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"""
Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR)
Copyright(c) 2023 lyuwenyu. All Rights Reserved.
"""
from typing import Any, Dict, List, Optional
import PIL
import PIL.Image
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms.v2 as T
import torchvision.transforms.v2.functional as F
from ...core import register
from .._misc import (
BoundingBoxes,
Image,
Mask,
SanitizeBoundingBoxes,
Video,
_boxes_keys,
convert_to_tv_tensor,
)
torchvision.disable_beta_transforms_warning()
RandomPhotometricDistort = register()(T.RandomPhotometricDistort)
RandomZoomOut = register()(T.RandomZoomOut)
RandomHorizontalFlip = register()(T.RandomHorizontalFlip)
Resize = register()(T.Resize)
# ToImageTensor = register()(T.ToImageTensor)
# ConvertDtype = register()(T.ConvertDtype)
# PILToTensor = register()(T.PILToTensor)
SanitizeBoundingBoxes = register(name="SanitizeBoundingBoxes")(SanitizeBoundingBoxes)
RandomCrop = register()(T.RandomCrop)
Normalize = register()(T.Normalize)
@register()
class EmptyTransform(T.Transform):
def __init__(
self,
) -> None:
super().__init__()
def forward(self, *inputs):
inputs = inputs if len(inputs) > 1 else inputs[0]
return inputs
@register()
class PadToSize(T.Pad):
_transformed_types = (
PIL.Image.Image,
Image,
Video,
Mask,
BoundingBoxes,
)
def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
sp = F.get_spatial_size(flat_inputs[0])
h, w = self.size[1] - sp[0], self.size[0] - sp[1]
self.padding = [0, 0, w, h]
return dict(padding=self.padding)
def __init__(self, size, fill=0, padding_mode="constant") -> None:
if isinstance(size, int):
size = (size, size)
self.size = size
super().__init__(0, fill, padding_mode)
def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
fill = self._fill[type(inpt)]
padding = params["padding"]
return F.pad(inpt, padding=padding, fill=fill, padding_mode=self.padding_mode) # type: ignore[arg-type]
def __call__(self, *inputs: Any) -> Any:
outputs = super().forward(*inputs)
if len(outputs) > 1 and isinstance(outputs[1], dict):
outputs[1]["padding"] = torch.tensor(self.padding)
return outputs
@register()
class RandomIoUCrop(T.RandomIoUCrop):
def __init__(
self,
min_scale: float = 0.3,
max_scale: float = 1,
min_aspect_ratio: float = 0.5,
max_aspect_ratio: float = 2,
sampler_options: Optional[List[float]] = None,
trials: int = 40,
p: float = 1.0,
):
super().__init__(
min_scale, max_scale, min_aspect_ratio, max_aspect_ratio, sampler_options, trials
)
self.p = p
def __call__(self, *inputs: Any) -> Any:
if torch.rand(1) >= self.p:
return inputs if len(inputs) > 1 else inputs[0]
return super().forward(*inputs)
@register()
class ConvertBoxes(T.Transform):
_transformed_types = (BoundingBoxes,)
def __init__(self, fmt="", normalize=False) -> None:
super().__init__()
self.fmt = fmt
self.normalize = normalize
def transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
return self._transform(inpt, params)
def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
spatial_size = getattr(inpt, _boxes_keys[1])
if self.fmt:
in_fmt = inpt.format.value.lower()
inpt = torchvision.ops.box_convert(inpt, in_fmt=in_fmt, out_fmt=self.fmt.lower())
inpt = convert_to_tv_tensor(
inpt, key="boxes", box_format=self.fmt.upper(), spatial_size=spatial_size
)
if self.normalize:
inpt = inpt / torch.tensor(spatial_size[::-1]).tile(2)[None]
return inpt
@register()
class ConvertPILImage(T.Transform):
_transformed_types = (PIL.Image.Image,)
def __init__(self, dtype="float32", scale=True) -> None:
super().__init__()
self.dtype = dtype
self.scale = scale
def transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
return self._transform(inpt, params)
def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
inpt = F.pil_to_tensor(inpt)
if self.dtype == "float32":
inpt = inpt.float()
if self.scale:
inpt = inpt / 255.0
inpt = Image(inpt)
return inpt
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