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- # Copyright (c) 2020 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
- from paddle.base import core
-
-
- def conv2d_forward_naive(
- input,
- filter,
- group,
- conv_param,
- padding_algorithm='EXPLICIT',
- data_format='NCHW',
- ):
- 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 ["NCHW", "NHWC"]:
- raise ValueError(
- "Unknown Attr(data_format): '%s' ."
- "It can only be 'NCHW' or 'NHWC'." % str(data_format)
- )
-
- channel_last = data_format == "NHWC"
- if channel_last:
- input = np.transpose(input, [0, 3, 1, 2])
-
- in_n, in_c, in_h, in_w = input.shape
- f_n, f_c, 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['dilation'],
- )
-
- # 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[2:4]
- if padding_algorithm == "VALID":
- pad = [0, 0, 0, 0]
- elif padding_algorithm == "SAME":
- dilation = [1, 1]
- input_data_shape = input.shape[2:4]
- pad = _get_padding_with_SAME(input_data_shape, ksize, stride)
-
- pad_h_0, pad_h_1 = pad[0], pad[0]
- pad_w_0, pad_w_1 = pad[1], pad[1]
- if len(pad) == 4:
- pad_h_0, pad_h_1 = pad[0], pad[1]
- pad_w_0, pad_w_1 = pad[2], pad[3]
- out_h = (
- 1
- + (in_h + pad_h_0 + pad_h_1 - (dilation[0] * (f_h - 1) + 1))
- // stride[0]
- )
- out_w = (
- 1
- + (in_w + pad_w_0 + pad_w_1 - (dilation[1] * (f_w - 1) + 1))
- // stride[1]
- )
- out = np.zeros((out_n, out_c, out_h, out_w))
-
- d_block_h = dilation[0] * (f_h - 1) + 1
- d_block_w = dilation[1] * (f_w - 1) + 1
-
- input_pad = np.pad(
- input,
- ((0, 0), (0, 0), (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_h, d_block_w))
- filter_dilation[
- :, :, 0 : d_block_h : dilation[0], 0 : d_block_w : dilation[1]
- ] = filter
-
- 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,
- i * stride[0] : i * stride[0] + d_block_h,
- j * stride[1] : j * stride[1] + d_block_w,
- ]
-
- f_sub = filter_dilation[
- g * sub_f_n : (g + 1) * sub_f_n, :, :, :
- ]
- # sub_f_n == sub_out_c
- for k in range(sub_out_c):
- # Multiplication of Corresponding Elements, then sum all
- out[:, g * sub_out_c + k, i, j] = np.sum(
- input_pad_masked * f_sub[k, :, :, :], axis=(1, 2, 3)
- )
-
- if channel_last:
- out = np.transpose(out, [0, 2, 3, 1])
-
- return out, in_n, out_h, out_w, out_c
-
-
- def create_test_channel_last_class(parent):
- class TestChannelLastCase(parent):
- def init_data_format(self):
- self.data_format = "NHWC"
-
- def init_test_case_2(self):
- N, C, H, W = self.input_size
- self.input_size = [N, H, W, C]
-
- cls_name = "{}_{}".format(parent.__name__, "ChannelLast")
- TestChannelLastCase.__name__ = cls_name
- globals()[cls_name] = TestChannelLastCase
-
-
- def create_test_padding_SAME_class(parent):
- class TestPaddingSAMECase(parent):
- def init_paddings(self):
- self.pad = [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]
- self.padding_algorithm = "VALID"
-
- cls_name = "{}_{}".format(parent.__name__, "PaddingVALIDOp")
- TestPaddingVALIDCase.__name__ = cls_name
- globals()[cls_name] = TestPaddingVALIDCase
-
-
- class XPUTestConv2DOp(XPUOpTestWrapper):
- def __init__(self):
- self.op_name = 'conv2d'
- self.use_dynamic_create_class = False
-
- class TestConv2DOp(XPUOpTest):
- def setUp(self):
- self.dtype = self.in_type
- self.place = paddle.XPUPlace(0)
- self.op_type = "conv2d"
- self.use_cudnn = False
- self.exhaustive_search = False
- self.use_cuda = False
- self.use_mkldnn = False
- self.fuse_relu_before_depthwise_conv = False
- self.data_format = "AnyLayout"
- self.init_kernel_type()
- self.init_group()
- self.init_dilation()
- self.init_test_case()
-
- conv2d_param = {
- 'stride': self.stride,
- 'pad': self.pad,
- 'dilation': self.dilations,
- }
-
- np.random.seed(100)
- input = np.random.random(self.input_size).astype(self.dtype)
- if not self.has_cuda():
- self.fuse_relu_before_depthwise_conv = False
- if self.fuse_relu_before_depthwise_conv:
- input = input - 0.5
- input -= (input < 0) * 0.1
- input += (input >= 0) * 0.1
- input2 = np.maximum(input, 0.0)
- else:
- input2 = input
- np.random.seed(1)
- filter = np.random.uniform(-1, 1, self.filter_size).astype(
- self.dtype
- )
-
- output, _, _, _, _ = conv2d_forward_naive(
- input2, filter, self.groups, conv2d_param
- )
- output = output.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,
- 'fuse_relu_before_depthwise_conv': self.fuse_relu_before_depthwise_conv,
- 'exhaustive_search': self.exhaustive_search,
- }
- self.outputs = {'Output': output}
-
- def has_cuda(self):
- return core.is_compiled_with_cuda() and (
- self.use_cudnn or self.use_cuda
- )
-
- def test_check_output(self):
- if core.is_compiled_with_xpu():
- paddle.enable_static()
- self.check_output_with_place(self.place)
-
- def test_check_grad(self):
- if hasattr(self, "no_need_check_grad") and self.no_need_check_grad:
- return
- if core.is_compiled_with_xpu():
- paddle.enable_static()
- self.check_grad_with_place(
- self.place, {'Input', 'Filter'}, 'Output'
- )
-
- def test_check_grad_no_filter(self):
- if hasattr(self, "no_need_check_grad") and self.no_need_check_grad:
- return
- if core.is_compiled_with_xpu():
- paddle.enable_static()
- self.check_grad_with_place(
- self.place, ['Input'], 'Output', no_grad_set={'Filter'}
- )
-
- def test_check_grad_no_input(self):
- if hasattr(self, "no_need_check_grad") and self.no_need_check_grad:
- return
- if core.is_compiled_with_xpu():
- paddle.enable_static()
- self.check_grad_with_place(
- self.place, ['Filter'], 'Output', no_grad_set={'Input'}
- )
-
- def init_test_case(self):
- self.pad = [0, 0]
- self.stride = [1, 1]
- self.input_size = [2, 3, 5, 5] # NCHW
- 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]
-
- def init_test_case_2(self):
- pass
-
- def init_dilation(self):
- self.dilations = [1, 1]
-
- def init_group(self):
- self.groups = 1
-
- def init_kernel_type(self):
- pass
-
- class TestWithPad(TestConv2DOp):
- def init_test_case(self):
- self.pad = [1, 1]
- self.stride = [1, 1]
- self.input_size = [2, 3, 5, 5] # NCHW
- 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]
-
- class TestWithStride(TestConv2DOp):
- def init_test_case(self):
- self.pad = [1, 1]
- self.stride = [2, 2]
- self.input_size = [2, 3, 6, 6] # NCHW
- 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]
-
- class TestWith1x1(TestConv2DOp):
- def init_test_case(self):
- self.pad = [0, 0]
- self.stride = [1, 1]
- self.input_size = [2, 3, 5, 5] # NCHW
- 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]
-
- def init_group(self):
- self.groups = 1
-
-
- # ---- test asymmetric padding ----
- class XPUTestConv2DOp_v2(XPUOpTestWrapper):
- def __init__(self):
- self.op_name = 'conv2d'
- self.use_dynamic_create_class = False
-
- class TestConv2DOp_v2(XPUOpTest):
- def setUp(self):
- self.dtype = self.in_type
- self.place = paddle.XPUPlace(0)
- self.op_type = "conv2d"
- self.use_cudnn = False
- self.exhaustive_search = False
- self.use_cuda = False
- self.use_mkldnn = False
- self.fuse_relu_before_depthwise_conv = False
- 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()
-
- conv2d_param = {
- 'stride': self.stride,
- 'pad': self.pad,
- 'dilation': self.dilations,
- }
-
- np.random.seed(100)
- input = np.random.random(self.input_size).astype(self.dtype)
- if not self.has_cuda():
- self.fuse_relu_before_depthwise_conv = False
- if self.fuse_relu_before_depthwise_conv:
- input = input - 0.5
- input -= (input < 0) * 0.1
- input += (input >= 0) * 0.1
- input2 = np.maximum(input, 0.0)
- else:
- input2 = input
- np.random.seed(8)
- filter = np.random.uniform(-1, 1, self.filter_size).astype(
- self.dtype
- )
- output, _, _, _, _ = conv2d_forward_naive(
- input2,
- filter,
- self.groups,
- conv2d_param,
- self.padding_algorithm,
- self.data_format,
- )
- output = output.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,
- 'fuse_relu_before_depthwise_conv': self.fuse_relu_before_depthwise_conv,
- 'exhaustive_search': self.exhaustive_search,
- }
- self.outputs = {'Output': output}
-
- def has_cuda(self):
- return core.is_compiled_with_cuda() and (
- self.use_cudnn or self.use_cuda
- )
-
- def test_check_output(self):
- # TODO(wangzhongpu): support onednn op in dygraph mode
- if core.is_compiled_with_xpu():
- paddle.enable_static()
- self.check_output_with_place(place=self.place)
-
- def test_check_grad(self):
- # TODO(wangzhongpu): support onednn op in dygraph mode
- if hasattr(self, "no_need_check_grad") and self.no_need_check_grad:
- return
- if core.is_compiled_with_xpu():
- paddle.enable_static()
- self.check_grad_with_place(
- self.place, {'Input', 'Filter'}, 'Output'
- )
-
- def test_check_grad_no_filter(self):
- # TODO(wangzhongpu): support onednn op in dygraph mode
- if hasattr(self, "no_need_check_grad") and self.no_need_check_grad:
- return
- if core.is_compiled_with_xpu():
- paddle.enable_static()
- self.check_grad_with_place(
- self.place, ['Input'], 'Output', no_grad_set={'Filter'}
- )
-
- def test_check_grad_no_input(self):
- # TODO(wangzhongpu): support onednn op in dygraph mode
- if hasattr(self, "no_need_check_grad") and self.no_need_check_grad:
- return
- if core.is_compiled_with_xpu():
- paddle.enable_static()
- self.check_grad_with_place(
- self.place, ['Filter'], 'Output', no_grad_set={'Input'}
- )
-
- def init_test_case(self):
- self.pad = [0, 0]
- self.stride = [1, 2]
- self.input_size = [2, 3, 5, 5] # NCHW
- assert np.mod(self.input_size[1], self.groups) == 0
- f_c = self.input_size[1] // self.groups
- self.filter_size = [6, f_c, 4, 3]
-
- def init_dilation(self):
- self.dilations = [1, 1]
-
- def init_group(self):
- self.groups = 1
-
- def init_kernel_type(self):
- pass
-
- def init_paddings(self):
- self.pad = [0, 0]
- self.padding_algorithm = "EXPLICIT"
-
- def init_data_format(self):
- self.data_format = "NCHW"
-
- def init_test_case_2(self):
- pass
-
- class TestConv2DOp_AsyPadding(TestConv2DOp_v2):
- def init_paddings(self):
- self.pad = [0, 0, 0, 0]
- self.padding_algorithm = "EXPLICIT"
-
- class TestWithPad_AsyPadding(TestConv2DOp_v2):
- def init_test_case(self):
- self.stride = [1, 1]
- self.input_size = [2, 3, 5, 5] # NCHW
- 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]
-
- def init_paddings(self):
- self.pad = [1, 1, 1, 1]
- self.padding_algorithm = "EXPLICIT"
-
- class TestWithStride_AsyPadding(TestConv2DOp_v2):
- def init_test_case(self):
- self.stride = [2, 2]
- self.input_size = [2, 3, 6, 6] # NCHW
- 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]
-
- def init_paddings(self):
- self.pad = [1, 1, 1, 1]
- self.padding_algorithm = "EXPLICIT"
-
-
- class XPUTestConv2DOp_NHWC(XPUOpTestWrapper):
- def __init__(self):
- self.op_name = 'conv2d'
- self.use_dynamic_create_class = False
-
- class TestConv2DOp_AsyPadding_NHWC(
- XPUTestConv2DOp_v2.TestConv2DOp_AsyPadding
- ):
- def init_data_format(self):
- self.data_format = "NHWC"
-
- def init_test_case_2(self):
- N, C, H, W = self.input_size
- self.input_size = [N, H, W, C]
-
- class TestWithPad_AsyPadding_NHWC(
- XPUTestConv2DOp_v2.TestWithPad_AsyPadding
- ):
- def init_data_format(self):
- self.data_format = "NHWC"
-
- def init_test_case_2(self):
- N, C, H, W = self.input_size
- self.input_size = [N, H, W, C]
-
-
- support_types = get_xpu_op_support_types('conv2d')
- for stype in ['float32']:
- create_test_class(globals(), XPUTestConv2DOp, stype)
- create_test_class(globals(), XPUTestConv2DOp_v2, stype)
- create_test_class(
- globals(),
- XPUTestConv2DOp_NHWC,
- stype,
- ignore_device_version=[core.XPUVersion.XPU1],
- )
-
- # ---------- test SAME VALID -----------
- # create_test_padding_SAME_class(TestConv2DOp_AsyPadding)
- # create_test_padding_SAME_class(TestWithPad_AsyPadding)
- # create_test_padding_SAME_class(TestWithStride_AsyPadding)
-
- # create_test_padding_VALID_class(TestConv2DOp_AsyPadding)
- # create_test_padding_VALID_class(TestWithPad_AsyPadding)
- # create_test_padding_VALID_class(TestWithStride_AsyPadding)
-
- if __name__ == '__main__':
- unittest.main()
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