Home
last modified time | relevance | path

Searched refs:in_depth (Results 1 – 25 of 41) sorted by relevance

12

/external/tensorflow/tensorflow/core/kernels/
Ddeep_conv2d.cc49 int out_tile_rows, int out_tile_cols, int in_depth, in GetDeepConvCost() argument
54 input_tile_spatial_size * input_tile_spatial_size * in_depth; in GetDeepConvCost()
57 const int64 product_cost = input_tile_spatial_size * in_depth * out_depth; in GetDeepConvCost()
74 static int64 GetDirectConvCost(int filter_rows, int filter_cols, int in_depth, 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()
146 const int64 vectorized_size = args.in_depth / kPacketSize; in operator ()()
147 const int64 scalar_size = args.in_depth % kPacketSize; in operator ()()
[all …]
Ddepthwise_conv_op_gpu.h80 const int in_depth = args.in_depth; in DepthwiseConv2dGPUKernelNHWC() local
126 in_depth * (in_col + in_width * (in_row + input_offset_temp)); in DepthwiseConv2dGPUKernelNHWC()
130 (in_channel + in_depth * (filter_col + filter_offset_temp)); in DepthwiseConv2dGPUKernelNHWC()
148 in_depth * (in_col + in_width * (in_row + input_offset_temp)); in DepthwiseConv2dGPUKernelNHWC()
152 (in_channel + in_depth * (filter_col + filter_offset_temp)); in DepthwiseConv2dGPUKernelNHWC()
187 const int in_depth = args.in_depth; in DepthwiseConv2dGPUKernelNHWCSmall() local
202 const int in_row_size = in_width * in_depth; in DepthwiseConv2dGPUKernelNHWCSmall()
213 const int batch_blocks = (in_depth + kBlockDepth - 1) / kBlockDepth; in DepthwiseConv2dGPUKernelNHWCSmall()
233 const int tensor_idx = thread_pix * in_depth + thread_depth; in DepthwiseConv2dGPUKernelNHWCSmall()
243 const int max_channel = in_depth - thread_depth; in DepthwiseConv2dGPUKernelNHWCSmall()
[all …]
Ddepthwise_conv_grad_op.cc98 const int64 in_depth = GetTensorDim(input_shape, data_format_, 'C'); \
99 OP_REQUIRES(context, in_depth == filter_shape.dim_size(2), \
111 context, (depth_multiplier * in_depth) == out_depth, \
136 args.in_depth = in_depth; \
147 << input_rows << ", " << input_cols << ", " << in_depth \
149 << in_depth << ", " << depth_multiplier << "]; stride = " << stride \
284 const int64 in_depth = args.in_depth; in ComputeBackpropInput() local
294 const int64 base_output_index = (in_r * args.in_cols + in_c) * in_depth; in ComputeBackpropInput()
351 for (int64 d = 0; d < in_depth; ++d) { in ComputeBackpropInput()
410 args.in_rows * args.in_cols * args.in_depth; in operator ()()
[all …]
Ddepthwise_conv_op.cc191 args.in_rows * args.in_cols * args.in_depth; in operator ()()
317 const int64 in_depth = GetTensorDim(input, data_format_, 'C'); in Compute() local
318 OP_REQUIRES(context, in_depth == filter.dim_size(2), in Compute()
320 "input and filter must have the same depth: ", in_depth, in Compute()
327 const int32 out_depth = in_depth * depth_multiplier; in Compute()
375 bool use_cudnn = use_cudnn_ && (in_depth == 1 || use_cudnn_grouped_conv_); in Compute()
379 << ", " << in_depth << "]; Filter: [" << filter_rows << ", " in Compute()
380 << filter_cols << ", " << in_depth << ", " << depth_multiplier in Compute()
416 args.in_depth = in_depth; in Compute()
Dconv_ops_3d.cc133 const int64 in_depth = GetTensorDim(input, data_format_, 'C'); in Compute() local
138 context, in_depth == filter.dim_size(3), in Compute()
217 const int64 in_depth = GetTensorDim(input, data_format, 'C'); in launch() local
245 const uint64 k = in_depth; in launch()
272 const uint64 k = in_planes * in_rows * in_cols * in_depth; in launch()
312 in_depth); in launch()
330 FORMAT_NCHW, in_batch, {{in_planes, in_rows, in_cols}}, in_depth); in launch()
331 if (in_depth > 1) { in launch()
353 .set_feature_map_count(in_depth) in launch()
369 .set_input_feature_map_count(in_depth) in launch()
[all …]
Deigen_spatial_convolutions_test.cc669 const int in_depth = 5; in TEST() local
678 const int out_depth = in_depth; in TEST()
682 Tensor<float, 4> input(in_channels, in_depth, in_rows, in_cols); in TEST()
711 c - off_c + k >= 0 && p - off_p + i < in_depth && in TEST()
730 const int in_depth = 5; in TEST() local
739 const int out_depth = in_depth; in TEST()
743 Tensor<float, 4, RowMajor> input(in_cols, in_rows, in_depth, in_channels); in TEST()
773 c - off_c + k >= 0 && p - off_p + i < in_depth && in TEST()
792 const int in_depth = 5; in TEST() local
805 Tensor<float, 4> input(in_channels, in_depth, in_rows, in_cols); in TEST()
[all …]
Dconv_ops.cc141 const int64 in_depth = GetTensorDim(input, data_format, 'C'); in operator ()() local
142 OP_REQUIRES(ctx, in_depth == filter.dim_size(2), in operator ()()
156 int input_cols, int in_depth, int filter_rows, in Run() argument
172 int input_cols, int in_depth, int filter_rows, in Run() argument
180 in_depth, out_depth, out_rows, out_cols)) { in Run()
188 args.in_depth = in_depth; in Run()
213 int input_cols, int in_depth, int filter_rows, in Run() argument
227 int input_cols, int in_depth, int filter_rows, in Run() argument
237 desc.C = in_depth; in Run()
359 const int in_depth = static_cast<int>(in_depth_raw); in ComputeConv2DDimension() local
[all …]
Ddeep_conv2d.h72 int in_depth; member
87 in_depth(0), in Conv2DArgs()
101 int filter_cols, int in_depth, int out_depth,
Ddepthwise_conv_op.h30 int in_depth; member
47 in_depth(0), in DepthwiseArgs()
200 (args.in_depth / kPacketSize) * kPacketSize;
201 const int64 input_scalar_size = args.in_depth % kPacketSize;
232 auto* in = input + (in_r * args.in_cols + in_c) * args.in_depth;
Dconv_grad_ops_3d.cc377 dims.spatial_dims[2].filter_size * dims.in_depth; in Compute()
452 dims.spatial_dims[2].input_size * dims.in_depth; in Compute()
491 Col2im<T>(col_buffer_data, dims.in_depth, in Compute()
544 Col2im<T>(im2col_buf, dims.in_depth, in Compute()
845 dims.spatial_dims[2].filter_size * dims.in_depth; in Compute()
914 dims.spatial_dims[2].input_size * dims.in_depth; in Compute()
957 Im2col<T>(input_data_shard, dims.in_depth, in Compute()
1175 const uint64 n = dims.in_depth; in Compute()
1204 dims.input_size(2) * dims.in_depth; in Compute()
1240 dims.in_depth, in Compute()
[all …]
Dconv_grad_filter_ops.cc140 auto in_depth = input.dimension(3); in operator ()() local
150 desc.C = in_depth; in operator ()()
298 dims.in_depth; in Compute()
336 dims.spatial_dims[1].input_size * dims.in_depth; in Compute()
378 input_data_shard, dims.in_depth, dims.spatial_dims[0].input_size, in Compute()
605 bool is_grouped_convolution = filter_shape.dim_size(2) != dims.in_depth; in operator ()()
612 const uint64 m = dims.in_depth; in operator ()()
655 dims.spatial_dims[1].input_size * dims.in_depth; in operator ()()
700 new_in_cols, dims.in_depth), in operator ()()
720 .set_feature_map_count(dims.in_depth) in operator ()()
[all …]
Dconv_grad_input_ops.cc147 auto in_depth = input_backward.dimension(3); in operator ()() local
156 desc.C = in_depth; in operator ()()
394 dims.in_depth; in Compute()
450 dims.spatial_dims[1].input_size * dims.in_depth; in Compute()
488 col_buffer_data, dims.in_depth, dims.spatial_dims[0].input_size, in Compute()
517 Col2im<T>(im2col_buf, dims.in_depth, in Compute()
744 bool is_grouped_convolution = filter_shape.dim_size(2) != dims.in_depth; in operator ()()
753 const uint64 n = dims.in_depth; in operator ()()
786 dims.spatial_dims[1].input_size * dims.in_depth; in operator ()()
824 data_format, dims.batch_size, new_in_rows, new_in_cols, dims.in_depth); in operator ()()
[all …]
Dconv_grad_ops.cc129 dims->in_depth = input_shape.dim_size(feature_dim); in ConvBackpropComputeDimensionsV2()
132 VLOG(2) << "input vs filter_in depth " << dims->in_depth << " " in ConvBackpropComputeDimensionsV2()
134 if (dims->in_depth % filter_shape.dim_size(num_dims - 2)) { in ConvBackpropComputeDimensionsV2()
Dconv_ops_test.cc1104 static Conv2DGraph Conv2D(int batch, int height, int width, int in_depth, in Conv2D() argument
1108 Tensor images_t = MakeRandomTensor({batch, height, width, in_depth}); in Conv2D()
1109 Tensor filter_t = MakeRandomTensor({filter_w, filter_h, in_depth, out_depth}); in Conv2D()
1128 int in_depth, int filter_w, in Conv2DWithBias() argument
1131 Conv2D(batch, height, width, in_depth, filter_w, filter_h, out_depth); in Conv2DWithBias()
1152 int width, int in_depth, in Conv2DWithBiasAndRelu() argument
1157 batch, height, width, in_depth, filter_w, filter_h, out_depth); in Conv2DWithBiasAndRelu()
1174 int width, int in_depth, in Conv2DWithBatchNorm() argument
1178 Conv2D(batch, height, width, in_depth, filter_w, filter_h, out_depth); in Conv2DWithBatchNorm()
1210 int batch, int height, int width, int in_depth, int filter_w, int filter_h, in Conv2DWithBatchNormAndRelu() argument
[all …]
Dconv_ops_using_gemm.cc479 const int64 in_depth = GetTensorDim(input, data_format_, 'C'); in Compute() local
480 OP_REQUIRES(context, in_depth == filter.dim_size(2), in Compute()
482 "input and filter must have the same depth: ", in_depth, in Compute()
535 VLOG(2) << "Conv2D: in_depth = " << in_depth in Compute()
550 in_depth, filter.flat<T>().data(), filter_rows, filter_cols, in Compute()
/external/tensorflow/tensorflow/python/kernel_tests/
Dconv_ops_3d_test.py368 self, batch, input_shape, filter_shape, in_depth, out_depth, stride, argument
374 input_shape = [batch, input_planes, input_rows, input_cols, in_depth]
376 filter_planes, filter_rows, filter_cols, in_depth, out_depth
471 in_depth=2,
483 in_depth=2,
495 in_depth=2,
507 in_depth=2,
519 in_depth=2,
531 in_depth=2,
543 in_depth=2,
[all …]
Dconv_ops_test.py1639 filter_cols, in_depth, out_depth, stride_rows, argument
1642 input_shape = [batch, input_rows, input_cols, in_depth]
1643 filter_shape = [filter_rows, filter_cols, in_depth, out_depth]
1725 in_depth=2,
1743 in_depth=2,
1761 in_depth=2,
1779 in_depth=2,
1797 in_depth=4,
1815 in_depth=2,
1833 in_depth=2,
[all …]
/external/tensorflow/tensorflow/core/kernels/neon/
Dneon_depthwise_conv_op.cc73 const int32 in_depth = input.dim_size(3); in Compute() local
74 OP_REQUIRES(context, in_depth == filter.dim_size(2), in Compute()
76 "input and filter must have the same depth: ", in_depth, in Compute()
86 const int32 out_depth = in_depth * depth_multiplier; in Compute()
111 << ", " << in_depth << "]; Filter: [" << filter_rows << ", " in Compute()
112 << filter_cols << ", " << in_depth << ", " << depth_multiplier in Compute()
/external/tensorflow/tensorflow/core/api_def/base_api/
Dapi_def_Conv3D.pbtxt6 Shape `[batch, in_depth, in_height, in_width, in_channels]`.
34 [batch, in_depth, in_height, in_width, in_channels].
36 [batch, in_channels, in_depth, in_height, in_width].
Dapi_def_MaxPool3D.pbtxt40 [batch, in_depth, in_height, in_width, in_channels].
42 [batch, in_channels, in_depth, in_height, in_width].
Dapi_def_AvgPool3D.pbtxt40 [batch, in_depth, in_height, in_width, in_channels].
42 [batch, in_channels, in_depth, in_height, in_width].
Dapi_def_MaxPool3DGrad.pbtxt46 [batch, in_depth, in_height, in_width, in_channels].
48 [batch, in_channels, in_depth, in_height, in_width].
Dapi_def_AvgPool3DGrad.pbtxt46 [batch, in_depth, in_height, in_width, in_channels].
48 [batch, in_channels, in_depth, in_height, in_width].
Dapi_def_MaxPool3DGradGrad.pbtxt52 [batch, in_depth, in_height, in_width, in_channels].
54 [batch, in_channels, in_depth, in_height, in_width].
/external/tensorflow/tensorflow/core/grappler/costs/
Dutils_test.cc72 int in_depth = 3; in TEST() local
80 CreateConstOp("input", {batch, rows, cols, in_depth}, input); in TEST()
83 CreateConstOp("filter", {filter_rows, filter_cols, in_depth, out_depth}, in TEST()
92 std::vector<int32>({batch, rows, cols, in_depth}), in TEST()
98 std::vector<int32>({filter_rows, filter_cols, in_depth, out_depth}), in TEST()

12