1 // clang-format off
2 // Generated file (from: transpose_conv2d_large.mod.py). Do not edit
CreateModel_quant8(Model * model)3 void CreateModel_quant8(Model *model) {
4   OperandType type0(Type::BOOL, {});
5   OperandType type10(Type::TENSOR_QUANT8_ASYMM, {25, 32, 32, 16}, 0.5f, 0);
6   OperandType type4(Type::TENSOR_INT32, {4});
7   OperandType type5(Type::INT32, {});
8   OperandType type7(Type::TENSOR_QUANT8_ASYMM, {25, 1, 1, 1}, 0.5f, 0);
9   OperandType type8(Type::TENSOR_QUANT8_ASYMM, {16, 1, 1, 1}, 0.5f, 0);
10   OperandType type9(Type::TENSOR_INT32, {16}, 0.25f, 0);
11   // Phase 1, operands
12   auto op1 = model->addOperand(&type7);
13   auto op2 = model->addOperand(&type8);
14   auto op3 = model->addOperand(&type9);
15   auto shape = model->addOperand(&type4);
16   auto param = model->addOperand(&type5);
17   auto param1 = model->addOperand(&type5);
18   auto param2 = model->addOperand(&type5);
19   auto act = model->addOperand(&type5);
20   auto layout = model->addOperand(&type0);
21   auto op4 = model->addOperand(&type10);
22   // Phase 2, operations
23   static uint8_t op2_init[] = {2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2};
24   model->setOperandValue(op2, op2_init, sizeof(uint8_t) * 16);
25   static int32_t op3_init[] = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
26   model->setOperandValue(op3, op3_init, sizeof(int32_t) * 16);
27   static int32_t shape_init[] = {25, 32, 32, 16};
28   model->setOperandValue(shape, shape_init, sizeof(int32_t) * 4);
29   static int32_t param_init[] = {1};
30   model->setOperandValue(param, param_init, sizeof(int32_t) * 1);
31   static int32_t param1_init[] = {32};
32   model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1);
33   static int32_t param2_init[] = {32};
34   model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1);
35   static int32_t act_init[] = {0};
36   model->setOperandValue(act, act_init, sizeof(int32_t) * 1);
37   static bool8 layout_init[] = {false};
38   model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
39   model->addOperation(ANEURALNETWORKS_TRANSPOSE_CONV_2D, {op1, op2, op3, shape, param, param1, param2, act, layout}, {op4});
40   // Phase 3, inputs and outputs
41   model->identifyInputsAndOutputs(
42     {op1},
43     {op4});
44   assert(model->isValid());
45 }
46 
is_ignored_quant8(int i)47 inline bool is_ignored_quant8(int i) {
48   static std::set<int> ignore = {};
49   return ignore.find(i) != ignore.end();
50 }
51 
CreateModel_channelQuant8(Model * model)52 void CreateModel_channelQuant8(Model *model) {
53   OperandType type0(Type::BOOL, {});
54   OperandType type11(Type::TENSOR_QUANT8_ASYMM, {25, 1, 1, 1}, 0.25f, 100);
55   OperandType type12(Type::TENSOR_QUANT8_SYMM_PER_CHANNEL, {16, 1, 1, 1}, 0.0f, 0, SymmPerChannelQuantParams({0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f},0));
56   OperandType type13(Type::TENSOR_INT32, {16}, 0.0f, 0);
57   OperandType type14(Type::TENSOR_QUANT8_ASYMM, {25, 32, 32, 16}, 0.5f, 80);
58   OperandType type4(Type::TENSOR_INT32, {4});
59   OperandType type5(Type::INT32, {});
60   // Phase 1, operands
61   auto op1 = model->addOperand(&type11);
62   auto op2 = model->addOperand(&type12);
63   auto op3 = model->addOperand(&type13);
64   auto shape = model->addOperand(&type4);
65   auto param = model->addOperand(&type5);
66   auto param1 = model->addOperand(&type5);
67   auto param2 = model->addOperand(&type5);
68   auto act = model->addOperand(&type5);
69   auto layout = model->addOperand(&type0);
70   auto op4 = model->addOperand(&type14);
71   // Phase 2, operations
72   static int8_t op2_init[] = {2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2};
73   model->setOperandValue(op2, op2_init, sizeof(int8_t) * 16);
74   static int32_t op3_init[] = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
75   model->setOperandValue(op3, op3_init, sizeof(int32_t) * 16);
76   static int32_t shape_init[] = {25, 32, 32, 16};
77   model->setOperandValue(shape, shape_init, sizeof(int32_t) * 4);
78   static int32_t param_init[] = {1};
79   model->setOperandValue(param, param_init, sizeof(int32_t) * 1);
80   static int32_t param1_init[] = {32};
81   model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1);
82   static int32_t param2_init[] = {32};
83   model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1);
84   static int32_t act_init[] = {0};
85   model->setOperandValue(act, act_init, sizeof(int32_t) * 1);
86   static bool8 layout_init[] = {false};
87   model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
88   model->addOperation(ANEURALNETWORKS_TRANSPOSE_CONV_2D, {op1, op2, op3, shape, param, param1, param2, act, layout}, {op4});
89   // Phase 3, inputs and outputs
90   model->identifyInputsAndOutputs(
91     {op1},
92     {op4});
93   assert(model->isValid());
94 }
95 
is_ignored_channelQuant8(int i)96 inline bool is_ignored_channelQuant8(int i) {
97   static std::set<int> ignore = {};
98   return ignore.find(i) != ignore.end();
99 }
100 
CreateModel_dynamic_output_shape_quant8(Model * model)101 void CreateModel_dynamic_output_shape_quant8(Model *model) {
102   OperandType type0(Type::BOOL, {});
103   OperandType type15(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 0.5f, 0);
104   OperandType type4(Type::TENSOR_INT32, {4});
105   OperandType type5(Type::INT32, {});
106   OperandType type7(Type::TENSOR_QUANT8_ASYMM, {25, 1, 1, 1}, 0.5f, 0);
107   OperandType type8(Type::TENSOR_QUANT8_ASYMM, {16, 1, 1, 1}, 0.5f, 0);
108   OperandType type9(Type::TENSOR_INT32, {16}, 0.25f, 0);
109   // Phase 1, operands
110   auto op1 = model->addOperand(&type7);
111   auto op2 = model->addOperand(&type8);
112   auto op3 = model->addOperand(&type9);
113   auto shape = model->addOperand(&type4);
114   auto param = model->addOperand(&type5);
115   auto param1 = model->addOperand(&type5);
116   auto param2 = model->addOperand(&type5);
117   auto act = model->addOperand(&type5);
118   auto layout = model->addOperand(&type0);
119   auto op4 = model->addOperand(&type15);
120   // Phase 2, operations
121   static uint8_t op2_init[] = {2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2};
122   model->setOperandValue(op2, op2_init, sizeof(uint8_t) * 16);
123   static int32_t op3_init[] = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
124   model->setOperandValue(op3, op3_init, sizeof(int32_t) * 16);
125   static int32_t shape_init[] = {25, 32, 32, 16};
126   model->setOperandValue(shape, shape_init, sizeof(int32_t) * 4);
127   static int32_t param_init[] = {1};
128   model->setOperandValue(param, param_init, sizeof(int32_t) * 1);
129   static int32_t param1_init[] = {32};
130   model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1);
131   static int32_t param2_init[] = {32};
132   model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1);
133   static int32_t act_init[] = {0};
134   model->setOperandValue(act, act_init, sizeof(int32_t) * 1);
135   static bool8 layout_init[] = {false};
136   model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
137   model->addOperation(ANEURALNETWORKS_TRANSPOSE_CONV_2D, {op1, op2, op3, shape, param, param1, param2, act, layout}, {op4});
138   // Phase 3, inputs and outputs
139   model->identifyInputsAndOutputs(
140     {op1},
141     {op4});
142   assert(model->isValid());
143 }
144 
is_ignored_dynamic_output_shape_quant8(int i)145 inline bool is_ignored_dynamic_output_shape_quant8(int i) {
146   static std::set<int> ignore = {};
147   return ignore.find(i) != ignore.end();
148 }
149 
CreateModel_dynamic_output_shape_channelQuant8(Model * model)150 void CreateModel_dynamic_output_shape_channelQuant8(Model *model) {
151   OperandType type0(Type::BOOL, {});
152   OperandType type11(Type::TENSOR_QUANT8_ASYMM, {25, 1, 1, 1}, 0.25f, 100);
153   OperandType type12(Type::TENSOR_QUANT8_SYMM_PER_CHANNEL, {16, 1, 1, 1}, 0.0f, 0, SymmPerChannelQuantParams({0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f},0));
154   OperandType type13(Type::TENSOR_INT32, {16}, 0.0f, 0);
155   OperandType type16(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 0.5f, 80);
156   OperandType type4(Type::TENSOR_INT32, {4});
157   OperandType type5(Type::INT32, {});
158   // Phase 1, operands
159   auto op1 = model->addOperand(&type11);
160   auto op2 = model->addOperand(&type12);
161   auto op3 = model->addOperand(&type13);
162   auto shape = model->addOperand(&type4);
163   auto param = model->addOperand(&type5);
164   auto param1 = model->addOperand(&type5);
165   auto param2 = model->addOperand(&type5);
166   auto act = model->addOperand(&type5);
167   auto layout = model->addOperand(&type0);
168   auto op4 = model->addOperand(&type16);
169   // Phase 2, operations
170   static int8_t op2_init[] = {2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2};
171   model->setOperandValue(op2, op2_init, sizeof(int8_t) * 16);
172   static int32_t op3_init[] = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
173   model->setOperandValue(op3, op3_init, sizeof(int32_t) * 16);
174   static int32_t shape_init[] = {25, 32, 32, 16};
175   model->setOperandValue(shape, shape_init, sizeof(int32_t) * 4);
176   static int32_t param_init[] = {1};
177   model->setOperandValue(param, param_init, sizeof(int32_t) * 1);
178   static int32_t param1_init[] = {32};
179   model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1);
180   static int32_t param2_init[] = {32};
181   model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1);
182   static int32_t act_init[] = {0};
183   model->setOperandValue(act, act_init, sizeof(int32_t) * 1);
184   static bool8 layout_init[] = {false};
185   model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
186   model->addOperation(ANEURALNETWORKS_TRANSPOSE_CONV_2D, {op1, op2, op3, shape, param, param1, param2, act, layout}, {op4});
187   // Phase 3, inputs and outputs
188   model->identifyInputsAndOutputs(
189     {op1},
190     {op4});
191   assert(model->isValid());
192 }
193 
is_ignored_dynamic_output_shape_channelQuant8(int i)194 inline bool is_ignored_dynamic_output_shape_channelQuant8(int i) {
195   static std::set<int> ignore = {};
196   return ignore.find(i) != ignore.end();
197 }
198 
199