[BUG] TypeError: forward() missing 1 required positional argument: 'target'

[Tomakko]

Hi, i am trying to run your efficient det on my custom dataset. In thee forward pass
TypeError: forward() missing 1 required positional argument: 'target' is thrown. I would kindly ask for your help with this issue.

This is how i instantiate the model:

def get_train_efficientdet():
    config = get_efficientdet_config('tf_efficientdet_d5')
    config.image_size = (IMG_SIZE, IMG_SIZE)
    config.norm_kwargs=dict(eps=.001, momentum=.01)
    
    net = create_model_from_config(
        config, bench_task='train',num_classes=num_cats,
        pretrained=True,
        )

    print('EfficientDet Config:', config)
    return DetBenchTrain(net, config)

This is how i call the forward pass from within the pytorch lightning framework:

def training_step(self, batch, batch_idx):
    images, targets = batch
    targets = [{k: v for k, v in t.items()} for t in targets]

    # separate losses
    images = torch.stack(images).float()
    targets2 = {}
    targets2["bbox"] = [
        target["boxes"].float() for target in targets
    ]  # variable number of instances, so the entire structure can be forced to tensor
    targets2["cls"] = [target["labels"].float() for target in targets]
    targets2["image_id"] = torch.tensor(
        [target["image_id"] for target in targets]
    ).float()
    targets2["img_scale"] = torch.tensor(
        [target["img_scale"] for target in targets]
    ).float()
    targets2["img_size"] = torch.tensor(
        [(IMG_SIZE, IMG_SIZE) for target in targets]
    ).float()
    print('targets2:')

    print(targets2)
    print('type of dict: ', type(targets2))
    losses_dict = self.forward(images, targets2)

   return {"loss": losses_dict["loss"], "log": losses_dict}

Printed targets2:
{'bbox': [tensor([[139.1957, 137.6129, 210.4661, 204.0420]])], 'cls': [tensor([[0., 0., 0., 1., 0., 0., 0., 0.]])], 'image_id': tensor([47727.]), 'img_scale': tensor([1.]), 'img_size': tensor([[256., 256.]])}

The full callstack is:

TypeError                                 Traceback (most recent call last)
<ipython-input-272-d56bad246dc1> in <module>
      6 # logger = loggers.TensorBoardLogger("logs", name="effdet-b5", version="fold_0")
      7 trainer = Trainer(fast_dev_run=True)
----> 8 trainer.fit(model)

~/object-detection-for-forgotten-items/venv/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py in fit(self, model, train_dataloader, val_dataloaders, datamodule)
    497 
    498         # dispath `start_training` or `start_testing` or `start_predicting`
--> 499         self.dispatch()
    500 
    501         # plugin will finalized fitting (e.g. ddp_spawn will load trained model)

~/object-detection-for-forgotten-items/venv/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py in dispatch(self)
    544 
    545         else:
--> 546             self.accelerator.start_training(self)
    547 
    548     def train_or_test_or_predict(self):

~/object-detection-for-forgotten-items/venv/lib/python3.8/site-packages/pytorch_lightning/accelerators/accelerator.py in start_training(self, trainer)
     71 
     72     def start_training(self, trainer):
---> 73         self.training_type_plugin.start_training(trainer)
     74 
     75     def start_testing(self, trainer):

~/object-detection-for-forgotten-items/venv/lib/python3.8/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py in start_training(self, trainer)
    112     def start_training(self, trainer: 'Trainer') -> None:
    113         # double dispatch to initiate the training loop
--> 114         self._results = trainer.run_train()
    115 
    116     def start_testing(self, trainer: 'Trainer') -> None:

~/object-detection-for-forgotten-items/venv/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py in run_train(self)
    635                 with self.profiler.profile("run_training_epoch"):
    636                     # run train epoch
--> 637                     self.train_loop.run_training_epoch()
    638 
    639                 if self.max_steps and self.max_steps <= self.global_step:

~/object-detection-for-forgotten-items/venv/lib/python3.8/site-packages/pytorch_lightning/trainer/training_loop.py in run_training_epoch(self)
    490             # ------------------------------------
    491             with self.trainer.profiler.profile("run_training_batch"):
--> 492                 batch_output = self.run_training_batch(batch, batch_idx, dataloader_idx)
    493 
    494             # when returning -1 from train_step, we end epoch early

~/object-detection-for-forgotten-items/venv/lib/python3.8/site-packages/pytorch_lightning/trainer/training_loop.py in run_training_batch(self, batch, batch_idx, dataloader_idx)
    652 
    653                         # optimizer step
--> 654                         self.optimizer_step(optimizer, opt_idx, batch_idx, train_step_and_backward_closure)
    655 
    656                     else:

~/object-detection-for-forgotten-items/venv/lib/python3.8/site-packages/pytorch_lightning/trainer/training_loop.py in optimizer_step(self, optimizer, opt_idx, batch_idx, train_step_and_backward_closure)
    423 
    424         # model hook
--> 425         model_ref.optimizer_step(
    426             self.trainer.current_epoch,
    427             batch_idx,

~/object-detection-for-forgotten-items/venv/lib/python3.8/site-packages/pytorch_lightning/core/lightning.py in optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx, optimizer_closure, on_tpu, using_native_amp, using_lbfgs)
   1388             # wraps into LightingOptimizer only for running step
   1389             optimizer = LightningOptimizer._to_lightning_optimizer(optimizer, self.trainer, optimizer_idx)
-> 1390         optimizer.step(closure=optimizer_closure)
   1391 
   1392     def optimizer_zero_grad(self, epoch: int, batch_idx: int, optimizer: Optimizer, optimizer_idx: int):

~/object-detection-for-forgotten-items/venv/lib/python3.8/site-packages/pytorch_lightning/core/optimizer.py in step(self, closure, *args, **kwargs)
    212             profiler_name = f"optimizer_step_and_closure_{self._optimizer_idx}"
    213 
--> 214         self.__optimizer_step(*args, closure=closure, profiler_name=profiler_name, **kwargs)
    215         self._total_optimizer_step_calls += 1
    216 

~/object-detection-for-forgotten-items/venv/lib/python3.8/site-packages/pytorch_lightning/core/optimizer.py in __optimizer_step(self, closure, profiler_name, **kwargs)
    132 
    133         with trainer.profiler.profile(profiler_name):
--> 134             trainer.accelerator.optimizer_step(optimizer, self._optimizer_idx, lambda_closure=closure, **kwargs)
    135 
    136     def step(self, *args, closure: Optional[Callable] = None, **kwargs):

~/object-detection-for-forgotten-items/venv/lib/python3.8/site-packages/pytorch_lightning/accelerators/accelerator.py in optimizer_step(self, optimizer, opt_idx, lambda_closure, **kwargs)
    275         )
    276         if make_optimizer_step:
--> 277             self.run_optimizer_step(optimizer, opt_idx, lambda_closure, **kwargs)
    278         self.precision_plugin.post_optimizer_step(optimizer, opt_idx)
    279         self.training_type_plugin.post_optimizer_step(optimizer, opt_idx, **kwargs)

~/object-detection-for-forgotten-items/venv/lib/python3.8/site-packages/pytorch_lightning/accelerators/accelerator.py in run_optimizer_step(self, optimizer, optimizer_idx, lambda_closure, **kwargs)
    280 
    281     def run_optimizer_step(self, optimizer: Optimizer, optimizer_idx: int, lambda_closure: Callable, **kwargs):
--> 282         self.training_type_plugin.optimizer_step(optimizer, lambda_closure=lambda_closure, **kwargs)
    283 
    284     def optimizer_zero_grad(self, current_epoch: int, batch_idx: int, optimizer: Optimizer, opt_idx: int) -> None:

~/object-detection-for-forgotten-items/venv/lib/python3.8/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py in optimizer_step(self, optimizer, lambda_closure, **kwargs)
    161 
    162     def optimizer_step(self, optimizer: torch.optim.Optimizer, lambda_closure: Callable, **kwargs):
--> 163         optimizer.step(closure=lambda_closure, **kwargs)

~/object-detection-for-forgotten-items/venv/lib/python3.8/site-packages/torch/optim/optimizer.py in wrapper(*args, **kwargs)
     87                 profile_name = "Optimizer.step#{}.step".format(obj.__class__.__name__)
     88                 with torch.autograd.profiler.record_function(profile_name):
---> 89                     return func(*args, **kwargs)
     90             return wrapper
     91 

~/object-detection-for-forgotten-items/venv/lib/python3.8/site-packages/torch/autograd/grad_mode.py in decorate_context(*args, **kwargs)
     25         def decorate_context(*args, **kwargs):
     26             with self.__class__():
---> 27                 return func(*args, **kwargs)
     28         return cast(F, decorate_context)
     29 

~/object-detection-for-forgotten-items/venv/lib/python3.8/site-packages/torch/optim/adamw.py in step(self, closure)
     63         if closure is not None:
     64             with torch.enable_grad():
---> 65                 loss = closure()
     66 
     67         for group in self.param_groups:

~/object-detection-for-forgotten-items/venv/lib/python3.8/site-packages/pytorch_lightning/trainer/training_loop.py in train_step_and_backward_closure()
    646 
    647                         def train_step_and_backward_closure():
--> 648                             result = self.training_step_and_backward(
    649                                 split_batch, batch_idx, opt_idx, optimizer, self.trainer.hiddens
    650                             )

~/object-detection-for-forgotten-items/venv/lib/python3.8/site-packages/pytorch_lightning/trainer/training_loop.py in training_step_and_backward(self, split_batch, batch_idx, opt_idx, optimizer, hiddens)
    740         with self.trainer.profiler.profile("training_step_and_backward"):
    741             # lightning module hook
--> 742             result = self.training_step(split_batch, batch_idx, opt_idx, hiddens)
    743             self._curr_step_result = result
    744 

~/object-detection-for-forgotten-items/venv/lib/python3.8/site-packages/pytorch_lightning/trainer/training_loop.py in training_step(self, split_batch, batch_idx, opt_idx, hiddens)
    291             model_ref._results = Result()
    292             with self.trainer.profiler.profile("training_step"):
--> 293                 training_step_output = self.trainer.accelerator.training_step(args)
    294                 self.trainer.accelerator.post_training_step()
    295 

~/object-detection-for-forgotten-items/venv/lib/python3.8/site-packages/pytorch_lightning/accelerators/accelerator.py in training_step(self, args)
    154 
    155         with self.precision_plugin.train_step_context(), self.training_type_plugin.train_step_context():
--> 156             return self.training_type_plugin.training_step(*args)
    157 
    158     def post_training_step(self):

~/object-detection-for-forgotten-items/venv/lib/python3.8/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py in training_step(self, *args, **kwargs)
    123 
    124     def training_step(self, *args, **kwargs):
--> 125         return self.lightning_module.training_step(*args, **kwargs)
    126 
    127     def post_training_step(self):

<ipython-input-271-6e3db7ea7bf0> in training_step(self, batch, batch_idx)
     78         print(targets2)
     79         print('type of dict: ', type(targets2))
---> 80         losses_dict = self.forward(images, targets2)
     81 
     82         return {"loss": losses_dict["loss"], "log": losses_dict}

<ipython-input-271-6e3db7ea7bf0> in forward(self, image, target)
     32         self.batch_size = 1
     33     def forward(self, image, target):
---> 34         return self.model(image, target)
     35 
     36     # Let's add the train and validation data loaders.

~/object-detection-for-forgotten-items/venv/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
    887             result = self._slow_forward(*input, **kwargs)
    888         else:
--> 889             result = self.forward(*input, **kwargs)
    890         for hook in itertools.chain(
    891                 _global_forward_hooks.values(),

~/object-detection-for-forgotten-items/venv/lib/python3.8/site-packages/effdet/bench.py in forward(self, x, target)
    108 
    109     def forward(self, x, target: Dict[str, torch.Tensor]):
--> 110         class_out, box_out = self.model(x)
    111         if self.anchor_labeler is None:
    112             # target should contain pre-computed anchor labels if labeler not present in bench

~/object-detection-for-forgotten-items/venv/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
    887             result = self._slow_forward(*input, **kwargs)
    888         else:
--> 889             result = self.forward(*input, **kwargs)
    890         for hook in itertools.chain(
    891                 _global_forward_hooks.values(),

TypeError: forward() missing 1 required positional argument: 'target'

Desktop (please complete the following information):

• Ubuntu 20.04
• effdet 0.2.1
• PyTorch 1.8.1