| /external/tensorflow/tensorflow/python/compiler/tensorrt/test/ |
| D | conv2d_test.py | 70 kernel_sizes, argument
74 for kernel_size in kernel_sizes:
93 kernel_sizes=[(3, 3), (3, 2)],
128 kernel_sizes=[(3, 3), (3, 2)],
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| /external/tensorflow/tensorflow/compiler/xla/service/ |
| D | convolution_group_converter.cc | 255 std::vector<int64> kernel_sizes(filter->shape().dimensions().begin(), in HandleBatchGroupCount() local
257 kernel_sizes[kernel_output_feature_dimension] /= batch_group_count; in HandleBatchGroupCount()
258 kernel_sizes.insert(kernel_sizes.begin() + kernel_output_feature_dimension, in HandleBatchGroupCount()
260 filter = MakeReshapeHlo(kernel_sizes, filter).ValueOrDie(); in HandleBatchGroupCount()
603 std::vector<int64> kernel_sizes(filter->shape().dimensions().begin(), in HandleConvolution() local
607 kernel_sizes[kernel_output_feature_dimension] /= group_count; in HandleConvolution()
608 kernel_sizes.insert(kernel_sizes.begin() + kernel_output_feature_dimension, in HandleConvolution()
610 filter = MakeReshapeHlo(kernel_sizes, filter).ValueOrDie(); in HandleConvolution()
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| D | space_to_batch_converter.cc | 2505 std::vector<int64> kernel_sizes(kernel_new->shape().dimensions().begin(), in PropagateOnBackpropFilterConv() local
2508 kernel_sizes.push_back(1); in PropagateOnBackpropFilterConv()
2509 TF_ASSIGN_OR_RETURN(kernel_new, MakeReshapeHlo(kernel_sizes, kernel_new)); in PropagateOnBackpropFilterConv()
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| /external/tensorflow/tensorflow/core/framework/ |
| D | common_shape_fns.cc | 1079 std::vector<int32> kernel_sizes; in AvgPoolShape() local
1080 TF_RETURN_IF_ERROR(c->GetAttr("ksize", &kernel_sizes)); in AvgPoolShape()
1081 if (kernel_sizes.size() != 4) { in AvgPoolShape()
1084 kernel_sizes.size()); in AvgPoolShape()
1089 int32 kernel_rows = GetTensorDim(kernel_sizes, data_format, 'H'); in AvgPoolShape()
1090 int32 kernel_cols = GetTensorDim(kernel_sizes, data_format, 'W'); in AvgPoolShape()
1520 std::vector<int32> kernel_sizes; in MaxPoolShapeImpl() local
1521 TF_RETURN_IF_ERROR(c->GetAttr("ksize", &kernel_sizes)); in MaxPoolShapeImpl()
1522 if (kernel_sizes.size() != 4) { in MaxPoolShapeImpl()
1525 kernel_sizes.size()); in MaxPoolShapeImpl()
[all …]
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| /external/eigen/bench/tensors/ |
| D | tensor_benchmarks.h | 422 Eigen::array<TensorIndex, 2> kernel_sizes; in convolution() local
423 kernel_sizes[0] = kernel_x; in convolution()
424 kernel_sizes[1] = kernel_y; in convolution()
425 TensorMap<Tensor<T, 2>, Eigen::Aligned> B(b_, kernel_sizes); in convolution()
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| /external/tensorflow/tensorflow/python/kernel_tests/ |
| D | atrous_convolution_test.py | 215 kernel_sizes = [1, 3] if padding == "SAME" else range(1, 3)
216 for kernel in kernel_sizes:
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| /external/tensorflow/tensorflow/python/keras/ |
| D | backend_test.py | 935 kernel_sizes = (kernel_size,) * dim
943 np.prod(kernel_sizes) * channels_in, filters)
947 output_shape + (channels_in, np.prod(kernel_sizes), filters))
952 conv_cf = backend.local_conv(inputs_cf, kernel_cf, kernel_sizes,
965 conv_cl = backend.local_conv(inputs_cl, kernel_cl, kernel_sizes,
979 def test_local_conv_1d_and_2d(self, input_shape, kernel_sizes, strides, argument
988 (np.prod(output_shape), np.prod(kernel_sizes) *
992 local_conv = backend.local_conv(inputs, kernel, kernel_sizes, strides,
995 local_conv_dim = backend.local_conv1d(inputs, kernel, kernel_sizes,
998 local_conv_dim = backend.local_conv2d(inputs, kernel, kernel_sizes,
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| /external/tensorflow/tensorflow/core/kernels/hexagon/ |
| D | graph_transferer.cc | 659 std::vector<int32> kernel_sizes; in RegisterNodeWithPaddingAndStrides() local
660 TF_CHECK_OK(context->GetAttr(KSIZE_ATTR_NAME, &kernel_sizes)); in RegisterNodeWithPaddingAndStrides()
661 const int ksize_id = RegisterConstantShape(kernel_sizes); in RegisterNodeWithPaddingAndStrides()
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