EfficientDet Pytorch-lightning with EfficientNet v2 backbone Blog Post.ipynb

EfficientDet Pytorch-lightning with EfficientNet v2 backbone Blog Post.ipynb

Hi @Chris-hughes10. thank you for this great job.
I'm new in this field. I have a dataset that is in Pascal VOC format with XML annotations. How can I use it to training? Can you please help me?

Dear @Chris-hughes10, Thank you for this amazing work.

Have you done any work as to convert the saved model from torch.save() to onnx?

I am asking this question because I got stuck trying to convert the saved model. The conversion script that I am using is this:

import os
import io
import numpy as np
import pandas as pd
from functools import partial

from custom_utils import widerface_data_adaptor
from custom_utils import effdet_data_module
from custom_utils import effdet_model

import torch
import torch.onnx

from effdet import get_efficientdet_config, EfficientDet, DetBenchPredict

model_checkpoint_path = "/home/soroush.tabadkani/projects/efficientdet-pytorch/checkpoints/trained_effdet.pt"
device = torch.device('cuda')

input_shape = (1, 3, 512, 512)
dummy_input = torch.randn(input_shape, dtype=torch.float32, requires_grad=True).to(device)

net = effdet_model.EfficientDetModel(
    num_classes=1,
    img_size=512
    )

net.load_state_dict(torch.load(model_checkpoint_path))
net.eval()

dynamic_axes = {out:{0:'batch_size'} for out in ['outputs']}
dynamic_axes.update({input: {0: 'batch_size'} for input in ['inputs']})
              
torch.onnx.export(net.cuda(),
                  (dummy_input),
                  'efficientdet-d0.onnx',
                  input_names = ['inputs'],
                  output_names = ['outputs'],
                  verbose=True,
                  dynamic_axes=dynamic_axes,
                  opset_version=12)

and the error I get is:

torch.onnx.export(net.cuda(),
  File "/home/soroush.tabadkani/projects/efficientdet-pytorch/env_test/lib/python3.8/site-packages/torch/onnx/__init__.py", line 271, in export
    return utils.export(model, args, f, export_params, verbose, training,
  File "/home/soroush.tabadkani/projects/efficientdet-pytorch/env_test/lib/python3.8/site-packages/torch/onnx/utils.py", line 88, in export
    _export(model, args, f, export_params, verbose, training, input_names, output_names,
  File "/home/soroush.tabadkani/projects/efficientdet-pytorch/env_test/lib/python3.8/site-packages/torch/onnx/utils.py", line 694, in _export
    _model_to_graph(model, args, verbose, input_names,
  File "/home/soroush.tabadkani/projects/efficientdet-pytorch/env_test/lib/python3.8/site-packages/torch/onnx/utils.py", line 457, in _model_to_graph
    graph, params, torch_out, module = _create_jit_graph(model, args,
  File "/home/soroush.tabadkani/projects/efficientdet-pytorch/env_test/lib/python3.8/site-packages/torch/onnx/utils.py", line 420, in _create_jit_graph
    graph, torch_out = _trace_and_get_graph_from_model(model, args)
  File "/home/soroush.tabadkani/projects/efficientdet-pytorch/env_test/lib/python3.8/site-packages/torch/onnx/utils.py", line 380, in _trace_and_get_graph_from_model
    torch.jit._get_trace_graph(model, args, strict=False, _force_outplace=False, _return_inputs_states=True)
  File "/home/soroush.tabadkani/projects/efficientdet-pytorch/env_test/lib/python3.8/site-packages/torch/jit/_trace.py", line 1139, in _get_trace_graph
    outs = ONNXTracedModule(f, strict, _force_outplace, return_inputs, _return_inputs_states)(*args, **kwargs)
  File "/home/soroush.tabadkani/projects/efficientdet-pytorch/env_test/lib/python3.8/site-packages/torch/nn/modules/module.py", line 891, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/home/soroush.tabadkani/projects/efficientdet-pytorch/env_test/lib/python3.8/site-packages/torch/jit/_trace.py", line 125, in forward
    graph, out = torch._C._create_graph_by_tracing(
  File "/home/soroush.tabadkani/projects/efficientdet-pytorch/env_test/lib/python3.8/site-packages/torch/jit/_trace.py", line 116, in wrapper
    outs.append(self.inner(*trace_inputs))
  File "/home/soroush.tabadkani/projects/efficientdet-pytorch/env_test/lib/python3.8/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
    result = self._slow_forward(*input, **kwargs)
  File "/home/soroush.tabadkani/projects/efficientdet-pytorch/env_test/lib/python3.8/site-packages/torch/nn/modules/module.py", line 862, in _slow_forward
    result = self.forward(*input, **kwargs)
  File "/home/soroush.tabadkani/projects/efficientdet-pytorch/env_test/lib/python3.8/site-packages/pytorch_lightning/core/decorators.py", line 62, in auto_transfer_args
    return fn(self, *args, **kwargs)
TypeError: forward() missing 1 required positional argument: 'targets'

No matter how many approaches I tried to solve this problem with, they all eventually resulted in the error above. Any help or guidance if you can kindly provide me with is deeply appreciated.