/external/tensorflow/tensorflow/contrib/lite/toco/graph_transformations/ |
H A D | drop_im2col_arrays.cc | 28 if (conv_op->outputs.size() < 2) { 34 CHECK_EQ(conv_op->outputs.size(), 2); 35 model->EraseArray(conv_op->outputs[1]); 36 conv_op->outputs.resize(1);
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H A D | unfuse_activation_functions.cc | 33 if ((op->type == OperatorType::kConv) && (op->outputs.size() == 2)) { 55 CHECK_EQ(op->outputs.size(), 1); 63 ac_op->outputs = op->outputs; 65 AvailableArrayName(*model, op->outputs[0] + "_unfused"); 69 op->outputs = {tmp_array_name};
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H A D | unroll_batch_matmul.cc | 64 matmul_op->outputs = batch_op->outputs; 82 std::string(batch_op->outputs[0]) + "_b" + std::to_string(batch); 94 slice_a_op->outputs = {AvailableArrayName(*model, batch_name + "/slice_a")}; 95 auto& slice_a_op_output = model->GetOrCreateArray(slice_a_op->outputs[0]); 102 slice_a_op->outputs[0], 105 slice_a_reshape_op->outputs = { 108 model->GetOrCreateArray(slice_a_reshape_op->outputs[0]); 121 slice_b_op->outputs = {AvailableArrayName(*model, batch_name + "/slice_b")}; 122 auto& slice_b_op_output = model->GetOrCreateArray(slice_b_op->outputs[ [all...] |
H A D | resolve_squeeze_attributes.cc | 34 DCHECK_EQ(squeeze_op->outputs.size(), 1); 37 if (CountOpsWithInput(*model, squeeze_op->outputs[0]) == 1) { 38 const auto* next_op = GetOpWithInput(*model, squeeze_op->outputs[0]);
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H A D | reorder_activation_functions.cc | 49 DCHECK_EQ(exchange_op->outputs[0], ac_op->inputs[0]); 51 const auto& intermediate_array = exchange_op->outputs[0]; 52 const auto& ac_op_output = ac_op->outputs[0]; 70 if (model->flags.output_arrays(i) == ac_op->outputs[0]) { 74 LogName(*exchange_op), ac_op->outputs[0]); 93 model->GetOrCreateArray(ac_op->outputs[0]).clear_shape(); 94 model->GetOrCreateArray(exchange_op->outputs[0]).clear_shape();
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H A D | identify_lstm_split_inputs.cc | 52 if (!model->GetArray(curr_op->outputs[LstmCellOperator::ACTIV_OUTPUT]) 66 model->GetArray(curr_op->outputs[LstmCellOperator::ACTIV_OUTPUT]) 79 curr_op->outputs[LstmCellOperator::ACTIV_OUTPUT], 80 curr_op->outputs[LstmCellOperator::STATE_OUTPUT])); 141 // Reorder LstmCell's outputs. 142 lstm_cell_op->outputs.resize(LstmCellOperator::NUM_OUTPUTS); 143 lstm_cell_op->outputs[kScratchBufferTensor] = 144 curr_op->outputs[LstmCellOperator::CONCAT_TEMP]; 145 lstm_cell_op->outputs[kOutputStateTensor] = 146 curr_op->outputs[LstmCellOperato [all...] |
/external/tensorflow/tensorflow/cc/client/ |
H A D | client_session_test.cc | 38 std::vector<Tensor> outputs; local 40 TF_EXPECT_OK(session.Run({c}, &outputs)); 41 test::ExpectTensorEqual<int>(outputs[0], test::AsTensor<int>({1, 1}, {1, 2})); 50 std::vector<Tensor> outputs; local 52 TF_EXPECT_OK(session.Run({{a, 1}, {b, 41}}, {c}, &outputs)); 53 test::ExpectTensorEqual<int>(outputs[0], test::AsTensor<int>({42}, {})); 61 std::vector<Tensor> outputs; local 63 TF_EXPECT_OK(session.Run({{a, {1, 1}}}, {c}, &outputs)); 64 test::ExpectTensorEqual<int>(outputs[0], test::AsTensor<int>({3, 3}, {2})); 67 outputs 93 std::vector<Tensor> outputs; local [all...] |
/external/tensorflow/tensorflow/contrib/rnn/python/ops/ |
H A D | fused_rnn_cell.py | 104 outputs, state = rnn.dynamic_rnn( 113 # Convert outputs back to list 114 outputs = array_ops.unstack(outputs) 118 outputs, state = rnn.static_rnn( 126 # Convert outputs back to tensor 127 outputs = array_ops.stack(outputs) 129 return outputs, state 176 outputs, stat [all...] |
/external/libvpx/libvpx/test/ |
H A D | svc_test.cc | 137 struct vpx_fixed_buf *const outputs, 153 outputs[*frame_received].buf = malloc(frame_size + 16); 154 ASSERT_TRUE(outputs[*frame_received].buf != NULL); 155 memcpy(outputs[*frame_received].buf, cx_pkt->data.frame.buf, 157 outputs[*frame_received].sz = frame_size; 165 struct vpx_fixed_buf *const outputs) { 169 ASSERT_TRUE(outputs != NULL); 191 StoreFrames(n, outputs, &frame_received); 199 StoreFrames(n, outputs, &frame_received); 450 vpx_fixed_buf outputs[ local 136 StoreFrames(const size_t max_frame_received, struct vpx_fixed_buf *const outputs, size_t *const frame_received) argument 163 Pass2EncodeNFrames(std::string *const stats_buf, const int n, const int layers, struct vpx_fixed_buf *const outputs) argument 464 vpx_fixed_buf outputs[10]; local 479 vpx_fixed_buf outputs[20]; local 494 vpx_fixed_buf outputs[10]; local 510 vpx_fixed_buf outputs[10]; local 536 vpx_fixed_buf outputs[20]; local 552 vpx_fixed_buf outputs[20]; local 585 vpx_fixed_buf outputs[10]; local 602 vpx_fixed_buf outputs[10]; local 622 vpx_fixed_buf outputs[10]; local 642 vpx_fixed_buf outputs[10]; local 666 vpx_fixed_buf outputs[10]; local 687 vpx_fixed_buf outputs[10]; local 705 vpx_fixed_buf outputs[10]; local 731 vpx_fixed_buf outputs[10]; local 757 vpx_fixed_buf outputs[10]; local 782 vpx_fixed_buf outputs[10]; local [all...] |
/external/mesa3d/src/gallium/drivers/r300/ |
H A D | r300_vs.c | 109 struct r300_shader_semantics* outputs = &vs->outputs; local 112 boolean any_bcolor_used = outputs->bcolor[0] != ATTR_UNUSED || 113 outputs->bcolor[1] != ATTR_UNUSED; 120 if (outputs->pos != ATTR_UNUSED) { 121 c->code->outputs[outputs->pos] = reg++; 127 if (outputs->psize != ATTR_UNUSED) { 128 c->code->outputs[outputs [all...] |
/external/tensorflow/tensorflow/contrib/specs/python/ |
H A D | specs_test.py | 44 outputs = specs.create_net(spec, inputs) 45 self.assertEqual(outputs.get_shape().as_list(), [1, 18, 19, 64]) 47 result = outputs.eval() 59 outputs = specs.create_net(spec, inputs) 60 self.assertEqual(outputs.get_shape().as_list(), [17, 55]) 62 result = outputs.eval() 69 outputs = specs.create_net(spec, inputs) 70 self.assertEqual(outputs.get_shape().as_list(), [17, 10]) 72 result = outputs.eval() 83 outputs [all...] |
H A D | summaries_test.py | 41 outputs = specs.create_net(spec, inputs) 43 result = outputs.eval() 54 outputs = specs.create_net(spec, inputs) 56 result = outputs.eval() 66 outputs = specs.create_net(spec, inputs) 68 result = outputs.eval() 76 outputs = specs.create_net(spec, inputs) 78 result = outputs.eval()
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/external/tensorflow/tensorflow/cc/tools/ |
H A D | freeze_saved_model.h | 28 // `inputs` and `outputs` consist of the union of all inputs and outputs in the 31 // `outputs`. All variables in the supplied SavedModelBundle are converted to 39 std::unordered_set<string>* outputs);
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/external/toybox/toys/posix/ |
H A D | tee.c | 1 /* tee.c - cat to multiple outputs. 26 void *outputs; 41 temp->next = TT.outputs; 43 TT.outputs = temp; 64 fdl = TT.outputs;
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/external/tensorflow/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/ |
H A D | Interpreter.java | 38 * <p>If a model takes multiple inputs or outputs: 50 * <p>Orders of inputs and outputs are determined when converting TensorFlow model to TensorFlowLite 91 Map<Integer, Object> outputs = new HashMap<>(); 92 outputs.put(0, output); 93 runForMultipleInputsOutputs(inputs, outputs); 97 * Runs model inference if the model takes multiple inputs, or returns multiple outputs. 104 * @param outputs a map mapping output indices to multidimensional arrays of output data. It only 105 * needs to keep entries for the outputs to be used. 108 @NotNull Object[] inputs, @NotNull Map<Integer, Object> outputs) { 113 if (outputs 107 runForMultipleInputsOutputs( @otNull Object[] inputs, @NotNull Map<Integer, Object> outputs) argument [all...] |
/external/tensorflow/tensorflow/cc/framework/ |
H A D | testutil.cc | 34 std::vector<Tensor> outputs; local 35 GetTensors(scope, {std::move(tensor)}, &outputs); 36 *out = outputs[0]; 48 std::vector<Tensor> outputs; local 49 GetTensors(scope, assign_vars, {std::move(tensor)}, &outputs); 50 *out = outputs[0];
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/external/tensorflow/tensorflow/contrib/tpu/python/tpu/ |
H A D | training_loop.py | 120 outputs = body(*(inputs + dequeue_ops)) 123 if not isinstance(outputs, (list, tuple)): 124 outputs = (outputs,) 126 outputs = [ 128 for o in outputs 132 output_operations = [o for o in outputs if isinstance(o, ops.Operation)] 133 output_tensors = [o for o in outputs 136 if outputs != output_tensors + output_operations: 206 outputs [all...] |
/external/tensorflow/tensorflow/core/kernels/ |
H A D | shape_op_test.cc | 86 std::vector<Tensor> outputs; local 93 Status s = session.Run({{input, variant_tensor}}, {shape_output}, &outputs); 104 Status s = session.Run({{input, variant_tensor}}, {shape_output}, &outputs); 117 &outputs)); 118 EXPECT_EQ(outputs[0].dims(), 1); // shape 119 EXPECT_EQ(vec_dim_value, outputs[0].vec<int32>()(0)); 120 EXPECT_EQ(outputs[1].dims(), 0); // rank 121 EXPECT_EQ(1, outputs[1].scalar<int32>()()); 122 EXPECT_EQ(outputs[2].dims(), 0); // size 123 EXPECT_EQ(vec_dim_value, outputs[ [all...] |
/external/tensorflow/tensorflow/cc/ops/ |
H A D | while_loop.cc | 35 std::vector<OutputTensor> ToOutputTensors(const std::vector<Output>& outputs) { argument 36 std::vector<OutputTensor> result(outputs.size()); 37 for (int i = 0; i < outputs.size(); ++i) { 38 result[i] = ToOutputTensor(outputs[i]); 44 std::vector<Node*> ToNodes(const std::vector<Output>& outputs) { argument 45 std::vector<Node*> result(outputs.size()); 46 for (int i = 0; i < outputs.size(); ++i) { 47 result[i] = outputs[i].node(); 119 // Create the body subgraph defined by `body`. `outputs` must be non-null and 123 std::vector<Output>* outputs) { 121 CreateBody(const Scope& scope, const BodyGraphBuilderFn& body, const std::vector<Output>& inputs, std::vector<Output>* outputs) argument 172 BuildWhileLoop(const Scope& scope, const std::vector<Output>& inputs, const CondGraphBuilderFn& cond, const BodyGraphBuilderFn& body, const string& frame_name, OutputList* outputs, bool create_while_ctx, Output* cond_output) argument [all...] |
/external/tensorflow/tensorflow/contrib/tensor_forest/kernels/v4/ |
H A D | candidate_graph_runner.cc | 72 std::vector<Tensor>* outputs) { 77 TF_CHECK_OK(session_->Run(inputs, output_tensor_names, op_name, outputs)) 98 std::vector<Tensor> outputs; local 99 RunOp(kNoOp, TensorNameValueList(), {kSplitScoreName}, &outputs); 100 return outputs[0].unaligned_flat<float>()(0); 104 std::vector<Tensor> outputs; local 105 RunOp(kNoOp, TensorNameValueList(), {kGetSplitName}, &outputs); 106 ParseProtoUnlimited(node, outputs[0].unaligned_flat<string>()(0)); 116 std::vector<Tensor> outputs; local 117 RunOp(kNoOp, TensorNameValueList(), {kGetLeftStatsName}, &outputs); 69 RunOp(const string& name, const TensorNameValueList& inputs, const std::vector<string>& output_tensor_names, std::vector<Tensor>* outputs) argument 126 std::vector<Tensor> outputs; local [all...] |
/external/deqp/modules/gles3/scripts/ |
H A D | gen-large-constant-arrays.py | 59 def __init__(self, name, array, inputs, outputs): 63 self.outputs = outputs 69 "VALUES": genValues(self.inputs, self.outputs), 104 outputs = [array[index] for index in indexes] variable 105 outType = outputs[0].typeString() 111 [("%s out0" % outType, outputs)])
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/external/tensorflow/tensorflow/contrib/batching/python/ops/ |
H A D | batch_ops.py | 37 op.outputs[-2], 38 op.outputs[-1], 122 outputs = f(*batched_tensors) 123 if isinstance(outputs, ops.Tensor): 124 outputs_list = [outputs] 126 outputs_list = outputs 133 if isinstance(outputs, ops.Tensor):
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/external/tensorflow/tensorflow/core/common_runtime/ |
H A D | graph_runner_test.cc | 50 std::vector<Tensor> outputs; local 51 Status s = graph_runner.Run(root.graph(), nullptr, {}, {c.name()}, &outputs); 53 ExpectEqual(42.0f, outputs[0].scalar<float>()()); 71 // Create and destroy the GraphRunner, and ensure that the outputs are 73 std::vector<Tensor> outputs; local 77 graph_runner.Run(root.graph(), nullptr, inputs, {"add:0"}, &outputs); 80 ExpectEqual(3.0f, outputs[0].scalar<float>()()); 88 std::vector<Tensor> outputs; local 90 &outputs); 92 ExpectEqual(42.0f, outputs[ 110 std::vector<Tensor> outputs; local [all...] |
/external/tensorflow/tensorflow/python/keras/_impl/keras/utils/ |
H A D | training_utils.py | 170 for i in range(len(model.outputs)): 194 outputs = model(inputs) 195 if not isinstance(outputs, list): 196 outputs = [outputs] 198 # Save the outputs for merging back together later. 199 for o in range(len(outputs)): 200 all_outputs[o].append(outputs[o]) 202 # Merge outputs on CPU. 205 for name, outputs i [all...] |
/external/tensorflow/tensorflow/python/saved_model/ |
H A D | signature_def_utils_impl.py | 33 def build_signature_def(inputs=None, outputs=None, method_name=None): 39 outputs: Outputs of the SignatureDef defined as a proto map of string to 50 if outputs is not None: 51 for item in outputs: 52 signature_def.outputs[item].CopyFrom(outputs[item]) 154 def predict_signature_def(inputs, outputs): 155 """Creates prediction signature from given inputs and outputs. 163 outputs: dict of string to `Tensor`. 169 ValueError: If inputs or outputs i [all...] |