| /external/mesa3d/src/mesa/drivers/dri/radeon/ |
| radeon_maos.h | 40 extern void radeonEmitArrays( struct gl_context *ctx, GLuint inputs );
|
| /external/libcxx/benchmarks/ |
| GenerateInput.hpp | 51 std::vector<IntT> inputs(N, static_cast<IntT>(-1));
52 return inputs;
57 std::vector<IntT> inputs; local
59 inputs.push_back(i);
60 return inputs;
65 std::vector<IntT> inputs; local
67 inputs.push_back(i + N);
69 return inputs;
74 std::vector<IntT> inputs = getSortedIntegerInputs<IntT>(N); local
75 for (auto& E : inputs) E <<= ((sizeof(IntT) / 2) * CHAR_BIT)
81 std::vector<IntT> inputs; local
101 std::vector<IntT> inputs; local
114 std::vector<std::string> inputs; local
122 std::vector<std::string> inputs = getRandomStringInputs(N); local
128 std::vector<std::string> inputs = getSortedStringInputs(N); local
134 static std::vector<std::string> inputs = getRandomStringInputs(N); local
[all...] |
| /prebuilts/ndk/r16/sources/cxx-stl/llvm-libc++/benchmarks/ |
| GenerateInput.hpp | 51 std::vector<IntT> inputs(N, static_cast<IntT>(-1));
52 return inputs;
57 std::vector<IntT> inputs; local
59 inputs.push_back(i);
60 return inputs;
65 std::vector<IntT> inputs; local
67 inputs.push_back(i + N);
69 return inputs;
74 std::vector<IntT> inputs = getSortedIntegerInputs<IntT>(N); local
75 for (auto& E : inputs) E <<= ((sizeof(IntT) / 2) * CHAR_BIT)
81 std::vector<IntT> inputs; local
101 std::vector<IntT> inputs; local
114 std::vector<std::string> inputs; local
122 std::vector<std::string> inputs = getRandomStringInputs(N); local
128 std::vector<std::string> inputs = getSortedStringInputs(N); local
134 static std::vector<std::string> inputs = getRandomStringInputs(N); local
[all...] |
| /frameworks/base/media/mca/filterfw/java/android/filterfw/core/ |
| Program.java | 27 public abstract void process(Frame[] inputs, Frame output);
30 Frame[] inputs = new Frame[1]; local
31 inputs[0] = input;
32 process(inputs, output);
|
| /external/tensorflow/tensorflow/contrib/lite/toco/graph_transformations/ |
| identify_lstm_split_inputs.cc | 36 // Already an extended LstmCell with kExtendedLstmInputCount of inputs,
37 // do not need to split cell inputs.
38 if (curr_op->inputs.size() == kExtendedLstmInputCount) {
45 *model, curr_op->inputs[LstmCellOperator::WEIGHTS_INPUT]) ||
47 *model, curr_op->inputs[LstmCellOperator::BIASES_INPUT])) {
57 // Emplace a new LstmCell operator with extended inputs (kernel/lstm.cc).
59 lstm_cell_op->inputs.resize(kExtendedLstmInputCount);
60 int num_input = model->GetArray(curr_op->inputs[LstmCellOperator::DATA_INPUT])
72 lstm_cell_op->inputs[kInputTensor] =
73 curr_op->inputs[LstmCellOperator::ACTIV_OUTPUT]
[all...] |
| convert_trivial_addn_to_add.cc | 21 // This pass will convert an AddN operator with only 2 inputs into a regular Add
29 CHECK_GE(addn_op->inputs.size(), 2);
33 if (addn_op->inputs.size() != 2) {
37 // Copy inputs & outputs to regular Add.
39 add_op->inputs.push_back(addn_op->inputs[0]);
40 add_op->inputs.push_back(addn_op->inputs[1]);
|
| identify_lstm_merge_inputs.cc | 36 // Already a compact LstmCell with LstmCellOperator::NUM_INPUTS of inputs,
37 // do not need to merge cell inputs.
38 if (src_op->inputs.size() == LstmCellOperator::NUM_INPUTS) {
42 // Identify prev_activ_input, prev_state_input as required Op inputs,
56 int num_cell = model->GetArray(src_op->inputs[kInputToInputWeightsTensor])
59 int num_input = model->GetArray(src_op->inputs[kInputToInputWeightsTensor])
63 model->GetArray(src_op->inputs[kRecurrentToInputWeightsTensor])
86 model->GetArray(src_op->inputs[kInputToInputWeightsTensor]), 0, 0);
89 model->GetArray(src_op->inputs[kInputToCellWeightsTensor]), num_cell, 0);
92 model->GetArray(src_op->inputs[kInputToForgetWeightsTensor])
[all...] |
| identify_l2_normalization.cc | 54 CHECK_EQ(div_or_mul_op->inputs.size(), 2);
56 GetOpWithOutput(*model, div_or_mul_op->inputs[0]),
57 GetOpWithOutput(*model, div_or_mul_op->inputs[1]),
65 CHECK_EQ(sqrt_or_rsqrt_op->inputs.size(), 1);
67 GetOpWithOutput(*model, sqrt_or_rsqrt_op->inputs[0]);
82 CHECK_EQ(op_producing_sqrt_or_rsqrt_input->inputs.size(), 2);
85 model->GetArray(op_producing_sqrt_or_rsqrt_input->inputs[i]);
101 op_producing_add_input = GetOpWithOutput(*model, add_op->inputs[1 - i]);
124 Operator* square_op = GetOpWithOutput(*model, sum_op->inputs[0]);
133 CHECK_EQ(square_op->inputs.size(), 1)
[all...] |
| resolve_constant_range.cc | 30 CHECK_EQ(op->inputs.size(), 3);
31 const auto& start_array = model->GetArray(op->inputs[0]);
36 const auto& limit_array = model->GetArray(op->inputs[1]);
41 const auto& delta_array = model->GetArray(op->inputs[2]);
47 for (const auto& input : op->inputs) {
62 << "Range op inputs must be scalar.";
64 << "Range op inputs must be scalar.";
66 << "Range op inputs must be scalar.";
69 << "Range op inputs must be int32.";
71 << "Range op inputs must be int32."
[all...] |
| resolve_slice_attributes.cc | 35 CHECK_EQ(op->inputs.size(), 3);
36 if (!IsConstantParameterArray(*model, op->inputs[1])) return false;
37 if (!IsConstantParameterArray(*model, op->inputs[2])) return false;
39 const auto& begin_array = model->GetArray(op->inputs[1]);
42 const auto& size_array = model->GetArray(op->inputs[2]);
48 // TODO(dkalenichenko): Delete the extra inputs?
|
| resolve_strided_slice_attributes.cc | 33 CHECK_EQ(op->inputs.size(), 4);
34 const auto& start_array = model->GetArray(op->inputs[1]);
41 const auto& stop_array = model->GetArray(op->inputs[2]);
44 const auto& stride_array = model->GetArray(op->inputs[3]);
47 if (!IsConstantParameterArray(*model, op->inputs[1])) return false;
48 if (!IsConstantParameterArray(*model, op->inputs[2])) return false;
49 if (!IsConstantParameterArray(*model, op->inputs[3])) return false;
|
| identify_l2_pool.cc | 48 CHECK_EQ(sqrt_op->inputs.size(), 1);
54 Operator* prev_to_sqrt_op = GetOpWithOutput(*model, sqrt_op->inputs[0]);
64 CHECK_EQ(avpool_op->inputs.size(), 1);
66 square_op = GetOpWithOutput(*model, avpool_op->inputs[0]);
67 CHECK_EQ(square_op->inputs.size(), 1);
79 l2pool_op->inputs = {square_op->inputs[0]};
95 model->EraseArray(avpool_op->inputs[0]);
96 model->EraseArray(sqrt_op->inputs[0]);
|
| /external/tensorflow/tensorflow/contrib/tpu/python/tpu/ |
| training_loop.py | 29 def while_loop(condition, body, inputs=None, infeed_queue=None, name=None):
32 The set of loop-carried tensors corresponds to `inputs`. Both
43 inputs: a list of initial values passed into the training loop, or
46 of arguments as inputs to condition.
56 # Converts inputs to Tensors.
57 inputs = [] if inputs is None else [ops.convert_to_tensor(x) for
58 x in inputs]
59 input_types = [x.dtype for x in inputs]
60 input_arity = len(inputs)
[all...] |
| /external/tensorflow/tensorflow/contrib/framework/python/ops/ |
| accumulate_n_v2.py | 31 def accumulate_n_v2(inputs, shape=None, tensor_dtype=None, name=None):
38 wait for all of its inputs to be ready before beginning to sum. This can
39 save memory if inputs are ready at different times, since minimum temporary
40 storage is proportional to the output size rather than the inputs size.
58 inputs: A list of `Tensor` objects, each with same shape and type.
59 shape: Shape of elements of `inputs`.
60 tensor_dtype: The type of `inputs`.
64 A `Tensor` of same shape and type as the elements of `inputs`.
67 ValueError: If `inputs` don't all have same shape and dtype or the shape
70 _INPUTS_ERR_MSG = ValueError("inputs must be a list of at least one Tensor
[all...] |
| /external/v8/tools/sanitizers/ |
| sancov_merger_test.py | 31 # Inputs for merge process with 2 cpus. The tuples contain:
75 inputs = sancov_merger.generate_inputs(
77 self.assertEquals(EXPECTED_INPUTS_2, inputs)
80 inputs = sancov_merger.generate_inputs(
82 self.assertEquals(EXPECTED_INPUTS_4, inputs)
|
| /external/tensorflow/tensorflow/python/ops/ |
| accumulate_n_benchmark.py | 41 def _AccumulateNTemplate(self, inputs, init, shape, validate_shape):
43 shape=shape, dtype=inputs[0].dtype.base_dtype)
47 ref, tensor, use_locking=True).op for tensor in inputs
53 def _AccumulateNInitializedWithFirst(self, inputs):
55 inputs,
56 init=array_ops.zeros_like(inputs[0]),
57 shape=inputs[0].get_shape(),
60 def _AccumulateNInitializedWithMerge(self, inputs):
62 inputs,
63 init=array_ops.zeros_like(gen_control_flow_ops._merge(inputs)[0])
[all...] |
| /external/tensorflow/tensorflow/python/kernel_tests/ |
| nth_element_op_test.py | 33 def _validateNthElement(self, inputs, dtype, n, reverse, expected_values):
36 inputs_op = ops.convert_to_tensor(inputs, dtype=dtype)
44 inputs = [2.2, 4.4, 1.1, 5.5, 3.3]
45 self._validateNthElement(inputs, dtypes.float32, 1, False, 2.2)
46 self._validateNthElement(inputs, dtypes.float32, 1, True, 4.4)
49 inputs = [[2.2, 4.4, 1.1], [5.5, 3.3, 6.6]]
50 self._validateNthElement(inputs, dtypes.float64, 2, False, [4.4, 6.6])
51 self._validateNthElement(inputs, dtypes.float64, 2, True, [1.1, 3.3])
54 inputs = [[[2, 4, 1], [5, -3, 6]],
56 self._validateNthElement(inputs, dtypes.int32, 0, False
[all...] |
| topk_op_test.py | 42 inputs,
50 values_op, indices_op = nn_ops.top_k(inputs, k, sorted=sorted)
73 np_inputs = np.array(inputs)
96 inputs = [[0.1, 0.3, 0.2, 0.4], [0.1, 0.3, 0.3, 0.2]]
97 self._validateTopK(inputs, 1, [[0.4], [0.3]], [[3], [1]])
100 inputs = [[0.1, 0.3, 0.2, 0.4], [0.1, 0.3, 0.4, 0.2]]
101 self._validateTopK(inputs, 2, [[0.4, 0.3], [0.4, 0.3]], [[3, 1], [2, 1]])
105 inputs = np.random.permutation(np.linspace(0, 100, 6140, dtype=np.float64))
106 indices = np.argsort(-inputs)[:k]
107 values = -np.sort(-inputs)[:k
[all...] |
| /build/make/tools/ |
| check_builds.sh | 61 local inputs=
64 inputs="$inputs $TEST_BUILD_DIR/golden-$1/installed-files.txt"
65 inputs="$inputs $TEST_BUILD_DIR/dist-$1/installed-files.txt"
68 build/make/tools/compare_fileslist.py $inputs > $TEST_BUILD_DIR/sizes.html
80 local inputs=
90 build/make/tools/compare_fileslist.py $inputs > $TEST_BUILD_DIR/sizes.html
|
| /frameworks/base/media/mca/filterpacks/native/imageproc/ |
| invert.c | 21 int invert_process(const char** inputs,
36 const char* input_ptr = inputs[0];
|
| to_rgba.c | 21 int gray_to_rgb_process(const char** inputs,
36 const char* input_ptr = inputs[0];
52 int rgba_to_rgb_process(const char** inputs,
67 const char* input_ptr = inputs[0];
84 int gray_to_rgba_process(const char** inputs,
99 const char* input_ptr = inputs[0];
116 int rgb_to_rgba_process(const char** inputs,
131 const char* input_ptr = inputs[0];
|
| /external/tensorflow/tensorflow/contrib/rnn/python/ops/ |
| fused_rnn_cell.py | 45 inputs,
50 """Run this fused RNN on inputs, starting from the given state.
53 inputs: `3-D` tensor with shape `[time_len x batch_size x input_size]`
62 sequence_length: Specifies the length of each sequence in inputs. An
74 to match the type of the `inputs`.
95 inputs,
100 is_list = isinstance(inputs, list)
103 inputs = array_ops.stack(inputs)
106 inputs,
[all...] |
| /external/tensorflow/tensorflow/contrib/specs/python/ |
| specs_test.py | 42 inputs = constant_op.constant(_rand(1, 18, 19, 5))
44 outputs = specs.create_net(spec, inputs)
50 summaries.tf_spec_structure(spec, inputs),
57 inputs = constant_op.constant(_rand(17, 55))
59 outputs = specs.create_net(spec, inputs)
67 inputs = constant_op.constant(_rand(17, 55))
69 outputs = specs.create_net(spec, inputs)
75 summaries.tf_spec_structure(spec, inputs),
81 inputs = constant_op.constant(_rand(1, 64, 64, 5))
83 outputs = specs.create_net(spec, inputs)
[all...] |
| /cts/suite/audio_quality/lib/src/ |
| BuiltinProcessing.cpp | 46 TaskGeneric::ExecutionResult BuiltinProcessing::rms_mva(void** inputs, void** outputs)
49 inputs[0], inputs[1], inputs[2], outputs[0]);
50 android::sp<Buffer>& data(*reinterpret_cast<android::sp<Buffer>*>(inputs[0]));
52 int64_t passMin = (reinterpret_cast<TaskCase::Value*>(inputs[1]))->getInt64();
53 int64_t passMax = (reinterpret_cast<TaskCase::Value*>(inputs[2]))->getInt64();
|
| /external/tensorflow/tensorflow/contrib/quantize/python/ |
| quant_ops.py | 33 def FixedQuantize(inputs, init_min=-6.0, init_max=6.0, scope=None):
37 inputs: a tensor containing values to be quantized.
44 with ops.name_scope(scope, 'FixedQuantize', values=[inputs]):
46 inputs, min=init_min, max=init_max)
50 def LastValueQuantize(inputs,
67 inputs: a tensor containing values to be quantized.
87 None, default_name=name_prefix, values=[inputs], reuse=reuse):
88 input_shape = inputs.get_shape()
112 inputs,
128 inputs, reduction_indices=reduce_dims, name='BatchMin'
[all...] |
