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/external/tensorflow/tensorflow/python/ops/ragged/
Dragged_where_op_test.py7 # http://www.apache.org/licenses/LICENSE-2.0
34 dict( # shape=[D1, (D2)]
38 dict( # shape=[D1, (D2)]
47 dict( # shape=[D1, (D2)]
54 # Coordinate-retrieval mode
56 dict( # shape=[D1]
59 dict( # shape=[D1, D2]
62 dict( # shape=[D1, (D2)]
66 dict( # shape=[D1, (D2), (D3)]
73 dict( # shape=[D1, (D2), D3]
[all …]
Dragged_boolean_mask_op_test.py7 # http://www.apache.org/licenses/LICENSE-2.0
61 descr='data.shape=[7]; mask.shape=[7]',
66 descr='data.shape=[5, 3]; mask.shape=[5]',
71 descr='data.shape=[5, 3]; mask.shape=[5, 3]',
77 descr='data.shape=[3, 2, 2]; mask.shape=[3]',
82 descr='data.shape=[3, 2, 2]; mask.shape=[3]',
87 descr='data.shape=[3, 2, 2]; mask.shape=[3, 2]',
94 descr='data.shape=[3, 2, 2]; mask.shape=[3, 2, 2]',
100 descr='data.shape=mask.shape=[2, 2, 2, 2]',
112 descr='data.shape=[5, (D2)]; mask.shape=[5, (D2)]',
[all …]
Dragged_reverse_op_test.py7 # http://www.apache.org/licenses/LICENSE-2.0
40 descr='data.shape=[5, (D2)]; axis=[0]',
45 descr='data.shape=[5, (D2)]; axis=[1]',
50 descr='data.shape=[5, (D2), (D3)]; axis=[0, -1]',
52 axis=[0, -1],
55 descr='data.shape=[2, (D2), 2]; axis=[2]',
61 descr='data.shape=[2, (D2), (D3)]; axis=[-1]',
63 axis=[-1],
66 descr='data.shape=[2, (D2), (D3)]; axis=[]',
Dragged_expand_dims_op_test.py7 # http://www.apache.org/licenses/LICENSE-2.0
29 # An example 4-d ragged tensor with shape [3, (D2), (D3), 2], and the
40 4: [[[[[d3] for d3 in d2] for d2 in d1] for d1 in d0] for d0 in EXAMPLE4D]
78 # 4D Ragged Inputs: [3, (D2), (D3), 2]
114 self.assertEqual(expanded.shape.ndims, rt.shape.ndims + 1)
116 self.assertEqual(expanded.shape.as_list(), expected_shape)
Dragged_array_ops.py7 # http://www.apache.org/licenses/LICENSE-2.0
64 mask: A potentially ragged boolean tensor. `mask`'s shape must be a prefix
65 of `data`'s shape. `rank(mask)` must be known statically.
73 * `output.ragged_rank = max(data.ragged_rank, rank(mask) - 1)`.
76 ValueError: if `rank(mask)` is not known statically; or if `mask.shape` is
77 not a prefix of `data.shape`.
108 if mask.shape.ndims is None:
109 raise ValueError('mask.shape.ndims must be known statically.')
110 elif mask.shape.ndims == 0:
113 # If mask is ragged, then recurse with a non-ragged mask.
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Dragged_map_fn_op_test.py7 # http://www.apache.org/licenses/LICENSE-2.0
41 # transformation that returns with shape:
42 # [d1, (d2)] -> [d1]
57 # [d1, (d2)] -> [d1, 2]
67 # [d1, (d2)] -> [d1, (d2)]
76 # [d1, (d2), d3] -> [d1, (d2), d3]
86 # [d1, (d2)] -> [d1, (d2), (d3)]
94 # [d1, (d2), (d3)] -> [d1, (d2), (d3)]
102 # [d1, (d2), (d3)] -> [d1, (d2)]
110 # [d1, (d2), (d3)] -> [d1, (d3)]
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Dragged_batch_gather_op_test.py7 # http://www.apache.org/licenses/LICENSE-2.0
154 indices=[[1, 2, -1], [], [], [0, 10]],
163 indices=[1, 100, 0, -1],
165 'Yahoo', '!', 'rejected', 'a', '47.5', '-', 'billion', '-',
172 'Yahoo', '!', 'rejected', 'a', '47.5', '-', 'billion',
173 '-', 'dollar', 'takeover', 'offer', 'from', 'Microsoft',
186 indices=[-1, 0, 1000],
193 descr='Test underbound indices of shape [1, (d2)]',
196 '!', 'rejected', 'a', '47.5', '-', 'billion', '-', 'dollar',
199 indices=[[8, -1]],
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Dragged_to_sparse_op_test.py7 # http://www.apache.org/licenses/LICENSE-2.0
119 self.assertEqual(st.indices.shape.as_list(), [7, 2])
120 self.assertEqual(st.values.shape.as_list(), [7])
121 self.assertEqual(st.dense_shape.shape.as_list(), [2])
126 self.assertEqual(st.indices.shape.as_list(), [4, 3])
127 self.assertEqual(st.values.shape.as_list(), [4])
128 self.assertEqual(st.dense_shape.shape.as_list(), [3])
132 self.assertEqual(st.indices.shape.as_list(), [7, 3])
133 self.assertEqual(st.values.shape.as_list(), [7])
134 self.assertEqual(st.dense_shape.shape.as_list(), [3])
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/external/perfetto/infra/perfetto.dev/
Dpnpm-lock.yaml25 fs-extra:
34 node-watch:
43 /@braintree/sanitize-url@6.0.4:
44 …resolution: {integrity: sha512-s3jaWicZd0pkP0jf5ysyHUI/RE7MHos6qlToFcGWXVp+ykHOy77OUMrfbgJ9it2C5bo…
48 …resolution: {integrity: sha512-j+gKExEuLmKwvz3OgROXtrJ2UG2x8Ch2YZUxahh+s1F2HZ+wAceUNLkvy6zKCPVRkU+…
52 …resolution: {integrity: sha512-AZkcAA5vnN/v4PDqKyMR5lx7hZttPDgClv83E//FMNhR2TMcLUhfRUBHCmSl0oi9zMg…
56 …resolution: {integrity: sha512-YyFaikqM5sH0ziFZCN3xDC7zeGaB/d0IUb9CATugHWbd1FRFwWwt4ld4OYMPWu5a3Xe…
60 …resolution: {integrity: sha512-j9ednRT81vYJ9OfVuXG6ERSTdEL1xVsNgqpkxMsbIabzSo3goCjDIveeGv5d03om39M…
64 …resolution: {integrity: sha512-lljVXpqXebpsijW71PZaCYeIcE5on1w5DlQy5WH6GLbFryLUrBD4932W/E2BSpfRJWs…
71 …resolution: {integrity: sha512-Ddb+kVXlXst9d+R9PfTIxh1EdNkgoRe5tOX6t01f1lYWOvJnSPDBlG241QLzcyPdoNT…
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/external/tensorflow/tensorflow/lite/tools/
Dvisualize.py8 # http://www.apache.org/licenses/LICENSE-2.0
29 # pylint: disable=g-import-not-at-top
43 body {font-family: sans-serif; background-color: #fa0;}
44 table {background-color: #eca;}
45 th {background-color: black; color: white;}
47 background-color: ffaa00;
55 border-style: solid;
56 border-color: black;
61 border-radius: 5px;
62 background-color: #fec;
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/external/tensorflow/tensorflow/core/kernels/linalg/
Dbanded_triangular_solve_op.cc7 http://www.apache.org/licenses/LICENSE-2.0
58 t->flat<Scalar>().data() + slice * t->dim_size(1) * t->dim_size(2), in TensorSliceToEigenMatrix()
59 t->dim_size(1), t->dim_size(2)); in TensorSliceToEigenMatrix()
80 // x_i = (b_i - sum a_ij * x_j) / a_ii, where the sum is from in Run()
81 // j = 0 to i - 1. in Run()
84 // then the sum goes from j = i - band_size to i - 1, since the other in Run()
95 (rhs.row(i) - matrix.block(1, i, i, 1).reverse().transpose() * in Run()
100 (rhs.row(i) - in Run()
101 matrix.block(1, i, num_bands - 1, 1).reverse().transpose() * in Run()
102 output.middleRows(i - (num_bands - 1), num_bands - 1)) / in Run()
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Dmatrix_triangular_solve_op_impl.h7 http://www.apache.org/licenses/LICENSE-2.0
75 t->flat<Scalar>().data() + slice * t->dim_size(1) * t->dim_size(2), in TensorSliceToEigenMatrix()
76 t->dim_size(1), t->dim_size(2)); in TensorSliceToEigenMatrix()
122 auto worker_threads = *(context->device()->tensorflow_cpu_worker_threads());
143 OP_REQUIRES_OK(context, context->GetAttr("lower", &lower_));
144 OP_REQUIRES_OK(context, context->GetAttr("adjoint", &adjoint_));
150 const Tensor& in0 = ctx->input(0);
151 const Tensor& in1 = ctx->input(1);
154 if (!ctx->status().ok()) {
158 MatMulBCast bcast(in0.shape().dim_sizes(), in1.shape().dim_sizes());
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/external/aws-sdk-java-v2/services/snowball/src/main/resources/codegen-resources/
Dservice-2.json4 "apiVersion":"2016-06-30",
13 "uid":"snowball-2016-06-30"
22 "input":{"shape":"CancelClusterRequest"}, string
23 "output":{"shape":"CancelClusterResult"}, string
25 {"shape":"KMSRequestFailedException"}, string
26 {"shape":"InvalidJobStateException"}, string
27 {"shape":"InvalidResourceException"} string
37 "input":{"shape":"CancelJobRequest"}, string
38 "output":{"shape":"CancelJobResult"}, string
40 {"shape":"InvalidResourceException"}, string
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/external/tensorflow/tensorflow/lite/kernels/
Dkernel_util.cc7 http://www.apache.org/licenses/LICENSE-2.0
45 if (context->tensors != nullptr) { in GetTensorAtIndex()
46 return &context->tensors[tensor_index]; in GetTensorAtIndex()
48 return context->GetTensor(context, tensor_index); in GetTensorAtIndex()
74 // Same as above but returns -1 for invalid inputs instead of status + logging
84 return -1; in ValidateTensorIndexing()
90 context, index, node->inputs->size, node->inputs->data); in GetMutableInput()
102 context, ValidateTensorIndexingSafe(context, index, node->inputs->size, in GetMutableInputSafe()
103 node->inputs->data, &tensor_index)); in GetMutableInputSafe()
124 return tensor->is_variable ? tensor : nullptr; in GetVariableInput()
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/external/tensorflow/tensorflow/compiler/xla/mlir_hlo/tests/Dialect/gml_st/
Dfusion.mlir1 // RUN: mlir-hlo-opt %s --split-input-file --gml-fusion | FileCheck %s
3 // CHECK-LABEL: @dynamic_broadcast_in_dim_at_tile
4 // CHECK-SAME: %[[ARG:.*]]: tensor<?x?xf32>, %[[SHAPE:.*]]: tensor<3xindex>, %[[TILE:.*]]: !gml_st…
6 %shape : tensor<3xindex>, %tile : !gml_st.tile<3x4x?>)
7 -> tensor<3x4x?xf32> {
8 // CHECK-DAG: %[[C0:.*]] = arith.constant 0
9 // CHECK-DAG: %[[C1:.*]] = arith.constant 1
10 // CHECK-DAG: %[[C2:.*]] = arith.constant 2
11 // CHECK-DAG: %[[S0:.*]] = tensor.extract %[[SHAPE]][%[[C0]]]
12 // CHECK-DAG: %[[S1:.*]] = tensor.extract %[[SHAPE]][%[[C1]]]
[all …]
/external/tensorflow/tensorflow/core/kernels/
Dmatmul_op_impl.h7 http://www.apache.org/licenses/LICENSE-2.0
76 // Parallel batch matmul kernel based on the multi-threaded tensor contraction
81 const Eigen::ThreadPoolDevice d = context->eigen_cpu_device(); in Conjugate()
82 auto z = out->tensor<Scalar, 3>(); in Conjugate()
93 auto Tz = out->tensor<Scalar, 3>(); in Run()
101 const Eigen::ThreadPoolDevice d = context->eigen_cpu_device(); in Run()
136 const Eigen::ThreadPoolDevice d = context->eigen_cpu_device();
142 auto Tz = out->flat_inner_dims<Scalar, 2>();
147 auto Tz = out->tensor<Scalar, 3>();
166 // better on vector-matrix and matrix-vector products.
[all …]
/external/libopus/dnn/training_tf2/
Drdovae.py8 - Redistributions of source code must retain the above copyright
11 - Redistributions in binary form must reproduce the above copyright
52 #return K.clip(p, -self.c, self.c)
62 #x = x - (.25/np.math.pi)*tf.math.sin(2*np.math.pi*x)
63 #x = x - (.25/np.math.pi)*tf.math.sin(2*np.math.pi*x)
64 #x = x - (.25/np.math.pi)*tf.math.sin(2*np.math.pi*x)
68 return soft_quantize(x + (K.random_uniform((128, 16, 80))-.5) )
73 return x + tf.stop_gradient(quantized - x)
78 y = x - d*tf.math.tanh(x/(.1+d))
83 n = y_pred.shape[-1]
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/external/ruy/ruy/
Dkernel_arm32.cc7 http://www.apache.org/licenses/LICENSE-2.0
77 // Float kernel for ARM32 out-of-order cores.
79 // use 16 128-bit NEON registers. This is a "first pass" kernel and not
80 // tuned. It is meant to run on out-of-order CPUs like the Krait 400 or A9.
87 // In ARM32 NEON, there are 16 128-bit "q" registers. These registers are in KernelFloat32Neon()
88 // each composed of two 64-bit "d" registers. The asm kernel below has the in KernelFloat32Neon()
90 // Registers q3 -- q10 are accumulators. During accumulation, in KernelFloat32Neon()
91 // q0 -- q2 (d0 -- d5) are used to load data from LHS and RHS. q0 and q1 in KernelFloat32Neon()
97 // /--------------------------| in KernelFloat32Neon()
99 // \--------------------------/ in KernelFloat32Neon()
[all …]
/external/tensorflow/tensorflow/core/common_runtime/eager/
Dtensor_handle_test.cc7 http://www.apache.org/licenses/LICENSE-2.0
30 EXPECT_TRUE(t.shape().IsSameSize(TensorShape({2, 2}))); in TEST()
31 for (int64_t a = 0; a < t.shape().dim_size(0); a++) { in TEST()
32 for (int64_t b = 0; b < t.shape().dim_size(1); b++) { in TEST()
48 EXPECT_TRUE(async_th->CopyInferenceShape(sync_th).ok()); in TEST()
52 EXPECT_TRUE(sync_th->Shape(&sync_shape).ok()); in TEST()
53 EXPECT_TRUE(async_th->Shape(&async_shape).ok()); in TEST()
56 int num_dims = -1; in TEST()
57 EXPECT_TRUE(async_th->NumDims(&num_dims).ok()); in TEST()
60 int64_t num_elements = -1; in TEST()
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/external/tensorflow/tensorflow/compiler/xla/
Dliteral_util.h7 http://www.apache.org/licenses/LICENSE-2.0
68 // and the compiler) these CreateR[0-2] methods should explicitly specify the
115 // primitive type. For floating-point types, returns -inf.
118 // primitive type. For floating-point types, returns inf.
121 // primitive type. Fail for non-inexact types. For complex types, returns a
124 // Creates a literal of the given shape where each element is `value`.
156 // Creates a linspace-populated literal with the given number of rows and
238 // Creates a literal with a new shape with the given new dimensions using the
246 // Creates a literal with the supplied shape, and uses the provided value
253 const Shape& shape,
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/external/tensorflow/tensorflow/compiler/xla/mlir_hlo/tests/Dialect/mhlo/
Dhlo-legalize-to-linalg.mlir1 // RUN: mlir-hlo-opt %s -hlo-legalize-to-linalg -split-input-file --canonicalize | FILECHECK_OPTS="…
3 // CHECK: #map = affine_map<(d0, d1) -> (d0, d1)>
4 // CHECK-LABEL: func @float_add
6 %rhs: tensor<2x2xf32>) -> tensor<2x2xf32> {
8 // CHECK-SAME: {someattr}
9 // CHECK: ^{{[a-z0-9_]*}}
10 // CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]*]]: f32
11 // CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]*]]: f32
12 // CHECK: %[[RESULT:[a-zA-Z0-9_]*]] = arith.addf %[[ARG0]], %[[ARG1]]
15 : (tensor<2x2xf32>, tensor<2x2xf32>) -> tensor<2x2xf32>
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/external/tensorflow/tensorflow/python/ops/distributions/
Dbijector_impl.py7 # http://www.apache.org/licenses/LICENSE-2.0
68 """Returns key used for caching X=g^{-1}(Y)."""
134 (one to one) function defined on an open subset of `R^n`. Some non-injective
161 first-order partial derivatives of the inverse function."
169 orientation-reversing transformation. It is ok for us to discard the sign
170 of the determinant because we only integrate everywhere-nonnegative
180 - Basic properties:
190 - Computing a log-likelihood:
198 - Transforming a random outcome:
207 - "Exponential"
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/external/tensorflow/tensorflow/compiler/mlir/lite/tests/flatbuffer2mlir/
Dtest_schema.fbs7 // http://www.apache.org/licenses/LICENSE-2.0
61 // f = scale * (q - zero_point)
73 // Specifies the dimension of the Tensor's shape that the scales and
86 // Reference: http://tensor-compiler.org/kjolstad-oopsla17-tensor-compiler.pdf
88 // To encode a conceptual n-dimensional dense tensor with dims (d0, ..., dn-1),
89 // potentially with a k-dimensional block (0 <= k <= n) with dims
90 // (dn, ..., dn+k-1), the format needs to specify:
91 // 1. In what order to traverse these dimensions. For example, to store a 2-D
94 // (d1, d0). If the 2-D matrix has a 2-D inner block, the traversal order
95 // could be (d0, d1, d2, d3).
[all …]
/external/tensorflow/tensorflow/lite/schema/
Dschema_v3a.fbs7 // http://www.apache.org/licenses/LICENSE-2.0
64 // f = scale * (q - zero_point)
76 // Specifies the dimension of the Tensor's shape that the scales and
89 // Reference: http://tensor-compiler.org/kjolstad-oopsla17-tensor-compiler.pdf
91 // To encode a conceptual n-dimensional dense tensor with dims (d0, ..., dn-1),
92 // potentially with a k-dimensional block (0 <= k <= n) with dims
93 // (dn, ..., dn+k-1), the format needs to specify:
94 // 1. In what order to traverse these dimensions. For example, to store a 2-D
97 // (d1, d0). If the 2-D matrix has a 2-D inner block, the traversal order
98 // could be (d0, d1, d2, d3).
[all …]
Dschema.fbs7 // http://www.apache.org/licenses/LICENSE-2.0
74 // f = scale * (q - zero_point)
86 // Specifies the dimension of the Tensor's shape that the scales and
99 // Reference: http://tensor-compiler.org/kjolstad-oopsla17-tensor-compiler.pdf
101 // To encode a conceptual n-dimensional dense tensor with dims (d0, ..., dn-1),
102 // potentially with a k-dimensional block (0 <= k <= n) with dims
103 // (dn, ..., dn+k-1), the format needs to specify:
104 // 1. In what order to traverse these dimensions. For example, to store a 2-D
107 // (d1, d0). If the 2-D matrix has a 2-D inner block, the traversal order
108 // could be (d0, d1, d2, d3).
[all …]

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