[][src]Struct darknet_sys::network

#[repr(C)]pub struct network {
    pub n: c_int,
    pub batch: c_int,
    pub seen: *mut u64,
    pub cur_iteration: *mut c_int,
    pub loss_scale: f32,
    pub t: *mut c_int,
    pub epoch: f32,
    pub subdivisions: c_int,
    pub layers: *mut layer,
    pub output: *mut f32,
    pub policy: learning_rate_policy,
    pub benchmark_layers: c_int,
    pub learning_rate: f32,
    pub learning_rate_min: f32,
    pub learning_rate_max: f32,
    pub batches_per_cycle: c_int,
    pub batches_cycle_mult: c_int,
    pub momentum: f32,
    pub decay: f32,
    pub gamma: f32,
    pub scale: f32,
    pub power: f32,
    pub time_steps: c_int,
    pub step: c_int,
    pub max_batches: c_int,
    pub num_boxes: c_int,
    pub train_images_num: c_int,
    pub seq_scales: *mut f32,
    pub scales: *mut f32,
    pub steps: *mut c_int,
    pub num_steps: c_int,
    pub burn_in: c_int,
    pub cudnn_half: c_int,
    pub adam: c_int,
    pub B1: f32,
    pub B2: f32,
    pub eps: f32,
    pub inputs: c_int,
    pub outputs: c_int,
    pub truths: c_int,
    pub notruth: c_int,
    pub h: c_int,
    pub w: c_int,
    pub c: c_int,
    pub max_crop: c_int,
    pub min_crop: c_int,
    pub max_ratio: f32,
    pub min_ratio: f32,
    pub center: c_int,
    pub flip: c_int,
    pub gaussian_noise: c_int,
    pub blur: c_int,
    pub mixup: c_int,
    pub label_smooth_eps: f32,
    pub resize_step: c_int,
    pub attention: c_int,
    pub adversarial: c_int,
    pub adversarial_lr: f32,
    pub letter_box: c_int,
    pub angle: f32,
    pub aspect: f32,
    pub exposure: f32,
    pub saturation: f32,
    pub hue: f32,
    pub random: c_int,
    pub track: c_int,
    pub augment_speed: c_int,
    pub sequential_subdivisions: c_int,
    pub init_sequential_subdivisions: c_int,
    pub current_subdivision: c_int,
    pub try_fix_nan: c_int,
    pub gpu_index: c_int,
    pub hierarchy: *mut tree,
    pub input: *mut f32,
    pub truth: *mut f32,
    pub delta: *mut f32,
    pub workspace: *mut f32,
    pub train: c_int,
    pub index: c_int,
    pub cost: *mut f32,
    pub clip: f32,
    pub delta_gpu: *mut f32,
    pub output_gpu: *mut f32,
    pub input_state_gpu: *mut f32,
    pub input_pinned_cpu: *mut f32,
    pub input_pinned_cpu_flag: c_int,
    pub input_gpu: *mut *mut f32,
    pub truth_gpu: *mut *mut f32,
    pub input16_gpu: *mut *mut f32,
    pub output16_gpu: *mut *mut f32,
    pub max_input16_size: *mut size_t,
    pub max_output16_size: *mut size_t,
    pub wait_stream: c_int,
    pub global_delta_gpu: *mut f32,
    pub state_delta_gpu: *mut f32,
    pub max_delta_gpu_size: size_t,
    pub optimized_memory: c_int,
    pub dynamic_minibatch: c_int,
    pub workspace_size_limit: size_t,
}

Fields

n: c_intbatch: c_intseen: *mut u64cur_iteration: *mut c_intloss_scale: f32t: *mut c_intepoch: f32subdivisions: c_intlayers: *mut layeroutput: *mut f32policy: learning_rate_policybenchmark_layers: c_intlearning_rate: f32learning_rate_min: f32learning_rate_max: f32batches_per_cycle: c_intbatches_cycle_mult: c_intmomentum: f32decay: f32gamma: f32scale: f32power: f32time_steps: c_intstep: c_intmax_batches: c_intnum_boxes: c_inttrain_images_num: c_intseq_scales: *mut f32scales: *mut f32steps: *mut c_intnum_steps: c_intburn_in: c_intcudnn_half: c_intadam: c_intB1: f32B2: f32eps: f32inputs: c_intoutputs: c_inttruths: c_intnotruth: c_inth: c_intw: c_intc: c_intmax_crop: c_intmin_crop: c_intmax_ratio: f32min_ratio: f32center: c_intflip: c_intgaussian_noise: c_intblur: c_intmixup: c_intlabel_smooth_eps: f32resize_step: c_intattention: c_intadversarial: c_intadversarial_lr: f32letter_box: c_intangle: f32aspect: f32exposure: f32saturation: f32hue: f32random: c_inttrack: c_intaugment_speed: c_intsequential_subdivisions: c_intinit_sequential_subdivisions: c_intcurrent_subdivision: c_inttry_fix_nan: c_intgpu_index: c_inthierarchy: *mut treeinput: *mut f32truth: *mut f32delta: *mut f32workspace: *mut f32train: c_intindex: c_intcost: *mut f32clip: f32delta_gpu: *mut f32output_gpu: *mut f32input_state_gpu: *mut f32input_pinned_cpu: *mut f32input_pinned_cpu_flag: c_intinput_gpu: *mut *mut f32truth_gpu: *mut *mut f32input16_gpu: *mut *mut f32output16_gpu: *mut *mut f32max_input16_size: *mut size_tmax_output16_size: *mut size_twait_stream: c_intglobal_delta_gpu: *mut f32state_delta_gpu: *mut f32max_delta_gpu_size: size_toptimized_memory: c_intdynamic_minibatch: c_intworkspace_size_limit: size_t

Trait Implementations

impl Clone for network[src]

impl Copy for network[src]

impl Debug for network[src]

Auto Trait Implementations

impl RefUnwindSafe for network

impl !Send for network

impl !Sync for network

impl Unpin for network

impl UnwindSafe for network

Blanket Implementations

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

impl<T, U> Into<U> for T where
    U: From<T>, 
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impl<T> ToOwned for T where
    T: Clone
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type Owned = T

The resulting type after obtaining ownership.

impl<T, U> TryFrom<U> for T where
    U: Into<T>, 
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type Error = Infallible

The type returned in the event of a conversion error.

impl<T, U> TryInto<U> for T where
    U: TryFrom<T>, 
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type Error = <U as TryFrom<T>>::Error

The type returned in the event of a conversion error.