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outline for loading old sleap config #162

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outline for loading old sleap config #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
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120 changes: 119 additions & 1 deletion 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 @@ -185,3 +185,121 @@ class DataConfig:
use_augmentations_train: bool = False
augmentation_config: Optional[AugmentationConfig] = None
skeletons: Optional[dict] = None


def data_mapper(legacy_config: dict) -> DataConfig:
train_labels_path = legacy_config.get("data", {}).get("labels", {}).get("training_labels", None) or MISSING
if train_labels_path is MISSING:
raise MissingMandatoryValue("train_labels_path is missing")
val_labels_path = legacy_config.get("data", {}).get("labels", {}).get("validation_labels", None) or MISSING
if val_labels_path is MISSING:
raise MissingMandatoryValue("val_labels_path is missing")

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"),
# 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=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=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=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=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"),
)
168 changes: 168 additions & 0 deletions sleap_nn/config/model_config.py
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -832,3 +832,171 @@ 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=CentroidConfMapsConfig(
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("loss_weight", None),
sigma=legacy_config.get("model", {})
.get("heads", {})
.get("multi_instance", {})
.get("sigma", 5.0),
output_stride=legacy_config.get("model", {})
.get("heads", {})
.get("multi_instance", {})
.get("output_stride", 1),
part_names=legacy_config.get("model", {})
.get("heads", {})
.get("multi_instance", {})
.get("part_names", None),
),
pafs=PAFConfig(
edges=legacy_config.get("model", {})
.get("heads", {})
.get("multi_instance", {})
.get("edges", None),
sigma=legacy_config.get("model", {})
.get("heads", {})
.get("multi_instance", {})
.get("sigma", 15.0),
output_stride=legacy_config.get("model", {})
.get("heads", {})
.get("multi_instance", {})
.get("output_stride", 1),
loss_weight=legacy_config.get("model", {})
.get("heads", {})
.get("multi_instance", {})
.get("loss_weight", None),
),
)
if legacy_config.get("model", {}).get("heads", {}).get("multi_instance")
else None
),
),
)
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