**Group cohesiveness quizletFlops for Gluon. GitHub Gist: instantly share code, notes, and snippets. AlexNet超参数： params AlexNext FLOPs 4M FC1000 4M 16M FC4096/ReLU 4M 37M FC4096/ReLU 37M Max Pool 3x3s2 ... tf.nn.conv2d卷积images ... **

nn.Conv2d(inp, hidden_dim ... 然而，通过一系列实验发现ShuffleNet v2仅仅依赖FLOPs是有问题的，FLOPs近似的网络会存在不同的速度 ...

conv2d_fft This is a GPU-only version of nnet.conv2d that uses an FFT transform to perform the work. It flips the kernel just like conv2d. conv2d_fft should not be used directly as it does not provide a gradient. Instead, use nnet.conv2d and allow Theano’s graph optimizer to replace it by the FFT version by setting ‘THEANO_FLAGS=optimizer ... 前微软研究员何凯明凭借着深度残差学习在Imagenet比赛的三个任务、以及COCO比赛的检测和分割任务上都获得了第一名，最新的CVPR2016最佳论文。 in <<TensorRT-Developer-Guide 5.pdf>>, the mentioned op includes Conv2d and DepthwiseCOnv2dNative. I guess 1x1 conv is treats as common Conv2d; not be optimized specifically. since 1x1 conv is widely used in current net models, could tensorRT do some optimize on it? for example: it may benefit if NHWC data format is supported.

Flops for Gluon. GitHub Gist: instantly share code, notes, and snippets.

2005 damon tuscanyMLModelScope: Evaluate and Profile ML Models at Scale and Across Stack Cheng Li*, Abdul Dakkak*, Jinjun Xiong†, Wen-Mei Hwu* {cli99, dakkak, w-hwu}@illinois.edu, [email protected] But the real operation is more like what you said: the kernel is multiplied by the input elements then "tiled" into the output with potential overlap (if kernel size is bigger than stride). I think the underlying math and gradient computing would be also different from the "conventional" Conv2D operation. $\endgroup$ – X.X Nov 1 '19 at 0:10

Dear Niko, did you make sure to pass in a frozen.pb into Model Optimizer --input_meta_graph ? There is more than one way to go about freezing a tensorflow model, one way is to use the "freeze_graph.py" script which comes with the tensorflow installation: