add_depthwise_conv2d_transpose

paddle/phi/backends/xpu/xpu2_op_list.cc

@@ -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,

paddle/phi/kernels/xpu/conv_transpose_grad_kernel.cc

@@ -97,10 +97,45 @@ 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,
                    XPU,
                    ALL_LAYOUT,
                    phi::Conv2dTransposeGradKernel,
                    float) {}
+PD_REGISTER_KERNEL(depthwise_conv2d_transpose_grad,
+                   XPU,
+                   ALL_LAYOUT,
+                   phi::DepthwiseConv2dTransposeGradKernel,
+                   float) {}

paddle/phi/kernels/xpu/conv_transpose_kernel.cc

@@ -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,

test/xpu/test_depthwise_conv2d_transpose_op_xpu.py

@@ -0,0 +1,298 @@
+#   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()