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- # Copyright (c) 2023 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 get_test_cover_info import (
- XPUOpTestWrapper,
- create_test_class,
- get_xpu_op_support_types,
- )
- from op_test_xpu import XPUOpTest
-
- import paddle
-
- paddle.enable_static()
-
-
- 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(
- "Unknown Attr(padding_algorithm): '%s'. "
- "It can only be 'SAME' or 'VALID'." % str(padding_algorithm)
- )
-
- 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
-
-
- class XPUTestPool3DOp(XPUOpTestWrapper):
- def __init__(self):
- self.op_name = 'pool3d'
- self.use_dynamic_create_class = False
-
- class TestPool3D_Op(XPUOpTest):
- def setUp(self):
- self.op_type = "pool3d"
- self.init_kernel_type()
- self.dtype = self.in_type
- 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': XPUOpTest.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,
- '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}
-
- def test_check_output(self):
- place = paddle.XPUPlace(0)
- self.check_output_with_place(place)
-
- def test_check_grad(self):
- if self.dtype == np.float16:
- return
-
- place = paddle.XPUPlace(0)
- self.check_grad_with_place(place, {'X'}, 'Out')
-
- 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"
-
- class TestAvgInclude(TestCase2):
- 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]
-
- class TestAvgInclude_AsyPadding(TestCase2):
- def init_exclusive(self):
- self.exclusive = False
-
- def init_paddings(self):
- self.paddings = [2, 2, 1, 1, 0, 0]
-
- class TestAvgPoolAdaptive_AsyPadding(TestCase1):
- def init_adaptive(self):
- self.adaptive = True
-
- def init_paddings(self):
- self.paddings = [1, 0, 2, 1, 2, 1]
-
- class TestCase5_Max(TestCase2):
- def init_pool_type(self):
- self.pool_type = "max"
-
- def test_check_grad(self):
- if self.dtype == np.float16:
- return
- place = paddle.XPUPlace(0)
- self.check_grad_with_place(place, {'X'}, 'Out')
-
-
- support_types = get_xpu_op_support_types('pool3d')
- for stype in ["float32"]:
- create_test_class(globals(), XPUTestPool3DOp, stype)
-
- if __name__ == '__main__':
- unittest.main()
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