Datasets:
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Experiment Configuration System
Handles experiment parameters for training and evaluation
"""
import ast
from dataclasses import dataclass
from typing import List, Dict, Any
import os
# Per-model defaults for pretrained_weights and single_target_strategy.
# These are sourced from the experimentation plan used in the published results.
# Users can override both via --pretrained_weights and --single_target_strategy CLI flags.
MODEL_DEFAULTS: Dict[str, Dict[str, str]] = {
"ResNet-18": {"pretrained_weights": "ImageNet", "single_target_strategy": "Direct classification head"},
"ResNet-34": {"pretrained_weights": "ImageNet", "single_target_strategy": "Direct classification head"},
"DenseNet-121":{"pretrained_weights": "ImageNet", "single_target_strategy": "Direct classification head"},
"EfficientNet-B0": {"pretrained_weights": "ImageNet", "single_target_strategy": "Direct classification head"},
"ViT-Small (DINOv2)": {"pretrained_weights": "DINOv2 (self-supervised)", "single_target_strategy": "CLS token classification"},
"Swin Transformer-Base": {"pretrained_weights": "ImageNet-22K", "single_target_strategy": "CLS token classification"},
"ResNet-50 (RadImageNet)": {"pretrained_weights": "RadImageNet", "single_target_strategy": "Direct classification head"},
}
DEFAULT_AUGMENTATION_PARAMS: Dict[str, Any] = {
"rotation": 15,
"horizontal_flip": True,
"random_crop": True,
"color_jitter": True,
"brightness": 0.2,
"contrast": 0.2,
"imagenet_norm": True,
}
DEFAULT_AUGMENTATIONS = DEFAULT_AUGMENTATION_PARAMS.copy()
def get_model_defaults(model_name: str) -> Dict[str, str]:
"""
Return the default pretrained_weights and single_target_strategy for a given model.
Falls back to ImageNet weights and Direct classification head for unknown models.
"""
return MODEL_DEFAULTS.get(
model_name,
{"pretrained_weights": "ImageNet", "single_target_strategy": "Direct classification head"}
)
@dataclass
class ExperimentConfig:
"""Configuration for a single experiment"""
# Model configuration
model: str
loss_function: str
must_include: bool
learning_rate: List[float]
batch_size: int
weight_decay: float
optimizer: str
scheduler: str
# Training configuration
image_augmentations: Dict[str, Any]
dropout: float
loss_specific_params: str
multi_target_strategy: str
single_target_strategy: str
pretrained_weights: str
fine_tuning_strategy: str
# System configuration
expected_gpu_memory: str
architectural_family: str
class_weighting: str
sampling_strategy: str
threshold_selection: str
# Additional configuration
experiment_name: str = ""
output_dir: str = ""
# GradNorm configuration
use_gradnorm: bool = False
gradnorm_alpha: float = 0.16
gradnorm_update_freq: int = 10
def __post_init__(self):
"""Process configuration after initialization"""
if isinstance(self.learning_rate, str):
try:
self.learning_rate = ast.literal_eval(self.learning_rate)
except (ValueError, SyntaxError):
try:
self.learning_rate = [float(self.learning_rate)]
except ValueError:
self.learning_rate = [1e-4]
if not isinstance(self.learning_rate, list):
self.learning_rate = [self.learning_rate]
self.image_augmentations = normalize_augmentation_params(self.image_augmentations)
if not self.experiment_name:
self.experiment_name = self._generate_experiment_name()
def _generate_experiment_name(self) -> str:
"""Generate a unique experiment name based on configuration"""
import datetime
model_clean = self.model.replace('/', '_').replace(' ', '_').replace('(', '').replace(')', '')
lr_str = f"lr{self.learning_rate[0]:.0e}" if len(self.learning_rate) == 1 else "lr_sweep"
batch_str = f"bs{self.batch_size}"
ft_suffix = ""
if "frozen" in self.fine_tuning_strategy.lower() or "probe" in self.fine_tuning_strategy.lower():
ft_suffix = "_frozen"
elif "partial" in self.fine_tuning_strategy.lower():
ft_suffix = "_partial"
timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
return f"{model_clean}_{lr_str}_{batch_str}{ft_suffix}_{timestamp}"
def get_output_directory(self, base_dir: str) -> str:
"""Get the output directory for this experiment"""
if self.output_dir:
return self.output_dir
return os.path.join(base_dir, self.experiment_name)
def to_dict(self) -> Dict[str, Any]:
"""Convert configuration to dictionary"""
learning_rate_value = self.learning_rate[0] if len(self.learning_rate) == 1 else self.learning_rate
return {
'model': self.model,
'loss_function': self.loss_function,
'must_include': self.must_include,
'learning_rate': learning_rate_value,
'batch_size': self.batch_size,
'weight_decay': self.weight_decay,
'optimizer': self.optimizer,
'scheduler': self.scheduler,
'image_augmentations': self.image_augmentations,
'dropout': self.dropout,
'loss_specific_params': self.loss_specific_params,
'multi_target_strategy': self.multi_target_strategy,
'single_target_strategy': self.single_target_strategy,
'pretrained_weights': self.pretrained_weights,
'fine_tuning_strategy': self.fine_tuning_strategy,
'expected_gpu_memory': self.expected_gpu_memory,
'architectural_family': self.architectural_family,
'class_weighting': self.class_weighting,
'sampling_strategy': self.sampling_strategy,
'threshold_selection': self.threshold_selection,
'experiment_name': self.experiment_name,
}
def normalize_augmentation_params(aug_input: Any) -> Dict[str, Any]:
"""Normalize augmentation params into a validated parameter dictionary."""
aug_params = DEFAULT_AUGMENTATION_PARAMS.copy()
if aug_input is None:
return aug_params
if isinstance(aug_input, str):
try:
parsed = ast.literal_eval(aug_input)
except (ValueError, SyntaxError) as exc:
raise ValueError(
"image_augmentations must be a dict (or a dict-like string), "
"not a free-form text description."
) from exc
aug_input = parsed
if not isinstance(aug_input, dict):
raise ValueError("image_augmentations must be a dictionary of augmentation params.")
aug_params.update(aug_input)
# Enforce expected types
aug_params["rotation"] = int(aug_params["rotation"])
aug_params["horizontal_flip"] = bool(aug_params["horizontal_flip"])
aug_params["random_crop"] = bool(aug_params["random_crop"])
aug_params["color_jitter"] = bool(aug_params["color_jitter"])
aug_params["brightness"] = float(aug_params["brightness"])
aug_params["contrast"] = float(aug_params["contrast"])
aug_params["imagenet_norm"] = bool(aug_params["imagenet_norm"])
return aug_params
def parse_augmentation_string(aug_input: Any) -> Dict[str, Any]:
"""Backward-compatible alias for older imports/call sites."""
return normalize_augmentation_params(aug_input)
def create_optimizer(model_parameters, config: 'ExperimentConfig'):
"""Create optimizer based on configuration"""
import torch.optim as optim
if config.optimizer == 'AdamW':
return optim.AdamW(
model_parameters,
lr=config.learning_rate[0],
weight_decay=config.weight_decay
)
elif config.optimizer == 'Adam':
return optim.Adam(
model_parameters,
lr=config.learning_rate[0],
weight_decay=config.weight_decay
)
elif config.optimizer == 'SGD':
return optim.SGD(
model_parameters,
lr=config.learning_rate[0],
weight_decay=config.weight_decay,
momentum=0.9
)
else:
raise ValueError(f"Unsupported optimizer: {config.optimizer}")
def create_scheduler(optimizer, config: 'ExperimentConfig', total_epochs: int):
"""Create learning rate scheduler based on configuration"""
import torch.optim.lr_scheduler as lr_scheduler
if config.scheduler == 'CosineAnnealing':
return lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_epochs)
elif config.scheduler == 'CosineAnnealingWarmRestarts':
return lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0=10, T_mult=2)
elif config.scheduler == 'ReduceLROnPlateau':
return lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', patience=10, factor=0.5)
elif config.scheduler == 'StepLR':
return lr_scheduler.StepLR(optimizer, step_size=30, gamma=0.1)
elif config.scheduler == 'ExponentialLR':
return lr_scheduler.ExponentialLR(optimizer, gamma=0.95)
else:
raise ValueError(f"Unsupported scheduler: {config.scheduler}")
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