diff --git a/engine.py b/engine.py index a6261bc..ba84630 100644 --- a/engine.py +++ b/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) diff --git a/main.py b/main.py index 01e8009..7bdcf68 100644 --- a/main.py +++ b/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 - parser.add_argument('--pruner', type=str, help='pruning criterion') - parser.add_argument('--sparsity', type=float, default=1.0, help = 'the sparisty level (ratio of unpruned weight)') - parser.add_argument('--custom-config', type=str, help='customized configuration of sparsity level for each linear layer') + # Sparsity Training Related Flag + parser.add_argument('--nas-config', type=str, help='configuration for supernet training') + parser.add_argument('--nas-mode', action='store_true') 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) @@ -262,7 +274,10 @@ def main(args): 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) - + + 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") diff --git a/model_sparse.py b/model_sparse.py index 91c7e7c..dc489c8 100644 --- a/model_sparse.py +++ b/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): diff --git a/models.py b/models.py index 5b22ef3..3ecd17e 100644 --- a/models.py +++ b/models.py @@ -1,82 +1,348 @@ -# Copyright (c) 2015-present, Facebook, Inc. -# All rights reserved. +""" Vision Transformer (ViT) in PyTorch + +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__ = [ - 'deit_tiny_patch16_224', 'deit_small_patch16_224', 'deit_base_patch16_224', - 'deit_tiny_distilled_patch16_224', 'deit_small_distilled_patch16_224', - 'deit_base_distilled_patch16_224', 'deit_base_patch16_384', - 'deit_base_distilled_patch16_384', -] +def _cfg(url='', **kwargs): + return { + 'url': url, + 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None, + '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): - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - self.dist_token = nn.Parameter(torch.zeros(1, 1, self.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() +default_cfgs = { + # patch models + 'vit_small_patch16_224': _cfg( + 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(), +} - trunc_normal_(self.dist_token, std=.02) - trunc_normal_(self.pos_embed, std=.02) - self.head_dist.apply(self._init_weights) - - 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_tokens = self.cls_token.expand(B, -1, -1) # stole cls_tokens impl from Phil Wang, thanks - dist_token = self.dist_token.expand(B, -1, -1) - x = torch.cat((cls_tokens, dist_token, x), dim=1) - - x = x + self.pos_embed - x = self.pos_drop(x) - - for blk in self.blocks: - x = blk(x) - - x = self.norm(x) - return x[:, 0], x[:, 1] +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, x_dist = self.forward_features(x) - x = self.head(x) - x_dist = self.head_dist(x_dist) - if self.training: - return x, x_dist + 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.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: - # during inference, return the average of both classifier predictions - return (x + x_dist) / 2 + 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) + 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): + x = self.patch_embed(x) + cls_token = self.cls_token.expand(x.shape[0], -1, -1) # stole cls_tokens impl from Phil Wang, thanks + if self.dist_token is None: + x = torch.cat((cls_token, x), dim=1) + else: + x = torch.cat((cls_token, self.dist_token.expand(x.shape[0], -1, -1), x), dim=1) + x = self.pos_drop(x + self.pos_embed) + x = self.blocks(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 = self.forward_features(x) + if self.head_dist is not None: + x, x_dist = self.head(x[0]), self.head_dist(x[1]) # x must be a tuple + if self.training and not torch.jit.is_scripting(): + # during inference, return the average of both classifier predictions + return x, x_dist + else: + return (x + x_dist) / 2 + else: + x = self.head(x) + return x + + + + + + @register_model -def deit_tiny_patch16_224(pretrained=False, **kwargs): - model = VisionTransformer( - patch_size=16, embed_dim=192, depth=12, num_heads=3, mlp_ratio=4, qkv_bias=True, +def deit_base_patch16_224(pretrained=False, **kwargs): + """ 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=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): - model = VisionTransformer( - 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_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( +def deit_tiny_patch16_224(pretrained=False, **kwargs): + """ 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=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