Fix av1_nn_predict_test MSVC build error
MSVC can't handle struct initialisation by named elements in array
initialisers. Furthermore, some implicit casts caused warnings. This
patch should resolve both of these issues.
BUG=aomedia:2250
Change-Id: I637717f5bb3305a12f03850b94dacc2f2c7949b4
diff --git a/test/av1_nn_predict_test.cc b/test/av1_nn_predict_test.cc
index bda8868..a193ab3 100644
--- a/test/av1_nn_predict_test.cc
+++ b/test/av1_nn_predict_test.cc
@@ -102,30 +102,25 @@
for (int iter = 0; iter < 10000 && !HasFatalFailure(); ++iter) {
for (int node = 0; node < shape->num_inputs; node++) {
- inputs[node] = (int32_t)rng_.Rand31() - (1 << 30);
- inputs[node] /= (1 << 31);
+ inputs[node] = ((float)rng_.Rand31() - (1 << 30)) / (1 << 31);
}
for (int layer = 0; layer < shape->num_hidden_layers; layer++) {
for (int node = 0; node < NN_MAX_NODES_PER_LAYER; node++) {
- bias[layer][node] = (int32_t)rng_.Rand31() - (1 << 30);
- bias[layer][node] /= (1 << 31);
+ bias[layer][node] = ((float)rng_.Rand31() - (1 << 30)) / (1 << 31);
}
for (int node = 0; node < NN_MAX_NODES_PER_LAYER * NN_MAX_NODES_PER_LAYER;
node++) {
- weights[layer][node] = (int32_t)rng_.Rand31() - (1 << 30);
- weights[layer][node] /= (1 << 31);
+ weights[layer][node] = ((float)rng_.Rand31() - (1 << 30)) / (1 << 31);
}
}
// Now the outputs:
int layer = shape->num_hidden_layers;
for (int node = 0; node < NN_MAX_NODES_PER_LAYER; node++) {
- bias[layer][node] = (int32_t)rng_.Rand31() - (1 << 30);
- bias[layer][node] /= (1 << 31);
+ bias[layer][node] = ((float)rng_.Rand31() - (1 << 30)) / (1 << 31);
}
for (int node = 0; node < NN_MAX_NODES_PER_LAYER * NN_MAX_NODES_PER_LAYER;
node++) {
- weights[layer][node] = (int32_t)rng_.Rand31() - (1 << 30);
- weights[layer][node] /= (1 << 31);
+ weights[layer][node] = ((float)rng_.Rand31() - (1 << 30)) / (1 << 31);
}
av1_nn_predict_c(inputs, &nn_config, outputs_ref);
@@ -192,90 +187,13 @@
// runs of the encoder. It also conveniently covers all the kernels
// implemented.
static const NN_CONFIG shapes[] = {
- { .num_inputs = 10,
- .num_outputs = 16,
- .num_hidden_layers = 1,
- .num_hidden_nodes = { 64 },
- .weights = { 0 },
- .bias = { 0 } },
- { .num_inputs = 12,
- .num_outputs = 1,
- .num_hidden_layers = 1,
- .num_hidden_nodes = { 12 },
- .weights = { 0 },
- .bias = { 0 } },
- { .num_inputs = 12,
- .num_outputs = 1,
- .num_hidden_layers = 1,
- .num_hidden_nodes = { 24 },
- .weights = { 0 },
- .bias = { 0 } },
- { .num_inputs = 12,
- .num_outputs = 1,
- .num_hidden_layers = 1,
- .num_hidden_nodes = { 32 },
- .weights = { 0 },
- .bias = { 0 } },
- { .num_inputs = 18,
- .num_outputs = 4,
- .num_hidden_layers = 1,
- .num_hidden_nodes = { 24 },
- .weights = { 0 },
- .bias = { 0 } },
- { .num_inputs = 18,
- .num_outputs = 4,
- .num_hidden_layers = 1,
- .num_hidden_nodes = { 32 },
- .weights = { 0 },
- .bias = { 0 } },
- { .num_inputs = 4,
- .num_outputs = 1,
- .num_hidden_layers = 1,
- .num_hidden_nodes = { 16 },
- .weights = { 0 },
- .bias = { 0 } },
- { .num_inputs = 8,
- .num_outputs = 1,
- .num_hidden_layers = 1,
- .num_hidden_nodes = { 16 },
- .weights = { 0 },
- .bias = { 0 } },
- { .num_inputs = 8,
- .num_outputs = 4,
- .num_hidden_layers = 1,
- .num_hidden_nodes = { 16 },
- .weights = { 0 },
- .bias = { 0 } },
- { .num_inputs = 8,
- .num_outputs = 1,
- .num_hidden_layers = 1,
- .num_hidden_nodes = { 24 },
- .weights = { 0 },
- .bias = { 0 } },
- { .num_inputs = 8,
- .num_outputs = 1,
- .num_hidden_layers = 1,
- .num_hidden_nodes = { 32 },
- .weights = { 0 },
- .bias = { 0 } },
- { .num_inputs = 8,
- .num_outputs = 1,
- .num_hidden_layers = 1,
- .num_hidden_nodes = { 64 },
- .weights = { 0 },
- .bias = { 0 } },
- { .num_inputs = 9,
- .num_outputs = 3,
- .num_hidden_layers = 1,
- .num_hidden_nodes = { 32 },
- .weights = { 0 },
- .bias = { 0 } },
- { .num_inputs = 4,
- .num_outputs = 4,
- .num_hidden_layers = 1,
- .num_hidden_nodes = { 8 },
- .weights = { 0 },
- .bias = { 0 } },
+ { 10, 16, 1, { 64 }, { 0 }, { 0 } }, { 12, 1, 1, { 12 }, { 0 }, { 0 } },
+ { 12, 1, 1, { 24 }, { 0 }, { 0 } }, { 12, 1, 1, { 32 }, { 0 }, { 0 } },
+ { 18, 4, 1, { 24 }, { 0 }, { 0 } }, { 18, 4, 1, { 32 }, { 0 }, { 0 } },
+ { 4, 1, 1, { 16 }, { 0 }, { 0 } }, { 8, 1, 1, { 16 }, { 0 }, { 0 } },
+ { 8, 4, 1, { 16 }, { 0 }, { 0 } }, { 8, 1, 1, { 24 }, { 0 }, { 0 } },
+ { 8, 1, 1, { 32 }, { 0 }, { 0 } }, { 8, 1, 1, { 64 }, { 0 }, { 0 } },
+ { 9, 3, 1, { 32 }, { 0 }, { 0 } }, { 4, 4, 1, { 8 }, { 0 }, { 0 } },
};
void NnPredictTest::runNnPredictTest_all(const NN_CONFIG *const shapes,
