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paddlepaddle--paddle/test/legacy_test/test_pool3d_op.py
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wehub-resource-sync e25996e7db chore: import upstream snapshot with attribution
2026-07-13 12:40:42 +08:00

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# 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, get_device_place, is_custom_device
import paddle
from paddle.base import core
from paddle.framework import in_dynamic_mode
def adaptive_start_index(index, input_size, output_size):
return int(np.floor(index * input_size / output_size))
def adaptive_end_index(index, input_size, output_size):
return int(np.ceil((index + 1) * input_size / output_size))
def pool3D_forward_naive(
x,
ksize,
strides,
paddings,
global_pool=0,
ceil_mode=False,
exclusive=True,
adaptive=False,
data_format='NCDHW',
pool_type='max',
padding_algorithm="EXPLICIT",
):
# update paddings
def _get_padding_with_SAME(input_shape, pool_size, pool_stride):
padding = []
for input_size, filter_size, stride_size in zip(
input_shape, pool_size, pool_stride
):
out_size = int((input_size + stride_size - 1) / stride_size)
pad_sum = np.max(
((out_size - 1) * stride_size + filter_size - input_size, 0)
)
pad_0 = int(pad_sum / 2)
pad_1 = int(pad_sum - pad_0)
padding.append(pad_0)
padding.append(pad_1)
return padding
if isinstance(padding_algorithm, str):
padding_algorithm = padding_algorithm.upper()
if padding_algorithm not in ["SAME", "VALID", "EXPLICIT"]:
raise ValueError(
f"Unknown Attr(padding_algorithm): '{padding_algorithm}'. "
"It can only be 'SAME' or 'VALID'."
)
if padding_algorithm == "VALID":
paddings = [0, 0, 0, 0, 0, 0]
if ceil_mode is not False:
raise ValueError(
'When Attr(pool_padding) is "VALID", Attr(ceil_mode)'
" must be False. "
"Received ceil_mode: True."
)
elif padding_algorithm == "SAME":
input_data_shape = []
if data_format == "NCDHW":
input_data_shape = x.shape[2:5]
elif data_format == "NDHWC":
input_data_shape = x.shape[1:4]
paddings = _get_padding_with_SAME(input_data_shape, ksize, strides)
assert len(paddings) == 3 or len(paddings) == 6
is_sys = True if len(paddings) == 3 else False
N = x.shape[0]
C, D, H, W = (
[x.shape[1], x.shape[2], x.shape[3], x.shape[4]]
if data_format == 'NCDHW'
else [x.shape[4], x.shape[1], x.shape[2], x.shape[3]]
)
if global_pool == 1:
ksize = [D, H, W]
paddings = [0 for _ in range(len(paddings))]
pad_d_forth = paddings[0] if is_sys else paddings[0]
pad_d_back = paddings[0] if is_sys else paddings[1]
pad_h_up = paddings[1] if is_sys else paddings[2]
pad_h_down = paddings[1] if is_sys else paddings[3]
pad_w_left = paddings[2] if is_sys else paddings[4]
pad_w_right = paddings[2] if is_sys else paddings[5]
if adaptive:
D_out, H_out, W_out = ksize
else:
D_out = (
(D - ksize[0] + pad_d_forth + pad_d_back + strides[0] - 1)
// strides[0]
+ 1
if ceil_mode
else (D - ksize[0] + pad_d_forth + pad_d_back) // strides[0] + 1
)
H_out = (
(H - ksize[1] + pad_h_up + pad_h_down + strides[1] - 1)
// strides[1]
+ 1
if ceil_mode
else (H - ksize[1] + pad_h_up + pad_h_down) // strides[1] + 1
)
W_out = (
(W - ksize[2] + pad_w_left + pad_w_right + strides[2] - 1)
// strides[2]
+ 1
if ceil_mode
else (W - ksize[2] + pad_w_left + pad_w_right) // strides[2] + 1
)
out = (
np.zeros((N, C, D_out, H_out, W_out))
if data_format == 'NCDHW'
else np.zeros((N, D_out, H_out, W_out, C))
)
for k in range(D_out):
if adaptive:
d_start = adaptive_start_index(k, D, ksize[0])
d_end = adaptive_end_index(k, D, ksize[0])
for i in range(H_out):
if adaptive:
h_start = adaptive_start_index(i, H, ksize[1])
h_end = adaptive_end_index(i, H, ksize[1])
for j in range(W_out):
if adaptive:
w_start = adaptive_start_index(j, W, ksize[2])
w_end = adaptive_end_index(j, W, ksize[2])
else:
d_start = k * strides[0] - pad_d_forth
d_end = np.min(
(
k * strides[0] + ksize[0] - pad_d_forth,
D + pad_d_back,
)
)
h_start = i * strides[1] - pad_h_up
h_end = np.min(
(i * strides[1] + ksize[1] - pad_h_up, H + pad_h_down)
)
w_start = j * strides[2] - pad_w_left
w_end = np.min(
(
j * strides[2] + ksize[2] - pad_w_left,
W + pad_w_right,
)
)
field_size = (
(d_end - d_start)
* (h_end - h_start)
* (w_end - w_start)
)
w_start = np.max((w_start, 0))
d_start = np.max((d_start, 0))
h_start = np.max((h_start, 0))
w_end = np.min((w_end, W))
d_end = np.min((d_end, D))
h_end = np.min((h_end, H))
if data_format == 'NCDHW':
x_masked = x[
:, :, d_start:d_end, h_start:h_end, w_start:w_end
]
if pool_type == 'avg':
if exclusive or adaptive:
field_size = (
(d_end - d_start)
* (h_end - h_start)
* (w_end - w_start)
)
out[:, :, k, i, j] = (
np.sum(x_masked, axis=(2, 3, 4)) / field_size
)
elif pool_type == 'max':
out[:, :, k, i, j] = np.max(x_masked, axis=(2, 3, 4))
elif data_format == 'NDHWC':
x_masked = x[
:, d_start:d_end, h_start:h_end, w_start:w_end, :
]
if pool_type == 'avg':
if exclusive or adaptive:
field_size = (
(d_end - d_start)
* (h_end - h_start)
* (w_end - w_start)
)
out[:, k, i, j, :] = (
np.sum(x_masked, axis=(1, 2, 3)) / field_size
)
elif pool_type == 'max':
out[:, k, i, j, :] = np.max(x_masked, axis=(1, 2, 3))
return out
def max_pool3D_forward_naive(
x,
ksize,
strides,
paddings,
global_pool=0,
ceil_mode=False,
exclusive=True,
adaptive=False,
):
out = pool3D_forward_naive(
x=x,
ksize=ksize,
strides=strides,
paddings=paddings,
global_pool=global_pool,
ceil_mode=ceil_mode,
exclusive=exclusive,
adaptive=adaptive,
data_format='NCDHW',
pool_type="max",
)
return out
def avg_pool3D_forward_naive(
x,
ksize,
strides,
paddings,
global_pool=0,
ceil_mode=False,
exclusive=True,
adaptive=False,
):
out = pool3D_forward_naive(
x=x,
ksize=ksize,
strides=strides,
paddings=paddings,
global_pool=global_pool,
ceil_mode=ceil_mode,
exclusive=exclusive,
adaptive=adaptive,
data_format='NCDHW',
pool_type="avg",
)
return out
def pool3d_wrapper_not_use_cudnn(
X,
ksize=[],
strides=[],
paddings=[],
ceil_mode=False,
exclusive=True,
data_format="NCDHW",
pooling_type="max",
global_pooling=False,
adaptive=False,
padding_algorithm="EXPLICIT",
):
if in_dynamic_mode():
X = X._use_gpudnn(False)
if data_format == "AnyLayout":
data_format = "NCDHW"
return paddle._C_ops.pool3d(
X,
ksize,
strides,
paddings,
ceil_mode,
exclusive,
data_format,
pooling_type,
global_pooling,
adaptive,
padding_algorithm,
)
def pool3d_wrapper_use_cudnn(
X,
ksize=[],
strides=[],
paddings=[],
ceil_mode=False,
exclusive=True,
data_format="NCDHW",
pooling_type="max",
global_pooling=False,
adaptive=False,
padding_algorithm="EXPLICIT",
):
if data_format == "AnyLayout":
data_format = "NCDHW"
return paddle._C_ops.pool3d(
X,
ksize,
strides,
paddings,
ceil_mode,
exclusive,
data_format,
pooling_type,
global_pooling,
adaptive,
padding_algorithm,
)
class TestPool3D_Op(OpTest):
def setUp(self):
self.op_type = "pool3d"
self.init_kernel_type()
self.dtype = np.float32 if core.is_compiled_with_rocm() else np.float64
self.init_test_case()
self.padding_algorithm = "EXPLICIT"
self.init_paddings()
self.init_global_pool()
self.init_kernel_type()
self.init_pool_type()
self.init_ceil_mode()
self.init_exclusive()
self.init_adaptive()
self.init_data_format()
self.init_shape()
paddle.enable_static()
input = np.random.random(self.shape).astype(self.dtype)
output = pool3D_forward_naive(
input,
self.ksize,
self.strides,
self.paddings,
self.global_pool,
self.ceil_mode,
self.exclusive,
self.adaptive,
self.data_format,
self.pool_type,
self.padding_algorithm,
).astype(self.dtype)
self.inputs = {'X': OpTest.np_dtype_to_base_dtype(input)}
self.attrs = {
'strides': self.strides,
'paddings': self.paddings,
'ksize': self.ksize,
'pooling_type': self.pool_type,
'global_pooling': self.global_pool,
'use_cudnn': self.use_cudnn,
'ceil_mode': self.ceil_mode,
'data_format': self.data_format,
'exclusive': self.exclusive,
'adaptive': self.adaptive,
"padding_algorithm": self.padding_algorithm,
}
self.outputs = {'Out': output}
if self.use_cudnn:
self.python_api = pool3d_wrapper_use_cudnn
else:
self.python_api = pool3d_wrapper_not_use_cudnn
def has_cudnn(self):
return (
core.is_compiled_with_cuda() or is_custom_device()
) and self.use_cudnn
def test_check_output(self):
if self.has_cudnn():
place = get_device_place()
self.check_output_with_place(place, atol=1e-5, check_pir=True)
else:
self.check_output(check_pir=True)
def test_check_grad(self):
if (
self.has_cudnn() or self.dtype == np.uint16
) and self.pool_type != "max":
place = get_device_place()
if core.is_compiled_with_rocm():
self.check_grad_with_place(
place, {'X'}, 'Out', max_relative_error=1e-2, check_pir=True
)
else:
self.check_grad_with_place(place, {'X'}, 'Out', check_pir=True)
elif self.pool_type != "max":
if core.is_compiled_with_rocm():
self.check_grad(
{'X'}, 'Out', max_relative_error=1e-2, check_pir=True
)
else:
self.check_grad({'X'}, 'Out', check_pir=True)
def init_data_format(self):
self.data_format = "NCDHW"
def init_shape(self):
self.shape = [1, 3, 5, 6, 5]
def init_test_case(self):
self.ksize = [2, 3, 1]
self.strides = [2, 2, 3]
def init_paddings(self):
self.paddings = [0, 0, 0]
self.padding_algorithm = "EXPLICIT"
def init_kernel_type(self):
self.use_cudnn = False
def init_pool_type(self):
self.pool_type = "avg"
def init_global_pool(self):
self.global_pool = True
def init_ceil_mode(self):
self.ceil_mode = False
def init_exclusive(self):
self.exclusive = True
def init_adaptive(self):
self.adaptive = False
class TestCase1(TestPool3D_Op):
def init_shape(self):
self.shape = [1, 3, 7, 7, 7]
def init_test_case(self):
self.ksize = [3, 3, 3]
self.strides = [1, 1, 1]
def init_paddings(self):
self.paddings = [0, 0, 0]
def init_pool_type(self):
self.pool_type = "avg"
def init_global_pool(self):
self.global_pool = False
class TestCase2(TestPool3D_Op):
def init_shape(self):
self.shape = [1, 3, 6, 7, 7]
def init_test_case(self):
self.ksize = [3, 3, 4]
self.strides = [1, 3, 2]
def init_paddings(self):
self.paddings = [1, 1, 1]
def init_pool_type(self):
self.pool_type = "avg"
def init_global_pool(self):
self.global_pool = False
class TestCase3(TestPool3D_Op):
def init_pool_type(self):
self.pool_type = "max"
class TestCase4(TestCase1):
def init_pool_type(self):
self.pool_type = "max"
class TestCase5(TestCase2):
def init_pool_type(self):
self.pool_type = "max"
# --------------------test pool3d cudnn--------------------
def create_test_cudnn_class(parent):
@unittest.skipIf(
not (core.is_compiled_with_cuda() or is_custom_device()),
"core is not compiled with CUDA",
)
class TestCUDNNCase(parent):
def init_kernel_type(self):
self.use_cudnn = True
cls_name = "{}_{}".format(parent.__name__, "CUDNNOp")
TestCUDNNCase.__name__ = cls_name
globals()[cls_name] = TestCUDNNCase
create_test_cudnn_class(TestPool3D_Op)
create_test_cudnn_class(TestCase1)
create_test_cudnn_class(TestCase2)
create_test_cudnn_class(TestCase3)
create_test_cudnn_class(TestCase4)
create_test_cudnn_class(TestCase5)
def create_test_cudnn_fp16_class(parent):
@unittest.skipIf(
not (core.is_compiled_with_cuda() or is_custom_device()),
"core is not compiled with CUDA",
)
class TestCUDNNFp16Case(parent):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.float16
def test_check_output(self):
if core.is_compiled_with_cuda() or is_custom_device():
place = get_device_place()
if core.is_float16_supported(place):
if core.is_compiled_with_rocm():
self.check_output_with_place(
place, atol=1e-2, check_pir=True
)
else:
self.check_output_with_place(
place, atol=1e-3, check_pir=True
)
cls_name = "{}_{}".format(parent.__name__, "CUDNNFp16Op")
TestCUDNNFp16Case.__name__ = cls_name
globals()[cls_name] = TestCUDNNFp16Case
def create_test_fp16_class(parent):
@unittest.skipIf(
not (core.is_compiled_with_cuda() or is_custom_device()),
"core is not compiled with CUDA",
)
class TestFp16Case(parent):
def init_kernel_type(self):
self.use_cudnn = False
self.dtype = np.float16
def test_check_output(self):
if core.is_compiled_with_cuda() or is_custom_device():
place = get_device_place()
if core.is_float16_supported(place):
self.check_output_with_place(
place, atol=1e-2, check_pir=True
)
cls_name = "{}_{}".format(parent.__name__, "Fp16Op")
TestFp16Case.__name__ = cls_name
globals()[cls_name] = TestFp16Case
def create_test_cudnn_bf16_class(parent):
@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 and not support the bfloat16",
)
class TestCUDNNBf16Case(parent):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.uint16
def test_check_output(self):
place = get_device_place()
self.check_output_with_place(place, check_pir=True)
cls_name = "{}_{}".format(parent.__name__, "CUDNNBf16Op")
TestCUDNNBf16Case.__name__ = cls_name
globals()[cls_name] = TestCUDNNBf16Case
def create_test_bf16_class(parent):
@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 and not support the bfloat16",
)
class TestBf16Case(parent):
def init_kernel_type(self):
self.use_cudnn = False
self.dtype = np.uint16
def test_check_output(self):
place = get_device_place()
self.check_output_with_place(place, check_pir=True)
cls_name = "{}_{}".format(parent.__name__, "Bf16Op")
TestBf16Case.__name__ = cls_name
globals()[cls_name] = TestBf16Case
create_test_cudnn_fp16_class(TestPool3D_Op)
create_test_cudnn_fp16_class(TestCase1)
create_test_cudnn_fp16_class(TestCase2)
create_test_cudnn_fp16_class(TestCase3)
create_test_cudnn_fp16_class(TestCase4)
create_test_cudnn_fp16_class(TestCase5)
create_test_fp16_class(TestPool3D_Op)
create_test_fp16_class(TestCase1)
create_test_fp16_class(TestCase2)
create_test_fp16_class(TestCase3)
create_test_fp16_class(TestCase4)
create_test_fp16_class(TestCase5)
create_test_cudnn_bf16_class(TestPool3D_Op)
create_test_cudnn_bf16_class(TestCase1)
create_test_cudnn_bf16_class(TestCase2)
create_test_cudnn_bf16_class(TestCase3)
create_test_cudnn_bf16_class(TestCase4)
create_test_cudnn_bf16_class(TestCase5)
create_test_bf16_class(TestPool3D_Op)
create_test_bf16_class(TestCase1)
create_test_bf16_class(TestCase2)
create_test_bf16_class(TestCase3)
create_test_bf16_class(TestCase4)
create_test_bf16_class(TestCase5)
# ---- test ceil mode ------
def create_test_cudnn_use_ceil_class(parent):
@unittest.skipIf(
not (core.is_compiled_with_cuda() or is_custom_device()),
"core is not compiled with CUDA",
)
class TestPool3DUseCeilCase(parent):
def init_kernel_type(self):
self.use_cudnn = True
def init_ceil_mode(self):
self.ceil_mode = True
cls_name = "{}_{}".format(parent.__name__, "CUDNNOpCeilMode")
TestPool3DUseCeilCase.__name__ = cls_name
globals()[cls_name] = TestPool3DUseCeilCase
create_test_cudnn_use_ceil_class(TestPool3D_Op)
create_test_cudnn_use_ceil_class(TestCase1)
def create_test_use_ceil_class(parent):
class TestPool3DUseCeilCase(parent):
def init_ceil_mode(self):
self.ceil_mode = True
cls_name = "{}_{}".format(parent.__name__, "CeilModeCast")
TestPool3DUseCeilCase.__name__ = cls_name
globals()[cls_name] = TestPool3DUseCeilCase
create_test_use_ceil_class(TestCase1)
create_test_use_ceil_class(TestCase2)
class TestAvgInclude(TestCase2):
def init_exclusive(self):
self.exclusive = False
@unittest.skipIf(
not (core.is_compiled_with_cuda() or is_custom_device()),
"core is not compiled with CUDA",
)
class TestCUDNNAvgInclude(TestCase2):
def init_kernel_type(self):
self.use_cudnn = True
def init_exclusive(self):
self.exclusive = False
class TestAvgPoolAdaptive(TestCase1):
def init_adaptive(self):
self.adaptive = True
class TestAvgPoolAdaptiveAsyOutSize(TestCase1):
def init_adaptive(self):
self.adaptive = True
def init_shape(self):
self.shape = [1, 3, 3, 4, 4]
def init_test_case(self):
self.ksize = [2, 2, 3]
self.strides = [1, 1, 1]
# -------test pool3d with asymmetric padding------
class TestPool3D_Op_AsyPadding(TestPool3D_Op):
def init_test_case(self):
self.ksize = [3, 4, 3]
self.strides = [1, 1, 2]
def init_paddings(self):
self.paddings = [0, 0, 0, 2, 3, 0]
def init_shape(self):
self.shape = [1, 3, 5, 5, 6]
class TestCase1_AsyPadding(TestCase1):
def init_test_case(self):
self.ksize = [3, 3, 4]
self.strides = [1, 1, 2]
def init_paddings(self):
self.paddings = [1, 0, 2, 1, 2, 1]
def init_shape(self):
self.shape = [1, 3, 7, 7, 6]
class TestCase2_AsyPadding(TestCase2):
def init_test_case(self):
self.ksize = [3, 3, 3]
self.strides = [1, 1, 1]
def init_paddings(self):
self.paddings = [1, 2, 1, 1, 1, 0]
def init_shape(self):
self.shape = [1, 3, 7, 7, 7]
class TestCase3_AsyPadding(TestCase3):
def init_test_case(self):
self.ksize = [3, 3, 3]
self.strides = [1, 1, 1]
def init_paddings(self):
self.paddings = [1, 0, 0, 0, 1, 0]
def init_shape(self):
self.shape = [1, 3, 5, 5, 5]
class TestCase4_AsyPadding(TestCase4):
def init_test_case(self):
self.ksize = [3, 3, 3]
self.strides = [1, 1, 1]
def init_paddings(self):
self.paddings = [1, 0, 2, 1, 2, 1]
def init_shape(self):
self.shape = [1, 3, 7, 7, 7]
class TestCase5_AsyPadding(TestCase5):
def init_test_case(self):
self.ksize = [3, 3, 3]
self.strides = [1, 1, 1]
def init_paddings(self):
self.paddings = [1, 2, 1, 1, 1, 0]
def init_shape(self):
self.shape = [1, 3, 7, 7, 7]
create_test_cudnn_class(TestPool3D_Op_AsyPadding)
create_test_cudnn_class(TestCase1_AsyPadding)
create_test_cudnn_class(TestCase2_AsyPadding)
create_test_cudnn_class(TestCase3_AsyPadding)
create_test_cudnn_class(TestCase4_AsyPadding)
create_test_cudnn_class(TestCase5_AsyPadding)
create_test_cudnn_fp16_class(TestPool3D_Op_AsyPadding)
create_test_cudnn_fp16_class(TestCase1_AsyPadding)
create_test_cudnn_fp16_class(TestCase2_AsyPadding)
create_test_cudnn_fp16_class(TestCase3_AsyPadding)
create_test_cudnn_fp16_class(TestCase4_AsyPadding)
create_test_cudnn_fp16_class(TestCase5_AsyPadding)
create_test_cudnn_bf16_class(TestPool3D_Op_AsyPadding)
create_test_cudnn_bf16_class(TestCase1_AsyPadding)
create_test_cudnn_bf16_class(TestCase2_AsyPadding)
create_test_cudnn_bf16_class(TestCase3_AsyPadding)
create_test_cudnn_bf16_class(TestCase4_AsyPadding)
create_test_cudnn_bf16_class(TestCase5_AsyPadding)
create_test_cudnn_use_ceil_class(TestPool3D_Op_AsyPadding)
create_test_cudnn_use_ceil_class(TestCase1_AsyPadding)
create_test_use_ceil_class(TestCase1_AsyPadding)
create_test_use_ceil_class(TestCase2_AsyPadding)
class TestAvgInclude_AsyPadding(TestCase2):
def init_exclusive(self):
self.exclusive = False
def init_paddings(self):
self.paddings = [2, 2, 1, 1, 0, 0]
@unittest.skipIf(
not (core.is_compiled_with_cuda() or is_custom_device()),
"core is not compiled with CUDA",
)
class TestCUDNNAvgInclude_AsyPadding(TestCase2):
def init_kernel_type(self):
self.use_cudnn = True
def init_exclusive(self):
self.exclusive = False
def init_paddings(self):
self.paddings = [1, 0, 0, 0, 0, 0]
def init_shape(self):
self.shape = [1, 3, 5, 5, 5]
class TestAvgPoolAdaptive_AsyPadding(TestCase1):
def init_adaptive(self):
self.adaptive = True
def init_paddings(self):
self.paddings = [1, 0, 2, 1, 2, 1]
# ------------ test channel_last --------------
class TestPool3D_channel_last(TestPool3D_Op):
def init_data_format(self):
self.data_format = "NDHWC"
def init_shape(self):
self.shape = [1, 5, 5, 6, 3]
class TestCase1_channel_last(TestCase1):
def init_data_format(self):
self.data_format = "NDHWC"
def init_shape(self):
self.shape = [1, 7, 7, 7, 3]
class TestCase2_channel_last(TestCase2):
def init_data_format(self):
self.data_format = "NDHWC"
def init_shape(self):
self.shape = [1, 7, 7, 5, 3]
class TestCase3_channel_last(TestCase3):
def init_data_format(self):
self.data_format = "NDHWC"
def init_shape(self):
self.shape = [1, 5, 6, 5, 3]
class TestCase4_channel_last(TestCase4):
def init_data_format(self):
self.data_format = "NDHWC"
def init_shape(self):
self.shape = [1, 7, 6, 7, 3]
class TestCase5_channel_last(TestCase5):
def init_data_format(self):
self.data_format = "NDHWC"
def init_shape(self):
self.shape = [1, 7, 7, 7, 3]
create_test_cudnn_class(TestPool3D_channel_last)
create_test_cudnn_class(TestCase1_channel_last)
create_test_cudnn_class(TestCase2_channel_last)
create_test_cudnn_class(TestCase3_channel_last)
create_test_cudnn_class(TestCase4_channel_last)
create_test_cudnn_class(TestCase5_channel_last)
create_test_cudnn_use_ceil_class(TestPool3D_channel_last)
create_test_cudnn_use_ceil_class(TestCase1_channel_last)
create_test_use_ceil_class(TestCase1_channel_last)
create_test_use_ceil_class(TestCase2_channel_last)
class TestCase5_Max(TestCase2):
def init_pool_type(self):
self.pool_type = "max"
def test_check_grad(self):
if self.dtype == np.float16:
return
if self.has_cudnn() and self.pool_type == "max":
place = get_device_place()
self.check_grad_with_place(
place, {'X'}, 'Out', max_relative_error=1.00, check_pir=True
)
elif self.pool_type == "max":
self.check_grad(
{'X'}, 'Out', max_relative_error=1.00, check_pir=True
)
class TestCase5_channel_last_Max(TestCase5_Max):
def init_data_format(self):
self.data_format = "NDHWC"
def init_shape(self):
self.shape = [1, 7, 7, 7, 3]
create_test_cudnn_class(TestCase5_Max)
create_test_cudnn_class(TestCase5_channel_last_Max)
class TestAvgInclude_channel_last(TestCase2_channel_last):
def init_exclusive(self):
self.exclusive = False
@unittest.skipIf(
not (core.is_compiled_with_cuda() or is_custom_device()),
"core is not compiled with CUDA",
)
class TestCUDNNAvgInclude_channel_last(TestCase2_channel_last):
def init_kernel_type(self):
self.use_cudnn = True
def init_exclusive(self):
self.exclusive = False
class TestAvgPoolAdaptive_channel_last(TestCase1_channel_last):
def init_adaptive(self):
self.adaptive = True
# --- asy padding
class TestPool3D_Op_AsyPadding_channel_last(TestPool3D_Op_AsyPadding):
def init_data_format(self):
self.data_format = "NDHWC"
def init_shape(self):
self.shape = [1, 5, 5, 6, 3]
class TestCase1_AsyPadding_channel_last(TestCase1_AsyPadding):
def init_data_format(self):
self.data_format = "NDHWC"
def init_shape(self):
self.shape = [1, 7, 6, 8, 3]
class TestCase2_AsyPadding_channel_last(TestCase2_AsyPadding):
def init_data_format(self):
self.data_format = "NDHWC"
def init_shape(self):
self.shape = [1, 6, 8, 7, 3]
class TestCase3_AsyPadding_channel_last(TestCase3_AsyPadding):
def init_data_format(self):
self.data_format = "NDHWC"
def init_shape(self):
self.shape = [1, 5, 7, 5, 3]
class TestCase4_AsyPadding_channel_last(TestCase4_AsyPadding):
def init_data_format(self):
self.data_format = "NDHWC"
def init_shape(self):
self.shape = [1, 6, 7, 7, 3]
class TestCase5_AsyPadding_channel_last(TestCase5_AsyPadding):
def init_data_format(self):
self.data_format = "NDHWC"
def init_shape(self):
self.shape = [1, 7, 8, 6, 3]
create_test_cudnn_class(TestPool3D_Op_AsyPadding_channel_last)
create_test_cudnn_class(TestCase1_AsyPadding_channel_last)
create_test_cudnn_class(TestCase2_AsyPadding_channel_last)
create_test_cudnn_class(TestCase3_AsyPadding_channel_last)
create_test_cudnn_class(TestCase4_AsyPadding_channel_last)
create_test_cudnn_class(TestCase5_AsyPadding_channel_last)
create_test_cudnn_use_ceil_class(TestPool3D_Op_AsyPadding_channel_last)
create_test_cudnn_use_ceil_class(TestCase1_AsyPadding_channel_last)
create_test_use_ceil_class(TestCase1_AsyPadding_channel_last)
create_test_use_ceil_class(TestCase2_AsyPadding_channel_last)
class TestAvgInclude_AsyPadding_channel_last(TestAvgInclude_AsyPadding):
def init_data_format(self):
self.data_format = "NDHWC"
@unittest.skipIf(
not (core.is_compiled_with_cuda() or is_custom_device()),
"core is not compiled with CUDA",
)
class TestCUDNNAvgInclude_AsyPadding_channel_last(
TestCUDNNAvgInclude_AsyPadding
):
def init_data_format(self):
self.data_format = "NDHWC"
class TestAvgPoolAdaptive_AsyPadding_channel_last(
TestAvgPoolAdaptive_AsyPadding
):
def init_data_format(self):
self.data_format = "NDHWC"
def init_shape(self):
self.shape = [1, 7, 7, 7, 3]
# test padding = SAME VALID
def create_test_padding_SAME_class(parent):
class TestPaddingSAMECase(parent):
def init_paddings(self):
self.paddings = [0, 0, 0]
self.padding_algorithm = "SAME"
cls_name = "{}_{}".format(parent.__name__, "PaddingSAMEOp")
TestPaddingSAMECase.__name__ = cls_name
globals()[cls_name] = TestPaddingSAMECase
create_test_padding_SAME_class(TestPool3D_Op)
create_test_padding_SAME_class(TestCase1)
create_test_padding_SAME_class(TestCase2)
create_test_padding_SAME_class(TestCase3)
create_test_padding_SAME_class(TestCase4)
create_test_padding_SAME_class(TestCase5)
create_test_padding_SAME_class(TestPool3D_channel_last)
create_test_padding_SAME_class(TestCase1_channel_last)
create_test_padding_SAME_class(TestCase2_channel_last)
create_test_padding_SAME_class(TestCase3_channel_last)
create_test_padding_SAME_class(TestCase4_channel_last)
create_test_padding_SAME_class(TestCase5_channel_last)
def create_test_cudnn_padding_SAME_class(parent):
@unittest.skipIf(
not (core.is_compiled_with_cuda() or is_custom_device()),
"core is not compiled with CUDA",
)
class TestCUDNNPaddingSAMECase(parent):
def init_kernel_type(self):
self.use_cudnn = True
def init_paddings(self):
self.paddings = [1, 1, 1]
self.padding_algorithm = "SAME"
cls_name = "{}_{}".format(parent.__name__, "CudnnPaddingSAMEOp")
TestCUDNNPaddingSAMECase.__name__ = cls_name
globals()[cls_name] = TestCUDNNPaddingSAMECase
create_test_cudnn_padding_SAME_class(TestPool3D_Op)
create_test_cudnn_padding_SAME_class(TestCase1)
create_test_cudnn_padding_SAME_class(TestCase2)
create_test_cudnn_padding_SAME_class(TestCase3)
create_test_cudnn_padding_SAME_class(TestCase4)
create_test_cudnn_padding_SAME_class(TestCase5)
create_test_cudnn_padding_SAME_class(TestPool3D_channel_last)
create_test_cudnn_padding_SAME_class(TestCase1_channel_last)
create_test_cudnn_padding_SAME_class(TestCase2_channel_last)
create_test_cudnn_padding_SAME_class(TestCase3_channel_last)
create_test_cudnn_padding_SAME_class(TestCase4_channel_last)
create_test_cudnn_padding_SAME_class(TestCase5_channel_last)
def create_test_padding_VALID_class(parent):
class TestPaddingVALIDCase(parent):
def init_paddings(self):
self.paddings = [1, 1, 1]
self.padding_algorithm = "VALID"
cls_name = "{}_{}".format(parent.__name__, "PaddingVALIDOp")
TestPaddingVALIDCase.__name__ = cls_name
globals()[cls_name] = TestPaddingVALIDCase
create_test_padding_VALID_class(TestPool3D_Op)
create_test_padding_VALID_class(TestCase1)
create_test_padding_VALID_class(TestCase2)
create_test_padding_VALID_class(TestCase3)
create_test_padding_VALID_class(TestCase4)
create_test_padding_VALID_class(TestCase5)
create_test_padding_VALID_class(TestPool3D_channel_last)
create_test_padding_VALID_class(TestCase1_channel_last)
create_test_padding_VALID_class(TestCase2_channel_last)
create_test_padding_VALID_class(TestCase3_channel_last)
create_test_padding_VALID_class(TestCase4_channel_last)
create_test_padding_VALID_class(TestCase5_channel_last)
def create_test_cudnn_padding_VALID_class(parent):
@unittest.skipIf(
not (core.is_compiled_with_cuda() or is_custom_device()),
"core is not compiled with CUDA",
)
class TestCUDNNPaddingVALIDCase(parent):
def init_kernel_type(self):
self.use_cudnn = True
def init_paddings(self):
self.paddings = [1, 1, 1]
self.padding_algorithm = "VALID"
cls_name = "{}_{}".format(parent.__name__, "CudnnPaddingVALIDOp")
TestCUDNNPaddingVALIDCase.__name__ = cls_name
globals()[cls_name] = TestCUDNNPaddingVALIDCase
create_test_cudnn_padding_VALID_class(TestPool3D_Op)
create_test_cudnn_padding_VALID_class(TestCase1)
create_test_cudnn_padding_VALID_class(TestCase2)
create_test_cudnn_padding_VALID_class(TestCase3)
create_test_cudnn_padding_VALID_class(TestCase4)
create_test_cudnn_padding_VALID_class(TestCase5)
create_test_cudnn_padding_VALID_class(TestPool3D_channel_last)
create_test_cudnn_padding_VALID_class(TestCase1_channel_last)
create_test_cudnn_padding_VALID_class(TestCase2_channel_last)
create_test_cudnn_padding_VALID_class(TestCase3_channel_last)
create_test_cudnn_padding_VALID_class(TestCase4_channel_last)
create_test_cudnn_padding_VALID_class(TestCase5_channel_last)
if __name__ == '__main__':
unittest.main()
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