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)