/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},
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hashtable_lookup_quant8.model.cpp | 7 .dimensions = {4}, 16 .dimensions = {3}, 25 .dimensions = {3, 2}, 34 .dimensions = {4, 2}, 43 .dimensions = {4},
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l2_pool_float.model.cpp | 7 .dimensions = {1, 2, 2, 1}, 16 .dimensions = {}, 25 .dimensions = {}, 34 .dimensions = {}, 43 .dimensions = {1, 2, 2, 1},
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lsh_projection.model.cpp | 7 .dimensions = {4, 2}, 16 .dimensions = {3, 2}, 25 .dimensions = {3}, 34 .dimensions = {}, 43 .dimensions = {8},
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lsh_projection_2.model.cpp | 7 .dimensions = {4, 2}, 16 .dimensions = {3, 2}, 25 .dimensions = {3}, 34 .dimensions = {}, 43 .dimensions = {4},
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lsh_projection_weights_as_inputs.model.cpp | 7 .dimensions = {4, 2}, 16 .dimensions = {3, 2}, 25 .dimensions = {3}, 34 .dimensions = {1}, 43 .dimensions = {8},
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max_pool_float_1.model.cpp | 7 .dimensions = {1, 2, 2, 1}, 16 .dimensions = {}, 25 .dimensions = {}, 34 .dimensions = {}, 43 .dimensions = {1, 2, 2, 1},
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max_pool_quant8_1.model.cpp | 7 .dimensions = {1, 2, 2, 1}, 16 .dimensions = {}, 25 .dimensions = {}, 34 .dimensions = {}, 43 .dimensions = {1, 2, 2, 1},
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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},
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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},
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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},
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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},
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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},
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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},
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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},
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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},
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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},
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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},
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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},
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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},
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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},
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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},
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/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);
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/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];
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