inference_resnet3d.py

import os.path as osp
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
import argparse
# from timm.data.mixup import Mixup
from sklearn.metrics import fbeta_score
from timesformer_pytorch import TimeSformer
import wandb

import torchvision.models as models
import random
import sys
import yaml

import numpy as np
import pandas as pd


import gc

import pytorch_lightning as pl
from pytorch_lightning import Trainer
from pytorch_lightning.callbacks import ModelCheckpoint, StochasticWeightAveraging,BackboneFinetuning
import numpy as np
import scipy.stats as st
import matplotlib.pyplot as plt
def gkern(kernlen=21, nsig=3):
    """Returns a 2D Gaussian kernel."""

    x = np.linspace(-nsig, nsig, kernlen+1)
    kern1d = np.diff(st.norm.cdf(x))
    kern2d = np.outer(kern1d, kern1d)
    return kern2d/kern2d.sum()

from sklearn.metrics import roc_auc_score, accuracy_score, f1_score, log_loss
import pickle
from torch.utils.data import DataLoader
from torch.cuda.amp import autocast, GradScaler
import warnings
import sys
import pandas as pd
import os
import gc
import sys
import math
import time
import random
import shutil
from pathlib import Path
from contextlib import contextmanager
from collections import defaultdict, Counter
import cv2

import scipy as sp
import numpy as np
import pandas as pd
import segmentation_models_pytorch as smp
import matplotlib.pyplot as plt
from tqdm.auto import tqdm
from functools import partial

import argparse
import importlib
import torch
import torch.nn as nn
from torch.optim import Adam, SGD, AdamW

import datetime
import numpy as np
from torch.utils.data import DataLoader, Dataset
import cv2
import torch
import os
import albumentations as A
from albumentations.pytorch import ToTensorV2
from albumentations import ImageOnlyTransform
import os
import albumentations as A
from albumentations.pytorch import ToTensorV2
from timm.models.convnext import convnextv2_femto,convnextv2_nano
import torch
import torch.nn as nn
from torch.nn.utils.rnn import pack_padded_sequence
import torch.nn.functional as F
import PIL.Image
from models.i3dallnl import InceptionI3d
from models.resnetall import generate_model
PIL.Image.MAX_IMAGE_PIXELS = 933120000
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
class CFG:
    # ============== comp exp name =============
    comp_name = 'vesuvius'

    comp_dir_path = './'
    comp_folder_name = './'
    comp_dataset_path = f'./'
    
    exp_name = 'pretraining_all'

    # ============== pred target =============
    target_size = 1

    # ============== model cfg =============
    model_name = 'Unet'
    backbone = 'efficientnet-b0'
    # backbone = 'se_resnext50_32x4d'

    in_chans = 30 # 65
    encoder_depth=5
    # ============== training cfg =============
    size = 64
    tile_size = 64
    stride = tile_size // 3

    train_batch_size = 256 # 32
    valid_batch_size = 256
    use_amp = True

    scheduler = 'GradualWarmupSchedulerV2'
    # scheduler = 'CosineAnnealingLR'
    epochs = 50 # 30

    # adamW warmupあり
    warmup_factor = 10
    # lr = 1e-4 / warmup_factor
    lr = 1e-4 / warmup_factor

    # ============== fold =============
    valid_id = 2

    # objective_cv = 'binary'  # 'binary', 'multiclass', 'regression'
    metric_direction = 'maximize'  # maximize, 'minimize'
    # metrics = 'dice_coef'

    # ============== fixed =============
    pretrained = True
    inf_weight = 'best'  # 'best'

    min_lr = 1e-6
    weight_decay = 1e-6
    max_grad_norm = 5

    print_freq = 50
    num_workers = 16

    seed = 42

    # ============== set dataset path =============
    print('set dataset path')

    outputs_path = f'./outputs/{comp_name}/{exp_name}/'

    submission_dir = outputs_path + 'submissions/'
    submission_path = submission_dir + f'submission_{exp_name}.csv'

    model_dir = outputs_path + \
        f'{comp_name}-models/'

    figures_dir = outputs_path + 'figures/'

    log_dir = outputs_path + 'logs/'
    log_path = log_dir + f'{exp_name}.txt'

    # ============== augmentation =============
    train_aug_list = [
        # A.RandomResizedCrop(
        #     size, size, scale=(0.85, 1.0)),
        A.Resize(size, size),
        A.HorizontalFlip(p=0.5),
        A.VerticalFlip(p=0.5),
        A.RandomBrightnessContrast(p=0.75),
        A.ShiftScaleRotate(rotate_limit=90,shift_limit=0.1,scale_limit=0.1,p=0.5),
        A.OneOf([
                A.GaussNoise(var_limit=[10, 50]),
                A.GaussianBlur(),
                A.MotionBlur(),
                ], p=0.4),
        A.GridDistortion(num_steps=2, distort_limit=0.3, p=0.4),
        A.CoarseDropout(max_holes=5, max_width=int(size * 0.05), max_height=int(size * 0.05), 
                        mask_fill_value=0, p=0.5),
        # A.Cutout(max_h_size=int(size * 0.6),
        #          max_w_size=int(size * 0.6), num_holes=1, p=1.0),
        A.Normalize(
            mean= [0] * in_chans,
            std= [1] * in_chans
        ),
        ToTensorV2(transpose_mask=True),
    ]

    valid_aug_list = [
        A.Resize(size, size),
        A.Normalize(
            mean= [0] * in_chans,
            std= [1] * in_chans
        ),
        ToTensorV2(transpose_mask=True),
    ]
class AverageMeter(object):
    """Computes and stores the average and current value"""

    def __init__(self):
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count
def init_logger(log_file):
    from logging import getLogger, INFO, FileHandler, Formatter, StreamHandler
    logger = getLogger(__name__)
    logger.setLevel(INFO)
    handler1 = StreamHandler()
    handler1.setFormatter(Formatter("%(message)s"))
    handler2 = FileHandler(filename=log_file)
    handler2.setFormatter(Formatter("%(message)s"))
    logger.addHandler(handler1)
    logger.addHandler(handler2)
    return logger

def set_seed(seed=None, cudnn_deterministic=True):
    if seed is None:
        seed = 42

    os.environ['PYTHONHASHSEED'] = str(seed)
    np.random.seed(seed)
    random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.backends.cudnn.deterministic = cudnn_deterministic
    torch.backends.cudnn.benchmark = False
def make_dirs(cfg):
    for dir in [cfg.model_dir, cfg.figures_dir, cfg.submission_dir, cfg.log_dir]:
        os.makedirs(dir, exist_ok=True)
def cfg_init(cfg, mode='train'):
    set_seed(cfg.seed)
    # set_env_name()
    # set_dataset_path(cfg)

    if mode == 'train':
        make_dirs(cfg)
cfg_init(CFG)

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

def read_image_mask(fragment_id,start_idx=18,end_idx=38,rotation=0):

    images = []

    # idxs = range(65)
    mid = 65 // 2
    start = mid - CFG.in_chans // 2
    end = mid + CFG.in_chans // 2
    idxs = range(start_idx, end_idx)
    # idxs = range(0, 65)

    for i in idxs:
        
        image = cv2.imread(f"./train_scrolls/{fragment_id}/layers/{i:02}.tif", 0)

        pad0 = (256 - image.shape[0] % 256)
        pad1 = (256 - image.shape[1] % 256)

        image = np.pad(image, [(0, pad0), (0, pad1)], constant_values=0)
        # image = ndimage.median_filter(image, size=5)
        image=np.clip(image,0,200)
        # image = cv2.flip(image, 0)

        images.append(image)
    images = np.stack(images, axis=2)
    if fragment_id in ['20230701020044','verso','20230901184804','20230901234823','20230531193658','20231007101615','20231005123333','20231011144857','20230522215721', '20230919113918', '20230625171244','20231022170900','20231012173610','20231016151000']:
        images=images[:,:,::-1]

    fragment_mask=None
    if os.path.exists(f'./train_scrolls/{fragment_id}/{fragment_id}_mask.png'):
        fragment_mask=cv2.imread(CFG.comp_dataset_path + f"train_scrolls/{fragment_id}/{fragment_id}_mask.png", 0)
        fragment_mask = np.pad(fragment_mask, [(0, pad0), (0, pad1)], constant_values=0)

    return images,fragment_mask

def get_img_splits(fragment_id,s,e,rotation=0):
    images = []
    xyxys = []
    image,fragment_mask = read_image_mask(fragment_id,s,e,rotation)
    x1_list = list(range(0, image.shape[1]-CFG.tile_size+1, CFG.stride))
    y1_list = list(range(0, image.shape[0]-CFG.tile_size+1, CFG.stride))
    for y1 in y1_list:
        for x1 in x1_list:
            y2 = y1 + CFG.tile_size
            x2 = x1 + CFG.tile_size
            if not np.any(fragment_mask[y1:y2, x1:x2]==0):
                images.append(image[y1:y2, x1:x2])
                xyxys.append([x1, y1, x2, y2])
    test_dataset = CustomDatasetTest(images,np.stack(xyxys), CFG,transform=A.Compose([
        A.Resize(CFG.size, CFG.size),
        A.Normalize(
            mean= [0] * CFG.in_chans,
            std= [1] * CFG.in_chans
        ),
        ToTensorV2(transpose_mask=True),
    ]))

    test_loader = DataLoader(test_dataset,
                              batch_size=CFG.valid_batch_size,
                              shuffle=False,
                              num_workers=CFG.num_workers, pin_memory=True, drop_last=False,
                              )
    return test_loader, np.stack(xyxys),(image.shape[0],image.shape[1]),fragment_mask

def get_transforms(data, cfg):
    if data == 'train':
        aug = A.Compose(cfg.train_aug_list)
    elif data == 'valid':
        aug = A.Compose(cfg.valid_aug_list)

    # print(aug)
    return aug

class CustomDataset(Dataset):
    def __init__(self, images ,cfg,xyxys=None, labels=None, transform=None):
        self.images = images
        self.cfg = cfg
        self.labels = labels
        self.transform = transform
        self.xyxys=xyxys
        self.kernel=gkern(64,2)
        self.kernel/=self.kernel.max()
        self.kernel=torch.FloatTensor(self.kernel)
    def __len__(self):
        # return len(self.df)
        return len(self.images)

    def __getitem__(self, idx):
        if self.xyxys is not None:
            image = self.images[idx]
            label = self.labels[idx]
            offset=4
            image=image[:,:,offset:offset+self.cfg.in_chans]
            xy=self.xyxys[idx]
            if self.transform:
                data = self.transform(image=image, mask=label)
                image = data['image'].unsqueeze(0)
                label= torch.mul(self.kernel,data['mask'])
                label = label.mean().type(torch.float32)

            return image, label,xy
        else:
            image = self.images[idx]
            label = self.labels[idx]
            # offset=random.choice([0,1,2,3,4])
            offset=4
            image=image[:,:,offset:offset+self.cfg.in_chans]
            if self.transform:
                data = self.transform(image=image, mask=label)
                image = data['image'].unsqueeze(0)
                label= torch.mul(self.kernel,data['mask'])
                label = label.mean().type(torch.float32)
            
            return image, label
class CustomDatasetTest(Dataset):
    def __init__(self, images,xyxys, cfg, transform=None):
        self.images = images
        self.xyxys=xyxys
        self.cfg = cfg
        self.transform = transform

    def __len__(self):
        # return len(self.df)
        return len(self.images)

    def __getitem__(self, idx):
        image = self.images[idx]
        xy=self.xyxys[idx]
        if self.transform:
            data = self.transform(image=image)
            image = data['image'].unsqueeze(0)

        return image,xy

    






class Decoder(nn.Module):
    def __init__(self, encoder_dims, upscale):
        super().__init__()
        self.convs = nn.ModuleList([
            nn.Sequential(
                nn.Conv2d(encoder_dims[i]+encoder_dims[i-1], encoder_dims[i-1], 3, 1, 1, bias=False),
                nn.BatchNorm2d(encoder_dims[i-1]),
                nn.ReLU(inplace=True)
            ) for i in range(1, len(encoder_dims))])

        self.logit = nn.Conv2d(encoder_dims[0], 1, 1, 1, 0)
        self.up = nn.Upsample(scale_factor=upscale, mode="bilinear")
        # for l in self.convs:
        #     for m in l._modules:
        #         init_weights(m)
    def forward(self, feature_maps):
        for i in range(len(feature_maps)-1, 0, -1):
            f_up = F.interpolate(feature_maps[i], scale_factor=2, mode="bilinear")
            f = torch.cat([feature_maps[i-1], f_up], dim=1)
            f_down = self.convs[i-1](f)
            feature_maps[i-1] = f_down

        x = self.logit(feature_maps[0])
        mask = self.up(x)
        return mask



from collections import OrderedDict




class RegressionPLModel(pl.LightningModule):
    def __init__(self,pred_shape,size=224,enc='',with_norm=False):
        super(RegressionPLModel, self).__init__()

        self.save_hyperparameters()
        self.mask_pred = np.zeros(self.hparams.pred_shape)
        self.mask_count = np.zeros(self.hparams.pred_shape)
        # self.backbone=SegModel(model_depth=50)
        self.loss_func1 = smp.losses.DiceLoss(mode='binary')
        # self.loss_func2= smp.losses.FocalLoss(mode='binary',gamma=2)
        self.loss_func2= smp.losses.SoftBCEWithLogitsLoss(smooth_factor=0.15)
        # self.loss_func=nn.HuberLoss(delta=5.0)
        self.loss_func= lambda x,y:0.5 * self.loss_func1(x,y)+0.5*self.loss_func2(x,y)
        
        # self.backbone = generate_model(model_depth=50, n_input_channels=1,forward_features=True,n_classes=700)
        if self.hparams.enc=='resnet34':
            self.backbone = generate_model(model_depth=34, n_input_channels=1,forward_features=True,n_classes=700)
            state_dict=torch.load('./r3d34_K_200ep.pth')["state_dict"]
            conv1_weight = state_dict['conv1.weight']
            state_dict['conv1.weight'] = conv1_weight.sum(dim=1, keepdim=True)
            self.backbone.load_state_dict(state_dict,strict=False)
        elif self.hparams.enc=='resnest101':
            self.backbone = generate_model(model_depth=101, n_input_channels=1,forward_features=True,n_classes=1039)
            state_dict=torch.load('./r3d101_KM_200ep.pth')["state_dict"]
            conv1_weight = state_dict['conv1.weight']
            state_dict['conv1.weight'] = conv1_weight.sum(dim=1, keepdim=True)
            self.backbone.load_state_dict(state_dict,strict=False)
        elif self.hparams.enc=='2p1d':
            self.backbone = generate_2p1d(model_depth=34, n_input_channels=1,n_classes=700)
            state_dict=torch.load('./r2p1d34_K_200ep.pth')["state_dict"]
            conv1_weight = state_dict['conv1_s.weight']
            state_dict['conv1_s.weight'] = conv1_weight.sum(dim=1, keepdim=True)
            self.backbone.load_state_dict(state_dict,strict=False)
        elif self.hparams.enc=='wide50':
            self.backbone = generate_wide(model_depth=50, n_input_channels=1,n_classes=700,forward_features=True,k=2)
        elif self.hparams.enc=='i3d':
            self.backbone=InceptionI3d(in_channels=1,num_classes=512,non_local=True)
        elif self.hparams.enc=='resnext101':
            self.backbone=resnext101(sample_size=112,
                                  sample_duration=16,
                                  shortcut_type='B',
                                  cardinality=32,
                                  num_classes=600)
            state_dict = torch.load('./kinetics_resnext_101_RGB_16_best.pth')['state_dict']
            checkpoint_custom = OrderedDict()
            for key_model, key_checkpoint in zip(self.backbone.state_dict().keys(), state_dict.keys()):
                checkpoint_custom.update({f'{key_model}': state_dict[f'{key_checkpoint}']})

            self.backbone.load_state_dict(checkpoint_custom, strict=True)
            self.backbone.conv1 = nn.Conv3d(1, 64, kernel_size=(7, 7, 7), stride=(1, 2, 2), padding=(3, 3, 3), bias=False)
        else:
            self.backbone = generate_model(model_depth=50, n_input_channels=1,forward_features=True,n_classes=700)
            state_dict=torch.load('./r3d50_K_200ep.pth')["state_dict"]
            conv1_weight = state_dict['conv1.weight']
            state_dict['conv1.weight'] = conv1_weight.sum(dim=1, keepdim=True)
            self.backbone.load_state_dict(state_dict,strict=False)
        
        self.decoder = Decoder(encoder_dims=[x.size(1) for x in self.backbone(torch.rand(1,1,20,256,256))], upscale=1)

        if self.hparams.with_norm:
            self.normalization=nn.BatchNorm3d(num_features=1)
    def forward(self, x):
        if x.ndim==4:
            x=x[:,None]
        if self.hparams.with_norm:
            x=self.normalization(x)
        feat_maps = self.backbone(x)
        feat_maps_pooled = [torch.max(f, dim=2)[0] for f in feat_maps]
        pred_mask = self.decoder(feat_maps_pooled)
        
        return pred_mask
    def training_step(self, batch, batch_idx):
        x, y = batch
        outputs = self(x)
        loss1 = self.loss_func(outputs, y)
        if torch.isnan(loss1):
            print("Loss nan encountered")
        self.log("train/Arcface_loss", loss1.item(),on_step=True, on_epoch=True, prog_bar=True)
        return {"loss": loss1}

    def validation_step(self, batch, batch_idx):
        x,y,xyxys= batch
        batch_size = x.size(0)
        outputs = self(x)
        loss1 = self.loss_func(outputs, y)
        y_preds = torch.sigmoid(outputs).to('cpu')
        for i, (x1, y1, x2, y2) in enumerate(xyxys):
            self.mask_pred[y1:y2, x1:x2] += F.interpolate(y_preds[i].unsqueeze(0).float(),scale_factor=4,mode='bilinear').squeeze(0).squeeze(0).numpy()
            self.mask_count[y1:y2, x1:x2] += np.ones((self.hparams.size, self.hparams.size))

        self.log("val/MSE_loss", loss1.item(),on_step=True, on_epoch=True, prog_bar=True)
        return {"loss": loss1}
    
    def on_validation_epoch_end(self):
        self.mask_pred = np.divide(self.mask_pred, self.mask_count, out=np.zeros_like(self.mask_pred), where=self.mask_count!=0)
        wandb_logger.log_image(key="masks", images=[np.clip(self.mask_pred,0,1)], caption=["probs"])

        #reset mask
        self.mask_pred = np.zeros(self.hparams.pred_shape)
        self.mask_count = np.zeros(self.hparams.pred_shape)
    def configure_optimizers(self):
        optimizer = AdamW(filter(lambda p: p.requires_grad, self.parameters()), lr=CFG.lr)    
        scheduler = get_scheduler(CFG, optimizer)
        return [optimizer]
import torch.nn as nn
import torch
import math
import time
import numpy as np
import torch

from warmup_scheduler import GradualWarmupScheduler


class GradualWarmupSchedulerV2(GradualWarmupScheduler):
    """
    https://www.kaggle.com/code/underwearfitting/single-fold-training-of-resnet200d-lb0-965
    """
    def __init__(self, optimizer, multiplier, total_epoch, after_scheduler=None):
        super(GradualWarmupSchedulerV2, self).__init__(
            optimizer, multiplier, total_epoch, after_scheduler)

    def get_lr(self):
        if self.last_epoch > self.total_epoch:
            if self.after_scheduler:
                if not self.finished:
                    self.after_scheduler.base_lrs = [
                        base_lr * self.multiplier for base_lr in self.base_lrs]
                    self.finished = True
                return self.after_scheduler.get_lr()
            return [base_lr * self.multiplier for base_lr in self.base_lrs]
        if self.multiplier == 1.0:
            return [base_lr * (float(self.last_epoch) / self.total_epoch) for base_lr in self.base_lrs]
        else:
            return [base_lr * ((self.multiplier - 1.) * self.last_epoch / self.total_epoch + 1.) for base_lr in self.base_lrs]

def get_scheduler(cfg, optimizer):
    scheduler_cosine = torch.optim.lr_scheduler.CosineAnnealingLR(
        optimizer, cfg.epochs, eta_min=1e-7)
    scheduler = GradualWarmupSchedulerV2(
        optimizer, multiplier=10, total_epoch=1, after_scheduler=scheduler_cosine)

    return scheduler

def scheduler_step(scheduler, avg_val_loss, epoch):
    scheduler.step(epoch)
   
import torch as tc
def TTA(x:tc.Tensor,model:nn.Module):
    #x.shape=(batch,c,h,w)
    shape=x.shape
    x=[x,*[tc.rot90(x,k=i,dims=(-2,-1)) for i in range(1,4)],]
    x=tc.cat(x,dim=0)
    x=model(x)
    # x=torch.sigmoid(x)
    # print(x.shape)
    x=x.reshape(4,shape[0],CFG.size//4,CFG.size//4)
    
    x=[tc.rot90(x[i],k=-i,dims=(-2,-1)) for i in range(4)]
    x=tc.stack(x,dim=0)
    return x.mean(0)

DiceLoss = smp.losses.DiceLoss(mode='binary')
BCELoss = smp.losses.SoftBCEWithLogitsLoss()
FocalLoss=smp.losses.FocalLoss(gamma=2.5,mode='binary')
alpha = 0.5
beta = 1 - alpha
TverskyLoss = smp.losses.TverskyLoss(
    mode='binary', log_loss=False, alpha=alpha, beta=beta)
MSELoss=nn.MSELoss()
HuberLoss=nn.HuberLoss(delta=5.0)
NNBCE=nn.BCEWithLogitsLoss(pos_weight=torch.ones([1]).to('cuda')*5 )
def criterion(y_pred, y_true):
    # return 0.5 * BCELoss(y_pred, y_true) + 0.5 * DiceLoss(y_pred, y_true)
    return HuberLoss(y_pred, y_true)
def normalization(x):
    """input.shape=(batch,f1,f2,...)"""
    #[batch,f1,f2]->dim[1,2]
    dim=list(range(1,x.ndim))
    mean=x.mean(dim=dim,keepdim=True)
    std=x.std(dim=dim,keepdim=True)
    return (x-mean)/(std+1e-9)
def predict_fn(test_loader, model, device, test_xyxys,pred_shape):
    mask_pred = np.zeros(pred_shape)
    mask_count = np.zeros(pred_shape)
    kernel=gkern(CFG.size,1)
    kernel=kernel/kernel.max()
    model.eval()

    for step, (images,xys) in tqdm(enumerate(test_loader),total=len(test_loader)):
        images = images.to(device)
        batch_size = images.size(0)
        with torch.no_grad():
            with torch.autocast(device_type="cuda"):
                y_preds = model(images)
            # y_preds =TTA(images,model)
        # y_preds = y_preds.to('cpu').numpy()

        y_preds = torch.sigmoid(y_preds).to('cpu')
        for i, (x1, y1, x2, y2) in enumerate(xys):
            mask_pred[y1:y2, x1:x2] += np.multiply(F.interpolate(y_preds[i].unsqueeze(0).float(),scale_factor=4,mode='bilinear').squeeze(0).squeeze(0).numpy(),kernel)
            # mask_pred[y1:y2, x1:x2] += F.interpolate(y_preds[i].unsqueeze(0).unsqueeze(0).float(),scale_factor=4,mode='bilinear').squeeze(0).squeeze(0).numpy()
            mask_count[y1:y2, x1:x2] += np.ones((CFG.size, CFG.size))

    mask_pred /= mask_count
    # mask_pred/=mask_pred.max()
    return mask_pred
    # return losses.avg,[]
from PIL import Image
from PIL.ImageOps import equalize,autocontrast
import gc
import time


for m in ['wild14_deduped_64_pretrained2_20231005123336_0_fr_i3depoch=23.ckpt']:
    # model=torch.jit.load(f'models_norm/{m}')
    model=RegressionPLModel.load_from_checkpoint(CFG.model_dir+m,strict=False,enc='resnest101')
    model.cuda()
    model.eval()
    wandb.init(
      # Set the project where this run will be logged
      project="vesivus", 
      # We pass a run name (otherwise it’ll be randomly assigned, like sunshine-lollypop-10)
      name=f"ALL_scrolls_tta_{m}", 
      # Track hyperparameters and run metadata
        )
    for fragment_id in ['20231005123336']:
    # for fragment_id in os.listdir('train_scrolls'):
        if os.path.exists(f"train_scrolls/{fragment_id}/layers/00.tif"):
            preds=[]
            for r in [0]:
                for i in [17]:
                    start_f=i
                    end_f=start_f+CFG.in_chans
                    test_loader,test_xyxz,test_shape,fragment_mask=get_img_splits(fragment_id,start_f,end_f,r)
                    mask_pred= predict_fn(test_loader, model, device, test_xyxz,test_shape)
                    mask_pred=np.clip(np.nan_to_num(mask_pred),a_min=0,a_max=1)
                    mask_pred/=mask_pred.max()

                    preds.append(mask_pred)

            img=wandb.Image(
            preds[0], 
            caption=f"{fragment_id}"
            )
            wandb.log({'predictions':img})

            # print("plot time: ",t5-t4)
            gc.collect()
        
    del mask_pred,test_loader,model
    torch.cuda.empty_cache()
    gc.collect()
    wandb.finish()