wehub/paddlepaddle--paddle
1
0
Fork 0
Code Issues Pull Requests Actions 2 Deployments Models Agent Notes Packages Projects Releases Wiki Activity
Files
main
paddlepaddle--paddle/test/legacy_test/test_nearest_interp_v2_op.py
T
wehub-resource-sync e25996e7db chore: import upstream snapshot with attribution
2026-07-13 12:40:42 +08:00

1117 lines
34 KiB
Python
Executable File
Raw Permalink Blame History

# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# 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 unittest
import numpy as np
from op_test import (
OpTest,
convert_float_to_uint16,
get_device_place,
is_custom_device,
)
import paddle
from paddle import base
from paddle.base import core
from paddle.nn.functional import interpolate
paddle.enable_static()
def create_test_case0(self):
self.interp_method = 'nearest'
self.input_shape = [2, 3, 4, 5]
self.out_h = 2
self.out_w = 2
self.scale = []
self.out_size = np.array([3, 3]).astype("int32")
self.align_corners = True
def create_test_case1(self):
self.interp_method = 'nearest'
self.input_shape = [4, 1, 1, 7, 8]
self.out_d = 1
self.out_h = 1
self.out_w = 1
self.scale = []
self.align_corners = True
def create_test_case2(self):
self.interp_method = 'nearest'
self.input_shape = [3, 3, 9, 6]
self.out_h = 12
self.out_w = 12
self.scale = []
self.align_corners = True
def create_test_case3(self):
self.interp_method = 'nearest'
self.input_shape = [1, 1, 32, 64]
self.out_h = 64
self.out_w = 32
self.scale = []
self.align_corners = True
def create_test_case4(self):
self.interp_method = 'nearest'
self.input_shape = [4, 1, 7, 8]
self.out_h = 1
self.out_w = 1
self.scale = []
self.out_size = np.array([2, 2]).astype("int32")
self.align_corners = True
def create_test_case5(self):
self.interp_method = 'nearest'
self.input_shape = [3, 3, 9, 6]
self.out_h = 12
self.out_w = 12
self.scale = []
self.out_size = np.array([11, 11]).astype("int32")
self.align_corners = True
def create_test_case6(self):
self.interp_method = 'nearest'
self.input_shape = [1, 1, 32, 64]
self.out_h = 64
self.out_w = 32
self.scale = []
self.out_size = np.array([65, 129]).astype("int32")
self.align_corners = True
def nearest_interp_test(
x,
OutSize=None,
SizeTensor=None,
Scale=None,
data_layout='NCHW',
out_d=-1,
out_h=-1,
out_w=-1,
scale=[],
interp_method='nearest',
align_corners=True,
align_mode=0,
):
if isinstance(scale, (float, int)):
scale_list = []
for _ in range(len(x.shape) - 2):
scale_list.append(scale)
scale = list(map(float, scale_list))
elif isinstance(scale, (list, tuple)):
scale = list(map(float, scale))
if SizeTensor is not None:
if not isinstance(SizeTensor, list) and not isinstance(
SizeTensor, tuple
):
SizeTensor = [SizeTensor]
return paddle._C_ops.nearest_interp(
x,
OutSize,
SizeTensor,
Scale,
data_layout,
out_d,
out_h,
out_w,
scale,
interp_method,
align_corners,
align_mode,
)
def nearest_neighbor_interp_np(
X,
out_h,
out_w,
scale_h=0,
scale_w=0,
out_size=None,
actual_shape=None,
align_corners=True,
data_layout='NCHW',
):
"""nearest neighbor interpolation implement in shape [N, C, H, W]"""
if data_layout == "NHWC":
X = np.transpose(X, (0, 3, 1, 2)) # NHWC => NCHW
if out_size is not None:
out_h = out_size[0]
out_w = out_size[1]
if actual_shape is not None:
out_h = actual_shape[0]
out_w = actual_shape[1]
n, c, in_h, in_w = X.shape
ratio_h = ratio_w = 0.0
if out_h > 1:
if align_corners:
ratio_h = (in_h - 1.0) / (out_h - 1.0)
else:
if scale_h > 0:
ratio_h = 1.0 / scale_h
else:
ratio_h = 1.0 * in_h / out_h
if out_w > 1:
if align_corners:
ratio_w = (in_w - 1.0) / (out_w - 1.0)
else:
if scale_w > 0:
ratio_w = 1.0 / scale_w
else:
ratio_w = 1.0 * in_w / out_w
out = np.zeros((n, c, out_h, out_w))
if align_corners:
for i in range(out_h):
in_i = int(ratio_h * i + 0.5)
for j in range(out_w):
in_j = int(ratio_w * j + 0.5)
out[:, :, i, j] = X[:, :, in_i, in_j]
else:
for i in range(out_h):
in_i = int(ratio_h * i)
for j in range(out_w):
in_j = int(ratio_w * j)
out[:, :, i, j] = X[:, :, in_i, in_j]
if data_layout == "NHWC":
out = np.transpose(out, (0, 2, 3, 1)) # NCHW => NHWC
# out = np.expand_dims(out, 2)
return out.astype(X.dtype)
def nearest_neighbor_interp3d_np(
X,
out_d,
out_h,
out_w,
scale_d=0,
scale_h=0,
scale_w=0,
out_size=None,
actual_shape=None,
align_corners=True,
data_layout='NCHW',
):
"""nearest neighbor interpolation implement in shape [N, C, H, W]"""
if data_layout == "NHWC":
X = np.transpose(X, (0, 4, 1, 2, 3)) # NDHWC => NCDHW
if out_size is not None:
out_d = out_size[0]
out_h = out_size[1]
out_w = out_size[2]
if actual_shape is not None:
out_d = actual_shape[0]
out_h = actual_shape[1]
out_w = actual_shape[2]
n, c, in_d, in_h, in_w = X.shape
ratio_d = ratio_h = ratio_w = 0.0
if out_d > 1:
if align_corners:
ratio_d = (in_d - 1.0) / (out_d - 1.0)
else:
if scale_d > 0:
ratio_d = 1.0 / scale_d
else:
ratio_d = 1.0 * in_d / out_d
if out_h > 1:
if align_corners:
ratio_h = (in_h - 1.0) / (out_h - 1.0)
else:
if scale_h > 0:
ratio_h = 1.0 / scale_h
else:
ratio_h = 1.0 * in_h / out_h
if out_w > 1:
if align_corners:
ratio_w = (in_w - 1.0) / (out_w - 1.0)
else:
if scale_w > 0:
ratio_w = 1.0 / scale_w
else:
ratio_w = 1.0 * in_w / out_w
out = np.zeros((n, c, out_d, out_h, out_w))
if align_corners:
for d in range(out_d):
in_d = int(ratio_d * d + 0.5)
for i in range(out_h):
in_i = int(ratio_h * i + 0.5)
for j in range(out_w):
in_j = int(ratio_w * j + 0.5)
out[:, :, d, i, j] = X[:, :, in_d, in_i, in_j]
else:
for d in range(out_d):
in_d = int(ratio_d * d)
for i in range(out_h):
in_i = int(ratio_h * i)
for j in range(out_w):
in_j = int(ratio_w * j)
out[:, :, d, i, j] = X[:, :, in_d, in_i, in_j]
if data_layout == "NDHWC":
out = np.transpose(out, (0, 2, 3, 4, 1)) # NCDHW => NDHWC
return out.astype(X.dtype)
class TestNearestInterpOp(OpTest):
def setUp(self):
self.python_api = nearest_interp_test
self.out_size = None
self.actual_shape = None
self.data_layout = 'NCHW'
self.dtype = np.float64
self.init_test_case()
self.op_type = "nearest_interp_v2"
input_np = np.random.random(self.input_shape).astype(self.dtype)
if self.data_layout == "NCHW" and len(self.input_shape) == 4:
in_d = 1
in_h = self.input_shape[2]
in_w = self.input_shape[3]
else:
in_d = 1
in_h = self.input_shape[1]
in_w = self.input_shape[2]
if self.data_layout == "NCDHW" and len(self.input_shape) == 5:
in_d = self.input_shape[2]
in_h = self.input_shape[3]
in_w = self.input_shape[4]
else:
in_d = self.input_shape[1]
in_h = self.input_shape[2]
in_w = self.input_shape[3]
scale_d = 0
scale_h = 0
scale_w = 0
if self.scale:
if isinstance(self.scale, (float, int)):
if self.scale > 0:
scale_d = scale_h = scale_w = float(self.scale)
if isinstance(self.scale, list) and len(self.scale) == 1:
scale_d = scale_w = scale_h = self.scale[0]
elif isinstance(self.scale, list) and len(self.scale) > 1:
if len(self.scale) == 5:
scale_w = self.scale[2]
scale_h = self.scale[1]
scale_d = self.scale[0]
else:
scale_w = self.scale[1]
scale_h = self.scale[0]
out_h = int(in_h * scale_h)
out_w = int(in_w * scale_w)
out_d = int(in_d * scale_d)
else:
if len(self.input_shape) == 5:
out_d = self.out_d
out_h = self.out_h
out_w = self.out_w
if len(self.input_shape) == 4:
output_np = nearest_neighbor_interp_np(
input_np,
out_h,
out_w,
scale_h,
scale_w,
self.out_size,
self.actual_shape,
self.align_corners,
self.data_layout,
)
elif len(self.input_shape) == 5:
output_np = nearest_neighbor_interp3d_np(
input_np,
out_d,
out_h,
out_w,
scale_d,
scale_h,
scale_w,
self.out_size,
self.actual_shape,
self.align_corners,
self.data_layout,
)
self.inputs = {'X': input_np}
if self.out_size is not None:
self.inputs['OutSize'] = self.out_size
if self.actual_shape is not None:
self.inputs['OutSize'] = self.actual_shape
if len(self.input_shape) == 5:
self.attrs = {
'out_d': self.out_d,
'out_h': self.out_h,
'out_w': self.out_w,
'interp_method': self.interp_method,
'align_corners': self.align_corners,
'data_layout': self.data_layout,
}
else:
self.attrs = {
'out_h': self.out_h,
'out_w': self.out_w,
'interp_method': self.interp_method,
'align_corners': self.align_corners,
'data_layout': self.data_layout,
}
if self.scale:
if isinstance(self.scale, (float, int)):
if self.scale > 0:
self.scale = [self.scale]
if isinstance(self.scale, list) and len(self.scale) == 1:
self.scale = [self.scale[0], self.scale[0]]
self.attrs['scale'] = self.scale
self.outputs = {'Out': output_np}
def test_check_output(self):
self.check_output(
check_pir=True, check_symbol_infer=(self.out_size is None)
)
def test_check_grad(self):
self.check_grad(['X'], 'Out', in_place=True, check_pir=True)
def init_test_case(self):
create_test_case0(self)
class TestNearestNeighborInterpCase1(TestNearestInterpOp):
def init_test_case(self):
create_test_case1(self)
class TestNearestNeighborInterpCase2(TestNearestInterpOp):
def init_test_case(self):
create_test_case2(self)
class TestNearestNeighborInterpCase3(TestNearestInterpOp):
def init_test_case(self):
create_test_case3(self)
class TestNearestNeighborInterpCase4(TestNearestInterpOp):
def init_test_case(self):
create_test_case4(self)
class TestNearestNeighborInterpCase5(TestNearestInterpOp):
def init_test_case(self):
create_test_case5(self)
class TestNearestNeighborInterpCase6(TestNearestInterpOp):
def init_test_case(self):
create_test_case6(self)
class TestNearestNeighborInterpSame(TestNearestInterpOp):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [2, 3, 32, 64]
self.out_h = 32
self.out_w = 64
self.scale = []
self.align_corners = True
class TestNearestNeighborInterpActualShape(TestNearestInterpOp):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [3, 2, 32, 16]
self.out_h = 64
self.out_w = 32
self.scale = []
self.out_size = np.array([66, 40]).astype("int32")
self.align_corners = True
class TestNearestInterpOpFP16(TestNearestInterpOp):
def test_check_output(self):
self.check_output(
check_pir=True, check_symbol_infer=(self.out_size is None)
)
def test_check_grad(self):
self.check_grad(['X'], 'Out', in_place=True, check_pir=True)
def init_test_case(self):
create_test_case0(self)
self.dtype = np.float16
class TestNearestNeighborInterpCase1FP16(TestNearestInterpOpFP16):
def init_test_case(self):
create_test_case1(self)
self.dtype = np.float16
class TestNearestNeighborInterpCase2FP16(TestNearestInterpOpFP16):
def init_test_case(self):
create_test_case2(self)
self.dtype = np.float16
class TestNearestNeighborInterpCase3FP16(TestNearestInterpOpFP16):
def init_test_case(self):
create_test_case3(self)
self.dtype = np.float16
class TestNearestNeighborInterpCase4FP16(TestNearestInterpOpFP16):
def init_test_case(self):
create_test_case4(self)
self.dtype = np.float16
class TestNearestNeighborInterpCase5FP16(TestNearestInterpOpFP16):
def init_test_case(self):
create_test_case5(self)
self.dtype = np.float16
class TestNearestNeighborInterpCase6FP16(TestNearestInterpOpFP16):
def init_test_case(self):
create_test_case6(self)
self.dtype = np.float16
@unittest.skipIf(
not (core.is_compiled_with_cuda() or is_custom_device())
or not core.is_bfloat16_supported(get_device_place()),
"core is not compiled with CUDA or not support the bfloat16",
)
class TestNearestInterpOpBF16(OpTest):
def setUp(self):
self.python_api = nearest_interp_test
self.out_size = None
self.actual_shape = None
self.data_layout = 'NCHW'
self.init_test_case()
self.op_type = "nearest_interp_v2"
self.dtype = np.uint16
input_np = np.random.random(self.input_shape).astype("float32")
if self.data_layout == "NCHW" and len(self.input_shape) == 4:
in_d = 1
in_h = self.input_shape[2]
in_w = self.input_shape[3]
else:
in_d = 1
in_h = self.input_shape[1]
in_w = self.input_shape[2]
if self.data_layout == "NCDHW" and len(self.input_shape) == 5:
in_d = self.input_shape[2]
in_h = self.input_shape[3]
in_w = self.input_shape[4]
else:
in_d = self.input_shape[1]
in_h = self.input_shape[2]
in_w = self.input_shape[3]
scale_d = 0
scale_h = 0
scale_w = 0
if self.scale:
if isinstance(self.scale, (float, int)):
if self.scale > 0:
scale_d = scale_h = scale_w = float(self.scale)
if isinstance(self.scale, list) and len(self.scale) == 1:
scale_d = scale_w = scale_h = self.scale[0]
elif isinstance(self.scale, list) and len(self.scale) > 1:
if len(self.scale) == 5:
scale_w = self.scale[2]
scale_h = self.scale[1]
scale_d = self.scale[0]
else:
scale_w = self.scale[1]
scale_h = self.scale[0]
out_h = int(in_h * scale_h)
out_w = int(in_w * scale_w)
out_d = int(in_d * scale_d)
else:
if len(self.input_shape) == 5:
out_d = self.out_d
out_h = self.out_h
out_w = self.out_w
if len(self.input_shape) == 4:
output_np = nearest_neighbor_interp_np(
input_np,
out_h,
out_w,
scale_h,
scale_w,
self.out_size,
self.actual_shape,
self.align_corners,
self.data_layout,
)
elif len(self.input_shape) == 5:
output_np = nearest_neighbor_interp3d_np(
input_np,
out_d,
out_h,
out_w,
scale_d,
scale_h,
scale_w,
self.out_size,
self.actual_shape,
self.align_corners,
self.data_layout,
)
self.inputs = {'X': convert_float_to_uint16(input_np)}
if self.out_size is not None:
self.inputs['OutSize'] = self.out_size
if self.actual_shape is not None:
self.inputs['OutSize'] = self.actual_shape
if len(self.input_shape) == 5:
self.attrs = {
'out_d': self.out_d,
'out_h': self.out_h,
'out_w': self.out_w,
'interp_method': self.interp_method,
'align_corners': self.align_corners,
'data_layout': self.data_layout,
}
else:
self.attrs = {
'out_h': self.out_h,
'out_w': self.out_w,
'interp_method': self.interp_method,
'align_corners': self.align_corners,
'data_layout': self.data_layout,
}
if self.scale:
if isinstance(self.scale, (float, int)):
if self.scale > 0:
self.scale = [self.scale]
if isinstance(self.scale, list) and len(self.scale) == 1:
self.scale = [self.scale[0], self.scale[0]]
self.attrs['scale'] = self.scale
self.outputs = {'Out': convert_float_to_uint16(output_np)}
def test_check_output(self):
self.check_output(
check_pir=True, check_symbol_infer=(self.out_size is None)
)
def test_check_grad(self):
self.check_grad(['X'], 'Out', in_place=True, check_pir=True)
def init_test_case(self):
create_test_case0(self)
class TestNearestNeighborInterpCase1BF16(TestNearestInterpOpBF16):
def init_test_case(self):
create_test_case1(self)
@unittest.skipIf(
not (core.is_compiled_with_cuda() or is_custom_device())
or not core.is_bfloat16_supported(get_device_place()),
"core is not compiled with CUDA or not support the bfloat16",
)
class TestNearestNeighborInterpCase2BF16(TestNearestInterpOpBF16):
def init_test_case(self):
create_test_case2(self)
@unittest.skipIf(
not (core.is_compiled_with_cuda() or is_custom_device())
or not core.is_bfloat16_supported(get_device_place()),
"core is not compiled with CUDA or not support the bfloat16",
)
class TestNearestNeighborInterpCase3BF16(TestNearestInterpOpBF16):
def init_test_case(self):
create_test_case3(self)
@unittest.skipIf(
not (core.is_compiled_with_cuda() or is_custom_device())
or not core.is_bfloat16_supported(get_device_place()),
"core is not compiled with CUDA or not support the bfloat16",
)
class TestNearestNeighborInterpCase4BF16(TestNearestInterpOpBF16):
def init_test_case(self):
create_test_case4(self)
@unittest.skipIf(
not (core.is_compiled_with_cuda() or is_custom_device())
or not core.is_bfloat16_supported(get_device_place()),
"core is not compiled with CUDA or not support the bfloat16",
)
class TestNearestNeighborInterpCase5BF16(TestNearestInterpOpBF16):
def init_test_case(self):
create_test_case5(self)
@unittest.skipIf(
not (core.is_compiled_with_cuda() or is_custom_device())
or not core.is_bfloat16_supported(get_device_place()),
"core is not compiled with CUDA or not support the bfloat16",
)
class TestNearestNeighborInterpCase6BF16(TestNearestInterpOpBF16):
def init_test_case(self):
create_test_case6(self)
class TestNearestNeighborInterpDataLayout(TestNearestInterpOp):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [2, 4, 4, 5]
self.out_h = 2
self.out_w = 2
self.scale = []
self.out_size = np.array([3, 8]).astype("int32")
self.align_corners = True
self.data_layout = "NHWC"
class TestNearestInterpOpUint8(OpTest):
def setUp(self):
self.python_api = nearest_interp_test
self.out_size = None
self.actual_shape = None
self.init_test_case()
self.op_type = "nearest_interp_v2"
input_np = np.random.randint(
low=0, high=256, size=self.input_shape
).astype("uint8")
if self.scale:
if isinstance(self.scale, (float, int)):
if self.scale > 0:
scale_h = scale_w = float(self.scale)
if isinstance(self.scale, list) and len(self.scale) == 1:
scale_w = scale_h = self.scale[0]
elif isinstance(self.scale, list) and len(self.scale) > 1:
scale_w = self.scale[1]
scale_h = self.scale[0]
out_h = int(self.input_shape[2] * scale_h)
out_w = int(self.input_shape[3] * scale_w)
else:
out_h = self.out_h
out_w = self.out_w
output_np = nearest_neighbor_interp_np(
input_np,
out_h,
out_w,
0,
0,
self.out_size,
self.actual_shape,
self.align_corners,
)
self.inputs = {'X': input_np}
if self.out_size is not None:
self.inputs['OutSize'] = self.out_size
self.attrs = {
'out_h': self.out_h,
'out_w': self.out_w,
'interp_method': self.interp_method,
'align_corners': self.align_corners,
}
if self.scale:
if isinstance(self.scale, (float, int)):
if self.scale > 0:
self.scale = [self.scale]
if isinstance(self.scale, list) and len(self.scale) == 1:
self.scale = [self.scale[0], self.scale[0]]
self.attrs['scale'] = self.scale
self.outputs = {'Out': output_np}
def test_check_output(self):
self.check_output_with_place(
place=core.CPUPlace(),
atol=1,
check_pir=True,
check_symbol_infer=(self.out_size is None),
)
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [1, 3, 9, 6]
self.out_h = 10
self.out_w = 9
self.scale = []
self.align_corners = True
class TestNearestNeighborInterpCase1Uint8(TestNearestInterpOpUint8):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [2, 3, 32, 64]
self.out_h = 80
self.out_w = 40
self.scale = []
self.align_corners = True
class TestNearestNeighborInterpCase2Uint8(TestNearestInterpOpUint8):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [4, 1, 7, 8]
self.out_h = 5
self.out_w = 13
self.scale = []
self.out_size = np.array([6, 15]).astype("int32")
self.align_corners = True
class TestNearestInterpWithoutCorners(TestNearestInterpOp):
def set_align_corners(self):
self.align_corners = False
class TestNearestNeighborInterpScale1(TestNearestInterpOp):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [3, 2, 7, 5]
self.out_h = 64
self.out_w = 32
self.scale = 2.0
self.out_size = np.array([66, 40]).astype("int32")
self.align_corners = True
class TestNearestNeighborInterpScale2(TestNearestInterpOp):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [3, 2, 5, 7]
self.out_h = 64
self.out_w = 32
self.scale = 1.5
self.out_size = np.array([66, 40]).astype("int32")
self.align_corners = True
class TestNearestNeighborInterpScale3(TestNearestInterpOp):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [3, 2, 7, 5]
self.out_h = 64
self.out_w = 32
self.scale = [2.0, 3.0]
self.out_size = np.array([66, 40]).astype("int32")
self.align_corners = True
class TestNearestNeighbor3DInterp(TestNearestInterpOp):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [3, 2, 4, 7, 5]
self.out_d = 8
self.out_h = 64
self.out_w = 32
self.scale = [4.0, 2.0, 3.0]
self.out_size = np.array([8, 66, 40]).astype("int32")
self.align_corners = True
class TestNearestInterpOp_attr_tensor(OpTest):
def setUp(self):
self.python_api = nearest_interp_test
self.out_size = None
self.actual_shape = None
self.shape_by_1Dtensor = False
self.scale_by_1Dtensor = False
self.scale_by_2Dtensor = False
self.init_test_case()
self.op_type = "nearest_interp_v2"
self.attrs = {
'interp_method': self.interp_method,
'align_corners': self.align_corners,
}
input_np = np.random.random(self.input_shape).astype("float64")
self.inputs = {'X': input_np}
if self.scale_by_1Dtensor:
self.inputs['Scale'] = np.array([self.scale]).astype("float32")
out_h = int(self.input_shape[2] * self.scale)
out_w = int(self.input_shape[3] * self.scale)
elif self.scale_by_2Dtensor:
self.inputs['Scale'] = np.array(self.scale).astype("float32")
out_h = int(self.input_shape[2] * self.scale[0])
out_w = int(self.input_shape[3] * self.scale[1])
elif self.scale:
if isinstance(self.scale, (float, int)):
if self.scale > 0:
scale_h = scale_w = float(self.scale)
if isinstance(self.scale, list) and len(self.scale) == 1:
scale_w = scale_h = self.scale[0]
elif isinstance(self.scale, list) and len(self.scale) > 1:
scale_w = self.scale[1]
scale_h = self.scale[0]
out_h = int(self.input_shape[2] * scale_h)
out_w = int(self.input_shape[3] * scale_w)
else:
out_h = self.out_h
out_w = self.out_w
if self.shape_by_1Dtensor:
self.inputs['OutSize'] = self.out_size
elif self.out_size is not None:
size_tensor = []
for index, ele in enumerate(self.out_size):
size_tensor.append(
("x" + str(index), np.ones(1).astype('int32') * ele)
)
self.inputs['SizeTensor'] = size_tensor
self.attrs['out_h'] = self.out_h
self.attrs['out_w'] = self.out_w
if self.scale:
if isinstance(self.scale, (float, int)):
if self.scale > 0:
self.scale = [self.scale]
if isinstance(self.scale, list) and len(self.scale) == 1:
self.scale = [self.scale[0], self.scale[0]]
self.attrs['scale'] = self.scale
output_np = nearest_neighbor_interp_np(
input_np,
out_h,
out_w,
0,
0,
self.out_size,
self.actual_shape,
self.align_corners,
)
self.outputs = {'Out': output_np}
def test_check_output(self):
self.check_output(
check_pir=True, check_symbol_infer=(self.out_size is None)
)
def test_check_grad(self):
self.check_grad(['X'], 'Out', in_place=True, check_pir=True)
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [2, 5, 4, 4]
self.out_h = 3
self.out_w = 3
self.scale = []
self.out_size = [3, 3]
self.align_corners = True
# out_size is a tensor list
class TestNearestInterp_attr_tensor_Case1(TestNearestInterpOp_attr_tensor):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [3, 3, 9, 6]
self.out_h = 12
self.out_w = 12
self.scale = []
self.out_size = [8, 12]
self.align_corners = True
# out_size is a 1-D tensor
class TestNearestInterp_attr_tensor_Case2(TestNearestInterpOp_attr_tensor):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [3, 2, 32, 16]
self.out_h = 64
self.out_w = 32
self.scale = []
self.out_size = np.array([66, 40]).astype("int32")
self.align_corners = True
self.shape_by_1Dtensor = True
# scale is a 1-D tensor
class TestNearestInterp_attr_tensor_Case3(TestNearestInterpOp_attr_tensor):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [3, 2, 32, 16]
self.out_h = 64
self.out_w = 32
self.scale = 2.0
self.out_size = None
self.align_corners = True
self.scale_by_1Dtensor = True
# scale is a 2-D tensor
class TestNearestInterp_attr_tensor_Case4(TestNearestInterpOp_attr_tensor):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [3, 2, 32, 16]
self.out_h = 64
self.out_w = 32
self.scale = [2.0, 2.0]
self.out_size = None
self.align_corners = True
self.scale_by_2Dtensor = True
class TestNearestInterpOpAPI_dy(unittest.TestCase):
def test_case(self):
import paddle
if core.is_compiled_with_cuda() or is_custom_device():
place = get_device_place()
else:
place = core.CPUPlace()
with base.dygraph.guard(place):
input_data = np.random.random((2, 3, 6, 6)).astype("int64")
scale_np = np.array([2, 2]).astype("int64")
input_x = paddle.to_tensor(input_data)
scale = paddle.to_tensor(scale_np)
expect_res = nearest_neighbor_interp_np(
input_data, out_h=12, out_w=12, align_corners=False
)
out = interpolate(
x=input_x,
scale_factor=scale,
mode="nearest",
align_corners=False,
)
np.testing.assert_allclose(out.numpy(), expect_res, rtol=1e-05)
class TestNearestInterp3DOpAPI_dy(unittest.TestCase):
def test_case(self):
import paddle
if core.is_compiled_with_cuda() or is_custom_device():
place = get_device_place()
else:
place = core.CPUPlace()
with base.dygraph.guard(place):
input_data = np.random.random((2, 2, 6, 6, 6)).astype("int64")
scale_np = np.array([2, 2, 2]).astype("int64")
input_x = paddle.to_tensor(input_data)
scale = paddle.to_tensor(scale_np)
expect_res = nearest_neighbor_interp3d_np(
input_data, out_d=12, out_h=12, out_w=12, align_corners=False
)
out = interpolate(
x=input_x,
scale_factor=scale,
mode="nearest",
align_corners=False,
data_format="NCDHW",
)
np.testing.assert_allclose(out.numpy(), expect_res, rtol=1e-05)
@unittest.skipIf(
not (base.core.is_compiled_with_cuda() or is_custom_device()),
"core is not compiled with CUDA",
)
class TestNearestInterp3DOpForFloat16(unittest.TestCase):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [2, 2, 6, 6, 6]
self.scale = [2, 2, 2]
self.align_corners = False
self.data_layout = 'NCDHW'
def check_main(self, x_np, dtype):
paddle.disable_static()
x_np = x_np.astype(dtype)
x = paddle.to_tensor(x_np)
x.stop_gradient = False
y = interpolate(
x,
scale_factor=self.scale,
mode=self.interp_method,
align_corners=self.align_corners,
data_format=self.data_layout,
)
x_g = paddle.grad(y, x)
y_np = y[0].numpy().astype('float32')
x_g_np = x_g[0].numpy().astype('float32')
paddle.enable_static()
return y_np, x_g_np
def test_main(self):
self.init_test_case()
x_np = np.random.random(self.input_shape).astype("float16")
y_np_1, x_g_np_1 = self.check_main(x_np, 'float16')
y_np_2, x_g_np_2 = self.check_main(x_np, 'float32')
# forward
np.testing.assert_allclose(y_np_1, y_np_2, rtol=1e-03)
# backward
np.testing.assert_allclose(x_g_np_1, x_g_np_2)
@unittest.skipIf(
not (base.core.is_compiled_with_cuda() or is_custom_device()),
"core is not compiled with CUDA",
)
class TestNearestInterpOpForFloat16(unittest.TestCase):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [2, 2, 6, 6]
self.scale = [2, 2]
self.align_corners = False
def check_main(self, x_np, dtype):
paddle.disable_static()
x_np = x_np.astype(dtype)
x = paddle.to_tensor(x_np)
x.stop_gradient = False
y = interpolate(
x,
scale_factor=self.scale,
mode=self.interp_method,
align_corners=self.align_corners,
)
x_g = paddle.grad(y, x)
y_np = y[0].numpy().astype('float32')
x_g_np = x_g[0].numpy().astype('float32')
paddle.enable_static()
return y_np, x_g_np
def test_main(self):
self.init_test_case()
x_np = np.random.random(self.input_shape).astype("float16")
y_np_1, x_g_np_1 = self.check_main(x_np, 'float16')
y_np_2, x_g_np_2 = self.check_main(x_np, 'float32')
# forward
np.testing.assert_allclose(y_np_1, y_np_2)
# backward
np.testing.assert_allclose(x_g_np_1, x_g_np_2)
if __name__ == "__main__":
unittest.main()
Reference in New Issue View Git Blame Copy Permalink