validation_protosam.py

"""
Validation script
"""
import math
import os
import pandas as pd
import csv
import shutil
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
import torchvision.transforms.functional as F
from torch.utils.data import DataLoader
import torch.backends.cudnn as cudnn
import numpy as np
import time
import matplotlib.pyplot as plt
from models.ProtoSAM import ProtoSAM,  ALPNetWrapper, SamWrapperWrapper, InputFactory, ModelWrapper, TYPE_ALPNET, TYPE_SAM
from models.ProtoMedSAM import ProtoMedSAM
from models.grid_proto_fewshot import FewShotSeg
from models.segment_anything.utils.transforms import ResizeLongestSide
from models.SamWrapper import SamWrapper
# from dataloaders.PolypDataset import get_polyp_dataset, get_vps_easy_unseen_dataset, get_vps_hard_unseen_dataset, PolypDataset, KVASIR, CVC300, COLON_DB, ETIS_DB, CLINIC_DB
from dataloaders.PolypDataset import get_polyp_dataset, PolypDataset
from dataloaders.PolypTransforms import get_polyp_transform
from dataloaders.SimpleDataset import SimpleDataset
from dataloaders.ManualAnnoDatasetv2 import get_nii_dataset
from dataloaders.common import ValidationDataset
from config_ssl_upload import ex

import tqdm
from tqdm.auto import tqdm
import cv2
from collections import defaultdict

# config pre-trained model caching path
os.environ['TORCH_HOME'] = "./pretrained_model"

# Supported Datasets
CHAOS = "chaos"
SABS = "sabs"
POLYPS = "polyps"

ALP_DS = [CHAOS, SABS]

ROT_DEG = 0

def get_bounding_box(segmentation_map):
    """Generate bounding box from a segmentation map. one bounding box to include the extreme points of the segmentation map."""
    if isinstance(segmentation_map, torch.Tensor):
        segmentation_map = segmentation_map.cpu().numpy()
    
    bbox = cv2.boundingRect(segmentation_map.astype(np.uint8))
    # plot bounding boxes for each contours
    # plt.figure()
    # x, y, w, h = bbox
    # plt.imshow(segmentation_map)
    # plt.gca().add_patch(plt.Rectangle((x, y), w, h, fill=False, edgecolor='r', linewidth=2))
    # plt.savefig("debug/bounding_boxes.png") 

    return bbox

def calc_iou(boxA, boxB):
    """
    boxA: [x, y, w, h]
    """
    xA = max(boxA[0], boxB[0])
    yA = max(boxA[1], boxB[1])
    xB = min(boxA[0] + boxA[2], boxB[0] + boxB[2])
    yB = min(boxA[1] + boxA[3], boxB[1] + boxB[3])

    interArea = max(0, xB - xA) * max(0, yB - yA)
    boxAArea = boxA[2] * boxA[3]
    boxBArea = boxB[2] * boxB[3]
    
    iou = interArea / float(boxAArea + boxBArea - interArea)
    return iou


def eval_detection(pred_list):
    """
    pred_list: list of dictionaries with keys 'pred_bbox', 'gt_bbox' and score (prediction confidence score).
    compute AP50, AP75, AP50:95:10
    """
    iou_thresholds = np.round(np.arange(0.5, 1.0, 0.05), 2)
    ap_dict = {iou: [] for iou in iou_thresholds}
    for iou_threshold in iou_thresholds:
        tp, fp = 0, 0
        
        for pred in pred_list:
            pred_bbox = pred['pred_bbox']
            gt_bbox = pred['gt_bbox']
            
            iou = calc_iou(pred_bbox, gt_bbox)
            
            if iou >= iou_threshold:
                tp += 1
            else:
                fp += 1

        precision = tp / (tp + fp)
        recall = tp / len(pred_list) 
        f1 = 2 * (precision * recall) / (precision + recall)        

        ap_dict[iou_threshold] = {
            'iou_threshold': iou_threshold,
            'tp': tp,
            'fp': fp,
            'n_gt': len(pred_list),
            'f1': f1,
            'precision': precision,
            'recall': recall
        }
    
    # Convert results to a DataFrame and save to CSV
    results = []
    for iou_threshold in iou_thresholds:
        results.append(ap_dict[iou_threshold])
    
    df = pd.DataFrame(results)
    return df


def plot_pred_gt_support(query_image, pred, gt, support_images, support_masks, score=None, save_path="debug/pred_vs_gt"):
    """
    Save 5 key images: support images, support mask, query, ground truth and prediction.
    Handles both grayscale and RGB images consistently with the same mask color.
    
    Args:
        query_image: Query image tensor (grayscale or RGB)
        pred: 2d tensor where 1 represents foreground and 0 represents background
        gt: 2d tensor where 1 represents foreground and 0 represents background
        support_images: Support image tensors (grayscale or RGB)
        support_masks: Support mask tensors
        score: Optional score to add to filename
        save_path: Base path without extension for saving images
    """
    # Create directory for this case
    os.makedirs(os.path.dirname(save_path), exist_ok=True)
    
    # Process query image - ensure HxWxC format for visualization
    query_image = query_image.clone().detach().cpu()
    if len(query_image.shape) == 3 and query_image.shape[0] <= 3:  # CHW format
        query_image = query_image.permute(1, 2, 0)
    
    # Handle grayscale vs RGB consistently
    if len(query_image.shape) == 2 or (len(query_image.shape) == 3 and query_image.shape[2] == 1):
        # For grayscale, use cmap='gray' for visualization
        is_grayscale = True
        if len(query_image.shape) == 3:
            query_image = query_image.squeeze(2)  # Remove channel dimension for grayscale
    else:
        is_grayscale = False
    
    # Normalize image for visualization
    query_image = (query_image - query_image.min()) / (query_image.max() - query_image.min() + 1e-8)
    
    # Convert pred and gt to numpy for visualization
    pred_np = pred.cpu().float().numpy()  # Ensure float before converting to numpy
    gt_np = gt.cpu().float().numpy()  # Ensure float before converting to numpy
    
    # Ensure binary masks
    pred_np = (pred_np > 0).astype(np.float32)
    gt_np = (gt_np > 0).astype(np.float32)
    
    # Set all positive values to 1.0 to ensure consistent red coloring in YlOrRd colormap
    pred_np[pred_np > 0] = 1.0
    gt_np[gt_np > 0] = 1.0
    
    # Create colormap for mask overlays - using the YlOrRd colormap as requested
    mask_cmap = plt.cm.get_cmap('YlOrRd')
    
    # Generate color masks with alpha values
    pred_rgba = mask_cmap(pred_np)
    pred_rgba[..., 3] = pred_np * 0.7  # Last channel is alpha - semitransparent where mask=1
    
    gt_rgba = mask_cmap(gt_np)
    gt_rgba[..., 3] = gt_np * 0.7  # Last channel is alpha - semitransparent where mask=1
    
    # 1. Save query image (original)
    plt.figure(figsize=(10, 10))
    if is_grayscale:
        plt.imshow(query_image, cmap='gray')
    else:
        plt.imshow(query_image)
    plt.axis('off')
    # Remove padding/whitespace
    plt.subplots_adjust(left=0, right=1, top=1, bottom=0, wspace=0, hspace=0)
    plt.savefig(f"{save_path}/query.png", bbox_inches='tight', pad_inches=0)
    plt.close()
    
    # 2. Save query image with prediction overlay
    plt.figure(figsize=(10, 10))
    if is_grayscale:
        plt.imshow(query_image, cmap='gray')
    else:
        plt.imshow(query_image)
    plt.imshow(pred_rgba)
    plt.axis('off')
    # Remove padding/whitespace
    plt.subplots_adjust(left=0, right=1, top=1, bottom=0, wspace=0, hspace=0)
    plt.savefig(f"{save_path}/pred.png", bbox_inches='tight', pad_inches=0)
    plt.close()
    
    # 3. Save query image with ground truth overlay
    plt.figure(figsize=(10, 10))
    if is_grayscale:
        plt.imshow(query_image, cmap='gray')
    else:
        plt.imshow(query_image)
    plt.imshow(gt_rgba)
    plt.axis('off')
    # Remove padding/whitespace
    plt.subplots_adjust(left=0, right=1, top=1, bottom=0, wspace=0, hspace=0)
    plt.savefig(f"{save_path}/gt.png", bbox_inches='tight', pad_inches=0)
    plt.close()
    
    # Process and save support images and masks (just the first one for brevity)
    if support_images is not None:
        if isinstance(support_images, list):
            support_images = torch.cat(support_images, dim=0).clone().detach()
        if isinstance(support_masks, list):
            support_masks = torch.cat(support_masks, dim=0).clone().detach()
        
        # Move to CPU for processing
        support_images = support_images.cpu()
        support_masks = support_masks.cpu()
        
        # Handle different dimensions of support images
        if len(support_images.shape) == 4:  # NCHW format
            # Convert to NHWC for visualization
            support_images = support_images.permute(0, 2, 3, 1)
        
        # Just process the first support image
        i = 0
        if support_images.shape[0] > 0:
            support_img = support_images[i].clone()
            support_mask = support_masks[i].clone()
            
            # Check if grayscale or RGB
            if support_img.shape[-1] == 1:  # Last dimension is channels
                support_img = support_img.squeeze(-1)  # Remove channel dimension
                support_is_gray = True
            elif support_img.shape[-1] == 3:
                support_is_gray = False
            else:  # Assume it's grayscale if not 1 or 3 channels
                support_is_gray = True
            
            # Normalize support image
            support_img = (support_img - support_img.min()) / (support_img.max() - support_img.min() + 1e-8)
            
            # 4. Save support image only
            plt.figure(figsize=(10, 10))
            if support_is_gray:
                plt.imshow(support_img, cmap='gray')
            else:
                plt.imshow(support_img)
            plt.axis('off')
            # Remove padding/whitespace
            plt.subplots_adjust(left=0, right=1, top=1, bottom=0, wspace=0, hspace=0)
            plt.savefig(f"{save_path}/support_1.png", bbox_inches='tight', pad_inches=0)
            plt.close()
            
            # 5. Save support mask only (direct mask visualization similar to gt/pred)
            plt.figure(figsize=(10, 10))
            
            # Process support mask with same approach
            support_mask_np = support_mask.cpu().float().numpy()
            support_mask_np = (support_mask_np > 0).astype(np.float32)
            support_mask_np[support_mask_np > 0] = 1.0  # Set to 1.0 for consistent coloring
            
            support_mask_rgba = mask_cmap(support_mask_np)
            support_mask_rgba[..., 3] = support_mask_np * 0.7  # Last channel is alpha - semitransparent where mask=1
            
            if is_grayscale:
                plt.imshow(support_img, cmap='gray')
            else:
                plt.imshow(support_img)
            plt.imshow(support_mask_rgba)
            plt.axis('off')
            # Remove padding/whitespace
            plt.subplots_adjust(left=0, right=1, top=1, bottom=0, wspace=0, hspace=0)
            plt.savefig(f"{save_path}/support_mask.png", bbox_inches='tight', pad_inches=0)
            plt.close()




def get_dice_iou_precision_recall(pred: torch.Tensor, gt: torch.Tensor):
    """
    pred: 2d tensor of shape (H, W) where 1 represents foreground and 0 represents background
    gt: 2d tensor of shape (H, W) where 1 represents foreground and 0 represents background
    """
    if gt.sum() == 0:
        print("gt is all background")
        return {"dice": 0, "precision": 0, "recall": 0}

    # Resize pred to match gt dimensions if they're different
    if pred.shape != gt.shape:
        print(f"Resizing prediction from {pred.shape} to match ground truth {gt.shape}")
        # Use interpolate to resize pred to match gt dimensions
        pred = torch.nn.functional.interpolate(
            pred.unsqueeze(0).unsqueeze(0).float(), 
            size=gt.shape, 
            mode='nearest'
        ).squeeze(0).squeeze(0)

    tp = (pred * gt).sum()
    fp = (pred * (1 - gt)).sum()
    fn = ((1 - pred) * gt).sum()
    dice = 2 * tp / (2 * tp + fp + fn + 1e-8)
    precision = tp / (tp + fp + 1e-8)
    recall = tp / (tp + fn + 1e-8)
    iou = tp / (tp + fp + fn + 1e-8)
    return {"dice": dice, "iou": iou, "precision": precision, "recall": recall}


def get_alpnet_model(_config) -> ModelWrapper:
    alpnet = FewShotSeg(
       _config["input_size"][0],
       _config["reload_model_path"],
       _config["model"]
    )
    alpnet.cuda()
    alpnet_wrapper = ALPNetWrapper(alpnet)
    
    return alpnet_wrapper

def get_sam_model(_config) -> ModelWrapper:
    sam_args = {
        "model_type": "vit_h",
        "sam_checkpoint": "pretrained_model/sam_vit_h.pth"
    }
    sam = SamWrapper(sam_args=sam_args).cuda()
    sam_wrapper = SamWrapperWrapper(sam)
    return sam_wrapper  

def get_model(_config) -> ProtoSAM:
    # Initial Segmentation Model
    if _config["base_model"] == TYPE_ALPNET:
        base_model = get_alpnet_model(_config)
    else:
        raise NotImplementedError(f"base model {_config['base_model']} not implemented")
    
    # ProtoSAM model
    if _config["protosam_sam_ver"] in  ("sam_h", "sam_b"):
        sam_h_checkpoint = "pretrained_model/sam_vit_h.pth"
        sam_b_checkpoint = "pretrained_model/sam_vit_b.pth"
        sam_checkpoint = sam_h_checkpoint if _config["protosam_sam_ver"] == "sam_h" else sam_b_checkpoint
        model = ProtoSAM(image_size = (1024, 1024),
                    coarse_segmentation_model=base_model,
                    use_bbox=_config["use_bbox"],
                    use_points=_config["use_points"],
                    use_mask=_config["use_mask"],
                    debug=_config["debug"],
                    num_points_for_sam=1,
                    use_cca=_config["do_cca"],
                    point_mode=_config["point_mode"],
                    use_sam_trans=True, 
                    coarse_pred_only=_config["coarse_pred_only"],
                    sam_pretrained_path=sam_checkpoint,
                    use_neg_points=_config["use_neg_points"],) 
    elif _config["protosam_sam_ver"] == "medsam":
        model = ProtoMedSAM(image_size = (1024, 1024),
                            coarse_segmentation_model=base_model,
                            debug=_config["debug"],
                            use_cca=_config["do_cca"],
        )
    else:
        raise NotImplementedError(f"protosam_sam_ver {_config['protosam_sam_ver']} not implemented")
    
    return model


def get_support_set_polyps(_config, dataset:PolypDataset):
    n_support = _config["n_support"]
    (support_images, support_labels, case) = dataset.get_support(n_support=n_support)
    
    return support_images, support_labels, case


def get_support_set_alpds(config, dataset:ValidationDataset):
    support_set = dataset.get_support_set(config)
    support_fg_masks = support_set["support_labels"]
    support_images = support_set["support_images"]
    support_scan_id = support_set["support_scan_id"]
    return support_images, support_fg_masks, support_scan_id


def get_support_set(_config, dataset):
    if _config["dataset"].lower() == POLYPS:
        support_images, support_fg_masks, case = get_support_set_polyps(_config, dataset)
    elif any(item in _config["dataset"].lower() for item in ALP_DS):
        support_images, support_fg_masks, support_scan_id = get_support_set_alpds(_config, dataset)
    else:
        raise NotImplementedError(f"dataset {_config['dataset']} not implemented")
    return support_images, support_fg_masks, support_scan_id


def update_support_set_by_scan_part(support_images, support_labels, qpart):
    qpart_support_images = [support_images[qpart]]
    qpart_support_labels = [support_labels[qpart]]
    
    return qpart_support_images, qpart_support_labels


def manage_support_sets(sample_batched, all_support_images, all_support_fg_mask, support_images, support_fg_mask, qpart=None):
    if sample_batched['part_assign'][0] != qpart:
        qpart = sample_batched['part_assign'][0]
        support_images, support_fg_mask = update_support_set_by_scan_part(all_support_images, all_support_fg_mask, qpart)
            
    return support_images, support_fg_mask, qpart


@ex.automain
def main(_run, _config, _log):
    if _run.observers:
        os.makedirs(f'{_run.observers[0].dir}/interm_preds', exist_ok=True)
        for source_file, _ in _run.experiment_info['sources']:
            os.makedirs(os.path.dirname(f'{_run.observers[0].dir}/source/{source_file}'),
                        exist_ok=True)
            _run.observers[0].save_file(source_file, f'source/{source_file}')
        print(f"####### created dir:{_run.observers[0].dir} #######")
        shutil.rmtree(f'{_run.observers[0].basedir}/_sources')
    print(f"config do_cca: {_config['do_cca']}, use_bbox: {_config['use_bbox']}")
    cudnn.enabled = True
    cudnn.benchmark = True
    torch.cuda.set_device(device=_config['gpu_id'])
    torch.set_num_threads(1)

    _log.info(f'###### Reload model {_config["reload_model_path"]} ######')
    print(f'###### Reload model {_config["reload_model_path"]} ######')
    model = get_model(_config)
    model = model.to(torch.device("cuda"))
    model.eval()
    
    sam_trans = ResizeLongestSide(1024)
    if _config["dataset"].lower() == POLYPS:
        tr_dataset, te_dataset = get_polyp_dataset(sam_trans=sam_trans, image_size=(1024, 1024))
    elif CHAOS in _config["dataset"].lower() or SABS in _config["dataset"].lower():
        tr_dataset, te_dataset = get_nii_dataset(_config, _config["input_size"][0]) 
    else:
        raise NotImplementedError(
            f"dataset {_config['dataset']} not implemented")

    # dataloaders
    testloader = DataLoader(
        te_dataset,
        batch_size=1,
        shuffle=False,
        num_workers=1,
        pin_memory=False,
        drop_last=False
    )

    _log.info('###### Starting validation ######')
    model.eval()

    mean_dice = []
    mean_prec = []
    mean_rec = []
    mean_iou = []
    
    mean_dice_cases = {}
    mean_iou_cases = {} 
    bboxes_w_scores = []
    
    curr_case = None
    supp_fts = None
    qpart = None
    support_images = support_fg_mask = None
    all_support_images, all_support_fg_mask, support_scan_id = None, None, None
    MAX_SUPPORT_IMAGES = 1
    is_alp_ds = any(item in _config["dataset"].lower() for item in ALP_DS)
    is_polyp_ds  = _config["dataset"].lower() == POLYPS
    
    if is_alp_ds:
        all_support_images, all_support_fg_mask, support_scan_id = get_support_set(_config, te_dataset)
    elif is_polyp_ds:
        support_images, support_fg_mask, case = get_support_set_polyps(_config, tr_dataset)
        
    with tqdm(testloader) as pbar: 
        for idx, sample_batched in enumerate(tqdm(testloader)):
            case = sample_batched['case'][0]
            if is_alp_ds: 
                support_images, support_fg_mask, qpart = manage_support_sets(
                                                            sample_batched,
                                                            all_support_images,
                                                            all_support_fg_mask,
                                                            support_images,
                                                            support_fg_mask,
                                                            qpart,
                )
            
            if is_alp_ds and sample_batched["scan_id"][0] in support_scan_id:
                continue
             
            query_images = sample_batched['image'].cuda()
            query_labels = torch.cat([sample_batched['label']], dim=0)
            if not 1 in query_labels and _config["skip_no_organ_slices"]:
                continue
            
            n_try = 1
            with torch.no_grad():
                coarse_model_input = InputFactory.create_input(
                                        input_type=_config["base_model"],
                                        query_image=query_images,
                                        support_images=support_images,
                                        support_labels=support_fg_mask,
                                        isval=True,
                                        val_wsize=_config["val_wsize"],
                                        original_sz=query_images.shape[-2:],
                                        img_sz=query_images.shape[-2:],
                                        gts=query_labels,
                )
                coarse_model_input.to(torch.device("cuda"))
                    
                query_pred, scores = model(
                        query_images, coarse_model_input, degrees_rotate=0)
            query_pred = query_pred.cpu().detach()
                
            if _config["debug"]:
                if is_alp_ds:
                    save_path = f'debug/preds/{case}_{sample_batched["z_id"].item()}_{idx}_{n_try}'
                    os.makedirs(save_path, exist_ok=True)
                elif is_polyp_ds:
                    save_path = f'debug/preds/{case}_{idx}_{n_try}'
                    os.makedirs(save_path, exist_ok=True)
                plot_pred_gt_support(query_images[0,0].cpu(), query_pred.cpu(), query_labels[0].cpu(),
                                    support_images, support_fg_mask, save_path=save_path, score=scores[0])

            # print(query_pred.shape)
            # print(query_labels[0].shape)
            metrics = get_dice_iou_precision_recall(
                query_pred, query_labels[0].to(query_pred.device))
            mean_dice.append(metrics["dice"])
            mean_prec.append(metrics["precision"])
            mean_rec.append(metrics["recall"])
            mean_iou.append(metrics["iou"])

            bboxes_w_scores.append({"pred_bbox": get_bounding_box(query_pred.cpu()),
                                    "gt_bbox": get_bounding_box(query_labels[0].cpu()),
                                    "score": np.mean(scores)})
            
            if case not in mean_dice_cases:
                mean_dice_cases[case] = []
                mean_iou_cases[case] = []
            mean_dice_cases[case].append(metrics["dice"])
            mean_iou_cases[case].append(metrics["iou"])

            if metrics["dice"] < 0.6 and _config["debug"]:
                path = f'{_run.observers[0].dir}/bad_preds/case_{case}_idx_{idx}_dice_{metrics["dice"]:.4f}'
                if _config["debug"]:
                    path = f'debug/bad_preds/case_{case}_idx_{idx}_dice_{metrics["dice"]:.4f}'
                os.makedirs(path, exist_ok=True)
                print(f"saving bad prediction to {path}")
                plot_pred_gt_support(query_images[0,0].cpu(), query_pred.cpu(), query_labels[0].cpu(
                    ), support_images, support_fg_mask, save_path=path, score=scores[0])
                
            pbar.set_postfix_str({"mdice": f"{np.mean(mean_dice):.4f}", "miou": f"{np.mean(mean_iou):.4f}, n_try: {n_try}"})
                

    for k in mean_dice_cases.keys():
        _run.log_scalar(f'mar_val_batches_meanDice_{k}', np.mean(mean_dice_cases[k]))
        _run.log_scalar(f'mar_val_batches_meanIOU_{k}', np.mean(mean_iou_cases[k]))
        _log.info(f'mar_val batches meanDice_{k}: {np.mean(mean_dice_cases[k])}')
        _log.info(f'mar_val batches meanIOU_{k}: {np.mean(mean_iou_cases[k])}') 
    
    # write validation result to log file
    m_meanDice = np.mean(mean_dice)
    m_meanPrec = np.mean(mean_prec)
    m_meanRec = np.mean(mean_rec)
    m_meanIOU = np.mean(mean_iou)

    _run.log_scalar('mar_val_batches_meanDice', m_meanDice)
    _run.log_scalar('mar_val_batches_meanPrec', m_meanPrec)
    _run.log_scalar('mar_val_al_batches_meanRec', m_meanRec)
    _run.log_scalar('mar_val_al_batches_meanIOU', m_meanIOU)
    _log.info(f'mar_val batches meanDice: {m_meanDice}')
    _log.info(f'mar_val batches meanPrec: {m_meanPrec}')
    _log.info(f'mar_val batches meanRec: {m_meanRec}')
    _log.info(f'mar_val batches meanIOU: {m_meanIOU}')
    print("============ ============")
    _log.info(f'End of validation')
    return 1