Hi all, I’m new to MXNet and coming across some issues.
I am surprised to find no answers yet in the list of available questions. It seems like a pretty common problem and pretty easy to solve in Tensorflow.
Put it simple, I’m loading a network, then trying to run inference with varying batch_sizes, height and width of image.
import mxnet as mx sym, arg, aux = mx.model.load_checkpoint("simple_pose_resnet50_v1d", 0) mod = mx.mod.Module(symbol=sym, label_names=None) mod.bind(for_training=False, data_shapes=[('data', (-1,3,-1,-1))]) mod.set_params(arg, aux) from collections import namedtuple Batch = namedtuple('Batch', ['data']) mod.forward(Batch([pose_input])) predicted_heatmap = mod.get_outputs()
I’ve highlighted -1 as a common notation in Tensorflow for unknown size and to be determined doing runtime. Is it possible to do this in MXNet? Please help