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

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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.cc109 const int32 out_depth = static_cast<int32>(out_depth_raw); \
111 context, (depth_multiplier * in_depth) == out_depth, \
145 args.out_depth = out_depth; \
152 << ", " << out_depth << "]";
212 const int64 vectorized_size = (args.out_depth / kPacketSize) * kPacketSize; in CopyOutputBackpropRegion()
213 const int64 scalar_size = args.out_depth % kPacketSize; in CopyOutputBackpropRegion()
224 out_backprop + (out_r * args.out_cols + out_c) * args.out_depth; in CopyOutputBackpropRegion()
286 const int64 out_depth = args.out_depth; in ComputeBackpropInput() local
290 const int64 output_vectorized_size = (out_depth / kPacketSize) * kPacketSize; in ComputeBackpropInput()
291 const int64 output_scalar_size = out_depth % kPacketSize; in ComputeBackpropInput()
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Ddepthwise_conv_op.cc89 const int64 out_depth = args.out_depth; in Run() local
91 const int64 output_scalar_size = out_depth % kPacketSize; in Run()
93 (out_depth / kPacketSize) * kPacketSize; in Run()
94 const int64 base_output_index = (out_r * args.out_cols + out_c) * out_depth; in Run()
167 const bool pad_filter = (args.out_depth % kPacketSize) == 0 ? false : true; in operator ()()
173 ((args.out_depth + kPacketSize - 1) / kPacketSize) * kPacketSize; in operator ()()
193 args.out_rows * args.out_cols * args.out_depth; in operator ()()
196 ((args.out_depth + kPacketSize - 1) / kPacketSize) * kPacketSize; in operator ()()
236 const int64 shard_cost = kCostMultiplier * args.out_cols * args.out_depth; in operator ()()
327 const int32 out_depth = in_depth * depth_multiplier; in Compute() local
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Deigen_spatial_convolutions_test.cc678 const int out_depth = in_depth; in TEST() local
685 Tensor<float, 4> result(kern_filters, out_depth, out_height, out_width); in TEST()
693 EXPECT_EQ(result.dimension(1), out_depth); in TEST()
702 for (int i = 0; i < out_depth; ++i) { in TEST()
739 const int out_depth = in_depth; in TEST() local
746 Tensor<float, 4, RowMajor> result(out_width, out_height, out_depth, in TEST()
755 EXPECT_EQ(result.dimension(2), out_depth); in TEST()
764 for (int i = 0; i < out_depth; ++i) { in TEST()
801 const int out_depth = 3; in TEST() local
808 Tensor<float, 4> result(kern_filters, out_depth, out_height, out_width); in TEST()
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Ddepthwise_conv_op_gpu.h92 const int out_depth = args.out_depth; in DepthwiseConv2dGPUKernelNHWC() local
96 const int out_channel = thread_id % out_depth; in DepthwiseConv2dGPUKernelNHWC()
97 const int out_col = (thread_id / out_depth) % out_width; in DepthwiseConv2dGPUKernelNHWC()
98 const int out_row = (thread_id / out_depth / out_width) % out_height; in DepthwiseConv2dGPUKernelNHWC()
99 const int batch = thread_id / out_depth / out_width / out_height; in DepthwiseConv2dGPUKernelNHWC()
332 const int out_depth = args.out_depth; in DepthwiseConv2dGPUKernelNCHW() local
344 const int out_channel = (thread_id / out_width / out_height) % out_depth; in DepthwiseConv2dGPUKernelNCHW()
345 const int batch = thread_id / out_width / out_height / out_depth; in DepthwiseConv2dGPUKernelNCHW()
612 args.batch * DivUp(args.out_depth, kBlockDepth) * kBlockDepth; in LaunchDepthwiseConv2dGPUSmall()
621 DivUp(args.batch * args.out_depth, kBlockDepth) * kBlockDepth; in LaunchDepthwiseConv2dGPUSmall()
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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_test.cc1105 int filter_w, int filter_h, int out_depth) { in Conv2D() argument
1109 Tensor filter_t = MakeRandomTensor({filter_w, filter_h, in_depth, out_depth}); in Conv2D()
1129 int filter_h, int out_depth) { in Conv2DWithBias() argument
1131 Conv2D(batch, height, width, in_depth, filter_w, filter_h, out_depth); in Conv2DWithBias()
1136 Tensor bias_t = MakeRandomTensor({out_depth}); in Conv2DWithBias()
1155 int out_depth) { in Conv2DWithBiasAndRelu() argument
1157 batch, height, width, in_depth, filter_w, filter_h, out_depth); in Conv2DWithBiasAndRelu()
1176 int out_depth) { in Conv2DWithBatchNorm() argument
1178 Conv2D(batch, height, width, in_depth, filter_w, filter_h, out_depth); in Conv2DWithBatchNorm()
1183 Tensor scale_t = MakeRandomTensor({out_depth}); in Conv2DWithBatchNorm()
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Dconv_ops_3d.cc136 const int64 out_depth = filter.dim_size(4); in Compute() local
161 data_format_, in_batch, {{out[0], out[1], out[2]}}, out_depth); in Compute()
222 const int64 out_depth = filter.dim_size(4); in launch() local
246 const uint64 n = out_depth; in launch()
273 const uint64 n = out_depth; in launch()
363 .set_feature_map_count(out_depth) in launch()
370 .set_output_feature_map_count(out_depth); in launch()
385 TensorShape({out_depth, in_depth, filter_planes, in launch()
400 {{out_planes, out_rows, out_cols}}, out_depth), in launch()
422 out_depth, in launch()
Dconv_grad_ops_3d.cc391 const int64 size_A = output_image_size * dims.out_depth; in Compute()
393 const int64 size_B = filter_total_size * dims.out_depth; in Compute()
457 dims.spatial_dims[2].output_size * dims.out_depth; in Compute()
486 output_image_size, dims.out_depth); in Compute()
487 ConstTensorMap B(filter_data, filter_total_size, dims.out_depth); in Compute()
539 ConstMatrixMap A(out_data, output_image_size, dims.out_depth); in Compute()
540 ConstMatrixMap B(filter_data, filter_total_size, dims.out_depth); in Compute()
866 const int64 size_B = output_image_size * dims.out_depth; in Compute()
868 const int64 size_C = filter_total_size * dims.out_depth; in Compute()
918 dims.spatial_dims[2].output_size * dims.out_depth; in Compute()
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Dconv_ops.cc174 int out_cols, int out_depth, int dilation_rows, in Run() argument
180 in_depth, out_depth, out_rows, out_cols)) { in Run()
195 args.out_depth = out_depth; in Run()
215 int out_cols, int out_depth, int stride_rows, int stride_cols, in Run() argument
229 int out_cols, int out_depth, int dilation_rows, in Run() argument
240 desc.K = out_depth; in Run()
367 const int out_depth = static_cast<int>(filter.dim_size(3)); in ComputeConv2DDimension() local
424 dimensions->out_depth = out_depth; in ComputeConv2DDimension()
467 dimensions.out_cols, dimensions.out_depth); in Compute()
484 << ", out_depth = " << dimensions.out_depth; in Compute()
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Dconv_grad_filter_ops.cc141 auto out_depth = output.dimension(3); in operator ()() local
153 desc.K = out_depth; in operator ()()
316 const size_t size_B = output_image_size * dims.out_depth; in Compute()
318 const size_t size_C = filter_total_size * dims.out_depth; in Compute()
339 dims.spatial_dims[1].output_size * dims.out_depth; in Compute()
353 TensorMap C(filter_backprop_data, filter_total_size, dims.out_depth); in Compute()
391 dims.out_depth); in Compute()
615 const uint64 n = dims.out_depth; in operator ()()
657 const uint64 n = dims.out_depth; in operator ()()
726 .set_feature_map_count(dims.out_depth) in operator ()()
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Dconv_grad_input_ops.cc148 auto out_depth = output_backward.dimension(3); in operator ()() local
159 desc.K = out_depth; in operator ()()
407 const size_t size_A = output_image_size * dims.out_depth; in Compute()
409 const size_t size_B = filter_total_size * dims.out_depth; in Compute()
453 dims.spatial_dims[1].output_size * dims.out_depth; in Compute()
482 output_image_size, dims.out_depth); in Compute()
483 ConstTensorMap B(filter_data, filter_total_size, dims.out_depth); in Compute()
515 output_image_size, dims.out_depth, im2col_buf); in Compute()
752 const uint64 k = dims.out_depth; in operator ()()
784 const uint64 k = dims.out_depth; in operator ()()
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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.h373 int out_depth; variable
382 out_depth = (filter_shape.dim_size(TF_2DFILTER_DIM_I) *
385 out_depth = filter_shape.dim_size(
448 out_depth)
450 {{out_planes, out_rows, out_cols}}, out_depth);
457 mkldnn_sizes[MklDnnDims::Dim_C] = out_depth;
464 mkldnn_sizes[MklDnnDims3D::Dim3d_C] = out_depth;
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.cc138 dims->out_depth = filter_shape.dim_size(num_dims - 1); in ConvBackpropComputeDimensionsV2()
139 if (dims->out_depth != out_backprop_shape.dim_size(feature_dim)) { in ConvBackpropComputeDimensionsV2()
/external/tensorflow/tensorflow/python/kernel_tests/
Dconv_ops_3d_test.py368 self, batch, input_shape, filter_shape, in_depth, out_depth, stride, argument
376 filter_planes, filter_rows, filter_cols, in_depth, out_depth
395 output_shape = [batch, output_planes, output_rows, output_cols, out_depth]
472 out_depth=3,
484 out_depth=3,
496 out_depth=3,
508 out_depth=3,
520 out_depth=3,
532 out_depth=3,
544 out_depth=1,
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Dconv_ops_test.py1639 filter_cols, in_depth, out_depth, stride_rows, argument
1643 filter_shape = [filter_rows, filter_cols, in_depth, out_depth]
1657 output_shape = [batch, output_rows, output_cols, out_depth]
1726 out_depth=3,
1744 out_depth=3,
1762 out_depth=3,
1780 out_depth=3,
1798 out_depth=5,
1816 out_depth=3,
1834 out_depth=3,
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/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()
Dhexagon_controller.c187 const uint32_t out_depth = output0->depth; in hexagon_controller_ExecuteGraphWithBuffer() local
203 out_batches * out_height * out_width * out_depth, OUT_RANKING_SIZE, in hexagon_controller_ExecuteGraphWithBuffer()
206 out_height, out_width, out_depth, out_data_size, out_buf_byte_size); in hexagon_controller_ExecuteGraphWithBuffer()
/external/tensorflow/tensorflow/contrib/quantize/python/
Dfold_batch_norms_test.py95 out_depth = 3 if with_bypass else 32
103 out_depth, [5, 5],
124 out_depth, [5, 5],
195 out_depth = 3
202 out_depth, [5, 5],
215 2 * out_depth, [5, 5],
290 out_depth = 3 if with_bypass else 32
296 out_depth, [5, 5],
363 out_depth = 256 if with_bypass else 128
371 out_depth,
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Dquantize_parameterized_test.py185 out_depth = 3 if with_bypass else 32
192 out_depth, [5, 5],
237 out_depth = 256 if with_bypass else 128
244 out_depth,
511 out_depth = 3 if with_bypass else 32
517 out_depth, [5, 5],
567 out_depth = 256 if with_bypass else 128
573 out_depth,
Dcommon_test.py101 out_depth = 32
105 out_depth, [2, 2],
/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()
/external/tensorflow/tensorflow/core/grappler/costs/
Dutils_test.cc73 int out_depth = 5; in TEST() local
83 CreateConstOp("filter", {filter_rows, filter_cols, in_depth, out_depth}, in TEST()
87 CreateConstOp("output_backprop", {batch, out_rows, out_cols, out_depth}, in TEST()
98 std::vector<int32>({filter_rows, filter_cols, in_depth, out_depth}), in TEST()

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