| /external/libchrome/mojo/public/interfaces/bindings/tests/ |
| D | test_structs.mojom | 2 // Use of this source code is governed by a BSD-style license that can be
12 array<Rect>? rects;
33 int8 f1;
55 array<string> f23;
56 array<string?> f24;
57 array<string>? f25;
58 array<string?>? f26;
70 int8 f1 = 100;
118 map<int8, int8> f1;
137 map<string, array<string>> f0;
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| D | test_unions.mojom | 2 // Use of this source code is governed by a BSD-style license that can be
19 int8 f_int8;
20 int8 f_int8_other;
36 int8 f_int8;
40 array<int8> f_array_int8;
41 map<string, int8> f_map_int8;
44 array<SmallStruct> f_small_structs;
64 int8 f_int8;
70 array<PodUnion>? pod_union_array;
71 array<PodUnion?>? nullable_pod_union_array;
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| /external/tensorflow/tensorflow/core/api_def/base_api/ |
| D | api_def_LeftShift.pbtxt | 3 summary: "Elementwise computes the bitwise left-shift of `x` and `y`."
14 dtype_list = [tf.int8, tf.int16, tf.int32, tf.int64]
17 lhs = tf.constant([-1, -5, -3, -14], dtype=dtype)
25 # tf.Tensor([ -32 -5 -128 0], shape=(4,), dtype=int8)
26 # tf.Tensor([ -32 -5 -384 -28672], shape=(4,), dtype=int16)
27 # tf.Tensor([ -32 -5 -384 -28672], shape=(4,), dtype=int32)
28 # tf.Tensor([ -32 -5 -384 -28672], shape=(4,), dtype=int64)
30 lhs = np.array([-2, 64, 101, 32], dtype=np.int8)
31 rhs = np.array([-1, -5, -3, -14], dtype=np.int8)
33 # <tf.Tensor: shape=(4,), dtype=int8, numpy=array([ -2, 64, 101, 32], dtype=int8)>
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| D | api_def_RightShift.pbtxt | 3 summary: "Elementwise computes the bitwise right-shift of `x` and `y`."
17 dtype_list = [tf.int8, tf.int16, tf.int32, tf.int64]
20 lhs = tf.constant([-1, -5, -3, -14], dtype=dtype)
28 # tf.Tensor([-1 -5 -1 -1], shape=(4,), dtype=int8)
29 # tf.Tensor([-1 -5 -1 -1], shape=(4,), dtype=int16)
30 # tf.Tensor([-1 -5 -1 -1], shape=(4,), dtype=int32)
31 # tf.Tensor([-1 -5 -1 -1], shape=(4,), dtype=int64)
33 lhs = np.array([-2, 64, 101, 32], dtype=np.int8)
34 rhs = np.array([-1, -5, -3, -14], dtype=np.int8)
36 # <tf.Tensor: shape=(4,), dtype=int8, numpy=array([ -2, 64, 101, 32], dtype=int8)>
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| /external/tensorflow/tensorflow/python/ops/ |
| D | bitwise_ops_test.py | 7 # http://www.apache.org/licenses/LICENSE-2.0
39 dtype_list = [dtypes.int8, dtypes.int16, dtypes.int32, dtypes.int64,
56 dtypes.int8, dtypes.int16, dtypes.int32, dtypes.int64, dtypes.uint8,
59 raw_inputs = [0, 1, -1, 3, -3, 5, -5, 14, -14,
61 2**31 - 1, 2**31, 2**32 - 1, 2**32, -2**32 + 1, -2**32,
62 -2**63 + 1, 2**63 - 1]
68 inputs = np.array(raw_inputs, dtype=dtype.as_numpy_dtype)
77 dtype_list = [dtypes.int8, dtypes.int16, dtypes.int32, dtypes.int64,
97 expected = [dtype.max - x for x in inputs]
102 dtype_list = [np.int8, np.int16, np.int32, np.int64,
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| D | math_ops_test.py | 7 # http://www.apache.org/licenses/LICENSE-2.0
44 x = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32)
57 self.assertAllClose(out_bf16, expected, 1e-3)
60 x = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32)
62 for axis in (0, -2):
65 for axis in (1, -1):
68 for axis in (None, (0, 1), (1, 0), (-1, 0), (0, -1), (-2, 1), (1, -2),
69 (-1, -2), (-2, -1)):
77 x = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32)
78 axis = np.array([[0], [1]])
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| /external/tensorflow/tensorflow/lite/toco/graph_transformations/ |
| D | ensure_uint8_weights_safe_for_fast_int8_kernels.cc | 7 http://www.apache.org/licenses/LICENSE-2.0
29 // TLDR: Some of our 8-bit arithmetic operations require uint8 weight values
40 // --allow_nudging_weights_to_use_fast_gemm_kernel.
44 // The present graph transformation implements both the default fatal-erroring
63 // In the multiply-add operation in (10), we first change the
64 // operands’ type from uint8 to int8 (which can be done by
65 // subtracting 128 from the quantized values and zero-points).
66 // Thus the core multiply-add becomes
68 // int32 += int8 * int8. (B.1)
72 // quantized as int8 values, never take the value −128. Hence,
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| /external/deqp-deps/amber/docs/ |
| D | memory_layout.md | 8 For the purposes of this document, all scalars are stored in little-endian. The
18 |------|-------|
19 | int8 | <kbd>[b]</kbd> |
35 |------|-------|
37 | vec3\<float> | <kbd>[bbbb][bbbb][bbbb][----]</kbd> |
39 | vec2\<int8> | <kbd>[bb]</kbd> |
40 | vec3\<int8> | <kbd>[bbb-]</kbd> |
41 | vec4\<int8> | <kbd>[bbbb]</kbd> |
43 | vec3\<int16> | <kbd>[bbbb][bb--]</kbd> |
46 | vec3\<int32> | <kbd>[bbbb][bbbb][bbbb][----]</kbd> |
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| /external/tensorflow/tensorflow/lite/g3doc/r1/convert/ |
| D | cmdline_reference.md | 3 This page is complete reference of command-line flags used by the TensorFlow
6 ## High-level flags
9 files. The flag `--output_file` is always required. Additionally, either
10 `--saved_model_dir`, `--keras_model_file` or `--graph_def_file` is required.
12 * `--output_file`. Type: string. Specifies the full path of the output file.
13 * `--saved_model_dir`. Type: string. Specifies the full path to the directory
15 * `--keras_model_file`. Type: string. Specifies the full path of the HDF5 file
17 * `--graph_def_file`. Type: string. Specifies the full path of the input
20 * `--output_format`. Type: string. Default: `TFLITE`. Specifies the format of
27 `--dump_graphviz_dir` instead to get a final visualization with all
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| /external/tensorflow/tensorflow/lite/python/ |
| D | util.py | 8 # http://www.apache.org/licenses/LICENSE-2.0
62 9: dtypes.int8,
71 dtypes.int8: {dtypes.int8, dtypes.uint8},
113 len(parts) - 1))
232 for array in input_arrays:
233 signature.inputs[array.name].name = array.name
234 signature.inputs[array.name].dtype = array.dtype.as_datatype_enum
235 signature.inputs[array.name].tensor_shape.CopyFrom(array.shape.as_proto())
237 for array in output_arrays:
238 signature.outputs[array.name].name = array.name
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| D | convert.py | 8 # http://www.apache.org/licenses/LICENSE-2.0
23 import enum # pylint: disable=g-bad-import-order
51 def _requires_input_stats(toco_flags: _toco_flags_pb2.TocoFlags()) -> bool:
69 tf_type: dtypes.DType, usage: str = "") -> _types_pb2.IODataType:
86 dtypes.int8: _types_pb2.INT8,
108 tf_type: dtypes.DType, usage: str = "") -> _types_pb2.IODataType:
124 dtypes.int8: _types_pb2.QUANTIZED_INT8,
172 # Convert model using only TensorFlow Lite quantized int8 operations.
177 # Convert model using only TensorFlow Lite operations with quantized int8
181 # This quantization mode may be used in models for super-resolution,
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| D | lite_test.py | 8 # http://www.apache.org/licenses/LICENSE-2.0
316 ('_INT8InputOutput', False, False, dtypes.int8),
319 ('_IntOnly_INT8InputOutput', True, False, dtypes.int8),
322 ('_IntOnly_INT8InputOutputMlirQuant', True, False, dtypes.int8, True),
332 # ceil kernel does not support int8 nor int16 types neither.
336 # ceil kernel does not support int8 nor int16 types neither.
343 np.random.uniform(-1, 1, size=(3)).astype(np.float32),
344 np.random.uniform(-1, 1, size=(3)).astype(np.float32)
381 # Allow float32 for fallback on non-quantizable op.
463 """Convert a model from an intermediate input array."""
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| /external/libchrome/mojo/public/tools/bindings/pylib/mojom/generate/ |
| D | pack_tests.py | 2 # Use of this source code is governed by a BSD-style license that can be
41 struct.AddField('testfield1', mojom.INT8)
71 (mojom.INT8, mojom.UINT8, mojom.INT32),
78 (mojom.INT8, mojom.INT32, mojom.UINT8),
84 kinds = (mojom.INT8, mojom.INT32, mojom.INT16, mojom.INT8, mojom.INT8)
96 mojom.Array().MakeNullableKind(),
98 mojom.Array(length=5).MakeNullableKind(),
110 (mojom.BOOL, mojom.INT8, mojom.STRING, mojom.UINT8,
114 mojom.UINT64, mojom.Struct('test'), mojom.Array(),
123 struct.AddField('testfield1', mojom.INT8)
|
| /external/tensorflow/tensorflow/core/api_def/python_api/ |
| D | api_def_Sub.pbtxt | 8 Both input and output have a range `(-inf, inf)`.
12 Subtract operation between an array and a scalar:
17 <tf.Tensor: shape=(5,), dtype=int32, numpy=array([0, 1, 2, 3, 4], dtype=int32)>
20 numpy=array([ 0, -1, -2, -3, -4], dtype=int32)>
22 Note that binary `-` operator can be used instead:
26 >>> x - y
27 <tf.Tensor: shape=(5,), dtype=int32, numpy=array([0, 1, 2, 3, 4], dtype=int32)>
29 Subtract operation between an array and a tensor of same shape:
35 numpy=array([ 4, 2, 0, -2, -4], dtype=int32)>
38 non-tensor, the non-tensor input will adopt (or get casted to) the data type
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| /external/dtc/tests/ |
| D | type-preservation.dts | 1 /dts-v1/;
4 #address-cells = <0x01>;
5 #size-cells = <0x00>;
10 int-array = <0x00 0x01>, int_value_label: <0x02 0x03>;
11 int8 = [56];
12 int8-array = [00 12 34 56] label:;
14 int16-array = /bits/ 16 <0x1234 0x5678 0x90ab 0xcdef>;
15 int16-matrix = /bits/ 16 <0x1234 0x5678>, <0x90ab 0xcdef>;
17 int64-array = /bits/ 64 <0x100000000 0x00> int64_array_label_end:;
18 a-string-with-nulls = "foo\0bar", "baz";
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| D | type-preservation.dt.yaml | 1 ---
2 - '#address-cells': [[0x1]]
3 '#size-cells': [[0x0]]
7 int-array: [[0x0, 0x1], [0x2, 0x3]]
8 int8: [!u8 [0x56]]
9 int8-array: [!u8 [0x0, 0x12, 0x34, 0x56]]
11 int16-array: [!u16 [0x1234, 0x5678, 0x90ab, 0xcdef]]
12 int16-matrix: [!u16 [0x1234, 0x5678], [0x90ab, 0xcdef]]
14 int64-array: [!u64 [0x100000000, 0x0]]
15 a-string-with-nulls: ["foo\0bar", "baz"]
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| /external/tensorflow/tensorflow/lite/kernels/internal/optimized/integer_ops/ |
| D | fully_connected.h | 7 http://www.apache.org/licenses/LICENSE-2.0
32 const int8* input_data, const RuntimeShape& filter_shape, in FullyConnected()
33 const int8* filter_data, const RuntimeShape& bias_shape, in FullyConnected()
34 const int32* bias_data, const RuntimeShape& output_shape, int8* output_data, in FullyConnected()
49 // but the current --variable_batch hack consists in overwriting the 3rd in FullyConnected()
51 // array of which dimension is the batch dimension in it. in FullyConnected()
54 const int batches = FlatSizeSkipDim(output_shape, output_dim_count - 1); in FullyConnected()
55 const int filter_rows = filter_shape.Dims(filter_dim_count - 2); in FullyConnected()
56 const int filter_cols = filter_shape.Dims(filter_dim_count - 1); in FullyConnected()
58 const int output_rows = output_shape.Dims(output_dim_count - 1); in FullyConnected()
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| /external/neven/Embedded/common/src/b_BasicEm/ |
| D | Int8Arr.h | 8 * http://www.apache.org/licenses/LICENSE-2.0
20 /* ---- includes ----------------------------------------------------------- */
25 /* ---- related objects --------------------------------------------------- */
27 /* ---- typedefs ----------------------------------------------------------- */
29 /* ---- constants ---------------------------------------------------------- */
31 /* ---- object definition -------------------------------------------------- */
33 /** byte array */
37 /* ---- private data --------------------------------------------------- */
42 /* ---- public data ---------------------------------------------------- */
44 /** pointer to array of int8 */
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| /external/armnn/python/pyarmnn/test/ |
| D | test_const_tensor.py | 2 # SPDX-License-Identifier: MIT
20 … (ann.DataType_QAsymmS8, np.random.randint(1, size=(2, 4)).astype(np.int8)),
21 … (ann.DataType_QSymmS8, np.random.randint(1, size=(2, 4)).astype(np.int8)),
24 … ], ids=['float32', 'float16', 'unsigned int8', 'signed int8', 'signed int8', 'int32', 'int16'])
40 … (ann.DataType_QAsymmS8, np.random.randint(1, size=(2, 2)).astype(np.int8)),
41 … (ann.DataType_QSymmS8, np.random.randint(1, size=(2, 2)).astype(np.int8)),
44 … ], ids=['float32', 'float16', 'unsigned int8', 'signed int8', 'signed int8', 'int32', 'int16'])
60 … (ann.DataType_QAsymmS8, np.random.randint(1, size=(2, 2, 3, 3)).astype(np.int8)),
61 … (ann.DataType_QSymmS8, np.random.randint(1, size=(2, 2, 3, 3)).astype(np.int8)),
64 … ], ids=['float32', 'float16', 'unsigned int8', 'signed int8', 'signed int8', 'int32', 'int16'])
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| /external/armnn/python/pyarmnn/src/pyarmnn/_tensor/ |
| D | const_tensor.py | 2 # SPDX-License-Identifier: MIT
42 >>> input_data = np.array(...)
55 input_data (ndarray): The numpy array will be transformed to a
66 # TensorInfo as first argument and numpy array as second
73 raise TypeError('Data must be provided as a numpy array.')
75 # copy constructor - reference to memory area is passed from copied const
88 def __copy__(self) -> 'ConstTensor':
129 … "Is your input array data type ({}) aligned with TensorInfo?".format(num_bytes, size_in_bytes,
142 data (ndarray): Input data as numpy array.
146 DataType_QAsymmS8: np.int8,
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| /external/tensorflow/tensorflow/lite/kernels/internal/optimized/ |
| D | optimized_ops.h | 7 http://www.apache.org/licenses/LICENSE-2.0
112 // Used to convert from old-style shifts (right) to new-style (left).
113 static constexpr int kReverseShift = -1;
115 // Make a local VectorMap typedef allowing to map a float array
135 // Make a local VectorMap typedef allowing to map a float array
149 const int rows = shape.Dims(dims_count - 1); in MapAsMatrixWithLastDimAsRows()
150 const int cols = FlatSizeSkipDim(shape, dims_count - 1); in MapAsMatrixWithLastDimAsRows()
165 Eigen::Map<const Eigen::Array<typename std::remove_const<Scalar>::type,
167 Eigen::Map<Eigen::Array<Scalar, Eigen::Dynamic, Eigen::Dynamic>>>::type;
173 const int rows = shape.Dims(dims_count - 1); in MapAsArrayWithLastDimAsRows()
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| /external/tensorflow/tensorflow/lite/python/optimize/ |
| D | calibrator_test.py | 8 # http://www.apache.org/licenses/LICENSE-2.0
36 # Activation type Int8
37 ('UseActivationTypeInt8', dtypes.int8),
59 # Activation type Int8
60 ('UseActivationTypeInt8', dtypes.int8),
105 yield [np.array(u'Test' + str(i))]
112 # Activation type Int8
113 ('UseActivationTypeInt8 - EnableMlirQuantizer', dtypes.int8),
115 ('UseActivationTypeInt16 - DisableEnableMlirQuantizer', dtypes.int16))
170 dtypes.float32, False, dtypes.int8,
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| /external/libchrome/mojo/public/tools/bindings/ |
| D | README.md | 46 ninja -C out/r services/widget/public/interfaces
62 [documentation for individual target languages](#Generated-Code-For-Target-Languages).
79 |-------------------------------|-------------------------------------------------------|
81 | `int8`, `uint8` | Signed or unsigned 8-bit integer.
82 | `int16`, `uint16` | Signed or unsigned 16-bit integer.
83 | `int32`, `uint32` | Signed or unsigned 32-bit integer.
84 | `int64`, `uint64` | Signed or unsigned 64-bit integer.
85 | `float`, `double` | 32- or 64-bit floating point number.
86 | `string` | UTF-8 encoded string.
87 | `array<T>` | Array of any Mojom type *T*; for example, `array<uint8>` or `arra…
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| /external/tensorflow/tensorflow/core/kernels/ |
| D | gpu_device_array.h | 7 http://www.apache.org/licenses/LICENSE-2.0
28 // Create an array of value on the host, to be sent to kernel using
57 // Out-of-line: allocate data that will be memcopied. in Init()
62 context_->allocate_temp(DT_INT8, TensorShape{total_bytes_}, in Init()
65 out_of_line_values_on_host_.flat<int8>().data()); in Init()
80 // Out-of-line - copy pointers to device. in Finalize()
81 auto stream = context_->op_device_context()->stream(); in Finalize()
83 TF_RETURN_IF_ERROR(context_->allocate_temp( in Finalize()
86 out_of_line_values_on_gpu_.flat<int8>().data(), in Finalize()
88 stream->ThenMemcpy(&output_values_base, in Finalize()
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| /external/tensorflow/tensorflow/python/framework/ |
| D | dtypes_test.py | 7 # http://www.apache.org/licenses/LICENSE-2.0
89 self.assertIs(dtypes.int8, dtypes.as_dtype(np.int8))
94 dtypes.as_dtype(np.array(["foo", "bar"]).dtype))
111 dtypes.float32, dtypes.float64, dtypes.bool, dtypes.uint8, dtypes.int8,
125 self.assertIs(dtypes.int8, dtypes.as_dtype("int8"))
165 self.assertEqual(dtypes.as_dtype("int8").is_integer, True)
185 self.assertEqual(dtypes.as_dtype("int8").is_floating, False)
205 self.assertEqual(dtypes.as_dtype("int8").is_complex, False)
225 self.assertEqual(dtypes.as_dtype("int8").is_unsigned, False)
260 print("%s: %s - %s" % (dtype, dtype.min, dtype.max))
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