test/check_pearson.py

import sys
import os
import glob
import re
from pathlib import Path

import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt
from omegaconf import OmegaConf

# Add src to the system path so we can import architecture and metrics
src_dir = Path(__file__).resolve().parent.parent / "src"
sys.path.append(str(src_dir))

from training import make_data_loaders, evaluate_stage1, evaluate_stage2, SUBJECTS
from medarc_architecture import MultiSubjectConvLinearEncoder
from stage2.CFM import CFM

def plot_heatmap(acc_map, title, save_path):
    """
    Plots a 1D tensor (brain region voxels) as a 2D wrapped heatmap grid.
    Pads the flat array with NaNs to make it square.
    """
    V = acc_map.shape[0]
    
    # Calculate square dimensions (e.g., 1000 -> 32 x 32 grid roughly)
    side = int(np.ceil(np.sqrt(V)))
    
    # Pad to make it perfectly square
    padded = np.pad(acc_map, (0, side * side - V), constant_values=np.nan)
    grid = padded.reshape(side, side)

    plt.figure(figsize=(8, 6))
    
    # Using 'viridis' or 'hot' for heatmap representation
    # vmin/vmax set to typical Pearson's R bounds for visuals or let it auto-scale
    im = plt.imshow(grid, cmap="viridis", aspect="auto")
    plt.colorbar(im, label="Pearson's r Correlation")
    
    plt.title(title)
    plt.tight_layout()
    plt.savefig(save_path, dpi=150)
    plt.close()
    print(f"Saved heatmap {title} to {save_path}")

def main():
    root_dir = Path("/workspace/code/flow_matching")
    
    # Assuming standard directory where training script drops them
    run_dir = root_dir / "output" / "two_stage_encoding"
    config_path = run_dir / "config.yaml"
    
    if not config_path.exists():
        print(f"Configuration file not found at {config_path}!")
        return

    cfg = OmegaConf.load(config_path)
    device = torch.device(cfg.device if torch.cuda.is_available() else "cpu")
    print(f"Using device: {device}")

    print("Building DataLoaders...")
    data_loaders = make_data_loaders(cfg)
    val_loader = data_loaders[cfg.val_set_name]
    
    sample_batch = next(iter(val_loader))
    feat_dims = [f.shape[-1] for f in sample_batch["features"]]
    target_dim = sample_batch["fmri"].shape[-1]
    subjects_list = cfg.get("subjects", SUBJECTS)

    print(f"Found target Voxel bounds (V): {target_dim}")
    print("--- Stage 1 Checkout ---")
    stage1_model = MultiSubjectConvLinearEncoder(
        num_subjects=len(subjects_list),
        feat_dims=feat_dims,
        **cfg.stage1.model
    ).to(device)

    stage1_ckpt = run_dir / "stage1_best.pt"
    if not stage1_ckpt.exists():
        print(f"Cannot find Stage 1 checkpoint at {stage1_ckpt}")
        return
        
    print(f"Loading Stage 1 Mean Anchor from {stage1_ckpt.name}...")
    stage1_model.load_state_dict(torch.load(stage1_ckpt, map_location=device))
    
    # Execute Stage 1 evaluate to compute Pearson map
    acc_s1, metrics_s1 = evaluate_stage1(
        epoch=0,
        model=stage1_model,
        val_loader=val_loader,
        device=device,
        subjects=subjects_list,
        ds_name=cfg.val_set_name
    )
    
    heatmap_dir = run_dir / "heatmaps"
    heatmap_dir.mkdir(exist_ok=True, parents=True)
    
    print(f"Stage 1 Overall Pearson's r: {acc_s1:.4f}")
    # Visualize stage 1 mean anchors 
    for sub in subjects_list:
        acc_map = metrics_s1[f"accmap_sub-{sub}"]
        mean_r = acc_map.mean().item() if isinstance(acc_map, torch.Tensor) else np.mean(acc_map)
        print(f"Stage 1 - Sub {sub} Mean Pearson's r: {mean_r:.4f}")
        plot_heatmap(acc_map, f"Stage 1 Best (Mean Anchor) - Sub {sub}", heatmap_dir / f"stage1_sub{sub}.png")

    print("\n--- Stage 2 Checkout ---")
    cfm_params = cfg.stage2.cfm
    velocity_net_params = cfg.stage2.velocity_net
    source_ve_params = cfg.stage2.source_ve
    transport_params = cfg.stage2.transport
    stage2_models = nn.ModuleDict()

    for sub in subjects_list:
        sub_key = str(sub)
        cfm_model = CFM(
            feat_dim=target_dim,
            cfm_params=cfm_params,
            velocity_net_params=velocity_net_params,
            source_ve_params=source_ve_params,
            transport_params=transport_params,
        ).to(device)
        stage2_models[sub_key] = cfm_model

    # Locate and sort all consecutive stage2 weights
    stage2_ckpts = list(run_dir.glob("stage2_epoch_*.pt"))
    def get_epoch(p):
        m = re.search(r"stage2_epoch_(\d+).pt", p.name)
        return int(m.group(1)) if m else -1
    
    stage2_ckpts.sort(key=get_epoch)
    
    if not stage2_ckpts:
        print("No Stage 2 configurations found to visualize!")
        return

    # Evaluate each stage 2 checkpoint consecutively and map visualizations
    for ckpt in stage2_ckpts:
        ep = get_epoch(ckpt)
        print(f"\nProcessing Vector Field {ckpt.name}...")
        
        stage2_models.load_state_dict(torch.load(ckpt, map_location=device))
        
        acc_s2, metrics_s2 = evaluate_stage2(
            epoch=ep,
            stage1_model=stage1_model,
            stage2_models=stage2_models,
            val_loader=val_loader,
            device=device,
            subjects=subjects_list,
            ds_name=cfg.val_set_name,
            n_timesteps=cfg.stage2.get("n_timesteps", 25)
        )
        
        print(f"Stage 2 Epoch {ep} Overall Pearson's r: {acc_s2:.4f}")
        for sub in subjects_list:
            acc_map = metrics_s2[f"accmap_sub-{sub}"]
            mean_r = acc_map.mean().item() if isinstance(acc_map, torch.Tensor) else np.mean(acc_map)
            print(f"Stage 2 Epoch {ep} - Sub {sub} Mean Pearson's r: {mean_r:.4f}")
            plot_heatmap(acc_map, f"Stage 2 Epoch {ep} - Sub {sub}", heatmap_dir / f"stage2_ep{ep}_sub{sub}.png")
            
    print("\nVisualizations effectively correlated & processed! 🧠")

if __name__ == "__main__":
    main()