/frameworks/ml/nn/common/operations/ |
D | SVDF.cpp | 87 const uint32_t memory_size = SizeOfDimension(weights_time, 1); in Prepare() local 99 stateShape->dimensions = {batch_size, memory_size * num_filters}; in Prepare() 174 const int memory_size = SizeOfDimension(weights_time_, 1); in EvalFloat32() local 176 memcpy(outputStateData, inputStateData, sizeof(float) * batch_size * memory_size * num_filters); in EvalFloat32() 179 float* state_ptr_batch = outputStateData + b * memory_size * num_filters; in EvalFloat32() 181 float* state_ptr = state_ptr_batch + c * memory_size; in EvalFloat32() 182 state_ptr[memory_size - 1] = 0.0; in EvalFloat32() 190 &outputStateData[memory_size - 1], memory_size); in EvalFloat32() 198 float* state_out_ptr_batch = outputStateData + b * memory_size * num_filters; in EvalFloat32() 201 weightsTimeData, state_out_ptr_batch, memory_size, num_filters, scratch_ptr_batch, in EvalFloat32() [all …]
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D | SVDFTest.cpp | 166 SVDFOpModel(uint32_t batches, uint32_t units, uint32_t input_size, uint32_t memory_size, in SVDFOpModel() argument 171 memory_size_(memory_size), in SVDFOpModel() 178 {batches_, memory_size * units_ * rank_}, // state in tensor in SVDFOpModel()
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/frameworks/ml/nn/runtime/test/specs/V1_2/ |
D | svdf_float16.mod.py | 22 memory_size = 10 variable 28 weights_time = Input("weights_time", "TENSOR_FLOAT16", "{%d, %d}" % (features, memory_size)) 30 state_in = Input("state_in", "TENSOR_FLOAT16", "{%d, %d}" % (batches, memory_size*features)) 33 state_out = IgnoredOutput("state_out", "TENSOR_FLOAT16", "{%d, %d}" % (batches, memory_size*feature… 60 state_in: [0 for _ in range(batches * memory_size * features)], 127 output0 = {state_out: [0 for _ in range(batches * memory_size * features)],
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D | svdf_bias_present_float16.mod.py | 22 memory_size = 10 variable 28 weights_time = Input("weights_time", "TENSOR_FLOAT16", "{%d, %d}" % (features, memory_size)) 30 state_in = Input("state_in", "TENSOR_FLOAT16", "{%d, %d}" % (batches, memory_size*features)) 33 state_out = IgnoredOutput("state_out", "TENSOR_FLOAT16", "{%d, %d}" % (batches, memory_size*feature… 60 state_in: [0 for _ in range(batches * memory_size * features)], 127 output0 = {state_out: [0 for _ in range(batches * memory_size * features)],
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D | svdf_state_float16.mod.py | 20 memory_size = 10 variable 26 weights_time = Input("weights_time", "TENSOR_FLOAT16", "{%d, %d}" % (units, memory_size)) 28 state_in = Input("state_in", "TENSOR_FLOAT16", "{%d, %d}" % (batches, memory_size*units)) 31 state_out = Output("state_out", "TENSOR_FLOAT16", "{%d, %d}" % (batches, memory_size*units))
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/frameworks/ml/nn/runtime/test/specs/V1_0/ |
D | svdf_bias_present.mod.py | 22 memory_size = 10 variable 28 weights_time = Input("weights_time", "TENSOR_FLOAT32", "{%d, %d}" % (features, memory_size)) 30 state_in = Input("state_in", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*features)) 33 state_out = IgnoredOutput("state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*feature… 60 state_in: [0 for _ in range(batches * memory_size * features)], 127 output0 = {state_out: [0 for _ in range(batches * memory_size * features)],
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D | svdf2.mod.py | 22 memory_size = 10 variable 28 weights_time = Input("weights_time", "TENSOR_FLOAT32", "{%d, %d}" % (features, memory_size)) 30 state_in = Input("state_in", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*features)) 33 state_out = IgnoredOutput("state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*feature… 75 state_in: [0 for _ in range(batches * memory_size * features)], 142 output0 = {state_out: [0 for _ in range(batches * memory_size * features)],
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D | svdf.mod.py | 22 memory_size = 10 variable 28 weights_time = Input("weights_time", "TENSOR_FLOAT32", "{%d, %d}" % (features, memory_size)) 30 state_in = Input("state_in", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*features)) 33 state_out = IgnoredOutput("state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*feature… 60 state_in: [0 for _ in range(batches * memory_size * features)], 127 output0 = {state_out: [0 for _ in range(batches * memory_size * features)],
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D | svdf_state.mod.py | 20 memory_size = 10 variable 26 weights_time = Input("weights_time", "TENSOR_FLOAT32", "{%d, %d}" % (units, memory_size)) 28 state_in = Input("state_in", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*units)) 31 state_out = Output("state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*units))
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/frameworks/ml/nn/runtime/test/specs/V1_1/ |
D | svdf2_relaxed.mod.py | 22 memory_size = 10 variable 28 weights_time = Input("weights_time", "TENSOR_FLOAT32", "{%d, %d}" % (features, memory_size)) 30 state_in = Input("state_in", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*features)) 33 state_out = IgnoredOutput("state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*feature… 76 state_in: [0 for _ in range(batches * memory_size * features)], 143 output0 = {state_out: [0 for _ in range(batches * memory_size * features)],
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D | svdf_relaxed.mod.py | 22 memory_size = 10 variable 28 weights_time = Input("weights_time", "TENSOR_FLOAT32", "{%d, %d}" % (features, memory_size)) 30 state_in = Input("state_in", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*features)) 33 state_out = IgnoredOutput("state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*feature… 61 state_in: [0 for _ in range(batches * memory_size * features)], 128 output0 = {state_out: [0 for _ in range(batches * memory_size * features)],
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D | svdf_bias_present_relaxed.mod.py | 22 memory_size = 10 variable 28 weights_time = Input("weights_time", "TENSOR_FLOAT32", "{%d, %d}" % (features, memory_size)) 30 state_in = Input("state_in", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*features)) 33 state_out = IgnoredOutput("state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*feature… 61 state_in: [0 for _ in range(batches * memory_size * features)], 128 output0 = {state_out: [0 for _ in range(batches * memory_size * features)],
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D | svdf_state_relaxed.mod.py | 20 memory_size = 10 variable 26 weights_time = Input("weights_time", "TENSOR_FLOAT32", "{%d, %d}" % (units, memory_size)) 28 state_in = Input("state_in", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*units)) 31 state_out = Output("state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*units))
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/frameworks/native/libs/vr/libbroadcastring/include/libbroadcastring/ |
D | broadcast_ring.h | 499 size_t memory_size = record_count() * record_size(); in ValidateGeometry() local 500 if (memory_size / record_size() != record_count()) return false; in ValidateGeometry() 501 if (memory_size + sizeof(Header) < memory_size) return false; in ValidateGeometry() 502 if (memory_size + sizeof(Header) > mmap_size) return false; in ValidateGeometry()
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/frameworks/minikin/tests/util/ |
D | FreeTypeMinikinFontForTest.cpp | 80 args.memory_size = mFontSize; in FreeTypeMinikinFontForTest()
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/frameworks/ml/nn/tools/api/ |
D | types.spec | 2266 * get pushed into a memory of fixed-size memory_size. 2267 * * stage 2 performs filtering on the "time" dimension of the memory_size 2308 * A 2-D tensor of shape [num_units, memory_size], where “memory_size” 2313 * A 2-D tensor of shape [batch_size, (memory_size - 1) * num_units * rank]. 2324 * [batch_size, (memory_size - 1) * num_units * rank].
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