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回退 'Pull Request !45880 : add bicubic mode in interpolate'
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@ -28,7 +28,7 @@ mindspore.ops.interpolate
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old_i = new_length != 0 ? new_i * old_length / new_length : 0 # if set to 'asymmetric'
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- **mode** (str) - 所使用的插值方式。目前支持"linear"，"bilinear"和"bicubic"插值方式。默认值："linear"。
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- **mode** (str) - 所使用的插值方式。目前支持"linear"和"bilinear"插值方式。默认值："linear"。
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返回：
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Tensor，数据类型与 `x` 相同。
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@ -1902,7 +1902,7 @@ def _check_interpolate_inputs(input_dims, roi, scales, sizes, coordinate_transfo
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validator.check_value_type("mode", mode, [str], prim_name)
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if mode == "linear":
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validator.check_int(input_dims, 3, Rel.EQ, "input dims", prim_name)
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elif mode in ["bilinear", "bicubic"]:
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elif mode == "bilinear":
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validator.check_int(input_dims, 4, Rel.EQ, "input dims", prim_name)
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else:
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raise ValueError(f"{msg_prefix} mode must be 'linear' or 'bilinear', but got {mode}")
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@ -1937,16 +1937,12 @@ def _interpolate_output_shape(shape, scales, sizes, mode):
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if sizes is not None:
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if mode == "bilinear":
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return sizes
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if mode == "bicubic":
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return Tensor(sizes, dtype=mstype.int32)
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return Tensor(sizes)
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ret = ()
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for i in range(2, len(shape)):
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ret = ret + (int(scales[i] * shape[i]),)
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if mode == "bilinear":
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return ret
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if mode == "bicubic":
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return Tensor(ret, dtype=mstype.int32)
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return Tensor(ret)
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@ -1989,7 +1985,7 @@ def interpolate(x, roi=None, scales=None, sizes=None, coordinate_transformation_
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old_i = new_length != 0 ? new_i * old_length / new_length : 0 # if set to 'asymmetric'
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mode (str): The method used to interpolate: 'linear' | 'bilinear' | 'bicubic'. Default is 'linear'.
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mode (str): The method used to interpolate: 'linear' | 'bilinear'. Default is 'linear'.
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Returns:
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Resized tensor, with the same data type as input `x`.
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@ -2030,26 +2026,26 @@ def interpolate(x, roi=None, scales=None, sizes=None, coordinate_transformation_
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raise TypeError("For interpolate, the input x must be tensor")
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input_shape = x.shape
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input_dims = len(input_shape)
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_check_interpolate_inputs(input_dims, roi, scales, sizes, coordinate_transformation_mode, mode, "interpolate")
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_check_interpolate_inputs(input_dims, roi, scales, sizes, coordinate_transformation_mode, mode,
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"interpolate")
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output_size = _interpolate_output_shape(input_shape, scales, sizes, mode)
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if mode == "linear":
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resize_linear_inner = _get_cache_prim(IMG.ResizeLinear1D)(
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coordinate_transformation_mode=coordinate_transformation_mode)
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return resize_linear_inner(x, output_size)
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if mode in ["bilinear", "bicubic"]:
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if mode == "bilinear":
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align_corners = False
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half_pixel_centers = False
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if coordinate_transformation_mode == "align_corners":
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align_corners = True
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elif coordinate_transformation_mode == "half_pixel":
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half_pixel_centers = True
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if mode == "bilinear":
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resize_bilinear_inner = _get_cache_prim(IMG.ResizeBilinearV2)(align_corners, half_pixel_centers)
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return resize_bilinear_inner(x, output_size)
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if mode == "bicubic":
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resize_bicubic_inner = _get_cache_prim(IMG.ResizeBicubic)(align_corners, half_pixel_centers)
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return resize_bicubic_inner(x.transpose((0, 2, 3, 1)), output_size).transpose((0, 3, 1, 2))
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raise TypeError("Input Error: For interpolate, {} mode is not support now".format(mode))
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resize_bilinear_inner = _get_cache_prim(IMG.ResizeBilinearV2)(align_corners, half_pixel_centers)
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return resize_bilinear_inner(x, output_size)
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raise TypeError(
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"Input Error: For interpolate, {} mode is not support now".format(mode))
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def softsign(x):
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@ -1,62 +0,0 @@
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# Copyright 2022 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import numpy as np
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import pytest
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import mindspore as ms
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import mindspore.nn as nn
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import mindspore.ops as ops
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class Net(nn.Cell):
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def __init__(self, coordinate_transformation_mode, mode):
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super(Net, self).__init__()
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self.coordinate_transformation_mode = coordinate_transformation_mode
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self.mode = mode
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def construct(self, x, scales=None, sizes=None):
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return ops.interpolate(x, None, scales, sizes, self.coordinate_transformation_mode, self.mode)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_arm_cpu
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('mode', [ms.GRAPH_MODE, ms.PYNATIVE_MODE])
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def test_interpolate_normal(mode):
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"""
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Feature: interpolate
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Description: Verify the result of interpolate
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Expectation: success
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"""
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ms.set_context(mode=mode)
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net = Net("align_corners", "bicubic")
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x = ops.arange(4, dtype=ms.float32).reshape((1, 1, 2, 2))
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out1 = net(x, sizes=(4, 6))
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expect_out1 = np.array([[[[0.0000, 0.1762, 0.3884, 0.6116, 0.8238, 1.0000],
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[0.6289, 0.8051, 1.0174, 1.2405, 1.4527, 1.6289],
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[1.3711, 1.5473, 1.7595, 1.9826, 2.1949, 2.3711],
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[2.0000, 2.1762, 2.3884, 2.6116, 2.8238, 3.0000]]]])
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assert np.allclose(out1.asnumpy(), expect_out1, rtol=1e-4)
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out2 = net(x, scales=(1.0, 1.0, 3.0, 3.0))
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expect_out2 = np.array([[[[0.0000, 0.1762, 0.3884, 0.6116, 0.8238, 1.0000],
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[0.3524, 0.5286, 0.7408, 0.9640, 1.1762, 1.3524],
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[0.7769, 0.9531, 1.1653, 1.3884, 1.6007, 1.7769],
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[1.2231, 1.3993, 1.6116, 1.8347, 2.0469, 2.2231],
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[1.6476, 1.8238, 2.0360, 2.2592, 2.4714, 2.6476],
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[2.0000, 2.1762, 2.3884, 2.6116, 2.8238, 3.0000]]]])
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assert np.allclose(out2.asnumpy(), expect_out2, rtol=1e-4)
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