How to convert to onnx model?

@toandaominh1997 Hi, thanks for your Great work. I have tried to convert efficientdet-b0.pth to onnx model, and my code is follow :

def to_onnx(checkpoint_name,save_onnx_name='efficientdet.onnx', size_image=(512, 512)):
    from torch.autograd import Variable
    import torch.onnx as onnx

    checkpoint = torch.load(checkpoint_name)#,map_location=lambda storage, loc: storage)
    num_class = checkpoint['num_class']
    network = checkpoint['network']
    model=EfficientDet(num_classes=num_class,
                     network=network,
                     W_bifpn=EFFICIENTDET[network]['W_bifpn'],
                     D_bifpn=EFFICIENTDET[network]['D_bifpn'],
                     D_class=EFFICIENTDET[network]['D_class'],
                     is_training=False
                     )

    state_dict = checkpoint['state_dict']
    model.load_state_dict(state_dict)
    model.train(False)

    dummpy_input = Variable(torch.randn(1,3,512,512))
    output = onnx.export(model, dummpy_input, save_onnx_name,verbose=True, export_params=True)
to_onnx('checkpoint_VOC_efficientdet-d0_206.pth')

I can get efficientdet.onnx file when I run the above code. Moreover I use onnx.checker.check_model to check the onnx model and no error.

But when I use the same image to run demo.py and onnx_test.py, demo.py has result(can predict object) , and onnx_test.py is not. my onnx_test.py is follow:

import onnxruntime as rt
import cv2
import numpy as np

def run(image_name, onnx_path):
    img=cv2.imread(image_name)
    img=cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
    img = cv2.resize(img, (512,512))
    img = img.reshape(1,3,512,512)
    img= img.astype(np.float32)

    sess = rt.InferenceSession(onnx_path)
    input_name = sess.get_inputs()[0].name
    output_name1 = sess.get_outputs()[0].name
    output_name2 = sess.get_outputs()[1].name
    output_name3 = sess.get_outputs()[2].name
    print(output_name1, output_name2, output_name3)
    output_name=[output_name1,output_name2, output_name3]
    pred_onnx = sess.run(output_name, {input_name: img})
    print(pred_onnx)

when run onnx_test.py, output :

2019-12-31 13:32:44.800826576 [W:onnxruntime:, graph.cc:2412 CleanUnusedInitializers] Removing initializer 'efficientnet._blocks.8._bn1.weight'. It is not used by any node and should be removed from the model.
2019-12-31 13:32:44.800834976 [W:onnxruntime:, graph.cc:2412 CleanUnusedInitializers] Removing initializer 'efficientnet._blocks.8._bn2.bias'. It is not used by any node and should be removed from the model.
2019-12-31 13:32:44.800843492 [W:onnxruntime:, graph.cc:2412 CleanUnusedInitializers] Removing initializer 'efficientnet._blocks.8._bn2.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-12-31 13:32:44.800852497 [W:onnxruntime:, graph.cc:2412 CleanUnusedInitializers] Removing initializer 'efficientnet._blocks.5._depthwise_conv.weight'. It is not used by any node and should be removed from the model.
2019-12-31 13:32:44.800861084 [W:onnxruntime:, graph.cc:2412 CleanUnusedInitializers] Removing initializer 'efficientnet._blocks.8._bn2.running_mean'. It is not used by any node and should be removed from the model.
459 460 461
[array([], dtype=float32), array([], dtype=float32), array([], shape=(0, 4), dtype=float32)]

Q1: Is there a problem with my converted code？
Q2: " 'efficientnet._blocks.8._bn1.weight'. It is not used by any node and should be removed from the model." , how this mean?
Q3: The size of checkpoint_xxx-b0_xxx.pth is about 38M,
Can i reduce the model file size?

Please take the time to answer the above three questions, thank you very much！

toandaominh1997 / EfficientDet.Pytorch

How to convert to onnx model? #53