| /frameworks/ml/nn/runtime/test/generated/vts_models/ |
| hashtable_lookup_float.model.cpp | 7 .dimensions = {4},
16 .dimensions = {3},
25 .dimensions = {3, 2},
34 .dimensions = {4, 2},
43 .dimensions = {4},
|
| hashtable_lookup_quant8.model.cpp | 7 .dimensions = {4},
16 .dimensions = {3},
25 .dimensions = {3, 2},
34 .dimensions = {4, 2},
43 .dimensions = {4},
|
| l2_pool_float.model.cpp | 7 .dimensions = {1, 2, 2, 1},
16 .dimensions = {},
25 .dimensions = {},
34 .dimensions = {},
43 .dimensions = {1, 2, 2, 1},
|
| lsh_projection.model.cpp | 7 .dimensions = {4, 2},
16 .dimensions = {3, 2},
25 .dimensions = {3},
34 .dimensions = {},
43 .dimensions = {8},
|
| lsh_projection_2.model.cpp | 7 .dimensions = {4, 2},
16 .dimensions = {3, 2},
25 .dimensions = {3},
34 .dimensions = {},
43 .dimensions = {4},
|
| lsh_projection_weights_as_inputs.model.cpp | 7 .dimensions = {4, 2},
16 .dimensions = {3, 2},
25 .dimensions = {3},
34 .dimensions = {1},
43 .dimensions = {8},
|
| max_pool_float_1.model.cpp | 7 .dimensions = {1, 2, 2, 1},
16 .dimensions = {},
25 .dimensions = {},
34 .dimensions = {},
43 .dimensions = {1, 2, 2, 1},
|
| max_pool_quant8_1.model.cpp | 7 .dimensions = {1, 2, 2, 1},
16 .dimensions = {},
25 .dimensions = {},
34 .dimensions = {},
43 .dimensions = {1, 2, 2, 1},
|
| conv_float.model.cpp | 7 .dimensions = {1, 3, 3, 1},
16 .dimensions = {1, 2, 2, 1},
25 .dimensions = {1},
34 .dimensions = {},
43 .dimensions = {},
52 .dimensions = {},
61 .dimensions = {1, 2, 2, 1},
|
| conv_float_channels.model.cpp | 7 .dimensions = {1, 1, 1, 3},
16 .dimensions = {3, 1, 1, 3},
25 .dimensions = {3},
34 .dimensions = {},
43 .dimensions = {},
52 .dimensions = {},
61 .dimensions = {1, 1, 1, 3},
|
| conv_float_channels_weights_as_inputs.model.cpp | 7 .dimensions = {1, 1, 1, 3},
16 .dimensions = {3, 1, 1, 3},
25 .dimensions = {3},
34 .dimensions = {},
43 .dimensions = {},
52 .dimensions = {},
61 .dimensions = {1, 1, 1, 3},
|
| conv_float_large.model.cpp | 7 .dimensions = {1, 2, 3, 3},
16 .dimensions = {3, 1, 1, 3},
25 .dimensions = {3},
34 .dimensions = {},
43 .dimensions = {},
52 .dimensions = {},
61 .dimensions = {1, 2, 3, 3},
|
| conv_float_large_weights_as_inputs.model.cpp | 7 .dimensions = {1, 2, 3, 3},
16 .dimensions = {3, 1, 1, 3},
25 .dimensions = {3},
34 .dimensions = {},
43 .dimensions = {},
52 .dimensions = {},
61 .dimensions = {1, 2, 3, 3},
|
| conv_float_weights_as_inputs.model.cpp | 7 .dimensions = {1, 3, 3, 1},
16 .dimensions = {1, 2, 2, 1},
25 .dimensions = {1},
34 .dimensions = {},
43 .dimensions = {},
52 .dimensions = {},
61 .dimensions = {1, 2, 2, 1},
|
| conv_quant8.model.cpp | 7 .dimensions = {1, 3, 3, 1},
16 .dimensions = {1, 2, 2, 1},
25 .dimensions = {1},
34 .dimensions = {},
43 .dimensions = {},
52 .dimensions = {},
61 .dimensions = {1, 2, 2, 1},
|
| conv_quant8_channels.model.cpp | 7 .dimensions = {1, 1, 1, 3},
16 .dimensions = {3, 1, 1, 3},
25 .dimensions = {3},
34 .dimensions = {},
43 .dimensions = {},
52 .dimensions = {},
61 .dimensions = {1, 1, 1, 3},
|
| conv_quant8_channels_weights_as_inputs.model.cpp | 7 .dimensions = {1, 1, 1, 3},
16 .dimensions = {3, 1, 1, 3},
25 .dimensions = {3},
34 .dimensions = {},
43 .dimensions = {},
52 .dimensions = {},
61 .dimensions = {1, 1, 1, 3},
|
| conv_quant8_large.model.cpp | 7 .dimensions = {1, 2, 3, 3},
16 .dimensions = {3, 1, 1, 3},
25 .dimensions = {3},
34 .dimensions = {},
43 .dimensions = {},
52 .dimensions = {},
61 .dimensions = {1, 2, 3, 3},
|
| conv_quant8_large_weights_as_inputs.model.cpp | 7 .dimensions = {1, 2, 3, 3},
16 .dimensions = {3, 1, 1, 3},
25 .dimensions = {3},
34 .dimensions = {},
43 .dimensions = {},
52 .dimensions = {},
61 .dimensions = {1, 2, 3, 3},
|
| conv_quant8_overflow.model.cpp | 7 .dimensions = {1, 2, 3, 3},
16 .dimensions = {3, 1, 1, 3},
25 .dimensions = {3},
34 .dimensions = {},
43 .dimensions = {},
52 .dimensions = {},
61 .dimensions = {1, 2, 3, 3},
|
| conv_quant8_overflow_weights_as_inputs.model.cpp | 7 .dimensions = {1, 2, 3, 3},
16 .dimensions = {3, 1, 1, 3},
25 .dimensions = {3},
34 .dimensions = {},
43 .dimensions = {},
52 .dimensions = {},
61 .dimensions = {1, 2, 3, 3},
|
| conv_quant8_weights_as_inputs.model.cpp | 7 .dimensions = {1, 3, 3, 1},
16 .dimensions = {1, 2, 2, 1},
25 .dimensions = {1},
34 .dimensions = {},
43 .dimensions = {},
52 .dimensions = {},
61 .dimensions = {1, 2, 2, 1},
|
| /art/runtime/mirror/ |
| array.cc | 42 // Recursively create an array with multiple dimensions. Elements may be
46 Handle<mirror::IntArray> dimensions)
48 int32_t array_length = dimensions->Get(current_dimension);
59 if (current_dimension + 1 < dimensions->GetLength()) {
65 current_dimension + 1, dimensions);
78 Handle<IntArray> dimensions) {
79 // Verify dimensions.
83 int num_dimensions = dimensions->GetLength();
88 int dimension = dimensions->Get(i);
105 for (int32_t i = 1; i < dimensions->GetLength(); ++i)
[all...] |
| /development/samples/browseable/HdrViewfinder/src/com.example.android.hdrviewfinder/ |
| ViewfinderProcessor.java | 55 public ViewfinderProcessor(RenderScript rs, Size dimensions) {
56 mSize = dimensions;
59 yuvTypeBuilder.setX(dimensions.getWidth());
60 yuvTypeBuilder.setY(dimensions.getHeight());
68 rgbTypeBuilder.setX(dimensions.getWidth());
69 rgbTypeBuilder.setY(dimensions.getHeight());
83 mHdrTask = new ProcessingTask(mInputHdrAllocation, dimensions.getWidth()/2, true);
|
| /frameworks/ml/nn/common/operations/ |
| RNN.cpp | 71 hiddenStateShape->dimensions = { batch_size, num_units };
75 outputShape->dimensions = { batch_size, num_units };
83 const uint32_t batch_size = input_->shape().dimensions[0];
84 const uint32_t num_units = weights_->shape().dimensions[0];
85 const uint32_t input_size = input_->shape().dimensions[1];
86 const uint32_t input_weights_stride = weights_->shape().dimensions[1];
88 recurrent_weights_->shape().dimensions[1];
|
