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@ -20,9 +20,9 @@ limitations under the License. */
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namespace paddle {
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enum TestType {
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FORWARD_TEST = 0,
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BACKWARD_INPUT_TEST = 1,
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BACKWARD_FILTER_TEST = 2,
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kForwardTest = 0,
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kBackwardInputTest = 1,
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kBackwardFilterTest = 2,
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};
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template <DeviceType DType1, DeviceType DType2>
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@ -43,16 +43,16 @@ public:
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if (padding >= filterSize) break;
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size_t outputSize =
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(inputSize - filterSize + 2 * padding + stride) / stride;
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LOG(INFO) << " batchSize=" << batchSize
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<< " inputChannels=" << inputChannels
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<< " inputHeight=" << inputSize
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<< " inputWidth=" << inputSize
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<< " outputChannels=" << outputChannels
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<< " filterHeight=" << filterSize
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<< " filterWidth=" << filterSize
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<< " outputHeight=" << outputSize
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<< " outputWidth=" << outputSize
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<< " stride=" << stride << " padding=" << padding;
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VLOG(3) << " batchSize=" << batchSize
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<< " inputChannels=" << inputChannels
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<< " inputHeight=" << inputSize
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<< " inputWidth=" << inputSize
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<< " outputChannels=" << outputChannels
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<< " filterHeight=" << filterSize
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<< " filterWidth=" << filterSize
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<< " outputHeight=" << outputSize
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<< " outputWidth=" << outputSize
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<< " stride=" << stride << " padding=" << padding;
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std::vector<size_t> paddings = {padding, padding};
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std::vector<size_t> strides = {stride, stride};
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@ -72,17 +72,17 @@ public:
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TensorShape output{
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batchSize, outputChannels, outputSize, outputSize};
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if (type == FORWARD_TEST) {
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if (type == kForwardTest) {
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test.addInputs(BufferArg(VALUE_TYPE_FLOAT, input));
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test.addInputs(BufferArg(VALUE_TYPE_FLOAT, filter));
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test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, output));
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test.run();
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} else if (type == BACKWARD_INPUT_TEST) {
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} else if (type == kBackwardInputTest) {
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test.addInputs(BufferArg(VALUE_TYPE_FLOAT, output));
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test.addInputs(BufferArg(VALUE_TYPE_FLOAT, filter));
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test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, input), ADD_TO);
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test.run();
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} else if (type == BACKWARD_FILTER_TEST) {
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} else if (type == kBackwardFilterTest) {
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test.addInputs(BufferArg(VALUE_TYPE_FLOAT, output));
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test.addInputs(BufferArg(VALUE_TYPE_FLOAT, input));
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test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, filter));
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@ -100,23 +100,23 @@ public:
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TEST(Forward, GEMM) {
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ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_CPU> test(
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"NaiveConv-CPU", "GemmConv-CPU", FORWARD_TEST);
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"NaiveConv-CPU", "GemmConv-CPU", kForwardTest);
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}
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#ifndef PADDLE_ONLY_CPU
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TEST(Forward, GEMM2) {
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ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test(
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"GemmConv-CPU", "GemmConv-GPU", FORWARD_TEST);
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"GemmConv-CPU", "GemmConv-GPU", kForwardTest);
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}
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TEST(BackwardInput, GEMM) {
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ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test(
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"GemmConvGradInput-CPU", "GemmConvGradInput-GPU", BACKWARD_INPUT_TEST);
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"GemmConvGradInput-CPU", "GemmConvGradInput-GPU", kBackwardInputTest);
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}
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TEST(BackwardFilter, GEMM) {
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ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test(
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"GemmConvGradFilter-CPU", "GemmConvGradFilter-GPU", BACKWARD_FILTER_TEST);
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"GemmConvGradFilter-CPU", "GemmConvGradFilter-GPU", kBackwardFilterTest);
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}
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#endif
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