def __interpolate(g, input, size, scale_factor, mode , align_corners):
mode = sym_help._maybe_get_const(mode, 's')
align_corners = sym_help._maybe_get_const(align_corners, 'b')
align_corners = False if _is_none(align_corners) else align_corners
coordinate_transformation_mode = "asymmetric" if mode == "nearest" \
else "align_corners" if align_corners else "pytorch_half_pixel"
# roi only takes effect whith coordinate_transformation_mode="tf_crop_and_resize"
roi = g.op("Constant", value_t=torch.tensor([], dtype=torch.float32))
if not sym_help._is_none(size) :
offsets = g.op("Constant", value_t=torch.ones(2, dtype=torch.int64))
size = g.op("Cast", size, to_i=sym_help.cast_pytorch_to_onnx["Long"])
size = g.op("Concat", offsets, size, axis_i=0)
scales = g.op("Constant", value_t=torch.tensor([], dtype=torch.float32))
elif not sym_help._is_none(scales) :
scales = sym_help._interpolate_get_scales(g, scale_factor, 4)
size = g.op("Constant", value_t=torch.tensor([], dtype=torch.int64))
return g.op("Resize",
input,
roi,
scales,
size,
coordinate_transformation_mode_s=coordinate_transformation_mode,
cubic_coeff_a_f=-0.75, # only valid when mode="cubic"
mode_s=mode, # nearest, linear, or cubic
nearest_mode_s="floor") # only valid when mode="nearest"
def __interpolate(g, input, size, scale_factor, mode, align_corners, recompute_scale_factor):
mode = sym_help._maybe_get_const(mode, 's')
if 'linear' in mode:
mode = 'linear'
if 'cubic' in mode:
mode = 'cubic'
align_corners = sym_help._maybe_get_const(align_corners, 'b')
align_corners = False if not isinstance(align_corners, bool) else align_corners
coordinate_transformation_mode = "asymmetric" if mode == "nearest" \
else "align_corners" if align_corners else "pytorch_half_pixel"
# roi only takes effect with coordinate_transformation_mode="tf_crop_and_resize"
roi = g.op("Constant", value_t=torch.tensor([], dtype=torch.float32))
if not sym_help._is_none(size) :
input_size = g.op("Shape", input)
input_size = sym_help._slice_helper(g, input_size, axes=[0], ends=[2], starts=[0])
# in some cases size is not a packed list but size is a scalar
# We need to also verify that (sym_help._maybe_get_const(size, 't').dim() == 0)
# but this information is not always available. Try to get the dim,
# and if not assume that it is not a scalar.
try:
is_scalar = not sym_help._is_packed_list(size) and ((sym_help._maybe_get_const(size, 't').dim() == 0))
except AttributeError:
is_scalar = not sym_help._is_packed_list(size)
if not is_scalar:
warnings.warn("Cannot verify if the output_size is a scalar "
"while exporting interpolate. Assuming that it is not a scalar.")
if is_scalar:
if not input.type().dim():
return sym_help._unimplemented("interpolate (with a scalar output_size)",
"missing input shape (try giving an array of output_size values)")
size = unsqueeze(g, size, 0)
size = [size for i in range(input.type().dim() - 2)]
size = g.op("Concat", *size, axis_i=0)
size = g.op("Cast", size, to_i=sym_help.cast_pytorch_to_onnx['Long'])
size = g.op("Concat", input_size, size, axis_i=0)
scales = g.op("Constant", value_t=torch.tensor([], dtype=torch.float32))
return g.op("Resize",
input,
roi,
scales,
size,
coordinate_transformation_mode_s=coordinate_transformation_mode,
cubic_coeff_a_f=-0.75, # only valid when mode="cubic"
mode_s=mode, # nearest, linear, or cubic
nearest_mode_s="floor")
else: # if not sym_help._is_none(scales)
if not input.type().dim():
return sym_help._unimplemented("interpolate (with scales)", "missing input shape")
scales = sym_help._interpolate_get_scales(g, scale_factor, input.type().dim())
return g.op("Resize",
input,
roi,
scales,
coordinate_transformation_mode_s=coordinate_transformation_mode,
cubic_coeff_a_f=-0.75, # only valid when mode="cubic"
mode_s=mode, # nearest, linear, or cubic
nearest_mode_s="floor") # only valid when mode="nearest"
def __interpolate(g, input, size, scale_factor, mode, align_corners):
mode = sym_help._maybe_get_const(mode, 's')
if 'linear' in mode:
mode = 'linear'
if 'cubic' in mode:
mode = 'cubic'
align_corners = sym_help._maybe_get_const(align_corners, 'b')
align_corners = False if sym_help._is_none(
align_corners) else align_corners
coordinate_transformation_mode = "asymmetric" if mode == "nearest" \
else "align_corners" if align_corners else "pytorch_half_pixel"
# roi only takes effect whith coordinate_transformation_mode="tf_crop_and_resize"
roi = g.op("Constant", value_t=torch.tensor([], dtype=torch.float32))
if not sym_help._is_none(size):
input_size = input.type().sizes()
input_size = g.op("Constant",
value_t=torch.tensor(input_size[0:2],
dtype=torch.int64))
is_scalar = ((sym_help._maybe_get_const(size, 't').dim() == 0))
if is_scalar:
size = unsqueeze(g, size, 0)
size = [size for i in range(input.type().dim() - 2)]
size = g.op("Concat", *size, axis_i=0)
size = g.op("Concat", input_size, size, axis_i=0)
scales = g.op("Constant",
value_t=torch.tensor([], dtype=torch.float32))
return g.op(
"Resize",
input,
roi,
scales,
size,
coordinate_transformation_mode_s=coordinate_transformation_mode,
cubic_coeff_a_f=-0.75, # only valid when mode="cubic"
mode_s=mode, # nearest, linear, or cubic
nearest_mode_s="floor")
else: # if not sym_help._is_none(scales)
scales = sym_help._interpolate_get_scales(g, scale_factor,
input.type().dim())
return g.op(
"Resize",
input,
roi,
scales,
coordinate_transformation_mode_s=coordinate_transformation_mode,
cubic_coeff_a_f=-0.75, # only valid when mode="cubic"
mode_s=mode, # nearest, linear, or cubic
nearest_mode_s="floor") # only valid when mode="nearest"
