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import functools
import torch
import torch.utils.data
from frame_field_learning import data_transforms
from lydorn_utils import print_utils
def inria_aerial_train_tile_filter(tile, train_val_split_point):
return tile["number"] <= train_val_split_point
def inria_aerial_val_tile_filter(tile, train_val_split_point):
return train_val_split_point < tile["number"]
def get_inria_aerial_folds(config, root_dir, folds):
from torch_lydorn.torchvision.datasets import InriaAerial
# --- Online transform done on the host (CPU):
online_cpu_transform = data_transforms.get_online_cpu_transform(config,
augmentations=config["data_aug_params"]["enable"])
mask_only = config["dataset_params"]["mask_only"]
kwargs = {
"pre_process": config["dataset_params"]["pre_process"],
"transform": online_cpu_transform,
"patch_size": config["dataset_params"]["data_patch_size"],
"patch_stride": config["dataset_params"]["input_patch_size"],
"pre_transform": data_transforms.get_offline_transform_patch(distances=not mask_only, sizes=not mask_only),
"small": config["dataset_params"]["small"],
"pool_size": config["num_workers"],
"gt_source": config["dataset_params"]["gt_source"],
"gt_type": config["dataset_params"]["gt_type"],
"gt_dirname": config["dataset_params"]["gt_dirname"],
"mask_only": mask_only,
}
train_val_split_point = config["dataset_params"]["train_fraction"] * 36
partial_train_tile_filter = functools.partial(inria_aerial_train_tile_filter, train_val_split_point=train_val_split_point)
partial_val_tile_filter = functools.partial(inria_aerial_val_tile_filter, train_val_split_point=train_val_split_point)
ds_list = []
for fold in folds:
if fold == "train":
ds = InriaAerial(root_dir, fold="train", tile_filter=partial_train_tile_filter, **kwargs)
ds_list.append(ds)
elif fold == "val":
ds = InriaAerial(root_dir, fold="train", tile_filter=partial_val_tile_filter, **kwargs)
ds_list.append(ds)
elif fold == "train_val":
ds = InriaAerial(root_dir, fold="train", **kwargs)
ds_list.append(ds)
elif fold == "test":
ds = InriaAerial(root_dir, fold="test", **kwargs)
ds_list.append(ds)
else:
print_utils.print_error("ERROR: fold \"{}\" not recognized, implement it in dataset_folds.py.".format(fold))
return ds_list
def get_luxcarta_buildings(config, root_dir, folds):
from torch_lydorn.torchvision.datasets import LuxcartaBuildings
# --- Online transform done on the host (CPU):
online_cpu_transform = data_transforms.get_online_cpu_transform(config,
augmentations=config["data_aug_params"]["enable"])
data_patch_size = config["dataset_params"]["data_patch_size"] if config["data_aug_params"]["enable"] else config[
"input_patch_size"]
ds = LuxcartaBuildings(root_dir,
transform=online_cpu_transform,
patch_size=data_patch_size,
patch_stride=config["dataset_params"]["input_patch_size"],
pre_transform=data_transforms.get_offline_transform_patch(),
fold="train",
pool_size=config["num_workers"])
torch.manual_seed(config["dataset_params"]["seed"]) # Ensure a seed is set
train_split_length = int(round(config["dataset_params"]["train_fraction"] * len(ds)))
val_split_length = len(ds) - train_split_length
train_ds, val_ds = torch.utils.data.random_split(ds, [train_split_length, val_split_length])
ds_list = []
for fold in folds:
if fold == "train":
ds_list.append(train_ds)
elif fold == "val":
ds_list.append(val_ds)
elif fold == "test":
# TODO: handle patching with multi-GPU processing
print_utils.print_error("WARNING: handle patching with multi-GPU processing")
ds = LuxcartaBuildings(root_dir,
transform=online_cpu_transform,
pre_transform=data_transforms.get_offline_transform_patch(),
fold="test",
pool_size=config["num_workers"])
ds_list.append(ds)
else:
print_utils.print_error("ERROR: fold \"{}\" not recognized, implement it in dataset_folds.py.".format(fold))
return ds_list
def get_mapping_challenge(config, root_dir, folds):
from torch_lydorn.torchvision.datasets import MappingChallenge
if "train" in folds or "val" in folds or "train_val" in folds:
train_online_cpu_transform = data_transforms.get_online_cpu_transform(config,
augmentations=config["data_aug_params"][
"enable"])
ds = MappingChallenge(root_dir,
transform=train_online_cpu_transform,
pre_transform=data_transforms.get_offline_transform_patch(),
small=config["dataset_params"]["small"],
fold="train",
pool_size=config["num_workers"])
torch.manual_seed(config["dataset_params"]["seed"]) # Ensure a seed is set
train_split_length = int(round(config["dataset_params"]["train_fraction"] * len(ds)))
val_split_length = len(ds) - train_split_length
train_ds, val_ds = torch.utils.data.random_split(ds, [train_split_length, val_split_length])
ds_list = []
for fold in folds:
if fold == "train":
ds_list.append(train_ds)
elif fold == "val":
ds_list.append(val_ds)
elif fold == "train_val":
ds_list.append(ds)
elif fold == "test":
# The val fold from the original challenge is used as test here
# because we don't have the ground truth for the test_images fold of the challenge:
test_online_cpu_transform = data_transforms.get_eval_online_cpu_transform()
test_ds = MappingChallenge(root_dir,
transform=test_online_cpu_transform,
pre_transform=data_transforms.get_offline_transform_patch(),
small=config["dataset_params"]["small"],
fold="val",
pool_size=config["num_workers"])
ds_list.append(test_ds)
else:
print_utils.print_error("ERROR: fold \"{}\" not recognized, implement it in dataset_folds.py.".format(fold))
exit()
return ds_list
def get_opencities_competition(config, root_dir, folds):
from torch_lydorn.torchvision.datasets import RasterizedOpenCities, OpenCitiesTestDataset
data_patch_size = config["dataset_params"]["data_patch_size"] if config["data_aug_params"]["enable"] else config[
"input_patch_size"]
ds_list = []
for fold in folds:
if fold == "train":
train_ds = RasterizedOpenCities(tier=1, augment=False, small_subset=False, resize_size=data_patch_size,
data_dir=root_dir, baseline_mode=False, val=False,
val_split=config["dataset_params"]["val_fraction"])
ds_list.append(train_ds)
elif fold == "val":
val_ds = RasterizedOpenCities(tier=1, augment=False, small_subset=False, resize_size=data_patch_size,
data_dir=root_dir, baseline_mode=False, val=True,
val_split=config["dataset_params"]["val_fraction"])
ds_list.append(val_ds)
elif fold == "test":
test_ds = OpenCitiesTestDataset(root_dir + "/test/", 1024)
ds_list.append(test_ds)
else:
print_utils.print_error("ERROR: fold \"{}\" not recognized, implement it in dataset_folds.py.".format(fold))
return ds_list
def get_xview2_dataset(config, root_dir, folds):
from torch_lydorn.torchvision.datasets import xView2Dataset
if "train" in folds or "val" in folds or "train_val" in folds:
train_online_cpu_transform = data_transforms.get_online_cpu_transform(config,
augmentations=config["data_aug_params"][
"enable"])
ds = xView2Dataset(root_dir, fold="train", pre_process=True,
patch_size=config["dataset_params"]["data_patch_size"],
pre_transform=data_transforms.get_offline_transform_patch(),
transform=train_online_cpu_transform,
small=config["dataset_params"]["small"], pool_size=config["num_workers"])
torch.manual_seed(config["dataset_params"]["seed"]) # Ensure a seed is set
train_split_length = int(round(config["dataset_params"]["train_fraction"] * len(ds)))
val_split_length = len(ds) - train_split_length
train_ds, val_ds = torch.utils.data.random_split(ds, [train_split_length, val_split_length])
ds_list = []
for fold in folds:
if fold == "train":
ds_list.append(train_ds)
elif fold == "val":
ds_list.append(val_ds)
elif fold == "train_val":
ds_list.append(ds)
elif fold == "test":
raise NotImplementedError("Test fold not yet implemented (skip pre-processing?)")
elif fold == "hold":
raise NotImplementedError("Hold fold not yet implemented (skip pre-processing?)")
else:
print_utils.print_error("ERROR: fold \"{}\" not recognized, implement it in dataset_folds.py.".format(fold))
exit()
return ds_list
def get_folds(config, root_dir, folds):
assert set(folds).issubset({"train", "val", "train_val", "test"}), \
'fold in folds should be in ["train", "val", "train_val", "test"]'
if config["dataset_params"]["root_dirname"] == "AerialImageDataset":
return get_inria_aerial_folds(config, root_dir, folds)
elif config["dataset_params"]["root_dirname"] == "luxcarta_precise_buildings":
return get_luxcarta_buildings(config, root_dir, folds)
elif config["dataset_params"]["root_dirname"] == "mapping_challenge_dataset":
return get_mapping_challenge(config, root_dir, folds)
elif config["dataset_params"]["root_dirname"] == "segbuildings":
return get_opencities_competition(config, root_dir, folds)
elif config["dataset_params"]["root_dirname"] == "xview2_xbd_dataset":
return get_xview2_dataset(config, root_dir, folds)
else:
print_utils.print_error("ERROR: config[\"data_root_partial_dirpath\"] = \"{}\" is an unknown dataset! "
"If it is a new dataset, add it in dataset_folds.py's get_folds() function.".format(
config["dataset_params"]["root_dirname"]))
exit()
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