of deep learning, Pete Warden Convolution • AlexNet 模型推理各个层计算比例 • 86.1% • 2.6% 来源: Learning Semantic Image Representations at a Large Scale, Yangqing Jia Convolution • ResNet-50 • PyTorch Profiler

depth Sobel Warp Perspective OTSU Thresholding Arithmetic addition Table lookup Custom convolution Fast corner Mean Shift Tracking (MST) Arithmetic subtraction Histogram LK Dense Optical Flow

depth Sobel Warp Perspective OTSU Thresholding Arithmetic addition Table lookup Custom convolution Fast corner Mean Shift Tracking (MST) Arithmetic subtraction Histogram LK Dense Optical Flow

depth Sobel Warp Perspective OTSU Thresholding Arithmetic addition Table lookup Custom convolution Fast corner Mean Shift Tracking (MST) Arithmetic subtraction Histogram LK Dense Optical Flow

loop the code is. • Disabled py_compile for pypy and python 3. 11.15 Version 2.6 (codename Convolution, released on July 24th 2011) • internal attributes now raise an internal attribute error now instead

batched(starmap(math.sumprod, product(m1, transpose(m2))), n) def convolve(signal, kernel): """Discrete linear convolution of two iterables. Equivalent to polynomial multiplication. Convolutions are mathematically commutative; consumed before the calculations begin. Article: https://betterexplained.com/articles/intuitive-convolution/ Video: https://www.youtube.com/watch?v=KuXjwB4LzSA """ # convolve([1, -1, -20], [1, -3]) → 1 Notwithstanding the foregoing, with regard to derivative works based on Python 1.6.1 that incorporate non-separable material that was previously distributed under the GNU General Public License (GPL), the law of

batched(starmap(math.sumprod, product(m1, transpose(m2))), n) def convolve(signal, kernel): """Discrete linear convolution of two iterables. Equivalent to polynomial multiplication. Convolutions are mathematically commutative; consumed before the calculations begin. Article: https://betterexplained.com/articles/intuitive-convolution/ Video: https://www.youtube.com/watch?v=KuXjwB4LzSA """ # convolve([1, -1, -20], [1, -3]) → 1 Notwithstanding the foregoing, with regard to derivative works based on Python 1.6.1 that incorporate non-separable material that was previously distributed under the GNU General Public License (GPL), the law of

batched(starmap(math.sumprod, product(m1, transpose(m2))), n) def convolve(signal, kernel): """Discrete linear convolution of two iterables. Equivalent to polynomial multiplication. Convolutions are mathematically commutative; consumed before the calculations begin. Article: https://betterexplained.com/articles/intuitive-convolution/ Video: https://www.youtube.com/watch?v=KuXjwB4LzSA """ # convolve([1, -1, -20], [1, -3]) → 1 Notwithstanding the foregoing, with regard to derivative works based on Python 1.6.1 that incorporate non-separable material that was previously distributed under the GNU General Public License (GPL), the law of

batched(starmap(math.sumprod, product(m1, transpose(m2))), n) def convolve(signal, kernel): """Discrete linear convolution of two iterables. Equivalent to polynomial multiplication. Convolutions are mathematically commutative; consumed before the calculations begin. Article: https://betterexplained.com/articles/intuitive-convolution/ Video: https://www.youtube.com/watch?v=KuXjwB4LzSA """ # convolve([1, -1, -20], [1, -3]) → 1 Notwithstanding the foregoing, with regard to derivative works based on Python 1.6.1 that incorporate non-separable material that was previously distributed under the GNU General Public License (GPL), the law of

transpose(m2))), n) def convolve(signal, kernel): # See: https://betterexplained.com/articles/intuitive-convolution/ # convolve(data, [0.25, 0.25, 0.25, 0.25]) --> Moving average (blur) # convolve(data, [1, -1]) Notwithstanding the foregoing, with regard to derivative works based on Python 1.6.1 that incorporate non-separable material that was previously distributed under the GNU General Public License (GPL), the law of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof