support roi_align & affine_channel for kunlun (#29561)
* support roi_align & affine_channel for kunlun * minorrevert-31562-mean
QingshuChen
4 years ago
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GitHub
parent
0cad1152f4
commit
79a41a9ed6
@ -0,0 +1,186 @@
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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Indicesou may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#ifdef PADDLE_WITH_XPU
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#include <string>
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#include <unordered_map>
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#include <vector>
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#include "paddle/fluid/framework/data_layout.h"
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#include "paddle/fluid/framework/eigen.h"
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#include "paddle/fluid/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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template <typename DeviceContext, typename T>
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class AffineChannelXPUKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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auto* x = ctx.Input<framework::Tensor>("X");
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auto* scale = ctx.Input<framework::Tensor>("Scale");
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auto* bias = ctx.Input<framework::Tensor>("Bias");
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auto* y = ctx.Output<framework::Tensor>("Out");
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y->mutable_data<T>(ctx.GetPlace());
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const framework::DataLayout layout =
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framework::StringToDataLayout(ctx.Attr<std::string>("data_layout"));
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auto dims = x->dims();
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int N = dims[0];
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int C = layout == framework::DataLayout::kNCHW ? dims[1]
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: dims[dims.size() - 1];
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int HxW = x->numel() / N / C;
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auto* scale_d = scale->data<T>();
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auto* bias_d = bias->data<T>();
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auto* x_d = x->data<T>();
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auto* y_d = y->data<T>();
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auto& dev_ctx = ctx.template device_context<DeviceContext>();
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std::vector<int> x_shape;
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std::vector<int> b_shape;
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if (layout == framework::DataLayout::kNCHW) {
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x_shape.push_back(N);
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x_shape.push_back(C);
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x_shape.push_back(HxW);
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b_shape.push_back(1);
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b_shape.push_back(C);
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b_shape.push_back(1);
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} else {
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x_shape.push_back(N * HxW);
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x_shape.push_back(C);
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b_shape.push_back(1);
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b_shape.push_back(C);
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}
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int r = 0;
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r = xpu::broadcast_mul(dev_ctx.x_context(), x_d, scale_d, y_d, x_shape,
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b_shape);
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PADDLE_ENFORCE_EQ(r, xpu::Error_t::SUCCESS,
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platform::errors::External(
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"The broadcast_mul XPU OP return wrong value[%d %s]",
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r, XPUAPIErrorMsg[r]));
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r = xpu::broadcast_add(dev_ctx.x_context(), y_d, bias_d, y_d, x_shape,
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b_shape);
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PADDLE_ENFORCE_EQ(r, xpu::Error_t::SUCCESS,
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platform::errors::External(
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"The broadcast_add XPU OP return wrong value[%d %s]",
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r, XPUAPIErrorMsg[r]));
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}
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};
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template <typename DeviceContext, typename T>
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class AffineChannelGradXPUKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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auto* x = ctx.Input<framework::Tensor>("X");
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auto* scale = ctx.Input<framework::Tensor>("Scale");
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auto* dy = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
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auto* dx = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
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auto* dscale =
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ctx.Output<framework::Tensor>(framework::GradVarName("Scale"));
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auto* dbias = ctx.Output<framework::Tensor>(framework::GradVarName("Bias"));
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const framework::DataLayout layout =
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framework::StringToDataLayout(ctx.Attr<std::string>("data_layout"));
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auto dims = x->dims();
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int N = dims[0];
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int C = layout == framework::DataLayout::kNCHW ? dims[1]
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: dims[dims.size() - 1];
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int HxW = x->numel() / N / C;
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auto* dy_d = dy->data<T>();
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auto* scale_d = scale->data<T>();
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T* dx_d = dx ? dx->mutable_data<T>(ctx.GetPlace()) : nullptr;
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T* dscale_d = dscale ? dscale->mutable_data<T>(ctx.GetPlace()) : nullptr;
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T* dbias_d = dbias ? dbias->mutable_data<T>(ctx.GetPlace()) : nullptr;
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auto& dev_ctx = ctx.template device_context<DeviceContext>();
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std::vector<int> x_shape;
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std::vector<int> b_shape;
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std::vector<int> rdims;
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if (layout == framework::DataLayout::kNCHW) {
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x_shape.push_back(N);
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x_shape.push_back(C);
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x_shape.push_back(HxW);
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b_shape.push_back(1);
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b_shape.push_back(C);
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b_shape.push_back(1);
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rdims.push_back(0);
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rdims.push_back(2);
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} else {
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x_shape.push_back(N * HxW);
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x_shape.push_back(C);
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b_shape.push_back(1);
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b_shape.push_back(C);
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rdims.push_back(0);
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}
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int r = 0;
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if (dscale_d && dbias_d) {
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r = xpu::reduce_sum<T>(dev_ctx.x_context(), dy_d, dbias_d, x_shape,
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rdims);
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PADDLE_ENFORCE_EQ(r, xpu::Error_t::SUCCESS,
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platform::errors::External(
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"The reduce_sum XPU OP return wrong value[%d %s]",
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r, XPUAPIErrorMsg[r]));
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T* tmp = nullptr;
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r = xpu_malloc(reinterpret_cast<void**>(&tmp), dy->numel() * sizeof(T));
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PADDLE_ENFORCE_EQ(r, xpu::Error_t::SUCCESS,
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platform::errors::External("no enough memory in xpu"));
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r = xpu::mul<T>(dev_ctx.x_context(), dy_d, x->data<T>(), tmp,
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dy->numel());
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PADDLE_ENFORCE_EQ(
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r, xpu::Error_t::SUCCESS,
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platform::errors::External("The mul XPU OP return wrong value[%d %s]",
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r, XPUAPIErrorMsg[r]));
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r = xpu::reduce_sum<T>(dev_ctx.x_context(), tmp, dscale_d, x_shape,
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rdims);
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PADDLE_ENFORCE_EQ(r, xpu::Error_t::SUCCESS,
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platform::errors::External(
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"The reduce_sum XPU OP return wrong value[%d %s]",
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r, XPUAPIErrorMsg[r]));
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if (dev_ctx.x_context()->xpu_stream) {
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dev_ctx.Wait();
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}
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xpu_free(tmp);
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}
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if (dx_d) {
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r = xpu::broadcast_mul(dev_ctx.x_context(), dy_d, scale_d, dx_d, x_shape,
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b_shape);
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PADDLE_ENFORCE_EQ(
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r, xpu::Error_t::SUCCESS,
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platform::errors::External(
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"The broadcast_mul XPU OP return wrong value[%d %s]", r,
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XPUAPIErrorMsg[r]));
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}
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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using XPU = paddle::platform::XPUDeviceContext;
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REGISTER_OP_XPU_KERNEL(affine_channel, ops::AffineChannelXPUKernel<XPU, float>);
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REGISTER_OP_XPU_KERNEL(affine_channel_grad,
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ops::AffineChannelGradXPUKernel<XPU, float>);
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#endif
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File diff suppressed because it is too large
Load Diff
@ -0,0 +1,148 @@
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Unit testing for affine_channel_op
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"""
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from __future__ import print_function
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import sys
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sys.path.append("..")
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import unittest
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import numpy as np
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from op_test_xpu import XPUOpTest
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import paddle
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import paddle.fluid.core as core
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import paddle.fluid as fluid
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def affine_channel(x, scale, bias, layout):
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C = x.shape[1] if layout == 'NCHW' else x.shape[-1]
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if len(x.shape) == 4:
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new_shape = (1, C, 1, 1) if layout == 'NCHW' else (1, 1, 1, C)
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else:
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new_shape = (1, C)
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scale = scale.reshape(new_shape)
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bias = bias.reshape(new_shape)
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return x * scale + bias
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class TestAffineChannelOp(XPUOpTest):
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def setUp(self):
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self.op_type = "affine_channel"
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self.init_test_case()
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x = np.random.random(self.shape).astype("float32")
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scale = np.random.random(self.C).astype("float32")
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bias = np.random.random(self.C).astype("float32")
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y = affine_channel(x, scale, bias, self.layout)
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self.inputs = {'X': x, 'Scale': scale, 'Bias': bias}
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self.attrs = {'data_layout': self.layout}
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self.outputs = {'Out': y}
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def test_check_output(self):
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if core.is_compiled_with_xpu():
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paddle.enable_static()
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place = paddle.XPUPlace(0)
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self.check_output_with_place(place)
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def test_check_grad(self):
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if core.is_compiled_with_xpu():
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paddle.enable_static()
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place = paddle.XPUPlace(0)
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self.check_grad_with_place(place, ['X', 'Scale', 'Bias'], 'Out')
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def test_check_grad_stopgrad_dx(self):
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if core.is_compiled_with_xpu():
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paddle.enable_static()
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place = paddle.XPUPlace(0)
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self.check_grad_with_place(
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place, ['Scale', 'Bias'], 'Out', no_grad_set=set('X'))
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def test_check_grad_stopgrad_dscale_dbias(self):
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if core.is_compiled_with_xpu():
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paddle.enable_static()
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place = paddle.XPUPlace(0)
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self.check_grad_with_place(
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place, ['X'], 'Out', no_grad_set=set(['Scale', 'Bias']))
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def init_test_case(self):
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self.shape = [2, 100, 3, 3]
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self.C = 100
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self.layout = 'NCHW'
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class TestAffineChannelOpError(unittest.TestCase):
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def test_errors(self):
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with fluid.program_guard(fluid.Program()):
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def test_x_type():
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input_data = np.random.random(2, 1, 2, 2).astype("float32")
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fluid.layers.affine_channel(input_data)
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self.assertRaises(TypeError, test_x_type)
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def test_x_dtype():
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x2 = fluid.layers.data(
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name='x2', shape=[None, 1, 2, 2], dtype='int32')
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fluid.layers.affine_channel(x2)
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self.assertRaises(TypeError, test_x_dtype)
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def test_scale_type():
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x3 = fluid.layers.data(
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name='x3', shape=[None, 1, 2, 2], dtype='float32')
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fluid.layers.affine_channel(x3, scale=1)
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self.assertRaises(TypeError, test_scale_type)
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def test_bias_type():
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x4 = fluid.layers.data(
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name='x4', shape=[None, 1, 2, 2], dtype='float32')
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fluid.layers.affine_channel(x4, bias=1)
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self.assertRaises(TypeError, test_bias_type)
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class TestAffineChannelNHWC(TestAffineChannelOp):
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def init_test_case(self):
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self.shape = [2, 3, 3, 100]
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self.C = 100
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self.layout = 'NHWC'
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def test_check_grad_stopgrad_dx(self):
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return
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def test_check_grad_stopgrad_dscale_dbias(self):
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return
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class TestAffineChannel2D(TestAffineChannelOp):
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def init_test_case(self):
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self.shape = [2, 100]
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self.C = 100
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self.layout = 'NCHW'
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def test_check_grad_stopgrad_dx(self):
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return
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def test_check_grad_stopgrad_dscale_dbias(self):
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return
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if __name__ == '__main__':
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unittest.main()
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