[NAS] Modify the infrasturcture for nas training

engine.py

@@ -28,6 +28,8 @@ def train_one_epoch(model: torch.nn.Module, criterion: DistillationLoss,
     print_freq = 10
 
     for samples, targets in metric_logger.log_every(data_loader, print_freq, header):
+        if args.nas_mode:
+            model.module.set_random_sample_config()
         samples = samples.to(device, non_blocking=True)
         targets = targets.to(device, non_blocking=True)
 

main.py

@@ -27,7 +27,8 @@ from augment import new_data_aug_generator
 
 import models
 import models_v2
-
+import model_sparse
+import random
 import utils
 
 from sparsity_factory.pruners import weight_pruner_loader, prune_weights_reparam, check_valid_pruner
@@ -173,7 +174,7 @@ def get_args_parser():
     parser.add_argument('--eval', action='store_true', help='Perform evaluation only')
     parser.add_argument('--eval-crop-ratio', default=0.875, type=float, help="Crop ratio for evaluation")
     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('--num_workers', default=16, 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',
@@ -185,13 +186,24 @@ def get_args_parser():
                         help='number of distributed processes')
     parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training')
 
-    # sparsity parameters
+    # Sparsity Training Related Flag
-    parser.add_argument('--pruner', type=str, help='pruning criterion')
+    parser.add_argument('--nas-config', type=str, help='configuration for supernet training')
-    parser.add_argument('--sparsity', type=float, default=1.0, help = 'the sparisty level (ratio of unpruned weight)')
+    parser.add_argument('--nas-mode', action='store_true')
-    parser.add_argument('--custom-config', type=str, help='customized configuration of sparsity level for each linear layer')
     return parser
 
 
+def gen_random_config_fn(config):
+    if utils.get_rank() == 0 : # print whether to use non_unifrom at initialization at main process
+        print(f"Set up the uniform sampling function")
+    def _fn_uni():
+        def weights(ratios):
+            return [1 for _ in ratios]
+        res = []
+        for ratios in config['sparsity']['choices']:
+            res.append(random.choices(ratios, weights(ratios))[0])
+        return res
+    return _fn_uni
+
 def main(args):
     utils.init_distributed_mode(args)
 
@@ -263,6 +275,9 @@ def main(args):
             prob=args.mixup_prob, switch_prob=args.mixup_switch_prob, mode=args.mixup_mode,
             label_smoothing=args.smoothing, num_classes=args.nb_classes)
     
+    with open(args.nas_config) as f:
+        nas_config = yaml.load(f, Loader=SafeLoader)  
+    
     print(f"Creating model: {args.model}")
     model = create_model(
         args.model,
@@ -274,29 +289,6 @@ def main(args):
         img_size=args.input_size
     )
 
-
-    
-    if args.pruner == 'custom':
-        if args.custom_config:
-            with open(args.custom_config) as f:
-                config = yaml.load(f, Loader=SafeLoader)  
-        else:
-            raise ValueError("Please provide the configuration file when using the custom mode")
-        
-        mode = config['sparsity']['mode']
-        sparsity_config = config['sparsity']['level']
-
-        pruner = weight_pruner_loader(args.pruner)
-        pruner(model, mode, sparsity_config)
-    elif check_valid_pruner(args.pruner):
-        pruner = weight_pruner_loader(args.pruner)
-        prune_weights_reparam(model)
-        pruner(model, args.sparsity)
-    else:
-        raise ValueError(f"Pruner '{args.pruner}' is not supported")
-    
-
-
     if args.finetune:
         if args.finetune.startswith('https'):
             checkpoint = torch.hub.load_state_dict_from_url(
@@ -370,6 +362,15 @@ def main(args):
     if args.distributed:
         model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
         model_without_ddp = model.module
+    
+    if args.nas_mode:
+        smallest_config = []
+        for ratios in nas_config['sparsity']['choices']:
+            smallest_config.append(ratios[0])
+        model_without_ddp.set_random_config_fn(gen_random_config_fn(nas_config))
+        model_without_ddp.set_sample_config(smallest_config)    
+        
+
     n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)
     print('number of params:', n_parameters)
     if not args.unscale_lr:
@@ -493,9 +494,6 @@ def main(args):
                      'epoch': epoch,
                      'n_parameters': n_parameters}
 
-
-
-
         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")

model_sparse.py

@@ -313,7 +313,6 @@ class SparseVisionTransformer(nn.Module):
 
     def set_sample_config(self, sparse_configs):
         for ratio, layer in zip(sparse_configs, filter(lambda x: isinstance(x, SparseLinearSuper), self.modules())):
-            #print(ratio, layer)
             layer.set_sample_config(ratio)
 
     def set_random_config_fn(self, fn):

models.py

@@ -1,82 +1,348 @@
-# Copyright (c) 2015-present, Facebook, Inc.
+""" Vision Transformer (ViT) in PyTorch
-# All rights reserved.
+
+A PyTorch implement of Vision Transformers as described in:
+
+'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale'
+    - https://arxiv.org/abs/2010.11929
+
+`How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers`
+    - https://arxiv.org/abs/2106.10270
+
+The official jax code is released and available at https://github.com/google-research/vision_transformer
+
+DeiT model defs and weights from https://github.com/facebookresearch/deit,
+paper `DeiT: Data-efficient Image Transformers` - https://arxiv.org/abs/2012.12877
+
+Acknowledgments:
+* The paper authors for releasing code and weights, thanks!
+* I fixed my class token impl based on Phil Wang's https://github.com/lucidrains/vit-pytorch ... check it out
+for some einops/einsum fun
+* Simple transformer style inspired by Andrej Karpathy's https://github.com/karpathy/minGPT
+* Bert reference code checks against Huggingface Transformers and Tensorflow Bert
+
+Hacked together by / Copyright 2020, Ross Wightman
+"""
+import math
+import logging
+from functools import partial
+from collections import OrderedDict
+from copy import deepcopy
+from statistics import mode
+
 import torch
 import torch.nn as nn
-from functools import partial
+import torch.nn.functional as F
 
-from timm.models.vision_transformer import VisionTransformer, _cfg
+from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
+from timm.models.helpers import load_pretrained
+from timm.models.layers import DropPath, to_2tuple, trunc_normal_
 from timm.models.registry import register_model
-from timm.models.layers import trunc_normal_
+
+from sparse_linear import SparseLinearSuper
+
+_logger = logging.getLogger(__name__)
 
 
-__all__ = [
+def _cfg(url='', **kwargs):
-    'deit_tiny_patch16_224', 'deit_small_patch16_224', 'deit_base_patch16_224',
+    return {
-    'deit_tiny_distilled_patch16_224', 'deit_small_distilled_patch16_224',
+        'url': url,
-    'deit_base_distilled_patch16_224', 'deit_base_patch16_384',
+        'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
-    'deit_base_distilled_patch16_384',
+        'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
-]
+        'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD,
+        'first_conv': 'patch_embed.proj', 'classifier': 'head',
+        **kwargs
+    }
 
 
-class DistilledVisionTransformer(VisionTransformer):
+default_cfgs = {
-    def __init__(self, *args, **kwargs):
+    # patch models
-        super().__init__(*args, **kwargs)
+    'vit_small_patch16_224': _cfg(
-        self.dist_token = nn.Parameter(torch.zeros(1, 1, self.embed_dim))
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/vit_small_p16_224-15ec54c9.pth',
+    ),
+    'vit_base_patch16_224': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_224-80ecf9dd.pth',
+        mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
+    ),
+    'vit_base_patch16_384': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_384-83fb41ba.pth',
+        input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),
+    'vit_base_patch32_384': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p32_384-830016f5.pth',
+        input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),
+    'vit_large_patch16_224': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p16_224-4ee7a4dc.pth',
+        mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
+    'vit_large_patch16_384': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p16_384-b3be5167.pth',
+        input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),
+    'vit_large_patch32_384': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p32_384-9b920ba8.pth',
+        input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),
+    'vit_huge_patch16_224': _cfg(),
+    'vit_huge_patch32_384': _cfg(input_size=(3, 384, 384)),
+    # hybrid models
+    'vit_small_resnet26d_224': _cfg(),
+    'vit_small_resnet50d_s3_224': _cfg(),
+    'vit_base_resnet26d_224': _cfg(),
+    'vit_base_resnet50d_224': _cfg(),
+}
+
+class LRMlpSuper(nn.Module):
+    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.act = act_layer()
+        self.drop = nn.Dropout(drop)
+        self.fc1 = SparseLinearSuper(in_features, hidden_features)
+        self.fc2 = SparseLinearSuper(hidden_features, out_features)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+class LRAttentionSuper(nn.Module):
+    def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = head_dim ** -0.5
+
+        self.proj = SparseLinearSuper(dim, dim)
+        self.qkv = SparseLinearSuper(dim, dim * 3, bias = qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x):
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv.unbind(0)   # make torchscript happy (cannot use tensor as tuple)
+
+        attn = (q @ k.transpose(-2, -1)) * self.scale
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+class Block(nn.Module):
+
+    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0.,
+                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.attn = LRAttentionSuper(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop, )
+        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = LRMlpSuper(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
+
+    def forward(self, x):
+        x = x + self.drop_path(self.attn(self.norm1(x)))
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+        return x
+
+    
+    
+class PatchEmbed(nn.Module):
+    """ Image to Patch Embedding
+    """
+    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
+        super().__init__()
+        img_size = to_2tuple(img_size)
+        patch_size = to_2tuple(patch_size)
+        num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.num_patches = num_patches
+
+        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
+
+    def forward(self, x):
+        B, C, H, W = x.shape
+        # FIXME look at relaxing size constraints
+        assert H == self.img_size[0] and W == self.img_size[1], \
+            f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
+        x = self.proj(x).flatten(2).transpose(1, 2)
+        return x
+    
+    def num_params(self):
+        return sum(p.numel() for p in self.parameters() if p.requires_grad)
+
+
+
+class SparseVisionTransformer(nn.Module):
+    """ Vision Transformer
+
+    A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`
+        - https://arxiv.org/abs/2010.11929
+
+    Includes distillation token & head support for `DeiT: Data-efficient Image Transformers`
+        - https://arxiv.org/abs/2012.12877
+    """
+
+    def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
+                 num_heads=12, mlp_ratio=4., qkv_bias=True, representation_size=None, distilled=False,
+                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0., embed_layer=PatchEmbed, norm_layer=None,
+                 act_layer=None, weight_init='', ):
+        """
+        Args:
+            img_size (int, tuple): input image size
+            patch_size (int, tuple): patch size
+            in_chans (int): number of input channels
+            num_classes (int): number of classes for classification head
+            embed_dim (int): embedding dimension
+            depth (int): depth of transformer
+            num_heads (int): number of attention heads
+            mlp_ratio (int): ratio of mlp hidden dim to embedding dim
+            qkv_bias (bool): enable bias for qkv if True
+            representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set
+            distilled (bool): model includes a distillation token and head as in DeiT models
+            drop_rate (float): dropout rate
+            attn_drop_rate (float): attention dropout rate
+            drop_path_rate (float): stochastic depth rate
+            embed_layer (nn.Module): patch embedding layer
+            norm_layer: (nn.Module): normalization layer
+            weight_init: (str): weight init scheme
+        """
+        super().__init__()
+        self.num_classes = num_classes
+        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
+        self.num_tokens = 2 if distilled else 1
+        norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
+        act_layer = act_layer or nn.GELU
+
+        self.patch_embed = embed_layer(
+            img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
         num_patches = self.patch_embed.num_patches
-        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 2, self.embed_dim))
-        self.head_dist = nn.Linear(self.embed_dim, self.num_classes) if self.num_classes > 0 else nn.Identity()
 
-        trunc_normal_(self.dist_token, std=.02)
+        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
+        self.dist_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) if distilled else None
+        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim))
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
+        self.blocks = nn.Sequential(*[
+            Block(
+                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop_rate,
+                attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, act_layer=act_layer)
+            for i in range(depth)])
+        self.norm = norm_layer(embed_dim)
+
+        # Representation layer
+        if representation_size and not distilled:
+            self.num_features = representation_size
+            self.pre_logits = nn.Sequential(OrderedDict([
+                ('fc', nn.Linear(embed_dim, representation_size)),
+                ('act', nn.Tanh())
+            ]))
+        else:
+            self.pre_logits = nn.Identity()
+
+        # Classifier head(s)
+        self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
+        self.head_dist = None
+        if distilled:
+            self.head_dist = nn.Linear(self.embed_dim, self.num_classes) if num_classes > 0 else nn.Identity()
+
         trunc_normal_(self.pos_embed, std=.02)
-        self.head_dist.apply(self._init_weights)
+        trunc_normal_(self.cls_token, std=.02)
+        self.apply(self._init_weights)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+
+
+
+    @torch.jit.ignore
+    def no_weight_decay(self):
+        return {'pos_embed', 'cls_token', 'dist_token'}
+
+    def get_classifier(self):
+        if self.dist_token is None:
+            return self.head
+        else:
+            return self.head, self.head_dist
+
+    def reset_classifier(self, num_classes, global_pool=''):
+        self.num_classes = num_classes
+        self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
+        if self.num_tokens == 2:
+            self.head_dist = nn.Linear(self.embed_dim, self.num_classes) if num_classes > 0 else nn.Identity()
 
     def forward_features(self, x):
-        # taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
-        # with slight modifications to add the dist_token
-        B = x.shape[0]
         x = self.patch_embed(x)
-
+        cls_token = self.cls_token.expand(x.shape[0], -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
-        cls_tokens = self.cls_token.expand(B, -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
+        if self.dist_token is None:
-        dist_token = self.dist_token.expand(B, -1, -1)
+            x = torch.cat((cls_token, x), dim=1)
-        x = torch.cat((cls_tokens, dist_token, x), dim=1)
+        else:
-
+            x = torch.cat((cls_token, self.dist_token.expand(x.shape[0], -1, -1), x), dim=1)
-        x = x + self.pos_embed
+        x = self.pos_drop(x + self.pos_embed)
-        x = self.pos_drop(x)
+        x = self.blocks(x)
-
-        for blk in self.blocks:
-            x = blk(x)
-
         x = self.norm(x)
+        if self.dist_token is None:
+            return self.pre_logits(x[:, 0])
+        else:
             return x[:, 0], x[:, 1]
 
     def forward(self, x):
-        x, x_dist = self.forward_features(x)
+        x = self.forward_features(x)
-        x = self.head(x)
+        if self.head_dist is not None:
-        x_dist = self.head_dist(x_dist)
+            x, x_dist = self.head(x[0]), self.head_dist(x[1])  # x must be a tuple
-        if self.training:
+            if self.training and not torch.jit.is_scripting():
+                # during inference, return the average of both classifier predictions
                 return x, x_dist
             else:
-            # during inference, return the average of both classifier predictions
                 return (x + x_dist) / 2
+        else:
+            x = self.head(x)
+        return x
+
+
+
+
+
+
 
 
 @register_model
-def deit_tiny_patch16_224(pretrained=False, **kwargs):
+def deit_base_patch16_224(pretrained=False, **kwargs):
-    model = VisionTransformer(
+    """ DeiT base model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).
-        patch_size=16, embed_dim=192, depth=12, num_heads=3, mlp_ratio=4, qkv_bias=True,
+    ImageNet-1k weights from https://github.com/facebookresearch/deit.
+    """
+    model =  SparseVisionTransformer(
+        patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True,
         norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
     model.default_cfg = _cfg()
     if pretrained:
         checkpoint = torch.hub.load_state_dict_from_url(
-            url="https://dl.fbaipublicfiles.com/deit/deit_tiny_patch16_224-a1311bcf.pth",
+            url="https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth",
             map_location="cpu", check_hash=True
         )
         model.load_state_dict(checkpoint["model"])
     return model
 
 
+
 @register_model
 def deit_small_patch16_224(pretrained=False, **kwargs):
-    model = VisionTransformer(
+    """ DeiT base model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).
+    ImageNet-1k weights from https://github.com/facebookresearch/deit.
+    """
+    model =  SparseVisionTransformer(
         patch_size=16, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4, qkv_bias=True,
         norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
     model.default_cfg = _cfg()
@@ -90,90 +356,25 @@ def deit_small_patch16_224(pretrained=False, **kwargs):
 
 
 @register_model
-def deit_base_patch16_224(pretrained=False, **kwargs):
+def deit_tiny_patch16_224(pretrained=False, **kwargs):
-    model = VisionTransformer(
+    """ DeiT base model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).
-        patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True,
+    ImageNet-1k weights from https://github.com/facebookresearch/deit.
-        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
+    """
-    model.default_cfg = _cfg()
+    model =  SparseVisionTransformer(
-    if pretrained:
-        checkpoint = torch.hub.load_state_dict_from_url(
-            url="https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth",
-            map_location="cpu", check_hash=True
-        )
-        model.load_state_dict(checkpoint["model"])
-    return model
-
-
-@register_model
-def deit_tiny_distilled_patch16_224(pretrained=False, **kwargs):
-    model = DistilledVisionTransformer(
         patch_size=16, embed_dim=192, depth=12, num_heads=3, mlp_ratio=4, qkv_bias=True,
         norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
     model.default_cfg = _cfg()
     if pretrained:
         checkpoint = torch.hub.load_state_dict_from_url(
-            url="https://dl.fbaipublicfiles.com/deit/deit_tiny_distilled_patch16_224-b40b3cf7.pth",
+            url="https://dl.fbaipublicfiles.com/deit/deit_tiny_patch16_224-a1311bcf.pth",
             map_location="cpu", check_hash=True
         )
         model.load_state_dict(checkpoint["model"])
     return model
 
 
-@register_model
-def deit_small_distilled_patch16_224(pretrained=False, **kwargs):
-    model = DistilledVisionTransformer(
-        patch_size=16, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4, qkv_bias=True,
-        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
-    model.default_cfg = _cfg()
-    if pretrained:
-        checkpoint = torch.hub.load_state_dict_from_url(
-            url="https://dl.fbaipublicfiles.com/deit/deit_small_distilled_patch16_224-649709d9.pth",
-            map_location="cpu", check_hash=True
-        )
-        model.load_state_dict(checkpoint["model"])
-    return model
 
 
-@register_model
-def deit_base_distilled_patch16_224(pretrained=False, **kwargs):
-    model = DistilledVisionTransformer(
-        patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True,
-        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
-    model.default_cfg = _cfg()
-    if pretrained:
-        checkpoint = torch.hub.load_state_dict_from_url(
-            url="https://dl.fbaipublicfiles.com/deit/deit_base_distilled_patch16_224-df68dfff.pth",
-            map_location="cpu", check_hash=True
-        )
-        model.load_state_dict(checkpoint["model"])
-    return model
 
 
-@register_model
-def deit_base_patch16_384(pretrained=False, **kwargs):
-    model = VisionTransformer(
-        img_size=384, patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True,
-        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
-    model.default_cfg = _cfg()
-    if pretrained:
-        checkpoint = torch.hub.load_state_dict_from_url(
-            url="https://dl.fbaipublicfiles.com/deit/deit_base_patch16_384-8de9b5d1.pth",
-            map_location="cpu", check_hash=True
-        )
-        model.load_state_dict(checkpoint["model"])
-    return model
 
-
-@register_model
-def deit_base_distilled_patch16_384(pretrained=False, **kwargs):
-    model = DistilledVisionTransformer(
-        img_size=384, patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True,
-        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
-    model.default_cfg = _cfg()
-    if pretrained:
-        checkpoint = torch.hub.load_state_dict_from_url(
-            url="https://dl.fbaipublicfiles.com/deit/deit_base_distilled_patch16_384-d0272ac0.pth",
-            map_location="cpu", check_hash=True
-        )
-        model.load_state_dict(checkpoint["model"])
-    return model