pytorch 反卷积可视化

我的是直接转换pt文件。

D RKNN output shape(batchnormalize): (0 16 16 320)

D Process convolution_at_input136.1_36 ...

D RKNN output shape(convolution): (0 16 16 1280)

D Process batch_norm_at_input137.1_35 ...

D RKNN output shape(batchnormalize): (0 16 16 1280)

D Process hardtanh__at_input138.1_34 ...

D RKNN output shape(relun): (0 16 16 1280)

D Process convolution_at_input139.1_33 ...

D RKNN output shape(convolution): (0 8 8 1280)

D Process batch_norm_at_input140.1_32 ...

D RKNN output shape(batchnormalize): (0 8 8 1280)

D Process relu__at_input141.1_31 ...

D RKNN output shape(relu): (0 8 8 1280)

D Process convolution_at_input142.1_30 ...

D RKNN output shape(convolution): (0 4 4 16)

D Process batch_norm_at_input143.1_29 ...

D RKNN output shape(batchnormalize): (0 4 4 16)

D Process relu__at_input144.1_28 ...

D RKNN output shape(relu): (0 4 4 16)

D Process convolution_at_input145.1_27 ...

D RKNN output shape(convolution): (0 2 2 16)

D Process batch_norm_at_input146.1_26 ...

D RKNN output shape(batchnormalize): (0 2 2 16)

D Process relu__at_input147.1_24 ...

D RKNN output shape(relu): (0 2 2 16)

D Process convolution_at_input148.1_18 ...

D RKNN output shape(convolution): (0 2 2 64)

D Process relu__at_input149.1_12 ...

D RKNN output shape(relu): (0 2 2 64)

D Process convolution_at_hm_x.1_6 ...

D Process output_of_convolution_at_2269_5 ...

D RKNN output shape(output): (0 2 2 2)

I Build mobilenet1 complete.

D Optimizing network with force_1d_tensor, swapper, merge_layer, auto_fill_bn, resize_nearest_transformer, auto_fill_multiply, merge_avgpool_conv1x1, auto_fill_zero_bias, proposal_opt_import

D Merge ['convolution_at_input145.1_27', 'batch_norm_at_input146.1_26'] (convolution)

D Merge ['convolution_at_input142.1_30', 'batch_norm_at_input143.1_29'] (convolution)

D Merge ['convolution_at_input139.1_33', 'batch_norm_at_input140.1_32'] (convolution)

E Catch exception when loading pytorch model: /Users/gyq/DeepLearning/CenterNet_in_mac/mobilenet1.pt!

E Traceback (most recent call last):

E File "rknn/api/rknn_base.py", line 567, in rknn.api.rknn_base.RKNNBase.load_pytorch

Load pytorch model failed!

E File "rknn/base/RKNNlib/app/importer/import_pytorch.py", line 121, in rknn.base.RKNNlib.app.importer.import_pytorch.ImportPytorch.run

E File "rknn/base/RKNNlib/app/helper/mergehelper.py", line 155, in rknn.base.RKNNlib.app.helper.mergehelper.MergeHelper.merge

E File "rknn/base/RKNNlib/optimize/optimizer.py", line 299, in rknn.base.RKNNlib.optimize.optimizer.Optimizer.apply

E File "rknn/base/RKNNlib/optimize/rules/merge_layer.py", line 81, in rknn.base.RKNNlib.optimize.rules.merge_layer.MergeLayer.apply

E File "rknn/base/RKNNlib/optimize/rules/merge_layer.py", line 105, in rknn.base.RKNNlib.optimize.rules.merge_layer.MergeLayer._loop

E File "rknn/base/RKNNlib/optimize/rules/merge_layer_ext_proc.py", line 86, in rknn.base.RKNNlib.optimize.rules.merge_layer_ext_proc.m_l1_bn

E ValueError: operands could not be broadcast together with shapes (4,4,16,1280) (16,)

pytorch 反卷积可视化_Toybrick相关推荐

pytorch 反卷积可视化_手推反卷积
先手推卷积热个身在推导反卷积之前,先推导一下卷积. 假设输入为 ,卷积核为 ,输出大小的计算公式为 .当时,输出为 . 将输入矩阵转成一个的列阵,卷积核扩展为的矩阵,即则 , 所以 . 用p ...
反卷积原理 + pytorch反卷积层参数output_padding
一般我们会认为2维卷积的计算分为了以下3类: 1.full 2.same 3. valid 参考:https://cn.mathworks.com/help/matlab/ref/conv2. ...
反卷积可视化工具--deconv-deep-vis-toolbox
https://www.cnblogs.com/byteHuang/p/6932772.html
tictoc正方形网络模型_反卷积：可视化并理解卷积网络
可视化与理解卷积网络的探讨卷积网络模型自问世以来,对AI技术的发展起了很大的推动作用.但其复杂的模型结构,庞大的参数规模也给模型优化带来很多问题.如果问某个参数的值问什么是3而不是6,很难得到有根据 ...
用反卷积（Deconvnet）可视化理解卷积神经网络还有使用tensorboard
『cs231n』卷积神经网络的可视化与进一步理解深度学习小白--卷积神经网络可视化(二) TensorBoard--TensorFlow可视化原文地址:http://blog.csdn.net/h ...
反卷积(Deconvolution)、上采样(UNSampling)与上池化(UnPooling)加入自己的思考（pytorch函数）（二）
ps之前写过反卷积(Deconvolution).上采样(UNSampling)与上池化(UnPooling)加入自己的思考(一),不过那是东拼西凑出来的别人的东西就是稍微加入点自己的理解.今天在看代 ...
pytorch之卷积模块、池化、激活函数（可视化）
pytorch之卷积模块卷积卷积在 pytorch 中有两种方式,一种是 torch.nn.Conv2d(),一种是torch.nn.functional.conv2d(),这两种形式本质都是使用 ...
反卷积在神经网络可视化上的成功应用
反卷积(Deconvolution)的概念第一次出现是Zeiler在2010年发表的论文Deconvolutional networks中,但是并没有指定反卷积这个名字,反卷积这个术语正式的使用是在其 ...
pytorch卷积可视化_使用Pytorch可视化卷积神经网络
pytorch卷积可视化 Filter and Feature map Image by the author 筛选和特征图作者提供的图像 When dealing with image's and ...

pytorch 反卷积可视化_Toybrick

pytorch 反卷积可视化_Toybrick相关推荐

最新文章

热门文章

pytorch 反卷积 可视化_Toybrick

pytorch 反卷积 可视化_Toybrick相关推荐

最新文章

热门文章

pytorch 反卷积可视化_Toybrick

pytorch 反卷积可视化_Toybrick相关推荐