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Fix PadToSize impl to follow Transform API after torchvision 0.21 #629

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Merged
merged 1 commit into from
Sep 4, 2025
Merged

Fix PadToSize impl to follow Transform API after torchvision 0.21 #629

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6 changes: 6 additions & 0 deletions rtdetr_pytorch/src/data/transforms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -81,6 +81,9 @@ def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
self.padding = [0, 0, w, h]
return dict(padding=self.padding)

def make_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
return self._get_params(flat_inputs)

def __init__(self, spatial_size, fill=0, padding_mode='constant') -> None:
if isinstance(spatial_size, int):
spatial_size = (spatial_size, spatial_size)
Expand All @@ -93,6 +96,9 @@ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
padding = params['padding']
return F.pad(inpt, padding=padding, fill=fill, padding_mode=self.padding_mode) # type: ignore[arg-type]

def transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
return self._transform(inpt, params)

def __call__(self, *inputs: Any) -> Any:
outputs = super().forward(*inputs)
if len(outputs) > 1 and isinstance(outputs[1], dict):
Expand Down
8 changes: 7 additions & 1 deletion rtdetrv2_pytorch/src/data/transforms/_transforms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -59,6 +59,9 @@ def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
self.padding = [0, 0, w, h]
return dict(padding=self.padding)

def make_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
return self._get_params(flat_inputs)

def __init__(self, size, fill=0, padding_mode='constant') -> None:
if isinstance(size, int):
size = (size, size)
Expand All @@ -70,6 +73,9 @@ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
padding = params['padding']
return F.pad(inpt, padding=padding, fill=fill, padding_mode=self.padding_mode) # type: ignore[arg-type]

def transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
return self._transform(inpt, params)

def __call__(self, *inputs: Any) -> Any:
outputs = super().forward(*inputs)
if len(outputs) > 1 and isinstance(outputs[1], dict):
Expand Down Expand Up @@ -139,4 +145,4 @@ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
return inpt

def transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
return self._transform(inpt, params)
return self._transform(inpt, params)