ViT里Embedding层的参数量是正比于图像尺寸的,以224*224图像为例,单patch像素点数为196,所以总参数量是196*C*D,C是输入通道数,D是Embedding维数,以3和768记的话为0.45M,远小于BERT-base。从下表可以看到同样尺寸的ViT参数量都小于对应的BERT。 不同尺寸ViT参数量 按论文的这种处理方式也有几个比较明显的问题,...
self).__init__()self.vit=ViTModel.from_pretrained('vit-base-patch16-224-in21k')self.fc=nn.Linear(768,10)# 10 classes for classificationdefforward(self,x):x=self.vit(x)x=self.fc(x.last_hidden_state[:,0,:])returnx
ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer. weights ported from official Google JAX impl: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_patch32_224_in21k-8db57226.pth """ model = VisionTransformer...
Using only architecture defaults to the first weights in the default_cfgs for that model architecture. In adding pretrained tags, many model names that existed to differentiate were renamed to use the tag (ex: vit_base_patch16_224_in21k -> vit_base_patch16_224.augreg_in21k). There are ...
Compose, Normalize, RandomHorizontalFlip, RandomResizedCrop, Resize, ToTensor) from transformers import ViTImageProcessor processor = ViTImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k") image_mean = processor.image_mean image_std = processor.image_std ...
3.2. Proposed Multi-Scale Vision Transformer The granularity of the patch size affects the accuracy and complexity of ViT; with fine-grained patch size, ViT can perform better but results in higher FLOPs and memory consumption. For example, the ViT with...
importosimportmathimportargparseimporttorchimporttorch.optimasoptimimporttorch.optim.lr_scheduleraslr_schedulerfromtorch.utils.tensorboardimportSummaryWriterfromtorchvisionimporttransformsfrommy_datasetimportMyDataSetfromtimm.models.vision_transformerimportvit_base_patch16_224_in21kascreate_modelfromutilsimportread_split...
224 224)即表示输入图片尺寸 print(input.shape) model = vit_base_patch16_224_in21k() ...
cd jigsaw-deit/ # run Jigsaw-ViT with DeiT-Base/16 backbone python -m torch.distributed.launch --nproc_per_node=8 --use_env main_jigsaw.py --model jigsaw_base_patch16_224 --batch-size 128 --data-path ./imagenet --lambda-jigsaw 0.1 --mask-ratio 0.5 --output_dir ./jigsaw_base_...
patch_size=16, embed_dim=768, depth=12, num_heads=12, representation_size=None, num_classes=num_classes) return model def vit_base_patch16_224_in21k(num_classes: int = 21843, has_logits: bool = True): """ ViT-Base model (ViT-B/16) from original paper (https://arxiv.org/abs/...