[ti-machine-learning/ti-machine-learning.git] / src / app / cnn / class / cifar10 / appCNNClassCIFAR10Testing.c
diff --git a/src/app/cnn/class/cifar10/appCNNClassCIFAR10Testing.c b/src/app/cnn/class/cifar10/appCNNClassCIFAR10Testing.c
index cac970cc308709517dd6ff58ffca6db17b8738c0..642e4ad73d1c8fd4fa54c2647d6cd75628f5d7ab 100644 (file)
#define MODEL_PATH "../../../../database/model/cifar10/databaseModelCIFAR10.m"
#define DATABASE_PATH "../../../../database/cifar10"
-#define IMAGE_PATH "../../../../database/cifar10/%1d.jpg"
-#define LABEL_PATH "../../../../database/cifar10/label.txt"
-#define IMAGE_NUM 3
#define TOP_N 1
#define IMAGE_ROW 32
#define IMAGE_COL 32
struct timespec endTime;
long testingTime;
int topN;
- int *label;
timlUtilImageSet training;
timlUtilImageSet testing;
- timlUtilImage image;
- char str[TIML_UTIL_MAX_STR];
- int i;
- FILE *fp;
- int read;
+ size_t mem1;
+ size_t mem2;
+ size_t mem3;
// init
err = 0;
// read the CNN config file
printf("1. Read the CNN config\n");
- timlConvNeuralNetwork *cnn = timlCNNReadFromFile(MODEL_PATH, 0);
+ timlConvNeuralNetwork *cnn = timlCNNReadFromFile(MODEL_PATH);
+ timlCNNAddAccuracyLayer(cnn, TOP_N);
+ timlCNNInitialize(cnn);
+ timlCNNLoadParamsFromFile(cnn, cnn->paramsFileName);
timlCNNSetMode(cnn, Util_Test);
- mem = timlCNNMemory(cnn);
timlCNNPrint(cnn);
- printf("CNN memory allocation = %.10f MB.\n", (float)mem/1024.0/1024.0);
- printf("CNN parameter # = %lu.\n", timlCNNGetParamsNum(cnn));
-
-// // read CIFAR10 database
-// printf("2. Read CIFAR10 database\n");
-// timlUtilReadCIFAR10(DATABASE_PATH, &training, &testing);
-
- testing.data = malloc(sizeof(float)*IMAGE_ROW*IMAGE_COL*IMAGE_CHANNEL*IMAGE_NUM);
- testing.label = malloc(sizeof(int)*IMAGE_NUM);
- testing.num = IMAGE_NUM;
- // read labels
- fp = fopen(LABEL_PATH, "rt");
- for (i = 0; i < IMAGE_NUM; i++) {
- read = fscanf(fp, "%d", testing.label + i);
- }
- fclose(fp);
-
- // read images
- for (i = 0; i < IMAGE_NUM; i++) {
- sprintf(str, IMAGE_PATH, i);
- image = timlUtilReadJPEG(str);
- cblas_scopy(dim, image.data, 1, testing.data + i*dim, 1);
- free(image.data);
- }
+
+ mem1 = cnn->forwardMemory + cnn->backwardMemory + cnn->fixedMemory + cnn->paramsMemory;
+ mem2 = cnn->forwardMemory + cnn->fixedMemory + cnn->paramsMemory;
+ mem3 = cnn->memPoolSize + cnn->fixedMemory + cnn->paramsMemory;
+ printf("CNN level 1 memory size = %10.4f MB.\n", (float)mem1/1024.0/1024.0);
+ printf("CNN level 2 memory size = %10.4f MB.\n", (float)mem2/1024.0/1024.0);
+ printf("CNN level 3 memory size = %10.4f MB.\n", (float)mem3/1024.0/1024.0);
+ printf("CNN forward memory size = %10.4f MB.\n", (float)cnn->forwardMemory/1024.0/1024.0);
+ printf("CNN memory pool size = %10.4f MB.\n", (float)cnn->memPoolSize/1024.0/1024.0);
+ printf("CNN params memory size = %10.4f MB.\n", (float)cnn->paramsMemory/1024.0/1024.0);
+
+ // read CIFAR10 database
+ printf("2. Read CIFAR10 database\n");
+ timlUtilReadCIFAR10(DATABASE_PATH, &training, &testing);
// testing
printf("3. Start testing\n");
- label = malloc(sizeof(int)*topN*testing.num);
clock_gettime(CLOCK_REALTIME, &startTime);
- timlCNNClassifyTopNBatchMode(cnn, testing.data, dim, testing.num, label, NULL, topN);
+ timlCNNClassifyAccuracy(cnn, testing.data, IMAGE_ROW, IMAGE_COL, IMAGE_CHANNEL, testing.label, 1, 1, testing.num, &classifyNum);
clock_gettime(CLOCK_REALTIME, &endTime);
testingTime = timlUtilDiffTime(startTime, endTime);
- classifyNum = timlUtilClassifyAccuracy(label, topN, testing.num, testing.label);
classifyPercent = (float)classifyNum/(float)testing.num;
printf("Testing time = %.3f s\n", testingTime/1000000.0);
printf("Classify accuracy = %.3f %%\n", classifyPercent*100.00);
// cleaning
printf("4. Clean up\n");
-// free(training.data);
-// free(training.label);
+ free(training.data);
+ free(training.label);
free(testing.data);
free(testing.label);
- free(label);
timlCNNDelete(cnn);
return err;