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Map legacy SLEAP json configs to SLEAP-NN OmegaConf objects #162

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Map legacy SLEAP json configs to SLEAP-NN OmegaConf objects #162

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53345e4
outline for loading old sleap config
gqcpm Mar 18, 2025
369dcff
initial mappers function
gqcpm Mar 20, 2025
5649f45
check geometric config and comment out all parameters not addressed i…
gqcpm Mar 21, 2025
cdc2c99
model_config map backbone, heads, and lint other files
gqcpm Mar 22, 2025
d5c871b
map train_loader, val_loader, ckpt, max_epochs, optimizer, lr_schedul…
gqcpm Mar 27, 2025
e642b41
test trainer_mapper function
gqcpm Mar 28, 2025
69a65d4
model_config tests and legacy config path adjustments
gqcpm Mar 28, 2025
3e86b38
add tests for data config and adjust paths to map legacy config
gqcpm Mar 28, 2025
ac9327c
test training_job_config with json file
gqcpm Apr 1, 2025
d4edbaa
linted.
gqcpm Apr 1, 2025
70a5052
uniformnoisemax set to max 1, missingerror on no train/val path
gqcpm Apr 3, 2025
4b82c4c
add different head types and adjust with tests
gqcpm Apr 3, 2025
e391eb2
output omegaconf, check for mandatory values, change default values
gqcpm Apr 3, 2025
4ab26ad
docstrings
gqcpm Apr 4, 2025
a198136
adjust skeletons parameter
gqcpm Apr 4, 2025
9293589
change docstring and headers
gqcpm Apr 4, 2025
a4ab4dd
rename file and function for bottomup_multiclass
gqcpm Apr 4, 2025
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129 changes: 127 additions & 2 deletions sleap_nn/config/data_config.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,7 @@
"""

from attrs import define, field, validators
from omegaconf import MISSING
from omegaconf import MISSING, MissingMandatoryValue
from typing import Optional, Tuple, Any, List
from loguru import logger

Expand Down Expand Up @@ -184,4 +184,129 @@ class DataConfig:
preprocessing: PreprocessingConfig = field(factory=PreprocessingConfig)
use_augmentations_train: bool = False
augmentation_config: Optional[AugmentationConfig] = None
skeletons: Optional[dict] = None
skeletons: Optional[list] = None


def data_mapper(legacy_config: dict) -> DataConfig:
return DataConfig(
train_labels_path=legacy_config.get("data", {})
.get("labels", {})
.get("training_labels", MISSING),
val_labels_path=legacy_config.get("data", {})
.get("labels", {})
.get("validation_labels", MISSING),
test_file_path=legacy_config.get("data", {})
.get("labels", {})
.get("test_labels", None),
# provider=legacy_config.get("provider", "LabelsReader"),
# user_instances_only=legacy_config.get("user_instances_only", True),
# data_pipeline_fw=legacy_config.get("data_pipeline_fw", "torch_dataset"),
# np_chunks_path=legacy_config.get("np_chunks_path"),
# litdata_chunks_path=legacy_config.get("litdata_chunks_path"),
# use_existing_chunks=legacy_config.get("use_existing_chunks", False),
# chunk_size=int(legacy_config.get("chunk_size", 100)),
# delete_chunks_after_training=legacy_config.get("delete_chunks_after_training", True),
preprocessing=PreprocessingConfig(
is_rgb=legacy_config.get("data", {})
.get("preprocessing", {})
.get("ensure_rgb", False),
max_height=legacy_config.get("data", {})
.get("preprocessing", {})
.get("target_height"),
max_width=legacy_config.get("data", {})
.get("preprocessing", {})
.get("target_width"),
scale=legacy_config.get("data", {})
.get("preprocessing", {})
.get("input_scaling", 1.0),
crop_hw=legacy_config.get("data", {})
.get("preprocessing", {})
.get("crop_size"),
min_crop_size=legacy_config.get("data", {})
.get("preprocessing", {})
.get("crop_size_detection_padding", 100),
),
# use_augmentations_train=legacy_config.get("use_augmentations_train", False),
augmentation_config=(
AugmentationConfig(
intensity=IntensityConfig(
uniform_noise_min=legacy_config.get("optimization", {})
.get("augmentation_config", {})
.get("uniform_noise_min_val", 0.0),
uniform_noise_max=min(
legacy_config.get("optimization", {})
.get("augmentation_config", {})
.get("uniform_noise_max_val", 1.0),
1.0,
),
uniform_noise_p=float(
legacy_config.get("optimization", {})
.get("augmentation_config", {})
.get("uniform_noise", 1.0)
),
gaussian_noise_mean=legacy_config.get("optimization", {})
.get("augmentation_config", {})
.get("gaussian_noise_mean", 0.0),
gaussian_noise_std=legacy_config.get("optimization", {})
.get("augmentation_config", {})
.get("gaussian_noise_stddev", 1.0),
gaussian_noise_p=float(
legacy_config.get("optimization", {})
.get("augmentation_config", {})
.get("gaussian_noise", 1.0)
),
contrast_min=legacy_config.get("optimization", {})
.get("augmentation_config", {})
.get("contrast_min_gamma", 0.5),
contrast_max=legacy_config.get("optimization", {})
.get("augmentation_config", {})
.get("contrast_max_gamma", 2.0),
contrast_p=float(
legacy_config.get("optimization", {})
.get("augmentation_config", {})
.get("contrast", 1.0)
),
brightness=(
legacy_config.get("optimization", {})
.get("augmentation_config", {})
.get("brightness_min_val", 1.0),
legacy_config.get("optimization", {})
.get("augmentation_config", {})
.get("brightness_max_val", 1.0),
),
brightness_p=float(
legacy_config.get("optimization", {})
.get("augmentation_config", {})
.get("brightness", 1.0)
),
),
geometric=GeometricConfig(
rotation=legacy_config.get("optimization", {})
.get("augmentation_config", {})
.get("rotation_max_angle", 180.0),
scale=(
legacy_config.get("optimization", {})
.get("augmentation_config", {})
.get("scale_min", None),
legacy_config.get("optimization", {})
.get("augmentation_config", {})
.get("scale_max", None),
),
# translate_width=legacy_config.get("optimization", {}).get("augmentation_config", {}).get("translate_width", 0.2),
# translate_height=legacy_config.get("optimization", {}).get("augmentation_config", {}).get("translate_height", 0.2),
# affine_p=legacy_config.get("optimization", {}).get("augmentation_config", {}).get("affine_p", 0.0),
# erase_scale_min=legacy_config.get("optimization", {}).get("augmentation_config", {}).get("erase_scale_min", 0.0001),
# erase_scale_max=legacy_config.get("optimization", {}).get("augmentation_config", {}).get("erase_scale_max", 0.01),
# erase_ratio_min=legacy_config.get("optimization", {}).get("augmentation_config", {}).get("erase_ratio_min", 1.0),
# erase_ratio_max=legacy_config.get("optimization", {}).get("augmentation_config", {}).get("erase_ratio_max", 1.0),
# erase_p=legacy_config.get("optimization", {}).get("augmentation_config", {}).get("erase_p", 0.0),
# mixup_lambda=legacy_config.get("optimization", {}).get("augmentation_config", {}).get("mixup_lambda", [0.01, 0.05]),
# mixup_p=legacy_config.get("optimization", {}).get("augmentation_config", {}).get("mixup_p", 0.0),
),
)
# if legacy_config.get("use_augmentations_train", False)
# else None
),
use_augmentations_train=True,
skeletons=legacy_config.get("data", {}).get("labels", {}).get("skeletons"),
)
176 changes: 176 additions & 0 deletions sleap_nn/config/model_config.py
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Original file line number Diff line number Diff line change
Expand Up @@ -832,3 +832,179 @@ def validate_pre_trained_weights(self, value):
message = "UNet does not support pre-trained weights."
logger.error(message)
raise ValueError(message)


def model_mapper(legacy_config: dict) -> ModelConfig:
return ModelConfig(
# init_weights=legacy_config.get("init_weights", "default"),
# pre_trained_weights not in old config
# pretrained_backbone_weights=legacy_config.get("PretrainedEncoderConfig")?? # i think its different
# pretrained_head_weights not in old config
backbone_config=BackboneConfig(
unet=(
UNetConfig(
# in_channels=legacy_config.get("backbone", {}).get("in_channels", 1),
# kernel_size=legacy_config.get("backbone", {}).get("kernel_size", 3),
filters=legacy_config.get("model", {})
.get("backbone", {})
.get("unet", {})
.get("filters", 32),
filters_rate=legacy_config.get("model", {})
.get("backbone", {})
.get("unet", {})
.get("filters_rate", 1.5),
max_stride=legacy_config.get("model", {})
.get("backbone", {})
.get("unet", {})
.get("max_stride", 16),
stem_stride=legacy_config.get("model", {})
.get("backbone", {})
.get("unet", {})
.get("stem_stride", 16),
middle_block=legacy_config.get("model", {})
.get("backbone", {})
.get("unet", {})
.get("middle_block", True),
up_interpolate=legacy_config.get("model", {})
.get("backbone", {})
.get("unet", {})
.get("up_interpolate", True),
stacks=legacy_config.get("model", {})
.get("backbone", {})
.get("unet", {})
.get("stacks", 1),
# convs_per_block=2,
output_stride=legacy_config.get("model", {})
.get("backbone", {})
.get("unet", {})
.get("output_stride", 1),
)
if legacy_config.get("model", {}).get("backbone", {}).get("unet")
else None
),
# convnext not in old config
# swint not in old config
),
head_configs=HeadConfig(
single_instance=(
(
SingleInstanceConfig(
confmaps=SingleInstanceConfMapsConfig(
part_names=legacy_config.get("model", {})
.get("heads", {})
.get("single_instance", {})
.get("part_names"),
sigma=legacy_config.get("model", {})
.get("heads", {})
.get("single_instance", {})
.get("sigma", 5.0),
output_stride=legacy_config.get("model", {})
.get("heads", {})
.get("single_instance", {})
.get("output_stride", 1),
)
)
)
if legacy_config.get("model", {})
.get("heads", {})
.get("single_instance")
else None
),
centroid=(
CentroidConfig(
confmaps=CentroidConfMapsConfig(
anchor_part=legacy_config.get("model", {})
.get("heads", {})
.get("centroid", {})
.get("anchor_part"),
sigma=legacy_config.get("model", {})
.get("heads", {})
.get("centroid", {})
.get("sigma", 5.0),
output_stride=legacy_config.get("model", {})
.get("heads", {})
.get("centroid", {})
.get("output_stride", 1),
)
)
if legacy_config.get("model", {}).get("heads", {}).get("centroid")
else None
),
centered_instance=(
CenteredInstanceConfig(
confmaps=CenteredInstanceConfMapsConfig(
anchor_part=legacy_config.get("model", {})
.get("heads", {})
.get("centered_instance", {})
.get("anchor_part"),
sigma=legacy_config.get("model", {})
.get("heads", {})
.get("centered_instance", {})
.get("sigma", 5.0),
output_stride=legacy_config.get("model", {})
.get("heads", {})
.get("centered_instance", {})
.get("output_stride", 1),
part_names=legacy_config.get("model", {})
.get("heads", {})
.get("centered_instance", {})
.get("part_names", None),
)
)
if legacy_config.get("model", {})
.get("heads", {})
.get("centered_instance")
else None
),
bottomup=(
BottomUpConfig(
confmaps=BottomUpConfMapsConfig(
loss_weight=legacy_config.get("model", {})
.get("heads", {})
.get("multi_instance", {})
.get("confmaps", {})
.get("loss_weight", None),
sigma=legacy_config.get("model", {})
.get("heads", {})
.get("multi_instance", {})
.get("confmaps", {})
.get("sigma", 5.0),
output_stride=legacy_config.get("model", {})
.get("heads", {})
.get("multi_instance", {})
.get("confmaps", {})
.get("output_stride", 1),
part_names=legacy_config.get("model", {})
.get("heads", {})
.get("multi_instance", {})
.get("confmaps", {})
.get("part_names", None),
),
pafs=PAFConfig(
edges=legacy_config.get("model", {})
.get("heads", {})
.get("multi_instance", {})
.get("pafs", {})
.get("edges", None),
sigma=legacy_config.get("model", {})
.get("heads", {})
.get("multi_instance", {})
.get("pafs", {})
.get("sigma", 15.0),
output_stride=legacy_config.get("model", {})
.get("heads", {})
.get("multi_instance", {})
.get("pafs", {})
.get("output_stride", 1),
loss_weight=legacy_config.get("model", {})
.get("heads", {})
.get("multi_instance", {})
.get("pafs", {})
.get("loss_weight", None),
),
)
if legacy_config.get("model", {}).get("heads", {}).get("multi_instance")
else None
),
),
)
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