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Searched refs:out_rows (Results 1 – 25 of 30) sorted by relevance

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/external/tensorflow/tensorflow/core/kernels/
Ddepthwise_conv_op.cc193 args.out_rows * args.out_cols * args.out_depth; in operator ()()
208 const int64 b = i / args.out_rows; in operator ()()
212 const int64 out_r = i % args.out_rows; in operator ()()
228 const int64 total_shards = args.batch * args.out_rows; in operator ()()
348 int64 out_rows = 0, out_cols = 0, pad_rows = 0, pad_cols = 0; in Compute() local
351 padding_, &out_rows, &pad_rows)); in Compute()
356 ShapeFromFormat(data_format_, batch, out_rows, out_cols, out_depth); in Compute()
381 << "]; Output: [" << batch << ", " << out_rows << ", " << out_cols in Compute()
423 args.out_rows = out_rows; in Compute()
Ddilation_ops.cc68 int64* out_rows, int64* out_cols) { in ParseSizes() argument
108 padding, out_rows, pad_top)); in ParseSizes()
129 int64 out_rows = 0, out_cols = 0; in Compute() local
131 &rate_rows, &rate_cols, &pad_top, &pad_left, &out_rows, in Compute()
138 const std::vector<int64> out_sizes = {batch, out_rows, out_cols, depth}; in Compute()
228 int64 out_rows = 0, out_cols = 0; in Compute() local
230 &rate_rows, &rate_cols, &pad_top, &pad_left, &out_rows, in Compute()
239 out_rows == out_backprop.dim_size(1) && in Compute()
348 int64 out_rows = 0, out_cols = 0; in Compute() local
350 &rate_rows, &rate_cols, &pad_top, &pad_left, &out_rows, in Compute()
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Ddepthwise_conv_grad_op.cc115 int64 out_rows = 0, out_cols = 0, pad_rows = 0, pad_cols = 0; \
118 padding_, &out_rows, &pad_rows)); \
123 context, output_rows == out_rows, \
126 "actual = ", output_rows, ", computed = ", out_rows)); \
143 args.out_rows = out_rows; \
151 << ", output: [" << batch << ", " << out_rows << ", " << out_cols \
192 const int64 out_rows = args.out_rows; in CopyOutputBackpropRegion() local
198 const int64 out_r_end = std::min(out_rows - 1, (in_r + pad_rows) / stride); in CopyOutputBackpropRegion()
412 args.out_rows * args.out_cols * args.out_depth; in operator ()()
478 std::min(args.out_rows - 1, (in_r + args.pad_rows) / stride); in DepthwiseConvBackpropInputReference()
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Dconv_ops.cc157 int filter_cols, int pad_rows, int pad_cols, int out_rows, in Run() argument
173 int filter_cols, int pad_rows, int pad_cols, int out_rows, in Run() argument
180 in_depth, out_depth, out_rows, out_cols)) { in Run()
193 args.out_rows = out_rows; in Run()
214 int filter_cols, int pad_rows, int pad_cols, int out_rows, in Run() argument
228 int filter_cols, int pad_rows, int pad_cols, int out_rows, in Run() argument
409 int64 out_rows = 0, out_cols = 0; in ComputeConv2DDimension() local
412 &out_rows, &pad_rows_before, &pad_rows_after)); in ComputeConv2DDimension()
429 dimensions->out_rows = out_rows; in ComputeConv2DDimension()
466 params_.data_format, dimensions.batch, dimensions.out_rows, in Compute()
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Ddeep_conv2d.h79 int out_rows; member
92 out_rows(0), in Conv2DArgs()
102 int out_rows, int out_cols);
Dextract_image_patches_op.cc86 int64 out_rows = 0, out_cols = 0; in Compute() local
90 padding_, &out_rows, &pad_rows)); in Compute()
95 const std::vector<int64> out_sizes = {batch, out_rows, out_cols, in Compute()
Dextract_volume_patches_op.cc109 int64 out_planes = 0, out_rows = 0, out_cols = 0; in Compute() local
116 padding_, &out_rows, &pad_rows)); in Compute()
122 batch, out_planes, out_rows, out_cols, in Compute()
Dmkl_conv_ops.h390 int64 out_rows = 0, out_cols = 0, out_planes = 0; variable
408 padding_type, &out_rows, &pad_top, &pad_bottom));
419 padding_, &out_rows, &pad_top, &pad_bottom));
447 ? ShapeFromFormat(data_format_, out_batch, out_rows, out_cols,
450 {{out_planes, out_rows, out_cols}}, out_depth);
458 mkldnn_sizes[MklDnnDims::Dim_H] = static_cast<int>(out_rows);
466 mkldnn_sizes[MklDnnDims3D::Dim3d_H] = static_cast<int>(out_rows);
Ddeep_conv2d.cc50 int out_depth, int out_rows, int out_cols) { in GetDeepConvCost() argument
65 const int64 row_tiles = (out_rows + out_tile_rows - 1) / out_tile_rows; in GetDeepConvCost()
75 int out_depth, int out_rows, int out_cols) { in GetDirectConvCost() argument
76 return filter_rows * filter_cols * in_depth * out_depth * out_rows * out_cols; in GetDirectConvCost()
99 int out_rows, int out_cols) { 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()
799 if (out_r_start < 0 || out_r_start >= args.out_rows || in operator ()()
809 if (out_r >= args.out_rows) continue; in operator ()()
1011 (args.out_rows + out_tile_rows - 1) / out_tile_rows + in operator ()()
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Dpooling_ops_3d_sycl.h35 const int out_rows, const int out_cols, in SYCL3DPoolParams()
51 out_rows_(out_rows), in SYCL3DPoolParams()
126 const int out_rows, const int out_cols, in MaxPool3DSYCL() argument
132 : p_(depth, batch, in_planes, in_rows, in_cols, out_planes, out_rows, in MaxPool3DSYCL()
187 const int out_rows = GetTensorDim(*output, data_format, '1');
208 out_planes, out_rows, out_cols, window, stride,
532 const int out_rows, const int out_cols,
538 : p_(depth, batch, in_planes, in_rows, in_cols, out_planes, out_rows,
593 const int out_rows = GetTensorDim(*output, data_format, '1');
614 out_planes, out_rows, out_cols, window, stride,
Dfractional_avg_pool_op.cc245 const int64 out_rows = out_backprop.dim_size(1); in Compute() local
277 out_cols * out_rows * out_batch); in Compute()
283 for (int64 r = 0; r < out_rows; ++r) { in Compute()
296 const int64 out_index = (b * out_rows + r) * out_cols + c; in Compute()
Dquantized_conv_ops.cc540 int64 out_rows = 0, out_cols = 0, pad_rows = 0, pad_cols = 0; in Compute() local
543 padding_, &out_rows, &pad_rows)); in Compute()
548 CHECK_GT(out_rows, 0); in Compute()
551 TensorShape out_shape({batch, out_rows, out_cols, out_depth}); in Compute()
564 padding_, output->flat<T3>().data(), out_rows, out_cols, in Compute()
Dconv_ops_using_gemm.cc520 int64 out_rows = 0, out_cols = 0, pad_rows = 0, pad_cols = 0; in Compute() local
523 padding_, &out_rows, &pad_rows)); in Compute()
528 ShapeFromFormat(data_format_, batch, out_rows, out_cols, out_depth); in Compute()
552 output->flat<T>().data(), out_rows, out_cols); in Compute()
Ddepthwise_conv_op_gpu.h47 args.in_cols <= 32 && args.in_rows == args.out_rows && in CanLaunchDepthwiseConv2dGPUSmall()
60 args.in_cols <= 32 && args.in_rows == args.out_rows && in CanLaunchDepthwiseConv2dBackpropFilterGPUSmall()
90 const int out_height = args.out_rows; in DepthwiseConv2dGPUKernelNHWC()
330 const int out_height = args.out_rows; in DepthwiseConv2dGPUKernelNCHW()
637 const int num_outputs = args.out_rows * args.out_cols * block_count; in LaunchDepthwiseConv2dGPUSmall()
760 args.batch * args.out_rows * args.out_cols * args.out_depth;
832 const int out_height = args.out_rows;
902 const int out_height = args.out_rows;
1052 const int out_height = args.out_rows;
1336 const int out_height = args.out_rows;
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Dconv_ops_3d.cc226 int64 out_rows = GetTensorDim(*output, data_format, '1'); in launch() local
233 0, (out_rows - 1) * strides[1] + filter_rows - in_rows); in launch()
361 .set_spatial_dim(DimIndex::Y, out_rows) in launch()
400 {{out_planes, out_rows, out_cols}}, out_depth), in launch()
Ddepthwise_conv_op.h39 int out_rows; member
54 out_rows(0), in DepthwiseArgs()
Dconv_ops_fused_impl.h593 const int64 out_rows = GetTensorDim(*output, params.data_format, 'H');
616 0, (out_rows - 1) * dimensions.stride_rows +
705 .set_height(out_rows)
728 ShapeFromFormat(FORMAT_NCHW, out_batch, out_rows,
905 params_.data_format, dimensions.batch, dimensions.out_rows,
Dconv_ops_fused_image_transform.cc825 int64 out_rows = 0, out_cols = 0, pad_rows = 0, pad_cols = 0; in Compute() local
828 padding_, &out_rows, &pad_rows)); in Compute()
833 ShapeFromFormat(FORMAT_NHWC, batch, out_rows, out_cols, out_depth); in Compute()
861 output->flat<T>().data(), out_rows, out_cols, st, top_padding, in Compute()
Dconv_ops.h98 int64 out_rows;
/external/tensorflow/tensorflow/python/framework/
Dcommon_shapes.py225 out_rows, out_cols = get2d_conv_output_size(in_rows, in_cols, filter_rows,
229 output_shape = [batch_size, out_rows, out_cols, depth_out]
286 out_rows, out_cols = get2d_conv_output_size(in_rows, in_cols, filter_rows,
290 return [tensor_shape.TensorShape([batch_size, out_rows, out_cols, depth_out])]
349 out_rows, out_cols = get2d_conv_output_size(in_rows, in_cols, filter_rows,
353 return [tensor_shape.TensorShape([batch_size, out_rows, out_cols, depth_out])]
412 out_rows, out_cols = get2d_conv_output_size(in_rows, in_cols, ksize_r,
416 output_shape = [batch_size, out_rows, out_cols, depth]
484 out_rows, out_cols = get2d_conv_output_size(in_rows, in_cols, ksize_r,
487 output_shape = [batch_size, out_rows, out_cols, depth]
/external/tensorflow/tensorflow/core/kernels/neon/
Dneon_depthwise_conv_op.cc90 int64 out_rows = 0, out_cols = 0, pad_rows = 0, pad_cols = 0; in Compute() local
93 padding_, &out_rows, &pad_rows)); in Compute()
97 TensorShape out_shape({batch, out_rows, out_cols, out_depth}); in Compute()
115 << out_rows << ", " << out_cols << ", " << out_depth << "]"; in Compute()
/external/tensorflow/tensorflow/core/api_def/base_api/
Dapi_def_ExtractVolumePatches.pbtxt12 5-D Tensor with shape `[batch, out_planes, out_rows, out_cols,
15 in the "depth" dimension. Note `out_planes`, `out_rows` and `out_cols`
Dapi_def_ExtractImagePatches.pbtxt12 4-D Tensor with shape `[batch, out_rows, out_cols, ksize_rows *
15 `out_rows` and `out_cols` are the dimensions of the output patches.
Dapi_def_Conv3DBackpropInput.pbtxt19 Backprop signal of shape `[batch, out_depth, out_rows, out_cols,
Dapi_def_Conv3DBackpropFilter.pbtxt19 Backprop signal of shape `[batch, out_depth, out_rows, out_cols,

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