o *jC.@sdZddlZddlmZddlmmZddlmZddl m Z zddl m Z Wne y8ddlmZ YnwdZe ZGdd d ejZd d ZGd d d ejjZGdddejjZGdddejjZGdddejjZdS)zx Part of the implementation is borrowed and modified from LaMa, publicly available at https://github.com/saic-mdal/lama N)models) get_logger)load_state_dict_from_url)load_urlzjhttps://github.com/mseitzer/pytorch-fid/releases/download/fid_weights/pt_inception-2015-12-05-6726825d.pthcsHeZdZdZdZdddddZegddddffd d Zd d ZZS) InceptionV3z5Pretrained InceptionV3 network returning feature mapsr)@TFc sRtt|||_||_t||_t||_|jdks Jdt |_ |r+t }nt jdd}|j|j|jt jdddg}|j t j||jdkrb|j|jt jdddg}|j t j||jdkr|j|j|j|j|j|j|j|jg} |j t j| |jdkr|j|j |j!t j"dd g} |j t j| |#D]} || _$qd S) aMBuild pretrained InceptionV3 Parameters ---------- output_blocks : list of int Indices of blocks to return features of. Possible values are: - 0: corresponds to output of first max pooling - 1: corresponds to output of second max pooling - 2: corresponds to output which is fed to aux classifier - 3: corresponds to output of final average pooling resize_input : bool If true, bilinearly resizes input to width and height 299 before feeding input to model. As the network without fully connected layers is fully convolutional, it should be able to handle inputs of arbitrary size, so resizing might not be strictly needed normalize_input : bool If true, scales the input from range (0, 1) to the range the pretrained Inception network expects, namely (-1, 1) requires_grad : bool If true, parameters of the model require gradients. Possibly useful for finetuning the network use_fid_inception : bool If true, uses the pretrained Inception model used in Tensorflow's FID implementation. If false, uses the pretrained Inception model available in torchvision. 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