Struct darknet_sys::network

source · 
#[repr(C)]
pub struct network {
Show 120 fields
    pub n: c_int,
    pub batch: c_int,
    pub seen: *mut u64,
    pub badlabels_reject_threshold: *mut f32,
    pub delta_rolling_max: *mut f32,
    pub delta_rolling_avg: *mut f32,
    pub delta_rolling_std: *mut f32,
    pub weights_reject_freq: c_int,
    pub equidistant_point: c_int,
    pub badlabels_rejection_percentage: f32,
    pub num_sigmas_reject_badlabels: f32,
    pub ema_alpha: f32,
    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 total_bbox: *mut c_int,
    pub rewritten_bbox: *mut 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 max_chart_loss: f32,
    pub letter_box: c_int,
    pub mosaic_bound: c_int,
    pub contrastive: c_int,
    pub contrastive_jit_flip: c_int,
    pub contrastive_color: c_int,
    pub unsupervised: 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 usize,
    pub max_output16_size: *mut usize,
    pub wait_stream: c_int,
    pub cuda_graph: *mut c_void,
    pub cuda_graph_exec: *mut c_void,
    pub use_cuda_graph: c_int,
    pub cuda_graph_ready: *mut c_int,
    pub global_delta_gpu: *mut f32,
    pub state_delta_gpu: *mut f32,
    pub max_delta_gpu_size: usize,
    pub optimized_memory: c_int,
    pub dynamic_minibatch: c_int,
    pub workspace_size_limit: usize,

}

Fields§

§n: c_int§batch: c_int§seen: *mut u64§badlabels_reject_threshold: *mut f32§delta_rolling_max: *mut f32§delta_rolling_avg: *mut f32§delta_rolling_std: *mut f32§weights_reject_freq: c_int§equidistant_point: c_int§badlabels_rejection_percentage: f32§num_sigmas_reject_badlabels: f32§ema_alpha: f32§cur_iteration: *mut c_int§loss_scale: f32§t: *mut c_int§epoch: f32§subdivisions: c_int§layers: *mut layer§output: *mut f32§policy: learning_rate_policy§benchmark_layers: c_int§total_bbox: *mut c_int§rewritten_bbox: *mut c_int§learning_rate: f32§learning_rate_min: f32§learning_rate_max: f32§batches_per_cycle: c_int§batches_cycle_mult: c_int§momentum: f32§decay: f32§gamma: f32§scale: f32§power: f32§time_steps: c_int§step: c_int§max_batches: c_int§num_boxes: c_int§train_images_num: c_int§seq_scales: *mut f32§scales: *mut f32§steps: *mut c_int§num_steps: c_int§burn_in: c_int§cudnn_half: c_int§adam: c_int§B1: f32§B2: f32§eps: f32§inputs: c_int§outputs: c_int§truths: c_int§notruth: c_int§h: c_int§w: c_int§c: c_int§max_crop: c_int§min_crop: c_int§max_ratio: f32§min_ratio: f32§center: c_int§flip: c_int§gaussian_noise: c_int§blur: c_int§mixup: c_int§label_smooth_eps: f32§resize_step: c_int§attention: c_int§adversarial: c_int§adversarial_lr: f32§max_chart_loss: f32§letter_box: c_int§mosaic_bound: c_int§contrastive: c_int§contrastive_jit_flip: c_int§contrastive_color: c_int§unsupervised: c_int§angle: f32§aspect: f32§exposure: f32§saturation: f32§hue: f32§random: c_int§track: c_int§augment_speed: c_int§sequential_subdivisions: c_int§init_sequential_subdivisions: c_int§current_subdivision: c_int§try_fix_nan: c_int§gpu_index: c_int§hierarchy: *mut tree§input: *mut f32§truth: *mut f32§delta: *mut f32§workspace: *mut f32§train: c_int§index: c_int§cost: *mut f32§clip: f32§delta_gpu: *mut f32§output_gpu: *mut f32§input_state_gpu: *mut f32§input_pinned_cpu: *mut f32§input_pinned_cpu_flag: c_int§input_gpu: *mut *mut f32§truth_gpu: *mut *mut f32§input16_gpu: *mut *mut f32§output16_gpu: *mut *mut f32§max_input16_size: *mut usize§max_output16_size: *mut usize§wait_stream: c_int§cuda_graph: *mut c_void§cuda_graph_exec: *mut c_void§use_cuda_graph: c_int§cuda_graph_ready: *mut c_int§global_delta_gpu: *mut f32§state_delta_gpu: *mut f32§max_delta_gpu_size: usize§optimized_memory: c_int§dynamic_minibatch: c_int§workspace_size_limit: usize

Trait Implementations§

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impl Clone for network

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fn clone(&self) -> network

Returns a copy of the value. Read more
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fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source. Read more
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impl Debug for network

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fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter. Read more
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impl Copy for network

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impl<T> Any for Twhere T: 'static + ?Sized,

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fn type_id(&self) -> TypeId

Gets the TypeId of self. Read more
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impl<T> Borrow<T> for Twhere T: ?Sized,

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fn borrow(&self) -> &T

Immutably borrows from an owned value. Read more
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impl<T> BorrowMut<T> for Twhere T: ?Sized,

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fn borrow_mut(&mut self) -> &mut T

Mutably borrows from an owned value. Read more
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impl<T> From<T> for T

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fn from(t: T) -> T

Returns the argument unchanged.

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impl<T, U> Into<U> for Twhere U: From<T>,

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fn into(self) -> U

Calls U::from(self).

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type Owned = T

The resulting type after obtaining ownership.
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Uses borrowed data to replace owned data, usually by cloning. Read more
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impl<T, U> TryFrom<U> for Twhere U: Into<T>,

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

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fn try_into(self) -> Result<U, <U as TryFrom<T>>::Error>

Performs the conversion.