scripts/explorer/datasets.py

"""
Dataset loading for the SAE Feature Explorer.

Both regular explorer_data.pt files and brain_meis.pt files are loaded
through a single function that produces the same dict schema. Derived
numpy arrays (freq, log_freq, live_mask, umap_backup, etc.) are
pre-computed at load time so callbacks never recompute them.
"""

import json
import os
from collections import OrderedDict

import numpy as np
import torch

from .args import args
from .state import _all_datasets


# ---------- Helpers ----------

def _build_basename_index(paths: list) -> dict:
    """Map both full basename and stem → image index for fast filename lookup."""
    idx = {}
    for i, p in enumerate(paths):
        base = os.path.basename(p)
        stem = os.path.splitext(base)[0]
        idx[base] = i
        idx[stem] = i
    return idx


# ---------- Core loader ----------

def _load_dataset(path: str, label: str, *,
                  sae_url: str | None = None,
                  is_brain: bool = False,
                  thumb_dir: str = "") -> dict | None:
    """
    Load one dataset file (.pt) and return a fully-populated dict.

    Both explorer_data.pt and brain_meis.pt files are handled here.
    The returned dict includes:
      - all raw tensors from the file
      - per-dataset derived arrays (freq, log_freq, live_mask, umap_backup, …)
      - feature names and auto-interp labels read from JSON sidecars
      - pre-computed heatmaps read from the _heatmaps.pt sidecar if present
    """
    print(f"Loading [{label}] from {path} ...")
    try:
        d = torch.load(path, map_location='cpu', weights_only=False)
    except Exception as err:
        print(f"  WARNING: failed to load {path}: {err}")
        return None

    # Resolve image paths for brain datasets where paths may be stored as basenames.
    raw_paths = d.get('image_paths', [])
    if is_brain and raw_paths and thumb_dir and (
        not os.path.isabs(raw_paths[0]) or not os.path.exists(raw_paths[0])
    ):
        image_paths = [os.path.join(thumb_dir, os.path.basename(p)) for p in raw_paths]
    else:
        image_paths = raw_paths

    d_model = d['d_model']
    nan2    = np.full((d_model, 2), np.nan, dtype=np.float32)
    stem    = os.path.splitext(path)[0]

    # Feature names (manual labels)
    names_file = (args.names_file if (path == args.data and args.names_file)
                  else stem + '_feature_names.json')
    feature_names = {}
    if os.path.exists(names_file):
        with open(names_file) as f:
            feature_names = {int(k): v for k, v in json.load(f).items()}

    # Auto-interp labels
    auto_interp_file = stem + '_auto_interp.json'
    auto_interp_names = {}
    if os.path.exists(auto_interp_file):
        with open(auto_interp_file) as f:
            auto_interp_names = {int(k): v for k, v in json.load(f).items()}
        print(f"  Loaded {len(auto_interp_names)} auto-interp labels")

    # Core tensors
    feature_frequency = d['feature_frequency']
    feature_mean_act  = d['feature_mean_act']
    umap_coords       = d['umap_coords'].numpy()
    dict_umap_coords  = (d['dict_umap_coords'].numpy()
                         if 'dict_umap_coords' in d else nan2)

    # Derived arrays — computed once, stored in the dict
    freq              = feature_frequency.numpy()
    mean_act          = feature_mean_act.numpy()
    log_freq          = np.log10(freq + 1)
    live_mask         = ~np.isnan(umap_coords[:, 0])
    live_indices      = np.where(live_mask)[0]
    dict_live_mask    = ~np.isnan(dict_umap_coords[:, 0])
    dict_live_indices = np.where(dict_live_mask)[0]
    active_feats      = [int(i) for i in range(d_model) if freq[i] > 0]
    umap_backup       = dict(
        act_x    = umap_coords[live_mask, 0].tolist(),
        act_y    = umap_coords[live_mask, 1].tolist(),
        act_feat = live_indices.tolist(),
        dict_x   = dict_umap_coords[dict_live_mask, 0].tolist(),
        dict_y   = dict_umap_coords[dict_live_mask, 1].tolist(),
        dict_feat= dict_live_indices.tolist(),
    )

    entry = {
        'label':              label,
        'path':               path,
        'image_paths':        image_paths,
        'basename_index':     _build_basename_index(image_paths),
        'd_model':            d_model,
        'n_images':           d.get('n_images', len(image_paths)),
        'patch_grid':         d.get('patch_grid', 16),
        'image_size':         d.get('image_size', 224),
        'backbone':           d.get('backbone', 'dinov2'),
        'top_img_idx':        d['top_img_idx'],
        'top_img_act':        d['top_img_act'],
        'mean_img_idx':       d.get('mean_img_idx', d['top_img_idx']),
        'mean_img_act':       d.get('mean_img_act', d['top_img_act']),
        'nsd_top_img_idx':    d.get('nsd_top_img_idx'),
        'nsd_top_img_act':    d.get('nsd_top_img_act'),
        'nsd_mean_img_idx':   d.get('nsd_mean_img_idx'),
        'nsd_mean_img_act':   d.get('nsd_mean_img_act'),
        'feature_frequency':  feature_frequency,
        'feature_mean_act':   feature_mean_act,
        'umap_coords':        umap_coords,
        'dict_umap_coords':   dict_umap_coords,
        'clip_embeds':        d.get('clip_feature_embeds'),
        'nsd_clip_embeds':    d.get('nsd_clip_feature_embeds'),
        'sae_url':            sae_url,
        'inference_cache':    OrderedDict(),
        'names_file':         names_file,
        'auto_interp_file':   auto_interp_file,
        'feature_names':      feature_names,
        'auto_interp_names':  auto_interp_names,
        # Pre-computed derived arrays
        'freq':               freq,
        'mean_act':           mean_act,
        'log_freq':           log_freq,
        'live_mask':          live_mask,
        'live_indices':       live_indices,
        'dict_live_mask':     dict_live_mask,
        'dict_live_indices':  dict_live_indices,
        'active_feats':       active_feats,
        'umap_backup':        umap_backup,
    }

    # Brain MEI sidecar (disabled — re-enable after running precompute_brain_response_meis.py)
    # brain_sidecar = stem + '_brain_meis.pt'
    # if os.path.exists(brain_sidecar):
    #     print(f"  Loading brain MEI sidecar {os.path.basename(brain_sidecar)} ...")
    #     bm = torch.load(brain_sidecar, map_location='cpu', weights_only=False)
    #     entry['brain_top_img_idx'] = bm.get('brain_top_img_idx')
    #     entry['brain_top_img_act'] = bm.get('brain_top_img_act')
    # else:
    #     entry['brain_top_img_idx'] = None
    #     entry['brain_top_img_act'] = None

    # Heatmaps sidecar
    sidecar = stem + '_heatmaps.pt'
    if os.path.exists(sidecar):
        print(f"  Loading heatmaps sidecar {os.path.basename(sidecar)} ...")
        hm = torch.load(sidecar, map_location='cpu', weights_only=not is_brain)
        entry['top_heatmaps']       = hm.get('top_heatmaps')
        entry['mean_heatmaps']      = hm.get('mean_heatmaps')
        entry['nsd_top_heatmaps']   = hm.get('nsd_top_heatmaps')
        entry['nsd_mean_heatmaps']  = hm.get('nsd_mean_heatmaps')
        entry['heatmap_patch_grid'] = hm.get('patch_grid', d.get('patch_grid', 16))
        has_hm = 'yes'
    else:
        entry['top_heatmaps']       = None
        entry['mean_heatmaps']      = None
        entry['nsd_top_heatmaps']   = None
        entry['nsd_mean_heatmaps']  = None
        entry['heatmap_patch_grid'] = d.get('patch_grid', 16)
        has_hm = 'no'

    # Brain render sidecar (precomputed compact phi map PNGs)
    brain_render_sidecar = stem + '_brain_renders.pt'
    if os.path.exists(brain_render_sidecar):
        print(f"  Loading brain render sidecar {os.path.basename(brain_render_sidecar)} ...")
        br = torch.load(brain_render_sidecar, map_location='cpu', weights_only=False)
        entry['phi_map_cache'] = {int(k): v for k, v in br.items()}
        print(f"  Cached {len(entry['phi_map_cache'])} phi map renders")
    else:
        entry['phi_map_cache'] = {}

    # Cortical profile sidecar (precomputed 4-view cortical profile PNGs)
    cortical_sidecar = stem + '_cortical_profiles.pt'
    if os.path.exists(cortical_sidecar):
        print(f"  Loading cortical profile sidecar {os.path.basename(cortical_sidecar)} ...")
        cp = torch.load(cortical_sidecar, map_location='cpu', weights_only=False)
        entry['cortical_profile_cache'] = {int(k): v for k, v in cp.items()}
        print(f"  Cached {len(entry['cortical_profile_cache'])} cortical profiles")
    else:
        entry['cortical_profile_cache'] = {}

    # GT brain render sidecar (precomputed fMRI response PNGs per NSD image)
    gt_brain_sidecar = stem + '_gt_brain_renders.pt'
    if os.path.exists(gt_brain_sidecar):
        print(f"  Loading GT brain render sidecar {os.path.basename(gt_brain_sidecar)} ...")
        gb = torch.load(gt_brain_sidecar, map_location='cpu', weights_only=False)
        entry['gt_brain_cache'] = {int(k): v for k, v in gb.items()}
        print(f"  Cached {len(entry['gt_brain_cache'])} GT brain renders")
    else:
        entry['gt_brain_cache'] = {}

    has_clip = 'yes' if entry['clip_embeds'] is not None else 'no'
    print(f"  d={d_model}, n={entry['n_images']}, backbone={entry['backbone']}, "
          f"clip={has_clip}, heatmaps={has_hm}")
    return entry


# ---------- Public entry point ----------

def load_all_datasets():
    """Load all datasets specified by CLI args into _all_datasets."""
    # Primary dataset (always required)
    _all_datasets.append(
        _load_dataset(args.data, args.primary_label, sae_url=args.sae_url)
    )

    # Optional NSD brain dataset
    if args.brain_data:
        if os.path.exists(args.brain_data):
            entry = _load_dataset(
                args.brain_data, args.brain_label,
                is_brain=True,
                thumb_dir=args.brain_thumbnails or '',
            )
            if entry is not None:
                _all_datasets.append(entry)
        else:
            print(f"[Brain] WARNING: --brain-data not found: {args.brain_data}")