rpotter6298
67 lines
2.7 KiB
Python
67 lines
2.7 KiB
Python
"""transforms — image transform utilities for v4 towers."""
|
|
from __future__ import annotations
|
|
|
|
from dataclasses import dataclass
|
|
from typing import Tuple
|
|
|
|
from torchvision import transforms
|
|
|
|
from v4.classes.accessory.backbones import BACKBONES
|
|
|
|
IMAGENET_MEAN: Tuple[float, float, float] = (0.485, 0.456, 0.406)
|
|
IMAGENET_STD: Tuple[float, float, float] = (0.229, 0.224, 0.225)
|
|
|
|
|
|
@dataclass
|
|
class ImageTransformConfig:
|
|
crop_size: int = 224
|
|
resize_size: int = 256
|
|
mean: Tuple[float, float, float] = IMAGENET_MEAN
|
|
std: Tuple[float, float, float] = IMAGENET_STD
|
|
augment: bool = True
|
|
rotation_deg: int = 15
|
|
color_jitter: Tuple[float, float, float, float] = (0.1, 0.1, 0.1, 0.05)
|
|
hflip: bool = True
|
|
vflip: bool = True
|
|
|
|
def build(self) -> transforms.Compose:
|
|
ops = [
|
|
transforms.Resize(self.resize_size),
|
|
transforms.CenterCrop(self.crop_size),
|
|
]
|
|
if self.augment:
|
|
if self.hflip:
|
|
ops.append(transforms.RandomHorizontalFlip())
|
|
if self.vflip:
|
|
ops.append(transforms.RandomVerticalFlip())
|
|
if self.rotation_deg:
|
|
ops.append(transforms.RandomRotation(self.rotation_deg))
|
|
if self.color_jitter:
|
|
ops.append(transforms.ColorJitter(*self.color_jitter))
|
|
ops += [
|
|
transforms.ToTensor(),
|
|
transforms.Normalize(mean=self.mean, std=self.std),
|
|
]
|
|
return transforms.Compose(ops)
|
|
|
|
|
|
def backbone_transform_config(backbone_name: str, augment: bool = True) -> ImageTransformConfig:
|
|
"""Build an ImageTransformConfig using the backbone's default normalisation stats."""
|
|
key = (backbone_name or "").lower()
|
|
if key not in BACKBONES:
|
|
raise ValueError(f"Unknown backbone '{backbone_name}'.")
|
|
spec = BACKBONES[key]
|
|
mean = getattr(spec.weights_default, "meta", {}).get("mean", IMAGENET_MEAN)
|
|
std = getattr(spec.weights_default, "meta", {}).get("std", IMAGENET_STD)
|
|
crop = 299 if key == "inception_v3" else 224
|
|
return ImageTransformConfig(crop_size=crop, mean=mean, std=std, augment=augment)
|
|
|
|
|
|
def build_backbone_transform(backbone_name: str, augment: bool = True) -> transforms.Compose:
|
|
return backbone_transform_config(backbone_name, augment=augment).build()
|
|
|
|
|
|
def build_eval_transform(backbone_name: str) -> transforms.Compose:
|
|
"""Deterministic eval transform — no augmentation, backbone-matched normalisation."""
|
|
return build_backbone_transform(backbone_name, augment=False)
|