def symbolic_fn(g, input, output_size, align_corners=None): sym_help._interpolate_warning(interpolate_mode) align_corners = sym_help._maybe_get_scalar(align_corners) if align_corners: return _unimplemented(name, "align_corners == True") scales = sym_help._interpolate_size_to_scales(g, input, output_size, dim) return g.op("Resize", input, scales, mode_s=interpolate_mode)
def symbolic_fn(g, input, output_size, *args): scales, align_corners = symbolic_helper._get_interpolate_attributes( g, interpolate_mode, args) symbolic_helper._interpolate_warning(interpolate_mode) align_corners = symbolic_helper._maybe_get_scalar(align_corners) if align_corners: return symbolic_helper._unimplemented(name, "align_corners == True") if scales is None: scales = symbolic_helper._interpolate_size_to_scales( g, input, output_size, dim) return g.op("Resize", input, scales, mode_s=interpolate_mode)
def symbolic_fn(g, input, output_size, align_corners=None): sym_help._interpolate_warning(interpolate_mode) align_corners = sym_help._maybe_get_scalar(align_corners) if align_corners: return _unimplemented(name, "align_corners == True") output_size = sym_help._maybe_get_const(output_size, 'is') if sym_help._is_value(output_size): return _unimplemented(name, "torch._C.Value (output_size) indexing") else: scales = [1. if i < 2 else float(output_size[-(dim - i)]) / float(input.type().sizes()[-(dim - i)]) for i in range(0, dim)] return g.op("Upsample", input, mode_s=interpolate_mode, scales_f=scales)
def symbolic_fn(g, input, output_size, *args): scales, align_corners = symbolic_helper._get_interpolate_attributes( g, interpolate_mode, args ) symbolic_helper._interpolate_warning(interpolate_mode) align_corners = symbolic_helper._maybe_get_scalar(align_corners) if align_corners: return symbolic_helper._unimplemented(name, "align_corners == True", input) output_size = symbolic_helper._maybe_get_const(output_size, "is") if symbolic_helper._is_value(output_size): return symbolic_helper._unimplemented( name, "torch._C.Value (output_size) indexing" ) if scales is None: scales = [ 1.0 if i < 2 else float(output_size[-(dim - i)]) / float(input.type().sizes()[-(dim - i)]) for i in range(0, dim) ] return g.op("Upsample", input, mode_s=interpolate_mode, scales_f=scales)