diff --git a/fastMONAI/_modidx.py b/fastMONAI/_modidx.py index 8e38850..7bad5ad 100644 --- a/fastMONAI/_modidx.py +++ b/fastMONAI/_modidx.py @@ -8,14 +8,24 @@ 'syms': { 'fastMONAI.dataset_info': { 'fastMONAI.dataset_info.MedDataset': ('dataset_info.html#meddataset', 'fastMONAI/dataset_info.py'), 'fastMONAI.dataset_info.MedDataset.__init__': ( 'dataset_info.html#meddataset.__init__', 'fastMONAI/dataset_info.py'), + 'fastMONAI.dataset_info.MedDataset._auto_cache_path': ( 'dataset_info.html#meddataset._auto_cache_path', + 'fastMONAI/dataset_info.py'), 'fastMONAI.dataset_info.MedDataset._create_data_frame': ( 'dataset_info.html#meddataset._create_data_frame', 'fastMONAI/dataset_info.py'), 'fastMONAI.dataset_info.MedDataset._get_data_info': ( 'dataset_info.html#meddataset._get_data_info', 'fastMONAI/dataset_info.py'), + 'fastMONAI.dataset_info.MedDataset._load_cache': ( 'dataset_info.html#meddataset._load_cache', + 'fastMONAI/dataset_info.py'), + 'fastMONAI.dataset_info.MedDataset._process_with_cache': ( 'dataset_info.html#meddataset._process_with_cache', + 'fastMONAI/dataset_info.py'), + 'fastMONAI.dataset_info.MedDataset._save_cache': ( 'dataset_info.html#meddataset._save_cache', + 'fastMONAI/dataset_info.py'), 'fastMONAI.dataset_info.MedDataset._visualize_single_case': ( 'dataset_info.html#meddataset._visualize_single_case', 'fastMONAI/dataset_info.py'), 'fastMONAI.dataset_info.MedDataset.calculate_target_size': ( 'dataset_info.html#meddataset.calculate_target_size', 'fastMONAI/dataset_info.py'), + 'fastMONAI.dataset_info.MedDataset.fingerprint': ( 'dataset_info.html#meddataset.fingerprint', + 'fastMONAI/dataset_info.py'), 'fastMONAI.dataset_info.MedDataset.get_size_statistics': ( 'dataset_info.html#meddataset.get_size_statistics', 'fastMONAI/dataset_info.py'), 'fastMONAI.dataset_info.MedDataset.get_suggestion': ( 'dataset_info.html#meddataset.get_suggestion', @@ -79,7 +89,7 @@ 'fastMONAI/utils.py'), 'fastMONAI.utils.ModelTrackingCallback._extract_training_params': ( 'utils.html#modeltrackingcallback._extract_training_params', 'fastMONAI/utils.py'), - 'fastMONAI.utils.ModelTrackingCallback._log_datasets': ( 'utils.html#modeltrackingcallback._log_datasets', + 'fastMONAI.utils.ModelTrackingCallback._log_split_df': ( 'utils.html#modeltrackingcallback._log_split_df', 'fastMONAI/utils.py'), 'fastMONAI.utils.ModelTrackingCallback._register_pytorch_model': ( 'utils.html#modeltrackingcallback._register_pytorch_model', 'fastMONAI/utils.py'), @@ -103,13 +113,9 @@ 'fastMONAI.utils._extract_loss_name': ('utils.html#_extract_loss_name', 'fastMONAI/utils.py'), 'fastMONAI.utils._extract_model_name': ('utils.html#_extract_model_name', 'fastMONAI/utils.py'), 'fastMONAI.utils._extract_patch_config': ('utils.html#_extract_patch_config', 'fastMONAI/utils.py'), - 'fastMONAI.utils._extract_patch_dataset_dfs': ( 'utils.html#_extract_patch_dataset_dfs', - 'fastMONAI/utils.py'), 'fastMONAI.utils._extract_size_from_transforms': ( 'utils.html#_extract_size_from_transforms', 'fastMONAI/utils.py'), 'fastMONAI.utils._extract_standard_config': ('utils.html#_extract_standard_config', 'fastMONAI/utils.py'), - 'fastMONAI.utils._extract_standard_dataset_dfs': ( 'utils.html#_extract_standard_dataset_dfs', - 'fastMONAI/utils.py'), 'fastMONAI.utils.create_mlflow_callback': ('utils.html#create_mlflow_callback', 'fastMONAI/utils.py'), 'fastMONAI.utils.load_patch_variables': ('utils.html#load_patch_variables', 'fastMONAI/utils.py'), 'fastMONAI.utils.load_variables': ('utils.html#load_variables', 'fastMONAI/utils.py'), @@ -473,6 +479,8 @@ 'fastMONAI/vision_patch.py'), 'fastMONAI.vision_patch.MedPatchDataLoaders.show_batch': ( 'vision_patch.html#medpatchdataloaders.show_batch', 'fastMONAI/vision_patch.py'), + 'fastMONAI.vision_patch.MedPatchDataLoaders.split_df': ( 'vision_patch.html#medpatchdataloaders.split_df', + 'fastMONAI/vision_patch.py'), 'fastMONAI.vision_patch.MedPatchDataLoaders.target_spacing': ( 'vision_patch.html#medpatchdataloaders.target_spacing', 'fastMONAI/vision_patch.py'), 'fastMONAI.vision_patch.MedPatchDataLoaders.to': ( 'vision_patch.html#medpatchdataloaders.to', diff --git a/fastMONAI/dataset_info.py b/fastMONAI/dataset_info.py index 98339cf..fad03e5 100644 --- a/fastMONAI/dataset_info.py +++ b/fastMONAI/dataset_info.py @@ -9,10 +9,14 @@ from sklearn.utils.class_weight import compute_class_weight from concurrent.futures import ThreadPoolExecutor +from pathlib import Path import pandas as pd import numpy as np import torch import glob +import hashlib +import os +import pickle import matplotlib.pyplot as plt # %% ../nbs/08_dataset_info.ipynb #7401beac @@ -23,7 +27,8 @@ class MedDataset: def __init__(self, dataframe=None, image_col:str=None, mask_col:str="mask_path", path=None, img_list=None, postfix:str='', apply_reorder:bool=True, - dtype:(MedImage, MedMask)=MedImage, max_workers:int=1): + dtype:(MedImage, MedMask)=MedImage, max_workers:int=1, + use_cache:bool=True, cache_path=None): """Constructs MedDataset object. Args: @@ -36,6 +41,12 @@ def __init__(self, dataframe=None, image_col:str=None, mask_col:str="mask_path", apply_reorder: Whether to reorder images to RAS+ orientation. dtype: MedImage for images or MedMask for segmentation masks. max_workers: Number of parallel workers for processing. + use_cache: Enable metadata caching. When True (default) and cache_path + is not provided, auto-generates a cache path in ~/.cache/fastmonai/. + Set to False to disable caching entirely. + cache_path: Explicit path to a pickle file for metadata caching. + Only used when use_cache=True. If None and use_cache=True, + a path is auto-generated from the file list and config. """ self.input_df = dataframe self.image_col = image_col @@ -46,6 +57,8 @@ def __init__(self, dataframe=None, image_col:str=None, mask_col:str="mask_path", self.apply_reorder = apply_reorder self.dtype = dtype self.max_workers = max_workers + self.use_cache = use_cache + self.cache_path = cache_path self.df = self._create_data_frame() def _create_data_frame(self): @@ -70,9 +83,16 @@ def _create_data_frame(self): print('Error: Must provide path, img_list, or dataframe with mask_col') return pd.DataFrame() - # Process images to extract metadata - with ThreadPoolExecutor(max_workers=self.max_workers) as executor: - data_info_dict = list(executor.map(self._get_data_info, file_list)) + # Resolve cache path + if self.use_cache and self.cache_path is None: + self.cache_path = self._auto_cache_path(file_list) + + # Process images to extract metadata (with optional caching) + if self.use_cache and self.cache_path is not None: + data_info_dict = self._process_with_cache(file_list) + else: + with ThreadPoolExecutor(max_workers=self.max_workers) as executor: + data_info_dict = list(executor.map(self._get_data_info, file_list)) df = pd.DataFrame(data_info_dict) @@ -85,6 +105,34 @@ def _create_data_frame(self): return df + def _auto_cache_path(self, file_list): + """Generate automatic cache path from file list and config.""" + try: + cache_dir = Path.home() / '.cache' / 'fastmonai' + cache_dir.mkdir(parents=True, exist_ok=True) + config_str = f"reorder={self.apply_reorder}|dtype={self.dtype.__name__}" + abs_paths = sorted(os.path.abspath(fn) for fn in file_list) + key_input = config_str + '|' + '|'.join(abs_paths) + key = hashlib.md5(key_input.encode()).hexdigest() + return str(cache_dir / f'med_dataset_{key}.pkl') + except Exception: + return None + + @property + def fingerprint(self): + """Compute a dataset fingerprint from per-file content hashes. + + Returns a deterministic MD5 hex digest representing the dataset contents. + Returns None if content hashes are not available (e.g., all files failed). + """ + if 'content_hash' not in self.df.columns: + return None + hashes = self.df['content_hash'].dropna().sort_values().tolist() + if not hashes: + return None + combined = ''.join(hashes) + return hashlib.md5(combined.encode()).hexdigest() + def summary(self): """Summary DataFrame of the dataset with example path for similar data.""" @@ -125,7 +173,10 @@ def _get_data_info(self, fn: str): try: _, o, _ = med_img_reader(fn, apply_reorder=self.apply_reorder, only_tensor=False, dtype=self.dtype) - info_dict = {'path': fn, 'dim_0': o.shape[1], 'dim_1': o.shape[2], 'dim_2': o.shape[3], + # Hash loaded tensor data (implicitly captures apply_reorder state) + content_hash = hashlib.md5(o.data.numpy().tobytes()).hexdigest() + + info_dict = {'path': fn, 'content_hash': content_hash, 'dim_0': o.shape[1], 'dim_1': o.shape[2], 'dim_2': o.shape[3], 'voxel_0': round(o.spacing[0], 4), 'voxel_1': round(o.spacing[1], 4), 'voxel_2': round(o.spacing[2], 4), 'orientation': f'{"".join(o.orientation)}+'} @@ -149,6 +200,97 @@ def _get_data_info(self, fn: str): print(f"Warning: Failed to process {fn}: {e}") return {'path': fn, 'error': str(e)} + def _load_cache(self): + """Load metadata cache from disk. Returns empty dict on any failure.""" + try: + with open(self.cache_path, 'rb') as f: + cache = pickle.load(f) + if not isinstance(cache, dict) or cache.get('version') != 1: + return {} + return cache.get('entries', {}) + except Exception: + return {} + + def _save_cache(self, entries): + """Save metadata cache to disk.""" + try: + cache = {'version': 1, 'entries': entries} + with open(self.cache_path, 'wb') as f: + pickle.dump(cache, f) + except Exception as e: + print(f"Warning: Failed to save metadata cache: {e}") + + def _process_with_cache(self, file_list): + """Process file list with per-file metadata caching. + + Loads existing cache, identifies files that need reprocessing + (cache misses), processes only those files, updates and saves + the cache, then returns all results in original order. + """ + entries = self._load_cache() + + cached_results = [] + files_to_process = [] + process_indices = [] + + for i, fn in enumerate(file_list): + abs_path = os.path.abspath(fn) + entry = entries.get(abs_path) + if entry is not None: + try: + stat = os.stat(fn) + if (entry.get('mtime') == stat.st_mtime + and entry.get('size') == stat.st_size + and entry.get('apply_reorder') == self.apply_reorder + and entry.get('dtype') == self.dtype.__name__): + cached_results.append((i, entry['info'])) + continue + except OSError: + pass + files_to_process.append(fn) + process_indices.append(i) + + # Process cache misses + if files_to_process: + with ThreadPoolExecutor(max_workers=self.max_workers) as executor: + fresh_results = list(executor.map(self._get_data_info, files_to_process)) + else: + fresh_results = [] + + # Update cache entries with fresh results (skip errors) + for fn, info in zip(files_to_process, fresh_results): + if 'error' not in info: + abs_path = os.path.abspath(fn) + try: + stat = os.stat(fn) + entries[abs_path] = { + 'mtime': stat.st_mtime, + 'size': stat.st_size, + 'apply_reorder': self.apply_reorder, + 'dtype': self.dtype.__name__, + 'info': info + } + except OSError: + pass + + self._save_cache(entries) + + # Reconstruct results in original file_list order + all_results = [None] * len(file_list) + for i, info in cached_results: + all_results[i] = info + for i, info in zip(process_indices, fresh_results): + all_results[i] = info + + n_cached = len(cached_results) + n_processed = len(files_to_process) + if n_cached > 0: + print(f"MedDataset cache: {n_cached} cached, {n_processed} processed") + elif n_processed > 0: + print(f"MedDataset: processed {n_processed} files (results cached)") + + return all_results + def calculate_target_size(self, target_spacing: list = None) -> list: """Calculate the target image size for the dataset. diff --git a/fastMONAI/utils.py b/fastMONAI/utils.py index 4e10165..a9ae159 100644 --- a/fastMONAI/utils.py +++ b/fastMONAI/utils.py @@ -10,13 +10,14 @@ from pathlib import Path import mlflow import mlflow.pytorch -import mlflow.data -mlflow.set_tracking_uri("sqlite:///mlruns.db") +try: + _mlruns_db = Path(__file__).resolve().parent.parent / 'mlruns.db' +except NameError: + _mlruns_db = Path('mlruns.db').resolve() +mlflow.set_tracking_uri(f"sqlite:///{_mlruns_db}") import os import tempfile import json -import warnings -import pandas as pd from datetime import datetime from fastai.callback.core import Callback from fastcore.foundation import L @@ -272,73 +273,6 @@ def _extract_model_name(learn) -> str: model = learn.model return model._get_name() if hasattr(model, '_get_name') else model.__class__.__name__ - -def _extract_standard_dataset_dfs(dls): - """Extract train/valid DataFrames from standard MedImageDataLoaders. - - For standard DataLoaders created via MedImageDataLoaders.from_df(), - the items attribute contains the original DataFrame split by train/valid. - - Args: - dls: Standard fastai DataLoaders (MedImageDataLoaders) - - Returns: - Tuple of (train_df, valid_df). Either can be None if items - are not DataFrames (e.g., manually constructed DataLoaders). - """ - train_df = None - valid_df = None - - train_items = getattr(getattr(dls, 'train_ds', None), 'items', None) - valid_items = getattr(getattr(dls, 'valid_ds', None), 'items', None) - - if isinstance(train_items, pd.DataFrame): - train_df = train_items - if isinstance(valid_items, pd.DataFrame): - valid_df = valid_items - - return train_df, valid_df - - -def _extract_patch_dataset_dfs(dls): - """Extract train/valid DataFrames from MedPatchDataLoaders. - - Reconstructs DataFrames from TorchIO SubjectsDataset by extracting - file paths from each Subject's image and (optionally) mask. - Uses _subjects directly to avoid triggering data loading. - - Args: - dls: MedPatchDataLoaders instance - - Returns: - Tuple of (train_df, valid_df). Either can be None if extraction fails. - """ - img_col = getattr(dls, '_img_col', 'image') - mask_col = getattr(dls, '_mask_col', None) - - def _subjects_to_df(subjects_dataset): - subjects = getattr(subjects_dataset, '_subjects', None) - if subjects is None: - return None - rows = [] - for s in subjects: - row = {} - img = s.get('image') - if img is not None and getattr(img, 'path', None) is not None: - row[img_col] = str(img.path) - if mask_col is not None: - mask = s.get('mask') - if mask is not None and getattr(mask, 'path', None) is not None: - row[mask_col] = str(mask.path) - if row: - rows.append(row) - return pd.DataFrame(rows) if rows else None - - train_df = _subjects_to_df(dls.train_ds) - valid_df = _subjects_to_df(dls.valid_ds) - - return train_df, valid_df - # %% ../nbs/07_utils.ipynb #030876d2 class ModelTrackingCallback(Callback): """ @@ -364,7 +298,9 @@ def __init__( auto_start: bool = False, patch_config: dict = None, extra_params: dict = None, - extra_tags: dict = None + extra_tags: dict = None, + dataset_version: str = None, + log_split: bool = True ): """ Initialize the MLflow tracking callback. @@ -382,6 +318,8 @@ def __init__( patch_config: Patch configuration dict for logging (from MedPatchDataLoaders) extra_params: Additional parameters to log extra_tags: MLflow tags to set on the run + dataset_version: Dataset version hash string for tracking + log_split: If True, auto-logs train/val split CSV when dls has split_df """ self.model_name = model_name self.loss_function = loss_function @@ -397,6 +335,8 @@ def __init__( self.patch_config = patch_config self.extra_params = extra_params or {} self.extra_tags = extra_tags or {} + self.dataset_version = dataset_version + self.log_split = log_split self._auto_started = False self.run_id = None @@ -473,42 +413,6 @@ def _extract_training_params(self) -> dict[str, Any]: return params - def _log_datasets(self) -> None: - """Log train/valid datasets to MLflow's Datasets tab. - - Uses mlflow.data.from_pandas() and mlflow.log_input() to register - dataset metadata so they appear in the MLflow UI Datasets tab. - Failures are silently caught to never break training. - """ - try: - dls = self.learn.dls - is_patch = _detect_patch_workflow(dls) - - if is_patch: - train_df, valid_df = _extract_patch_dataset_dfs(dls) - else: - train_df, valid_df = _extract_standard_dataset_dfs(dls) - - source = self.experiment_name or self.model_name or "fastMONAI" - - with warnings.catch_warnings(): - warnings.simplefilter("ignore") - - if train_df is not None and not train_df.empty: - train_dataset = mlflow.data.from_pandas( - train_df, source=source, name="train" - ) - mlflow.log_input(train_dataset, context="training") - - if valid_df is not None and not valid_df.empty: - valid_dataset = mlflow.data.from_pandas( - valid_df, source=source, name="validation" - ) - mlflow.log_input(valid_dataset, context="validation") - - except Exception: - pass # Dataset logging must never break training - def _extract_epoch_metrics(self) -> dict[str, float]: """Extract metrics from the current epoch.""" recorder = self.learn.recorder @@ -549,46 +453,55 @@ def _extract_epoch_metrics(self) -> dict[str, float]: def _save_model_artifacts(self, temp_dir: Path) -> None: """Save model weights, learner, and configuration as artifacts.""" - weights_path = temp_dir / "weights" + import shutil + + # Save final epoch weights + weights_path = temp_dir / "final_weights" self.learn.save(str(weights_path)) - weights_file = f"{weights_path}.pth" if os.path.exists(weights_file): mlflow.log_artifact(weights_file, "model") - - # Auto-detect SaveModelCallback and log best model + + # Auto-detect SaveModelCallback and log best model weights from fastai.callback.tracker import SaveModelCallback best_model_cb = None for cb in self.learn.cbs: if isinstance(cb, SaveModelCallback): - best_model_path = self.learn.path/self.learn.model_dir/f'{cb.fname}.pth' + best_model_path = self.learn.path / self.learn.model_dir / f'{cb.fname}.pth' if best_model_path.exists(): - mlflow.log_artifact(str(best_model_path), "model") - print(f"Logged best model artifact: {cb.fname}.pth") + best_weights_dest = temp_dir / "best_weights.pth" + shutil.copy2(str(best_model_path), str(best_weights_dest)) + mlflow.log_artifact(str(best_weights_dest), "model") + print(f"Logged best model weights: best_weights.pth") best_model_cb = cb break - # Load best model weights before export if SaveModelCallback was used + # Remove ModelTrackingCallback before exporting learners + original_cbs = self.learn.cbs.copy() + filtered_cbs = L([cb for cb in self.learn.cbs + if not isinstance(cb, ModelTrackingCallback)]) + self.learn.cbs = filtered_cbs + + # Export final epoch learner (before loading best weights) + final_learner_path = temp_dir / "final_learner.pkl" + self.learn.export(str(final_learner_path)) + mlflow.log_artifact(str(final_learner_path), "model") + print("Logged final epoch learner: final_learner.pkl") + + # If best model exists, load best weights and export best learner if best_model_cb is not None: self.learn.load(best_model_cb.fname) - print(f"Loaded best model weights ({best_model_cb.fname}) for learner export") + print(f"Loaded best model weights ({best_model_cb.fname}) for best learner export") - # Remove MLflow callbacks before exporting learner for inference - # This prevents the callback from being triggered during inference - original_cbs = self.learn.cbs.copy() # Save original callbacks - - # Remove ModelTrackingCallback instances from learner using proper collection type - filtered_cbs = L([cb for cb in self.learn.cbs if not isinstance(cb, ModelTrackingCallback)]) - self.learn.cbs = filtered_cbs - - # Export clean learner without MLflow callbacks - learner_path = temp_dir / "learner.pkl" - self.learn.export(str(learner_path)) - mlflow.log_artifact(str(learner_path), "model") - - # Restore original callbacks for current session + best_learner_path = temp_dir / "best_learner.pkl" + self.learn.export(str(best_learner_path)) + mlflow.log_artifact(str(best_learner_path), "model") + print("Logged best epoch learner: best_learner.pkl") + + # Restore original callbacks self.learn.cbs = original_cbs - + + # Save inference config config_path = temp_dir / "inference_settings.pkl" store_variables(config_path, self.size, self.apply_reorder, self.target_spacing) mlflow.log_artifact(str(config_path), "config") @@ -599,7 +512,29 @@ def _register_pytorch_model(self) -> None: pytorch_model=self.learn.model, registered_model_name=self.model_name ) - + + def _log_split_df(self) -> None: + """Log train/validation split as CSV artifact if available.""" + dls = self.learn.dls + if not hasattr(dls, 'split_df'): + return + try: + split_df = dls.split_df + cols = [] + img_col = getattr(dls, '_img_col', None) + mask_col = getattr(dls, '_mask_col', None) + if img_col and img_col in split_df.columns: + cols.append(img_col) + if mask_col and mask_col in split_df.columns: + cols.append(mask_col) + cols.append('is_valid') + split_df = split_df[cols] + + mlflow.log_text(split_df.to_csv(index=False), "data/train_val_split.csv") + print(f"Logged train/val split ({len(split_df)} rows) to MLflow artifacts") + except Exception as e: + print(f"Warning: Could not log train/val split: {e}") + def before_fit(self) -> None: """Log hyperparameters before training starts. Auto-start run if configured.""" # Auto-start run if requested @@ -610,6 +545,10 @@ def before_fit(self) -> None: mlflow.start_run(run_name=self.run_name) self._auto_started = True + # Log dataset version tag if provided + if self.dataset_version is not None: + mlflow.set_tag("dataset_version", self.dataset_version) + # Set tags if self.extra_tags: mlflow.set_tags(self.extra_tags) @@ -618,9 +557,10 @@ def before_fit(self) -> None: params = self._extract_training_params() params.update(self.extra_params) mlflow.log_params(params) - - # Log datasets - self._log_datasets() + + # Log train/val split + if self.log_split: + self._log_split_df() def after_epoch(self) -> None: """Log metrics after each epoch.""" @@ -763,6 +703,8 @@ def create_mlflow_callback( model_name: str = None, extra_params: dict = None, extra_tags: dict = None, + dataset_version: str = None, + log_split: bool = True, ) -> ModelTrackingCallback: """Create MLflow tracking callback with auto-extracted configuration. @@ -783,6 +725,8 @@ def create_mlflow_callback( model_name: Override auto-extracted model name for registration. extra_params: Additional parameters to log (e.g., {'dropout': 0.5}). extra_tags: MLflow tags to set on the run. + dataset_version: Dataset version hash string for tracking. + log_split: If True, auto-logs train/val split CSV when dls has split_df. Returns: ModelTrackingCallback ready to use with learn.fit() @@ -840,6 +784,8 @@ def create_mlflow_callback( patch_config=config['patch_config'], extra_params=extra_params, extra_tags=extra_tags, + dataset_version=dataset_version, + log_split=log_split, ) # %% ../nbs/07_utils.ipynb #a3d2d585 @@ -860,7 +806,7 @@ def __init__(self): self.thread = None self.port = 5001 self.host = '0.0.0.0' - self.backend_store_uri = 'sqlite:///mlruns.db' + self.backend_store_uri = f'sqlite:///{_mlruns_db}' self._owns_process = False self._reusing_external = False diff --git a/fastMONAI/vision_core.py b/fastMONAI/vision_core.py index a038778..075f47a 100644 --- a/fastMONAI/vision_core.py +++ b/fastMONAI/vision_core.py @@ -209,7 +209,8 @@ def item_preprocessing(cls, target_spacing: (list, int, tuple), apply_reorder: b cls.target_spacing = target_spacing cls.apply_reorder = apply_reorder - def show(self, ctx=None, channel: int = 0, slice_index: int = None, anatomical_plane: int = 0, **kwargs): + def show(self, ctx=None, channel: int = 0, slice_index: int = None, + anatomical_plane: int = 0, voxel_size=None, **kwargs): """ Displays the Medimage using `merge(self._show_args, kwargs)`. @@ -222,15 +223,19 @@ def show(self, ctx=None, channel: int = 0, slice_index: int = None, anatomical_p Index of the images to be displayed. Defaults to None. anatomical_plane : int, optional Anatomical plane of the image to be displayed. Defaults to 0. + voxel_size : list or None, optional + Voxel size for aspect ratio calculation. Overrides + class-level target_spacing when provided. Defaults to None. kwargs : dict, optional Additional parameters for the show function. Returns: Shown image. """ + _voxel_size = voxel_size if voxel_size is not None else self.target_spacing return show_med_img( self, ctx=ctx, channel=channel, slice_index=slice_index, - anatomical_plane=anatomical_plane, voxel_size=self.target_spacing, + anatomical_plane=anatomical_plane, voxel_size=_voxel_size, **merge(self._show_args, kwargs) ) diff --git a/fastMONAI/vision_patch.py b/fastMONAI/vision_patch.py index d589d82..60706e4 100644 --- a/fastMONAI/vision_patch.py +++ b/fastMONAI/vision_patch.py @@ -734,6 +734,8 @@ def from_df( instance._target_spacing = _target_spacing instance._ensure_affine_consistency = ensure_affine_consistency instance._patch_config = patch_config + instance._train_source_df = train_df + instance._valid_source_df = valid_df return instance @property @@ -781,6 +783,13 @@ def patch_config(self): """The PatchConfig used for this DataLoaders.""" return getattr(self, '_patch_config', None) + @property + def split_df(self): + """DataFrame recording train/valid split for reproducibility logging.""" + train = self._train_source_df.assign(is_valid=False) + valid = self._valid_source_df.assign(is_valid=True) + return pd.concat([train, valid], ignore_index=True) + def to(self, device): """Move DataLoaders to device.""" self._device = device @@ -868,14 +877,15 @@ def show_batch(self, dl_idx=0, max_n=6, figsize=None, channel=0, imgs.extend(im_channels) slice_idxs.extend([idx] * len(im_channels)) - with warnings.catch_warnings(): - warnings.filterwarnings('ignore', message='Voxel size not defined') - ctxs = [im.show(ax=ax, slice_index=idx, anatomical_plane=anatomical_plane) - for im, ax, idx in zip(imgs, flat_axs, slice_idxs)] + voxel_size = self.target_spacing + ctxs = [im.show(ax=ax, slice_index=idx, anatomical_plane=anatomical_plane, + voxel_size=voxel_size) + for im, ax, idx in zip(imgs, flat_axs, slice_idxs)] - if overlay and has_mask: - for mask, ax, idx in zip(masks_for_overlay, flat_axs, slice_idxs): - mask.show(ax=ax, slice_index=idx, anatomical_plane=anatomical_plane) + if overlay and has_mask: + for mask, ax, idx in zip(masks_for_overlay, flat_axs, slice_idxs): + mask.show(ax=ax, slice_index=idx, anatomical_plane=anatomical_plane, + voxel_size=voxel_size) plt.tight_layout() plt.show() @@ -927,7 +937,6 @@ def __del__(self): except Exception: pass - # %% ../nbs/10_vision_patch.ipynb #cell-17 import numbers @@ -1007,7 +1016,7 @@ class PatchInferenceEngine: >>> # Option 2: From raw PyTorch model (recommended for deployment) >>> model = UNet(spatial_dims=3, in_channels=1, out_channels=2, ...) - >>> model.load_state_dict(torch.load('weights.pth')) + >>> model.load_state_dict(torch.load('final_weights.pth')) >>> model.cuda().eval() >>> engine = PatchInferenceEngine(model, config, pre_inference_tfms=[ZNormalization()]) >>> pred = engine.predict('image.nii.gz') diff --git a/nbs/01_vision_core.ipynb b/nbs/01_vision_core.ipynb index 288af09..f986805 100644 --- a/nbs/01_vision_core.ipynb +++ b/nbs/01_vision_core.ipynb @@ -219,123 +219,7 @@ "id": "10916a66", "metadata": {}, "outputs": [], - "source": [ - "#| export\n", - "class MedBase(torch.Tensor, metaclass=MetaResolver):\n", - " \"\"\"A class that represents an image object.\n", - " Metaclass casts `x` to this class if it is of type `cls._bypass_type`.\"\"\"\n", - " \n", - " _bypass_type = torch.Tensor\n", - " _show_args = {'cmap':'gray'}\n", - " target_spacing, apply_reorder = None, False\n", - " affine_matrix = None\n", - "\n", - " @classmethod\n", - " def create(cls, fn: (Path, str, L, list, torch.Tensor), **kwargs) -> torch.Tensor: \n", - " \"\"\"\n", - " Opens a medical image and casts it to MedBase object.\n", - " If `fn` is a torch.Tensor, it's cast to MedBase object.\n", - "\n", - " Args:\n", - " fn : (Path, str, torch.Tensor)\n", - " Image path or a 4D torch.Tensor.\n", - " kwargs : dict\n", - " Additional parameters for the medical image reader.\n", - "\n", - " Returns:\n", - " torch.Tensor : A 4D tensor as a MedBase object.\n", - " \"\"\"\n", - " if isinstance(fn, torch.Tensor):\n", - " return cls(fn)\n", - "\n", - " return med_img_reader(fn, target_spacing=cls.target_spacing, apply_reorder=cls.apply_reorder, dtype=cls)\n", - "\n", - " def __new__(cls, x, **kwargs):\n", - " \"\"\"Creates a new instance of MedBase from a tensor.\"\"\"\n", - " if isinstance(x, torch.Tensor):\n", - " # Create tensor of the same type and copy data\n", - " res = torch.Tensor._make_subclass(cls, x.data, x.requires_grad)\n", - " # Copy any additional attributes\n", - " if hasattr(x, 'affine_matrix'):\n", - " res.affine_matrix = x.affine_matrix\n", - " return res\n", - " else:\n", - " # Handle other types by converting to tensor first\n", - " tensor = torch.as_tensor(x, **kwargs)\n", - " return cls.__new__(cls, tensor)\n", - "\n", - " def new_empty(self, size, **kwargs):\n", - " \"\"\"Create a new empty tensor of the same type.\"\"\"\n", - " # Create new tensor with same type and device/dtype\n", - " kwargs.setdefault('dtype', self.dtype)\n", - " kwargs.setdefault('device', self.device)\n", - " new_tensor = torch.empty(size, **kwargs)\n", - " # Use __new__ to create proper subclass instance\n", - " return self.__class__.__new__(self.__class__, new_tensor)\n", - "\n", - " def __copy__(self):\n", - " \"\"\"Shallow copy implementation.\"\"\"\n", - " copied = self.__class__.__new__(self.__class__, self.clone())\n", - " # Copy class attributes\n", - " if hasattr(self, 'affine_matrix'):\n", - " copied.affine_matrix = self.affine_matrix\n", - " return copied\n", - "\n", - " def __deepcopy__(self, memo):\n", - " \"\"\"Deep copy implementation.\"\"\"\n", - " # Create a deep copy of the tensor data\n", - " copied_data = self.clone()\n", - " copied = self.__class__.__new__(self.__class__, copied_data)\n", - " # Deep copy class attributes\n", - " if hasattr(self, 'affine_matrix') and self.affine_matrix is not None:\n", - " copied.affine_matrix = copy.deepcopy(self.affine_matrix, memo)\n", - " else:\n", - " copied.affine_matrix = None\n", - " return copied\n", - "\n", - " @classmethod\n", - " def item_preprocessing(cls, target_spacing: (list, int, tuple), apply_reorder: bool):\n", - " \"\"\"\n", - " Changes the values for the class variables `target_spacing` and `apply_reorder`.\n", - "\n", - " Args:\n", - " target_spacing : (list, int, tuple)\n", - " A list with voxel spacing.\n", - " apply_reorder : bool\n", - " Whether to reorder the data to be closest to canonical (RAS+) orientation.\n", - " \"\"\"\n", - " cls.target_spacing = target_spacing\n", - " cls.apply_reorder = apply_reorder\n", - "\n", - " def show(self, ctx=None, channel: int = 0, slice_index: int = None, anatomical_plane: int = 0, **kwargs):\n", - " \"\"\"\n", - " Displays the Medimage using `merge(self._show_args, kwargs)`.\n", - "\n", - " Args:\n", - " ctx : Any, optional\n", - " Context to use for the display. Defaults to None.\n", - " channel : int, optional\n", - " The channel of the image to be displayed. Defaults to 0.\n", - " slice_index : int or None, optional\n", - " Index of the images to be displayed. Defaults to None.\n", - " anatomical_plane : int, optional\n", - " Anatomical plane of the image to be displayed. Defaults to 0.\n", - " kwargs : dict, optional\n", - " Additional parameters for the show function.\n", - "\n", - " Returns:\n", - " Shown image.\n", - " \"\"\"\n", - " return show_med_img(\n", - " self, ctx=ctx, channel=channel, slice_index=slice_index, \n", - " anatomical_plane=anatomical_plane, voxel_size=self.target_spacing, \n", - " **merge(self._show_args, kwargs)\n", - " )\n", - "\n", - " def __repr__(self) -> str:\n", - " \"\"\"Returns the string representation of the MedBase instance.\"\"\"\n", - " return f'{self.__class__.__name__} mode={self.mode} size={\"x\".join([str(d) for d in self.size])}'" - ] + "source": "#| export\nclass MedBase(torch.Tensor, metaclass=MetaResolver):\n \"\"\"A class that represents an image object.\n Metaclass casts `x` to this class if it is of type `cls._bypass_type`.\"\"\"\n \n _bypass_type = torch.Tensor\n _show_args = {'cmap':'gray'}\n target_spacing, apply_reorder = None, False\n affine_matrix = None\n\n @classmethod\n def create(cls, fn: (Path, str, L, list, torch.Tensor), **kwargs) -> torch.Tensor: \n \"\"\"\n Opens a medical image and casts it to MedBase object.\n If `fn` is a torch.Tensor, it's cast to MedBase object.\n\n Args:\n fn : (Path, str, torch.Tensor)\n Image path or a 4D torch.Tensor.\n kwargs : dict\n Additional parameters for the medical image reader.\n\n Returns:\n torch.Tensor : A 4D tensor as a MedBase object.\n \"\"\"\n if isinstance(fn, torch.Tensor):\n return cls(fn)\n\n return med_img_reader(fn, target_spacing=cls.target_spacing, apply_reorder=cls.apply_reorder, dtype=cls)\n\n def __new__(cls, x, **kwargs):\n \"\"\"Creates a new instance of MedBase from a tensor.\"\"\"\n if isinstance(x, torch.Tensor):\n # Create tensor of the same type and copy data\n res = torch.Tensor._make_subclass(cls, x.data, x.requires_grad)\n # Copy any additional attributes\n if hasattr(x, 'affine_matrix'):\n res.affine_matrix = x.affine_matrix\n return res\n else:\n # Handle other types by converting to tensor first\n tensor = torch.as_tensor(x, **kwargs)\n return cls.__new__(cls, tensor)\n\n def new_empty(self, size, **kwargs):\n \"\"\"Create a new empty tensor of the same type.\"\"\"\n # Create new tensor with same type and device/dtype\n kwargs.setdefault('dtype', self.dtype)\n kwargs.setdefault('device', self.device)\n new_tensor = torch.empty(size, **kwargs)\n # Use __new__ to create proper subclass instance\n return self.__class__.__new__(self.__class__, new_tensor)\n\n def __copy__(self):\n \"\"\"Shallow copy implementation.\"\"\"\n copied = self.__class__.__new__(self.__class__, self.clone())\n # Copy class attributes\n if hasattr(self, 'affine_matrix'):\n copied.affine_matrix = self.affine_matrix\n return copied\n\n def __deepcopy__(self, memo):\n \"\"\"Deep copy implementation.\"\"\"\n # Create a deep copy of the tensor data\n copied_data = self.clone()\n copied = self.__class__.__new__(self.__class__, copied_data)\n # Deep copy class attributes\n if hasattr(self, 'affine_matrix') and self.affine_matrix is not None:\n copied.affine_matrix = copy.deepcopy(self.affine_matrix, memo)\n else:\n copied.affine_matrix = None\n return copied\n\n @classmethod\n def item_preprocessing(cls, target_spacing: (list, int, tuple), apply_reorder: bool):\n \"\"\"\n Changes the values for the class variables `target_spacing` and `apply_reorder`.\n\n Args:\n target_spacing : (list, int, tuple)\n A list with voxel spacing.\n apply_reorder : bool\n Whether to reorder the data to be closest to canonical (RAS+) orientation.\n \"\"\"\n cls.target_spacing = target_spacing\n cls.apply_reorder = apply_reorder\n\n def show(self, ctx=None, channel: int = 0, slice_index: int = None,\n anatomical_plane: int = 0, voxel_size=None, **kwargs):\n \"\"\"\n Displays the Medimage using `merge(self._show_args, kwargs)`.\n\n Args:\n ctx : Any, optional\n Context to use for the display. Defaults to None.\n channel : int, optional\n The channel of the image to be displayed. Defaults to 0.\n slice_index : int or None, optional\n Index of the images to be displayed. Defaults to None.\n anatomical_plane : int, optional\n Anatomical plane of the image to be displayed. Defaults to 0.\n voxel_size : list or None, optional\n Voxel size for aspect ratio calculation. Overrides\n class-level target_spacing when provided. Defaults to None.\n kwargs : dict, optional\n Additional parameters for the show function.\n\n Returns:\n Shown image.\n \"\"\"\n _voxel_size = voxel_size if voxel_size is not None else self.target_spacing\n return show_med_img(\n self, ctx=ctx, channel=channel, slice_index=slice_index, \n anatomical_plane=anatomical_plane, voxel_size=_voxel_size, \n **merge(self._show_args, kwargs)\n )\n\n def __repr__(self) -> str:\n \"\"\"Returns the string representation of the MedBase instance.\"\"\"\n return f'{self.__class__.__name__} mode={self.mode} size={\"x\".join([str(d) for d in self.size])}'" }, { "cell_type": "code", diff --git a/nbs/07_utils.ipynb b/nbs/07_utils.ipynb index 36974f1..98ad7b5 100644 --- a/nbs/07_utils.ipynb +++ b/nbs/07_utils.ipynb @@ -16,25 +16,7 @@ "id": "ac941446-cf7e-4f5c-ace0-a36fb078022f", "metadata": {}, "outputs": [], - "source": [ - "#| export\n", - "import pickle\n", - "import torch\n", - "from pathlib import Path\n", - "import mlflow\n", - "import mlflow.pytorch\n", - "import mlflow.data\n", - "mlflow.set_tracking_uri(\"sqlite:///mlruns.db\")\n", - "import os\n", - "import tempfile\n", - "import json\n", - "import warnings\n", - "import pandas as pd\n", - "from datetime import datetime\n", - "from fastai.callback.core import Callback\n", - "from fastcore.foundation import L\n", - "from typing import Any" - ] + "source": "#| export\nimport pickle\nimport torch\nfrom pathlib import Path\nimport mlflow\nimport mlflow.pytorch\ntry:\n _mlruns_db = Path(__file__).resolve().parent.parent / 'mlruns.db'\nexcept NameError:\n _mlruns_db = Path('mlruns.db').resolve()\nmlflow.set_tracking_uri(f\"sqlite:///{_mlruns_db}\")\nimport os\nimport tempfile\nimport json\nfrom datetime import datetime\nfrom fastai.callback.core import Callback\nfrom fastcore.foundation import L\nfrom typing import Any" }, { "cell_type": "markdown", @@ -217,206 +199,7 @@ "id": "czquspt567w", "metadata": {}, "outputs": [], - "source": [ - "#| export\n", - "def _detect_patch_workflow(dls) -> bool:\n", - " \"\"\"Detect if DataLoaders are patch-based (MedPatchDataLoaders).\n", - " \n", - " Args:\n", - " dls: DataLoaders instance\n", - " \n", - " Returns:\n", - " True if dls is a MedPatchDataLoaders instance\n", - " \"\"\"\n", - " return hasattr(dls, 'patch_config') or hasattr(dls, '_patch_config')\n", - "\n", - "\n", - "def _extract_size_from_transforms(tfms) -> list | None:\n", - " \"\"\"Extract target size from PadOrCrop transform if present.\n", - " \n", - " Args:\n", - " tfms: List of transforms\n", - " \n", - " Returns:\n", - " Target size as list, or None if not found\n", - " \"\"\"\n", - " if tfms is None:\n", - " return None\n", - " for tfm in tfms:\n", - " if hasattr(tfm, 'pad_or_crop') and hasattr(tfm.pad_or_crop, 'target_shape'):\n", - " return list(tfm.pad_or_crop.target_shape)\n", - " return None\n", - "\n", - "\n", - "def _extract_standard_config(learn) -> dict:\n", - " \"\"\"Extract config from standard MedDataBlock workflow.\n", - " \n", - " Args:\n", - " learn: fastai Learner instance\n", - " \n", - " Returns:\n", - " Dictionary with extracted configuration\n", - " \"\"\"\n", - " from fastMONAI.vision_core import MedBase\n", - " dls = learn.dls\n", - "\n", - " # Get preprocessing from MedBase class attributes\n", - " apply_reorder = MedBase.apply_reorder\n", - " target_spacing = MedBase.target_spacing\n", - "\n", - " # Extract item_tfms from DataLoaders pipeline\n", - " item_tfms = []\n", - " if hasattr(dls, 'after_item') and dls.after_item:\n", - " item_tfms = list(dls.after_item.fs)\n", - "\n", - " # Extract size from PadOrCrop transform\n", - " size = _extract_size_from_transforms(item_tfms)\n", - "\n", - " return {\n", - " 'apply_reorder': apply_reorder,\n", - " 'target_spacing': target_spacing,\n", - " 'size': size,\n", - " 'item_tfms': item_tfms,\n", - " 'batch_size': dls.bs,\n", - " 'patch_config': None,\n", - " }\n", - "\n", - "\n", - "def _extract_patch_config(learn) -> dict:\n", - " \"\"\"Extract config from MedPatchDataLoaders workflow.\n", - " \n", - " Args:\n", - " learn: fastai Learner instance\n", - " \n", - " Returns:\n", - " Dictionary with extracted configuration including patch-specific params\n", - " \"\"\"\n", - " dls = learn.dls\n", - " patch_config = getattr(dls, '_patch_config', None) or getattr(dls, 'patch_config', None)\n", - "\n", - " config = {\n", - " 'apply_reorder': getattr(dls, '_apply_reorder', patch_config.apply_reorder if patch_config else False),\n", - " 'target_spacing': getattr(dls, '_target_spacing', patch_config.target_spacing if patch_config else None),\n", - " 'size': patch_config.patch_size if patch_config else None,\n", - " 'item_tfms': getattr(dls, '_pre_patch_tfms', []) or [],\n", - " 'batch_size': dls.bs,\n", - " }\n", - "\n", - " # Add patch-specific params for logging\n", - " if patch_config:\n", - " config['patch_config'] = {\n", - " 'patch_size': patch_config.patch_size,\n", - " 'patch_overlap': patch_config.patch_overlap,\n", - " 'samples_per_volume': patch_config.samples_per_volume,\n", - " 'sampler_type': patch_config.sampler_type,\n", - " 'label_probabilities': str(patch_config.label_probabilities) if patch_config.label_probabilities else None,\n", - " 'queue_length': patch_config.queue_length,\n", - " 'aggregation_mode': patch_config.aggregation_mode,\n", - " 'padding_mode': patch_config.padding_mode,\n", - " 'keep_largest_component': patch_config.keep_largest_component,\n", - " }\n", - " else:\n", - " config['patch_config'] = None\n", - "\n", - " return config\n", - "\n", - "\n", - "def _extract_loss_name(learn) -> str:\n", - " \"\"\"Extract loss function name from Learner.\n", - " \n", - " Args:\n", - " learn: fastai Learner instance\n", - " \n", - " Returns:\n", - " Name of the loss function\n", - " \"\"\"\n", - " loss_func = learn.loss_func\n", - " # Handle CustomLoss wrapper\n", - " if hasattr(loss_func, 'loss_func'):\n", - " inner = loss_func.loss_func\n", - " return inner._get_name() if hasattr(inner, '_get_name') else inner.__class__.__name__\n", - " return loss_func._get_name() if hasattr(loss_func, '_get_name') else loss_func.__class__.__name__\n", - "\n", - "\n", - "def _extract_model_name(learn) -> str:\n", - " \"\"\"Extract model architecture name from Learner.\n", - " \n", - " Args:\n", - " learn: fastai Learner instance\n", - " \n", - " Returns:\n", - " Name of the model architecture\n", - " \"\"\"\n", - " model = learn.model\n", - " return model._get_name() if hasattr(model, '_get_name') else model.__class__.__name__\n", - "\n", - "\n", - "def _extract_standard_dataset_dfs(dls):\n", - " \"\"\"Extract train/valid DataFrames from standard MedImageDataLoaders.\n", - " \n", - " For standard DataLoaders created via MedImageDataLoaders.from_df(),\n", - " the items attribute contains the original DataFrame split by train/valid.\n", - " \n", - " Args:\n", - " dls: Standard fastai DataLoaders (MedImageDataLoaders)\n", - " \n", - " Returns:\n", - " Tuple of (train_df, valid_df). Either can be None if items\n", - " are not DataFrames (e.g., manually constructed DataLoaders).\n", - " \"\"\"\n", - " train_df = None\n", - " valid_df = None\n", - " \n", - " train_items = getattr(getattr(dls, 'train_ds', None), 'items', None)\n", - " valid_items = getattr(getattr(dls, 'valid_ds', None), 'items', None)\n", - " \n", - " if isinstance(train_items, pd.DataFrame):\n", - " train_df = train_items\n", - " if isinstance(valid_items, pd.DataFrame):\n", - " valid_df = valid_items\n", - " \n", - " return train_df, valid_df\n", - "\n", - "\n", - "def _extract_patch_dataset_dfs(dls):\n", - " \"\"\"Extract train/valid DataFrames from MedPatchDataLoaders.\n", - " \n", - " Reconstructs DataFrames from TorchIO SubjectsDataset by extracting\n", - " file paths from each Subject's image and (optionally) mask.\n", - " Uses _subjects directly to avoid triggering data loading.\n", - " \n", - " Args:\n", - " dls: MedPatchDataLoaders instance\n", - " \n", - " Returns:\n", - " Tuple of (train_df, valid_df). Either can be None if extraction fails.\n", - " \"\"\"\n", - " img_col = getattr(dls, '_img_col', 'image')\n", - " mask_col = getattr(dls, '_mask_col', None)\n", - " \n", - " def _subjects_to_df(subjects_dataset):\n", - " subjects = getattr(subjects_dataset, '_subjects', None)\n", - " if subjects is None:\n", - " return None\n", - " rows = []\n", - " for s in subjects:\n", - " row = {}\n", - " img = s.get('image')\n", - " if img is not None and getattr(img, 'path', None) is not None:\n", - " row[img_col] = str(img.path)\n", - " if mask_col is not None:\n", - " mask = s.get('mask')\n", - " if mask is not None and getattr(mask, 'path', None) is not None:\n", - " row[mask_col] = str(mask.path)\n", - " if row:\n", - " rows.append(row)\n", - " return pd.DataFrame(rows) if rows else None\n", - " \n", - " train_df = _subjects_to_df(dls.train_ds)\n", - " valid_df = _subjects_to_df(dls.valid_ds)\n", - " \n", - " return train_df, valid_df" - ] + "source": "#| export\ndef _detect_patch_workflow(dls) -> bool:\n \"\"\"Detect if DataLoaders are patch-based (MedPatchDataLoaders).\n \n Args:\n dls: DataLoaders instance\n \n Returns:\n True if dls is a MedPatchDataLoaders instance\n \"\"\"\n return hasattr(dls, 'patch_config') or hasattr(dls, '_patch_config')\n\n\ndef _extract_size_from_transforms(tfms) -> list | None:\n \"\"\"Extract target size from PadOrCrop transform if present.\n \n Args:\n tfms: List of transforms\n \n Returns:\n Target size as list, or None if not found\n \"\"\"\n if tfms is None:\n return None\n for tfm in tfms:\n if hasattr(tfm, 'pad_or_crop') and hasattr(tfm.pad_or_crop, 'target_shape'):\n return list(tfm.pad_or_crop.target_shape)\n return None\n\n\ndef _extract_standard_config(learn) -> dict:\n \"\"\"Extract config from standard MedDataBlock workflow.\n \n Args:\n learn: fastai Learner instance\n \n Returns:\n Dictionary with extracted configuration\n \"\"\"\n from fastMONAI.vision_core import MedBase\n dls = learn.dls\n\n # Get preprocessing from MedBase class attributes\n apply_reorder = MedBase.apply_reorder\n target_spacing = MedBase.target_spacing\n\n # Extract item_tfms from DataLoaders pipeline\n item_tfms = []\n if hasattr(dls, 'after_item') and dls.after_item:\n item_tfms = list(dls.after_item.fs)\n\n # Extract size from PadOrCrop transform\n size = _extract_size_from_transforms(item_tfms)\n\n return {\n 'apply_reorder': apply_reorder,\n 'target_spacing': target_spacing,\n 'size': size,\n 'item_tfms': item_tfms,\n 'batch_size': dls.bs,\n 'patch_config': None,\n }\n\n\ndef _extract_patch_config(learn) -> dict:\n \"\"\"Extract config from MedPatchDataLoaders workflow.\n \n Args:\n learn: fastai Learner instance\n \n Returns:\n Dictionary with extracted configuration including patch-specific params\n \"\"\"\n dls = learn.dls\n patch_config = getattr(dls, '_patch_config', None) or getattr(dls, 'patch_config', None)\n\n config = {\n 'apply_reorder': getattr(dls, '_apply_reorder', patch_config.apply_reorder if patch_config else False),\n 'target_spacing': getattr(dls, '_target_spacing', patch_config.target_spacing if patch_config else None),\n 'size': patch_config.patch_size if patch_config else None,\n 'item_tfms': getattr(dls, '_pre_patch_tfms', []) or [],\n 'batch_size': dls.bs,\n }\n\n # Add patch-specific params for logging\n if patch_config:\n config['patch_config'] = {\n 'patch_size': patch_config.patch_size,\n 'patch_overlap': patch_config.patch_overlap,\n 'samples_per_volume': patch_config.samples_per_volume,\n 'sampler_type': patch_config.sampler_type,\n 'label_probabilities': str(patch_config.label_probabilities) if patch_config.label_probabilities else None,\n 'queue_length': patch_config.queue_length,\n 'aggregation_mode': patch_config.aggregation_mode,\n 'padding_mode': patch_config.padding_mode,\n 'keep_largest_component': patch_config.keep_largest_component,\n }\n else:\n config['patch_config'] = None\n\n return config\n\n\ndef _extract_loss_name(learn) -> str:\n \"\"\"Extract loss function name from Learner.\n \n Args:\n learn: fastai Learner instance\n \n Returns:\n Name of the loss function\n \"\"\"\n loss_func = learn.loss_func\n # Handle CustomLoss wrapper\n if hasattr(loss_func, 'loss_func'):\n inner = loss_func.loss_func\n return inner._get_name() if hasattr(inner, '_get_name') else inner.__class__.__name__\n return loss_func._get_name() if hasattr(loss_func, '_get_name') else loss_func.__class__.__name__\n\n\ndef _extract_model_name(learn) -> str:\n \"\"\"Extract model architecture name from Learner.\n \n Args:\n learn: fastai Learner instance\n \n Returns:\n Name of the model architecture\n \"\"\"\n model = learn.model\n return model._get_name() if hasattr(model, '_get_name') else model.__class__.__name__" }, { "cell_type": "code", @@ -424,422 +207,7 @@ "id": "030876d2", "metadata": {}, "outputs": [], - "source": [ - "#| export\n", - "class ModelTrackingCallback(Callback):\n", - " \"\"\"\n", - " A FastAI callback for comprehensive MLflow experiment tracking.\n", - " \n", - " This callback automatically logs hyperparameters, metrics, model artifacts,\n", - " and configuration to MLflow during training. If a SaveModelCallback is present,\n", - " the best model checkpoint will also be logged as an artifact.\n", - " \n", - " Supports auto-managed runs when created via `create_mlflow_callback()`.\n", - " \"\"\"\n", - " \n", - " def __init__(\n", - " self, \n", - " model_name: str, \n", - " loss_function: str, \n", - " item_tfms: list[Any],\n", - " size: list[int], \n", - " target_spacing: list[float], \n", - " apply_reorder: bool,\n", - " experiment_name: str = None,\n", - " run_name: str = None,\n", - " auto_start: bool = False,\n", - " patch_config: dict = None,\n", - " extra_params: dict = None,\n", - " extra_tags: dict = None\n", - " ):\n", - " \"\"\"\n", - " Initialize the MLflow tracking callback.\n", - " \n", - " Args:\n", - " model_name: Name of the model architecture for registration\n", - " loss_function: Name of the loss function being used\n", - " item_tfms: List of item transforms\n", - " size: Model input dimensions\n", - " target_spacing: Resampling dimensions\n", - " apply_reorder: Whether reordering augmentation is applied\n", - " experiment_name: MLflow experiment name (used with auto_start)\n", - " run_name: MLflow run name (auto-generated if None)\n", - " auto_start: If True, auto-starts/stops MLflow run\n", - " patch_config: Patch configuration dict for logging (from MedPatchDataLoaders)\n", - " extra_params: Additional parameters to log\n", - " extra_tags: MLflow tags to set on the run\n", - " \"\"\"\n", - " self.model_name = model_name\n", - " self.loss_function = loss_function\n", - " self.item_tfms = item_tfms\n", - " self.size = size\n", - " self.target_spacing = target_spacing\n", - " self.apply_reorder = apply_reorder\n", - " \n", - " # New auto-management fields\n", - " self.experiment_name = experiment_name\n", - " self.run_name = run_name\n", - " self.auto_start = auto_start\n", - " self.patch_config = patch_config\n", - " self.extra_params = extra_params or {}\n", - " self.extra_tags = extra_tags or {}\n", - " self._auto_started = False\n", - " self.run_id = None\n", - " \n", - " self.config = self._build_config()\n", - " \n", - " def extract_all_params(self, tfm):\n", - " \"\"\"\n", - " Extract all parameters from a transform object for detailed logging.\n", - " \n", - " Args:\n", - " tfm: Transform object to extract parameters from\n", - " \n", - " Returns:\n", - " dict: Dictionary with 'name' and 'params' keys containing transform details\n", - " \"\"\"\n", - " class_name = tfm.__class__.__name__\n", - " params = {}\n", - " \n", - " for key, value in tfm.__dict__.items():\n", - " if not key.startswith('_') and key != '__signature__':\n", - " if hasattr(value, '__dict__') and hasattr(value, 'target_shape'):\n", - " params['target_shape'] = value.target_shape\n", - " elif hasattr(value, '__dict__') and not key.startswith('_'):\n", - " nested_params = {k: v for k, v in value.__dict__.items() \n", - " if not k.startswith('_') and isinstance(v, (int, float, str, bool, tuple, list))}\n", - " params.update(nested_params)\n", - " elif isinstance(value, (int, float, str, bool, tuple, list)):\n", - " params[key] = value\n", - " \n", - " return {\n", - " 'name': class_name,\n", - " 'params': params\n", - " }\n", - " \n", - " def _build_config(self) -> dict[str, Any]:\n", - " \"\"\"Build configuration dictionary from initialization parameters.\"\"\"\n", - " # Extract detailed transform information\n", - " transform_details = [self.extract_all_params(tfm) for tfm in self.item_tfms]\n", - " \n", - " return {\n", - " \"model_name\": self.model_name,\n", - " \"loss_function\": self.loss_function,\n", - " \"transform_details\": transform_details,\n", - " \"size\": self.size,\n", - " \"target_spacing\": self.target_spacing,\n", - " \"apply_reorder\": self.apply_reorder,\n", - " }\n", - " \n", - " def _extract_training_params(self) -> dict[str, Any]:\n", - " \"\"\"Extract training hyperparameters from the learner.\"\"\"\n", - " params = {}\n", - " \n", - " params[\"epochs\"] = self.learn.n_epoch\n", - " params[\"learning_rate\"] = float(self.learn.lr)\n", - " params[\"optimizer\"] = self.learn.opt_func.__name__\n", - " params[\"batch_size\"] = self.learn.dls.bs\n", - " \n", - " params[\"loss_function\"] = self.config[\"loss_function\"]\n", - " params[\"size\"] = self.config[\"size\"]\n", - " params[\"target_spacing\"] = self.config[\"target_spacing\"]\n", - " params[\"apply_reorder\"] = self.config[\"apply_reorder\"]\n", - " \n", - " params[\"transformations\"] = json.dumps(\n", - " self.config[\"transform_details\"], \n", - " indent=2, \n", - " separators=(',', ': ')\n", - " )\n", - " \n", - " # Add patch-specific params if present\n", - " if self.patch_config:\n", - " for key, value in self.patch_config.items():\n", - " if value is not None:\n", - " params[f\"patch_{key}\"] = value if isinstance(value, (int, float, str, bool)) else str(value)\n", - " \n", - " return params\n", - " \n", - " def _log_datasets(self) -> None:\n", - " \"\"\"Log train/valid datasets to MLflow's Datasets tab.\n", - " \n", - " Uses mlflow.data.from_pandas() and mlflow.log_input() to register\n", - " dataset metadata so they appear in the MLflow UI Datasets tab.\n", - " Failures are silently caught to never break training.\n", - " \"\"\"\n", - " try:\n", - " dls = self.learn.dls\n", - " is_patch = _detect_patch_workflow(dls)\n", - " \n", - " if is_patch:\n", - " train_df, valid_df = _extract_patch_dataset_dfs(dls)\n", - " else:\n", - " train_df, valid_df = _extract_standard_dataset_dfs(dls)\n", - " \n", - " source = self.experiment_name or self.model_name or \"fastMONAI\"\n", - " \n", - " with warnings.catch_warnings():\n", - " warnings.simplefilter(\"ignore\")\n", - " \n", - " if train_df is not None and not train_df.empty:\n", - " train_dataset = mlflow.data.from_pandas(\n", - " train_df, source=source, name=\"train\"\n", - " )\n", - " mlflow.log_input(train_dataset, context=\"training\")\n", - " \n", - " if valid_df is not None and not valid_df.empty:\n", - " valid_dataset = mlflow.data.from_pandas(\n", - " valid_df, source=source, name=\"validation\"\n", - " )\n", - " mlflow.log_input(valid_dataset, context=\"validation\")\n", - " \n", - " except Exception:\n", - " pass # Dataset logging must never break training\n", - " \n", - " def _extract_epoch_metrics(self) -> dict[str, float]:\n", - " \"\"\"Extract metrics from the current epoch.\"\"\"\n", - " recorder = self.learn.recorder\n", - " \n", - " # Get custom metric names and values (skip 'epoch' and 'time')\n", - " metric_names = recorder.metric_names[2:]\n", - " raw_metric_values = recorder.log[2:]\n", - " \n", - " metrics = {}\n", - " \n", - " # Process each metric, handling both scalars and tensors\n", - " for name, val in zip(metric_names, raw_metric_values):\n", - " if val is None:\n", - " continue # Skip None values during inference\n", - " if isinstance(val, torch.Tensor):\n", - " if val.numel() == 1:\n", - " # Single value tensor (like binary dice score)\n", - " metrics[name] = float(val)\n", - " else:\n", - " # Multi-element tensor (like multiclass dice scores)\n", - " val_list = val.tolist() if hasattr(val, 'tolist') else list(val)\n", - " # Log individual class scores\n", - " for i, class_score in enumerate(val_list):\n", - " metrics[f\"{name}_class_{i+1}\"] = float(class_score)\n", - " # Log mean across classes\n", - " metrics[f\"{name}_mean\"] = float(torch.mean(val))\n", - " else:\n", - " metrics[name] = float(val)\n", - " \n", - " # Handle loss values\n", - " if len(recorder.log) >= 2:\n", - " if recorder.log[1] is not None:\n", - " metrics['train_loss'] = float(recorder.log[1])\n", - " if len(recorder.log) >= 3 and recorder.log[2] is not None:\n", - " metrics['valid_loss'] = float(recorder.log[2])\n", - " \n", - " return metrics\n", - " \n", - " def _save_model_artifacts(self, temp_dir: Path) -> None:\n", - " \"\"\"Save model weights, learner, and configuration as artifacts.\"\"\"\n", - " weights_path = temp_dir / \"weights\"\n", - " self.learn.save(str(weights_path))\n", - " \n", - " weights_file = f\"{weights_path}.pth\"\n", - " if os.path.exists(weights_file):\n", - " mlflow.log_artifact(weights_file, \"model\")\n", - " \n", - " # Auto-detect SaveModelCallback and log best model\n", - " from fastai.callback.tracker import SaveModelCallback\n", - " best_model_cb = None\n", - " for cb in self.learn.cbs:\n", - " if isinstance(cb, SaveModelCallback):\n", - " best_model_path = self.learn.path/self.learn.model_dir/f'{cb.fname}.pth'\n", - " if best_model_path.exists():\n", - " mlflow.log_artifact(str(best_model_path), \"model\")\n", - " print(f\"Logged best model artifact: {cb.fname}.pth\")\n", - " best_model_cb = cb\n", - " break\n", - "\n", - " # Load best model weights before export if SaveModelCallback was used\n", - " if best_model_cb is not None:\n", - " self.learn.load(best_model_cb.fname)\n", - " print(f\"Loaded best model weights ({best_model_cb.fname}) for learner export\")\n", - "\n", - " # Remove MLflow callbacks before exporting learner for inference\n", - " # This prevents the callback from being triggered during inference\n", - " original_cbs = self.learn.cbs.copy() # Save original callbacks\n", - " \n", - " # Remove ModelTrackingCallback instances from learner using proper collection type\n", - " filtered_cbs = L([cb for cb in self.learn.cbs if not isinstance(cb, ModelTrackingCallback)])\n", - " self.learn.cbs = filtered_cbs\n", - " \n", - " # Export clean learner without MLflow callbacks\n", - " learner_path = temp_dir / \"learner.pkl\"\n", - " self.learn.export(str(learner_path))\n", - " mlflow.log_artifact(str(learner_path), \"model\")\n", - " \n", - " # Restore original callbacks for current session\n", - " self.learn.cbs = original_cbs\n", - " \n", - " config_path = temp_dir / \"inference_settings.pkl\"\n", - " store_variables(config_path, self.size, self.apply_reorder, self.target_spacing)\n", - " mlflow.log_artifact(str(config_path), \"config\")\n", - " \n", - " def _register_pytorch_model(self) -> None:\n", - " \"\"\"Register the PyTorch model with MLflow.\"\"\"\n", - " mlflow.pytorch.log_model(\n", - " pytorch_model=self.learn.model,\n", - " registered_model_name=self.model_name\n", - " )\n", - " \n", - " def before_fit(self) -> None:\n", - " \"\"\"Log hyperparameters before training starts. Auto-start run if configured.\"\"\"\n", - " # Auto-start run if requested\n", - " if self.auto_start:\n", - " if self.experiment_name:\n", - " mlflow.set_experiment(self.experiment_name)\n", - " self.run_name = self.run_name or f\"run_{datetime.now().strftime('%Y%m%d_%H%M%S')}\"\n", - " mlflow.start_run(run_name=self.run_name)\n", - " self._auto_started = True\n", - " \n", - " # Set tags\n", - " if self.extra_tags:\n", - " mlflow.set_tags(self.extra_tags)\n", - " \n", - " # Log params\n", - " params = self._extract_training_params()\n", - " params.update(self.extra_params)\n", - " mlflow.log_params(params)\n", - " \n", - " # Log datasets\n", - " self._log_datasets()\n", - " \n", - " def after_epoch(self) -> None:\n", - " \"\"\"Log metrics after each epoch.\"\"\"\n", - " metrics = self._extract_epoch_metrics()\n", - " if metrics:\n", - " mlflow.log_metrics(metrics, step=self.learn.epoch)\n", - " \n", - " def after_fit(self) -> None:\n", - " \"\"\"Log model artifacts after training completion.\"\"\"\n", - " print(\"\\nTraining finished. Logging model artifacts to MLflow...\")\n", - " \n", - " with tempfile.TemporaryDirectory() as temp_dir:\n", - " temp_path = Path(temp_dir)\n", - " \n", - " self._save_model_artifacts(temp_path)\n", - " \n", - " self._register_pytorch_model()\n", - " \n", - " self.run_id = mlflow.active_run().info.run_id\n", - " print(f\"MLflow run completed. Run ID: {self.run_id}\")\n", - " \n", - " # End run if auto-started\n", - " if self._auto_started:\n", - " mlflow.end_run()\n", - " self._auto_started = False\n", - "\n", - " def log_metrics(self, metrics: dict) -> None:\n", - " \"\"\"Log additional metrics to the completed MLflow run.\n", - "\n", - " Reopens the run to log metrics, then closes it. Useful for\n", - " post-training evaluation (e.g., validation set patch inference results).\n", - " NaN values are silently skipped (MLflow rejects them).\n", - "\n", - " Args:\n", - " metrics: Dictionary of metric name -> value pairs to log.\n", - " \"\"\"\n", - " if self.run_id is None:\n", - " raise RuntimeError(\"No MLflow run found. Train the model first.\")\n", - "\n", - " import math\n", - " valid = {k: v for k, v in metrics.items()\n", - " if not (isinstance(v, float) and math.isnan(v))}\n", - "\n", - " if valid:\n", - " with mlflow.start_run(run_id=self.run_id):\n", - " mlflow.log_metrics(valid)\n", - " print(f\"Logged {len(valid)} metric(s) to MLflow run {self.run_id}\")\n", - "\n", - " def log_metrics_table(self, df, metrics=None, key='validation_metrics', display=False):\n", - " \"\"\"Log a summary table of metrics as an image to the MLflow Images tab.\n", - "\n", - " Creates a styled matplotlib table with Metric, Mean, and Std columns\n", - " from the given DataFrame and logs it via mlflow.log_image() so it\n", - " appears in the MLflow UI Images tab.\n", - "\n", - " Args:\n", - " df: DataFrame containing per-sample metric values.\n", - " metrics: List of column names in df to summarize. If None,\n", - " all numeric columns are used automatically.\n", - " key: Image key for MLflow Images tab.\n", - " display: If True, display the table inline in the notebook.\n", - " \"\"\"\n", - " import math\n", - " import io\n", - " import matplotlib.pyplot as plt\n", - " from PIL import Image\n", - "\n", - " if self.run_id is None:\n", - " raise RuntimeError(\"No MLflow run found. Train the model first.\")\n", - "\n", - " if metrics is None:\n", - " metrics = df.select_dtypes(include='number').columns.tolist()\n", - "\n", - " summary_data = []\n", - " for metric in metrics:\n", - " mean = df[metric].mean()\n", - " std = df[metric].std()\n", - " std_str = f'{std:.4f}' if not math.isnan(std) else 'N/A'\n", - " summary_data.append([metric, f'{mean:.4f}', std_str])\n", - "\n", - " fig, ax = plt.subplots(figsize=(5, len(metrics) * 0.8 + 0.5))\n", - " ax.axis('off')\n", - "\n", - " table = ax.table(\n", - " cellText=summary_data,\n", - " colLabels=['Metric', 'Mean', 'Std'],\n", - " cellLoc='center',\n", - " loc='center'\n", - " )\n", - " table.auto_set_font_size(False)\n", - " table.set_fontsize(14)\n", - " table.scale(1.5, 2.2)\n", - "\n", - " for col in range(3):\n", - " table[0, col].set_facecolor('#4472C4')\n", - " table[0, col].set_text_props(color='white', fontweight='bold')\n", - "\n", - " fig.tight_layout()\n", - "\n", - " buf = io.BytesIO()\n", - " fig.savefig(buf, format='png', bbox_inches='tight', dpi=250)\n", - " buf.seek(0)\n", - " pil_image = Image.open(buf)\n", - "\n", - " with mlflow.start_run(run_id=self.run_id):\n", - " mlflow.log_image(pil_image, key=key)\n", - "\n", - " buf.close()\n", - " if display: plt.show()\n", - " plt.close(fig)\n", - " print(f\"Logged metrics table to MLflow run {self.run_id}\")\n", - "\n", - " def log_dataframe(self, df, artifact_name='validation_results.csv', artifact_path='results'):\n", - " \"\"\"Log a DataFrame as a CSV artifact to the MLflow run.\n", - "\n", - " Args:\n", - " df: DataFrame to save.\n", - " artifact_name: Filename for the CSV.\n", - " artifact_path: Artifact subdirectory in MLflow.\n", - " \"\"\"\n", - " if self.run_id is None:\n", - " raise RuntimeError(\"No MLflow run found. Train the model first.\")\n", - "\n", - " with tempfile.NamedTemporaryFile(suffix='.csv', delete=False, mode='w') as f:\n", - " df.to_csv(f, index=False)\n", - " temp_path = f.name\n", - "\n", - " with mlflow.start_run(run_id=self.run_id):\n", - " mlflow.log_artifact(temp_path, artifact_path)\n", - "\n", - " os.remove(temp_path)\n", - " print(f\"Logged DataFrame ({len(df)} rows) to MLflow run {self.run_id}\")" - ] + "source": "#| export\nclass ModelTrackingCallback(Callback):\n \"\"\"\n A FastAI callback for comprehensive MLflow experiment tracking.\n \n This callback automatically logs hyperparameters, metrics, model artifacts,\n and configuration to MLflow during training. If a SaveModelCallback is present,\n the best model checkpoint will also be logged as an artifact.\n \n Supports auto-managed runs when created via `create_mlflow_callback()`.\n \"\"\"\n \n def __init__(\n self, \n model_name: str, \n loss_function: str, \n item_tfms: list[Any],\n size: list[int], \n target_spacing: list[float], \n apply_reorder: bool,\n experiment_name: str = None,\n run_name: str = None,\n auto_start: bool = False,\n patch_config: dict = None,\n extra_params: dict = None,\n extra_tags: dict = None,\n dataset_version: str = None,\n log_split: bool = True\n ):\n \"\"\"\n Initialize the MLflow tracking callback.\n \n Args:\n model_name: Name of the model architecture for registration\n loss_function: Name of the loss function being used\n item_tfms: List of item transforms\n size: Model input dimensions\n target_spacing: Resampling dimensions\n apply_reorder: Whether reordering augmentation is applied\n experiment_name: MLflow experiment name (used with auto_start)\n run_name: MLflow run name (auto-generated if None)\n auto_start: If True, auto-starts/stops MLflow run\n patch_config: Patch configuration dict for logging (from MedPatchDataLoaders)\n extra_params: Additional parameters to log\n extra_tags: MLflow tags to set on the run\n dataset_version: Dataset version hash string for tracking\n log_split: If True, auto-logs train/val split CSV when dls has split_df\n \"\"\"\n self.model_name = model_name\n self.loss_function = loss_function\n self.item_tfms = item_tfms\n self.size = size\n self.target_spacing = target_spacing\n self.apply_reorder = apply_reorder\n \n # New auto-management fields\n self.experiment_name = experiment_name\n self.run_name = run_name\n self.auto_start = auto_start\n self.patch_config = patch_config\n self.extra_params = extra_params or {}\n self.extra_tags = extra_tags or {}\n self.dataset_version = dataset_version\n self.log_split = log_split\n self._auto_started = False\n self.run_id = None\n \n self.config = self._build_config()\n \n def extract_all_params(self, tfm):\n \"\"\"\n Extract all parameters from a transform object for detailed logging.\n \n Args:\n tfm: Transform object to extract parameters from\n \n Returns:\n dict: Dictionary with 'name' and 'params' keys containing transform details\n \"\"\"\n class_name = tfm.__class__.__name__\n params = {}\n \n for key, value in tfm.__dict__.items():\n if not key.startswith('_') and key != '__signature__':\n if hasattr(value, '__dict__') and hasattr(value, 'target_shape'):\n params['target_shape'] = value.target_shape\n elif hasattr(value, '__dict__') and not key.startswith('_'):\n nested_params = {k: v for k, v in value.__dict__.items() \n if not k.startswith('_') and isinstance(v, (int, float, str, bool, tuple, list))}\n params.update(nested_params)\n elif isinstance(value, (int, float, str, bool, tuple, list)):\n params[key] = value\n \n return {\n 'name': class_name,\n 'params': params\n }\n \n def _build_config(self) -> dict[str, Any]:\n \"\"\"Build configuration dictionary from initialization parameters.\"\"\"\n # Extract detailed transform information\n transform_details = [self.extract_all_params(tfm) for tfm in self.item_tfms]\n \n return {\n \"model_name\": self.model_name,\n \"loss_function\": self.loss_function,\n \"transform_details\": transform_details,\n \"size\": self.size,\n \"target_spacing\": self.target_spacing,\n \"apply_reorder\": self.apply_reorder,\n }\n \n def _extract_training_params(self) -> dict[str, Any]:\n \"\"\"Extract training hyperparameters from the learner.\"\"\"\n params = {}\n \n params[\"epochs\"] = self.learn.n_epoch\n params[\"learning_rate\"] = float(self.learn.lr)\n params[\"optimizer\"] = self.learn.opt_func.__name__\n params[\"batch_size\"] = self.learn.dls.bs\n \n params[\"loss_function\"] = self.config[\"loss_function\"]\n params[\"size\"] = self.config[\"size\"]\n params[\"target_spacing\"] = self.config[\"target_spacing\"]\n params[\"apply_reorder\"] = self.config[\"apply_reorder\"]\n \n params[\"transformations\"] = json.dumps(\n self.config[\"transform_details\"], \n indent=2, \n separators=(',', ': ')\n )\n \n # Add patch-specific params if present\n if self.patch_config:\n for key, value in self.patch_config.items():\n if value is not None:\n params[f\"patch_{key}\"] = value if isinstance(value, (int, float, str, bool)) else str(value)\n \n return params\n \n def _extract_epoch_metrics(self) -> dict[str, float]:\n \"\"\"Extract metrics from the current epoch.\"\"\"\n recorder = self.learn.recorder\n \n # Get custom metric names and values (skip 'epoch' and 'time')\n metric_names = recorder.metric_names[2:]\n raw_metric_values = recorder.log[2:]\n \n metrics = {}\n \n # Process each metric, handling both scalars and tensors\n for name, val in zip(metric_names, raw_metric_values):\n if val is None:\n continue # Skip None values during inference\n if isinstance(val, torch.Tensor):\n if val.numel() == 1:\n # Single value tensor (like binary dice score)\n metrics[name] = float(val)\n else:\n # Multi-element tensor (like multiclass dice scores)\n val_list = val.tolist() if hasattr(val, 'tolist') else list(val)\n # Log individual class scores\n for i, class_score in enumerate(val_list):\n metrics[f\"{name}_class_{i+1}\"] = float(class_score)\n # Log mean across classes\n metrics[f\"{name}_mean\"] = float(torch.mean(val))\n else:\n metrics[name] = float(val)\n \n # Handle loss values\n if len(recorder.log) >= 2:\n if recorder.log[1] is not None:\n metrics['train_loss'] = float(recorder.log[1])\n if len(recorder.log) >= 3 and recorder.log[2] is not None:\n metrics['valid_loss'] = float(recorder.log[2])\n \n return metrics\n \n def _save_model_artifacts(self, temp_dir: Path) -> None:\n \"\"\"Save model weights, learner, and configuration as artifacts.\"\"\"\n import shutil\n\n # Save final epoch weights\n weights_path = temp_dir / \"final_weights\"\n self.learn.save(str(weights_path))\n weights_file = f\"{weights_path}.pth\"\n if os.path.exists(weights_file):\n mlflow.log_artifact(weights_file, \"model\")\n\n # Auto-detect SaveModelCallback and log best model weights\n from fastai.callback.tracker import SaveModelCallback\n best_model_cb = None\n for cb in self.learn.cbs:\n if isinstance(cb, SaveModelCallback):\n best_model_path = self.learn.path / self.learn.model_dir / f'{cb.fname}.pth'\n if best_model_path.exists():\n best_weights_dest = temp_dir / \"best_weights.pth\"\n shutil.copy2(str(best_model_path), str(best_weights_dest))\n mlflow.log_artifact(str(best_weights_dest), \"model\")\n print(f\"Logged best model weights: best_weights.pth\")\n best_model_cb = cb\n break\n\n # Remove ModelTrackingCallback before exporting learners\n original_cbs = self.learn.cbs.copy()\n filtered_cbs = L([cb for cb in self.learn.cbs\n if not isinstance(cb, ModelTrackingCallback)])\n self.learn.cbs = filtered_cbs\n\n # Export final epoch learner (before loading best weights)\n final_learner_path = temp_dir / \"final_learner.pkl\"\n self.learn.export(str(final_learner_path))\n mlflow.log_artifact(str(final_learner_path), \"model\")\n print(\"Logged final epoch learner: final_learner.pkl\")\n\n # If best model exists, load best weights and export best learner\n if best_model_cb is not None:\n self.learn.load(best_model_cb.fname)\n print(f\"Loaded best model weights ({best_model_cb.fname}) for best learner export\")\n\n best_learner_path = temp_dir / \"best_learner.pkl\"\n self.learn.export(str(best_learner_path))\n mlflow.log_artifact(str(best_learner_path), \"model\")\n print(\"Logged best epoch learner: best_learner.pkl\")\n\n # Restore original callbacks\n self.learn.cbs = original_cbs\n\n # Save inference config\n config_path = temp_dir / \"inference_settings.pkl\"\n store_variables(config_path, self.size, self.apply_reorder, self.target_spacing)\n mlflow.log_artifact(str(config_path), \"config\")\n \n def _register_pytorch_model(self) -> None:\n \"\"\"Register the PyTorch model with MLflow.\"\"\"\n mlflow.pytorch.log_model(\n pytorch_model=self.learn.model,\n registered_model_name=self.model_name\n )\n\n def _log_split_df(self) -> None:\n \"\"\"Log train/validation split as CSV artifact if available.\"\"\"\n dls = self.learn.dls\n if not hasattr(dls, 'split_df'):\n return\n try:\n split_df = dls.split_df\n cols = []\n img_col = getattr(dls, '_img_col', None)\n mask_col = getattr(dls, '_mask_col', None)\n if img_col and img_col in split_df.columns:\n cols.append(img_col)\n if mask_col and mask_col in split_df.columns:\n cols.append(mask_col)\n cols.append('is_valid')\n split_df = split_df[cols]\n\n mlflow.log_text(split_df.to_csv(index=False), \"data/train_val_split.csv\")\n print(f\"Logged train/val split ({len(split_df)} rows) to MLflow artifacts\")\n except Exception as e:\n print(f\"Warning: Could not log train/val split: {e}\")\n\n def before_fit(self) -> None:\n \"\"\"Log hyperparameters before training starts. Auto-start run if configured.\"\"\"\n # Auto-start run if requested\n if self.auto_start:\n if self.experiment_name:\n mlflow.set_experiment(self.experiment_name)\n self.run_name = self.run_name or f\"run_{datetime.now().strftime('%Y%m%d_%H%M%S')}\"\n mlflow.start_run(run_name=self.run_name)\n self._auto_started = True\n \n # Log dataset version tag if provided\n if self.dataset_version is not None:\n mlflow.set_tag(\"dataset_version\", self.dataset_version)\n \n # Set tags\n if self.extra_tags:\n mlflow.set_tags(self.extra_tags)\n \n # Log params\n params = self._extract_training_params()\n params.update(self.extra_params)\n mlflow.log_params(params)\n\n # Log train/val split\n if self.log_split:\n self._log_split_df()\n \n def after_epoch(self) -> None:\n \"\"\"Log metrics after each epoch.\"\"\"\n metrics = self._extract_epoch_metrics()\n if metrics:\n mlflow.log_metrics(metrics, step=self.learn.epoch)\n \n def after_fit(self) -> None:\n \"\"\"Log model artifacts after training completion.\"\"\"\n print(\"\\nTraining finished. Logging model artifacts to MLflow...\")\n \n with tempfile.TemporaryDirectory() as temp_dir:\n temp_path = Path(temp_dir)\n \n self._save_model_artifacts(temp_path)\n \n self._register_pytorch_model()\n \n self.run_id = mlflow.active_run().info.run_id\n print(f\"MLflow run completed. Run ID: {self.run_id}\")\n \n # End run if auto-started\n if self._auto_started:\n mlflow.end_run()\n self._auto_started = False\n\n def log_metrics(self, metrics: dict) -> None:\n \"\"\"Log additional metrics to the completed MLflow run.\n\n Reopens the run to log metrics, then closes it. Useful for\n post-training evaluation (e.g., validation set patch inference results).\n NaN values are silently skipped (MLflow rejects them).\n\n Args:\n metrics: Dictionary of metric name -> value pairs to log.\n \"\"\"\n if self.run_id is None:\n raise RuntimeError(\"No MLflow run found. Train the model first.\")\n\n import math\n valid = {k: v for k, v in metrics.items()\n if not (isinstance(v, float) and math.isnan(v))}\n\n if valid:\n with mlflow.start_run(run_id=self.run_id):\n mlflow.log_metrics(valid)\n print(f\"Logged {len(valid)} metric(s) to MLflow run {self.run_id}\")\n\n def log_metrics_table(self, df, metrics=None, key='validation_metrics', display=False):\n \"\"\"Log a summary table of metrics as an image to the MLflow Images tab.\n\n Creates a styled matplotlib table with Metric, Mean, and Std columns\n from the given DataFrame and logs it via mlflow.log_image() so it\n appears in the MLflow UI Images tab.\n\n Args:\n df: DataFrame containing per-sample metric values.\n metrics: List of column names in df to summarize. If None,\n all numeric columns are used automatically.\n key: Image key for MLflow Images tab.\n display: If True, display the table inline in the notebook.\n \"\"\"\n import math\n import io\n import matplotlib.pyplot as plt\n from PIL import Image\n\n if self.run_id is None:\n raise RuntimeError(\"No MLflow run found. Train the model first.\")\n\n if metrics is None:\n metrics = df.select_dtypes(include='number').columns.tolist()\n\n summary_data = []\n for metric in metrics:\n mean = df[metric].mean()\n std = df[metric].std()\n std_str = f'{std:.4f}' if not math.isnan(std) else 'N/A'\n summary_data.append([metric, f'{mean:.4f}', std_str])\n\n fig, ax = plt.subplots(figsize=(5, len(metrics) * 0.8 + 0.5))\n ax.axis('off')\n\n table = ax.table(\n cellText=summary_data,\n colLabels=['Metric', 'Mean', 'Std'],\n cellLoc='center',\n loc='center'\n )\n table.auto_set_font_size(False)\n table.set_fontsize(14)\n table.scale(1.5, 2.2)\n\n for col in range(3):\n table[0, col].set_facecolor('#4472C4')\n table[0, col].set_text_props(color='white', fontweight='bold')\n\n fig.tight_layout()\n\n buf = io.BytesIO()\n fig.savefig(buf, format='png', bbox_inches='tight', dpi=250)\n buf.seek(0)\n pil_image = Image.open(buf)\n\n with mlflow.start_run(run_id=self.run_id):\n mlflow.log_image(pil_image, key=key)\n\n buf.close()\n if display: plt.show()\n plt.close(fig)\n print(f\"Logged metrics table to MLflow run {self.run_id}\")\n\n def log_dataframe(self, df, artifact_name='validation_results.csv', artifact_path='results'):\n \"\"\"Log a DataFrame as a CSV artifact to the MLflow run.\n\n Args:\n df: DataFrame to save.\n artifact_name: Filename for the CSV.\n artifact_path: Artifact subdirectory in MLflow.\n \"\"\"\n if self.run_id is None:\n raise RuntimeError(\"No MLflow run found. Train the model first.\")\n\n with tempfile.NamedTemporaryFile(suffix='.csv', delete=False, mode='w') as f:\n df.to_csv(f, index=False)\n temp_path = f.name\n\n with mlflow.start_run(run_id=self.run_id):\n mlflow.log_artifact(temp_path, artifact_path)\n\n os.remove(temp_path)\n print(f\"Logged DataFrame ({len(df)} rows) to MLflow run {self.run_id}\")" }, { "cell_type": "code", @@ -847,95 +215,7 @@ "id": "38avhd96apq", "metadata": {}, "outputs": [], - "source": [ - "#| export\n", - "def create_mlflow_callback(\n", - " learn,\n", - " experiment_name: str = None,\n", - " run_name: str = None,\n", - " auto_start: bool = True,\n", - " model_name: str = None,\n", - " extra_params: dict = None,\n", - " extra_tags: dict = None,\n", - ") -> ModelTrackingCallback:\n", - " \"\"\"Create MLflow tracking callback with auto-extracted configuration.\n", - " \n", - " This factory function automatically extracts configuration from the Learner,\n", - " eliminating the need to manually specify parameters like size, transforms,\n", - " loss function, etc.\n", - " \n", - " Auto-extracts from Learner:\n", - " - Preprocessing: apply_reorder, target_spacing, size/patch_size\n", - " - Transforms: item_tfms or pre_patch_tfms\n", - " - Training: loss_func, model architecture\n", - " \n", - " Args:\n", - " learn: fastai Learner instance\n", - " experiment_name: MLflow experiment name. If None, uses model name.\n", - " run_name: MLflow run name. If None, auto-generates with timestamp.\n", - " auto_start: If True, auto-starts/stops MLflow run in before_fit/after_fit.\n", - " model_name: Override auto-extracted model name for registration.\n", - " extra_params: Additional parameters to log (e.g., {'dropout': 0.5}).\n", - " extra_tags: MLflow tags to set on the run.\n", - " \n", - " Returns:\n", - " ModelTrackingCallback ready to use with learn.fit()\n", - " \n", - " Example:\n", - " >>> # Instead of this (6 manual params):\n", - " >>> # mlflow_callback = ModelTrackingCallback(\n", - " >>> # model_name=f\"{task}_{model._get_name()}\",\n", - " >>> # loss_function=loss_func.loss_func._get_name(),\n", - " >>> # item_tfms=item_tfms,\n", - " >>> # size=size,\n", - " >>> # target_spacing=target_spacing,\n", - " >>> # apply_reorder=True,\n", - " >>> # )\n", - " >>> # with mlflow.start_run(run_name=\"training\"):\n", - " >>> # learn.fit_one_cycle(30, lr, cbs=[mlflow_callback])\n", - " >>> \n", - " >>> # Do this (zero manual params):\n", - " >>> callback = create_mlflow_callback(learn, experiment_name=\"Task02_Heart\")\n", - " >>> learn.fit_one_cycle(30, lr, cbs=[callback, save_best])\n", - " \"\"\"\n", - " # Detect workflow and extract config\n", - " if _detect_patch_workflow(learn.dls):\n", - " config = _extract_patch_config(learn)\n", - " else:\n", - " config = _extract_standard_config(learn)\n", - " \n", - " # Extract model/loss info\n", - " _model_name = model_name or _extract_model_name(learn)\n", - " _loss_name = _extract_loss_name(learn)\n", - " \n", - " # Set experiment name\n", - " if experiment_name is None:\n", - " experiment_name = _model_name\n", - " \n", - " # Validate size was extracted\n", - " if config['size'] is None:\n", - " raise ValueError(\n", - " \"Could not auto-extract 'size'. Either:\\n\"\n", - " \"1. Add PadOrCrop to item_tfms, or\\n\"\n", - " \"2. Use MedPatchDataLoaders with PatchConfig, or\\n\"\n", - " \"3. Use ModelTrackingCallback directly with manual params\"\n", - " )\n", - " \n", - " return ModelTrackingCallback(\n", - " model_name=_model_name,\n", - " loss_function=_loss_name,\n", - " item_tfms=config['item_tfms'],\n", - " size=config['size'],\n", - " target_spacing=config['target_spacing'],\n", - " apply_reorder=config['apply_reorder'],\n", - " experiment_name=experiment_name,\n", - " run_name=run_name,\n", - " auto_start=auto_start,\n", - " patch_config=config['patch_config'],\n", - " extra_params=extra_params,\n", - " extra_tags=extra_tags,\n", - " )" - ] + "source": "#| export\ndef create_mlflow_callback(\n learn,\n experiment_name: str = None,\n run_name: str = None,\n auto_start: bool = True,\n model_name: str = None,\n extra_params: dict = None,\n extra_tags: dict = None,\n dataset_version: str = None,\n log_split: bool = True,\n) -> ModelTrackingCallback:\n \"\"\"Create MLflow tracking callback with auto-extracted configuration.\n \n This factory function automatically extracts configuration from the Learner,\n eliminating the need to manually specify parameters like size, transforms,\n loss function, etc.\n \n Auto-extracts from Learner:\n - Preprocessing: apply_reorder, target_spacing, size/patch_size\n - Transforms: item_tfms or pre_patch_tfms\n - Training: loss_func, model architecture\n \n Args:\n learn: fastai Learner instance\n experiment_name: MLflow experiment name. If None, uses model name.\n run_name: MLflow run name. If None, auto-generates with timestamp.\n auto_start: If True, auto-starts/stops MLflow run in before_fit/after_fit.\n model_name: Override auto-extracted model name for registration.\n extra_params: Additional parameters to log (e.g., {'dropout': 0.5}).\n extra_tags: MLflow tags to set on the run.\n dataset_version: Dataset version hash string for tracking.\n log_split: If True, auto-logs train/val split CSV when dls has split_df.\n \n Returns:\n ModelTrackingCallback ready to use with learn.fit()\n \n Example:\n >>> # Instead of this (6 manual params):\n >>> # mlflow_callback = ModelTrackingCallback(\n >>> # model_name=f\"{task}_{model._get_name()}\",\n >>> # loss_function=loss_func.loss_func._get_name(),\n >>> # item_tfms=item_tfms,\n >>> # size=size,\n >>> # target_spacing=target_spacing,\n >>> # apply_reorder=True,\n >>> # )\n >>> # with mlflow.start_run(run_name=\"training\"):\n >>> # learn.fit_one_cycle(30, lr, cbs=[mlflow_callback])\n >>> \n >>> # Do this (zero manual params):\n >>> callback = create_mlflow_callback(learn, experiment_name=\"Task02_Heart\")\n >>> learn.fit_one_cycle(30, lr, cbs=[callback, save_best])\n \"\"\"\n # Detect workflow and extract config\n if _detect_patch_workflow(learn.dls):\n config = _extract_patch_config(learn)\n else:\n config = _extract_standard_config(learn)\n \n # Extract model/loss info\n _model_name = model_name or _extract_model_name(learn)\n _loss_name = _extract_loss_name(learn)\n \n # Set experiment name\n if experiment_name is None:\n experiment_name = _model_name\n \n # Validate size was extracted\n if config['size'] is None:\n raise ValueError(\n \"Could not auto-extract 'size'. Either:\\n\"\n \"1. Add PadOrCrop to item_tfms, or\\n\"\n \"2. Use MedPatchDataLoaders with PatchConfig, or\\n\"\n \"3. Use ModelTrackingCallback directly with manual params\"\n )\n \n return ModelTrackingCallback(\n model_name=_model_name,\n loss_function=_loss_name,\n item_tfms=config['item_tfms'],\n size=config['size'],\n target_spacing=config['target_spacing'],\n apply_reorder=config['apply_reorder'],\n experiment_name=experiment_name,\n run_name=run_name,\n auto_start=auto_start,\n patch_config=config['patch_config'],\n extra_params=extra_params,\n extra_tags=extra_tags,\n dataset_version=dataset_version,\n log_split=log_split,\n )" }, { "cell_type": "code", @@ -996,331 +276,13 @@ "print(\"All auto-extraction helper tests passed!\")" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "whmmqugsr1", - "metadata": {}, - "outputs": [], - "source": [ - "# Test _extract_standard_dataset_dfs\n", - "import pandas as pd\n", - "\n", - "class MockStdDs:\n", - " def __init__(self, items):\n", - " self.items = items\n", - "\n", - "class MockStdDls:\n", - " def __init__(self, train_items, valid_items):\n", - " self.train_ds = MockStdDs(train_items)\n", - " self.valid_ds = MockStdDs(valid_items)\n", - "\n", - "# Happy path: DataFrame items\n", - "train_df = pd.DataFrame({'image': ['/a.nii', '/b.nii'], 'label': [0, 1]})\n", - "valid_df = pd.DataFrame({'image': ['/c.nii'], 'label': [1]})\n", - "mock_dls = MockStdDls(train_df, valid_df)\n", - "result_train, result_valid = _extract_standard_dataset_dfs(mock_dls)\n", - "test_eq(len(result_train), 2)\n", - "test_eq(len(result_valid), 1)\n", - "test_eq(list(result_train.columns), ['image', 'label'])\n", - "\n", - "# Non-DataFrame items: graceful fallback\n", - "mock_dls_nodf = MockStdDls(['a', 'b'], ['c'])\n", - "result_train, result_valid = _extract_standard_dataset_dfs(mock_dls_nodf)\n", - "test_eq(result_train, None)\n", - "test_eq(result_valid, None)\n", - "\n", - "# Test _extract_patch_dataset_dfs\n", - "from pathlib import Path\n", - "\n", - "class MockTioImage:\n", - " def __init__(self, path):\n", - " self.path = Path(path) if path else None\n", - "\n", - "class MockSubject(dict):\n", - " pass\n", - "\n", - "class MockSubjectsDs:\n", - " def __init__(self, subjects):\n", - " self._subjects = subjects\n", - "\n", - "class MockPatchDlsForDataset:\n", - " _img_col = 'image_path'\n", - " _mask_col = 'mask_path'\n", - " patch_config = MockPatchConfig()\n", - " \n", - " def __init__(self, train_subjects, valid_subjects):\n", - " self._train_ds = MockSubjectsDs(train_subjects)\n", - " self._valid_ds = MockSubjectsDs(valid_subjects)\n", - " \n", - " @property\n", - " def train_ds(self):\n", - " return self._train_ds\n", - " \n", - " @property\n", - " def valid_ds(self):\n", - " return self._valid_ds\n", - "\n", - "# Create mock subjects with image and mask\n", - "s1 = MockSubject(image=MockTioImage('/data/img1.nii.gz'), mask=MockTioImage('/data/mask1.nii.gz'))\n", - "s2 = MockSubject(image=MockTioImage('/data/img2.nii.gz'), mask=MockTioImage('/data/mask2.nii.gz'))\n", - "s3 = MockSubject(image=MockTioImage('/data/img3.nii.gz'), mask=MockTioImage('/data/mask3.nii.gz'))\n", - "\n", - "mock_patch_dls = MockPatchDlsForDataset([s1, s2], [s3])\n", - "result_train, result_valid = _extract_patch_dataset_dfs(mock_patch_dls)\n", - "test_eq(len(result_train), 2)\n", - "test_eq(len(result_valid), 1)\n", - "test_eq(list(result_train.columns), ['image_path', 'mask_path'])\n", - "test_eq(result_train.iloc[0]['image_path'], '/data/img1.nii.gz')\n", - "\n", - "# Test without mask (classification workflow)\n", - "class MockPatchDlsNoMask:\n", - " _img_col = 'image'\n", - " _mask_col = None\n", - " patch_config = MockPatchConfig()\n", - " \n", - " def __init__(self, train_subjects, valid_subjects):\n", - " self._train_ds = MockSubjectsDs(train_subjects)\n", - " self._valid_ds = MockSubjectsDs(valid_subjects)\n", - " \n", - " @property\n", - " def train_ds(self):\n", - " return self._train_ds\n", - " \n", - " @property\n", - " def valid_ds(self):\n", - " return self._valid_ds\n", - "\n", - "s_no_mask = MockSubject(image=MockTioImage('/data/img1.nii.gz'))\n", - "mock_no_mask = MockPatchDlsNoMask([s_no_mask], [])\n", - "result_train, result_valid = _extract_patch_dataset_dfs(mock_no_mask)\n", - "test_eq(len(result_train), 1)\n", - "test_eq(list(result_train.columns), ['image'])\n", - "test_eq(result_valid, None)\n", - "\n", - "print(\"All dataset extraction helper tests passed!\")" - ] - }, { "cell_type": "code", "execution_count": null, "id": "a3d2d585", "metadata": {}, "outputs": [], - "source": [ - "#| export\n", - "\n", - "import subprocess\n", - "import threading\n", - "import time\n", - "import socket\n", - "import os\n", - "from IPython.display import display, HTML, clear_output\n", - "from IPython.core.magic import register_line_magic\n", - "from IPython import get_ipython\n", - "import requests\n", - "import shutil\n", - "\n", - "class MLflowUIManager:\n", - " def __init__(self):\n", - " self.process = None\n", - " self.thread = None\n", - " self.port = 5001\n", - " self.host = '0.0.0.0'\n", - " self.backend_store_uri = 'sqlite:///mlruns.db'\n", - " self._owns_process = False\n", - " self._reusing_external = False\n", - " \n", - " def is_port_available(self, port):\n", - " \"\"\"Check if a port is available.\"\"\"\n", - " with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:\n", - " try:\n", - " s.bind(('localhost', port))\n", - " return True\n", - " except OSError:\n", - " return False\n", - " \n", - " def is_mlflow_running(self):\n", - " \"\"\"Check if MLflow UI is actually responding.\"\"\"\n", - " try:\n", - " response = requests.get(f'http://localhost:{self.port}', timeout=2)\n", - " return response.status_code == 200\n", - " except (requests.RequestException, ConnectionError, OSError):\n", - " return False\n", - " \n", - " def _find_running_mlflow(self):\n", - " \"\"\"Find an existing MLflow UI in our port range. Returns port or None.\"\"\"\n", - " for port in range(self.port, self.port + 10):\n", - " if not self.is_port_available(port):\n", - " try:\n", - " response = requests.get(f'http://localhost:{port}', timeout=2)\n", - " if response.status_code == 200 and 'mlflow' in response.text.lower():\n", - " return port\n", - " except (requests.RequestException, ConnectionError, OSError):\n", - " continue\n", - " return None\n", - " \n", - " def find_available_port(self, start_port=5001):\n", - " \"\"\"Find an available port starting from start_port.\"\"\"\n", - " for port in range(start_port, start_port + 10):\n", - " if self.is_port_available(port):\n", - " return port\n", - " return None\n", - " \n", - " def check_mlflow_installed(self):\n", - " \"\"\"Check if MLflow is installed.\"\"\"\n", - " return shutil.which('mlflow') is not None\n", - " \n", - " def start_ui(self, auto_open=True, quiet=False):\n", - " \"\"\"Start MLflow UI with better error handling and user feedback.\"\"\"\n", - " \n", - " # Check if MLflow is installed\n", - " if not self.check_mlflow_installed():\n", - " if not quiet:\n", - " display(HTML('
❌ MLflow not installed. Run: pip install mlflow
'))\n", - " return False\n", - " \n", - " # Check for existing MLflow UI before launching a new one\n", - " existing_port = self._find_running_mlflow()\n", - " if existing_port is not None:\n", - " self.port = existing_port\n", - " self._owns_process = False\n", - " self._reusing_external = True\n", - " if not quiet:\n", - " display(HTML(f'''\n", - "
\n", - "
\n", - " Reusing existing MLflow UI on port {self.port}\n", - "
\n", - " \n", - " Open MLflow UI\n", - " \n", - "
\n", - "
URL: http://localhost:{self.port}
\n", - "
\n", - "
\n", - " '''))\n", - " else:\n", - " display(HTML(f'''\n", - " \n", - " MLflow UI (Port {self.port}, reused)\n", - " \n", - " '''))\n", - " return True\n", - " \n", - " # Find available port\n", - " available_port = self.find_available_port(self.port)\n", - " if available_port is None:\n", - " if not quiet:\n", - " display(HTML('
No available ports found (5001-5010)
'))\n", - " return False\n", - " \n", - " self.port = available_port\n", - " \n", - " # Start MLflow UI in a separate thread\n", - " def run_mlflow():\n", - " try:\n", - " self.process = subprocess.Popen([\n", - " 'mlflow', 'ui', \n", - " '--host', self.host,\n", - " '--port', str(self.port),\n", - " '--backend-store-uri', self.backend_store_uri\n", - " ], stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)\n", - " self.process.wait()\n", - " except Exception as e:\n", - " if not quiet:\n", - " display(HTML(f'
Error: {str(e)}
'))\n", - " \n", - " self.thread = threading.Thread(target=run_mlflow, daemon=True)\n", - " self.thread.start()\n", - " \n", - " # Wait and check if server started successfully\n", - " max_wait = 10\n", - " for i in range(max_wait):\n", - " time.sleep(1)\n", - " if self.is_mlflow_running():\n", - " self._owns_process = True\n", - " self._reusing_external = False\n", - " if quiet:\n", - " # Bright, visible link for quiet mode\n", - " display(HTML(f'''\n", - " \n", - " MLflow UI (Port {self.port})\n", - " \n", - " '''))\n", - " else:\n", - " # Success message with high contrast colors\n", - " display(HTML(f'''\n", - "
\n", - "
\n", - " MLflow UI is running successfully!\n", - "
\n", - " \n", - " Open MLflow UI\n", - " \n", - "
\n", - "
URL: http://localhost:{self.port}
\n", - "
\n", - "
\n", - " '''))\n", - " return True\n", - " \n", - " # If we get here, server didn't start properly\n", - " if not quiet:\n", - " display(HTML('
Failed to start MLflow UI
'))\n", - " return False\n", - " \n", - " def stop(self):\n", - " \"\"\"Stop the MLflow UI server.\"\"\"\n", - " if self.process and self._owns_process:\n", - " self.process.terminate()\n", - " self.process = None\n", - " self._owns_process = False\n", - " display(HTML('''\n", - "
\n", - " MLflow UI stopped\n", - "
\n", - " '''))\n", - " elif self._reusing_external:\n", - " self._reusing_external = False\n", - " display(HTML('''\n", - "
\n", - " MLflow UI was started externally — not stopping it\n", - "
\n", - " '''))\n", - " else:\n", - " display(HTML('''\n", - "
\n", - " MLflow UI is not currently running\n", - "
\n", - " '''))\n", - " \n", - " def status(self):\n", - " \"\"\"Check MLflow UI status.\"\"\"\n", - " if self.is_mlflow_running():\n", - " display(HTML(f'''\n", - "
\n", - "
MLflow UI is running
\n", - " \n", - " http://localhost:{self.port}\n", - " \n", - "
\n", - " '''))\n", - " else:\n", - " display(HTML('''\n", - "
\n", - "
MLflow UI is not running
\n", - "
\n", - " Run mlflow_ui.start_ui() to start it.\n", - "
\n", - "
\n", - " '''))" - ] + "source": "#| export\n\nimport subprocess\nimport threading\nimport time\nimport socket\nimport os\nfrom IPython.display import display, HTML, clear_output\nfrom IPython.core.magic import register_line_magic\nfrom IPython import get_ipython\nimport requests\nimport shutil\n\nclass MLflowUIManager:\n def __init__(self):\n self.process = None\n self.thread = None\n self.port = 5001\n self.host = '0.0.0.0'\n self.backend_store_uri = f'sqlite:///{_mlruns_db}'\n self._owns_process = False\n self._reusing_external = False\n \n def is_port_available(self, port):\n \"\"\"Check if a port is available.\"\"\"\n with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:\n try:\n s.bind(('localhost', port))\n return True\n except OSError:\n return False\n \n def is_mlflow_running(self):\n \"\"\"Check if MLflow UI is actually responding.\"\"\"\n try:\n response = requests.get(f'http://localhost:{self.port}', timeout=2)\n return response.status_code == 200\n except (requests.RequestException, ConnectionError, OSError):\n return False\n \n def _find_running_mlflow(self):\n \"\"\"Find an existing MLflow UI in our port range. Returns port or None.\"\"\"\n for port in range(self.port, self.port + 10):\n if not self.is_port_available(port):\n try:\n response = requests.get(f'http://localhost:{port}', timeout=2)\n if response.status_code == 200 and 'mlflow' in response.text.lower():\n return port\n except (requests.RequestException, ConnectionError, OSError):\n continue\n return None\n \n def find_available_port(self, start_port=5001):\n \"\"\"Find an available port starting from start_port.\"\"\"\n for port in range(start_port, start_port + 10):\n if self.is_port_available(port):\n return port\n return None\n \n def check_mlflow_installed(self):\n \"\"\"Check if MLflow is installed.\"\"\"\n return shutil.which('mlflow') is not None\n \n def start_ui(self, auto_open=True, quiet=False):\n \"\"\"Start MLflow UI with better error handling and user feedback.\"\"\"\n \n # Check if MLflow is installed\n if not self.check_mlflow_installed():\n if not quiet:\n display(HTML('
❌ MLflow not installed. Run: pip install mlflow
'))\n return False\n \n # Check for existing MLflow UI before launching a new one\n existing_port = self._find_running_mlflow()\n if existing_port is not None:\n self.port = existing_port\n self._owns_process = False\n self._reusing_external = True\n if not quiet:\n display(HTML(f'''\n
\n
\n Reusing existing MLflow UI on port {self.port}\n
\n \n Open MLflow UI\n \n
\n
URL: http://localhost:{self.port}
\n
\n
\n '''))\n else:\n display(HTML(f'''\n \n MLflow UI (Port {self.port}, reused)\n \n '''))\n return True\n \n # Find available port\n available_port = self.find_available_port(self.port)\n if available_port is None:\n if not quiet:\n display(HTML('
No available ports found (5001-5010)
'))\n return False\n \n self.port = available_port\n \n # Start MLflow UI in a separate thread\n def run_mlflow():\n try:\n self.process = subprocess.Popen([\n 'mlflow', 'ui', \n '--host', self.host,\n '--port', str(self.port),\n '--backend-store-uri', self.backend_store_uri\n ], stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)\n self.process.wait()\n except Exception as e:\n if not quiet:\n display(HTML(f'
Error: {str(e)}
'))\n \n self.thread = threading.Thread(target=run_mlflow, daemon=True)\n self.thread.start()\n \n # Wait and check if server started successfully\n max_wait = 10\n for i in range(max_wait):\n time.sleep(1)\n if self.is_mlflow_running():\n self._owns_process = True\n self._reusing_external = False\n if quiet:\n # Bright, visible link for quiet mode\n display(HTML(f'''\n \n MLflow UI (Port {self.port})\n \n '''))\n else:\n # Success message with high contrast colors\n display(HTML(f'''\n
\n
\n MLflow UI is running successfully!\n
\n \n Open MLflow UI\n \n
\n
URL: http://localhost:{self.port}
\n
\n
\n '''))\n return True\n \n # If we get here, server didn't start properly\n if not quiet:\n display(HTML('
Failed to start MLflow UI
'))\n return False\n \n def stop(self):\n \"\"\"Stop the MLflow UI server.\"\"\"\n if self.process and self._owns_process:\n self.process.terminate()\n self.process = None\n self._owns_process = False\n display(HTML('''\n
\n MLflow UI stopped\n
\n '''))\n elif self._reusing_external:\n self._reusing_external = False\n display(HTML('''\n
\n MLflow UI was started externally — not stopping it\n
\n '''))\n else:\n display(HTML('''\n
\n MLflow UI is not currently running\n
\n '''))\n \n def status(self):\n \"\"\"Check MLflow UI status.\"\"\"\n if self.is_mlflow_running():\n display(HTML(f'''\n
\n
MLflow UI is running
\n \n http://localhost:{self.port}\n \n
\n '''))\n else:\n display(HTML('''\n
\n
MLflow UI is not running
\n
\n Run mlflow_ui.start_ui() to start it.\n
\n
\n '''))" } ], "metadata": { diff --git a/nbs/08_dataset_info.ipynb b/nbs/08_dataset_info.ipynb index cf622b8..5be7e82 100644 --- a/nbs/08_dataset_info.ipynb +++ b/nbs/08_dataset_info.ipynb @@ -27,19 +27,7 @@ "id": "027f016a-a80c-4842-b9dc-0bddb358a00c", "metadata": {}, "outputs": [], - "source": [ - "#| export \n", - "from fastMONAI.vision_core import *\n", - "from fastMONAI.vision_plot import find_max_slice\n", - "\n", - "from sklearn.utils.class_weight import compute_class_weight\n", - "from concurrent.futures import ThreadPoolExecutor\n", - "import pandas as pd\n", - "import numpy as np\n", - "import torch\n", - "import glob\n", - "import matplotlib.pyplot as plt" - ] + "source": "#| export\nfrom fastMONAI.vision_core import *\nfrom fastMONAI.vision_plot import find_max_slice\n\nfrom sklearn.utils.class_weight import compute_class_weight\nfrom concurrent.futures import ThreadPoolExecutor\nfrom pathlib import Path\nimport pandas as pd\nimport numpy as np\nimport torch\nimport glob\nimport hashlib\nimport os\nimport pickle\nimport matplotlib.pyplot as plt" }, { "cell_type": "code", @@ -47,7 +35,7 @@ "id": "7401beac", "metadata": {}, "outputs": [], - "source": "#| export\nimport warnings\n\nclass MedDataset:\n \"\"\"A class to extract and present information about the dataset.\"\"\"\n\n def __init__(self, dataframe=None, image_col:str=None, mask_col:str=\"mask_path\",\n path=None, img_list=None, postfix:str='', apply_reorder:bool=True,\n dtype:(MedImage, MedMask)=MedImage, max_workers:int=1):\n \"\"\"Constructs MedDataset object.\n\n Args:\n dataframe: DataFrame containing image paths.\n image_col: Column name for image paths (used for visualization).\n mask_col: Column name for mask/label paths when using dataframe mode.\n path: Directory path containing images.\n img_list: List of image file paths to analyze.\n postfix: File postfix filter when using path mode.\n apply_reorder: Whether to reorder images to RAS+ orientation.\n dtype: MedImage for images or MedMask for segmentation masks.\n max_workers: Number of parallel workers for processing.\n \"\"\"\n self.input_df = dataframe\n self.image_col = image_col\n self.mask_col = mask_col\n self.path = path\n self.img_list = img_list\n self.postfix = postfix\n self.apply_reorder = apply_reorder\n self.dtype = dtype\n self.max_workers = max_workers\n self.df = self._create_data_frame()\n\n def _create_data_frame(self):\n \"\"\"Private method that returns a dataframe with information about the dataset.\"\"\"\n\n # Handle img_list (simple list of paths)\n if self.img_list is not None:\n file_list = self.img_list\n\n # Handle path-based initialization\n elif self.path:\n file_list = glob.glob(f'{self.path}/*{self.postfix}*')\n if not file_list:\n print('Could not find images. Check the image path')\n return pd.DataFrame()\n\n # Handle dataframe-based initialization\n elif self.input_df is not None and self.mask_col in self.input_df.columns:\n file_list = self.input_df[self.mask_col].tolist()\n\n else:\n print('Error: Must provide path, img_list, or dataframe with mask_col')\n return pd.DataFrame()\n\n # Process images to extract metadata\n with ThreadPoolExecutor(max_workers=self.max_workers) as executor:\n data_info_dict = list(executor.map(self._get_data_info, file_list))\n\n df = pd.DataFrame(data_info_dict)\n\n if len(df) > 0 and df.orientation.nunique() > 1 and not self.apply_reorder:\n raise ValueError(\n 'Mixed orientations detected in dataset. '\n 'Please recreate MedDataset with apply_reorder=True to get correct resample values: '\n 'MedDataset(..., apply_reorder=True)'\n )\n\n return df\n\n def summary(self):\n \"\"\"Summary DataFrame of the dataset with example path for similar data.\"\"\"\n\n columns = ['dim_0', 'dim_1', 'dim_2', 'voxel_0', 'voxel_1', 'voxel_2', 'orientation']\n\n return self.df.groupby(columns, as_index=False).agg(\n example_path=('path', 'min'), total=('path', 'size')\n ).sort_values('total', ascending=False)\n\n def get_suggestion(self, include_patch_size: bool = False):\n \"\"\"Returns suggested preprocessing parameters as a dictionary.\n\n The returned target_spacing is derived from the mode (most common value)\n of voxel spacings in the dataset.\n\n Note:\n apply_reorder is NOT included in the return value because it is not\n data-derived. Access dataset.apply_reorder directly if needed.\n\n Args:\n include_patch_size: If True, includes suggested patch_size for\n patch-based training. Requires vision_patch module.\n\n Returns:\n dict: {'target_spacing': [voxel_0, voxel_1, voxel_2]}\n If include_patch_size=True, also includes 'patch_size': [dim_0, dim_1, dim_2]\n \"\"\"\n target_spacing = [float(self.df.voxel_0.mode()[0]), float(self.df.voxel_1.mode()[0]), float(self.df.voxel_2.mode()[0])]\n result = {'target_spacing': target_spacing}\n\n if include_patch_size:\n result['patch_size'] = suggest_patch_size(self)\n\n return result\n\n def _get_data_info(self, fn: str):\n \"\"\"Private method to collect information about an image file.\"\"\"\n try:\n _, o, _ = med_img_reader(fn, apply_reorder=self.apply_reorder, only_tensor=False, dtype=self.dtype)\n\n info_dict = {'path': fn, 'dim_0': o.shape[1], 'dim_1': o.shape[2], 'dim_2': o.shape[3],\n 'voxel_0': round(o.spacing[0], 4), 'voxel_1': round(o.spacing[1], 4), 'voxel_2': round(o.spacing[2], 4),\n 'orientation': f'{\"\".join(o.orientation)}+'}\n\n if self.dtype is MedMask:\n # Calculate voxel volume in mm³\n voxel_volume = o.spacing[0] * o.spacing[1] * o.spacing[2]\n\n # Get voxel counts for each label\n mask_labels_dict = o.count_labels()\n\n # Calculate volumes for each label > 0 (skip background)\n for key, voxel_count in mask_labels_dict.items():\n label_int = int(key)\n if label_int > 0 and voxel_count > 0: # Skip background (label 0)\n volume_mm3 = voxel_count * voxel_volume\n info_dict[f'label_{label_int}_volume_mm3'] = round(volume_mm3, 4)\n\n return info_dict\n\n except Exception as e:\n print(f\"Warning: Failed to process {fn}: {e}\")\n return {'path': fn, 'error': str(e)}\n\n def calculate_target_size(self, target_spacing: list = None) -> list:\n \"\"\"Calculate the target image size for the dataset.\n\n .. deprecated::\n Use `get_size_statistics(target_spacing)['max']` instead for consistency\n with other size statistics methods.\n\n Args:\n target_spacing: If provided, calculates size after resampling to this spacing.\n If None, returns original dimensions.\n\n Returns:\n list: [dim_0, dim_1, dim_2] largest dimensions in dataset.\n \"\"\"\n warnings.warn(\n \"calculate_target_size() is deprecated. \"\n \"Use get_size_statistics(target_spacing)['max'] instead.\",\n DeprecationWarning,\n stacklevel=2\n )\n if target_spacing is not None:\n org_voxels = self.df[[\"voxel_0\", \"voxel_1\", 'voxel_2']].values\n org_dims = self.df[[\"dim_0\", \"dim_1\", 'dim_2']].values\n\n ratio = org_voxels/target_spacing\n new_dims = (org_dims * ratio).T\n # Use floor() to match TorchIO's Resample dimension calculation\n dims = [float(np.floor(new_dims[0].max())), float(np.floor(new_dims[1].max())), float(np.floor(new_dims[2].max()))]\n else:\n dims = [float(self.df.dim_0.max()), float(self.df.dim_1.max()), float(self.df.dim_2.max())]\n\n return dims\n\n def get_size_statistics(self, target_spacing: list = None) -> dict:\n \"\"\"Calculate comprehensive size statistics for the dataset.\n\n Args:\n target_spacing: If provided, calculates statistics after\n simulating resampling to this spacing.\n\n Returns:\n dict with keys: 'median', 'min', 'max', 'std', 'percentile_10', 'percentile_90'\n Each value is a list [dim_0, dim_1, dim_2].\n \"\"\"\n if len(self.df) == 0:\n raise ValueError(\"Dataset is empty - cannot calculate statistics\")\n\n if target_spacing is not None:\n # Simulate resampled dimensions\n org_voxels = self.df[[\"voxel_0\", \"voxel_1\", \"voxel_2\"]].values\n org_dims = self.df[[\"dim_0\", \"dim_1\", \"dim_2\"]].values\n ratio = org_voxels / np.array(target_spacing)\n dims = np.floor(org_dims * ratio)\n else:\n dims = self.df[[\"dim_0\", \"dim_1\", \"dim_2\"]].values\n\n return {\n 'median': [float(np.median(dims[:, i])) for i in range(3)],\n 'min': [float(np.min(dims[:, i])) for i in range(3)],\n 'max': [float(np.max(dims[:, i])) for i in range(3)],\n 'std': [float(np.std(dims[:, i])) for i in range(3)],\n 'percentile_10': [float(np.percentile(dims[:, i], 10)) for i in range(3)],\n 'percentile_90': [float(np.percentile(dims[:, i], 90)) for i in range(3)],\n }\n\n def get_volume_summary(self):\n \"\"\"Returns DataFrame with volume statistics for each label.\n\n Returns:\n DataFrame with columns: label, count, mean_mm3, median_mm3, min_mm3, max_mm3\n Returns None if no volume columns found (dtype was not MedMask).\n \"\"\"\n volume_cols = [col for col in self.df.columns if col.endswith('_volume_mm3')]\n\n if not volume_cols:\n return None\n\n summary_data = []\n for col in volume_cols:\n non_zero = self.df[self.df[col] > 0][col]\n if len(non_zero) > 0:\n summary_data.append({\n 'label': col.replace('_volume_mm3', ''),\n 'count': len(non_zero),\n 'mean_mm3': non_zero.mean(),\n 'median_mm3': non_zero.median(),\n 'min_mm3': non_zero.min(),\n 'max_mm3': non_zero.max()\n })\n\n return pd.DataFrame(summary_data) if summary_data else None\n\n def _visualize_single_case(self, img_path, mask_path, case_id, anatomical_plane=2, cmap='hot', figsize=(12, 5)):\n \"\"\"Helper method to visualize a single case.\"\"\"\n try:\n # Create MedImage and MedMask with current preprocessing settings\n suggestion = self.get_suggestion()\n MedBase.item_preprocessing(target_spacing=suggestion['target_spacing'], apply_reorder=self.apply_reorder)\n\n img = MedImage.create(img_path)\n mask = MedMask.create(mask_path)\n\n # Find optimal slice using explicit function\n mask_data = mask.numpy()[0] # Remove channel dimension\n optimal_slice = find_max_slice(mask_data, anatomical_plane)\n\n # Create subplot\n fig, axes = plt.subplots(1, 2, figsize=figsize)\n\n # Show image\n img.show(ctx=axes[0], anatomical_plane=anatomical_plane, slice_index=optimal_slice)\n axes[0].set_title(f\"{case_id} - Image (slice {optimal_slice})\")\n\n # Show overlay\n img.show(ctx=axes[1], anatomical_plane=anatomical_plane, slice_index=optimal_slice)\n mask.show(ctx=axes[1], anatomical_plane=anatomical_plane, slice_index=optimal_slice,\n alpha=0.3, cmap=cmap)\n axes[1].set_title(f\"{case_id} - Overlay (slice {optimal_slice})\")\n\n # Adjust spacing to bring plots closer\n plt.subplots_adjust(wspace=0.1)\n plt.tight_layout()\n plt.show()\n\n except Exception as e:\n print(f\"Failed to visualize case {case_id}: {e}\")\n\n def visualize_cases(self, n_cases=4, anatomical_plane=2, cmap='hot', figsize=(12, 5)):\n \"\"\"Visualize cases from the dataset.\n\n Args:\n n_cases: Number of cases to show.\n anatomical_plane: 0=sagittal, 1=coronal, 2=axial\n cmap: Colormap for mask overlay\n figsize: Figure size for each case\n \"\"\"\n if self.input_df is None:\n print(\"Error: No dataframe provided. Cannot visualize cases.\")\n return\n\n if self.image_col is None:\n print(\"Error: No image_col specified. Cannot visualize cases.\")\n return\n\n # Check if required columns exist\n if self.image_col not in self.input_df.columns:\n print(f\"Error: Column '{self.image_col}' not found in dataframe.\")\n return\n\n if self.mask_col not in self.input_df.columns:\n print(f\"Error: Column '{self.mask_col}' not found in dataframe.\")\n return\n\n for idx in range(min(n_cases, len(self.input_df))):\n row = self.input_df.iloc[idx]\n case_id = row.get('case_id', f'Case_{idx}') # Fallback if no case_id\n img_path = row[self.image_col]\n mask_path = row[self.mask_col]\n\n self._visualize_single_case(img_path, mask_path, case_id, anatomical_plane, cmap, figsize)\n print(\"-\" * 60)" + "source": "#| export\nimport warnings\n\nclass MedDataset:\n \"\"\"A class to extract and present information about the dataset.\"\"\"\n\n def __init__(self, dataframe=None, image_col:str=None, mask_col:str=\"mask_path\",\n path=None, img_list=None, postfix:str='', apply_reorder:bool=True,\n dtype:(MedImage, MedMask)=MedImage, max_workers:int=1,\n use_cache:bool=True, cache_path=None):\n \"\"\"Constructs MedDataset object.\n\n Args:\n dataframe: DataFrame containing image paths.\n image_col: Column name for image paths (used for visualization).\n mask_col: Column name for mask/label paths when using dataframe mode.\n path: Directory path containing images.\n img_list: List of image file paths to analyze.\n postfix: File postfix filter when using path mode.\n apply_reorder: Whether to reorder images to RAS+ orientation.\n dtype: MedImage for images or MedMask for segmentation masks.\n max_workers: Number of parallel workers for processing.\n use_cache: Enable metadata caching. When True (default) and cache_path\n is not provided, auto-generates a cache path in ~/.cache/fastmonai/.\n Set to False to disable caching entirely.\n cache_path: Explicit path to a pickle file for metadata caching.\n Only used when use_cache=True. If None and use_cache=True,\n a path is auto-generated from the file list and config.\n \"\"\"\n self.input_df = dataframe\n self.image_col = image_col\n self.mask_col = mask_col\n self.path = path\n self.img_list = img_list\n self.postfix = postfix\n self.apply_reorder = apply_reorder\n self.dtype = dtype\n self.max_workers = max_workers\n self.use_cache = use_cache\n self.cache_path = cache_path\n self.df = self._create_data_frame()\n\n def _create_data_frame(self):\n \"\"\"Private method that returns a dataframe with information about the dataset.\"\"\"\n\n # Handle img_list (simple list of paths)\n if self.img_list is not None:\n file_list = self.img_list\n\n # Handle path-based initialization\n elif self.path:\n file_list = glob.glob(f'{self.path}/*{self.postfix}*')\n if not file_list:\n print('Could not find images. Check the image path')\n return pd.DataFrame()\n\n # Handle dataframe-based initialization\n elif self.input_df is not None and self.mask_col in self.input_df.columns:\n file_list = self.input_df[self.mask_col].tolist()\n\n else:\n print('Error: Must provide path, img_list, or dataframe with mask_col')\n return pd.DataFrame()\n\n # Resolve cache path\n if self.use_cache and self.cache_path is None:\n self.cache_path = self._auto_cache_path(file_list)\n\n # Process images to extract metadata (with optional caching)\n if self.use_cache and self.cache_path is not None:\n data_info_dict = self._process_with_cache(file_list)\n else:\n with ThreadPoolExecutor(max_workers=self.max_workers) as executor:\n data_info_dict = list(executor.map(self._get_data_info, file_list))\n\n df = pd.DataFrame(data_info_dict)\n\n if len(df) > 0 and df.orientation.nunique() > 1 and not self.apply_reorder:\n raise ValueError(\n 'Mixed orientations detected in dataset. '\n 'Please recreate MedDataset with apply_reorder=True to get correct resample values: '\n 'MedDataset(..., apply_reorder=True)'\n )\n\n return df\n\n def _auto_cache_path(self, file_list):\n \"\"\"Generate automatic cache path from file list and config.\"\"\"\n try:\n cache_dir = Path.home() / '.cache' / 'fastmonai'\n cache_dir.mkdir(parents=True, exist_ok=True)\n config_str = f\"reorder={self.apply_reorder}|dtype={self.dtype.__name__}\"\n abs_paths = sorted(os.path.abspath(fn) for fn in file_list)\n key_input = config_str + '|' + '|'.join(abs_paths)\n key = hashlib.md5(key_input.encode()).hexdigest()\n return str(cache_dir / f'med_dataset_{key}.pkl')\n except Exception:\n return None\n\n @property\n def fingerprint(self):\n \"\"\"Compute a dataset fingerprint from per-file content hashes.\n\n Returns a deterministic MD5 hex digest representing the dataset contents.\n Returns None if content hashes are not available (e.g., all files failed).\n \"\"\"\n if 'content_hash' not in self.df.columns:\n return None\n hashes = self.df['content_hash'].dropna().sort_values().tolist()\n if not hashes:\n return None\n combined = ''.join(hashes)\n return hashlib.md5(combined.encode()).hexdigest()\n\n def summary(self):\n \"\"\"Summary DataFrame of the dataset with example path for similar data.\"\"\"\n\n columns = ['dim_0', 'dim_1', 'dim_2', 'voxel_0', 'voxel_1', 'voxel_2', 'orientation']\n\n return self.df.groupby(columns, as_index=False).agg(\n example_path=('path', 'min'), total=('path', 'size')\n ).sort_values('total', ascending=False)\n\n def get_suggestion(self, include_patch_size: bool = False):\n \"\"\"Returns suggested preprocessing parameters as a dictionary.\n\n The returned target_spacing is derived from the mode (most common value)\n of voxel spacings in the dataset.\n\n Note:\n apply_reorder is NOT included in the return value because it is not\n data-derived. Access dataset.apply_reorder directly if needed.\n\n Args:\n include_patch_size: If True, includes suggested patch_size for\n patch-based training. Requires vision_patch module.\n\n Returns:\n dict: {'target_spacing': [voxel_0, voxel_1, voxel_2]}\n If include_patch_size=True, also includes 'patch_size': [dim_0, dim_1, dim_2]\n \"\"\"\n target_spacing = [float(self.df.voxel_0.mode()[0]), float(self.df.voxel_1.mode()[0]), float(self.df.voxel_2.mode()[0])]\n result = {'target_spacing': target_spacing}\n\n if include_patch_size:\n result['patch_size'] = suggest_patch_size(self)\n\n return result\n\n def _get_data_info(self, fn: str):\n \"\"\"Private method to collect information about an image file.\"\"\"\n try:\n _, o, _ = med_img_reader(fn, apply_reorder=self.apply_reorder, only_tensor=False, dtype=self.dtype)\n\n # Hash loaded tensor data (implicitly captures apply_reorder state)\n content_hash = hashlib.md5(o.data.numpy().tobytes()).hexdigest()\n\n info_dict = {'path': fn, 'content_hash': content_hash, 'dim_0': o.shape[1], 'dim_1': o.shape[2], 'dim_2': o.shape[3],\n 'voxel_0': round(o.spacing[0], 4), 'voxel_1': round(o.spacing[1], 4), 'voxel_2': round(o.spacing[2], 4),\n 'orientation': f'{\"\".join(o.orientation)}+'}\n\n if self.dtype is MedMask:\n # Calculate voxel volume in mm³\n voxel_volume = o.spacing[0] * o.spacing[1] * o.spacing[2]\n\n # Get voxel counts for each label\n mask_labels_dict = o.count_labels()\n\n # Calculate volumes for each label > 0 (skip background)\n for key, voxel_count in mask_labels_dict.items():\n label_int = int(key)\n if label_int > 0 and voxel_count > 0: # Skip background (label 0)\n volume_mm3 = voxel_count * voxel_volume\n info_dict[f'label_{label_int}_volume_mm3'] = round(volume_mm3, 4)\n\n return info_dict\n\n except Exception as e:\n print(f\"Warning: Failed to process {fn}: {e}\")\n return {'path': fn, 'error': str(e)}\n\n def _load_cache(self):\n \"\"\"Load metadata cache from disk. Returns empty dict on any failure.\"\"\"\n try:\n with open(self.cache_path, 'rb') as f:\n cache = pickle.load(f)\n if not isinstance(cache, dict) or cache.get('version') != 1:\n return {}\n return cache.get('entries', {})\n except Exception:\n return {}\n\n def _save_cache(self, entries):\n \"\"\"Save metadata cache to disk.\"\"\"\n try:\n cache = {'version': 1, 'entries': entries}\n with open(self.cache_path, 'wb') as f:\n pickle.dump(cache, f)\n except Exception as e:\n print(f\"Warning: Failed to save metadata cache: {e}\")\n\n def _process_with_cache(self, file_list):\n \"\"\"Process file list with per-file metadata caching.\n\n Loads existing cache, identifies files that need reprocessing\n (cache misses), processes only those files, updates and saves\n the cache, then returns all results in original order.\n \"\"\"\n entries = self._load_cache()\n\n cached_results = []\n files_to_process = []\n process_indices = []\n\n for i, fn in enumerate(file_list):\n abs_path = os.path.abspath(fn)\n entry = entries.get(abs_path)\n if entry is not None:\n try:\n stat = os.stat(fn)\n if (entry.get('mtime') == stat.st_mtime\n and entry.get('size') == stat.st_size\n and entry.get('apply_reorder') == self.apply_reorder\n and entry.get('dtype') == self.dtype.__name__):\n cached_results.append((i, entry['info']))\n continue\n except OSError:\n pass\n files_to_process.append(fn)\n process_indices.append(i)\n\n # Process cache misses\n if files_to_process:\n with ThreadPoolExecutor(max_workers=self.max_workers) as executor:\n fresh_results = list(executor.map(self._get_data_info, files_to_process))\n else:\n fresh_results = []\n\n # Update cache entries with fresh results (skip errors)\n for fn, info in zip(files_to_process, fresh_results):\n if 'error' not in info:\n abs_path = os.path.abspath(fn)\n try:\n stat = os.stat(fn)\n entries[abs_path] = {\n 'mtime': stat.st_mtime,\n 'size': stat.st_size,\n 'apply_reorder': self.apply_reorder,\n 'dtype': self.dtype.__name__,\n 'info': info\n }\n except OSError:\n pass\n\n self._save_cache(entries)\n\n # Reconstruct results in original file_list order\n all_results = [None] * len(file_list)\n for i, info in cached_results:\n all_results[i] = info\n for i, info in zip(process_indices, fresh_results):\n all_results[i] = info\n\n n_cached = len(cached_results)\n n_processed = len(files_to_process)\n if n_cached > 0:\n print(f\"MedDataset cache: {n_cached} cached, {n_processed} processed\")\n elif n_processed > 0:\n print(f\"MedDataset: processed {n_processed} files (results cached)\")\n\n return all_results\n\n def calculate_target_size(self, target_spacing: list = None) -> list:\n \"\"\"Calculate the target image size for the dataset.\n\n .. deprecated::\n Use `get_size_statistics(target_spacing)['max']` instead for consistency\n with other size statistics methods.\n\n Args:\n target_spacing: If provided, calculates size after resampling to this spacing.\n If None, returns original dimensions.\n\n Returns:\n list: [dim_0, dim_1, dim_2] largest dimensions in dataset.\n \"\"\"\n warnings.warn(\n \"calculate_target_size() is deprecated. \"\n \"Use get_size_statistics(target_spacing)['max'] instead.\",\n DeprecationWarning,\n stacklevel=2\n )\n if target_spacing is not None:\n org_voxels = self.df[[\"voxel_0\", \"voxel_1\", 'voxel_2']].values\n org_dims = self.df[[\"dim_0\", \"dim_1\", 'dim_2']].values\n\n ratio = org_voxels/target_spacing\n new_dims = (org_dims * ratio).T\n # Use floor() to match TorchIO's Resample dimension calculation\n dims = [float(np.floor(new_dims[0].max())), float(np.floor(new_dims[1].max())), float(np.floor(new_dims[2].max()))]\n else:\n dims = [float(self.df.dim_0.max()), float(self.df.dim_1.max()), float(self.df.dim_2.max())]\n\n return dims\n\n def get_size_statistics(self, target_spacing: list = None) -> dict:\n \"\"\"Calculate comprehensive size statistics for the dataset.\n\n Args:\n target_spacing: If provided, calculates statistics after\n simulating resampling to this spacing.\n\n Returns:\n dict with keys: 'median', 'min', 'max', 'std', 'percentile_10', 'percentile_90'\n Each value is a list [dim_0, dim_1, dim_2].\n \"\"\"\n if len(self.df) == 0:\n raise ValueError(\"Dataset is empty - cannot calculate statistics\")\n\n if target_spacing is not None:\n # Simulate resampled dimensions\n org_voxels = self.df[[\"voxel_0\", \"voxel_1\", \"voxel_2\"]].values\n org_dims = self.df[[\"dim_0\", \"dim_1\", \"dim_2\"]].values\n ratio = org_voxels / np.array(target_spacing)\n dims = np.floor(org_dims * ratio)\n else:\n dims = self.df[[\"dim_0\", \"dim_1\", \"dim_2\"]].values\n\n return {\n 'median': [float(np.median(dims[:, i])) for i in range(3)],\n 'min': [float(np.min(dims[:, i])) for i in range(3)],\n 'max': [float(np.max(dims[:, i])) for i in range(3)],\n 'std': [float(np.std(dims[:, i])) for i in range(3)],\n 'percentile_10': [float(np.percentile(dims[:, i], 10)) for i in range(3)],\n 'percentile_90': [float(np.percentile(dims[:, i], 90)) for i in range(3)],\n }\n\n def get_volume_summary(self):\n \"\"\"Returns DataFrame with volume statistics for each label.\n\n Returns:\n DataFrame with columns: label, count, mean_mm3, median_mm3, min_mm3, max_mm3\n Returns None if no volume columns found (dtype was not MedMask).\n \"\"\"\n volume_cols = [col for col in self.df.columns if col.endswith('_volume_mm3')]\n\n if not volume_cols:\n return None\n\n summary_data = []\n for col in volume_cols:\n non_zero = self.df[self.df[col] > 0][col]\n if len(non_zero) > 0:\n summary_data.append({\n 'label': col.replace('_volume_mm3', ''),\n 'count': len(non_zero),\n 'mean_mm3': non_zero.mean(),\n 'median_mm3': non_zero.median(),\n 'min_mm3': non_zero.min(),\n 'max_mm3': non_zero.max()\n })\n\n return pd.DataFrame(summary_data) if summary_data else None\n\n def _visualize_single_case(self, img_path, mask_path, case_id, anatomical_plane=2, cmap='hot', figsize=(12, 5)):\n \"\"\"Helper method to visualize a single case.\"\"\"\n try:\n # Create MedImage and MedMask with current preprocessing settings\n suggestion = self.get_suggestion()\n MedBase.item_preprocessing(target_spacing=suggestion['target_spacing'], apply_reorder=self.apply_reorder)\n\n img = MedImage.create(img_path)\n mask = MedMask.create(mask_path)\n\n # Find optimal slice using explicit function\n mask_data = mask.numpy()[0] # Remove channel dimension\n optimal_slice = find_max_slice(mask_data, anatomical_plane)\n\n # Create subplot\n fig, axes = plt.subplots(1, 2, figsize=figsize)\n\n # Show image\n img.show(ctx=axes[0], anatomical_plane=anatomical_plane, slice_index=optimal_slice)\n axes[0].set_title(f\"{case_id} - Image (slice {optimal_slice})\")\n\n # Show overlay\n img.show(ctx=axes[1], anatomical_plane=anatomical_plane, slice_index=optimal_slice)\n mask.show(ctx=axes[1], anatomical_plane=anatomical_plane, slice_index=optimal_slice,\n alpha=0.3, cmap=cmap)\n axes[1].set_title(f\"{case_id} - Overlay (slice {optimal_slice})\")\n\n # Adjust spacing to bring plots closer\n plt.subplots_adjust(wspace=0.1)\n plt.tight_layout()\n plt.show()\n\n except Exception as e:\n print(f\"Failed to visualize case {case_id}: {e}\")\n\n def visualize_cases(self, n_cases=4, anatomical_plane=2, cmap='hot', figsize=(12, 5)):\n \"\"\"Visualize cases from the dataset.\n\n Args:\n n_cases: Number of cases to show.\n anatomical_plane: 0=sagittal, 1=coronal, 2=axial\n cmap: Colormap for mask overlay\n figsize: Figure size for each case\n \"\"\"\n if self.input_df is None:\n print(\"Error: No dataframe provided. Cannot visualize cases.\")\n return\n\n if self.image_col is None:\n print(\"Error: No image_col specified. Cannot visualize cases.\")\n return\n\n # Check if required columns exist\n if self.image_col not in self.input_df.columns:\n print(f\"Error: Column '{self.image_col}' not found in dataframe.\")\n return\n\n if self.mask_col not in self.input_df.columns:\n print(f\"Error: Column '{self.mask_col}' not found in dataframe.\")\n return\n\n for idx in range(min(n_cases, len(self.input_df))):\n row = self.input_df.iloc[idx]\n case_id = row.get('case_id', f'Case_{idx}') # Fallback if no case_id\n img_path = row[self.image_col]\n mask_path = row[self.mask_col]\n\n self._visualize_single_case(img_path, mask_path, case_id, anatomical_plane, cmap, figsize)\n print(\"-\" * 60)" }, { "cell_type": "code", diff --git a/nbs/10_vision_patch.ipynb b/nbs/10_vision_patch.ipynb index 27716c3..92067c3 100644 --- a/nbs/10_vision_patch.ipynb +++ b/nbs/10_vision_patch.ipynb @@ -764,391 +764,7 @@ "id": "cell-15", "metadata": {}, "outputs": [], - "source": [ - "#| export\n", - "class MedPatchDataLoaders:\n", - " \"\"\"fastai-compatible DataLoaders for patch-based training with LAZY loading.\n", - "\n", - " This class provides train and validation DataLoaders that work with\n", - " fastai's Learner for patch-based training on 3D medical images.\n", - "\n", - " Memory-efficient: Volumes are loaded on-demand by Queue workers,\n", - " keeping memory usage constant (~150 MB) regardless of dataset size.\n", - "\n", - " Note: Validation uses the same sampling as training (pseudo Dice).\n", - " For true validation metrics, use PatchInferenceEngine with GridSampler\n", - " for full-volume sliding window inference.\n", - "\n", - " Example:\n", - " >>> import torchio as tio\n", - " >>>\n", - " >>> # New pattern: preprocessing params in config (DRY)\n", - " >>> config = PatchConfig(\n", - " ... patch_size=[96, 96, 96],\n", - " ... apply_reorder=True,\n", - " ... target_spacing=[0.5, 0.5, 0.5]\n", - " ... )\n", - " >>> dls = MedPatchDataLoaders.from_df(\n", - " ... df, img_col='image', mask_col='label',\n", - " ... valid_pct=0.2,\n", - " ... patch_config=config,\n", - " ... pre_patch_tfms=[tio.ZNormalization()],\n", - " ... bs=4\n", - " ... )\n", - " >>> learn = Learner(dls, model, loss_func=DiceLoss())\n", - " \"\"\"\n", - "\n", - " def __init__(\n", - " self,\n", - " train_dl: MedPatchDataLoader,\n", - " valid_dl: MedPatchDataLoader,\n", - " device: torch.device = None\n", - " ):\n", - " self._train_dl = train_dl\n", - " self._valid_dl = valid_dl\n", - " self._device = device or _get_default_device()\n", - "\n", - " # Move to device\n", - " self._train_dl.to(self._device)\n", - " self._valid_dl.to(self._device)\n", - "\n", - " # Track cleanup state\n", - " self._closed = False\n", - "\n", - " @classmethod\n", - " def from_df(\n", - " cls,\n", - " df: pd.DataFrame,\n", - " img_col: str,\n", - " mask_col: str = None,\n", - " valid_pct: float = 0.2,\n", - " valid_col: str = None,\n", - " patch_config: PatchConfig = None,\n", - " pre_patch_tfms: list = None,\n", - " patch_tfms: list = None,\n", - " apply_reorder: bool = None,\n", - " target_spacing: list = None,\n", - " bs: int = 4,\n", - " seed: int = None,\n", - " device: torch.device = None,\n", - " ensure_affine_consistency: bool = True\n", - " ) -> 'MedPatchDataLoaders':\n", - " \"\"\"Create train/valid DataLoaders from DataFrame with LAZY loading.\n", - "\n", - " Memory-efficient: Only file paths are stored at creation time.\n", - " Volumes are loaded on-demand by Queue workers during training.\n", - "\n", - " Note: Both train and valid use the same sampling strategy from patch_config.\n", - " This gives pseudo Dice during training. For true validation metrics,\n", - " use PatchInferenceEngine with full-volume sliding window inference.\n", - "\n", - " Args:\n", - " df: DataFrame with image paths.\n", - " img_col: Column name for image paths.\n", - " mask_col: Column name for mask paths.\n", - " valid_pct: Fraction of data for validation.\n", - " valid_col: Column name for train/valid split (if pre-defined).\n", - " patch_config: PatchConfig instance. Preprocessing params (apply_reorder,\n", - " target_spacing) can be set here for DRY usage with PatchInferenceEngine.\n", - " pre_patch_tfms: TorchIO transforms applied before patch extraction\n", - " (after reorder/resample). Example: [tio.ZNormalization()].\n", - " Accepts both fastMONAI wrappers and raw TorchIO transforms.\n", - " patch_tfms: TorchIO transforms applied to extracted patches (training only).\n", - " apply_reorder: If True, reorder to RAS+ orientation. If None, uses\n", - " patch_config.apply_reorder. Explicit value overrides config.\n", - " target_spacing: Target voxel spacing [x, y, z]. If None, uses\n", - " patch_config.target_spacing. Explicit value overrides config.\n", - " bs: Batch size.\n", - " seed: Random seed for splitting.\n", - " device: Device to use.\n", - " ensure_affine_consistency: If True and mask_col is provided, automatically\n", - " adds tio.CopyAffine(target='image') as the first transform to prevent\n", - " spatial metadata mismatch errors. Defaults to True.\n", - "\n", - " Returns:\n", - " MedPatchDataLoaders instance.\n", - "\n", - " Example:\n", - " >>> # New pattern: config contains preprocessing params\n", - " >>> config = PatchConfig(\n", - " ... patch_size=[96, 96, 96],\n", - " ... apply_reorder=True,\n", - " ... target_spacing=[0.5, 0.5, 0.5],\n", - " ... label_probabilities={0: 0.1, 1: 0.9}\n", - " ... )\n", - " >>> dls = MedPatchDataLoaders.from_df(\n", - " ... df, img_col='image', mask_col='label',\n", - " ... patch_config=config,\n", - " ... pre_patch_tfms=[tio.ZNormalization()],\n", - " ... patch_tfms=[tio.RandomAffine(degrees=10), tio.RandomFlip()],\n", - " ... bs=4\n", - " ... )\n", - " >>> # Memory: ~150 MB (queue buffer only)\n", - " \"\"\"\n", - " if patch_config is None:\n", - " patch_config = PatchConfig()\n", - "\n", - " # Use config values, allow explicit overrides for backward compatibility\n", - " _apply_reorder = apply_reorder if apply_reorder is not None else patch_config.apply_reorder\n", - " _target_spacing = target_spacing if target_spacing is not None else patch_config.target_spacing\n", - "\n", - " # Warn if both config and explicit args provided with different values\n", - " _warn_config_override('apply_reorder', patch_config.apply_reorder, apply_reorder)\n", - " _warn_config_override('target_spacing', patch_config.target_spacing, target_spacing)\n", - "\n", - " # Split data\n", - " if valid_col is not None:\n", - " train_df = df[df[valid_col] == False].reset_index(drop=True)\n", - " valid_df = df[df[valid_col] == True].reset_index(drop=True)\n", - " else:\n", - " if seed is not None:\n", - " np.random.seed(seed)\n", - " n = len(df)\n", - " valid_idx = np.random.choice(n, size=int(n * valid_pct), replace=False)\n", - " train_idx = np.setdiff1d(np.arange(n), valid_idx)\n", - " train_df = df.iloc[train_idx].reset_index(drop=True)\n", - " valid_df = df.iloc[valid_idx].reset_index(drop=True)\n", - "\n", - " # Build preprocessing transforms\n", - " all_pre_tfms = []\n", - "\n", - " # Add reorder transform (reorder to RAS+ orientation)\n", - " if _apply_reorder:\n", - " all_pre_tfms.append(tio.ToCanonical())\n", - "\n", - " # Add resample transform\n", - " if _target_spacing is not None:\n", - " all_pre_tfms.append(tio.Resample(_target_spacing))\n", - "\n", - " # Add user-provided transforms (normalize to raw TorchIO transforms)\n", - " if pre_patch_tfms:\n", - " all_pre_tfms.extend(normalize_patch_transforms(pre_patch_tfms))\n", - "\n", - " # Create subjects datasets with lazy loading (paths only, ~0 MB)\n", - " train_subjects = create_subjects_dataset(\n", - " train_df, img_col, mask_col,\n", - " pre_tfms=all_pre_tfms if all_pre_tfms else None,\n", - " ensure_affine_consistency=ensure_affine_consistency\n", - " )\n", - " valid_subjects = create_subjects_dataset(\n", - " valid_df, img_col, mask_col,\n", - " pre_tfms=all_pre_tfms if all_pre_tfms else None,\n", - " ensure_affine_consistency=ensure_affine_consistency\n", - " )\n", - "\n", - " # Create DataLoaders (both use same patch_config for consistent sampling)\n", - " train_dl = MedPatchDataLoader(\n", - " train_subjects, patch_config, bs,\n", - " patch_tfms=patch_tfms, shuffle=True, drop_last=True\n", - " )\n", - " valid_dl = MedPatchDataLoader(\n", - " valid_subjects, patch_config, bs,\n", - " patch_tfms=None, # No augmentation for validation\n", - " shuffle=False, drop_last=False\n", - " )\n", - "\n", - " # Create instance and store metadata\n", - " instance = cls(train_dl, valid_dl, device)\n", - " instance._img_col = img_col\n", - " instance._mask_col = mask_col\n", - " instance._pre_patch_tfms = pre_patch_tfms\n", - " instance._apply_reorder = _apply_reorder\n", - " instance._target_spacing = _target_spacing\n", - " instance._ensure_affine_consistency = ensure_affine_consistency\n", - " instance._patch_config = patch_config\n", - " return instance\n", - "\n", - " @property\n", - " def train(self):\n", - " \"\"\"Training DataLoader.\"\"\"\n", - " return self._train_dl\n", - "\n", - " @property\n", - " def valid(self):\n", - " \"\"\"Validation DataLoader.\"\"\"\n", - " return self._valid_dl\n", - "\n", - " @property\n", - " def train_ds(self):\n", - " \"\"\"Training subjects dataset.\"\"\"\n", - " return self._train_dl.subjects_dataset\n", - "\n", - " @property\n", - " def valid_ds(self):\n", - " \"\"\"Validation subjects dataset.\"\"\"\n", - " return self._valid_dl.subjects_dataset\n", - "\n", - " @property\n", - " def device(self):\n", - " \"\"\"Current device.\"\"\"\n", - " return self._device\n", - "\n", - " @property\n", - " def bs(self):\n", - " \"\"\"Batch size.\"\"\"\n", - " return self._train_dl.bs\n", - "\n", - " @property\n", - " def apply_reorder(self):\n", - " \"\"\"Whether reordering to RAS+ is enabled.\"\"\"\n", - " return getattr(self, '_apply_reorder', False)\n", - "\n", - " @property\n", - " def target_spacing(self):\n", - " \"\"\"Target voxel spacing for resampling.\"\"\"\n", - " return getattr(self, '_target_spacing', None)\n", - "\n", - " @property\n", - " def patch_config(self):\n", - " \"\"\"The PatchConfig used for this DataLoaders.\"\"\"\n", - " return getattr(self, '_patch_config', None)\n", - "\n", - " def to(self, device):\n", - " \"\"\"Move DataLoaders to device.\"\"\"\n", - " self._device = device\n", - " self._train_dl.to(device)\n", - " self._valid_dl.to(device)\n", - " return self\n", - "\n", - " def __iter__(self):\n", - " \"\"\"Iterate over training DataLoader.\"\"\"\n", - " return iter(self._train_dl)\n", - "\n", - " def one_batch(self):\n", - " \"\"\"Return one batch from the training DataLoader.\n", - "\n", - " Required for fastai Learner compatibility - used for device\n", - " detection and batch shape validation.\n", - " \"\"\"\n", - " return self._train_dl.one_batch()\n", - "\n", - " def __len__(self):\n", - " \"\"\"Return number of batches in training DataLoader.\"\"\"\n", - " return len(self._train_dl)\n", - "\n", - " def __getitem__(self, idx):\n", - " \"\"\"Get DataLoader by index. Required for fastai Learner compatibility.\n", - "\n", - " Args:\n", - " idx: 0 for training DataLoader, 1 for validation DataLoader.\n", - "\n", - " Returns:\n", - " MedPatchDataLoader instance.\n", - " \"\"\"\n", - " if idx == 0:\n", - " return self._train_dl\n", - " elif idx == 1:\n", - " return self._valid_dl\n", - " else:\n", - " raise IndexError(f\"Index {idx} out of range. Use 0 (train) or 1 (valid).\")\n", - "\n", - " def cuda(self):\n", - " \"\"\"Move DataLoaders to CUDA device.\"\"\"\n", - " return self.to(torch.device('cuda'))\n", - "\n", - " def cpu(self):\n", - " \"\"\"Move DataLoaders to CPU.\"\"\"\n", - " return self.to(torch.device('cpu'))\n", - "\n", - " def show_batch(self, dl_idx=0, max_n=6, figsize=None, channel=0,\n", - " slice_index=None, anatomical_plane=0, overlay=False, **kwargs):\n", - " \"\"\"Show a batch of patch samples for visualization.\"\"\"\n", - "\n", - " dl = self[dl_idx]\n", - " x, y = dl.one_batch()\n", - " x = x.cpu()\n", - " if y is not None: y = y.cpu()\n", - "\n", - " nrows = min(x.shape[0], max_n)\n", - " has_mask = y is not None\n", - "\n", - " if overlay and has_mask:\n", - " ncols = x.shape[1]\n", - " else:\n", - " ncols = x.shape[1] + (1 if has_mask else 0)\n", - "\n", - " if figsize is None:\n", - " figsize = (ncols * 3, nrows * 3)\n", - " fig, axs = plt.subplots(nrows, ncols, figsize=figsize, squeeze=False)\n", - " flat_axs = axs.flatten()\n", - "\n", - " imgs, masks_for_overlay, slice_idxs = [], [], []\n", - " for i in range(nrows):\n", - " img = x[i]\n", - " im_channels = [MedImage(c_img[None]) for c_img in img]\n", - "\n", - " if has_mask:\n", - " mask = y[i]\n", - " idx = find_max_slice(mask[0].numpy(), anatomical_plane) if slice_index is None else slice_index\n", - " if overlay:\n", - " masks_for_overlay.extend([MedMask(mask)] * len(im_channels))\n", - " else:\n", - " im_channels.append(MedMask(mask))\n", - " else:\n", - " idx = slice_index\n", - "\n", - " imgs.extend(im_channels)\n", - " slice_idxs.extend([idx] * len(im_channels))\n", - "\n", - " with warnings.catch_warnings():\n", - " warnings.filterwarnings('ignore', message='Voxel size not defined')\n", - " ctxs = [im.show(ax=ax, slice_index=idx, anatomical_plane=anatomical_plane)\n", - " for im, ax, idx in zip(imgs, flat_axs, slice_idxs)]\n", - "\n", - " if overlay and has_mask:\n", - " for mask, ax, idx in zip(masks_for_overlay, flat_axs, slice_idxs):\n", - " mask.show(ax=ax, slice_index=idx, anatomical_plane=anatomical_plane)\n", - "\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - " def new_empty(self):\n", - " \"\"\"Create a new empty version of self for learner export.\n", - "\n", - " Required for fastai Learner.export() compatibility - creates a\n", - " lightweight placeholder that can be pickled without the full dataset.\n", - "\n", - " Returns:\n", - " A minimal MedPatchDataLoaders-like object with no data.\n", - " \"\"\"\n", - " class EmptyMedPatchDataLoaders:\n", - " \"\"\"Minimal placeholder for exported learner.\"\"\"\n", - " def __init__(self, device):\n", - " self._device = device\n", - " @property\n", - " def device(self): return self._device\n", - " def to(self, device):\n", - " self._device = device\n", - " return self\n", - " def cpu(self):\n", - " \"\"\"Move to CPU. Required for load_learner compatibility.\"\"\"\n", - " return self.to(torch.device('cpu'))\n", - "\n", - " return EmptyMedPatchDataLoaders(self._device)\n", - "\n", - " def close(self):\n", - " \"\"\"Shut down all DataLoader workers. Safe to call multiple times.\"\"\"\n", - " if self._closed:\n", - " return\n", - " self._closed = True\n", - " if hasattr(self, '_train_dl') and self._train_dl is not None:\n", - " self._train_dl.close()\n", - " if hasattr(self, '_valid_dl') and self._valid_dl is not None:\n", - " self._valid_dl.close()\n", - "\n", - " def __enter__(self):\n", - " return self\n", - "\n", - " def __exit__(self, exc_type, exc_val, exc_tb):\n", - " self.close()\n", - " return False\n", - "\n", - " def __del__(self):\n", - " try:\n", - " self.close()\n", - " except Exception:\n", - " pass\n" - ] + "source": "#| export\nclass MedPatchDataLoaders:\n \"\"\"fastai-compatible DataLoaders for patch-based training with LAZY loading.\n\n This class provides train and validation DataLoaders that work with\n fastai's Learner for patch-based training on 3D medical images.\n\n Memory-efficient: Volumes are loaded on-demand by Queue workers,\n keeping memory usage constant (~150 MB) regardless of dataset size.\n\n Note: Validation uses the same sampling as training (pseudo Dice).\n For true validation metrics, use PatchInferenceEngine with GridSampler\n for full-volume sliding window inference.\n\n Example:\n >>> import torchio as tio\n >>>\n >>> # New pattern: preprocessing params in config (DRY)\n >>> config = PatchConfig(\n ... patch_size=[96, 96, 96],\n ... apply_reorder=True,\n ... target_spacing=[0.5, 0.5, 0.5]\n ... )\n >>> dls = MedPatchDataLoaders.from_df(\n ... df, img_col='image', mask_col='label',\n ... valid_pct=0.2,\n ... patch_config=config,\n ... pre_patch_tfms=[tio.ZNormalization()],\n ... bs=4\n ... )\n >>> learn = Learner(dls, model, loss_func=DiceLoss())\n \"\"\"\n\n def __init__(\n self,\n train_dl: MedPatchDataLoader,\n valid_dl: MedPatchDataLoader,\n device: torch.device = None\n ):\n self._train_dl = train_dl\n self._valid_dl = valid_dl\n self._device = device or _get_default_device()\n\n # Move to device\n self._train_dl.to(self._device)\n self._valid_dl.to(self._device)\n\n # Track cleanup state\n self._closed = False\n\n @classmethod\n def from_df(\n cls,\n df: pd.DataFrame,\n img_col: str,\n mask_col: str = None,\n valid_pct: float = 0.2,\n valid_col: str = None,\n patch_config: PatchConfig = None,\n pre_patch_tfms: list = None,\n patch_tfms: list = None,\n apply_reorder: bool = None,\n target_spacing: list = None,\n bs: int = 4,\n seed: int = None,\n device: torch.device = None,\n ensure_affine_consistency: bool = True\n ) -> 'MedPatchDataLoaders':\n \"\"\"Create train/valid DataLoaders from DataFrame with LAZY loading.\n\n Memory-efficient: Only file paths are stored at creation time.\n Volumes are loaded on-demand by Queue workers during training.\n\n Note: Both train and valid use the same sampling strategy from patch_config.\n This gives pseudo Dice during training. For true validation metrics,\n use PatchInferenceEngine with full-volume sliding window inference.\n\n Args:\n df: DataFrame with image paths.\n img_col: Column name for image paths.\n mask_col: Column name for mask paths.\n valid_pct: Fraction of data for validation.\n valid_col: Column name for train/valid split (if pre-defined).\n patch_config: PatchConfig instance. Preprocessing params (apply_reorder,\n target_spacing) can be set here for DRY usage with PatchInferenceEngine.\n pre_patch_tfms: TorchIO transforms applied before patch extraction\n (after reorder/resample). Example: [tio.ZNormalization()].\n Accepts both fastMONAI wrappers and raw TorchIO transforms.\n patch_tfms: TorchIO transforms applied to extracted patches (training only).\n apply_reorder: If True, reorder to RAS+ orientation. If None, uses\n patch_config.apply_reorder. Explicit value overrides config.\n target_spacing: Target voxel spacing [x, y, z]. If None, uses\n patch_config.target_spacing. Explicit value overrides config.\n bs: Batch size.\n seed: Random seed for splitting.\n device: Device to use.\n ensure_affine_consistency: If True and mask_col is provided, automatically\n adds tio.CopyAffine(target='image') as the first transform to prevent\n spatial metadata mismatch errors. Defaults to True.\n\n Returns:\n MedPatchDataLoaders instance.\n\n Example:\n >>> # New pattern: config contains preprocessing params\n >>> config = PatchConfig(\n ... patch_size=[96, 96, 96],\n ... apply_reorder=True,\n ... target_spacing=[0.5, 0.5, 0.5],\n ... label_probabilities={0: 0.1, 1: 0.9}\n ... )\n >>> dls = MedPatchDataLoaders.from_df(\n ... df, img_col='image', mask_col='label',\n ... patch_config=config,\n ... pre_patch_tfms=[tio.ZNormalization()],\n ... patch_tfms=[tio.RandomAffine(degrees=10), tio.RandomFlip()],\n ... bs=4\n ... )\n >>> # Memory: ~150 MB (queue buffer only)\n \"\"\"\n if patch_config is None:\n patch_config = PatchConfig()\n\n # Use config values, allow explicit overrides for backward compatibility\n _apply_reorder = apply_reorder if apply_reorder is not None else patch_config.apply_reorder\n _target_spacing = target_spacing if target_spacing is not None else patch_config.target_spacing\n\n # Warn if both config and explicit args provided with different values\n _warn_config_override('apply_reorder', patch_config.apply_reorder, apply_reorder)\n _warn_config_override('target_spacing', patch_config.target_spacing, target_spacing)\n\n # Split data\n if valid_col is not None:\n train_df = df[df[valid_col] == False].reset_index(drop=True)\n valid_df = df[df[valid_col] == True].reset_index(drop=True)\n else:\n if seed is not None:\n np.random.seed(seed)\n n = len(df)\n valid_idx = np.random.choice(n, size=int(n * valid_pct), replace=False)\n train_idx = np.setdiff1d(np.arange(n), valid_idx)\n train_df = df.iloc[train_idx].reset_index(drop=True)\n valid_df = df.iloc[valid_idx].reset_index(drop=True)\n\n # Build preprocessing transforms\n all_pre_tfms = []\n\n # Add reorder transform (reorder to RAS+ orientation)\n if _apply_reorder:\n all_pre_tfms.append(tio.ToCanonical())\n\n # Add resample transform\n if _target_spacing is not None:\n all_pre_tfms.append(tio.Resample(_target_spacing))\n\n # Add user-provided transforms (normalize to raw TorchIO transforms)\n if pre_patch_tfms:\n all_pre_tfms.extend(normalize_patch_transforms(pre_patch_tfms))\n\n # Create subjects datasets with lazy loading (paths only, ~0 MB)\n train_subjects = create_subjects_dataset(\n train_df, img_col, mask_col,\n pre_tfms=all_pre_tfms if all_pre_tfms else None,\n ensure_affine_consistency=ensure_affine_consistency\n )\n valid_subjects = create_subjects_dataset(\n valid_df, img_col, mask_col,\n pre_tfms=all_pre_tfms if all_pre_tfms else None,\n ensure_affine_consistency=ensure_affine_consistency\n )\n\n # Create DataLoaders (both use same patch_config for consistent sampling)\n train_dl = MedPatchDataLoader(\n train_subjects, patch_config, bs,\n patch_tfms=patch_tfms, shuffle=True, drop_last=True\n )\n valid_dl = MedPatchDataLoader(\n valid_subjects, patch_config, bs,\n patch_tfms=None, # No augmentation for validation\n shuffle=False, drop_last=False\n )\n\n # Create instance and store metadata\n instance = cls(train_dl, valid_dl, device)\n instance._img_col = img_col\n instance._mask_col = mask_col\n instance._pre_patch_tfms = pre_patch_tfms\n instance._apply_reorder = _apply_reorder\n instance._target_spacing = _target_spacing\n instance._ensure_affine_consistency = ensure_affine_consistency\n instance._patch_config = patch_config\n instance._train_source_df = train_df\n instance._valid_source_df = valid_df\n return instance\n\n @property\n def train(self):\n \"\"\"Training DataLoader.\"\"\"\n return self._train_dl\n\n @property\n def valid(self):\n \"\"\"Validation DataLoader.\"\"\"\n return self._valid_dl\n\n @property\n def train_ds(self):\n \"\"\"Training subjects dataset.\"\"\"\n return self._train_dl.subjects_dataset\n\n @property\n def valid_ds(self):\n \"\"\"Validation subjects dataset.\"\"\"\n return self._valid_dl.subjects_dataset\n\n @property\n def device(self):\n \"\"\"Current device.\"\"\"\n return self._device\n\n @property\n def bs(self):\n \"\"\"Batch size.\"\"\"\n return self._train_dl.bs\n\n @property\n def apply_reorder(self):\n \"\"\"Whether reordering to RAS+ is enabled.\"\"\"\n return getattr(self, '_apply_reorder', False)\n\n @property\n def target_spacing(self):\n \"\"\"Target voxel spacing for resampling.\"\"\"\n return getattr(self, '_target_spacing', None)\n\n @property\n def patch_config(self):\n \"\"\"The PatchConfig used for this DataLoaders.\"\"\"\n return getattr(self, '_patch_config', None)\n\n @property\n def split_df(self):\n \"\"\"DataFrame recording train/valid split for reproducibility logging.\"\"\"\n train = self._train_source_df.assign(is_valid=False)\n valid = self._valid_source_df.assign(is_valid=True)\n return pd.concat([train, valid], ignore_index=True)\n\n def to(self, device):\n \"\"\"Move DataLoaders to device.\"\"\"\n self._device = device\n self._train_dl.to(device)\n self._valid_dl.to(device)\n return self\n\n def __iter__(self):\n \"\"\"Iterate over training DataLoader.\"\"\"\n return iter(self._train_dl)\n\n def one_batch(self):\n \"\"\"Return one batch from the training DataLoader.\n\n Required for fastai Learner compatibility - used for device\n detection and batch shape validation.\n \"\"\"\n return self._train_dl.one_batch()\n\n def __len__(self):\n \"\"\"Return number of batches in training DataLoader.\"\"\"\n return len(self._train_dl)\n\n def __getitem__(self, idx):\n \"\"\"Get DataLoader by index. Required for fastai Learner compatibility.\n\n Args:\n idx: 0 for training DataLoader, 1 for validation DataLoader.\n\n Returns:\n MedPatchDataLoader instance.\n \"\"\"\n if idx == 0:\n return self._train_dl\n elif idx == 1:\n return self._valid_dl\n else:\n raise IndexError(f\"Index {idx} out of range. Use 0 (train) or 1 (valid).\")\n\n def cuda(self):\n \"\"\"Move DataLoaders to CUDA device.\"\"\"\n return self.to(torch.device('cuda'))\n\n def cpu(self):\n \"\"\"Move DataLoaders to CPU.\"\"\"\n return self.to(torch.device('cpu'))\n\n def show_batch(self, dl_idx=0, max_n=6, figsize=None, channel=0,\n slice_index=None, anatomical_plane=0, overlay=False, **kwargs):\n \"\"\"Show a batch of patch samples for visualization.\"\"\"\n\n dl = self[dl_idx]\n x, y = dl.one_batch()\n x = x.cpu()\n if y is not None: y = y.cpu()\n\n nrows = min(x.shape[0], max_n)\n has_mask = y is not None\n\n if overlay and has_mask:\n ncols = x.shape[1]\n else:\n ncols = x.shape[1] + (1 if has_mask else 0)\n\n if figsize is None:\n figsize = (ncols * 3, nrows * 3)\n fig, axs = plt.subplots(nrows, ncols, figsize=figsize, squeeze=False)\n flat_axs = axs.flatten()\n\n imgs, masks_for_overlay, slice_idxs = [], [], []\n for i in range(nrows):\n img = x[i]\n im_channels = [MedImage(c_img[None]) for c_img in img]\n\n if has_mask:\n mask = y[i]\n idx = find_max_slice(mask[0].numpy(), anatomical_plane) if slice_index is None else slice_index\n if overlay:\n masks_for_overlay.extend([MedMask(mask)] * len(im_channels))\n else:\n im_channels.append(MedMask(mask))\n else:\n idx = slice_index\n\n imgs.extend(im_channels)\n slice_idxs.extend([idx] * len(im_channels))\n\n voxel_size = self.target_spacing\n ctxs = [im.show(ax=ax, slice_index=idx, anatomical_plane=anatomical_plane,\n voxel_size=voxel_size)\n for im, ax, idx in zip(imgs, flat_axs, slice_idxs)]\n\n if overlay and has_mask:\n for mask, ax, idx in zip(masks_for_overlay, flat_axs, slice_idxs):\n mask.show(ax=ax, slice_index=idx, anatomical_plane=anatomical_plane,\n voxel_size=voxel_size)\n\n plt.tight_layout()\n plt.show()\n\n def new_empty(self):\n \"\"\"Create a new empty version of self for learner export.\n\n Required for fastai Learner.export() compatibility - creates a\n lightweight placeholder that can be pickled without the full dataset.\n\n Returns:\n A minimal MedPatchDataLoaders-like object with no data.\n \"\"\"\n class EmptyMedPatchDataLoaders:\n \"\"\"Minimal placeholder for exported learner.\"\"\"\n def __init__(self, device):\n self._device = device\n @property\n def device(self): return self._device\n def to(self, device):\n self._device = device\n return self\n def cpu(self):\n \"\"\"Move to CPU. Required for load_learner compatibility.\"\"\"\n return self.to(torch.device('cpu'))\n\n return EmptyMedPatchDataLoaders(self._device)\n\n def close(self):\n \"\"\"Shut down all DataLoader workers. Safe to call multiple times.\"\"\"\n if self._closed:\n return\n self._closed = True\n if hasattr(self, '_train_dl') and self._train_dl is not None:\n self._train_dl.close()\n if hasattr(self, '_valid_dl') and self._valid_dl is not None:\n self._valid_dl.close()\n\n def __enter__(self):\n return self\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n self.close()\n return False\n\n def __del__(self):\n try:\n self.close()\n except Exception:\n pass" }, { "cell_type": "markdown", @@ -1244,7 +860,7 @@ " \n", " >>> # Option 2: From raw PyTorch model (recommended for deployment)\n", " >>> model = UNet(spatial_dims=3, in_channels=1, out_channels=2, ...)\n", - " >>> model.load_state_dict(torch.load('weights.pth'))\n", + " >>> model.load_state_dict(torch.load('final_weights.pth'))\n", " >>> model.cuda().eval()\n", " >>> engine = PatchInferenceEngine(model, config, pre_inference_tfms=[ZNormalization()])\n", " >>> pred = engine.predict('image.nii.gz')\n", diff --git a/nbs/11f_tutorial_inference.ipynb b/nbs/11f_tutorial_inference.ipynb index 0b1bf60..19d0fbf 100644 --- a/nbs/11f_tutorial_inference.ipynb +++ b/nbs/11f_tutorial_inference.ipynb @@ -97,27 +97,7 @@ "id": "f2dd02b4", "metadata": {}, "outputs": [], - "source": [ - "import mlflow\n", - "from mlflow.tracking import MlflowClient\n", - "\n", - "client = MlflowClient()\n", - "experiment = mlflow.get_experiment_by_name(task)\n", - "\n", - "# Get latest run\n", - "runs = mlflow.search_runs(\n", - " experiment_ids=[experiment.experiment_id],\n", - " order_by=[\"start_time DESC\"], \n", - " max_results=1\n", - ")\n", - "\n", - "latest_run_id = runs.iloc[0].run_id\n", - "print(f\"Loading artifacts from run: {latest_run_id}\")\n", - "\n", - "# Download artifacts\n", - "learner_path = client.download_artifacts(latest_run_id, \"model/learner.pkl\")\n", - "config_path = client.download_artifacts(latest_run_id, \"config/inference_settings.pkl\")" - ] + "source": "import mlflow\nfrom mlflow.tracking import MlflowClient\n\nclient = MlflowClient()\nexperiment = mlflow.get_experiment_by_name(task)\n\n# Get latest run\nruns = mlflow.search_runs(\n experiment_ids=[experiment.experiment_id],\n order_by=[\"start_time DESC\"], \n max_results=1\n)\n\nlatest_run_id = runs.iloc[0].run_id\nprint(f\"Loading artifacts from run: {latest_run_id}\")\n\n# Download artifacts\nlearner_path = client.download_artifacts(latest_run_id, \"model/best_learner.pkl\")\nconfig_path = client.download_artifacts(latest_run_id, \"config/inference_settings.pkl\")" }, { "cell_type": "code", diff --git a/nbs/12a_tutorial_patch_training.ipynb b/nbs/12a_tutorial_patch_training.ipynb index f91d9ef..58d6651 100644 --- a/nbs/12a_tutorial_patch_training.ipynb +++ b/nbs/12a_tutorial_patch_training.ipynb @@ -107,9 +107,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "2026-02-10 13:47:22,352 - INFO - Verified 'Task02_Heart.tar', md5: 06ee59366e1e5124267b774dbd654057.\n", - "2026-02-10 13:47:22,353 - INFO - File exists: ../data/Task02_Heart.tar, skipped downloading.\n", - "2026-02-10 13:47:22,353 - INFO - Non-empty folder exists in ../data/Task02_Heart, skipped extracting.\n" + "2026-02-12 14:35:02,956 - INFO - Verified 'Task02_Heart.tar', md5: 06ee59366e1e5124267b774dbd654057.\n", + "2026-02-12 14:35:02,956 - INFO - File exists: ../data/Task02_Heart.tar, skipped downloading.\n", + "2026-02-12 14:35:02,957 - INFO - Non-empty folder exists in ../data/Task02_Heart, skipped extracting.\n" ] } ], @@ -186,7 +186,15 @@ "execution_count": null, "id": "cell-meddataset", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MedDataset: processed 14 files (results cached)\n" + ] + } + ], "source": [ "med_dataset = MedDataset(img_list=train_df.label.tolist(), dtype=MedMask, max_workers=12)" ] @@ -219,6 +227,7 @@ " \n", " \n", " path\n", + " content_hash\n", " dim_0\n", " dim_1\n", " dim_2\n", @@ -233,6 +242,7 @@ " \n", " 0\n", " ../data/Task02_Heart/labelsTr/la_023.nii.gz\n", + " a4b0e976cf03b0f1ffdbf79ae91281c4\n", " 320\n", " 320\n", " 110\n", @@ -245,6 +255,7 @@ " \n", " 1\n", " ../data/Task02_Heart/labelsTr/la_004.nii.gz\n", + " db5dcb7cce258526726054f56bf015ef\n", " 320\n", " 320\n", " 110\n", @@ -257,6 +268,7 @@ " \n", " 2\n", " ../data/Task02_Heart/labelsTr/la_007.nii.gz\n", + " 4b1c1bf9d0d1adfb12d287c7aa200edc\n", " 320\n", " 320\n", " 130\n", @@ -269,6 +281,7 @@ " \n", " 3\n", " ../data/Task02_Heart/labelsTr/la_022.nii.gz\n", + " f9c20e542980932d2ea19a56f3bc7a60\n", " 320\n", " 320\n", " 110\n", @@ -281,6 +294,7 @@ " \n", " 4\n", " ../data/Task02_Heart/labelsTr/la_011.nii.gz\n", + " f1dd0a596e4daf898c3287441971cc06\n", " 320\n", " 320\n", " 120\n", @@ -295,19 +309,26 @@ "" ], "text/plain": [ - " path dim_0 dim_1 dim_2 voxel_0 \\\n", - "0 ../data/Task02_Heart/labelsTr/la_023.nii.gz 320 320 110 1.25 \n", - "1 ../data/Task02_Heart/labelsTr/la_004.nii.gz 320 320 110 1.25 \n", - "2 ../data/Task02_Heart/labelsTr/la_007.nii.gz 320 320 130 1.25 \n", - "3 ../data/Task02_Heart/labelsTr/la_022.nii.gz 320 320 110 1.25 \n", - "4 ../data/Task02_Heart/labelsTr/la_011.nii.gz 320 320 120 1.25 \n", + " path \\\n", + "0 ../data/Task02_Heart/labelsTr/la_023.nii.gz \n", + "1 ../data/Task02_Heart/labelsTr/la_004.nii.gz \n", + "2 ../data/Task02_Heart/labelsTr/la_007.nii.gz \n", + "3 ../data/Task02_Heart/labelsTr/la_022.nii.gz \n", + "4 ../data/Task02_Heart/labelsTr/la_011.nii.gz \n", "\n", - " voxel_1 voxel_2 orientation label_1_volume_mm3 \n", - "0 1.25 1.37 RAS+ 92483.5628 \n", - "1 1.25 1.37 RAS+ 125173.0473 \n", - "2 1.25 1.37 RAS+ 118684.8129 \n", - "3 1.25 1.37 RAS+ 71820.1096 \n", - "4 1.25 1.37 RAS+ 125130.2348 " + " content_hash dim_0 dim_1 dim_2 voxel_0 voxel_1 \\\n", + "0 a4b0e976cf03b0f1ffdbf79ae91281c4 320 320 110 1.25 1.25 \n", + "1 db5dcb7cce258526726054f56bf015ef 320 320 110 1.25 1.25 \n", + "2 4b1c1bf9d0d1adfb12d287c7aa200edc 320 320 130 1.25 1.25 \n", + "3 f9c20e542980932d2ea19a56f3bc7a60 320 320 110 1.25 1.25 \n", + "4 f1dd0a596e4daf898c3287441971cc06 320 320 120 1.25 1.25 \n", + "\n", + " voxel_2 orientation label_1_volume_mm3 \n", + "0 1.37 RAS+ 92483.5628 \n", + "1 1.37 RAS+ 125173.0473 \n", + "2 1.37 RAS+ 118684.8129 \n", + "3 1.37 RAS+ 71820.1096 \n", + "4 1.37 RAS+ 125130.2348 " ] }, "execution_count": null, @@ -560,7 +581,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -686,7 +707,7 @@ { "data": { "text/plain": [ - "SuggestedLRs(valley=0.0010000000474974513)" + "SuggestedLRs(valley=0.0012022644514217973)" ] }, "execution_count": null, @@ -695,7 +716,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -744,8 +765,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "2026/02/10 13:48:08 INFO alembic.runtime.migration: Context impl SQLiteImpl.\n", - "2026/02/10 13:48:08 INFO alembic.runtime.migration: Will assume non-transactional DDL.\n" + "2026/02/12 14:35:47 INFO alembic.runtime.migration: Context impl SQLiteImpl.\n", + "2026/02/12 14:35:47 INFO alembic.runtime.migration: Will assume non-transactional DDL.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Logged train/val split (14 rows) to MLflow artifacts\n" ] }, { @@ -791,284 +819,214 @@ " \n", " \n", " 0\n", - " 1.188972\n", - " 1.150066\n", - " 0.072936\n", + " 1.406590\n", + " 1.364680\n", + " 0.047128\n", " 00:09\n", " \n", " \n", " 1\n", - " 1.128688\n", - " 1.059523\n", - " 0.100490\n", - " 00:11\n", + " 1.329838\n", + " 1.237999\n", + " 0.093652\n", + " 00:10\n", " \n", " \n", " 2\n", - " 1.060407\n", - " 0.966478\n", - " 0.164882\n", + " 1.238961\n", + " 1.107408\n", + " 0.159831\n", " 00:10\n", " \n", " \n", " 3\n", - 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" 0.113019\n", - " 0.212477\n", - " 0.722688\n", - " 00:10\n", + " 0.122979\n", + " 0.238442\n", + " 0.701188\n", + " 00:11\n", " \n", " \n", " 25\n", - " 0.111935\n", - " 0.200070\n", - " 0.771948\n", + " 0.117919\n", + " 0.259488\n", + " 0.705504\n", " 00:10\n", " \n", " \n", " 26\n", - " 0.112475\n", - " 0.219445\n", - " 0.722223\n", + " 0.112155\n", + " 0.148297\n", + " 0.792503\n", " 00:11\n", " \n", " \n", " 27\n", - " 0.106262\n", - " 0.215529\n", - " 0.725434\n", + " 0.115003\n", + " 0.154213\n", + " 0.782339\n", " 00:10\n", " \n", " \n", " 28\n", - " 0.107414\n", - " 0.144815\n", - " 0.779320\n", - " 00:11\n", - " \n", - " \n", - " 29\n", - " 0.105382\n", - " 0.217872\n", - " 0.691926\n", - " 00:11\n", - " \n", - " \n", - " 30\n", - " 0.098850\n", - " 0.214837\n", - " 0.706129\n", - " 00:11\n", - " \n", - " \n", - " 31\n", - " 0.093996\n", - " 0.146677\n", - " 0.778879\n", + " 0.109797\n", + " 0.156226\n", + " 0.779197\n", " 00:10\n", " \n", " \n", - " 32\n", - " 0.092643\n", - " 0.153536\n", - " 0.773694\n", - " 00:11\n", - " \n", - " \n", - " 33\n", - " 0.090032\n", - " 0.153435\n", - " 0.836407\n", - " 00:10\n", - " \n", - " \n", - " 34\n", - " 0.092880\n", - " 0.123455\n", - " 0.812076\n", - " 00:11\n", - " \n", - " \n", - " 35\n", - " 0.088473\n", - " 0.173280\n", - " 0.741809\n", - " 00:10\n", - " \n", - " \n", - " 36\n", - " 0.085035\n", - " 0.203046\n", - " 0.729246\n", + " 29\n", + " 0.110347\n", + " 0.197927\n", + " 0.770012\n", " 00:11\n", " \n", - " \n", - " 37\n", - " 0.085146\n", - " 0.170847\n", - " 0.751540\n", - " 00:10\n", - " \n", - " \n", - " 38\n", - " 0.085042\n", - " 0.202479\n", - " 0.740759\n", - " 00:10\n", - " \n", - " \n", - " 39\n", - " 0.085334\n", - " 0.146925\n", - " 0.776810\n", - " 00:10\n", - " \n", " \n", "" ], @@ -1083,24 +1041,23 @@ "name": "stdout", "output_type": "stream", "text": [ - "Better model found at epoch 0 with accumulated_dice value: 0.07293620124642437.\n", - "Better model found at epoch 1 with accumulated_dice value: 0.10049037011369508.\n", - "Better model found at epoch 2 with accumulated_dice value: 0.1648821815160829.\n", - "Better model found at epoch 3 with accumulated_dice value: 0.22023152807830138.\n", - "Better model found at epoch 4 with accumulated_dice value: 0.30003705728318625.\n", - "Better model found at epoch 5 with accumulated_dice value: 0.40939961223457266.\n", - "Better model found at epoch 6 with accumulated_dice value: 0.5805066636399605.\n", - "Better model found at epoch 11 with accumulated_dice value: 0.6370960058087665.\n", - "Better model found at epoch 14 with accumulated_dice value: 0.6561856876528258.\n", - "Better model found at epoch 18 with accumulated_dice value: 0.6736515428257281.\n", - "Better model found at epoch 22 with accumulated_dice value: 0.7323807910242516.\n", - "Better model found at epoch 25 with accumulated_dice value: 0.7719477930468283.\n", - "Better model found at epoch 28 with accumulated_dice value: 0.7793202232422927.\n", - "Better model found at epoch 33 with accumulated_dice value: 0.8364068593639333.\n", + "Better model found at epoch 0 with accumulated_dice value: 0.04712842815224207.\n", + "Better model found at epoch 1 with accumulated_dice value: 0.09365233388678425.\n", + "Better model found at epoch 2 with accumulated_dice value: 0.1598308554480334.\n", + "Better model found at epoch 3 with accumulated_dice value: 0.2534747373036856.\n", + "Better model found at epoch 4 with accumulated_dice value: 0.34191410015430584.\n", + "Better model found at epoch 5 with accumulated_dice value: 0.4028456312132039.\n", + "Better model found at epoch 6 with accumulated_dice value: 0.507275803722496.\n", + "Better model found at epoch 10 with accumulated_dice value: 0.5763428130740373.\n", + "Better model found at epoch 13 with accumulated_dice value: 0.6083813148059677.\n", + "Better model found at epoch 16 with accumulated_dice value: 0.6903457644226796.\n", + "Better model found at epoch 18 with accumulated_dice value: 0.7561720218272072.\n", + "Better model found at epoch 20 with accumulated_dice value: 0.7845047648278451.\n", + "Better model found at epoch 26 with accumulated_dice value: 0.7925034936382713.\n", "\n", "Training finished. Logging model artifacts to MLflow...\n", - "Logged best model artifact: best_heart_patch.pth\n", - "Loaded best model weights (best_heart_patch) for learner export\n" + "Logged best model weights: best_weights.pth\n", + "Logged final epoch learner: final_learner.pkl\n" ] }, { @@ -1108,14 +1065,16 @@ "output_type": "stream", "text": [ "Saved file doesn't contain an optimizer state.\n", - "2026/02/10 13:55:26 WARNING mlflow.pytorch: Saving pytorch model by Pickle or CloudPickle format requires exercising caution because these formats rely on Python's object serialization mechanism, which can execute arbitrary code during deserialization.The recommended safe alternative is to set 'export_model' to True to save the pytorch model using the safe graph model format.\n" + "2026/02/12 14:41:19 WARNING mlflow.pytorch: Saving pytorch model by Pickle or CloudPickle format requires exercising caution because these formats rely on Python's object serialization mechanism, which can execute arbitrary code during deserialization.The recommended safe alternative is to set 'export_model' to True to save the pytorch model using the safe graph model format.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "MLflow run completed. Run ID: 8454cd7886814580a8439d1e27141c36\n" + "Loaded best model weights (best_heart_patch) for best learner export\n", + "Logged best epoch learner: best_learner.pkl\n", + "MLflow run completed. Run ID: 62148b02243a480a9f54fa4df9193617\n" ] }, { @@ -1123,13 +1082,13 @@ "output_type": "stream", "text": [ "Registered model 'UNet' already exists. Creating a new version of this model...\n", - "Created version '9' of model 'UNet'.\n" + "Created version '14' of model 'UNet'.\n" ] } ], "source": [ "# All params (size, transforms, loss, model name, etc.) are extracted from learn automatically\n", - "mlflow_callback = create_mlflow_callback(learn, experiment_name=\"Task02_Heart_Patch\")\n", + "mlflow_callback = create_mlflow_callback(learn, experiment_name=\"Task02_Heart_Patch\", dataset_version=med_dataset.fingerprint)\n", "learn.fit_one_cycle(30 , lr, cbs=[mlflow_callback, save_best])" ] }, @@ -1141,7 +1100,7 @@ "outputs": [ { "data": { - "image/png": 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gNjYWb2+Ns6yKsp5lRb6/ndpyk5WVRefOnZkwYUK5yi9evJhLLrmEX3/9lbi4OC688EKGDh3K2rVrqznSigv19+Jfl7YG4M3ft3E8q+xBVyIiIuIYTh3xdNlll3HZZZeVu/w/974YP348P/74Iz/99BNdu3Z1cHRVN6p3U6at3MfW5AzenLeNl67u6OyQREREXF6dHnNjt9vJyMggJCTkjGXy8vJIT08v8aop7m5Wnh3aHoApK/ZpcLGIiEgNqNPJzRtvvEFmZiY33HDDGcu88sorBAYGFr+KlpauKX1ahHJd9yYYBjw+Yz25BWefciciIiKVV2eTm6lTp/L888/z7bffEh4efsZyTz31FGlpacWvxMTEGozS9PSQdoQ38GL3kSz+N297jd9fRESkPqmTyc306dO5/fbb+fbbbxk4cGCZZb28vAgICCjxqmmBvh6MH2aOt/lkyW7W7jte4zGIiIjUF3UuuZk2bRpjx45l2rRpDBkyxNnhlNvAdhFc3aUx9hPdU3mF6p4SERGpDk5NbjIzM4mPjyc+Ph6AhIQE4uPjixcReuqppxg9enRx+alTpzJ69GjefPNNevfuTXJyMsnJyaSlpTkj/Ap7dmh7wvy92HEok3fn73B2OCIiIi7JqcnN6tWr6dq1a/E07nHjxtG1a1eeeeYZAJKSkkqslvjxxx9TWFjIfffdR6NGjYpfDz30kFPir6hgP09euroDAB8u2s2G/XUjKRMREalLnJrcDBgwAMMwSr0+//xzAD7//HMWLlxYXH7hwoVllq8LBneI5IpOjbDZDR6bsY78Qm3NICIidVNMTEyJNegsFguzZs06Y/k9e/ZgsViKe2yqS50bc+MKnr+yPaF+nmxNzuD9BTudHY6IiIhDJCUlVWhx3uqi5MYJQv29eP4qc3G/DxbsZOMBdU+JiEjdFxkZiZeXl7PDUHLjLEM6NuLyjpEU2g0e/W6dZk+JiEiN+vjjj2ncuDF2e8nhEVdddRW33noru3bt4qqrriIiIgJ/f3969uzJH3/8UWad/+yWWrlyJV27dsXb25sePXrU2F6QSm6cxGKx8OJVHYq7p975Q7OnRERcgmFAfpZzXoZR7jCvv/56jh49yoIFC4rPHTt2jLlz5zJq1CgyMzO5/PLLmT9/PmvXrmXw4MEMHTq0xESfsmRmZnLFFVfQrl074uLieO6553j00Ucr/Dgrw6kbZ9Z3of5evDysI3d/HcdHi3czpFMj2jcOdHZYIiJSFQXZML6xc+7974Pg6VeuosHBwVx22WVMnTqViy++GIAZM2YQFhbGhRdeiNVqpXPnzsXlX3zxRX744Qdmz57N/ffff9b6p06dit1u57PPPsPb25v27duzf/9+7rnnnsp9tgpQy42TDe4QyZCO5uypJ75fT6FNs6dERKRmjBo1iu+//568vDwApkyZwo033ojVaiUzM5NHH32Utm3bEhQUhL+/P1u2bCl3y82WLVvo1KkT3t7exef69OlTLZ/jn9RyU5PsdtjxGxxLgD73Fp9+9sp2LN15hI0H0pn0VwJ3nt/CiUGKiEiVePiaLSjOuncFDB06FMMw+OWXX+jZsydLlizhf//7HwCPPvoo8+bN44033qBly5b4+Phw3XXXkZ+fXx2RO5SSm5p0cC1MuxHcvKDTDeAXBkB4A2/+M6Qtj89Yz1vztjOofSTNQsvXrCgiIrWMxVLuriFn8/b25pprrmHKlCns3LmT1q1b061bNwD++usvxowZw7BhwwBzDM2ePXvKXXfbtm356quvyM3NLW69Wb58ucM/w+moW6omRXWDxt3Algdxn5d46/ruTejXMpTcAjtPzdyAUYFBYSIiIpU1atQofvnlFyZNmsSoUaOKz7dq1YqZM2cSHx/PunXrGDlyZKmZVWUZOXIkFouFO+64g82bN/Prr7/yxhtvVMdHKEXJTU2yWKD33ebxqs/AVnDKWxbGD+uIt4eVv3cd5bvV+50UpIiI1CcXXXQRISEhbNu2jZEjRxaff+uttwgODqZv374MHTqUQYMGFbfqlIe/vz8//fQTGzZsoGvXrvznP//hv//9b3V8hFIsRj1rIkhPTycwMJC0tDQCAgJqPoDCPPhfB8g6BNdNhg7XlHj748W7GP/rVgK83flj3AWEB3ifoSIREakNcnNzSUhIIDY2tsTgWam4sp5lRb6/1XJT09y9oMet5vGKj0q9fWu/WDpGBZKeW8izszfVcHAiIiJ1n5IbZ+gxFqwekLjcHGR8Cnc3K69e2xE3q4U5G5P5c2uKk4IUERGpm5TcOEODSGhvjj5nxcel3m7fOJDb+scC8OzsTeQWaGsGERGR8lJy4yznnhhYvHEGZB4q9fZDF7eiUaA3icdymLhwVw0HJyIiUncpuXGWqO7QpCfY8ktNCwfw83Ln6SvaATBx0S4Sj2XXcIAiIlIR9Wx+TrVw1DNUcuNMp04LLyy94uNlHSLp2yKU/EI7L/68uYaDExGR8vDw8AAgO1v/CK2qotWP3dzcqlSPVih2prZXgn8kZCbDltnQ8boSb1ssFp6/sj2XvbOE3zensHDbIQa0DndSsCIicjpubm4EBQVx6JA5xMDX1xeLxeLkqOoeu93O4cOH8fX1xd29aumJkhtncveEnrfBgpdhxYelkhuAVhENGNM3hk+XJvDCT5vp0yIUL/eqZbQiIuJYkZGRAMUJjlSO1WqladOmVU4Oldw4W/cxsPh12L8K9sdBk+6lijw0sBU/rjvI7iNZfLY0gXsHtKz5OEVE5IwsFguNGjUiPDycgoKCs18gp+Xp6YnVWvURM0punM0/HDpcC+umma03TT4pVaSBtwdPXdaGcd+u4735O7m6SxSNg3ycEKyIiJTFzc2tyuNFpOo0oLg26H2X+XPTD5CRfNoiw7pG0aNZMDkFNg0uFhERKYOSm9qgcVeIPhfsBbB68mmLWCwWXry6Q/HKxQu3qV9XRETkdJTc1BZFrTerJ5mba55G20YBjOkbA8D4X7dgs2tNBRERkX9SclNbtB0KDRqbu4VvmnXGYg9e1IpAHw+2p2Ty/Zr9NRefiIhIHaHkprZw8zCnhQOsmAhnWKUx0NeD+y80Z0v9b9527TslIiLyD0puapPuY8DNy9wpfP/qMxa7uU8zGgd6k5SWy+d/76mx8EREROoCJTe1iV8YdLzePF4x8YzFvD3cGHdpawA+WLCTtGytqSAiIlJEyU1t0/tO8+fmHyH94BmLDesaReuIBqTnFjJxkXYNFxERKaLkprZp1Bma9gV7oTlz6gzcrBYeG2S23kz+K4HktNyailBERKRWU3JTG517Yrfw1ZOh4MxJy8Vtw+nRLJi8QjvvzN9RQ8GJiIjUbkpuaqPWQyCgCWQfgU0zz1jMYrHwxGVtAPh2dSK7DmfWVIQiIiK1lpKb2sjNHXrdbh4vP/O0cICeMSFc3CYcm93grd+311CAIiIitZeSm9qq2y3g7g3J62Hf8jKLPja4NRYL/LIhifX7U2smPhERkVpKyU1t5Rtyclr4qtI7hZ+qTWQAw7pGAfDfuVurOzIREZFaTclNbdbrDvPn5h/PuFt4kUcGnoOnm5W/dh5l6Y4jNRCciIhI7aTkpjZr1Bmie5vTwuO+KLNodIgvo85tCpitN3ZtqikiIvWUkpvarueJ1pu4yWAreyXi+y9siZ+nGxsOpPHrxqQaCE5ERKT2UXJT27W7EvwaQkYSbP2lzKKh/l7ccX5zAN78fTsFNntNRCgiIlKrKLmp7dy9zJlTAKs+PWvx289rTqifJwlHsvh2dWI1ByciIlL7KLmpC3qMBYsV9iyBQ1vKLOrv5c4DF7UE4J0/dpCTb6uJCEVERGoNJTd1QWATaH25eVyO1psRvZvSJNiHQxl5TP47oZqDExERqV2U3NQVvU7sFr5uOuSml1nUy92Nf116DgATF+4iNTu/uqMTERGpNZTc1BWx50NYa8jPNBOcs7iqcxRtIhuQkVvIxIW7aiBAERGR2kHJTV1hsUDPE/tNrfq0zP2mAKxWC48Pbg3A53/vISktp7ojFBERqRWU3NQlnW8ET384sg0SFp+1+IWtw+kVE0JeoZ135++ogQBFREScT8lNXeIdAJ2Gm8dn2W8KwGKx8MRlZuvNd6v3s/94dnVGJyIiUis4NblZvHgxQ4cOpXHjxlgsFmbNmnXWaxYuXEi3bt3w8vKiZcuWfP7559UeZ61StN/U1l8hbf9Zi3dvFkK/lqEU2g2NvRERkXrBqclNVlYWnTt3ZsKECeUqn5CQwJAhQ7jwwguJj4/n4Ycf5vbbb+e3336r5khrkfC2EHMeGDZYPblclzxwUSvAbL1JTsutzuhERESczqnJzWWXXcZLL73EsGHDylX+ww8/JDY2ljfffJO2bdty//33c9111/G///2vmiOtZYoGFq/5Agrzzlr83Oah9IoJId9m58NFar0RERHXVqfG3CxbtoyBAweWODdo0CCWLVt2xmvy8vJIT08v8arz2gyBBo0g6zBsnl2uSx642Fy1eNrKfRxKV+uNiIi4rjqV3CQnJxMREVHiXEREBOnp6eTknH6q8yuvvEJgYGDxKzo6uiZCrV5uHtB9rHlcjoHFAP1bhtGtaRB5hXY+Wry7GoMTERFxrjqV3FTGU089RVpaWvErMdFFNpPsfgtY3SFxBSStO2txi8XCQwPNVYunrNjL4Yyzd2eJiIjURXUquYmMjCQlJaXEuZSUFAICAvDx8TntNV5eXgQEBJR4uYQGkdDuKvN4Zflab85vFUaX6CByC+x8vFhjb0RExDXVqeSmT58+zJ8/v8S5efPm0adPHydF5GQ9T0wL3zADco6ftbjFYuHhgebMqa+Wq/VGRERck1OTm8zMTOLj44mPjwfMqd7x8fHs27cPMLuURo8eXVz+7rvvZvfu3Tz++ONs3bqVDz74gG+//ZZHHnnEGeE7X9NzIaIDFObA2inluuSCcxoWt958pJlTIiLigpya3KxevZquXbvStWtXAMaNG0fXrl155plnAEhKSipOdABiY2P55ZdfmDdvHp07d+bNN9/k008/ZdCgQU6J3+n+ud+U3V6OSyw8cok59uar5Xs1c0pERFyOxTDOsgOji0lPTycwMJC0tDTXGH+TnwVvtoW8NBj1PbQaeNZLDMPgug+XEbf3OLf3j+X/rmhXA4GKiIhUXkW+v+vUmBs5DU8/6DLSPC7ntHCLxcL9F5nr3kxduY/jWfnVFZ2IiEiNU3LjCoq6prb/Bsf3lOuSAec0pF2jALLzbXyxrHzXiIiI1AVKblxBWEtocRFgwKrPynWJxWLhngEtAPj87z1k5RVWY4AiIiI1R8mNqyiaFr72Kyg4/WrN/3R5x0bEhPqSml3AtJX7zn6BiIhIHaDkxlWcMwgCm5rr3WycWa5L3KwW7rrAbL35dEkC+YVnn20lIiJS2ym5cRVWN+hRsf2mAK7pFkV4Ay+S03P5Mf5ANQUnIiJSc5TcuJJuo8HNEw6uhf1x5brEy92NW/vHAvDZ0gTq2coAIiLigpTcuBK/MOhwrXm88uNyXzaiZ1N8PNzYmpzB37uOVlNwIiIiNUPJjaspGli8aSZkHSnXJYG+HtzQowkAny7ZXV2RiYiI1AglN66mSXdo3BVs+bDmy3JfNrZfLBYLLNh2mJ2HMqoxQBERkeql5MYVFbXerJ4Mdlu5LokJ82Ng2wjAHHsjIiJSVym5cUUdrgGfYEjbZ65aXE53nt8cgO/XHOBwRl51RSciIlKtlNy4Ig8f6HqzeVyBaeE9mgXTtWkQ+YV2vtKWDCIiUkcpuXFVPW8DLLDrTziys1yXWCwW7jjPbL35avlecvLL16UlIiJSmyi5cVXBMeaqxQCrPi33ZYPaRxId4sPx7AJmxCVWT2wiIiLVSMmNKysaWBw/FfKzynWJm9XCbf3MRf0+XrKbQpu2ZBARkbpFyY0ra3ERhDSHvDRY/225Lxvesykhfp4kHsvhlw1J1RigiIiI4ym5cWVWK/S4zTxe9SmUc2sFH083xvaNAWDiwl3akkFEROoUJTeurusocPeBlI2wb1m5LxvdJwY/T3NLhoXbDldjgCIiIo6l5MbV+QRDx+vM45XlnxYe6OvBqHObAfDBwvLNthIREakNlNzUB71ODCzeMhsykst92W39Y/F0s7Jqz3Hi9h6rpuBEREQcS8lNfdCoM0T3BnshxH1R7ssiArwZ1jUKgIkLtaGmiIjUDUpu6ouiaeFxk8FWUO7L7rygORYL/LElhR0p2lBTRERqPyU39UW7K8GvIWQkwdZfyn1Zi4b+DGoXCcDERbuqKzoRERGHUXJTX7h7QbdbzOMKDCwGuGdACwBmxx9k//FsR0cmIiLiUEpu6pMeY8Fihb1LIWVzuS/rHB1Ev5ahFNoNPl2SUI0BioiIVJ2Sm/oksAm0GWIeV2C/KYB7LmgJwPRV+ziamefoyERERBxGyU19UzSweP03kJte7sv6tQylY1QguQV2vvh7T/XEJiIi4gBKbuqb2PMhrDXkZ8K66eW+zGKxcO+JsTdfLNtLZl5hdUUoIiJSJUpu6huLBXrebh6v+qTc+00BXNo+kuZhfqTlFDBtxb5qClBERKRqlNzUR51vBA8/OLIdEleU+zI3q4W7LmgOwKdLd5NXaKuuCEVERCpNyU195B0A7a82j9d+XaFLh3VtQmSANynpeUxfmej42ERERKpIyU191WWU+XPTD5CfVe7LPN2t3H+ROXPqvT93kKWxNyIiUssouamvmvWFkObmwOLNP1bo0uE9o2kW6suRzHw+18wpERGpZZTc1FcWC3QZaR6vnVKhSz3crDw8sBUAHy3aRVpO+feqEhERqW5KbuqzziMAi7li8bGK7fp9ZecoWoX7k55byGdLtGO4iIjUHkpu6rPAJtDiIvM4fmqFLnWzWhh3yTkATP5rj1pvRESk1lByU991PTGwOH4q2Cs2tXtQ+0jOifAnI6+Qr5fvrYbgREREKk7JTX3Xegh4B0H6Adi9sEKXWq0W7h1gzpz6bGkCOfla90ZERJxPyU195+ENHa83jyu45g3AFZ0aER3iw7GsfKav0qrFIiLifEpuBLreZP7c+gtkH6vQpe5uVu6+wNxz6uPFu8kvtDs6OhERkQpRciPQqDNEdABbHmz8vsKXX9utCeENvEhKy2XW2gPVEKCIiEj5KbkRc82botabSnRNeXu4ccd55p5TExftwmYv/2acIiIijqbkRkwdbwCrByTFQ/LGCl8+sndTAn08SDiSxZyNSY6PT0REpJyU3IjJLxRaX2Yex1dsxWIAPy93xvaLAWDCgl0YhlpvRETEOZTcyElFXVPrv4HC/ApfPqZvDL6ebmxJSmfhtsMODk5ERKR8lNzISS0uBv9IyD4K2+dW+PIgX09uOrcZAO8v2KnWGxERcQqnJzcTJkwgJiYGb29vevfuzcqVK8ss//bbb9O6dWt8fHyIjo7mkUceITc3t4aidXFu7tD5RvO4EgOLAW7vH4unm5W4vcdZmVCxaeUiIiKO4NTk5ptvvmHcuHE8++yzrFmzhs6dOzNo0CAOHTp02vJTp07lySef5Nlnn2XLli189tlnfPPNN/z73/+u4chdWFHX1M55kF7xgcHhAd5c36MJABMW7nJkZCIiIuXi1OTmrbfe4o477mDs2LG0a9eODz/8EF9fXyZNmnTa8n///Tf9+vVj5MiRxMTEcOmllzJixIiztvZIBYS1gujeYNhh/fRKVXHX+S1ws1pYvP0wG/anOThAERGRsjktucnPzycuLo6BAweeDMZqZeDAgSxbtuy01/Tt25e4uLjiZGb37t38+uuvXH755We8T15eHunp6SVechbFa95MgUqMm2ka6svQTo0AmPxXgiMjExEROSunJTdHjhzBZrMRERFR4nxERATJycmnvWbkyJG88MIL9O/fHw8PD1q0aMGAAQPK7JZ65ZVXCAwMLH5FR0c79HO4pPbDwMMXju6AxMq1io3tFwvAz+uTOJKZ58joREREyuT0AcUVsXDhQsaPH88HH3zAmjVrmDlzJr/88gsvvvjiGa956qmnSEtLK34lJibWYMR1lFcDaHe1eRxfuYHFnaOD6BwdRL7NzvSV2lBTRERqjtOSm7CwMNzc3EhJSSlxPiUlhcjIyNNe8/TTT3PzzTdz++2307FjR4YNG8b48eN55ZVXsNtPv2Gjl5cXAQEBJV5SDl1HmT83/gD5WZWqYkxfc1r418v3aUNNERGpMU5Lbjw9PenevTvz588vPme325k/fz59+vQ57TXZ2dlYrSVDdnNzA9CaKo7WrB8Ex0J+BmyeXakqLu/YiDB/L5LTc/l6+V4HBygiInJ6Tu2WGjduHJ988glffPEFW7Zs4Z577iErK4uxY8cCMHr0aJ566qni8kOHDmXixIlMnz6dhIQE5s2bx9NPP83QoUOLkxxxEIsFupxovanEdgwAXu5uPHJJKwA+XbKbQptab0REpPq5O/Pmw4cP5/DhwzzzzDMkJyfTpUsX5s6dWzzIeN++fSVaav7v//4Pi8XC//3f/3HgwAEaNmzI0KFDefnll531EVxblxGw4GXYswSOJUBIbIWruLZbE976fTsH03KZszGZoZ0bV0OgIiIiJ1mMetafk56eTmBgIGlpaRp/Ux5fDYNdf8L5j8NF/6lUFW//sZ23/9hB5yaBzLqvHxaLxcFBioiIq6vI93edmi0lTlC05k38VLDbKlXFTec2w9Pdyrr9acTtPe7A4EREREpTciNlaz0EvAMhfT/sXlipKsL8vbi2WxQAny7Ron4iIlK9lNxI2Ty8oeMN5vHarypdza0nFvX7bXMye49Wbmq5iIhIeSi5kbPrNtr8ufUXyDpaqSpaRTRgQOuGGAZM/muP42ITERH5ByU3cnaNOkGjzmDLh/XfVLqa2/s3B+Db1Ykcy8p3VHQiIiIlKLmR8ilqvVnzZaU20wTo1zKUDlEBZOfb+GjxLgcGJyIicpKSGymfDteBuzcc3gIH4ipVhcViYdwl5wDw5d97OZyhDTVFRMTxlNxI+fgEndxMc80Xla7mwtbhdIkOIqfAxmdLNXNKREQcT8mNlF9R19TGmZCXWakqLBYL91/YEoApy/eSnlvgqOhEREQAJTdSEc36QkgLyM+ETT9UupqL2oTTKtyfjLxCpizf58AARURElNxIRVgs0O1m87gKa95YrRbuvqAFAJ8tTSC3oHIrH4uIiJyOkhupmM4jweIGiSvg8LZKV3Nll8Y0DvTmSGYeP6w94MAARUSkvlNyIxXTIALOGWwer/my0tV4uFm5tb+5avEni3djt9er/VtFRKQaKbmRiivqmlo3DQorvxjfjb2aEuDtzu4jWczbkuKg4EREpL5TciMV1/IS8I+E7KOwfU6lq/H3cufmPs0A+HDRLoxKLg4oIiJyKiU3UnFu7tBlpHlcha4pgFv6xuDpbmXtvlRW7z3ugOBERKS+U3IjldP1JvPnzvmQtr/S1YQ38Obabk0A+GiRtmQQEZGqU3IjlRPaAmLOAwxYO6VKVd1xXiwWC/yx5RA7UjIcE5+IiNRbSm6k8opWLF77Ndjtla6meUN/Lm0XAcDHi3c7IjIREanHlNxI5bUdCt6BkLYPEhZWqaq7TizqNyv+ACnpuQ4ITkRE6islN1J5Hj7Q8QbzeE3lVywG6NY0mF4xIRTYDCb9pQ01RUSk8pTcSNUUdU1t/RmyjlapqrsuaA7A1OX7tKGmiIhUmpIbqZpGnaBRZ7Dlw/pvqlTVha1Pbqg5eekex8QnIiL1jpIbqbrigcVfQRUW4rNaLTw0sBUAHy/exdHMPEdEJyIi9UylkpsvvviCX375pfj3xx9/nKCgIPr27cvevXsdFpzUER2uA3dvOLQZDsRVqarLOzSiY1QgWfk2JizQujciIlJxlUpuxo8fj4+PDwDLli1jwoQJvPbaa4SFhfHII484NECpA3yCoN3V5nEVVyy2Wi08Prg1AF8v38shzZwSEZEKqlRyk5iYSMuWLQGYNWsW1157LXfeeSevvPIKS5YscWiAUkcUbaa58XvIy6xSVf1bhtG9WTD5NjtfLVdLoIiIVEylkht/f3+OHjVnxvz+++9ccsklAHh7e5OTk+O46KTuaNYPQppDfiZsnlWlqiwWC7f1jwVgyop95BbYHBCgiIjUF5VKbi655BJuv/12br/9drZv387ll18OwKZNm4iJiXFkfFJXWCwnBxZXsWsK4NJ2EUQF+XAsK59Zaw9UuT4REak/KpXcTJgwgT59+nD48GG+//57QkNDAYiLi2PEiBEODVDqkM4jweIGiSvg8LYqVeXuZmVsvxgAPluagFGFWVgiIlK/WIx69q2Rnp5OYGAgaWlpBAQEODsc1zNtJGz7BfrcD4NerlJV6bkF9H3lTzLzCpk8picXtgl3UJAiIlLXVOT7u1ItN3PnzmXp0qXFv0+YMIEuXbowcuRIjh8/XpkqxVUUdU3FT4WCqs10CvD2YESvaADemb9DrTciIlIulUpuHnvsMdLT0wHYsGED//rXv7j88stJSEhg3LhxDg1Q6phWl0BAE8g5Bpt/rHJ1d5zXHB8PN+ITU5mzMdkBAYqIiKurVHKTkJBAu3btAPj++++54oorGD9+PBMmTGDOnDkODVDqGKsbdB9jHq+eVOXqwgO8ueN8c8+p1+ZuxWZX642IiJStUsmNp6cn2dnZAPzxxx9ceumlAISEhBS36Eg91u1msLpD4nJI2VTl6u46vznBvh7sOZrNvM0pDghQRERcWaWSm/79+zNu3DhefPFFVq5cyZAhQwDYvn07TZo0cWiAUgc1iIQ25n8TrJ5c5er8vNwZ0aspAJP+SqhyfSIi4toqldy8//77uLu7M2PGDCZOnEhUVBQAc+bMYfDgwQ4NUOqoHreaP9dNr/KKxQA392mGu9XCyoRjrNmnQesiInJmmgou1cNuh/d7wLFdMPSdk+NwquDR79YxI24/3ZsF8/09faseo4iI1BnVPhUcwGaz8f333/PSSy/x0ksv8cMPP2CzaZl8OcFqhR5jzeNVn4EDcujHB7XG081K3N7jxO09VuX6RETENVUqudm5cydt27Zl9OjRzJw5k5kzZ3LTTTfRvn17du3a5egYpa7qMgrcvCB5PRxcU+XqwgO8GdbV7AL9ePHuKtcnIiKuqVLJzYMPPkiLFi1ITExkzZo1rFmzhn379hEbG8uDDz7o6BilrvINgfbDzGMHTAsHuON8c0PN3zensPtw1cfyiIiI66lUcrNo0SJee+01QkJCis+Fhoby6quvsmjRIocFJy6gaGDxhu8hp+oDgVuGN+DiNuEYBny6VDOnRESktEolN15eXmRkZJQ6n5mZiaenZ5WDEhcS3QvC20NhDqz7xiFV3nliUb/v4/ZzJDPPIXWKiIjrqFRyc8UVV3DnnXeyYsUKDMPAMAyWL1/O3XffzZVXXunoGKUus1hODixePckhA4t7xYbQuUkgeYV2vly2t8r1iYiIa6lUcvPuu+/SokUL+vTpg7e3N97e3vTt25eWLVvy9ttvOzhEqfM6DQcPPziyDfb+XeXqLBYLd57fAoCvlu0hO7+wynWKiIjrcK/MRUFBQfz444/s3LmTLVu2ANC2bVtatmzp0ODERXgHQKfrIe5zs/Umpl+VqxzUPoJmob7sPZrNG79t55mh7aoep4iIuIRyL+JXkd2+33rrrUoHVN20iJ+TJK2Dj84HqweM2wL+Datc5cJthxgzeRUWC8x75HxahjdwQKAiIlIbVcsifmvXri3XKz4+vkLBTpgwgZiYGLy9venduzcrV64ss3xqair33XcfjRo1wsvLi3POOYdff/21QvcUJ2jUGaK6g70A4r92SJUDWodzSbsIDAM+WqR1b0RExFTubqkFCxY4/ObffPMN48aN48MPP6R37968/fbbDBo0iG3bthEeHl6qfH5+Ppdccgnh4eHMmDGDqKgo9u7dS1BQkMNjk2rQ41Y4EGduptn3IXMV4yq6Z0AL5m1O4Ye1B3jgolY0DfV1QKAiIlKXVf3bpQreeust7rjjDsaOHUu7du348MMP8fX1ZdKk0y/4NmnSJI4dO8asWbPo168fMTExXHDBBXTu3LmGI5dKaX8NeAdC6l7Y/adDquzWNJjzWoVRaDd4Z/4Oh9QpIiJ1m9OSm/z8fOLi4hg4cODJYKxWBg4cyLJly057zezZs+nTpw/33XcfERERdOjQgfHjx2tPq7rC0xc6jzSPVzlmxWKAf13aGoCZa/cTn5jqsHpFRKRuclpyc+TIEWw2GxERESXOR0REkJycfNprdu/ezYwZM7DZbPz66688/fTTvPnmm7z00ktnvE9eXh7p6eklXuJERWvebJ8DaQccUmWX6CCu6RqFYcCzP27Ebq9XG92LiMg/OLVbqqLsdjvh4eF8/PHHdO/eneHDh/Of//yHDz/88IzXvPLKKwQGBha/oqOjazBiKaVha2jWHww7rPnSYdU+eXkb/L3cWbc/jW9XJzqsXhERqXucltyEhYXh5uZGSkpKifMpKSlERkae9ppGjRpxzjnn4ObmVnyubdu2JCcnk5+ff9prnnrqKdLS0opfiYn64nO6otabNV+AzTEL8IU38Obhga0A+O/craRmn/6/BxERcX1OS248PT3p3r078+fPLz5nt9uZP38+ffr0Oe01/fr1Y+fOndjt9uJz27dvp1GjRmfc08rLy4uAgIASL3GytkPBNwwykmD7XIdVe0vfGM6J8Od4dgFv/r7dYfWKiEjd4tRuqXHjxvHJJ5/wxRdfsGXLFu655x6ysrIYO9b8l/3o0aN56qmnisvfc889HDt2jIceeojt27fzyy+/MH78eO677z5nfQSpDHcv6HqTebz6M4dV6+Fm5bkr2wMwbeU+9h/PdljdIiJSdzg1uRk+fDhvvPEGzzzzDF26dCE+Pp65c+cWDzLet28fSUlJxeWjo6P57bffWLVqFZ06deLBBx/koYce4sknn3TWR5DK6j4GsMCuP+GY4xbg69sijL4tQim0G3y8WAv7iYjUR+XefsFVaPuFWuTra2HnH9DvIbjkBYdV+/fOI4z8dAXeHlb+euIiQv29HFa3iIg4R7VsvyDicD1uNX8u+wDmPgXZxxxSbZ8WoXRqEkhugZ0v/t7jkDpFRKTuUHIjztNqELS5wtxvavkH8E5nWPIm5FdtrIzFYuGeC1oA8NHi3VrYT0SknlFyI87j5g7Dv4abvoeIjpCXDvNfgPe6m2vgVGGa+KXtI+nRLJi8QjuPfBNPoc1+9otERMQlKLkR57JYoOVAuGsxDPsYAptCxkGY/QB82A+2zYFKDAtzs1qYNLYnwb4eJBzJYs7G0696LSIirkfJjdQOVit0Hg73r4JLXwafYDi8FabdCJMvh8RVFa4ywNuDW/rGAPDhol3Us7HzIiL1lpIbqV08vKHv/fBgPPR7GNy9Yd/f8NlA+OZmOLKzQtXd0icGHw83Nh1MZ+nOI9USsoiI1C5KbqR28gmCS56HB9aYC/5ZrLBlNkzsC8kby11NsJ8nw3ua+4l9uGhXNQUrIiK1iZIbqd0Co+CqCXD3XxDVA2x5sHxihaq4/bxY3KwW/tp5lA3706opUBERqS2U3EjdENEOBr1sHm/8HnJSy31pk2BfruzcGIAPF6v1RkTE1Sm5kbojujc0bAuFObD+mwpdetcFzQGYsyGJvUezqiM6ERGpJZTcSN1hsUAPc1NVVk+u0BTxNpEBXNi6IXYD7TklIuLilNxI3dJpOLj7wOEtkLiiQpfefWLV4u/i9nM4I686ohMRkVpAyY3ULT5B0OFa83j15Apd2is2hK5Ng8gvtPP53wmOj01ERGoFJTdS9xR1TW36oUKbbVosluLWm6+W7SUzr/LbO4iISO2l5Ebqnqju5l5UtjxYN71Cl17SNoLmDf1Izy1k2op91RSgiIg4k5IbqXssFugxxjyOq9jAYqvVwt3nm603ny1NIL9QG2qKiLgaJTdSN3W8ATz84Mh22Pt3hS69qmtjIgK8SE7PZUbc/moKUEREnEXJjdRN3gHQ8cTA4riKDSz2cnfjrhOtN+/M306hTa03IiKuRMmN1F3dTwws3vwjZB2t0KU3nduMYF8PUtLzWKINNUVEXIqSG6m7orpBo85gy4d1Uyt0qae7lau6RAHw0aJd2O3lH7cjIiK1m5Ibqdt63Gr+jPu8QgOLAUb1boqnm5Xlu4/xx5YUx8cmIiJOoeRG6rYO14FnAzi6E/YsqdClrSIacEvfZgB8syqxOqITEREnUHIjdZuXP3S63jxePanCl9/YqykAC7YdIiktx5GRiYiIkyi5kbqvaGDxlp8h83CFLm3R0J9esSHYDZixWtPCRURcgZIbqfsadTJXLbYXQPzXFb78xp7RAHyzOlEDi0VEXICSG3ENRa03cZ+DvWLr1lzesRENvN3ZfzyHpZoWLiJS5ym5EdfQ4RrwCoDjeyBhYYUu9fZw45qu5rTwqdpvSkSkzlNyI67B0w86DTePV1dsxWKAkb3NWVPztqRoYLGISB2n5EZcR48TXVPbfoWMiq1b0zqyAb1jQ7DZDd6dv7MaghMRkZqi5EZcR0R7aNIL7IWw9qsKX/7ooNYAfLs6kQOpar0REamrlNyIaylasXjNFxUeWNwzJoRzm5utN/d+HUdadkE1BCgiItVNyY24lvZXg3cQpO6DXX9W+PIHLmqFu9XCuv1p/O+P7Q4PT0REqp+SG3EtHj7QeYR5HFfxgcX9Wobx/siuAMyI28/xrHxHRiciIjVAyY24nuKBxXMgPanClw9qH0mLhn5k5hXyr+/WOTg4ERGpbkpuxPU0bA1N+4Jhg8WvV3i3cIvFwls3dMHDzcKfWw+xYNuhagpURESqg5IbcU197zd/rv4Mfvt3hROcztFBjOkbA8CLP28mv7Big5NFRMR5lNyIa2ozBC5/wzxe/gH8/EiFZ0/df1ErQv082X04iy/+3uP4GEVEpFoouRHX1esOuPJ9wGIOLp51D9gKy315oI8Hjw821755/bdtbNifVk2BioiIIym5EdfW7Wa49lOwuMH66fD9bVBY/hlQ13eP5oJzGpJvs/Phol3VGKiIiDiKkhtxfR2vgxu+BDdP2DwLvh0NBbnlutRqtfDU5W0AmLspWSsXi4jUAUpupH5oewXcOA3cvWH7HJh2I+Rnl+vSNpEB9G0Ris1u8OWyPdUbp4iIVJmSG6k/Wg2EUTPAww92L4Cvr4Xc9HJdOrZfLADTVuwjO7/843ZERKTmKbmR+iX2PBg9C7wCYd/f8NXVkHP8rJdd1CacZqG+pOcW8t3q/dUepoiIVJ6SG6l/onvBLbPBJwQOxMEXQyHrSJmXuFkt3NbfbL35bGkCNnvF1s0REZGao+RG6qfGXWDML+AXDskbYPLlZ92q4bruTQjy9WDfsWx+25RcM3GKiEiFKbmR+iuiHYydAwFRcGQbfDwA4qeecbE/X093RveJAeC9P3diVHDVYxERqRlKbqR+C2tpJjihrSAz2Vzo75MLYe/fpy1+a78YvNytbElKJz4xtWZjFRGRcqkVyc2ECROIiYnB29ub3r17s3LlynJdN336dCwWC1dffXX1BiiuLbgZ3PMXXPICeAVAUjxMvsxcD+f4nhJFg3w9GdKxEQDTVu6r+VhFROSsnJ7cfPPNN4wbN45nn32WNWvW0LlzZwYNGsShQ2XvxLxnzx4effRRzjvvvBqKVFyauxf0ewgeWAPdx4LFCpt/hPd7wrxnS0wZH9G7KQA/rUsiPbfAWRGLiMgZOD25eeutt7jjjjsYO3Ys7dq148MPP8TX15dJkyad8RqbzcaoUaN4/vnnad68eQ1GKy7PvyEMfRvuXgrNB4AtH/56G97rBnGfg91Gj2bBtAr3J6fAxo/xB50br4iIlOLU5CY/P5+4uDgGDhxYfM5qtTJw4ECWLVt2xuteeOEFwsPDue2222oiTKmPItrDzbNgxDcQ2hKyDsNPD8FH52NJWMSIXmbrzZTle7FrWriISK3i1OTmyJEj2Gw2IiIiSpyPiIggOfn0U22XLl3KZ599xieffFKue+Tl5ZGenl7iJVIuFgu0Hgz3LIPBr4J3EKRshC+v4qZ9/0eAh8HW5AxmrNGifiIitYnTu6UqIiMjg5tvvplPPvmEsLCwcl3zyiuvEBgYWPyKjo6u5ijF5bh7wrn3wINrodddYHHDc/vPvN1uBwCTliZoWriISC3i1OQmLCwMNzc3UlJSSpxPSUkhMjKyVPldu3axZ88ehg4diru7O+7u7nz55ZfMnj0bd3d3du3aVeqap556irS0tOJXYmJitX0ecXG+IXD5a3DevwA4z1iNt4eVrckZrNpz9i0cRESkZjg1ufH09KR79+7Mnz+/+Jzdbmf+/Pn06dOnVPk2bdqwYcMG4uPji19XXnklF154IfHx8adtlfHy8iIgIKDES6RKWl8GgMeehVzTsSGgaeEiIrWJu7MDGDduHLfccgs9evSgV69evP3222RlZTF27FgARo8eTVRUFK+88gre3t506NChxPVBQUEApc6LVJtGXcA/EjKTuS36AFPXuPPLhiSeHdqOIF9PZ0cnIlLvOX3MzfDhw3njjTd45pln6NKlC/Hx8cydO7d4kPG+fftISip7zx+RGmW1wjmDAGh+bAltGwWQX2hn5poDNRtHbjoc3l6z9xQRqQMsRj0bCZmenk5gYCBpaWnqopLK2zYHpt0IgdF8de7PPP3jJpoE+7Dw0QG4u9XAvxlshfDpReamn3cuhEadq/+eIiJOVJHvb6e33IjUSbEXgLs3pCVyfXQ6IX6e7D+ew4Jth2vm/mu/hKR1YNhhy881c08RkTpCyY1IZXj6misYA967f+O67k0AmLpib/XfOzcN/nz55O+75p+5rIhIPaTkRqSyzhls/tw2t3jF4oXbD7PnSFb13nfxG5B9BAJPzA48sAayj1XvPUVE6hAlNyKVVZTcHIgj1juLC1s3xDDg87/3VN89j+6C5RPN4yFvQcO2gAEJi6rvniIidYySG5HKCmhkTgvHgO2/cVt/cxPXaSv3VV/rzbxnwF4ALS6GVpdAi4vM8zvVNSUiUkTJjUhVnFjQj+1z6dcylHaNAsgrtPPod+scf6+EJbD1Z7C4waCXzb2vWp5IbnYtgPo18VFE5IyU3IhURVHX1K4/sRTm8f7Irni6WVm99zir9jhwHIzdBr89ZR73GAvhbc3jpn3BzQvS98ORHY67n4hIHabkRqQqGnWGBo2hIBsSFtO8oT/Xnpg5NXFh6b3OKi1+irmmjVcgDPj3yfOevtDsxFYlmjUlIgIouRGpGosFWp9ovdk+B4C7zm+O1QJ/bj3E1uT0qt8jLwPmv2geD3gC/EJLvt/iYvPnrj+rfi8REReg5Eakqs4pGnfzGxgGMWF+XNaxEQAfLdpd9fqXvAVZhyCkBfS8o/T7RYOK9yyFwryq309EpI5TciNSVbHng4cvpB+A5PUA3H1+CwB+Xn+QQxm5la/7+B5YNsE8vvQlcD/NxpwR7cE/wuwa27e88vcSEXERSm5EqsrDG5pfaB5vmwtAxyaBdGsaRIHN4L35Oytf97xnwZZnbvdQNDPrnyyWk603zuiayjoKyRtr/r4iImeg5EbEEf4x7gbgsUFtAPhmVSJJaTkVr3Pv37B5FlisMGi8mcScSXFyU8ODiu02+PJK+LAfxH1es/cWETkDJTcijtBqkPnz4FpITwKgT4tQeseGkG+zV3zsjd0Oc09M/e42GiI7lF2+qOUoeQNkHqrYvapi0w+QcqLV5udHiluuXErWEZhwLsx+0NmRiEg5KbkRcYQGERDV3Tze8Vvx6QcuagWYqxZXaOzN+umQFA+eDeDC/zt7ef+GENnJPN69sPz3qQq7DRa/bh4HRps7lH83Bvavrpn715RVn8HhLbD2K+3hJVJHKLkRcZSiWVOntF70axlKl+gg8grtfLokoXz15GXCH8+bxxc8ZiYu5VHTWzFsngWHt4J3INy5CFpeAoU5MOV6OFKFcUa1SWE+rJ5kHht2TbcXqSOU3Ig4StG4m90LocAcY2OxWHjoYrP15qtle8vXevPX25CZDMEx0Pvu8t//1EHF1b0Vg90Oi14zj8+911x75/rPoXFXyDkGX18DGSnVG0NN2DLb/N+iyI7fnReLiJSbkhsRR4noAAFNzNaL3Sd36R7QuiFdooPIKbCdfdXi1ET4+z3z+JIXwd2r/Pdveq45JT3rEKRsqsQHqICiVhuvwJMJmJc/jPwOgmMhdS9Mvd5cgLAuW/GR+TP2AvPnzj/M7jgRqdWU3Ig4ymlWKzZPW3j00tYATFm+j4OpZcyc+uM5KMyFZv2h7dCK3d/dC2L6m8fVOWuqRKvNPeATdPI9/4Zw0/fgGwZJ6+Db0WArqL5YqtOBNbB/JVg94OqJ4BUA2UfNQeMiUqspuRFxpFPH3djtxaf7tTw5c+qJ79dTaLOXvnbNl7BxBmCBwWeZ+n0mNbHezZYfzQG2XoFmcvNPoS1g1LdmK9KuP2H2A3Vzx/KVH5s/2w+DwChocWJGmrqmRGo9JTcijhTTHzz8zHEaSfHFpy0WC/8Z0hZvDytLdhzhg392T235CX56yDw+71/mhpyVUbTP1N5lkJ9duTrKUqLV5u6SrTaniuoO138BFjdYNw3mv+D4WKpT5mHY+L15XNTt1upS86eSG5FaT8mNiCN5eJ/8F/72kmu+dGoSxKvXmNO131+wk+0pJ8ajJCyBGbeZs3G6jYaLyjH1+0zCWpnjfmx55iKAjrZlNhzabHbRnK7V5lTnXApXvmseL30LVn7i+Hiqy5rPwZZvJmlNTkzxbznQ/Hlwbc2uJSQiFabkRsTRirZJ2Dan1FtXdWnMea3CyC+0M+Lj5cye8yvGtBvNZKTNFTDkf5XrjipisZxMrhzdNVVqrE3w2a/petPJdXp+fcxsoartbAXm2jYAve46eb5B5MkWtZqabi8ilaLkRsTRWg0CLOYmmmkHSrxlsVh458autG8cQIPsvfRdfheW/EwKovvBtZ+Bm3vV719d4262/gSHNpWv1eZU5z8K3ccChtlCtXeZY+NytC0/QUYS+IVD+6tLvqeuKZE6QcmNiKP5N4QmPczj7aW3Iwjx82TGqFi+93+dMEs6G+0xPMBjGBWZ9l2W5gMAizno9x/JVaWd2mrT++7ytdoUsVjg8jeg9eVmC9W0G+HwdsfEVR2Kpn/3GFt6Kn5RcrNrPtgKazYuESk3JTci1aGoa+o0yQ05x/H55npCC5LJD4jhDvuTzN2RTZun57LrcGbV7+0bAlHdzOPdC6peH8DWn809pDwbVKzVpoibu9ky1aQX5KbW3hlUSesgcTlY3aHHraXfj+puJna5abB/Vc3HJyLlouRGpDoUTQnfvQjys06ez8+GqcPNQbn+kXiOnc09Q/oAkFdoZ/Dbi5m7Manq93dk19Q/Z0j5hlSuHk9fuOELc4p44nJz083aZsWJ6d/trjbH2PyT1e3kjDR1TYnUWkpuRKpDeFsIamp2wxRtZGkrgO9ugcQV5n5MN/8Awc0Y3SeGKzs3BqDAZvDg9HhmrztYtfsXfQHvWlD1FXW3/QIpG0602txbtboCGkO/E1Pe5z0LBRXYTLS6ZR2BDd+Zx73vOnO54nE386o/JhGpFCU3ItXBYjllQb85ZuvHj/eZ/9p39zG3KYhoV1z83RFd2TX+ci5uE05+oZ0Hp62l58t/8O2qxMrdv0kPMxnJOWZ2tVSWYcCi/5rHve+qfKvNqfo+CAFRkLYPlk+oen1Fdi+EJW9WPmFa84WZjDbuCk16nrlcy4sBi5nwpVcxCRWRaqHkRqS6FG/F8Bv89m9Y/425qN0NX0LT3qWKu1ktfHhzd4Z1jQLgcEYej3+/nq+X7634vd08IPZ887gqXVNbf4HkDeDpD33uq3w9p/L0hYufNY+XvOWYDTaTN5i7kc9/wRywXFDGFhenYyssOf27rOn4fmHm2BtQ641ILaXkRqS6NOtvtp5kHYIVE81zV080F7c7Aw83K/8b3oV1z1zKkI6NAPi/WRuZtbYSs55aVnHcjWHAolfNY0e12hTpeL2ZIORnwp8vVq2ughz4/g5z0T0wB1FPvaHkWKez2fozpB8Av4bQ4Zqzly/qmtqp5EakNlJyI1Jd3D1PJhgAg16BzsPLdWmgrwfjr+lITKgvAE/P2kjisQpup1A0qDhxReV259726ymtNvdX/PqyWK0w+ETitPZrSFpf+brmPWtOe/cLh+FTzHgTFsOUGyCvnLPPivaR6j6mfDuxt7rE/LlrIRTmVyZqEalGSm5EqlOP28zWmwv/A30qNhg30MeDP8ZdQPdmwWTkFTLu23hs9gpMnw5pDsExYC80t3ioCMOAhSeSj153OrbVpkh0L+hwLWCY3XaVmRq+/XdYeWJdmqsnQtsrzIHaXgGwdylMue7siV3yBtj715mnf59Ooy5mK09+hjnzS0RqFSU3ItWp+QXwVCJc8HilLnd3s/L28C74e7mzas9xPlu6u2IVFM+aqmDX1LY55grLHn6Ob7U51cDnwN0b9iwxx/dUROZh+PFEwtj7bmh1Yu+n6F5w8yxzRtq+ZfDVNea6NGdStGhf2yvN2VzlYbVCyxOtN5oSLlLrKLkRqW5V2SsKiA7x5f+GtAXgjd+3syOlAl1MlVnvJvsYLBxvHve+E/xCy39tRQU1PZk8/f5/UJhXvusMw5x9lnUYwtvBwOdLvt+kO4z+EbyDYP9K+GoY5KSWrif7WPmmf59OUdeUBhWL1DpKbkTqgOE9oxnQuiH5hXYu+d9ivltdziniseeZM7SO7YLje8oum3nYHL/ydkezq8bDD/o8UOXYz6r/I+AfAccTTo59OZtVn8KO38DNC6791NyN/Z8ad4VbfgKfEDgQB19eZSYzp1rzBRTmmhtiRpeewVamFheCxQqHt8LxSsxoE5Fqo+RGpA6wWCy8ek0n/DzdAHji+/X8sbkcU6i9A81uGjhz6036QZj7lJnU/PW2OYMpoiOM/KZ6W22KePnDxc+Yx4teMxfTK8uhrWYrD8Alz0NE+zOXbdQJxvwMvmGQFA9fXglZR833KjL9+3R8gk8mRJo1JVKrKLkRqSMiA72ZeW8/Gni5YzfggWlr2bC/jLEkRc7UNZW6D34eB+90huUfQGEONO4GI6bD3UvMVp+a0nkkRHaCvHRYMP7M5Qrz4PvbzdaWFhebScnZRLQ3Exy/cLNF6ouhZgK17VdISwTf0BMDmytBXVMitZKSG5E6pHVkA9Y8cwnntQojp8DGrV+sYv/xs0wRL0pudi82WyuO7jLHq7zbFVZ/Zq4P07QP3DQT7vjT3PSziuOEKuzUqeFxkyFl8+nLzX/BXBnYNxSu/sC8rjzC28KYX8A/Eg5tgs+HmK1UYE7/Pl23VnkUrXeze1Ht2kpCpJ5TciNSx3i4WflgVDfaRDbgcEYed30VR15hGftHNe5qDqzNS4Mp18L7Pcy1ZeyF0HyA+aV/61xzW4GaTmpOFdPPnLFk2E8/NXzXAlj2vnl85fun39iyLA3PMT9rg8bmOJkDceZ4pB63VT7miA7QoJHZ6rX3r8rXIyIOpeRGpA5q4O3BpDE9Cfb1YNPBdP49cyMFNvvpC1vdzCQGzP2XDDu0GgS3zTNnFMX0r6mwz+6SF8DN01xl+NQp1tnHYNY95nH3sdDm8srVH9YSxv4CAU3M39teAYFRlY/XYlHXlEgtpORGpI5qHOTDmzd0BuD7Nft5YsZ6jDMthNfrDrMrp80VcOciGPXtyYHGtUlILJx7Ion57T/mTuqGAbMfgIwkCG0Fg16u4j2amy1VFzxxsiusKop3Cdd6NyK1hcU4419D15Senk5gYCBpaWkEBAQ4OxyRKvth7X4e/W49NrtBRIAXD1zUipvObebssCovNx3e62auYTP4v+ZGm7MfAKsH3P4HNO7i7AhLyk2H15qDvQAeWAOhLZwdkYhLqsj3t1puROq4YV2b8PLVHQBISc/j/2ZtZEbcfidHVQXeAXDRianeC8fDnCfM44v+r/YlNmDG2/Rc81hdUyK1gpIbERdwY6+mLHh0QPFO4o/NWMeXy/Y4N6iq6HqzOVg3Nw0KsiHmPOj7oLOjOjN1TYnUKu7ODkBEHCM2zI/3RnQl0NeDqSv28cyPmziYmssTg1tjceYsqMqwusGg8eaie95BMOzD8k/7doZWl8K8p2HPUsjPAk8/Z0dUfvlZ5orPW34yd1Rv0MiciVb8OvG7f0T5dkyvUizZ5oKIHr7mZq2+oeYK014NnDuTT+ocJTciLsRqtfDy1R1oHOjNG79v58NFu0hJz+XVazvi5e7m7PAqpvkFMOZX80s1sImzoylbw9YQ2BTS9pk7sLce7OyIzi4vE1Z9An+/B9lHy3eNT8jJZKfrqMovfngmvz4G8V+XPm/1MBMd39CTSU/Rq91VENnBsXFInafkRsTFWCwW7r+oFeEB3jw1cwM/rD1AZl4h44d1JNTPE6u1Dv0LOKafsyMon6Ip4as/M7umHJncFOTC/lVmq1DiCjPZa3uFuUKzp2/F68vLgJUnkpqcE3tthTSHvg+Au485Ky0zxfyZkXziZwrY8szyOcfMhRD3/mVuP+GoxDNlM8RPMY8jO5obnWYfNbsl7QWQmWy+/inuc3hwjdm6I3JCrUhuJkyYwOuvv05ycjKdO3fmvffeo1ev009T/eSTT/jyyy/ZuHEjAN27d2f8+PFnLC9SX93QI5qIAG/u+HI18zanMG9zCiF+nowf1oHBHRo5OzzX0+pSM7nZOc+cvl7ZbpSCnJPJzJ6/zGPbP3ZLXz/dTERaXmwufHjOIPAJKrve3HRzY9Jl70POcfNcSAu44HHocB24lfF1YBjmNUXJzsJXzLj+fBmGTazc5/yn+S8AhtkSc8OXJ8/nZ5sJVfbRE69jJ3/GTzVby5a8BQOfdUwc4hKcPhX8m2++YfTo0Xz44Yf07t2bt99+m++++45t27YRHh5eqvyoUaPo168fffv2xdvbm//+97/88MMPbNq0iaiosy/GpangUt/8uTWFB6fFk5lXCJjfuee3akhMqC83nduMVhENsNkNCu32utd1VZvkZ8F/Y81E5L6VZldVeZRIZpaeSGbyS5bxjzQXW2x6LhxLgK0/mXuDFbG6Q+z50HYotB4CDSJOvpebDis/gmUTTiY1oS3h/MfNbqWykpoz2R8Hn14EWMx9yCI7VryOU+1bDpMGmStG37cCwlqV77qtv8L0Eebu8PevhOCYqsVR19lt5ng1F1WR72+nJze9e/emZ8+evP++uay63W4nOjqaBx54gCeffPKs19tsNoKDg3n//fcZPXr0WcsruZH6KCffRm6BjadmbmDuppJN+/1bhrEuMZWMvEKahfry78vbMqh9Bbc2ENNX18Cu+XDpS2Y3zz8V5EDKJkhad/J1aPPpk5nY88yEJuY8s9vo1JYgw4Dk9eYg4C0/w+Etp1xsMbuL2l5hdmktex9yU823QludaKm5tupfgt+NhU0zofmFMHpW5esxDJh8GexbBt1ugSvfrdi1X14FCYtKt/jUN/FTYc6TZgJ8zUfmrvWOYBjmxrqbfoDL/gtR3R1TbyXUmeQmPz8fX19fZsyYwdVXX118/pZbbiE1NZUff/zxrHVkZGQQHh7Od999xxVXXFHq/by8PPLyTjbppqenEx0dreRG6qUCm53v4/azcNvhUknOP7VrFMBr13WiQ1RgDUXnApZPhLlPmq0oN041dyE/NZE5vA2M0+wD1qDRiUTmDMnM2RzZabbmbPnJ3DPrn8LOMVdkbj/Mcf+yP5YA7/c0x8Pc9D20HFi5erb/BlNvAHdveHAtBDSu2PUpm+DD/ua2ImN+rTvjtBylIMcciL32q5PnQlrAyG/K3wJWVt2zH4AN35m/BzWDu5eaazs5QZ1Jbg4ePEhUVBR///03ffr0KT7/+OOPs2jRIlasWHHWOu69915+++03Nm3ahLd36Z19n3vuOZ5//vlS55XcSH1nGAaT/9rDy79uwd/Lnbdu6MyXy/ayaPvh4jKeblbeH9mVS9WSUz5Hd5mrK5fFN8xcjLBRZ4jsZP4MjnHcVOe0A7D1F9j6MxTmmVtvODKpOdXcf8PyCRDe3uyequg97Db48DxzgHK/h8y9xSrj50dg9STzed650KW7Zko4ugu+vQVSNgAWOPde2DIb0hLBKxCun1T5pDNtP0wfaSblFjezJSj7CHQZBVd/4NCPUV71Jrl59dVXee2111i4cCGdOnU6bRm13IiU7UhmHp7uVgK8PQBYvz+VpTuP8MfmFNbsS8XDzcLHN/fgwjalx8DJaUzsf+LLBnODzkadodGJJKZRZ7OVxlXWbMk+Bu92MRdbvGoCdL2pYtev+wZ+uBO8A+GhdZXvSsk6Au92hbz0ysVRF235CWbda35m31C49lNocRFkHoZvbza7+SxWuPRlc7+2ivw3t3eZWUfWYbPu678wE8bPh5gtZDd8aXYD1rA6k9xUpVvqjTfe4KWXXuKPP/6gR48e5b6nxtyIlE+hzc5D38Tzy/okLBa487zmtI5swMB2EcWJkJxG5mE4vBXC24JfmLOjqX5/vWsuYNigkbm3Vnmnpxfmwfs9zIHRFz8L542rWhx/vwe//585Vf6BONedGm4rgD+eM8dSAUSfC9dPLtmdV5gHv4yDtSfWDOp6Mwx5C9w9z17/6slmN5e9ACI6wo1TIPjEXnV/PA9L3zKT0HuWQUDNzrqsM3tLeXp60r17d+bPn198zm63M3/+/BItOf/02muv8eKLLzJ37twKJTYiUn7ublb+d0MXrukahWHAR4t3M+7bdQx5dwmTliZQaLM7O8Tayb+hORi4PiQ2AL3uNBcwzEgyu6jKK+5zM7Hxj4TedzsgjrvMsUqZKebUcFeUfhA+v+JkYtPnfhjzc+lxSu5ecOX7MOgVs/Vm7Vfmat+Zh0vXWaQwH34eBz8/bCY27a6G2347mdgADHjKbH3MOQ4/3gv22vs3wOnrmY8bN45PPvmEL774gi1btnDPPfeQlZXF2LFjARg9ejRPPfVUcfn//ve/PP3000yaNImYmBiSk5NJTk4mMzPTWR9BxGV5ult584bO3HVBc/w8zXEMicdyeOHnzQx6ezEfLdpFWnZBcfn03AK+XLaHH+MPsO9oNk6ejCk1wcMbLn7GPF76TtlfoEXyMmDRa+bxgCcqtxjhP7l7ml0wYE57P76n6nXWJrsWmOOTEpeDVwAM/xoGvQxuZ2hFtVigz70w8juz/L5l8MlFkLyxdNnMw/DV1eY6TVjgoqfh+s9LbyPi7gnXfGIO/t71p7nCdS3l9KngAO+//37xIn5dunTh3XffpXfv3gAMGDCAmJgYPv/8cwBiYmLYu3dvqTqeffZZnnvuubPeS91SIpWXnJbLjLhEPlmSQFqOmdQ08HYnNswPN6uFtftSS5RvFurLm9d3pkdMiBOilRpjt8MnF0JSPPS8HYa8WXb5hf81d3wPaWGua3OmL+iKcsWp4XY7LH7dXDgRw1xT6IYvzVaq8jq8HaYNh2O7wcMPrvnYXCoAzAHD00ZC+n7wbGCO3TnbCtsrP4FfHzWTnDsXQXibSn+8iqgzY26cQcmNSNVl5BYwbeU+PlmSwOGMvDLLentYeeP6zvy96ygrdh/lX5e25rIOkXVvM08pW8IS+OKKsy/El3UE3ukM+Zlw3WTocI1j43ClqeHZx+D72821k8BcB+iy/4KHT+XqmjEWdi80f7/oaXOW3o/3Q2GOmWiOmFa+xScNA6ZcBzv/MJOt2/8s33ieKlJyUwYlNyKOk1tg46tle9l1OJPuzYKJT0ylV2wI5zYP5VB6Hq/9tpUlO46c9tqGDbx47dpOmoXlSqYOh+1zoc0V5kDU05nzJKyYCI26wB0Lqme3d1eYGl6Yb85O2r/S3Grjiv9BlxFVq9NWCL/921yx+lQtB8K1n519C49TZSTDB33MrTH6PwIDn6tabOWg5KYMSm5Eak6Bzc4zP25k2spEANpENmBrckapcrf0acZDA88hxK/6//Un1ejQVpjYx2w1GTsHmvUt+f7xveYMKVs+3PyDOXW5OmQdgXe7QV6aObC2283Vc5/q9Otj5l5gXoEw9lfH7ny+erLZrWQvNNcXuvjZyiWAW36Cb24CLDDml2pvJVNyUwYlNyI1yzAMFm4/TKifJ52aBJF4LJvfN6fw26Zk4vYex2Y3/wTFhPry0tUd6d08BA83p891kMr66WGImwxRPeD2P0qur/LD3bBuGsReALfMrt44/n4ffv8P+IXXvV3Di9b/ARjxjWN3mS9yaIu571jT3lWr58f7zCnngU3hnqXmmkXVRMlNGZTciNQeRzLzmLsxmYkLd3EgNQeA6BAfbukTwzXdmqglpy7KSDEX1CvIKjmmJmUzTOwLGHDHn9W/R1FhPnzQ2xxE239c3dk1PHkjfDrQHAdz/uNw0X+cHVHZ8jLMMU7H90CnG819rapJnVnnRkTqtzB/L246txk/3t+PEb2a0sDbncRjObz0yxbOHT+fiQt3aTp5XdMgwuzqAJj/vLmgHMD8FwDDnMVUE5sv1sWp4TmpZjdPYQ60uBgGnH3zaKfzagDDPjbX01k/HTbOdHZEgFpunB2OiJwiNTufx2es5/fNKcXngn09CPHzZEjHRtx5QQv8vdydGKGUS36WOeYlM9lcSC6qG0wadPaZVI5mGOb6LbsXnn1quK0Qju0yZ1sd2mxuDGqcWKTOYgEsZzjG/Fxtr4A2Qyofq91u7uW0fY7ZxXPXIvCtQ0so/PmSOWXdOwju+RsCoxx+C3VLlUHJjUjtV7Sp5+u/bSOn4OQu2rFhfjx5WRsMA1pF+PPj2gPsPJzJ/Re2ol1j/f+5Von7An560FyqP6S5uVt5t1vgyndrNo5/Tg1v1tec6XNok9lVdmizWebwNrCVvazBWfW602wtqsy06MWvmwmCm5e5MnDjrlWLpabZCuCzS+DgWnNM1c2zHD4TTslNGZTciNQdqdn5TPprD39uTSE5LZcjmflnLHtDjyZc1z2azLwCcgvs9IwJodBuJ6KBN1ar1tSpcXYbTOwHh7eYv7t7w4NrS28VUBOKpob7hppJTs7x05fz8DP3BItoB6GtzG0MDAM48TV5puNjCSdW98UcSH3DFxDYpPzx7ZwPX19r1ldXZ3cBHNlhrqJcmGO22PW516HVK7kpg5IbkbrpWFY+r/+2jbkbkzh+YssHTzcr+TY7FsuJ75rTCPTxoH3jAPq3CuO67k0Ib+Bdg1HXc9t/h6nXm8f9HoJLXnBOHFlH4L1u5u7lYI4PCW0J4e0gov3Jn0HNKt/asG2uOcMpNw18QsyVfltefPbrju+Fjy8wEy5ntGw52qpP4Zd/mRuYPhjvmK01TlByUwYlNyJ1W6HNju3Eny13qxU3q4WVCcd4+dctrEtMLfNaTzcrl7SPoEWYH3mFdro2DSav0EbPmBAaB1Vi1Vcpm2GYK+Ae3gI3fW92UTnLoS2QvMFcgTestbknlqMd3wPfjja3NMBibjR5/mNnTpgKcs2xSEnxZjfU2LnVE1dNMgxzq4hutzh83I2SmzIouRFxXUcy8/DzdMdqhV2Hsth7NAuLxcL+49n8siGp1N5Xp2oe5kfzhv6kZucT5u9F84Z+dIkOoluzYML8vWruQ0jdVpALcx6HNV+Yv7e42Nxs0i+0dNnZD8CaL82WnrsWQVDTmo21jlFyUwYlNyL1k2EY/L45hY0H0vh9UwrbUkqvlHwmLRr6cW33JgT7etKhcSAdogK0N5aULX4q/DzOHH8SGA3XfwFNTpkCXzTgGgvcPLP6Vmt2IUpuyqDkRkSKpKTnEujjwZHMPJbvPkbc3mPkFdqJCfXjwPEc1uw7zo5DmaWuaxLsw5BOjRjWNYo2kfo7ImeQvBG+vdlcSNDqAYNfMXdNP7gWJg02Z2dd9DSc/6izI60TlNyUQcmNiFTEwdQcflh7gDV7j5OSkcuuQ1klpqf3igkhMtCbIZ0aMbBtBEez8nht7jbObR7KNV2jTjtTKzu/kPxCOwdTc2ka6qu1e1xZbpq5RcGWn8zfO1wLiSshLRFaD4HhX1fP5qEuSMlNGZTciEhV5OTbWLDtELPjD/L75mTsp/wFbRzozcG03OLfm4b4ckvfGAa0bsi6xFTc3ax8H7efRdsPF5dp4OXOsG5RDO8ZTfvG1bcvjziRYcCy92Hes2CcSIxDmps7llfjXkyuRslNGZTciIij7D2axYKth9hzNJsf1h4gLaegSvV1ahLIDT2iaRTozR9bDtG9WTAXtm5IqAY0u4a9f8N3Y81xOGPnmNPPpdyU3JRByY2IVIfcAhuLtx9mc1I6MaF+XNQ2nB/jD/LF33vYeWLcjr+XO5l5hfSMCWbiTd0J9vXk711HmL4qkd83JVNgK/3n2NPNyuUdIxncIZKBbSNwP82O6Xa7waGMPCICvIoHOucV2nh61kaW7jhCVLAPN/SIZkDrcBo2UKLkVLYCKMgBb33/VJSSmzIouRGRmrbxQBp+Xu7EhvmRV2jDy92tVJljWfnMXLOf6asSSU7LJTbMj8y8QhKOZBWXaeDtTqifJ/7e7iSlmoOh3ayW4kHPsWF+NAv1pVGgN8t2HWXP0ewS97BYIMTXE093K81CfQnx82RdYhoWC+w/nkOv2BCG94imZ0wITUMdt/iaiCMouSmDkhsRqUvW7jvOrxuSmLnmAEezzrz9xJn0igmhd/MQZsTtJ+mU8UBnY7VATKgfnu5WLmoTTmyYH/uOZdMzJoQ+LUJJzS4gJ99GVLAPbqcMmjYMg3X70/D2sOJutWK1QKNAH3w8SyZ0NrvBlqR09h/PZu/RbLo1C6ZHs2BNsZczUnJTBiU3IlIX5RXaWJeYxh9bUsjILaBhA2+ig33YnpLBrsNZ3HxuM9JzCzicYU5r3388m1ev7USX6KDiOg6m5vDHlhQ83KwkpeVyOCOXQ+l59GkRyp6jWeQX2tmWksmG/aklBkqXpVmoL7FhfgAkp+WyNbn0+kGBPh70bxlGi4Z+/LXrKMlpuRxIzSlVrkmwD/1bhtEy3J92jQPo1CRIM8mkmJKbMii5EREp2/GsfI5l55OclktyWi7TVu4jbt9xDAPC/D3L3MC0stytFgr/kVF5uls5v1UYvWNDCfHzpGvTIKJDfPE4zbgjcX1Kbsqg5EZEpOLyC+14uJkJyMqEY4T4eeJutbDpYDp5hTb2H8/ht03JGAbcdUELLmkXQUZuAQU2gyOZeazYfZS4vccJ8fNiYNtwjmblc27zEJqF+uFutZCVb+PvnUdYtvso+4/nsPlg+mlbd9ysFpoE++DlbiXQx4Mu0UGcE9GALtFBxIT5YTeM045pkrpPyU0ZlNyIiNR+hmGwPSWTuRuTWbc/lZT0XHYdziS3wF7mdRYLnBPegAFtzLWFcgvsNA/zo31UIN4eVgoK7fh7e3A0M4+4vceLr4sK9qFr0+ATe4z54et5sjssLbsAm2EQ4udZHJt5r4qNDyq0mQs3entYCQ8wN8hMTsvF18uNAG+PCtVVHym5KYOSGxGRuskwDFLS80g4ksXB1ByOZ+ez6WA6uw5nsvNQJtn5trNXUk5RQT60DPfnQGpO8VT+2DA/gnw92JGSiYebhQ5RgZzbPJSL2oQTE2oOuPb1dGN7SgYtGvrTNMSXvEI7//tjO4u3Hy4xHqldowD8vd1ZmXAMMMcbnRPRAB8PN7o2DaJnTAgdogJLDNau75TclEHJjYiI68ktsLFkxxEiArzYczSbRdsOs/NwJl2jg/D2cGNLUjqJx7Lx8nAjr9CGzW6w92g255/TkJ7NgjmYlsv2lAwSjmRxrBKz0k7HzWrBVt6R2acR4udJ/5ZhNArypqG/F57uVhr6exFwYgkAm90gvIEXTYJ9S81Gq6rs/ELWJabROTqwRCtWWdJzC/DzdK+2hEzJTRmU3IiISFlSs/OLN1Jt2MALH093BpzTkJ2HM1m7L5WGDbzoGBXIhv2p/LHlECsSjpbqLvNyt5JXePJc8zA/7ruwJZ2jAwnx8+KPzSlsSU5nSMdGtAz3Z2tyBluT0jmUkceWpHRW7zlORl5huWMOb+BF+8YBdIwKpEW4P/GJqXy5bC+NAr2JDfPDarGwOSkdm92gRUM/CmwG6TkFBPp6cCg9D3c3C3kFdiICvQny8WD9/lSOZxfg6W6lSZAPHm5Wwhp40qd5KF2bBrMy4RjHs/PJzC0kKtiHX9YnsftIFl7uVlo09OeRS87hknYRDvvfBJTclEnJjYiIOFKhzU5Wvg1fTzcOHM8hPMALTzcrmw6mk5KeS/OG/sSE+p52dekzKbDZWb3nOGv2HedoZj57j2aRkVtIVn4hqdkFpOcU4OFupcBmJyO3/ElQTflkdA8lNzVJyY2IiLiStOwCdh3JZMP+NDYfTGf7oQyOZubTMtyf4T2jOXA8h2NZ+RzNyqNLdBA+nu54u1vx8nDjYGoO+YV2moX6si05g2BfTywWOJyZx6XtInCzWjmUnktmXiEHUnNYtsuc9Wa1WGjfOID2jQNISc/DajXHKQ3p1JhdhzLp1iy4eAC2oyi5KYOSGxERkbqnIt/fWglJREREXIqSGxEREXEpSm5ERETEpSi5EREREZei5EZERERcipIbERERcSlKbkRERMSlKLkRERERl6LkRkRERFyKkhsRERFxKUpuRERExKUouRERERGXouRGREREXIqSGxEREXEp7s4OoKYZhgGYW6eLiIhI3VD0vV30PV6WepfcZGRkABAdHe3kSERERKSiMjIyCAwMLLOMxShPCuRC7HY7Bw8epEGDBlgsFofUmZ6eTnR0NImJiQQEBDikTjHp2VYfPdvqo2dbffRsq09tf7aGYZCRkUHjxo2xWsseVVPvWm6sVitNmjSplroDAgJq5X8QrkDPtvro2VYfPdvqo2dbfWrzsz1bi00RDSgWERERl6LkRkRERFyKkhsH8PLy4tlnn8XLy8vZobgcPdvqo2dbffRsq4+ebfVxpWdb7wYUi4iIiGtTy42IiIi4FCU3IiIi4lKU3IiIiIhLUXIjIiIiLkXJTRVNmDCBmJgYvL296d27NytXrnR2SLXeK6+8Qs+ePWnQoAHh4eFcffXVbNu2rUSZ3Nxc7rvvPkJDQ/H39+faa68lJSWlRJl9+/YxZMgQfH19CQ8P57HHHqOwsLAmP0qt9uqrr2KxWHj44YeLz+m5Vs2BAwe46aabCA0NxcfHh44dO7J69eri9w3D4JlnnqFRo0b4+PgwcOBAduzYUaKOY8eOMWrUKAICAggKCuK2224jMzOzpj9KrWKz2Xj66aeJjY3Fx8eHFi1a8OKLL5bYQ0jPtnwWL17M0KFDady4MRaLhVmzZpV431HPcf369Zx33nl4e3sTHR3Na6+9Vt0frWIMqbTp06cbnp6exqRJk4xNmzYZd9xxhxEUFGSkpKQ4O7RabdCgQcbkyZONjRs3GvHx8cbll19uNG3a1MjMzCwuc/fddxvR0dHG/PnzjdWrVxvnnnuu0bdv3+L3CwsLjQ4dOhgDBw401q5da/z6669GWFiY8dRTTznjI9U6K1euNGJiYoxOnToZDz30UPF5PdfKO3bsmNGsWTNjzJgxxooVK4zdu3cbv/32m7Fz587iMq+++qoRGBhozJo1y1i3bp1x5ZVXGrGxsUZOTk5xmcGDBxudO3c2li9fbixZssRo2bKlMWLECGd8pFrj5ZdfNkJDQ42ff/7ZSEhIML777jvD39/feOedd4rL6NmWz6+//mr85z//MWbOnGkAxg8//FDifUc8x7S0NCMiIsIYNWqUsXHjRmPatGmGj4+P8dFHH9XUxzwrJTdV0KtXL+O+++4r/t1msxmNGzc2XnnlFSdGVfccOnTIAIxFixYZhmEYqamphoeHh/Hdd98Vl9myZYsBGMuWLTMMw/w/sNVqNZKTk4vLTJw40QgICDDy8vJq9gPUMhkZGUarVq2MefPmGRdccEFxcqPnWjVPPPGE0b9//zO+b7fbjcjISOP1118vPpeammp4eXkZ06ZNMwzDMDZv3mwAxqpVq4rLzJkzx7BYLMaBAweqL/habsiQIcatt95a4tw111xjjBo1yjAMPdvK+mdy46jn+MEHHxjBwcEl/iY88cQTRuvWrav5E5WfuqUqKT8/n7i4OAYOHFh8zmq1MnDgQJYtW+bEyOqetLQ0AEJCQgCIi4ujoKCgxLNt06YNTZs2LX62y5Yto2PHjkRERBSXGTRoEOnp6WzatKkGo6997rvvPoYMGVLi+YGea1XNnj2bHj16cP311xMeHk7Xrl355JNPit9PSEggOTm5xPMNDAykd+/eJZ5vUFAQPXr0KC4zcOBArFYrK1asqLkPU8v07duX+fPns337dgDWrVvH0qVLueyyywA9W0dx1HNctmwZ559/Pp6ensVlBg0axLZt2zh+/HgNfZqy1buNMx3lyJEj2Gy2El8CABEREWzdutVJUdU9drudhx9+mH79+tGhQwcAkpOT8fT0JCgoqETZiIgIkpOTi8uc7tkXvVdfTZ8+nTVr1rBq1apS7+m5Vs3u3buZOHEi48aN49///jerVq3iwQcfxNPTk1tuuaX4+Zzu+Z36fMPDw0u87+7uTkhISL1+vk8++STp6em0adMGNzc3bDYbL7/8MqNGjQLQs3UQRz3H5ORkYmNjS9VR9F5wcHC1xF8RSm7Eqe677z42btzI0qVLnR1KnZeYmMhDDz3EvHnz8Pb2dnY4Lsdut9OjRw/Gjx8PQNeuXdm4cSMffvght9xyi5Ojq9u+/fZbpkyZwtSpU2nfvj3x8fE8/PDDNG7cWM9WKkXdUpUUFhaGm5tbqZkmKSkpREZGOimquuX+++/n559/ZsGCBTRp0qT4fGRkJPn5+aSmppYof+qzjYyMPO2zL3qvPoqLi+PQoUN069YNd3d33N3dWbRoEe+++y7u7u5ERETouVZBo0aNaNeuXYlzbdu2Zd++fcDJ51PW34TIyEgOHTpU4v3CwkKOHTtWr5/vY489xpNPPsmNN95Ix44dufnmm3nkkUd45ZVXAD1bR3HUc6wLfyeU3FSSp6cn3bt3Z/78+cXn7HY78+fPp0+fPk6MrPYzDIP777+fH374gT///LNU82b37t3x8PAo8Wy3bdvGvn37ip9tnz592LBhQ4n/E86bN4+AgIBSX0D1xcUXX8yGDRuIj48vfvXo0YNRo0YVH+u5Vl6/fv1KLVmwfft2mjVrBkBsbCyRkZElnm96ejorVqwo8XxTU1OJi4srLvPnn39it9vp3bt3DXyK2ik7OxurteTXkZubG3a7HdCzdRRHPcc+ffqwePFiCgoKisvMmzeP1q1b14ouKUBTwati+vTphpeXl/H5558bmzdvNu68804jKCioxEwTKe2ee+4xAgMDjYULFxpJSUnFr+zs7OIyd999t9G0aVPjzz//NFavXm306dPH6NOnT/H7RVOWL730UiM+Pt6YO3eu0bBhQ01Z/odTZ0sZhp5rVaxcudJwd3c3Xn75ZWPHjh3GlClTDF9fX+Prr78uLvPqq68aQUFBxo8//misX7/euOqqq047zbZr167GihUrjKVLlxqtWrWqd9OV/+mWW24xoqKiiqeCz5w50wgLCzMef/zx4jJ6tuWTkZFhrF271li7dq0BGG+99Zaxdu1aY+/evYZhOOY5pqamGhEREcbNN99sbNy40Zg+fbrh6+urqeCu5L333jOaNm1qeHp6Gr169TKWL1/u7JBqPeC0r8mTJxeXycnJMe69914jODjY8PX1NYYNG2YkJSWVqGfPnj3GZZddZvj4+BhhYWHGv/71L6OgoKCGP03t9s/kRs+1an766SejQ4cOhpeXl9GmTRvj448/LvG+3W43nn76aSMiIsLw8vIyLr74YmPbtm0lyhw9etQYMWKE4e/vbwQEBBhjx441MjIyavJj1Drp6enGQw89ZDRt2tTw9vY2mjdvbvznP/8pMdVYz7Z8FixYcNq/r7fccothGI57juvWrTP69+9veHl5GVFRUcarr75aUx+xXCyGccoSkCIiIiJ1nMbciIiIiEtRciMiIiIuRcmNiIiIuBQlNyIiIuJSlNyIiIiIS1FyIyIiIi5FyY2IiIi4FCU3IiIi4lKU3IhIrTdmzBiuvvpqZ4chInWEkhsRERFxKUpuRKTWmDFjBh07dsTHx4fQ0FAGDhzIY489xhdffMGPP/6IxWLBYrGwcOFCABITE7nhhhsICgoiJCSEq666ij179hTXV9Ti8/zzz9OwYUMCAgK4++67yc/PL/OeWVlZNfzJRcSR3J0dgIgIQFJSEiNGjOC1115j2LBhZGRksGTJEkaPHs2+fftIT09n8uTJAISEhFBQUMCgQYPo06cPS5Yswd3dnZdeeonBgwezfv16PD09AZg/fz7e3t4sXLiQPXv2MHbsWEJDQ3n55ZfPeE9tuSdStym5EZFaISkpicLCQq655hqaNWsGQMeOHQHw8fEhLy+PyMjI4vJff/01drudTz/9FIvFAsDkyZMJCgpi4cKFXHrppQB4enoyadIkfH19ad++PS+88AKPPfYYL774Ypn3FJG6S91SIlIrdO7cmYsvvpiOHTty/fXX88knn3D8+PEzll+3bh07d+6kQYMG+Pv74+/vT0hICLm5uezatatEvb6+vsW/9+nTh8zMTBITEyt8TxGpG5TciEit4Obmxrx585gzZw7t2rXjvffeo3Xr1iQkJJy2fGZmJt27dyc+Pr7Ea/v27YwcObJa7ikidYOSGxGpNSwWC/369eP5559n7dq1eHp68sMPP+Dp6YnNZitRtlu3buzYsYPw8HBatmxZ4hUYGFhcbt26deTk5BT/vnz5cvz9/YmOji7zniJSdym5EZFaYcWKFYwfP57Vq1ezb98+Zs6cyeHDh2nbti0xMTGsX7+ebdu2ceTIEQoKChg1ahRhYWFcddVVLFmyhISEBBYuXMiDDz7I/v37i+vNz8/ntttuY/Pmzfz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g7JJERETKBYWcYubuZuXju1vRpnoQienZ3PP5WvadSnJ2WSIiIi5PIacE+Hq6M3loW5pGBnAuJZO7P1vLuZTLP3YnIiIiRePUkLNs2TL69OlDREQEFouFWbNm5fvYlStX4u7uTosWLYqtPkfy9/bgy3vbUatyBeIS03nh520Fmn5bRERECsapISclJYXmzZszYcKEAh0XHx/PoEGDuPbaa4upsuIRXMGTt+9ojrvVwpwtJ3h3wR5nlyQiIuKynDpbUe/evendu3eBjxs+fDgDBgzAzc2tQL0/pUGrakGMvaUp//lpCx8s2kf1ShW4vXVVZ5clIiLicsrcmJwvvviCAwcOMGbMGGeXUmh3to3ikW61ARg9Ywur9p91ckUiIiKup0yFnL179/Lss8/yzTff4O6ev06ojIwMEhMTc71Kgyd71OeGplXIshk88PV6dp4oHXWJiIi4ijITcmw2GwMGDODll1+mXr16+T5u3LhxBAQE5LyioqKKscr8s1otvHNnc9rVCCYpPZvBk9dyLD7N2WWJiIi4jDITcpKSkli/fj2PPvoo7u7uuLu788orr7B582bc3d1ZtGhRnseNHj2ahISEnNeRI0dKuPJL8/ZwY9KgNtQPq8ippAyGf72B9KwrL5QmIiJSGtSoUYPx48fnfH2lJ6UPHTqExWIhJiam2GuDMrQKub+/P1u3bs313kcffcSiRYuYPn06NWvWzPM4Ly8vvLy8SqLEQgnw9eDzIW3o88EKth5L4MWft/HGbc2wWLRquYiIlC0nTpwgKKj0LF/k1JCTnJzMvn37cr4+ePAgMTExBAcHU61aNUaPHs2xY8f46quvsFqtNGnSJNfxoaGheHt7X/R+WVM1yJf3+7dk8OS1/LD+KC2ighjQvpqzyxIRESmQ8PBwZ5eQi1NvV61fv56WLVvSsmVLAEaNGkXLli158cUXATMRXmpFVFfTpW5lnu7VAIAxs7exMfa8kysSERFX9umnnxIREYHdbs/1/k033cS9997L/v37uemmmwgLC8PPz4+2bdvyxx9/XLbNf9+uWrt2LS1btsTb25s2bdqwadOm4riUS3JqyOnatSuGYVz0mjJlCgBTpkxhyZIllzz+pZdeKrH7eiVh+NW16N0knCybwUPfbOB0UoazSxIRkYIyDMhMcc6rADPp33HHHZw9e5bFixfnvHfu3DnmzZvHwIEDSU5O5vrrr2fhwoVs2rSJ6667jj59+uS78yE5OZkbb7yRRo0asWHDBl566SWeeuqpAn87i6LMjMkpDywWC2/d0Zy9p5LZdyqZR77byLf3tcfDrcyMDxcRkaxUGBvhnHM/dxw8K+Rr16CgIHr37s13332Xs4LA9OnTCQkJoVu3blitVpo3b56z/6uvvsrMmTOZPXs2jz766BXb/+6777Db7Xz++ed4e3vTuHFjjh49ykMPPVS4aysE/fZ0lqx0WDsJfs79H4qflzuf3NMaPy931h48x9jfdjqpQBERcXUDBw7kp59+IiPDvHPw7bffctddd2G1WklOTuapp56iYcOGBAYG4ufnx86dO/Pdk7Nz506aNWuGt7d3znvR0dHFch2Xop4cZ0mOg7nPgGGDNkMhsnXOR7Ur+/Hunc154OsNfLHyEM2rBnJzy0gnFisiIvnm4Wv2qDjr3AXQp08fDMNgzpw5tG3bluXLl/N///d/ADz11FMsWLCAt99+mzp16uDj48Ptt99OZmZmcVReLBRynCWoBjS7EzZ/D8vegf7f5fq4Z+NwHrumDh8s2sezM7ZQL6wijSL8nVOriIjkn8WS71tGzubt7c2tt97Kt99+y759+6hfvz6tWrUCYOXKlQwZMoRbbrkFMMfYHDp0KN9tN2zYkK+//pr09PSc3pzVq1c7/BouR7ernKnzKMACu+fAye0XfTyyez2urleZ9Cw7D36znvjUspOeRUSkbBg4cCBz5sxh8uTJDBw4MOf9unXrMmPGDGJiYti8eTMDBgy46EmsyxkwYAAWi4X777+fHTt28Ntvv/H2228XxyVckkKOM1WuB41uMreXv3vRx25WC+/d1YJqwb4cOZfGyGkx2O35HzkvIiJyJddccw3BwcHs3r2bAQMG5Lz/7rvvEhQURMeOHenTpw+9evXK6eXJDz8/P3755Re2bt1Ky5Ytef7553njjTeK4xIuyWIYBXjezAUkJiYSEBBAQkIC/v6l4PbPiS3wSRewWOHR9VCp9kW77DieyK0fryQ9y87I7nUZ2T3/a3eJiEjxSk9P5+DBg9SsWTPXIFspuMt9Lwvz+1s9Oc5WpRnU7QWGHVZc3JsD0CjCn9dubgrAewv3smzP6ZKsUEREpExSyCkNrvprcqTNUyE+7wVEb2tdlQHtq2EYMOqHGE0UKCIicgUKOaVBVDuoeRXYs+HP9y+524s3NqJ+WEXOJGfy9PTNlLM7jSIiIgWikFNadPmrN2fDl5B0Ms9dvD3c+GBAS7zcrSzZfZov/zxUcvWJiIiUMQo5pUXNq6BqW7BlwKoPL7lbvbCKPHd9QwDGzt3FjuOJJVWhiIhchnrXi87R30OFnNLCYoGrnja310+G1HOX3HVQdHW6NwwlM9vOo99vJDUzu4SKFBGRf/Pw8AAgNTXVyZWUfRdmU3Zzc3NIe5rxuDSp2xPCm0LcVlgzEbo9l+duFouFN29vTu/3lnHgdAovzd7Om7c3z3NfEREpXm5ubgQGBnLq1CkAfH19sVgsTq6q7LHb7Zw+fRpfX1/c3R0TTzRPTmmzfSb8OAS8A2DkNvC+dI2r9p9lwGerMQx4v39L+jZ30qq3IiLlnGEYxMXFER8f7+xSyjSr1UrNmjXx9PS86LPC/P5WT05p07AvhNSDM3tg/efQ+YlL7hpduxKPdavD+4v28fyMrbSpHkREoE8JFisiImD2sFepUoXQ0FCysrKcXU6Z5enpidXquJE06skpjWK+h1nDoUJlGLEFPC+9qmy2zc6dn6xiY2w8XetX5oshbdVNKiIiLkczHruKprdDYDVIOQ0bv7rsru5uVt68vRmebuZj5TM3HSuhIkVEREo3hZzSyM3j79tUf74P2ZdffbxOaEVGdK8LwCu/7tBsyCIiIijklF4tBkLFKpB4DDZ/f8XdH7iqFo2q+BOfmsVLs7eXQIEiIiKlm0JOaeXuBR0fM7dXvAu2y8+F4/HXbSs3q4U5W08wb1tcCRQpIiJSeinklGath4BvJTh/CLbPuOLuTSIDGH51LQBe+HkbCaka4S8iIuWXQk5p5lkBOjxsbi9/B+z2Kx7y2DV1qV25AqeTMnh1zo5iLlBERKT0Usgp7drdD14BcHoX7Pr1irt7e7jx5u3NsFhg+oajLNyZ92KfIiIirk4hp7TzDjCDDsDytyEf0xq1rh7MvZ1qAvDkj5s5Hp9WnBWKiIiUSgo5ZUGHh8HDF05shn0L83XIf66rT7OqAXraSkREyi2FnLKgQiVoc6+5veytfPXmeLm78c4dzXGzWvh9x0n+3HemmIsUEREpXRRyyoroR8HNE46shsMr83VI3bCK3N2+GgD/nbWN9CxbcVYoIiJSqijklBX+VaDlPeb2srfzfdioHvUJrejFgTMpvD1/dzEVJyIiUvoo5JQlnUaAxQ0OLIajG/J1SICvB+NubQrA5ysPsuHwueKsUEREpNRQyClLgqpDs37m9vL89+Zc2zCMW1tFYhjw9PQtum0lIiLlgkJOWdNlFGCB3b/Byfw/NTXmxsbmbavTKfzfH3uKrz4REZFSQiGnrAmpC41vNreXv5PvwwJ8PRh7i3nbatKyA2yKPV8MxYmIiJQeCjllUZcnzT+3zYAz+/J9WPdGYdzSMhK7Ac/+tJUs25WXiRARESmrFHLKovCmUO86wIA/3y/QoS/e2IjgCp7sPpnElJWHiqU8ERGR0kAhp6zq/IT55+bvISku34cFVfDk2d4NAPi/P/ZwIkFLPoiIiGtSyCmrqnWAqA5gy4TVHxXo0NtbVaVN9SBSM228NmdnMRUoIiLiXAo5ZdmF3px1kyEtPt+HWa0WXr6pMVYL/LrlBGsOnC2e+kRERJxIIacsq9sTKjeEzCRYP7lAhzaOCOCuduaSDy/9sgOb/crrYYmIiJQlTg05y5Yto0+fPkRERGCxWJg1a9Zl958xYwY9evSgcuXK+Pv7Ex0dzfz580um2NLIaoXOI83t1R9DVsHG1zzVsz7+3u7sPJHI92tjHV+fiIiIEzk15KSkpNC8eXMmTJiQr/2XLVtGjx49+O2339iwYQPdunWjT58+bNq0qZgrLcWa3AYBUZByCmK+K9ChwRU8GdWjHgDv/L6b+NTM4qhQRETEKSyGYZSK+xQWi4WZM2dy8803F+i4xo0b069fP1588cV87Z+YmEhAQAAJCQn4+/sXotJSaPVEmPcMBNWARzeAm3u+D8222bn+/eXsOZnMjc2q8EH/llgsluKrVUREpBAK8/u7TI/JsdvtJCUlERwcfMl9MjIySExMzPVyOa3uAZ9gOH8Idv5coEPd3ay8cVsz3K0Wft1ygp82HiueGkVEREpYmQ45b7/9NsnJydx5552X3GfcuHEEBATkvKKiokqwwhLiWQHaDze3V/wfFLBzrmW1IJ7467bV2N92cjopw9EVioiIlLgyG3K+++47Xn75ZX744QdCQ0Mvud/o0aNJSEjIeR05cqQEqyxB7e4HD1+I2wr7FxX48AeuqkW9MD/OpWTy1I+bKSV3MUVERAqtTIacqVOnct999/HDDz/QvXv3y+7r5eWFv79/rpdL8g2G1kPM7RX/V+DDPdysfDSwFZ5uVpbuOc3cbfmfRVlERKQ0KnMh5/vvv2fo0KF8//333HDDDc4up3SJfgSs7nBoORzdUODD64RWZHjX2gC88ssOkjOyHV2hiIhIiXFqyElOTiYmJoaYmBgADh48SExMDLGx5pwto0ePZtCgQTn7f/fddwwaNIh33nmH9u3bExcXR1xcHAkJCc4ov/QJqApN/xqftLLgvTkAD3etTVSwD3GJ6by/cK8DixMRESlZTg0569evp2XLlrRs2RKAUaNG0bJly5zHwU+cOJETeAA+/fRTsrOzeeSRR6hSpUrOa8SIEU6pv1Tq9Nf3YuevcKbgIcXbw41X+jYB4PMVB9kdl+TI6kREREpMqZknp6S45Dw5//Z9f9j9G7S8B276sFBNPPj1euZvP0m7GsFMe7CD5s4RERGnKnfz5MglXFi4c/NUSDxeqCZe7NMYHw831h46xwzNnSMiImWQQo4rimoH1TuBPQtW5W/JjH+LDPTh8WvrAubcOQmpWY6sUEREpNgp5LiqTiPNPzdMgbTzhWpiWOea1An142xKJm//vtthpYmIiJQEhRxXVbcHhDaGzGRY91mhmvB0t/LqTeYg5G/WHGbL0XgHFigiIlK8FHJclcUCnUea26snQlZaoZqJrl2Jm1tEYBjw31nbsNnL1Th1EREpwxRyXFnjWyGwGqSegU3fFLqZ525oSEUvd7YcTeD7tbFXPkBERKQUUMhxZW7u0PFxc/vP98FWuBmMQyt682RPcwHPN+ft4myyFvAUEZHSTyHH1bUYCL6VID4WdswqdDN3d6hO4wh/EtOz+WDRPsfVJyIiUkwUclydpy+0f8jcXvF/UMi5H93drDx/fUMAvl1zmNizqY6qUEREpFgo5JQH7e4DTz84uQ32/VHoZjrWCaFL3RCybAbvaV0rEREp5RRyygOfIGg9xNxeMb5ITT3Vsz4AMzcd5eCZlKLVJSIiUowUcsqLDg+D1QMOr4AjawvdTPOoQK5pEIrdgA/UmyMiIqWYQk55ERAJzfuZ20XszRnZ3VzuYVbMMfad0irlIiJSOinklCcdRwAW2D0HThd+mYZmVQPp0SgMuwGvz9VyDyIiUjop5JQnletBgxvM7ZXvFampZ65rgJvVwh87T7L24DkHFCciIuJYCjnlTecnzD+3TIOEo4Vupk6oH/3aRgEw/o89jqhMRETEoRRyypuqbaBGF7Bnw+qPi9TUI93q4OFm4c/9Z1lz4KyDChQREXEMhZzy6MJSDxu+hPSEQjcTGejDnW3M3hzNmyMiIqWNQk55VLcHVG4ImUmw/osiNfWwenNERKSUUsgpjywW6PiYub1mImRnFrop9eaIiEhppZBTXjW9AypWgaQTsG16kZpSb46IiJRGCjnllbsntH/Q3P7zg0Iv3AnqzRERkdJJIac8az3UXLjz1I4iLdwJ6s0REZHSRyGnPPMJ/HvhziJODvjP3pw35u3CKELPkIiIiCMo5JR3HR4CqzscWg7HNxWpqcevrYu3h5WNsfGs0SzIIiLiZAo55V1AVWh8q7n95wdFairM35vbWlUF4PW5u7DZ1ZsjIiLOo5Aj0OmvyQG3z4Lzh4vU1KPX1KGilzsxR+L5dcvxotcmIiJSSAo5AuFNoVY3MGyw+qMiNVUlwIfhXWsD8N4fe8m22R1RoYiISIEp5IjpQm/Oxq8gtWjjaQZ3rEGQrwcHzqTwc4x6c0RExDkUcsRUqxuENYWsVFg/uUhN+Xm588BVZm/O+4v2kqXeHBERcQKFHDHlWurhE8hKL1Jzg6KrU6mCJ4fPpjJz4zEHFCgiIlIwCjnytya3gn9VSDkFW6YVqakKXu4Mv/rv3pzMbPXmiIhIyVLIkb+5eZjz5oD5OLm9aMHk7g7VCfHz4uj5NKZvOOqAAkVERPJPIUdyaz0YvALg7F7YO79ITfl4uvHwX09afbhoLxnZNkdUKCIiki8KOZKbV0VoM8TcXvl+kZsb0L4aYf5eHE9I54f16s0REZGSo5AjF2v/EFg9IPZPOLq+SE15e7jxSLc6AExYtI/0LPXmiIhIyVDIkYv5V4Fmd5rbRVy4E6Bf2yiqBHgTl5jO1LWxRW5PREQkPxRyJG8XHiff+Quc3V+kprzc3Xj0mr96c5bsV2+OiIiUCIUcyVtoQ6jbEzCKvNQDwB2to4gM9OF0UgbfrC7a+lgiIiL54dSQs2zZMvr06UNERAQWi4VZs2Zd8ZglS5bQqlUrvLy8qFOnDlOmTCn2OsutC705m76FlLNFasrT3crj15q9OROX7ic1M7uo1YmIiFyWU0NOSkoKzZs3Z8KECfna/+DBg9xwww1069aNmJgYRo4cyX333cf8+UV71FkuoUYXqNICstNg3aQiN3drq6pUC/blTHImX/6p3hwRESleFsMwDGcXAWCxWJg5cyY333zzJfd55plnmDNnDtu2bct576677iI+Pp558+bl6zyJiYkEBASQkJCAv79/Uct2fdt+gun3gm8lGLkNPH2L1NxPG47y5I+bCfT1YPl/ulHR28NBhYqIiCsrzO/vMjUmZ9WqVXTv3j3Xe7169WLVqlWXPCYjI4PExMRcLymAhjdBYDVIPQubvy9ycze3jKR25QrEp2YxecWhotcnIiJyCWUq5MTFxREWFpbrvbCwMBITE0lLS8vzmHHjxhEQEJDzioqKKolSXYebO3R4xNxe9SHYi/ZklJvVwhM96gHw2fIDxKdmFrVCERGRPJWpkFMYo0ePJiEhIed15MgRZ5dU9rS8G7wD4dwB2DWnyM1d36QKDcIrkpSRzafLDhS9PhERkTyUqZATHh7OyZMnc7138uRJ/P398fHxyfMYLy8v/P39c72kgLz8oO195vaf70MRh3FZrRae7FkfgC9WHuJMckZRKxQREblImQo50dHRLFy4MNd7CxYsIDo62kkVlSPtHwQ3Lzi6DmJXF7m57g1DaV41gLQsGx8vKdpkgyIiInlxashJTk4mJiaGmJgYwHxEPCYmhthYc+r/0aNHM2jQoJz9hw8fzoEDB/jPf/7Drl27+Oijj/jhhx944oknnFF++eIXCs3vMrf//KDIzVksf/fmfL36MHEJ6UVuU0RE5J+cGnLWr19Py5YtadmyJQCjRo2iZcuWvPjiiwCcOHEiJ/AA1KxZkzlz5rBgwQKaN2/OO++8w2effUavXr2cUn+5E/2o+efu3+DM3iI316VuCO1qBJOZbefDxUVvT0RE5J9KzTw5JUXz5BTR9/3NkNPmXrjx/4rc3OoDZ7nr09V4uFlY+nQ3IgLzHlslIiLlm8vPkyOlQIeHzT83T4W080VvrlYlOtQKJstmaGyOiIg4lEKOFEyNzhDWBLJSYeNXDmlyxLXmvDnT1h3hRELe8x2JiIgUlEKOFIzFAh0eMrfXfAq2oi+0GV27Eu1rBpNps6s3R0REHEYhRwquye3gGwKJR2HXrw5pckT3ugBMXXtET1qJiIhDKORIwXl4Q5uh5vaaiQ5pMrpWJfNJK5udiUvVmyMiIkWnkCOF02YYWN0hdhUc31Tk5iwWCyP/6s35bm2senNERKTIFHKkcPyrQONbze3VDurNqV0pZ96cj5fsc0ibIiJSfinkSOF1GG7+ue0nSDp5+X3z4Z+9Od9rbI6IiBSRQo4UXmRrqNoO7FmwfrJDmszpzbHZ+Ui9OSIiUgQKOVI0F3pz1n8O2UVfTdxisTCyx99PWmneHBERKSyFHCmahn2hYgSknIZtMxzSZHStSrT7a96cjxbrSSsRESkchRwpGjcPaHe/ub36I3DAUmgWi4Unuv89C/LxePXmiIhIwSnkSNG1HgLuPhC3xXyk3AH+OQuyxuaIiEhhKORI0fkGQ7M7ze3VHzus2ZH/6M05pt4cEREpIIUccYz2fw1A3vUrxMc6pMno2n+vUD52zk6HtCkiIuWHQo44RlgjqNUVDDus/dRhzb5wYyPcrBbmbD3Bkt2nHNauiIi4PoUccZz2f61OvvEryExxSJONIwIY0rEGAOP/2IvhgIHNIiJSPijkiOPU7QnBtSA9ATZ/77Bmh19dG093KzFH4vllywmHtSsiIq5NIUccx2qFdg+a22s+AbvdIc1WrujFw11rAzB2zk7Ss2wOaVdERFybQo44VosB4FkRzuyBA4sc1uzwq2sTGehDXGI6U/485LB2RUTEdSnkiGN5+0Ore8xtBz5O7u3hxqge5iPlHy3eR0JqlsPaFhER16SQI47X7gHAAvv+gNN7HNbszS0jqR9WkcT0bD5aqgkCRUTk8hRyxPGCa0L93ub22k8c1qyb1cJ/rqsPwBcrD7HnZJLD2hYREdejkCPF48LkgDHfQ1q8w5q9pkEo3epXJjPbzguztumRchERuSSFHCkeNa+C0MaQlQKbvnZYsxaLhdduaYqnu5U1B8+xfO8Zh7UtIiKuRSFHiofFAh3+6s1Z8ynYsh3WdESgD/d0qA7AW/N3qzdHRETypJAjxafpHeATDAmxsPs3hzb9cNfaVPB0Y+uxBOZui3No2yIi4hoUcqT4ePhAm6Hm9pqJDm26kp8Xw7rUAuDdBXuw2dWbIyIiuSnkSPFqex9Y3eHwSjix2aFN39elJgE+Huw7lcysTccc2raIiJR9CjlSvPwjoNHN5vZqx/bm+Ht7MPxqc7mH8Qv3kJntmGUkRETENSjkSPHr8Nfq5NumQ/IphzY9uGN1Qvy8OHIujR/WH3Fo2yIiUrYp5Ejxq9oGItuALRPWf+HQpn093Xm0m9mb88GivVq8U0REcijkSMm40Juz/nPIznRo0/3bVyMy0IeTiRl8veqwQ9sWEZGySyFHSkajm6BiFUg+CdtnOLRpL3c3RnSvC8BHS/aRlK7FO0VERCFHSoqbB7S739xe/i7YHXtb6daWkdSqXIHzqVlMXnHIoW2LiEjZpJAjJaft/eAdCGd2wzbH9ua4u1kZ1aMeAJOWH+B8imNviYmISNlTqJDz5ZdfMmfOnJyv//Of/xAYGEjHjh05fFhjIuQSvP2h0+Pm9tLXHbrUA8D1TarQqIo/yRnZTFy636Fti4hI2VOokDN27Fh8fHwAWLVqFRMmTODNN98kJCSEJ554wqEFiotp9wD4VoKz+2Drjw5t2mq18HSv+gB8ueoQJxPTHdq+iIiULYUKOUeOHKFOnToAzJo1i9tuu40HHniAcePGsXz58gK1NWHCBGrUqIG3tzft27dn7dq1l91//Pjx1K9fHx8fH6KionjiiSdIT9cvszLDqyJ0GmFuL30DbI4dJNy1fmVaVw8iPcvOh4v2ObRtEREpWwoVcvz8/Dh79iwAv//+Oz169ADA29ubtLS0fLczbdo0Ro0axZgxY9i4cSPNmzenV69enDqV94Rx3333Hc8++yxjxoxh586dfP7550ybNo3nnnuuMJchztL2PqhQGc4fhM1THdq0xfJ3b873a2OJPZvq0PZFRKTsKFTI6dGjB/fddx/33Xcfe/bs4frrrwdg+/bt1KhRI9/tvPvuu9x///0MHTqURo0aMXHiRHx9fZk8eXKe+//555906tSJAQMGUKNGDXr27En//v2v2PsjpYxnBeg00txe9qbD583pUKsSXeqGkG03GL9wj0PbFhGRsqNQIWfChAlER0dz+vRpfvrpJypVqgTAhg0b6N+/f77ayMzMZMOGDXTv3v3vYqxWunfvzqpVq/I8pmPHjmzYsCEn1Bw4cIDffvstJ2TlJSMjg8TExFwvKQXa3At+YRAfCzHfOrz5C705MzcdY3dcksPbFxGR0s+9MAcFBgby4YcfXvT+yy+/nO82zpw5g81mIywsLNf7YWFh7Nq1K89jBgwYwJkzZ+jcuTOGYZCdnc3w4cMve7tq3LhxBapLSoinL3QeBfOegWVvQ4sB4O7lsOabVQ2kd5Nw5m6L4+3fdzNpUBuHtS0iImVDoXpy5s2bx4oVK3K+njBhAi1atGDAgAGcP3/eYcX925IlSxg7diwfffQRGzduZMaMGcyZM4dXX331kseMHj2ahISEnNeRI1rEsdRoPcScBTnxKGz8yuHNP9mzPlYLLNhxkg2Hi++/SxERKZ0KFXKefvrpnNs+W7du5cknn+T666/n4MGDjBo1Kl9thISE4ObmxsmTJ3O9f/LkScLDw/M85oUXXuCee+7hvvvuo2nTptxyyy2MHTuWcePGYbfb8zzGy8sLf3//XC8pJTy8ocuT5vbydyDLsU/J1Qn1447WUQC8MW8XhmE4tH0RESndChVyDh48SKNGjQD46aefuPHGGxk7diwTJkxg7ty5+WrD09OT1q1bs3Dhwpz37HY7CxcuJDo6Os9jUlNTsVpzl+zm5gagX2BlVatB4F8Vkk7AhikOb35E97p4ultZe/AcS/ecdnj7IiJSehUq5Hh6epKaaj6a+8cff9CzZ08AgoODCzSwd9SoUUyaNIkvv/ySnTt38tBDD5GSksLQoUMBGDRoEKNHj87Zv0+fPnz88cdMnTqVgwcPsmDBAl544QX69OmTE3akjHH3gqueMrdXvAuZjn3kOyLQh8HR1QF4c95u7HaFYRGR8qJQA487d+7MqFGj6NSpE2vXrmXatGkA7Nmzh6pVq+a7nX79+nH69GlefPFF4uLiaNGiBfPmzcsZjBwbG5ur5+a///0vFouF//73vxw7dozKlSvTp08fXnvttcJchpQWLQaaASc+FtZPho6POrT5h7vWYeraI+w4kcgvW45zU4tIh7YvIiKlk8UoxH2e2NhYHn74YY4cOcLjjz/OsGHDAHjiiSew2Wy8//77Di/UURITEwkICCAhIUHjc0qTjV/D7EfBNwRGbjHn0nGgDxbu5Z0Fe6gW7MvvT1yFt4d6/kREypLC/P4uVMgpyxRySilbFnzY1pwFufvL0HmkQ5tPycim29tLOJWUwd0dqvHqTU2wWCwOPYeIiBSfwvz+LtSYHACbzcZPP/3E//73P/73v/8xc+ZMbDZbYZuT8s7NA65+xtxe+R5kOHYCvwpe7oy9pSkA36yO5ZNlBxzavoiIlD6FCjn79u2jYcOGDBo0iBkzZjBjxgzuvvtuGjduzP79+x1do5QXTe+ASnUg7Rys+cThzXdvFMaYPuZTge/8vpt9p5Idfg4RESk9ChVyHn/8cWrXrs2RI0fYuHEjGzduJDY2lpo1a/L44487ukYpL9zc4epnze0/P4D0BIefYkjHGnSrX5ksm8F/Z23V1AMiIi6sUCFn6dKlvPnmmwQHB+e8V6lSJV5//XWWLl3qsOKkHGpyK4TUh/R4WD3R4c1bLBZeuakJ3h5WVh84x8xNxxx+DhERKR0KFXK8vLxISrp4zERycjKenp5FLkrKMasbdP2rN2fVBEhz/HIMUcG+PH5tXQBem7OT+FTHroIuIiKlQ6FCzo033sgDDzzAmjVrMAwDwzBYvXo1w4cPp2/fvo6uUcqbRjdDaCPISIBVHxXLKe7rXIu6oX6cTcnkjXl5LwgrIiJlW6FCzvvvv0/t2rWJjo7G29sbb29vOnbsSJ06dRg/fryDS5Ryx2qFrn/NdL36Y0g95/BTeLpbee2vp62+X3tEC3iKiLigIs2Ts2/fPnbu3AlAw4YNqVOnjsMKKy6aJ6eMsNvh06sgbit0HgXdxxTLaZ7+cTM/bjhKk0h/fn6kM25WzZ0jIlIaFetkgPldXRzg3Xffzfe+JU0hpwzZ9RtM7Q8eFcxZkCuEOPwUZ5IzuObtJSSmZ/PqzU24p0N1h59DRESKrjC/v/O9dtWmTZvytZ9mkRWHqd8bqrSAEzHmBIE9X3X4KUL8vHiyZ33GzN7O2/N3c0PTKgRX0OB5ERFXoGUdpHTb8zt8dwe4+5i9OX6hDj9Fts1Onw9XsvNEIne1jeL125o5/BwiIlI0Jbqsg0iJqNsDIttAdhqsGF8sp3B3s/LqTY0BmLb+CDFH4ovlPCIiUrIUcqR0s1ig23Pm9vrPISmuWE7TpkYwt7aKxDDgxZ+3YbOXqw5OERGXpJAjpV/tayCqA2Snw7K3i+00o3s3pKKXO1uOJvDThqPFdh4RESkZCjlS+lkscM1/ze31k+Hk9mI5TeWKXjkzIb+zYDdpmbZiOY+IiJQMhRwpG2p2gUY3gWGD3/4DxTReflDH6lQN8uFkYgbvLthdLOcQEZGSoZAjZUfP18ynrA6vgG0/FcspvNzdePHGRgBMWn6QTbGaCVlEpKxSyJGyIzAKrnrS3P79v5CRXCyn6dk4nNtaVQXgyR82k5SeVSznERGR4qWQI2VL9GMQVBOSTsCyt4rtNM/f0JCIAG8OnEnh+Znbiu08IiJSfBRypGzx8Ibeb5jbqybAmb3FcprgCp5MGNgKqwVmbz7OukOOXyRURESKl0KOlD31ekHdXmDPgrnFNwi5ZbUg+rWtBsD/ft2BXXPniIiUKQo5UjZdNw7cPGH/Itg1p9hOM6pHPSp4urH5aAI/bz5WbOcRERHHU8iRsqlSbej4uLk9bzRkpRXLaSpX9OLhbnUAeHOe5s4RESlLFHKk7OoyCvyrQkJssa1rBTCsc00iA304kZDOZ8sPFNt5RETEsRRypOzyrAC9XjO3V/wfnDtYLKfx9nDjmd4NAPh46X5OJBRPr5GIiDiWQo6UbY1ugppXgy0D5j9fbKfp06wKraoFkppp48kfNmsQsohIGaCQI2WbxQK93wSrO+yeA3sXFNNpLLx1R3N8PNz4c/9ZPtVtKxGRUk8hR8q+0AbQfri5PfcZyM4oltPUruzHS33NJR/enr+bzUfii+U8IiLiGAo54hqufgb8wuDcfnOSwGJyZ5sorm8aTrbdYMTUTaRmZhfbuUREpGgUcsQ1ePtDj1fN7WVvQULxzGljsVgYd0szIgK8OXQ2lQmL9xXLeUREpOgUcsR1NLsTqkVDVioseKHYThPg68GYvo0BmLTsIIfOpBTbuUREpPAUcsR1XBiEbLHCtp/g4PJiO1XPRmFcVa8ymTY7r/y6o9jOIyIihaeQI66lSjNoM8zcnvsfsGUVy2ksFgtj+jTCw83Col2nWLjzZLGcR0RECk8hR1xPt+fAJxhO7YB1nxXbaWpX9uPezjUBeOXXHaRnackHEZHSRCFHXI9vMHQfY24vHgvJp4rtVI9dU5fQil4cPpvK5yuKZ8ZlEREpHIUccU0t74GIlpCRCH+8VGyn8fNy5/kbGgLw4aJ9HI/Xkg8iIqWFQo64JqsbXP+2uR3zLRxZW2yn6ts8gnY1gknLsvHKLzswDC35ICJSGijkiOuq2gZa3m1u//YU2ItnzIzFYuGlvo1xs1qYtz2OnzYWzxw9IiJSME4PORMmTKBGjRp4e3vTvn171q69/L+44+PjeeSRR6hSpQpeXl7Uq1eP3377rYSqlTLn2pfAKwBObIb1k4vtNI0i/Hmie10AXpq9nTPJxbO0hIiI5J9TQ860adMYNWoUY8aMYePGjTRv3pxevXpx6lTeA0UzMzPp0aMHhw4dYvr06ezevZtJkyYRGRlZwpVLmeFXGa75a3Xyuc/A6o+hmG4nPdS1Dk0jA0jOyOaDhXuL5RwiIpJ/FsOJAwjat29P27Zt+fDDDwGw2+1ERUXx2GOP8eyzz160/8SJE3nrrbfYtWsXHh4ehTpnYmIiAQEBJCQk4O/vX6T6pYyw2+DnR2Hzd+bXLe6GG98Fdy+Hn+rPfWcY8Nka3K0Wfn60E40jAhx+DhGR8qgwv7+d1pOTmZnJhg0b6N69+9/FWK10796dVatW5XnM7NmziY6O5pFHHiEsLIwmTZowduxYbLZLj7XIyMggMTEx10vKGasb3PwR9BprzoYc8w1MuQGS4hx+qo51QujZKIxsu8Ej324kKb14JiMUEZErc1rIOXPmDDabjbCwsFzvh4WFEReX9y+fAwcOMH36dGw2G7/99hsvvPAC77zzDv/73/8ueZ5x48YREBCQ84qKinLodUgZYbFA9CNw90/gHQhH18GnXeHYBoef6o3b/l7A83+/7nR4+yIikj9OH3hcEHa7ndDQUD799FNat25Nv379eP7555k4ceIljxk9ejQJCQk5ryNHjpRgxVLq1L4G7l8EIfUh6QRM7g2bpzr0FEEVPBl/V0ssFpi2/giLdxffZIQiInJpTgs5ISEhuLm5cfJk7jV/Tp48SXh4eJ7HVKlShXr16uHm5pbzXsOGDYmLiyMzMzPPY7y8vPD398/1knKuUm247w+ofz3YMmDmgzD/ebBlO+wU7WoGM7SjueTDM9O3cEyTBIqIlDinhRxPT09at27NwoULc96z2+0sXLiQ6OjoPI/p1KkT+/btw26357y3Z88eqlSpgqenZ7HXLC7E2x/6fQtXPW1+vepD+O4OSDvvsFM83as+dUL9OJWUwcPfbCDbZr/yQSIi4jBOvV01atQoJk2axJdffsnOnTt56KGHSElJYejQoQAMGjSI0aNH5+z/0EMPce7cOUaMGMGePXuYM2cOY8eO5ZFHHnHWJUhZZrXCNf+FO6aAhy/sXwSTroHTux3SvI+nG1/e246K3u5sPprAZ1rbSkSkRDk15PTr14+3336bF198kRYtWhATE8O8efNyBiPHxsZy4sSJnP2joqKYP38+69ato1mzZjz++OOMGDEiz8fNRfKt8S1w73wIqAbnDsCka2H3PIc0HRnowws3NgLg3QV72Hcq2SHtiojIlTl1nhxn0Dw5ckkpZ+CHwXB4BWAxe3m6PGk+mVUEhmEw+It1LNtzmlbVAvlxeEfcrEVrU0SkvClT8+SIlDoVQmDQLGgzDDBg0aswfShkphSpWYvFwuu3NsXPy52NsfG898ceh5QrIiKXp5Aj8k9uHuZsyDf+H1jdYftMmNwL4mOL1GxEoA8v9jFvW72/aB+TNT5HRKTYKeSI5KXNvTD4F/ANgbitMKED/P4CJJ8udJN3toni6V71AXhj3i6OnEt1VLUiIpIHhRyRS6neER5YAlXbQlYK/Pk+jG9qzqmTdPKKh+fl4a61ia5ViYxsO6/N0WzIIiLFSSFH5HICo2DYAug/DSJaQnaaOafOe81g3ugCr39lsVgY07cRblYL87bH8ceOwoUlERG5MoUckSuxWKD+dXD/Yhg4HSLbQHY6rP4I3msOc5+BxOP5bq5BuD+DoqsD8Nj3m3TbSkSkmCjkiOSXxQJ1e5hLQtz9E1RtZ4adNRPhvRYw5ylIOJavpkb3bki7GsGkZdkYM3s75WwmBxGREqGQI1JQFgvU6Q7Dfod7ZkG1aHMNrHWT4P0W8OsoiL/8QrCe7lbG3toUDzcLi3ad4oNF+xR0REQcTCFHpLAsFqjdDYbOhUGzoXonsGXC+s/h/Zbwywg4f/iSh9cJ9eOZ6xoA5mzIn+uxchERh1LIESkqiwVqXQ1Df4Mhc6BGF7BnwYYp8EErs2cnI+/lHIZ1rpnzWPlb83dzXKuVi4g4jEKOiCPV6AxDfjV7d2peDfZss2fn06vhxJaLdrdYLDzctTbtagaTkW3nxZ+36baViIiDKOSIFIfqHWHwbPM2VsUIOLsPPusOayfBv0KMxWLhpT6N8XSz8sfOU0xYrPE5IiKOoJAjUpxqXQ0PrYR6vc3Byb89BdPuhrTzuXZrFOHPc9eb43Pe/n0PL/+ywxnVioi4FIUckeLmGwz9v4frXgerB+z6FSZ2gdg1uXYb3LEGz1/fEIsFpvx5iPnbCzbRoIiI5KaQI1ISLBbo8BDctwCCakLCEfiiNyx/F+z2v3axcP9VtXjwqtoAPD9zG+dTMp1ZtYhImaaQI1KSIlrCg8ugye1g2GDhy/DNrZB8KmeXkd3rUjfUjzPJGQyZso7E9CwnFiwiUnYp5IiUNG9/uO0z6PsBuPvAgcXwcSfYv9j82MON/+vXggAfDzYfiWfE95uw2TUQWUSkoBRyRJzBYoFWg+CBxVC5IaScgq9vgYWvgC2bJpEBfDOsPV7uVhbvPs2b83c5u2IRkTJHIUfEmUIbwv2LoPUQwIDl78CUGyD+CE2rBvDWHc0B+GTpAWZsPOrUUkVEyhqFHBFn8/SFPu/B7V+Alz8cWQ0TO8POX+nbPIJHupkDkZ+dsZVNseev0JiIiFygkCNSWjS51RyUHNEK0uNh2kCY+yxPXluH7g3DyMy28+DXG4hLSHd2pSIiZYJCjkhpElwT7p0P0Y+aX6/5GOv6zxh/VwvqhflxKimDB79eT3qWzbl1ioiUAQo5IqWNuyf0eg16v2V+vfBl/FKP8tmgtgT6erD5aILWuBIRyQeFHJHSqu19UL0zZKXCLyOoFuzDB/1bYrXAD+uP8u2aWGdXKCJSqinkiJRWViv0fR/cveHAEtj0DV3qVubpXuYaVy/N3s6P6484t0YRkVJMIUekNKtUG7o9b27Pfx4STzD86lrc1qoq2XaD0TO2suHwOefWKCJSSinkiJR2HR42n7jKSIA5T2IB3r6jGTc2q0K23eDR7zZxNjnD2VWKiJQ6CjkipZ2bO9z0obmC+e45sH0mFouF129rRq3KFTiRkM7IaTFa+kFE5F8UckTKgrDG0OVJc/u3pyHlLH5e7nw8sDXeHlaW7z3Dh4v2ObdGEZFSRiFHpKzo8iSENoLUMzB/NAD1wyvyv5ubAjB+4R5W7T/rzApFREoVhRyRssLdE/p+CBYrbJkGe34H4PbWVbmjdVUMA578IYaEtKyinScj2Xyay64JB0WkbFPIESlLqrY2ByID/DoS0hMBGNO3MdWCfTmekM6TP2zGXtjxOecPwaRr4KubYN5oh5QsIuIsCjkiZU235yGoJiQegz/GAODn5c4H/Vvi6W7lj50nufmjlQV/4uroBvisO5zZbX699hM4vMrBxYuIlByFHJGyxtMX+n5gbq+fDAeXA9A8KpDXb22K1QJbjiYw/JsNZGbb89fmzl9gyg2QchrCm0Kjm833Zz8KWWmOvwYRkRKgkCNSFtXsAq2Hmtu/PA6ZqQDc2qoqsx/tTEUvd9YdOs9/Z229/BpXhgGrJsC0eyA7Der2hKFzoc974BcOZ/fBknElcEEiIo6nkCNSVvV4GSpGwLkDsGRszttNIgP4YMDfa1y9MW933mN0bNnm4+jznwMMc62su74Hr4rgEwg3/p+5358fwLGNJXJJIiKOpJAjUlZ5B0Cf8eb2qglwbEPOR13rh/LijY0AmLh0P8O+XMfek0l/H5uRDFMHwLpJgAV6vgbXv21OPHhBg+uhyW1g2OHnRyE7s/ivSUTEgRRyRMqyer2g6R1/BZHHcgWRIZ1q8tbtzXC3Wli8+zS3fPQnG2PPQ+IJ+KI37J1vLv5551fQ8VGwWC5uv/eb4FsJTm2HFf9XghcmIlJ0pSLkTJgwgRo1auDt7U379u1Zu3Ztvo6bOnUqFouFm2++uXgLFCnNrnsDfEP+CiLv5vrojjZRTHuwA3VC/UjOyGbMp9NI/agrxG0xjxkyBxr1vXTbFULMoAOw7C04uaP4rkNExMGcHnKmTZvGqFGjGDNmDBs3bqR58+b06tWLU6dOXfa4Q4cO8dRTT9GlS5cSqlSklKpQCa6/EETeviiItK4ezM+PdGJE9Vi+c3sJ3/Q4Ev1qwn1/QNU2V26/yW1QrzfYs8ynrTRJoIiUEU4POe+++y73338/Q4cOpVGjRkycOBFfX18mT558yWNsNhsDBw7k5ZdfplatWiVYrUgp1fhWqH/9JYNIha1fM/LU81S0pLHK1oirzo5m8akK+WvbYoEb3wUvf3Pcz+qPiuECREQcz6khJzMzkw0bNtC9e/ec96xWK927d2fVqktPQvbKK68QGhrKsGHDSqJMkdLPYoEb3gWvgL+CyMfm+3Y7LBgDv47EYtgwmvVjdtMPiDf8eOz7Tez552Dky/GPgJ7/M7cX/Q/O7i+e6xARcSCnhpwzZ85gs9kICwvL9X5YWBhxcXF5HrNixQo+//xzJk2alK9zZGRkkJiYmOsl4pL8q0DPV83tRf8zb1v9dC+sHG++13U0lls+4ZXbWtGhVjDJGdkM+3Id51Ly+dRUq0FQ82rITofZj5sBSkSkFHP67aqCSEpK4p577mHSpEmEhITk65hx48YREBCQ84qKiirmKkWcqNUgqHmVObHfJ1fB9plg9YCbJ0LXZ8FiwcPNyscDW1Mt2Jcj59Lo/+lqTiWlX7ltiwX6vg8evnB4BWy49C1lEZHSwKkhJyQkBDc3N06ePJnr/ZMnTxIeHn7R/vv37+fQoUP06dMHd3d33N3d+eqrr5g9ezbu7u7s339xF/ro0aNJSEjIeR05cqTYrkfE6SwW6PNXELFnmXPp3DMTWvTPtVtQBU8mD2lD5Ype7D6ZxIBJaziTn7WugmrAteZ6WSwYA/H6/0lESi+nhhxPT09at27NwoULc96z2+0sXLiQ6Ojoi/Zv0KABW7duJSYmJufVt29funXrRkxMTJ69NF5eXvj7++d6ibi04Jpw2+fQ+BYYtsBcAiIPdUIrMn14NFUCvNl3KpkBk1ZzOikfQafdAxDVHjKTzZXQL7dshIiIEzn9dtWoUaOYNGkSX375JTt37uShhx4iJSWFoUPNdXkGDRrE6NGjAfD29qZJkya5XoGBgVSsWJEmTZrg6enpzEsRKT0aXA93TIHK9S+7W/VKFfju/g6E+Xux52Qyw7/ZQGpm9uXbtlqh74fg5gX7/oDNUx1Xt4iIAzk95PTr14+3336bF198kRYtWhATE8O8efNyBiPHxsZy4sQJJ1cp4rpqhlTg+/s7UNHbnQ2Hz3PXp6uvPBi5cj3o+oy5Pe9ZSDp5+f1FRJzAYlx2iWLXk5iYSEBAAAkJCbp1JfIPG2PPM2zKOs6nZtGwij/fDGtHJT+vSx9gy4JJ15izJzfsC/2+LrliRaTcKczvb6f35IhI6dCqWhA/Do8mxM+LnScSueWjPzmZeJmnrtw84KYJYHWHnbNhx88lV6yISD4o5IhIjjqhFZn6QAeign2IPZfK4MlrSUzPuvQBVZpBp5Hm9pynIPVcidQpIpIfCjkikkudUD++HdaBED8vdsUlcf+X60nPusx6VVf/B0LqQ8opmDe65AoVEbkChRwRuUi1Sr5MGdoWPy931hw8R+c3FvPD+iNk2fKY5djdy7xthQW2TIU9v5d4vSIieVHIEZE8NYkM4NNBrXG3WjiTnMF/pm+h29tLWL739MVhJ6otdHjY3P51JKSXseVTDEOrq4u4IIUcEbmkjrVDWPJ0V1pXDwLg6Pk07vl8LdHjFhJzJD73ztf815wROfEYfHs7nD9c4vUWSuwaGN8UJveC7Hyu4yUiZYJCjohcVtUgX356qCObX+xJj0bm/FVnkjMZMGk1K/ed+XtHT1+45RPwrAhH1sDEzrDlRydVnU+bvoUvb4SEI3B0HcR84+yKRMSBFHJEJF8CfD2YNKgN21/uRac6lUjNtDH0i3XM2/aPyTqrdYCHVpjLPmQkwoz74Kf7IT3BeYXnxW6D3/8LPz8MtkwIrm2+v/QtyMrHYqUiUiYo5IhIgVTwcmfykLb0bhJOps3O8G828uQPm/9+AiuoBgz5Dbo+BxY32PoDfNwZYlc7te4c6YnwfX/48wPz66v+Aw+tBP+qkHQc1mt1dRFXoZAjIgXm5e7GhwNa0b+duSjuTxuP0uXNxbwxbxcJaVng5m4u+3DvPAisDgmx8EVvWDwWbFdYG6s4nTsIn/eAvfPB3dtcyPSa58HDx3wUHmDFu5CR7LwaRcRhFHJEpFDcrBbG3dqMT+9pTQVPN04nZfDxkv3c8tHKvwclR7WD4Sug+QAw7LD0DfjiOjh3oOQLPrjcXIbi9C7wC4ehv0HT2//+vMUACKoJKadh7SclX5+IOJxCjogUSc/G4cx8pBMNwisCcOB0CgMmrebPC4OSvf3hlo/h9sngFWAO8J3YBWK+Mx/dLgkbpsDXN0PaOYhoCQ8shsjWufdx84Buz5nbK9+DtPiSqU1Eio1CjogUWb2wiswbeRXr/9udLnVDSM20MezL9WyMPf/3Tk1uM8e+VO8Emckw6yGYPhTSzl+64aKyZcPcZ+CXEWDPhsa3muOF/CPy3r/JbVC5gTlQevVHxVeXiJQIhRwRcZgQPy8+G9yGLnVDSMsyn75auuf03zsERsHgX+DaF82FPbfPNAclH1rh+GLSzpvz9ayZaH7d7b9mb5Kn76WPsbr93ZuzagKknHV8XSJSYhRyRMShvNzd+OSe1rSsFkhCWhaDJ69l0rJ/jMGxukGXJ2HY7+aj24lHYcqN8MfLjpuM78w++Kw7HFgMHr5w59dw9dNgsVz52IZ9IbyZ2du0crxj6hERp1DIERGH8/V058t72zGwfTUAXvttJ6OmxXAu5R8hJrI1PLgMWg0CDPOppsk9Yet0OLIOkk4WbszO/sXw2TVwdp/5WPi986FR3/wfb7HANS+Y22snQVJcwWsQkVLBYhglNfKvdEhMTCQgIICEhAT8/f2dXY6Iyxs3dyefLDV7chpH+PP9Ax3w9/bIvdOOn2H245Aen/t9d28IiILAav96VTf/9Av9u3fGMGDdZ+YYHMMGVdtCv2+hYljBizYM+LwnHF0L7R6A698qeBsi4lCF+f2tkCMixcowDKZvOMorv+4gKT2bdjWD+erednh7uOXeMeEYLH8bTu2C+FhzYj4jj1XP/+mfIchihX0LzPeb94cbx4OHd+ELP7gMvuwDVg94fKN5DhFxGoWcfFDIEXGObccS6P/papIysulcJ4SP725FxX/36PxTdqa52Gd8bN6vxGPAv//6skCPl6Hj4/kbf3MlX/Yxw07Le+CmD4venogUmkJOPijkiDjP2oPnGPLFWlIzbTSO8OfLe9sR4udVuMb+HYISj0PNLlC9o+MKPrLWnCHZ4gaProNKtR3XtogUiEJOPijkiDjXlqPxDP1iHWdTMgmu4MlLfRvTt/kl5q0pDb6901wGoumdcNskZ1cjUm4V5ve3nq4SkRLVrGogPw6PJirYh3MpmTz+/Sb+O2sraZk2Z5eWtwvz5mz9EU7ucG4tIlIgCjkiUuJqVfZjwRNXM6RjDQC+WR3LXZ+u4nRShnMLy0tEC3PuHAxYMtbZ1YhIASjkiIhTeHu48VLfxnwzrD1Bvh5sPprATR+u4KtVh0pfr0635wEL7PwFjm9ydjUikk8akyMiTnfwTApDv1jLobOpAEQEeDO4Yw3qh1ckMtCHmiEVcHdz8r/JZjwAW6ZBnR5w93Tn1iJSDmngcT4o5IiUTskZ2Xy96jBT/jzIycTct61qhVRgytB2VKt0mXWnitvZ/fBhW3OiwXvnQ7UORW8z/ggkn4Kqra+8r0g5p4HHIlJm+Xm581DX2vzyaGc61AqmVkgFGoRXxNfTjQNnUrht4p/EHIl3XoGVakPLu83tRf8rWltp8TD/eXi/pbkExZYfi1yeiFxMPTkiUqqdSkxn0OS17IpLwsvdylt3NHfeI+fxR+CDVmDLhEE/Q62uBTvelgXrv4Al4yDt3N/ve/nD8OUQVMOR1YqzpZ03x3FlJJvLg7i5O7uiMk09OSLickL9vZn+UEe6NwwlI9vO499vosazc/hh/ZGSLyYwCloPNbcX/S//C4gaBuyZDx93hLlPmwGncgMY8ANEdYCMRPjpPrBlF1/t/3TuICx8xbxVVpJSzsLM4bBjdsmetyRlJJs9c9/dBW/VhdmPwfzRsP5zZ1dWLqknR0TKBJvd4M15u/hk2YGc9x7qWpsne9Qr2UHJSSfhveaQnQb9p0H96y6/f9w2+P15OLDE/No3xJx7p9Vg81/28bHwcWfISICr/gPXPF+89aecNW+RnT9k9kTdM8sxS2Dkx/R7YdtP4OYJw36HiJYlc97ilpVurpu27SfYPc/8b+MC/0hzZm6fYBgRA94BTiuzrFNPjoi4LDerhdHXN+Tjga2ICDAX3vx4yX7u+Xxtyc6vUzEM2j9gbi/+H9gvsYho0knzX/GfdDEDjpsndBphLvbZdtjfty4Cq8GN75rby9+GQyuLr/bsTPhhkBlwwKxr8/fFd75/2jPfDAFg3u77YbB5O6essmXB3j9g5kPwdl2Ydjdsn2kGnOBacNXT8PBqGLEFQuqZvXcrxju76nJHPTkiUib9uuU4/5m+hdRMG2H+Xnw0sBWtqweXzMlTz8H4ZpCZBHd8CY1v/vuzrDRYNQFW/B9kJpvvNb4Fur90+TE3sx6GmG/Bvyo8tAJ8ghxbs2HAryNhwxTw9IOmt5vb3oHmulx+oY493z9lJMGEDpB41OzBOrAE4g9D/Rvgrm9LriepqOx2iP3TDGs7fobUs39/5h8JTW6FJrdBlRa5r2nXbzC1P7h7w2MbIKBqiZfuCvQIeT4o5Ii4jn2nkhj+zUb2nUrG3Wqhb/MI+revRtsaJRB2Fo+Dpa9DSH14eBVYrOYvvz9egoS/xgtFtILrxuXvcfOMJPjkKjh3ABrdZIYnR/7yX/OpOR4IC/SfCnW6w6RuELfFDGF3THHcuf5t7jOwZqLZa/XwajizBz7vafbo9PwfdHys+M5dVHY7HNtg9tJsnwFJJ/7+rEJlaHSzGWyi2oP1EjdHDAOm3ACHV0KLgXDzRyVSuqtRyMkHhRwR15KSkc0zP23h1y1///Lp0SiMUT3q0bBKMf4/np5g9uakx0OnkXBoBRxbb37mX9XsuWly26V/8eXl2Abzl789G/p+CK3ucUyt+xfDN7eZc/x0fxk6jzTfP7EZPu1mvn/X99Dgesec75+OrDNXcseAu2dAnWvN99d9BnOeNFd4H/qbY+YdchRbFhxaDjt/hV1zIDnu78+8A6BhH/NnW+Oq/D8xdXSDORYKi/kkXXjTYindlSnk5INCjojrMQyDr1Yd5tNlBzgW//egz/7toqgWXIFqwb60rRnEifh06odXxNvDzTEnXv4uLHz57689KkCXJ6DDI+BZyIkLV/yf2Rvk4QsPLoOQukWr8ex+s8cmPQGa3QW3TMzdQ7RgDKwcDxUj4JHVjh0Ym51p9k6d3gnN+5vnvsAwzCfKtk03zz18OVQIcdy5CyozBfYthF2/wp555vfrAs+KUK+XeYuv9jXg7lW4c/w41OwNqn0t3DPDMXWXIwo5+aCQI+La9pxM4o25u1i4K+/Ho0P8vBh+dS3uia6Ol3sRw05mCkxoDwlHzYkCr/kvVAwvWpt2O3x9ExxcBlWaw7A/wN2zcG2lxcNn3eHsXqjaFgb/Ch7euffJSjMfbT93ANrcCzf+X9Hq/6elb5mDs30rwSProEKl3J9nJJsB7MweMzwMnA5WBwXQ/Eg9Zw6I3vkL7F+U+6moCpWh/vVmr03NqwofbP7p3EFz1mx7Ftwz07xmyTeFnHxQyBEpH5btOc03qw+Tnm3n8NkUDp9NxcvdSka2+TRUjUq+NAj3Jz4tkyyb+ddgYloW1zQI5e4O1YkKzmdPTOo5MygERDqu+MTj8HEn84mcjo9Dz1cL3oYtG767E/YvNAfF3r/YfDIsLweXwZd9zO2hc6F6x8LXfsHpPTCxkznu5tbPoNkdee93cgdMusYMGF2fg67PFP3cl5N43LwFtfMX8xaj8Y/FYAOrm6GmwY0Q1a54Ate80bD6IwhravbUFeR2ZjmnkJMPCjki5Y/dbnA+NZOK3h7M2HiUt3/fw5nkSz927uFmoX+7avRuUoUOtYKxOOPpn11zYOoAc/ueWVC7W8GOv/DL1MMX7p1n9gpdzuzHYONXUKkODF95cY9PQdjt5kDb2D/NBU0H/nj5QdQx38GshwALDJpV8Jmkr1iPDdZPNh+XP7Yh92dhTcxQ0/BGc7u4f9ap5+C9Fua8SDdPhBb9i/d8LkQhJx8UckQkIS2LxbtOkZSRzYn4NDzcrHh7uFHBy43ftp5g9YG/l1xoUz2IG5tV4dbWVfH1cCvZiQd/HWXOlOsXBg/9mf8xKxu+hF8eN7f//Yj7paTFm7fekuOgy5Nw7YuFrdoMFL8+YY5RemS1+VTVlfz8KGz62rxN9OBy8K9S+PP/U/wRc5blwyv+esNi9tI07AMNbjDntClpK8bDH2PMAeqPrQcPn5KvoQwqsyFnwoQJvPXWW8TFxdG8eXM++OAD2rVrl+e+kyZN4quvvmLbtm0AtG7dmrFjx15y/39TyBGRK1m86xSfrTjAuoPnybT9Pdmf1QJNIwPwcnejR6Mw+rWLwt/bo/gKyUw1x6yc3gX1rjMf/b5ST8PhP+HLvua4j4Le/tn5izmpndUdHlhSuCeAEk/AhHbmUhXXvQ4dHsrfcVlp8FkPOLkVqnWEwb8Ufa2nbT/BL0+YvSaefuZM001uv/Rtu5KSlQYftDHnDer+EnR+wrn1lBFlMuRMmzaNQYMGMXHiRNq3b8/48eP58ccf2b17N6GhF09ONXDgQDp16kTHjh3x9vbmjTfeYObMmWzfvp3IyCvfE1fIEZH8Oh6fxsxNx5i27gix51Lz3Kd6JV+61Q9lxLV1CapQyAHClxO3zRyzYsuA69+Gdvdfet/zh81QlHrWnPvm9i8Kfvtl2t1m2IloaQ56LmjQmDrQfEIpsjUMW1CwcS1n98MnV5uTLHYaCT1evuIheUpPhN+ehi1Tza8j28Btk5zTa3Mpm6fCzAfNxVkfj7l4ULZcpEyGnPbt29O2bVs+/PBDAOx2O1FRUTz22GM8++yzVzzeZrMRFBTEhx9+yKBBg664v0KOiBRUls3OrhNJuLtZ2H86mbPJmUz58xAHz6Tk7BPu783wq2txW+uqVHR0787qiTDvGXDzMntYwhpdvE9GEnzeC05tN8ffDJ1XuMfYk+LMnpj0hIJP1LdjNvxwj9kT9OAyCGtc8PNvnwU/Dja387M22L/FroEZ95szKlus5vIKVz0NbsXY41YYdjt8ehXEbYX2D0Hv151dUalX5tauyszMZMOGDXTv3j3nPavVSvfu3Vm1alW+2khNTSUrK4vg4LxnOM3IyCAxMTHXS0SkIDzcrDStGkDDKv7c2CyCwR1rsPiprqx4phtP96pPZKAPcYnpvPTLDq5+awk/rj+Cze7Afz+2fxDq9jR7c34aZt7u+Ce7HWY8YAYcvzBzYr/CztNTMdwMNwCLXjMfe86PtHiz9wTMXpjCBBwwxw+1H25uz3zQ7J3KD1s2LB4LX1xnBpzAauaTYt2eK30BB8ynqnr89dTcus/MR/hdgWHApm/NHshSwKkh58yZM9hsNsLCct8fDQsLIy4u7hJH5fbMM88QERGRKyj907hx4wgICMh5RUVFFbluERGAqkG+PNKtDn+MuppXb25CzZAKnEvJ5OnpW2g/diFP/7iZQ//o7Sk0iwVu+ggqhMKpHbDgX4OCF70Ku38ze3ru+q7oj7O3vAdqdDEf6/51pPmL60r+GGMOWq5Ux+w5KYoer5q3u9Lj4cchkH2FBVjPHYDJvWDpG2DYzYkHh68sXbMo56V2N3NiQHsWLHzF2dUUXfIp84nAnx82B3tnZzq7orK9Cvnrr7/O1KlTmTlzJt7eeT/uOHr0aBISEnJeR44cKeEqRcTV+Xi6cU+H6vz+xFU827sBAT4enEnO4McNR+n69hLu/mwNO08UsRfZrzLc8rG5vfZT2D3P3N7yA6z4axXzvh9A1TZFOw+YoarPe+aCkgeWmAuHXs6hleZinwB93i/a4+dgTn54xxRz8dDjG+H3/+a934Veg4ldzCU1vALg9snmzMreZWQ4Qo9XAIu5NtbR9c6upvB2/AwfdfgrbHtC09tKdmLHS3BqyAkJCcHNzY2TJ0/mev/kyZOEh19+1tC3336b119/nd9//51mzZpdcj8vLy/8/f1zvUREioOHm5XhV9dm5bPX8Ok9relYuxIWC6zYd4br31/OyKmbWH/o3JUbupQ63SH6UXP754fNtZV+/uvrzk9A835Fv4gLKtWGrqPN7fnPQ9LJvPfLSv/7cfXWQ6BGJ8ecP7Aa3Pqpub32U9j2r2UQUs+ZY3d+fthc7b16Z3hopbmmVFkS3gRa/DUf0u8v5K/XrDRJOw8/3Q8/DDIHvIc1NceNdX5CIcfT05PWrVuzcOHCnPfsdjsLFy4kOjr6kse9+eabvPrqq8ybN482bRzwrxYREQfy83KnZ+Nwvru/A8ue7sYNzapgGDAr5ji3T1zFfV+uZ9+p5MI1fu2L5qPdqWdh2kBznE693nBNEea1uZToR81BzOnxMPc/ee+z7C04uw/8ws3FPx2pXq+/H6+e/Tic2WduH1hqzgi942dzkPO1Y2DwbAgso8MRuj1v9prF/gm75zq7mvzb9wd81BG2/mAO8u7yFNy/qPDjsYqB05+umjZtGoMHD+aTTz6hXbt2jB8/nh9++IFdu3YRFhbGoEGDiIyMZNy4cQC88cYbvPjii3z33Xd06vT3vxj8/Pzw8/O74vn0dJWIOMPmI/F8u+YwP208ljMo+ZoGofRrG0Xtyn5U8HKjSkA+J4U7vcdc+DI7DUIbwbDfwati8RSea6Xy78wJ9C44ud2sw54Nd34Njfo6/vy2bPiqLxxeCaGNoc418OeHgGGO/7ntM/Nx97Luj5fN244h9eChVUWfI6g4ZSTDghfMSR/B/Dnc8oljbpVeRpl8hBzgww8/zJkMsEWLFrz//vu0b98egK5du1KjRg2mTJkCQI0aNTh8+OLR9mPGjOGll1664rkUckTEmfadSuL1ubv5Y2fu2z9uVgvd6ofSJNKfvs0jqFX5Cv9o2/sHbP0Rrnk+fzMKF0XOSuVV4JE15krldht83sNcJqHBjXDXFcbtFEVSHEzsDCmn/36v9VDo9Rp4Vii+85ak9AR4v6XZQ3fj/5mLpZZGh1fBrOFw/pD5dfvhZk9aYZ/mK4AyG3JKkkKOiJQGB04n892aWH7dcoK4xPRcn1kscG2DMO7rUpP2NZ20dtY/ZaWZt4fO7TfDRZ/xf8/d4+UPj6x13DIMl3JgKXxzmzmguO8HuXuUXMWaT8zbghVC4fFN4HXluxMlJisdFr8Gf34AGBAQBTdNgFpXl1gJCjn5oJAjIqWJ3W6Qnm1j36lkftpwlJ1xSaw9+Pfg5CaR/gy/ujbRtSpRyc/LeYUeWmEuuglwy6fm2lRZKSXb65BwzAw5xXVrztmyM+Gj9uYj8Vc/C91GO7si0/EY85Hw0zvNr1vcDdeNNXv0SpBCTj4o5IhIabf/dDKTVxzkp41HSc/6e+2semF+9G0ewU0tIqkS4F2yi4WCOfh345d/f12tIwyZY05sJ45xYcZnjwrw+EZzckZnsWWb44SWvmGOu6oQak4t0OB6p5SjkJMPCjkiUlacT8nkiz8P8dOGoxxPSMv1dLGnu5U21YOIrlWJfu2iCK1YxLlp8uOfK5W7eZoro4fULf7zlieGYY51OrrOfCS/z3vOqeP0brP35vhG8+tGN8EN/+fUNbYUcvJBIUdEyqKEtCzmb4vjp41HWfOP21kAHm4WWlUL4rZWVbmxeRV8PQv+ZM7OE4n8tvVEzrnCA7xpEhFAu5rBeHv8Y76TvX+YS0t0ex7aP1Cka5JLOLzKXJ7CYoWHV0Pl+lc+xm4zB2YnxZkzDyfHmV9npUF2ujmmJvufr4y/Psswn9LLzvjHfhnmIqmG3bwldf070PT2gi/26mAKOfmgkCMiZd3Z5Ax2n0xi+d4zrDlwlo2x8Tmf+Xm507V+ZdrXDCa6dgih/l7457Fg6PH4NKatO8KS3afIshnsOZlEdh7rbXl7WOlcJ4Q720RxTYPQkr9FVl5dWM293nXQ+01IPvlXgPnHn//cTjlthhJHqtPdHOTtH+HYdgtJIScfFHJExNXsP53Mgh0nmbo2lkNnU3N95ma10LVeZe6Ork6n2iH8uuU4K/edZcamoxdNrtuoij+h/l45q6ivO3gu15NfPh5uNKsaQPtalbi6XggNq/jn6jU6Hp9GaqYNX083Qit6KRAVxZm95q1Bw5b/YyxWqFAZ/ELNyRn9QsHD11xmw/0fr0t+7QPuXuDhYz6a78zxQHlQyMkHhRwRcVV2u8Hqg2dZd/A8S/ecytXDk5cG4RXp3aQK1Sr5UNHLg671K+cKJoZhsPNEEj9vPsa0dUeIT83Kdbynu5WOtStRNciHLUcT2HosISc4hfh5cnW9UM6mZHDgdApVg3zo3SScNjWCqV3ZD093BaArujA/kZsXVAz7O7hUDDe3//2eb0jpnkSwiBRy8kEhR0TKC7vd4MCZFL5fG8tPG4/mhJQwfy9e6tOY3k3zP7eN2VYyGw6f5/ftJ9l8NIEzyXmvDm61QB53vnKJDPShapAPFb09iK5diQ61ggnz9yYt00aYv7dC0AWZqWbPirPnSioFFHLyQSFHRMqjzGw7K/edwW4YXNswrMjtGYbBvlPJLNx1isNnU6lRyZfOdUNoGO6PzTBYse8MS3efJiPbRu3KfqRn2Vi46xTbjyeSmX35sSO+nm60rh5Ey6hArq5fmRZRQbhZ9Uu+vFPIyQeFHBER58my2TlyLpUTCemcS8nkREIay/eeYePh86Rk2rBYLl6IO8TPixZRgdQOrUClCp7UDa1Iwyr+hPmbkyM6fUZoKREKOfmgkCMiUjolpGXh6+nG3pPJbIg9z9qD51i6+xSJ6dl57u9utWAzDBqE+9O2RhBtawRzPjWTHccTyci2cyY5g2PxaWBARKAPHetUolPtEJpEmjP1GoZRoMHR51IyMQyD4AqeClZOoJCTDwo5IiJlR2a2ndUHzrL/dDK745JITM9id1wSh86m5qzmXlD+3u5k2QxshmE+UVbRi01H4rHZDSr7eREZ5EP98IrsOpHIrrgkqlfypVqwLz/HHCcj207VIB/aVA+igpc7aVk2alaqQKe6ITSLDMBqsXA2JZMQPwUhR1PIyQeFHBGRsi8j28aZ5EwANh+JZ+3Bc6w7dI49J5PoUKsSneqEkJltp2lkAJ7uVvaeTGLl/rOsPnCWpEv0DBWVr6cbqZnmI9+hFb1oXT2IVtWCaFU9iCaR/ni5u12hhfw7nZTB+kPnaFU9iDD/EpjtuhRQyMkHhRwRkfIr22ZnV1wSXu5WPNysbD4az664JGqFVKBhFX+Ox6cRey6VvSeTWXXgLPXCKtK9YSgbDp8nKtiXuztUZ9X+s+w+mcTuuETcrBZsdoPVB86RkJZ1yfN6ullpEulPq2pBhFT0Yv+pZOZvj6OClzuBvp6cSkwnxM+LBlUqUtnPi0YR/gT4eLDzRCJBFTyJCvLFbhhsio1nw+HzrNh3JqftBuEVaV41kMT0LOJTswjz9yIty0aAjwdd64fSpW5IztxHWTY7VotZ87mUTML8vcpMj5NCTj4o5IiIiKPZ7QY7TiRy9HwqraoFcehsKhsOn2dj7Hk2Hj7P2ZTMYjlvXgO1/83DzULTyAD8vD1Y99eSIGlZZo9TRW93/L098PKw0rZ6MG1qBGGxWFi1/yx7TiZhsYDNbo5D8vf2INDXgzqhfiSmZWOz2/H38aBKgA8pGdmsPnCWhLQsqlXyZXB0DWqEVHDotSrk5INCjoiIlCTDMDj8V+jZcjSehLQs4tOyuKVlJJGBPuw+mYSflzt+Xu7sikviTHIGm4/E/zUeqAJVArw5dj6N4wlp1AqpQOPIAK6qG0KPRuEkpGWxYt8ZDp5OIS3LRpbNjp+XO2dTMrAbsHr/WQ6cSSnxa/71sc45A7wdRSEnHxRyRESkLDAMwyG3kg6cTmbrsQROJ2UQGehD9UoVOJuSQYuoQI6eT+NUUgZZ2XbWHz7PukPnOB6fRqtqQXSoFUxkkA8Wi4WzyZkkpmVxOjmDg6dT8PF0w8/LndNJGZxNycDDzUpKpo0ONYMxgMevrYufl2NnX1bIyQeFHBERkbKnML+/NW+2iIiIuCSFHBEREXFJCjkiIiLikhRyRERExCUp5IiIiIhLUsgRERERl6SQIyIiIi5JIUdERERckkKOiIiIuCSFHBEREXFJCjkiIiLikhRyRERExCUp5IiIiIhLUsgRERERl+Tu7AJKmmEYgLlku4iIiJQNF35vX/g9nh/lLuQkJSUBEBUV5eRKREREpKCSkpIICAjI174WoyCRyAXY7XaOHz9OxYoVsVgsRW4vMTGRqKgojhw5gr+/vwMqLJ10na6lvFwnlJ9r1XW6Fl3nxQzDICkpiYiICKzW/I22KXc9OVarlapVqzq8XX9/f5f+D/ECXadrKS/XCeXnWnWdrkXXmVt+e3Au0MBjERERcUkKOSIiIuKSFHKKyMvLizFjxuDl5eXsUoqVrtO1lJfrhPJzrbpO16LrdIxyN/BYREREygf15IiIiIhLUsgRERERl6SQIyIiIi5JIUdERERckkJOEUyYMIEaNWrg7e1N+/btWbt2rbNLKpBly5bRp08fIiIisFgszJo1K9fnhmHw4osvUqVKFXx8fOjevTt79+7Ntc+5c+cYOHAg/v7+BAYGMmzYMJKTk0vwKq5s3LhxtG3blooVKxIaGsrNN9/M7t27c+2Tnp7OI488QqVKlfDz8+O2227j5MmTufaJjY3lhhtuwNfXl9DQUJ5++mmys7NL8lIu6+OPP6ZZs2Y5k2pFR0czd+7cnM9d4Rrz8vrrr2OxWBg5cmTOe65yrS+99BIWiyXXq0GDBjmfu8p1Ahw7doy7776bSpUq4ePjQ9OmTVm/fn3O567w91GNGjUu+nlaLBYeeeQRwHV+njabjRdeeIGaNWvi4+ND7dq1efXVV3OtOVViP09DCmXq1KmGp6enMXnyZGP79u3G/fffbwQGBhonT550dmn59ttvvxnPP/+8MWPGDAMwZs6cmevz119/3QgICDBmzZplbN682ejbt69Rs2ZNIy0tLWef6667zmjevLmxevVqY/ny5UadOnWM/v37l/CVXF6vXr2ML774wti2bZsRExNjXH/99Ua1atWM5OTknH2GDx9uREVFGQsXLjTWr19vdOjQwejYsWPO59nZ2UaTJk2M7t27G5s2bTJ+++03IyQkxBg9erQzLilPs2fPNubMmWPs2bPH2L17t/Hcc88ZHh4exrZt2wzDcI1r/Le1a9caNWrUMJo1a2aMGDEi531XudYxY8YYjRs3Nk6cOJHzOn36dM7nrnKd586dM6pXr24MGTLEWLNmjXHgwAFj/vz5xr59+3L2cYW/j06dOpXrZ7lgwQIDMBYvXmwYhuv8PF977TWjUqVKxq+//mocPHjQ+PHHHw0/Pz/jvffey9mnpH6eCjmF1K5dO+ORRx7J+dpmsxkRERHGuHHjnFhV4f075NjtdiM8PNx46623ct6Lj483vLy8jO+//94wDMPYsWOHARjr1q3L2Wfu3LmGxWIxjh07VmK1F9SpU6cMwFi6dKlhGOZ1eXh4GD/++GPOPjt37jQAY9WqVYZhmIHQarUacXFxOft8/PHHhr+/v5GRkVGyF1AAQUFBxmeffeaS15iUlGTUrVvXWLBggXH11VfnhBxXutYxY8YYzZs3z/MzV7rOZ555xujcufMlP3fVv49GjBhh1K5d27Db7S7187zhhhuMe++9N9d7t956qzFw4EDDMEr256nbVYWQmZnJhg0b6N69e857VquV7t27s2rVKidW5jgHDx4kLi4u1zUGBATQvn37nGtctWoVgYGBtGnTJmef7t27Y7VaWbNmTYnXnF8JCQkABAcHA7BhwwaysrJyXWuDBg2oVq1armtt2rQpYWFhOfv06tWLxMREtm/fXoLV54/NZmPq1KmkpKQQHR3tktf4yCOPcMMNN+S6JnC9n+fevXuJiIigVq1aDBw4kNjYWMC1rnP27Nm0adOGO+64g9DQUFq2bMmkSZNyPnfFv48yMzP55ptvuPfee7FYLC718+zYsSMLFy5kz549AGzevJkVK1bQu3dvoGR/nuVugU5HOHPmDDabLdd/aABhYWHs2rXLSVU5VlxcHECe13jhs7i4OEJDQ3N97u7uTnBwcM4+pY3dbmfkyJF06tSJJk2aAOZ1eHp6EhgYmGvff19rXt+LC5+VFlu3biU6Opr09HT8/PyYOXMmjRo1IiYmxmWuEWDq1Kls3LiRdevWXfSZK/0827dvz5QpU6hfvz4nTpzg5ZdfpkuXLmzbts2lrvPAgQN8/PHHjBo1iueee45169bx+OOP4+npyeDBg13y76NZs2YRHx/PkCFDANf67/bZZ58lMTGRBg0a4Obmhs1m47XXXmPgwIFAyf5+UciRcuWRRx5h27ZtrFixwtmlFIv69esTExNDQkIC06dPZ/DgwSxdutTZZTnUkSNHGDFiBAsWLMDb29vZ5RSrC//yBWjWrBnt27enevXq/PDDD/j4+DixMsey2+20adOGsWPHAtCyZUu2bdvGxIkTGTx4sJOrKx6ff/45vXv3JiIiwtmlONwPP/zAt99+y3fffUfjxo2JiYlh5MiRRERElPjPU7erCiEkJAQ3N7eLRr2fPHmS8PBwJ1XlWBeu43LXGB4ezqlTp3J9np2dzblz50rl9+HRRx/l119/ZfHixVStWjXn/fDwcDIzM4mPj8+1/7+vNa/vxYXPSgtPT0/q1KlD69atGTduHM2bN+e9995zqWvcsGEDp06dolWrVri7u+Pu7s7SpUt5//33cXd3JywszGWu9d8CAwOpV68e+/btc6mfaZUqVWjUqFGu9xo2bJhza87V/j46fPgwf/zxB/fdd1/Oe67083z66ad59tlnueuuu2jatCn33HMPTzzxBOPGjQNK9uepkFMInp6etG7dmoULF+a8Z7fbWbhwIdHR0U6szHFq1qxJeHh4rmtMTExkzZo1OdcYHR1NfHw8GzZsyNln0aJF2O122rdvX+I1X4phGDz66KPMnDmTRYsWUbNmzVyft27dGg8Pj1zXunv3bmJjY3Nd69atW3P9T7dgwQL8/f0v+su5NLHb7WRkZLjUNV577bVs3bqVmJiYnFebNm0YOHBgzrarXOu/JScns3//fqpUqeJSP9NOnTpdNK3Dnj17qF69OuBafx8BfPHFF4SGhnLDDTfkvOdKP8/U1FSs1tzxws3NDbvdDpTwz7MIA6jLtalTpxpeXl7GlClTjB07dhgPPPCAERgYmGvUe2mXlJRkbNq0ydi0aZMBGO+++66xadMm4/Dhw4ZhmI/4BQYGGj///LOxZcsW46abbsrzEb+WLVsaa9asMVasWGHUrVu3VD2yaRiG8dBDDxkBAQHGkiVLcj2+mZqamrPP8OHDjWrVqhmLFi0y1q9fb0RHRxvR0dE5n194dLNnz55GTEyMMW/ePKNy5cql6tHNZ5991li6dKlx8OBBY8uWLcazzz5rWCwW4/fffzcMwzWu8VL++XSVYbjOtT755JPGkiVLjIMHDxorV640unfvboSEhBinTp0yDMN1rnPt2rWGu7u78dprrxl79+41vv32W8PX19f45ptvcvZxlb+PbDabUa1aNeOZZ5656DNX+XkOHjzYiIyMzHmEfMaMGUZISIjxn//8J2efkvp5KuQUwQcffGBUq1bN8PT0NNq1a2esXr3a2SUVyOLFiw3gotfgwYMNwzAf83vhhReMsLAww8vLy7j22muN3bt352rj7NmzRv/+/Q0/Pz/D39/fGDp0qJGUlOSEq7m0vK4RML744oucfdLS0oyHH37YCAoKMnx9fY1bbrnFOHHiRK52Dh06ZPTu3dvw8fExQkJCjCeffNLIysoq4au5tHvvvdeoXr264enpaVSuXNm49tprcwKOYbjGNV7Kv0OOq1xrv379jCpVqhienp5GZGSk0a9fv1xzx7jKdRqGYfzyyy9GkyZNDC8vL6NBgwbGp59+mutzV/n7aP78+QZwUe2G4To/z8TERGPEiBFGtWrVDG9vb6NWrVrG888/n+sx95L6eVoM4x9TEIqIiIi4CI3JEREREZekkCMiIiIuSSFHREREXJJCjoiIiLgkhRwRERFxSQo5IiIi4pIUckRERMQlKeSIiIiIS1LIEZFSb8iQIdx8883OLkNEyhiFHBEREXFJCjkiUmpMnz6dpk2b4uPjQ6VKlejevTtPP/00X375JT///DMWiwWLxcKSJUsAOHLkCHfeeSeBgYEEBwdz0003cejQoZz2LvQAvfzyy1SuXBl/f3+GDx9OZmbmZc+ZkpJSwlcuIsXB3dkFiIgAnDhxgv79+/Pmm29yyy23kJSUxPLlyxk0aBCxsbEkJibyxRdfABAcHExWVha9evUiOjqa5cuX4+7uzv/+9z+uu+46tmzZgqenJwALFy7E29ubJUuWcOjQIYYOHUqlSpV47bXXLnlOLekn4hoUckSkVDhx4gTZ2dnceuutVK9eHYCmTZsC4OPjQ0ZGBuHh4Tn7f/PNN9jtdj777DMsFgsAX3zxBYGBgSxZsoSePXsC4OnpyeTJk/H19aVx48a88sorPP3007z66quXPaeIlH26XSUipULz5s259tpradq0KXfccQeTJk3i/Pnzl9x/8+bN7Nu3j4oVK+Ln54efnx/BwcGkp6ezf//+XO36+vrmfB0dHU1ycjJHjhwp8DlFpGxRyBGRUsHNzY0FCxYwd+5cGjVqxAcffED9+vU5ePBgnvsnJyfTunVrYmJicr327NnDgAEDiuWcIlK2KOSISKlhsVjo1KkTL7/8Mps2bcLT05OZM2fi6emJzWbLtW+rVq3Yu3cvoaGh1KlTJ9crICAgZ7/NmzeTlpaW8/Xq1avx8/MjKirqsucUkbJPIUdESoU1a9YwduxY1q9fT2xsLDNmzOD06dM0bNiQGjVqsGXLFnbv3s2ZM2fIyspi4MCBhISEcNNNN7F8+XIOHjzIkiVLePzxxzl69GhOu5mZmQwbNowdO3bw22+/MWbMGB599FGsVutlzykiZZ8GHotIqeDv78+yZcsYP348iYmJVK9enXfeeYfevXvTpk0blixZQps2bUhOTmbx4sV07dqVZcuW8cwzz3DrrbeSlJREZGQk1157Lf7+/jntXnvttdStW5errrqKjIwM+vfvz0svvXTFc4pI2Wcx9KykiLioIUOGEB8fz6xZs5xdiog4gW5XiYiIiEtSyBERERGXpNtVIiIi4pLUkyMiIiIuSSFHREREXJJCjoiIiLgkhRwRERFxSQo5IiIi4pIUckRERMQlKeSIiIiIS1LIEREREZekkCMiIiIu6f8BRmGOYQDDNGsAAAAASUVORK5CYII=", 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" ] @@ -1265,7 +1224,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "99fb343b435542598223775107c4be42", + "model_id": "c6d5114f62d44344aa5d4e6d585dd7e5", "version_major": 2, "version_minor": 0 }, @@ -1289,7 +1248,7 @@ "# Load the best model (saved locally by SaveModelCallback)\n", "learn.load(best_model_fname)\n", "print(f\"Loaded best model: {best_model_fname}\")\n", - "learn.export(f\"models/learner.pkl\")\n", + "learn.export(f\"models/best_learner.pkl\")\n", "\n", "save_dir = Path('predictions/patch_heart_val')\n", "\n", @@ -1365,7 +1324,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1377,9 +1336,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Logged metrics table to MLflow run 8454cd7886814580a8439d1e27141c36\n", - "Logged 5 metric(s) to MLflow run 8454cd7886814580a8439d1e27141c36\n", - "Logged DataFrame (1 rows) to MLflow run 8454cd7886814580a8439d1e27141c36\n" + "Logged metrics table to MLflow run 62148b02243a480a9f54fa4df9193617\n", + "Logged 5 metric(s) to MLflow run 62148b02243a480a9f54fa4df9193617\n", + "Logged DataFrame (1 rows) to MLflow run 62148b02243a480a9f54fa4df9193617\n" ] } ], @@ -1400,7 +1359,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1523,7 +1482,7 @@ "text/html": [ "\n", "
\n", - " ℹMLflow UI was started externally — not stopping it\n", + " MLflow UI was started externally — not stopping it\n", "
\n", " " ], diff --git a/nbs/12b_tutorial_patch_inference.ipynb b/nbs/12b_tutorial_patch_inference.ipynb index 811dee6..dfe838e 100644 --- a/nbs/12b_tutorial_patch_inference.ipynb +++ b/nbs/12b_tutorial_patch_inference.ipynb @@ -258,60 +258,15 @@ "cell_type": "markdown", "id": "cell-model-header", "metadata": {}, - "source": [ - "### Load trained model\n", - "\n", - "Load the exported learner which contains both the model architecture and best weights.\n", - "\n", - "**Two options:**\n", - "1. **Local file**: Load `learner.pkl` exported during training\n", - "2. **MLflow**: Download from MLflow artifacts (for experiment tracking workflows)" - ] + "source": "### Load trained model\n\nLoad the exported learner which contains both the model architecture and best weights.\n\n**Two options:**\n1. **Local file**: Load `best_learner.pkl` exported during training\n2. **MLflow**: Download from MLflow artifacts (for experiment tracking workflows)" }, { "cell_type": "code", "execution_count": null, "id": "cell-learner", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "load_learner` uses Python's insecure pickle module, which can execute malicious arbitrary code when loading. Only load files you trust.\n", - "If you only need to load model weights and optimizer state, use the safe `Learner.load` instead.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loaded model: UNet\n", - "Device: cuda\n" - ] - } - ], - "source": [ - "from fastai.learner import load_learner\n", - "import torch\n", - "\n", - "# Option 1: Load from local file\n", - "learn = load_learner('models/learner.pkl')\n", - "\n", - "# Option 2: Load from MLflow (uncomment to use)\n", - "# import mlflow\n", - "# run_id = \"your_run_id\" # Get from MLflow UI\n", - "# mlflow.artifacts.download_artifacts(run_id=run_id, artifact_path=\"model/learner.pkl\", dst_path=\"./\")\n", - "# learn = load_learner('learner.pkl')\n", - "\n", - "model = learn.model\n", - "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", - "model.to(device)\n", - "model.eval()\n", - "\n", - "print(f\"Loaded model: {model.__class__.__name__}\")\n", - "print(f\"Device: {device}\")" - ] + "outputs": [], + "source": "from fastai.learner import load_learner\nimport torch\n\n# Option 1: Load from local file\nlearn = load_learner('models/best_learner.pkl')\n\n# Option 2: Load from MLflow (uncomment to use)\n# import mlflow\n# run_id = \"your_run_id\" # Get from MLflow UI\n# mlflow.artifacts.download_artifacts(run_id=run_id, artifact_path=\"model/best_learner.pkl\", dst_path=\"./\")\n# learn = load_learner('best_learner.pkl')\n\nmodel = learn.model\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\nmodel.to(device)\nmodel.eval()\n\nprint(f\"Loaded model: {model.__class__.__name__}\")\nprint(f\"Device: {device}\")" }, { "cell_type": "markdown",