| /external/XNNPACK/test/ |
| D | convolution-nhwc.cc | 17 .kernel_size(1, 1)
27 .kernel_size(1, 1)
38 .kernel_size(1, 1)
49 .kernel_size(1, 1)
60 .kernel_size(1, 1)
72 .kernel_size(1, 1)
83 .kernel_size(1, 1)
93 .kernel_size(1, 1) in TEST()
104 .kernel_size(1, 1) in TEST()
116 .kernel_size(1, 1) in TEST()
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| D | convolution-nchw.cc | 17 .kernel_size(1, 1)
29 .kernel_size(1, 1)
42 .kernel_size(1, 1)
56 .kernel_size(1, 1)
70 .kernel_size(1, 1)
84 .kernel_size(1, 1)
96 .kernel_size(1, 1)
108 .kernel_size(1, 1)
121 .kernel_size(1, 1)
136 .kernel_size(1, 1) in TEST()
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| D | deconvolution-nhwc.cc | 25 .kernel_size(1, 1)
37 .kernel_size(1, 1)
50 .kernel_size(1, 1)
63 .kernel_size(1, 1)
76 .kernel_size(1, 1)
88 .kernel_size(1, 1)
100 .kernel_size(1, 1)
112 .kernel_size(1, 1)
124 .kernel_size(1, 1)
137 .kernel_size(1, 1)
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| D | f32-conv-hwc.cc | 19 .kernel_size(3) in TEST()
34 .kernel_size(3) in TEST()
50 .kernel_size(3) in TEST()
66 .kernel_size(3) in TEST()
83 .kernel_size(3) in TEST()
101 .kernel_size(3) in TEST()
119 .kernel_size(3) in TEST()
138 .kernel_size(3) in TEST()
158 .kernel_size(3) in TEST()
178 .kernel_size(3) in TEST()
[all …]
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| D | f32-conv-hwc2chw.cc | 19 .kernel_size(3) in TEST()
34 .kernel_size(3) in TEST()
50 .kernel_size(3) in TEST()
66 .kernel_size(3) in TEST()
83 .kernel_size(3) in TEST()
101 .kernel_size(3) in TEST()
119 .kernel_size(3) in TEST()
138 .kernel_size(3) in TEST()
158 .kernel_size(3) in TEST()
178 .kernel_size(3) in TEST()
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| /external/tensorflow/tensorflow/compiler/xla/client/lib/ |
| D | pooling_test.cc | 35 XlaOp input, absl::Span<const int64> kernel_size, in MakeGeneralPadding() argument
42 return MakeSpatialPadding(input_size, kernel_size, stride, padding, in MakeGeneralPadding()
71 auto kernel_size = ExpandWithBatchAndFeatureDimensions({2, 2}, data_format); in XLA_TEST_F() local
72 auto stride = kernel_size; in XLA_TEST_F()
73 MaxPool(input, kernel_size, stride, Padding::kValid, data_format); in XLA_TEST_F()
84 auto kernel_size = ExpandWithBatchAndFeatureDimensions({2, 2}, data_format); in XLA_TEST_F() local
85 auto stride = kernel_size; in XLA_TEST_F()
86 MaxPool(input, kernel_size, stride, Padding::kSame, data_format); in XLA_TEST_F()
97 auto kernel_size = ExpandWithBatchAndFeatureDimensions({2, 2}, data_format); in XLA_TEST_F() local
99 MaxPool(input, kernel_size, stride, Padding::kSame, data_format); in XLA_TEST_F()
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| D | pooling.cc | 74 absl::Span<const int64> kernel_size, in ComputeSums() argument
83 return ReduceWindow(operand, init_value, add_computation, kernel_size, in ComputeSums()
134 XlaOp MaxPool(XlaOp operand, absl::Span<const int64> kernel_size, in MaxPool() argument
143 return ReduceWindow(operand, init_value, max_computation, kernel_size, in MaxPool()
148 XlaOp AvgPool(XlaOp operand, absl::Span<const int64> kernel_size, in AvgPool() argument
160 const int num_dims = kernel_size.size(); in AvgPool()
165 auto pooled = ComputeSums(padded_operand, init_value, kernel_size, stride, in AvgPool()
167 return AvgPoolDivideByCount(pooled, input_size, kernel_size, stride, in AvgPool()
174 absl::Span<const int64> input_size, absl::Span<const int64> kernel_size, in MakeSpatialPadding() argument
177 const int num_spatial_dims = kernel_size.size() - 2; in MakeSpatialPadding()
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| D | pooling.h | 52 XlaOp MaxPool(XlaOp operand, absl::Span<const int64> kernel_size,
57 XlaOp AvgPool(XlaOp operand, absl::Span<const int64> kernel_size,
66 absl::Span<const int64> input_size, absl::Span<const int64> kernel_size,
72 absl::Span<const int64> kernel_size,
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| /external/gemmlowp/meta/ |
| D | single_thread_transform.h | 24 template <typename Params, int kernel_size>
31 template <typename P, int kernel_size, int leftovers>
34 typename P::Kernel, kernel_size, in ExecuteDispatch1D()
40 template <typename E, typename P, int kernel_size, int variable_leftovers>
45 std::cout << "Dispatch(1): " << kernel_size << ":" << variable_leftovers in Execute()
51 E::template ExecuteDispatch1D<P, kernel_size, variable_leftovers>(params); in Execute()
53 Dispatch1D<E, P, kernel_size, variable_leftovers - 1>::Execute(params, in Execute()
59 template <typename E, typename P, int kernel_size>
60 struct Dispatch1D<E, P, kernel_size, 0> {
64 std::cout << "Dispatch(1): " << kernel_size << ": 0" << std::endl
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| D | transform_kernels.h | 70 template <typename InType, typename OutType, int kernel_size, int leftovers>
71 class Transform1DKernel<InType, OutType, Quantize, kernel_size, leftovers> {
79 << kernel_size << "x" << leftovers << std::endl; in Transform()
88 template <typename InType, typename OutType, int kernel_size, int leftovers>
89 class Transform1DKernel<InType, OutType, Dequantize, kernel_size, leftovers> {
97 << kernel_size << "x" << leftovers << std::endl; in Transform()
106 template <typename InType, typename OutType, int kernel_size, int leftovers>
107 class Transform1DKernel<InType, OutType, Requantize, kernel_size, leftovers> {
115 << kernel_size << "x" << leftovers << std::endl; in Transform()
124 template <typename InType, typename OutType, int kernel_size, int leftovers,
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| D | multi_thread_transform.h | 30 const Params& params, int kernel_size, in PrepareTransform1DTasks() argument
64 template <typename Params, int kernel_size>
68 void Run() override { Transform1D<Params, kernel_size>(params); } in Run()
75 template <typename MultiThreadingContext, typename Params, int kernel_size>
78 typedef internal::Transform1DTaskRunner<Params, kernel_size> TaskRunnerType; in MultiThreadTransform1D()
82 context, params, kernel_size, &task_params)) { in MultiThreadTransform1D()
83 Transform1D<Params, kernel_size>(params); in MultiThreadTransform1D()
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| /external/tensorflow/tensorflow/compiler/tf2xla/kernels/ |
| D | image_resize_ops.cc | 81 std::vector<int64> kernel_size; // k member
92 dims.kernel_size.resize(num_spatial_dims); in ComputeResizeConvolutionParameters()
98 dims.stride[i] = dims.kernel_size[i] = 1; in ComputeResizeConvolutionParameters()
102 dims.stride[i] = dims.kernel_size[i] = 1; in ComputeResizeConvolutionParameters()
112 dims.kernel_size[i] = out_size_factor / gcd; in ComputeResizeConvolutionParameters()
124 int64 CalculateUpperPadding(int64 in_size, int64 out_size, int64 kernel_size, in CalculateUpperPadding() argument
126 int64 padding = (2 * kernel_size - 1) + (out_size - 1) * stride - in CalculateUpperPadding()
127 (kernel_size - 1) - 1 - (kernel_size * (in_size - 1)); in CalculateUpperPadding()
177 absl::Span<const int64> kernel_size, in MakeGeneralResizeKernel() argument
183 (2 * kernel_size[0] - 1), (2 * kernel_size[1] - 1), channels, 1}; in MakeGeneralResizeKernel()
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| D | extract_image_patches_op.cc | 108 int64 kernel_size = 1; in Compile() local
113 kernel_size *= ksizes_[input_dim]; in Compile()
116 kernel_shape[num_spatial_dims + 1] = kernel_size * depth; in Compile()
118 xla::ShapeUtil::MakeShape(xla::S32, {kernel_size, depth, kernel_size}); in Compile()
163 conv_dims.push_back(kernel_size); in Compile()
166 conv_dims.back() *= kernel_size; in Compile()
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| /external/tensorflow/tensorflow/python/keras/legacy_tf_layers/ |
| D | convolutional.py | 85 kernel_size, argument
104 kernel_size=kernel_size,
125 kernel_size, argument
207 kernel_size=kernel_size,
290 kernel_size, argument
309 kernel_size=kernel_size,
330 kernel_size, argument
419 kernel_size=kernel_size,
503 kernel_size, argument
522 kernel_size=kernel_size,
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| /external/tensorflow/tensorflow/compiler/xla/tests/ |
| D | conv_depthwise_test.cc | 47 int64 kernel_size = option[2]; in GetConv2DTestCases() local
53 config.window = kernel_size; in GetConv2DTestCases()
58 config.kernel_dims = {kernel_size, kernel_size, 1, feature}; in GetConv2DTestCases()
62 if (activation_size == 1 && kernel_size == 2) { in GetConv2DTestCases()
65 config.output_dims = {batch, activation_size + kernel_size - 1, in GetConv2DTestCases()
66 activation_size + kernel_size, feature}; in GetConv2DTestCases()
73 activation_size - kernel_size + 1, feature}; in GetConv2DTestCases()
76 config.output_dims = {batch, activation_size - kernel_size + 1, in GetConv2DTestCases()
77 activation_size - kernel_size + 1, feature}; in GetConv2DTestCases()
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| D | conv_depthwise_backprop_filter_test.cc | 65 int64 kernel_size = option[2]; in GetConv2DTestCases() local
71 config.window = kernel_size; in GetConv2DTestCases()
76 config.kernel_dims = {batch, kernel_size, kernel_size, in GetConv2DTestCases()
82 int64 output_space_size = 3 + activation_size - kernel_size; in GetConv2DTestCases()
95 if (activation_size % 2 == 0 && activation_size == kernel_size) { in GetConv2DTestCases()
99 config.window = kernel_size / 2; in GetConv2DTestCases()
102 config.kernel_dims = {batch, kernel_size / 2, kernel_size / 2, feature}; in GetConv2DTestCases()
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| D | grouped_convolution_test.cc | 68 int64 kernel_size = option[2]; in GetConv2DTestCases() local
78 config.window = kernel_size; in GetConv2DTestCases()
84 config.kernel_dims = {kernel_size, kernel_size, group_size, output_feature}; in GetConv2DTestCases()
87 if (activation_size == 1 && kernel_size == 2) { in GetConv2DTestCases()
90 config.output_dims = {batch, activation_size + kernel_size - 1, in GetConv2DTestCases()
91 activation_size + kernel_size, output_feature}; in GetConv2DTestCases()
98 activation_size - kernel_size + 1, output_feature}; in GetConv2DTestCases()
101 config.output_dims = {batch, activation_size - kernel_size + 1, in GetConv2DTestCases()
102 activation_size - kernel_size + 1, output_feature}; in GetConv2DTestCases()
110 if (kernel_size % 2 == 0) { in GetConv2DTestCases()
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| /external/XNNPACK/bench/ |
| D | f32-conv-hwc2chw.cc | 44 const size_t kernel_size = 3; in DConvHWC2CHW3X3S2P1Benchmark() local
48 const size_t output_height = (input_height + 2 * padding - kernel_size) / subsampling + 1; in DConvHWC2CHW3X3S2P1Benchmark()
49 const size_t output_width = (input_width + 2 * padding - kernel_size) / subsampling + 1; in DConvHWC2CHW3X3S2P1Benchmark()
53 std::vector<float> kernel(output_channels * kernel_size * kernel_size * input_channels); in DConvHWC2CHW3X3S2P1Benchmark()
60 const size_t weights_elements = (kernel_size * kernel_size * input_channels + 1) * in DConvHWC2CHW3X3S2P1Benchmark()
71 kernel_size /* kernel height */, kernel_size /* kernel width */, in DConvHWC2CHW3X3S2P1Benchmark()
113 kernel_size * kernel_size, in DConvHWC2CHW3X3S2P1Benchmark()
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| D | f32-conv-hwc.cc | 42 const size_t kernel_size = 3; in DConv3X3S2P1Benchmark() local
46 const size_t output_height = (input_height + 2 * padding - kernel_size) / subsampling + 1; in DConv3X3S2P1Benchmark()
47 const size_t output_width = (input_width + 2 * padding - kernel_size) / subsampling + 1; in DConv3X3S2P1Benchmark()
51 std::vector<float> kernel(output_channels * kernel_size * kernel_size * input_channels); in DConv3X3S2P1Benchmark()
58 const size_t weights_elements = (kernel_size * kernel_size * input_channels + 1) * in DConv3X3S2P1Benchmark()
69 kernel_size /* kernel height */, kernel_size /* kernel width */, in DConv3X3S2P1Benchmark()
111 kernel_size * kernel_size, in DConv3X3S2P1Benchmark()
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| D | f32-im2col-gemm.cc | 38 const size_t kernel_size = kernel_height * kernel_width; in Im2ColGEMMBenchmark() local
68 const size_t w_elements = (kernel_size * kc_stride + 1) * nc_stride; in Im2ColGEMMBenchmark()
76 xnn_pack_f32_gemm_goi_w(1 /* groups */, group_output_channels, group_input_channels * kernel_size, in Im2ColGEMMBenchmark()
82 …std::vector<float> im2col_buffer(output_size * group_input_channels * kernel_size * group_output_c… in Im2ColGEMMBenchmark()
98 if (kernel_size != 1 || subsampling != 1) { in Im2ColGEMMBenchmark()
116 mb, nb, kernel_size * group_input_channels * sizeof(float), in Im2ColGEMMBenchmark()
117 …inputData + m * kernel_size * group_input_channels, kernel_size * group_input_channels * sizeof(fl… in Im2ColGEMMBenchmark()
118 w.data() + (buffer_index * nc_stride + n) * (kernel_size * kc_stride + 1), in Im2ColGEMMBenchmark()
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| /external/tensorflow/tensorflow/lite/delegates/gpu/common/tasks/ |
| D | convolution_transposed_thin.cc | 54 const int2& kernel_size) { in GenerateConvolutionTransposedCode() argument
91 c += " " + accum_type + " r[" + std::to_string(kernel_size.y) + "][" + in GenerateConvolutionTransposedCode()
92 std::to_string(kernel_size.x) + "];\n"; in GenerateConvolutionTransposedCode()
96 for (int y = 0; y < kernel_size.y; ++y) { in GenerateConvolutionTransposedCode()
97 for (int x = 0; x < kernel_size.x; ++x) { in GenerateConvolutionTransposedCode()
113 for (int y = 0; y < kernel_size.y; ++y) { in GenerateConvolutionTransposedCode()
114 for (int x = 0; x < kernel_size.x; ++x) { in GenerateConvolutionTransposedCode()
126 c += " X *= " + std::to_string(kernel_size.x) + ";\n"; in GenerateConvolutionTransposedCode()
127 c += " Y *= " + std::to_string(kernel_size.y) + ";\n"; in GenerateConvolutionTransposedCode()
128 for (int y = 0; y < kernel_size.y; ++y) { in GenerateConvolutionTransposedCode()
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| /external/tensorflow/tensorflow/python/keras/layers/ |
| D | local.py | 121 kernel_size, argument
138 self.kernel_size = conv_utils.normalize_tuple(kernel_size, 1, 'kernel_size')
170 self.kernel_size[0],
175 self.kernel_shape = (self.output_length, self.kernel_size[0] * input_dim,
202 kernel_shape=self.kernel_size,
215 kernel_shape=self.kernel_size,
256 length = conv_utils.conv_output_length(input_length, self.kernel_size[0],
266 output = K.local_conv(inputs, self.kernel, self.kernel_size, self.strides,
293 self.kernel_size,
423 kernel_size, argument
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| D | convolutional.py | 116 kernel_size, argument
146 self.kernel_size = conv_utils.normalize_tuple(
147 kernel_size, rank, 'kernel_size')
178 if not all(self.kernel_size):
180 'Received: %s' % (self.kernel_size,))
196 kernel_shape = self.kernel_size + (input_channel // self.groups,
285 self.kernel_size[i],
313 self.kernel_size,
348 left_pad = self.dilation_rate[0] * (self.kernel_size[0] - 1)
486 kernel_size, argument
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| /external/gemmlowp/meta/generators/ |
| D | transform_kernels_common.py | 38 def Check(self, in_type, out_type, kernel_size, leftovers): argument
41 assert kernel_size is 16
82 def Check(self, in_type, out_type, kernel_size, leftovers): argument
85 assert kernel_size is 16
151 def Check(self, in_type, out_type, kernel_size, leftovers): argument
154 assert kernel_size is 16
220 def Check(self, in_type, out_type, kernel_size, leftovers): argument
223 assert kernel_size is 16
313 def EmitTransform(self, in_type, out_type, kernel_size, leftovers): argument
315 self.transformation.Check(in_type, out_type, kernel_size, leftovers)
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| /external/tensorflow/tensorflow/python/keras/applications/ |
| D | imagenet_utils.py | 388 def correct_pad(inputs, kernel_size): argument
400 if isinstance(kernel_size, int):
401 kernel_size = (kernel_size, kernel_size)
406 correct = (kernel_size[0] // 2, kernel_size[1] // 2)
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