a Sic@sdZddlmmZddlmZddlmZddlm Z ddl m Z ddl m Z ddl mZdd lmZd Zd d d ddddddddddddddddddd d!d"d#Zgd$gd%d&d'd(d)gd*gd+d,d'd(d)gd-gd.d/d'd(d)gd0gd1d,d'd(d)gd2gd3d/d'd(d)gd4gd5d6d'd(d)gd7gd8d9d'd(d)gd:gd;dd?d'd(d)gd@gdAdBd'd(d)gdCgdDdEd'd(d)gdFgdGdHd'd(d)gd$gd%d&d'dId)gdJgdKd&d'dId)gdLgdMd,d'dId)gdNgdOd,d'dId)gdPgdQd/d'dId)gdRgdSd/d'dId)gdTgdUdVd'dId)gdWgdXdYd'dId)gd:gdZd@i)r/r@r0r/)r:rBrMi@r2)r/r#rCr/)r;rTir>)r?rHrIi)r/r"rGr!)r(rHii )r/r6r0r!)iipirVa Instantiates the {name} architecture. Reference: - [Designing Network Design Spaces](https://arxiv.org/abs/2003.13678) (CVPR 2020) For image classification use cases, see [this page for detailed examples]( https://keras.io/api/applications/#usage-examples-for-image-classification-models). For transfer learning use cases, make sure to read the [guide to transfer learning & fine-tuning]( https://keras.io/guides/transfer_learning/). Note: Each Keras Application expects a specific kind of input preprocessing. For Regnets, preprocessing is included in the model using a `Rescaling` layer. RegNet models expect their inputs to be float or uint8 tensors of pixels with values in the [0-255] range. The naming of models is as follows: `RegNet` where `block_type` is one of `(X, Y)` and `flops` signifies hundred million floating point operations. For example RegNetY064 corresponds to RegNet with Y block and 6.4 giga flops (64 hundred million flops). Args: include_top: Whether to include the fully-connected layer at the top of the network. Defaults to True. weights: One of `None` (random initialization), `"imagenet"` (pre-training on ImageNet), or the path to the weights file to be loaded. Defaults to `"imagenet"`. input_tensor: Optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model. input_shape: Optional shape tuple, only to be specified if `include_top` is False. It should have exactly 3 inputs channels. pooling: Optional pooling mode for feature extraction when `include_top` is `False`. Defaults to None. - `None` means that the output of the model will be the 4D tensor output of the last convolutional layer. - `avg` means that global average pooling will be applied to the output of the last convolutional layer, and thus the output of the model will be a 2D tensor. - `max` means that global max pooling will be applied. classes: Optional number of classes to classify images into, only to be specified if `include_top` is True, and if no `weights` argument is specified. Defaults to 1000 (number of ImageNet classes). classifier_activation: A `str` or callable. The activation function to use on the "top" layer. Ignored unless `include_top=True`. Set `classifier_activation=None` to return the logits of the "top" layer. Defaults to `"softmax"`. When loading pretrained weights, `classifier_activation` can only be `None` or `"softmax"`. Returns: A `keras.Model` instance. cs*durdttdfdd}|S)zRescales and normalizes inputs to [0,1] and ImageNet mean and std. Args: name: name prefix Returns: Rescaled and normalized tensor Nprestemcstjddd|}|S)Ngp?Z_prestem_rescaling)scalename)r RescalingxrYU/var/www/html/django/DPS/env/lib/python3.9/site-packages/keras/applications/regnet.pyapplys zPreStem..applystrrget_uidrYr`r^r]r_PreStem~s  recs*durdttdfdd}|S)zImplementation of RegNet stem. (Common to all model variants) Args: name: name prefix Returns: Output tensor of the Stem Nstemc sPtjdddddddd|}tjd d d d |}tjd d|}|S)Nr1r5r5r/Fsame he_normal _stem_conv)stridesuse_biaspaddingkernel_initializerrY?h㈵>Z_stem_bnmomentumepsilonrYZ _stem_relur])rConv2DBatchNormalizationReLUr[r]r^r_r`s$  zStem..applyrardr^r]r_Stems  rwcs*durttdfdd}|S)zImplements the Squeeze and excite block (https://arxiv.org/abs/1709.01507). Args: filters_in: input filters to the block se_filters: filters to squeeze to name: name prefix Returns: A function object NZsqueeze_and_excitecs`tjddd|}tjddddd|}tjdd dd d|}tj||}|S) NZ_squeeze_and_excite_gapT)rYkeepdimsr!r!reluriZ_squeeze_and_excite_squeeze) activationrnrYsigmoidZ_squeeze_and_excite_excite)rGlobalAveragePooling2Drttfmathmultiply)inputsr\ filters_inrY se_filtersr^r_r`s2z$SqueezeAndExciteBlock..applyra)rrrYr`r^rr_SqueezeAndExciteBlocks rr!cs.durttdfdd}|S)aYImplementation of X Block. Reference: [Designing Network Design Spaces](https://arxiv.org/abs/2003.13678) Args: filters_in: filters in the input tensor filters_out: filters in the output tensor group_width: group width stride: stride name: name prefix Returns: Output tensor of the block NZxblockc sdkr2dkr2tddddd }dkrztjddd d d |}tjd d dd|}n|}tjddd dd|}tjd d dd|}tjdd|}tjdd|dd dd|}tjd d dd|}tjdd|}tjddd dd|}tjd d dd|}tjdd||}|SNr!Input filters() and output filters(z) are not equal for stride 4. Input and output filters must be equal for stride=.ryFriZ _skip_1x1)rkrlrnrYrorpZ_skip_bnrq _conv_1x1_1rlrnrY_conv_1x1_1_bnZ_conv_1x1_1_relur]rgrh _conv_3x3rlrkgroupsrmrnrY _conv_3x3_bnZ_conv_3x3_relu _conv_1x1_2_conv_1x1_2_bnZ _exit_relu) ValueErrorrrtrurv)rrskipr\r filters_outr,rYstrider^r_r`s      zXBlock..applyra)rrr,rrYr`r^rr_XBlocksIr?cs0durttdfdd}|S)aImplementation of Y Block. Reference: [Designing Network Design Spaces](https://arxiv.org/abs/2003.13678) Args: filters_in: filters in the input tensor filters_out: filters in the output tensor group_width: group width stride: stride squeeze_excite_ratio: expansion ration for Squeeze and Excite block name: name prefix Returns: Output tensor of the block NZyblockc skr2dkr2tddddd }t}dkrtjddd d d |}tjd d dd|}n|}tjddd dd|}tjd d dd|}tjdd|}tjdd|dd dd|}tjd d dd|}tjdd|}t|d|}tjddd dd|}tjd d dd|}tjdd||}|Sr)rintrrtrurvr)rrrrr\rrr,rYsqueeze_excite_ratiorr^r_r`Os       zYBlock..applyra)rrr,rrrYr`r^rr_YBlock6sLrcs2durttdfdd}|S)aImplementation of Z block Reference: [Fast and Accurate Model Scaling](https://arxiv.org/abs/2103.06877). Args: filters_in: filters in the input tensor filters_out: filters in the output tensor group_width: group width stride: stride squeeze_excite_ratio: expansion ration for Squeeze and Excite block bottleneck_ratio: inverted bottleneck ratio name: name prefix Returns: Output tensor of the block NZzblockc s4kr2dkr2tddddd }t}t}tj|ddd d d |}tjd d dd|}tj|}tj|dd|dd dd|}tjd d dd|}tj|}t||d}tjddd dd |}tjd d dd|}dkr(|S||SdS)Nr!rrz)are not equal for stride rrryFrirrrorprrqrgrhrrrr]rr) rrrrtrur~nnsilur)rrrZinv_btlneck_filtersr\bottleneck_ratiorrr,rYrrr^r_r`s|          zZBlock..applyra)rrr,rrrrYr`r^rr_ZBlocks?rcs0durttdfdd}|S)auImplementation of Stage in RegNet. Args: block_type: must be one of "X", "Y", "Z" depth: depth of stage, number of blocks to use group_width: group width of all blocks in this stage filters_in: input filters to this stage filters_out: output filters from this stage name: name prefix Returns: Output tensor of Stage Nstagecs|}dkrXtddd|}tdD]"}td|d|}q2ndkrtdd d|}tdD]"}td |d|}qndd krtdd d|}tdD]"}td |d|}qntdd|S)Nr)r/Z _XBlock_0)rrYr!Z_XBlock_r]rOZ _YBlock_0Z_YBlock_ZZ _ZBlock_0Z_ZBlock_z Block type `z<` not recognized.block_type must be one of (`X`, `Y`, `Z`). )rrangerrNotImplementedError)rr\ir.depthrrr,rYr^r_r` s|    zStage..applyra)r.rr,rrrYr`r^rr_Stages6rcs(durttdfdd}|S)zImplementation of classification head of RegNet. Args: num_classes: number of classes for Dense layer name: name prefix Returns: Output logits tensor. Nheadcs.tjdd|}tjdd|}|S)N _head_gapr]Z head_dense)rr}Denser[rY num_classesr^r_r`QszHead..applyra)rrYr`r^rr_HeadDs rregnetTimagenetsoftmaxc Cs|dvstjj|std|dkr:|r:| dkr:tdtj| |dt||d} | durjt j | d }nt | st j | | d }n| }| durt | }n|}|}|rt|d |}t|d |}d}td D]<}||}||}t||||||d t|d |}|}q|r0t| d|}t| |n.| dkrHt |}n| dkr^t |}tj|||d}|dkr|rd}t|ddd}nd}t|ddd}||}tj|t|d|d}||n|dur|||S)a Instantiates RegNet architecture given specific configuration. Args: depths: An iterable containing depths for each individual stages. widths: An iterable containing output channel width of each individual stages group_width: Number of channels to be used in each group. See grouped convolutions for more information. block_type: Must be one of `{"X", "Y", "Z"}`. For more details see the papers "Designing network design spaces" and "Fast and Accurate Model Scaling" default_size: Default input image size. model_name: An optional name for the model. include_preprocessing: boolean denoting whther to include preprocessing in the model include_top: Boolean denoting whether to include classification head to the model. weights: one of `None` (random initialization), "imagenet" (pre-training on ImageNet), or the path to the weights file to be loaded. input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model. input_shape: optional shape tuple, only to be specified if `include_top` is False. It should have exactly 3 inputs channels. pooling: optional pooling mode for feature extraction when `include_top` is `False`. - `None` means that the output of the model will be the 4D tensor output of the last convolutional layer. - `avg` means that global average pooling will be applied to the output of the last convolutional layer, and thus the output of the model will be a 2D tensor. - `max` means that global max pooling will be applied. classes: optional number of classes to classify images into, only to be specified if `include_top` is True, and if no `weights` argument is specified. classifier_activation: A `str` or callable. The activation function to use on the "top" layer. Ignored unless `include_top=True`. Set `classifier_activation=None` to return the logits of the "top" layer. Returns: A `keras.Model` instance. Raises: ValueError: in case of invalid argument for `weights`, or invalid input shape. ValueError: if `classifier_activation` is not `softmax` or `None` when using a pretrained top layer. ValueError: if `include_top` is True but `num_classes` is not 1000. 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