Cuda实现Radon变换

本文探讨了如何利用CUDA技术实现Radon变换。通过详细阐述算法,并提供类似于C语言的编程方式,展示了如何在CUDA中设置block和thread,每个block包含numAngles(如181个)线程来执行变换,提升计算效率。

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有关radon变换的算法的详情,写在这里:http://blog.youkuaiyun.com/celte/article/details/9826505

用Cuda实现radon变换,可以类似C语言程序操作。。。

这里可以简单的使用一个block,每个block包含numAngles个thread(在下面的代码中是181个thread)

#include <stdio.h>
#include "math.h"
#include "cuda_runtime.h"
#include "device_launch_parameters.h"

   
__global__ static void radon_cuda_core(float *gpuOutput,float *gpuInput,float *gpuAngles,int M,int N,int xOrgin,int yOrgin,int numAngles,int rFirst,int rSize)
{
	const int tid=threadIdx.x;	
	float angle=gpuAngles[tid];
	float *pOutput=gpuOutput+tid*rSize;		
	float sine=sin(angle);
	float cosine=cos(angle);
	int m,n;

	for(m=0;m<rSize;m++)
		pOutput[m]=0.0;

	float *pInput=gpuInput;
	for(n=0;n<N;n++
``` def _load_optimizer_state(self): main_checkpoint = find_resume_checkpoint() or self.resume_checkpoint opt_checkpoint = bf.join( bf.dirname(main_checkpoint), f"opt{self.resume_step:06}.pt" ) if bf.exists(opt_checkpoint): logger.log(f"loading optimizer state from checkpoint: {opt_checkpoint}") state_dict = dist_util.load_state_dict( opt_checkpoint, map_location=dist_util.dev() ) self.opt.load_state_dict(state_dict) def _setup_fp16(self): self.master_params = make_master_params(self.model_params) self.model.convert_to_fp16() def run_loop(self): lpip_loss = lpips.LPIPS(net="alex").to(dist_util.dev()) ############## ssim_loss = SSIM(win_size=7, win_sigma=1.5, data_range=1, size_average=False, channel=1) if parallel: radon_288_736 = para_prepare_parallel(2.5) radon_144_736 = para_prepare_parallel(4.5) radon_72_736 = para_prepare_parallel(8.5) radon_36_736 = para_prepare_parallel(16.5) else: radon_288_736 = para_prepare(2.5) radon_36_736 = para_prepare(16.5) helper = {"fbp_para_288_736": radon_288_736, "fbp_para_36_736": radon_36_736, "fbp_para_72_736": radon_72_736, "fbp_para_144_736": radon_144_736} ######################### "fbp_para_36_512": radon_36_51 while ( not self.lr_anneal_steps or self.step + self.resume_step < self.lr_anneal_steps ): batch, cond = next(self.data) timestep = np.random.randint(low=3, high=None, size=None, dtype='l') t = th.tensor([timestep,timestep]).to("cuda") if timestep == 2: cond["x_t"] = F.interpolate(F.interpolate(batch, (36, 736), mode="nearest"), (288, 736), mode="nearest") elif timestep == 1: cond["x_t"] = F.interpolate(F.interpolate(batch, (72, 736), mode="nearest"), (288, 736), mode="nearest") elif timestep == 0: cond["x_t"] = F.interpolate(F.interpolate(batch, (144, 736), mode="nearest"), (288, 736), mode="nearest") model_output = self.run_step(batch, cond, t, ssim_loss, lpip_loss, helper) # for i in range(2,-1,-1): # t = th.tensor([i,i]).to("cuda") # if i == 2: # cond["x_t"] = cond["low_res"] # model_output = self.run_step(batch, cond, t, ssim_loss, lpip_loss, helper) # else: # cond["x_t"] = model_output # model_output = self.run_step(batch, cond, t, ssim_loss, lpip_loss, helper) if self.step % self.log_interval == 0: logger.dumpkvs() if self.step % self.save_interval == 0: self.save() # Run for a finite amount of time in integration tests. if os.environ.get("DIFFUSION_TRAINING_TEST", "") and self.step > 0: return self.step += 1 # Save the last checkpoint if it wasn't already saved. if (self.step - 1) % self.save_interval != 0: self.save() def run_step(self, batch, cond, t, ssim_loss=None, lpip_loss=None, helper=None): model_output = self.forward_backward(batch, cond, t, ssim_loss, lpip_loss, helper) if self.use_fp16: self.optimize_fp16() else: self.optimize_normal() self.log_step() return model_output```解释代码内容
03-26
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