Searched defs:out_depth (Results 1 - 25 of 30) sorted by relevance

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/external/tensorflow/tensorflow/core/kernels/
H A Dconv_grad_ops.h37 // where both "X" and "Y" are in_depth x out_depth
45 // where each "a", "b", etc is batch x out_depth
236 int64 in_depth, out_depth; member in struct:tensorflow::ConvBackpropDimensions
H A Ddeep_conv2d.h26 // in_depth * out_depth product) convolutions (see deep_conv2d.cc for details).
81 int out_depth; member in struct:tensorflow::Conv2DArgs
94 out_depth(0) {}
101 int filter_cols, int in_depth, int out_depth,
H A Deigen_cuboid_convolution.h121 TensorIndex out_depth; local
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;
H A Ddepthwise_conv_op.h41 int out_depth; member in struct:tensorflow::DepthwiseArgs
56 out_depth(0) {}
137 const int64 filter_inner_dim_size = args.out_depth;
211 const int64 output_scalar_size = args.out_depth % kPacketSize;
H A Deigen_spatial_convolutions_test.cc676 const int out_depth = in_depth; local
683 Tensor<float, 4> result(kern_filters, out_depth, out_height, out_width);
691 EXPECT_EQ(result.dimension(1), out_depth);
700 for (int i = 0; i < out_depth; ++i) {
737 const int out_depth = in_depth; local
744 Tensor<float, 4, RowMajor> result(out_width, out_height, out_depth,
753 EXPECT_EQ(result.dimension(2), out_depth);
762 for (int i = 0; i < out_depth; ++i) {
799 const int out_depth = 3; local
806 Tensor<float, 4> result(kern_filters, out_depth, out_heigh
851 const int out_depth = 3; local
905 const int out_depth = in_depth; local
971 const int out_depth = in_depth; local
1037 const int out_depth = 3; local
1092 const int out_depth = 3; local
1149 const int out_depth = ceil_div(in_depth, stride); local
1219 const int out_depth = ceil_div(in_depth, stride); local
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H A Dpooling_ops_common.h66 int out_depth; member in struct:tensorflow::PoolParameters
H A Dconv_grad_ops_3d.cc77 const int64 out_depth = filter_shape.dim_size(4); \
79 context, out_depth == GetTensorDim(out_backprop, data_format_, 'C'), \
81 label, ": filter and out_backprop must have the same out_depth")); \
191 padded_out_cols, out_depth});
205 // out_depth for the filter and reverse the planes, rows and cols.
207 {filter_size[0], filter_size[1], filter_size[2], out_depth, in_depth});
309 // [batch, out_z, out_y, out_x, out_depth]
311 // [out_depth, out_x, out_y, out_z, batch]
313 TensorShape padded_out_shape({out_depth, padded_out_planes, padded_out_rows,
346 // [out_depth, filter_siz
581 .set_output_feature_map_count(out_depth); variable
942 .set_output_feature_map_count(out_depth); variable
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H A Dconv_ops_3d.cc106 const int64 out_depth = filter.dim_size(4); variable
126 data_format_, in_batch, {{out[0], out[1], out[2]}}, out_depth);
184 const int64 out_depth = filter.dim_size(4); local
207 const uint64 n = out_depth;
234 const uint64 n = out_depth;
324 .set_feature_map_count(out_depth)
331 .set_output_feature_map_count(out_depth);
343 TensorShape({out_depth, in_depth, filter_planes,
357 {{out_planes, out_rows, out_cols}}, out_depth),
378 out_depth,
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H A Ddepthwise_conv_op.cc88 const int64 out_depth = args.out_depth; local
90 const int64 output_scalar_size = out_depth % kPacketSize;
92 (out_depth / kPacketSize) * kPacketSize;
93 const int64 base_output_index = (out_r * args.out_cols + out_c) * out_depth;
166 const bool pad_filter = (args.out_depth % kPacketSize) == 0 ? false : true;
172 ((args.out_depth + kPacketSize - 1) / kPacketSize) * kPacketSize;
192 args.out_rows * args.out_cols * args.out_depth;
195 ((args.out_depth + kPacketSize - 1) / kPacketSize) * kPacketSize;
235 const int64 shard_cost = kCostMultiplier * args.out_cols * args.out_depth;
320 const int32 out_depth = in_depth * depth_multiplier; variable
366 << out_rows << ", " << out_cols << ", " << out_depth << "]"; variable
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H A Dfractional_avg_pool_op.cc247 const int64 out_depth = out_backprop.dim_size(3); variable
276 out_depth,
303 for (int64 d = 0; d < out_depth; ++d) {
H A Dconv_ops_using_gemm.cc459 // [ filter_rows, filter_cols, in_depth, out_depth]
485 // The last dimension for filter is out_depth.
486 const int out_depth = static_cast<int>(filter.dim_size(3)); variable
528 ShapeFromFormat(data_format_, batch, out_rows, out_cols, out_depth);
531 // [ in_batch, out_rows, out_cols, out_depth ]
542 << ", out_depth = " << out_depth; variable
551 out_depth, stride_rows, stride_cols, padding_,
H A Dmkl_conv_ops.h164 // TF filter is always in (rows, cols, in_depth, out_depth) order.
168 int out_depth = static_cast<int>(filter_shape.dim_size(3)); local
171 // OIHW = (out_depth, in_depth, rows, cols)
173 mkldnn_sizes[MklDnnDims::Dim_O] = out_depth;
239 int out_depth = filter_shape.dim_size(3); local
253 ShapeFromFormat(data_format_, out_batch, out_rows, out_cols, out_depth);
259 mkldnn_sizes[MklDnnDims::Dim_C] = out_depth;
H A Dmkl_pooling_ops_common.h54 int out_depth; member in struct:tensorflow::MklPoolParameters
76 out_depth(0),
148 mkl_pool_params.out_depth,
H A Dnn_ops_test.cc106 int out_depth, int filter_rows, int filter_cols,
137 num_ops = static_cast<int64>(batch * in_depth * out_depth) *
144 num_ops = static_cast<int64>(batch * in_depth * out_depth) *
151 SetConstOp("filter", {filter_rows, filter_cols, in_depth, out_depth},
153 SetConstOp("output_backprop", {batch, out_rows, out_cols, out_depth},
160 std::vector<int32>({filter_rows, filter_cols, in_depth, out_depth}),
500 int out_depth, int filter_rows,
555 SetConstOp("output_backprop", {batch, out_rows, out_cols, out_depth},
105 BM_ConvFloat(int iters, int batch, int rows, int cols, int in_depth, int out_depth, int filter_rows, int filter_cols, CONV_OP op, int num_threads, int stride, Padding padding, bool use_gpu, DataType data_type, const string& label) argument
498 BM_ConvFloatDepthwise(int iters, int batch, int rows, int cols, int in_depth, int depth_multiplier, int out_depth, int filter_rows, int filter_cols, DEPTHWISE_CONV_OP op, int num_threads, int stride, Padding padding, bool use_gpu, const string& label) argument
H A Dconv_grad_filter_ops.cc133 auto out_depth = output.dimension(3); local
145 desc.K = out_depth;
410 const size_t size_B = output_image_size * dims.out_depth;
412 const size_t size_C = filter_total_size * dims.out_depth;
433 dims.spatial_dims[1].output_size * dims.out_depth;
447 TensorMap C(filter_backprop_data, filter_total_size, dims.out_depth);
485 dims.out_depth);
699 const uint64 n = dims.out_depth;
702 // [batch, out_rows, out_cols, out_depth]
714 // [1, 1, in_depth, out_depth]
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H A Dconv_grad_input_ops.cc139 auto out_depth = output_backward.dimension(3); local
150 desc.K = out_depth;
428 const size_t size_A = output_image_size * dims.out_depth;
430 const size_t size_B = filter_total_size * dims.out_depth;
474 dims.spatial_dims[1].output_size * dims.out_depth;
503 output_image_size, dims.out_depth);
504 ConstTensorMap B(filter_data, filter_total_size, dims.out_depth);
544 ConstMatrixMap A(out_data, output_image_size, dims.out_depth);
545 ConstMatrixMap B(filter_data, filter_total_size, dims.out_depth);
768 const uint64 k = dims.out_depth;
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H A Dconv_ops.cc139 int /*out_cols*/, int /*out_depth*/, int /*dilation_rows*/,
155 int out_cols, int out_depth, int dilation_rows,
161 in_depth, out_depth, out_rows, out_cols)) {
176 args.out_depth = out_depth;
196 int out_cols, int out_depth, int stride_rows, int stride_cols,
210 int out_cols, int out_depth, int dilation_rows,
221 desc.K = out_depth;
308 // [ filter_rows, filter_cols, in_depth, out_depth]
334 // The last dimension for filter is out_depth
151 Run(OpKernelContext* ctx, const Tensor& input, const Tensor& filter, int batch, int input_rows, int input_cols, int in_depth, int filter_rows, int filter_cols, int pad_rows, int pad_cols, int out_rows, int out_cols, int out_depth, int dilation_rows, int dilation_cols, int stride_rows, int stride_cols, Tensor* output, TensorFormat data_format) argument
192 Run(OpKernelContext* ctx, const Tensor& input, const Tensor& filter, int batch, int input_rows, int input_cols, int in_depth, int filter_rows, int filter_cols, int pad_rows, int pad_cols, int out_rows, int out_cols, int out_depth, int stride_rows, int stride_cols, int dilation_rows, int dilation_cols, Tensor* output, TensorFormat data_format) argument
206 Run(OpKernelContext* ctx, const Tensor& input, const Tensor& filter, int batch, int input_rows, int input_cols, int in_depth, int filter_rows, int filter_cols, int pad_rows, int pad_cols, int out_rows, int out_cols, int out_depth, int dilation_rows, int dilation_cols, int stride_rows, int stride_cols, Tensor* output, TensorFormat data_format) argument
335 const int out_depth = static_cast<int>(filter.dim_size(3)); variable
397 << ", out_depth = " << out_depth; variable
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H A Dconv_ops_fused.cc759 // [ filter_rows, filter_cols, in_depth, out_depth]
787 // The last dimension for filter is out_depth.
788 const int out_depth = static_cast<int>(filter.dim_size(3)); variable
832 ShapeFromFormat(FORMAT_NHWC, batch, out_rows, out_cols, out_depth);
837 // [ in_batch, out_rows, out_cols, out_depth ]
850 << ", out_depth = " << out_depth << ", DoResize=" << DoResize;
859 filter_cols, out_depth, stride_rows, stride_cols, padding_,
H A Ddepthwise_conv_grad_op.cc106 const int32 out_depth = static_cast<int32>(out_depth_raw); \
108 context, (depth_multiplier * in_depth) == out_depth, \
110 label, ": depth_multiplier * in_depth not equal to out_depth")); \
142 args.out_depth = out_depth; \
149 << ", " << out_depth << "]";
162 // 'out_backprop': [batch, out_rows, out_cols, out_depth]
209 const int64 vectorized_size = (args.out_depth / kPacketSize) * kPacketSize;
210 const int64 scalar_size = args.out_depth % kPacketSize;
221 out_backprop + (out_r * args.out_cols + out_c) * args.out_depth;
283 const int64 out_depth = args.out_depth; local
653 const int64 out_depth = args.out_depth; local
800 const int64 out_depth = args.out_depth; local
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H A Dmkl_conv_ops.cc132 // The last dimension for filter is out_depth.
133 const int out_depth = static_cast<int>(filter.dim_size(3)); variable
182 ShapeFromFormat(data_format_, batch, out_rows, out_cols, out_depth);
185 // [ in_batch, out_rows, out_cols, out_depth ]
220 mkl_context.out_sizes[MklDims::C] = static_cast<size_t>(out_depth);
234 // TF filter dimension order (out_depth, in_depth, cols, rows) ->
235 // MKL filter dimension order (out_depth, in_depth, rows, cols)
239 mkl_context.filter_sizes[3] = filter.dim_size(3); // out_depth
241 // TF filter layout - (rows, cols, in_depth, out_depth)
247 mkl_context.filter_strides[3] = 1; // out_depth
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/external/tensorflow/tensorflow/core/grappler/costs/
H A Dutils_test.cc67 int out_depth = 5; local
77 CreateConstOp("filter", {filter_rows, filter_cols, in_depth, out_depth},
81 CreateConstOp("output_backprop", {batch, out_rows, out_cols, out_depth},
92 std::vector<int32>({filter_rows, filter_cols, in_depth, out_depth}),
/external/tensorflow/tensorflow/core/kernels/neon/
H A Dneon_depthwise_conv_op.cc86 const int32 out_depth = in_depth * depth_multiplier; variable
97 TensorShape out_shape({batch, out_rows, out_cols, out_depth});
105 // [ in_batch, out_rows, out_cols, out_depth ]
115 << out_rows << ", " << out_cols << ", " << out_depth << "]"; variable
/external/tensorflow/tensorflow/contrib/hvx/hexagon_controller/src_impl/
H A Dgraph_functions_wrapper.c216 uint32_t* out_width, uint32_t* out_depth, uint8_t* out_vals,
246 *out_depth = output.depth;
252 *out_width, *out_depth, *out_data_byte_size);
259 uint32_t out_batches, out_height, out_width, out_depth; local
268 &out_batches, &out_height, &out_width, &out_depth,
273 s_output_values, out_batches * out_height * out_width * out_depth,
276 out_width, out_depth, out_data_size);
279 out_batches * out_height * out_width * out_depth));
212 hexagon_controller_ExecuteGraph( const uint32_t nn_id, const uint32_t batches, const uint32_t height, const uint32_t width, const uint32_t depth, uint8_t* int_data, const uint32_t int_data_size, uint32_t* out_batches, uint32_t* out_height, uint32_t* out_width, uint32_t* out_depth, uint8_t* out_vals, const uint32_t output_val_byte_size, uint32_t* out_data_byte_size) argument
H A Dhexagon_controller.c187 const uint32_t out_depth = output0->depth; local
203 out_batches * out_height * out_width * out_depth, OUT_RANKING_SIZE,
206 out_height, out_width, out_depth, out_data_size, out_buf_byte_size);
/external/mesa3d/src/mesa/state_tracker/
H A Dst_cb_drawpixels.c124 struct ureg_dst out_color, out_depth, out_stencil; local
152 out_depth = ureg_DECL_output(ureg, TGSI_SEMANTIC_POSITION, 0);
172 ureg_TEX(ureg, ureg_writemask(out_depth, TGSI_WRITEMASK_Z),

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