| /frameworks/ml/nn/runtime/test/specs/V1_0/ |
| relu6_quant8_2.mod.py | 27 output = Output("output", "TENSOR_QUANT8_ASYMM", "{%d, %d, %d, %d}, 1.f, 128" % (d0, d1, d2, d3)) variable
29 model = model.Operation("RELU6", i0).To(output)
36 output0 = {output: output_values}
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| relu_float_2.mod.py | 27 output = Output("output", "TENSOR_FLOAT32", "{%d, %d, %d, %d}" % (d0, d1, d2, d3)) variable
29 model = model.Operation("RELU", i0).To(output)
36 output0 = {output: output_values}
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| relu_quant8_2.mod.py | 27 output = Output("output", "TENSOR_QUANT8_ASYMM", "{%d, %d, %d, %d}, 1.f, 128" % (d0, d1, d2, d3)) variable
29 model = model.Operation("RELU", i0).To(output)
36 output0 = {output: output_values}
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| rnn_state.mod.py | 32 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (batches, units)) variable
35 activation_param).To([hidden_state_out, output])
116 output0[output] = [
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| svdf_state.mod.py | 31 state_out = Output("state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*units))
32 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (batches, units)) variable
35 rank_param, activation_param).To([state_out, output])
108 output : [
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| /frameworks/ml/nn/runtime/test/specs/V1_1/ |
| concat_float_2_relaxed.mod.py | 28 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (output_row, col)) # output variable
29 model = model.Operation("CONCATENATION", input1, input2, axis0).To(output)
39 output0 = {output: output_values}
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| concat_float_3_relaxed.mod.py | 28 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (row, output_col)) # output variable
29 model = model.Operation("CONCATENATION", input1, input2, axis1).To(output)
45 output0 = {output: output_values}
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| depthwise_conv2d_float_2_relaxed.mod.py | 25 output = Output("op4", "TENSOR_FLOAT32", "{1, 2, 1, 4}") variable
31 cm, act_none).To(output)
40 output0 = {output: # output 0
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| depthwise_conv2d_float_large_2_relaxed.mod.py | 25 output = Output("op4", "TENSOR_FLOAT32", "{1, 1, 1, 4}") variable
31 cm, act).To(output)
43 output0 = {output: # output 0
|
| depthwise_conv2d_float_large_2_weights_as_inputs_relaxed.mod.py | 25 output = Output("op4", "TENSOR_FLOAT32", "{1, 1, 1, 4}") variable
31 cm, act).To(output)
50 output0 = {output: # output 0
|
| depthwise_conv2d_float_large_relaxed.mod.py | 25 output = Output("op4", "TENSOR_FLOAT32", "{1, 1, 1, 2}") variable
31 cm, act).To(output)
41 output0 = {output: # output 0
|
| depthwise_conv2d_float_large_weights_as_inputs_relaxed.mod.py | 25 output = Output("op4", "TENSOR_FLOAT32", "{1, 1, 1, 2}") variable
31 cm, act).To(output)
46 output0 = {output: # output 0
|
| depthwise_conv2d_float_relaxed.mod.py | 25 output = Output("op4", "TENSOR_FLOAT32", "{1, 2, 2, 4}") variable
31 cm, act).To(output)
45 output0 = {output: # output 0
|
| depthwise_conv2d_float_weights_as_inputs_relaxed.mod.py | 25 output = Output("op4", "TENSOR_FLOAT32", "{1, 2, 2, 4}") variable
31 cm, act).To(output)
52 output0 = {output: # output 0
|
| embedding_lookup_relaxed.mod.py | 31 output = Output("output", "TENSOR_FLOAT32", "{%d, %d, %d}" % (lookups, columns, features)) variable
32 model = model.Operation("EMBEDDING_LOOKUP", index, value).To(output)
38 output0 = {output:
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| hashtable_lookup_float_relaxed.mod.py | 32 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (lookups, features)) variable
33 hits = Output("hits", "TENSOR_QUANT8_ASYMM", "{%d}, 1.f, 0" % (lookups))
34 model = model.Operation("HASHTABLE_LOOKUP", lookup, key, value).To([output, hits])
41 output0 = {output:
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| logistic_float_2_relaxed.mod.py | 27 output = Output("output", "TENSOR_FLOAT32", "{%d, %d, %d, %d}" % (d0, d1, d2, d3)) variable
29 model = model.Operation("LOGISTIC", i0).To(output)
37 output0 = {output: output_values}
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| lsh_projection_2_relaxed.mod.py | 28 output = Output("output", "TENSOR_INT32", "{%d}" % (num_hash)) variable
30 type_param).To(output)
40 output0 = {output: [1, 2, 2, 0]}
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| lsh_projection_relaxed.mod.py | 28 output = Output("output", "TENSOR_INT32", "{%d}" % (num_hash * num_bits)) variable
30 type_param).To(output)
38 output0 = {output: [1, 1, 1, 0, 1, 1, 1, 0]}
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| lsh_projection_weights_as_inputs_relaxed.mod.py | 27 output = Output("output", "TENSOR_INT32", "{%d}" % (num_hash * num_bits)) variable
28 model = model.Operation("LSH_PROJECTION", hhash, lookup, weight, type_param).To(output)
36 output0 = {output: [1, 1, 1, 0, 1, 1, 1, 0]}
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| relu1_float_2_relaxed.mod.py | 27 output = Output("output", "TENSOR_FLOAT32", "{%d, %d, %d, %d}" % (d0, d1, d2, d3)) variable
29 model = model.Operation("RELU1", i0).To(output)
37 output0 = {output: output_values}
|
| relu6_float_2_relaxed.mod.py | 27 output = Output("output", "TENSOR_FLOAT32", "{%d, %d, %d, %d}" % (d0, d1, d2, d3)) variable
29 model = model.Operation("RELU6", i0).To(output)
37 output0 = {output: output_values}
|
| relu_float_2_relaxed.mod.py | 27 output = Output("output", "TENSOR_FLOAT32", "{%d, %d, %d, %d}" % (d0, d1, d2, d3)) variable
29 model = model.Operation("RELU", i0).To(output)
37 output0 = {output: output_values}
|
| rnn_state_relaxed.mod.py | 32 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (batches, units)) variable
35 activation_param).To([hidden_state_out, output])
117 output0[output] = [
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| strided_slice.mod.py | 10 output = Output("output", "TENSOR_FLOAT32", "{1, 2}") variable
12 model = model.Operation("STRIDED_SLICE", i1, begins, ends, strides, beginMask, endMask, shrinkAxisMask).To(output)
19 output0 = {output: # output 0
|
