o i e9@sdZddlmZddlmZddlmZddlmZddlmZddlmZe dd d Z e d d d Z e dddZ e dddZ e dddZddZe dddZdS)z*Contains Gradient functions for image ops.)dtypes)ops) array_ops)array_ops_stack) gen_image_ops)math_opsResizeNearestNeighborcCsh|jd}|ddr|dd}n t|dd}tj|||d|dd}|dgS)zThe 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. r align_cornershalf_pixel_centersr r N)inputs get_shapeis_fully_definedrshaperresize_nearest_neighbor_gradget_attr)opgradimage image_shapegradsrO/var/www/myenv/lib/python3.10/site-packages/tensorflow/python/ops/image_grad.py_ResizeNearestNeighborGrads rResizeBilinearcCs,tj||jd|d|dd}|dgS)zThe 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)rresize_bilinear_gradrrrrgrad0rrr_ResizeBilinearGrad2s r ScaleAndTranslatec Cs@tj||jd|jd|jd|d|dd}|dddgS)zThe 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. rr kernel_type antialias)r#r$N)rscale_and_translate_gradrrrrrr_ScaleAndTranslateGradEs  r& ResizeBicubiccCsLtjtjg}d}|jdj|vr"tj||jd|d|dd}|dgS)zThe derivatives for bicubic resizing. Args: op: The ResizeBicubic op. grad: The tensor representing the gradient w.r.t. the output. Returns: The gradients w.r.t. the input. Nrr r r )rfloat32float64rdtyperresize_bicubic_gradr)rr allowed_typesrrrr_ResizeBicubicGrad[s r- CropAndResizec Cs|jd}|r|}nt|}tjtjtj g}|jdj |vr?t j ||jd|jd|| d| dd}nd}t ||jd|jd|jd}||ddgS)aThe derivatives for crop_and_resize. We back-propagate to the image only when the input image tensor has floating point dtype but we always back-propagate to the input boxes tensor. Args: op: The CropAndResize op. grad: The tensor representing the gradient w.r.t. the output. Returns: The gradients w.r.t. the input image, boxes, as well as the always-None gradients w.r.t. box_ind and crop_size. rr r"Tmethod)r/r0N)rrras_listrrrfloat16r(r)r*rcrop_and_resize_grad_imagercrop_and_resize_grad_boxes)rrrrr,rgrad1rrr_CropAndResizeGradqs    r6cCsttd|j|S)zWrapper function around `math_ops.div_no_nan()` to perform a "safe" reciprocal incase the input is zero. Avoids divide by zero and NaNs. Input: x -> input tensor to be reciprocat-ed. Returns: x_reciprocal -> reciprocal of x without NaNs. g?)r div_no_nancastr*)xrrr_CustomReciprocalsr:RGBToHSVc* Cs|jdd}|jdd}|jdd}|jdd}|jdd}|jdj}t||k||k@|}t||k||k@|} t||k||k@|} t||k||k@|} t||k||k@|} t||k||k@|} |}| }| }t|dk|t|t| || |tt|| dt| || |}t|dk|t| t| || |tt|| dt||| |}t|dk|t| t| || |tt|| dt| |||}dt|dk||d||tt|tt|}dt|dk|| | ||tt||}dt|dk|| | dt||}dt|dk|| | ||tt||}dt|dk|| | t||}|||||}|d}dt|dk|| d||tt|tt|}dt|dk||| ||tt||}dt|dk||| t||}dt|dk|| | ||tt||}dt|dk|| | dt||}|||||}|d}dt|dk|| d||tt|tt|} dt|dk||| ||tt||}!dt|dk||| dt||}"dt|dk|| | ||tt||}#dt|dk|| | t||}$| |!|"|#|$}%|%d}%tj |d||d||d|gdd}&tj |d||d||d|gdd}'tj |d||d||d|%gdd}(tt|&|'|(})|)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 ).r"<ih)axis) routputsr*rr8addr:squarerstack)*rrredsgreensblues saturationvaluer* red_biggest green_biggest blue_biggest red_smallestgreen_smallest blue_smallestdv_drdv_dgdv_dbds_drds_dgds_dbdh_dr_1dh_dr_2dh_dr_3dh_dr_4dh_dr_5dh_drdh_dg_1dh_dg_2dh_dg_3dh_dg_4dh_dg_5dh_dgdh_db_1dh_db_2dh_db_3dh_db_4dh_db_5dh_dbdv_drgbds_drgbdh_drgbgradient_inputrrr _RGBToHSVGradsl                          rjN)__doc__tensorflow.python.frameworkrrtensorflow.python.opsrrrrRegisterGradientrr r&r-r6r:rjrrrrs(           *