train_mps-2.py

# %%
import argparse
import os
import random
from datetime import datetime
from glob import glob
from os import makedirs
from os.path import join, isfile, basename
from time import time

import cv2
import monai
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from matplotlib import pyplot as plt
from torch.utils.data import Dataset, DataLoader
from tqdm import tqdm

from segment_anything.modeling import MaskDecoder, PromptEncoder, TwoWayTransformer
from tiny_vit_sam import TinyViT

# %%
parser = argparse.ArgumentParser()
parser.add_argument("-task_name", type=str, default="MedSAM-lite")
parser.add_argument("-model_type", type=str, default="vit_tiny")
parser.add_argument(
    "-data_root", type=str, default="./data/npy",
    help="Path to the npy data root."
)
parser.add_argument(
    "-pretrained_checkpoint", type=str, default="lite_medsam.pth",
    help="Path to the pretrained Lite-MedSAM checkpoint."
)
parser.add_argument(
    "-resume", type=str, default='workdir/medsam_lite_latest.pth',
    help="Path to the checkpoint to continue training."
)
parser.add_argument(
    "-work_dir", type=str, default="./workdir",
    help="Path to the working directory where checkpoints and logs will be saved."
)
parser.add_argument(
    "-num_epochs", type=int, default=10,
    help="Number of epochs to train."
)
parser.add_argument(
    "-batch_size", type=int, default=4,
    help="Batch size."
)
parser.add_argument(
    "-num_workers", type=int, default=8,
    help="Number of workers for dataloader."
)
parser.add_argument(
    "-device", type=str, default="mps",
    help="Device to train on."
)  # cuda:0
parser.add_argument(
    "-bbox_shift", type=int, default=5,
    help="Perturbation to bounding box coordinates during training."
)
parser.add_argument(
    "-lr", type=float, default=0.00005,
    help="Learning rate."
)
parser.add_argument(
    "-weight_decay", type=float, default=0.01,
    help="Weight decay."
)
parser.add_argument(
    "-iou_loss_weight", type=float, default=1.0,
    help="Weight of IoU loss."
)
parser.add_argument(
    "-seg_loss_weight", type=float, default=1.0,
    help="Weight of segmentation loss."
)
parser.add_argument(
    "-ce_loss_weight", type=float, default=1.0,
    help="Weight of cross entropy loss."
)
parser.add_argument(
    "--sanity_check", action="store_true",
    help="Whether to do sanity check for dataloading."
)
parser.add_argument(
    "-use_wandb", type=bool, default=True,
    help="use wandb to monitor training"
)  # default=False
args = parser.parse_args()

run_id = datetime.now().strftime("%Y%m%d-%H%M")
model_save_path = join(args.work_dir, args.task_name + "-" + run_id)

# %%
work_dir = args.work_dir
data_root = args.data_root
medsam_lite_checkpoint = args.pretrained_checkpoint
num_epochs = args.num_epochs
batch_size = args.batch_size
num_workers = args.num_workers
device = args.device
bbox_shift = args.bbox_shift
lr = args.lr
weight_decay = args.weight_decay
iou_loss_weight = args.iou_loss_weight
seg_loss_weight = args.seg_loss_weight
ce_loss_weight = args.ce_loss_weight
do_sancheck = args.sanity_check
checkpoint = args.resume

makedirs(work_dir, exist_ok=True)

# %%
torch.cuda.empty_cache()
os.environ["OMP_NUM_THREADS"] = "4"  # export OMP_NUM_THREADS=4
os.environ["OPENBLAS_NUM_THREADS"] = "4"  # export OPENBLAS_NUM_THREADS=4
os.environ["MKL_NUM_THREADS"] = "6"  # export MKL_NUM_THREADS=6
os.environ["VECLIB_MAXIMUM_THREADS"] = "4"  # export VECLIB_MAXIMUM_THREADS=4
os.environ["NUMEXPR_NUM_THREADS"] = "6"  # export NUMEXPR_NUM_THREADS=6


def show_mask(mask, ax, random_color=False):
    if random_color:
        color = np.concatenate([np.random.random(3), np.array([0.45])], axis=0)
    else:
        color = np.array([251 / 255, 252 / 255, 30 / 255, 0.45])
    h, w = mask.shape[-2:]
    mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
    ax.imshow(mask_image)


def show_box(box, ax):
    x0, y0 = box[0], box[1]
    w, h = box[2] - box[0], box[3] - box[1]
    ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='blue', facecolor=(0, 0, 0, 0), lw=2))


def cal_iou(result, reference):
    intersection = torch.count_nonzero(torch.logical_and(result, reference), dim=[i for i in range(1, result.ndim)])
    union = torch.count_nonzero(torch.logical_or(result, reference), dim=[i for i in range(1, result.ndim)])

    iou = intersection.float() / union.float()

    return iou.unsqueeze(1)


# %%
class NpyDataset(Dataset):
    def __init__(self, data_root, image_size=256, bbox_shift=5, data_aug=True):
        self.data_root = data_root
        self.gt_path = join(data_root, 'gts')
        self.img_path = join(data_root, 'imgs')
        self.gt_path_files = sorted(glob(join(self.gt_path, '*.npy'), recursive=True))
        self.gt_path_files = [
            file for file in self.gt_path_files
            if isfile(join(self.img_path, basename(file)))
        ]
        self.image_size = image_size
        self.target_length = image_size
        self.bbox_shift = bbox_shift
        self.data_aug = data_aug

    def __len__(self):
        return len(self.gt_path_files)

    def __getitem__(self, index):
        img_name = basename(self.gt_path_files[index])
        assert img_name == basename(self.gt_path_files[index]), 'img gt name error' + self.gt_path_files[index] + \
                                                                self.npy_files[index]
        img_3c = np.load(join(self.img_path, img_name), 'r', allow_pickle=True)  # (H, W, 3)
        img_resize = self.resize_longest_side(img_3c)
        # Resizing
        img_resize = (img_resize - img_resize.min()) / np.clip(img_resize.max() - img_resize.min(), a_min=1e-8,
                                                               a_max=None)  # normalize to [0, 1], (H, W, 3
        img_padded = self.pad_image(img_resize)  # (256, 256, 3)
        # convert the shape to (3, H, W)
        img_padded = np.transpose(img_padded, (2, 0, 1))  # (3, 256, 256)
        assert np.max(img_padded) <= 1.0 and np.min(img_padded) >= 0.0, 'image should be normalized to [0, 1]'
        gt = np.load(self.gt_path_files[index], 'r', allow_pickle=True)  # multiple labels [0, 1,4,5...], (256,256)
        gt = cv2.resize(
            gt,
            (img_resize.shape[1], img_resize.shape[0]),
            interpolation=cv2.INTER_NEAREST
        ).astype(np.uint8)
        gt = self.pad_image(gt)  # (256, 256)
        label_ids = np.unique(gt)[1:]
        try:
            gt2D = np.uint8(gt == random.choice(label_ids.tolist()))  # only one label, (256, 256)
        except:
            print(img_name, 'label_ids.tolist()', label_ids.tolist())
            gt2D = np.uint8(gt == np.max(gt))  # only one label, (256, 256)
        # add data augmentation: random fliplr and random flipud
        if self.data_aug:
            if random.random() > 0.5:
                img_padded = np.ascontiguousarray(np.flip(img_padded, axis=-1))
                gt2D = np.ascontiguousarray(np.flip(gt2D, axis=-1))
                # print('DA with flip left right')
            if random.random() > 0.5:
                img_padded = np.ascontiguousarray(np.flip(img_padded, axis=-2))
                gt2D = np.ascontiguousarray(np.flip(gt2D, axis=-2))
                # print('DA with flip upside down')
        gt2D = np.uint8(gt2D > 0)
        y_indices, x_indices = np.where(gt2D > 0)
        x_min, x_max = np.min(x_indices), np.max(x_indices)
        y_min, y_max = np.min(y_indices), np.max(y_indices)
        # add perturbation to bounding box coordinates
        H, W = gt2D.shape
        x_min = max(0, x_min - random.randint(0, self.bbox_shift))
        x_max = min(W, x_max + random.randint(0, self.bbox_shift))
        y_min = max(0, y_min - random.randint(0, self.bbox_shift))
        y_max = min(H, y_max + random.randint(0, self.bbox_shift))
        bboxes = np.array([x_min, y_min, x_max, y_max])
        return {
            "image": torch.tensor(img_padded).float(),
            "gt2D": torch.tensor(gt2D[None, :, :]).long(),
            "bboxes": torch.tensor(bboxes[None, None, ...]).float(),  # (B, 1, 4)
            "image_name": img_name,
            "new_size": torch.tensor(np.array([img_resize.shape[0], img_resize.shape[1]])).long(),
            "original_size": torch.tensor(np.array([img_3c.shape[0], img_3c.shape[1]])).long()
        }

    def resize_longest_side(self, image):
        """
        Expects a numpy array with shape HxWxC in uint8 format.
        """
        long_side_length = self.target_length
        oldh, oldw = image.shape[0], image.shape[1]
        scale = long_side_length * 1.0 / max(oldh, oldw)
        newh, neww = oldh * scale, oldw * scale
        neww, newh = int(neww + 0.5), int(newh + 0.5)
        target_size = (neww, newh)

        return cv2.resize(image, target_size, interpolation=cv2.INTER_AREA)

    def pad_image(self, image):
        """
        Expects a numpy array with shape HxWxC in uint8 format.
        """
        # Pad
        h, w = image.shape[0], image.shape[1]
        padh = self.image_size - h
        padw = self.image_size - w
        if len(image.shape) == 3:  ## Pad image
            image_padded = np.pad(image, ((0, padh), (0, padw), (0, 0)))
        else:  ## Pad gt mask
            image_padded = np.pad(image, ((0, padh), (0, padw)))

        return image_padded


# %% sanity test of dataset class
if do_sancheck:
    tr_dataset = NpyDataset(data_root, data_aug=True)
    tr_dataloader = DataLoader(tr_dataset, batch_size=8, shuffle=True)
    for step, batch in enumerate(tr_dataloader):
        # show the example
        _, axs = plt.subplots(1, 2, figsize=(10, 10))
        idx = random.randint(0, 4)

        image = batch["image"]
        gt = batch["gt2D"]
        bboxes = batch["bboxes"]
        names_temp = batch["image_name"]

        axs[0].imshow(image[idx].cpu().permute(1, 2, 0).numpy())
        show_mask(gt[idx].cpu().squeeze().numpy(), axs[0])
        show_box(bboxes[idx].numpy().squeeze(), axs[0])
        axs[0].axis('off')
        # set title
        axs[0].set_title(names_temp[idx])
        idx = random.randint(4, 7)
        axs[1].imshow(image[idx].cpu().permute(1, 2, 0).numpy())
        show_mask(gt[idx].cpu().squeeze().numpy(), axs[1])
        show_box(bboxes[idx].numpy().squeeze(), axs[1])
        axs[1].axis('off')
        # set title
        axs[1].set_title(names_temp[idx])
        plt.subplots_adjust(wspace=0.01, hspace=0)
        plt.savefig(
            join(work_dir, 'medsam_lite-train_bbox_prompt_sanitycheck_DA.png'),
            bbox_inches='tight',
            dpi=300
        )
        plt.close()
        break


# %%
class MedSAM_Lite(nn.Module):
    def __init__(self,
                 image_encoder,
                 mask_decoder,
                 prompt_encoder
                 ):
        super().__init__()
        self.image_encoder = image_encoder
        self.mask_decoder = mask_decoder
        self.prompt_encoder = prompt_encoder

    def forward(self, image, boxes):
        image_embedding = self.image_encoder(image)  # (B, 256, 64, 64)

        sparse_embeddings, dense_embeddings = self.prompt_encoder(
            points=None,
            boxes=boxes,
            masks=None,
        )
        low_res_masks, iou_predictions = self.mask_decoder(
            image_embeddings=image_embedding,  # (B, 256, 64, 64)
            image_pe=self.prompt_encoder.get_dense_pe(),  # (1, 256, 64, 64)
            sparse_prompt_embeddings=sparse_embeddings,  # (B, 2, 256)
            dense_prompt_embeddings=dense_embeddings,  # (B, 256, 64, 64)
            multimask_output=False,
        )  # (B, 1, 256, 256)

        return low_res_masks, iou_predictions

    @torch.no_grad()
    def postprocess_masks(self, masks, new_size, original_size):
        """
        Do cropping and resizing
        """
        # Crop
        masks = masks[:, :, :new_size[0], :new_size[1]]
        # Resize
        masks = F.interpolate(
            masks,
            size=(original_size[0], original_size[1]),
            mode="bilinear",
            align_corners=False,
        )

        return masks


# %%


def main():
    import wandb

    wandb.login()
    wandb.init(
        project=args.task_name,
        name="run-0424-2",
        config={
            "lr": args.lr,
            "batch_size": args.batch_size,
            "data_path": args.data_root,
            "epochs": args.num_epochs,
        },
    )

    train_losses = []
    for epoch in range(start_epoch + 1, num_epochs):
        epoch_loss = [1e10 for _ in range(len(train_loader))]
        epoch_start_time = time()
        pbar = tqdm(train_loader)
        for step, batch in enumerate(pbar):
            image = batch["image"]
            gt2D = batch["gt2D"]
            boxes = batch["bboxes"]
            optimizer.zero_grad()
            image, gt2D, boxes = image.to(device), gt2D.to(device), boxes.to(device)
            logits_pred, iou_pred = medsam_lite_model(image, boxes)
            l_seg = seg_loss(logits_pred, gt2D)
            l_ce = ce_loss(logits_pred, gt2D.float())
            # mask_loss = l_seg + l_ce
            mask_loss = seg_loss_weight * l_seg + ce_loss_weight * l_ce
            iou_gt = cal_iou(torch.sigmoid(logits_pred) > 0.5, gt2D.bool())
            l_iou = iou_loss(iou_pred, iou_gt)
            # loss = mask_loss + l_iou
            loss = mask_loss + iou_loss_weight * l_iou
            epoch_loss[step] = loss.item()
            loss.backward()
            optimizer.step()
            optimizer.zero_grad()
            # if args.use_wandb:
            wandb.log({"seg_loss_weight": seg_loss_weight})
            wandb.log({"l_seg": l_seg})
            wandb.log({"ce_loss_weight": ce_loss_weight})
            wandb.log({"l_ce": l_ce})
            wandb.log({"mask_loss": mask_loss})

            wandb.log({"iou_gt": iou_gt})

            wandb.log({"iou_loss_weight": iou_loss_weight})
            wandb.log({"l_iou": l_iou})
            wandb.log({"loss": loss})

            wandb.log({"epoch_loss[step]": epoch_loss[step]})

            wandb.log({"loss.item()": loss.item()})

            pbar.set_description(
                f"Epoch {epoch} at {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}, loss: {loss.item():.4f}")

        epoch_end_time = time()
        epoch_loss_reduced = sum(epoch_loss) / len(epoch_loss)
        wandb.log({"epoch_loss": epoch_loss_reduced})
        train_losses.append(epoch_loss_reduced)
        wandb.log({"train_losses": train_losses})
        lr_scheduler.step(epoch_loss_reduced)
        model_weights = medsam_lite_model.state_dict()
        checkpoint = {
            "model": model_weights,
            "epoch": epoch,
            "optimizer": optimizer.state_dict(),
            "loss": epoch_loss_reduced,
            "best_loss": best_loss,
        }
        torch.save(checkpoint, join(work_dir, "medsam_lite_latest.pth"))
        if epoch_loss_reduced < best_loss:
            print(f"New best loss: {best_loss:.4f} -> {epoch_loss_reduced:.4f}")
            best_loss = epoch_loss_reduced
            checkpoint["best_loss"] = best_loss
            torch.save(checkpoint, join(work_dir, "medsam_lite_best.pth"))

        epoch_loss_reduced = 1e10
        # %% plot loss
        plt.plot(train_losses)
        plt.title("Dice + Binary Cross Entropy + IoU Loss")
        plt.xlabel("Epoch")
        plt.ylabel("Loss")
        plt.savefig(join(model_save_path, args.task_name, work_dir, "train_loss.png"))
        plt.close()

    wandb.finish()


if __name__ == "__main__":
    # %%
    # medsam_lite_image_encoder = TinyViT(
    medsam_lite_image_encoder = TinyViT(
        img_size=256,
        in_chans=3,
        embed_dims=[
            64,  ## (64, 256, 256)
            128,  ## (128, 128, 128)
            160,  ## (160, 64, 64)
            320  ## (320, 64, 64)
        ],
        depths=[2, 2, 6, 2],
        num_heads=[2, 4, 5, 10],
        window_sizes=[7, 7, 14, 7],
        mlp_ratio=4.,
        drop_rate=0.,
        drop_path_rate=0.0,
        use_checkpoint=False,
        mbconv_expand_ratio=4.0,
        local_conv_size=3,
        layer_lr_decay=0.8
    )

    medsam_lite_prompt_encoder = PromptEncoder(
        embed_dim=256,
        image_embedding_size=(64, 64),
        input_image_size=(256, 256),
        mask_in_chans=16
    )

    medsam_lite_mask_decoder = MaskDecoder(
        num_multimask_outputs=3,
        transformer=TwoWayTransformer(
            depth=2,
            embedding_dim=256,
            mlp_dim=2048,
            num_heads=8,
        ),
        transformer_dim=256,
        iou_head_depth=3,
        iou_head_hidden_dim=256,
    )

    medsam_lite_model = MedSAM_Lite(
        image_encoder=medsam_lite_image_encoder,
        mask_decoder=medsam_lite_mask_decoder,
        prompt_encoder=medsam_lite_prompt_encoder
    )

    if medsam_lite_checkpoint is not None:
        if isfile(medsam_lite_checkpoint):
            print(f"Finetuning with pretrained weights {medsam_lite_checkpoint}")
            medsam_lite_ckpt = torch.load(
                medsam_lite_checkpoint,
                map_location="cpu"
            )
            medsam_lite_model.load_state_dict(medsam_lite_ckpt, strict=True)
        else:
            print(f"Pretained weights {medsam_lite_checkpoint} not found, training from scratch")

    medsam_lite_model = medsam_lite_model.to(device)
    medsam_lite_model.train()

    # %%
    print(f"MedSAM Lite size: {sum(p.numel() for p in medsam_lite_model.parameters())}")
    # %%
    optimizer = optim.AdamW(
        medsam_lite_model.parameters(),
        lr=lr,
        betas=(0.9, 0.999),
        eps=1e-08,
        weight_decay=weight_decay,
    )
    lr_scheduler = optim.lr_scheduler.ReduceLROnPlateau(
        optimizer,
        mode='min',
        factor=0.9,
        patience=5,
        cooldown=0
    )
    seg_loss = monai.losses.DiceLoss(sigmoid=True, squared_pred=True, reduction='mean')
    ce_loss = nn.BCEWithLogitsLoss(reduction='mean')
    iou_loss = nn.MSELoss(reduction='mean')
    # %%
    train_dataset = NpyDataset(data_root=data_root, data_aug=True)
    train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers,
                              pin_memory=True)

    if checkpoint and isfile(checkpoint):
        print(f"Resuming from checkpoint {checkpoint}")
        checkpoint = torch.load(checkpoint)
        medsam_lite_model.load_state_dict(checkpoint["model"], strict=True)
        optimizer.load_state_dict(checkpoint["optimizer"])
        start_epoch = checkpoint["epoch"]
        best_loss = checkpoint["loss"]
        print(f"Loaded checkpoint from epoch {start_epoch}")
    else:
        start_epoch = 0
        best_loss = 1e10

    main()