EfficientDet pretrained arch transfer learning

📓 New <Tutorial/Example>

Request for an example

What is the task? Object detection using transfer learning for the whole architecture. Are there some defined methods to load fastai model and change its head to a different number of classes, similar to this?

I was able to run the Faster-RCNN example using this example trained on COCO dataset and evaluate its mAP.

The EfficientDet workflow seems not to be yet ready. Has there been some update on that?

I was able to create EfficientDet with pretrained encoder and train it myself on COCO. I’m now trying to do transfer learning for a different number of classes. Loading model through fastai, expectedly, throws an error:

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-22-cf184f01fec3> in <module>
----> 1 learn.load('coco_local')

~/anaconda3/envs/icevision/lib/python3.8/site-packages/fastai/learner.py in load(self, file, with_opt, device, **kwargs)
    293         if self.opt is None: self.create_opt()
    294         file = join_path_file(file, self.path/self.model_dir, ext='.pth')
--> 295         load_model(file, self.model, self.opt, device=device, **kwargs)
    296         return self
    297 

~/anaconda3/envs/icevision/lib/python3.8/site-packages/fastai/learner.py in load_model(file, model, opt, with_opt, device, strict)
     47     hasopt = set(state)=={'model', 'opt'}
     48     model_state = state['model'] if hasopt else state
---> 49     get_model(model).load_state_dict(model_state, strict=strict)
     50     if hasopt and with_opt:
     51         try: opt.load_state_dict(state['opt'])

~/anaconda3/envs/icevision/lib/python3.8/site-packages/torch/nn/modules/module.py in load_state_dict(self, state_dict, strict)
   1049 
   1050         if len(error_msgs) > 0:
-> 1051             raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
   1052                                self.__class__.__name__, "\n\t".join(error_msgs)))
   1053         return _IncompatibleKeys(missing_keys, unexpected_keys)

RuntimeError: Error(s) in loading state_dict for DetBenchTrain:
	size mismatch for model.class_net.predict.conv_pw.weight: copying a param with shape torch.Size([819, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([99, 64, 1, 1]).
	size mismatch for model.class_net.predict.conv_pw.bias: copying a param with shape torch.Size([819]) from checkpoint, the shape in current model is torch.Size([99]).

Is this example for a specific model? EfficientDet

Is this example for a specific dataset? COCO transfer learning

---

Don’t remove Main issue for examples: #39

[potipot]

COCOMetric gives correct results for validation with this change:

def build_valid_batch(records, batch_tfms=None):
    (images, targets), records = build_train_batch(
        records=records, batch_tfms=batch_tfms
    )

-   img_sizes = [(r["height"], r["width"]) for r in records]
-   targets["img_size"] = tensor(img_sizes, dtype=torch.float)
+   # passing the size of transformed image to efficientdet, necessary for its own scaling and resizing, see
+   # https://github.com/rwightman/efficientdet-pytorch/blob/645d84a6f0cd837703f98f48179a06c354902515/effdet/bench.py#L100
+   targets["img_size"] = tensor([image.shape[-2:] for image in images], dtype=torch.float)
    targets["img_scale"] = tensor([1] * len(records), dtype=torch.float)

    return (images, targets), records

[lgvaz]

Amazing work @potipot, I was going to investigate this right now but you already solved it! hahahah

So, if I understood correctly, the error is on this line? Here we are setting the image size without padding, but instead we need to set the image size with padding? If so we just need to take the image size from the images tensor images.shape[2:]