// Make FeatureShape from input. Note that feature in locomotiv represented as NHWC
loco::FeatureShape input_shape;
assert(input_buf->shape().rank() == 4);
- input_shape.count() = loco::make_dimension(input_buf->shape().dim(0));
- input_shape.height() = loco::make_dimension(input_buf->shape().dim(1));
- input_shape.width() = loco::make_dimension(input_buf->shape().dim(2));
- input_shape.depth() = loco::make_dimension(input_buf->shape().dim(3));
+ input_shape.count() = input_buf->shape().dim(0);
+ input_shape.height() = input_buf->shape().dim(1);
+ input_shape.width() = input_buf->shape().dim(2);
+ input_shape.depth() = input_buf->shape().dim(3);
loco::TensorShape node_shape = decoder->shape(input_shape);
assert(input_shape.rank() == 4);
for (uint32_t i = 0; i < input_shape.rank(); ++i)
{
- input_shape.dim(i) = loco::make_dimension(input_buf->shape().dim(i));
+ input_shape.dim(i) = input_buf->shape().dim(i);
}
loco::FeatureShape node_shape = encoder->shape(input_shape);
assert(input_shape.rank() == 4);
for (uint32_t i = 0; i < input_shape.rank(); ++i)
{
- input_shape.dim(i) = loco::make_dimension(input_buf->shape().dim(i));
+ input_shape.dim(i) = input_buf->shape().dim(i);
}
loco::FilterShape node_shape = encoder->shape(input_shape);
auto pull = g->nodes()->create<loco::Pull>();
pull->dtype(loco::DataType::FLOAT32);
pull->rank(1);
- pull->dim(0) = loco::make_dimension(1);
+ pull->dim(0) = 1;
g->inputs()->create()->node(pull);
// Make good data
auto pull_node = g->nodes()->create<loco::Pull>();
pull_node->dtype(loco::DataType::FLOAT32);
pull_node->rank(1);
- pull_node->dim(0) = loco::make_dimension(1);
+ pull_node->dim(0) = 1;
// Push node
auto push_node = g->nodes()->create<loco::Push>();
auto pull = g->nodes()->create<loco::Pull>();
pull->dtype(loco::DataType::FLOAT32);
pull->rank(1);
- pull->dim(0) = loco::make_dimension(1);
+ pull->dim(0) = 1;
// Input
auto input = g->inputs()->create();