PANSAM/fine_tune_good.py

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()