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multispectral.py
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multispectral.py
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# AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/62_multispectral.ipynb.
# %% ../nbs/62_multispectral.ipynb 1
from __future__ import annotations
# %% auto 0
__all__ = ['BandInputs', 'MSDescriptor', 'createSentinel2Descriptor', 'MSData', 'MaskData', 'MSAugment', 'GSUnetModel', 'FastGS']
# %% ../nbs/62_multispectral.ipynb 3
from typing import Callable
from dataclasses import dataclass
from fastai.vision.all import *
from .vision.core import *
from .vision.augment import *
from .vision.learner import *
from .vision.load import *
# %% ../nbs/62_multispectral.ipynb 6
@dataclass
class BandInputs:
ids: list[str]
idxs: list[int]
@classmethod
def from_ids(cls, ids: list[str]):
return cls(ids, [i for i in range(len(ids))])
# %% ../nbs/62_multispectral.ipynb 10
@patch
def _get_index(self: BandInputs, id: str) -> int:
return self.idxs[self.ids.index(id)]
# %% ../nbs/62_multispectral.ipynb 13
@patch
def _get_bands(self: BandInputs, ids: list[str]) -> tuple[int]:
assert set(ids).issubset(set(self.ids))
return tuple(self._get_index(id) for id in ids)
# %% ../nbs/62_multispectral.ipynb 16
@patch
def get_bands_list(self: BandInputs, ids_list: list[list[str]]) -> list[tuple[int]]:
return [self._get_bands(ids) for ids in ids_list]
# %% ../nbs/62_multispectral.ipynb 19
@patch
def get_captions(self: BandInputs, ids_list: list[list[str]]) -> list[tuple[int]]:
return [','.join(ids) for ids in ids_list]
# %% ../nbs/62_multispectral.ipynb 23
class MSDescriptor:
pass
# %% ../nbs/62_multispectral.ipynb 24
@patch
def __init__(self: MSDescriptor, band_ids, brgtX, res_m, rgb_combo): store_attr()
# %% ../nbs/62_multispectral.ipynb 26
@patch(cls_method=True)
def from_all(
cls: MSDescriptor,
band_ids: tuple(str),
brgtX: list(float),
res_m: list[int],
rgb_combo: dict[str, list[str]]={}
):
return cls(band_ids,brgtX,res_m,rgb_combo)
@patch(cls_method=True)
def from_band_brgt(cls: MSDescriptor, band_ids: tuple(str), brgtX: list[int]):
return cls.from_all(band_ids,brgtX,[None]*len(band_ids))
@patch(cls_method=True)
def from_bands(cls: MSDescriptor, band_ids: tuple(str)):
return cls.from_band_brgt(band_ids,[1.0]*len(band_ids))
# %% ../nbs/62_multispectral.ipynb 30
def createSentinel2Descriptor() -> MSDescriptor:
return MSDescriptor.from_all(
["B01","B02","B03","B04","B05","B06","B07","B08","B8A","B09","B10","B11","B12","AOT"],
[2.5,4.75,4.25,3.75,3,2,1.7,1.7,2.5,2.5,1.6,1.6,2.2,30],
[60,10,10,10,20,20,20,10,20,60,60,20,20,20],
{# https://gisgeography.com/sentinel-2-bands-combinations/
"natural_color": ["B04","B03","B02"],
"color_infrared": ["B08","B04","B03"],
"short_wave_infrared": ["B12","B8a","B04"],
"agriculture": ["B11","B08","B02"],
"geology": ["B12","B11","B02"],
"bathymetric": ["B04","B03","B01"]
}
)
# %% ../nbs/62_multispectral.ipynb 33
@patch
def get_res_ids(self: MSDescriptor, res: int) -> list[str]:
indices = [i for i,r in enumerate(self.res_m) if r == res]
return [self.band_ids[i] for i in indices]
# %% ../nbs/62_multispectral.ipynb 36
@patch
def get_brgtX(self: MSDescriptor, ids: list[str]) -> list[float]:
indices = [self.band_ids.index(id) for id in ids]
return [self.brgtX[i] for i in indices]
# %% ../nbs/62_multispectral.ipynb 39
@patch
def get_brgtX_list(self: MSDescriptor, ids_list: list[list[str]]) -> list[list[float]]:
return [self.get_brgtX(ids) for ids in ids_list]
# %% ../nbs/62_multispectral.ipynb 42
class MSData:
pass
# %% ../nbs/62_multispectral.ipynb 43
@patch
def __init__(
self: MSData,
ms_descriptor: MSDescriptor,
bands: BandInputs,
chn_grp_ids: list[list[str]],
tensor_getter: MSTensorGetter
):
store_attr()
# %% ../nbs/62_multispectral.ipynb 44
@patch(cls_method=True)
def from_files(
cls: MSData,
ms_descriptor: MSDescriptor,
band_ids: list[str],
chn_grp_ids: list[list[str]],
files_getter: Callable[[list[str], Any], list[str]],
chan_io_fn: Callable[[list[str]], Tensor]
):
tensor_getter = MSTensorGetter.from_files(files_getter,chan_io_fn)
return cls(ms_descriptor,BandInputs.from_ids(band_ids),chn_grp_ids,tensor_getter)
@patch(cls_method=True)
def from_loader(
cls: MSData,
ms_descriptor: MSDescriptor,
band_ids: list[str],
chn_grp_ids: list[list[str]],
tg_fn: Callable[[list[str], Any], Tensor]
):
tensor_getter = MSTensorGetter.from_delegate(tg_fn)
return cls(ms_descriptor,BandInputs.from_ids(band_ids),chn_grp_ids,tensor_getter)
# %% ../nbs/62_multispectral.ipynb 53
@patch
def _load_image(self: MSData, img_id, cls: TensorImage) -> TensorImage:
ids_list = self.chn_grp_ids
bands = self.bands.get_bands_list(ids_list)
captions = self.bands.get_captions(ids_list)
brgtX = self.ms_descriptor.get_brgtX_list(ids_list)
return cls(self.tensor_getter.load_tensor(self.bands.ids, img_id), bands=bands, captions=captions, brgtX=brgtX)
@patch
def load_image(self: MSData, img_id) -> TensorImageMS:
return self._load_image(img_id, TensorImageMS)
# %% ../nbs/62_multispectral.ipynb 54
@patch
def num_channels(self: MSData) -> int:
return len(self.bands.ids)
# %% ../nbs/62_multispectral.ipynb 59
class MaskData:
pass
# %% ../nbs/62_multispectral.ipynb 60
@patch
def __init__(
self: MaskData,
mask_id: str,
mask_getter: Callable[[list[str], Any], TensorMask],
mask_codes: list[str]
):
store_attr()
# %% ../nbs/62_multispectral.ipynb 61
@patch
def load_mask(self: MaskData, img_id) -> TensorMask:
return self.mask_getter.load_mask(self.mask_id, img_id)
# %% ../nbs/62_multispectral.ipynb 62
@patch
def num_channels(self: MaskData) -> int:
return len(self.mask_codes)
# %% ../nbs/62_multispectral.ipynb 63
@patch(cls_method=True)
def from_files(
cls: MaskData,
mask_id: str,
files_getter: Callable[[list[str], Any], list[str]],
mask_io_fn: Callable[[list[str]], Tensor],
mask_codes: list[str]
):
mask_getter = MSMaskGetter.from_files(files_getter,mask_io_fn)
return cls(mask_id,mask_getter,mask_codes)
@patch(cls_method=True)
def from_loader(
cls: MaskData,
mask_id: str,
tg_fn: Callable[[list[str], Any], Tensor],
mask_codes: list[str]
):
mask_getter = MSMaskGetter.from_delegate(tg_fn)
return cls(mask_id,mask_getter,mask_codes)
# %% ../nbs/62_multispectral.ipynb 66
class MSAugment:
pass
# %% ../nbs/62_multispectral.ipynb 67
@patch
def __init__(self: MSAugment,train_aug,valid_aug): store_attr()
# %% ../nbs/62_multispectral.ipynb 68
@patch(cls_method=True)
def from_augs(
cls: MSAugment,
train_aug=None,
valid_aug=None
):
return cls(train_aug=train_aug, valid_aug=valid_aug)
# %% ../nbs/62_multispectral.ipynb 70
@patch
def create_xform_block(self: MSData) -> DataBlock:
return TransformBlock(type_tfms=[
partial(MSData.load_image,self),
])
# %% ../nbs/62_multispectral.ipynb 71
@patch
def create_xform_block(self: MaskData) -> DataBlock:
return TransformBlock(type_tfms=[
partial(MaskData.load_mask,self),
AddMaskCodes(codes=self.mask_codes),
])
# %% ../nbs/62_multispectral.ipynb 72
@patch
def create_item_xforms(self: MSAugment) -> list(ItemTransform):
if self.train_aug is None and self.valid_aug is None: return []
elif self.valid_aug is None: return [TrainMSSAT(self.train_aug)]
elif self.train_aug is None: return [ValidMSSAT(self.valid_aug)]
else: return [TrainMSSAT(self.train_aug),ValidMSSAT(self.valid_aug)]
# %% ../nbs/62_multispectral.ipynb 74
class GSUnetModel:
pass
@patch(cls_method=True)
def from_all(
cls:GSUnetModel,
model,
ms_data:MSData,
mask_codes:[str],
loss_func=CrossEntropyLossFlat(axis=1),
metrics=Dice(axis=1)
):
return cls(model,len(ms_data.bands.ids),len(mask_codes),loss_func,metrics)
@patch
def __init__(self: GSUnetModel, model, n_in, n_out, loss_func, metrics):
store_attr()
@patch
def create_learner(self:GSUnetModel,dl,pretrained=False,**kwargs):
learner = unet_learner(dl,self.model,n_in=self.n_in,n_out=self.n_out,pretrained=pretrained,loss_func=self.loss_func,metrics=self.metrics)
if pretrained:
learner.model[0][0].fastgs_reinit_weights(reweight=reweight)
return learner
@patch
def load_learner(self:GSUnetModel,model_path:str,dl):
learner = unet_learner(dl,self.model,n_in=self.n_in,n_out=self.n_out,pretrained=False,loss_func=self.loss_func,metrics=self.metrics)
learner.load(model_path)
return learner
# %% ../nbs/62_multispectral.ipynb 76
class FastGS:
pass
# %% ../nbs/62_multispectral.ipynb 77
@patch
def __init__(self:FastGS, model:GSUnetModel, ms_data:MSData, mask_data:MaskData, ms_aug:MSAugment):
store_attr()
# %% ../nbs/62_multispectral.ipynb 78
@patch(cls_method=True)
def for_training(
cls:FastGS,
ms_data:MSData,
mask_data:MaskData,
ms_aug:MSAugment=MSAugment.from_augs()
):
model = GSUnetModel.from_all(resnet18,ms_data,mask_data.mask_codes)
return cls(model,ms_data,mask_data,ms_aug)
@patch(cls_method=True)
def for_inference(
cls:FastGS,
ms_data:MSData,
mask_codes:[str]
):
model = GSUnetModel.from_all(resnet18,ms_data,mask_codes)
return cls(model,ms_data,None,None)
# %% ../nbs/62_multispectral.ipynb 80
@patch
def create_data_block(self: FastGS, splitter=RandomSplitter(valid_pct=0.2, seed=107)) -> DataBlock:
return DataBlock(
blocks=(self.ms_data.create_xform_block(),self.mask_data.create_xform_block()),
splitter=splitter,
item_tfms=self.ms_aug.create_item_xforms()
)
# %% ../nbs/62_multispectral.ipynb 82
@patch
def create_learner(self:FastGS,dl,reweight="avg") -> Learner:
return self.model.create_learner(dl,pretrained=reweight is None,reweight=reweight)
@patch
def load_learner(self:FastGS,model_path,dl) -> Learner:
return self.model.load_learner(model_path,dl)