浙江省建设厅继续教育官方网站,微信里面的小程序怎么设置,做企划的网站,建设通一年多少钱背景#xff1a;transformer在CV领域的应用论文下载链接#xff1a;https://arxiv.org/abs/2010.11929Pytorch实现代码#xff1a; pytorch_classification/vision_transformer(太阳花的小绿豆博主实现的代码)有一些大神在研究关于CNNtransformer或者纯用transformer实现。原…背景transformer在CV领域的应用论文下载链接https://arxiv.org/abs/2010.11929Pytorch实现代码 pytorch_classification/vision_transformer(太阳花的小绿豆博主实现的代码)有一些大神在研究关于CNNtransformer或者纯用transformer实现。原文的摘要说We show that this reliance on CNNs is not necessary and a pure transformer applied directly to sequences of image patches can perform very well on image classification tasks.(我们展示这种对 CNN 的依赖是不必要的直接应用于图像块序列的纯变换器可以很好地执行图像分类任务) 比较具体的内容请看太阳花的小绿豆博主的《Vision Transformer详解》,相关的图片是这个博主的我这里直接用ONNX的模型结构进行说明可能更加直观一点(不喜勿碰哈)VIT整体结构图VIT形状变化pytorch的api:summary(model, (3, 224, 224))----------------------------------------------------------------Layer (type) Output Shape Param #(1) 前处理Conv2d-1 [-1, 768, 14, 14] 590,592Identity-2 [-1, 196, 768] 0PatchEmbed-3 [-1, 196, 768] 0Dropout-4 [-1, 197, 768] 0
(2) transformer encoderblock 1LayerNorm-5 [-1, 197, 768] 1,536Linear-6 [-1, 197, 2304] 1,771,776Dropout-7 [-1, 12, 197, 197] 0Linear-8 [-1, 197, 768] 590,592Dropout-9 [-1, 197, 768] 0Attention-10 [-1, 197, 768] 0Identity-11 [-1, 197, 768] 0LayerNorm-12 [-1, 197, 768] 1,536Linear-13 [-1, 197, 3072] 2,362,368GELU-14 [-1, 197, 3072] 0Dropout-15 [-1, 197, 3072] 0Linear-16 [-1, 197, 768] 2,360,064Dropout-17 [-1, 197, 768] 0Mlp-18 [-1, 197, 768] 0Identity-19 [-1, 197, 768] 0Block-20 [-1, 197, 768] 0
block 2LayerNorm-21 [-1, 197, 768] 1,536Linear-22 [-1, 197, 2304] 1,771,776Dropout-23 [-1, 12, 197, 197] 0Linear-24 [-1, 197, 768] 590,592Dropout-25 [-1, 197, 768] 0Attention-26 [-1, 197, 768] 0Identity-27 [-1, 197, 768] 0LayerNorm-28 [-1, 197, 768] 1,536Linear-29 [-1, 197, 3072] 2,362,368GELU-30 [-1, 197, 3072] 0Dropout-31 [-1, 197, 3072] 0Linear-32 [-1, 197, 768] 2,360,064Dropout-33 [-1, 197, 768] 0Mlp-34 [-1, 197, 768] 0Identity-35 [-1, 197, 768] 0Block-36 [-1, 197, 768] 0
block 3LayerNorm-37 [-1, 197, 768] 1,536Linear-38 [-1, 197, 2304] 1,771,776Dropout-39 [-1, 12, 197, 197] 0Linear-40 [-1, 197, 768] 590,592Dropout-41 [-1, 197, 768] 0Attention-42 [-1, 197, 768] 0Identity-43 [-1, 197, 768] 0LayerNorm-44 [-1, 197, 768] 1,536Linear-45 [-1, 197, 3072] 2,362,368GELU-46 [-1, 197, 3072] 0Dropout-47 [-1, 197, 3072] 0Linear-48 [-1, 197, 768] 2,360,064Dropout-49 [-1, 197, 768] 0Mlp-50 [-1, 197, 768] 0Identity-51 [-1, 197, 768] 0Block-52 [-1, 197, 768] 0
block 4LayerNorm-53 [-1, 197, 768] 1,536Linear-54 [-1, 197, 2304] 1,771,776Dropout-55 [-1, 12, 197, 197] 0Linear-56 [-1, 197, 768] 590,592Dropout-57 [-1, 197, 768] 0Attention-58 [-1, 197, 768] 0Identity-59 [-1, 197, 768] 0LayerNorm-60 [-1, 197, 768] 1,536Linear-61 [-1, 197, 3072] 2,362,368GELU-62 [-1, 197, 3072] 0Dropout-63 [-1, 197, 3072] 0Linear-64 [-1, 197, 768] 2,360,064Dropout-65 [-1, 197, 768] 0Mlp-66 [-1, 197, 768] 0Identity-67 [-1, 197, 768] 0Block-68 [-1, 197, 768] 0
block 5LayerNorm-69 [-1, 197, 768] 1,536Linear-70 [-1, 197, 2304] 1,771,776Dropout-71 [-1, 12, 197, 197] 0Linear-72 [-1, 197, 768] 590,592Dropout-73 [-1, 197, 768] 0Attention-74 [-1, 197, 768] 0Identity-75 [-1, 197, 768] 0LayerNorm-76 [-1, 197, 768] 1,536Linear-77 [-1, 197, 3072] 2,362,368GELU-78 [-1, 197, 3072] 0Dropout-79 [-1, 197, 3072] 0Linear-80 [-1, 197, 768] 2,360,064Dropout-81 [-1, 197, 768] 0Mlp-82 [-1, 197, 768] 0Identity-83 [-1, 197, 768] 0Block-84 [-1, 197, 768] 0
block 6LayerNorm-85 [-1, 197, 768] 1,536Linear-86 [-1, 197, 2304] 1,771,776Dropout-87 [-1, 12, 197, 197] 0Linear-88 [-1, 197, 768] 590,592Dropout-89 [-1, 197, 768] 0Attention-90 [-1, 197, 768] 0Identity-91 [-1, 197, 768] 0LayerNorm-92 [-1, 197, 768] 1,536Linear-93 [-1, 197, 3072] 2,362,368GELU-94 [-1, 197, 3072] 0Dropout-95 [-1, 197, 3072] 0Linear-96 [-1, 197, 768] 2,360,064Dropout-97 [-1, 197, 768] 0Mlp-98 [-1, 197, 768] 0Identity-99 [-1, 197, 768] 0Block-100 [-1, 197, 768] 0
block 7LayerNorm-101 [-1, 197, 768] 1,536Linear-102 [-1, 197, 2304] 1,771,776Dropout-103 [-1, 12, 197, 197] 0Linear-104 [-1, 197, 768] 590,592Dropout-105 [-1, 197, 768] 0Attention-106 [-1, 197, 768] 0Identity-107 [-1, 197, 768] 0LayerNorm-108 [-1, 197, 768] 1,536Linear-109 [-1, 197, 3072] 2,362,368GELU-110 [-1, 197, 3072] 0Dropout-111 [-1, 197, 3072] 0Linear-112 [-1, 197, 768] 2,360,064Dropout-113 [-1, 197, 768] 0Mlp-114 [-1, 197, 768] 0Identity-115 [-1, 197, 768] 0Block-116 [-1, 197, 768] 0
block 8LayerNorm-117 [-1, 197, 768] 1,536Linear-118 [-1, 197, 2304] 1,771,776Dropout-119 [-1, 12, 197, 197] 0Linear-120 [-1, 197, 768] 590,592Dropout-121 [-1, 197, 768] 0Attention-122 [-1, 197, 768] 0Identity-123 [-1, 197, 768] 0LayerNorm-124 [-1, 197, 768] 1,536Linear-125 [-1, 197, 3072] 2,362,368GELU-126 [-1, 197, 3072] 0Dropout-127 [-1, 197, 3072] 0Linear-128 [-1, 197, 768] 2,360,064Dropout-129 [-1, 197, 768] 0Mlp-130 [-1, 197, 768] 0Identity-131 [-1, 197, 768] 0Block-132 [-1, 197, 768] 0
block 9LayerNorm-133 [-1, 197, 768] 1,536Linear-134 [-1, 197, 2304] 1,771,776Dropout-135 [-1, 12, 197, 197] 0Linear-136 [-1, 197, 768] 590,592Dropout-137 [-1, 197, 768] 0Attention-138 [-1, 197, 768] 0Identity-139 [-1, 197, 768] 0LayerNorm-140 [-1, 197, 768] 1,536Linear-141 [-1, 197, 3072] 2,362,368GELU-142 [-1, 197, 3072] 0Dropout-143 [-1, 197, 3072] 0Linear-144 [-1, 197, 768] 2,360,064Dropout-145 [-1, 197, 768] 0Mlp-146 [-1, 197, 768] 0Identity-147 [-1, 197, 768] 0Block-148 [-1, 197, 768] 0
block 10LayerNorm-149 [-1, 197, 768] 1,536Linear-150 [-1, 197, 2304] 1,771,776Dropout-151 [-1, 12, 197, 197] 0Linear-152 [-1, 197, 768] 590,592Dropout-153 [-1, 197, 768] 0Attention-154 [-1, 197, 768] 0Identity-155 [-1, 197, 768] 0LayerNorm-156 [-1, 197, 768] 1,536Linear-157 [-1, 197, 3072] 2,362,368GELU-158 [-1, 197, 3072] 0Dropout-159 [-1, 197, 3072] 0Linear-160 [-1, 197, 768] 2,360,064Dropout-161 [-1, 197, 768] 0Mlp-162 [-1, 197, 768] 0Identity-163 [-1, 197, 768] 0Block-164 [-1, 197, 768] 0
block 11LayerNorm-165 [-1, 197, 768] 1,536Linear-166 [-1, 197, 2304] 1,771,776Dropout-167 [-1, 12, 197, 197] 0Linear-168 [-1, 197, 768] 590,592Dropout-169 [-1, 197, 768] 0Attention-170 [-1, 197, 768] 0Identity-171 [-1, 197, 768] 0LayerNorm-172 [-1, 197, 768] 1,536Linear-173 [-1, 197, 3072] 2,362,368GELU-174 [-1, 197, 3072] 0Dropout-175 [-1, 197, 3072] 0Linear-176 [-1, 197, 768] 2,360,064Dropout-177 [-1, 197, 768] 0Mlp-178 [-1, 197, 768] 0Identity-179 [-1, 197, 768] 0Block-180 [-1, 197, 768] 0
block 12LayerNorm-181 [-1, 197, 768] 1,536Linear-182 [-1, 197, 2304] 1,771,776Dropout-183 [-1, 12, 197, 197] 0Linear-184 [-1, 197, 768] 590,592Dropout-185 [-1, 197, 768] 0Attention-186 [-1, 197, 768] 0Identity-187 [-1, 197, 768] 0LayerNorm-188 [-1, 197, 768] 1,536Linear-189 [-1, 197, 3072] 2,362,368GELU-190 [-1, 197, 3072] 0Dropout-191 [-1, 197, 3072] 0Linear-192 [-1, 197, 768] 2,360,064Dropout-193 [-1, 197, 768] 0Mlp-194 [-1, 197, 768] 0Identity-195 [-1, 197, 768] 0Block-196 [-1, 197, 768] 0
(3)后处理LayerNorm-197 [-1, 197, 768] 1,536Identity-198 [-1, 768] 0Linear-199 [-1, 5] 3,845Total params: 85,650,437
Trainable params: 85,650,437
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 408.54
Params size (MB): 326.73
Estimated Total Size (MB): 735.84
----------------------------------------------------------------3. 数据前处理3*224*224经过768个16*16的卷积输出768*14*14将输出flatten768*19614*14调整通道196*768添加class_num(分类信息)1*768拼接196*768成197*768添加位置信息pos,add(shape还是197*768)4.数据输入到transformer encoder的onnx结构图关于ONNX里面的op说实话有点hold不住layernorm层搞得很复杂融合暂时还没有看(后续会研究的暂时没有时间)反正这个就是transformer encoder(我不管这个就是) LayerNorm-5 [-1, 197, 768] Linear-6 [-1, 197, 2304] Dropout-7 [-1, 12, 197, 197] Linear-8 [-1, 197, 768] Dropout-9 [-1, 197, 768] Attention-10 [-1, 197, 768] Identity-11 [-1, 197, 768] LayerNorm-12 [-1, 197, 768] Linear-13 [-1, 197, 3072] GELU-14 [-1, 197, 3072] Dropout-15 [-1, 197, 3072] Linear-16 [-1, 197, 768] Dropout-17 [-1, 197, 768] Mlp-18 [-1, 197, 768] Identity-19 [-1, 197, 768] Block-20 [-1, 197, 768] 5.后处理 LayerNorm-197 [-1, 197, 768] Identity-198 [-1, 768] Linear-199 [-1, 5] 那 你看这就是layernorm的op操作不忍吐槽 最后接上全连接层输出结果总结 其实从OP来看VIT并没有添加新的算子只是一些层的拼接但是效果却是很好真的朴实无华的结构做着深奥的内容哎继续学习吧学无止境相关的ONNX代码感兴趣的读者多的话后续可以上传供大家试用请关注或者评论(⊙o⊙)哦class: daisy prob: 0.995class: dandelion prob: 0.00298class: roses prob: 0.000599class: sunflowers prob: 0.000633class: tulips prob: 0.000771
