car-detection-bayes/utils/onnx2coreml.py

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2018-12-26 14:46:39 +00:00
import os
from onnx import onnx_pb
from onnx_coreml import convert
import glob
# https://github.com/onnx/onnx-coreml
# http://machinethink.net/blog/mobilenet-ssdlite-coreml/
2018-12-26 14:57:18 +00:00
# https://github.com/hollance/YOLO-CoreML-MPSNNGraph
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def main():
os.system('rm -rf saved_models && mkdir saved_models')
files = glob.glob('saved_models/*.onnx') + glob.glob('../yolov3/weights/*.onnx')
for f in files:
# 1. ONNX to CoreML
name = 'saved_models/' + f.split('/')[-1].replace('.onnx', '')
model_file = open(f, 'rb')
model_proto = onnx_pb.ModelProto()
model_proto.ParseFromString(model_file.read())
coreml_model = convert(model_proto, image_input_names=['0'])
# coreml_model.save(model_out)
# 2. Reduce model to FP16, change outputs to DOUBLE and save
import coremltools
spec = coreml_model.get_spec()
for i in range(2):
spec.description.output[i].type.multiArrayType.dataType = \
coremltools.proto.FeatureTypes_pb2.ArrayFeatureType.ArrayDataType.Value('DOUBLE')
spec = coremltools.utils.convert_neural_network_spec_weights_to_fp16(spec)
coreml_model = coremltools.models.MLModel(spec)
num_classes = 80
num_anchors = 507
spec.description.output[0].type.multiArrayType.shape.append(num_classes)
spec.description.output[0].type.multiArrayType.shape.append(num_anchors)
spec.description.output[1].type.multiArrayType.shape.append(4)
spec.description.output[1].type.multiArrayType.shape.append(num_anchors)
coreml_model.save(name + '.mlmodel')
print(spec.description)
# 3. Create NMS protobuf
import numpy as np
nms_spec = coremltools.proto.Model_pb2.Model()
nms_spec.specificationVersion = 3
for i in range(2):
decoder_output = coreml_model._spec.description.output[i].SerializeToString()
nms_spec.description.input.add()
nms_spec.description.input[i].ParseFromString(decoder_output)
nms_spec.description.output.add()
nms_spec.description.output[i].ParseFromString(decoder_output)
nms_spec.description.output[0].name = 'confidence'
nms_spec.description.output[1].name = 'coordinates'
output_sizes = [num_classes, 4]
for i in range(2):
ma_type = nms_spec.description.output[i].type.multiArrayType
ma_type.shapeRange.sizeRanges.add()
ma_type.shapeRange.sizeRanges[0].lowerBound = 0
ma_type.shapeRange.sizeRanges[0].upperBound = -1
ma_type.shapeRange.sizeRanges.add()
ma_type.shapeRange.sizeRanges[1].lowerBound = output_sizes[i]
ma_type.shapeRange.sizeRanges[1].upperBound = output_sizes[i]
del ma_type.shape[:]
nms = nms_spec.nonMaximumSuppression
nms.confidenceInputFeatureName = '133' # 1x507x80
nms.coordinatesInputFeatureName = '134' # 1x507x4
nms.confidenceOutputFeatureName = 'confidence'
nms.coordinatesOutputFeatureName = 'coordinates'
nms.iouThresholdInputFeatureName = 'iouThreshold'
nms.confidenceThresholdInputFeatureName = 'confidenceThreshold'
nms.iouThreshold = 0.6
nms.confidenceThreshold = 0.4
nms.pickTop.perClass = True
labels = np.loadtxt('../yolov3/data/coco.names', dtype=str, delimiter='\n')
nms.stringClassLabels.vector.extend(labels)
nms_model = coremltools.models.MLModel(nms_spec)
nms_model.save(name + '_nms.mlmodel')
# 4. Pipeline models togethor
from coremltools.models import datatypes
# from coremltools.models import neural_network
from coremltools.models.pipeline import Pipeline
input_features = [('image', datatypes.Array(3, 416, 416)),
('iouThreshold', datatypes.Double()),
('confidenceThreshold', datatypes.Double())]
output_features = ['confidence', 'coordinates']
pipeline = Pipeline(input_features, output_features)
# Add 3rd dimension of size 1 (apparently not needed, produces error on compile)
# ssd_output = coreml_model._spec.description.output
# ssd_output[0].type.multiArrayType.shape[:] = [num_classes, num_anchors, 1]
# ssd_output[1].type.multiArrayType.shape[:] = [4, num_anchors, 1]
# And now we can add the three models, in order:
pipeline.add_model(coreml_model)
pipeline.add_model(nms_model)
# Correct datatypes
pipeline.spec.description.input[0].ParseFromString(coreml_model._spec.description.input[0].SerializeToString())
pipeline.spec.description.output[0].ParseFromString(nms_model._spec.description.output[0].SerializeToString())
pipeline.spec.description.output[1].ParseFromString(nms_model._spec.description.output[1].SerializeToString())
# Update metadata
pipeline.spec.description.metadata.versionString = 'yolov3-tiny.pt imported from PyTorch'
pipeline.spec.description.metadata.shortDescription = 'https://github.com/ultralytics/yolov3'
pipeline.spec.description.metadata.author = 'glenn.jocher@ultralytics.com'
pipeline.spec.description.metadata.license = 'https://github.com/ultralytics/yolov3'
user_defined_metadata = {'classes': ','.join(labels),
'iou_threshold': str(nms.iouThreshold),
'confidence_threshold': str(nms.confidenceThreshold)}
pipeline.spec.description.metadata.userDefined.update(user_defined_metadata)
# Save the model
pipeline.spec.specificationVersion = 3
final_model = coremltools.models.MLModel(pipeline.spec)
final_model.save((name + '_pipelined.mlmodel'))
if __name__ == '__main__':
main()