| /external/tensorflow/tensorflow/contrib/image/kernels/ |
| adjust_hsv_in_yiq_op.cc | 40 Tensor* output = nullptr; member in struct:tensorflow::AdjustHsvInYiqOpBase::ComputeOptions
73 Tensor* output = nullptr; variable
75 context->allocate_output(0, input.shape(), &output));
84 options.output = output;
103 Tensor* output = options.output; variable
109 auto output_data = output->shaped<float, 2>({channel_count, kChannelSize});
159 delta_h, scale_s, scale_v, options.output);
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| /external/tensorflow/tensorflow/contrib/lite/kernels/ |
| concatenation.cc | 63 // Output dimensions will match input dimensions, except 'axis', which
88 TfLiteTensor* output = &context->tensors[node->outputs->data[0]]; local
89 TF_LITE_ENSURE_EQ(context, output->type, input_type);
91 TF_LITE_ENSURE_EQ(context, output->params.zero_point,
93 TF_LITE_ENSURE_EQ(context, output->params.scale, t0->params.scale);
96 return context->ResizeTensor(context, output, output_size);
104 TfLiteTensor* output = &context->tensors[node->outputs->data[0]]; local
105 if (axis < 0) axis += output->dims->size;
114 RemapDim(NumDimensions(output), axis), all_inputs.data(), \
115 all_inputs.dims(), node->inputs->size, GetTensorData<scalar>(output), \
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| div.cc | 46 TfLiteTensor* output = GetOutput(context, node, kOutputTensor); local
54 TF_LITE_ENSURE_EQ(context, input1->type, output->type);
55 TF_LITE_ENSURE_EQ(context, input2->type, output->type);
58 return context->ResizeTensor(context, output, output_size);
64 TfLiteTensor* input2, TfLiteTensor* output) {
72 GetTensorData<float>(output), GetTensorDims(output))
87 TfLiteTensor* output = GetOutput(context, node, kOutputTensor); local
89 if (output->type == kTfLiteFloat32) {
90 EvalDivFloat<kernel_type>(context, node, params, input1, input2, output);
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| gather.cc | 40 TfLiteTensor* output = GetOutput(context, node, kOutputTensor); local
43 // Check that input and output types match.
44 TF_LITE_ENSURE_EQ(context, input->type, output->type);
80 return context->ResizeTensor(context, output, output_shape);
86 TfLiteTensor* output = GetOutput(context, node, kOutputTensor); local
92 GetTensorData<data_type>(output), GetTensorDims(output));
113 buffer.WriteToTensor(output);
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| hashtable_lookup.cc | 25 // Output:
26 // Output[0].dim[0] == Tensor[0].dim[0], num of lookups
27 // Each item in output is a raw bytes copy of corresponding item in input.
30 // Output[1].dim = { Tensor[0].dim[0] }, num of lookups
84 TfLiteTensor* output = GetOutput(context, node, 0); local
85 TF_LITE_ENSURE_EQ(context, value->type, output->type);
88 if (output->type != kTfLiteString) {
94 status = context->ResizeTensor(context, output, outputSize);
103 TfLiteTensor* output = GetOutput(context, node, 0); local
124 if (output->type == kTfLiteString)
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| hashtable_lookup_test.cc | 80 TfLiteTensor* output = interpreter_->tensor(output_); local
81 int num = GetStringCount(output);
84 auto ref = GetString(output, i);
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| pad.cc | 40 output = GetOutput(context, node, 0);
45 TfLiteTensor* output; member in struct:tflite::ops::builtin::pad::PadContext
49 // Resizes output array based on the input size and padding size. This function
59 // Determines the size of the output tensor.
75 return context->ResizeTensor(context, op_context->output, output_size);
83 TF_LITE_ENSURE_EQ(context, op_context.input->type, op_context.output->type);
88 // Exit early if paddings is a non-const tensor. Set output tensor to
89 // dynamic so output size can be determined in Eval.
91 SetTensorToDynamic(op_context.output);
101 // Resize the output tensor if the output tensor is dynamic
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| resize_bilinear.cc | 40 TfLiteTensor* size, TfLiteTensor* output) {
47 return context->ResizeTensor(context, output, output_size);
56 TfLiteTensor* output = GetOutput(context, node, kOutputTensor); local
67 output->type = kTfLiteFloat32;
70 SetTensorToDynamic(output);
73 return ResizeOutputTensor(context, input, size, output);
82 TfLiteTensor* output = GetOutput(context, node, kOutputTensor); local
85 if (IsDynamicTensor(output)) {
87 ResizeOutputTensor(context, input, size, output));
90 if (output->type == kTfLiteFloat32)
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| space_to_depth.cc | 46 TfLiteTensor* output = GetOutput(context, node, kOutputTensor); local
50 auto data_type = output->type;
54 TF_LITE_ENSURE_EQ(context, input->type, output->type);
71 return context->ResizeTensor(context, output, output_size);
80 TfLiteTensor* output = GetOutput(context, node, kOutputTensor); local
85 GetTensorData<scalar>(output), GetTensorDims(output))
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| split.cc | 64 TfLiteTensor* output = GetOutput(context, node, i); local
65 TF_LITE_ENSURE_STATUS(context->ResizeTensor(context, output, output_dims));
98 // When the 'axis' tensor is non-const we can't resize output tensors in
|
| sub.cc | 46 TfLiteTensor* output = GetOutput(context, node, kOutputTensor); local
54 TF_LITE_ENSURE_EQ(context, input1->type, output->type);
55 TF_LITE_ENSURE_EQ(context, input2->type, output->type);
58 return context->ResizeTensor(context, output, output_size);
64 TfLiteTensor* input2, TfLiteTensor* output) {
72 GetTensorData<float>(output), GetTensorDims(output))
87 TfLiteTensor* output = GetOutput(context, node, kOutputTensor); local
89 if (output->type == kTfLiteFloat32) {
90 EvalSubFloat<kernel_type>(context, node, params, input1, input2, output);
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| transpose.cc | 38 output = GetOutput(context, node, 0);
42 TfLiteTensor* output; member in struct:tflite::ops::builtin::transpose::TransposeContext
58 // Determine size of output tensor.
65 return context->ResizeTensor(context, op_context->output, output_size);
77 TF_LITE_ENSURE_EQ(context, op_context.input->type, op_context.output->type);
80 SetTensorToDynamic(op_context.output);
90 // Resize the output tensor if the output tensor is dynamic.
91 if (IsDynamicTensor(op_context.output)) {
113 GetTensorData<scalar>(op_context.output), \
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| /external/tensorflow/tensorflow/contrib/periodic_resample/kernels/ |
| periodic_resample_op.h | 41 // un-rasterize the output index
169 // Create an output tensor and attach it to the current context
173 auto output = output_tensor->flat<InputDataT>(); local
181 // Fill output tensor with periodically resampled input tensor values
184 output(output_index) = input(compute_input_index(
|
| /external/tensorflow/tensorflow/contrib/reduce_slice_ops/kernels/ |
| reduce_slice_ops.cc | 56 typename TTypes<T, 3>::Tensor output) { \
58 Index dim1 = output.dimension(0); \
59 Index dim2 = output.dimension(1); \
60 Index dim3 = output.dimension(2); \
75 output(x, y, z) = zero; \
79 output(x, y, z) = reduceop(output(x, y, z), data(x, i, z)); \
143 Tensor* output = nullptr; variable
144 OP_REQUIRES_OK(context, context->allocate_output(0, output_shape, &output));
148 output->flat_inner_outer_dims<T, 3>(axis - 1))
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| /external/tensorflow/tensorflow/contrib/tensorrt/ |
| tensorrt_test.cc | 63 const char* kOutputTensor = "output";
80 // Mark the output.
81 auto output = layer->getOutput(0); local
82 output->setName(kOutputTensor);
83 network->markOutput(*output);
99 float* output) {
102 // We have two bindings: input and output.
114 // Copy the input to the GPU, execute the network, and copy the output back.
122 ASSERT_EQ(0, cudaMemcpyAsync(output, buffers[output_index], sizeof(float),
145 float output; local
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| /external/tensorflow/tensorflow/core/grappler/costs/ |
| op_level_cost_estimator_test.cc | 130 auto output = op_context.op_info.add_outputs(); local
131 auto shape = output->mutable_shape();
136 output->set_dtype(DT_FLOAT);
|
| /external/tensorflow/tensorflow/core/kernels/ |
| argmax_op.cc | 76 Tensor* output = nullptr; variable
77 OP_REQUIRES_OK(context, context->allocate_output(0, output_shape, &output));
83 output->tensor<Tout, NDIM - 1>()); \
158 const int32 dimension, typename TTypes<Tout, Dims - 1>::Tensor output); \
162 const int32 dimension, typename TTypes<Tout, Dims - 1>::Tensor output);
|
| betainc_op.cc | 73 Tensor* output = nullptr; variable
74 OP_REQUIRES_OK(ctx, ctx->allocate_output(0, merged_shape, &output));
79 x.flat<T>(), output->flat<T>());
101 output->shaped<T, NDIM>(a_shaper.y_reshape())); \
134 typename TTypes<T, NDIM>::Tensor output); \
143 typename TTypes<T, NDIM>::Tensor output); \
|
| bincount_op.cc | 41 typename TTypes<T, 1>::Tensor& output) {
42 int size = output.size();
53 // Allocate partial output bin sums for each worker thread. Worker ids in
82 output.device(context->eigen_cpu_device()) = partial_bins.sum(reduce_dims);
109 auto output = output_t->flat<T>(); variable
111 ctx, arr, weights, output));
|
| concat_op_test.cc | 182 bfloat16* output = &result[j * dim2]; local
187 memcpy(output, inputs[j], dim2 * sizeof(bfloat16));
189 output += dim2 * kNumCopies;
216 bfloat16* output = result; local
222 memcpy(output, inputs[j], dim2 * sizeof(bfloat16));
224 output += dim2;
|
| decode_bmp_op.cc | 132 Tensor* output = nullptr; variable
135 0, TensorShape({abs_height, width, channels_}), &output));
139 Decode(bmp_pixels, row_size, output->flat<uint8>().data(), width,
143 uint8* Decode(const uint8* input, const int row_size, uint8* const output,
153 uint8* const output, const int width,
171 output[dst_pos] = input[src_pos];
175 output[dst_pos] = input[src_pos + 2];
176 output[dst_pos + 1] = input[src_pos + 1];
177 output[dst_pos + 2] = input[src_pos];
181 output[dst_pos] = input[src_pos + 2]
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| dequantize_op.cc | 71 Tensor* output = nullptr; variable
72 OP_REQUIRES_OK(ctx, ctx->allocate_output(0, input.shape(), &output));
79 float* out_ptr = output->flat<float>().data();
92 min_range, max_range, output->flat<float>().data());
96 output);
115 float* out_ptr = output->flat<float>().data();
|
| diag_op.cc | 98 Tensor* output = nullptr; variable
99 OP_REQUIRES_OK(context, context->allocate_output(0, out_shape, &output));
102 tensor.flat<T>().data(), output->flat<T>().data());
110 // `output[i1,..., ik, i1,..., ik] = input[i1,..., ik]`,
116 // Let new_index is the offset of output's pointer with coordinate
|
| draw_bounding_box_op.cc | 80 Tensor* output; variable
84 0, TensorShape({batch_size, height, width, depth}), &output));
86 output->tensor<T, 4>() = images.tensor<T, 4>();
87 auto canvas = output->tensor<T, 4>();
|
| image_resizer_state.h | 16 // This is a helper struct to package up the input and output
54 // height_scale and width_scale, and calculates the output size.
84 errors::InvalidArgument("output dimensions must be positive"));
107 // Calculates all the required variables, and allocates the output.
115 &output));
126 Tensor* output = nullptr; member in struct:tensorflow::ImageResizerState
151 // Allocate output and initialize to zeros.
170 output = nullptr;
175 &output));
186 Tensor* output; member in struct:tensorflow::ImageResizerGradientState
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