first commit

Docs/Docs.txt

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+the docs file repo saves here

Inference_individually.py

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+from pathlib import Path
+import numpy as np
+import matplotlib.pyplot as plt
+from utils import sample_prompt
+from collections import defaultdict
+import torchvision.transforms as transforms
+import torch
+from torch import nn
+import torch.nn.functional as F
+from segment_anything.utils.transforms import ResizeLongestSide
+import albumentations as A
+from albumentations.pytorch import ToTensorV2
+from einops import rearrange
+import random
+from tqdm import tqdm
+from time import sleep
+from data import *
+from time import time
+from PIL import Image
+from sklearn.model_selection import KFold
+from shutil import copyfile
+from args import get_arguments
+# import wandb_handler
+args = get_arguments()
+
+def save_img(img, dir):
+    img = img.clone().cpu().numpy() + 100
+
+    if len(img.shape) == 3:
+        img = rearrange(img, "c h w -> h w c")
+        img_min = np.amin(img, axis=(0, 1), keepdims=True)
+        img = img - img_min
+
+        img_max = np.amax(img, axis=(0, 1), keepdims=True)
+        img = (img / img_max * 255).astype(np.uint8)
+        img = Image.fromarray(img)
+
+    else:
+        img_min = img.min()
+        img = img - img_min
+        img_max = img.max()
+        if img_max != 0:
+            img = img / img_max * 255
+        img = Image.fromarray(img).convert("L")
+
+    img.save(dir)
+
+
+
+class loss_fn(torch.nn.Module):
+    def __init__(self, alpha=0.7, gamma=2.0, epsilon=1e-5):
+        super(loss_fn, self).__init__()
+        self.alpha = alpha
+        self.gamma = gamma
+        self.epsilon = epsilon
+        
+    def dice_loss(self, logits, gt, eps=1):
+
+        probs = torch.sigmoid(logits)
+
+        probs = probs.view(-1)
+        gt = gt.view(-1)
+
+        intersection = (probs * gt).sum()
+
+        dice_coeff = (2.0 * intersection + eps) / (probs.sum() + gt.sum() + eps)
+
+        loss = 1 - dice_coeff
+        return loss
+
+    def focal_loss(self, logits, gt, gamma=2):
+        logits = logits.reshape(-1, 1)
+        gt = gt.reshape(-1, 1)
+        logits = torch.cat((1 - logits, logits), dim=1)
+
+        probs = torch.sigmoid(logits)
+        pt = probs.gather(1, gt.long())
+
+        modulating_factor = (1 - pt) ** gamma
+    
+        focal_loss = -modulating_factor * torch.log(pt + 1e-12)
+
+        loss = focal_loss.mean()
+        return loss  # Store as a Python number to save memory
+
+    def forward(self, logits, target):
+        logits = logits.squeeze(1)
+        target = target.squeeze(1)
+        # Dice Loss
+        # prob = F.softmax(logits, dim=1)[:, 1, ...]
+
+        dice_loss = self.dice_loss(logits, target)
+
+        # Focal Loss
+        focal_loss = self.focal_loss(logits, target.squeeze(-1))
+        alpha = 20.0
+        # Combined Loss
+        combined_loss = alpha * focal_loss + dice_loss
+        return combined_loss
+
+
+def img_enhance(img2, coef=0.2):
+    img_mean = np.mean(img2)
+    img_max = np.max(img2)
+    val = (img_max - img_mean) * coef + img_mean
+    img2[img2 < img_mean * 0.7] = img_mean * 0.7
+    img2[img2 > val] = val
+    return img2
+
+
+def dice_coefficient(logits, gt):
+    
+    eps=1
+    binary_mask = logits>0
+    
+    intersection = (binary_mask * gt).sum(dim=(-2,-1))
+    dice_scores = (2.0 * intersection + eps) / (binary_mask.sum(dim=(-2,-1)) + gt.sum(dim=(-2,-1)) + eps)
+    
+    return dice_scores.mean()
+
+
+def calculate_recall(pred, target):
+    smooth = 1
+    batch_size = pred.shape[0]
+    recall_scores = []
+    binary_mask = pred>0
+
+    for i in range(batch_size):
+        true_positive = ((binary_mask[i] == 1) & (target[i] == 1)).sum().item()
+        false_negative = ((binary_mask[i] == 0) & (target[i] == 1)).sum().item()
+        recall = (true_positive + smooth) / ((true_positive + false_negative) + smooth)
+        recall_scores.append(recall)
+
+    return sum(recall_scores) / len(recall_scores)
+
+def calculate_precision(pred, target):
+    smooth = 1
+    batch_size = pred.shape[0]
+    precision_scores = []
+    binary_mask = pred>0
+
+    for i in range(batch_size):
+        true_positive = ((binary_mask[i] == 1) & (target[i] == 1)).sum().item()
+        false_positive = ((binary_mask[i] == 1) & (target[i] == 0)).sum().item()
+        precision = (true_positive + smooth) / ((true_positive + false_positive) + smooth)
+        precision_scores.append(precision)
+
+    return sum(precision_scores) / len(precision_scores)
+
+def calculate_jaccard(pred, target):
+    smooth = 1
+    batch_size = pred.shape[0]
+    jaccard_scores = []
+    binary_mask = pred>0
+    
+
+    for i in range(batch_size):
+        true_positive = ((binary_mask[i] == 1) & (target[i] == 1)).sum().item()
+        false_positive = ((binary_mask[i] == 1) & (target[i] == 0)).sum().item()
+        false_negative = ((binary_mask[i] == 0) & (target[i] == 1)).sum().item()
+        jaccard = (true_positive + smooth) / (true_positive + false_positive + false_negative + smooth)
+        jaccard_scores.append(jaccard)
+
+    return sum(jaccard_scores) / len(jaccard_scores)
+
+def calculate_specificity(pred, target):
+    smooth = 1
+    batch_size = pred.shape[0]
+    specificity_scores = []
+    binary_mask = pred>0
+    
+
+    for i in range(batch_size):
+        true_negative = ((binary_mask[i] == 0) & (target[i] == 0)).sum().item()
+        false_positive = ((binary_mask[i] == 1) & (target[i] == 0)).sum().item()
+        specificity = (true_negative + smooth) / (true_negative + false_positive + smooth)
+        specificity_scores.append(specificity)
+
+    return sum(specificity_scores) / len(specificity_scores)
+
+def what_the_f(low_res_masks,label):
+            
+    low_res_label = F.interpolate(label, low_res_masks.shape[-2:])
+    dice = dice_coefficient(
+        low_res_masks, low_res_label
+    )
+    recall=calculate_recall(low_res_masks, low_res_label)
+    precision =calculate_precision(low_res_masks, low_res_label)
+    jaccard = calculate_jaccard(low_res_masks, low_res_label)
+    
+    return dice , precision , recall , jaccard 
+
+accumaltive_batch_size = 8
+batch_size = 1
+num_workers = 2
+slice_per_image = 1
+num_epochs = 40
+sample_size = 3660
+# sample_size = 43300
+# image_size=sam_model.image_encoder.img_size
+image_size = 1024
+exp_id = 0
+found = 0
+
+
+
+layer_n = 4
+L = layer_n
+a = np.full(L, layer_n)
+params = {"M": 255, "a": a, "p": 0.35}
+
+
+model_type = "vit_h"
+checkpoint = "checkpoints/sam_vit_h_4b8939.pth"
+device = "cuda:0"
+
+
+from segment_anything import SamPredictor, sam_model_registry
+
+
+
+##################################main model#######################################
+
+
+
+class panc_sam(nn.Module):
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+        
+        #Promptless
+        sam = torch.load(args.pointbasemodel).sam
+        
+        self.prompt_encoder = sam.prompt_encoder
+        
+        self.mask_decoder = sam.mask_decoder
+        for param in self.prompt_encoder.parameters():
+            param.requires_grad = False
+            
+        for param in self.mask_decoder.parameters():
+            param.requires_grad = False
+        
+        #with Prompt
+        sam = torch.load(
+            args.promptprovider
+        ).sam
+        self.image_encoder = sam.image_encoder
+        self.prompt_encoder2 = sam.prompt_encoder
+        self.mask_decoder2 = sam.mask_decoder
+        
+        for param in self.image_encoder.parameters():
+            param.requires_grad = False
+            
+        for param in self.prompt_encoder2.parameters():
+            param.requires_grad = False
+            
+
+        
+
+    def forward(self, input_images,box=None):
+        
+
+        # input_images = torch.stack([x["image"] for x in batched_input], dim=0)
+        # raise ValueError(input_images.shape)
+        with torch.no_grad():
+            image_embeddings = self.image_encoder(input_images).detach()
+
+            
+        outputs_prompt = []
+        outputs = []
+        
+        for curr_embedding in image_embeddings:
+            
+            with torch.no_grad():
+                sparse_embeddings, dense_embeddings = self.prompt_encoder(
+                points=None,
+                boxes=None,
+                masks=None,
+                )
+            
+            low_res_masks, _ = self.mask_decoder(
+                image_embeddings=curr_embedding,
+                image_pe=self.prompt_encoder.get_dense_pe().detach(),
+                sparse_prompt_embeddings=sparse_embeddings.detach(),
+                dense_prompt_embeddings=dense_embeddings.detach(),
+                multimask_output=False,
+            )
+            outputs_prompt.append(low_res_masks)
+            # raise ValueError(low_res_masks)
+            # points, point_labels = sample_prompt((low_res_masks > 0).float())
+            points, point_labels = sample_prompt(low_res_masks)
+            
+            
+            points = points * 4
+            points = (points, point_labels)
+
+            with torch.no_grad():
+                sparse_embeddings, dense_embeddings = self.prompt_encoder2(
+                     points=points,
+                     boxes=None,
+                     masks=None,
+                 )
+            
+            low_res_masks, _ = self.mask_decoder2(
+                image_embeddings=curr_embedding,
+                image_pe=self.prompt_encoder2.get_dense_pe().detach(),
+                sparse_prompt_embeddings=sparse_embeddings.detach(),
+                dense_prompt_embeddings=dense_embeddings.detach(),
+                multimask_output=False,
+            )
+            
+            outputs.append(low_res_masks)
+        low_res_masks_promtp = torch.cat(outputs_prompt, dim=0)
+        low_res_masks = torch.cat(outputs, dim=0)
+        
+
+        return low_res_masks, low_res_masks_promtp
+##################################end#######################################
+
+##################################Augmentation#######################################
+
+augmentation = A.Compose(
+    [
+        A.Rotate(limit=30, p=0.5),
+        A.RandomBrightnessContrast(brightness_limit=0.3, contrast_limit=0.3, p=1),
+        A.RandomResizedCrop(1024, 1024, scale=(0.9, 1.0), p=1),
+        A.HorizontalFlip(p=0.5),
+        A.CLAHE(clip_limit=2.0, tile_grid_size=(8, 8), p=0.5),
+        A.CoarseDropout(
+            max_holes=8,
+            max_height=16,
+            max_width=16,
+            min_height=8,
+            min_width=8,
+            fill_value=0,
+            p=0.5,
+        ),
+        A.RandomScale(scale_limit=0.3, p=0.5),
+        # A.GaussNoise(var_limit=(10.0, 50.0), p=0.5),
+        # A.GridDistortion(p=0.5),
+    ]
+)
+##################model load#####################
+panc_sam_instance = panc_sam()
+
+# for param in panc_sam_instance_point.parameters():
+#     param.requires_grad = False
+panc_sam_instance.to(device)
+panc_sam_instance.train()
+
+
+##################load data#######################
+
+
+test_dataset = PanDataset(
+    [args.test_dir],
+    [args.test_labels_dir],
+        
+    [["NIH_PNG",1]],
+
+    image_size,
+    
+    slice_per_image=slice_per_image,
+    train=False,
+) 
+
+test_loader = DataLoader(
+    test_dataset,
+    batch_size=batch_size,
+    collate_fn=test_dataset.collate_fn,
+    shuffle=False,
+    drop_last=False,
+    num_workers=num_workers,
+)
+##################end load data#######################
+
+lr = 1e-4
+max_lr = 5e-5
+wd = 5e-4
+
+optimizer_main = torch.optim.Adam(
+    # parameters,
+    list(panc_sam_instance.mask_decoder2.parameters()),
+    
+    lr=lr,
+    weight_decay=wd,
+)
+scheduler_main = torch.optim.lr_scheduler.OneCycleLR(
+    optimizer_main,
+    max_lr=max_lr,
+    epochs=num_epochs,
+    steps_per_epoch=sample_size // (accumaltive_batch_size // batch_size),
+)
+#####################################################
+
+from statistics import mean
+
+from tqdm import tqdm
+from torch.nn.functional import threshold, normalize
+
+loss_function = loss_fn(alpha=0.5, gamma=2.0)
+loss_function.to(device)
+
+from time import time
+import time as s_time
+
+
+
+def process_model(main_model , data_loader, train=0, save_output=0):
+    epoch_losses = []
+    results=[]
+    index = 0
+    results = torch.zeros((2, 0, 256, 256))
+    #############################
+    total_dice = 0.0
+    total_precision = 0.0
+    total_recall =0.0
+    total_jaccard = 0.0
+    #############################
+    num_samples = 0
+    #############################
+    total_dice_main =0.0
+    total_precision_main = 0.0
+    total_recall_main =0.0
+    total_jaccard_main = 0.0
+
+    counterb = 0
+    for image, label in tqdm(data_loader, total=sample_size):
+        num_samples += 1
+        counterb += 1
+        index += 1
+        image = image.to(device)
+        label = label.to(device).float()
+        
+        ############################model and dice########################################
+        box = torch.tensor([[200, 200, 750, 800]]).to(device)
+        low_res_masks_main,low_res_masks_prompt = main_model(image,box)
+        
+        low_res_label = F.interpolate(label, low_res_masks_main.shape[-2:]) 
+        
+        dice_prompt,  precisio_prompt , recall_prompt , jaccard_prompt  = what_the_f(low_res_masks_prompt,low_res_label)
+        dice_main , precision_main , recall_main , jaccard_main  = what_the_f(low_res_masks_main,low_res_label)
+
+        binary_mask = normalize(threshold(low_res_masks_main, 0.0,0))
+        ##############prompt###############
+        total_dice += dice_prompt
+        total_precision += precisio_prompt
+        total_recall += recall_prompt
+        total_jaccard += jaccard_prompt
+        average_dice = total_dice / num_samples
+        average_precision = total_precision /num_samples
+        average_recall = total_recall /num_samples
+        average_jaccard = total_jaccard /num_samples
+        
+        ##############main##################
+        total_dice_main+=dice_main
+        total_precision_main +=precision_main
+        total_recall_main +=recall_main
+        total_jaccard_main += jaccard_main
+        
+        
+        average_dice_main = total_dice_main / num_samples
+        average_precision_main = total_precision_main /num_samples
+        average_recall_main = total_recall_main /num_samples
+        average_jaccard_main = total_jaccard_main /num_samples
+        
+        ###################################
+    
+        # result = torch.cat(
+        #     (
+        #         # low_res_masks_main[0].detach().cpu().reshape(1, 1, 256, 256),
+        #         binary_mask[0].detach().cpu().reshape(1, 1, 256, 256),
+        #     ),
+        #     dim=0,
+        # )
+        # results = torch.cat((results, result), dim=1)
+
+        if counterb == sample_size and train:
+            break
+        elif counterb == sample_size and not train:
+            break
+
+    return epoch_losses, results, average_dice,average_precision ,average_recall, average_jaccard,average_dice_main,average_precision_main,average_recall_main,average_jaccard_main
+
+
+
+def train_model( test_loader, K_fold=False, N_fold=7, epoch_num_start=7):
+    print("Train model started.")
+
+    test_losses = []
+    test_epochs = []
+    dice = []
+    dice_main = []
+    dice_test = []
+    dice_test_main =[]
+    results = []
+    index = 0
+
+    print("Testing:")
+    test_epoch_losses, epoch_results, average_dice_test,average_precision ,average_recall, average_jaccard,average_dice_test_main,average_precision_main,average_recall_main,average_jaccard_main = process_model(
+        panc_sam_instance,test_loader
+    )
+    import torchvision.transforms.functional as TF
+
+
+
+
+    dice_test.append(average_dice_test)
+    dice_test_main.append(average_dice_test_main)
+    print("######################Prompt##########################")
+    print(f"Test Dice : {average_dice_test}")
+    print(f"Test presision : {average_precision}")
+    print(f"Test recall : {average_recall}")
+    print(f"Test jaccard : {average_jaccard}")
+    
+    print("######################Main##########################")
+    print(f"Test Dice main : {average_dice_test_main}")
+    print(f"Test presision main : {average_precision_main}")
+    print(f"Test recall main : {average_recall_main}")
+    print(f"Test jaccard main : {average_jaccard_main}")
+    
+    
+    # results.append(epoch_results)
+    # del epoch_results
+    del average_dice_test
+            
+
+    # return train_losses,  results
+
+
+train_model(test_loader)
+

README.md

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+# Pancreas Segmentation in CT Scan Images: Harnessing the Power of SAM
+<p align="center">
+  <img width="100%" src="Docs/ffpip.jpg">
+</p>
+In this repositpry we describe the code impelmentation of the paper: "Pancreas Segmentation in CT Scan Images: Harnessing the Power of SAM"
+
+## Requirments
+Frist step is install [requirements.txt](/requirements.txt) bakages in a conda eviroment.
+
+Clone the [SAM](https://github.com/facebookresearch/segment-anything) repository.
+
+Use the code below to download the suggested checkpoint of SAM:
+```
+!wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth"
+```
+
+## Dadaset and data loader description
+For this segmentation report we used to populare pancreas datas:
+- [NIH pancreas CT](https://wiki.cancerimagingarchive.net/display/Public/Pancreas-CT)
+- [AbdomenCT-1K](https://github.com/JunMa11/AbdomenCT-1K)
+
+After downloading and allocating datasets, we used a specefice data format (.npy) and for this step [save.py](/data_handler/save.py) provided. `save_dir` and `labels_save_dir` should modify.
+
+As defualt `data.py` and `data_loader_group.py` are used in the desired codes.
+
+Address can be modify in [args.py](args.py). 
+
+Due to anonymous code submitiom we haven't share our Model Weights.  
+
+## Train model
+For train model we use the files [fine_tune_good.py](fine_tune_good.py) and [fine_tune_good_unet.py](fine_tune_good_unet.py) and the command bellow is an example for start training with some costume settings.
+```
+python3 fine_tune_good_unet.py --sample_size 66 --accumulative_batch_size 4 --num_epochs 60 --num_workers 8 --batch_step_one 20 --batch_step_two 30 --lr 3e-4 --inference
+
+```
+## Inference Model
+To infrence both types of decoders just run the [double_decoder_infrence.py](double_decoder_infrence.py)
+
+To get individually infrence SAM with or without prompt use [Inference_individually.py](Inference_individually)
+
+## 3D Aggregator
+
+To run the `3D Aggregator` codes are available in [kernel](/kernel) folder and just run the [run.sh](kernel/run.sh) file.
+
+becuase of opening so many files, the `u -limit` thresh hold should be increased using:
+
+```
+u -limit 15000
+
+```
+

SAM_with_prompt.py

@@ -0,0 +1,662 @@
+debug = 0
+from pathlib import Path
+import numpy as np
+import matplotlib.pyplot as plt
+
+# import cv2
+from collections import defaultdict
+import torchvision.transforms as transforms
+import torch
+from torch import nn
+
+import torch.nn.functional as F
+from segment_anything.utils.transforms import ResizeLongestSide
+import albumentations as A
+from albumentations.pytorch import ToTensorV2
+import numpy as np
+from einops import rearrange
+import random
+from tqdm import tqdm
+from time import sleep
+from data import *
+from time import time
+from PIL import Image
+from sklearn.model_selection import KFold
+from shutil import copyfile
+# import monai
+from tqdm import tqdm
+from utils import main_prompt,main_prompt_for_ground_true
+from torch.autograd import Variable
+from args import get_arguments
+
+# import wandb_handler
+args = get_arguments()
+
+def save_img(img, dir):
+    img = img.clone().cpu().numpy() + 100
+    if len(img.shape) == 3:
+        img = rearrange(img, "c h w -> h w c")
+        img_min = np.amin(img, axis=(0, 1), keepdims=True)
+        img = img - img_min
+
+        img_max = np.amax(img, axis=(0, 1), keepdims=True)
+        img = (img / img_max * 255).astype(np.uint8)
+        grey_img = Image.fromarray(img[:, :, 0])
+        img = Image.fromarray(img)
+
+    else:
+        img_min = img.min()
+        img = img - img_min
+        img_max = img.max()
+        if img_max != 0:
+            img = img / img_max * 255
+        img = Image.fromarray(img).convert("L")
+
+    img.save(dir)
+
+
+class FocalLoss(nn.Module):
+    def __init__(self, gamma=2.0, alpha=0.25):
+        super(FocalLoss, self).__init__()
+        self.gamma = gamma
+        self.alpha = alpha
+
+    def dice_loss(self, logits, gt, eps=1):
+        # Convert logits to probabilities
+        # Flatten the tensors
+        # probs = probs.view(-1)
+        # gt = gt.view(-1)
+
+        probs = torch.sigmoid(logits)
+
+        # Compute Dice coefficient
+        intersection = (probs * gt).sum()
+
+        dice_coeff = (2.0 * intersection + eps) / (probs.sum() + gt.sum() + eps)
+
+        # Compute Dice Los[s
+        loss = 1 - dice_coeff
+        return loss
+
+    def focal_loss(self, pred, mask):
+        """
+        pred: [B, 1, H, W]
+        mask: [B, 1, H, W]
+        """
+        # pred=pred.reshape(-1,1)
+        # mask = mask.reshape(-1,1)
+        # assert pred.shape == mask.shape, "pred and mask should have the same shape."
+        p = torch.sigmoid(pred)
+        num_pos = torch.sum(mask)
+        num_neg = mask.numel() - num_pos
+        w_pos = (1 - p) ** self.gamma
+        w_neg = p**self.gamma
+
+        loss_pos = -self.alpha * mask * w_pos * torch.log(p + 1e-12)
+        loss_neg = -(1 - self.alpha) * (1 - mask) * w_neg * torch.log(1 - p + 1e-12)
+
+        loss = (torch.sum(loss_pos) + torch.sum(loss_neg)) / (num_pos + num_neg + 1e-12)
+
+        return loss
+
+    def forward(self, logits, target):
+        logits = logits.squeeze(1)
+        target = target.squeeze(1)
+        # Dice Loss
+        # prob = F.softmax(logits, dim=1)[:, 1, ...]
+
+        dice_loss = self.dice_loss(logits, target)
+
+        # Focal Loss
+        focal_loss = self.focal_loss(logits, target.squeeze(-1))
+        alpha = 20.0
+        # Combined Loss
+        combined_loss = alpha * focal_loss + dice_loss
+        return combined_loss
+
+
+class loss_fn(torch.nn.Module):
+    def __init__(self, alpha=0.7, gamma=2.0, epsilon=1e-5):
+        super(loss_fn, self).__init__()
+        self.alpha = alpha
+        self.gamma = gamma
+        self.epsilon = epsilon
+
+    def tversky_loss(self, y_pred, y_true, alpha=0.8, beta=0.2, smooth=1e-2):
+        y_pred = torch.sigmoid(y_pred)
+        # raise ValueError(y_pred)
+        y_true_pos = torch.flatten(y_true)
+        y_pred_pos = torch.flatten(y_pred)
+        true_pos = torch.sum(y_true_pos * y_pred_pos)
+        false_neg = torch.sum(y_true_pos * (1 - y_pred_pos))
+        false_pos = torch.sum((1 - y_true_pos) * y_pred_pos)
+        tversky_index = (true_pos + smooth) / (
+            true_pos + alpha * false_neg + beta * false_pos + smooth
+        )
+        return 1 - tversky_index
+
+    def focal_tversky(self, y_pred, y_true, gamma=0.75):
+        pt_1 = self.tversky_loss(y_pred, y_true)
+        return torch.pow((1 - pt_1), gamma)
+    def dice_loss(self, logits, gt, eps=1):
+        # Convert logits to probabilities
+        # Flatten the tensorsx
+        probs = torch.sigmoid(logits)
+
+        probs = probs.view(-1)
+        gt = gt.view(-1)
+
+        # Compute Dice coefficient
+        intersection = (probs * gt).sum()
+
+        dice_coeff = (2.0 * intersection + eps) / (probs.sum() + gt.sum() + eps)
+
+        # Compute Dice Los[s
+        loss = 1 - dice_coeff
+        return loss
+
+    def focal_loss(self, logits, gt, gamma=2):
+        logits = logits.reshape(-1, 1)
+        gt = gt.reshape(-1, 1)
+        logits = torch.cat((1 - logits, logits), dim=1)
+
+        probs = torch.sigmoid(logits)
+        pt = probs.gather(1, gt.long())
+
+        modulating_factor = (1 - pt) ** gamma
+        # pt_false= pt<=0.5
+        # modulating_factor[pt_false] *= 2
+        focal_loss = -modulating_factor * torch.log(pt + 1e-12)
+
+        # Compute the mean focal loss
+        loss = focal_loss.mean()
+        return loss  # Store as a Python number to save memory
+
+    def forward(self, logits, target):
+        logits = logits.squeeze(1)
+        target = target.squeeze(1)
+        # Dice Loss
+        # prob = F.softmax(logits, dim=1)[:, 1, ...]
+
+        dice_loss = self.dice_loss(logits, target)
+        tversky_loss = self.tversky_loss(logits, target)
+
+        # Focal Loss
+        focal_loss = self.focal_loss(logits, target.squeeze(-1))
+        alpha = 20.0
+        # Combined Loss
+        combined_loss = alpha * focal_loss + dice_loss
+        return combined_loss
+
+
+def img_enhance(img2, coef=0.2):
+    img_mean = np.mean(img2)
+    img_max = np.max(img2)
+    val = (img_max - img_mean) * coef + img_mean
+    img2[img2 < img_mean * 0.7] = img_mean * 0.7
+    img2[img2 > val] = val
+    return img2
+
+
+def dice_coefficient(pred, target):
+    smooth = 1  # Smoothing constant to avoid division by zero
+    dice = 0
+    pred_index = pred
+    target_index = target
+    intersection = (pred_index * target_index).sum()
+    union = pred_index.sum() + target_index.sum()
+    dice += (2.0 * intersection + smooth) / (union + smooth)
+    return dice.item()
+
+
+num_workers = 4
+slice_per_image = 1
+num_epochs = 80
+sample_size = 2000
+# image_size=sam_model.image_encoder.img_size
+image_size = 1024
+exp_id = 0
+found = 0
+if debug:
+    user_input = "debug"
+else:
+    user_input = input("Related changes: ")
+while found == 0:
+    try:
+        os.makedirs(f"exps/{exp_id}-{user_input}")
+        found = 1
+    except:
+        exp_id = exp_id + 1
+copyfile(os.path.realpath(__file__), f"exps/{exp_id}-{user_input}/code.py")
+
+
+layer_n = 4
+L = layer_n
+a = np.full(L, layer_n)
+params = {"M": 255, "a": a, "p": 0.35}
+
+
+device = "cuda:0"
+
+
+from segment_anything import SamPredictor, sam_model_registry
+
+
+# //////////////////
+class panc_sam(nn.Module):
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+        sam=sam_model_registry[args.model_type](args.checkpoint)
+
+    def forward(self, batched_input):
+        # with torch.no_grad():
+        # raise ValueError(10)
+        input_images = torch.stack([x["image"] for x in batched_input], dim=0)
+        
+        with torch.no_grad():
+            image_embeddings = self.sam.image_encoder(input_images).detach()
+        
+        outputs = []
+        
+        for image_record, curr_embedding in zip(batched_input, image_embeddings):
+            if "point_coords" in image_record:
+                points = (image_record["point_coords"].unsqueeze(0), image_record["point_labels"].unsqueeze(0))
+                # raise ValueError(points)
+            else:
+                raise ValueError('what the f?')
+                points = None
+            # raise ValueError(image_record["point_coords"].shape)
+            with torch.no_grad():
+                 sparse_embeddings, dense_embeddings = self.sam.prompt_encoder(
+                     points=points,
+                     boxes=image_record.get("boxes", None),
+                     masks=image_record.get("mask_inputs", None),
+                 )
+
+            
+            low_res_masks, _ = self.sam.mask_decoder(
+                image_embeddings=curr_embedding.unsqueeze(0),
+                image_pe=self.sam.prompt_encoder.get_dense_pe().detach(),
+                sparse_prompt_embeddings=sparse_embeddings.detach(),
+                dense_prompt_embeddings=dense_embeddings.detach(),
+                multimask_output=False,
+            )
+            outputs.append(
+                {
+                    "low_res_logits": low_res_masks,
+                }
+            )
+        low_res_masks = torch.stack([x["low_res_logits"] for x in outputs], dim=0)
+
+        return low_res_masks.squeeze(1)
+
+
+# ///////////////
+
+augmentation = A.Compose(
+    [
+        A.Rotate(limit=90, p=0.5),
+        A.RandomBrightnessContrast(brightness_limit=0.3, contrast_limit=0.3, p=1),
+        A.RandomResizedCrop(1024, 1024, scale=(0.9, 1.0), p=1),
+        A.HorizontalFlip(p=0.5),
+        A.CLAHE(clip_limit=2.0, tile_grid_size=(8, 8), p=0.5),
+        A.CoarseDropout(
+            max_holes=8,
+            max_height=16,
+            max_width=16,
+            min_height=8,
+            min_width=8,
+            fill_value=0,
+            p=0.5,
+        ),
+        A.RandomScale(scale_limit=0.1, p=0.5),
+
+    ]
+)
+panc_sam_instance = panc_sam()
+
+panc_sam_instance.to(device)
+panc_sam_instance.train()
+
+train_dataset = PanDataset(
+    [args.train_dir],
+    [args.train_labels_dir],
+
+    [["NIH_PNG",1]],
+    
+    image_size,
+    
+    slice_per_image=slice_per_image,
+    train=True,
+    augmentation=augmentation,
+)
+val_dataset = PanDataset(
+    [args.val_dir],
+    [args.train_dir],
+        
+    [["NIH_PNG",1]],
+
+    image_size,
+    
+    slice_per_image=slice_per_image,
+    train=False,
+)
+train_loader = DataLoader(
+    train_dataset,
+    batch_size=args.batch_size,
+    collate_fn=train_dataset.collate_fn,
+    shuffle=True,
+    drop_last=False,
+    num_workers=num_workers,
+)
+val_loader = DataLoader(
+    val_dataset,
+    batch_size=args.batch_size,
+    collate_fn=val_dataset.collate_fn,
+    shuffle=False,
+    drop_last=False,
+    num_workers=num_workers,
+)
+
+
+# Set up the optimizer, hyperparameter tuning will improve performance here
+lr = 1e-4
+max_lr = 5e-5
+wd = 5e-4
+
+
+optimizer = torch.optim.Adam(
+    # parameters,
+    list(panc_sam_instance.sam.mask_decoder.parameters()),
+    # list(panc_sam_instance.mask_decoder.parameters()),
+    lr=lr,
+    weight_decay=wd,
+)
+scheduler = torch.optim.lr_scheduler.OneCycleLR(
+    optimizer,
+    max_lr=max_lr,
+    epochs=num_epochs,
+    steps_per_epoch=sample_size // (args.accumulative_batch_size // args.batch_size),
+)
+
+from statistics import mean
+
+from tqdm import tqdm
+from torch.nn.functional import threshold, normalize
+
+loss_function = loss_fn(alpha=0.5, gamma=2.0)
+loss_function.to(device)
+
+from time import time
+import time as s_time
+
+log_file = open(f"exps/{exp_id}-{user_input}/log.txt", "a")
+
+
+def process_model(data_loader, train=0, save_output=0):
+    epoch_losses = []
+
+    index = 0
+    results = torch.zeros((2, 0, 256, 256))
+    total_dice = 0.0
+    num_samples = 0
+
+    counterb = 0
+    for image, label in tqdm(data_loader, total=sample_size):
+        s_time.sleep(0.6)
+        counterb += 1
+
+        index += 1
+        image = image.to(device)
+        label = label.to(device).float()
+
+        input_size = (1024, 1024)
+
+        box = torch.tensor([[200, 200, 750, 800]]).to(device)
+        points, point_labels = main_prompt_for_ground_true(label)
+        # raise ValueError(points)
+        batched_input = []
+        for ibatch in range(args.batch_size):
+            
+            batched_input.append(
+                {
+                    "image": image[ibatch],
+                    "point_coords": points[ibatch],
+                    "point_labels": point_labels[ibatch],
+                    "original_size": (1024, 1024)
+                    # 'original_size': image1.shape[:2]
+                },
+            )
+            # raise ValueError(batched_input)
+
+        low_res_masks = panc_sam_instance(batched_input)
+        low_res_label = F.interpolate(label, low_res_masks.shape[-2:])    
+        binary_mask = normalize(threshold(low_res_masks, 0.0,0))
+        loss = loss_function(low_res_masks, low_res_label)
+            
+        loss /= (args.accumulative_batch_size / args.batch_size)
+        opened_binary_mask = torch.zeros_like(binary_mask).cpu()
+
+        for j, mask in enumerate(binary_mask[:, 0]):
+            numpy_mask = mask.detach().cpu().numpy().astype(np.uint8)
+
+            opened_binary_mask[j][0] = torch.from_numpy(numpy_mask)
+
+        dice = dice_coefficient(
+            opened_binary_mask.numpy(), low_res_label.cpu().detach().numpy()
+        )
+        # print(dice)
+        total_dice += dice
+        num_samples += 1
+        average_dice = total_dice / num_samples
+        log_file.write(str(average_dice) + "\n")
+        log_file.flush()
+        if train:
+            loss.backward()
+
+            if index % (args.accumulative_batch_size / args.batch_size) == 0:
+                # print(loss)
+                optimizer.step()
+                scheduler.step()
+                optimizer.zero_grad()
+                index = 0
+
+        else:
+            result = torch.cat(
+                (
+                    low_res_masks[0].detach().cpu().reshape(1, 1, 256, 256),
+                    opened_binary_mask[0].reshape(1, 1, 256, 256),
+                ),
+                dim=0,
+            )
+            results = torch.cat((results, result), dim=1)
+        if index % (args.accumulative_batch_size / args.batch_size) == 0:
+            epoch_losses.append(loss.item())
+        if counterb == sample_size and train:
+            break
+        elif counterb == sample_size // 5 and not train:
+            break
+
+    return epoch_losses, results, average_dice
+
+
+def train_model(train_loader, val_loader, K_fold=False, N_fold=7, epoch_num_start=7):
+    print("Train model started.")
+
+    train_losses = []
+    train_epochs = []
+    val_losses = []
+    val_epochs = []
+    dice = []
+    dice_val = []
+    results = []
+    if debug==0:
+        index = 0
+
+
+
+
+    ## training with k-fold cross validation:
+    last_best_dice = 0
+    for epoch in range(num_epochs):
+        if epoch > epoch_num_start:
+            kf = KFold(n_splits=N_fold, shuffle=True)
+            for i, (train_index, val_index) in enumerate(kf.split(train_loader)):
+                print(
+                    f"=====================EPOCH: {epoch} fold: {i}====================="
+                )
+                print("Training:")
+                x_train, x_val = (
+                    train_loader[train_index],
+                    train_loader[val_index],
+                )
+
+                train_epoch_losses, epoch_results, average_dice = process_model(
+                    x_train, train=1
+                )
+
+                dice.append(average_dice)
+                train_losses.append(train_epoch_losses)
+                if (average_dice) > 0.6:
+                    print("validating:")
+                    (
+                        val_epoch_losses,
+                        epoch_results,
+                        average_dice_val,
+                    ) = process_model(x_val)
+
+                    val_losses.append(val_epoch_losses)
+                    for i in tqdm(range(len(epoch_results[0]))):
+                        if not os.path.exists(f"ims/batch_{i}"):
+                            os.mkdir(f"ims/batch_{i}")
+
+                        save_img(
+                            epoch_results[0, i].clone(),
+                            f"ims/batch_{i}/prob_epoch_{epoch}.png",
+                        )
+                        save_img(
+                            epoch_results[1, i].clone(),
+                            f"ims/batch_{i}/pred_epoch_{epoch}.png",
+                        )
+                train_mean_losses = [mean(x) for x in train_losses]
+                val_mean_losses = [mean(x) for x in val_losses]
+
+                np.save("train_losses.npy", train_mean_losses)
+                np.save("val_losses.npy", val_mean_losses)
+
+                print(f"Train Dice: {average_dice}")
+                print(f"Mean train loss: {mean(train_epoch_losses)}")
+
+                try:
+                    dice_val.append(average_dice_val)
+                    print(f"val Dice : {average_dice_val}")
+                    print(f"Mean val loss: {mean(val_epoch_losses)}")
+
+                    results.append(epoch_results)
+                    val_epochs.append(epoch)
+                    train_epochs.append(epoch)
+                    plt.plot(
+                        val_epochs,
+                        val_mean_losses,
+                        train_epochs,
+                        train_mean_losses,
+                    )
+                    if average_dice_val > last_best_dice:
+                        torch.save(
+                            panc_sam_instance,
+                            f"exps/{exp_id}-{user_input}/sam_tuned_save.pth",
+                        )
+
+                        last_best_dice = average_dice_val
+                    del epoch_results
+                    del average_dice_val
+                except:
+                    train_epochs.append(epoch)
+                    plt.plot(train_epochs, train_mean_losses)
+                    print(
+                        f"=================End of EPOCH: {epoch} Fold :{i}==================\n"
+                    )
+
+            plt.yscale("log")
+            plt.title("Mean epoch loss")
+            plt.xlabel("Epoch Number")
+            plt.ylabel("Loss")
+            plt.savefig("result")
+
+        else:
+            print(f"=====================EPOCH: {epoch}=====================")
+            last_best_dice = 0
+            print("Training:")
+            train_epoch_losses, epoch_results, average_dice = process_model(
+                train_loader, train=1
+            )
+
+            dice.append(average_dice)
+            train_losses.append(train_epoch_losses)
+            if (average_dice) > 0.6:
+                print("validating:")
+                val_epoch_losses, epoch_results, average_dice_val = process_model(
+                    val_loader
+                )
+
+                val_losses.append(val_epoch_losses)
+                # for i in tqdm(range(len(epoch_results[0]))):
+                #     if not os.path.exists(f"ims/batch_{i}"):
+                #         os.mkdir(f"ims/batch_{i}")
+
+                #     save_img(
+                #         epoch_results[0, i].clone(),
+                #         f"ims/batch_{i}/prob_epoch_{epoch}.png",
+                #     )
+                #     save_img(
+                #         epoch_results[1, i].clone(),
+                #         f"ims/batch_{i}/pred_epoch_{epoch}.png",
+                #     )
+
+            train_mean_losses = [mean(x) for x in train_losses]
+            val_mean_losses = [mean(x) for x in val_losses]
+
+            np.save("train_losses.npy", train_mean_losses)
+            np.save("val_losses.npy", val_mean_losses)
+
+            print(f"Train Dice: {average_dice}")
+            print(f"Mean train loss: {mean(train_epoch_losses)}")
+
+            try:
+                dice_val.append(average_dice_val)
+                print(f"val Dice : {average_dice_val}")
+                print(f"Mean val loss: {mean(val_epoch_losses)}")
+
+                results.append(epoch_results)
+                val_epochs.append(epoch)
+                train_epochs.append(epoch)
+                plt.plot(
+                    val_epochs, val_mean_losses, train_epochs, train_mean_losses
+                )
+                if average_dice_val > last_best_dice:
+                    torch.save(
+                        panc_sam_instance,
+                        f"exps/{exp_id}-{user_input}/sam_tuned_save.pth",
+                    )
+
+                    last_best_dice = average_dice_val
+                del epoch_results
+                del average_dice_val
+            except:
+                train_epochs.append(epoch)
+                plt.plot(train_epochs, train_mean_losses)
+                print(f"=================End of EPOCH: {epoch}==================\n")
+
+            plt.yscale("log")
+            plt.title("Mean epoch loss")
+            plt.xlabel("Epoch Number")
+            plt.ylabel("Loss")
+            plt.savefig("result")
+
+    return train_losses, val_losses, results
+
+
+train_losses, val_losses, results = train_model(train_loader, val_loader)
+log_file.close()
+
+# train and also test the model

SAM_without_prompt.py

@@ -0,0 +1,456 @@
+from pathlib import Path
+import numpy as np
+import matplotlib.pyplot as plt
+
+# import cv2
+from collections import defaultdict
+import torchvision.transforms as transforms
+import torch
+from torch import nn
+
+import torch.nn.functional as F
+from segment_anything.utils.transforms import ResizeLongestSide
+import albumentations as A
+from albumentations.pytorch import ToTensorV2
+import numpy as np
+from einops import rearrange
+import random
+from tqdm import tqdm
+from time import sleep
+from data import *
+from time import time
+from PIL import Image
+from sklearn.model_selection import KFold
+from shutil import copyfile
+from args import get_arguments
+
+# import wandb_handler
+args = get_arguments()
+
+def save_img(img, dir):
+    img = img.clone().cpu().numpy() + 100
+
+    if len(img.shape) == 3:
+        img = rearrange(img, "c h w -> h w c")
+        img_min = np.amin(img, axis=(0, 1), keepdims=True)
+        img = img - img_min
+
+        img_max = np.amax(img, axis=(0, 1), keepdims=True)
+        img = (img / img_max * 255).astype(np.uint8)
+        grey_img = Image.fromarray(img[:, :, 0])
+        img = Image.fromarray(img)
+
+    else:
+        img_min = img.min()
+        img = img - img_min
+        img_max = img.max()
+        if img_max != 0:
+            img = img / img_max * 255
+        img = Image.fromarray(img).convert("L")
+
+    img.save(dir)
+
+
+
+
+
+class loss_fn(torch.nn.Module):
+    def __init__(self, alpha=0.7, gamma=2.0, epsilon=1e-5):
+        super(loss_fn, self).__init__()
+        self.alpha = alpha
+        self.gamma = gamma
+        self.epsilon = epsilon
+
+
+    def dice_loss(self, logits, gt, eps=1):
+        # Convert logits to probabilities
+        # Flatten the tensorsx
+        probs = torch.sigmoid(logits)
+
+        probs = probs.view(-1)
+        gt = gt.view(-1)
+
+        # Compute Dice coefficient
+        intersection = (probs * gt).sum()
+
+        dice_coeff = (2.0 * intersection + eps) / (probs.sum() + gt.sum() + eps)
+
+        # Compute Dice Los[s
+        loss = 1 - dice_coeff
+        return loss
+
+    def focal_loss(self, logits, gt, gamma=2):
+        logits = logits.reshape(-1, 1)
+        gt = gt.reshape(-1, 1)
+        logits = torch.cat((1 - logits, logits), dim=1)
+
+        probs = torch.sigmoid(logits)
+        pt = probs.gather(1, gt.long())
+
+        modulating_factor = (1 - pt) ** gamma
+        # pt_false= pt<=0.5
+        # modulating_factor[pt_false] *= 2
+        focal_loss = -modulating_factor * torch.log(pt + 1e-12)
+
+        # Compute the mean focal loss
+        loss = focal_loss.mean()
+        return loss  # Store as a Python number to save memory
+
+    def forward(self, logits, target):
+        logits = logits.squeeze(1)
+        target = target.squeeze(1)
+        # Dice Loss
+        # prob = F.softmax(logits, dim=1)[:, 1, ...]
+
+        dice_loss = self.dice_loss(logits, target)
+
+        # Focal Loss
+        focal_loss = self.focal_loss(logits, target.squeeze(-1))
+        alpha = 20.0
+        # Combined Loss
+        combined_loss = alpha * focal_loss + dice_loss
+        return combined_loss
+
+
+def img_enhance(img2, coef=0.2):
+    img_mean = np.mean(img2)
+    img_max = np.max(img2)
+    val = (img_max - img_mean) * coef + img_mean
+    img2[img2 < img_mean * 0.7] = img_mean * 0.7
+    img2[img2 > val] = val
+    return img2
+
+def dice_coefficient(logits, gt):
+    eps=1
+    binary_mask = logits>0
+    intersection = (binary_mask * gt).sum(dim=(-2,-1))
+    dice_scores = (2.0 * intersection + eps) / (binary_mask.sum(dim=(-2,-1)) + gt.sum(dim=(-2,-1)) + eps)
+    
+    return dice_scores.mean()
+
+def what_the_f(low_res_masks,label):
+            
+    low_res_label = F.interpolate(label, low_res_masks.shape[-2:])
+    dice = dice_coefficient(
+        low_res_masks, low_res_label
+    )
+    return dice
+
+
+
+
+accumaltive_batch_size = 8
+batch_size = 1
+num_workers = 4
+slice_per_image = 1
+num_epochs = 80
+sample_size = 2000
+
+image_size = 1024
+exp_id = 0
+found=0
+debug = 0
+
+if debug:
+    user_input='debug'
+else:    
+    user_input = input("Related changes: ")
+while found == 0:
+    try:
+        os.makedirs(f"exps/{exp_id}-{user_input}/")
+        found = 1
+    except:
+        exp_id = exp_id + 1
+copyfile(os.path.realpath(__file__), f"exps/{exp_id}-{user_input}/code.py")
+
+
+
+
+device = "cuda:1"
+
+
+from segment_anything import SamPredictor, sam_model_registry
+
+
+# //////////////////
+class panc_sam(nn.Module):
+    def __init__(self, *args, **kwargs) -> None: 
+        super().__init__(*args, **kwargs)
+          
+        sam=sam_model_registry[args.model_type](args.checkpoint)
+        # self.sam  = torch.load('exps/sam_tuned_save.pth').sam
+        self.prompt_encoder =  self.sam.prompt_encoder
+        for param in self.prompt_encoder.parameters():
+            param.requires_grad = False
+
+
+        
+    def forward(self, image ,box):
+        with torch.no_grad():
+            image_embedding = self.sam.image_encoder(image).detach()
+            
+        outputs_prompt = []
+
+        for curr_embedding in image_embedding:
+            with torch.no_grad():
+                sparse_embeddings, dense_embeddings = self.sam.prompt_encoder(
+                points=None,
+                boxes=None,
+                masks=None,
+            )
+            
+            low_res_masks, _ = self.sam.mask_decoder(
+                image_embeddings=curr_embedding,
+                image_pe=self.sam.prompt_encoder.get_dense_pe().detach(),
+                sparse_prompt_embeddings=sparse_embeddings.detach(),
+                dense_prompt_embeddings=dense_embeddings.detach(),
+                multimask_output=False,
+            )
+            outputs_prompt.append(low_res_masks)
+
+        low_res_masks_promtp = torch.cat(outputs_prompt, dim=0)
+        # raise ValueError(low_res_masks_promtp)
+
+        return low_res_masks_promtp
+
+# ///////////////
+
+
+augmentation = A.Compose(
+    [
+        A.Rotate(limit=90, p=0.5),
+        A.RandomBrightnessContrast(brightness_limit=0.3, contrast_limit=0.3, p=1),
+        A.RandomResizedCrop(1024, 1024, scale=(0.9, 1.0), p=1),
+        A.HorizontalFlip(p=0.5),
+        A.CLAHE(clip_limit=2.0, tile_grid_size=(8, 8), p=0.5),
+        A.CoarseDropout(max_holes=8, max_height=16, max_width=16, min_height=8, min_width=8, fill_value=0, p=0.5),
+        A.RandomScale(scale_limit=0.3, p=0.5),
+        A.GaussNoise(var_limit=(10.0, 50.0), p=0.5),
+        A.GridDistortion(p=0.5),
+        
+    ]
+)
+panc_sam_instance=panc_sam()
+
+
+panc_sam_instance.to(device)
+panc_sam_instance.train()
+train_dataset = PanDataset(
+    [args.train_dir],
+    [args.train_labels_dir],
+
+    [["NIH_PNG",1]],
+    
+    image_size,
+    
+    slice_per_image=slice_per_image,
+    train=True,
+    augmentation=augmentation,
+)
+val_dataset = PanDataset(
+    [args.val_dir],
+    [args.val_labels_dir],
+        
+    [["NIH_PNG",1]],
+
+    image_size,
+    
+    slice_per_image=slice_per_image,
+    train=False,
+)
+train_loader = DataLoader(
+    train_dataset,
+    batch_size=batch_size,
+    collate_fn=train_dataset.collate_fn,
+    shuffle=True,
+    drop_last=False,
+    num_workers=num_workers,
+)
+val_loader = DataLoader(
+    val_dataset,
+    batch_size=batch_size,
+    collate_fn=val_dataset.collate_fn,
+    shuffle=False,
+    drop_last=False,
+    num_workers=num_workers,
+)
+
+
+# Set up the optimizer, hyperparameter tuning will improve performance here
+lr = 1e-4
+
+max_lr = 5e-5 
+wd = 5e-4
+
+
+optimizer = torch.optim.Adam(
+    # parameters,
+    list(panc_sam_instance.sam.mask_decoder.parameters()),
+    lr=lr, weight_decay=wd
+)
+scheduler = torch.optim.lr_scheduler.OneCycleLR(
+    optimizer,
+    max_lr=max_lr,
+    epochs=num_epochs,
+    steps_per_epoch=sample_size // (accumaltive_batch_size // batch_size),
+)
+
+
+
+from statistics import mean
+
+from tqdm import tqdm
+from torch.nn.functional import threshold, normalize
+
+loss_function = loss_fn(alpha=0.5, gamma=2.0)
+loss_function.to(device)
+
+from time import time
+import time as s_time
+
+log_file = open(f"exps/{exp_id}-{user_input}/log.txt", "a")
+
+
+def process_model(data_loader, train=0, save_output=0):
+    epoch_losses = []
+
+    index = 0
+    results = torch.zeros((2, 0, 256, 256))
+    total_dice = 0.0
+    num_samples = 0
+
+    counterb = 0
+    for image, label in tqdm(data_loader, total=sample_size):
+        counterb += 1
+        num_samples += 1
+        index += 1
+        image = image.to(device)
+        label = label.to(device).float()
+
+        input_size = (1024, 1024)
+
+        box = torch.tensor([[200, 200, 750, 800]]).to(device)         
+        low_res_masks = panc_sam_instance(image,box)
+        low_res_label = F.interpolate(label, low_res_masks.shape[-2:])
+        dice = what_the_f(low_res_masks,low_res_label)
+
+
+        binary_mask = normalize(threshold(low_res_masks, 0.0, 0))
+
+        
+        total_dice += dice
+        average_dice = total_dice / num_samples
+        log_file.write(str(average_dice) + "\n")
+        log_file.flush()
+        loss = loss_function.forward(low_res_masks, low_res_label)
+
+        loss /= accumaltive_batch_size / batch_size
+        if train:
+            
+            loss.backward()
+
+            if index % (accumaltive_batch_size / batch_size) == 0:
+                # print(loss)
+                optimizer.step()
+                scheduler.step()
+                optimizer.zero_grad()
+                index = 0
+
+        else:
+            pass
+
+        if index % (accumaltive_batch_size / batch_size) == 0:
+            epoch_losses.append(loss.item())
+        if counterb == sample_size and train:
+            break
+        elif counterb == sample_size / 10  and not train:
+            break
+
+    return epoch_losses, results, average_dice
+
+def train_model(train_loader, val_loader, K_fold=False, N_fold=7, epoch_num_start=7):
+    print("Train model started.")
+
+    train_losses = []
+    train_epochs = []
+    val_losses = []
+    val_epochs = []
+    dice = []
+    dice_val = []
+    results = []
+
+    index = 0
+
+    last_best_dice = 0
+    for epoch in range(num_epochs):
+        print(f"=====================EPOCH: {epoch + 1}=====================")
+        log_file.write(
+            f"=====================EPOCH: {epoch + 1}===================\n"
+        )
+        print("Training:")
+        train_epoch_losses, epoch_results, average_dice = process_model(
+            train_loader, train=1
+        )
+        
+        dice.append(average_dice)
+        train_losses.append(train_epoch_losses)
+        if (average_dice) > 0.5:
+            print("valing:")
+            val_epoch_losses, epoch_results, average_dice_val = process_model(
+                val_loader
+            )
+
+            val_losses.append(val_epoch_losses)
+            for i in tqdm(range(len(epoch_results[0]))):
+                if not os.path.exists(f"ims/batch_{i}"):
+                    os.mkdir(f"ims/batch_{i}")
+
+                save_img(epoch_results[0, i].clone(), f"ims/batch_{i}/prob_epoch_{epoch}.png")
+                save_img(epoch_results[1, i].clone(), f"ims/batch_{i}/pred_epoch_{epoch}.png")
+
+        train_mean_losses = [mean(x) for x in train_losses]
+        # raise ValueError(average_dice)
+        val_mean_losses = [mean(x) for x in val_losses]
+        np.save("train_losses.npy", train_mean_losses)
+        np.save("val_losses.npy", val_mean_losses)
+
+        print(f"Train Dice: {average_dice}")
+        print(f"Mean train loss: {mean(train_epoch_losses)}")
+
+        try:
+            dice_val.append(average_dice_val)
+            print(f"val Dice : {average_dice_val}")
+            print(f"Mean val loss: {mean(val_epoch_losses)}")
+
+            results.append(epoch_results)
+            val_epochs.append(epoch)
+            train_epochs.append(epoch)
+            plt.plot(val_epochs, val_mean_losses, train_epochs, train_mean_losses)
+            print(last_best_dice)
+            log_file.write(f'bestwieght:{last_best_dice}')
+            if average_dice_val > last_best_dice:
+                torch.save(panc_sam_instance, f"exps/{exp_id}-{user_input}/sam_tuned_save.pth")
+
+                last_best_dice = average_dice_val
+            del epoch_results
+            del average_dice_val
+        except:
+            train_epochs.append(epoch)
+            plt.plot(train_epochs, train_mean_losses)
+            print(f"=================End of EPOCH: {epoch}==================\n")
+
+        plt.yscale("log")
+        plt.title("Mean epoch loss")
+        plt.xlabel("Epoch Number")
+        plt.ylabel("Loss")
+        plt.savefig("result")
+
+    
+
+    return train_losses, val_losses, results
+
+
+train_losses, val_losses, results = train_model(train_loader, val_loader)
+log_file.close()
+
+# train and also val the model

args.py

@@ -0,0 +1,36 @@
+import argparse
+
+def get_arguments():
+    parser = argparse.ArgumentParser(description="Your program's description here")
+
+    parser.add_argument('--debug', action='store_true', help='Enable debug mode')
+    parser.add_argument('--accumulative_batch_size', type=int, default=2, help='Accumulative batch size')
+    parser.add_argument('--batch_size', type=int, default=1, help='Batch size')
+    parser.add_argument('--num_workers', type=int, default=1, help='Number of workers')
+    parser.add_argument('--slice_per_image', type=int, default=1, help='Slices per image')
+    parser.add_argument('--num_epochs', type=int, default=40, help='Number of epochs')
+    parser.add_argument('--sample_size', type=int, default=4, help='Sample size')
+    parser.add_argument('--image_size', type=int, default=1024, help='Image size')
+    parser.add_argument('--run_name', type=str, default='debug', help='The name of the run')
+    parser.add_argument('--lr', type=float, default=1e-3, help='Learning Rate')
+    parser.add_argument('--batch_step_one', type=int, default=15, help='Batch one')
+    parser.add_argument('--batch_step_two', type=int, default=25, help='Batch two')
+    parser.add_argument('--conv_model', type=str, default=None, help='Path to convolution model')
+    parser.add_argument('--custom_bias', type=float, default=0, help='Learning Rate')
+    parser.add_argument("--inference", action="store_true", help="Set for inference")
+    ########################################################################################
+    parser.add_argument(" --promptprovider" , type=str , help="path weight of prompt provider")
+    parser.add_argument(" --pointbasemodel" , type=str , help="path weight of prompt provider")
+    
+    parser.add_argument("--train_dir",type=str, help="Path to the training data")
+    parser.add_argument("--test_dir",type=str, help="Path to the test data")
+    parser.add_argument("--test_labels_dir",type=str, help="Path to the test data")
+    parser.add_argument("--train_labels_dir",type=str, help="Path to the test data")
+    parser.add_argument("--images_dir",type=str, help="Path to the test data")
+    parser.add_argument("--checkpoint",type=str, help="Path to the test data")
+    parser.add_argument("--model_type",type=str, help="Path to the test data",default="vit_h")
+    
+    
+    
+
+    return parser.parse_args()

data.py

@@ -0,0 +1,291 @@
+import matplotlib.pyplot as plt
+import os
+import numpy as np
+import random
+from segment_anything.utils.transforms import ResizeLongestSide
+from einops import rearrange
+import torch
+from segment_anything import SamPredictor, sam_model_registry
+from torch.utils.data import DataLoader
+from time import time
+import torch.nn.functional as F
+import cv2
+from PIL import Image
+import cv2
+
+from kernel.pre_processer import PreProcessing
+
+
+def apply_median_filter(input_matrix, kernel_size=5, sigma=0):
+    # Apply the Gaussian filter
+    filtered_matrix = cv2.medianBlur(input_matrix.astype(np.uint8), kernel_size)
+
+    return filtered_matrix.astype(np.float32)
+
+
+def apply_guassain_filter(input_matrix, kernel_size=(7, 7), sigma=0):
+    smoothed_matrix = cv2.blur(input_matrix, kernel_size)
+
+    return smoothed_matrix.astype(np.float32)
+
+
+def img_enhance(img2, over_coef=0.8, under_coef=0.7):
+    img2 = apply_median_filter(img2)
+    img_blure = apply_guassain_filter(img2)
+
+    img2 = img2 - 0.8 * img_blure
+
+    img_mean = np.mean(img2, axis=(1, 2))
+
+    img_max = np.amax(img2, axis=(1, 2))
+
+    val = (img_max - img_mean) * over_coef + img_mean
+
+    img2 = (img2 < img_mean * under_coef).astype(np.float32) * img_mean * under_coef + (
+        (img2 >= img_mean * under_coef).astype(np.float32)
+    ) * img2
+
+    img2 = (img2 <= val).astype(np.float32) * img2 + (img2 > val).astype(
+        np.float32
+    ) * val
+
+    return img2
+
+
+def normalize_and_pad(x, img_size):
+    """Normalize pixel values and pad to a square input."""
+
+    pixel_mean = torch.tensor([[[[123.675]], [[116.28]], [[103.53]]]])
+    pixel_std = torch.tensor([[[[58.395]], [[57.12]], [[57.375]]]])
+
+    # Normalize colors
+    x = (x - pixel_mean) / pixel_std
+
+    # Pad
+    h, w = x.shape[-2:]
+    padh = img_size - h
+    padw = img_size - w
+    x = F.pad(x, (0, padw, 0, padh))
+    return x
+
+
+def preprocess(img_enhanced, img_enhance_times=1, over_coef=0.4, under_coef=0.5):
+    # img_enhanced = img_enhanced+0.1
+
+    img_enhanced -= torch.amin(img_enhanced, dim=(1, 2), keepdim=True)
+    img_max = torch.amax(img_enhanced, axis=(1, 2), keepdims=True)
+    img_max[img_max == 0] = 1
+    img_enhanced = img_enhanced / img_max
+    # raise ValueError(img_max)
+    img_enhanced = img_enhanced.unsqueeze(1)
+
+    img_enhanced = PreProcessing.CLAHE(img_enhanced, clip_limit=9.0, grid_size=(4, 4))
+    img_enhanced = img_enhanced[0]
+
+    # for i in range(img_enhance_times):
+    #     img_enhanced=img_enhance(img_enhanced.astype(np.float32), over_coef=over_coef,under_coef=under_coef)
+
+    img_enhanced -= torch.amin(img_enhanced, dim=(1, 2), keepdim=True)
+    larg_imag = (
+        img_enhanced / torch.amax(img_enhanced, axis=(1, 2), keepdims=True) * 255
+    ).type(torch.uint8)
+
+    return larg_imag
+
+
+def prepare(larg_imag, target_image_size):
+    # larg_imag = 255 - larg_imag
+    larg_imag = rearrange(larg_imag, "S H W -> S 1 H W")
+    larg_imag = torch.tensor(
+        np.concatenate([larg_imag, larg_imag, larg_imag], axis=1)
+    ).float()
+    transform = ResizeLongestSide(target_image_size)
+    larg_imag = transform.apply_image_torch(larg_imag)
+    larg_imag = normalize_and_pad(larg_imag, target_image_size)
+    return larg_imag
+
+
+def process_single_image(image_path, target_image_size):
+    # Load the image
+    if image_path.endswith(".png") or image_path.endswith(".jpg"):
+        data = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE).squeeze()
+    else:
+        data = np.load(image_path)
+    x = rearrange(data, "H W -> 1 H W")
+    x = torch.tensor(x)
+
+    # Apply preprocessing
+    x = preprocess(x)
+    x = prepare(x, target_image_size)
+
+    return x
+
+
+class PanDataset:
+    def __init__(
+        self,
+        images_dirs,
+        labels_dirs,
+        datasets,
+        target_image_size,
+        slice_per_image,
+        train=True,
+        ratio=0.9,
+        augmentation=None,
+    ):
+        self.data_set_names = []
+        self.labels_path = []
+        self.images_path = []
+        for labels_dir, images_dir, dataset_name in zip(
+            labels_dirs, images_dirs, datasets
+        ):
+            if train == True:
+                self.data_set_names.extend(
+                    sorted([dataset_name[0] for _ in os.listdir(labels_dir)[:int(len(os.listdir(labels_dir)) * ratio)]])
+                )
+
+                self.labels_path.extend(
+                    sorted([os.path.join(labels_dir, item) for item in os.listdir(labels_dir)[:int(len(os.listdir(labels_dir)) * ratio)]])
+                )
+                self.images_path.extend(
+                    sorted([os.path.join(images_dir, item) for item in os.listdir(images_dir)[:int(len(os.listdir(images_dir)) * ratio)]])
+                )
+            else:
+                self.data_set_names.extend(
+                sorted([dataset_name[0] for _ in os.listdir(labels_dir)[int(len(os.listdir(labels_dir)) * ratio):]])
+                )
+
+                self.labels_path.extend(
+                    sorted([os.path.join(labels_dir, item) for item in os.listdir(labels_dir)[int(len(os.listdir(labels_dir)) * ratio):]])
+                )
+                self.images_path.extend(
+                    sorted([os.path.join(images_dir, item) for item in os.listdir(images_dir)[int(len(os.listdir(images_dir)) * ratio):]])
+                )
+                
+
+        self.target_image_size = target_image_size
+        self.datasets = datasets
+        self.slice_per_image = slice_per_image
+        self.augmentation = augmentation
+
+    def __getitem__(self, idx):
+        data = np.load(self.images_path[idx])
+        raw_data = data
+
+        labels = np.load(self.labels_path[idx])
+        
+
+        if self.data_set_names[idx] == "NIH_PNG":
+            x = rearrange(data.T, "H W -> 1 H W")
+
+            y = rearrange(labels.T, "H W -> 1 H W")
+            y = (y == 1).astype(np.uint8)
+            
+            
+            
+
+        elif self.data_set_names[idx] == "Abdment1kPNG":
+            x = rearrange(data, "H W -> 1 H W")
+
+            y = rearrange(labels, "H W -> 1 H W")
+            y = (y == 4).astype(np.uint8)
+        else:
+            raise ValueError("Incorect dataset name")
+
+        x = torch.tensor(x)
+        y = torch.tensor(y)
+        x = preprocess(x)
+        
+
+
+        x, y = self.apply_augmentation(x.numpy(), y.numpy())
+
+        y = F.interpolate(y.unsqueeze(1), size=self.target_image_size)
+
+        x = prepare(x, self.target_image_size)
+        return x, y ,raw_data
+
+    def collate_fn(self, data):
+        images, labels , raw_data = zip(*data)
+        images = torch.cat(images, dim=0)
+        labels = torch.cat(labels, dim=0)
+        # raw_data = torch.cat(raw_data, dim=0)
+        
+        return images, labels , raw_data 
+
+    def __len__(self):
+        return len(self.images_path)
+
+    def apply_augmentation(self, image, label):
+        if self.augmentation:
+            # If image and label are tensors, convert them to numpy arrays
+            # raise ValueError(label.shape)
+            augmented = self.augmentation(image=image[0], mask=label[0])
+
+            image = torch.tensor(augmented["image"])
+            label = torch.tensor(augmented["mask"])
+
+            # You might want to convert back to torch.Tensor after the transformation
+            image = image.unsqueeze(0)
+            label = label.unsqueeze(0)
+
+        else:
+            image = torch.Tensor(image)
+            label = torch.Tensor(label)
+
+        return image, label
+
+
+import albumentations as A
+
+if __name__ == "__main__":
+    model_type = "vit_h"
+    batch_size = 4
+    num_workers = 4
+    slice_per_image = 1
+    image_size = 1024
+
+    checkpoint = "checkpoints/sam_vit_h_4b8939.pth"
+    panc_sam_instance = sam_model_registry[model_type](checkpoint=checkpoint)
+
+    augmentation = A.Compose(
+        [
+            A.Rotate(limit=10, p=0.5),
+            A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2, p=1),
+            A.RandomResizedCrop(1024, 1024, scale=(0.9, 1.0), p=1),
+        ]
+    )
+    train_dataset = PanDataset(
+        "bath image",
+        "bath label",
+        image_size,
+        slice_per_image=slice_per_image,
+        train=True,
+        augmentation=None,
+    )
+
+    train_loader = DataLoader(
+        train_dataset,
+        batch_size=batch_size,
+        collate_fn=train_dataset.collate_fn,
+        shuffle=True,
+        drop_last=False,
+        num_workers=num_workers,
+    )
+    # x, y = dataset[7]
+    # print(x.shape, y.shape)
+
+    now = time()
+    for images, labels in train_loader:
+        # pass
+        image_numpy = images[0].permute(1, 2, 0).cpu().numpy()
+
+        # Ensure that the values are in the correct range [0, 255] and cast to uint8
+        image_numpy = (image_numpy * 255).astype(np.uint8)
+
+        # Save the image using OpenCV
+        cv2.imwrite("image2.png", image_numpy[:, :, 1])
+
+        break
+
+    # print((time() - now) / batch_size / slice_per_image)

data_handler/save.py

@@ -0,0 +1,166 @@
+import matplotlib.pyplot as plt
+import os
+import numpy as np
+import random
+from segment_anything.utils.transforms import ResizeLongestSide
+from einops import rearrange
+import torch
+
+import os
+from segment_anything import SamPredictor, sam_model_registry
+from torch.utils.data import DataLoader
+from time import time
+import torch.nn.functional as F
+import cv2
+
+
+    
+# def preprocess(image_paths, label_paths):
+#     preprocessed_images = []
+#     preprocessed_labels = []
+#     for image_path, label_path in zip(image_paths, label_paths):
+#         # Load image and label from paths
+#         image = plt.imread(image_path)
+#         label = plt.imread(label_path)
+        
+#         # Perform preprocessing steps here
+#         # ...
+        
+#         preprocessed_images.append(image)
+#         preprocessed_labels.append(label)
+    
+#     return preprocessed_images, preprocessed_labels
+
+
+class PanDataset:
+    def __init__(self, images_dir, labels_dir, slice_per_image, train=True,**kwargs):
+        #for Abdonomial
+        self.images_path = sorted([os.path.join(images_dir, item[:item.rindex('.')] + '_0000.npz') for item in os.listdir(labels_dir) if item.endswith('.npz') and not item.startswith('.')])
+        self.labels_path = sorted([os.path.join(labels_dir, item) for item in os.listdir(labels_dir) if item.endswith('.npz') and not item.startswith('.')])
+        #for NIH
+        # self.images_path = sorted([os.path.join(images_dir, item) for item in os.listdir(labels_dir) if item.endswith('.npy')])
+        # self.labels_path = sorted([os.path.join(labels_dir, item) for item in os.listdir(labels_dir) if item.endswith('.npy')])
+        
+
+        N = len(self.images_path)
+        n = int(N * 0.8)
+        self.train = train 
+        self.slice_per_image = slice_per_image
+        
+        if train:
+            
+            self.labels_path = self.labels_path[:n]
+            self.images_path = self.images_path[:n]
+            
+        else:
+            self.labels_path = self.labels_path[n:]
+            self.images_path = self.images_path[n:]            
+        self.args=kwargs['args']
+
+        
+    def __getitem__(self, idx):
+        now = time()
+        # for abdoment
+        data = np.load(self.images_path[idx])['arr_0']
+        labels = np.load(self.labels_path[idx])['arr_0']
+        #for nih
+        # data = np.load(self.images_path[idx])
+        # labels = np.load(self.labels_path[idx])
+        H, W, C = data.shape
+        positive_slices = np.any(labels == 1, axis=(0, 1))
+        # print("Load from file time = ", time() - now)
+        now = time()
+
+        # Find the first and last positive slices
+        first_positive_slice = np.argmax(positive_slices)
+        last_positive_slice = labels.shape[2] - np.argmax(positive_slices[::-1]) - 1
+        dist=last_positive_slice-first_positive_slice
+
+        if self.train:
+            
+            save_dir = self.args.images_dir # data address here
+            labels_save_dir = self.args.labels_dir #  label address here
+        else :
+            save_dir = self.args.test_images_dir  # data address here
+            labels_save_dir = self.args.test_labels_dir  # label address here
+            
+        j=0
+        for j in range(1):
+            slice = range(len(labels[0,0,:]))
+            # raise ValueError(labels.shape)
+            image_paths = []
+            label_paths = []
+
+            for i, slc_idx in enumerate(slice):
+                # Saving Image Slices
+                image_array = data[:, :, slc_idx]
+                
+                # Resize the array to 512x512
+                resized_image_array = cv2.resize(image_array, (512, 512))
+                
+                min_val = resized_image_array.min()
+                max_val = resized_image_array.max()
+                normalized_image_array = ((resized_image_array - min_val) / (max_val - min_val) * 255).astype(np.uint8)
+                image_paths.append(f"slice_{i}_{idx}.npy")
+
+                if normalized_image_array.max()>0:
+                    np.save(os.path.join(save_dir, image_paths[-1]), normalized_image_array)
+                    
+                    # Saving Corresponding Label Slices
+                    label_array = labels[:, :, slc_idx]
+                    
+                    # Resize the array to 512x512
+                    resized_label_array = cv2.resize(label_array, (512, 512))
+                    
+                    min_val = resized_label_array.min()
+                    max_val = resized_label_array.max()
+                    # raise ValueError(np.unique(resized_label_array))
+                    # normalized_label_array = ((resized_label_array - min_val) / (max_val - min_val) * 255).astype(np.uint8)
+                    label_paths.append(f"label_{i}_{idx}.npy")
+                    np.save(os.path.join(labels_save_dir, label_paths[-1]), resized_label_array)
+                
+        
+
+        
+        
+        return data
+    
+        
+
+    
+    def collate_fn(self, data):
+        
+        return data
+
+    def __len__(self):
+        return len(self.images_path)
+
+if __name__ == '__main__':
+    model_type = 'vit_b'
+    batch_size = 4
+    num_workers = 4
+    slice_per_image = 1
+    
+    dataset = PanDataset('../../Data/AbdomenCT-1K/numpy/images', '../../Data/AbdomenCT-1K/numpy/labels',
+                         slice_per_image=slice_per_image)
+    # x, y = dataset[7]
+    # # print(x.shape, y.shape)
+    dataloader = DataLoader(dataset, batch_size=batch_size, collate_fn=dataset.collate_fn, shuffle=True, drop_last=False, num_workers=num_workers)
+
+    now = time()
+    for data in dataloader:
+        # pass
+        # print(images.shape, labels.shape)
+        continue
+    dataset = PanDataset(f'{args.train_dir}/numpy/images', f'{args.train_dir}/numpy/labels', 
+                         train = False , slice_per_image=slice_per_image)
+    # x, y = dataset[7]
+    # # print(x.shape, y.shape)
+    dataloader = DataLoader(dataset, batch_size=batch_size, collate_fn=dataset.collate_fn, shuffle=True, drop_last=False, num_workers=num_workers)
+
+    now = time()
+    for data in dataloader:
+        # pass
+        # print(images.shape, labels.shape)
+        continue
+    

double_decoder_infrence.py

@@ -0,0 +1,434 @@
+from pathlib import Path
+import numpy as np
+import matplotlib.pyplot as plt
+from utils import   main_prompt , sample_prompt
+from collections import defaultdict
+import torchvision.transforms as transforms
+import torch
+from torch import nn
+import torch.nn.functional as F
+from segment_anything.utils.transforms import ResizeLongestSide
+import albumentations as A
+from albumentations.pytorch import ToTensorV2
+from einops import rearrange
+import random
+from tqdm import tqdm
+from time import sleep
+from data import *
+from time import time
+from PIL import Image
+from sklearn.model_selection import KFold
+from shutil import copyfile
+from torch.nn.functional import threshold, normalize
+import torchvision.transforms.functional as TF
+from args import get_arguments
+
+args = get_arguments()
+
+def dice_coefficient(logits, gt):
+    
+    eps=1
+    binary_mask = logits>0
+    
+    intersection = (binary_mask * gt).sum(dim=(-2,-1))
+    dice_scores = (2.0 * intersection + eps) / (binary_mask.sum(dim=(-2,-1)) + gt.sum(dim=(-2,-1)) + eps)
+    
+    return dice_scores.mean()
+
+
+def calculate_recall(pred, target):
+    smooth = 1
+    batch_size = pred.shape[0]
+    recall_scores = []
+    binary_mask = pred>0
+
+    for i in range(batch_size):
+        true_positive = ((binary_mask[i] == 1) & (target[i] == 1)).sum().item()
+        false_negative = ((binary_mask[i] == 0) & (target[i] == 1)).sum().item()
+        recall = (true_positive + smooth) / ((true_positive + false_negative) + smooth)
+        recall_scores.append(recall)
+
+    return sum(recall_scores) / len(recall_scores)
+
+def calculate_precision(pred, target):
+    smooth = 1
+    batch_size = pred.shape[0]
+    precision_scores = []
+    binary_mask = pred>0
+
+    for i in range(batch_size):
+        true_positive = ((binary_mask[i] == 1) & (target[i] == 1)).sum().item()
+        false_positive = ((binary_mask[i] == 1) & (target[i] == 0)).sum().item()
+        precision = (true_positive + smooth) / ((true_positive + false_positive) + smooth)
+        precision_scores.append(precision)
+
+    return sum(precision_scores) / len(precision_scores)
+
+def calculate_jaccard(pred, target):
+    smooth = 1
+    batch_size = pred.shape[0]
+    jaccard_scores = []
+    binary_mask = pred>0
+    
+
+    for i in range(batch_size):
+        true_positive = ((binary_mask[i] == 1) & (target[i] == 1)).sum().item()
+        false_positive = ((binary_mask[i] == 1) & (target[i] == 0)).sum().item()
+        false_negative = ((binary_mask[i] == 0) & (target[i] == 1)).sum().item()
+        jaccard = (true_positive + smooth) / (true_positive + false_positive + false_negative + smooth)
+        jaccard_scores.append(jaccard)
+
+    return sum(jaccard_scores) / len(jaccard_scores)
+
+def calculate_specificity(pred, target):
+    smooth = 1
+    batch_size = pred.shape[0]
+    specificity_scores = []
+    binary_mask = pred>0
+    
+
+    for i in range(batch_size):
+        true_negative = ((binary_mask[i] == 0) & (target[i] == 0)).sum().item()
+        false_positive = ((binary_mask[i] == 1) & (target[i] == 0)).sum().item()
+        specificity = (true_negative + smooth) / (true_negative + false_positive + smooth)
+        specificity_scores.append(specificity)
+
+    return sum(specificity_scores) / len(specificity_scores)
+
+def what_the_f(low_res_masks,label):
+            
+    low_res_label = F.interpolate(label, low_res_masks.shape[-2:])
+    dice = dice_coefficient(
+        low_res_masks, low_res_label
+    )
+    recall=calculate_recall(low_res_masks, low_res_label)
+    precision =calculate_precision(low_res_masks, low_res_label)
+    jaccard = calculate_jaccard(low_res_masks, low_res_label)
+    
+    return dice , precision , recall , jaccard 
+def save_img(img, dir):
+    img = img.clone().cpu().numpy() 
+
+    if len(img.shape) == 3:
+        img = rearrange(img, "c h w -> h w c")
+        img_min = np.amin(img, axis=(0, 1), keepdims=True)
+        img = img - img_min
+
+        img_max = np.amax(img, axis=(0, 1), keepdims=True)
+        img = (img / img_max * 255).astype(np.uint8)
+        img = Image.fromarray(img)
+
+    else:
+        img_min = img.min()
+        img = img - img_min
+        img_max = img.max()
+        if img_max != 0:
+            img = img / img_max * 255
+        img = Image.fromarray(img).convert("L")
+
+    img.save(dir)
+
+slice_per_image = 1
+image_size = 1024
+class panc_sam(nn.Module):
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+        
+        #Promptless
+        sam = torch.load("sam_tuned_save.pth").sam
+        
+        self.prompt_encoder = sam.prompt_encoder
+        
+        self.mask_decoder = sam.mask_decoder
+        for param in self.prompt_encoder.parameters():
+            param.requires_grad = False
+            
+        for param in self.mask_decoder.parameters():
+            param.requires_grad = False
+        
+        #with Prompt
+        sam=sam_model_registry[model_type](checkpoint=checkpoint)
+
+        # sam = torch.load(
+        #     "sam_tuned_save.pth"
+        # ).sam
+        self.image_encoder = sam.image_encoder
+        self.prompt_encoder2 = sam.prompt_encoder
+        self.mask_decoder2 = sam.mask_decoder
+        
+        for param in self.image_encoder.parameters():
+            param.requires_grad = False
+            
+        for param in self.prompt_encoder2.parameters():
+            param.requires_grad = False
+
+            
+
+        
+
+    def forward(self, input_images,box=None):
+        
+
+        # input_images = torch.stack([x["image"] for x in batched_input], dim=0)
+        with torch.no_grad():
+            image_embeddings = self.image_encoder(input_images).detach()
+
+            
+        outputs_prompt = []
+        outputs = []
+        
+        for curr_embedding in image_embeddings:
+            
+            with torch.no_grad():
+                sparse_embeddings, dense_embeddings = self.prompt_encoder(
+                points=None,
+                boxes=None,
+                masks=None,
+                )
+            
+            low_res_masks, _ = self.mask_decoder(
+                image_embeddings=curr_embedding,
+                image_pe=self.prompt_encoder.get_dense_pe().detach(),
+                sparse_prompt_embeddings=sparse_embeddings.detach(),
+                dense_prompt_embeddings=dense_embeddings.detach(),
+                multimask_output=False,
+            )
+            outputs_prompt.append(low_res_masks)
+            
+            # points, point_labels = sample_prompt((low_res_masks > 0).float())
+            points, point_labels = main_prompt(low_res_masks)
+            
+            
+            points = points * 4
+            points = (points, point_labels)
+
+            with torch.no_grad():
+                sparse_embeddings, dense_embeddings = self.prompt_encoder2(
+                     points=points,
+                     boxes=None,
+                     masks=None,
+                 )
+            
+            low_res_masks, _ = self.mask_decoder2(
+                image_embeddings=curr_embedding,
+                image_pe=self.prompt_encoder2.get_dense_pe().detach(),
+                sparse_prompt_embeddings=sparse_embeddings.detach(),
+                dense_prompt_embeddings=dense_embeddings.detach(),
+                multimask_output=False,
+            )
+            
+            outputs.append(low_res_masks)
+        low_res_masks_promtp = torch.cat(outputs_prompt, dim=0)
+        low_res_masks = torch.cat(outputs, dim=0)
+        
+
+        return low_res_masks, low_res_masks_promtp
+    
+test_dataset = PanDataset(
+    [
+        
+     args.test_dir
+     ],
+    [
+     args.test_labels_dir
+     ],
+        
+    [["NIH_PNG",1]],
+
+    image_size,
+    
+    slice_per_image=slice_per_image,
+    train=False,
+) 
+device = "cuda:0"
+
+x = torch.load('sam_tuned_save.pth', map_location='cpu')
+# raise ValueError(x)
+x.to(device)
+# num_samples = 0
+# counterb=0
+# index=0
+# for image, label in tqdm(test_dataset, total=len(test_dataset)):
+#     num_samples += 1
+#     counterb += 1
+#     index += 1
+#     image = image.to(device)
+#     label = label.to(device).float()
+    
+#     ############################model and dice########################################
+#     box = torch.tensor([[200, 200, 750, 800]]).to(device)
+#     low_res_masks_main,low_res_masks_prompt = x(image,box)
+
+def process_model(main_model , test_dataset, train=0, save_output=0):
+    epoch_losses = []
+    results=[]
+    results_prompt=[]
+    index = 0
+    results = torch.zeros((2, 0, 256, 256))
+    results_prompt = torch.zeros((2, 0, 256, 256))
+    
+    #############################
+    total_dice = 0.0
+    total_precision = 0.0
+    total_recall =0.0
+    total_jaccard = 0.0
+    #############################
+    num_samples = 0
+    #############################
+    total_dice_main =0.0
+    total_precision_main = 0.0
+    total_recall_main =0.0
+    total_jaccard_main = 0.0
+    counterb = 0
+    for image, label , raw_data in tqdm(test_dataset, total=len(test_dataset)):
+        # raise ValueError(image.shape , label.shape , raw_data.shape)
+        num_samples += 1
+        counterb += 1
+        index += 1
+        image = image.to(device)
+        label = label.to(device).float()
+        
+        ############################model and dice########################################
+        box = torch.tensor([[200, 200, 750, 800]]).to(device)
+        low_res_masks_main,low_res_masks_prompt = main_model(image,box)
+        
+        low_res_label = F.interpolate(label, low_res_masks_main.shape[-2:]) 
+        
+        dice_prompt,  precisio_prompt , recall_prompt , jaccard_prompt  = what_the_f(low_res_masks_prompt,low_res_label)
+        dice_main , precision_main , recall_main , jaccard_main  = what_the_f(low_res_masks_main,low_res_label)
+
+        # binary_mask = normalize(threshold(low_res_masks_main, 0.0,0))
+        ##############prompt###############
+        total_dice += dice_prompt
+        total_precision += precisio_prompt
+        total_recall += recall_prompt
+        total_jaccard += jaccard_prompt
+        average_dice = total_dice / num_samples
+        average_precision = total_precision /num_samples
+        average_recall = total_recall /num_samples
+        average_jaccard = total_jaccard /num_samples
+        
+        ##############main##################
+        total_dice_main+=dice_main
+        total_precision_main +=precision_main
+        total_recall_main +=recall_main
+        total_jaccard_main += jaccard_main
+        
+        
+        average_dice_main = total_dice_main / num_samples
+        average_precision_main = total_precision_main /num_samples
+        average_recall_main = total_recall_main /num_samples
+        average_jaccard_main = total_jaccard_main /num_samples
+        binary_mask = normalize(threshold(low_res_masks_main, 0.0,0))
+        binary_mask_mask = normalize(threshold(low_res_masks_prompt, 0.0,0))
+        
+
+        
+        ###################################
+    
+        result = torch.cat(
+            (
+                low_res_masks_main[0].detach().cpu().reshape(1, 1, 256, 256),
+                binary_mask[0].detach().cpu().reshape(1, 1, 256, 256),
+            ),
+            dim=0,
+        )
+        results = torch.cat((results, result), dim=1)
+        
+        result_prompt = torch.cat(
+            (
+                low_res_masks_prompt[0].detach().cpu().reshape(1, 1, 256, 256),
+                binary_mask_mask[0].detach().cpu().reshape(1, 1, 256, 256),
+            ),
+            dim=0,
+        )
+        results_prompt = torch.cat((results_prompt, result_prompt), dim=1)
+        # if counterb == len(test_dataset)-5:
+        #     break
+        if counterb == 200:
+            break
+        # elif counterb == sample_size and not train:
+        #     break
+
+    return epoch_losses, results,results_prompt, average_dice,average_precision ,average_recall, average_jaccard,average_dice_main,average_precision_main,average_recall_main,average_jaccard_main
+
+
+print("Testing:")
+test_epoch_losses, epoch_results , results_prompt, average_dice_test,average_precision ,average_recall, average_jaccard,average_dice_test_main,average_precision_main,average_recall_main,average_jaccard_main = process_model(
+    x,test_dataset
+)
+# raise ValueError(len(epoch_results[0]))
+train_losses = []
+train_epochs = []
+test_losses = []
+test_epochs = []
+dice = []
+dice_main = []
+dice_test = []
+dice_test_main =[]
+results = []
+index = 0
+##############################save image#########################################
+for image, label , raw_data in tqdm(test_dataset):
+    
+    if index < 200:
+        if not os.path.exists(f"result_img/batch_{index}"):
+            os.mkdir(f"result_img/batch_{index}")
+
+        save_img(
+            image[0],
+            f"result_img/batch_{index}/img.png",
+        )
+        tensor_raw = torch.tensor(raw_data)
+        save_img(
+            tensor_raw.T,
+            f"result_img/batch_{index}/raw_img.png",
+        )
+        
+        model_result_resized = TF.resize(epoch_results, size=(1024, 1024))
+        result_canvas = torch.zeros_like(image[0])
+        result_canvas[1] = label[0][0]
+        result_canvas[0] = model_result_resized[1, index]
+        blended_result = 0.2 * image[0] + 0.5 * result_canvas
+        
+        ###################################################################
+        
+        model_result_resized_prompt = TF.resize(results_prompt, size=(1024, 1024))
+        result_canvas_prompt = torch.zeros_like(image[0])
+        result_canvas_prompt[1] = label[0][0]
+        # raise ValueError(model_result_resized_prompt.shape ,model_result_resized.shape )
+        result_canvas_prompt[0] = model_result_resized_prompt[1, index]
+        blended_result_prompt = 0.2 * image[0] + 0.5 * result_canvas_prompt
+        
+        
+
+        save_img(blended_result, f"result_img/batch_{index}/comb.png")
+        save_img(blended_result_prompt, f"result_img/batch_{index}/comb_prompt.png")
+        
+        
+        save_img(
+        epoch_results[1, index].clone(),f"result_img/batch_{index}/modelresult.png",
+        )
+        save_img(
+                epoch_results[0, index].clone(),f"result_img/batch_{index}/prob_epoch_{index}.png",)
+        
+
+    index += 1
+    if index == 200:
+        break
+
+
+dice_test.append(average_dice_test)
+dice_test_main.append(average_dice_test_main)
+print("######################Prompt##########################")
+print(f"Test Dice : {average_dice_test}")
+print(f"Test presision : {average_precision}")
+print(f"Test recall : {average_recall}")
+print(f"Test jaccard : {average_jaccard}")
+
+print("######################Main##########################")
+print(f"Test Dice main : {average_dice_test_main}")
+print(f"Test presision main : {average_precision_main}")
+print(f"Test recall main : {average_recall_main}")
+print(f"Test jaccard main : {average_jaccard_main}")
+

fine_tune_good.py

@@ -0,0 +1,541 @@
+debug = 0
+from pathlib import Path
+import numpy as np
+import matplotlib.pyplot as plt
+from utils import sample_prompt , main_prompt 
+from collections import defaultdict
+import torchvision.transforms as transforms
+import torch
+from torch import nn
+import torch.nn.functional as F
+from segment_anything.utils.transforms import ResizeLongestSide
+import albumentations as A
+from albumentations.pytorch import ToTensorV2
+import numpy as np
+from einops import rearrange
+import random
+from tqdm import tqdm
+from time import sleep
+from data import *
+from time import time
+from PIL import Image
+from sklearn.model_selection import KFold
+from shutil import copyfile
+from args import get_arguments
+
+args = get_arguments()
+def save_img(img, dir):
+    img = img.clone().cpu().numpy() + 100
+
+    if len(img.shape) == 3:
+        img = rearrange(img, "c h w -> h w c")
+        img_min = np.amin(img, axis=(0, 1), keepdims=True)
+        img = img - img_min
+
+        img_max = np.amax(img, axis=(0, 1), keepdims=True)
+        img = (img / img_max * 255).astype(np.uint8)
+        # grey_img = Image.fromarray(img[:, :, 0])
+        img = Image.fromarray(img)
+
+    else:
+        img_min = img.min()
+        img = img - img_min
+        img_max = img.max()
+        if img_max != 0:
+            img = img / img_max * 255
+        img = Image.fromarray(img).convert("L")
+
+    img.save(dir)
+
+
+
+class loss_fn(torch.nn.Module):
+    def __init__(self, alpha=0.7, gamma=2.0, epsilon=1e-5):
+        super(loss_fn, self).__init__()
+        self.alpha = alpha
+        self.gamma = gamma
+        self.epsilon = epsilon
+
+    def tversky_loss(self, y_pred, y_true, alpha=0.8, beta=0.2, smooth=1e-2):
+        y_pred = torch.sigmoid(y_pred)
+        y_true_pos = torch.flatten(y_true)
+        y_pred_pos = torch.flatten(y_pred)
+        true_pos = torch.sum(y_true_pos * y_pred_pos)
+        false_neg = torch.sum(y_true_pos * (1 - y_pred_pos))
+        false_pos = torch.sum((1 - y_true_pos) * y_pred_pos)
+        tversky_index = (true_pos + smooth) / (
+            true_pos + alpha * false_neg + beta * false_pos + smooth
+        )
+        return 1 - tversky_index
+
+    def focal_tversky(self, y_pred, y_true, gamma=0.75):
+        pt_1 = self.tversky_loss(y_pred, y_true)
+        return torch.pow((1 - pt_1), gamma)
+
+    def dice_loss(self, logits, gt, eps=1):
+        # Convert logits to probabilities
+        # Flatten the tensorsx
+        probs = torch.sigmoid(logits)
+
+        probs = probs.view(-1)
+        gt = gt.view(-1)
+
+        # Compute Dice coefficient
+        intersection = (probs * gt).sum()
+
+        dice_coeff = (2.0 * intersection + eps) / (probs.sum() + gt.sum() + eps)
+
+        # Compute Dice Los[s
+        loss = 1 - dice_coeff
+        return loss
+
+    def focal_loss(self, logits, gt, gamma=2):
+        logits = logits.reshape(-1, 1)
+        gt = gt.reshape(-1, 1)
+        logits = torch.cat((1 - logits, logits), dim=1)
+
+        probs = torch.sigmoid(logits)
+        pt = probs.gather(1, gt.long())
+
+        modulating_factor = (1 - pt) ** gamma
+        # pt_false= pt<=0.5
+        # modulating_factor[pt_false] *= 2
+        focal_loss = -modulating_factor * torch.log(pt + 1e-12)
+
+        # Compute the mean focal loss
+        loss = focal_loss.mean()
+        return loss  # Store as a Python number to save memory
+
+    def forward(self, logits, target):
+        logits = logits.squeeze(1)
+        target = target.squeeze(1)
+        # Dice Loss
+        # prob = F.softmax(logits, dim=1)[:, 1, ...]
+
+        dice_loss = self.dice_loss(logits, target)
+        tversky_loss = self.tversky_loss(logits, target)
+
+        # Focal Loss
+        focal_loss = self.focal_loss(logits, target.squeeze(-1))
+        alpha = 20.0
+        # Combined Loss
+        combined_loss = alpha * focal_loss + dice_loss
+        return combined_loss
+
+
+def img_enhance(img2, coef=0.2):
+    img_mean = np.mean(img2)
+    img_max = np.max(img2)
+    val = (img_max - img_mean) * coef + img_mean
+    img2[img2 < img_mean * 0.7] = img_mean * 0.7
+    img2[img2 > val] = val
+    return img2
+
+
+def dice_coefficient(logits, gt):
+    eps=1
+    binary_mask = logits>0
+    intersection = (binary_mask * gt).sum(dim=(-2,-1))
+    dice_scores = (2.0 * intersection + eps) / (binary_mask.sum(dim=(-2,-1)) + gt.sum(dim=(-2,-1)) + eps)
+    
+    return dice_scores.mean()
+
+def what_the_f(low_res_masks,label):
+            
+    low_res_label = F.interpolate(label, low_res_masks.shape[-2:])
+    dice = dice_coefficient(
+        low_res_masks, low_res_label
+    )
+    return dice
+
+
+image_size = args.image_size
+exp_id = 0
+found = 0
+if debug:
+    user_input='debug'
+else:    
+    user_input = input("Related changes: ")
+while found == 0:
+    try:
+        os.makedirs(f"exps/{exp_id}-{user_input}")
+        found = 1
+    except:
+        exp_id = exp_id + 1
+copyfile(os.path.realpath(__file__), f"exps/{exp_id}-{user_input}/code.py")
+layer_n = 4
+L = layer_n
+a = np.full(L, layer_n)
+params = {"M": 255, "a": a, "p": 0.35}
+
+device = "cuda:1"
+from segment_anything import SamPredictor, sam_model_registry
+class panc_sam(nn.Module):
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+        
+        #Promptless
+        sam = torch.load(args.promptprovider)
+        
+        self.prompt_encoder = sam.prompt_encoder
+        
+        self.mask_decoder = sam.mask_decoder
+        for param in self.prompt_encoder.parameters():
+            param.requires_grad = False
+            
+        for param in self.mask_decoder.parameters():
+            param.requires_grad = False
+        
+        #with Prompt
+        sam=sam_model_registry[args.model_type](args.checkpoint)
+        self.image_encoder = sam.image_encoder
+        self.prompt_encoder2 = sam.prompt_encoder
+        self.mask_decoder2 = sam.mask_decoder
+        
+        for param in self.image_encoder.parameters():
+            param.requires_grad = False
+            
+        for param in self.prompt_encoder2.parameters():
+            param.requires_grad = False
+
+    def forward(self, input_images,box=None):
+        
+
+        # input_images = torch.stack([x["image"] for x in batched_input], dim=0)
+        with torch.no_grad():
+            image_embeddings = self.image_encoder(input_images).detach()
+
+            
+        outputs_prompt = []
+        outputs = []
+        
+        for curr_embedding in image_embeddings:
+            
+            with torch.no_grad():
+                sparse_embeddings, dense_embeddings = self.prompt_encoder(
+                points=None,
+                boxes=None,
+                masks=None,
+                )
+            
+            low_res_masks, _ = self.mask_decoder(
+                image_embeddings=curr_embedding,
+                image_pe=self.prompt_encoder.get_dense_pe().detach(),
+                sparse_prompt_embeddings=sparse_embeddings.detach(),
+                dense_prompt_embeddings=dense_embeddings.detach(),
+                multimask_output=False,
+            )
+            outputs_prompt.append(low_res_masks)
+            
+            points, point_labels = main_prompt(low_res_masks)
+            points_sconed, point_labels_sconed = sample_prompt(low_res_masks)
+            points = torch.cat([points, points_sconed], dim=1)  # Adjust dimensions as necessary
+            point_labels = torch.cat([point_labels, point_labels_sconed], dim=1)    
+            
+            
+            
+            points = points * 4
+            points = (points, point_labels)
+
+            with torch.no_grad():
+                sparse_embeddings, dense_embeddings = self.prompt_encoder2(
+                     points=points,
+                     boxes=None,
+                     masks=None,
+                 )
+            
+            low_res_masks, _ = self.mask_decoder2(
+                image_embeddings=curr_embedding,
+                image_pe=self.prompt_encoder2.get_dense_pe().detach(),
+                sparse_prompt_embeddings=sparse_embeddings.detach(),
+                dense_prompt_embeddings=dense_embeddings.detach(),
+                multimask_output=False,
+            )
+            
+            outputs.append(low_res_masks)
+        low_res_masks_promtp = torch.cat(outputs_prompt, dim=0)
+        low_res_masks = torch.cat(outputs, dim=0)
+        
+
+        return low_res_masks, low_res_masks_promtp
+
+augmentation = A.Compose(
+    [
+        A.Rotate(limit=30, p=0.5),
+        A.RandomBrightnessContrast(brightness_limit=0.3, contrast_limit=0.3, p=1),
+        A.RandomResizedCrop(1024, 1024, scale=(0.9, 1.0), p=1),
+        A.HorizontalFlip(p=0.5),
+        A.CLAHE(clip_limit=2.0, tile_grid_size=(8, 8), p=0.5),
+        A.CoarseDropout(
+            max_holes=8,
+            max_height=16,
+            max_width=16,
+            min_height=8,
+            min_width=8,
+            fill_value=0,
+            p=0.5,
+        ),
+        A.RandomScale(scale_limit=0.3, p=0.5),
+            
+    ]
+)
+panc_sam_instance = panc_sam()
+panc_sam_instance.to(device)
+panc_sam_instance.train()
+train_dataset = PanDataset(
+    [args.dir_train],
+    [args.dir_labels],
+
+    [["NIH_PNG",1]],
+    
+    args.image_size,
+    
+    slice_per_image=args.slice_per_image,
+    train=True,
+    augmentation=augmentation,
+)
+val_dataset = PanDataset(
+    [args.dir_train],
+    [args.dir_labels],
+        
+    [["NIH_PNG",1]],
+
+    image_size,
+    
+    slice_per_image=args.slice_per_image,
+    train=False,
+) 
+train_loader = DataLoader(
+    train_dataset,
+    batch_size=args.batch_size,
+    collate_fn=train_dataset.collate_fn,
+    shuffle=True,
+    drop_last=False,
+    num_workers=args.num_workers,
+)
+val_loader = DataLoader(
+    val_dataset,
+    batch_size=args.batch_size,
+    collate_fn=val_dataset.collate_fn,
+    shuffle=True,
+    drop_last=False,
+    num_workers=args.num_workers,
+)
+
+lr = 1e-4
+#1e-3
+max_lr = 3e-4
+wd = 5e-4
+optimizer_main = torch.optim.Adam(
+    # parameters,
+    list(panc_sam_instance.mask_decoder2.parameters()),
+    
+    lr=lr,
+    weight_decay=wd,
+)
+scheduler_main = torch.optim.lr_scheduler.OneCycleLR(
+    optimizer_main,
+    max_lr=max_lr,
+    epochs=args.num_epochs,
+    steps_per_epoch=args.sample_size // (args.accumulative_batch_size // args.batch_size),
+)
+#####################################################
+
+from statistics import mean
+
+from tqdm import tqdm
+from torch.nn.functional import threshold, normalize
+
+loss_function = loss_fn(alpha=0.5, gamma=4.0)
+loss_function.to(device)
+
+from time import time
+import time as s_time
+
+log_file = open(f"exps/{exp_id}-{user_input}/log.txt", "a")
+
+
+def process_model(main_model , data_loader, train=0, save_output=0):
+    epoch_losses = []
+    index = 0
+    results = torch.zeros((2, 0, 256, 256))
+    total_dice = 0.0
+    num_samples = 0
+    total_dice_main =0.0
+
+    counterb = 0
+    for image, label,raw_data in tqdm(data_loader, total=args.sample_size):
+        num_samples += 1
+        counterb += 1
+        index += 1
+        image = image.to(device)
+        label = label.to(device).float()
+        
+        ############################promt########################################
+        box = torch.tensor([[200, 200, 750, 800]]).to(device)
+        low_res_masks_main,low_res_masks_prompt = main_model(image,box)
+        
+        low_res_label = F.interpolate(label, low_res_masks_main.shape[-2:]) 
+        
+        dice_prompt = what_the_f(low_res_masks_prompt,low_res_label)
+        dice_main = what_the_f(low_res_masks_main,low_res_label)
+        
+        binary_mask = normalize(threshold(low_res_masks_main, 0.0,0))
+        total_dice += dice_prompt
+        total_dice_main+=dice_main
+        
+        average_dice = total_dice / num_samples
+        average_dice_main = total_dice_main / num_samples
+        log_file.write(str(average_dice) + "\n")
+        log_file.flush()
+        loss = loss_function.forward(low_res_masks_main, low_res_label)
+        loss /= args.accumulative_batch_size / args.batch_size
+        
+        if train:
+            loss.backward()
+        
+
+            if index % (args.accumulative_batch_size / args.batch_size) == 0:
+                # print(loss)
+                optimizer_main.step()
+                scheduler_main.step()
+                optimizer_main.zero_grad()
+                index = 0
+
+        else:
+            result = torch.cat(
+                (
+                    low_res_masks_main[0].detach().cpu().reshape(1, 1, 256, 256),
+                    binary_mask[0].detach().cpu().reshape(1, 1, 256, 256),
+                ),
+                dim=0,
+            )
+            results = torch.cat((results, result), dim=1)
+        if index % (args.accumulative_batch_size / args.batch_size) == 0:
+            epoch_losses.append(loss.item())
+        if counterb == args.sample_size and train:
+            break
+        elif counterb == args.sample_size //2  and not train:
+            break
+
+    return epoch_losses, results, average_dice ,average_dice_main
+
+
+def train_model(train_loader, val_loader, K_fold=False, N_fold=7, epoch_num_start=7):
+    print("Train model started.")
+
+    train_losses = []
+    train_epochs = []
+    val_losses = []
+    val_epochs = []
+    dice = []
+    dice_main = []
+    dice_val = []
+    dice_val_main =[]
+    results = []
+#########################save image##################################
+    index = 0
+    if debug==0:
+        for image, label , raw_data in tqdm(val_loader):
+            if index < 100:
+                if not os.path.exists(f"ims/batch_{index}"):
+                    os.mkdir(f"ims/batch_{index}")
+
+                save_img(
+                    image[0],
+                    f"ims/batch_{index}/img_0.png",
+                )
+                save_img(0.2 * image[0][0] + label[0][0], f"ims/batch_{index}/gt_0.png")
+
+            index += 1
+            if index == 100:
+                break
+
+    # In each epoch we will train the model and the val it
+    # training without k_fold cross validation:
+    last_best_dice = 0
+    if K_fold == False:
+        for epoch in range(args.num_epochs):
+            print(f"=====================EPOCH: {epoch + 1}=====================")
+            log_file.write(
+                f"=====================EPOCH: {epoch + 1}===================\n"
+            )
+            print("Training:")
+            train_epoch_losses, epoch_results, average_dice ,average_dice_main = process_model(panc_sam_instance,train_loader, train=1)
+
+            dice.append(average_dice)
+            dice_main.append(average_dice_main)
+            train_losses.append(train_epoch_losses)
+            if (average_dice) > 0.5:
+                print("validating:")
+                val_epoch_losses, epoch_results, average_dice_val,average_dice_val_main = process_model(
+                    panc_sam_instance,val_loader
+                )
+
+                val_losses.append(val_epoch_losses)
+                for i in tqdm(range(len(epoch_results[0]))):
+                    if not os.path.exists(f"ims/batch_{i}"):
+                        os.mkdir(f"ims/batch_{i}")
+
+                    save_img(
+                        epoch_results[0, i].clone(),
+                        f"ims/batch_{i}/prob_epoch_{epoch}.png",
+                    )
+                    save_img(
+                        epoch_results[1, i].clone(),
+                        f"ims/batch_{i}/pred_epoch_{epoch}.png",
+                    )
+
+            train_mean_losses = [mean(x) for x in train_losses]
+            val_mean_losses = [mean(x) for x in val_losses]
+            np.save("train_losses.npy", train_mean_losses)
+            np.save("val_losses.npy", val_mean_losses)
+
+            print(f"Train Dice: {average_dice}")
+            print(f"Train Dice main: {average_dice_main}")
+            
+            
+            print(f"Mean train loss: {mean(train_epoch_losses)}")
+
+            try:
+                dice_val.append(average_dice_val)
+                dice_val_main.append(average_dice_val_main)
+                print(f"val Dice : {average_dice_val}")
+                print(f"val Dice main : {average_dice_val_main}")
+                
+                print(f"Mean val loss: {mean(val_epoch_losses)}")
+
+                results.append(epoch_results)
+                val_epochs.append(epoch)
+                train_epochs.append(epoch)
+                plt.plot(val_epochs, val_mean_losses, train_epochs, train_mean_losses)
+                if average_dice_val_main > last_best_dice:
+                    torch.save(
+                        panc_sam_instance,
+                        f"exps/{exp_id}-{user_input}/sam_tuned_save.pth",
+                    )
+
+                    last_best_dice = average_dice_val
+                del epoch_results
+                del average_dice_val
+            except:
+                train_epochs.append(epoch)
+                plt.plot(train_epochs, train_mean_losses)
+                print(f"=================End of EPOCH: {epoch}==================\n")
+
+            plt.yscale("log")
+            plt.title("Mean epoch loss")
+            plt.xlabel("Epoch Number")
+            plt.ylabel("Loss")
+            plt.savefig("result")
+
+    ## training with k-fold cross validation:
+    
+
+    return train_losses, val_losses, results
+
+
+train_losses, val_losses, results = train_model(train_loader, val_loader)
+log_file.close()
+
+

kernel/Models/model_handler.py

@@ -0,0 +1,202 @@
+import torch
+from torch import nn
+from torch.nn import functional as F
+from utils import create_prompt_main
+
+device = 'cuda:0'
+
+
+from segment_anything import SamPredictor, sam_model_registry
+
+
+class panc_sam(nn.Module):
+    
+    def forward(self, batched_input, device):
+        box = torch.tensor([[200, 200, 750, 800]]).to(device)
+        outputs = []
+        outputs_prompt = []
+        for image_record in batched_input:
+            image_embeddings = image_record["image_embedd"].to(device)
+            if "point_coords" in image_record:
+                point_coords = image_record["point_coords"].to(device)
+                point_labels = image_record["point_labels"].to(device)
+                points = (point_coords.unsqueeze(0), point_labels.unsqueeze(0))
+
+            else:
+                raise ValueError("what the f?")
+            # input_images = torch.stack([x["image"] for x in batched_input], dim=0)
+
+            with torch.no_grad():
+                sparse_embeddings, dense_embeddings = self.prompt_encoder(
+                    points=None,
+                    boxes=box,
+                    masks=None,
+                )
+                sparse_embeddings = sparse_embeddings
+                dense_embeddings = dense_embeddings
+                # raise ValueError(image_embeddings.shape)
+                #####################################################
+
+                low_res_masks, _ = self.mask_decoder(
+                    image_embeddings=image_embeddings,
+                    image_pe=self.prompt_encoder.get_dense_pe().detach(),
+                    sparse_prompt_embeddings=sparse_embeddings.detach(),
+                    dense_prompt_embeddings=dense_embeddings.detach(),
+                    multimask_output=False,
+                )
+
+                outputs.append(low_res_masks)
+
+                # points, point_labels = create_prompt((low_res_masks > 0).float())
+                # points, point_labels = create_prompt(low_res_masks)
+                points, point_labels = create_prompt_main(low_res_masks)
+
+
+                points = points * 4
+                points = (points, point_labels)
+
+                with torch.no_grad():
+                    sparse_embeddings, dense_embeddings = self.prompt_encoder2(
+                        points=points,
+                        boxes=None,
+                        masks=None,
+                    )
+
+                low_res_masks, _ = self.mask_decoder2(
+                    image_embeddings=image_embeddings,
+                    image_pe=self.prompt_encoder2.get_dense_pe().detach(),
+                    sparse_prompt_embeddings=sparse_embeddings.detach(),
+                    dense_prompt_embeddings=dense_embeddings.detach(),
+                    multimask_output=False,
+                )
+
+                outputs_prompt.append(low_res_masks)
+
+        low_res_masks_promtp = torch.cat(outputs_prompt, dim=1)
+        low_res_masks = torch.cat(outputs, dim=1)
+
+        return low_res_masks, low_res_masks_promtp
+
+
+
+
+
+def double_conv_3d(in_channels, out_channels):
+    return nn.Sequential(
+        nn.Conv3d(in_channels, out_channels, kernel_size=(1, 3, 3), padding=(0, 1, 1)),
+        nn.ReLU(inplace=True),
+        nn.Conv3d(out_channels, out_channels, kernel_size=(3, 1, 1), padding=(1, 0, 0)),
+        nn.ReLU(inplace=True),
+    )
+
+#Was not used
+class UNet3D(nn.Module):
+
+    def __init__(self):
+        super(UNet3D, self).__init__()
+
+        self.dconv_down1 = double_conv_3d(1, 32)
+        self.dconv_down2 = double_conv_3d(32, 64)
+        self.dconv_down3 = double_conv_3d(64, 96)
+
+        self.maxpool = nn.MaxPool3d((1, 2, 2))
+        self.upsample = nn.Upsample(
+            scale_factor=(1, 2, 2), mode="trilinear", align_corners=True
+        )
+
+        self.dconv_up2 = double_conv_3d(64 + 96, 64)
+        self.dconv_up1 = double_conv_3d(64 + 32, 32)
+
+        self.conv_last = nn.Conv3d(32, 1, kernel_size=1)
+
+    def forward(self, x):
+        x = x.unsqueeze(1)
+        conv1 = self.dconv_down1(x)
+
+        x = self.maxpool(conv1)
+
+        conv2 = self.dconv_down2(x)
+
+        x = self.maxpool(conv2)
+        x = self.dconv_down3(x)
+
+
+        x = self.upsample(x)
+
+        x = torch.cat([x, conv2], dim=1)
+        x = self.dconv_up2(x)
+        x = self.upsample(x)
+        x = torch.cat([x, conv1], dim=1)
+
+        x = self.dconv_up1(x)
+        out = self.conv_last(x)
+        return out
+
+class Conv3DFilter(nn.Module):
+    def __init__(
+        self,
+        in_channels=1,
+        out_channels=1,
+        kernel_size=[(3, 1, 1), (3, 1, 1), (3, 1, 1), (3, 1, 1)],
+        padding_sizes=None,
+        custom_bias=0,
+    ):
+        super(Conv3DFilter, self).__init__()
+        self.custom_bias = custom_bias
+        
+        self.bias = 1e-8
+        # Convolutional layer with padding to maintain input spatial dimensions
+        self.convs = nn.ModuleList(
+            [
+                nn.Sequential(
+                    nn.Conv3d(
+                        in_channels,
+                        out_channels,
+                        kernel_size[0],
+                        padding=padding_sizes[0],
+                    ),
+
+                    nn.ReLU(),
+                    nn.Conv3d(
+                        out_channels,
+                        out_channels,
+                        kernel_size[0],
+                        padding=padding_sizes[0],
+                    ),
+
+                    nn.ReLU(),
+                )
+            ]
+        )
+        for kernel, padding in zip(kernel_size[1:-1], padding_sizes[1:-1]):
+
+            self.convs.extend(
+                [
+                    nn.Sequential(
+                        nn.Conv3d(
+                            out_channels, out_channels, kernel, padding=padding
+                        ),
+
+                        nn.ReLU(),
+                        nn.Conv3d(
+                            out_channels, out_channels, kernel, padding=padding
+                        ),
+
+                        nn.ReLU(),
+                    )
+                ]
+            )
+        self.output_conv = nn.Conv3d(
+            out_channels, 1, kernel_size[-1], padding=padding_sizes[-1]
+        )
+            
+        # self.m = nn.LeakyReLU(0.1)
+
+    def forward(self, input):
+        x = input.unsqueeze(1)
+
+        for module in self.convs:
+            x = module(x) + x
+        x = self.output_conv(x)
+        x = torch.sigmoid(x).squeeze(1)
+        return x

kernel/Models/utils.py

@@ -0,0 +1,170 @@
+import torch
+import torch.nn as nn
+import numpy as np
+
+
+def create_prompt_simple(masks, forground=2, background=2):
+    kernel_size = 9
+    kernel = nn.Conv2d(
+        in_channels=1,
+        bias=False,
+        out_channels=1,
+        kernel_size=kernel_size,
+        stride=1,
+        padding=kernel_size // 2,
+    )
+    # print(kernel.weight.shape)
+    kernel.weight = nn.Parameter(
+        torch.zeros(1, 1, kernel_size, kernel_size).to(masks.device),
+        requires_grad=False,
+    )
+    kernel.weight[0, 0] = 1.0
+    eroded_masks = kernel(masks).squeeze(1)//(kernel_size**2)
+    masks = masks.squeeze(1)
+    use_eroded = (eroded_masks.sum(dim=(1, 2), keepdim=True) >= forground).float()
+    new_masks = (eroded_masks * use_eroded) + (masks * (1 - use_eroded))
+    all_points = []
+    all_labels = []
+    for i in range(len(new_masks)):
+        new_background = background
+        points = []
+        labels = []
+        new_mask = new_masks[i]
+        nonzeros = torch.nonzero(new_mask, as_tuple=False)
+        n_nonzero = len(nonzeros)
+        if n_nonzero >= forground:
+            indices = np.random.choice(
+                np.arange(n_nonzero), size=forground, replace=False
+            ).tolist()
+            # raise ValueError(nonzeros[:, [0, 1]][indices])
+            points.append(nonzeros[:, [1,0]][indices])
+            labels.append(torch.ones(forground))
+        else:
+            if n_nonzero > 0:
+                points.append(nonzeros)
+                labels.append(torch.ones(n_nonzero))
+            new_background += forground - n_nonzero
+        # print(points, new_background)
+        zeros = torch.nonzero(1 - masks[i], as_tuple=False)
+        n_zero = len(zeros)
+        indices = np.random.choice(
+            np.arange(n_zero), size=new_background, replace=False
+        ).tolist()
+        points.append(zeros[:, [1, 0]][indices])
+        labels.append(torch.zeros(new_background))
+        points = torch.cat(points, dim=0)
+        labels = torch.cat(labels, dim=0)
+        all_points.append(points)
+        all_labels.append(labels)
+    all_points = torch.stack(all_points, dim=0)
+    all_labels = torch.stack(all_labels, dim=0)
+    return all_points, all_labels 
+
+
+
+def distance_to_edge(point, image_shape):
+    y, x = point
+    height, width = image_shape
+    distance_top = y
+    distance_bottom = height - y
+    distance_left = x
+    distance_right = width - x
+    return min(distance_top, distance_bottom, distance_left, distance_right)
+
+def create_prompt(probabilities, foreground=2, background=2):
+    kernel_size = 9
+    kernel = nn.Conv2d(
+        in_channels=1,
+        bias=False,
+        out_channels=1,
+        kernel_size=kernel_size,
+        stride=1,
+        padding=kernel_size // 2,
+    )
+    kernel.weight = nn.Parameter(
+        torch.zeros(1, 1, kernel_size, kernel_size).to(probabilities.device),
+        requires_grad=False,
+    )
+    kernel.weight[0, 0] = 1.0
+    eroded_probs = kernel(probabilities).squeeze(1) / (kernel_size ** 2)
+    probabilities = probabilities.squeeze(1)
+
+    all_points = []
+    all_labels = []
+
+    for i in range(len(probabilities)):
+        points = []
+        labels = []
+
+        prob_mask = probabilities[i]
+
+        if torch.max(prob_mask) > 0.01:
+            foreground_indices = torch.topk(prob_mask.view(-1), k=foreground, dim=0).indices
+            foreground_points = torch.nonzero(prob_mask > 0, as_tuple=False)
+            n_foreground = len(foreground_points)
+            if n_foreground >= foreground:
+                # Get the index of the point with the highest probability
+                top_prob_idx = torch.topk(prob_mask.view(-1), k=1).indices[0]
+                # Convert the flat index to 2D coordinates
+                top_prob_point = np.unravel_index(top_prob_idx.item(), prob_mask.shape)
+                top_prob_point = torch.tensor(top_prob_point, device=probabilities.device)  # Move to the same device
+
+                # Add the point with the highest probability to the points list
+                points.append(torch.tensor([top_prob_point[1], top_prob_point[0]], device=probabilities.device).unsqueeze(0))
+                labels.append(torch.ones(1, device=probabilities.device))
+
+                # Exclude the top probability point when finding the point closest to the edge
+                remaining_foreground_points = foreground_points[(foreground_points != top_prob_point.unsqueeze(0)).all(dim=1)]
+                if remaining_foreground_points.numel() > 0:
+                    distances = [distance_to_edge(point.cpu().numpy(), prob_mask.shape) for point in remaining_foreground_points]
+                    edge_point_idx = np.argmin(distances)
+                    edge_point = remaining_foreground_points[edge_point_idx]
+
+                    # Add the edge point to the points list
+                    points.append(edge_point[[1, 0]].unsqueeze(0))
+                    labels.append(torch.ones(1, device=probabilities.device))
+                    # raise ValueError(points , labels)
+            else:
+                if n_foreground > 0:
+                    points.append(foreground_points[:, [1, 0]])
+                    labels.append(torch.ones(n_foreground))
+
+
+
+            # Select 2 background points, one from 0 to -15 and one less than -15
+            background_indices_1 = torch.nonzero((prob_mask < 0) & (prob_mask > -15), as_tuple=False)
+            background_indices_2 = torch.nonzero(prob_mask < -15, as_tuple=False)
+
+            # Randomly sample from each set of background points
+            indices_1 = np.random.choice(np.arange(len(background_indices_1)), size=1, replace=False).tolist()
+            indices_2 = np.random.choice(np.arange(len(background_indices_2)), size=1, replace=False).tolist()
+
+            points.append(background_indices_1[indices_1])
+            points.append(background_indices_2[indices_2])
+            labels.append(torch.zeros(2))
+        else:
+            # If no probability is greater than 0, return 4 background points
+            # print(prob_mask.unique())
+            background_indices_1 = torch.nonzero(prob_mask < 0, as_tuple=False)
+
+            indices_1 = np.random.choice(np.arange(len(background_indices_1)), size=4, replace=False).tolist()
+        
+            points.append(background_indices_1[indices_1])
+            labels.append(torch.zeros(4))
+            
+        labels = [label.to(probabilities.device) for label in labels]
+        points = torch.cat(points, dim=0)
+
+        all_points.append(points)
+        all_labels.append(torch.cat(labels, dim=0))
+
+
+    all_points = torch.stack(all_points, dim=0)
+    all_labels = torch.stack(all_labels, dim=0)
+    # print(all_points, all_labels)
+
+    return all_points, all_labels
+
+
+
+

kernel/args.py

@@ -0,0 +1,30 @@
+import argparse
+
+def get_arguments():
+    parser = argparse.ArgumentParser(description="Your program's description here")
+
+    parser.add_argument('--debug', action='store_true', help='Enable debug mode')
+    parser.add_argument('--accumulative_batch_size', type=int, default=2, help='Accumulative batch size')
+    parser.add_argument('--batch_size', type=int, default=1, help='Batch size')
+    parser.add_argument('--num_workers', type=int, default=1, help='Number of workers')
+    parser.add_argument('--slice_per_image', type=int, default=1, help='Slices per image')
+    parser.add_argument('--num_epochs', type=int, default=40, help='Number of epochs')
+    parser.add_argument('--sample_size', type=int, default=4, help='Sample size')
+    parser.add_argument('--image_size', type=int, default=1024, help='Image size')
+    parser.add_argument('--run_name', type=str, default='debug', help='The name of the run')
+    parser.add_argument('--lr', type=float, default=1e-3, help='Learning Rate')
+    parser.add_argument('--batch_step_one', type=int, default=15, help='Batch one')
+    parser.add_argument('--batch_step_two', type=int, default=25, help='Batch two')
+    parser.add_argument('--conv_model', type=str, default=None, help='Path to convolution model')
+    parser.add_argument('--custom_bias', type=float, default=0, help='Learning Rate')
+    parser.add_argument("--inference", action="store_true", help="Set for inference")
+    parser.add_argument("--conv_path", type=str, help="Path for convolutional model path (normally found in exps folder)")
+    
+    parser.add_argument("--train_dir",type=str, help="Path to the training data")
+    parser.add_argument("--test_dir",type=str, help="Path to the test data")
+    parser.add_argument("--test_labels_dir",type=str, help="Path to the test data")
+    parser.add_argument("--train_labels_dir",type=str, help="Path to the test data")
+
+    parser.add_argument("--model_path",type=str, help="Path to the test data")
+
+    return parser.parse_args()

kernel/data_load_group.py

@@ -0,0 +1,282 @@
+import matplotlib.pyplot as plt
+import os
+import numpy as np
+import random
+from segment_anything.utils.transforms import ResizeLongestSide
+from einops import rearrange
+import torch
+from segment_anything import SamPredictor, sam_model_registry
+from torch.utils.data import DataLoader
+from time import time
+import torch.nn.functional as F
+import cv2
+from PIL import Image
+import cv2
+from utils import create_prompt_simple
+from pre_processer import PreProcessing
+from tqdm import tqdm
+from args import get_arguments
+
+
+def apply_median_filter(input_matrix, kernel_size=5, sigma=0):
+    # Apply the Gaussian filter
+    filtered_matrix = cv2.medianBlur(input_matrix.astype(np.uint8), kernel_size)
+
+    return filtered_matrix.astype(np.float32)
+
+
+def apply_guassain_filter(input_matrix, kernel_size=(7, 7), sigma=0):
+    smoothed_matrix = cv2.blur(input_matrix, kernel_size)
+
+    return smoothed_matrix.astype(np.float32)
+
+
+def img_enhance(img2, over_coef=0.8, under_coef=0.7):
+    img2 = apply_median_filter(img2)
+    img_blure = apply_guassain_filter(img2)
+
+    img2 = img2 - 0.8 * img_blure
+
+    img_mean = np.mean(img2, axis=(1, 2))
+
+    img_max = np.amax(img2, axis=(1, 2))
+
+    val = (img_max - img_mean) * over_coef + img_mean
+
+    img2 = (img2 < img_mean * under_coef).astype(np.float32) * img_mean * under_coef + (
+        (img2 >= img_mean * under_coef).astype(np.float32)
+    ) * img2
+
+    img2 = (img2 <= val).astype(np.float32) * img2 + (img2 > val).astype(
+        np.float32
+    ) * val
+
+    return img2
+
+
+def normalize_and_pad(x, img_size):
+    """Normalize pixel values and pad to a square input."""
+
+    pixel_mean = torch.tensor([[[[123.675]], [[116.28]], [[103.53]]]])
+    pixel_std = torch.tensor([[[[58.395]], [[57.12]], [[57.375]]]])
+
+    # Normalize colors
+    x = (x - pixel_mean) / pixel_std
+
+    # Pad
+    h, w = x.shape[-2:]
+    padh = img_size - h
+    padw = img_size - w
+    x = F.pad(x, (0, padw, 0, padh))
+    return x
+
+
+def preprocess(img_enhanced, img_enhance_times=1, over_coef=0.4, under_coef=0.5):
+    # img_enhanced = img_enhanced+0.1
+
+    img_enhanced -= torch.min(img_enhanced)
+    img_max = torch.max(img_enhanced)
+    if img_max > 0:
+        img_enhanced = img_enhanced / img_max
+        # raise ValueError(img_max)
+        img_enhanced = img_enhanced.unsqueeze(1)
+        img_enhanced = img_enhanced.unsqueeze(1)
+        img_enhanced = PreProcessing.CLAHE(
+            img_enhanced, clip_limit=9.0, grid_size=(4, 4)
+        )
+        raise ValueError(img_enhanced.shape)
+        img_enhanced = img_enhanced[0]
+
+    # for i in range(img_enhance_times):
+    #     img_enhanced=img_enhance(img_enhanced.astype(np.float32), over_coef=over_coef,under_coef=under_coef)
+
+    img_enhanced -= torch.amin(img_enhanced, dim=(1, 2), keepdim=True)
+    larg_imag = (
+        img_enhanced / torch.amax(img_enhanced, axis=(1, 2), keepdims=True) * 255
+    ).type(torch.uint8)
+
+    return larg_imag
+
+
+def prepare(larg_imag, target_image_size):
+    # larg_imag = 255 - larg_imag
+    larg_imag = rearrange(larg_imag, "S H W -> S 1 H W")
+    larg_imag = torch.tensor(
+        np.concatenate([larg_imag, larg_imag, larg_imag], axis=1)
+    ).float()
+    transform = ResizeLongestSide(target_image_size)
+    larg_imag = transform.apply_image_torch(larg_imag)
+    larg_imag = normalize_and_pad(larg_imag, target_image_size)
+    return larg_imag
+
+
+def process_single_image(image_path, target_image_size):
+    # Load the image
+    if image_path.endswith(".png") or image_path.endswith(".jpg"):
+        data = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE).squeeze()
+    else:
+        data = np.load(image_path)
+    x = rearrange(data, "H W -> 1 H W")
+    x = torch.tensor(x)
+
+    # Apply preprocessing
+    x = preprocess(x)
+    x = prepare(x, target_image_size)
+
+    return x
+
+
+class PanDataset:
+    def __init__(
+        self,
+        dirs,
+        datasets,
+        target_image_size,
+        slice_per_image,
+        split_ratio=0.9,
+        train=True,
+        val=False,
+        augmentation=None,
+    ):
+        self.data_set_names = []
+        self.labels_path = []
+        self.images_path = []
+        self.embedds_path = []
+        self.labels_indexes = []
+        self.individual_index = []
+        for dir, dataset_name in zip(dirs, datasets):
+            labels_dir = dir + "/labels"
+            npy_files = [file for file in os.listdir(labels_dir)]
+            items_label = sorted(
+                npy_files,
+                key=lambda x: (
+                    int(x.split("_")[2].split(".")[0]),
+                    int(x.split("_")[1]),
+                ),
+            )
+
+            images_dir = dir + "/images"
+            npy_files = [file for file in os.listdir(images_dir)]
+            items_image = sorted(
+                npy_files,
+                key=lambda x: (
+                    int(x.split("_")[2].split(".")[0]),
+                    int(x.split("_")[1]),
+                ),
+            )
+            try:
+                embedds_dir = dir + "/embeddings"
+                npy_files = [file for file in os.listdir(embedds_dir)]
+                items_embedds = sorted(
+                    npy_files,
+                    key=lambda x: (
+                        int(x.split("_")[2].split(".")[0]),
+                        int(x.split("_")[1]),
+                    ),
+                )
+                self.embedds_path.extend(
+                    [os.path.join(embedds_dir, item) for item in items_embedds]
+                )
+            except:
+                a = 1
+
+            # raise ValueError(items_label[990].split('_')[2].split('.')[0])
+            subject_indexes = set()
+            for item in items_label:
+                subject_indexes.add(int(item.split("_")[2].split(".")[0]))
+            indexes = list(subject_indexes)
+
+            self.labels_indexes.extend(indexes)
+
+            self.individual_index.extend(
+                [int(item.split("_")[2].split(".")[0]) for item in items_label]
+            )
+
+            self.data_set_names.extend([dataset_name[0] for _ in items_label])
+
+            self.labels_path.extend(
+                [os.path.join(labels_dir, item) for item in items_label]
+            )
+            self.images_path.extend(
+                [os.path.join(images_dir, item) for item in items_image]
+            )
+
+        self.target_image_size = target_image_size
+        self.datasets = datasets
+        self.slice_per_image = slice_per_image
+        self.augmentation = augmentation
+        self.individual_index = torch.tensor(self.individual_index)
+        if val:
+            self.labels_indexes=self.labels_indexes[int(split_ratio*len(self.labels_indexes)):]
+        elif train:
+            self.labels_indexes=self.labels_indexes[:int(split_ratio*len(self.labels_indexes))]
+        
+
+    def __getitem__(self, idx):
+
+        indexes = (self.individual_index == self.labels_indexes[idx]).nonzero()
+        
+        images_list = []
+        labels_list = []
+        batched_input = []
+        for index in indexes:
+
+            data = np.load(self.images_path[index])
+            embedd = np.load(self.embedds_path[index])
+
+            labels = np.load(self.labels_path[index])
+
+            if self.data_set_names[index] == "NIH_PNG":
+                x = data.T
+                y = rearrange(labels.T, "H W -> 1 H W")
+                y = (y == 1).astype(np.uint8)
+
+            elif self.data_set_names[index] == "Abdment1k-npy":
+                x = data
+
+                y = rearrange(labels, "H W -> 1 H W")
+                y = (y == 4).astype(np.uint8)
+            else:
+                raise ValueError("Incorect dataset name")
+
+            x = torch.tensor(x)
+            
+            embedd = torch.tensor(embedd)
+            y = torch.tensor(y)
+
+
+            current_image_size = y.shape[-1]
+
+            points, point_labels = create_prompt_simple(y[:, ::2, ::2].squeeze(1).float())
+            
+            points *= self.target_image_size // y[:, ::2, ::2].shape[-1]
+            
+            y = F.interpolate(y.unsqueeze(1), size=self.target_image_size)
+            batched_input.append(
+                {
+                    "image_embedd": embedd,
+                    "image": x,
+                    "label": y,
+                    "point_coords": points[0],
+                    "point_labels": point_labels[0],
+                    "original_size": (1024, 1024),
+                },
+            )
+
+
+        return batched_input
+
+    def collate_fn(self, data):
+        batched_input = zip(*data)
+
+
+        return data
+
+    def __len__(self):
+        return len(self.labels_indexes)
+
+  
+
+
+
+

kernel/fine_tune_good_unet.py

@@ -0,0 +1,564 @@
+from pathlib import Path
+import numpy as np
+import matplotlib.pyplot as plt
+
+# import cv2
+from collections import defaultdict
+import torchvision.transforms as transforms
+import torch
+from torch import nn
+
+import torch.nn.functional as F
+from segment_anything.utils.transforms import ResizeLongestSide
+import albumentations as A
+from albumentations.pytorch import ToTensorV2
+import numpy as np
+
+import random
+from tqdm import tqdm
+from time import sleep
+from data_load_group import *
+from time import time
+from PIL import Image
+from sklearn.model_selection import KFold
+from shutil import copyfile
+# import monai
+from utils import *
+from torch.autograd import Variable
+from Models.model_handler import panc_sam
+from Models.model_handler import UNet3D
+from Models.model_handler import Conv3DFilter
+from loss import *
+from args import get_arguments
+from segment_anything import SamPredictor, sam_model_registry
+from statistics import mean
+from copy import deepcopy
+from torch.nn.functional import threshold, normalize
+def calculate_recall(pred, target):
+    smooth = 1
+    batch_size = 1
+    recall_scores = []
+    
+    binary_mask = pred>0
+
+    true_positive = ((pred == 1) & (target == 1)).sum().item()
+    false_negative = ((pred == 0) & (target == 1)).sum().item()
+    recall = (true_positive + smooth) / ((true_positive + false_negative) + smooth)
+    
+
+    
+    return recall
+
+def calculate_precision(pred, target):
+    smooth = 1
+    batch_size = 1
+    recall_scores = []
+    
+    binary_mask = pred>0
+
+    true_positive = ((pred == 1) & (target == 1)).sum().item()
+    false_negative = ((pred == 1) & (target == 0)).sum().item()
+    recall = (true_positive + smooth) / ((true_positive + false_negative) + smooth)
+    
+
+    
+    return recall
+
+def calculate_jaccard(pred, target):
+    smooth = 1
+    batch_size = pred.shape[0]
+    jaccard_scores = []
+    binary_mask = pred>0
+    true_positive = ((pred == 1) & (target == 1)).sum().item()
+    false_negative = ((pred == 0) & (target == 1)).sum().item()
+    true_positive = ((pred == 1) & (target == 1)).sum().item()
+    false_positive = ((pred == 1) & (target == 0)).sum().item()
+    jaccard = (true_positive + smooth) / (true_positive + false_positive + false_negative + smooth)
+    # jaccard_scores.append(jaccard)
+    # for i in range(batch_size):
+    #     true_positive = ((binary_mask[i] == 1) & (target[i] == 1)).sum().item()
+    #     false_positive = ((binary_mask[i] == 1) & (target[i] == 0)).sum().item()
+    #     false_negative = ((binary_mask[i] == 0) & (target[i] == 1)).sum().item()
+        
+
+    return jaccard
+
+def save_img(img, dir):
+    img = img.clone().cpu().numpy() + 100
+
+    if len(img.shape) == 3:
+        img = rearrange(img, "c h w -> h w c")
+        img_min = np.amin(img, axis=(0, 1), keepdims=True)
+        img = img - img_min
+
+        img_max = np.amax(img, axis=(0, 1), keepdims=True)
+        img = (img / img_max * 255).astype(np.uint8)
+        # grey_img = Image.fromarray(img[:, :, 0])
+        
+        img = Image.fromarray(img)
+
+    else:
+        img_min = img.min()
+        img = img - img_min
+        img_max = img.max()
+        if img_max != 0:
+            img = img / img_max * 255
+        
+        img = Image.fromarray(img).convert("L")
+
+    img.save(dir)
+    
+def seed_everything(seed: int):
+    import random, os
+    import numpy as np
+    import torch
+    
+    random.seed(seed)
+    os.environ['PYTHONHASHSEED'] = str(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = True
+    
+seed_everything(2024)
+
+
+global optimizer
+args = get_arguments()
+
+
+exp_id = 0
+found = 0
+user_input = args.run_name
+while found == 0:
+    try:
+        os.makedirs(f"exps/{exp_id}-{user_input}")
+        found = 1
+    except:
+        exp_id = exp_id + 1
+copyfile(os.path.realpath(__file__), f"exps/{exp_id}-{user_input}/code.py")
+
+augmentation = A.Compose(
+    [
+        A.Rotate(limit=100, p=0.7),
+        A.RandomScale(scale_limit=0.3, p=0.5),
+    ]
+)
+device = "cuda:0"
+
+
+
+panc_sam_instance = torch.load(args.model_path)
+panc_sam_instance.to(device)
+
+conv3d_instance = UNet3D()
+kernel_size = [(1, 5, 5), (5, 5, 5), (5, 5, 5), (5, 5, 5)]
+
+conv3d_instance = Conv3DFilter(
+    1,
+    5,
+    kernel_size,
+    np.array(kernel_size) // 2,
+    custom_bias=args.custom_bias,
+)
+
+conv3d_instance.to(device)
+conv3d_instance.train()
+
+train_dataset = PanDataset(
+    dirs=[f"{args.train_dir}/train"],
+    datasets=[["NIH_PNG", 1]],
+    target_image_size=args.image_size,
+    slice_per_image=args.slice_per_image,
+    train=True,
+    val=False,  # Enable validation data splitting
+    augmentation=augmentation,
+)
+
+val_dataset = PanDataset(
+    [f"{args.train_dir}/train"],
+    [["NIH_PNG", 1]],
+    args.image_size,
+    slice_per_image=args.slice_per_image,
+    val=True
+)
+
+test_dataset = PanDataset(
+    [f"{args.test_dir}/test"],
+    [["NIH_PNG", 1]],
+    args.image_size,
+    slice_per_image=args.slice_per_image,
+    train=False,
+)
+train_loader = DataLoader(
+    train_dataset,
+    batch_size=args.batch_size,
+    collate_fn=train_dataset.collate_fn,
+    shuffle=True,
+    drop_last=False,
+    num_workers=args.num_workers,
+)
+
+
+val_loader = DataLoader(
+    val_dataset,
+    batch_size=args.batch_size,
+    collate_fn=val_dataset.collate_fn,
+    shuffle=False,
+    drop_last=False,
+    num_workers=args.num_workers,
+)
+
+test_loader = DataLoader(
+    test_dataset,
+    batch_size=args.batch_size,
+    collate_fn=test_dataset.collate_fn,
+    shuffle=False,
+    drop_last=False,
+    num_workers=args.num_workers,
+)
+
+
+# Set up the optimizer, hyperparameter tuning will improve performance here
+lr = args.lr
+max_lr = 3e-4
+wd = 5e-4
+
+
+all_parameters = list(conv3d_instance.parameters())
+
+optimizer = torch.optim.Adam(
+    all_parameters,
+    lr=lr,
+    weight_decay=wd,
+)
+
+
+scheduler = torch.optim.lr_scheduler.StepLR(
+    optimizer, step_size=10, gamma=0.7, verbose=True
+)
+
+loss_function = loss_fn(alpha=0.5, gamma=2.0)
+loss_function.to(device)
+
+from time import time
+import time as s_time
+
+log_file = open(f"exps/{exp_id}-{user_input}/log.txt", "a")
+
+
+def process_model(data_loader, train=0, save_output=0, epoch=None, scheduler=None):
+    epoch_losses = []
+
+    index = 0
+    results = []
+    dice_sam_lists = []
+    dice_sam_prompt_lists = []
+    dice_lists = []
+    dice_prompt_lists = []
+
+    num_samples = 0
+
+    counterb = 0
+
+    for batch in tqdm(data_loader, total=args.sample_size // args.batch_size):
+
+        for batched_input in batch:
+            
+            num_samples += 1
+
+            # raise ValueError(len(batched_input))
+            low_res_masks = torch.zeros((1, 1, 0, 256, 256))
+            # s_time.sleep(0.6)
+            counterb += len(batch)
+
+            index += 1
+            label = []
+            label = [i["label"] for i in batched_input]
+
+            # Only correct if gray scale
+            label = torch.cat(label, dim=1)
+            # raise ValueError(la)
+            label = label.float()
+
+            true_indexes = torch.where((torch.amax(label, dim=(2, 3)) > 0).view(-1))[0]
+
+            low_res_label = F.interpolate(label, low_res_masks.shape[-2:]).to("cuda:0")
+            low_res_masks, low_res_masks_promtp = panc_sam_instance(
+                batched_input, device
+            )
+            
+            low_res_shape = low_res_masks.shape[-2:]
+            low_res_label_prompt=low_res_label
+            if train:
+                transformed = augmentation(
+                    image=low_res_masks_promtp[0].permute(1, 2, 0).cpu().numpy(),
+                    mask=low_res_label[0].permute(1, 2, 0).cpu().numpy(),
+                )
+
+                low_res_masks_promtp = (
+                    torch.tensor(transformed["image"])
+                    .permute(2, 0, 1)
+                    .unsqueeze(0)
+                    .to(device)
+                )
+
+                low_res_label_prompt = (
+                    torch.tensor(transformed["mask"])
+                    .permute(2, 0, 1)
+                    .unsqueeze(0)
+                    .to(device)
+                )
+
+                transformed = augmentation(
+                    image=low_res_masks[0].permute(1, 2, 0).cpu().numpy(),
+                    mask=low_res_label[0].permute(1, 2, 0).cpu().numpy(),
+                )
+
+                low_res_masks = (
+                    torch.tensor(transformed["image"])
+                    .permute(2, 0, 1)
+                    .unsqueeze(0)
+                    .to(device)
+                )
+
+                low_res_label = (
+                    torch.tensor(transformed["mask"])
+                    .permute(2, 0, 1)
+                    .unsqueeze(0)
+                    .to(device)
+                )
+                low_res_masks = F.interpolate(low_res_masks, low_res_shape).to(device)
+                low_res_label = F.interpolate(low_res_label, low_res_shape).to(device)
+
+                low_res_masks = F.interpolate(low_res_masks, low_res_shape).to(device)
+                low_res_label = F.interpolate(low_res_label, low_res_shape).to(device)
+                low_res_masks_promtp = F.interpolate(
+                    low_res_masks_promtp, low_res_shape
+                ).to(device)
+                low_res_label_prompt = F.interpolate(
+                    low_res_label_prompt, low_res_shape
+                ).to(device)
+            low_res_masks = low_res_masks.detach()
+            low_res_masks_promtp = low_res_masks_promtp.detach()
+
+            dice_sam = dice_coefficient(low_res_masks , low_res_label).detach().cpu()
+            dice_sam_prompt = (
+                dice_coefficient(low_res_masks_promtp, low_res_label_prompt)
+                .detach()
+                .cpu()
+            )
+            low_res_masks_promtp = conv3d_instance(
+                low_res_masks_promtp.detach().to(device)
+            )
+
+            loss = loss_function(low_res_masks_promtp, low_res_masks_promtp)
+            loss /= args.accumulative_batch_size / args.batch_size
+
+            binary_mask = low_res_masks > 0.5
+            binary_mask_prompt = low_res_masks_promtp > 0.5
+
+            dice = dice_coefficient(binary_mask, low_res_label).detach().cpu()
+            dice_prompt = (
+                dice_coefficient(binary_mask_prompt, low_res_label_prompt)
+                .detach()
+                .cpu()
+            )
+
+            dice_sam_lists.append(dice_sam)
+            dice_sam_prompt_lists.append(dice_sam_prompt)
+            dice_lists.append(dice)
+            dice_prompt_lists.append(dice_prompt)
+
+            log_file.flush()
+            if train:
+                loss.backward()
+                if index % (args.accumulative_batch_size / args.batch_size) == 0:
+
+                    optimizer.step()
+                    # if epoch==40:
+                    #     scheduler.step()
+                    optimizer.zero_grad()
+                    index = 0
+
+            else:
+
+                result = torch.cat(
+                    (
+                        low_res_masks[:, ::10].detach().cpu().reshape(1, -1, 256, 256),
+                        binary_mask[:, ::10].detach().cpu().reshape(1, -1, 256, 256),
+                    ),
+                    dim=0,
+                )
+                results.append(result)
+
+        if index % (args.accumulative_batch_size / args.batch_size) == 0:
+            epoch_losses.append(loss.item())
+        if counterb == (args.sample_size // args.batch_size) and train:
+            break
+
+
+    return (
+        epoch_losses,
+        results,
+        dice_lists,
+        dice_prompt_lists,
+        dice_sam_lists,
+        dice_sam_prompt_lists,
+    )
+
+
+def train_model(train_loader, val_loader,test_loader, K_fold=False, N_fold=7, epoch_num_start=7):
+    global optimizer
+    index=0
+    if args.inference:
+        with torch.no_grad():
+            conv = torch.load(f'{args.conv_path}')
+        
+        recall_list=[]
+        percision_list=[]
+        jaccard_list=[]
+        
+        for input in tqdm(test_loader):
+            
+            
+            low_res_masks_sam, low_res_masks_promtp_sam = panc_sam_instance(
+                                                    input[0], device
+                                                )
+            low_res_masks_sam = F.interpolate(low_res_masks_sam, 512).cpu()
+            low_res_masks_promtp_sam = F.interpolate(low_res_masks_promtp_sam, 512).cpu()
+            low_res_masks_promtp = conv(low_res_masks_promtp_sam.to(device)).detach().cpu()
+            
+            for slice_id,(batched_input,mask_sam,mask_prompt_sam,mask_prompt) in enumerate(zip(input[0],low_res_masks_sam[0],low_res_masks_promtp_sam[0],low_res_masks_promtp[0])):
+                
+                if not os.path.exists(f"ims/batch_{index}"):
+                    os.mkdir(f"ims/batch_{index}")
+                image = batched_input["image"]
+                
+                
+                label = batched_input["label"][0,0,::2,::2].to(bool)
+                binary_mask_sam = (mask_sam > 0)
+                binary_mask_prompt_sam = (mask_prompt_sam > 0)
+                binary_mask_prompt = (mask_prompt > 0.5)
+                recall = calculate_recall(label, binary_mask_prompt)
+                percision = calculate_precision(label, binary_mask_prompt)
+                jaccard = calculate_jaccard(label, binary_mask_prompt)
+                percision_list.append(percision)
+                recall_list.append(recall)
+                jaccard_list.append(jaccard)
+                image_mask = image.clone().to(torch.long)
+                image_label = image.clone().to(torch.long)
+                image_mask[binary_mask_sam]=255
+                image_label[label]=255
+                save_img(
+                    torch.stack((image_mask,image_label,image),dim=0),
+                    f"ims/batch_{index}/sam{slice_id}.png",
+                )
+                
+                image_mask = image.clone().to(torch.long)
+                image_mask[binary_mask_prompt_sam]=255
+
+                save_img(
+                    torch.stack((image_mask,image_label,image),dim=0),
+                    f"ims/batch_{index}/sam_prompt{slice_id}.png",
+                )
+                
+                image_mask = image.clone().to(torch.long)
+                image_mask[binary_mask_prompt]=255
+                save_img(
+                    torch.stack((image_mask,image_label,image),dim=0),
+                    f"ims/batch_{index}/prompt_{slice_id}.png",
+                )
+            print(f'Recall={np.mean(recall_list)}')
+            print(f'Percision={np.mean(percision_list)}')
+            print(f'Jaccard={np.mean(jaccard_list)}')
+            index += 1
+        print(f'Recall={np.mean(recall_list)}')
+        print(f'Percision={np.mean(percision_list)}')
+        print(f'Jaccard={np.mean(jaccard_list)}')
+    else:
+        
+        print("Train model started.")
+
+        train_losses = []
+        train_epochs = []
+        val_losses = []
+        val_epochs = []
+        dice = []
+        dice_val = []
+        results = []
+        last_best_dice=0
+        for epoch in range(args.num_epochs):
+
+            print(f"=====================EPOCH: {epoch + 1}=====================")
+            log_file.write(f"=====================EPOCH: {epoch + 1}===================\n")
+
+            print("Training:")
+            (
+                train_epoch_losses,
+                results,
+                dice_list,
+                dice_prompt_list,
+                dice_sam_list,
+                dice_sam_prompt_list,
+            ) = process_model(train_loader, train=1, epoch=epoch, scheduler=scheduler)
+
+            dice_mean = np.mean(dice_list)
+            dice_prompt_mean = np.mean(dice_prompt_list)
+            dice_sam_mean = np.mean(dice_sam_list)
+            dice_sam_prompt_mean = np.mean(dice_sam_prompt_list)
+
+            print("Validating:")
+            (
+                _,
+                _,
+                val_dice_list,
+                val_dice_prompt_list,
+                val_dice_sam_list,
+                val_dice_sam_prompt_list,
+            ) = process_model(val_loader)
+            val_dice_mean = np.mean(val_dice_list)
+            val_dice_prompt_mean = np.mean(val_dice_prompt_list)
+            val_dice_sam_mean = np.mean(val_dice_sam_list)
+            val_dice_sam_prompt_mean = np.mean(val_dice_sam_prompt_list)
+
+            train_mean_losses = [mean(x) for x in train_losses]
+
+
+            logs = ""
+
+            logs += f"Train Dice_sam: {dice_sam_mean}\n"
+            logs += f"Train Dice: {dice_mean}\n"
+            logs += f"Train Dice_sam_prompt: {dice_sam_prompt_mean}\n"
+            logs += f"Train Dice_prompt: {dice_prompt_mean}\n"
+            logs += f"Mean train loss: {mean(train_epoch_losses)}\n"
+
+            
+
+            logs += f"val Dice_sam: {val_dice_sam_mean}\n"
+            logs += f"val Dice: {val_dice_mean}\n"
+            logs += f"val Dice_sam_prompt: {val_dice_sam_prompt_mean}\n"
+            logs += f"val Dice_prompt: {val_dice_prompt_mean}\n"
+
+                # plt.plot(val_epochs, val_mean_losses, train_epochs, train_mean_losses)
+            if val_dice_prompt_mean > last_best_dice:
+                torch.save(
+                    conv3d_instance,
+                    f"exps/{exp_id}-{user_input}/conv_save.pth",
+                )
+                print("Model saved")
+                last_best_dice = val_dice_prompt_mean
+
+
+            print(logs)
+            log_file.write(logs)
+            scheduler.step()
+    ## training with k-fold cross validation:
+
+
+fff = time()
+train_model(train_loader, val_loader,test_loader)
+log_file.close()
+
+# train and also test the model

kernel/loss.py

@@ -0,0 +1,90 @@
+import torch
+from torch import nn
+import numpy as np
+
+
+
+class loss_fn(torch.nn.Module):
+    def __init__(self, alpha=0.7, gamma=2.0, epsilon=1e-5):
+        super(loss_fn, self).__init__()
+        self.alpha = alpha
+        self.gamma = gamma
+        self.epsilon = epsilon
+
+
+    def focal_tversky(self, y_pred, y_true, gamma=0.75):
+        pt_1 = self.tversky_loss(y_pred, y_true)
+        return torch.pow((1 - pt_1), gamma)
+    def dice_loss(self, probs, gt, eps=1):
+        
+        intersection = (probs * gt).sum(dim=(-2,-1))
+        dice_coeff = (2.0 * intersection + eps) / (probs.sum(dim=(-2,-1)) + gt.sum(dim=(-2,-1)) + eps)
+        loss = 1 - dice_coeff.mean()
+        return loss
+
+    def focal_loss(self, probs, gt, gamma=4):
+        probs = probs.reshape(-1, 1)
+        gt = gt.reshape(-1, 1)
+        probs = torch.cat((1 - probs, probs), dim=1)
+
+        pt = probs.gather(1, gt.long())
+        modulating_factor = (1 - pt) ** gamma
+        # modulating_factor = (3**(10*((1-pt)-0.5)))*(1 - pt) ** gamma
+        modulating_factor[pt>0.55] = 0.1*modulating_factor[pt>0.55] 
+
+        focal_loss = -modulating_factor * torch.log(pt + 1e-12)
+        
+        # Compute the mean focal loss
+        loss = focal_loss.mean()
+        return loss  # Store as a Python number to save memory
+
+    def forward(self, probs, target):
+        
+        self.gamma=8
+        dice_loss = self.dice_loss(probs, target)
+        # tversky_loss = self.tversky_loss(logits, target)
+
+        # Focal Loss
+        focal_loss = self.focal_loss(probs, target,self.gamma)
+        alpha = 20.0
+        # Combined Loss
+        combined_loss = alpha * focal_loss + dice_loss
+        return combined_loss
+
+def img_enhance(img2, coef=0.2):
+    img_mean = np.mean(img2)
+    img_max = np.max(img2)
+    val = (img_max - img_mean) * coef + img_mean
+    img2[img2 < img_mean * 0.7] = img_mean * 0.7
+    img2[img2 > val] = val
+    return img2
+
+
+
+def dice_coefficient(logits, gt):
+    eps=1
+    binary_mask = logits>0
+    # raise ValueError( binary_mask.shape,gt.shape)
+    intersection = (binary_mask * gt).sum(dim=(-2,-1))
+    dice_scores = (2.0 * intersection + eps) / (binary_mask.sum(dim=(-2,-1)) + gt.sum(dim=(-2,-1)) + eps)
+    # raise ValueError(intersection.shape , binary_mask.shape,gt.shape)
+    
+    return dice_scores.mean()
+
+def calculate_accuracy(pred, target):
+    correct = (pred == target).sum().item()
+    total = target.numel()
+    return correct / total
+
+def calculate_sensitivity(pred, target): 
+    smooth = 1
+    # Also known as recall
+    true_positive = ((pred == 1) & (target == 1)).sum().item()
+    false_negative = ((pred == 0) & (target == 1)).sum().item()
+    return (true_positive + smooth) / ((true_positive + false_negative) + smooth)
+
+def calculate_specificity(pred, target):
+    smooth = 1
+    true_negative = ((pred == 0) & (target == 0)).sum().item()
+    false_positive = ((pred == 1) & (target == 0)).sum().item()
+    return (true_negative + smooth) / ((true_negative + false_positive ) + smooth)

kernel/pre_processer.py

@@ -0,0 +1,501 @@
+# This code has been taken from monai
+import matplotlib.pyplot as plt
+from typing import Tuple, Optional
+import os
+import math
+import torch.nn.functional as F
+import glob
+from pprint import pprint
+import tempfile
+import shutil
+
+import torchvision.transforms as transforms
+import pandas as pd
+import numpy as np
+import torch
+from einops import rearrange
+import nibabel as nib
+
+
+###############################
+from typing import Collection, Hashable, Iterable, Sequence, TypeVar, Union, Mapping, Callable, Generator
+from enum import Enum
+from abc import ABC, abstractmethod
+from typing import Any, TypeVar
+from warnings import warn
+
+def _map_luts(interp_tiles: torch.Tensor, luts: torch.Tensor) -> torch.Tensor:
+    r"""Assign the required luts to each tile.
+
+    Args:
+        interp_tiles (torch.Tensor): set of interpolation tiles. (B, 2GH, 2GW, C, TH/2, TW/2)
+        luts (torch.Tensor): luts for each one of the original tiles. (B, GH, GW, C, 256)
+
+    Returns:
+        torch.Tensor: mapped luts (B, 2GH, 2GW, 4, C, 256)
+
+    """
+    assert interp_tiles.dim() == 6, "interp_tiles tensor must be 6D."
+    assert luts.dim() == 5, "luts tensor must be 5D."
+
+    # gh, gw -> 2x the number of tiles used to compute the histograms
+    # th, tw -> /2 the sizes of the tiles used to compute the histograms
+    num_imgs, gh, gw, c, th, tw = interp_tiles.shape
+
+    # precompute idxs for non corner regions (doing it in cpu seems sligthly faster)
+    j_idxs = torch.ones(gh - 2, 4, dtype=torch.long) * torch.arange(1, gh - 1).reshape(gh - 2, 1)
+    i_idxs = torch.ones(gw - 2, 4, dtype=torch.long) * torch.arange(1, gw - 1).reshape(gw - 2, 1)
+    j_idxs = j_idxs // 2 + j_idxs % 2
+    j_idxs[:, 0:2] -= 1
+    i_idxs = i_idxs // 2 + i_idxs % 2
+    # i_idxs[:, [0, 2]] -= 1  # this slicing is not supported by jit
+    i_idxs[:, 0] -= 1
+    i_idxs[:, 2] -= 1
+
+    # selection of luts to interpolate each patch
+    # create a tensor with dims: interp_patches height and width x 4 x num channels x bins in the histograms
+    # the tensor is init to -1 to denote non init hists
+    luts_x_interp_tiles: torch.Tensor = -torch.ones(
+        num_imgs, gh, gw, 4, c, luts.shape[-1], device=interp_tiles.device)  # B x GH x GW x 4 x C x 256
+    # corner regions
+    luts_x_interp_tiles[:, 0::gh - 1, 0::gw - 1, 0] = luts[:, 0::max(gh // 2 - 1, 1), 0::max(gw // 2 - 1, 1)]
+    # border region (h)
+    luts_x_interp_tiles[:, 1:-1, 0::gw - 1, 0] = luts[:, j_idxs[:, 0], 0::max(gw // 2 - 1, 1)]
+    luts_x_interp_tiles[:, 1:-1, 0::gw - 1, 1] = luts[:, j_idxs[:, 2], 0::max(gw // 2 - 1, 1)]
+    # border region (w)
+    luts_x_interp_tiles[:, 0::gh - 1, 1:-1, 0] = luts[:, 0::max(gh // 2 - 1, 1), i_idxs[:, 0]]
+    luts_x_interp_tiles[:, 0::gh - 1, 1:-1, 1] = luts[:, 0::max(gh // 2 - 1, 1), i_idxs[:, 1]]
+    # internal region
+    luts_x_interp_tiles[:, 1:-1, 1:-1, :] = luts[
+        :, j_idxs.repeat(max(gh - 2, 1), 1, 1).permute(1, 0, 2), i_idxs.repeat(max(gw - 2, 1), 1, 1)]
+
+    return luts_x_interp_tiles
+
+def marginal_pdf(values: torch.Tensor, bins: torch.Tensor, sigma: torch.Tensor,
+                 epsilon: float = 1e-10) -> Tuple[torch.Tensor, torch.Tensor]:
+    """Function that calculates the marginal probability distribution function of the input tensor
+        based on the number of histogram bins.
+
+    Args:
+        values (torch.Tensor): shape [BxNx1].
+        bins (torch.Tensor): shape [NUM_BINS].
+        sigma (torch.Tensor): shape [1], gaussian smoothing factor.
+        epsilon: (float), scalar, for numerical stability.
+
+    Returns:
+        Tuple[torch.Tensor, torch.Tensor]:
+          - torch.Tensor: shape [BxN].
+          - torch.Tensor: shape [BxNxNUM_BINS].
+
+    """
+
+    if not isinstance(values, torch.Tensor):
+        raise TypeError("Input values type is not a torch.Tensor. Got {}"
+                        .format(type(values)))
+
+    if not isinstance(bins, torch.Tensor):
+        raise TypeError("Input bins type is not a torch.Tensor. Got {}"
+                        .format(type(bins)))
+
+    if not isinstance(sigma, torch.Tensor):
+        raise TypeError("Input sigma type is not a torch.Tensor. Got {}"
+                        .format(type(sigma)))
+
+    if not values.dim() == 3:
+        raise ValueError("Input values must be a of the shape BxNx1."
+                         " Got {}".format(values.shape))
+
+    if not bins.dim() == 1:
+        raise ValueError("Input bins must be a of the shape NUM_BINS"
+                         " Got {}".format(bins.shape))
+
+    if not sigma.dim() == 0:
+        raise ValueError("Input sigma must be a of the shape 1"
+                         " Got {}".format(sigma.shape))
+
+    residuals = values - bins.unsqueeze(0).unsqueeze(0)
+    kernel_values = torch.exp(-0.5 * (residuals / sigma).pow(2))
+
+    pdf = torch.mean(kernel_values, dim=1)
+    normalization = torch.sum(pdf, dim=1).unsqueeze(1) + epsilon
+    pdf = pdf / normalization
+
+    return (pdf, kernel_values)
+
+def histogram(x: torch.Tensor, bins: torch.Tensor, bandwidth: torch.Tensor,
+              epsilon: float = 1e-10) -> torch.Tensor:
+    """Function that estimates the histogram of the input tensor.
+
+    The calculation uses kernel density estimation which requires a bandwidth (smoothing) parameter.
+
+    Args:
+        x (torch.Tensor): Input tensor to compute the histogram with shape :math:`(B, D)`.
+        bins (torch.Tensor): The number of bins to use the histogram :math:`(N_{bins})`.
+        bandwidth (torch.Tensor): Gaussian smoothing factor with shape shape [1].
+        epsilon (float): A scalar, for numerical stability. Default: 1e-10.
+
+    Returns:
+        torch.Tensor: Computed histogram of shape :math:`(B, N_{bins})`.
+
+    Examples:
+        >>> x = torch.rand(1, 10)
+        >>> bins = torch.torch.linspace(0, 255, 128)
+        >>> hist = histogram(x, bins, bandwidth=torch.tensor(0.9))
+        >>> hist.shape
+        torch.Size([1, 128])
+    """
+
+    pdf, _ = marginal_pdf(x.unsqueeze(2), bins, bandwidth, epsilon)
+
+    return pdf
+
+def _compute_tiles(imgs: torch.Tensor, grid_size: Tuple[int, int], even_tile_size: bool = False
+                   ) -> Tuple[torch.Tensor, torch.Tensor]:
+    r"""Compute tiles on an image according to a grid size.
+
+    Note that padding can be added to the image in order to crop properly the image.
+    So, the grid_size (GH, GW) x tile_size (TH, TW) >= image_size (H, W)
+
+    Args:
+        imgs (torch.Tensor): batch of 2D images with shape (B, C, H, W) or (C, H, W).
+        grid_size (Tuple[int, int]): number of tiles to be cropped in each direction (GH, GW)
+        even_tile_size (bool, optional): Determine if the width and height of the tiles must be even. Default: False.
+
+    Returns:
+        torch.Tensor: tensor with tiles (B, GH, GW, C, TH, TW). B = 1 in case of a single image is provided.
+        torch.Tensor: tensor with the padded batch of 2D imageswith shape (B, C, H', W')
+
+    """
+    
+    batch: torch.Tensor = _to_bchw(imgs)  # B x C x H x W
+    
+    # compute stride and kernel size
+    h, w = batch.shape[-2:]
+    # raise ValueError(batch.shape)
+    kernel_vert: int = math.ceil(h / grid_size[0])
+    kernel_horz: int = math.ceil(w / grid_size[1])
+    
+    if even_tile_size:
+        kernel_vert += 1 if kernel_vert % 2 else 0
+        kernel_horz += 1 if kernel_horz % 2 else 0
+
+    # add padding (with that kernel size we could need some extra cols and rows...)
+    pad_vert = kernel_vert * grid_size[0] - h
+    pad_horz = kernel_horz * grid_size[1] - w
+    # raise ValueError(pad_horz)
+    # add the padding in the last coluns and rows
+    
+    if pad_vert > 0 or pad_horz > 0:
+        
+
+        batch = F.pad(batch, (0, pad_horz, 0, pad_vert), mode='reflect')  # B x C x H' x W'
+
+
+    # compute tiles
+    c: int = batch.shape[-3]
+    tiles: torch.Tensor = (batch.unfold(1, c, c)  # unfold(dimension, size, step)
+                                .unfold(2, kernel_vert, kernel_vert)
+                                .unfold(3, kernel_horz, kernel_horz)
+                                .squeeze(1))  # GH x GW x C x TH x TW
+    assert tiles.shape[-5] == grid_size[0]  # check the grid size
+    assert tiles.shape[-4] == grid_size[1]
+    return tiles, batch
+
+def _to_bchw(tensor: torch.Tensor, color_channel_num: Optional[int] = None) -> torch.Tensor:
+    """Converts a PyTorch tensor image to BCHW format.
+
+    Args:
+        tensor (torch.Tensor): image of the form :math:`(H, W)`, :math:`(C, H, W)`, :math:`(H, W, C)` or
+            :math:`(B, C, H, W)`.
+        color_channel_num (Optional[int]): Color channel of the input tensor.
+            If None, it will not alter the input channel.
+
+    Returns:
+        torch.Tensor: input tensor of the form :math:`(B, C, H, W)`.
+    """
+    if not isinstance(tensor, torch.Tensor):
+        raise TypeError(f"Input type is not a torch.Tensor. Got {type(tensor)}")
+
+    if len(tensor.shape) > 4 or len(tensor.shape) < 2:
+        raise ValueError(f"Input size must be a two, three or four dimensional tensor. Got {tensor.shape}")
+
+    if len(tensor.shape) == 2:
+        tensor = tensor.unsqueeze(0)
+
+    if len(tensor.shape) == 3:
+        tensor = tensor.unsqueeze(0)
+
+    # TODO(jian): this function is never used. Besides is not feasible for torchscript.
+    # In addition, the docs must be updated. I don't understand what is doing.
+    # if color_channel_num is not None and color_channel_num != 1:
+    #    channel_list = [0, 1, 2, 3]
+    #    channel_list.insert(1, channel_list.pop(color_channel_num))
+    #    tensor = tensor.permute(*channel_list)
+    return tensor
+
+def _compute_interpolation_tiles(padded_imgs: torch.Tensor, tile_size: Tuple[int, int]) -> torch.Tensor:
+    r"""Compute interpolation tiles on a properly padded set of images.
+
+    Note that images must be padded. So, the tile_size (TH, TW) * grid_size (GH, GW) = image_size (H, W)
+
+    Args:
+        padded_imgs (torch.Tensor): batch of 2D images with shape (B, C, H, W) already padded to extract tiles
+                                    of size (TH, TW).
+        tile_size (Tuple[int, int]): shape of the current tiles (TH, TW).
+
+    Returns:
+        torch.Tensor: tensor with the interpolation tiles (B, 2GH, 2GW, C, TH/2, TW/2).
+
+    """
+    assert padded_imgs.dim() == 4, "Images Tensor must be 4D."
+    assert padded_imgs.shape[-2] % tile_size[0] == 0, "Images are not correctly padded."
+    assert padded_imgs.shape[-1] % tile_size[1] == 0, "Images are not correctly padded."
+
+    # tiles to be interpolated are built by dividing in 4 each alrady existing
+    interp_kernel_vert: int = tile_size[0] // 2
+    interp_kernel_horz: int = tile_size[1] // 2
+
+    c: int = padded_imgs.shape[-3]
+    interp_tiles: torch.Tensor = (padded_imgs.unfold(1, c, c)
+                                             .unfold(2, interp_kernel_vert, interp_kernel_vert)
+                                             .unfold(3, interp_kernel_horz, interp_kernel_horz)
+                                             .squeeze(1))  # 2GH x 2GW x C x TH/2 x TW/2
+    assert interp_tiles.shape[-3] == c
+    assert interp_tiles.shape[-2] == tile_size[0] / 2
+    assert interp_tiles.shape[-1] == tile_size[1] / 2
+    return interp_tiles
+
+def _compute_luts(tiles_x_im: torch.Tensor, num_bins: int = 256, clip: float = 40., diff: bool = False) -> torch.Tensor:
+    r"""Compute luts for a batched set of tiles.
+
+    Same approach as in OpenCV (https://github.com/opencv/opencv/blob/master/modules/imgproc/src/clahe.cpp)
+
+    Args:
+        tiles_x_im (torch.Tensor): set of tiles per image to apply the lut. (B, GH, GW, C, TH, TW)
+        num_bins (int, optional): number of bins. default: 256
+        clip (float): threshold value for contrast limiting. If it is 0 then the clipping is disabled. Default: 40.
+        diff (bool, optional): denote if the differentiable histagram will be used. Default: False
+
+    Returns:
+        torch.Tensor: Lut for each tile (B, GH, GW, C, 256)
+
+    """
+    assert tiles_x_im.dim() == 6, "Tensor must be 6D."
+
+    b, gh, gw, c, th, tw = tiles_x_im.shape
+    pixels: int = th * tw
+    tiles: torch.Tensor = tiles_x_im.reshape(-1, pixels)  # test with view  # T x (THxTW)
+    histos: torch.Tensor = torch.empty((tiles.shape[0], num_bins), device=tiles.device)
+    if not diff:
+        for i in range(tiles.shape[0]):
+            
+            histos[i] = torch.histc(tiles[i], bins=num_bins, min=0, max=1)
+    else:
+        bins: torch.Tensor = torch.linspace(0, 1, num_bins, device=tiles.device)
+        histos = histogram(tiles, bins, torch.tensor(0.001)).squeeze()
+        histos *= pixels
+
+    # clip limit (TODO: optimice the code)
+    if clip > 0.:
+        clip_limit: torch.Tensor = torch.tensor(
+            max(clip * pixels // num_bins, 1), dtype=histos.dtype, device=tiles.device)
+
+        clip_idxs: torch.Tensor = histos > clip_limit
+        for i in range(histos.shape[0]):
+            hist: torch.Tensor = histos[i]
+            idxs = clip_idxs[i]
+            if idxs.any():
+                clipped: float = float((hist[idxs] - clip_limit).sum().item())
+                hist = torch.where(idxs, clip_limit, hist)
+
+                redist: float = clipped // num_bins
+                hist += redist
+
+                residual: float = clipped - redist * num_bins
+                if residual:
+                    hist[0:int(residual)] += 1
+                histos[i] = hist
+
+    lut_scale: float = (num_bins - 1) / pixels
+    luts: torch.Tensor = torch.cumsum(histos, 1) * lut_scale
+    luts = luts.clamp(0, num_bins - 1).floor()  # to get the same values as converting to int maintaining the type
+    luts = luts.view((b, gh, gw, c, num_bins))
+    return luts
+
+def _compute_equalized_tiles(interp_tiles: torch.Tensor, luts: torch.Tensor) -> torch.Tensor:
+    r"""Equalize the tiles.
+
+    Args:
+        interp_tiles (torch.Tensor): set of interpolation tiles, values must be in the range [0, 1].
+                                     (B, 2GH, 2GW, C, TH/2, TW/2)
+        luts (torch.Tensor): luts for each one of the original tiles. (B, GH, GW, C, 256)
+
+    Returns:
+        torch.Tensor: equalized tiles (B, 2GH, 2GW, C, TH/2, TW/2)
+
+    """
+    assert interp_tiles.dim() == 6, "interp_tiles tensor must be 6D."
+    assert luts.dim() == 5, "luts tensor must be 5D."
+
+    mapped_luts: torch.Tensor = _map_luts(interp_tiles, luts)  # Bx2GHx2GWx4xCx256
+
+    # gh, gw -> 2x the number of tiles used to compute the histograms
+    # th, tw -> /2 the sizes of the tiles used to compute the histograms
+    num_imgs, gh, gw, c, th, tw = interp_tiles.shape
+    # print(interp_tiles.max())
+    # equalize tiles
+    flatten_interp_tiles: torch.Tensor = (interp_tiles * 255).long().flatten(-2, -1)  # B x GH x GW x 4 x C x (THxTW)
+    flatten_interp_tiles = flatten_interp_tiles.unsqueeze(-3).expand(num_imgs, gh, gw, 4, c, th * tw)
+    # raise ValueError(flatten_interp_tiles.max())
+    k=torch.gather(mapped_luts, 5, flatten_interp_tiles)
+    preinterp_tiles_equalized = torch.gather(mapped_luts, 5, flatten_interp_tiles).reshape(num_imgs, gh, gw, 4, c, th, tw)  # B x GH x GW x 4 x C x TH x TW
+    
+    # interp tiles
+    tiles_equalized: torch.Tensor = torch.zeros_like(interp_tiles, dtype=torch.long)
+
+    # compute the interpolation weights (shapes are 2 x TH x TW because they must be applied to 2 interp tiles)
+    ih = torch.arange(2 * th - 1, -1, -1, device=interp_tiles.device).div(
+        2. * th - 1)[None].transpose(-2, -1).expand(2 * th, tw)
+    ih = ih.unfold(0, th, th).unfold(1, tw, tw)  # 2 x 1 x TH x TW
+    iw = torch.arange(2 * tw - 1, -1, -1, device=interp_tiles.device).div(2. * tw - 1).expand(th, 2 * tw)
+    iw = iw.unfold(0, th, th).unfold(1, tw, tw)  # 1 x 2 x TH x TW
+
+    # compute row and column interpolation weigths
+    tiw = iw.expand((gw - 2) // 2, 2, th, tw).reshape(gw - 2, 1, th, tw).unsqueeze(0)  # 1 x GW-2 x 1 x TH x TW
+    tih = ih.repeat((gh - 2) // 2, 1, 1, 1).unsqueeze(1)  # GH-2 x 1 x 1 x TH x TW
+
+    # internal regions
+    tl, tr, bl, br = preinterp_tiles_equalized[:, 1:-1, 1:-1].unbind(3)
+    t = tiw * (tl - tr) + tr
+    b = tiw * (bl - br) + br
+    tiles_equalized[:, 1:-1, 1:-1] = tih * (t - b) + b
+
+    # corner regions
+    tiles_equalized[:, 0::gh - 1, 0::gw - 1] = preinterp_tiles_equalized[:, 0::gh - 1, 0::gw - 1, 0]
+
+    # border region (h)
+    t, b, _, _ = preinterp_tiles_equalized[:, 1:-1, 0].unbind(2)
+    tiles_equalized[:, 1:-1, 0] = tih.squeeze(1) * (t - b) + b
+    t, b, _, _ = preinterp_tiles_equalized[:, 1:-1, gh - 1].unbind(2)
+    tiles_equalized[:, 1:-1, gh - 1] = tih.squeeze(1) * (t - b) + b
+
+    # border region (w)
+    l, r, _, _ = preinterp_tiles_equalized[:, 0, 1:-1].unbind(2)
+    tiles_equalized[:, 0, 1:-1] = tiw * (l - r) + r
+    l, r, _, _ = preinterp_tiles_equalized[:, gw - 1, 1:-1].unbind(2)
+    tiles_equalized[:, gw - 1, 1:-1] = tiw * (l - r) + r
+
+    # same type as the input
+    return tiles_equalized.to(interp_tiles).div(255.)
+
+def equalize_clahe(input: torch.Tensor, clip_limit: float = 40., grid_size: Tuple[int, int] = (8, 8)) -> torch.Tensor:
+    r"""Apply clahe equalization on the input tensor.
+
+    NOTE: Lut computation uses the same approach as in OpenCV, in next versions this can change.
+
+    Args:
+        input (torch.Tensor): images tensor to equalize with values in the range [0, 1] and shapes like
+                              :math:`(C, H, W)` or :math:`(B, C, H, W)`.
+        clip_limit (float): threshold value for contrast limiting. If 0 clipping is disabled. Default: 40.
+        grid_size (Tuple[int, int]): number of tiles to be cropped in each direction (GH, GW). Default: (8, 8).
+
+    Returns:
+        torch.Tensor: Equalized image or images with shape as the input.
+
+    Examples:
+        >>> img = torch.rand(1, 10, 20)
+        >>> res = equalize_clahe(img)
+        >>> res.shape
+        torch.Size([1, 10, 20])
+
+        >>> img = torch.rand(2, 3, 10, 20)
+        >>> res = equalize_clahe(img)
+        >>> res.shape
+        torch.Size([2, 3, 10, 20])
+
+    """
+    
+    if not isinstance(input, torch.Tensor):
+        raise TypeError(f"Input input type is not a torch.Tensor. Got {type(input)}")
+
+    if input.dim() not in [3, 4]:
+        raise ValueError(f"Invalid input shape, we expect CxHxW or BxCxHxW. Got: {input.shape}")
+    if input.dim() ==3 and len(input) not in [1,3]:
+        raise ValueError(f'What type of image is this? The first dimension should be batch or channel number')
+    if input.numel() == 0:
+        raise ValueError("Invalid input tensor, it is empty.")
+
+    if not isinstance(clip_limit, float):
+        raise TypeError(f"Input clip_limit type is not float. Got {type(clip_limit)}")
+
+    if not isinstance(grid_size, tuple):
+        raise TypeError(f"Input grid_size type is not Tuple. Got {type(grid_size)}")
+
+    if len(grid_size) != 2:
+        raise TypeError(f"Input grid_size is not a Tuple with 2 elements. Got {len(grid_size)}")
+
+    if isinstance(grid_size[0], float) or isinstance(grid_size[1], float):
+        raise TypeError("Input grid_size type is not valid, must be a Tuple[int, int].")
+
+    if grid_size[0] <= 0 or grid_size[1] <= 0:
+        raise ValueError("Input grid_size elements must be positive. Got {grid_size}")
+
+    imgs: torch.Tensor = _to_bchw(input)  # B x C x H x W
+    
+    # hist_tiles: torch.Tensor  # B x GH x GW x C x TH x TW  # not supported by JIT
+    # img_padded: torch.Tensor  # B x C x H' x W'  # not supported by JIT
+    # the size of the tiles must be even in order to divide them into 4 tiles for the interpolation
+    hist_tiles, img_padded = _compute_tiles(imgs, grid_size, True)
+    tile_size: Tuple[int, int] = (hist_tiles.shape[-2], hist_tiles.shape[-1])
+    # print(imgs.max())
+    
+    interp_tiles: torch.Tensor = (
+        _compute_interpolation_tiles(img_padded, tile_size))  # B x 2GH x 2GW x C x TH/2 x TW/2
+    luts: torch.Tensor = _compute_luts(hist_tiles, clip=clip_limit)  # B x GH x GW x C x B
+    equalized_tiles: torch.Tensor = _compute_equalized_tiles(interp_tiles, luts)  # B x 2GH x 2GW x C x TH/2 x TW/2
+
+    # reconstruct the images form the tiles
+    eq_imgs: torch.Tensor = torch.cat(equalized_tiles.unbind(2), 4)
+    eq_imgs = torch.cat(eq_imgs.unbind(1), 2)
+    h, w = imgs.shape[-2:]
+    eq_imgs = eq_imgs[..., :h, :w]  # crop imgs if they were padded
+
+    # remove batch if the input was not in batch form
+    if input.dim() != eq_imgs.dim():
+        eq_imgs = eq_imgs.squeeze(0)
+    return eq_imgs
+
+##############
+
+
+
+def histo(image):
+    # Calculate the histogram
+    min=np.min(image)
+    max=np.max(image)
+    histogram, bins = np.histogram(image.flatten(), bins=np.linspace(min,max,100))
+
+    # Plot the histogram
+    plt.figure()
+    plt.title('Histogram')
+    plt.xlabel('Pixel Value')
+    plt.ylabel('Frequency')
+    plt.bar(bins[:-1], histogram, width=1)
+
+    # Display the histogram
+    # plt.show()
+
+## class for data pre-processing
+class PreProcessing:
+    
+
+    def CLAHE(img, clip_limit=40.0,grid_size=(8,8)):
+        
+        img=equalize_clahe( img,clip_limit,grid_size)
+        return img
+
+    def INTERPOLATE(img, size=(64),mode='linear',align_corners=False):
+        img=F.interpolate( img,size=size, mode=mode, align_corners=False)
+        return img
+
+

kernel/run.sh

@@ -0,0 +1,11 @@
+#!/bin/bash
+
+rm -r ims
+
+mkdir ims
+python3 fine_tune_good_unet.py --sample_size 4 --accumulative_batch_size 4\
+ --num_epochs 60 --num_workers 8 --batch_step_one 20\
+ --batch_step_two 30 --lr 1e-3\
+ --train_dir "The path to your train data"\
+ --test_dir "The path to your test data"\
+ --model_path "The path to pre-trained sam model"

kernel/utils.py

@@ -0,0 +1,122 @@
+import torch
+import torch.nn as nn
+import numpy as np
+import torch.nn.functional as F
+
+def create_prompt_simple(masks, forground=2, background=2):
+    kernel_size = 9
+    kernel = nn.Conv2d(
+        in_channels=1,
+        bias=False,
+        out_channels=1,
+        kernel_size=kernel_size,
+        stride=1,
+        padding=kernel_size // 2,
+    )
+    # print(kernel.weight.shape)
+    kernel.weight = nn.Parameter(
+        torch.zeros(1, 1, kernel_size, kernel_size).to(masks.device),
+        requires_grad=False,
+    )
+    kernel.weight[0, 0] = 1.0
+    eroded_masks = kernel(masks).squeeze(1)//(kernel_size**2)
+    masks = masks.squeeze(1)
+    use_eroded = (eroded_masks.sum(dim=(1, 2), keepdim=True) >= forground).float()
+    new_masks = (eroded_masks * use_eroded) + (masks * (1 - use_eroded))
+    all_points = []
+    all_labels = []
+    for i in range(len(new_masks)):
+        new_background = background
+        points = []
+        labels = []
+        new_mask = new_masks[i]
+        nonzeros = torch.nonzero(new_mask, as_tuple=False)
+        n_nonzero = len(nonzeros)
+        if n_nonzero >= forground:
+            indices = np.random.choice(
+                np.arange(n_nonzero), size=forground, replace=False
+            ).tolist()
+            # raise ValueError(nonzeros[:, [0, 1]][indices])
+            points.append(nonzeros[:, [1,0]][indices])
+            labels.append(torch.ones(forground))
+        else:
+            if n_nonzero > 0:
+                points.append(nonzeros)
+                labels.append(torch.ones(n_nonzero))
+            new_background += forground - n_nonzero
+        # print(points, new_background)
+        zeros = torch.nonzero(1 - masks[i], as_tuple=False)
+        n_zero = len(zeros)
+        indices = np.random.choice(
+            np.arange(n_zero), size=new_background, replace=False
+        ).tolist()
+        points.append(zeros[:, [1, 0]][indices])
+        labels.append(torch.zeros(new_background))
+        points = torch.cat(points, dim=0)
+        labels = torch.cat(labels, dim=0)
+        all_points.append(points)
+        all_labels.append(labels)
+    all_points = torch.stack(all_points, dim=0)
+    all_labels = torch.stack(all_labels, dim=0)
+    return all_points, all_labels 
+
+
+
+
+#
+device = "cuda:0"
+def create_prompt_main(probabilities):
+    probabilities = probabilities.sigmoid()
+
+    # Thresholding function
+    def threshold(tensor, thresh):
+        return (tensor > thresh).float()
+
+    # Morphological operations
+    def morphological_op(tensor, operation, kernel_size):
+        kernel = torch.ones(1, 1, kernel_size[0], kernel_size[1]).to(tensor.device)
+        if kernel_size[0] % 2 == 0:  
+            padding = [(k - 1) // 2 for k in kernel_size]
+            extra_pad = [0, 2, 0, 2]  
+        else:
+            padding = [(k - 1) // 2 for k in kernel_size]
+            extra_pad = [0, 0, 0, 0]  
+
+        if operation == 'erode':
+            tensor = F.conv2d(F.pad(tensor, extra_pad), kernel, padding=padding).clamp(max=1)
+        elif operation == 'dilate':
+            tensor = F.max_pool2d(F.pad(tensor, extra_pad), kernel_size, stride=1, padding=padding).clamp(max=1)
+
+        if kernel_size[0] % 2 == 0:  
+            tensor = tensor[:, :, :tensor.shape[2] - 1, :tensor.shape[3] - 1]
+
+        return tensor.squeeze(1)
+
+    # Foreground prompts
+    th_O = threshold(probabilities, 0.5)
+    M_f = morphological_op(morphological_op(th_O, 'erode', (10, 10)), 'dilate', (5, 5))
+    foreground_indices = torch.nonzero(M_f.squeeze(0), as_tuple=False)
+    n_for = 2 if len(foreground_indices) >= 2 else len(foreground_indices)
+    n_back = 4 - n_for
+    # Background prompts
+    M_b1 = 1 - morphological_op(threshold(probabilities, 0.5), 'dilate', (10, 10))
+    M_b2 = 1 - threshold(probabilities, 0.4)
+    M_b2 = M_b2.squeeze(1)
+
+    M_b = M_b1 * M_b2
+    M_b = M_b.squeeze(0)
+    background_indices = torch.nonzero(M_b, as_tuple=False)
+
+    if n_for > 0:
+        indices = torch.concat([foreground_indices[np.random.choice(np.arange(len(foreground_indices)), size=n_for)],
+                                background_indices[np.random.choice(np.arange(len(background_indices)), size=n_back)]
+                                ])
+        values = torch.tensor([1] * n_for + [0] * n_back)
+    else:
+        indices = background_indices[np.random.choice(np.arange(len(background_indices)), size=4)]
+        values = torch.tensor([0] * 4)
+    # raise ValueError(indices, values)
+    return indices.unsqueeze(0), values.unsqueeze(0)
+
+
+

requirements.txt

@@ -0,0 +1,148 @@
+addict==2.4.0
+albumentations==1.3.1
+aliyun-python-sdk-core==2.14.0
+aliyun-python-sdk-kms==2.16.2
+asttokens @ file:///home/conda/feedstock_root/build_artifacts/asttokens_1694046349000/work
+backcall @ file:///home/conda/feedstock_root/build_artifacts/backcall_1592338393461/work
+backports.functools-lru-cache @ file:///home/conda/feedstock_root/build_artifacts/backports.functools_lru_cache_1687772187254/work
+brotlipy==0.7.0
+certifi @ file:///croot/certifi_1690232220950/work/certifi
+cffi==1.16.0
+charset-normalizer @ file:///tmp/build/80754af9/charset-normalizer_1630003229654/work
+click==8.1.7
+cmake==3.27.5
+colorama==0.4.6
+coloredlogs==15.0.1
+comm @ file:///home/conda/feedstock_root/build_artifacts/comm_1691044910542/work
+contourpy @ file:///work/ci_py311/contourpy_1676827066340/work
+crcmod==1.7
+cryptography @ file:///croot/cryptography_1694444244250/work
+cycler @ file:///tmp/build/80754af9/cycler_1637851556182/work
+debugpy @ file:///croot/debugpy_1690905042057/work
+decorator @ file:///home/conda/feedstock_root/build_artifacts/decorator_1641555617451/work
+einops==0.6.1
+exceptiongroup @ file:///home/conda/feedstock_root/build_artifacts/exceptiongroup_1692026125334/work
+executing @ file:///home/conda/feedstock_root/build_artifacts/executing_1667317341051/work
+filelock==3.12.4
+flatbuffers==23.5.26
+fonttools==4.25.0
+gmpy2 @ file:///work/ci_py311/gmpy2_1676839849213/work
+humanfriendly==10.0
+idna @ file:///work/ci_py311/idna_1676822698822/work
+imageio==2.31.4
+importlib-metadata @ file:///home/conda/feedstock_root/build_artifacts/importlib-metadata_1688754491823/work
+ipykernel @ file:///home/conda/feedstock_root/build_artifacts/ipykernel_1693880262622/work
+ipython @ file:///home/conda/feedstock_root/build_artifacts/ipython_1693579759651/work
+jedi @ file:///home/conda/feedstock_root/build_artifacts/jedi_1690896916983/work
+Jinja2 @ file:///work/ci_py311/jinja2_1676823587943/work
+jmespath==0.10.0
+joblib==1.3.2
+jupyter_client @ file:///home/conda/feedstock_root/build_artifacts/jupyter_client_1693317508789/work
+jupyter_core @ file:///home/conda/feedstock_root/build_artifacts/jupyter_core_1695827980501/work
+kiwisolver @ file:///work/ci_py311/kiwisolver_1676827230232/work
+lazy_loader==0.3
+lit==17.0.1
+Markdown==3.4.4
+markdown-it-py==3.0.0
+MarkupSafe @ file:///work/ci_py311/markupsafe_1676823507015/work
+mat4py==0.5.0
+matplotlib @ file:///croot/matplotlib-suite_1693812469450/work
+matplotlib-inline @ file:///home/conda/feedstock_root/build_artifacts/matplotlib-inline_1660814786464/work
+mdurl==0.1.2
+mkl-fft @ file:///croot/mkl_fft_1695058164594/work
+mkl-random @ file:///croot/mkl_random_1695059800811/work
+mkl-service==2.4.0
+mmcv==2.0.1
+-e git+https://github.com/open-mmlab/mmdetection.git@f78af7785ada87f1ced75a2313746e4ba3149760#egg=mmdet
+mmengine==0.8.5
+mmpretrain==1.0.2
+model-index==0.1.11
+modelindex==0.0.2
+motmetrics==1.4.0
+mpmath @ file:///croot/mpmath_1690848262763/work
+munkres==1.1.4
+nest-asyncio @ file:///home/conda/feedstock_root/build_artifacts/nest-asyncio_1664684991461/work
+networkx @ file:///croot/networkx_1690561992265/work
+nibabel @ file:///home/conda/feedstock_root/build_artifacts/nibabel_1680908467684/work
+numpy @ file:///croot/numpy_and_numpy_base_1691091611330/work
+nvidia-cublas-cu11==11.10.3.66
+nvidia-cuda-cupti-cu11==11.7.101
+nvidia-cuda-nvrtc-cu11==11.7.99
+nvidia-cuda-runtime-cu11==11.7.99
+nvidia-cudnn-cu11==8.5.0.96
+nvidia-cufft-cu11==10.9.0.58
+nvidia-curand-cu11==10.2.10.91
+nvidia-cusolver-cu11==11.4.0.1
+nvidia-cusparse-cu11==11.7.4.91
+nvidia-nccl-cu11==2.14.3
+nvidia-nvtx-cu11==11.7.91
+onnx==1.14.1
+onnxruntime==1.16.0
+opencv-python-headless==4.8.1.78
+opendatalab==0.0.10
+openmim==0.3.9
+openxlab==0.0.26
+ordered-set==4.1.0
+oss2==2.17.0
+packaging @ file:///croot/packaging_1693575174725/work
+pandas==2.1.1
+parso @ file:///home/conda/feedstock_root/build_artifacts/parso_1638334955874/work
+pexpect @ file:///home/conda/feedstock_root/build_artifacts/pexpect_1667297516076/work
+pickleshare @ file:///home/conda/feedstock_root/build_artifacts/pickleshare_1602536217715/work
+Pillow @ file:///croot/pillow_1695134008276/work
+platformdirs @ file:///home/conda/feedstock_root/build_artifacts/platformdirs_1690813113769/work
+ply==3.11
+prompt-toolkit @ file:///home/conda/feedstock_root/build_artifacts/prompt-toolkit_1688565951714/work
+protobuf==4.24.3
+psutil @ file:///work/ci_py311_2/psutil_1679337388738/work
+ptyprocess @ file:///home/conda/feedstock_root/build_artifacts/ptyprocess_1609419310487/work/dist/ptyprocess-0.7.0-py2.py3-none-any.whl
+pure-eval @ file:///home/conda/feedstock_root/build_artifacts/pure_eval_1642875951954/work
+pycocotools==2.0.7
+pycparser @ file:///tmp/build/80754af9/pycparser_1636541352034/work
+pycryptodome==3.19.0
+pydicom @ file:///home/conda/feedstock_root/build_artifacts/pydicom_1692139723652/work
+Pygments @ file:///home/conda/feedstock_root/build_artifacts/pygments_1691408637400/work
+pyOpenSSL @ file:///croot/pyopenssl_1690223430423/work
+pyparsing @ file:///work/ci_py311/pyparsing_1677811559502/work
+PyQt5-sip==12.11.0
+PySocks @ file:///work/ci_py311/pysocks_1676822712504/work
+python-dateutil @ file:///tmp/build/80754af9/python-dateutil_1626374649649/work
+pytz==2023.3.post1
+PyWavelets==1.4.1
+PyYAML==6.0.1
+pyzmq @ file:///croot/pyzmq_1686601365461/work
+qudida==0.0.4
+requests==2.28.2
+rich==13.4.2
+scikit-image==0.21.0
+scikit-learn==1.3.1
+scipy==1.11.2
+seaborn==0.13.0
+-e git+ssh://[email protected]/facebookresearch/segment-anything.git@6fdee8f2727f4506cfbbe553e23b895e27956588#egg=segment_anything
+shapely==2.0.1
+sip @ file:///work/ci_py311/sip_1676825117084/work
+six @ file:///tmp/build/80754af9/six_1644875935023/work
+stack-data @ file:///home/conda/feedstock_root/build_artifacts/stack_data_1669632077133/work
+sympy @ file:///work/ci_py311_2/sympy_1679339311852/work
+tabulate==0.9.0
+termcolor==2.3.0
+terminaltables==3.1.10
+threadpoolctl==3.2.0
+tifffile==2023.9.26
+toml @ file:///tmp/build/80754af9/toml_1616166611790/work
+tomli==2.0.1
+torch==2.0.1+cu118
+torchaudio==2.0.2
+torchvision==0.15.2
+tornado @ file:///croot/tornado_1690848263220/work
+tqdm==4.65.2
+trackeval @ git+https://github.com/JonathonLuiten/TrackEval.git@12c8791b303e0a0b50f753af204249e622d0281a
+traitlets @ file:///home/conda/feedstock_root/build_artifacts/traitlets_1695739569237/work
+triton==2.0.0
+typing_extensions @ file:///home/conda/feedstock_root/build_artifacts/typing_extensions_1695040754690/work
+tzdata==2023.3
+urllib3 @ file:///croot/urllib3_1686163155763/work
+wcwidth @ file:///home/conda/feedstock_root/build_artifacts/wcwidth_1673864653149/work
+xmltodict==0.13.0
+yapf==0.40.2
+zipp @ file:///home/conda/feedstock_root/build_artifacts/zipp_1695255097490/work

test_inference.py

@@ -0,0 +1,520 @@
+debug = 1
+from pathlib import Path
+import numpy as np
+import matplotlib.pyplot as plt
+
+# import cv2
+from collections import defaultdict
+import torchvision.transforms as transforms
+import torch
+from torch import nn
+
+import torch.nn.functional as F
+from segment_anything.utils.transforms import ResizeLongestSide
+import albumentations as A
+from albumentations.pytorch import ToTensorV2
+import numpy as np
+from einops import rearrange
+import random
+from tqdm import tqdm
+from time import sleep
+from data import *
+from time import time
+from PIL import Image
+from sklearn.model_selection import KFold
+from shutil import copyfile
+import monai
+from tqdm import tqdm
+from utils import sample_prompt
+from torch.autograd import Variable
+from args import get_arguments
+# import wandb_handler
+
+args = get_arguments()
+def save_img(img, dir):
+    img = img.clone().cpu().numpy() + 100
+    if len(img.shape) == 3:
+        img = rearrange(img, "c h w -> h w c")
+        img_min = np.amin(img, axis=(0, 1), keepdims=True)
+        img = img - img_min
+
+        img_max = np.amax(img, axis=(0, 1), keepdims=True)
+        img = (img / img_max * 255).astype(np.uint8)
+        grey_img = Image.fromarray(img[:, :, 0])
+        img = Image.fromarray(img)
+
+    else:
+        img_min = img.min()
+        img = img - img_min
+        img_max = img.max()
+        if img_max != 0:
+            img = img / img_max * 255
+        img = Image.fromarray(img).convert("L")
+
+    img.save(dir)
+    
+    
+
+
+
+class FocalLoss(nn.Module):
+    def __init__(self, gamma=2.0, alpha=0.25):
+        super(FocalLoss, self).__init__()
+        self.gamma = gamma
+        self.alpha = alpha
+
+    def dice_loss(self, logits, gt, eps=1):
+        # Convert logits to probabilities
+        # Flatten the tensors
+        # probs = probs.view(-1)
+        # gt = gt.view(-1)
+
+        probs = torch.sigmoid(logits)
+
+        # Compute Dice coefficient
+        intersection = (probs * gt).sum()
+
+        dice_coeff = (2.0 * intersection + eps) / (probs.sum() + gt.sum() + eps)
+
+        # Compute Dice Los[s
+        loss = 1 - dice_coeff
+        return loss
+
+    def focal_loss(self, pred, mask):
+        """
+        pred: [B, 1, H, W]
+        mask: [B, 1, H, W]
+        """
+        # pred=pred.reshape(-1,1)
+        # mask = mask.reshape(-1,1)
+        # assert pred.shape == mask.shape, "pred and mask should have the same shape."
+        p = torch.sigmoid(pred)
+        num_pos = torch.sum(mask)
+        num_neg = mask.numel() - num_pos
+        w_pos = (1 - p) ** self.gamma
+        w_neg = p**self.gamma
+
+        loss_pos = -self.alpha * mask * w_pos * torch.log(p + 1e-12)
+        loss_neg = -(1 - self.alpha) * (1 - mask) * w_neg * torch.log(1 - p + 1e-12)
+
+        loss = (torch.sum(loss_pos) + torch.sum(loss_neg)) / (num_pos + num_neg + 1e-12)
+
+        return loss
+
+    def forward(self, logits, target):
+        logits = logits.squeeze(1)
+        target = target.squeeze(1)
+        # Dice Loss
+        # prob = F.softmax(logits, dim=1)[:, 1, ...]
+
+        dice_loss = self.dice_loss(logits, target)
+
+        # Focal Loss
+        focal_loss = self.focal_loss(logits, target.squeeze(-1))
+        alpha = 20.0
+        # Combined Loss
+        combined_loss = alpha * focal_loss + dice_loss
+        return combined_loss
+
+
+class loss_fn(torch.nn.Module):
+    def __init__(self, alpha=0.7, gamma=2.0, epsilon=1e-5):
+        super(loss_fn, self).__init__()
+        self.alpha = alpha
+        self.gamma = gamma
+        self.epsilon = epsilon
+
+
+    def dice_loss(self, logits, gt, eps=1):
+        # Convert logits to probabilities
+        # Flatten the tensorsx
+        probs = torch.sigmoid(logits)
+
+        probs = probs.view(-1)
+        gt = gt.view(-1)
+
+        # Compute Dice coefficient
+        intersection = (probs * gt).sum()
+
+        dice_coeff = (2.0 * intersection + eps) / (probs.sum() + gt.sum() + eps)
+
+        # Compute Dice Los[s
+        loss = 1 - dice_coeff
+        return loss
+
+    def focal_loss(self, logits, gt, gamma=4):
+        logits = logits.reshape(-1, 1)
+        gt = gt.reshape(-1, 1)
+        logits = torch.cat((1 - logits, logits), dim=1)
+
+        probs = torch.sigmoid(logits)
+        pt = probs.gather(1, gt.long())
+
+        modulating_factor = (1 - pt) ** gamma
+        # pt_false= pt<=0.5
+        # modulating_factor[pt_false] *= 2
+        focal_loss = -modulating_factor * torch.log(pt + 1e-12)
+
+        # Compute the mean focal loss
+        loss = focal_loss.mean()
+        return loss  # Store as a Python number to save memory
+
+    def forward(self, logits, target):
+        logits = logits.squeeze(1)
+        target = target.squeeze(1)
+        # Dice Loss
+        # prob = F.softmax(logits, dim=1)[:, 1, ...]
+
+        dice_loss = self.dice_loss(logits, target)
+
+        # Focal Loss
+        focal_loss = self.focal_loss(logits, target.squeeze(-1))
+        alpha = 20.0
+        # Combined Loss
+        combined_loss = alpha * focal_loss + dice_loss
+        return combined_loss
+
+
+def img_enhance(img2, coef=0.2):
+    img_mean = np.mean(img2)
+    img_max = np.max(img2)
+    val = (img_max - img_mean) * coef + img_mean
+    img2[img2 < img_mean * 0.7] = img_mean * 0.7
+    img2[img2 > val] = val
+    return img2
+
+
+def dice_coefficient(pred, target):
+    smooth = 1  # Smoothing constant to avoid division by zero
+    dice = 0
+    pred_index = pred
+    target_index = target
+    intersection = (pred_index * target_index).sum()
+    union = pred_index.sum() + target_index.sum()
+    dice += (2.0 * intersection + smooth) / (union + smooth)
+    return dice.item()
+
+def calculate_accuracy(pred, target):
+    correct = (pred == target).sum().item()
+    total = target.numel()
+    return correct / total
+
+def calculate_sensitivity(pred, target): 
+    smooth = 1
+    # Also known as recall
+    true_positive = ((pred == 1) & (target == 1)).sum().item()
+    false_negative = ((pred == 0) & (target == 1)).sum().item()
+    return (true_positive + smooth) / ((true_positive + false_negative) + smooth)
+
+def calculate_specificity(pred, target):
+    smooth = 1
+    true_negative = ((pred == 0) & (target == 0)).sum().item()
+    false_positive = ((pred == 1) & (target == 0)).sum().item()
+    return (true_negative + smooth) / ((true_negative + false_positive ) + smooth)
+
+# def calculate_recall(pred, target):  # Same as sensitivity
+#     return calculate_sensitivity(pred, target)
+
+
+
+accumaltive_batch_size = 512
+batch_size = 2
+num_workers = 4
+slice_per_image = 1
+num_epochs = 40
+sample_size = 1800
+# image_size=sam_model.image_encoder.img_size
+image_size = 1024
+exp_id = 0
+found = 0
+# if debug:
+#     user_input = "debug"
+# else:
+#     user_input = input("Related changes: ")
+# while found == 0:
+#     try:
+#         os.makedirs(f"exps/{exp_id}-{user_input}")
+#         found = 1
+#     except:
+#         exp_id = exp_id + 1
+# copyfile(os.path.realpath(__file__), f"exps/{exp_id}-{user_input}/code.py")
+
+
+layer_n = 4
+L = layer_n
+a = np.full(L, layer_n)
+params = {"M": 255, "a": a, "p": 0.35}
+
+
+model_type = "vit_h"
+checkpoint = "checkpoints/sam_vit_h_4b8939.pth"
+device = "cuda:0"
+
+
+from segment_anything import SamPredictor, sam_model_registry
+
+
+# //////////////////
+class panc_sam(nn.Module):
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+        # self.sam = sam_model_registry[model_type](checkpoint=checkpoint)
+        self.sam = torch.load(
+            "sam_tuned_save.pth"
+        ).sam
+
+
+    def forward(self, batched_input):
+        # with torch.no_grad():
+        input_images = torch.stack([x["image"] for x in batched_input], dim=0)
+        
+        with torch.no_grad():
+            image_embeddings = self.sam.image_encoder(input_images).detach()
+        
+        outputs = []
+        
+        for image_record, curr_embedding in zip(batched_input, image_embeddings):
+            if "point_coords" in image_record:
+                points = (image_record["point_coords"].unsqueeze(0), image_record["point_labels"].unsqueeze(0))
+            else:
+                raise ValueError('what the f?')
+                points = None
+            # raise ValueError(image_record["point_coords"].shape)
+            with torch.no_grad():
+                sparse_embeddings, dense_embeddings = self.sam.prompt_encoder(
+                     points=points,
+                     boxes=image_record.get("boxes", None),
+                     masks=image_record.get("mask_inputs", None),
+                 )
+                #sparse_embeddings, dense_embeddings = self.sam.prompt_encoder(
+                #    points=None,
+                #    boxes=None,
+                #    masks=None,
+                #)
+                sparse_embeddings = sparse_embeddings / 5
+                dense_embeddings = dense_embeddings / 5
+            # raise ValueError(image_embeddings.shape)
+            low_res_masks, _ = self.sam.mask_decoder(
+                image_embeddings=curr_embedding.unsqueeze(0),
+                image_pe=self.sam.prompt_encoder.get_dense_pe().detach(),
+                sparse_prompt_embeddings=sparse_embeddings.detach(),
+                dense_prompt_embeddings=dense_embeddings.detach(),
+                multimask_output=False,
+            )
+            outputs.append(
+                {
+                    "low_res_logits": low_res_masks,
+                }
+            )
+        low_res_masks = torch.stack([x["low_res_logits"] for x in outputs], dim=0)
+
+        return low_res_masks.squeeze(1)
+    
+    
+    
+panc_sam_instance = panc_sam()
+
+panc_sam_instance.to(device)
+panc_sam_instance.eval()  # Set the model to evaluation mode
+
+test_dataset = PanDataset(
+    [args.test_dir],
+    [args.test_labels_dir],
+    [["NIH_PNG",1]],
+    image_size,
+    slice_per_image=slice_per_image,
+    train=False,
+)
+test_loader = DataLoader(
+    test_dataset,
+    batch_size=batch_size,
+    collate_fn=test_dataset.collate_fn,
+    shuffle=False,
+    drop_last=False,
+    num_workers=num_workers,)
+
+lr = 1e-4
+max_lr = 1e-3
+wd = 5e-4
+
+optimizer = torch.optim.Adam(
+    # parameters,
+    list(panc_sam_instance.sam.mask_decoder.parameters()),
+    lr=lr,
+    weight_decay=wd,
+)
+scheduler = torch.optim.lr_scheduler.OneCycleLR(
+    optimizer,
+    max_lr=max_lr,
+    epochs=num_epochs,
+    steps_per_epoch=sample_size // (accumaltive_batch_size // batch_size),
+)
+loss_function = loss_fn(alpha=0.5, gamma=2.0)
+loss_function.to(device)
+from statistics import mean
+
+from tqdm import tqdm
+from torch.nn.functional import threshold, normalize
+
+def process_model(data_loader, train=False, save_output=0):
+    epoch_losses = []
+
+    index = 0
+    results = torch.zeros((2, 0, 256, 256))
+    total_dice = 0.0
+    total_accuracy = 0.0
+    total_sensitivity = 0.0
+    total_specificity = 0.0
+    num_samples = 0
+
+    counterb = 0
+    for image, label in tqdm(data_loader, total=sample_size):
+        counterb += 1
+
+        index += 1
+        image = image.to(device)
+        label = label.to(device).float()
+        points, point_labels = sample_prompt(label)
+
+        batched_input = []
+        for ibatch in range(batch_size):
+            batched_input.append(
+                {
+                    "image": image[ibatch],
+                    "point_coords": points[ibatch],
+                    "point_labels": point_labels[ibatch],
+                    "original_size": (1024, 1024)
+                },
+            )
+
+        low_res_masks = panc_sam_instance(batched_input)
+        low_res_label = F.interpolate(label, low_res_masks.shape[-2:])    
+        binary_mask = normalize(threshold(low_res_masks, 0.0,0))
+        loss = loss_function(low_res_masks, low_res_label)
+            
+        loss /= (accumaltive_batch_size / batch_size)
+        opened_binary_mask = torch.zeros_like(binary_mask).cpu()
+
+        for j, mask in enumerate(binary_mask[:, 0]):
+            numpy_mask = mask.detach().cpu().numpy().astype(np.uint8)
+
+            opened_binary_mask[j][0] = torch.from_numpy(numpy_mask)
+
+        dice = dice_coefficient(
+            opened_binary_mask.numpy(), low_res_label.cpu().detach().numpy()
+        )
+        accuracy = calculate_accuracy(binary_mask, low_res_label)
+        sensitivity = calculate_sensitivity(binary_mask, low_res_label)
+        
+        specificity = calculate_specificity(binary_mask, low_res_label)
+        total_accuracy += accuracy
+        total_sensitivity += sensitivity
+        total_specificity += specificity
+        total_dice += dice
+        num_samples += 1
+        average_dice = total_dice / num_samples
+        average_accuracy = total_accuracy / num_samples
+        average_sensitivity = total_sensitivity / num_samples
+        average_specificity = total_specificity / num_samples
+
+        if train:
+            loss.backward()
+
+            if index % (accumaltive_batch_size / batch_size) == 0:
+                optimizer.step()
+                scheduler.step()
+                optimizer.zero_grad()
+                index = 0
+
+        else:
+            result = torch.cat(
+                (
+                    low_res_masks[0].detach().cpu().reshape(1, 1, 256, 256),
+                    opened_binary_mask[0].reshape(1, 1, 256, 256),
+                ),
+                dim=0,
+            )
+            results = torch.cat((results, result), dim=1)
+        if index % (accumaltive_batch_size / batch_size) == 0:
+            epoch_losses.append(loss.item())
+        if counterb == sample_size:
+            break
+    
+
+    return epoch_losses, results, average_dice , average_accuracy , average_sensitivity ,average_specificity
+
+def test_model(test_loader):
+    print("Testing started.")
+    test_losses = []
+    dice_test = []
+    results = []
+
+    test_epoch_losses, epoch_results, average_dice_test, average_accuracy_test, average_sensitivity_test, average_specificity_test = process_model(test_loader)
+
+    test_losses.append(test_epoch_losses)
+    dice_test.append(average_dice_test)
+    print(dice_test)
+
+    # Handling the results as needed
+
+    return test_losses, results
+
+test_losses, results = test_model(test_loader)
+
+
+import torch
+import torch.nn as nn
+
+def double_conv_3d(in_channels, out_channels):
+    return nn.Sequential(
+        nn.Conv3d(in_channels, out_channels, kernel_size=3, padding=1),
+        nn.ReLU(inplace=True),
+        nn.Conv3d(out_channels, out_channels, kernel_size=3, padding=1),
+        nn.ReLU(inplace=True)
+    )
+
+class UNet3D(nn.Module):
+    def __init__(self):
+        super(UNet3D, self).__init__()
+
+        self.dconv_down1 = double_conv_3d(3, 64)
+        self.dconv_down2 = double_conv_3d(64, 128)
+        self.dconv_down3 = double_conv_3d(128, 256)
+        self.dconv_down4 = double_conv_3d(256, 512)
+
+        self.maxpool = nn.MaxPool3d(2)
+        self.upsample = nn.Upsample(scale_factor=2, mode='trilinear', align_corners=True)
+        
+        self.dconv_up3 = double_conv_3d(256 + 512, 256)
+        self.dconv_up2 = double_conv_3d(128 + 256, 128)
+        self.dconv_up1 = double_conv_3d(128 + 64, 64)
+        
+        self.conv_last = nn.Conv3d(64, 1, kernel_size=1)
+
+    def forward(self, x):
+        conv1 = self.dconv_down1(x)
+        x = self.maxpool(conv1)
+
+        conv2 = self.dconv_down2(x)
+        x = self.maxpool(conv2)
+        
+        conv3 = self.dconv_down3(x)
+        x = self.maxpool(conv3)
+        
+        x = self.dconv_down4(x)
+        
+        x = self.upsample(x)
+        x = torch.cat([x, conv3], dim=1)
+        
+        x = self.dconv_up3(x)
+        x = self.upsample(x)
+        x = torch.cat([x, conv2], dim=1)
+        
+        x = self.dconv_up2(x)
+        x = self.upsample(x)
+        x = torch.cat([x, conv1], dim=1)
+        
+        x = self.dconv_up1(x)
+        out = self.conv_last(x)
+        
+        return out

utils.py

@@ -0,0 +1,158 @@
+import torch
+import torch.nn as nn
+import numpy as np
+import torch.nn.functional as F
+
+
+def distance_to_edge(point, image_shape):
+    y, x = point
+    height, width = image_shape
+    distance_top = y
+    distance_bottom = height - y
+    distance_left = x
+    distance_right = width - x
+    return min(distance_top, distance_bottom, distance_left, distance_right)
+
+def sample_prompt(probabilities, forground=2, background=2):
+    kernel_size = 9
+    kernel = nn.Conv2d(
+        in_channels=1,
+        bias=False,
+        out_channels=1,
+        kernel_size=kernel_size,
+        stride=1,
+        padding=kernel_size // 2,
+    )
+    kernel.weight = nn.Parameter(
+        torch.zeros(1, 1, kernel_size, kernel_size).to(probabilities.device),
+        requires_grad=False,
+    )
+    kernel.weight[0, 0] = 1.0
+    eroded_probs = kernel(probabilities).squeeze(1) / (kernel_size ** 2)
+    probabilities = probabilities.squeeze(1)
+
+    all_points = []
+    all_labels = []
+
+    for i in range(len(probabilities)):
+        points = []
+        labels = []
+
+        prob_mask = probabilities[i]
+
+        if torch.max(prob_mask) > 0.01:
+            foreground_indices = torch.topk(prob_mask.view(-1), k=forground, dim=0).indices
+            foreground_points = torch.nonzero(prob_mask > 0, as_tuple=False)
+            n_foreground = len(foreground_points)
+
+            if n_foreground >= forground:
+                # Calculate distance to edge for each point
+                distances = [distance_to_edge(point.cpu().numpy(), prob_mask.shape) for point in foreground_points]
+
+                # Find the point with minimum distance to edge
+                edge_point_idx = np.argmin(distances)
+                edge_point = foreground_points[edge_point_idx]
+
+                # Append the point closest to the edge and another random point
+                points.append(edge_point[[1, 0]].unsqueeze(0))
+                indices_foreground = np.random.choice(np.arange(n_foreground), size=forground-1, replace=False).tolist()
+                selected_foreground = foreground_points[indices_foreground]
+                points.append(selected_foreground[:, [1, 0]])
+                labels.append(torch.ones(forground))
+            else:
+                if n_foreground > 0:
+                    points.append(foreground_points[:, [1, 0]])
+                    labels.append(torch.ones(n_foreground))
+
+
+
+            # Select 2 background points, one from 0 to -15 and one less than -15
+            background_indices_1 = torch.nonzero((prob_mask < 0) & (prob_mask > -15), as_tuple=False)
+            background_indices_2 = torch.nonzero(prob_mask < -15, as_tuple=False)
+
+            # Randomly sample from each set of background points
+            indices_1 = np.random.choice(np.arange(len(background_indices_1)), size=1, replace=False).tolist()
+            indices_2 = np.random.choice(np.arange(len(background_indices_2)), size=1, replace=False).tolist()
+
+            points.append(background_indices_1[indices_1])
+            points.append(background_indices_2[indices_2])
+            labels.append(torch.zeros(2))
+        else:
+            # If no probability is greater than 0, return 4 background points
+            # print(prob_mask.unique())
+            background_indices_1 = torch.nonzero(prob_mask < 0, as_tuple=False)
+
+            indices_1 = np.random.choice(np.arange(len(background_indices_1)), size=4, replace=False).tolist()
+        
+            points.append(background_indices_1[indices_1])
+            labels.append(torch.zeros(4))
+
+        points = torch.cat(points, dim=0)
+        labels = torch.cat(labels, dim=0)
+
+        all_points.append(points)
+        all_labels.append(labels)
+
+    all_points = torch.stack(all_points, dim=0)
+    all_labels = torch.stack(all_labels, dim=0)
+    # print(all_points, all_labels)
+
+    return all_points, all_labels
+
+
+
+device = "cuda:0"
+def main_prompt(probabilities):
+    probabilities = probabilities.sigmoid()
+
+    # Thresholding function
+    def threshold(tensor, thresh):
+        return (tensor > thresh).float()
+
+    # Morphological operations
+    def morphological_op(tensor, operation, kernel_size):
+        kernel = torch.ones(1, 1, kernel_size[0], kernel_size[1]).to(tensor.device)
+        if kernel_size[0] % 2 == 0:  
+            padding = [(k - 1) // 2 for k in kernel_size]
+            extra_pad = [0, 2, 0, 2]  
+        else:
+            padding = [(k - 1) // 2 for k in kernel_size]
+            extra_pad = [0, 0, 0, 0]  
+
+        if operation == 'erode':
+            tensor = F.conv2d(F.pad(tensor, extra_pad), kernel, padding=padding).clamp(max=1)
+        elif operation == 'dilate':
+            tensor = F.max_pool2d(F.pad(tensor, extra_pad), kernel_size, stride=1, padding=padding).clamp(max=1)
+
+        if kernel_size[0] % 2 == 0:  
+            tensor = tensor[:, :, :tensor.shape[2] - 1, :tensor.shape[3] - 1]
+
+        return tensor.squeeze(1)
+
+    # Foreground prompts
+    th_O = threshold(probabilities, 0.5)
+    M_f = morphological_op(morphological_op(th_O, 'erode', (10, 10)), 'dilate', (5, 5))
+    foreground_indices = torch.nonzero(M_f.squeeze(0), as_tuple=False)
+    n_for = 2 if len(foreground_indices) >= 2 else len(foreground_indices)
+    n_back = 4 - n_for
+    # Background prompts
+    M_b1 = 1 - morphological_op(threshold(probabilities, 0.5), 'dilate', (10, 10))
+    M_b2 = 1 - threshold(probabilities, 0.4)
+    M_b2 = M_b2.squeeze(1)
+
+    M_b = M_b1 * M_b2
+    M_b = M_b.squeeze(0)
+    background_indices = torch.nonzero(M_b, as_tuple=False)
+
+    if n_for > 0:
+        indices = torch.concat([foreground_indices[np.random.choice(np.arange(len(foreground_indices)), size=n_for)],
+                                background_indices[np.random.choice(np.arange(len(background_indices)), size=n_back)]
+                                ])
+        values = torch.tensor([1] * n_for + [0] * n_back)
+    else:
+        indices = background_indices[np.random.choice(np.arange(len(background_indices)), size=4)]
+        values = torch.tensor([0] * 4)
+    # raise ValueError(indices, values)
+    return indices.unsqueeze(0), values.unsqueeze(0)
+
+