| /external/tensorflow/tensorflow/core/kernels/ |
| conv_grad_ops.h | 37 // where both "X" and "Y" are in_depth x out_depth
45 // where each "a", "b", etc is batch x out_depth
242 int64 in_depth, out_depth; member in struct:tensorflow::ConvBackpropDimensions
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| deep_conv2d.h | 26 // 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,
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| conv_ops.h | 90 int out_depth; member in struct:tensorflow::Conv2DDimensions
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| depthwise_conv_op.h | 41 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;
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| pooling_ops_common.h | 66 int out_depth; member in struct:tensorflow::PoolParameters
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| conv_ops_3d.cc | 136 const int64 out_depth = filter.dim_size(4); variable
161 data_format_, in_batch, {{out[0], out[1], out[2]}}, out_depth);
222 const int64 out_depth = filter.dim_size(4); local
246 const uint64 n = out_depth;
273 const uint64 n = out_depth;
363 .set_feature_map_count(out_depth)
370 .set_output_feature_map_count(out_depth);
385 TensorShape({out_depth, in_depth, filter_planes,
400 {{out_planes, out_rows, out_cols}}, out_depth),
422 out_depth,
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| conv_ops_using_gemm.cc | 459 // [ 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_,
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| depthwise_conv_op.cc | 89 const int64 out_depth = args.out_depth; local
91 const int64 output_scalar_size = out_depth % kPacketSize;
93 (out_depth / kPacketSize) * kPacketSize;
94 const int64 base_output_index = (out_r * args.out_cols + out_c) * out_depth;
167 const bool pad_filter = (args.out_depth % kPacketSize) == 0 ? false : true;
173 ((args.out_depth + kPacketSize - 1) / kPacketSize) * kPacketSize;
193 args.out_rows * args.out_cols * args.out_depth;
196 ((args.out_depth + kPacketSize - 1) / kPacketSize) * kPacketSize;
236 const int64 shard_cost = kCostMultiplier * args.out_cols * args.out_depth;
327 const int32 out_depth = in_depth * depth_multiplier; variable
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| fractional_avg_pool_op.cc | 247 const int64 out_depth = out_backprop.dim_size(3); variable
276 out_depth,
303 for (int64 d = 0; d < out_depth; ++d) {
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| mkl_conv_ops.h | 213 // TF filter is always in (rows, cols, in_depth, out_depth) order.
224 // OIHW = (out_depth, in_depth, rows, cols)
225 // GOIHW = (group, out_depth, in_depth, rows, cols)
251 // TF filter is always in (planes, rows, cols, in_depth, out_depth) order.
264 // OIDHW = (out_depth, in_depth, planes, rows, cols)
373 int out_depth; local
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);
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| eigen_spatial_convolutions_test.cc | 678 const int out_depth = in_depth; local
685 Tensor<float, 4> result(kern_filters, out_depth, out_height, out_width);
693 EXPECT_EQ(result.dimension(1), out_depth);
702 for (int i = 0; i < out_depth; ++i) {
739 const int out_depth = in_depth; local
746 Tensor<float, 4, RowMajor> result(out_width, out_height, out_depth,
755 EXPECT_EQ(result.dimension(2), out_depth);
764 for (int i = 0; i < out_depth; ++i) {
801 const int out_depth = 3; local
808 Tensor<float, 4> result(kern_filters, out_depth, out_height, out_width)
853 const int out_depth = 3; local
907 const int out_depth = in_depth; local
973 const int out_depth = in_depth; local
1039 const int out_depth = 3; local
1094 const int out_depth = 3; local
1151 const int out_depth = ceil_div(in_depth, stride); local
1221 const int out_depth = ceil_div(in_depth, stride); local
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| quantized_conv_ops.cc | 488 // [ filter_rows, filter_cols, in_depth, out_depth]
519 // The last dimension for filter is out_depth.
520 const int64 out_depth = filter.dim_size(3); variable
550 CHECK_GT(out_depth, 0);
551 TensorShape out_shape({batch, out_rows, out_cols, out_depth});
554 // [ in_batch, out_rows, out_cols, out_depth ]
563 filter_rows, filter_cols, out_depth, offset_filter, stride,
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| conv_grad_filter_ops.cc | 141 auto out_depth = output.dimension(3); local
153 desc.K = out_depth;
316 const size_t size_B = output_image_size * dims.out_depth;
318 const size_t size_C = filter_total_size * dims.out_depth;
339 dims.spatial_dims[1].output_size * dims.out_depth;
353 TensorMap C(filter_backprop_data, filter_total_size, dims.out_depth);
391 dims.out_depth);
615 const uint64 n = dims.out_depth;
618 // [batch, out_rows, out_cols, out_depth]
630 // [1, 1, in_depth, out_depth]
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| conv_grad_input_ops.cc | 148 auto out_depth = output_backward.dimension(3); local
159 desc.K = out_depth;
407 const size_t size_A = output_image_size * dims.out_depth;
409 const size_t size_B = filter_total_size * dims.out_depth;
453 dims.spatial_dims[1].output_size * dims.out_depth;
482 output_image_size, dims.out_depth);
483 ConstTensorMap B(filter_data, filter_total_size, dims.out_depth);
515 output_image_size, dims.out_depth, im2col_buf);
752 const uint64 k = dims.out_depth;
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| conv_ops.cc | 158 int /*out_cols*/, int /*out_depth*/, int /*dilation_rows*/,
174 int out_cols, int out_depth, int dilation_rows,
180 in_depth, out_depth, out_rows, out_cols)) {
195 args.out_depth = out_depth;
215 int out_cols, int out_depth, int stride_rows, int stride_cols,
229 int out_cols, int out_depth, int dilation_rows,
240 desc.K = out_depth;
366 // The last dimension for filter is out_depth.
367 const int out_depth = static_cast<int>(filter.dim_size(3)) local
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| conv_ops_fused_image_transform.cc | 789 const int out_depth = static_cast<int>(filter.dim_size(3)); variable
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| deep_conv2d.cc | 30 // large 'in_depth' and 'out_depth' product. See cost models below for details).
50 int out_depth, int out_rows, int out_cols) {
57 const int64 product_cost = input_tile_spatial_size * in_depth * out_depth;
62 output_tile_spatial_size * input_tile_spatial_size * out_depth;
75 int out_depth, int out_rows, int out_cols) {
76 return filter_rows * filter_cols * in_depth * out_depth * out_rows * out_cols;
98 int filter_cols, int in_depth, int out_depth,
117 t.output_shape().cols, in_depth, out_depth, out_rows, out_cols);
119 filter_rows, filter_cols, in_depth, out_depth, out_rows, out_cols);
135 // [filter_rows, filter_cols, in_depth, out_depth]
382 const int64 out_depth = args.out_depth; local
532 const int64 out_depth = args.out_depth; local
891 const int64 out_depth = args.out_depth; local
944 const int64 out_depth = args.out_depth; local
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| depthwise_conv_grad_op.cc | 109 const int32 out_depth = static_cast<int32>(out_depth_raw); \
111 context, (depth_multiplier * in_depth) == out_depth, \
113 label, ": depth_multiplier * in_depth not equal to out_depth")); \
145 args.out_depth = out_depth; \
152 << ", " << out_depth << "]";
165 // 'out_backprop': [batch, out_rows, out_cols, out_depth]
212 const int64 vectorized_size = (args.out_depth / kPacketSize) * kPacketSize;
213 const int64 scalar_size = args.out_depth % kPacketSize;
224 out_backprop + (out_r * args.out_cols + out_c) * args.out_depth;
286 const int64 out_depth = args.out_depth; local
754 const int64 out_depth = args.out_depth; local
901 const int64 out_depth = args.out_depth; local
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| mkl_pooling_ops_common.h | 385 int out_depth; member in struct:tensorflow::MklPoolParameters
413 out_depth(0),
495 mkl_pool_params.out_depth,
501 mkl_pool_params.out_depth,
568 pool_params->out_depth};
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| mkl_conv_ops.cc | 483 // The last dimension for filter is out_depth.
484 const int out_depth = static_cast<int>(filter.dim_size(3)); variable
533 ShapeFromFormat(data_format_, batch, out_rows, out_cols, out_depth);
536 // [ in_batch, out_rows, out_cols, out_depth ]
571 mkl_context.out_sizes[MklDims::C] = static_cast<size_t>(out_depth);
585 // TF filter dimension order (out_depth, in_depth, cols, rows) ->
586 // MKL filter dimension order (out_depth, in_depth, rows, cols)
590 mkl_context.filter_sizes[3] = filter.dim_size(3); // out_depth
592 // TF filter layout - (rows, cols, in_depth, out_depth)
598 mkl_context.filter_strides[3] = 1; // out_depth
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| /external/tensorflow/tensorflow/core/grappler/costs/ |
| utils_test.cc | 73 int out_depth = 5; local
83 CreateConstOp("filter", {filter_rows, filter_cols, in_depth, out_depth},
87 CreateConstOp("output_backprop", {batch, out_rows, out_cols, out_depth},
98 std::vector<int32>({filter_rows, filter_cols, in_depth, out_depth}),
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| /external/tensorflow/tensorflow/core/kernels/neon/ |
| neon_depthwise_conv_op.cc | 86 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
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| /external/tensorflow/tensorflow/contrib/hvx/hexagon_controller/src_impl/ |
| graph_functions_wrapper.c | 216 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));
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| hexagon_controller.c | 187 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);
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| /external/mesa3d/src/mesa/state_tracker/ |
| st_cb_drawpixels.c | 125 struct ureg_dst out_color, out_depth, out_stencil; local
153 out_depth = ureg_DECL_output(ureg, TGSI_SEMANTIC_POSITION, 0);
173 ureg_TEX(ureg, ureg_writemask(out_depth, TGSI_WRITEMASK_Z),
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