o ®Ÿ?eŠ9ć@sÄdZddlmZddlmZddlmZddlmZddlmZddlmZe  d”d d „ƒZ e  d ”d d „ƒZ e  d”dd„ƒZ e  d”dd„ƒZ e  d”dd„ƒZdd„Ze  d”dd„ƒZdS)z*Contains Gradient functions for image ops.é)Śdtypes)Śops)Ś array_ops)Śarray_ops_stack)Ś gen_image_ops)Śmath_opsZResizeNearestNeighborcCsh|jd}| ”dd… ”r| ”dd…}n t |”dd…}tj||| d”| d”d}|dgS)zįThe derivatives for nearest neighbor resizing. Args: op: The ResizeNearestNeighbor op. grad: The tensor representing the gradient w.r.t. the output. Returns: The gradients w.r.t. the input and the output. rééŚ align_cornersŚhalf_pixel_centers©r r N)ŚinputsŚ get_shapeŚis_fully_definedrŚshaperZresize_nearest_neighbor_gradŚget_attr)ŚopŚgradŚimageŚ image_shapeZgrads©rśa/home/www/facesmatcher.com/pyenv/lib/python3.10/site-packages/tensorflow/python/ops/image_grad.pyŚ_ResizeNearestNeighborGrads ürZResizeBilinearcCs,tj||jd| d”| d”d}|dgS)zĆThe derivatives for bilinear resizing. Args: op: The ResizeBilinear op. grad: The tensor representing the gradient w.r.t. the output. Returns: The gradients w.r.t. the input. rr r r N)rZresize_bilinear_gradr r©rrŚgrad0rrrŚ_ResizeBilinearGrad2s ürZScaleAndTranslatec Cs@tj||jd|jd|jd| d”| d”d}|dddgS)zŲThe derivatives for ScaleAndTranslate transformation op. Args: op: The ScaleAndTranslate op. grad: The tensor representing the gradient w.r.t. the output. Returns: The gradients w.r.t. the input. rér Ś kernel_typeŚ antialias)rrN)rZscale_and_translate_gradr rrrrrŚ_ScaleAndTranslateGradEs ś rZ ResizeBicubiccCsLtjtjg}d}|jdj|vr"tj||jd| d”| d”d}|dgS)zĮThe derivatives for bicubic resizing. Args: op: The ResizeBicubic op. grad: The tensor representing the gradient w.r.t. the output. Returns: The gradients w.r.t. the input. Nrr r r )rŚfloat32Śfloat64r ŚdtyperZresize_bicubic_gradr)rrŚ allowed_typesrrrrŚ_ResizeBicubicGrad[s ür$Z CropAndResizec Cs°|jd}| ” ”r| ” ”}nt |”}tjtjtj g}|jdj |vr?t j ||jd|jd||  d”|  d”d}nd}t  ||jd|jd|jd”}||ddgS)aŖThe derivatives for crop_and_resize. We back-propagate to the image only when the input image tensor has floating point dtype but we always back-propagate to the input boxes tensor. Args: op: The CropAndResize op. grad: The tensor representing the gradient w.r.t. the output. Returns: The gradients w.r.t. the input image, boxes, as well as the always-None gradients w.r.t. box_ind and crop_size. rrrŚTŚmethod)r%r&N)r rrŚas_listrrrZfloat16r r!r"rZcrop_and_resize_grad_imagerZcrop_and_resize_grad_boxes)rrrrr#rZgrad1rrrŚ_CropAndResizeGradqs   ž’ r(cCst t d|j”|”S)zżWrapper function around `math_ops.div_no_nan()` to perform a "safe" reciprocal incase the input is zero. Avoids divide by zero and NaNs. Input: x -> input tensor to be reciprocat-ed. Returns: x_reciprocal -> reciprocal of x without NaNs. gš?)rZ div_no_nanŚcastr")ŚxrrrŚ_CustomReciprocalœsr+ZRGBToHSVc* Csą|jdd}|jdd}|jdd}|jdd}|jdd}|jdj}t ||k||k@|”}t ||k||k@|”} t ||k||k@|”} t ||k||k@|”} t ||k||k@|”} t ||k||k@|”} |}| }| }t |dk|”t |t | || |”tt |”ƒ| dt| || |ƒ”}t |dk|”t | t | || |”tt |”ƒ| dt||| |ƒ”}t |dk|”t | t | || |”tt |”ƒ| dt| |||ƒ”}dt |dk|”|d||tt |”ƒtt |”ƒ}dt |dk|”| | ||tt ||”ƒ}dt |dk|”| | dt||ƒ}dt |dk|”| | ||tt ||”ƒ}dt |dk|”| | t||ƒ}|||||}|d}dt |dk|”| d||tt |”ƒtt |”ƒ}dt |dk|”|| ||tt ||”ƒ}dt |dk|”|| t||ƒ}dt |dk|”| | ||tt ||”ƒ}dt |dk|”| | dt||ƒ}|||||}|d}dt |dk|”| d||tt |”ƒtt |”ƒ} dt |dk|”|| ||tt ||”ƒ}!dt |dk|”|| dt||ƒ}"dt |dk|”| | ||tt ||”ƒ}#dt |dk|”| | t||ƒ}$| |!|"|#|$}%|%d}%tj |d||d||d|gdd}&tj |d||d||d|gdd}'tj |d||d||d|%gdd}(t t |&|'”|(”})|)S) a6The gradients for `rgb_to_hsv` operation. This function is a piecewise continuous function as defined here: https://en.wikipedia.org/wiki/HSL_and_HSV#From_RGB We perform the multivariate derivative and compute all partial derivatives separately before adding them in the end. Formulas are given before each partial derivative calculation. Args: op: The `rgb_to_hsv` `Operation` that we are differentiating. grad: Gradient with respect to the output of the `rgb_to_hsv` op. Returns: Gradients with respect to the input of `rgb_to_hsv`. r).r).r).ré’’’’é<ih)Zaxis) r Zoutputsr"rr)Śaddr+ZsquarerŚstack)*rrZredsZgreensZbluesZ saturationŚvaluer"Z red_biggestZ green_biggestZ blue_biggestZ red_smallestZgreen_smallestZ blue_smallestZdv_drZdv_dgZdv_dbZds_drZds_dgZds_dbZdh_dr_1Zdh_dr_2Zdh_dr_3Zdh_dr_4Zdh_dr_5Zdh_drZdh_dg_1Zdh_dg_2Zdh_dg_3Zdh_dg_4Zdh_dg_5Zdh_dgZdh_db_1Zdh_db_2Zdh_db_3Zdh_db_4Zdh_db_5Zdh_dbZdv_drgbZds_drgbZdh_drgbZgradient_inputrrrŚ _RGBToHSVGrad§sl  ’’ ’’ ’’ ’’ ’’ ’’  ’’ž ’’ž ’’ž’ ’ ž’’’’ ’’’’’ ’’’ ’ ž’’’’ ’’’’’ ’’’ ’ ž’’’’ ’’’’’ ’’ ž ž žr1N)Ś__doc__Ztensorflow.python.frameworkrrZtensorflow.python.opsrrrrZRegisterGradientrrrr$r(r+r1rrrrŚs(           *