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