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""""by lyuwenyu
"""
import torch
import torch.nn as nn
import torchvision
torchvision.disable_beta_transforms_warning()
from torchvision import datapoints
import torchvision.transforms.v2 as T
import torchvision.transforms.v2.functional as F
from PIL import Image
from typing import Any, Dict, List, Optional
from src.core import register, GLOBAL_CONFIG
__all__ = ['Compose', ]
RandomPhotometricDistort = register(T.RandomPhotometricDistort)
RandomZoomOut = register(T.RandomZoomOut)
# RandomIoUCrop = register(T.RandomIoUCrop)
RandomHorizontalFlip = register(T.RandomHorizontalFlip)
Resize = register(T.Resize)
ToImageTensor = register(T.ToImageTensor)
ConvertDtype = register(T.ConvertDtype)
SanitizeBoundingBox = register(T.SanitizeBoundingBox)
RandomCrop = register(T.RandomCrop)
Normalize = register(T.Normalize)
@register
class Compose(T.Compose):
def __init__(self, ops) -> None:
transforms = []
if ops is not None:
for op in ops:
if isinstance(op, dict):
name = op.pop('type')
transfom = getattr(GLOBAL_CONFIG[name]['_pymodule'], name)(**op)
transforms.append(transfom)
# op['type'] = name
elif isinstance(op, nn.Module):
transforms.append(op)
else:
raise ValueError('')
else:
transforms =[EmptyTransform(), ]
super().__init__(transforms=transforms)
@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 = (
Image.Image,
datapoints.Image,
datapoints.Video,
datapoints.Mask,
datapoints.BoundingBox,
)
def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
sz = F.get_spatial_size(flat_inputs[0])
h, w = self.spatial_size[0] - sz[0], self.spatial_size[1] - sz[1]
self.padding = [0, 0, w, h]
return dict(padding=self.padding)
def __init__(self, spatial_size, fill=0, padding_mode='constant') -> None:
if isinstance(spatial_size, int):
spatial_size = (spatial_size, spatial_size)
self.spatial_size = spatial_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 ConvertBox(T.Transform):
_transformed_types = (
datapoints.BoundingBox,
)
def __init__(self, out_fmt='', normalize=False) -> None:
super().__init__()
self.out_fmt = out_fmt
self.normalize = normalize
self.data_fmt = {
'xyxy': datapoints.BoundingBoxFormat.XYXY,
'cxcywh': datapoints.BoundingBoxFormat.CXCYWH
}
def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
if self.out_fmt:
spatial_size = inpt.spatial_size
in_fmt = inpt.format.value.lower()
inpt = torchvision.ops.box_convert(inpt, in_fmt=in_fmt, out_fmt=self.out_fmt)
inpt = datapoints.BoundingBox(inpt, format=self.data_fmt[self.out_fmt], spatial_size=spatial_size)
if self.normalize:
inpt = inpt / torch.tensor(inpt.spatial_size[::-1]).tile(2)[None]
return inpt
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