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[NAS] Modify the infrasturcture for nas training
This commit is contained in:
brian1009
parent
0e1a79af52
commit
afadc46e64
@ -28,6 +28,8 @@ def train_one_epoch(model: torch.nn.Module, criterion: DistillationLoss,
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print_freq = 10
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for samples, targets in metric_logger.log_every(data_loader, print_freq, header):
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if args.nas_mode:
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model.module.set_random_sample_config()
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samples = samples.to(device, non_blocking=True)
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targets = targets.to(device, non_blocking=True)
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64
main.py
64
main.py
@ -27,7 +27,8 @@ from augment import new_data_aug_generator
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import models
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import models_v2
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import model_sparse
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import random
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import utils
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from sparsity_factory.pruners import weight_pruner_loader, prune_weights_reparam, check_valid_pruner
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@ -173,7 +174,7 @@ def get_args_parser():
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parser.add_argument('--eval', action='store_true', help='Perform evaluation only')
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parser.add_argument('--eval-crop-ratio', default=0.875, type=float, help="Crop ratio for evaluation")
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parser.add_argument('--dist-eval', action='store_true', default=False, help='Enabling distributed evaluation')
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parser.add_argument('--num_workers', default=10, type=int)
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parser.add_argument('--num_workers', default=16, type=int)
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parser.add_argument('--pin-mem', action='store_true',
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help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.')
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parser.add_argument('--no-pin-mem', action='store_false', dest='pin_mem',
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@ -185,13 +186,24 @@ def get_args_parser():
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help='number of distributed processes')
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parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training')
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# sparsity parameters
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parser.add_argument('--pruner', type=str, help='pruning criterion')
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parser.add_argument('--sparsity', type=float, default=1.0, help = 'the sparisty level (ratio of unpruned weight)')
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parser.add_argument('--custom-config', type=str, help='customized configuration of sparsity level for each linear layer')
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# Sparsity Training Related Flag
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parser.add_argument('--nas-config', type=str, help='configuration for supernet training')
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parser.add_argument('--nas-mode', action='store_true')
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return parser
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def gen_random_config_fn(config):
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if utils.get_rank() == 0 : # print whether to use non_unifrom at initialization at main process
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print(f"Set up the uniform sampling function")
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def _fn_uni():
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def weights(ratios):
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return [1 for _ in ratios]
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res = []
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for ratios in config['sparsity']['choices']:
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res.append(random.choices(ratios, weights(ratios))[0])
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return res
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return _fn_uni
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def main(args):
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utils.init_distributed_mode(args)
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@ -262,7 +274,10 @@ def main(args):
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mixup_alpha=args.mixup, cutmix_alpha=args.cutmix, cutmix_minmax=args.cutmix_minmax,
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prob=args.mixup_prob, switch_prob=args.mixup_switch_prob, mode=args.mixup_mode,
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label_smoothing=args.smoothing, num_classes=args.nb_classes)
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with open(args.nas_config) as f:
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nas_config = yaml.load(f, Loader=SafeLoader)
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print(f"Creating model: {args.model}")
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model = create_model(
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args.model,
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@ -274,29 +289,6 @@ def main(args):
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img_size=args.input_size
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)
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if args.pruner == 'custom':
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if args.custom_config:
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with open(args.custom_config) as f:
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config = yaml.load(f, Loader=SafeLoader)
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else:
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raise ValueError("Please provide the configuration file when using the custom mode")
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mode = config['sparsity']['mode']
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sparsity_config = config['sparsity']['level']
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pruner = weight_pruner_loader(args.pruner)
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pruner(model, mode, sparsity_config)
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elif check_valid_pruner(args.pruner):
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pruner = weight_pruner_loader(args.pruner)
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prune_weights_reparam(model)
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pruner(model, args.sparsity)
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else:
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raise ValueError(f"Pruner '{args.pruner}' is not supported")
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if args.finetune:
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if args.finetune.startswith('https'):
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checkpoint = torch.hub.load_state_dict_from_url(
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@ -370,6 +362,15 @@ def main(args):
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if args.distributed:
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model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
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model_without_ddp = model.module
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if args.nas_mode:
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smallest_config = []
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for ratios in nas_config['sparsity']['choices']:
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smallest_config.append(ratios[0])
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model_without_ddp.set_random_config_fn(gen_random_config_fn(nas_config))
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model_without_ddp.set_sample_config(smallest_config)
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n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)
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print('number of params:', n_parameters)
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if not args.unscale_lr:
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@ -493,9 +494,6 @@ def main(args):
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'epoch': epoch,
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'n_parameters': n_parameters}
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if args.output_dir and utils.is_main_process():
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with (output_dir / "log.txt").open("a") as f:
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f.write(json.dumps(log_stats) + "\n")
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@ -313,7 +313,6 @@ class SparseVisionTransformer(nn.Module):
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def set_sample_config(self, sparse_configs):
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for ratio, layer in zip(sparse_configs, filter(lambda x: isinstance(x, SparseLinearSuper), self.modules())):
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#print(ratio, layer)
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layer.set_sample_config(ratio)
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def set_random_config_fn(self, fn):
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447
models.py
447
models.py
@ -1,82 +1,348 @@
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# Copyright (c) 2015-present, Facebook, Inc.
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# All rights reserved.
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""" Vision Transformer (ViT) in PyTorch
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A PyTorch implement of Vision Transformers as described in:
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'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale'
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- https://arxiv.org/abs/2010.11929
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`How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers`
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- https://arxiv.org/abs/2106.10270
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The official jax code is released and available at https://github.com/google-research/vision_transformer
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DeiT model defs and weights from https://github.com/facebookresearch/deit,
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paper `DeiT: Data-efficient Image Transformers` - https://arxiv.org/abs/2012.12877
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Acknowledgments:
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* The paper authors for releasing code and weights, thanks!
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* I fixed my class token impl based on Phil Wang's https://github.com/lucidrains/vit-pytorch ... check it out
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for some einops/einsum fun
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* Simple transformer style inspired by Andrej Karpathy's https://github.com/karpathy/minGPT
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* Bert reference code checks against Huggingface Transformers and Tensorflow Bert
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Hacked together by / Copyright 2020, Ross Wightman
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"""
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import math
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import logging
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from functools import partial
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from collections import OrderedDict
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from copy import deepcopy
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from statistics import mode
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import torch
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import torch.nn as nn
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from functools import partial
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import torch.nn.functional as F
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from timm.models.vision_transformer import VisionTransformer, _cfg
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from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
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from timm.models.helpers import load_pretrained
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from timm.models.layers import DropPath, to_2tuple, trunc_normal_
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from timm.models.registry import register_model
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from timm.models.layers import trunc_normal_
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from sparse_linear import SparseLinearSuper
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_logger = logging.getLogger(__name__)
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__all__ = [
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'deit_tiny_patch16_224', 'deit_small_patch16_224', 'deit_base_patch16_224',
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'deit_tiny_distilled_patch16_224', 'deit_small_distilled_patch16_224',
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'deit_base_distilled_patch16_224', 'deit_base_patch16_384',
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'deit_base_distilled_patch16_384',
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]
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def _cfg(url='', **kwargs):
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return {
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'url': url,
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'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
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'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
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'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD,
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'first_conv': 'patch_embed.proj', 'classifier': 'head',
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**kwargs
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}
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class DistilledVisionTransformer(VisionTransformer):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.dist_token = nn.Parameter(torch.zeros(1, 1, self.embed_dim))
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num_patches = self.patch_embed.num_patches
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self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 2, self.embed_dim))
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self.head_dist = nn.Linear(self.embed_dim, self.num_classes) if self.num_classes > 0 else nn.Identity()
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default_cfgs = {
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# patch models
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'vit_small_patch16_224': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/vit_small_p16_224-15ec54c9.pth',
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),
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'vit_base_patch16_224': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_224-80ecf9dd.pth',
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mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
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),
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'vit_base_patch16_384': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_384-83fb41ba.pth',
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input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),
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'vit_base_patch32_384': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p32_384-830016f5.pth',
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input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),
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'vit_large_patch16_224': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p16_224-4ee7a4dc.pth',
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mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
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'vit_large_patch16_384': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p16_384-b3be5167.pth',
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input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),
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'vit_large_patch32_384': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p32_384-9b920ba8.pth',
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input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),
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'vit_huge_patch16_224': _cfg(),
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'vit_huge_patch32_384': _cfg(input_size=(3, 384, 384)),
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# hybrid models
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'vit_small_resnet26d_224': _cfg(),
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'vit_small_resnet50d_s3_224': _cfg(),
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'vit_base_resnet26d_224': _cfg(),
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'vit_base_resnet50d_224': _cfg(),
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}
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trunc_normal_(self.dist_token, std=.02)
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trunc_normal_(self.pos_embed, std=.02)
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self.head_dist.apply(self._init_weights)
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def forward_features(self, x):
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# taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
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# with slight modifications to add the dist_token
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B = x.shape[0]
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x = self.patch_embed(x)
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cls_tokens = self.cls_token.expand(B, -1, -1) # stole cls_tokens impl from Phil Wang, thanks
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dist_token = self.dist_token.expand(B, -1, -1)
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x = torch.cat((cls_tokens, dist_token, x), dim=1)
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x = x + self.pos_embed
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x = self.pos_drop(x)
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for blk in self.blocks:
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x = blk(x)
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x = self.norm(x)
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return x[:, 0], x[:, 1]
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class LRMlpSuper(nn.Module):
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def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
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super().__init__()
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out_features = out_features or in_features
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hidden_features = hidden_features or in_features
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self.act = act_layer()
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self.drop = nn.Dropout(drop)
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self.fc1 = SparseLinearSuper(in_features, hidden_features)
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self.fc2 = SparseLinearSuper(hidden_features, out_features)
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def forward(self, x):
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x, x_dist = self.forward_features(x)
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x = self.head(x)
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x_dist = self.head_dist(x_dist)
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if self.training:
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return x, x_dist
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x = self.fc1(x)
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x = self.act(x)
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x = self.drop(x)
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x = self.fc2(x)
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x = self.drop(x)
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return x
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class LRAttentionSuper(nn.Module):
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def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.):
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super().__init__()
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self.num_heads = num_heads
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head_dim = dim // num_heads
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self.scale = head_dim ** -0.5
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self.proj = SparseLinearSuper(dim, dim)
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self.qkv = SparseLinearSuper(dim, dim * 3, bias = qkv_bias)
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self.attn_drop = nn.Dropout(attn_drop)
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self.proj_drop = nn.Dropout(proj_drop)
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def forward(self, x):
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B, N, C = x.shape
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qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
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q, k, v = qkv.unbind(0) # make torchscript happy (cannot use tensor as tuple)
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attn = (q @ k.transpose(-2, -1)) * self.scale
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attn = attn.softmax(dim=-1)
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attn = self.attn_drop(attn)
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x = (attn @ v).transpose(1, 2).reshape(B, N, C)
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x = self.proj(x)
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x = self.proj_drop(x)
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return x
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class Block(nn.Module):
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def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0.,
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drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
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super().__init__()
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self.norm1 = norm_layer(dim)
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self.attn = LRAttentionSuper(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop, )
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# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
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self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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self.norm2 = norm_layer(dim)
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mlp_hidden_dim = int(dim * mlp_ratio)
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self.mlp = LRMlpSuper(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
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def forward(self, x):
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x = x + self.drop_path(self.attn(self.norm1(x)))
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x = x + self.drop_path(self.mlp(self.norm2(x)))
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return x
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class PatchEmbed(nn.Module):
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""" Image to Patch Embedding
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"""
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def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
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super().__init__()
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img_size = to_2tuple(img_size)
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patch_size = to_2tuple(patch_size)
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num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
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self.img_size = img_size
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self.patch_size = patch_size
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self.num_patches = num_patches
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self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
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def forward(self, x):
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B, C, H, W = x.shape
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# FIXME look at relaxing size constraints
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assert H == self.img_size[0] and W == self.img_size[1], \
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f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
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x = self.proj(x).flatten(2).transpose(1, 2)
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return x
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def num_params(self):
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return sum(p.numel() for p in self.parameters() if p.requires_grad)
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class SparseVisionTransformer(nn.Module):
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""" Vision Transformer
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A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`
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- https://arxiv.org/abs/2010.11929
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Includes distillation token & head support for `DeiT: Data-efficient Image Transformers`
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- https://arxiv.org/abs/2012.12877
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"""
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def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
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num_heads=12, mlp_ratio=4., qkv_bias=True, representation_size=None, distilled=False,
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drop_rate=0., attn_drop_rate=0., drop_path_rate=0., embed_layer=PatchEmbed, norm_layer=None,
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act_layer=None, weight_init='', ):
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"""
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Args:
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img_size (int, tuple): input image size
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patch_size (int, tuple): patch size
|
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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
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user