import os
import onnx
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/
# https://github.com/hollance/YOLO-CoreML-MPSNNGraph
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', '')
# # Load the ONNX model
model = onnx.load(f)
# Check that the IR is well formed
print(onnx.checker.check_model(model))
# Print a human readable representation of the graph
print(onnx.helper.printable_graph(model.graph))
model_file = open(f, 'rb')
model_proto = onnx_pb.ModelProto()
model_proto.ParseFromString(model_file.read())
yolov3_model = convert(model_proto, image_input_names=['0'], preprocessing_args={'image_scale': 1. / 255})
# 2. Reduce model to FP16, change outputs to DOUBLE and save
import coremltools
spec = yolov3_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)
yolov3_model = coremltools.models.MLModel(spec)
name_out0 = spec.description.output[0].name
name_out1 = spec.description.output[1].name
num_classes = 80
num_anchors = 507 # 507 for yolov3-tiny,
spec.description.output[0].type.multiArrayType.shape.append(num_anchors)
spec.description.output[0].type.multiArrayType.shape.append(num_classes)
# spec.description.output[0].type.multiArrayType.shape.append(1)
spec.description.output[1].type.multiArrayType.shape.append(num_anchors)
spec.description.output[1].type.multiArrayType.shape.append(4)
# spec.description.output[1].type.multiArrayType.shape.append(1)
# rename
# input_mlmodel = input_tensor.replace(":", "__").replace("/", "__")
# class_output_mlmodel = class_output_tensor.replace(":", "__").replace("/", "__")
# bbox_output_mlmodel = bbox_output_tensor.replace(":", "__").replace("/", "__")
#
# for i in range(len(spec.neuralNetwork.layers)):
# if spec.neuralNetwork.layers[i].input[0] == input_mlmodel:
# spec.neuralNetwork.layers[i].input[0] = 'image'
# if spec.neuralNetwork.layers[i].output[0] == class_output_mlmodel:
# spec.neuralNetwork.layers[i].output[0] = 'scores'
# if spec.neuralNetwork.layers[i].output[0] == bbox_output_mlmodel:
# spec.neuralNetwork.layers[i].output[0] = 'boxes'
spec.neuralNetwork.preprocessing[0].featureName = '0'
yolov3_model.save(name + '.mlmodel')
# yolov3_model.visualize_spec()
print(spec.description)
# 2.5. Try to Predict:
from PIL import Image
img = Image.open('../yolov3/data/samples/zidane_416.jpg')
out = yolov3_model.predict({'0': img}, useCPUOnly=True)
print(out[name_out0].shape, out[name_out1].shape)
# 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 = yolov3_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 = name_out0 # 1x507x80
nms.coordinatesInputFeatureName = name_out1 # 1x507x4
nms.confidenceOutputFeatureName = 'confidence'
nms.coordinatesOutputFeatureName = 'coordinates'
nms.iouThresholdInputFeatureName = 'iouThreshold'
nms.confidenceThresholdInputFeatureName = 'confidenceThreshold'
nms.iouThreshold = 0.4
nms.confidenceThreshold = 0.5
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')
# out_nms = nms_model.predict({
# '143': out['143'].squeeze().reshape((80, 507)),
# '144': out['144'].squeeze().reshape((4, 507))
# })
# print(out_nms['confidence'].shape, out_nms['coordinates'].shape)
# # # 3.5 Add Softmax model
# from coremltools.models import datatypes
# from coremltools.models import neural_network
#
# input_features = [
# ("141", datatypes.Array(num_anchors, num_classes, 1)),
# ("143", datatypes.Array(num_anchors, 4, 1))
# ]
#
# output_features = [
# ("141", datatypes.Array(num_anchors, num_classes, 1)),
# ("143", datatypes.Array(num_anchors, 4, 1))
# ]
#
# builder = neural_network.NeuralNetworkBuilder(input_features, output_features)
# builder.add_softmax(name="softmax_pcls",
# dim=(0, 3, 2, 1),
# input_name="scores",
# output_name="permute_scores_output")
# softmax_model = coremltools.models.MLModel(builder.spec)
# softmax_model.save("softmax.mlmodel")
# 4. Pipeline models togethor
from coremltools.models import datatypes
# from coremltools.models import neural_network
from coremltools.models.pipeline import Pipeline
input_features = [('0', 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)
yolov3_output = yolov3_model._spec.description.output
yolov3_output[0].type.multiArrayType.shape[:] = [num_anchors, num_classes, 1]
yolov3_output[1].type.multiArrayType.shape[:] = [num_anchors, 4, 1]
nms_input = nms_model._spec.description.input
for i in range(2):
nms_input[i].type.multiArrayType.shape[:] = yolov3_output[i].type.multiArrayType.shape[:]
# And now we can add the three models, in order:
pipeline.add_model(yolov3_model)
pipeline.add_model(nms_model)
# Correct datatypes
pipeline.spec.description.input[0].ParseFromString(yolov3_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()