(2)在Self-Attention模块内部,部分方法放弃了K和V的部分表达,相邻嵌入的键/值经常被合并,以降低成本。因此,即使嵌入同时具有小尺度和大尺度特征,合并操作也会丢失每个单个嵌入的小尺度(细粒度)特征,从而使跨尺度注意力失效。例如,Swin-Transformer将self-attention操作的范围限制在每个window内,这一定程度上放弃了全局...
为了解决上述问题,作者提出了Cross-scale Embedding Layer(CEL) 和Long Short Distance Attention(LSDA) 两个模块。其中 CEL 模块将不同尺度的特征进行融合,为 self-attention 模块提供了跨尺度的特征;LSDA 模块将 selff-attention 模块分为 short-distance 和 long-distance 两个部分,不仅减少了计算的负担,还保留了...
相同的参数下,GPU单P的loss如下: NPU单P的loss如下: 测试结论:前几个step,NPU的loss比GPU低,但由于下降速率慢,导致NPU中,后期loss偏高,最终精度偏低,不达标 测试算子在CPU与NPU中的精度差,都是小于万分之一 2、使用msaccucmp.py脚本,测试算子在GPU与NPU中的精度差,得到的CosineSimilarity都是1或者NAN 测试结论...
8, 16, 32].in_chans (int): Number of input image channels. Default: 3.embed_dim (int): Number of linear projection output channels. Default: 96.norm_layer (nn.Module, optional): Normalization layer. Default: None'''def__init__(self,img_size...
In particular, CEL blends each embedding with multiple patches of different scales, providing the model with cross-scale embeddings. LSDA splits the self-attention module into a short-distance and long-distance one, also lowering the cost but keeping both small-scale and large-scale features in ...
In this paper, we propose a cross-scale attention\n(CSA) model, which explicitly integrates features from different scales to form\nthe final representation. Moreover, we propose the adoption of the attention\nmechanism to specify the weights of local and global features based on the\nspatial ...
[浙江大学-PyTorch离线推理]Cross-scale-non-attention模型,onnx转om后,PRelu算子导致精度变低 DONE #I48MK6 推理问题 pika 创建于 2021-09-04 11:38 一、问题现象(附报错日志上下文): 直接测试onnx的精度,结果是达标的。把onnx转换为om之后,推理精度低很多,om推理得到的结果,后半部分有很多0 二、软件版本:...
Deep convolution-based single image super-resolution (SISR) networks embrace the benefits of learning from large-scale external image resources for local recovery, yet most existing works have ignored the long-range feature-wise similarities in natural images. Some recent works have successfully leverage...
CrossFormer: A Versatile Vision Transformer Hinging on Cross-scale Attention https://arxiv.org/abs/2108.00154 https://github.com/cheerss/CrossFormer 这是视觉的Transformer 演进过程:VIT---PVT---CrossFormer VIT没有考虑多尺度信息 PVT通过特征下采样集成了多尺度信息...
CROSSFORMER: A VERSATILE VISION TRANSFORMER BASED ON CROSS-SCALE ATTENTION,程序员大本营,技术文章内容聚合第一站。