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/packages/modules/NeuralNetworks/runtime/test/specs/V1_2/
Dbidirectional_sequence_rnn.mod.py84 input_size = 8 variable
210 num_batches, max_time, input_size)),
212 fw_num_units, input_size)),
220 bw_num_units, input_size)),
260 max_time, num_batches, input_size)),
262 fw_num_units, input_size)),
270 bw_num_units, input_size)),
292 [num_batches, max_time, input_size]),
313 max_time, num_batches, input_size)),
315 fw_num_units, input_size)),
[all …]
Dunidirectional_sequence_rnn.mod.py39 def convert_to_time_major(tensor, num_batches, max_time, input_size): argument
41 input_size]).transpose([1, 0, 2]).flatten().tolist()
46 input_size = 8 variable
142 num_batches, max_time, input_size)),
144 num_units, input_size)),
164 max_time, num_batches, input_size)),
166 num_units, input_size)),
177 input_size),
Drnn_float16.mod.py19 input_size = 8 variable
23 input = Input("input", "TENSOR_FLOAT16", "{%d, %d}" % (batches, input_size))
24 weights = Input("weights", "TENSOR_FLOAT16", "{%d, %d}" % (units, input_size))
184 input_sequence_size = int(len(test_inputs) / input_size / batches)
189 input_begin = i * input_size
190 input_end = input_begin + input_size
Dsvdf_bias_present_float16.mod.py21 input_size = 3 variable
26 input = Input("input", "TENSOR_FLOAT16", "{%d, %d}" % (batches, input_size))
27 weights_feature = Input("weights_feature", "TENSOR_FLOAT16", "{%d, %d}" % (features, input_size))
132 batch_start = i * input_size * batches
133 batch_end = batch_start + input_size * batches
Dsvdf_float16.mod.py21 input_size = 3 variable
26 input = Input("input", "TENSOR_FLOAT16", "{%d, %d}" % (batches, input_size))
27 weights_feature = Input("weights_feature", "TENSOR_FLOAT16", "{%d, %d}" % (features, input_size))
132 batch_start = i * input_size * batches
133 batch_end = batch_start + input_size * batches
Dsvdf_state_float16.mod.py19 input_size = 3 variable
24 input = Input("input", "TENSOR_FLOAT16", "{%d, %d}" % (batches, input_size))
25 weights_feature = Input("weights_feature", "TENSOR_FLOAT16", "{%d, %d}" % (units, input_size))
/packages/modules/NeuralNetworks/runtime/test/specs/V1_3/
Dbidirectional_sequence_rnn_state_output.mod.py92 input_size = 8 variable
221 "{{ {}, {}, {} }}".format(num_batches, max_time, input_size)),
223 "{{ {}, {} }}".format(fw_num_units, input_size)),
231 "{{ {}, {} }}".format(bw_num_units, input_size)),
277 "{{ {}, {}, {} }}".format(max_time, num_batches, input_size)),
279 "{{ {}, {} }}".format(fw_num_units, input_size)),
287 "{{ {}, {} }}".format(bw_num_units, input_size)),
313 [num_batches, max_time, input_size]),
336 "{{ {}, {}, {} }}".format(max_time, num_batches, input_size)),
338 "{{ {}, {} }}".format(fw_num_units, input_size)),
[all …]
Dbidirectional_sequence_rnn_1_3.mod.py90 input_size = 8 variable
216 "{{ {}, {}, {} }}".format(num_batches, max_time, input_size)),
218 "{{ {}, {} }}".format(fw_num_units, input_size)),
226 "{{ {}, {} }}".format(bw_num_units, input_size)),
235 "{{ {}, {}, {} }}".format(num_batches, max_time, input_size)),
248 fw_weights_data=[0] * fw_num_units * input_size,
266 "{{ {}, {}, {} }}".format(max_time, num_batches, input_size)),
268 "{{ {}, {} }}".format(fw_num_units, input_size)),
276 "{{ {}, {} }}".format(bw_num_units, input_size)),
285 "{{ {}, {}, {} }}".format(max_time, num_batches, input_size)),
[all …]
Dunidirectional_sequence_rnn.mod.py42 def convert_to_time_major(tensor, num_batches, max_time, input_size): argument
43 return np.array(tensor).reshape([num_batches, max_time, input_size
49 input_size = 8 variable
180 "{{{}, {}, {}}}".format(num_batches, max_time, input_size)),
182 "{{{}, {}}}".format(num_units, input_size)),
206 "{{{}, {}, {}}}".format(max_time, num_batches, input_size)),
208 "{{{}, {}}}".format(num_units, input_size)),
221 input_size),
/packages/modules/Virtualization/libs/apkverify/src/
Dhashtree.rs33 input_size: usize, in from()
39 let tree = generate_hash_tree(input, input_size, &salt, block_size, algorithm)?; in from()
67 input_size: usize, in generate_hash_tree()
73 let levels = calc_hash_levels(input_size, block_size, digest_size); in generate_hash_tree()
82 let pad_size = round_to_multiple(input_size, block_size) - input_size; in generate_hash_tree()
87 let mut num_blocks = (input_size + block_size - 1) / block_size; in generate_hash_tree()
131 fn calc_hash_levels(input_size: usize, block_size: usize, digest_size: usize) -> Vec<Range> { in calc_hash_levels()
137 let input_size = *level_sizes.last().unwrap_or(&input_size); in calc_hash_levels() localVariable
138 if input_size <= block_size { in calc_hash_levels()
141 let num_blocks = (input_size + block_size - 1) / block_size; in calc_hash_levels()
/packages/modules/NeuralNetworks/runtime/test/specs/V1_1/
Drnn_relaxed.mod.py19 input_size = 8 variable
23 input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (batches, input_size))
24 weights = Input("weights", "TENSOR_FLOAT32", "{%d, %d}" % (units, input_size))
185 input_sequence_size = int(len(test_inputs) / input_size / batches)
190 input_begin = i * input_size
191 input_end = input_begin + input_size
Dsvdf2_relaxed.mod.py21 input_size = 3 variable
26 input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (batches, input_size))
27 weights_feature = Input("weights_feature", "TENSOR_FLOAT32", "{%d, %d}" % (features, input_size))
148 batch_start = i * input_size * batches
149 batch_end = batch_start + input_size * batches
Dsvdf_relaxed.mod.py21 input_size = 3 variable
26 input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (batches, input_size))
27 weights_feature = Input("weights_feature", "TENSOR_FLOAT32", "{%d, %d}" % (features, input_size))
133 batch_start = i * input_size * batches
134 batch_end = batch_start + input_size * batches
Dsvdf_bias_present_relaxed.mod.py21 input_size = 3 variable
26 input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (batches, input_size))
27 weights_feature = Input("weights_feature", "TENSOR_FLOAT32", "{%d, %d}" % (features, input_size))
133 batch_start = i * input_size * batches
134 batch_end = batch_start + input_size * batches
Drnn_state_relaxed.mod.py19 input_size = 8 variable
23 input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (batches, input_size))
24 weights = Input("weights", "TENSOR_FLOAT32", "{%d, %d}" % (units, input_size))
Dsvdf_state_relaxed.mod.py19 input_size = 3 variable
24 input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (batches, input_size))
25 weights_feature = Input("weights_feature", "TENSOR_FLOAT32", "{%d, %d}" % (units, input_size))
/packages/modules/NeuralNetworks/runtime/test/specs/V1_0/
Drnn.mod.py19 input_size = 8 variable
23 input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (batches, input_size))
24 weights = Input("weights", "TENSOR_FLOAT32", "{%d, %d}" % (units, input_size))
184 input_sequence_size = int(len(test_inputs) / input_size / batches)
189 input_begin = i * input_size
190 input_end = input_begin + input_size
Dsvdf.mod.py21 input_size = 3 variable
26 input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (batches, input_size))
27 weights_feature = Input("weights_feature", "TENSOR_FLOAT32", "{%d, %d}" % (features, input_size))
132 batch_start = i * input_size * batches
133 batch_end = batch_start + input_size * batches
Dsvdf_bias_present.mod.py21 input_size = 3 variable
26 input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (batches, input_size))
27 weights_feature = Input("weights_feature", "TENSOR_FLOAT32", "{%d, %d}" % (features, input_size))
132 batch_start = i * input_size * batches
133 batch_end = batch_start + input_size * batches
Dsvdf2.mod.py21 input_size = 3 variable
26 input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (batches, input_size))
27 weights_feature = Input("weights_feature", "TENSOR_FLOAT32", "{%d, %d}" % (features, input_size))
147 batch_start = i * input_size * batches
148 batch_end = batch_start + input_size * batches
Drnn_state.mod.py19 input_size = 8 variable
23 input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (batches, input_size))
24 weights = Input("weights", "TENSOR_FLOAT32", "{%d, %d}" % (units, input_size))
Dsvdf_state.mod.py19 input_size = 3 variable
24 input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (batches, input_size))
25 weights_feature = Input("weights_feature", "TENSOR_FLOAT32", "{%d, %d}" % (units, input_size))
/packages/modules/NeuralNetworks/common/types/operations/src/
DFullyConnected.cpp44 uint32_t input_size = getSizeOfDimension(weights, 1u); in validateShapes() local
47 if (input_size != 0) { in validateShapes()
48 NN_RET_CHECK_EQ(input_n_elements % input_size, 0u); in validateShapes()
49 batch_size = input_n_elements / input_size; in validateShapes()
57 NN_RET_CHECK_GT(input_size, 0u); in validateShapes()
/packages/modules/NeuralNetworks/common/cpu_operations/
DRNNTest.cpp188 uint32_t input_size() const { return input_size_; } in input_size() function in android::nn::wrapper::BasicRNNOpModel
284 sizeof(rnn_input) / sizeof(float) / (rnn.input_size() * rnn.num_batches()); in TEST()
287 float* batch_start = rnn_input + i * rnn.input_size(); in TEST()
288 float* batch_end = batch_start + rnn.input_size(); in TEST()
290 rnn.SetInput(rnn.input_size(), batch_start, batch_end); in TEST()
DSVDFTest.cpp168 SVDFOpModel(uint32_t batches, uint32_t units, uint32_t input_size, uint32_t memory_size, in SVDFOpModel() argument
172 input_size_(input_size), in SVDFOpModel()
295 int input_size() const { return input_size_; } in input_size() function in android::nn::wrapper::SVDFOpModel
339 const int svdf_input_size = svdf.input_size(); in TEST()
398 const int svdf_input_size = svdf.input_size(); in TEST()

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