[−][src]Struct darknet_sys::layer

#[repr(C)]pub struct layer {
    pub type_: LAYER_TYPE,
    pub activation: ACTIVATION,
    pub lstm_activation: ACTIVATION,
    pub cost_type: COST_TYPE,
    pub forward: Option<unsafe extern "C" fn(arg1: layer, arg2: network_state)>,
    pub backward: Option<unsafe extern "C" fn(arg1: layer, arg2: network_state)>,
    pub update: Option<unsafe extern "C" fn(arg1: layer, arg2: c_int, arg3: f32, arg4: f32, arg5: f32)>,
    pub forward_gpu: Option<unsafe extern "C" fn(arg1: layer, arg2: network_state)>,
    pub backward_gpu: Option<unsafe extern "C" fn(arg1: layer, arg2: network_state)>,
    pub update_gpu: Option<unsafe extern "C" fn(arg1: layer, arg2: c_int, arg3: f32, arg4: f32, arg5: f32, arg6: f32)>,
    pub share_layer: *mut layer,
    pub train: c_int,
    pub avgpool: c_int,
    pub batch_normalize: c_int,
    pub shortcut: c_int,
    pub batch: c_int,
    pub dynamic_minibatch: c_int,
    pub forced: c_int,
    pub flipped: c_int,
    pub inputs: c_int,
    pub outputs: c_int,
    pub mean_alpha: f32,
    pub nweights: c_int,
    pub nbiases: c_int,
    pub extra: c_int,
    pub truths: c_int,
    pub h: c_int,
    pub w: c_int,
    pub c: c_int,
    pub out_h: c_int,
    pub out_w: c_int,
    pub out_c: c_int,
    pub n: c_int,
    pub max_boxes: c_int,
    pub truth_size: c_int,
    pub groups: c_int,
    pub group_id: c_int,
    pub size: c_int,
    pub side: c_int,
    pub stride: c_int,
    pub stride_x: c_int,
    pub stride_y: c_int,
    pub dilation: c_int,
    pub antialiasing: c_int,
    pub maxpool_depth: c_int,
    pub maxpool_zero_nonmax: c_int,
    pub out_channels: c_int,
    pub reverse: f32,
    pub coordconv: c_int,
    pub flatten: c_int,
    pub spatial: c_int,
    pub pad: c_int,
    pub sqrt: c_int,
    pub flip: c_int,
    pub index: c_int,
    pub scale_wh: c_int,
    pub binary: c_int,
    pub xnor: c_int,
    pub peephole: c_int,
    pub use_bin_output: c_int,
    pub keep_delta_gpu: c_int,
    pub optimized_memory: c_int,
    pub steps: c_int,
    pub history_size: c_int,
    pub bottleneck: c_int,
    pub time_normalizer: f32,
    pub state_constrain: c_int,
    pub hidden: c_int,
    pub truth: c_int,
    pub smooth: f32,
    pub dot: f32,
    pub deform: c_int,
    pub grad_centr: c_int,
    pub sway: c_int,
    pub rotate: c_int,
    pub stretch: c_int,
    pub stretch_sway: c_int,
    pub angle: f32,
    pub jitter: f32,
    pub resize: f32,
    pub saturation: f32,
    pub exposure: f32,
    pub shift: f32,
    pub ratio: f32,
    pub learning_rate_scale: f32,
    pub clip: f32,
    pub focal_loss: c_int,
    pub classes_multipliers: *mut f32,
    pub label_smooth_eps: f32,
    pub noloss: c_int,
    pub softmax: c_int,
    pub classes: c_int,
    pub detection: c_int,
    pub embedding_layer_id: c_int,
    pub embedding_output: *mut f32,
    pub embedding_size: c_int,
    pub sim_thresh: f32,
    pub track_history_size: c_int,
    pub dets_for_track: c_int,
    pub dets_for_show: c_int,
    pub track_ciou_norm: f32,
    pub coords: c_int,
    pub background: c_int,
    pub rescore: c_int,
    pub objectness: c_int,
    pub does_cost: c_int,
    pub joint: c_int,
    pub noadjust: c_int,
    pub reorg: c_int,
    pub log: c_int,
    pub tanh: c_int,
    pub mask: *mut c_int,
    pub total: c_int,
    pub bflops: f32,
    pub adam: c_int,
    pub B1: f32,
    pub B2: f32,
    pub eps: f32,
    pub t: c_int,
    pub alpha: f32,
    pub beta: f32,
    pub kappa: f32,
    pub coord_scale: f32,
    pub object_scale: f32,
    pub noobject_scale: f32,
    pub mask_scale: f32,
    pub class_scale: f32,
    pub bias_match: c_int,
    pub random: f32,
    pub ignore_thresh: f32,
    pub truth_thresh: f32,
    pub iou_thresh: f32,
    pub thresh: f32,
    pub focus: f32,
    pub classfix: c_int,
    pub absolute: c_int,
    pub assisted_excitation: c_int,
    pub onlyforward: c_int,
    pub stopbackward: c_int,
    pub train_only_bn: c_int,
    pub dont_update: c_int,
    pub burnin_update: c_int,
    pub dontload: c_int,
    pub dontsave: c_int,
    pub dontloadscales: c_int,
    pub numload: c_int,
    pub temperature: f32,
    pub probability: f32,
    pub dropblock_size_rel: f32,
    pub dropblock_size_abs: c_int,
    pub dropblock: c_int,
    pub scale: f32,
    pub receptive_w: c_int,
    pub receptive_h: c_int,
    pub receptive_w_scale: c_int,
    pub receptive_h_scale: c_int,
    pub cweights: *mut c_char,
    pub indexes: *mut c_int,
    pub input_layers: *mut c_int,
    pub input_sizes: *mut c_int,
    pub layers_output: *mut *mut f32,
    pub layers_delta: *mut *mut f32,
    pub weights_type: WEIGHTS_TYPE_T,
    pub weights_normalization: WEIGHTS_NORMALIZATION_T,
    pub map: *mut c_int,
    pub counts: *mut c_int,
    pub sums: *mut *mut f32,
    pub rand: *mut f32,
    pub cost: *mut f32,
    pub labels: *mut c_int,
    pub class_ids: *mut c_int,
    pub contrastive_neg_max: c_int,
    pub cos_sim: *mut f32,
    pub exp_cos_sim: *mut f32,
    pub p_constrastive: *mut f32,
    pub contrast_p_gpu: *mut contrastive_params,
    pub state: *mut f32,
    pub prev_state: *mut f32,
    pub forgot_state: *mut f32,
    pub forgot_delta: *mut f32,
    pub state_delta: *mut f32,
    pub combine_cpu: *mut f32,
    pub combine_delta_cpu: *mut f32,
    pub concat: *mut f32,
    pub concat_delta: *mut f32,
    pub binary_weights: *mut f32,
    pub biases: *mut f32,
    pub bias_updates: *mut f32,
    pub scales: *mut f32,
    pub scale_updates: *mut f32,
    pub weights: *mut f32,
    pub weight_updates: *mut f32,
    pub scale_x_y: f32,
    pub objectness_smooth: c_int,
    pub max_delta: f32,
    pub uc_normalizer: f32,
    pub iou_normalizer: f32,
    pub obj_normalizer: f32,
    pub cls_normalizer: f32,
    pub delta_normalizer: f32,
    pub iou_loss: IOU_LOSS,
    pub iou_thresh_kind: IOU_LOSS,
    pub nms_kind: NMS_KIND,
    pub beta_nms: f32,
    pub yolo_point: YOLO_POINT,
    pub align_bit_weights_gpu: *mut c_char,
    pub mean_arr_gpu: *mut f32,
    pub align_workspace_gpu: *mut f32,
    pub transposed_align_workspace_gpu: *mut f32,
    pub align_workspace_size: c_int,
    pub align_bit_weights: *mut c_char,
    pub mean_arr: *mut f32,
    pub align_bit_weights_size: c_int,
    pub lda_align: c_int,
    pub new_lda: c_int,
    pub bit_align: c_int,
    pub col_image: *mut f32,
    pub delta: *mut f32,
    pub output: *mut f32,
    pub activation_input: *mut f32,
    pub delta_pinned: c_int,
    pub output_pinned: c_int,
    pub loss: *mut f32,
    pub squared: *mut f32,
    pub norms: *mut f32,
    pub spatial_mean: *mut f32,
    pub mean: *mut f32,
    pub variance: *mut f32,
    pub mean_delta: *mut f32,
    pub variance_delta: *mut f32,
    pub rolling_mean: *mut f32,
    pub rolling_variance: *mut f32,
    pub x: *mut f32,
    pub x_norm: *mut f32,
    pub m: *mut f32,
    pub v: *mut f32,
    pub bias_m: *mut f32,
    pub bias_v: *mut f32,
    pub scale_m: *mut f32,
    pub scale_v: *mut f32,
    pub z_cpu: *mut f32,
    pub r_cpu: *mut f32,
    pub h_cpu: *mut f32,
    pub stored_h_cpu: *mut f32,
    pub prev_state_cpu: *mut f32,
    pub temp_cpu: *mut f32,
    pub temp2_cpu: *mut f32,
    pub temp3_cpu: *mut f32,
    pub dh_cpu: *mut f32,
    pub hh_cpu: *mut f32,
    pub prev_cell_cpu: *mut f32,
    pub cell_cpu: *mut f32,
    pub f_cpu: *mut f32,
    pub i_cpu: *mut f32,
    pub g_cpu: *mut f32,
    pub o_cpu: *mut f32,
    pub c_cpu: *mut f32,
    pub stored_c_cpu: *mut f32,
    pub dc_cpu: *mut f32,
    pub binary_input: *mut f32,
    pub bin_re_packed_input: *mut u32,
    pub t_bit_input: *mut c_char,
    pub input_layer: *mut layer,
    pub self_layer: *mut layer,
    pub output_layer: *mut layer,
    pub reset_layer: *mut layer,
    pub update_layer: *mut layer,
    pub state_layer: *mut layer,
    pub input_gate_layer: *mut layer,
    pub state_gate_layer: *mut layer,
    pub input_save_layer: *mut layer,
    pub state_save_layer: *mut layer,
    pub input_state_layer: *mut layer,
    pub state_state_layer: *mut layer,
    pub input_z_layer: *mut layer,
    pub state_z_layer: *mut layer,
    pub input_r_layer: *mut layer,
    pub state_r_layer: *mut layer,
    pub input_h_layer: *mut layer,
    pub state_h_layer: *mut layer,
    pub wz: *mut layer,
    pub uz: *mut layer,
    pub wr: *mut layer,
    pub ur: *mut layer,
    pub wh: *mut layer,
    pub uh: *mut layer,
    pub uo: *mut layer,
    pub wo: *mut layer,
    pub vo: *mut layer,
    pub uf: *mut layer,
    pub wf: *mut layer,
    pub vf: *mut layer,
    pub ui: *mut layer,
    pub wi: *mut layer,
    pub vi: *mut layer,
    pub ug: *mut layer,
    pub wg: *mut layer,
    pub softmax_tree: *mut tree,
    pub workspace_size: size_t,
    pub indexes_gpu: *mut c_int,
    pub z_gpu: *mut f32,
    pub r_gpu: *mut f32,
    pub h_gpu: *mut f32,
    pub stored_h_gpu: *mut f32,
    pub bottelneck_hi_gpu: *mut f32,
    pub bottelneck_delta_gpu: *mut f32,
    pub temp_gpu: *mut f32,
    pub temp2_gpu: *mut f32,
    pub temp3_gpu: *mut f32,
    pub dh_gpu: *mut f32,
    pub hh_gpu: *mut f32,
    pub prev_cell_gpu: *mut f32,
    pub prev_state_gpu: *mut f32,
    pub last_prev_state_gpu: *mut f32,
    pub last_prev_cell_gpu: *mut f32,
    pub cell_gpu: *mut f32,
    pub f_gpu: *mut f32,
    pub i_gpu: *mut f32,
    pub g_gpu: *mut f32,
    pub o_gpu: *mut f32,
    pub c_gpu: *mut f32,
    pub stored_c_gpu: *mut f32,
    pub dc_gpu: *mut f32,
    pub m_gpu: *mut f32,
    pub v_gpu: *mut f32,
    pub bias_m_gpu: *mut f32,
    pub scale_m_gpu: *mut f32,
    pub bias_v_gpu: *mut f32,
    pub scale_v_gpu: *mut f32,
    pub combine_gpu: *mut f32,
    pub combine_delta_gpu: *mut f32,
    pub forgot_state_gpu: *mut f32,
    pub forgot_delta_gpu: *mut f32,
    pub state_gpu: *mut f32,
    pub state_delta_gpu: *mut f32,
    pub gate_gpu: *mut f32,
    pub gate_delta_gpu: *mut f32,
    pub save_gpu: *mut f32,
    pub save_delta_gpu: *mut f32,
    pub concat_gpu: *mut f32,
    pub concat_delta_gpu: *mut f32,
    pub binary_input_gpu: *mut f32,
    pub binary_weights_gpu: *mut f32,
    pub bin_conv_shortcut_in_gpu: *mut f32,
    pub bin_conv_shortcut_out_gpu: *mut f32,
    pub mean_gpu: *mut f32,
    pub variance_gpu: *mut f32,
    pub m_cbn_avg_gpu: *mut f32,
    pub v_cbn_avg_gpu: *mut f32,
    pub rolling_mean_gpu: *mut f32,
    pub rolling_variance_gpu: *mut f32,
    pub variance_delta_gpu: *mut f32,
    pub mean_delta_gpu: *mut f32,
    pub col_image_gpu: *mut f32,
    pub x_gpu: *mut f32,
    pub x_norm_gpu: *mut f32,
    pub weights_gpu: *mut f32,
    pub weight_updates_gpu: *mut f32,
    pub weight_deform_gpu: *mut f32,
    pub weight_change_gpu: *mut f32,
    pub weights_gpu16: *mut f32,
    pub weight_updates_gpu16: *mut f32,
    pub biases_gpu: *mut f32,
    pub bias_updates_gpu: *mut f32,
    pub bias_change_gpu: *mut f32,
    pub scales_gpu: *mut f32,
    pub scale_updates_gpu: *mut f32,
    pub scale_change_gpu: *mut f32,
    pub input_antialiasing_gpu: *mut f32,
    pub output_gpu: *mut f32,
    pub output_avg_gpu: *mut f32,
    pub activation_input_gpu: *mut f32,
    pub loss_gpu: *mut f32,
    pub delta_gpu: *mut f32,
    pub cos_sim_gpu: *mut f32,
    pub rand_gpu: *mut f32,
    pub drop_blocks_scale: *mut f32,
    pub drop_blocks_scale_gpu: *mut f32,
    pub squared_gpu: *mut f32,
    pub norms_gpu: *mut f32,
    pub gt_gpu: *mut f32,
    pub a_avg_gpu: *mut f32,
    pub input_sizes_gpu: *mut c_int,
    pub layers_output_gpu: *mut *mut f32,
    pub layers_delta_gpu: *mut *mut f32,
    pub srcTensorDesc: *mut c_void,
    pub dstTensorDesc: *mut c_void,
    pub srcTensorDesc16: *mut c_void,
    pub dstTensorDesc16: *mut c_void,
    pub dsrcTensorDesc: *mut c_void,
    pub ddstTensorDesc: *mut c_void,
    pub dsrcTensorDesc16: *mut c_void,
    pub ddstTensorDesc16: *mut c_void,
    pub normTensorDesc: *mut c_void,
    pub normDstTensorDesc: *mut c_void,
    pub normDstTensorDescF16: *mut c_void,
    pub weightDesc: *mut c_void,
    pub weightDesc16: *mut c_void,
    pub dweightDesc: *mut c_void,
    pub dweightDesc16: *mut c_void,
    pub convDesc: *mut c_void,
    pub fw_algo: UNUSED_ENUM_TYPE,
    pub fw_algo16: UNUSED_ENUM_TYPE,
    pub bd_algo: UNUSED_ENUM_TYPE,
    pub bd_algo16: UNUSED_ENUM_TYPE,
    pub bf_algo: UNUSED_ENUM_TYPE,
    pub bf_algo16: UNUSED_ENUM_TYPE,
    pub poolingDesc: *mut c_void,
}

Fields

type_: LAYER_TYPEactivation: ACTIVATIONlstm_activation: ACTIVATIONcost_type: COST_TYPEforward: Option<unsafe extern "C" fn(arg1: layer, arg2: network_state)>backward: Option<unsafe extern "C" fn(arg1: layer, arg2: network_state)>update: Option<unsafe extern "C" fn(arg1: layer, arg2: c_int, arg3: f32, arg4: f32, arg5: f32)>forward_gpu: Option<unsafe extern "C" fn(arg1: layer, arg2: network_state)>backward_gpu: Option<unsafe extern "C" fn(arg1: layer, arg2: network_state)>update_gpu: Option<unsafe extern "C" fn(arg1: layer, arg2: c_int, arg3: f32, arg4: f32, arg5: f32, arg6: f32)>share_layer: *mut layertrain: c_intavgpool: c_intbatch_normalize: c_intshortcut: c_intbatch: c_intdynamic_minibatch: c_intforced: c_intflipped: c_intinputs: c_intoutputs: c_intmean_alpha: f32nweights: c_intnbiases: c_intextra: c_inttruths: c_inth: c_intw: c_intc: c_intout_h: c_intout_w: c_intout_c: c_intn: c_intmax_boxes: c_inttruth_size: c_intgroups: c_intgroup_id: c_intsize: c_intside: c_intstride: c_intstride_x: c_intstride_y: c_intdilation: c_intantialiasing: c_intmaxpool_depth: c_intmaxpool_zero_nonmax: c_intout_channels: c_intreverse: f32coordconv: c_intflatten: c_intspatial: c_intpad: c_intsqrt: c_intflip: c_intindex: c_intscale_wh: c_intbinary: c_intxnor: c_intpeephole: c_intuse_bin_output: c_intkeep_delta_gpu: c_intoptimized_memory: c_intsteps: c_inthistory_size: c_intbottleneck: c_inttime_normalizer: f32state_constrain: c_inthidden: c_inttruth: c_intsmooth: f32dot: f32deform: c_intgrad_centr: c_intsway: c_introtate: c_intstretch: c_intstretch_sway: c_intangle: f32jitter: f32resize: f32saturation: f32exposure: f32shift: f32ratio: f32learning_rate_scale: f32clip: f32focal_loss: c_intclasses_multipliers: *mut f32label_smooth_eps: f32noloss: c_intsoftmax: c_intclasses: c_intdetection: c_intembedding_layer_id: c_intembedding_output: *mut f32embedding_size: c_intsim_thresh: f32track_history_size: c_intdets_for_track: c_intdets_for_show: c_inttrack_ciou_norm: f32coords: c_intbackground: c_intrescore: c_intobjectness: c_intdoes_cost: c_intjoint: c_intnoadjust: c_intreorg: c_intlog: c_inttanh: c_intmask: *mut c_inttotal: c_intbflops: f32adam: c_intB1: f32B2: f32eps: f32t: c_intalpha: f32beta: f32kappa: f32coord_scale: f32object_scale: f32noobject_scale: f32mask_scale: f32class_scale: f32bias_match: c_intrandom: f32ignore_thresh: f32truth_thresh: f32iou_thresh: f32thresh: f32focus: f32classfix: c_intabsolute: c_intassisted_excitation: c_intonlyforward: c_intstopbackward: c_inttrain_only_bn: c_intdont_update: c_intburnin_update: c_intdontload: c_intdontsave: c_intdontloadscales: c_intnumload: c_inttemperature: f32probability: f32dropblock_size_rel: f32dropblock_size_abs: c_intdropblock: c_intscale: f32receptive_w: c_intreceptive_h: c_intreceptive_w_scale: c_intreceptive_h_scale: c_intcweights: *mut c_charindexes: *mut c_intinput_layers: *mut c_intinput_sizes: *mut c_intlayers_output: *mut *mut f32layers_delta: *mut *mut f32weights_type: WEIGHTS_TYPE_Tweights_normalization: WEIGHTS_NORMALIZATION_Tmap: *mut c_intcounts: *mut c_intsums: *mut *mut f32rand: *mut f32cost: *mut f32labels: *mut c_intclass_ids: *mut c_intcontrastive_neg_max: c_intcos_sim: *mut f32exp_cos_sim: *mut f32p_constrastive: *mut f32contrast_p_gpu: *mut contrastive_paramsstate: *mut f32prev_state: *mut f32forgot_state: *mut f32forgot_delta: *mut f32state_delta: *mut f32combine_cpu: *mut f32combine_delta_cpu: *mut f32concat: *mut f32concat_delta: *mut f32binary_weights: *mut f32biases: *mut f32bias_updates: *mut f32scales: *mut f32scale_updates: *mut f32weights: *mut f32weight_updates: *mut f32scale_x_y: f32objectness_smooth: c_intmax_delta: f32uc_normalizer: f32iou_normalizer: f32obj_normalizer: f32cls_normalizer: f32delta_normalizer: f32iou_loss: IOU_LOSSiou_thresh_kind: IOU_LOSSnms_kind: NMS_KINDbeta_nms: f32yolo_point: YOLO_POINTalign_bit_weights_gpu: *mut c_charmean_arr_gpu: *mut f32align_workspace_gpu: *mut f32transposed_align_workspace_gpu: *mut f32align_workspace_size: c_intalign_bit_weights: *mut c_charmean_arr: *mut f32align_bit_weights_size: c_intlda_align: c_intnew_lda: c_intbit_align: c_intcol_image: *mut f32delta: *mut f32output: *mut f32activation_input: *mut f32delta_pinned: c_intoutput_pinned: c_intloss: *mut f32squared: *mut f32norms: *mut f32spatial_mean: *mut f32mean: *mut f32variance: *mut f32mean_delta: *mut f32variance_delta: *mut f32rolling_mean: *mut f32rolling_variance: *mut f32x: *mut f32x_norm: *mut f32m: *mut f32v: *mut f32bias_m: *mut f32bias_v: *mut f32scale_m: *mut f32scale_v: *mut f32z_cpu: *mut f32r_cpu: *mut f32h_cpu: *mut f32stored_h_cpu: *mut f32prev_state_cpu: *mut f32temp_cpu: *mut f32temp2_cpu: *mut f32temp3_cpu: *mut f32dh_cpu: *mut f32hh_cpu: *mut f32prev_cell_cpu: *mut f32cell_cpu: *mut f32f_cpu: *mut f32i_cpu: *mut f32g_cpu: *mut f32o_cpu: *mut f32c_cpu: *mut f32stored_c_cpu: *mut f32dc_cpu: *mut f32binary_input: *mut f32bin_re_packed_input: *mut u32t_bit_input: *mut c_charinput_layer: *mut layerself_layer: *mut layeroutput_layer: *mut layerreset_layer: *mut layerupdate_layer: *mut layerstate_layer: *mut layerinput_gate_layer: *mut layerstate_gate_layer: *mut layerinput_save_layer: *mut layerstate_save_layer: *mut layerinput_state_layer: *mut layerstate_state_layer: *mut layerinput_z_layer: *mut layerstate_z_layer: *mut layerinput_r_layer: *mut layerstate_r_layer: *mut layerinput_h_layer: *mut layerstate_h_layer: *mut layerwz: *mut layeruz: *mut layerwr: *mut layerur: *mut layerwh: *mut layeruh: *mut layeruo: *mut layerwo: *mut layervo: *mut layeruf: *mut layerwf: *mut layervf: *mut layerui: *mut layerwi: *mut layervi: *mut layerug: *mut layerwg: *mut layersoftmax_tree: *mut treeworkspace_size: size_tindexes_gpu: *mut c_intz_gpu: *mut f32r_gpu: *mut f32h_gpu: *mut f32stored_h_gpu: *mut f32bottelneck_hi_gpu: *mut f32bottelneck_delta_gpu: *mut f32temp_gpu: *mut f32temp2_gpu: *mut f32temp3_gpu: *mut f32dh_gpu: *mut f32hh_gpu: *mut f32prev_cell_gpu: *mut f32prev_state_gpu: *mut f32last_prev_state_gpu: *mut f32last_prev_cell_gpu: *mut f32cell_gpu: *mut f32f_gpu: *mut f32i_gpu: *mut f32g_gpu: *mut f32o_gpu: *mut f32c_gpu: *mut f32stored_c_gpu: *mut f32dc_gpu: *mut f32m_gpu: *mut f32v_gpu: *mut f32bias_m_gpu: *mut f32scale_m_gpu: *mut f32bias_v_gpu: *mut f32scale_v_gpu: *mut f32combine_gpu: *mut f32combine_delta_gpu: *mut f32forgot_state_gpu: *mut f32forgot_delta_gpu: *mut f32state_gpu: *mut f32state_delta_gpu: *mut f32gate_gpu: *mut f32gate_delta_gpu: *mut f32save_gpu: *mut f32save_delta_gpu: *mut f32concat_gpu: *mut f32concat_delta_gpu: *mut f32binary_input_gpu: *mut f32binary_weights_gpu: *mut f32bin_conv_shortcut_in_gpu: *mut f32bin_conv_shortcut_out_gpu: *mut f32mean_gpu: *mut f32variance_gpu: *mut f32m_cbn_avg_gpu: *mut f32v_cbn_avg_gpu: *mut f32rolling_mean_gpu: *mut f32rolling_variance_gpu: *mut f32variance_delta_gpu: *mut f32mean_delta_gpu: *mut f32col_image_gpu: *mut f32x_gpu: *mut f32x_norm_gpu: *mut f32weights_gpu: *mut f32weight_updates_gpu: *mut f32weight_deform_gpu: *mut f32weight_change_gpu: *mut f32weights_gpu16: *mut f32weight_updates_gpu16: *mut f32biases_gpu: *mut f32bias_updates_gpu: *mut f32bias_change_gpu: *mut f32scales_gpu: *mut f32scale_updates_gpu: *mut f32scale_change_gpu: *mut f32input_antialiasing_gpu: *mut f32output_gpu: *mut f32output_avg_gpu: *mut f32activation_input_gpu: *mut f32loss_gpu: *mut f32delta_gpu: *mut f32cos_sim_gpu: *mut f32rand_gpu: *mut f32drop_blocks_scale: *mut f32drop_blocks_scale_gpu: *mut f32squared_gpu: *mut f32norms_gpu: *mut f32gt_gpu: *mut f32a_avg_gpu: *mut f32input_sizes_gpu: *mut c_intlayers_output_gpu: *mut *mut f32layers_delta_gpu: *mut *mut f32srcTensorDesc: *mut c_voiddstTensorDesc: *mut c_voidsrcTensorDesc16: *mut c_voiddstTensorDesc16: *mut c_voiddsrcTensorDesc: *mut c_voidddstTensorDesc: *mut c_voiddsrcTensorDesc16: *mut c_voidddstTensorDesc16: *mut c_voidnormTensorDesc: *mut c_voidnormDstTensorDesc: *mut c_voidnormDstTensorDescF16: *mut c_voidweightDesc: *mut c_voidweightDesc16: *mut c_voiddweightDesc: *mut c_voiddweightDesc16: *mut c_voidconvDesc: *mut c_voidfw_algo: UNUSED_ENUM_TYPEfw_algo16: UNUSED_ENUM_TYPEbd_algo: UNUSED_ENUM_TYPEbd_algo16: UNUSED_ENUM_TYPEbf_algo: UNUSED_ENUM_TYPEbf_algo16: UNUSED_ENUM_TYPEpoolingDesc: *mut c_void

Trait Implementations

impl Clone for layer[src]

impl Copy for layer[src]

impl Debug for layer[src]

Auto Trait Implementations

Blanket Implementations

impl<T> Any for T where
    T: 'static + ?Sized, [src]

impl<T> Borrow<T> for T where
    T: ?Sized, [src]

impl<T> BorrowMut<T> for T where
    T: ?Sized, [src]

impl<T> From<T> for T[src]

impl<T, U> Into<U> for T where
    U: From<T>, [src]

impl<T> ToOwned for T where
    T: Clone, [src]

type Owned = T

The resulting type after obtaining ownership.

impl<T, U> TryFrom<U> for T where
    U: Into<T>, [src]

type Error = Infallible

The type returned in the event of a conversion error.

impl<T, U> TryInto<U> for T where
    U: TryFrom<T>, [src]

type Error = <U as TryFrom<T>>::Error

The type returned in the event of a conversion error.