v1.0

engine.py

+"""
+Train and eval functions used in main.py
+"""
+import math
+import sys
+from typing import Iterable, Optional
+
+import torch
+
+from timm.data import Mixup
+from timm.utils import accuracy, ModelEma
+
+from losses import DistillationLoss
+import utils
+
+def set_bn_state(model):
+    for m in model.modules():
+        if isinstance(m, torch.nn.modules.batchnorm._BatchNorm):
+            m.eval()
+
+def train_one_epoch(model: torch.nn.Module, criterion: DistillationLoss,
+                    data_loader: Iterable, optimizer: torch.optim.Optimizer,
+                    device: torch.device, epoch: int, loss_scaler,
+                    clip_grad: float = 0,
+                    clip_mode: str = 'norm',
+                    model_ema: Optional[ModelEma] = None, mixup_fn: Optional[Mixup] = None,
+                    set_training_mode=True,
+                    set_bn_eval=False,):
+    model.train(set_training_mode)
+    if set_bn_eval:
+        set_bn_state(model)
+    metric_logger = utils.MetricLogger(delimiter="  ")
+    metric_logger.add_meter('lr', utils.SmoothedValue(
+        window_size=1, fmt='{value:.6f}'))
+    header = 'Epoch: [{}]'.format(epoch)
+    print_freq = 100
+
+    for samples, targets in metric_logger.log_every(
+            data_loader, print_freq, header):
+        samples = samples.to(device, non_blocking=True)
+        targets = targets.to(device, non_blocking=True)
+
+        if mixup_fn is not None:
+            samples, targets = mixup_fn(samples, targets)
+
+        with torch.cuda.amp.autocast():
+            outputs = model(samples)
+            loss = criterion(samples, outputs, targets)
+
+        loss_value = loss.item()
+
+        if not math.isfinite(loss_value):
+            print("Loss is {}, stopping training".format(loss_value))
+            sys.exit(1)
+
+        optimizer.zero_grad()
+
+        # this attribute is added by timm on one optimizer (adahessian)
+        is_second_order = hasattr(
+            optimizer, 'is_second_order') and optimizer.is_second_order
+        loss_scaler(loss, optimizer, clip_grad=clip_grad, clip_mode=clip_mode,
+                    parameters=model.parameters(), create_graph=is_second_order)
+
+        torch.cuda.synchronize()
+        if model_ema is not None:
+            model_ema.update(model)
+
+        metric_logger.update(loss=loss_value)
+        metric_logger.update(lr=optimizer.param_groups[0]["lr"])
+    # gather the stats from all processes
+    metric_logger.synchronize_between_processes()
+    print("Averaged stats:", metric_logger)
+    return {k: meter.global_avg for k, meter in metric_logger.meters.items()}
+
+
+@torch.no_grad()
+def evaluate(data_loader, model, device):
+    criterion = torch.nn.CrossEntropyLoss()
+
+    metric_logger = utils.MetricLogger(delimiter="  ")
+    header = 'Test:'
+
+    # switch to evaluation mode
+    model.eval()
+
+    for images, target in metric_logger.log_every(data_loader, 10, header):
+        images = images.to(device, non_blocking=True)
+        target = target.to(device, non_blocking=True)
+
+        # compute output
+        with torch.cuda.amp.autocast():
+            output = model(images)
+            loss = criterion(output, target)
+
+        acc1, acc5 = accuracy(output, target, topk=(1, 5))
+
+        batch_size = images.shape[0]
+        metric_logger.update(loss=loss.item())
+        metric_logger.meters['acc1'].update(acc1.item(), n=batch_size)
+        metric_logger.meters['acc5'].update(acc5.item(), n=batch_size)
+    # gather the stats from all processes
+    metric_logger.synchronize_between_processes()
+    print('* Acc@1 {top1.global_avg:.3f} Acc@5 {top5.global_avg:.3f} loss {losses.global_avg:.3f}'
+          .format(top1=metric_logger.acc1, top5=metric_logger.acc5, losses=metric_logger.loss))
+
+    return {k: meter.global_avg for k, meter in metric_logger.meters.items()}

eval.sh

+python main.py --eval --model repvit_m1_1 --resume pretrain/repvit_m1_1_distill_300e.pth --data-path ~/imagenet
\ No newline at end of file

export_coreml.py

+import torch
+
+from timm import create_model
+import model
+
+import utils
+
+import torch
+import torchvision
+from argparse import ArgumentParser
+
+parser = ArgumentParser()
+
+parser.add_argument('--model', default='repvit_m1_1', type=str)
+parser.add_argument('--resolution', default=224, type=int)
+parser.add_argument('--ckpt', default=None, type=str)
+
+if __name__ == "__main__":
+    # Load a pre-trained version of MobileNetV2
+    args = parser.parse_args()
+    model = create_model(args.model, distillation=True)
+    if args.ckpt:
+        model.load_state_dict(torch.load(args.ckpt)['model'])
+    utils.replace_batchnorm(model)
+    model.eval()
+
+    # Trace the model with random data.
+    resolution = args.resolution
+    example_input = torch.rand(1, 3, resolution, resolution) 
+    traced_model = torch.jit.trace(model, example_input)
+    out = traced_model(example_input)
+
+    import coremltools as ct
+
+    # Using image_input in the inputs parameter:
+    # Convert to Core ML neural network using the Unified Conversion API.
+    model = ct.convert(
+        traced_model,
+        inputs=[ct.ImageType(shape=example_input.shape)]
+    )
+
+    # Save the converted model.
+    model.save(f"coreml/{args.model}_{resolution}.mlmodel")
\ No newline at end of file

flops.py

+import torch
+import time
+from timm import create_model
+import model
+import utils
+from fvcore.nn import FlopCountAnalysis
+
+T0 = 5
+T1 = 10
+
+for n, batch_size, resolution in [
+    ('repvit_m0_9', 1024, 224),
+]:
+    inputs = torch.randn(1, 3, resolution,
+                            resolution)
+    model = create_model(n, num_classes=1000)
+    utils.replace_batchnorm(model)
+    n_parameters = sum(p.numel()
+                       for p in model.parameters() if p.requires_grad)
+    print('number of params:', n_parameters / 1e6)
+    flops = FlopCountAnalysis(model, inputs)
+    print("flops: ", flops.total() / 1e9)
\ No newline at end of file

losses.py

+"""
+Implements the knowledge distillation loss, proposed in deit
+"""
+import torch
+from torch.nn import functional as F
+
+
+class DistillationLoss(torch.nn.Module):
+    """
+    This module wraps a standard criterion and adds an extra knowledge distillation loss by
+    taking a teacher model prediction and using it as additional supervision.
+    """
+
+    def __init__(self, base_criterion: torch.nn.Module, teacher_model: torch.nn.Module,
+                 distillation_type: str, alpha: float, tau: float):
+        super().__init__()
+        self.base_criterion = base_criterion
+        self.teacher_model = teacher_model
+        assert distillation_type in ['none', 'soft', 'hard']
+        self.distillation_type = distillation_type
+        self.alpha = alpha
+        self.tau = tau
+
+    def forward(self, inputs, outputs, labels):
+        """
+        Args:
+            inputs: The original inputs that are feed to the teacher model
+            outputs: the outputs of the model to be trained. It is expected to be
+                either a Tensor, or a Tuple[Tensor, Tensor], with the original output
+                in the first position and the distillation predictions as the second output
+            labels: the labels for the base criterion
+        """
+        outputs_kd = None
+        if not isinstance(outputs, torch.Tensor):
+            # assume that the model outputs a tuple of [outputs, outputs_kd]
+            outputs, outputs_kd = outputs
+        base_loss = self.base_criterion(outputs, labels)
+        if self.distillation_type == 'none':
+            return base_loss
+
+        if outputs_kd is None:
+            raise ValueError("When knowledge distillation is enabled, the model is "
+                             "expected to return a Tuple[Tensor, Tensor] with the output of the "
+                             "class_token and the dist_token")
+        # don't backprop throught the teacher
+        with torch.no_grad():
+            teacher_outputs = self.teacher_model(inputs)
+
+        if self.distillation_type == 'soft':
+            T = self.tau
+            # taken from https://github.com/peterliht/knowledge-distillation-pytorch/blob/master/model/net.py#L100
+            # with slight modifications
+            distillation_loss = F.kl_div(
+                F.log_softmax(outputs_kd / T, dim=1),
+                F.log_softmax(teacher_outputs / T, dim=1),
+                reduction='sum',
+                log_target=True
+            ) * (T * T) / outputs_kd.numel()
+        elif self.distillation_type == 'hard':
+            distillation_loss = F.cross_entropy(
+                outputs_kd, teacher_outputs.argmax(dim=1))
+
+        loss = base_loss * (1 - self.alpha) + distillation_loss * self.alpha
+        return loss

main.py

+import argparse
+import datetime
+import numpy as np
+import time
+import torch
+import torch.backends.cudnn as cudnn
+import json
+import os
+
+from pathlib import Path
+
+from timm.data import Mixup
+from timm.models import create_model
+from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy
+from timm.scheduler import create_scheduler
+from timm.optim import create_optimizer
+from timm.utils import NativeScaler, get_state_dict, ModelEma
+
+from data.samplers import RASampler
+from data.datasets import build_dataset
+from data.threeaugment import new_data_aug_generator
+from engine import train_one_epoch, evaluate
+from losses import DistillationLoss
+
+import model
+import utils
+
+
+def get_args_parser():
+    parser = argparse.ArgumentParser(
+        'RepViT training and evaluation script', add_help=False)
+    parser.add_argument('--batch-size', default=256, type=int)
+    parser.add_argument('--epochs', default=300, type=int)
+
+    # Model parameters
+    parser.add_argument('--model', default='repvit_m1_1', type=str, metavar='MODEL',
+                        help='Name of model to train')
+    parser.add_argument('--input-size', default=224,
+                        type=int, help='images input size')
+
+    parser.add_argument('--model-ema', action='store_true')
+    parser.add_argument(
+        '--no-model-ema', action='store_false', dest='model_ema')
+    parser.set_defaults(model_ema=True)
+    parser.add_argument('--model-ema-decay', type=float,
+                        default=0.99996, help='')
+    parser.add_argument('--model-ema-force-cpu',
+                        action='store_true', default=False, help='')
+
+    # Optimizer parameters
+    parser.add_argument('--opt', default='adamw', type=str, metavar='OPTIMIZER',
+                        help='Optimizer (default: "adamw"')
+    parser.add_argument('--opt-eps', default=1e-8, type=float, metavar='EPSILON',
+                        help='Optimizer Epsilon (default: 1e-8)')
+    parser.add_argument('--opt-betas', default=None, type=float, nargs='+', metavar='BETA',
+                        help='Optimizer Betas (default: None, use opt default)')
+    parser.add_argument('--clip-grad', type=float, default=0.02, metavar='NORM',
+                        help='Clip gradient norm (default: None, no clipping)')
+    parser.add_argument('--clip-mode', type=str, default='agc',
+                        help='Gradient clipping mode. One of ("norm", "value", "agc")')
+    parser.add_argument('--momentum', type=float, default=0.9, metavar='M',
+                        help='SGD momentum (default: 0.9)')
+    parser.add_argument('--weight-decay', type=float, default=0.025,
+                        help='weight decay (default: 0.025)')
+
+    # Learning rate schedule parameters
+    parser.add_argument('--sched', default='cosine', type=str, metavar='SCHEDULER',
+                        help='LR scheduler (default: "cosine"')
+    parser.add_argument('--lr', type=float, default=1e-3, metavar='LR',
+                        help='learning rate (default: 1e-3)')
+    parser.add_argument('--lr-noise', type=float, nargs='+', default=None, metavar='pct, pct',
+                        help='learning rate noise on/off epoch percentages')
+    parser.add_argument('--lr-noise-pct', type=float, default=0.67, metavar='PERCENT',
+                        help='learning rate noise limit percent (default: 0.67)')
+    parser.add_argument('--lr-noise-std', type=float, default=1.0, metavar='STDDEV',
+                        help='learning rate noise std-dev (default: 1.0)')
+    parser.add_argument('--warmup-lr', type=float, default=1e-6, metavar='LR',
+                        help='warmup learning rate (default: 1e-6)')
+    parser.add_argument('--min-lr', type=float, default=1e-5, metavar='LR',
+                        help='lower lr bound for cyclic schedulers that hit 0 (1e-5)')
+    parser.add_argument('--decay-epochs', type=float, default=30, metavar='N',
+                        help='epoch interval to decay LR')
+    parser.add_argument('--warmup-epochs', type=int, default=5, metavar='N',
+                        help='epochs to warmup LR, if scheduler supports')
+    parser.add_argument('--cooldown-epochs', type=int, default=10, metavar='N',
+                        help='epochs to cooldown LR at min_lr, after cyclic schedule ends')
+    parser.add_argument('--patience-epochs', type=int, default=10, metavar='N',
+                        help='patience epochs for Plateau LR scheduler (default: 10')
+    parser.add_argument('--decay-rate', '--dr', type=float, default=0.1, metavar='RATE',
+                        help='LR decay rate (default: 0.1)')
+
+    # Augmentation parameters
+    parser.add_argument('--ThreeAugment', action='store_true')
+    parser.add_argument('--color-jitter', type=float, default=0.4, metavar='PCT',
+                        help='Color jitter factor (default: 0.4)')
+    parser.add_argument('--aa', type=str, default='rand-m9-mstd0.5-inc1', metavar='NAME',
+                        help='Use AutoAugment policy. "v0" or "original". " + \
+                             "(default: rand-m9-mstd0.5-inc1)'),
+    parser.add_argument('--smoothing', type=float, default=0.1,
+                        help='Label smoothing (default: 0.1)')
+    parser.add_argument('--train-interpolation', type=str, default='bicubic',
+                        help='Training interpolation (random, bilinear, bicubic default: "bicubic")')
+    parser.add_argument('--repeated-aug', action='store_true')
+    parser.add_argument('--no-repeated-aug',
+                        action='store_false', dest='repeated_aug')
+    parser.set_defaults(repeated_aug=True)
+
+    # Random Erase params
+    parser.add_argument('--reprob', type=float, default=0.25, metavar='PCT',
+                        help='Random erase prob (default: 0.25)')
+    parser.add_argument('--remode', type=str, default='pixel',
+                        help='Random erase mode (default: "pixel")')
+    parser.add_argument('--recount', type=int, default=1,
+                        help='Random erase count (default: 1)')
+    parser.add_argument('--resplit', action='store_true', default=False,
+                        help='Do not random erase first (clean) augmentation split')
+
+    # Mixup params
+    parser.add_argument('--mixup', type=float, default=0.8,
+                        help='mixup alpha, mixup enabled if > 0. (default: 0.8)')
+    parser.add_argument('--cutmix', type=float, default=1.0,
+                        help='cutmix alpha, cutmix enabled if > 0. (default: 1.0)')
+    parser.add_argument('--cutmix-minmax', type=float, nargs='+', default=None,
+                        help='cutmix min/max ratio, overrides alpha and enables cutmix if set (default: None)')
+    parser.add_argument('--mixup-prob', type=float, default=1.0,
+                        help='Probability of performing mixup or cutmix when either/both is enabled')
+    parser.add_argument('--mixup-switch-prob', type=float, default=0.5,
+                        help='Probability of switching to cutmix when both mixup and cutmix enabled')
+    parser.add_argument('--mixup-mode', type=str, default='batch',
+                        help='How to apply mixup/cutmix params. Per "batch", "pair", or "elem"')
+
+    # Distillation parameters
+    parser.add_argument('--teacher-model', default='regnety_160', type=str, metavar='MODEL',
+                        help='Name of teacher model to train (default: "regnety_160"')
+    parser.add_argument('--teacher-path', type=str,
+                        default='https://dl.fbaipublicfiles.com/deit/regnety_160-a5fe301d.pth')
+    parser.add_argument('--distillation-type', default='hard',
+                        choices=['none', 'soft', 'hard'], type=str, help="")
+    parser.add_argument('--distillation-alpha',
+                        default=0.5, type=float, help="")
+    parser.add_argument('--distillation-tau', default=1.0, type=float, help="")
+
+    # Finetuning params
+    parser.add_argument('--finetune', default='',
+                        help='finetune from checkpoint')
+    parser.add_argument('--set_bn_eval', action='store_true', default=False,
+                        help='set BN layers to eval mode during finetuning.')
+
+    # Dataset parameters
+    parser.add_argument('--data-path', default='/root/FastBaseline/data/imagenet', type=str,
+                        help='dataset path')
+    parser.add_argument('--data-set', default='IMNET', choices=['CIFAR', 'IMNET', 'INAT', 'INAT19'],
+                        type=str, help='Image Net dataset path')
+    parser.add_argument('--inat-category', default='name',
+                        choices=['kingdom', 'phylum', 'class', 'order',
+                                 'supercategory', 'family', 'genus', 'name'],
+                        type=str, help='semantic granularity')
+    parser.add_argument('--output_dir', default='checkpoints',
+                        help='path where to save, empty for no saving')
+    parser.add_argument('--device', default='cuda',
+                        help='device to use for training / testing')
+    parser.add_argument('--seed', default=0, type=int)
+    parser.add_argument('--resume', default='', help='resume from checkpoint')
+    parser.add_argument('--start_epoch', default=0, type=int, metavar='N',
+                        help='start epoch')
+    parser.add_argument('--eval', action='store_true',
+                        help='Perform evaluation only')
+    parser.add_argument('--dist-eval', action='store_true',
+                        default=False, help='Enabling distributed evaluation')
+    parser.add_argument('--num_workers', default=10, type=int)
+    parser.add_argument('--pin-mem', action='store_true',
+                        help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.')
+    parser.add_argument('--no-pin-mem', action='store_false', dest='pin_mem',
+                        help='')
+    parser.set_defaults(pin_mem=True)
+
+    # training parameters
+    parser.add_argument('--world_size', default=1, type=int,
+                        help='number of distributed processes')
+    parser.add_argument('--dist_url', default='env://',
+                        help='url used to set up distributed training')
+    parser.add_argument('--save_freq', default=1, type=int,
+                        help='frequency of model saving')
+    
+    parser.add_argument('--deploy', action='store_true', default=False)
+    parser.add_argument('--project', default='repvit', type=str)
+    return parser
+
+import wandb
+
+def main(args):
+    
+    utils.init_distributed_mode(args)
+
+    if utils.is_main_process() and not args.eval:
+        wandb.init(project=args.project, config=args)
+        wandb.run.log_code('model')
+    if args.distillation_type != 'none' and args.finetune and not args.eval:
+        raise NotImplementedError(
+            "Finetuning with distillation not yet supported")
+
+    device = torch.device(args.device)
+
+    # fix the seed for reproducibility
+    seed = args.seed + utils.get_rank()
+    torch.manual_seed(seed)
+    np.random.seed(seed)
+    # random.seed(seed)
+
+    cudnn.benchmark = True
+
+    dataset_train, args.nb_classes = build_dataset(is_train=True, args=args)
+    dataset_val, _ = build_dataset(is_train=False, args=args)
+
+    if True:  # args.distributed:
+        num_tasks = utils.get_world_size()
+        global_rank = utils.get_rank()
+        if args.repeated_aug:
+            sampler_train = RASampler(
+                dataset_train, num_replicas=num_tasks, rank=global_rank, shuffle=True
+            )
+        else:
+            sampler_train = torch.utils.data.DistributedSampler(
+                dataset_train, num_replicas=num_tasks, rank=global_rank, shuffle=True
+            )
+        if args.dist_eval:
+            if len(dataset_val) % num_tasks != 0:
+                print('Warning: Enabling distributed evaluation with an eval dataset not divisible by process number. '
+                      'This will slightly alter validation results as extra duplicate entries are added to achieve '
+                      'equal num of samples per-process.')
+            sampler_val = torch.utils.data.DistributedSampler(
+                dataset_val, num_replicas=num_tasks, rank=global_rank, shuffle=False)
+        else:
+            sampler_val = torch.utils.data.SequentialSampler(dataset_val)
+    else:
+        sampler_train = torch.utils.data.RandomSampler(dataset_train)
+        sampler_val = torch.utils.data.SequentialSampler(dataset_val)
+
+    data_loader_train = torch.utils.data.DataLoader(
+        dataset_train, sampler=sampler_train,
+        batch_size=args.batch_size,
+        num_workers=args.num_workers,
+        pin_memory=args.pin_mem,
+        drop_last=True,
+    )
+
+    if args.ThreeAugment:
+        data_loader_train.dataset.transform = new_data_aug_generator(args)
+        
+    data_loader_val = torch.utils.data.DataLoader(
+        dataset_val, sampler=sampler_val,
+        batch_size=int(1.5 * args.batch_size),
+        num_workers=args.num_workers,
+        pin_memory=args.pin_mem,
+        drop_last=False
+    )
+
+    mixup_fn = None
+    mixup_active = args.mixup > 0 or args.cutmix > 0. or args.cutmix_minmax is not None
+    if mixup_active:
+        mixup_fn = Mixup(
+            mixup_alpha=args.mixup, cutmix_alpha=args.cutmix, cutmix_minmax=args.cutmix_minmax,
+            prob=args.mixup_prob, switch_prob=args.mixup_switch_prob, mode=args.mixup_mode,
+            label_smoothing=args.smoothing, num_classes=args.nb_classes)
+
+    print(f"Creating model: {args.model}")
+    model = create_model(
+        args.model,
+        num_classes=args.nb_classes,
+        distillation=(args.distillation_type != 'none'),
+        pretrained=False,
+    )
+    export_onnx(model, args.output_dir)
+
+    if args.finetune:
+        if args.finetune.startswith('https'):
+            checkpoint = torch.hub.load_state_dict_from_url(
+                args.finetune, map_location='cpu', check_hash=True)
+        else:
+            print("Loading local checkpoint at {}".format(args.finetune))
+            checkpoint = torch.load(args.finetune, map_location='cpu')
+
+        checkpoint_model = checkpoint['model']
+        state_dict = model.state_dict()
+        for k in ['head.l.weight', 'head.l.bias',
+                  'head_dist.l.weight', 'head_dist.l.bias']:
+            if k in checkpoint_model and checkpoint_model[k].shape != state_dict[k].shape:
+                print(f"Removing key {k} from pretrained checkpoint")
+                del checkpoint_model[k]
+
+        msg = model.load_state_dict(checkpoint_model, strict=False)
+        print(msg)
+
+    model.to(device)
+
+    model_ema = None
+    if args.model_ema:
+        # Important to create EMA model after cuda(), DP wrapper, and AMP but
+        # before SyncBN and DDP wrapper
+        model_ema = ModelEma(
+            model,
+            decay=args.model_ema_decay,
+            device='cpu' if args.model_ema_force_cpu else '',
+            resume='')
+
+    model_without_ddp = model
+    if args.distributed:
+        model = torch.nn.parallel.DistributedDataParallel(
+            model, device_ids=[args.gpu])
+        model_without_ddp = model.module
+    n_parameters = sum(p.numel()
+                       for p in model.parameters() if p.requires_grad)
+    print('number of params:', n_parameters)
+
+    linear_scaled_lr = args.lr * args.batch_size * utils.get_world_size() / 512.0
+    args.lr = linear_scaled_lr
+    optimizer = create_optimizer(args, model_without_ddp)
+    loss_scaler = NativeScaler()
+
+    lr_scheduler, _ = create_scheduler(args, optimizer)
+
+    criterion = LabelSmoothingCrossEntropy()
+
+    if args.mixup > 0.:
+        # smoothing is handled with mixup label transform
+        criterion = SoftTargetCrossEntropy()
+    elif args.smoothing:
+        criterion = LabelSmoothingCrossEntropy(smoothing=args.smoothing)
+    else:
+        criterion = torch.nn.CrossEntropyLoss()
+
+    teacher_model = None
+    if args.distillation_type != 'none':
+        assert args.teacher_path, 'need to specify teacher-path when using distillation'
+        print(f"Creating teacher model: {args.teacher_model}")
+        teacher_model = create_model(
+            args.teacher_model,
+            pretrained=False,
+            num_classes=args.nb_classes,
+            global_pool='avg',
+        )
+        if args.teacher_path.startswith('https'):
+            checkpoint = torch.hub.load_state_dict_from_url(
+                args.teacher_path, map_location='cpu', check_hash=True)
+        else:
+            checkpoint = torch.load(args.teacher_path, map_location='cpu')
+        teacher_model.load_state_dict(checkpoint['model'])
+        teacher_model.to(device)
+        teacher_model.eval()
+
+    # wrap the criterion in our custom DistillationLoss, which
+    # just dispatches to the original criterion if args.distillation_type is
+    # 'none'
+    criterion = DistillationLoss(
+        criterion, teacher_model, args.distillation_type, args.distillation_alpha, args.distillation_tau
+    )
+
+    output_dir = Path(args.output_dir)
+    if args.output_dir and utils.is_main_process():
+        with (output_dir / "model.txt").open("a") as f:
+            f.write(str(model))
+            print(str(model))
+    if args.output_dir and utils.is_main_process():
+        with (output_dir / "args.txt").open("a") as f:
+            f.write(json.dumps(args.__dict__, indent=2) + "\n")
+            print(json.dumps(args.__dict__, indent=2) + "\n")
+    if args.resume:
+        if args.resume.startswith('https'):
+            checkpoint = torch.hub.load_state_dict_from_url(
+                args.resume, map_location='cpu', check_hash=True)
+        else:
+            print("Loading local checkpoint at {}".format(args.resume))
+            checkpoint = torch.load(args.resume, map_location='cpu')
+        msg = model_without_ddp.load_state_dict(checkpoint['model'], strict=True)
+        print(msg)
+        if not args.eval and 'optimizer' in checkpoint and 'lr_scheduler' in checkpoint and 'epoch' in checkpoint:
+            optimizer.load_state_dict(checkpoint['optimizer'])
+            lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
+            args.start_epoch = checkpoint['epoch'] + 1
+            if args.model_ema:
+                utils._load_checkpoint_for_ema(
+                    model_ema, checkpoint['model_ema'])
+            if 'scaler' in checkpoint:
+                loss_scaler.load_state_dict(checkpoint['scaler'])
+    if args.eval:
+        utils.replace_batchnorm(model) # Users may choose whether to merge Conv-BN layers during eval
+        print(f"Evaluating model: {args.model}")
+        test_stats = evaluate(data_loader_val, model, device)
+        print(
+            f"Accuracy of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%")
+        return
+
+    print(f"Start training for {args.epochs} epochs")
+    start_time = time.time()
+    max_accuracy = 0.0
+    max_accuracy_ema = 0.0
+    for epoch in range(args.start_epoch, args.epochs):
+        if args.distributed:
+            data_loader_train.sampler.set_epoch(epoch)
+
+        train_stats = train_one_epoch(
+            model, criterion, data_loader_train,
+            optimizer, device, epoch, loss_scaler,
+            args.clip_grad, args.clip_mode, model_ema, mixup_fn,
+            # set_training_mode=args.finetune == ''  # keep in eval mode during finetuning
+            set_training_mode=True,
+            set_bn_eval=args.set_bn_eval, # set bn to eval if finetune
+        )
+
+        lr_scheduler.step(epoch)
+
+        test_stats = evaluate(data_loader_val, model, device)
+        print(
+            f"Accuracy of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%")
+        
+        if args.output_dir:
+            ckpt_path = os.path.join(output_dir, 'checkpoint_'+str(epoch)+'.pth')
+            checkpoint_paths = [ckpt_path]
+            print("Saving checkpoint to {}".format(ckpt_path))
+            for checkpoint_path in checkpoint_paths:
+                utils.save_on_master({
+                    'model': model_without_ddp.state_dict(),
+                    'optimizer': optimizer.state_dict(),
+                    'lr_scheduler': lr_scheduler.state_dict(),
+                    'epoch': epoch,
+                    'model_ema': get_state_dict(model_ema),
+                    'scaler': loss_scaler.state_dict(),
+                    'args': args,
+                }, checkpoint_path)
+            remove_epoch = epoch - 3
+            if remove_epoch >= 0 and utils.is_main_process():
+                os.remove(os.path.join(output_dir, 'checkpoint_'+str(remove_epoch)+'.pth'))
+
+        if max_accuracy < test_stats["acc1"]:
+            utils.save_on_master({
+                    'model': model_without_ddp.state_dict(),
+                    'optimizer': optimizer.state_dict(),
+                    'lr_scheduler': lr_scheduler.state_dict(),
+                    'epoch': epoch,
+                    'model_ema': get_state_dict(model_ema),
+                    'scaler': loss_scaler.state_dict(),
+                    'args': args,
+                }, os.path.join(output_dir, 'checkpoint_best.pth'))
+        max_accuracy = max(max_accuracy, test_stats["acc1"])
+        
+        print(f'Max accuracy: {max_accuracy:.2f}%')
+        
+        log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
+                        **{f'test_{k}': v for k, v in test_stats.items()},
+                        'epoch': epoch,
+                        'n_parameters': n_parameters}
+        if utils.is_main_process():
+            wandb.log({**{f'train_{k}': v for k, v in train_stats.items()},
+                    **{f'test_{k}': v for k, v in test_stats.items()},
+                    'epoch': epoch,
+                    'max_accuracy': max_accuracy}, step=epoch)
+        if args.output_dir and utils.is_main_process():
+            with (output_dir / "log.txt").open("a") as f:
+                f.write(json.dumps(log_stats) + "\n")
+
+    total_time = time.time() - start_time
+    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
+    print('Training time {}'.format(total_time_str))
+    if utils.is_main_process():
+        wandb.finish()
+
+def export_onnx(model, output_dir):
+    # if utils.is_main_process():
+    #     dummy_input = torch.randn(1, 3, 224, 224)
+    #     torch.onnx.export(model, dummy_input, f"{output_dir}/model.onnx")
+    #     wandb.save(f"{output_dir}/model.onnx")
+    pass
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(
+        'RepViT training and evaluation script', parents=[get_args_parser()])
+    args = parser.parse_args()
+    if args.resume and not args.eval:
+        args.output_dir = '/'.join(args.resume.split('/')[:-1])
+    elif args.output_dir:
+        args.output_dir = args.output_dir + f"/{args.model}/" + datetime.datetime.now().strftime('%Y_%m_%d_%H_%M_%S')
+        Path(args.output_dir).mkdir(parents=True, exist_ok=True)
+    else:
+        assert(False)
+    main(args)

model.properties

+# 模型编码
+modelCode=698
+# 模型名称
+modelName=repvit_pytorch
+# 模型描述
+modelDescription=RepViT在iPhone 12上以1ms的延迟实现了超过80%的top-1准确率，为当前多个SOTA实例分割算法的backbone。
+# 应用场景
+appScenario=训练,制造,电商,医疗,能源,教育
+# 框架类型
+frameType=pytorch

model/__init__.py

+import model.repvit
\ No newline at end of file

model/repvit.py

+import torch.nn as nn
+
+def _make_divisible(v, divisor, min_value=None):
+    """
+    This function is taken from the original tf repo.
+    It ensures that all layers have a channel number that is divisible by 8
+    It can be seen here:
+    https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
+    :param v:
+    :param divisor:
+    :param min_value:
+    :return:
+    """
+    if min_value is None:
+        min_value = divisor
+    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
+    # Make sure that round down does not go down by more than 10%.
+    if new_v < 0.9 * v:
+        new_v += divisor
+    return new_v
+
+from timm.models.layers import SqueezeExcite
+
+import torch
+
+class Conv2d_BN(torch.nn.Sequential):
+    def __init__(self, a, b, ks=1, stride=1, pad=0, dilation=1,
+                 groups=1, bn_weight_init=1, resolution=-10000):
+        super().__init__()
+        self.add_module('c', torch.nn.Conv2d(
+            a, b, ks, stride, pad, dilation, groups, bias=False))
+        self.add_module('bn', torch.nn.BatchNorm2d(b))
+        torch.nn.init.constant_(self.bn.weight, bn_weight_init)
+        torch.nn.init.constant_(self.bn.bias, 0)
+
+    @torch.no_grad()
+    def fuse(self):
+        c, bn = self._modules.values()
+        w = bn.weight / (bn.running_var + bn.eps)**0.5
+        w = c.weight * w[:, None, None, None]
+        b = bn.bias - bn.running_mean * bn.weight / \
+            (bn.running_var + bn.eps)**0.5
+        m = torch.nn.Conv2d(w.size(1) * self.c.groups, w.size(
+            0), w.shape[2:], stride=self.c.stride, padding=self.c.padding, dilation=self.c.dilation, groups=self.c.groups,
+            device=c.weight.device)
+        m.weight.data.copy_(w)
+        m.bias.data.copy_(b)
+        return m
+
+class Residual(torch.nn.Module):
+    def __init__(self, m, drop=0.):
+        super().__init__()
+        self.m = m
+        self.drop = drop
+
+    def forward(self, x):
+        if self.training and self.drop > 0:
+            return x + self.m(x) * torch.rand(x.size(0), 1, 1, 1,
+                                              device=x.device).ge_(self.drop).div(1 - self.drop).detach()
+        else:
+            return x + self.m(x)
+    
+    @torch.no_grad()
+    def fuse(self):
+        if isinstance(self.m, Conv2d_BN):
+            m = self.m.fuse()
+            assert(m.groups == m.in_channels)
+            identity = torch.ones(m.weight.shape[0], m.weight.shape[1], 1, 1)
+            identity = torch.nn.functional.pad(identity, [1,1,1,1])
+            m.weight += identity.to(m.weight.device)
+            return m
+        elif isinstance(self.m, torch.nn.Conv2d):
+            m = self.m
+            assert(m.groups != m.in_channels)
+            identity = torch.ones(m.weight.shape[0], m.weight.shape[1], 1, 1)
+            identity = torch.nn.functional.pad(identity, [1,1,1,1])
+            m.weight += identity.to(m.weight.device)
+            return m
+        else:
+            return self
+
+
+class RepVGGDW(torch.nn.Module):
+    def __init__(self, ed) -> None:
+        super().__init__()
+        self.conv = Conv2d_BN(ed, ed, 3, 1, 1, groups=ed)
+        self.conv1 = torch.nn.Conv2d(ed, ed, 1, 1, 0, groups=ed)
+        self.dim = ed
+        self.bn = torch.nn.BatchNorm2d(ed)
+    
+    def forward(self, x):
+        return self.bn((self.conv(x) + self.conv1(x)) + x)
+    
+    @torch.no_grad()
+    def fuse(self):
+        conv = self.conv.fuse()
+        conv1 = self.conv1
+        
+        conv_w = conv.weight
+        conv_b = conv.bias
+        conv1_w = conv1.weight
+        conv1_b = conv1.bias
+        
+        conv1_w = torch.nn.functional.pad(conv1_w, [1,1,1,1])
+
+        identity = torch.nn.functional.pad(torch.ones(conv1_w.shape[0], conv1_w.shape[1], 1, 1, device=conv1_w.device), [1,1,1,1])
+
+        final_conv_w = conv_w + conv1_w + identity
+        final_conv_b = conv_b + conv1_b
+
+        conv.weight.data.copy_(final_conv_w)
+        conv.bias.data.copy_(final_conv_b)
+
+        bn = self.bn
+        w = bn.weight / (bn.running_var + bn.eps)**0.5
+        w = conv.weight * w[:, None, None, None]
+        b = bn.bias + (conv.bias - bn.running_mean) * bn.weight / \
+            (bn.running_var + bn.eps)**0.5
+        conv.weight.data.copy_(w)
+        conv.bias.data.copy_(b)
+        return conv
+
+
+class RepViTBlock(nn.Module):
+    def __init__(self, inp, hidden_dim, oup, kernel_size, stride, use_se, use_hs):
+        super(RepViTBlock, self).__init__()
+        assert stride in [1, 2]
+
+        self.identity = stride == 1 and inp == oup
+        assert(hidden_dim == 2 * inp)
+
+        if stride == 2:
+            self.token_mixer = nn.Sequential(
+                Conv2d_BN(inp, inp, kernel_size, stride, (kernel_size - 1) // 2, groups=inp),
+                SqueezeExcite(inp, 0.25) if use_se else nn.Identity(),
+                Conv2d_BN(inp, oup, ks=1, stride=1, pad=0)
+            )
+            self.channel_mixer = Residual(nn.Sequential(
+                    # pw
+                    Conv2d_BN(oup, 2 * oup, 1, 1, 0),
+                    nn.GELU() if use_hs else nn.GELU(),
+                    # pw-linear
+                    Conv2d_BN(2 * oup, oup, 1, 1, 0, bn_weight_init=0),
+                ))
+        else:
+            assert(self.identity)
+            self.token_mixer = nn.Sequential(
+                RepVGGDW(inp),
+                SqueezeExcite(inp, 0.25) if use_se else nn.Identity(),
+            )
+            self.channel_mixer = Residual(nn.Sequential(
+                    # pw
+                    Conv2d_BN(inp, hidden_dim, 1, 1, 0),
+                    nn.GELU() if use_hs else nn.GELU(),
+                    # pw-linear
+                    Conv2d_BN(hidden_dim, oup, 1, 1, 0, bn_weight_init=0),
+                ))
+
+    def forward(self, x):
+        return self.channel_mixer(self.token_mixer(x))
+
+from timm.models.vision_transformer import trunc_normal_
+class BN_Linear(torch.nn.Sequential):
+    def __init__(self, a, b, bias=True, std=0.02):
+        super().__init__()
+        self.add_module('bn', torch.nn.BatchNorm1d(a))
+        self.add_module('l', torch.nn.Linear(a, b, bias=bias))
+        trunc_normal_(self.l.weight, std=std)
+        if bias:
+            torch.nn.init.constant_(self.l.bias, 0)
+
+    @torch.no_grad()
+    def fuse(self):
+        bn, l = self._modules.values()
+        w = bn.weight / (bn.running_var + bn.eps)**0.5
+        b = bn.bias - self.bn.running_mean * \
+            self.bn.weight / (bn.running_var + bn.eps)**0.5
+        w = l.weight * w[None, :]
+        if l.bias is None:
+            b = b @ self.l.weight.T
+        else:
+            b = (l.weight @ b[:, None]).view(-1) + self.l.bias
+        m = torch.nn.Linear(w.size(1), w.size(0), device=l.weight.device)
+        m.weight.data.copy_(w)
+        m.bias.data.copy_(b)
+        return m
+
+class Classfier(nn.Module):
+    def __init__(self, dim, num_classes, distillation=True):
+        super().__init__()
+        self.classifier = BN_Linear(dim, num_classes) if num_classes > 0 else torch.nn.Identity()
+        self.distillation = distillation
+        if distillation:
+            self.classifier_dist = BN_Linear(dim, num_classes) if num_classes > 0 else torch.nn.Identity()
+
+    def forward(self, x):
+        if self.distillation:
+            x = self.classifier(x), self.classifier_dist(x)
+            if not self.training:
+                x = (x[0] + x[1]) / 2
+        else:
+            x = self.classifier(x)
+        return x
+
+    @torch.no_grad()
+    def fuse(self):
+        classifier = self.classifier.fuse()
+        if self.distillation:
+            classifier_dist = self.classifier_dist.fuse()
+            classifier.weight += classifier_dist.weight
+            classifier.bias += classifier_dist.bias
+            classifier.weight /= 2
+            classifier.bias /= 2
+            return classifier
+        else:
+            return classifier
+
+class RepViT(nn.Module):
+    def __init__(self, cfgs, num_classes=1000, distillation=False):
+        super(RepViT, self).__init__()
+        # setting of inverted residual blocks
+        self.cfgs = cfgs
+
+        # building first layer
+        input_channel = self.cfgs[0][2]
+        patch_embed = torch.nn.Sequential(Conv2d_BN(3, input_channel // 2, 3, 2, 1), torch.nn.GELU(),
+                           Conv2d_BN(input_channel // 2, input_channel, 3, 2, 1))
+        layers = [patch_embed]
+        # building inverted residual blocks
+        block = RepViTBlock
+        for k, t, c, use_se, use_hs, s in self.cfgs:
+            output_channel = _make_divisible(c, 8)
+            exp_size = _make_divisible(input_channel * t, 8)
+            layers.append(block(input_channel, exp_size, output_channel, k, s, use_se, use_hs))
+            input_channel = output_channel
+        self.features = nn.ModuleList(layers)
+        self.classifier = Classfier(output_channel, num_classes, distillation)
+        
+    def forward(self, x):
+        # x = self.features(x)
+        for f in self.features:
+            x = f(x)
+        x = torch.nn.functional.adaptive_avg_pool2d(x, 1).flatten(1)
+        x = self.classifier(x)
+        return x
+
+from timm.models import register_model
+
+
+@register_model
+def repvit_m0_6(pretrained=False, num_classes = 1000, distillation=False):
+    """
+    Constructs a MobileNetV3-Large model
+    """
+    cfgs = [
+        [3,   2,  40, 1, 0, 1],
+        [3,   2,  40, 0, 0, 1],
+        [3,   2,  80, 0, 0, 2],
+        [3,   2,  80, 1, 0, 1],
+        [3,   2,  80, 0, 0, 1],
+        [3,   2,  160, 0, 1, 2],
+        [3,   2, 160, 1, 1, 1],
+        [3,   2, 160, 0, 1, 1],
+        [3,   2, 160, 1, 1, 1],
+        [3,   2, 160, 0, 1, 1],
+        [3,   2, 160, 1, 1, 1],
+        [3,   2, 160, 0, 1, 1],
+        [3,   2, 160, 1, 1, 1],
+        [3,   2, 160, 0, 1, 1],
+        [3,   2, 160, 0, 1, 1],
+        [3,   2, 320, 0, 1, 2],
+        [3,   2, 320, 1, 1, 1],
+    ]
+    return RepViT(cfgs, num_classes=num_classes, distillation=distillation)
+
+@register_model
+def repvit_m0_9(pretrained=False, num_classes = 1000, distillation=False):
+    """
+    Constructs a MobileNetV3-Large model
+    """
+    cfgs = [
+        # k, t, c, SE, HS, s 
+        [3,   2,  48, 1, 0, 1],
+        [3,   2,  48, 0, 0, 1],
+        [3,   2,  48, 0, 0, 1],
+        [3,   2,  96, 0, 0, 2],
+        [3,   2,  96, 1, 0, 1],
+        [3,   2,  96, 0, 0, 1],
+        [3,   2,  96, 0, 0, 1],
+        [3,   2,  192, 0, 1, 2],
+        [3,   2,  192, 1, 1, 1],
+        [3,   2,  192, 0, 1, 1],
+        [3,   2,  192, 1, 1, 1],
+        [3,   2, 192, 0, 1, 1],
+        [3,   2, 192, 1, 1, 1],
+        [3,   2, 192, 0, 1, 1],
+        [3,   2, 192, 1, 1, 1],
+        [3,   2, 192, 0, 1, 1],
+        [3,   2, 192, 1, 1, 1],
+        [3,   2, 192, 0, 1, 1],
+        [3,   2, 192, 1, 1, 1],
+        [3,   2, 192, 0, 1, 1],
+        [3,   2, 192, 1, 1, 1],
+        [3,   2, 192, 0, 1, 1],
+        [3,   2, 192, 0, 1, 1],
+        [3,   2, 384, 0, 1, 2],
+        [3,   2, 384, 1, 1, 1],
+        [3,   2, 384, 0, 1, 1]
+    ]
+    return RepViT(cfgs, num_classes=num_classes, distillation=distillation)
+
+@register_model
+def repvit_m1_0(pretrained=False, num_classes = 1000, distillation=False):
+    """
+    Constructs a MobileNetV3-Large model
+    """
+    cfgs = [
+        # k, t, c, SE, HS, s 
+        [3,   2,  56, 1, 0, 1],
+        [3,   2,  56, 0, 0, 1],
+        [3,   2,  56, 0, 0, 1],
+        [3,   2,  112, 0, 0, 2],
+        [3,   2,  112, 1, 0, 1],
+        [3,   2,  112, 0, 0, 1],
+        [3,   2,  112, 0, 0, 1],
+        [3,   2,  224, 0, 1, 2],
+        [3,   2,  224, 1, 1, 1],
+        [3,   2,  224, 0, 1, 1],
+        [3,   2,  224, 1, 1, 1],
+        [3,   2, 224, 0, 1, 1],
+        [3,   2, 224, 1, 1, 1],
+        [3,   2, 224, 0, 1, 1],
+        [3,   2, 224, 1, 1, 1],
+        [3,   2, 224, 0, 1, 1],
+        [3,   2, 224, 1, 1, 1],
+        [3,   2, 224, 0, 1, 1],
+        [3,   2, 224, 1, 1, 1],
+        [3,   2, 224, 0, 1, 1],
+        [3,   2, 224, 1, 1, 1],
+        [3,   2, 224, 0, 1, 1],
+        [3,   2, 224, 0, 1, 1],
+        [3,   2, 448, 0, 1, 2],
+        [3,   2, 448, 1, 1, 1],
+        [3,   2, 448, 0, 1, 1]
+    ]
+    return RepViT(cfgs, num_classes=num_classes, distillation=distillation)
+
+
+@register_model
+def repvit_m1_1(pretrained=False, num_classes = 1000, distillation=False):
+    """
+    Constructs a MobileNetV3-Large model
+    """
+    cfgs = [
+        # k, t, c, SE, HS, s 
+        [3,   2,  64, 1, 0, 1],
+        [3,   2,  64, 0, 0, 1],
+        [3,   2,  64, 0, 0, 1],
+        [3,   2,  128, 0, 0, 2],
+        [3,   2,  128, 1, 0, 1],
+        [3,   2,  128, 0, 0, 1],
+        [3,   2,  128, 0, 0, 1],
+        [3,   2,  256, 0, 1, 2],
+        [3,   2,  256, 1, 1, 1],
+        [3,   2,  256, 0, 1, 1],
+        [3,   2,  256, 1, 1, 1],
+        [3,   2, 256, 0, 1, 1],
+        [3,   2, 256, 1, 1, 1],
+        [3,   2, 256, 0, 1, 1],
+        [3,   2, 256, 1, 1, 1],
+        [3,   2, 256, 0, 1, 1],
+        [3,   2, 256, 1, 1, 1],
+        [3,   2, 256, 0, 1, 1],
+        [3,   2, 256, 1, 1, 1],
+        [3,   2, 256, 0, 1, 1],
+        [3,   2, 256, 0, 1, 1],
+        [3,   2, 512, 0, 1, 2],
+        [3,   2, 512, 1, 1, 1],
+        [3,   2, 512, 0, 1, 1]
+    ]
+    return RepViT(cfgs, num_classes=num_classes, distillation=distillation)
+
+
+@register_model
+def repvit_m1_5(pretrained=False, num_classes = 1000, distillation=False):
+    """
+    Constructs a MobileNetV3-Large model
+    """
+    cfgs = [
+        # k, t, c, SE, HS, s 
+        [3,   2,  64, 1, 0, 1],
+        [3,   2,  64, 0, 0, 1],
+        [3,   2,  64, 1, 0, 1],
+        [3,   2,  64, 0, 0, 1],
+        [3,   2,  64, 0, 0, 1],
+        [3,   2,  128, 0, 0, 2],
+        [3,   2,  128, 1, 0, 1],
+        [3,   2,  128, 0, 0, 1],
+        [3,   2,  128, 1, 0, 1],
+        [3,   2,  128, 0, 0, 1],
+        [3,   2,  128, 0, 0, 1],
+        [3,   2,  256, 0, 1, 2],
+        [3,   2,  256, 1, 1, 1],
+        [3,   2,  256, 0, 1, 1],
+        [3,   2,  256, 1, 1, 1],
+        [3,   2,  256, 0, 1, 1],
+        [3,   2,  256, 1, 1, 1],
+        [3,   2,  256, 0, 1, 1],
+        [3,   2,  256, 1, 1, 1],
+        [3,   2, 256, 0, 1, 1],
+        [3,   2, 256, 1, 1, 1],
+        [3,   2, 256, 0, 1, 1],
+        [3,   2, 256, 1, 1, 1],
+        [3,   2, 256, 0, 1, 1],
+        [3,   2, 256, 1, 1, 1],
+        [3,   2, 256, 0, 1, 1],
+        [3,   2, 256, 1, 1, 1],
+        [3,   2, 256, 0, 1, 1],
+        [3,   2, 256, 1, 1, 1],
+        [3,   2, 256, 0, 1, 1],
+        [3,   2, 256, 1, 1, 1],
+        [3,   2, 256, 0, 1, 1],
+        [3,   2, 256, 1, 1, 1],
+        [3,   2, 256, 0, 1, 1],
+        [3,   2, 256, 1, 1, 1],
+        [3,   2, 256, 0, 1, 1],
+        [3,   2, 256, 0, 1, 1],
+        [3,   2, 512, 0, 1, 2],
+        [3,   2, 512, 1, 1, 1],
+        [3,   2, 512, 0, 1, 1],
+        [3,   2, 512, 1, 1, 1],
+        [3,   2, 512, 0, 1, 1]
+    ]
+    return RepViT(cfgs, num_classes=num_classes, distillation=distillation)
+
+
+
+@register_model
+def repvit_m2_3(pretrained=False, num_classes = 1000, distillation=False):
+    """
+    Constructs a MobileNetV3-Large model
+    """
+    cfgs = [
+        # k, t, c, SE, HS, s 
+        [3,   2,  80, 1, 0, 1],
+        [3,   2,  80, 0, 0, 1],
+        [3,   2,  80, 1, 0, 1],
+        [3,   2,  80, 0, 0, 1],
+        [3,   2,  80, 1, 0, 1],
+        [3,   2,  80, 0, 0, 1],
+        [3,   2,  80, 0, 0, 1],
+        [3,   2,  160, 0, 0, 2],
+        [3,   2,  160, 1, 0, 1],
+        [3,   2,  160, 0, 0, 1],
+        [3,   2,  160, 1, 0, 1],
+        [3,   2,  160, 0, 0, 1],
+        [3,   2,  160, 1, 0, 1],
+        [3,   2,  160, 0, 0, 1],
+        [3,   2,  160, 0, 0, 1],
+        [3,   2,  320, 0, 1, 2],
+        [3,   2,  320, 1, 1, 1],
+        [3,   2,  320, 0, 1, 1],
+        [3,   2,  320, 1, 1, 1],
+        [3,   2,  320, 0, 1, 1],
+        [3,   2,  320, 1, 1, 1],
+        [3,   2,  320, 0, 1, 1],
+        [3,   2,  320, 1, 1, 1],
+        [3,   2, 320, 0, 1, 1],
+        [3,   2, 320, 1, 1, 1],
+        [3,   2, 320, 0, 1, 1],
+        [3,   2, 320, 1, 1, 1],
+        [3,   2, 320, 0, 1, 1],
+        [3,   2, 320, 1, 1, 1],
+        [3,   2, 320, 0, 1, 1],
+        [3,   2, 320, 1, 1, 1],
+        [3,   2, 320, 0, 1, 1],
+        [3,   2, 320, 1, 1, 1],
+        [3,   2, 320, 0, 1, 1],
+        [3,   2, 320, 1, 1, 1],
+        [3,   2, 320, 0, 1, 1],
+        [3,   2, 320, 1, 1, 1],
+        [3,   2, 320, 0, 1, 1],
+        [3,   2, 320, 1, 1, 1],
+        [3,   2, 320, 0, 1, 1],
+        [3,   2, 320, 1, 1, 1],
+        [3,   2, 320, 0, 1, 1],
+        [3,   2, 320, 1, 1, 1],
+        [3,   2, 320, 0, 1, 1],
+        [3,   2, 320, 1, 1, 1],
+        [3,   2, 320, 0, 1, 1],
+        [3,   2, 320, 1, 1, 1],
+        [3,   2, 320, 0, 1, 1],
+        [3,   2, 320, 1, 1, 1],
+        [3,   2, 320, 0, 1, 1],
+        # [3,   2, 320, 1, 1, 1],
+        # [3,   2, 320, 0, 1, 1],
+        [3,   2, 320, 0, 1, 1],
+        [3,   2, 640, 0, 1, 2],
+        [3,   2, 640, 1, 1, 1],
+        [3,   2, 640, 0, 1, 1],
+        # [3,   2, 640, 1, 1, 1],
+        # [3,   2, 640, 0, 1, 1]
+    ]    
+    return RepViT(cfgs, num_classes=num_classes, distillation=distillation)
\ No newline at end of file

requirements.txt

+torch
+timm==0.5.4 
+fvcore
\ No newline at end of file

sam/.gitignore

+*.pyc
+*.pyo
+*.pyd
+__py
+**/__pycache__/
+repvit_sam.egg-info
+weights/*.pt
+*.pt
+*.onnx
\ No newline at end of file

sam/CODE_OF_CONDUCT.md

+# Code of Conduct
+
+## Our Pledge
+
+In the interest of fostering an open and welcoming environment, we as
+contributors and maintainers pledge to make participation in our project and
+our community a harassment-free experience for everyone, regardless of age, body
+size, disability, ethnicity, sex characteristics, gender identity and expression,
+level of experience, education, socio-economic status, nationality, personal
+appearance, race, religion, or sexual identity and orientation.
+
+## Our Standards
+
+Examples of behavior that contributes to creating a positive environment
+include:
+
+* Using welcoming and inclusive language
+* Being respectful of differing viewpoints and experiences
+* Gracefully accepting constructive criticism
+* Focusing on what is best for the community
+* Showing empathy towards other community members
+
+Examples of unacceptable behavior by participants include:
+
+* The use of sexualized language or imagery and unwelcome sexual attention or
+  advances
+* Trolling, insulting/derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or electronic
+  address, without explicit permission
+* Other conduct which could reasonably be considered inappropriate in a
+  professional setting
+
+## Our Responsibilities
+
+Project maintainers are responsible for clarifying the standards of acceptable
+behavior and are expected to take appropriate and fair corrective action in
+response to any instances of unacceptable behavior.
+
+Project maintainers have the right and responsibility to remove, edit, or
+reject comments, commits, code, wiki edits, issues, and other contributions
+that are not aligned to this Code of Conduct, or to ban temporarily or
+permanently any contributor for other behaviors that they deem inappropriate,
+threatening, offensive, or harmful.
+
+## Scope
+
+This Code of Conduct applies within all project spaces, and it also applies when
+an individual is representing the project or its community in public spaces.
+Examples of representing a project or community include using an official
+project e-mail address, posting via an official social media account, or acting
+as an appointed representative at an online or offline event. Representation of
+a project may be further defined and clarified by project maintainers.
+
+This Code of Conduct also applies outside the project spaces when there is a
+reasonable belief that an individual's behavior may have a negative impact on
+the project or its community.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported by contacting the project team at <opensource-conduct@fb.com>. All
+complaints will be reviewed and investigated and will result in a response that
+is deemed necessary and appropriate to the circumstances. The project team is
+obligated to maintain confidentiality with regard to the reporter of an incident.
+Further details of specific enforcement policies may be posted separately.
+
+Project maintainers who do not follow or enforce the Code of Conduct in good
+faith may face temporary or permanent repercussions as determined by other
+members of the project's leadership.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
+available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
+
+[homepage]: https://www.contributor-covenant.org
+
+For answers to common questions about this code of conduct, see
+https://www.contributor-covenant.org/faq

sam/CONTRIBUTING.md

+# Contributing to segment-anything
+We want to make contributing to this project as easy and transparent as
+possible.
+
+## Pull Requests
+We actively welcome your pull requests.
+
+1. Fork the repo and create your branch from `main`.
+2. If you've added code that should be tested, add tests.
+3. If you've changed APIs, update the documentation.
+4. Ensure the test suite passes.
+5. Make sure your code lints, using the `linter.sh` script in the project's root directory. Linting requires `black==23.*`, `isort==5.12.0`, `flake8`, and `mypy`.
+6. If you haven't already, complete the Contributor License Agreement ("CLA").
+
+## Contributor License Agreement ("CLA")
+In order to accept your pull request, we need you to submit a CLA. You only need
+to do this once to work on any of Facebook's open source projects.
+
+Complete your CLA here: <https://code.facebook.com/cla>
+
+## Issues
+We use GitHub issues to track public bugs. Please ensure your description is
+clear and has sufficient instructions to be able to reproduce the issue.
+
+Facebook has a [bounty program](https://www.facebook.com/whitehat/) for the safe
+disclosure of security bugs. In those cases, please go through the process
+outlined on that page and do not file a public issue.
+
+## License
+By contributing to segment-anything, you agree that your contributions will be licensed
+under the LICENSE file in the root directory of this source tree.

sam/LICENSE

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sam/README.md

+# [RepViT-SAM: Towards Real-Time Segmenting Anything](https://arxiv.org/abs/2312.05760)
+
+Official PyTorch implementation of **RepViT-SAM**, from the following paper:
+
+[RepViT-SAM: Towards Real-Time Segmenting Anything](https://arxiv.org/abs/2312.05760).\
+Ao Wang, Hui Chen, Zijia Lin, Jungong Han, and Guiguang Ding\
+[[`arXiv`](https://arxiv.org/abs/2312.05760)]
+
+<p align="center">
+  <img src="figures/comparison.png" width=70%> <br>
+  Models are deployed on iPhone 12 with Core ML Tools to get latency.
+</p>
+
+<details>
+  <summary>
+  <font size="+1">Abstract</font>
+  </summary>
+Segment Anything Model (SAM) has shown impressive zero-shot transfer performance for various computer vision tasks recently. However, its heavy computation costs remain daunting for practical applications. MobileSAM proposes to replace the heavyweight image encoder in SAM with TinyViT by employing distillation, which results in a significant reduction in computational requirements. However, its deployment on resource-constrained mobile devices still encounters challenges due to the substantial memory and computational overhead caused by self-attention mechanisms. Recently, RepViT achieves the state-of-the-art performance and latency trade-off on mobile devices by incorporating efficient architectural designs of ViTs into CNNs. Here, to achieve real-time segmenting anything on mobile devices, following, we replace the heavyweight image encoder in SAM with RepViT model, ending up with the RepViT-SAM model. Extensive experiments show that RepViT-SAM can enjoy significantly better zero-shot transfer capability than MobileSAM, along with nearly $10\times$ faster inference speed.
+</details>
+
+<br/>
+
+## Installation
+```bash
+pip install -e .
+# download pretrained checkpoint
+mkdir weights && cd weights
+wget https://github.com/THU-MIG/RepViT/releases/download/v1.0/repvit_sam.pt
+```
+
+## Demo
+Our Hugging Face demo is [here](https://huggingface.co/spaces/jameslahm/repvit-sam)
+```
+python app/app.py
+```
+
+## CoreML export
+Please refer to [coreml_example.ipynb](./notebooks/coreml_example.ipynb)
+
+
+## Latency comparisons
+Comparison between RepViT-SAM and others in terms of latency. The latency (ms) is measured with the standard resolution of 1024 $\times$ 1024 on iPhone 12 and Macbook M1 Pro by Core ML Tools. OOM means out of memory.
+
+<table class="tg">
+<thead>
+  <tr>
+    <th class="tg-c3ow" rowspan="2">Platform</th>
+    <th class="tg-c3ow" colspan="3">Image encoder</th>
+    <th class="tg-c3ow" rowspan="2">Mask decoder</th>
+  </tr>
+  <tr>
+    <th class="tg-c3ow" rowspan="1">RepViT-SAM</th>
+    <th class="tg-c3ow" rowspan="1">MobileSAM</th>
+    <th class="tg-c3ow" rowspan="1">ViT-B-SAM</th>
+  </tr>
+</thead>
+<tbody>
+  <tr>
+    <td class="tg-c3ow">iPhone</td>
+    <td class="tg-c3ow"><b>48.9ms</b></td>
+    <td class="tg-c3ow">OOM</td>
+    <td class="tg-c3ow">OOM</td>
+    <td class="tg-c3ow">11.6ms</td>
+  </tr>
+  <tr>
+    <td class="tg-c3ow">Macbook</td>
+    <td class="tg-c3ow"><b>44.8ms</b></td>
+    <td class="tg-c3ow">482.2ms</td>
+    <td class="tg-c3ow">6249.5ms</td>
+    <td class="tg-c3ow">11.8ms</td>
+  </tr>
+</tbody>
+</table>
+
+
+## Zero-shot edge detection
+
+Comparison results on BSDS500.
+
+<table class="tg">
+<thead>
+  <tr>
+    <th class="tg-c3ow" rowspan="2">Model</th>
+    <th class="tg-c3ow" colspan="3">zero-shot edge detection</th>
+  </tr>
+  <tr>
+    <th class="tg-c3ow">ODS</th>
+    <th class="tg-c3ow">OIS</th>
+    <th class="tg-c3ow">AP</th>
+  </tr>
+</thead>
+<tbody>
+  <tr>
+    <td class="tg-c3ow">ViT-H-SAM</td>
+    <td class="tg-c3ow"><b>.768</b></td>
+    <td class="tg-c3ow"><b>.786</b></td>
+    <td class="tg-c3ow"><b>.794</b></td>
+  </tr>
+  <tr>
+    <td class="tg-c3ow">ViT-B-SAM</td>
+    <td class="tg-c3ow">.743</td>
+    <td class="tg-c3ow">.764</td>
+    <td class="tg-c3ow">.726</td>
+  </tr>
+  <tr>
+    <td class="tg-c3ow">MobileSAM</td>
+    <td class="tg-c3ow">.756</td>
+    <td class="tg-c3ow">.768</td>
+    <td class="tg-c3ow">.746</td>
+  </tr>
+  <tr>
+    <td class="tg-c3ow">RepViT-SAM</td>
+    <td class="tg-c3ow"><ins>.764</ins></td>
+    <td class="tg-c3ow"><ins>.786</ins></td>
+    <td class="tg-c3ow"><ins>.773</ins></td>
+  </tr>
+</tbody>
+</table>
+
+
+## Zero-shot instance segmentation and SegInW
+Comparison results on COCO and SegInW.
+
+<table class="tg">
+<thead>
+  <tr>
+    <th class="tg-c3ow" rowspan="2">Model</th>
+    <th class="tg-c3ow" colspan="4">zero-shot instance segmentation</th>
+    <th class="tg-c3ow">SegInW</th>
+  </tr>
+  <tr>
+    <th class="tg-c3ow">AP</th>
+    <th class="tg-c3ow">$AP^{S}$</th>
+    <th class="tg-c3ow">$AP^{M}$</th>
+    <th class="tg-c3ow">$AP^{L}$</th>
+    <th class="tg-c3ow">Mean AP</th>
+  </tr>
+</thead>
+<tbody>
+  <tr>
+    <td class="tg-c3ow">ViT-H-SAM</td>
+    <td class="tg-c3ow"><b>46.8</b></td>
+    <td class="tg-c3ow"><b>31.8</b></td>
+    <td class="tg-c3ow"><b>51.0</b></td>
+    <td class="tg-c3ow"><b>63.6</b></td>
+    <td class="tg-c3ow"><b>48.7</b></td>
+  </tr>
+  <tr>
+    <td class="tg-c3ow">ViT-B-SAM</td>
+    <td class="tg-c3ow">42.5</td>
+    <td class="tg-c3ow"><ins>29.8</ins></td>
+    <td class="tg-c3ow">47.0</td>
+    <td class="tg-c3ow">56.8</td>
+    <td class="tg-c3ow">44.8</td>
+  </tr>
+  <tr>
+    <td class="tg-c3ow">MobileSAM</td>
+    <td class="tg-c3ow">42.7</td>
+    <td class="tg-c3ow">27.0</td>
+    <td class="tg-c3ow">46.5</td>
+    <td class="tg-c3ow">61.1</td>
+    <td class="tg-c3ow">43.9</td>
+  </tr>
+  <tr>
+    <td class="tg-c3ow">RepViT-SAM</td>
+    <td class="tg-c3ow"><ins>44.4</ins></td>
+    <td class="tg-c3ow">29.1</td>
+    <td class="tg-c3ow"><ins>48.6</ins></td>
+    <td class="tg-c3ow"><ins>61.4</ins></td>
+    <td class="tg-c3ow"><ins>46.1</ins></td>
+  </tr>
+</tbody>
+</table>
+
+## Zero-shot video object/instance segmentation
+Comparison results on DAVIS 2017 and UVO.
+
+<table class="tg">
+<thead>
+  <tr>
+    <th class="tg-c3ow" rowspan="2">Model</th>
+    <th class="tg-c3ow" colspan="3">z.s. VOS</th>
+    <th class="tg-c3ow">z.s. VIS</th>
+  </tr>
+  <tr>
+    <th class="tg-c3ow">$\mathcal{J\&amp;F}$</th>
+    <th class="tg-c3ow">$\mathcal{J}$</th>
+    <th class="tg-c3ow">$\mathcal{F}$</th>
+    <th class="tg-c3ow">AR100</th>
+  </tr>
+</thead>
+<tbody>
+  <tr>
+    <td class="tg-c3ow">ViT-H-SAM</td>
+    <td class="tg-c3ow"><b>77.4</b></td>
+    <td class="tg-c3ow"><b>74.6</b></td>
+    <td class="tg-c3ow"><b>80.2</b></td>
+    <td class="tg-c3ow"><b>28.8</b></td>
+  </tr>
+  <tr>
+    <td class="tg-c3ow">ViT-B-SAM</td>
+    <td class="tg-c3ow">71.3</td>
+    <td class="tg-c3ow">68.5</td>
+    <td class="tg-c3ow">74.1</td>
+    <td class="tg-c3ow">19.1</td>
+  </tr>
+  <tr>
+    <td class="tg-c3ow">MobileSAM</td>
+    <td class="tg-c3ow">71.1</td>
+    <td class="tg-c3ow">68.6</td>
+    <td class="tg-c3ow">73.6</td>
+    <td class="tg-c3ow">22.7</td>
+  </tr>
+  <tr>
+    <td class="tg-c3ow">RepViT-SAM</td>
+    <td class="tg-c3ow"><ins>73.5</ins></td>
+    <td class="tg-c3ow"><ins>71.0</ins></td>
+    <td class="tg-c3ow"><ins>76.1</ins></td>
+    <td class="tg-c3ow"><ins>25.3</ins></td>
+  </tr>
+</tbody>
+</table>
+
+## Zero-shot salient object segmentation
+Comparison results on DUTS.
+## Zero-shot anomaly detection
+Comparison results on MVTec.
+<table class="tg">
+<thead>
+  <tr>
+    <th class="tg-c3ow" rowspan="2">Model</th>
+    <th class="tg-c3ow">z.s. s.o.s.</th>
+    <th class="tg-c3ow">z.s. a.d.</th>
+  </tr>
+  <tr>
+    <th class="tg-c3ow">$\mathcal{M}$ $\downarrow$</th>
+    <th class="tg-c3ow">$\mathcal{F}_{p}$</th>
+  </tr>
+</thead>
+<tbody>
+  <tr>
+    <td class="tg-c3ow">ViT-H-SAM</td>
+    <td class="tg-c3ow"><b>0.046</b></td>
+    <td class="tg-c3ow"><ins>37.65</ins></td>
+  </tr>
+  <tr>
+    <td class="tg-c3ow">ViT-B-SAM</td>
+    <td class="tg-c3ow">0.121</td>
+    <td class="tg-c3ow">36.62</td>
+  </tr>
+  <tr>
+    <td class="tg-c3ow">MobileSAM</td>
+    <td class="tg-c3ow">0.147</td>
+    <td class="tg-c3ow">36.44</td>
+  </tr>
+  <tr>
+    <td class="tg-c3ow">RepViT-SAM</td>
+    <td class="tg-c3ow"><ins>0.066</ins></td>
+    <td class="tg-c3ow"><b>37.96</b></td>
+  </tr>
+</tbody>
+</table>
+
+## Acknowledgement
+
+The code base is partly built with [SAM](https://github.com/facebookresearch/segment-anything) and [MobileSAM](https://github.com/ChaoningZhang/MobileSAM). 
+
+Thanks for the great implementations! 
+
+## Citation
+
+If our code or models help your work, please cite our paper:
+```BibTeX
+@misc{wang2023repvitsam,
+      title={RepViT-SAM: Towards Real-Time Segmenting Anything}, 
+      author={Ao Wang and Hui Chen and Zijia Lin and Jungong Han and Guiguang Ding},
+      year={2023},
+      eprint={2312.05760},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```

sam/app/.gitattributes

+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+assets/sa_1309.jpg filter=lfs diff=lfs merge=lfs -text
+assets/sa_192.jpg filter=lfs diff=lfs merge=lfs -text
+assets/sa_414.jpg filter=lfs diff=lfs merge=lfs -text
+assets/sa_862.jpg filter=lfs diff=lfs merge=lfs -text

sam/app/app.py

+import os
+
+import gradio as gr
+import numpy as np
+import torch
+from repvit_sam import SamAutomaticMaskGenerator, SamPredictor, sam_model_registry
+from PIL import ImageDraw
+from utils.tools import box_prompt, format_results, point_prompt
+from utils.tools_gradio import fast_process
+
+# Most of our demo code is from [FastSAM Demo](https://huggingface.co/spaces/An-619/FastSAM). Huge thanks for AN-619.
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Load the pre-trained model
+sam_checkpoint = "weights/repvit_sam.pt"
+model_type = "repvit"
+
+repvit_sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)
+repvit_sam = repvit_sam.to(device=device)
+repvit_sam.eval()
+
+mask_generator = SamAutomaticMaskGenerator(repvit_sam)
+predictor = SamPredictor(repvit_sam)
+
+# Description
+title = "<center><strong><font size='8'>RepViT-SAM<font></strong></center>"
+
+description_e = """This is a demo of [RepViT-SAM](https://github.com/THU-MIG/RepViT).
+
+                   We will provide box mode soon. 
+
+                   Enjoy!
+                
+              """
+
+description_p = """ Instructions for point mode
+
+                0. Restart by click the Restart button
+                1. Select a point with Add Mask for the foreground (Must)
+                2. Select a point with Remove Area for the background (Optional)
+                3. Click the Start Segmenting.
+
+                Github [link](https://github.com/THU-MIG/RepViT)
+
+              """
+
+examples = [
+    ["app/assets/picture3.jpg"],
+    ["app/assets/picture4.jpg"],
+    ["app/assets/picture6.jpg"],
+    ["app/assets/picture1.jpg"],
+]
+
+default_example = examples[0]
+
+css = "h1 { text-align: center } .about { text-align: justify; padding-left: 10%; padding-right: 10%; }"
+
+def segment_with_points(
+    image,
+    original_image,
+    input_size=1024,
+    better_quality=False,
+    withContours=True,
+    use_retina=True,
+    mask_random_color=True,
+):
+    global global_points
+    global global_point_label
+
+    input_size = int(input_size)
+    w, h = image.size
+    scale = input_size / max(w, h)
+    new_w = int(w * scale)
+    new_h = int(h * scale)
+    image = image.resize((new_w, new_h))
+
+    scaled_points = np.array(
+        [[int(x * scale) for x in point] for point in global_points]
+    )
+    scaled_point_label = np.array(global_point_label)
+
+    if scaled_points.size == 0 and scaled_point_label.size == 0:
+        print("No points selected")
+        return image, image
+
+    nd_image = np.array(original_image.resize((new_w, new_h)))
+    predictor.set_image(nd_image)
+    masks, scores, logits = predictor.predict(
+        point_coords=scaled_points,
+        point_labels=scaled_point_label,
+        multimask_output=False,
+    )
+
+    results = format_results(masks, scores, logits, 0)
+
+    annotations, _ = point_prompt(
+        results, scaled_points, scaled_point_label, new_h, new_w
+    )
+    annotations = np.array([annotations])
+
+    fig = fast_process(
+        annotations=annotations,
+        image=image,
+        device=device,
+        scale=(1024 // input_size),
+        better_quality=better_quality,
+        mask_random_color=mask_random_color,
+        bbox=None,
+        use_retina=use_retina,
+        withContours=withContours,
+    )
+
+    global_points = []
+    global_point_label = []
+    # return fig, None
+    return fig, original_image.resize((new_w, new_h))
+
+
+def get_points_with_draw(image, label, evt: gr.SelectData):
+    global global_points
+    global global_point_label
+
+    x, y = evt.index[0], evt.index[1]
+    point_radius, point_color = 15 * ((max(image.width, image.height)) / 1024), (255, 255, 0) if label == "Add Mask" else (
+        255,
+        0,
+        255,
+    )
+    global_points.append([x, y])
+    global_point_label.append(1 if label == "Add Mask" else 0)
+
+    # 创建一个可以在图像上绘图的对象
+    draw = ImageDraw.Draw(image)
+    draw.ellipse(
+        [(x - point_radius, y - point_radius), (x + point_radius, y + point_radius)],
+        fill=point_color,
+    )
+    return image
+
+
+cond_img_e = gr.Image(label="Input", value=default_example[0], type="pil")
+cond_img_p = gr.Image(label="Input with points", value=default_example[0], type="pil")
+
+segm_img_e = gr.Image(label="Segmented Image", interactive=False, type="pil")
+segm_img_p = gr.Image(
+    label="Segmented Image with points", interactive=True, type="pil"
+)
+
+global_points = []
+global_point_label = []
+
+input_size_slider = gr.components.Slider(
+    minimum=512,
+    maximum=1024,
+    value=1024,
+    step=64,
+    label="Input_size",
+    info="Our model was trained on a size of 1024",
+)
+
+with gr.Blocks(css=css, title="RepViT-SAM") as demo:
+    from PIL import Image
+    original_image = gr.State(value=Image.open(default_example[0]).convert('RGB'))
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            # Title
+            gr.Markdown(title)
+
+    with gr.Tab("Point mode"):
+        # Images
+        with gr.Row(variant="panel"):
+            with gr.Column(scale=1):
+                cond_img_p.render()
+
+            with gr.Column(scale=1):
+                segm_img_p.render()
+
+        # Submit & Clear
+        with gr.Row():
+            with gr.Column():
+                with gr.Row():
+                    add_or_remove = gr.Radio(
+                        ["Add Mask", "Remove Area"],
+                        value="Add Mask",
+                    )
+
+                    with gr.Column():
+                        segment_btn_p = gr.Button(
+                            "Start segmenting!", variant="primary"
+                        )
+                        clear_btn_p = gr.Button("Restart", variant="secondary")
+
+                gr.Markdown("Try some of the examples below ⬇️")
+
+                gr.Examples(
+                    examples=examples,
+                    inputs=[cond_img_p],
+                    fn=lambda x: x,
+                    outputs=[original_image],
+                    # fn=segment_with_points,
+                    # cache_examples=True,
+                    examples_per_page=4,
+                    run_on_click=True
+                )
+
+            with gr.Column():
+                # Description
+                gr.Markdown(description_p)
+
+    cond_img_p.select(get_points_with_draw, [cond_img_p, add_or_remove], cond_img_p)
+    cond_img_p.upload(lambda x: x, inputs=[cond_img_p], outputs=[original_image])
+
+    # segment_btn_e.click(
+    #     segment_everything,
+    #     inputs=[
+    #         cond_img_e,
+    #         input_size_slider,
+    #         mor_check,
+    #         contour_check,
+    #         retina_check,
+    #     ],
+    #     outputs=segm_img_e,
+    # )
+
+    segment_btn_p.click(
+        segment_with_points, inputs=[cond_img_p, original_image], outputs=[segm_img_p, cond_img_p]
+    )
+
+    def clear():
+        return None, None
+
+    def clear_text():
+        return None, None, None
+
+    # clear_btn_e.click(clear, outputs=[cond_img_e, segm_img_e])
+    clear_btn_p.click(clear, outputs=[cond_img_p, segm_img_p])
+
+demo.queue()
+demo.launch()