o . i8$@sddlmZddlZddlmZddlmZmZeddZe ddd#d$ddZ e ddd#d$ddZ e ddd%d&ddZ e ddd'd(dd Z Gd!d"d"ejZdS))) annotationsN)ComponentStoreunsqueeze_rightNoiseSchedulesz%Functions to generate noise schedules linear_betazLinear beta schedule-C6?{Gz?num_train_timestepsint beta_startfloatbeta_endcCstj|||tjdS)a Linear beta noise schedule function. Args: num_train_timesteps: number of timesteps beta_start: start of beta range, default 1e-4 beta_end: end of beta range, default 2e-2 Returns: betas: beta schedule tensor dtypetorchlinspacefloat32r r r re/home/dell461/cl/sdc2/last_ska_mid/HISourceFinder-master-l/src/monai/networks/schedulers/scheduler.py _linear_beta+s rZscaled_linear_betazScaled linear beta schedulecCs tj|d|d|tjddS)a  Scaled linear beta noise schedule function. Args: num_train_timesteps: number of timesteps beta_start: start of beta range, default 1e-4 beta_end: end of beta range, default 2e-2 Returns: betas: beta schedule tensor ?rrrrrr_scaled_linear_beta;s rZ sigmoid_betazSigmoid beta schedule sig_rangecCs&t| ||}t||||S)aB Sigmoid beta noise schedule function. Args: num_train_timesteps: number of timesteps beta_start: start of beta range, default 1e-4 beta_end: end of beta range, default 2e-2 sig_range: pos/neg range of sigmoid input, default 6 Returns: betas: beta schedule tensor )rrsigmoid)r r r rbetasrrr _sigmoid_betaKsrcosinezCosine scheduleMb?scCstd||d}t|||d|tjdd}||d}d|dd|dd}t|dd }d|}tj|dd }|||fS) z Cosine noise schedule, see https://arxiv.org/abs/2102.09672 Args: num_train_timesteps: number of timesteps s: smoothing factor, default 8e-3 (see referenced paper) Returns: (betas, alphas, alpha_cumprod) values rrr?Ngg+?dim)rrcospiitemclipcumprod)r r"xalphas_cumprodralphasrrr _cosine_beta]s ( r0cs8eZdZdZddfd d ZdddZdddZZS) Schedulera Base class for other schedulers based on a noise schedule function. This class is meant as the base for other schedulers which implement their own way of sampling or stepping. Here the class defines beta, alpha, and alpha_cumprod values from a noise schedule function named with `schedule`, which is the name of a component in NoiseSchedules. These components must all be callables which return either the beta schedule alone or a triple containing (betas, alphas, alphas_cumprod) values. New schedule functions can be provided by using the NoiseSchedules.add_def, for example: .. code-block:: python from monai.networks.schedulers import NoiseSchedules, DDPMScheduler @NoiseSchedules.add_def("my_beta_schedule", "Some description of your function") def _beta_function(num_train_timesteps, beta_start=1e-4, beta_end=2e-2): return torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32) scheduler = DDPMScheduler(num_train_timesteps=1000, schedule="my_beta_schedule") All such functions should have an initial positional integer argument `num_train_timesteps` stating the number of timesteps the schedule is for, otherwise any other arguments can be given which will be passed by keyword through the constructor's `schedule_args` value. To see what noise functions are available, print the object NoiseSchedules to get a listing of stored objects with their docstring descriptions. Note: in previous versions of the schedulers the argument `schedule_beta` was used to state the beta schedule type, this now replaced with `schedule` and most names used with the previous argument now have "_beta" appended to them, eg. 'schedule_beta="linear"' -> 'schedule="linear_beta"'. The `beta_start` and `beta_end` arguments are still used for some schedules but these are provided as keyword arguments now. Args: num_train_timesteps: number of diffusion steps used to train the model. schedule: member of NoiseSchedules, a named function returning the beta tensor or (betas, alphas, alphas_cumprod) triple schedule_args: arguments to pass to the schedule function rr r schedulestrreturnNonec st||d<t|di|}t|tr |\|_|_|_n||_d|j|_tj |jdd|_||_ t d|_ d|_ t|ddd|_dS)Nr r$rr&r#r%r)super__init__r isinstancetuplerr/r.rr,r tensoronenum_inference_stepsarange timesteps)selfr r3 schedule_argsZ noise_sched __class__rrr8s    zScheduler.__init__original_samples torch.Tensornoiser?cCsf|jj|j|jd|_||j}t|j|d|j}td|j|d|j}||||}|S)aB Add noise to the original samples. Args: original_samples: original samples noise: noise to add to samples timesteps: timesteps tensor indicating the timestep to be computed for each sample. Returns: noisy_samples: sample with added noise devicerrr#r.torHrrndim)r@rDrFr?Zsqrt_alpha_cumprodsqrt_one_minus_alpha_prodZ noisy_samplesrrr add_noises zScheduler.add_noisesamplecCsf|jj|j|jd|_||j}t|j|d|j}td|j|d|j}||||}|S)NrGrr#rI)r@rNrFr?Zsqrt_alpha_prodrLZvelocityrrr get_velocitys zScheduler.get_velocity)r2r)r r r3r4r5r6)rDrErFrEr?rEr5rE)rNrErFrEr?rEr5rE)__name__ __module__ __qualname____doc__r8rMrO __classcell__rrrBrr1ss $ r1)rr)r r r r r r )rrr)r r r r r r rr )r!)r r r"r ) __future__rrtorch.nnnn monai.utilsrrradd_defrrrr0Moduler1rrrrs