From d0d06274d97a03d2fe24fdabb9f30a4a06448c12 Mon Sep 17 00:00:00 2001 From: Wenbing Li <10278425+wenbingl@users.noreply.github.com> Date: Fri, 20 Dec 2024 08:49:09 -0800 Subject: [PATCH] enable OS native codecs by default for all pipelines (#855) * trying to enable OS native codec by default * one more try for iOS build * adjust MSE threshold * fix test issue on MacOS * undo the CoreService change * more corrections * remove iOS support * Revert "remove iOS support" This reverts commit ff16c1352713034d74f4f962c544ac540516a017. * recover * podspec * more fixes --------- Co-authored-by: Chester Liu <4710575+skyline75489@users.noreply.github.com> --- CMakeLists.txt | 8 +- .../ppp_vision/create_boxdrawing_model.py | 3 - test/data/wolves_with_fastestDet.jpg | Bin 103675 -> 0 bytes test/data/wolves_with_fastestDet.png | Bin 0 -> 369911 bytes test/pp_api_test/test_imgcodec.cc | 4 +- ..._tools_add_pre_post_processing_to_model.py | 75 +++++++++++------- tools/ios/podspec.template | 2 + 7 files changed, 56 insertions(+), 36 deletions(-) delete mode 100644 test/data/wolves_with_fastestDet.jpg create mode 100644 test/data/wolves_with_fastestDet.png diff --git a/CMakeLists.txt b/CMakeLists.txt index d848cf698..fd8b40d5f 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -71,7 +71,7 @@ option(OCOS_ENABLE_BERT_TOKENIZER "Enable the BertTokenizer building" ON) option(OCOS_ENABLE_BLINGFIRE "Enable operators depending on the Blingfire library" ON) option(OCOS_ENABLE_MATH "Enable math tensor operators building" ON) option(OCOS_ENABLE_DLIB "Enable operators like Inverse depending on DLIB" ON) -option(OCOS_ENABLE_VENDOR_IMAGE_CODECS "Enable and use vendor image codecs if supported over libpng & libjpeg" OFF) +option(OCOS_ENABLE_VENDOR_IMAGE_CODECS "Enable and use vendor image codecs if supported over libpng & libjpeg" ON) option(OCOS_ENABLE_OPENCV_CODECS "Enable cv2 and vision operators that require opencv imgcodecs." OFF) option(OCOS_ENABLE_CV2 "Enable the operators in `operators/cv2`" OFF) option(OCOS_ENABLE_VISION "Enable the operators in `operators/vision`" ON) @@ -666,7 +666,11 @@ if(OCOS_ENABLE_VISION) elseif(CMAKE_SYSTEM_NAME STREQUAL "Darwin" OR CMAKE_SYSTEM_NAME STREQUAL "iOS") # Use ImageIO on Apple platforms set(_DEFAULT_CODEC_ENABLE OFF) - target_link_libraries(ocos_operators PRIVATE "-framework CoreFoundation" "-framework CoreGraphics" "-framework CoreServices" "-framework ImageIO") + set(_APPLE_FRAMEWORKS "-framework CoreFoundation" "-framework CoreGraphics" "-framework ImageIO" "-framework CoreServices") + if(CMAKE_SYSTEM_NAME STREQUAL "iOS") + list(APPEND _APPLE_FRAMEWORKS "-framework MobileCoreServices") + endif() + target_link_libraries(ocos_operators PRIVATE ${_APPLE_FRAMEWORKS}) endif() endif() diff --git a/test/data/ppp_vision/create_boxdrawing_model.py b/test/data/ppp_vision/create_boxdrawing_model.py index c0d4f4a4b..ed829a805 100644 --- a/test/data/ppp_vision/create_boxdrawing_model.py +++ b/test/data/ppp_vision/create_boxdrawing_model.py @@ -5,9 +5,6 @@ # NOTE: This assumes you have created an editable pip install for onnxruntime_extensions by running # `pip install -e .` from the repo root. from onnxruntime_extensions.tools.pre_post_processing import * -from onnxruntime_extensions import get_library_path - -import onnxruntime def create_model(output_file: Path, **kwargs): diff --git a/test/data/wolves_with_fastestDet.jpg b/test/data/wolves_with_fastestDet.jpg deleted file mode 100644 index f1523dfed9f4c9b1aa1c9fbf05c7ff40a3f33291..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 103675 zcmbTdWl$VX)IPfC26uN0?(QK3S=`+M1Okh@1$UPK2@b(wAq01KcY?E6@WnS+{`}sz z>fSH+!@aksx@)GUr{_G?Gf$m9&*^_F|F!|RO7b9i00II4fbe<%{;dII0f-3ymH%4A z{|YkFe=Q0!G7>T>3M%UVR3O?LR3I7<74;3q8#MI)%Ig^>20G?{7yk|NzmFoKAR(ck z15tth)8+qD^REYh_Xa@?;RXqT9)O64fP{zeZva5^YA4G7nD=Vz{}u#9BxDp+;Hw!J 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zXuNf<)e=&5QAW0qg_MkuU9=?vNM!MH;%x9 z)6pJ$sH9uMQMdNJc92Ituipw9#3q2A)u(?D0m{}R)YmFrOvg%_!cFVvT?ZjwU!jLc zv<7y!JCBWn2jlmq=7D~@jVdR0=1_B&3mtmYDuHAdC}XY@AiSaZ107lN1aM|sz>J^n zvB`nHrv7ZICP7jiZU^{Z93P7=UsTW$nt*zC4lCRl0!S~8WbvX>UPWd^QAr746HqbZ z`l^FzG}9_@{@Y+WJv85Ym$tR;MF|!%pK+2)OV2z#UCirwCgg5j^WPUbQ-z&mjM?_@N+|NeC$)r15u zi3T3gzq1Jy_jg7n;Afn)xH!fry;85>pL=3*y>)DjHcn=UP%9UGyX~sMkJRfLM~Sxh z6ZY2SpW*ecH}+pAC*`dYEm+&iV=DBI#^tBjrDoFKE8Hk2d20f6)ZRNd@aVZeH-^_Q z+`M@qalCp_P+oSJ;uQ}yu1rej7QUBbcCQYcp=~OO`xDn~B_?I@G_3>52DmeCE?F@) zc4mCw`4+smIkDYGvRuvFK_K>Kt6x*rFIHg9U-(UumtUE!-(i8W6_;V+!dm_v47@lz zRzk%ZH{<#3hP7Ve&&3BTV?{8Sn}DLwR%DR_}HWJzRr=M4wnspzRH zjvO@!+?RMYb?<vGZJh{Cz85hJnX z7~k7OkHMYo9A2M*wlM3V+JGqldOjX!6i-Z)kY(j-1r01@4XI9w!QQSO4u@v`O>cUZ z4eN|2960@cv&(jMWGG`^`1PLcwfJz+?ycESmhpcYJ;r+^>Sfr+AMN{4TPb^p%D!Urv!J5}@_3NZj5P&9z=XZnI(out_range, out_range + 12), - std::vector({225, 236, 222, 225, 236, 222, 221, 219, 196, 203, 201, 178})); + std::vector({225, 236, 222, 228, 235, 219, 218, 220, 199, 203, 201, 178})); out_range = out_tensor.Data() + 438 * width * 3; ASSERT_EQ(std::vector(out_range, out_range + 12), - std::vector({84, 68, 53, 86, 70, 55, 92, 77, 58, 101, 86, 67})); + std::vector({84, 68, 53, 86, 70, 55, 92, 76, 59, 101, 86, 65})); out_range = out_tensor.Data() + 875 * width * 3 + 1296 * 3; ASSERT_EQ(std::vector(out_range, out_range + 12), diff --git a/test/test_tools_add_pre_post_processing_to_model.py b/test/test_tools_add_pre_post_processing_to_model.py index bec043b17..6d92f924a 100644 --- a/test/test_tools_add_pre_post_processing_to_model.py +++ b/test/test_tools_add_pre_post_processing_to_model.py @@ -17,7 +17,7 @@ from onnxruntime_extensions.tools import add_pre_post_processing_to_model as add_ppp from onnxruntime_extensions.tools import add_HuggingFace_CLIPImageProcessor_to_model as add_clip_feature from onnxruntime_extensions.tools import pre_post_processing as pre_post_processing -from onnxruntime_extensions.tools.pre_post_processing import * +from onnxruntime_extensions.tools.pre_post_processing import * # noqa script_dir = os.path.dirname(os.path.realpath(__file__)) @@ -25,6 +25,21 @@ test_data_dir = os.path.join(ort_ext_root, "test", "data", "ppp_vision") +def compare_two_images_mse(image1, image2): + # decoding it firstly to avoid any format issues + image1 = Image.open(io.BytesIO(image1)) + image2 = Image.open(io.BytesIO(image2)) + if image1.size != image2.size: + return 10 # arbitrary large value + # check if the images are similar by MSE + return np.mean(np.square(np.array(image1) - np.array(image2))) + + +def load_image_file(file_path): + with open(file_path, "rb") as f: + return f.read() + + # Function to read the mobilenet labels and adjust for PT vs TF training if needed # def _get_labels(is_pytorch: bool = True): # labels_file = os.path.join(test_data_dir, "TF.ImageNetLabels.txt") @@ -103,9 +118,9 @@ def test_pytorch_mobilenet_using_clip_feature(self): output_model = os.path.join(test_data_dir, "pytorch_mobilenet_v2.updated.onnx") input_image_path = os.path.join(test_data_dir, "wolves.jpg") - add_clip_feature.clip_image_processor(Path(input_model), Path(output_model), opset=16, do_resize=True, + add_clip_feature.clip_image_processor(Path(input_model), Path(output_model), opset=16, do_resize=True, do_center_crop=True, do_normalize=True, do_rescale=True, - do_convert_rgb=True, size=256, crop_size=224, + do_convert_rgb=True, size=256, crop_size=224, rescale_factor=1/255, image_mean=[0.485, 0.456, 0.406], image_std=[0.229, 0.224, 0.225]) @@ -346,10 +361,10 @@ def test_hfbert_tokenizer(self): self.assertEqual(np.allclose(result[0], ref_output[0]), True) self.assertEqual(np.allclose(result[1], ref_output[2]), True) self.assertEqual(np.allclose(result[2], ref_output[1]), True) - + def test_hfbert_tokenizer_optional_output(self): output_model = (self.temp4onnx / "hfbert_tokenizer_optional_output.onnx").resolve() - + ref_output = ([ np.array([[2, 236, 118, 16, 1566, 875, 643, 3, 236, 118, 978, 1566, 875, 643, 3]]), np.array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]), @@ -368,7 +383,7 @@ def test_hfbert_tokenizer_optional_output(self): s = ort.InferenceSession(str(output_model), so, providers=["CPUExecutionProvider"]) result = s.run(None, {s.get_inputs()[0].name: np.array([[input_text[0], input_text[1]]])}) - + self.assertEqual(len(result), 2) self.assertEqual(np.allclose(result[0], ref_output[0]), True) @@ -416,7 +431,7 @@ def draw_boxes_on_image(self, output_model, test_boxes): so = ort.SessionOptions() so.register_custom_ops_library(get_library_path()) ort_sess = ort.InferenceSession(str(output_model), providers=['CPUExecutionProvider'], sess_options=so) - image = np.frombuffer(open(Path(test_data_dir)/'wolves.jpg', 'rb').read(), dtype=np.uint8) + image = np.frombuffer(load_image_file(Path(test_data_dir)/'wolves.jpg'), dtype=np.uint8) return ort_sess.run(None, {'image': image, "boxes_in": test_boxes})[0] @@ -432,9 +447,9 @@ def test_draw_box_crop_pad(self): for idx, is_crop in enumerate([True, False]): output_img = (Path(test_data_dir) / f"../{ref_img[idx]}").resolve() create_boxdrawing_model.create_model(output_model, is_crop=is_crop) - image_ref = np.frombuffer(open(output_img, 'rb').read(), dtype=np.uint8) + image_ref = np.frombuffer(load_image_file(output_img), dtype=np.uint8) output = self.draw_boxes_on_image(output_model, test_boxes[idx]) - self.assertEqual((image_ref == output).all(), True) + self.assertLess(compare_two_images_mse(image_ref, output), 0.2) def test_draw_box_share_border(self): import sys @@ -453,8 +468,8 @@ def test_draw_box_share_border(self): output = self.draw_boxes_on_image(output_model, test_boxes) output_img = (Path(test_data_dir) / f"../wolves_with_box_share_borders.jpg").resolve() - image_ref = np.frombuffer(open(output_img, 'rb').read(), dtype=np.uint8) - self.assertEqual((image_ref == output).all(), True) + image_ref = np.frombuffer(load_image_file(output_img), dtype=np.uint8) + self.assertLess(compare_two_images_mse(image_ref, output), 0.2) def test_draw_box_off_boundary_box(self): import sys @@ -473,8 +488,8 @@ def test_draw_box_off_boundary_box(self): output = self.draw_boxes_on_image(output_model, test_boxes) output_img = (Path(test_data_dir) / f"../wolves_with_box_off_boundary_box.jpg").resolve() - image_ref = np.frombuffer(open(output_img, 'rb').read(), dtype=np.uint8) - self.assertEqual((image_ref == output).all(), True) + image_ref = np.frombuffer(load_image_file(output_img), dtype=np.uint8) + self.assertLess(compare_two_images_mse(image_ref, output), 0.1) def test_draw_box_more_box_by_class_than_colors(self): import sys @@ -503,8 +518,8 @@ def test_draw_box_more_box_by_class_than_colors(self): output = self.draw_boxes_on_image(output_model, test_boxes) output_img = (Path(test_data_dir) / f"../wolves_with_box_more_box_than_colors.jpg").resolve() - image_ref = np.frombuffer(open(output_img, 'rb').read(), dtype=np.uint8) - self.assertEqual((image_ref == output).all(), True) + image_ref = np.frombuffer(load_image_file(output_img), dtype=np.uint8) + self.assertLess(compare_two_images_mse(image_ref, output), 0.1) def test_draw_box_more_box_by_score_than_colors(self): import sys @@ -534,8 +549,8 @@ def test_draw_box_more_box_by_score_than_colors(self): output = self.draw_boxes_on_image(output_model, test_boxes) output_img = (Path(test_data_dir) / f"../wolves_with_box_more_box_than_colors_score.jpg").resolve() - image_ref = np.frombuffer(open(output_img, 'rb').read(), dtype=np.uint8) - self.assertEqual((image_ref == output).all(), True) + image_ref = np.frombuffer(load_image_file(output_img), dtype=np.uint8) + self.assertLess(compare_two_images_mse(image_ref, output), 0.1) # a box with higher score should be drawn over a box with lower score @@ -556,8 +571,8 @@ def test_draw_box_overlapping_with_priority(self): output = self.draw_boxes_on_image(output_model, test_boxes) output_img = (Path(test_data_dir) / f"../wolves_with_box_overlapping.jpg").resolve() - image_ref = np.frombuffer(open(output_img, 'rb').read(), dtype=np.uint8) - self.assertEqual((image_ref == output).all(), True) + image_ref = np.frombuffer(load_image_file(output_img), dtype=np.uint8) + self.assertLess(compare_two_images_mse(image_ref, output), 0.1) def test_draw_box_with_large_thickness(self): import sys @@ -576,8 +591,8 @@ def test_draw_box_with_large_thickness(self): output = self.draw_boxes_on_image(output_model, test_boxes) output_img = (Path(test_data_dir) / f"../wolves_with_solid_box.jpg").resolve() - image_ref = np.frombuffer(open(output_img, 'rb').read(), dtype=np.uint8) - self.assertEqual((image_ref == output).all(), True) + image_ref = np.frombuffer(load_image_file(output_img), dtype=np.uint8) + self.assertLess(compare_two_images_mse(image_ref, output), 0.1) def _create_pipeline_and_run_for_nms(self, output_model: Path, has_conf_value: bool, @@ -612,7 +627,7 @@ def _create_pipeline_and_run_for_nms(self, output_model: Path, graph_def = onnx.parser.parse_graph( f"""\ identity (float[num_boxes,{length}] _input) - => (float[num_boxes,{length}] _output) + => (float[num_boxes,{length}] _output) {{ _output = Identity(_input) }} @@ -683,7 +698,7 @@ def get_model_output(): out = ort_sess.run(None, {'_input': input_data})[0] return out - + expected_size = [24,12,6,18,12,6,18,12,6,] idx = 0 for iou_threshold in [0.9, 0.75, 0.5]: @@ -801,7 +816,7 @@ def _create_pipeline_and_run_for_nms_and_scaling(self, output_model: Path, onnx_opset = 16 graph_text = \ - f"""pass_through ({', '.join(graph_input_strings)}) => ({', '.join(graph_output_strings)}) + f"""pass_through ({', '.join(graph_input_strings)}) => ({', '.join(graph_output_strings)}) {{ {graph_nodes} }}""" @@ -991,17 +1006,19 @@ def test_FastestDet(self): output_model = os.path.join(test_data_dir, "FastestDet.updated.onnx") input_image_path = os.path.join(test_data_dir, "wolves.jpg") - add_ppp.yolo_detection(Path(input_model), Path(output_model), input_shape=(352, 352)) + add_ppp.yolo_detection(Path(input_model), Path(output_model), output_format='png', input_shape=(352, 352)) so = ort.SessionOptions() so.register_custom_ops_library(get_library_path()) ort_sess = ort.InferenceSession(str(output_model), providers=['CPUExecutionProvider'], sess_options=so) - image = np.frombuffer(open(Path(test_data_dir)/'wolves.jpg', 'rb').read(), dtype=np.uint8) + image = np.frombuffer(load_image_file(input_image_path), dtype=np.uint8) output = ort_sess.run(None, {'image': image})[0] - output_img = (Path(test_data_dir) / f"../wolves_with_fastestDet.jpg").resolve() - image_ref = np.frombuffer(open(output_img, 'rb').read(), dtype=np.uint8) - self.assertEqual((image_ref == output).all(), True) + output_img = (Path(test_data_dir) / "../wolves_with_fastestDet.png").resolve() + # output.tofile(str(output_img) + "actual.png") + + image_ref = np.frombuffer(load_image_file(output_img), dtype=np.uint8) + self.assertLess(compare_two_images_mse(image_ref, output), 0.2) if __name__ == "__main__": diff --git a/tools/ios/podspec.template b/tools/ios/podspec.template index 6e477e859..914edd67a 100644 --- a/tools/ios/podspec.template +++ b/tools/ios/podspec.template @@ -14,6 +14,8 @@ Pod::Spec.new do |spec| spec.osx.deployment_target = macosx_deployment_target end + spec.ios.framework = 'MobileCoreServices' + spec.osx.framework = 'CoreServices' spec.vendored_frameworks = "@XCFRAMEWORK_DIR@" spec.static_framework = true spec.source_files = "@PUBLIC_HEADERS_DIR@/*.h"