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Searched refs:filter_rows (Results 1 – 25 of 32) sorted by relevance

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/external/tensorflow/tensorflow/core/util/
Duse_cudnn.cc73 bool IsCudnnSupportedFilterSize(const int32 filter_rows, in IsCudnnSupportedFilterSize() argument
76 return in_depth == out_depth && filter_rows == filter_cols && in IsCudnnSupportedFilterSize()
77 (filter_rows == 1 || filter_rows == 3 || filter_rows == 5 || in IsCudnnSupportedFilterSize()
78 filter_rows == 7); in IsCudnnSupportedFilterSize()
Duse_cudnn.h35 bool IsCudnnSupportedFilterSize(const int32 filter_rows,
/external/tensorflow/tensorflow/core/kernels/
Ddilation_ops_gpu.cu.cc41 int depth, int filter_rows, int filter_cols, int output_rows, in DilationKernel() argument
55 for (int h = 0; h < filter_rows; ++h) { in DilationKernel()
80 int batch, int input_rows, int input_cols, int depth, int filter_rows, in DilationBackpropInputKernel() argument
100 for (int h = 0; h < filter_rows; ++h) { in DilationBackpropInputKernel()
130 int batch, int input_rows, int input_cols, int depth, int filter_rows, in DilationBackpropFilterKernel() argument
150 for (int h = 0; h < filter_rows; ++h) { in DilationBackpropFilterKernel()
190 const int filter_rows = filter.dimension(0); in operator ()() local
202 batch, input_rows, input_cols, depth, filter_rows, filter_cols, in operator ()()
221 const int filter_rows = filter.dimension(0); in operator ()() local
244 input_cols, depth, filter_rows, filter_cols, output_rows, output_cols, in operator ()()
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Ddepthwise_conv_grad_op.cc87 const int64 filter_rows = filter_shape.dim_size(0); \
129 input_rows, filter_rows, stride_, padding_, \
149 args.filter_rows = filter_rows; \
160 << "]; Filter: [" << filter_rows << ", " << filter_cols << ", " \
200 const int64 filter_rows = args.filter_rows; in CopyOutputBackpropRegion() local
209 static_cast<int64>(0), (in_r - filter_rows + pad_rows + stride) / stride); in CopyOutputBackpropRegion()
216 const int64 filter_spatial_size = args.filter_rows * args.filter_cols; in CopyOutputBackpropRegion()
217 if ((out_r_end - out_r_start + 1) < args.filter_rows || in CopyOutputBackpropRegion()
299 const int64 filter_spatial_size = args.filter_rows * args.filter_cols; in ComputeBackpropInput()
401 const int64 filter_spatial_size = args.filter_rows * args.filter_cols; in operator ()()
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Ddeep_conv2d.h73 int filter_rows; member
88 filter_rows(0), in Conv2DArgs()
100 bool CanUseDeepConv2D(int stride_rows, int stride_cols, int filter_rows,
Ddepthwise_conv_op.cc95 const int64 filter_spatial_size = args.filter_rows * args.filter_cols; in Run()
176 const int64 filter_spatial_size = args.filter_rows * args.filter_cols; in operator ()()
199 const int64 filter_spatial_size = args.filter_rows * args.filter_cols; in operator ()()
361 const int32 filter_rows = filter.dim_size(0); in Compute() local
383 input_rows, filter_rows, stride_, padding_, in Compute()
411 IsCudnnSupportedFilterSize(/*filter_rows=*/filter_rows, in Compute()
418 << ", " << in_depth << "]; Filter: [" << filter_rows << ", " in Compute()
436 TensorShape{filter_rows, filter_cols, filter_in_depth, out_depth}; in Compute()
456 args.filter_rows = filter_rows; in Compute()
Ddilation_ops.cc92 const int filter_rows = filter.dim_size(0); in ParseSizes() local
102 filter_rows + (filter_rows - 1) * (*rate_rows - 1); in ParseSizes()
173 const int filter_rows = filter.dimension(0); in operator ()() local
188 for (int h = 0; h < filter_rows; ++h) { in operator ()()
282 const int filter_rows = filter.dimension(0); in operator ()() local
305 for (int h = 0; h < filter_rows; ++h) { in operator ()()
402 const int filter_rows = filter.dimension(0); in operator ()() local
425 for (int h = 0; h < filter_rows; ++h) { in operator ()()
Ddepthwise_conv_op.h31 int filter_rows; member
48 filter_rows(0), in DepthwiseArgs()
145 const int64 filter_spatial_size = args.filter_rows * args.filter_cols;
218 for (int64 f_r = 0; f_r < args.filter_rows; ++f_r) {
272 for (int64 f_r = 0; f_r < args.filter_rows; ++f_r) {
305 for (int64 f_r = 0; f_r < args.filter_rows; ++f_r) {
Ddepthwise_conv_op_gpu.h59 args.pad_rows < args.filter_rows && args.pad_cols >= 0 &&
61 args.filter_rows * args.filter_cols <=
72 args.pad_rows < args.filter_rows && args.pad_cols >= 0 &&
74 args.filter_rows * args.filter_cols <= args.in_cols * block_height;
95 kKnownFilterHeight < 0 ? args.filter_rows : kKnownFilterHeight;
206 kKnownFilterHeight < 0 ? args.filter_rows : kKnownFilterHeight;
342 kKnownFilterHeight < 0 ? args.filter_rows : kKnownFilterHeight;
497 kKnownFilterHeight < 0 ? args.filter_rows : kKnownFilterHeight;
657 const int tile_height = block_height * 2 + args.filter_rows - 1;
659 const int filter_pixels = args.filter_rows * args.filter_cols;
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Dconv_ops.cc223 int input_cols, int in_depth, int filter_rows, in Run() argument
239 int input_cols, int in_depth, int filter_rows, in Run() argument
246 !CanUseDeepConv2D(stride_rows, stride_cols, filter_rows, filter_cols, in Run()
256 args.filter_rows = filter_rows; in Run()
280 int input_cols, int in_depth, int filter_rows, in Run() argument
294 int input_cols, int in_depth, int filter_rows, in Run() argument
308 desc.R = filter_rows; in Run()
442 const int filter_rows = static_cast<int>(filter.dim_size(0)); in ComputeConv2DDimension() local
478 input_rows, filter_rows, dilation_rows, stride_rows, params.padding, in ComputeConv2DDimension()
488 dimensions->filter_rows = filter_rows; in ComputeConv2DDimension()
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Deigen_benchmark.h126 Eigen::Index filter_rows = filter_dims[0]; in SpatialConvolutionBackwardKernel() local
145 input, output_backward, filter_rows, filter_cols); in SpatialConvolutionBackwardKernel()
261 Eigen::Index filter_rows = filter_dims[0]; in CuboidConvolutionBackwardKernel() local
281 input, output_backward, filter_planes, filter_rows, filter_cols); in CuboidConvolutionBackwardKernel()
Dconv_ops_using_gemm.cc498 const int filter_rows = static_cast<int>(filter.dim_size(0)); in Compute() local
524 GetWindowedOutputSize(input_rows, filter_rows, stride_rows, in Compute()
541 << ", filter_rows = " << filter_rows in Compute()
552 in_depth, filter.flat<T>().data(), filter_rows, filter_cols, in Compute()
Ddeep_conv2d.cc74 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()
97 bool CanUseDeepConv2D(int stride_rows, int stride_cols, int filter_rows, in CanUseDeepConv2D() argument
102 if (stride_rows > 1 || stride_cols > 1 || filter_rows != 3 || in CanUseDeepConv2D()
119 filter_rows, filter_cols, in_depth, out_depth, out_rows, out_cols); in CanUseDeepConv2D()
297 std::max(int64{0}, args.filter_rows - base_filter_rows); in operator ()()
334 if (f_r >= args.filter_rows) continue; in operator ()()
483 const int64 shard_cost = args.filter_rows * args.filter_cols * in_depth * in operator ()()
957 std::max(int64{0}, args.filter_rows - base_filter_rows); in operator ()()
Dconv_ops_3d.cc229 const int64 filter_rows = filter.dim_size(1); in launch() local
243 0, (out_rows - 1) * strides[1] + filter_rows - in_rows); in launch()
251 if (!is_grouped_convolution && filter_planes == 1 && filter_rows == 1 && in launch()
279 filter_rows == in_rows && filter_cols == in_cols && in launch()
413 .set_spatial_dim(DimIndex::Y, filter_rows) in launch()
487 {{filter_planes, filter_rows, filter_cols}}, in launch()
Dnn_ops_test.cc108 int filter_rows, int filter_cols, CONV_OP op, in BM_ConvFloat() argument
129 TF_CHECK_OK(GetWindowedOutputSize(rows, filter_rows, stride, padding, in BM_ConvFloat()
140 static_cast<int64>(filter_rows * filter_cols) * in BM_ConvFloat()
147 static_cast<int64>(filter_rows * filter_cols) * in BM_ConvFloat()
153 SetConstOp("filter", {filter_rows, filter_cols, in_depth, out_depth}, in BM_ConvFloat()
162 std::vector<int32>({filter_rows, filter_cols, in_depth, out_depth}), in BM_ConvFloat()
517 int filter_rows, int filter_cols, in BM_ConvFloatDepthwise() argument
538 TF_CHECK_OK(GetWindowedOutputSize(rows, filter_rows, stride, padding, in BM_ConvFloatDepthwise()
550 static_cast<int64>(filter_rows * filter_cols) * in BM_ConvFloatDepthwise()
561 static_cast<int64>(filter_rows * filter_cols) * in BM_ConvFloatDepthwise()
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Dconv_ops.h87 int filter_rows;
Dquantized_conv_ops.cc525 const int64 filter_rows = filter.dim_size(0); in Compute() local
542 GetWindowedOutputSize(input_rows, filter_rows, stride, in Compute()
563 filter_rows, filter_cols, out_depth, offset_filter, stride, in Compute()
Dconv_ops_fused_image_transform.cc796 const int filter_rows = static_cast<int>(filter.dim_size(0)); in Compute() local
824 GetWindowedOutputSize(padded_rows, filter_rows, stride_rows, in Compute()
845 << ", filter_rows = " << filter_rows in Compute()
856 padded_cols, in_depth, filter.flat<T>().data(), filter_rows, in Compute()
Deigen_spatial_convolutions_test.cc1384 int filter_count, int filter_cols, int filter_rows, in PackRhsHelper() argument
1486 filter_rows, filter_cols, // in PackRhsHelper()
1504 numext::ceil((input_rows_eff - filter_rows + 1.f) / row_strides); in PackRhsHelper()
1512 reshape_dims[0] = input_depth * filter_rows * filter_cols; // patch size in PackRhsHelper()
1582 int filter_count, int filter_cols, int filter_rows, in PackLhsHelper() argument
1589 eigen_assert(block_cols <= input_depth * filter_rows * filter_cols); in PackLhsHelper()
1595 Dimensions filter_dims(filter_count, filter_rows, filter_cols, input_depth); in PackLhsHelper()
1651 reshape_dims[1] = input_depth * filter_rows * filter_cols; in PackLhsHelper()
1710 const Index max_col = filter_rows * filter_cols * input_depth; in PackLhsHelper()
1739 "filter: count=", filter_count, " dims=", filter_rows, "x", filter_cols, in PackLhsHelper()
/external/tensorflow/tensorflow/lite/kernels/internal/optimized/integer_ops/
Dfully_connected.h55 const int filter_rows = filter_shape.Dims(filter_dim_count - 2); in FullyConnected() local
57 TFLITE_DCHECK_EQ(filter_shape.FlatSize(), filter_rows * filter_cols); in FullyConnected()
59 TFLITE_DCHECK_EQ(output_rows, filter_rows); in FullyConnected()
65 lhs_params.rows = filter_rows; in FullyConnected()
75 dst_params.rows = filter_rows; in FullyConnected()
Dconv.h85 const int filter_rows = filter_shape.Dims(0); in ConvPerChannel() local
92 TFLITE_DCHECK_EQ(output_rows, filter_rows); in ConvPerChannel()
98 lhs_params.rows = filter_rows; in ConvPerChannel()
/external/tensorflow/tensorflow/core/kernels/mkl/
Dmkl_conv_ops.h220 int filter_rows = in GetFilterSizeInMklOrder() local
238 mkldnn_sizes[MKL_GROUP_FILTER_DIM_H] = filter_rows; in GetFilterSizeInMklOrder()
246 mkldnn_sizes[MklDnnDims::Dim_H] = filter_rows; in GetFilterSizeInMklOrder()
260 int filter_rows = in GetFilterSizeInMklOrder() local
275 mkldnn_sizes[MklDnnDims3D::Dim3d_H] = filter_rows; in GetFilterSizeInMklOrder()
349 int filter_planes, filter_rows, filter_cols; variable
351 filter_rows = filter_shape.dim_size(TF_2DFILTER_DIM_H);
355 filter_rows = filter_shape.dim_size(TF_3DFILTER_DIM_H);
413 input_rows, filter_rows, dilation_rows, stride_rows,
426 input_rows, filter_rows, dilation_rows, stride_rows,
/external/tensorflow/tensorflow/python/kernel_tests/
Dconv_ops_test.py858 filter_rows=3,
880 filter_rows=3,
1854 filter_rows, argument
1868 filter_shape = [filter_rows, filter_cols, in_depth // num_groups, out_depth]
1871 output_rows = (input_rows - filter_rows + stride_rows) // stride_rows
1878 output_rows = (input_rows + padding[1][0] + padding[1][1] - filter_rows +
1948 filter_rows=3,
1966 filter_rows=2,
1984 filter_rows=3,
2002 filter_rows=2,
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/external/tensorflow/tensorflow/core/kernels/neon/
Dneon_depthwise_conv_op.cc82 const int32 filter_rows = filter.dim_size(0); in Compute() local
92 GetWindowedOutputSize(input_rows, filter_rows, stride, in Compute()
111 << ", " << in_depth << "]; Filter: [" << filter_rows << ", " in Compute()
/external/tensorflow/tensorflow/core/grappler/costs/
Dutils_test.cc68 int filter_rows = 3; in TEST() local
83 CreateConstOp("filter", {filter_rows, filter_cols, in_depth, out_depth}, in TEST()
98 std::vector<int32>({filter_rows, filter_cols, in_depth, out_depth}), in TEST()

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