| /external/XNNPACK/src/operators/ |
| D | convolution-nhwc.c | 114 xnn_operator_t convolution_op) in generate_gemms_up_to_max_mr() argument
117 if (convolution_op->code_cache == NULL) { in generate_gemms_up_to_max_mr()
120 convolution_op->ukernel.gemm.gemm_cases[0].generated_code_offset[XNN_UARCH_DEFAULT] = in generate_gemms_up_to_max_mr()
122 log2_input_element_size, convolution_op->code_cache); in generate_gemms_up_to_max_mr()
124 convolution_op->ukernel.gemm.gemm_cases[mr - 1].generated_code_offset[XNN_UARCH_DEFAULT] = in generate_gemms_up_to_max_mr()
126 log2_input_element_size, convolution_op->code_cache); in generate_gemms_up_to_max_mr()
180 xnn_operator_t convolution_op) in generate_igemms_up_to_max_mr() argument
183 if (convolution_op->code_cache == NULL) { in generate_igemms_up_to_max_mr()
186 convolution_op->ukernel.igemm.igemm_cases[0].generated_code_offset[XNN_UARCH_DEFAULT] = in generate_igemms_up_to_max_mr()
188 log2_input_element_size, kernel_size, 1, convolution_op->code_cache); in generate_igemms_up_to_max_mr()
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| D | convolution-nchw.c | 51 xnn_operator_t convolution_op = NULL; in xnn_create_convolution2d_nchw_f32() local
214 convolution_op = xnn_allocate_zero_simd_memory(sizeof(struct xnn_operator)); in xnn_create_convolution2d_nchw_f32()
215 if (convolution_op == NULL) { in xnn_create_convolution2d_nchw_f32()
223 convolution_op->weights_cache = caches->weights_cache; in xnn_create_convolution2d_nchw_f32()
300 convolution_op->packed_weights.pointer = xnn_allocate_simd_memory(packed_weights_size); in xnn_create_convolution2d_nchw_f32()
301 if (convolution_op->packed_weights.pointer == NULL) { in xnn_create_convolution2d_nchw_f32()
307 convolution_op->num_nonzero_values = num_nonzero_values; in xnn_create_convolution2d_nchw_f32()
308 convolution_op->num_nonzero_blocks = num_nonzero_blocks; in xnn_create_convolution2d_nchw_f32()
309 convolution_op->num_output_channel_blocks = num_output_channel_blocks; in xnn_create_convolution2d_nchw_f32()
311 float* nonzero_values = convolution_op->packed_weights.pointer; in xnn_create_convolution2d_nchw_f32()
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| /external/XNNPACK/bench/ |
| D | f16-igemm.cc | 98 xnn_operator convolution_op = { }; in f16_igemm() local
99 convolution_op.indirection_buffer = reinterpret_cast<const void**>(i.data()); in f16_igemm()
100 convolution_op.input = a.data(); in f16_igemm()
101 convolution_op.input_pixel_stride = input_pixel_stride; in f16_igemm()
102 convolution_op.zero_buffer = z.data(); in f16_igemm()
103 convolution_op.groups = 1; in f16_igemm()
104 convolution_op.group_input_channels = group_input_channels; in f16_igemm()
105 convolution_op.batch_size = 1; in f16_igemm()
106 convolution_op.input_height = input_height; in f16_igemm()
107 convolution_op.input_width = input_width; in f16_igemm()
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| D | f16-dwconv.cc | 103 xnn_operator convolution_op = { }; in f16_dwconv() local
104 convolution_op.indirection_buffer = reinterpret_cast<const void**>(i.data()); in f16_dwconv()
105 convolution_op.input = a.data(); in f16_dwconv()
106 convolution_op.input_pixel_stride = channels; in f16_dwconv()
107 convolution_op.zero_buffer = z.data(); in f16_dwconv()
108 convolution_op.input_height = input_height; in f16_dwconv()
109 convolution_op.input_width = input_width; in f16_dwconv()
110 convolution_op.output_height = output_height; in f16_dwconv()
111 convolution_op.output_width = output_width; in f16_dwconv()
112 convolution_op.kernel_height = kernel_height; in f16_dwconv()
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| D | f32-igemm.cc | 95 xnn_operator convolution_op = { }; in f32_igemm() local
96 convolution_op.indirection_buffer = reinterpret_cast<const void**>(i.data()); in f32_igemm()
97 convolution_op.input = a.data(); in f32_igemm()
98 convolution_op.input_pixel_stride = input_pixel_stride; in f32_igemm()
99 convolution_op.zero_buffer = z.data(); in f32_igemm()
100 convolution_op.groups = 1; in f32_igemm()
101 convolution_op.group_input_channels = group_input_channels; in f32_igemm()
102 convolution_op.batch_size = 1; in f32_igemm()
103 convolution_op.input_height = input_height; in f32_igemm()
104 convolution_op.input_width = input_width; in f32_igemm()
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| D | f32-dwconv.cc | 95 xnn_operator convolution_op = { }; in f32_dwconv() local
96 convolution_op.indirection_buffer = reinterpret_cast<const void**>(i.data()); in f32_dwconv()
97 convolution_op.input = a.data(); in f32_dwconv()
98 convolution_op.input_pixel_stride = channels; in f32_dwconv()
99 convolution_op.zero_buffer = z.data(); in f32_dwconv()
100 convolution_op.input_height = input_height; in f32_dwconv()
101 convolution_op.input_width = input_width; in f32_dwconv()
102 convolution_op.output_height = output_height; in f32_dwconv()
103 convolution_op.output_width = output_width; in f32_dwconv()
104 convolution_op.kernel_height = kernel_height; in f32_dwconv()
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| D | qs8-dwconv.cc | 101 xnn_operator convolution_op = { }; in DWConvBenchmark() local
102 convolution_op.indirection_buffer = reinterpret_cast<const void**>(i.data()); in DWConvBenchmark()
103 convolution_op.input = a.data(); in DWConvBenchmark()
104 convolution_op.input_pixel_stride = channels; in DWConvBenchmark()
105 convolution_op.zero_buffer = z.data(); in DWConvBenchmark()
106 convolution_op.input_height = input_height; in DWConvBenchmark()
107 convolution_op.input_width = input_width; in DWConvBenchmark()
108 convolution_op.output_height = output_height; in DWConvBenchmark()
109 convolution_op.output_width = output_width; in DWConvBenchmark()
110 convolution_op.kernel_height = kernel_height; in DWConvBenchmark()
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| D | convolution.cc | 84 for (xnn_operator_t& convolution_op : convolution_operators) { in xnnpack_convolution_qu8()
96 0 /* flags */, NULL, &convolution_op); in xnnpack_convolution_qu8()
130 for (xnn_operator_t& convolution_op : convolution_operators) { in xnnpack_convolution_qu8()
131 status = xnn_delete_operator(convolution_op); in xnnpack_convolution_qu8()
136 convolution_op = nullptr; in xnnpack_convolution_qu8()
205 for (xnn_operator_t& convolution_op : convolution_operators) { in xnnpack_convolution_qs8()
216 0 /* flags */, NULL, &convolution_op); in xnnpack_convolution_qs8()
250 for (xnn_operator_t& convolution_op : convolution_operators) { in xnnpack_convolution_qs8()
251 status = xnn_delete_operator(convolution_op); in xnnpack_convolution_qs8()
256 convolution_op = nullptr; in xnnpack_convolution_qs8()
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| /external/XNNPACK/test/ |
| D | convolution-operator-tester.h | 668 xnn_operator_t convolution_op = nullptr; in TestNHWCxQC8() local
692 &convolution_op); in TestNHWCxQC8()
697 ASSERT_NE(nullptr, convolution_op); in TestNHWCxQC8()
704 …tr<xnn_operator, decltype(&xnn_delete_operator)> auto_convolution_op(convolution_op, xnn_delete_op… in TestNHWCxQC8()
708 convolution_op, in TestNHWCxQC8()
714 xnn_run_operator(convolution_op, nullptr /* thread pool */)); in TestNHWCxQC8()
884 xnn_operator_t convolution_op = nullptr; in TestNHWCxQS8() local
908 &convolution_op); in TestNHWCxQS8()
913 ASSERT_NE(nullptr, convolution_op); in TestNHWCxQS8()
920 …tr<xnn_operator, decltype(&xnn_delete_operator)> auto_convolution_op(convolution_op, xnn_delete_op… in TestNHWCxQS8()
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| /external/XNNPACK/include/ |
| D | xnnpack.h | 1696 xnn_operator_t convolution_op,
2121 xnn_operator_t convolution_op,
2420 xnn_operator_t convolution_op,
2923 xnn_operator_t convolution_op,
2988 xnn_operator_t convolution_op,
3296 xnn_operator_t convolution_op,
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| /external/tensorflow/tensorflow/tools/api/golden/v1/ |
| D | tensorflow.layers.-conv2-d-transpose.pbtxt | 192 name: "convolution_op"
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| D | tensorflow.layers.-separable-conv1-d.pbtxt | 192 name: "convolution_op"
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| D | tensorflow.layers.-conv2-d.pbtxt | 191 name: "convolution_op"
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| D | tensorflow.layers.-conv3-d-transpose.pbtxt | 192 name: "convolution_op"
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| D | tensorflow.layers.-conv3-d.pbtxt | 191 name: "convolution_op"
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| D | tensorflow.layers.-separable-conv2-d.pbtxt | 192 name: "convolution_op"
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| D | tensorflow.layers.-conv1-d.pbtxt | 191 name: "convolution_op"
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| /external/tensorflow/ |
| D | RELEASE.md | 1300 * `tf.keras.layers.Conv` now includes a public `convolution_op` method.
1306 return self.convolution_op(inputs, (self.kernel - mean) / tf.sqrt(var +
1307 1e-10))` Alternatively, you can override `convolution_op`: `python class
1308 StandardizedConv2D(tf.keras.Layer): def convolution_op(self, inputs,
1311 super().convolution_op(inputs, (kernel - mean) / tf.sqrt(var + 1e-10))`
|