.path.join(data_dir, filename)
         if not os.path.exists(file_path):
             print("\nDownloading test file: " + file_path + "\n")
             urllib.request.urlretrieve(file, file_path)
 
+    path, filename = ntpath.split(libarmnn_url)
+    file_path = os.path.join(data_dir, filename)
+    os.system(f"tar -xvzf {file_path} -C {data_dir} ")
 
     return data_dir
diff --git a/python/pyarmnn/examples/tests/context.py b/python/pyarmnn/examples/tests/context.py
index a678f94178..6b9439d353 100644
--- a/python/pyarmnn/examples/tests/context.py
+++ b/python/pyarmnn/examples/tests/context.py
@@ -10,6 +10,8 @@ sys.path.insert(0, os.path.join(script_dir, '..'))
 
 import common.cv_utils as cv_utils
 import common.network_executor as network_executor
+import common.network_executor_tflite as network_executor_tflite
+
 import common.utils as utils
 import common.audio_capture as audio_capture
 import common.mfcc as mfcc
@@ -17,6 +19,4 @@ import common.mfcc as mfcc
 import speech_recognition.wav2letter_mfcc as wav2letter_mfcc
 import speech_recognition.audio_utils as audio_utils
 
-
-
-
+import object_detection.style_transfer as style_transfer
diff --git a/python/pyarmnn/examples/tests/test_common_utils.py b/python/pyarmnn/examples/tests/test_common_utils.py
index 28d68ea235..254eba63f8 100644
--- a/python/pyarmnn/examples/tests/test_common_utils.py
+++ b/python/pyarmnn/examples/tests/test_common_utils.py
@@ -2,9 +2,13 @@
 # SPDX-License-Identifier: MIT
 
 import os
+import time
 
+import cv2
+import numpy as np
 from context import cv_utils
 from context import utils
+from utils import Profiling
 
 
 def test_get_source_encoding(test_data_folder):
@@ -17,3 +21,22 @@ def test_read_existing_labels_file(test_data_folder):
     label_file = os.path.join(test_data_folder, "labelmap.txt")
     labels_map = utils.dict_labels(label_file)
     assert labels_map is not None
+
+
+def test_preprocess(test_data_folder):
+    content_image = "messi5.jpg"
+    target_shape = (1, 256, 256, 3)
+    padding = True
+    image = cv2.imread(os.path.join(test_data_folder, content_image))
+    image = cv_utils.preprocess(image, np.float32, target_shape, True, padding)
+
+    assert image.shape == target_shape
+
+
+def test_profiling():
+    profiler = Profiling(True)
+    profiler.profiling_start()
+    time.sleep(1)
+    period = profiler.profiling_stop_and_print_us("Sleep for 1 second")
+    assert (1_000_000 < period < 1_002_000)
+
diff --git a/python/pyarmnn/examples/tests/test_network_executor.py b/python/pyarmnn/examples/tests/test_network_executor.py
index c124b11382..f266c16537 100644
--- a/python/pyarmnn/examples/tests/test_network_executor.py
+++ b/python/pyarmnn/examples/tests/test_network_executor.py
@@ -2,23 +2,35 @@
 # SPDX-License-Identifier: MIT
 
 import os
-
+import pytest
 import cv2
+import numpy as np
 
 from context import network_executor
+from context import network_executor_tflite
 from context import cv_utils
 
-
-def test_execute_network(test_data_folder):
+@pytest.mark.parametrize("executor_name", ["armnn", "tflite"])
+def test_execute_network(test_data_folder, executor_name):
     model_path = os.path.join(test_data_folder, "ssd_mobilenet_v1.tflite")
     backends = ["CpuAcc", "CpuRef"]
+    if executor_name == "armnn":
+        executor = network_executor.ArmnnNetworkExecutor(model_path, backends)
+    elif executor_name == "tflite":
+        delegate_path = os.path.join(test_data_folder, "libarmnnDelegate.so")
+        executor = network_executor_tflite.TFLiteNetworkExecutor(model_path, backends, delegate_path)
+    else:
+        raise f"unsupported executor_name: {executor_name}"
 
-    executor = network_executor.ArmnnNetworkExecutor(model_path, backends)
     img = cv2.imread(os.path.join(test_data_folder, "messi5.jpg"))
-    input_tensors = cv_utils.preprocess(img, executor.input_binding_info, True)
+    resized_img = cv_utils.preprocess(img, executor.get_data_type(), executor.get_shape(), True)
 
-    output_result = executor.run(input_tensors)
+    output_result = executor.run([resized_img])
 
     # Ensure it detects a person
     classes = output_result[1]
     assert classes[0][0] == 0
+
+    # Unit tests for network executor class functions - specifically for ssd_mobilenet_v1.tflite network
+    assert executor.get_data_type() == np.uint8
+    assert executor.get_shape() == (1, 300, 300, 3)
diff --git a/python/pyarmnn/examples/tests/test_style_transfer.py b/python/pyarmnn/examples/tests/test_style_transfer.py
new file mode 100644
index 0000000000..fae4a427f0
--- /dev/null
+++ b/python/pyarmnn/examples/tests/test_style_transfer.py
@@ -0,0 +1,70 @@
+# Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
+# SPDX-License-Identifier: MIT
+
+import os
+import cv2
+import numpy as np
+
+from context import style_transfer
+from context import cv_utils
+
+
+def test_style_transfer_postprocess(test_data_folder):
+    content_image = "messi5.jpg"
+    target_shape = (1,256,256,3)
+    keep_aspect_ratio = False
+    image = cv2.imread(os.path.join(test_data_folder, content_image))
+    original_shape = image.shape
+    preprocessed_image = cv_utils.preprocess(image, np.float32, target_shape, False, keep_aspect_ratio)
+    assert preprocessed_image.shape == target_shape
+
+    postprocess_image = style_transfer.style_transfer_postprocess(preprocessed_image, original_shape)
+    assert postprocess_image.shape == original_shape
+
+
+def test_style_transfer(test_data_folder):
+    style_predict_model_path = os.path.join(test_data_folder, "style_predict.tflite")
+    style_transfer_model_path = os.path.join(test_data_folder, "style_transfer.tflite")
+    backends = ["CpuAcc", "CpuRef"]
+    delegate_path = os.path.join(test_data_folder, "libarmnnDelegate.so")
+    image = cv2.imread(os.path.join(test_data_folder, "messi5.jpg"))
+
+    style_transfer_executor = style_transfer.StyleTransfer(style_predict_model_path, style_transfer_model_path,
+                                                           image, backends, delegate_path)
+
+    assert style_transfer_executor.get_style_predict_executor_shape() == (1, 256, 256, 3)
+
+def test_run_style_transfer(test_data_folder):
+    style_predict_model_path = os.path.join(test_data_folder, "style_predict.tflite")
+    style_transfer_model_path = os.path.join(test_data_folder, "style_transfer.tflite")
+    backends = ["CpuAcc", "CpuRef"]
+    delegate_path = os.path.join(test_data_folder, "libarmnnDelegate.so")
+    style_image = cv2.imread(os.path.join(test_data_folder, "messi5.jpg"))
+    content_image = cv2.imread(os.path.join(test_data_folder, "basketball1.png"))
+
+    style_transfer_executor = style_transfer.StyleTransfer(style_predict_model_path, style_transfer_model_path,
+                                                           style_image, backends, delegate_path)
+
+    stylized_image = style_transfer_executor.run_style_transfer(content_image)
+    assert stylized_image.shape == content_image.shape
+
+
+def test_create_stylized_detection(test_data_folder):
+    style_predict_model_path = os.path.join(test_data_folder, "style_predict.tflite")
+    style_transfer_model_path = os.path.join(test_data_folder, "style_transfer.tflite")
+    backends = ["CpuAcc", "CpuRef"]
+    delegate_path = os.path.join(test_data_folder, "libarmnnDelegate.so")
+
+    style_image = cv2.imread(os.path.join(test_data_folder, "messi5.jpg"))
+    content_image = cv2.imread(os.path.join(test_data_folder, "basketball1.png"))
+    detections = [(0.0, [0.16745174, 0.15101701, 0.5371381, 0.74165875], 0.87597656)]
+    labels = {0: ('person', (50.888902345757494, 129.61878417939724, 207.2891028294508)),
+             1: ('bicycle', (55.055339686943654, 55.828708219750574, 43.550389695374676)),
+             2: ('car', (95.33096265662336, 194.872841553212, 218.58516479057758))}
+    style_transfer_executor = style_transfer.StyleTransfer(style_predict_model_path, style_transfer_model_path,
+                                                           style_image, backends, delegate_path)
+
+    stylized_image = style_transfer.create_stylized_detection(style_transfer_executor, 'person', content_image,
+                                                              detections, 720, labels)
+
+    assert stylized_image.shape == content_image.shape