def symbolic_fn(g, input, output_size, *args):
scales, align_corners = sym_help._get_interpolate_attributes(g, interpolate_mode, args)
align_corners = sym_help._maybe_get_scalar(align_corners)
coordinate_transformation_mode = "asymmetric" if interpolate_mode == "nearest" \
else "align_corners" if align_corners else "pytorch_half_pixel"
empty_tensor = g.op("Constant", value_t=torch.tensor([], dtype=torch.float32))
if scales is None:
input_size = g.op("Shape", input)
input_size_beg = sym_help._slice_helper(g, input_size, axes=[0], ends=[2], starts=[0])
output_size = g.op("Cast", output_size, to_i=sym_help.cast_pytorch_to_onnx["Long"])
output_size = g.op("Concat", input_size_beg, output_size, axis_i=0)
scales = g.op("Constant", value_t=torch.tensor([], dtype=torch.float32))
return g.op("Resize",
input,
empty_tensor, # roi only takes effect whith coordinate_transformation_mode="tf_crop_and_resize"
scales, # scales is not needed since we are sending out_size
output_size,
coordinate_transformation_mode_s=coordinate_transformation_mode,
cubic_coeff_a_f=-0.75, # only valid when mode="cubic"
mode_s=interpolate_mode, # nearest, linear, or cubic
nearest_mode_s="floor") # only valid when mode="nearest"
else:
return g.op("Resize",
input,
empty_tensor, # roi only takes effect with coordinate_transformation_mode="tf_crop_and_resize"
scales, # scales is not needed since we are sending out_size
coordinate_transformation_mode_s=coordinate_transformation_mode,
cubic_coeff_a_f=-0.75, # only valid when mode="cubic"
mode_s=interpolate_mode, # nearest, linear, or cubic
nearest_mode_s="floor") # only valid when mode="nearest"
def symbolic_fn(g, input, output_size, *args):
scales, align_corners = sym_help._get_interpolate_attributes(g, interpolate_mode, args)
sym_help._interpolate_warning(interpolate_mode)
align_corners = sym_help._maybe_get_scalar(align_corners)
if align_corners:
return _unimplemented(name, "align_corners == True")
if scales is None:
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 = sym_help._get_interpolate_attributes(g, interpolate_mode, args)
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")
if scales is None:
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)