int main(int argc, char** argv)
{
ccv_nnc_init();
ccv_convnet_t* convnet = ccv_convnet_read(0, argv[2]);
ccv_dense_matrix_t* image = 0;
ccv_read(argv[1], &image, CCV_IO_ANY_FILE | CCV_IO_RGB_COLOR);
if (image != 0)
{
ccv_dense_matrix_t* input = 0;
ccv_convnet_input_formation(convnet->input, image, &input);
ccv_matrix_free(image);
ccv_dense_matrix_t* sliced = 0;
ccv_slice(input, (ccv_matrix_t**)&sliced, 0, (input->rows - 225) / 2, (input->cols - 225) / 2, 225, 225);
ccv_matrix_free(input);
ccv_dense_matrix_t* b = 0;
unsigned int elapsed_time = get_current_time();
ccv_convnet_encode(convnet, &sliced, &b, 1);
printf("ccv_convnet_encode %u ms\n", get_current_time() - elapsed_time);
ccv_nnc_tensor_t* c = ccv_nnc_tensor_new(0, ONE_CPU_TENSOR(1000), 0);
ccv_nnc_graph_exec_t source, dest;
ccv_array_t* tensors = ccv_array_new(sizeof(ccv_nnc_tensor_t*), 1, 0);
ccv_nnc_graph_t* graph = ccv_nnc_simple_graph(convnet, (ccv_nnc_tensor_t*)sliced, c, &source, &dest, tensors);
elapsed_time = get_current_time();
ccv_nnc_graph_run(graph, 0, &source, 1, &dest, 1);
printf("ccv_nnc_graph_run %u ms\n", get_current_time() - elapsed_time);
int i;
for (i = 0; i < 1000; i++)
if (fabsf(b->data.f32[i] - c->data.f32[i]) > 1e-4)
printf("mis-match at %d: %f %f\n", i, b->data.f32[i], c->data.f32[i]);
ccv_nnc_tensor_free(c);
ccv_matrix_free(sliced);
ccv_matrix_free(b);
ccv_nnc_graph_free(graph);
for (i = 0; i < tensors->rnum; i++)
ccv_nnc_tensor_free(*(ccv_nnc_tensor_t**)ccv_array_get(tensors, i));
ccv_array_free(tensors);
}
ccv_convnet_free(convnet);
return 0;
}
int main(int argc, char** argv)
{
static struct option image_net_options[] = {
/* help */
{"help", 0, 0, 0},
/* required parameters */
{"train-list", 1, 0, 0},
{"test-list", 1, 0, 0},
{"working-dir", 1, 0, 0},
/* optional parameters */
{"base-dir", 1, 0, 0},
{"max-epoch", 1, 0, 0},
{"iterations", 1, 0, 0},
{0, 0, 0, 0}
};
char* train_list = 0;
char* test_list = 0;
char* working_dir = 0;
char* base_dir = 0;
ccv_convnet_train_param_t train_params = {
.max_epoch = 100,
.mini_batch = 64,
.sgd_frequency = 1, // do sgd every sgd_frequency batches (mini_batch * device_count * sgd_frequency)
.iterations = 50000,
.device_count = 4,
.peer_access = 1,
.symmetric = 1,
.image_manipulation = 0.2,
.color_gain = 0.001,
.input = {
.min_dim = 257,
.max_dim = 257,
},
};
int i, c;
while (getopt_long_only(argc, argv, "", image_net_options, &c) != -1)
{
switch (c)
{
case 0:
exit_with_help();
case 1:
train_list = optarg;
break;
case 2:
test_list = optarg;
break;
case 3:
working_dir = optarg;
break;
case 4:
base_dir = optarg;
break;
case 5:
train_params.max_epoch = atoi(optarg);
break;
case 6:
train_params.iterations = atoi(optarg);
break;
}
}
if (!train_list || !test_list || !working_dir)
exit_with_help();
ccv_enable_default_cache();
FILE *r0 = fopen(train_list, "r");
assert(r0 && "train-list doesn't exists");
FILE* r1 = fopen(test_list, "r");
assert(r1 && "test-list doesn't exists");
char* file = (char*)malloc(1024);
int dirlen = (base_dir != 0) ? strlen(base_dir) + 1 : 0;
ccv_array_t* categorizeds = ccv_array_new(sizeof(ccv_categorized_t), 64, 0);
while (fscanf(r0, "%d %s", &c, file) != EOF)
{
char* filename = (char*)ccmalloc(1024);
if (base_dir != 0)
{
strncpy(filename, base_dir, 1024);
filename[dirlen - 1] = '/';
}
strncpy(filename + dirlen, file, 1024 - dirlen);
ccv_file_info_t file_info = {
.filename = filename,
};
// imageNet's category class starts from 1, thus, minus 1 to get 0-index
ccv_categorized_t categorized = ccv_categorized(c - 1, 0, &file_info);
ccv_array_push(categorizeds, &categorized);
}
fclose(r0);
ccv_array_t* tests = ccv_array_new(sizeof(ccv_categorized_t), 64, 0);
while (fscanf(r1, "%d %s", &c, file) != EOF)
{
char* filename = (char*)ccmalloc(1024);
if (base_dir != 0)
{
strncpy(filename, base_dir, 1024);
filename[dirlen - 1] = '/';
}
strncpy(filename + dirlen, file, 1024 - dirlen);
ccv_file_info_t file_info = {
.filename = filename,
};
// imageNet's category class starts from 1, thus, minus 1 to get 0-index
ccv_categorized_t categorized = ccv_categorized(c - 1, 0, &file_info);
ccv_array_push(tests, &categorized);
}
fclose(r1);
free(file);
// #define model_params vgg_d_params
#define model_params matt_c_params
int depth = sizeof(model_params) / sizeof(ccv_convnet_layer_param_t);
ccv_convnet_t* convnet = ccv_convnet_new(1, ccv_size(257, 257), model_params, depth);
if (ccv_convnet_verify(convnet, 1000) == 0)
{
ccv_convnet_layer_train_param_t layer_params[depth];
memset(layer_params, 0, sizeof(layer_params));
for (i = 0; i < depth; i++)
{
layer_params[i].w.decay = 0.0005;
layer_params[i].w.learn_rate = 0.01;
layer_params[i].w.momentum = 0.9;
layer_params[i].bias.decay = 0;
layer_params[i].bias.learn_rate = 0.01;
layer_params[i].bias.momentum = 0.9;
}
// set the two full connect layers to last with dropout rate at 0.5
for (i = depth - 3; i < depth - 1; i++)
layer_params[i].dor = 0.5;
train_params.layer_params = layer_params;
ccv_set_cli_output_levels(ccv_cli_output_level_and_above(CCV_CLI_INFO));
ccv_convnet_supervised_train(convnet, categorizeds, tests, working_dir, train_params);
} else {
PRINT(CCV_CLI_ERROR, "Invalid convnet configuration\n");
}
ccv_convnet_free(convnet);
ccv_disable_cache();
return 0;
}
ofxCcv::~ofxCcv() {
if(convnet) {
ccv_convnet_free(convnet);
}
ccv_drain_cache();
}
