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Searched refs:out_depth (Results 1 – 25 of 48) sorted by relevance

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/external/tensorflow/tensorflow/python/kernel_tests/
Dconv_ops_3d_test.py275 self, batch, input_shape, filter_shape, in_depth, out_depth, stride, argument
283 filter_planes, filter_rows, filter_cols, in_depth, out_depth
302 output_shape = [batch, output_planes, output_rows, output_cols, out_depth]
376 out_depth=3,
387 out_depth=3,
398 out_depth=3,
409 out_depth=3,
420 out_depth=3,
431 out_depth=3,
442 out_depth=1,
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Dconv_ops_test.py1116 filter_cols, in_depth, out_depth, stride_rows, argument
1120 filter_shape = [filter_rows, filter_cols, in_depth, out_depth]
1128 output_shape = [batch, output_rows, output_cols, out_depth]
1193 out_depth=3,
1210 out_depth=3,
1227 out_depth=3,
1244 out_depth=3,
1261 out_depth=5,
1278 out_depth=3,
1295 out_depth=3,
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/external/tensorflow/tensorflow/core/kernels/
Ddeep_conv2d.cc50 int out_depth, int out_rows, int out_cols) { in GetDeepConvCost() argument
57 const int64 product_cost = input_tile_spatial_size * in_depth * out_depth; in GetDeepConvCost()
62 output_tile_spatial_size * input_tile_spatial_size * out_depth; in GetDeepConvCost()
75 int out_depth, int out_rows, int out_cols) { in GetDirectConvCost() argument
76 return filter_rows * filter_cols * in_depth * out_depth * out_rows * out_cols; in GetDirectConvCost()
98 int filter_cols, int in_depth, int out_depth, in CanUseDeepConv2D() argument
117 t.output_shape().cols, in_depth, out_depth, out_rows, out_cols); in CanUseDeepConv2D()
119 filter_rows, filter_cols, in_depth, out_depth, out_rows, out_cols); in CanUseDeepConv2D()
148 const int64 input_stride = args.out_depth * kPacketSize; in operator ()()
153 args.out_depth); in operator ()()
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Ddepthwise_conv_grad_op.cc106 const int32 out_depth = static_cast<int32>(out_depth_raw); \
108 context, (depth_multiplier * in_depth) == out_depth, \
142 args.out_depth = out_depth; \
149 << ", " << out_depth << "]";
209 const int64 vectorized_size = (args.out_depth / kPacketSize) * kPacketSize; in CopyOutputBackpropRegion()
210 const int64 scalar_size = args.out_depth % kPacketSize; in CopyOutputBackpropRegion()
221 out_backprop + (out_r * args.out_cols + out_c) * args.out_depth; in CopyOutputBackpropRegion()
283 const int64 out_depth = args.out_depth; in ComputeBackpropInput() local
287 const int64 output_vectorized_size = (out_depth / kPacketSize) * kPacketSize; in ComputeBackpropInput()
288 const int64 output_scalar_size = out_depth % kPacketSize; in ComputeBackpropInput()
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Ddepthwise_conv_op.cc88 const int64 out_depth = args.out_depth; in Run() local
90 const int64 output_scalar_size = out_depth % kPacketSize; in Run()
92 (out_depth / kPacketSize) * kPacketSize; in Run()
93 const int64 base_output_index = (out_r * args.out_cols + out_c) * out_depth; in Run()
166 const bool pad_filter = (args.out_depth % kPacketSize) == 0 ? false : true; in operator ()()
172 ((args.out_depth + kPacketSize - 1) / kPacketSize) * kPacketSize; in operator ()()
192 args.out_rows * args.out_cols * args.out_depth; in operator ()()
195 ((args.out_depth + kPacketSize - 1) / kPacketSize) * kPacketSize; in operator ()()
235 const int64 shard_cost = kCostMultiplier * args.out_cols * args.out_depth; in operator ()()
320 const int32 out_depth = in_depth * depth_multiplier; in Compute() local
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Deigen_spatial_convolutions_test.cc676 const int out_depth = in_depth; in TEST() local
683 Tensor<float, 4> result(kern_filters, out_depth, out_height, out_width); in TEST()
691 EXPECT_EQ(result.dimension(1), out_depth); in TEST()
700 for (int i = 0; i < out_depth; ++i) { in TEST()
737 const int out_depth = in_depth; in TEST() local
744 Tensor<float, 4, RowMajor> result(out_width, out_height, out_depth, in TEST()
753 EXPECT_EQ(result.dimension(2), out_depth); in TEST()
762 for (int i = 0; i < out_depth; ++i) { in TEST()
799 const int out_depth = 3; in TEST() local
806 Tensor<float, 4> result(kern_filters, out_depth, out_height, out_width); in TEST()
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Deigen_cuboid_convolution.h121 TensorIndex out_depth; variable
126 out_depth = Eigen::divup(inputPlanes - kernelDepth + 1,
134 out_depth =
141 out_depth = 0;
164 pre_contract_dims[1] = out_depth * out_height * out_width;
171 pre_contract_dims[0] = out_depth * out_height * out_width;
190 post_contract_dims[1] = out_depth;
198 post_contract_dims[NumDims - 2] = out_depth;
Ddepthwise_conv_op_gpu.cu.cc89 const int out_depth = args.out_depth; in DepthwiseConv2dGPUKernelNHWC() local
93 const int out_channel = thread_id % out_depth; in DepthwiseConv2dGPUKernelNHWC()
94 const int out_col = (thread_id / out_depth) % out_width; in DepthwiseConv2dGPUKernelNHWC()
95 const int out_row = (thread_id / out_depth / out_width) % out_height; in DepthwiseConv2dGPUKernelNHWC()
96 const int batch = thread_id / out_depth / out_width / out_height; in DepthwiseConv2dGPUKernelNHWC()
325 const int out_depth = args.out_depth; in DepthwiseConv2dGPUKernelNCHW() local
337 const int out_channel = (thread_id / out_width / out_height) % out_depth; in DepthwiseConv2dGPUKernelNCHW()
338 const int batch = thread_id / out_width / out_height / out_depth; in DepthwiseConv2dGPUKernelNCHW()
601 args.batch * DivUp(args.out_depth, kBlockDepth) * kBlockDepth; in LaunchDepthwiseConv2dGPUSmall()
610 DivUp(args.batch * args.out_depth, kBlockDepth) * kBlockDepth; in LaunchDepthwiseConv2dGPUSmall()
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Dconv_grad_ops_3d.cc77 const int64 out_depth = filter_shape.dim_size(4); \
79 context, out_depth == GetTensorDim(out_backprop, data_format_, 'C'), \
191 padded_out_cols, out_depth}); in Compute()
207 {filter_size[0], filter_size[1], filter_size[2], out_depth, in_depth}); in Compute()
313 TensorShape padded_out_shape({out_depth, padded_out_planes, padded_out_rows, in Compute()
353 {out_depth, filter_size[0], filter_size[1], filter_size[2], in_depth}); in Compute()
477 const uint64 k = out_depth; in Compute()
505 const uint64 k = out_depth; in Compute()
574 .set_feature_map_count(out_depth) in Compute()
581 .set_output_feature_map_count(out_depth); in Compute()
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Ddeep_conv2d.h81 int out_depth; member
94 out_depth(0) {} in Conv2DArgs()
101 int filter_cols, int in_depth, int out_depth,
Dconv_ops_3d.cc106 const int64 out_depth = filter.dim_size(4); in Compute() local
126 data_format_, in_batch, {{out[0], out[1], out[2]}}, out_depth); in Compute()
184 const int64 out_depth = filter.dim_size(4); in launch() local
207 const uint64 n = out_depth; in launch()
234 const uint64 n = out_depth; in launch()
324 .set_feature_map_count(out_depth) in launch()
331 .set_output_feature_map_count(out_depth); in launch()
343 TensorShape({out_depth, in_depth, filter_planes, in launch()
357 {{out_planes, out_rows, out_cols}}, out_depth), in launch()
378 out_depth, in launch()
Dconv_grad_input_ops.cc139 auto out_depth = output_backward.dimension(3); in operator ()() local
150 desc.K = out_depth; in operator ()()
428 const size_t size_A = output_image_size * dims.out_depth; in Compute()
430 const size_t size_B = filter_total_size * dims.out_depth; in Compute()
474 dims.spatial_dims[1].output_size * dims.out_depth; in Compute()
503 output_image_size, dims.out_depth); in Compute()
504 ConstTensorMap B(filter_data, filter_total_size, dims.out_depth); in Compute()
544 ConstMatrixMap A(out_data, output_image_size, dims.out_depth); in Compute()
545 ConstMatrixMap B(filter_data, filter_total_size, dims.out_depth); in Compute()
768 const uint64 k = dims.out_depth; in operator ()()
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Dconv_grad_filter_ops.cc133 auto out_depth = output.dimension(3); in operator ()() local
145 desc.K = out_depth; in operator ()()
410 const size_t size_B = output_image_size * dims.out_depth; in Compute()
412 const size_t size_C = filter_total_size * dims.out_depth; in Compute()
433 dims.spatial_dims[1].output_size * dims.out_depth; in Compute()
447 TensorMap C(filter_backprop_data, filter_total_size, dims.out_depth); in Compute()
485 dims.out_depth); in Compute()
699 const uint64 n = dims.out_depth; in operator ()()
740 const uint64 n = dims.out_depth; in operator ()()
797 .set_feature_map_count(dims.out_depth) in operator ()()
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Dconv_ops.cc155 int out_cols, int out_depth, int dilation_rows, in Run() argument
161 in_depth, out_depth, out_rows, out_cols)) { in Run()
176 args.out_depth = out_depth; in Run()
196 int out_cols, int out_depth, int stride_rows, int stride_cols, in Run() argument
210 int out_cols, int out_depth, int dilation_rows, in Run() argument
221 desc.K = out_depth; in Run()
335 const int out_depth = static_cast<int>(filter.dim_size(3)); in Compute() local
381 ShapeFromFormat(data_format_, batch, out_rows, out_cols, out_depth); in Compute()
397 << ", out_depth = " << out_depth; in Compute()
408 out_depth, dilation_rows, dilation_cols, stride_rows, stride_cols, in Compute()
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Ddepthwise_conv_op.h41 int out_depth; member
56 out_depth(0) {} in DepthwiseArgs()
137 const int64 filter_inner_dim_size = args.out_depth;
211 const int64 output_scalar_size = args.out_depth % kPacketSize;
Dmkl_conv_ops.h168 int out_depth = static_cast<int>(filter_shape.dim_size(3)); in GetFilterSizeInMklOrder() local
173 mkldnn_sizes[MklDnnDims::Dim_O] = out_depth; in GetFilterSizeInMklOrder()
239 int out_depth = filter_shape.dim_size(3); in GetOutputAndPadSizeInMklOrder() local
253 ShapeFromFormat(data_format_, out_batch, out_rows, out_cols, out_depth); in GetOutputAndPadSizeInMklOrder()
259 mkldnn_sizes[MklDnnDims::Dim_C] = out_depth; in GetOutputAndPadSizeInMklOrder()
Dmkl_conv_grad_filter_ops.cc169 mkl_context.out_sizes[2] = static_cast<size_t>(backprop_dims.out_depth); in Compute()
189 mkl_context.filter_sizes[3] = backprop_dims.out_depth; in Compute()
196 backprop_dims.out_depth * backprop_dims.in_depth; in Compute()
197 mkl_context.filter_strides[1] = backprop_dims.out_depth * in Compute()
200 mkl_context.filter_strides[2] = backprop_dims.out_depth; in Compute()
Dconv_ops_using_gemm.cc486 const int out_depth = static_cast<int>(filter.dim_size(3)); in Compute() local
528 ShapeFromFormat(data_format_, batch, out_rows, out_cols, out_depth); in Compute()
542 << ", out_depth = " << out_depth; in Compute()
551 out_depth, stride_rows, stride_cols, padding_, in Compute()
Dconv_grad_ops.cc134 dims->out_depth = filter_shape.dim_size(num_dims - 1); in ConvBackpropComputeDimensionsV2()
135 if (dims->out_depth != out_backprop_shape.dim_size(feature_dim)) { in ConvBackpropComputeDimensionsV2()
Dmkl_pooling_ops_common.cc123 out_depth = depth; // output will have the same depth as the input in Init()
145 out_depth = depth / depth_window; in Init()
/external/tensorflow/tensorflow/contrib/hvx/hexagon_controller/src_impl/
Dgraph_functions_wrapper.c216 uint32_t* out_width, uint32_t* out_depth, uint8_t* out_vals, in hexagon_controller_ExecuteGraph() argument
246 *out_depth = output.depth; in hexagon_controller_ExecuteGraph()
252 *out_width, *out_depth, *out_data_byte_size); in hexagon_controller_ExecuteGraph()
259 uint32_t out_batches, out_height, out_width, out_depth; in hexagon_controller_ExecuteInceptionDummyData() local
268 &out_batches, &out_height, &out_width, &out_depth, in hexagon_controller_ExecuteInceptionDummyData()
273 s_output_values, out_batches * out_height * out_width * out_depth, in hexagon_controller_ExecuteInceptionDummyData()
276 out_width, out_depth, out_data_size); in hexagon_controller_ExecuteInceptionDummyData()
279 out_batches * out_height * out_width * out_depth)); in hexagon_controller_ExecuteInceptionDummyData()
/external/tensorflow/tensorflow/contrib/quantize/python/
Dfold_batch_norms_test.py86 out_depth = 3 if with_bypass else 32
92 out_depth, [5, 5],
155 out_depth = 3 if with_bypass else 32
161 out_depth, [5, 5],
221 out_depth = 256 if with_bypass else 128
226 out_depth,
364 out_depth = 3 if with_bypass else 32
370 out_depth, [5, 5],
Dquantize_parameterized_test.py78 out_depth = 3 if with_bypass else 32
81 node = conv2d(inputs, out_depth, [5, 5], stride=stride, padding='SAME',
154 out_depth = 256 if with_bypass else 128
157 node = fully_connected(inputs, out_depth,
343 out_depth = 3 if with_bypass else 32
347 out_depth, [5, 5],
428 out_depth = 256 if with_bypass else 128
432 out_depth,
/external/tensorflow/tensorflow/core/grappler/costs/
Dutils_test.cc67 int out_depth = 5; in TEST_F() local
77 CreateConstOp("filter", {filter_rows, filter_cols, in_depth, out_depth}, in TEST_F()
81 CreateConstOp("output_backprop", {batch, out_rows, out_cols, out_depth}, in TEST_F()
92 std::vector<int32>({filter_rows, filter_cols, in_depth, out_depth}), in TEST_F()
/external/tensorflow/tensorflow/core/kernels/neon/
Dneon_depthwise_conv_op.cc86 const int32 out_depth = in_depth * depth_multiplier; in Compute() local
97 TensorShape out_shape({batch, out_rows, out_cols, out_depth}); in Compute()
115 << out_rows << ", " << out_cols << ", " << out_depth << "]"; in Compute()

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