Struct darknet_sys::layer
source · #[repr(C)]pub struct layer {Show 415 fields
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_ema: *mut f32,
pub biases_ema: *mut f32,
pub scales_ema: *mut f32,
pub weights: *mut f32,
pub weight_updates: *mut f32,
pub scale_x_y: f32,
pub objectness_smooth: c_int,
pub new_coords: c_int,
pub show_details: 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: usize,
pub indexes_gpu: *mut c_int,
pub stream: c_int,
pub wait_stream_id: 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_TYPE§activation: ACTIVATION§lstm_activation: ACTIVATION§cost_type: COST_TYPE§forward: 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)>§train: c_int§avgpool: c_int§batch_normalize: c_int§shortcut: c_int§batch: c_int§dynamic_minibatch: c_int§forced: c_int§flipped: c_int§inputs: c_int§outputs: c_int§mean_alpha: f32§nweights: c_int§nbiases: c_int§extra: c_int§truths: c_int§h: c_int§w: c_int§c: c_int§out_h: c_int§out_w: c_int§out_c: c_int§n: c_int§max_boxes: c_int§truth_size: c_int§groups: c_int§group_id: c_int§size: c_int§side: c_int§stride: c_int§stride_x: c_int§stride_y: c_int§dilation: c_int§antialiasing: c_int§maxpool_depth: c_int§maxpool_zero_nonmax: c_int§out_channels: c_int§reverse: f32§coordconv: c_int§flatten: c_int§spatial: c_int§pad: c_int§sqrt: c_int§flip: c_int§index: c_int§scale_wh: c_int§binary: c_int§xnor: c_int§peephole: c_int§use_bin_output: c_int§keep_delta_gpu: c_int§optimized_memory: c_int§steps: c_int§history_size: c_int§bottleneck: c_int§time_normalizer: f32§state_constrain: c_int§truth: c_int§smooth: f32§dot: f32§deform: c_int§grad_centr: c_int§sway: c_int§rotate: c_int§stretch: c_int§stretch_sway: c_int§angle: f32§jitter: f32§resize: f32§saturation: f32§exposure: f32§shift: f32§ratio: f32§learning_rate_scale: f32§clip: f32§focal_loss: c_int§classes_multipliers: *mut f32§label_smooth_eps: f32§noloss: c_int§softmax: c_int§classes: c_int§detection: c_int§embedding_layer_id: c_int§embedding_output: *mut f32§embedding_size: c_int§sim_thresh: f32§track_history_size: c_int§dets_for_track: c_int§dets_for_show: c_int§track_ciou_norm: f32§coords: c_int§background: c_int§rescore: c_int§objectness: c_int§does_cost: c_int§joint: c_int§noadjust: c_int§reorg: c_int§log: c_int§tanh: c_int§mask: *mut c_int§total: c_int§bflops: f32§adam: c_int§B1: f32§B2: f32§eps: f32§t: c_int§alpha: f32§beta: f32§kappa: f32§coord_scale: f32§object_scale: f32§noobject_scale: f32§mask_scale: f32§class_scale: f32§bias_match: c_int§random: f32§ignore_thresh: f32§truth_thresh: f32§iou_thresh: f32§thresh: f32§focus: f32§classfix: c_int§absolute: c_int§assisted_excitation: c_int§onlyforward: c_int§stopbackward: c_int§train_only_bn: c_int§dont_update: c_int§burnin_update: c_int§dontload: c_int§dontsave: c_int§dontloadscales: c_int§numload: c_int§temperature: f32§probability: f32§dropblock_size_rel: f32§dropblock_size_abs: c_int§dropblock: c_int§scale: f32§receptive_w: c_int§receptive_h: c_int§receptive_w_scale: c_int§receptive_h_scale: c_int§cweights: *mut c_char§indexes: *mut c_int§input_layers: *mut c_int§input_sizes: *mut c_int§layers_output: *mut *mut f32§layers_delta: *mut *mut f32§weights_type: WEIGHTS_TYPE_T§weights_normalization: WEIGHTS_NORMALIZATION_T§map: *mut c_int§counts: *mut c_int§sums: *mut *mut f32§rand: *mut f32§cost: *mut f32§labels: *mut c_int§class_ids: *mut c_int§contrastive_neg_max: c_int§cos_sim: *mut f32§exp_cos_sim: *mut f32§p_constrastive: *mut f32§contrast_p_gpu: *mut contrastive_params§state: *mut f32§prev_state: *mut f32§forgot_state: *mut f32§forgot_delta: *mut f32§state_delta: *mut f32§combine_cpu: *mut f32§combine_delta_cpu: *mut f32§concat: *mut f32§concat_delta: *mut f32§binary_weights: *mut f32§biases: *mut f32§bias_updates: *mut f32§scales: *mut f32§scale_updates: *mut f32§weights_ema: *mut f32§biases_ema: *mut f32§scales_ema: *mut f32§weights: *mut f32§weight_updates: *mut f32§scale_x_y: f32§objectness_smooth: c_int§new_coords: c_int§show_details: c_int§max_delta: f32§uc_normalizer: f32§iou_normalizer: f32§obj_normalizer: f32§cls_normalizer: f32§delta_normalizer: f32§iou_loss: IOU_LOSS§iou_thresh_kind: IOU_LOSS§nms_kind: NMS_KIND§beta_nms: f32§yolo_point: YOLO_POINT§align_bit_weights_gpu: *mut c_char§mean_arr_gpu: *mut f32§align_workspace_gpu: *mut f32§transposed_align_workspace_gpu: *mut f32§align_workspace_size: c_int§align_bit_weights: *mut c_char§mean_arr: *mut f32§align_bit_weights_size: c_int§lda_align: c_int§new_lda: c_int§bit_align: c_int§col_image: *mut f32§delta: *mut f32§output: *mut f32§activation_input: *mut f32§delta_pinned: c_int§output_pinned: c_int§loss: *mut f32§squared: *mut f32§norms: *mut f32§spatial_mean: *mut f32§mean: *mut f32§variance: *mut f32§mean_delta: *mut f32§variance_delta: *mut f32§rolling_mean: *mut f32§rolling_variance: *mut f32§x: *mut f32§x_norm: *mut f32§m: *mut f32§v: *mut f32§bias_m: *mut f32§bias_v: *mut f32§scale_m: *mut f32§scale_v: *mut f32§z_cpu: *mut f32§r_cpu: *mut f32§h_cpu: *mut f32§stored_h_cpu: *mut f32§prev_state_cpu: *mut f32§temp_cpu: *mut f32§temp2_cpu: *mut f32§temp3_cpu: *mut f32§dh_cpu: *mut f32§hh_cpu: *mut f32§prev_cell_cpu: *mut f32§cell_cpu: *mut f32§f_cpu: *mut f32§i_cpu: *mut f32§g_cpu: *mut f32§o_cpu: *mut f32§c_cpu: *mut f32§stored_c_cpu: *mut f32§dc_cpu: *mut f32§binary_input: *mut f32§bin_re_packed_input: *mut u32§t_bit_input: *mut c_char§input_layer: *mut layer§self_layer: *mut layer§output_layer: *mut layer§reset_layer: *mut layer§update_layer: *mut layer§state_layer: *mut layer§input_gate_layer: *mut layer§state_gate_layer: *mut layer§input_save_layer: *mut layer§state_save_layer: *mut layer§input_state_layer: *mut layer§state_state_layer: *mut layer§input_z_layer: *mut layer§state_z_layer: *mut layer§input_r_layer: *mut layer§state_r_layer: *mut layer§input_h_layer: *mut layer§state_h_layer: *mut layer§wz: *mut layer§uz: *mut layer§wr: *mut layer§ur: *mut layer§wh: *mut layer§uh: *mut layer§uo: *mut layer§wo: *mut layer§vo: *mut layer§uf: *mut layer§wf: *mut layer§vf: *mut layer§ui: *mut layer§wi: *mut layer§vi: *mut layer§ug: *mut layer§wg: *mut layer§softmax_tree: *mut tree§workspace_size: usize§indexes_gpu: *mut c_int§stream: c_int§wait_stream_id: c_int§z_gpu: *mut f32§r_gpu: *mut f32§h_gpu: *mut f32§stored_h_gpu: *mut f32§bottelneck_hi_gpu: *mut f32§bottelneck_delta_gpu: *mut f32§temp_gpu: *mut f32§temp2_gpu: *mut f32§temp3_gpu: *mut f32§dh_gpu: *mut f32§hh_gpu: *mut f32§prev_cell_gpu: *mut f32§prev_state_gpu: *mut f32§last_prev_state_gpu: *mut f32§last_prev_cell_gpu: *mut f32§cell_gpu: *mut f32§f_gpu: *mut f32§i_gpu: *mut f32§g_gpu: *mut f32§o_gpu: *mut f32§c_gpu: *mut f32§stored_c_gpu: *mut f32§dc_gpu: *mut f32§m_gpu: *mut f32§v_gpu: *mut f32§bias_m_gpu: *mut f32§scale_m_gpu: *mut f32§bias_v_gpu: *mut f32§scale_v_gpu: *mut f32§combine_gpu: *mut f32§combine_delta_gpu: *mut f32§forgot_state_gpu: *mut f32§forgot_delta_gpu: *mut f32§state_gpu: *mut f32§state_delta_gpu: *mut f32§gate_gpu: *mut f32§gate_delta_gpu: *mut f32§save_gpu: *mut f32§save_delta_gpu: *mut f32§concat_gpu: *mut f32§concat_delta_gpu: *mut f32§binary_input_gpu: *mut f32§binary_weights_gpu: *mut f32§bin_conv_shortcut_in_gpu: *mut f32§bin_conv_shortcut_out_gpu: *mut f32§mean_gpu: *mut f32§variance_gpu: *mut f32§m_cbn_avg_gpu: *mut f32§v_cbn_avg_gpu: *mut f32§rolling_mean_gpu: *mut f32§rolling_variance_gpu: *mut f32§variance_delta_gpu: *mut f32§mean_delta_gpu: *mut f32§col_image_gpu: *mut f32§x_gpu: *mut f32§x_norm_gpu: *mut f32§weights_gpu: *mut f32§weight_updates_gpu: *mut f32§weight_deform_gpu: *mut f32§weight_change_gpu: *mut f32§weights_gpu16: *mut f32§weight_updates_gpu16: *mut f32§biases_gpu: *mut f32§bias_updates_gpu: *mut f32§bias_change_gpu: *mut f32§scales_gpu: *mut f32§scale_updates_gpu: *mut f32§scale_change_gpu: *mut f32§input_antialiasing_gpu: *mut f32§output_gpu: *mut f32§output_avg_gpu: *mut f32§activation_input_gpu: *mut f32§loss_gpu: *mut f32§delta_gpu: *mut f32§cos_sim_gpu: *mut f32§rand_gpu: *mut f32§drop_blocks_scale: *mut f32§drop_blocks_scale_gpu: *mut f32§squared_gpu: *mut f32§norms_gpu: *mut f32§gt_gpu: *mut f32§a_avg_gpu: *mut f32§input_sizes_gpu: *mut c_int§layers_output_gpu: *mut *mut f32§layers_delta_gpu: *mut *mut f32§srcTensorDesc: *mut c_void§dstTensorDesc: *mut c_void§srcTensorDesc16: *mut c_void§dstTensorDesc16: *mut c_void§dsrcTensorDesc: *mut c_void§ddstTensorDesc: *mut c_void§dsrcTensorDesc16: *mut c_void§ddstTensorDesc16: *mut c_void§normTensorDesc: *mut c_void§normDstTensorDesc: *mut c_void§normDstTensorDescF16: *mut c_void§weightDesc: *mut c_void§weightDesc16: *mut c_void§dweightDesc: *mut c_void§dweightDesc16: *mut c_void§convDesc: *mut c_void§fw_algo: UNUSED_ENUM_TYPE§fw_algo16: UNUSED_ENUM_TYPE§bd_algo: UNUSED_ENUM_TYPE§bd_algo16: UNUSED_ENUM_TYPE§bf_algo: UNUSED_ENUM_TYPE§bf_algo16: UNUSED_ENUM_TYPE§poolingDesc: *mut c_voidTrait Implementations§
Auto Trait Implementations§
impl RefUnwindSafe for layer
impl !Send for layer
impl !Sync for layer
impl Unpin for layer
impl UnwindSafe for layer
Blanket Implementations§
source§impl<T> BorrowMut<T> for Twhere
T: ?Sized,
impl<T> BorrowMut<T> for Twhere T: ?Sized,
source§fn borrow_mut(&mut self) -> &mut T
fn borrow_mut(&mut self) -> &mut T
Mutably borrows from an owned value. Read more