|
- # Copyright (c) 2022 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
- from op_test_xpu import XPUOpTest
-
- import paddle
-
-
- def conv3d_forward_naive(
- input,
- filter,
- group,
- conv_param,
- padding_algorithm='EXPLICIT',
- data_format="NCDHW",
- ):
- 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 data_format not in ["NCDHW", "NDHWC"]:
- raise ValueError(
- "Unknown Attr(data_format): '%s' ."
- "It can only be 'NCDHW' or 'NDHWC'." % str(data_format)
- )
-
- channel_last = data_format == "NDHWC"
- if channel_last:
- input = np.transpose(input, [0, 4, 1, 2, 3])
-
- in_n, in_c, in_d, in_h, in_w = input.shape
-
- f_n, f_c, f_d, f_h, f_w = filter.shape
- out_n = in_n
- out_c = f_n
- assert f_c * group == in_c
- assert np.mod(out_c, group) == 0
- sub_out_c = out_c // group
- sub_f_n = f_n // group
-
- stride, pad, dilation = (
- conv_param['stride'],
- conv_param['pad'],
- conv_param['dilations'],
- )
-
- # update pad and dilation
- 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
-
- ksize = filter.shape[2:5]
- if padding_algorithm == "VALID":
- pad = [0, 0, 0, 0, 0, 0]
- elif padding_algorithm == "SAME":
- dilation = [1, 1, 1]
- input_data_shape = input.shape[2:5]
- pad = _get_padding_with_SAME(input_data_shape, ksize, stride)
-
- pad_d_0, pad_d_1 = pad[0], pad[0]
- pad_h_0, pad_h_1 = pad[1], pad[1]
- pad_w_0, pad_w_1 = pad[2], pad[2]
- if len(pad) == 6:
- pad_d_0, pad_d_1 = pad[0], pad[1]
- pad_h_0, pad_h_1 = pad[2], pad[3]
- pad_w_0, pad_w_1 = pad[4], pad[5]
-
- out_d = (
- 1
- + (in_d + pad_d_0 + pad_d_1 - (dilation[0] * (f_d - 1) + 1))
- // stride[0]
- )
- out_h = (
- 1
- + (in_h + pad_h_0 + pad_h_1 - (dilation[1] * (f_h - 1) + 1))
- // stride[1]
- )
- out_w = (
- 1
- + (in_w + pad_w_0 + pad_w_1 - (dilation[2] * (f_w - 1) + 1))
- // stride[2]
- )
-
- out = np.zeros((in_n, out_c, out_d, out_h, out_w))
-
- d_block_d = dilation[0] * (f_d - 1) + 1
- d_block_h = dilation[1] * (f_h - 1) + 1
- d_block_w = dilation[2] * (f_w - 1) + 1
-
- input_pad = np.pad(
- input,
- (
- (0, 0),
- (0, 0),
- (pad_d_0, pad_d_1),
- (pad_h_0, pad_h_1),
- (pad_w_0, pad_w_1),
- ),
- mode='constant',
- constant_values=0,
- )
-
- filter_dilation = np.zeros((f_n, f_c, d_block_d, d_block_h, d_block_w))
- filter_dilation[
- :,
- :,
- 0 : d_block_d : dilation[0],
- 0 : d_block_h : dilation[1],
- 0 : d_block_w : dilation[2],
- ] = filter
-
- for d in range(out_d):
- for i in range(out_h):
- for j in range(out_w):
- for g in range(group):
- input_pad_masked = input_pad[
- :,
- g * f_c : (g + 1) * f_c,
- d * stride[0] : d * stride[0] + d_block_d,
- i * stride[1] : i * stride[1] + d_block_h,
- j * stride[2] : j * stride[2] + d_block_w,
- ]
-
- f_sub = filter_dilation[
- g * sub_f_n : (g + 1) * sub_f_n, :, :, :, :
- ]
- for k in range(sub_out_c):
- out[:, g * sub_out_c + k, d, i, j] = np.sum(
- input_pad_masked * f_sub[k, :, :, :, :],
- axis=(1, 2, 3, 4),
- )
- if channel_last:
- out = np.transpose(out, [0, 2, 3, 4, 1])
- return out
-
-
- def create_test_padding_SAME_class(parent):
- class TestPaddingSAMECase(parent):
- def init_paddings(self):
- self.pad = [0, 0, 0]
- self.padding_algorithm = "SAME"
-
- cls_name = "{}_{}".format(parent.__name__, "PaddingSAMEOp")
- TestPaddingSAMECase.__name__ = cls_name
- globals()[cls_name] = TestPaddingSAMECase
-
-
- def create_test_padding_VALID_class(parent):
- class TestPaddingVALIDCase(parent):
- def init_paddings(self):
- self.pad = [1, 1, 1]
- self.padding_algorithm = "VALID"
-
- cls_name = "{}_{}".format(parent.__name__, "PaddingVALIDOp")
- TestPaddingVALIDCase.__name__ = cls_name
- globals()[cls_name] = TestPaddingVALIDCase
-
-
- def create_test_channel_last_class(parent):
- class TestChannelLastCase(parent):
- def init_data_format(self):
- self.data_format = "NDHWC"
-
- def init_test_case_2(self):
- N, C, D, H, W = self.input_size
- self.input_size = [N, D, H, W, C]
-
- cls_name = "{}_{}".format(parent.__name__, "ChannelLast")
- TestChannelLastCase.__name__ = cls_name
- globals()[cls_name] = TestChannelLastCase
-
-
- paddle.enable_static()
-
-
- class XPUTestConv3DOp(XPUOpTestWrapper):
- def __init__(self):
- self.op_name = 'conv3d'
- self.use_dynamic_create_class = False
-
- class TestConv3DOp(XPUOpTest):
- def setUp(self):
- self.dtype = self.in_type
- self.op_type = "conv3d"
- self.use_cudnn = False
- self.use_mkldnn = False
- self.data_format = "AnyLayout"
- self.init_kernel_type()
- self.init_group()
- self.init_dilation()
- self.init_test_case()
-
- conv3d_param = {
- 'stride': self.stride,
- 'pad': self.pad,
- 'dilations': self.dilations,
- }
-
- np.random.seed(100)
- input = np.random.random(self.input_size).astype(self.dtype)
- filter = np.random.random(self.filter_size).astype(self.dtype)
- output = conv3d_forward_naive(
- input,
- filter,
- self.groups,
- conv3d_param,
- ).astype(self.dtype)
-
- self.inputs = {
- 'Input': XPUOpTest.np_dtype_to_base_dtype(input),
- 'Filter': XPUOpTest.np_dtype_to_base_dtype(filter),
- }
- self.attrs = {
- 'strides': self.stride,
- 'paddings': self.pad,
- 'groups': self.groups,
- 'dilations': self.dilations,
- 'use_cudnn': self.use_cudnn,
- 'use_mkldnn': self.use_mkldnn,
- 'data_format': self.data_format,
- }
- self.outputs = {'Output': output}
-
- def test_check_output(self):
- place = paddle.XPUPlace(0)
- self.check_output_with_place(place)
-
- def test_check_grad(self):
- place = paddle.XPUPlace(0)
- # TODO(wangzhongpu): support onednn op in dygraph mode
- self.check_grad_with_place(
- place,
- {'Input', 'Filter'},
- 'Output',
- max_relative_error=0.03,
- )
-
- def test_check_grad_no_filter(self):
- place = paddle.XPUPlace(0)
- # TODO(wangzhongpu): support onednn op in dygraph mode
- self.check_grad_with_place(
- place,
- ['Input'],
- 'Output',
- max_relative_error=0.03,
- no_grad_set={'Filter'},
- )
-
- def test_check_grad_no_input(self):
- place = paddle.XPUPlace(0)
- # TODO(wangzhongpu): support onednn op in dygraph mode
- self.check_grad_with_place(
- place,
- ['Filter'],
- 'Output',
- max_relative_error=0.03,
- no_grad_set={'Input'},
- )
-
- def init_test_case(self):
- self.pad = [0, 0, 0]
- self.stride = [1, 1, 1]
- self.input_size = [2, 3, 4, 4, 4] # NCDHW
- assert np.mod(self.input_size[1], self.groups) == 0
- f_c = self.input_size[1] // self.groups
- self.filter_size = [6, f_c, 3, 3, 3]
-
- def init_test_case_2(self):
- pass
-
- def init_dilation(self):
- self.dilations = [1, 1, 1]
-
- def init_group(self):
- self.groups = 1
-
- def init_kernel_type(self):
- pass
-
- class TestCase1(TestConv3DOp):
- def init_test_case(self):
- self.pad = [1, 1, 1]
- self.stride = [1, 1, 1]
- self.input_size = [2, 3, 4, 4, 4] # NCDHW
- assert np.mod(self.input_size[1], self.groups) == 0
- f_c = self.input_size[1] // self.groups
- self.filter_size = [6, f_c, 3, 3, 3]
-
- class TestWithGroup1(TestConv3DOp):
- def init_group(self):
- self.groups = 3
-
- class TestWithGroup2(TestCase1):
- def init_group(self):
- self.groups = 3
-
- class TestWith1x1(TestConv3DOp):
- def init_test_case(self):
- self.pad = [0, 0, 0]
- self.stride = [1, 1, 1]
- self.input_size = [2, 3, 4, 4, 4]
- assert np.mod(self.input_size[1], self.groups) == 0
- f_c = self.input_size[1] // self.groups
- self.filter_size = [120, f_c, 1, 1, 1]
-
- def init_dilation(self):
- self.dilations = [1, 1, 1]
-
- def init_group(self):
- self.groups = 3
-
- class TestWithInput1x1Filter1x1(TestConv3DOp):
- def init_test_case(self):
- self.pad = [0, 0, 0]
- self.stride = [1, 1, 1]
- self.input_size = [40, 3, 1, 1, 1]
- assert np.mod(self.input_size[1], self.groups) == 0
- f_c = self.input_size[1] // self.groups
- self.filter_size = [120, f_c, 1, 1, 1]
-
- def init_dilation(self):
- self.dilations = [1, 1, 1]
-
- def init_group(self):
- self.groups = 3
-
- class TestWithDilation(TestConv3DOp):
- def init_test_case(self):
- self.pad = [0, 0, 0]
- self.stride = [1, 1, 1]
- self.input_size = [2, 3, 6, 6, 6]
- assert np.mod(self.input_size[1], self.groups) == 0
- f_c = self.input_size[1] // self.groups
- self.filter_size = [24, f_c, 2, 2, 2]
-
- def init_dilation(self):
- self.dilations = [2, 2, 2]
-
- def init_group(self):
- self.groups = 3
-
-
- # ---- test asymmetric padding ----
- class XPUTestConv3DOp_v2(XPUOpTestWrapper):
- def __init__(self):
- self.op_name = 'conv3d'
- self.use_dynamic_create_class = False
-
- class TestConv3DOp_2(XPUOpTest):
- def setUp(self):
- self.dtype = self.in_type
- self.op_type = "conv3d"
- self.use_cudnn = False
- self.use_mkldnn = False
- self.data_format = "NCDHW"
- self.init_kernel_type()
- self.init_group()
- self.init_dilation()
- self.init_data_format()
- self.init_test_case()
- self.init_paddings()
-
- self.init_test_case_2()
-
- conv3d_param = {
- 'stride': self.stride,
- 'pad': self.pad,
- 'dilations': self.dilations,
- }
-
- np.random.seed(100)
- input = np.random.random(self.input_size).astype(self.dtype)
- filter = np.random.random(self.filter_size).astype(self.dtype)
- output = conv3d_forward_naive(
- input,
- filter,
- self.groups,
- conv3d_param,
- self.padding_algorithm,
- self.data_format,
- ).astype(self.dtype)
-
- self.inputs = {
- 'Input': XPUOpTest.np_dtype_to_base_dtype(input),
- 'Filter': XPUOpTest.np_dtype_to_base_dtype(filter),
- }
- self.attrs = {
- 'strides': self.stride,
- 'paddings': self.pad,
- 'padding_algorithm': self.padding_algorithm,
- 'groups': self.groups,
- 'dilations': self.dilations,
- 'use_cudnn': self.use_cudnn,
- 'use_mkldnn': self.use_mkldnn,
- 'data_format': self.data_format,
- }
- self.outputs = {'Output': output}
-
- def test_check_output(self):
- place = paddle.XPUPlace(0)
- self.check_output_with_place(place)
-
- def test_check_grad(self):
- place = paddle.XPUPlace(0)
- self.check_grad_with_place(
- place, {'Input', 'Filter'}, 'Output', max_relative_error=0.03
- )
-
- def test_check_grad_no_filter(self):
- place = paddle.XPUPlace(0)
- self.check_grad_with_place(
- place,
- ['Input'],
- 'Output',
- max_relative_error=0.03,
- no_grad_set={'Filter'},
- )
-
- def test_check_grad_no_input(self):
- place = paddle.XPUPlace(0)
- self.check_grad_with_place(
- place,
- ['Filter'],
- 'Output',
- max_relative_error=0.03,
- no_grad_set={'Input'},
- )
-
- def init_test_case(self):
- self.stride = [1, 1, 1]
- self.input_size = [2, 3, 4, 4, 4] # NCDHW
- assert np.mod(self.input_size[1], self.groups) == 0
- f_c = self.input_size[1] // self.groups
- self.filter_size = [6, f_c, 3, 3, 3]
-
- def init_test_case_2(self):
- pass
-
- def init_dilation(self):
- self.dilations = [1, 1, 1]
-
- def init_group(self):
- self.groups = 1
-
- def init_kernel_type(self):
- pass
-
- def init_paddings(self):
- self.pad = [0, 0, 0]
- self.padding_algorithm = "EXPLICIT"
-
- def init_data_format(self):
- self.data_format = "NCDHW"
-
- class TestConv3DOp_AsyPadding(TestConv3DOp_2):
- def init_test_case(self):
- self.stride = [1, 1, 2]
- self.input_size = [2, 3, 4, 4, 4] # NCDHW
- assert np.mod(self.input_size[1], self.groups) == 0
- f_c = self.input_size[1] // self.groups
- self.filter_size = [6, f_c, 3, 3, 3]
-
- def init_paddings(self):
- self.pad = [1, 0, 1, 0, 0, 2]
- self.padding_algorithm = "EXPLICIT"
-
- class TestConv3DOp_DiffDataInDiffDim(TestConv3DOp_2):
- def init_test_case(self):
- self.stride = [1, 1, 2]
- self.input_size = [2, 3, 4, 5, 5] # NCDHW
- assert np.mod(self.input_size[1], self.groups) == 0
- f_c = self.input_size[1] // self.groups
- self.filter_size = [6, f_c, 3, 4, 3]
-
- def init_paddings(self):
- self.pad = [1, 0, 1, 0, 0, 2]
- self.padding_algorithm = "EXPLICIT"
-
- class TestCase1_AsyPadding(TestConv3DOp_2):
- def init_test_case(self):
- self.stride = [1, 1, 1]
- self.input_size = [2, 3, 4, 4, 4] # NCDHW
- assert np.mod(self.input_size[1], self.groups) == 0
- f_c = self.input_size[1] // self.groups
- self.filter_size = [6, f_c, 3, 3, 3]
-
- def init_paddings(self):
- self.pad = [0, 0, 1, 0, 0, 2]
- self.padding_algorithm = "EXPLICIT"
-
- class TestWithGroup1_AsyPadding(TestConv3DOp_2):
- def init_group(self):
- self.groups = 3
-
- def init_paddings(self):
- self.pad = [1, 1, 1, 0, 0, 2]
- self.padding_algorithm = "EXPLICIT"
-
- class TestWithGroup2_AsyPadding(TestConv3DOp_2):
- def init_test_case(self):
- self.stride = [1, 1, 1]
- self.input_size = [2, 3, 4, 4, 4] # NCDHW
- assert np.mod(self.input_size[1], self.groups) == 0
- f_c = self.input_size[1] // self.groups
- self.filter_size = [6, f_c, 3, 3, 3]
-
- def init_group(self):
- self.groups = 3
-
- def init_paddings(self):
- self.pad = [1, 1, 0, 1, 0, 2]
- self.padding_algorithm = "EXPLICIT"
-
- class TestWithDilation_AsyPadding(TestConv3DOp_2):
- def init_test_case(self):
- self.stride = [1, 1, 1]
- self.input_size = [2, 3, 6, 6, 6]
- assert np.mod(self.input_size[1], self.groups) == 0
- f_c = self.input_size[1] // self.groups
- self.filter_size = [24, f_c, 2, 2, 2]
-
- def init_dilation(self):
- self.dilations = [2, 2, 2]
-
- def init_group(self):
- self.groups = 3
-
- def init_paddings(self):
- self.pad = [0, 0, 1, 0, 1, 0]
- self.padding_algorithm = "EXPLICIT"
-
-
- # --------- test python API ---------------
- class TestConv3DAPI(unittest.TestCase):
- def test_api(self):
- input_NDHWC = paddle.static.data(
- name="input_NDHWC",
- shape=[2, 5, 5, 5, 3],
- dtype="float32",
- )
-
- input_NCDHW = paddle.static.data(
- name="input_NCDHW",
- shape=[2, 3, 5, 5, 3],
- dtype="float32",
- )
-
- paddle.static.nn.conv3d(
- input=input_NDHWC,
- num_filters=3,
- filter_size=[3, 3, 3],
- stride=[1, 1, 1],
- padding=0,
- dilation=[1, 1, 1],
- groups=1,
- data_format="NCDHW",
- )
-
- paddle.static.nn.conv3d(
- input=input_NCDHW,
- num_filters=3,
- filter_size=[3, 3, 3],
- stride=[1, 1, 1],
- padding=[1, 2, 1, 0, 1, 0],
- dilation=[1, 1, 1],
- groups=1,
- data_format="NCDHW",
- )
-
- paddle.static.nn.conv3d(
- input=input_NCDHW,
- num_filters=3,
- filter_size=[3, 3, 3],
- stride=[1, 1, 1],
- padding=[[0, 0], [0, 0], [1, 1], [1, 1], [1, 1]],
- dilation=[1, 1, 1],
- groups=1,
- data_format="NCDHW",
- )
-
- paddle.static.nn.conv3d(
- input=input_NDHWC,
- num_filters=3,
- filter_size=[3, 3, 3],
- stride=[1, 1, 1],
- padding=[[0, 0], [1, 1], [1, 1], [1, 1], [0, 0]],
- dilation=[1, 1, 1],
- groups=1,
- data_format="NDHWC",
- )
-
- paddle.static.nn.conv3d(
- input=input_NCDHW,
- num_filters=3,
- filter_size=[3, 3, 3],
- stride=[1, 1, 1],
- padding="SAME",
- dilation=[1, 1, 1],
- groups=1,
- data_format="NCDHW",
- )
-
- paddle.static.nn.conv3d(
- input=input_NCDHW,
- num_filters=3,
- filter_size=[3, 3, 3],
- stride=[1, 1, 1],
- padding="VALID",
- dilation=[1, 1, 1],
- groups=1,
- data_format="NCDHW",
- )
-
-
- class TestConv3DAPI_Error(unittest.TestCase):
- def test_api(self):
- input = paddle.static.data(
- name="input",
- shape=[2, 5, 5, 5, 4],
- dtype="float32",
- )
-
- # ValueError: cudnn
- def run_1():
- paddle.static.nn.conv3d(
- input=input,
- num_filters=3,
- filter_size=3,
- stride=1,
- padding=0,
- dilation=1,
- groups=1,
- use_cudnn=[0],
- data_format="NCDHW",
- )
-
- self.assertRaises(ValueError, run_1)
-
- # ValueError: data_format
- def run_2():
- paddle.static.nn.conv3d(
- input=input,
- num_filters=3,
- filter_size=[3, 3, 3],
- stride=[1, 1, 1],
- padding=0,
- dilation=[1, 1, 1],
- groups=1,
- use_cudnn=False,
- data_format="NCHWC",
- )
-
- self.assertRaises(ValueError, run_2)
-
- # ValueError: padding
- def run_3():
- paddle.static.nn.conv3d(
- input=input,
- num_filters=3,
- filter_size=3,
- stride=1,
- padding="SAMEE",
- dilation=1,
- groups=1,
- use_cudnn=False,
- data_format="NCDHW",
- )
-
- self.assertRaises(ValueError, run_3)
-
- def run_4():
- paddle.static.nn.conv3d(
- input=input,
- num_filters=3,
- filter_size=3,
- stride=1,
- padding=[[0, 1], [0, 0], [0, 1], [0, 1], [0, 1]],
- dilation=1,
- groups=1,
- use_cudnn=False,
- data_format="NCDHW",
- )
-
- self.assertRaises(ValueError, run_4)
-
- def run_5():
- paddle.static.nn.conv3d(
- input=input,
- num_filters=3,
- filter_size=0,
- stride=0,
- padding=[[0, 1], [0, 1], [0, 1], [0, 1], [0, 1]],
- dilation=1,
- groups=1,
- use_cudnn=False,
- data_format="NDHWC",
- )
-
- self.assertRaises(ValueError, run_5)
-
- # ValueError: channel dimmention
- x = paddle.static.data(
- name="x",
- shape=[2, 5, 5, 5, -1],
- dtype="float32",
- )
-
- def run_6():
- paddle.static.nn.conv3d(
- input=x,
- num_filters=3,
- filter_size=3,
- stride=1,
- padding=0,
- dilation=1,
- groups=1,
- use_cudnn=False,
- data_format="NDHWC",
- )
-
- self.assertRaises(ValueError, run_6)
-
- # ValueError: groups
- def run_7():
- paddle.static.nn.conv3d(
- input=input,
- num_filters=3,
- filter_size=3,
- stride=1,
- padding=0,
- dilation=1,
- groups=3,
- use_cudnn=False,
- data_format="NDHWC",
- )
-
- self.assertRaises(ValueError, run_7)
-
- # ValueError: filter num
- def run_8():
- paddle.static.nn.conv3d(
- input=input,
- num_filters=0,
- filter_size=0,
- stride=0,
- padding=0,
- dilation=0,
- groups=1,
- use_cudnn=False,
- data_format="NDHWC",
- )
-
- self.assertRaises(ValueError, run_8)
-
-
- for stype in ["float32"]:
- create_test_class(globals(), XPUTestConv3DOp, stype)
- create_test_class(globals(), XPUTestConv3DOp_v2, stype)
- if __name__ == '__main__':
- unittest.main()
|