未验证 提交 08b6f5d6 编写于 作者: S SaltFish11 提交者: GitHub

[XPU] add depthwise_conv2d_transpose (#53680)

* add_depthwise_conv2d_transpose

* Update test_depthwise_conv2d_transpose_op_xpu.py

删除print语句
上级 6ec8d856
......@@ -185,6 +185,9 @@ XPUOpMap& get_kl2_ops() {
{"deformable_conv_v1", XPUKernelSet({phi::DataType::FLOAT32})},
{"depthwise_conv2d_grad", XPUKernelSet({phi::DataType::FLOAT32})},
{"depthwise_conv2d", XPUKernelSet({phi::DataType::FLOAT32})},
{"depthwise_conv2d_transpose_grad",
XPUKernelSet({phi::DataType::FLOAT32})},
{"depthwise_conv2d_transpose", XPUKernelSet({phi::DataType::FLOAT32})},
{"diag_v2",
XPUKernelSet({phi::DataType::FLOAT32,
phi::DataType::FLOAT16,
......
......@@ -97,6 +97,36 @@ void Conv2dTransposeGradKernel(const Context& ctx,
PADDLE_ENFORCE_XDNN_SUCCESS(r, "conv2d_transpose_grad");
}
template <typename T, typename Context>
void DepthwiseConv2dTransposeGradKernel(const Context& ctx,
const DenseTensor& x,
const DenseTensor& filter,
const DenseTensor& dout,
const std::vector<int>& strides,
const std::vector<int>& paddings,
const std::vector<int>& output_padding,
const IntArray& output_size,
const std::string& padding_algorithm,
int groups,
const std::vector<int>& dilations,
const std::string& data_format,
DenseTensor* dx,
DenseTensor* dfilter) {
Conv2dTransposeGradKernel<T, Context>(ctx,
x,
filter,
dout,
strides,
paddings,
output_padding,
output_size,
padding_algorithm,
groups,
dilations,
data_format,
dx,
dfilter);
}
} // namespace phi
PD_REGISTER_KERNEL(conv2d_transpose_grad,
......@@ -104,3 +134,8 @@ PD_REGISTER_KERNEL(conv2d_transpose_grad,
ALL_LAYOUT,
phi::Conv2dTransposeGradKernel,
float) {}
PD_REGISTER_KERNEL(depthwise_conv2d_transpose_grad,
XPU,
ALL_LAYOUT,
phi::DepthwiseConv2dTransposeGradKernel,
float) {}
......@@ -145,8 +145,39 @@ void Conv2dTransposeKernel(const Context& ctx,
PADDLE_ENFORCE_XDNN_SUCCESS(r, "conv2d_transpose_v2");
}
}
template <typename T, typename Context>
void DepthwiseConv2dTransposeKernel(const Context& ctx,
const DenseTensor& x,
const DenseTensor& filter,
const std::vector<int>& strides,
const std::vector<int>& paddings,
const std::vector<int>& output_padding,
const IntArray& output_size,
const std::string& padding_algorithm,
int groups,
const std::vector<int>& dilations,
const std::string& data_format,
DenseTensor* out) {
Conv2dTransposeKernel<T, Context>(ctx,
x,
filter,
strides,
paddings,
output_padding,
output_size,
padding_algorithm,
groups,
dilations,
data_format,
out);
}
} // namespace phi
PD_REGISTER_KERNEL(depthwise_conv2d_transpose,
XPU,
ALL_LAYOUT,
phi::DepthwiseConv2dTransposeKernel,
float) {}
PD_REGISTER_KERNEL(conv2d_transpose,
XPU,
......
# 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,
get_xpu_op_support_types,
)
from op_test_xpu import XPUOpTest
import paddle
paddle.enable_static()
def depthwiseconv2dtranspose_forward_naive(input_, filter_, attrs):
padding_algorithm = attrs['padding_algorithm']
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 attrs['data_format'] == 'NHWC':
input_ = np.transpose(input_, [0, 3, 1, 2])
in_n, in_c, in_h, in_w = input_.shape
f_c, f_out_c, f_h, f_w = filter_.shape
groups = attrs['groups']
assert in_c == f_c
out_c = f_out_c * groups
sub_in_c = in_c // groups
stride, pad, dilations = (
attrs['strides'],
attrs['paddings'],
attrs['dilations'],
)
# update pad and dilation
def _get_padding_with_SAME(input_shape, kernel_size, kernel_stride):
padding = []
for input_size, filter_size, stride_size in zip(
input_shape, kernel_size, kernel_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":
dilations = [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]
d_bolck_h = dilations[0] * (f_h - 1) + 1
d_bolck_w = dilations[1] * (f_w - 1) + 1
out_h = (in_h - 1) * stride[0] + d_bolck_h
out_w = (in_w - 1) * stride[1] + d_bolck_w
if 'output_size' in attrs:
output_size = attrs['output_size']
out_h = output_size[0] + pad_h_0 + pad_h_1
out_w = output_size[1] + pad_w_0 + pad_w_1
out_pad_h = 0
out_pad_w = 0
if 'output_padding' in attrs:
out_pad_h = attrs['output_padding'][0]
out_pad_w = attrs['output_padding'][1]
out = np.zeros(
(in_n, out_c, out_h + out_pad_h, out_w + out_pad_w), dtype=input_.dtype
)
for n in range(in_n):
for i in range(in_h):
for j in range(in_w):
for g in range(groups):
input_masked = input_[
n, g * sub_in_c : (g + 1) * sub_in_c, i, j
] # (c)
input_masked = np.reshape(input_masked, (sub_in_c, 1, 1))
input_masked = np.tile(input_masked, (1, f_h, f_w))
for k in range(f_out_c):
tmp_out = np.sum(
input_masked
* filter_[
g * sub_in_c : (g + 1) * sub_in_c, k, :, :
],
axis=0,
)
i1, i2 = i * stride[0], i * stride[0] + d_bolck_h
j1, j2 = j * stride[1], j * stride[1] + d_bolck_w
out[
n,
g * f_out_c + k,
i1 : i2 : dilations[0],
j1 : j2 : dilations[1],
] += tmp_out
out = out[
:,
:,
pad_h_0 : out_h - pad_h_1 + out_pad_h,
pad_w_0 : out_w - pad_w_1 + out_pad_w,
]
if attrs['data_format'] == 'NHWC':
out = np.transpose(out, [0, 2, 3, 1])
return out
class XPUTestDepthwiseConv2DTransposeOp(XPUOpTestWrapper):
def __init__(self):
self.op_name = 'depthwise_conv2d_transpose'
self.use_dynamic_create_class = False
class TestDepthwiseConv2DTransposeOp(XPUOpTest):
def setUp(self):
# init as conv transpose
self.need_check_grad = True
self.is_test = False
self.use_cudnn = False
self.use_mkldnn = False
self.output_size = None
self.output_padding = []
self.data_format = "NCHW"
self.pad = [0, 0]
self.padding_algorithm = "EXPLICIT"
self.init_op_type()
self.init_test_case()
self.__class__.op_type = "depthwise_conv2d_transpose"
input_ = np.random.random(self.input_size).astype(self.dtype)
filter_ = np.random.random(self.filter_size).astype(self.dtype)
self.inputs = {'Input': input_, 'Filter': 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,
'is_test': self.is_test,
'use_mkldnn': self.use_mkldnn,
'data_format': self.data_format,
}
if self.output_size is not None:
self.attrs['output_size'] = self.output_size
if len(self.output_padding) > 0:
self.attrs['output_padding'] = self.output_padding
output = depthwiseconv2dtranspose_forward_naive(
input_, filter_, self.attrs
).astype(self.dtype)
self.outputs = {'Output': output}
def test_check_output(self):
self.check_output_with_place(self.place)
def test_check_grad_no_input(self):
if self.need_check_grad:
self.check_grad_with_place(
self.place, ['Filter'], 'Output', no_grad_set={'Input'}
)
def test_check_grad_no_filter(self):
if self.need_check_grad:
self.check_grad_with_place(
self.place, ['Input'], 'Output', no_grad_set={'Filter'}
)
def test_check_grad(self):
if self.need_check_grad:
self.check_grad_with_place(
self.place, {'Input', 'Filter'}, 'Output'
)
def init_test_case(self):
self.pad = [0, 0]
self.stride = [1, 1]
self.dilations = [1, 1]
self.groups = 1
self.input_size = [2, 3, 5, 5] # NCHW
f_c = self.input_size[1]
self.filter_size = [f_c, 6, 3, 3]
def init_op_type(self):
self.dtype = self.in_type
self.place = paddle.XPUPlace(0)
self.op_type = "depthwise_conv2d_transpose"
class TestWithSymmetricPad(TestDepthwiseConv2DTransposeOp):
def init_test_case(self):
self.pad = [1, 1]
self.stride = [1, 1]
self.dilations = [1, 1]
self.groups = 1
self.input_size = [2, 3, 5, 5] # NCHW
f_c = self.input_size[1]
self.filter_size = [f_c, 6, 3, 3]
class TestWithAsymmetricPad(TestDepthwiseConv2DTransposeOp):
def init_test_case(self):
self.pad = [1, 0, 1, 2]
self.stride = [1, 1]
self.dilations = [1, 1]
self.groups = 1
self.input_size = [2, 3, 5, 5] # NCHW
f_c = self.input_size[1]
self.filter_size = [f_c, 6, 3, 3]
class TestWithSAMEPad(TestDepthwiseConv2DTransposeOp):
def init_test_case(self):
self.stride = [2, 1]
self.dilations = [1, 2]
self.groups = 1
self.input_size = [2, 3, 6, 5] # NCHW
f_c = self.input_size[1]
self.filter_size = [f_c, 6, 4, 3]
self.padding_algorithm = 'SAME'
class TestWithVALIDPad(TestDepthwiseConv2DTransposeOp):
def init_test_case(self):
self.stride = [1, 1]
self.dilations = [1, 1]
self.groups = 1
self.input_size = [2, 3, 5, 5] # NCHW
f_c = self.input_size[1]
self.filter_size = [f_c, 6, 3, 3]
self.padding_algorithm = 'VALID'
class TestWithGroups(TestDepthwiseConv2DTransposeOp):
def init_test_case(self):
self.pad = [1, 1]
self.stride = [1, 1]
self.dilations = [1, 1]
self.groups = 2
self.input_size = [2, 4, 5, 5] # NCHW
f_c = self.input_size[1]
self.filter_size = [f_c, 3, 3, 3]
class TestWithStride(TestDepthwiseConv2DTransposeOp):
def init_test_case(self):
self.pad = [1, 1]
self.stride = [2, 2]
self.dilations = [1, 1]
self.groups = 1
self.input_size = [2, 3, 5, 5] # NCHW
f_c = self.input_size[1]
self.filter_size = [f_c, 6, 3, 3]
class TestWithEvenUpsample(TestDepthwiseConv2DTransposeOp):
def init_test_case(self):
self.pad = [2, 2]
self.stride = [2, 2]
self.groups = 1
self.dilations = [1, 1]
self.output_size = [14, 14]
self.input_size = [2, 3, 7, 7] # NCHW
f_c = self.input_size[1]
self.filter_size = [f_c, 6, 5, 5]
support_types = get_xpu_op_support_types('depthwise_conv2d_transpose')
for stype in support_types:
create_test_class(globals(), XPUTestDepthwiseConv2DTransposeOp, stype)
if __name__ == '__main__':
unittest.main()
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