Source code for matx.vision.tv_transforms.resize

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from typing import Tuple, Union, Sequence, Dict, Any, List
import sys
matx = sys.modules['matx']
from .. import ASYNC, RESIZE_NOT_SMALLER, RESIZE_DEFAULT
from .. import INTER_NEAREST, INTER_LINEAR, INTER_CUBIC, INTER_LANCZOS4
from .. import ResizeOp, RandomResizedCropOp
from ._base import BaseInterfaceClass, BatchBaseClass
from ._common import _setup_size, _torch_interp_mode, create_device


[docs]class Resize(BaseInterfaceClass):
[docs] def __init__(self, size: List[int], interpolation: str = "bilinear", resize_mode: str = "", max_size: int = 0, device_id: int = -2, sync: int = ASYNC) -> None: super().__init__(device_id=device_id, sync=sync) # check size if len(size) == 1: self._size: Tuple[int, int] = (size[0], size[0]) if not resize_mode: self._resize_mode: str = RESIZE_NOT_SMALLER else: self._resize_mode: str = resize_mode elif len(size) == 2: self._size: Tuple[int, int] = (size[0], size[1]) if not resize_mode: self._resize_mode: str = RESIZE_DEFAULT else: self._resize_mode: str = resize_mode else: assert False, "Resize size value should be an integer or a list/tuple with length 2." self._max_size: int = max_size # check interpolation mode assert interpolation in ["nearest", "bilinear", "bicubic", "lanczos"], "interpolation_mode should be nearest, bilinear, bicubic or lanczos." torch_interp_mode: Dict[str, str] = { "nearest": INTER_NEAREST, "bilinear": INTER_LINEAR, "bicubic": INTER_CUBIC, "lanczos": INTER_LANCZOS4 } self._interpolation_mode: str = torch_interp_mode[interpolation]
[docs] def __call__(self, device: Any, device_str: str, sync: int) -> Any: return ResizeImpl( device, device_str, self._size, self._max_size, self._resize_mode, self._interpolation_mode, sync)
class ResizeImpl(BatchBaseClass): def __init__(self, device: Any, device_str: str, size: Tuple[int, int], max_size: int, resize_mode: str, interpolation_mode: str, sync: int = ASYNC) -> None: super().__init__() self.device_str: str = device_str self.size: Tuple[int, int] = size self.max_size: int = max_size self.resize_mode: str = resize_mode self.interpolation_mode: str = interpolation_mode self.sync: int = sync self.op: ResizeOp = ResizeOp( device, self.size, self.max_size, self.interpolation_mode, self.resize_mode) self.name: str = "ResizeImpl" def _process_resize_op(self, imgs: List[matx.NDArray]) -> List[matx.NDArray]: imgs = self.op(imgs, sync=self.sync) return imgs def _process(self, imgs: List[matx.NDArray]) -> List[matx.NDArray]: return self._process_resize_op(imgs) def __repr__(self) -> str: return self.name + '(size={0}, interpolation={1}, max_size={2}, device={3}, sync={4})'.format( self.size, self.interpolation_mode, self.max_size, self.device_str, self.sync)
[docs]class RandomResizedCrop(BaseInterfaceClass):
[docs] def __init__(self, size: List[int], scale: Tuple[float, float] = (0.08, 1.0), ratio: Tuple[float, float] = (3. / 4., 4. / 3.), interpolation: str = "bilinear", device_id: int = -2, sync: int = ASYNC) -> None: super().__init__(device_id=device_id, sync=sync) # check size if len(size) == 1: self._size: Tuple[int, int] = (size[0], size[0]) elif len(size) == 2: self._size: Tuple[int, int] = (size[0], size[1]) else: assert False, "Resize size value should be an integer or a list/tuple with length 2." # check scale if len(scale) == 2: assert scale[0] < scale[1], "Scale should be of kind [min, max]." self._scale: List[float] = [scale[0], scale[1]] else: assert False, "Scale should be a tuple with length 2." # check ratio if len(ratio) == 2: assert ratio[0] < ratio[1], "Ratio should be of kind [min, max]." self._ratio: List[float] = [ratio[0], ratio[1]] else: assert False, "Ratio should be a tuple with length 2." # check interpolation mode assert interpolation in ["nearest", "bilinear", "bicubic", "lanczos"], "interpolation_mode should be nearest, bilinear, bicubic or lanczos." torch_interp_mode: Dict[str, str] = { "nearest": INTER_NEAREST, "bilinear": INTER_LINEAR, "bicubic": INTER_CUBIC, "lanczos": INTER_LANCZOS4 } self._interpolation_mode: str = torch_interp_mode[interpolation]
[docs] def __call__(self, device: Any, device_str: str, sync: int) -> Any: return RandomResizedCropImpl( device, device_str, self._size, self._scale, self._ratio, self._interpolation_mode, sync)
class RandomResizedCropImpl(BatchBaseClass): def __init__(self, device: Any, device_str: str, size: Tuple[int, int], scale: List[float], ratio: List[float], interpolation_mode: str, sync: int) -> None: self.device_str: str = device_str self.size: Tuple[int, int] = size self.scale: List[float] = scale self.ratio: List[float] = ratio self.interpolation_mode: str = interpolation_mode self.sync: int = sync self.op: RandomResizedCropOp = RandomResizedCropOp( device, self.size, self.scale, self.ratio, self.interpolation_mode) self.name: str = "RandomResizedCropImpl" def _process_random_resized_crop_op(self, imgs: List[matx.NDArray]) -> List[matx.NDArray]: imgs = self.op(imgs, sync=self.sync) return imgs def _process(self, imgs: List[matx.NDArray]) -> List[matx.NDArray]: return self._process_random_resized_crop_op(imgs) def __repr__(self) -> str: return self.name + '(size={0}, scale={1}, ratio={2}, interpolation={3}, device={4}, sync={5})'.format( self.size, self.scale, self.ratio, self.interpolation_mode, self.device_str, self.sync)