def args_check(cls, node, **kwargs):
# update cast_map base on auto_cast flag
cls.indices_cast_map[
tf.uint64] = tf.int64 if sys_config.auto_cast else None
cls.depth_cast_map[
tf.uint32] = tf.int32 if sys_config.auto_cast else None
cls.depth_cast_map[
tf.uint64] = tf.int32 if sys_config.auto_cast else None
cls.depth_cast_map[
tf.int64] = tf.int32 if sys_config.auto_cast else None
tensor_dict = kwargs["tensor_dict"]
indices = tensor_dict[node.inputs[0]]
depth = tensor_dict[node.inputs[1]]
indices_dtype = indices.dtype
depth_dtype = depth.dtype
if indices_dtype in cls.indices_cast_map and cls.indices_cast_map[
indices_dtype] is None:
exception.DTYPE_NOT_CAST_EXCEPT(
"OneHot input " + node.inputs[0] + " with data type '" +
data_type.tf_to_np_str(indices_dtype) + "'",
data_type.tf_to_np_str_list(cls.indices_supported_type))
if depth_dtype in cls.depth_cast_map and cls.depth_cast_map[
depth_dtype] is None:
exception.DTYPE_NOT_CAST_EXCEPT(
"OneHot input " + node.inputs[1] + " with data type '" +
data_type.tf_to_np_str(depth_dtype) + "'",
data_type.tf_to_np_str_list(cls.depth_supported_type))
def args_check(cls, node, **kwargs):
dtype = kwargs["tensor_dict"][node.inputs[0]].dtype
if dtype in cls.cast_map and cls.cast_map[dtype] is None:
exception.DTYPE_NOT_CAST_EXCEPT(
"Sub input " + node.inputs[0] + " with data type '" +
data_type.tf_to_np_str(dtype) + "'",
data_type.tf_to_np_str_list(cls.supported_types))
def args_check(cls, node, **kwargs):
x = kwargs["tensor_dict"][node.inputs[0]]
# throw an error if the data type is not natively supported by
# Tensorflow, cannot be safely cast, and auto-cast option is False
if x.dtype in cls.cast_map and cls.cast_map[x.dtype] is None:
exception.DTYPE_NOT_CAST_EXCEPT(
"CumSum input " + node.inputs[0] + " with data type '" +
data_type.tf_to_np_str(x.dtype) + "'",
data_type.tf_to_np_str_list(cls.supported_types))
def args_check(cls, node, **kwargs):
# update cast map based on the auto_cast config option
cls.cast_map[tf.uint64] = tf.int64 if sys_config.auto_cast else None
x = kwargs["tensor_dict"][node.inputs[0]]
y = kwargs["tensor_dict"][node.inputs[1]]
# throw an error if the data type is not natively supported by
# Tensorflow, cannot be safely cast, and auto_cast option is False
if x.dtype in cls.cast_map and cls.cast_map[x.dtype] is None:
exception.DTYPE_NOT_CAST_EXCEPT(
"LessOrEqual input " + node.inputs[0] + " with data type '" +
data_type.tf_to_np_str(x.dtype) + "'",
data_type.tf_to_np_str_list(cls.supported_types))
if y.dtype in cls.cast_map and cls.cast_map[y.dtype] is None:
exception.DTYPE_NOT_CAST_EXCEPT(
"LessOrEqual input " + node.inputs[1] + " with data type '" +
data_type.tf_to_np_str(y.dtype) + "'",
data_type.tf_to_np_str_list(cls.supported_types))
def args_check(cls, node, **kwargs):
# update cast map based on the auto_cast config option
cls.cast_map[tf.uint64] = tf.int64 if sys_config.auto_cast else None
inp_dtype = kwargs["tensor_dict"][node.inputs[0]].dtype
if inp_dtype in cls.cast_map and cls.cast_map[inp_dtype] is None:
exception.DTYPE_NOT_CAST_EXCEPT(
"Min input " + node.inputs[0] + " with data type '" +
data_type.tf_to_np_str(inp_dtype) + "'",
data_type.tf_to_np_str_list(cls.supported_types))
def args_check(cls, node, **kwargs):
x = kwargs["tensor_dict"][node.inputs[0]]
y = kwargs["tensor_dict"][node.inputs[1]]
# throw an error if the data type is not natively supported by
# Tensorflow, cannot be safely cast, and auto-cast option is False
if x.dtype in cls.cast_map and cls.cast_map[x.dtype] is None:
exception.DTYPE_NOT_CAST_EXCEPT(
"Mod input " + node.inputs[0] + " with data type '" +
data_type.tf_to_np_str(x.dtype) + "'",
data_type.tf_to_np_str_list(cls.supported_types))
# throw an error if inputs A and B are not in the same data type
if x.dtype != y.dtype:
exception.OP_UNSUPPORTED_EXCEPT("Mod with inputs in different data types",
"Tensorflow")
def args_check(cls, node, **kwargs):
# update cast maps based on the auto_cast config option
cls.x_cast_map[tf.uint64] = tf.int64 if sys_config.auto_cast else None
cls.x_cast_map[
tf.complex64] = tf.float64 if sys_config.auto_cast else None
cls.x_cast_map[
tf.complex128] = tf.float64 if sys_config.auto_cast else None
cls.roi_cast_map[
tf.float64] = tf.float32 if sys_config.auto_cast else None
x = kwargs["tensor_dict"][node.inputs[0]]
x_shape = x.get_shape().as_list()
x_dtype = x.dtype
if len(x_shape) != 4:
exception.OP_UNSUPPORTED_EXCEPT("Resize required 4D input",
"Tensorflow")
if x_dtype in cls.x_cast_map and cls.x_cast_map[x_dtype] is None:
exception.DTYPE_NOT_CAST_EXCEPT(
"Resize input " + node.inputs[0] + " with data type '" +
data_type.tf_to_np_str(x_dtype) + "'",
data_type.tf_to_np_str_list(cls.x_supported_types))
if cls.SINCE_VERSION >= 11:
# supported attributes combination
# ____________________________________________________________________________________________________________________________________________________
# | mode | coordinate_transformation_mode | cubic_coeff_a | exclude_outside | extrapolation_value | nearest_mode | scales | sizes |
# |_________|________________________________|_______________|_________________|_____________________|___________________|_______________|___________|
# | nearest | align_corners | not apply | 0 | not apply | round_prefer_ceil | supported (1) | supported |
# |---------|--------------------------------|---------------|-----------------|---------------------|-------------------|---------------|-----------|
# | nearest | asymmetric | not apply | 0 | not apply | floor | supported (1) | supported |
# |---------|--------------------------------|---------------|-----------------|---------------------|-------------------|---------------|-----------|
# | nearest | tf_half_pixel_for_nn | not apply | 0 | not apply | floor | supported (1) | supported |
# |---------|--------------------------------|---------------|-----------------|---------------------|-------------------|---------------|-----------|
# | linear | align_corners | not apply | 0 | not apply | not apply | supported (1) | supported |
# |---------|--------------------------------|---------------|-----------------|---------------------|-------------------|---------------|-----------|
# | linear | asymmetric | not apply | 0 | not apply | not apply | supported (1) | supported |
# |---------|--------------------------------|---------------|-----------------|---------------------|-------------------|---------------|-----------|
# | linear | half_pixel | not apply | 0 | not apply | not apply | supported (1) | supported |
# |---------|--------------------------------|---------------|-----------------|---------------------|-------------------|---------------|-----------|
# | cubic | align_corners | -0.5 | 1 | not apply | not apply | supported (1) | supported |
# |---------|--------------------------------|---------------|-----------------|---------------------|-------------------|---------------|-----------|
# | cubic | asymmetric | -0.5 | 1 | not apply | not apply | supported (1) | supported |
# |---------|--------------------------------|---------------|-----------------|---------------------|-------------------|---------------|-----------|
# | cubic | half_pixel | -0.5 | 1 | not apply | not apply | supported (1) | supported |
# |---------|--------------------------------|---------------|-----------------|---------------------|-------------------|---------------|-----------|
# | nearest | tf_crop_and_resize | not apply | 0 | any float value | round_prefer_ceil | supported | supported |
# |---------|--------------------------------|---------------|-----------------|---------------------|-------------------|---------------|-----------|
# | linear | tf_crop_and_resize | not apply | 0 | any float value | not apply | supported | supported |
# |---------|--------------------------------|---------------|-----------------|---------------------|-------------------|---------------|-----------|
# Note:
# 1. The accuracy of your model will go down, if the height and the width of the new sizes(scales * origial sizes) are not in whole numbers.
coordinate_transformation_mode = node.attrs.get(
"coordinate_transformation_mode", "half_pixel")
cubic_coeff_a = node.attrs.get("cubic_coeff_a", -0.75)
exclude_outside = node.attrs.get("exclude_outside", 0)
mode = node.attrs.get("mode", "nearest")
nearest_mode = node.attrs.get("nearest_mode", "round_prefer_floor")
if coordinate_transformation_mode == "tf_crop_and_resize":
if x_dtype in cls.cr_x_cast_map and cls.cr_x_cast_map[
x_dtype] is None:
exception.DTYPE_NOT_CAST_EXCEPT(
"Resize input " + node.inputs[0] +
" with data type '" + data_type.tf_to_np_str(x_dtype) +
"'",
data_type.tf_to_np_str_list(cls.cr_x_supported_types))
roi = kwargs["tensor_dict"][node.inputs[1]]
roi_dtype = roi.dtype
if roi_dtype in cls.roi_cast_map and cls.roi_cast_map[
roi_dtype] is None:
exception.DTYPE_NOT_CAST_EXCEPT(
"Resize input " + node.inputs[1] +
" with data type '" +
data_type.tf_to_np_str(roi_dtype) + "'",
data_type.tf_to_np_str_list(cls.roi_supported_types))
if coordinate_transformation_mode == "pytorch_half_pixel":
exception.OP_UNSUPPORTED_EXCEPT(
"Resize coordinate_transformation_mode=pytorch_half_pixel",
"Tensorflow")
if (coordinate_transformation_mode == "half_pixel"
and mode == "nearest"
) or (coordinate_transformation_mode == "tf_half_pixel_for_nn"
and mode in ["linear", "cubic"]) or (
coordinate_transformation_mode
== "tf_crop_and_resize" and mode == "cubic"):
exception.OP_UNSUPPORTED_EXCEPT(
"Resize coordinate_transformation_mode=" +
coordinate_transformation_mode + " and mode=" + mode,
"Tensorflow")
if (exclude_outside == 1 and mode in ["nearest", "linear"]) or (
exclude_outside == 0 and mode == "cubic"):
exception.OP_UNSUPPORTED_EXCEPT(
"Resize mode=" + mode + " and exclude_outside=" +
str(exclude_outside), "Tensorflow")
if cubic_coeff_a != -0.5 and mode == "cubic":
exception.OP_UNSUPPORTED_EXCEPT(
"Resize mode=cubic and cubic_coeff_a=" + cubic_coeff_a,
"Tensorflow")
if mode == "nearest":
if (nearest_mode in [
"round_prefer_floor", "ceil"
]) or (coordinate_transformation_mode
in ["align_corners", "tf_crop_and_resize"]
and nearest_mode == "floor") or (
coordinate_transformation_mode
in ["asymmetric", "tf_half_pixel_for_nn"]
and nearest_mode == "round_prefer_ceil"):
exception.OP_UNSUPPORTED_EXCEPT(
"Resize coordinate_transformation_mode=" +
coordinate_transformation_mode +
", mode=nearest and nearest_mode=" + nearest_mode,
"Tensorflow")
