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5b6b189 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | import numbers
import random
import warnings
from dataclasses import dataclass, asdict
from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
import torch
# import torchvision.transforms.functional as F
# from torchvision.transforms import Normalize, Compose, RandomResizedCrop, InterpolationMode, ToTensor, Resize, \
# CenterCrop, ColorJitter, Grayscale
from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD
from .utils import to_2tuple
@dataclass
class PreprocessCfg:
size: Union[int, Tuple[int, int]] = 224
mode: str = 'RGB'
mean: Tuple[float, ...] = OPENAI_DATASET_MEAN
std: Tuple[float, ...] = OPENAI_DATASET_STD
interpolation: str = 'bicubic'
resize_mode: str = 'shortest'
fill_color: int = 0
def __post_init__(self):
assert self.mode in ('RGB',)
@property
def num_channels(self):
return 3
@property
def input_size(self):
return (self.num_channels,) + to_2tuple(self.size)
_PREPROCESS_KEYS = set(asdict(PreprocessCfg()).keys())
def merge_preprocess_dict(
base: Union[PreprocessCfg, Dict],
overlay: Dict,
):
""" Merge overlay key-value pairs on top of base preprocess cfg or dict.
Input dicts are filtered based on PreprocessCfg fields.
"""
if isinstance(base, PreprocessCfg):
base_clean = asdict(base)
else:
base_clean = {k: v for k, v in base.items() if k in _PREPROCESS_KEYS}
if overlay:
overlay_clean = {k: v for k, v in overlay.items() if k in _PREPROCESS_KEYS and v is not None}
base_clean.update(overlay_clean)
return base_clean
def merge_preprocess_kwargs(base: PreprocessCfg, **kwargs):
return merge_preprocess_dict(base, kwargs)
@dataclass
class AugmentationCfg:
scale: Tuple[float, float] = (0.9, 1.0)
ratio: Optional[Tuple[float, float]] = None
color_jitter: Optional[Union[float, Tuple[float, float, float], Tuple[float, float, float, float]]] = None
re_prob: Optional[float] = None
re_count: Optional[int] = None
use_timm: bool = False
# params for simclr_jitter_gray
color_jitter_prob: float = None
gray_scale_prob: float = None
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