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