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

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/external/tensorflow/tensorflow/core/kernels/
Ddepthwise_conv_grad_op.cc115 int64 out_rows = 0, out_cols = 0, pad_rows = 0, pad_cols = 0; \
121 padding_, &out_cols, &pad_cols)); \
128 context, output_cols == out_cols, \
131 "actual = ", output_cols, ", computed = ", out_cols)); \
144 args.out_cols = out_cols; \
151 << ", output: [" << batch << ", " << out_rows << ", " << out_cols \
193 const int64 out_cols = args.out_cols; in CopyOutputBackpropRegion() local
201 const int64 out_c_end = std::min(out_cols - 1, (in_c + pad_cols) / stride); in CopyOutputBackpropRegion()
224 out_backprop + (out_r * args.out_cols + out_c) * args.out_depth; in CopyOutputBackpropRegion()
412 args.out_rows * args.out_cols * args.out_depth; in operator ()()
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Ddepthwise_conv_op.cc94 const int64 base_output_index = (out_r * args.out_cols + out_c) * out_depth; in Run()
193 args.out_rows * args.out_cols * args.out_depth; in operator ()()
214 for (int64 out_c = 0; out_c < args.out_cols; ++out_c) { in operator ()()
236 const int64 shard_cost = kCostMultiplier * args.out_cols * args.out_depth; in operator ()()
348 int64 out_rows = 0, out_cols = 0, pad_rows = 0, pad_cols = 0; in Compute() local
354 padding_, &out_cols, &pad_cols)); in Compute()
356 ShapeFromFormat(data_format_, batch, out_rows, out_cols, out_depth); in Compute()
381 << "]; Output: [" << batch << ", " << out_rows << ", " << out_cols in Compute()
424 args.out_cols = out_cols; in Compute()
Ddilation_ops.cc68 int64* out_rows, int64* out_cols) { in ParseSizes() argument
111 padding, out_cols, pad_left)); in ParseSizes()
129 int64 out_rows = 0, out_cols = 0; in Compute() local
132 &out_cols); 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
231 &out_cols); in Compute()
240 out_cols == out_backprop.dim_size(2) && in Compute()
348 int64 out_rows = 0, out_cols = 0; in Compute() local
351 &out_cols); in Compute()
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Ddeep_conv2d.h80 int out_cols; member
93 out_cols(0), in Conv2DArgs()
102 int out_rows, int out_cols);
Dconv_ops.cc174 int out_cols, int out_depth, int dilation_rows, in Run() argument
180 in_depth, out_depth, out_rows, out_cols)) { in Run()
194 args.out_cols = out_cols; in Run()
215 int out_cols, int out_depth, int stride_rows, int stride_cols, in Run() argument
229 int out_cols, int out_depth, int dilation_rows, in Run() argument
409 int64 out_rows = 0, out_cols = 0; in ComputeConv2DDimension() local
415 &out_cols, &pad_cols_before, &pad_cols_after)); in ComputeConv2DDimension()
430 dimensions->out_cols = out_cols; in ComputeConv2DDimension()
467 dimensions.out_cols, dimensions.out_depth); in Compute()
498 dimensions.out_cols, dimensions.out_depth, dimensions.dilation_rows, in Compute()
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Dextract_image_patches_op.cc86 int64 out_rows = 0, out_cols = 0; in Compute() local
93 padding_, &out_cols, &pad_cols)); 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
119 padding_, &out_cols, &pad_cols)); 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
412 padding_type, &out_cols, &pad_left, &pad_right));
422 padding_, &out_cols, &pad_left, &pad_right));
447 ? ShapeFromFormat(data_format_, out_batch, out_rows, out_cols,
450 {{out_planes, out_rows, out_cols}}, out_depth);
459 mkldnn_sizes[MklDnnDims::Dim_W] = static_cast<int>(out_cols);
467 mkldnn_sizes[MklDnnDims3D::Dim3d_W] = static_cast<int>(out_cols);
Ddeep_conv2d.cc50 int out_depth, int out_rows, int out_cols) { in GetDeepConvCost() argument
66 const int64 col_tiles = (out_cols + out_tile_cols - 1) / out_tile_cols; 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()
800 out_c_start < 0 || out_c_start >= args.out_cols) { in operator ()()
813 if (out_c >= args.out_cols) continue; in operator ()()
823 args.out_depth * (out_r * args.out_cols + out_c) + od; in operator ()()
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Dpooling_ops_3d_sycl.h35 const int out_rows, const int out_cols, in SYCL3DPoolParams()
52 out_cols_(out_cols), in SYCL3DPoolParams()
126 const int out_rows, const int out_cols, in MaxPool3DSYCL() argument
133 out_cols, window, stride, padding), in MaxPool3DSYCL()
188 const int out_cols = GetTensorDim(*output, data_format, '2');
208 out_planes, out_rows, out_cols, window, stride,
532 const int out_rows, const int out_cols,
539 out_cols, window, stride, padding),
594 const int out_cols = GetTensorDim(*output, data_format, '2');
614 out_planes, out_rows, out_cols, window, stride,
Dfractional_avg_pool_op.cc246 const int64 out_cols = out_backprop.dim_size(2); in Compute() local
277 out_cols * out_rows * out_batch); in Compute()
288 for (int64 c = 0; c < out_cols; ++c) { 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
546 padding_, &out_cols, &pad_cols)); in Compute()
549 CHECK_GT(out_cols, 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
526 padding_, &out_cols, &pad_cols)); 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.h48 args.in_cols == args.out_cols && args.pad_rows >= 0 && in CanLaunchDepthwiseConv2dGPUSmall()
61 args.in_cols == args.out_cols && args.pad_rows >= 0 && in CanLaunchDepthwiseConv2dBackpropFilterGPUSmall()
91 const int out_width = args.out_cols; in DepthwiseConv2dGPUKernelNHWC()
331 const int out_width = args.out_cols; 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;
833 const int out_width = args.out_cols;
903 const int out_width = args.out_cols;
1053 const int out_width = args.out_cols;
1337 const int out_width = args.out_cols;
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Dconv_ops_3d.cc227 int64 out_cols = GetTensorDim(*output, data_format, '2'); in launch() local
235 0, (out_cols - 1) * strides[2] + filter_cols - in_cols); in launch()
360 .set_spatial_dim(DimIndex::X, out_cols) in launch()
400 {{out_planes, out_rows, out_cols}}, out_depth), in launch()
Ddepthwise_conv_op.h40 int out_cols; member
55 out_cols(0), in DepthwiseArgs()
Dconv_ops_fused_impl.h594 const int64 out_cols = GetTensorDim(*output, params.data_format, 'W');
619 0, (out_cols - 1) * dimensions.stride_cols +
706 .set_width(out_cols)
729 out_cols, out_depths),
906 dimensions.out_cols, dimensions.out_depth);
Dconv_ops_fused_image_transform.cc825 int64 out_rows = 0, out_cols = 0, pad_rows = 0, pad_cols = 0; in Compute() local
831 padding_, &out_cols, &pad_cols)); 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.h99 int64 out_cols;
/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
96 padding_, &out_cols, &pad_cols)); 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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