/frameworks/ml/nn/runtime/test/specs/V1_0/ |
H A 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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H A 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*features)) 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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H A 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*features)) 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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/frameworks/ml/nn/runtime/test/specs/V1_1/ |
H A 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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H A 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*features)) 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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H A 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*features)) 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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/frameworks/ml/nn/common/operations/ |
H A D | SVDF.cpp | 79 const uint32_t memory_size = SizeOfDimension(weights_time, 1); local 92 stateShape->dimensions = { batch_size, memory_size * num_filters }; 111 const int memory_size = SizeOfDimension(weights_time_, 1); local 114 sizeof(float) * batch_size * memory_size * num_filters); 117 float* state_ptr_batch = GetBuffer<float>(state_out_) + b * memory_size * num_filters; 119 float* state_ptr = state_ptr_batch + c * memory_size; 120 state_ptr[memory_size - 1] = 0.0; 124 // is achieved by starting at state->data.f[memory_size - 1] and having the 125 // stride equal to memory_size. 129 &GetBuffer<float>(state_out_)[memory_size 141 GetBuffer<float>(weights_time_), state_out_ptr_batch, memory_size, num_filters, local [all...] |
H A D | SVDFTest.cpp | 187 uint32_t memory_size, uint32_t rank) 191 memory_size_(memory_size), 198 {batches_, memory_size * units_ * rank_}, // state in tensor 340 /*memory_size=*/10, /*rank=*/1); 387 /*memory_size=*/10, /*rank=*/2); 186 SVDFOpModel(uint32_t batches, uint32_t units, uint32_t input_size, uint32_t memory_size, uint32_t rank) argument
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