在我的configureStore.dev.js文件中使用此代码，我在添加import {createStore, compose, applyMiddleware} from 'redux';
import rootReducer from '../reducers';
import reduxImmutableStateInvariant from 'redux-immutable-state-invariant';
export default function configureStore(initialState) {
const store = createStore(rootReducer, initialState, compose(
// Add other middleware on this line...
applyMiddleware(reduxImmutableStateInvariant),
window.devToolsExtension ? window.devToolsExtension() : f => f // add support for Redux dev tools
)
);
if (module.hot) {
// Enable Webpack hot module replacement for reducers
module.hot.accept('../reducers', () => {
const nextReducer = require('../reducers').default; // eslint-disable-line global-require
store.replaceReducer(nextReducer);
});
}
return store;
}
时收到$ cat t1241.cu
#include <cufft.h>
#include <stdexcept>
#include <iostream>
#include <numeric>
#include <vector>
#define ck(cmd) if ( cmd) { std::cerr << "error at line " << __LINE__ << std::endl;exit(1);}
__global__
void fill_input(cufftComplex * buf, int batch,int nbins,int stride,int seed)
{
for (int i = blockDim.y * blockIdx.y + threadIdx.y; i< batch;i += gridDim.y*blockDim.y)
for (int j = blockDim.x * blockIdx.x + threadIdx.x; j< nbins;j += gridDim.x*blockDim.x)
buf[i*stride + j] = make_cuFloatComplex( (i+seed)%101 - 50,(j+seed)%41-20);
}
__global__
void check_output(const float * buf1,const float * buf2,int batch, int nfft, int stride, int * errors)
{
for (int i = blockDim.y * blockIdx.y + threadIdx.y; i< batch;i += gridDim.y*blockDim.y) {
for (int j = blockDim.x * blockIdx.x + threadIdx.x; j< nfft;j += gridDim.x*blockDim.x) {
float e=buf1[i*stride+j] - buf2[i*stride+j];
if (e*e > 1) // gross error
atomicAdd(errors,1);
}
}
}
void demo(bool reuse_plan)
{
if (reuse_plan)
std::cout << "Reusing the same fft plan with multiple stream via cufftSetStream ... ";
else
std::cout << "Giving each stream its own dedicated fft plan ... ";
int nfft = 1024;
int batch = 1024;
int nstreams = 8;
int nbins = nfft/2+1;
int nit=100;
size_t inpitch,outpitch;
std::vector<cufftComplex*> inbufs(nstreams);
std::vector<float*> outbufs(nstreams);
std::vector<float*> checkbufs(nstreams);
std::vector<cudaStream_t> streams(nstreams);
std::vector<cufftHandle> plans(nstreams);
// if plan reuse, set up independent work areas
std::vector<char *> wk_areas(nstreams);
for (int i=0;i<nstreams;++i) {
ck( cudaStreamCreate(&streams[i]));
ck( cudaMallocPitch((void**)&inbufs[i],&inpitch,nbins*sizeof(cufftComplex),batch) );
ck( cudaMallocPitch((void**)&outbufs[i],&outpitch,nfft*sizeof(float),batch));
ck( cudaMallocPitch((void**)&checkbufs[i],&outpitch,nfft*sizeof(float),batch) );
if (i==0 || reuse_plan==false)
ck ( cufftPlanMany(&plans[i],1,&nfft,&nbins,1,inpitch/sizeof(cufftComplex),&nfft,1,outpitch/sizeof(float),CUFFT_C2R,batch) );
}
if (reuse_plan){
size_t ws;
ck(cufftGetSize(plans[0], &ws));
for (int i = 0; i < nstreams; i++)
ck(cudaMalloc(&(wk_areas[i]), ws));
ck(cufftSetAutoAllocation(plans[0], 0));
ck(cufftSetWorkArea(plans[0], wk_areas[0]));
}
// fill the input buffers and FFT them to get a baseline for comparison
for (int i=0;i<nstreams;++i) {
fill_input<<<20,dim3(32,32)>>>(inbufs[i],batch,nbins,inpitch/sizeof(cufftComplex),i);
ck (cudaGetLastError());
if (reuse_plan) {
ck (cufftExecC2R(plans[0],inbufs[i],checkbufs[i]));
}else{
ck (cufftExecC2R(plans[i],inbufs[i],checkbufs[i]));
ck( cufftSetStream(plans[i],streams[i]) ); // only need to set the stream once
}
ck( cudaDeviceSynchronize());
}
// allocate a buffer for the error count
int * errors;
cudaMallocHost((void**)&errors,sizeof(int)*nit);
memset(errors,0,sizeof(int)*nit);
// perform the FFTs and check the outputs on streams
for (int it=0;it<nit;++it) {
int k = it % nstreams;
ck( cudaStreamSynchronize(streams[k]) ); // make sure any prior kernels have completed
if (reuse_plan) {
ck(cufftSetStream(plans[0],streams[k]));
ck(cufftSetWorkArea(plans[0], wk_areas[k])); // update work area pointer in plan
ck(cufftExecC2R(plans[0],inbufs[k],outbufs[k]));
}else{
ck(cufftExecC2R(plans[k],inbufs[k],outbufs[k]));
}
check_output<<<100,dim3(32,32),0,streams[k]>>>(outbufs[k],checkbufs[k],batch,nfft,outpitch/sizeof(float),&errors[it]);
ck (cudaGetLastError());
}
ck(cudaDeviceSynchronize());
// report number of errors
int errcount=0;
for (int it=0;it<nit;++it)
if (errors[it])
++errcount;
std::cout << errcount << " of " << nit << " transforms had errors\n";
for (int i=0;i<nstreams;++i) {
cudaFree(inbufs[i]);
cudaFree(outbufs[i]);
cudaFree(wk_areas[i]);
cudaStreamDestroy(streams[i]);
if (i==0 || reuse_plan==false)
cufftDestroy(plans[i]);
}
}
int main(int argc,char ** argv)
{
demo(false);
demo(true);
return 0;
}
$ nvcc -o t1241 t1241.cu -lcufft
$ ./t1241
Giving each stream its own dedicated fft plan ... 0 of 100 transforms had errors
Reusing the same fft plan with multiple stream via cufftSetStream ... 0 of 100 transforms had errors
$
。当我删除这个添加的中间件时，我的项目运行正常。添加此中间件的正确方法是什么？这是完整的文件：
<div class="demo-container">
<div id="placeholder" class="demo-placeholder" style="width:800px; height:500px;"></div>
</div>
答案 0 :(得分：14)
reduxImmutableStateInvariant 是在将其传递给applyMiddleware之前需要调用的函数。
SELECT
l.id,
l.user_id,
l.latitude,
l.longitude
FROM
Location l
LEFT JOIN Task t
ON l.user_id = t.user_id
WHERE
t.id IS NULL /* No record in tasks table */
OR (t.id IS NOT NULL AND l.status = 1) /* if records exists then all of them must have status equals to 1 */
在github README docs中，在导入（通过require）const store = createStore(rootReducer, initialState, compose(
// Add other middleware on this line...
applyMiddleware(reduxImmutableStateInvariant()),
window.devToolsExtension ? window.devToolsExtension() : f => f // add support for Redux dev tools
)
);
后调用。见下面的第三行：
reduxImmutableStateInvariant在thunk中间件中，thunk函数为called before it is returned。
// Be sure to ONLY add this middleware in development!
const middleware = process.env.NODE_ENV !== 'production' ?
[require('redux-immutable-state-invariant')(), thunk] :
[thunk];
// Note passing middleware as the last argument to createStore requires redux@>=3.1.0
const store = createStore(
reducer,
applyMiddleware(...middleware)
);
基于代码，看起来您可以传入一个函数（const thunk = createThunkMiddleware();
thunk.withExtraArgument = createThunkMiddleware;
export default thunk;
），该函数用于确定redux状态中哪些属性是不可变的。我认为提供自己的isImmutable函数是允许这个中间件与其他不可变库很好地协同工作的。
isImmutable