a d/ @sHUddlZddlmZmZmZmZmZddlZddlm Z ddl m m Z ddlm Z ddlmZddlmZddlmZddlmZdd lmZd d d d dddedddiZiZeeefed<ded<Gddde jZGddde jZGddde j Z!d#e"e"e"e"e"e"dddZ#Gdd d e jZ$Gd!d"d"e jZ%dS)$N)CallableDictListOptionalTuple)Tensor)SpatialGradient)NonMaximaSuppression2d)pyrdown)laf_from_center_scale_ori)PassLAFKeyNet_default_config) num_filters num_levels kernel_sizenms_sizepyramid_levels up_levelsscale_factor_levelss_multurlszXhttps://github.com/axelBarroso/Key.Net-Pytorch/raw/main/model/weights/keynet_pytorch.pthkeynetcs0eZdZdZfddZeedddZZS)_FeatureExtractorzVHelper class for KeyNet. 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It defines the learnable blocks within the Key.Net ) in_channelscs(tt||_t|_t|_dSr!)r"r#_KeyNetConvBlockconv0conv1conv2)r)r?r*r,r-r#Rs  z_LearnableBlock.__init__r.cCs||||}|Sr!)rCrBrA)r)r/r,r,r-r1Ysz_LearnableBlock.forward)r>) r3r4r5r6intr#rr1r7r,r,r*r-r&Lsr&r? out_channelsrZstridepaddingZdilationc Cs,tt||||||t|tjddS)zWHelper function for KeyNet. Default learnable convolutional block for KeyNet. TZinplace)nn SequentialConv2dZ BatchNorm2dReLUrEr,r,r-r@^s  r@csBeZdZdZdedfeedfdd Zeeddd Z Z S) KeyNeta7Key.Net model definition -- local feature detector (response function). This is based on the original code from paper "Key.Net: Keypoint Detection by Handcrafted and Learned CNN Filters". See :cite:`KeyNet2019` for more details. Args: pretrained: Download and set pretrained weights to the model. keynet_conf: Dict with initiliazation parameters. Do not pass it, unless you know what you are doing`. Returns: KeyNet response score. Shape: - Input: :math:`(B, 1, H, W)` - Output: :math:`(B, 1, H, W)` Fr) pretrained keynet_confcst|d}|d|_|d}|d}t|_ttj||jd||dtjdd|_ |rd d }t j j t d |d }|j|d dd|dS)Nrrrrr )r?rFrrGTrHcSs|Sr!r,)Zstoragelocr,r,r-z!KeyNet.__init__..r)Z map_locationZ state_dict)strict)r"r#rr feature_extractorrIrJrKrL last_convr<ZhubZload_state_dict_from_urlrZload_state_dicteval)r)rNrOrrrGZ storage_fcnZpretrained_dictr*r,r-r#s"   zKeyNet.__init__r.cCsx|j}||g}td|jD]@}t|dd}||}tj||d|dfdd}||q|t j |dd}|S) z! x - input image r g333333?factorrrbilinear)sizemoder:) shaperTrangerr F interpolateappendrUr<r=)r)r/Zshape_imZfeatsiZfeats_iscoresr,r,r-r1s    zKeyNet.forward) r3r4r5r6 keynet_configboolrr#rr1r7r,r,r*r-rMqsrMcseZdZdZddedeefeeee j e j dfdd Z ded d d Z e eeeefee e fd d dZde ee ee e fdddZde ee ee e fdddZZS)KeyNetDetectoraZMulti-scale feature detector based on KeyNet. This is based on the original code from paper "Key.Net: Keypoint Detection by Handcrafted and Learned CNN Filters". 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First the location and scale of interest points are determined by detect function. Then affine shape and orientation. Args: img: image to extract features with shape [1xCxHxW]. KeyNetDetector does not support batch processing, because the number of detections is different on each image. mask: a mask with weights where to apply the response function. The shape must be the same as the input image. Returns: lafs: shape [1xNx2x3]. Detected local affine frames. responses: shape [1xNx1]. Response function values for corresponding lafs rr z'KeyNet supports only single-image input)r\ ValueErrorrrorn)r)rrqrrr,r,r-r1%s   zKeyNetDetector.forward)r)N)N)r3r4r5r6rcr rdrDrrIModuler#rrrrrrrrr1r7r,r,r*r-res6  = re)rrrr rr )&mathtypingrrrrrr<Ztorch.nnrIZtorch.nn.functionalZ functionalr^rZkornia.filtersrZkornia.geometry.subpixr Zkornia.geometry.transformr Zlafr Z orientationr sqrtrcrstr__annotations__rr r$rJr&rDr@rMrer,r,r,r-sT         6