#
# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import numbers
from dataclasses import dataclass
from typing import Optional, Sequence
from tripy import constraints, export
from tripy.common.exception import raise_error
from tripy.frontend import utils as frontend_utils
from tripy.frontend.trace.ops.base import BaseTraceOp
from tripy.types import ShapeLike
import tripy.frontend.trace.ops.utils as op_utils
@dataclass(repr=False)
class Resize(BaseTraceOp):
mode: str
scales: Optional[Sequence[float]]
align_corners: bool
infer_rank = op_utils.InferRankPolicies.same_as_input()
def infer_dtypes(self):
self.outputs[0].dtype = self.inputs[0].dtype
def to_flat_ir(self, inputs, outputs):
from tripy.flat_ir.ops import ResizeCubicOp, ResizeLinearOp, ResizeNearestOp
if self.scales:
from tripy.common.datatype import float32, int32
from tripy.flat_ir.ops import ConstantOp, ConvertOp, MulOp
from tripy.flat_ir.tensor import FlatIRTensor
# construct output_shape using scales
# inputs[1] is input[0].shape
# output_shape = (inputs[1].cast(fp32) * scales).cast(int32)
out_shape = (inputs[0].rank,)
scales_tensor = FlatIRTensor.build(
shape=out_shape,
rank=1,
dtype=float32,
device=outputs[0].device,
reason_details=[f"create scales tensor in resize op."],
)
ConstantOp.build([], [scales_tensor], data=self.scales)
input_shape_f32 = FlatIRTensor.build(
shape=out_shape,
rank=1,
dtype=float32,
device=outputs[0].device,
reason_details=[f"convert input shape tensor to float32 in resize op."],
)
ConvertOp.build([inputs[1]], [input_shape_f32])
out_shape_f32 = FlatIRTensor.build(
shape=out_shape,
rank=1,
dtype=float32,
device=outputs[0].device,
reason_details=[f"compute output shape in resize op."],
)
MulOp.build([input_shape_f32, scales_tensor], [out_shape_f32])
out_shape_tensor = FlatIRTensor.build(
shape=out_shape,
rank=1,
dtype=int32,
device=outputs[0].device,
reason_details=[f"convert output shape to int32 in resize op."],
)
ConvertOp.build([out_shape_f32], [out_shape_tensor])
inputs[1] = out_shape_tensor
if self.mode == "nearest":
ResizeNearestOp.build(inputs, outputs)
elif self.mode == "cubic":
ResizeCubicOp.build(inputs, outputs, self.align_corners, cubic_coeff=-0.75)
else:
ResizeLinearOp.build(inputs, outputs, self.align_corners)
def _check_mode(mode: str, align_corners: bool):
supported_modes = ("cubic", "linear", "nearest")
if mode not in supported_modes:
raise_error(
"Unsupported resize mode.",
[f"Supported modes are {supported_modes}, but got {mode}."],
)
if align_corners and mode not in ("cubic", "linear"):
raise_error("align_corners can only be set with `cubic` or `linear` mode.")
@export.public_api(document_under="operations/functions")
@frontend_utils.convert_to_tensors()
@constraints.dtypes(
constraints={"input": "T1", constraints.RETURN_VALUE: "T1"},
variables={"T1": ["float32", "float16", "int8"]},
)
def resize(input: "tripy.Tensor", mode: str, output_shape: ShapeLike, align_corners: bool = False) -> "tripy.Tensor":
r"""
Resizes the input tensor.
Args:
input: The input tensor.
mode: The resize operation's algorithm. Must be one of: ["cubic", linear", "nearest"].
output_shape: The output shape of the resize operation.
align_corners: If set to ``True``, the input and output tensors are aligned by the
center points of their corner pixels, preserving the values at the corner pixels.
If set to ``False``, the input and output tensors are aligned by the corner points of
their corner pixels. Only in effect when ``mode`` is ``"cubic"`` or ``"linear"``.
Returns:
The output tensor after the resize operation.
.. code-block:: python
:linenos:
:caption: Example
input = tp.reshape(tp.arange(16, dtype=tp.float32), (1, 1, 4, 4))
output = tp.resize(input, "nearest", output_shape=(1, 1, 8, 8))
input_torch = torch.arange(16, dtype=torch.float32).reshape((1, 1, 4, 4)) # doc: omit
expected = torch.nn.functional.interpolate(input_torch, scale_factor=2.0, mode="nearest") # doc: omit
assert torch.allclose(torch.from_dlpack(output).to("cpu"), expected)
"""
_check_mode(mode, align_corners)
return Resize.build([input, output_shape], mode, scales=None, align_corners=align_corners)
[docs]
@export.public_api(document_under="operations/functions")
@constraints.dtypes(
constraints={"input": "T1", constraints.RETURN_VALUE: "T1"},
variables={"T1": ["float32", "float16", "int8"]},
)
def resize(
input: "tripy.Tensor", mode: str, scales: Sequence[numbers.Number], align_corners: bool = False
) -> "tripy.Tensor":
r"""
Resizes the input tensor.
Args:
input: The input tensor.
mode: The resize operation's algorithm. Must be one of: ["cubic", linear", "nearest"].
scales: A sequence of scale factors for each dimension. Must have
the same length as input tensor's rank.
align_corners: If set to ``True``, the input and output tensors are aligned by the
center points of their corner pixels, preserving the values at the corner pixels.
If set to ``False``, the input and output tensors are aligned by the corner points of
their corner pixels. Only in effect when ``mode`` is ``"cubic"`` or ``"linear"``.
Returns:
The output tensor after the resize operation.
.. code-block:: python
:linenos:
:caption: Example
input = tp.reshape(tp.arange(16, dtype=tp.float32), (1, 1, 4, 4))
output = tp.resize(input, "nearest", scales=(1, 1, 2, 2))
input_torch = torch.arange(16, dtype=torch.float32).reshape((1, 1, 4, 4)) # doc: omit
expected = torch.nn.functional.interpolate(input_torch, scale_factor=2.0, mode="nearest") # doc: omit
assert torch.allclose(torch.from_dlpack(output).to("cpu"), expected)
"""
_check_mode(mode, align_corners)
return Resize.build([input, frontend_utils.tensor_from_shape_like(input.shape)], mode, scales, align_corners)