| /external/tensorflow/tensorflow/core/kernels/ |
| deep_conv2d.h | 26 // in_depth * out_depth product) convolutions (see deep_conv2d.cc for details).
72 int in_depth; member in struct:tensorflow::Conv2DArgs
87 in_depth(0),
101 int filter_cols, int in_depth, int out_depth,
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| deep_conv2d.cc | 30 // large 'in_depth' and 'out_depth' product. See cost models below for details).
49 int out_tile_rows, int out_tile_cols, int in_depth,
54 input_tile_spatial_size * input_tile_spatial_size * in_depth;
57 const int64 product_cost = input_tile_spatial_size * in_depth * out_depth;
74 static int64 GetDirectConvCost(int filter_rows, int filter_cols, int in_depth,
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);
132 // Copies data from 'filter_in' to 'filter_buf' along 'in_depth' dimension
205 const int64 in_depth = args.in_depth; local
381 const int64 in_depth = args.in_depth; local
530 const int64 in_depth = args.in_depth; local
889 const int64 in_depth = args.in_depth; local
942 const int64 in_depth = args.in_depth; local
[all...] |
| depthwise_conv_op_gpu.cu.cc | 77 const int in_depth = args.in_depth; local
123 in_depth * (in_col + in_width * (in_row + input_offset_temp));
127 (in_channel + in_depth * (filter_col + filter_offset_temp));
145 in_depth * (in_col + in_width * (in_row + input_offset_temp));
149 (in_channel + in_depth * (filter_col + filter_offset_temp));
181 const int in_depth = args.in_depth;
196 const int in_row_size = in_width * in_depth;
207 const int batch_blocks = (in_depth + kBlockDepth - 1) / kBlockDepth
[all...] |
| depthwise_conv_op.cc | 61 // in_depth = 3, depth_multiplier = 2, filter [2, 2], register_width = 4
64 // input_buffer [rows, cols, in_depth, depth_multiplier]
68 // filter [rows, cols, in_depth, depth_multiplier]
72 // First output register [in_depth, depth_multiplier]
150 // 'in_depth' is a multiple of register width, and 'depth_multipler' is one.
190 args.in_rows * args.in_cols * args.in_depth;
287 // For special case when in_depth == 1.
294 // [ batch, in_rows, in_cols, in_depth ]
298 // [ filter_rows, filter_cols, in_depth, depth_multiplier]
309 // in_depth for input and filter must match
310 const int64 in_depth = GetTensorDim(input, data_format_, 'C'); variable
[all...] |
| depthwise_conv_op.h | 30 int in_depth; member in struct:tensorflow::DepthwiseArgs
47 in_depth(0),
112 // filter_inner_dim_size = in_depth * depth_multiplier
114 // [filter_rows, filter_cols, in_depth, depth_multiplier]
118 // in_depth = 3, depth_multiplier = 2, filter [2, 2], register_width = 4
121 // filter [rows, cols, in_depth, depth_multiplier]
125 // padded_filter [rows, cols, in_depth, depth_multiplier]
175 // in_depth = 3, depth_multiplier = 2, filter [2, 2], register_width = 4
177 // input: [batch, in_rows, in_cols, in_depth]
198 // Calculate vectorized and scalar (residual) lengths for 'in_depth'
[all...] |
| eigen_spatial_convolutions_test.cc | 667 const int in_depth = 5; local
676 const int out_depth = in_depth;
680 Tensor<float, 4> input(in_channels, in_depth, in_rows, in_cols);
709 c - off_c + k >= 0 && p - off_p + i < in_depth &&
728 const int in_depth = 5; local
737 const int out_depth = in_depth;
741 Tensor<float, 4, RowMajor> input(in_cols, in_rows, in_depth, in_channels);
771 c - off_c + k >= 0 && p - off_p + i < in_depth &&
790 const int in_depth = 5; local
803 Tensor<float, 4> input(in_channels, in_depth, in_rows, in_cols)
842 const int in_depth = 5; local
896 const int in_depth = 5; local
962 const int in_depth = 5; local
1028 const int in_depth = 8; local
1083 const int in_depth = 8; local
1139 const int in_depth = 8; local
1209 const int in_depth = 8; local
[all...] |
| conv_ops_3d.cc | 103 const int64 in_depth = GetTensorDim(input, data_format_, 'C'); variable
108 context, in_depth == filter.dim_size(3),
179 const int64 in_depth = GetTensorDim(input, data_format, 'C'); local
206 const uint64 k = in_depth;
233 const uint64 k = in_planes * in_rows * in_cols * in_depth;
273 in_depth);
291 FORMAT_NCHW, in_batch, {{in_planes, in_rows, in_cols}}, in_depth);
292 if (in_depth > 1) {
314 .set_feature_map_count(in_depth)
330 .set_input_feature_map_count(in_depth)
[all...] |
| conv_grad_ops_3d.cc | 67 const int64 in_depth = GetTensorDim(input_shape, data_format_, 'C'); \
74 OP_REQUIRES(context, in_depth == filter_shape.dim_size(3), \
204 // Fill a new "reverted" filter. We need to transpose the in_depth and
207 {filter_size[0], filter_size[1], filter_size[2], out_depth, in_depth});
328 // [batch, in_z, in_y, in_x, in_depth]
330 // [in_z, in_y, in_x, batch, in_depth]
333 {input_size[0], input_size[1], input_size[2], batch, in_depth});
346 // [out_depth, filter_size[2], filter_size[1], filter_size[0], in_depth]
348 // [filter_size[2], filter_size[1], filter_size[0], in_depth, out_depth];
353 {out_depth, filter_size[0], filter_size[1], filter_size[2], in_depth});
[all...] |
| depthwise_conv_grad_op.cc | 95 const int64 in_depth = GetTensorDim(input_shape, data_format_, 'C'); \
96 OP_REQUIRES(context, in_depth == filter_shape.dim_size(2), \
98 label, ": input and filter must have the same in_depth")); \
108 context, (depth_multiplier * in_depth) == out_depth, \
110 label, ": depth_multiplier * in_depth not equal to out_depth")); \
133 args.in_depth = in_depth; \
144 << input_rows << ", " << input_cols << ", " << in_depth \
146 << in_depth << ", " << depth_multiplier << "]; stride = " << stride \
160 // in_depth = 3, depth_multiplier = 2, filter [2, 2], register_width =
281 const int64 in_depth = args.in_depth; local
[all...] |
| conv_ops.cc | 137 int input_cols, int in_depth, int filter_rows,
153 int input_cols, int in_depth, int filter_rows,
161 in_depth, out_depth, out_rows, out_cols)) {
169 args.in_depth = in_depth;
194 int input_cols, int in_depth, int filter_rows,
208 int input_cols, int in_depth, int filter_rows,
218 desc.C = in_depth;
303 // [ batch, in_rows, in_cols, in_depth ]
308 // [ filter_rows, filter_cols, in_depth, out_depth
328 const int64 in_depth = GetTensorDim(input, data_format_, 'C'); variable
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| conv_ops_using_gemm.cc | 139 | \in_depth
455 // [ batch, in_rows, in_cols, in_depth ]
459 // [ filter_rows, filter_cols, in_depth, out_depth]
477 // The last dimension for input is in_depth. It must be the same as the
478 // filter's in_depth.
479 const int64 in_depth = GetTensorDim(input, data_format_, 'C'); variable
480 OP_REQUIRES(context, in_depth == filter.dim_size(2),
482 "input and filter must have the same depth: ", in_depth,
535 VLOG(2) << "Conv2D: in_depth = " << in_depth
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| conv_grad_input_ops.cc | 56 // filter_width, in_depth). Implementation by Yangqing Jia (jiayq).
138 auto in_depth = input_backward.dimension(3); local
147 desc.C = in_depth;
415 dims.in_depth;
471 dims.spatial_dims[1].input_size * dims.in_depth;
509 col_buffer_data, dims.in_depth, dims.spatial_dims[0].input_size,
549 Col2im<T>(im2col_buf, dims.in_depth,
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| conv_grad_filter_ops.cc | 132 auto in_depth = input.dimension(3); local
142 desc.C = in_depth;
392 dims.in_depth;
430 dims.spatial_dims[1].input_size * dims.in_depth;
472 input_data_shard, dims.in_depth, dims.spatial_dims[0].input_size,
696 const uint64 m = dims.in_depth;
708 // [batch, in_rows, in_cols, in_depth],
714 // [1, 1, in_depth, out_depth]
738 dims.spatial_dims[1].input_size * dims.in_depth;
[all...] |
| conv_grad_ops.h | 33 // where each "A", "B", etc is batch x in_depth
37 // where both "X" and "Y" are in_depth x out_depth
236 int64 in_depth, out_depth; member in struct:tensorflow::ConvBackpropDimensions
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| conv_grad_ops.cc | 127 dims->in_depth = input_shape.dim_size(feature_dim);
130 if (dims->in_depth != filter_shape.dim_size(num_dims - 2)) {
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| mkl_conv_grad_filter_ops.cc | 163 mkl_context.in_sizes[2] = static_cast<size_t>(backprop_dims.in_depth);
188 mkl_context.filter_sizes[2] = backprop_dims.in_depth;
193 // Note TF filter layout : (rows, cols, in_depth, out_depth),
196 backprop_dims.out_depth * backprop_dims.in_depth;
198 backprop_dims.in_depth *
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| quantized_conv_ops.cc | 116 | \in_depth
484 // [ batch, in_rows, in_cols, in_depth ]
488 // [ filter_rows, filter_cols, in_depth, out_depth]
511 // The last dimension for input is in_depth. It must be the same as the
512 // filter's in_depth.
513 const int64 in_depth = input.dim_size(3); variable
514 OP_REQUIRES(context, in_depth == filter.dim_size(2),
516 "input and filter must have the same depth: ", in_depth,
562 input_cols, in_depth, offset_input, filter.flat<T2>().data(),
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| nn_ops_test.cc | 105 static void BM_ConvFloat(int iters, int batch, int rows, int cols, int in_depth,
135 // BATCH x OUT_ROW X OUT_COL X IN_DEPTH X PATCH_ROW X PATH_COL X OUT_DEPTH
137 num_ops = static_cast<int64>(batch * in_depth * out_depth) *
142 // BATCH x IN_ROW X IN_COL X IN_DEPTH X PATCH_ROW X PATCH_COL X OUT_DEPTH
144 num_ops = static_cast<int64>(batch * in_depth * out_depth) *
149 SetConstOp("input", {batch, rows, cols, in_depth}, data_type,
151 SetConstOp("filter", {filter_rows, filter_cols, in_depth, out_depth},
156 std::vector<int32>({batch, rows, cols, in_depth}),
160 std::vector<int32>({filter_rows, filter_cols, in_depth, out_depth}),
499 int in_depth, int depth_multiplier
[all...] |
| conv_ops_fused.cc | 646 // [ batch, in_rows, in_cols, in_depth ]
781 const int64 in_depth = padded_shape.dim_size(3); variable
[all...] |
| /external/tensorflow/tensorflow/python/kernel_tests/ |
| conv_ops_3d_test.py | 275 self, batch, input_shape, filter_shape, in_depth, out_depth, stride,
281 input_shape = [batch, input_planes, input_rows, input_cols, in_depth]
283 filter_planes, filter_rows, filter_cols, in_depth, out_depth
375 in_depth=2,
386 in_depth=2,
397 in_depth=2,
408 in_depth=2,
419 in_depth=2,
430 in_depth=2,
441 in_depth=2
[all...] |
| conv_ops_test.py | [all...] |
| /external/tensorflow/tensorflow/core/kernels/neon/ |
| neon_depthwise_conv_op.cc | 73 const int32 in_depth = input.dim_size(3); variable
74 OP_REQUIRES(context, in_depth == filter.dim_size(2),
76 "input and filter must have the same depth: ", in_depth,
86 const int32 out_depth = in_depth * depth_multiplier;
111 << ", " << in_depth << "]; Filter: [" << filter_rows << ", "
112 << filter_cols << ", " << in_depth << ", " << depth_multiplier
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| /external/tensorflow/tensorflow/core/grappler/costs/ |
| utils_test.cc | 66 int in_depth = 3; local
74 CreateConstOp("input", {batch, rows, cols, in_depth}, input);
77 CreateConstOp("filter", {filter_rows, filter_cols, in_depth, out_depth},
86 std::vector<int32>({batch, rows, cols, in_depth}),
92 std::vector<int32>({filter_rows, filter_cols, in_depth, out_depth}),
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| /prebuilts/ndk/r16/sources/third_party/shaderc/third_party/spirv-tools/source/util/ |
| huffman_codec.h | 349 Context(uint32_t in_node, uint64_t in_bits, size_t in_depth)
350 : node(in_node), bits(in_bits), depth(in_depth) {}
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| /external/tensorflow/tensorflow/compiler/tf2xla/kernels/ |
| conv_ops.cc | 224 // [ filter_rows, filter_cols, ..., in_depth, out_depth]
238 const int64 in_depth = filter_shape.dim_size(num_spatial_dims_); variable
240 // The 'C' dimension for input is in_depth. It must be the same as
241 // the filter's in_depth.
242 OP_REQUIRES(ctx, in_depth == input_shape.dim_size(feature_dim),
244 "input and filter must have the same depth: ", in_depth,
556 // Activations have shape: [batch, in_rows, in_cols, ..., in_depth]
559 // Each spatial entry has size in_depth * batch
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