From 1105111108b81fc983704263a1be36c93c1b25ce Mon Sep 17 00:00:00 2001 From: Yuantao Feng Date: Wed, 25 May 2022 20:05:17 +0800 Subject: [PATCH] add palm detector from mediapipe (#51) --- README.md | 7 +- .../config/palm_detection_mediapipe.yaml | 21 + benchmark/download_data.py | 8 +- models/__init__.py | 2 + models/palm_detection_mediapipe/LICENSE | 202 ++ models/palm_detection_mediapipe/README.md | 28 + models/palm_detection_mediapipe/demo.py | 120 + models/palm_detection_mediapipe/mp_palmdet.py | 3028 +++++++++++++++++ ...tion_mediapipe_2022may-int8-quantized.onnx | 3 + .../palm_detection_mediapipe_2022may.onnx | 3 + tools/quantize/README.md | 27 +- tools/quantize/quantize-inc.py | 71 + .../quantize/{quantize.py => quantize-ort.py} | 40 +- 13 files changed, 3518 insertions(+), 42 deletions(-) create mode 100644 benchmark/config/palm_detection_mediapipe.yaml create mode 100644 models/palm_detection_mediapipe/LICENSE create mode 100644 models/palm_detection_mediapipe/README.md create mode 100644 models/palm_detection_mediapipe/demo.py create mode 100644 models/palm_detection_mediapipe/mp_palmdet.py create mode 100644 models/palm_detection_mediapipe/palm_detection_mediapipe_2022may-int8-quantized.onnx create mode 100644 models/palm_detection_mediapipe/palm_detection_mediapipe_2022may.onnx create mode 100644 tools/quantize/quantize-inc.py rename tools/quantize/{quantize.py => quantize-ort.py} (71%) diff --git a/README.md b/README.md index 2772233..d6e8fe3 100644 --- a/README.md +++ b/README.md @@ -23,12 +23,15 @@ Guidelines: | [CRNN-EN](./models/text_recognition_crnn) | 100x32 | 50.21 | 234.32 | 196.15 | 125.30 | --- | | [CRNN-CN](./models/text_recognition_crnn) | 100x32 | 73.52 | 322.16 | 239.76 | 166.79 | --- | | [PP-ResNet](./models/image_classification_ppresnet) | 224x224 | 56.05 | 602.58 | 98.64 | 75.45 | --- | -| [MobileNet-V1](./models/image_classification_mobilenet)| 224x224 | 9.04 | 92.25 | 33.18 | 145.66 (per-channel) | --- | -| [MobileNet-V2](./models/image_classification_mobilenet)| 224x224 | 8.86 | 74.03 | 31.92 | 146.31 (per-channel) | --- | +| [MobileNet-V1](./models/image_classification_mobilenet)| 224x224 | 9.04 | 92.25 | 33.18 | 145.66\* | --- | +| [MobileNet-V2](./models/image_classification_mobilenet)| 224x224 | 8.86 | 74.03 | 31.92 | 146.31\* | --- | | [PP-HumanSeg](./models/human_segmentation_pphumanseg) | 192x192 | 19.92 | 105.32 | 67.97 | 74.77 | --- | | [WeChatQRCode](./models/qrcode_wechatqrcode) | 100x100 | 7.04 | 37.68 | --- | --- | --- | | [DaSiamRPN](./models/object_tracking_dasiamrpn) | 1280x720 | 36.15 | 705.48 | 76.82 | --- | --- | | [YoutuReID](./models/person_reid_youtureid) | 128x256 | 35.81 | 521.98 | 90.07 | 44.61 | --- | +| [MPPalmDet](./models/palm_detection_mediapipe) | 256x256 | 15.57 | 89.41 | 50.64 | 145.56\* | --- | + +\*: Models are quantized in per-channel mode, which run slower than per-tensor quantized models on NPU. Hardware Setup: - `INTEL-CPU`: [Intel Core i7-5930K](https://www.intel.com/content/www/us/en/products/sku/82931/intel-core-i75930k-processor-15m-cache-up-to-3-70-ghz/specifications.html) @ 3.50GHz, 6 cores, 12 threads. diff --git a/benchmark/config/palm_detection_mediapipe.yaml b/benchmark/config/palm_detection_mediapipe.yaml new file mode 100644 index 0000000..da7fa08 --- /dev/null +++ b/benchmark/config/palm_detection_mediapipe.yaml @@ -0,0 +1,21 @@ +Benchmark: + name: "Palm Detection Benchmark" + type: "Detection" + data: + path: "benchmark/data/palm_detection" + files: ["palm1.jpg", "palm2.jpg", "palm3.jpg"] + sizes: # [[w1, h1], ...], Omit to run at original scale + - [256, 256] + metric: + warmup: 30 + repeat: 10 + reduction: "median" + backend: "default" + target: "cpu" + +Model: + name: "MPPalmDet" + modelPath: "models/palm_detection_mediapipe/palm_detection_mediapipe_2022may.onnx" + scoreThreshold: 0.5 + nmsThreshold: 0.3 + diff --git a/benchmark/download_data.py b/benchmark/download_data.py index 275b27f..0e08ff1 100644 --- a/benchmark/download_data.py +++ b/benchmark/download_data.py @@ -196,7 +196,11 @@ data_downloaders = dict( person_reid=Downloader(name='person_reid', url='https://drive.google.com/u/0/uc?id=1G8FkfVo5qcuyMkjSs4EA6J5e16SWDGI2&export=download', sha='5b741fbf34c1fbcf59cad8f2a65327a5899e66f1', - filename='person_reid.zip') + filename='person_reid.zip'), + palm_detection=Downloader(name='palm_detection', + url='https://drive.google.com/u/0/uc?id=1qScOzehV8OIzJJLuD_LMvZq15YcWd_VV&export=download', + sha='c0d4f811d38c6f833364b9196a719307598213a1', + filename='palm_detection.zip'), ) if __name__ == '__main__': @@ -214,4 +218,4 @@ if __name__ == '__main__': download_failed.append(downloader._name) if download_failed: - print('Data have not been downloaded: {}'.format(str(download_failed))) \ No newline at end of file + print('Data have not been downloaded: {}'.format(str(download_failed))) diff --git a/models/__init__.py b/models/__init__.py index 0e03a05..f932a20 100644 --- a/models/__init__.py +++ b/models/__init__.py @@ -9,6 +9,7 @@ from .object_tracking_dasiamrpn.dasiamrpn import DaSiamRPN from .person_reid_youtureid.youtureid import YoutuReID from .image_classification_mobilenet.mobilenet_v1 import MobileNetV1 from .image_classification_mobilenet.mobilenet_v2 import MobileNetV2 +from .palm_detection_mediapipe.mp_palmdet import MPPalmDet class Registery: def __init__(self, name): @@ -33,4 +34,5 @@ MODELS.register(DaSiamRPN) MODELS.register(YoutuReID) MODELS.register(MobileNetV1) MODELS.register(MobileNetV2) +MODELS.register(MPPalmDet) diff --git a/models/palm_detection_mediapipe/LICENSE b/models/palm_detection_mediapipe/LICENSE new file mode 100644 index 0000000..d645695 --- /dev/null +++ b/models/palm_detection_mediapipe/LICENSE @@ -0,0 +1,202 @@ + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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Score threshold needs to be adjusted as well for the quantized model, which is empirically 0.49. + +## License + +All files in this directory are licensed under [Apache 2.0 License](./LICENSE). + +## Reference + +- MediaPipe Handpose: https://github.com/tensorflow/tfjs-models/tree/master/handpose diff --git a/models/palm_detection_mediapipe/demo.py b/models/palm_detection_mediapipe/demo.py new file mode 100644 index 0000000..048e1fe --- /dev/null +++ b/models/palm_detection_mediapipe/demo.py @@ -0,0 +1,120 @@ +import argparse + +import numpy as np +import cv2 as cv + +from mp_palmdet import MPPalmDet + +def str2bool(v): + if v.lower() in ['on', 'yes', 'true', 'y', 't']: + return True + elif v.lower() in ['off', 'no', 'false', 'n', 'f']: + return False + else: + raise NotImplementedError + +backends = [cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_BACKEND_CUDA] +targets = [cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_CUDA, cv.dnn.DNN_TARGET_CUDA_FP16] +help_msg_backends = "Choose one of the computation backends: {:d}: OpenCV implementation (default); {:d}: CUDA" +help_msg_targets = "Chose one of the target computation devices: {:d}: CPU (default); {:d}: CUDA; {:d}: CUDA fp16" +try: + backends += [cv.dnn.DNN_BACKEND_TIMVX] + targets += [cv.dnn.DNN_TARGET_NPU] + help_msg_backends += "; {:d}: TIMVX" + help_msg_targets += "; {:d}: NPU" +except: + print('This version of OpenCV does not support TIM-VX and NPU. Visit https://gist.github.com/fengyuentau/5a7a5ba36328f2b763aea026c43fa45f for more information.') + +parser = argparse.ArgumentParser(description='Hand Detector from MediaPipe') +parser.add_argument('--input', '-i', type=str, help='Path to the input image. Omit for using default camera.') +parser.add_argument('--model', '-m', type=str, default='./palm_detection_mediapipe_2022may.onnx', help='Path to the model.') +parser.add_argument('--backend', '-b', type=int, default=backends[0], help=help_msg_backends.format(*backends)) +parser.add_argument('--target', '-t', type=int, default=targets[0], help=help_msg_targets.format(*targets)) +parser.add_argument('--score_threshold', type=float, default=0.99, help='Filter out faces of confidence < conf_threshold. An empirical score threshold for the quantized model is 0.49.') +parser.add_argument('--nms_threshold', type=float, default=0.3, help='Suppress bounding boxes of iou >= nms_threshold.') +parser.add_argument('--save', '-s', type=str, default=False, help='Set true to save results. This flag is invalid when using camera.') +parser.add_argument('--vis', '-v', type=str2bool, default=True, help='Set true to open a window for result visualization. This flag is invalid when using camera.') +args = parser.parse_args() + +def visualize(image, score, palm_box, palm_landmarks, fps=None): + output = image.copy() + + if fps is not None: + cv.putText(output, 'FPS: {:.2f}'.format(fps), (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255)) + + # put score + palm_box = palm_box.astype(np.int32) + cv.putText(output, '{:.4f}'.format(score), (palm_box[0], palm_box[1]+12), cv.FONT_HERSHEY_DUPLEX, 0.5, (0, 255, 0)) + + # draw box + cv.rectangle(output, (palm_box[0], palm_box[1]), (palm_box[2], palm_box[3]), (0, 255, 0), 2) + + # draw points + palm_landmarks = palm_landmarks.astype(np.int32) + for p in palm_landmarks: + cv.circle(output, p, 2, (0, 0, 255), 2) + + return output + +if __name__ == '__main__': + # Instantiate MPPalmDet + model = MPPalmDet(modelPath=args.model, + nmsThreshold=args.nms_threshold, + scoreThreshold=args.score_threshold, + backendId=args.backend, + targetId=args.target) + + # If input is an image + if args.input is not None: + image = cv.imread(args.input) + + # Inference + score, palm_box, palm_landmarks = model.infer(image) + if score is None or palm_box is None or palm_landmarks is None: + print('Hand not detected') + else: + # Print results + print('score: {:.2f}'.format(score)) + print('palm box: {}'.format(palm_box)) + print('palm_landmarks: ') + for plm in enumerate(palm_landmarks): + print('\t{}'.format(plm)) + + # Draw results on the input image + image = visualize(image, score, palm_box, palm_landmarks) + + # Save results if save is true + if args.save: + print('Resutls saved to result.jpg\n') + cv.imwrite('result.jpg', image) + + # Visualize results in a new window + if args.vis: + cv.namedWindow(args.input, cv.WINDOW_AUTOSIZE) + cv.imshow(args.input, image) + cv.waitKey(0) + else: # Omit input to call default camera + deviceId = 0 + cap = cv.VideoCapture(deviceId) + + tm = cv.TickMeter() + while cv.waitKey(1) < 0: + hasFrame, frame = cap.read() + if not hasFrame: + print('No frames grabbed!') + break + + # Inference + tm.start() + score, palm_box, palm_landmarks = model.infer(frame) + tm.stop() + + # Draw results on the input image + if score is not None and palm_box is not None and palm_landmarks is not None: + frame = visualize(frame, score, palm_box, palm_landmarks, fps=tm.getFPS()) + + # Visualize results in a new Window + cv.imshow('MPPalmDet Demo', frame) + + tm.reset() + diff --git a/models/palm_detection_mediapipe/mp_palmdet.py b/models/palm_detection_mediapipe/mp_palmdet.py new file mode 100644 index 0000000..b2bc313 --- /dev/null +++ b/models/palm_detection_mediapipe/mp_palmdet.py @@ -0,0 +1,3028 @@ +import numpy as np +import cv2 as cv + +class MPPalmDet: + def __init__(self, modelPath, nmsThreshold=0.3, scoreThreshold=0.5, backendId=0, targetId=0): + self.model_path = modelPath + self.nms_threshold = nmsThreshold + self.score_threshold = scoreThreshold + self.backend_id = backendId + self.target_id = targetId + + self.input_size = np.array([256, 256]) # wh + + self.model = cv.dnn.readNet(self.model_path) + self.model.setPreferableBackend(self.backend_id) + self.model.setPreferableTarget(self.target_id) + + self.anchors = self._load_anchors() + + @property + def name(self): + return self.__class__.__name__ + + def setBackend(self, backendId): + self.backend_id = backendId + self.model.setPreferableBackend(self.backend_id) + + def setTarget(self, targetId): + self.target_id = targetId + self.model.setPreferableTarget(self.target_id) + + def _preprocess(self, image): + if image.shape[0] != self.input_size[0] or image.shape[1] != self.input_size[1]: + image = cv.resize(image, dsize=self.input_size) + image = cv.cvtColor(image, cv.COLOR_BGR2RGB) + image = image.astype(np.float32) / 255.0 # norm + image = (image - 0.5) * 2 # [0, 1] -> [-1, 1] + return image[np.newaxis, :, :, :] # hwc -> nhwc + + def infer(self, image): + h, w, _ = image.shape + + # Preprocess + input_blob = self._preprocess(image) + + # Forward + self.model.setInput(input_blob) + output_blob = self.model.forward() + + # Postprocess + score, palm_box, palm_landmarks = self._postprocess(output_blob, np.array([w, h])) + + return (score, palm_box, palm_landmarks) + + def _postprocess(self, output_blob, original_shape): + score = output_blob[0, :, 0] + box_delta = output_blob[0, :, 1:5] + landmark_delta = output_blob[0, :, 5:] + + # get scores + score = score.astype(np.float64) + score = 1 / (1 + np.exp(-score)) + + # get boxes + cxy_delta = box_delta[:, :2] / self.input_size + wh_delta = box_delta[:, 2:] / self.input_size + xy1 = (cxy_delta - wh_delta / 2 + self.anchors[:, :2]) * original_shape + xy2 = (cxy_delta + wh_delta / 2 + self.anchors[:, :2]) * original_shape + boxes = np.concatenate([xy1, xy2], axis=1) + # NMS + keep_idx = cv.dnn.NMSBoxes(boxes, score, self.score_threshold, self.nms_threshold, top_k=1) + if len(keep_idx) == 0: + return None, None, None + selected_score = score[keep_idx][0] + selected_box = boxes[keep_idx][0] + + # get landmarks + selected_landmarks = landmark_delta[keep_idx].reshape(7, 2) + selected_landmarks = (selected_landmarks / self.input_size + self.anchors[keep_idx]) * original_shape + + return (selected_score, selected_box, selected_landmarks) + + def _load_anchors(self): + return np.array([[0.015625, 0.015625], + [0.015625, 0.015625], + [0.046875, 0.015625], + [0.046875, 0.015625], + [0.078125, 0.015625], + [0.078125, 0.015625], + [0.109375, 0.015625], + [0.109375, 0.015625], + [0.140625, 0.015625], + [0.140625, 0.015625], + [0.171875, 0.015625], + [0.171875, 0.015625], + [0.203125, 0.015625], + [0.203125, 0.015625], + [0.234375, 0.015625], + [0.234375, 0.015625], + [0.265625, 0.015625], + [0.265625, 0.015625], + [0.296875, 0.015625], + [0.296875, 0.015625], + [0.328125, 0.015625], + [0.328125, 0.015625], + [0.359375, 0.015625], + [0.359375, 0.015625], + [0.390625, 0.015625], + [0.390625, 0.015625], + [0.421875, 0.015625], + [0.421875, 0.015625], + [0.453125, 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+version https://git-lfs.github.com/spec/v1 +oid sha256:c113e5a55e2b535baa4760a5c71a2d77be5f18c5bf555ac3459722c0c4633df3 +size 3164384 diff --git a/models/palm_detection_mediapipe/palm_detection_mediapipe_2022may.onnx b/models/palm_detection_mediapipe/palm_detection_mediapipe_2022may.onnx new file mode 100644 index 0000000..fa92139 --- /dev/null +++ b/models/palm_detection_mediapipe/palm_detection_mediapipe_2022may.onnx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:daa93134c3c0a7edc90b9e3a3494ec0c730f185b2ad55c30498620161655a96c +size 7105372 diff --git a/tools/quantize/README.md b/tools/quantize/README.md index a76f0ec..8a0d813 100644 --- a/tools/quantize/README.md +++ b/tools/quantize/README.md @@ -11,21 +11,24 @@ pip install -r requirements.txt Quantize all models in the Zoo: ```shell -python quantize.py +python quantize-ort.py +python quantize-inc.py ``` Quantize one of the models in the Zoo: ```shell # python quantize.py -python quantize.py yunet +python quantize-ort.py yunet +python quantize-inc.py mobilenetv1 ``` Customizing quantization configs: ```python -# add model into `models` dict in quantize.py +# Quantize with ONNXRUNTIME +# 1. add your model into `models` dict in quantize-ort.py models = dict( # ... - model1=Quantize(model_path='/path/to/model1.onnx' + model1=Quantize(model_path='/path/to/model1.onnx', calibration_image_dir='/path/to/images', transforms=Compose([''' transforms ''']), # transforms can be found in transforms.py per_channel=False, # set False to quantize in per-tensor style @@ -33,6 +36,18 @@ models = dict( wt_type='int8' # available types: 'int8', 'uint8' ) ) -# quantize the added models -python quantize.py model1 +# 2. quantize your model +python quantize-ort.py model1 + + +# Quantize with Intel Neural Compressor +# 1. add your model into `models` dict in quantize-inc.py +models = dict( + # ... + model1=Quantize(model_path='/path/to/model1.onnx', + config_path='/path/to/model1.yaml'), +) +# 2. prepare your YAML config model1.yaml (see configs in ./inc_configs) +# 3. quantize your model +python quantize-inc.py model1 ``` diff --git a/tools/quantize/quantize-inc.py b/tools/quantize/quantize-inc.py new file mode 100644 index 0000000..792c3a8 --- /dev/null +++ b/tools/quantize/quantize-inc.py @@ -0,0 +1,71 @@ +import os +import sys +import numpy as ny +import cv2 as cv + +import onnx +from neural_compressor.experimental import Quantization, common as nc_Quantization, nc_common + +class Quantize: + def __init__(self, model_path, config_path, custom_dataset=None): + self.model_path = model_path + self.config_path = config_path + self.custom_dataset = custom_dataset + + def run(self): + print('Quantizing (int8) with Intel\'s Neural Compressor:') + print('\tModel: {}'.format(self.model_path)) + print('\tConfig: {}'.format(self.config_path)) + + output_name = '{}-int8-quantized.onnx'.format(self.model_path[:-5]) + + model = onnx.load(self.model_path) + quantizer = nc_Quantization(self.config_path) + if self.custom_dataset is not None: + quantizer.calib_dataloader = common.DataLoader(self.custom_dataset) + quantizer.model = common.Model(model) + q_model = quantizer() + q_model.save(output_name) + +class Dataset: + def __init__(self, root): + self.root = root + self.image_list = self.load_image_list(self.root) + + def load_image_list(self, path): + image_list = [] + for f in os.listdir(path): + if not f.endswith('.jpg'): + continue + image_list.append(f) + return image_list + + def __getitem__(self, idx): + img = cv.imread(self.image_list[idx]) + return img, 1 + + def __len__(self): + return len(self.image_list) + +models=dict( + mobilenetv1=Quantize(model_path='../../models/image_classification_mobilenet/image_classification_mobilenetv1_2022apr.onnx', + config_path='./inc_configs/mobilenet.yaml'), + mobilenetv2=Quantize(model_path='../../models/image_classification_mobilenet/image_classification_mobilenetv2_2022apr.onnx', + config_path='./inc_configs/mobilenet.yaml'), + mppalm_det=Quantize(model_path='../../models/palm_detection_mediapipe/palm_detection_mediapipe_2022may.onnx', + config_path='./inc_configs/mppalmdet.yaml', + custom_dataset=Dataset(root='../../benchmark/data/palm_detection')) +) + +if __name__ == '__main__': + selected_models = [] + for i in range(1, len(sys.argv)): + selected_models.append(sys.argv[i]) + if not selected_models: + selected_models = list(models.keys()) + print('Models to be quantized: {}'.format(str(selected_models))) + + for selected_model_name in selected_models: + q = models[selected_model_name] + q.run() + diff --git a/tools/quantize/quantize.py b/tools/quantize/quantize-ort.py similarity index 71% rename from tools/quantize/quantize.py rename to tools/quantize/quantize-ort.py index e8943f7..f3b351d 100644 --- a/tools/quantize/quantize.py +++ b/tools/quantize/quantize-ort.py @@ -10,7 +10,6 @@ import numpy as ny import cv2 as cv import onnx -from neural_compressor.experimental import Quantization, common as nc_Quantization, nc_common from onnx import version_converter import onnxruntime from onnxruntime.quantization import quantize_static, CalibrationDataReader, QuantType @@ -40,7 +39,7 @@ class DataReader(CalibrationDataReader): blobs.append(blob) return blobs -class ORT_Quantize: +class Quantize: def __init__(self, model_path, calibration_image_dir, transforms=Compose(), per_channel=False, act_type='int8', wt_type='int8'): self.type_dict = {"uint8" : QuantType.QUInt8, "int8" : QuantType.QInt8} @@ -78,51 +77,28 @@ class ORT_Quantize: os.remove('{}-opt.onnx'.format(self.model_path[:-5])) print('\tQuantized model saved to {}'.format(output_name)) -class INC_Quantize: - def __init__(self, model_path, config_path): - self.model_path = model_path - self.config_path = config_path - - def run(self): - print('Quantizing (int8) with Intel\'s Neural Compressor:') - print('\tModel: {}'.format(self.model_path)) - print('\tConfig: {}'.format(self.config_path)) - - output_name = '{}-int8-quantized.onnx'.format(self.model_path[:-5]) - - model = onnx.load(self.model_path) - quantizer = nc_Quantization(self.config_path) - quantizer.model = common.Model(model) - q_model = quantizer() - q_model.save(output_name) - - models=dict( - yunet=ORT_Quantize(model_path='../../models/face_detection_yunet/face_detection_yunet_2022mar.onnx', + yunet=Quantize(model_path='../../models/face_detection_yunet/face_detection_yunet_2022mar.onnx', calibration_image_dir='../../benchmark/data/face_detection', transforms=Compose([Resize(size=(160, 120))])), - sface=ORT_Quantize(model_path='../../models/face_recognition_sface/face_recognition_sface_2021dec.onnx', + sface=Quantize(model_path='../../models/face_recognition_sface/face_recognition_sface_2021dec.onnx', calibration_image_dir='../../benchmark/data/face_recognition', transforms=Compose([Resize(size=(112, 112))])), - pphumenseg=ORT_Quantize(model_path='../../models/human_segmentation_pphumanseg/human_segmentation_pphumanseg_2021oct.onnx', + pphumenseg=Quantize(model_path='../../models/human_segmentation_pphumanseg/human_segmentation_pphumanseg_2021oct.onnx', calibration_image_dir='../../benchmark/data/human_segmentation', transforms=Compose([Resize(size=(192, 192))])), - ppresnet50=ORT_Quantize(model_path='../../models/image_classification_ppresnet/image_classification_ppresnet50_2022jan.onnx', + ppresnet50=Quantize(model_path='../../models/image_classification_ppresnet/image_classification_ppresnet50_2022jan.onnx', calibration_image_dir='../../benchmark/data/image_classification', transforms=Compose([Resize(size=(224, 224))])), - mobilenetv1=INC_Quantize(model_path='../../models/image_classification_mobilenet/image_classification_mobilenetv1_2022apr.onnx', - config_path='./inc_configs/mobilenet.yaml'), - mobilenetv2=INC_Quantize(model_path='../../models/image_classification_mobilenet/image_classification_mobilenetv2_2022apr.onnx', - config_path='./inc_configs/mobilenet.yaml'), # TBD: DaSiamRPN - youtureid=ORT_Quantize(model_path='../../models/person_reid_youtureid/person_reid_youtu_2021nov.onnx', + youtureid=Quantize(model_path='../../models/person_reid_youtureid/person_reid_youtu_2021nov.onnx', calibration_image_dir='../../benchmark/data/person_reid', transforms=Compose([Resize(size=(128, 256))])), # TBD: DB-EN & DB-CN - crnn_en=ORT_Quantize(model_path='../../models/text_recognition_crnn/text_recognition_CRNN_EN_2021sep.onnx', + crnn_en=Quantize(model_path='../../models/text_recognition_crnn/text_recognition_CRNN_EN_2021sep.onnx', calibration_image_dir='../../benchmark/data/text', transforms=Compose([Resize(size=(100, 32)), ColorConvert(ctype=cv.COLOR_BGR2GRAY)])), - crnn_cn=ORT_Quantize(model_path='../../models/text_recognition_crnn/text_recognition_CRNN_CN_2021nov.onnx', + crnn_cn=Quantize(model_path='../../models/text_recognition_crnn/text_recognition_CRNN_CN_2021nov.onnx', calibration_image_dir='../../benchmark/data/text', transforms=Compose([Resize(size=(100, 32))])) ) -- GitLab