Bug Summary

File:nnc/ccv_nnc_micro_interpret.c
Warning:line 186, column 65
The left expression of the compound assignment is an uninitialized value. The computed value will also be garbage

Annotated Source Code

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clang -cc1 -cc1 -triple x86_64-unknown-linux-gnu -analyze -disable-free -clear-ast-before-backend -disable-llvm-verifier -discard-value-names -main-file-name ccv_nnc_micro_interpret.c -analyzer-checker=core -analyzer-checker=apiModeling -analyzer-checker=unix -analyzer-checker=deadcode -analyzer-checker=security.insecureAPI.UncheckedReturn -analyzer-checker=security.insecureAPI.getpw -analyzer-checker=security.insecureAPI.gets -analyzer-checker=security.insecureAPI.mktemp -analyzer-checker=security.insecureAPI.mkstemp -analyzer-checker=security.insecureAPI.vfork -analyzer-checker=nullability.NullPassedToNonnull -analyzer-checker=nullability.NullReturnedFromNonnull -analyzer-output plist -w -setup-static-analyzer -mrelocation-model pic -pic-level 2 -pic-is-pie -mframe-pointer=none -fmath-errno -ffp-contract=on -fno-rounding-math -mconstructor-aliases -funwind-tables=2 -target-cpu x86-64 -target-feature +sse2 -tune-cpu generic -debugger-tuning=gdb -fdebug-compilation-dir=/home/liu/actions-runner/_work/ccv/ccv/lib/nnc -fcoverage-compilation-dir=/home/liu/actions-runner/_work/ccv/ccv/lib/nnc -resource-dir /usr/local/lib/clang/19 -I ../ -I /usr/local/cuda/include -D HAVE_CBLAS -D HAVE_LIBPNG -D HAVE_LIBJPEG -D HAVE_FFTW3 -D HAVE_PTHREAD -D HAVE_LIBLINEAR -D HAVE_TESSERACT -D HAVE_AVCODEC -D HAVE_AVFORMAT -D HAVE_AVUTIL -D HAVE_SWSCALE -D HAVE_SSE2 -D HAVE_GSL -D HAVE_CUDA -D HAVE_CUDNN -D HAVE_NCCL -D USE_SYSTEM_CUB -I /usr/local/include -internal-isystem /usr/local/lib/clang/19/include -internal-isystem /usr/local/include -internal-isystem /usr/lib/gcc/x86_64-linux-gnu/12/../../../../x86_64-linux-gnu/include -internal-externc-isystem /usr/include/x86_64-linux-gnu -internal-externc-isystem /include -internal-externc-isystem /usr/include -O3 -ferror-limit 19 -fgnuc-version=4.2.1 -fskip-odr-check-in-gmf -vectorize-loops -vectorize-slp -analyzer-output=html -faddrsig -D__GCC_HAVE_DWARF2_CFI_ASM=1 -o /home/liu/actions-runner/_work/ccv/ccv/_analyze/2024-11-11-114144-300406-1 -x c ccv_nnc_micro_interpret.c
1#include "ccv_nnc.h"
2#include "ccv_nnc_easy.h"
3#include "ccv_nnc_internal.h"
4#include "ccv_internal.h"
5#include "_ccv_nnc_micro.h"
6
7// MARK - Level-1 API
8
9static int _ccv_nnc_micro_index_interpret(const ccv_nnc_micro_loop_index_term_t index, const int* const loop_counter, const int* const shapes, const ccv_nnc_micro_scalar_t* const values, const int parameter_size)
10{
11 switch (index.type)
12 {
13 case CCV_NNC_MICRO_LOOP_INDEX_TYPE_VAL:
14 return index.immediate_value;
15 case CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID:
16 switch (index.id.type)
17 {
18 case CCV_NNC_MICRO_AXIS_SIZE_ID:
19 return shapes[index.id.id * CCV_NNC_MAX_DIM_ALLOC(12) + index.id.d];
20 case CCV_NNC_MICRO_LOOP_ID:
21 return loop_counter[index.id.id];
22 case CCV_NNC_MICRO_SCALAR_ID:
23 switch (values[index.id.id].type)
24 {
25 case CCV_8U:
26 return values[index.id.id].u8;
27 case CCV_32S:
28 return values[index.id.id].i32;
29 case CCV_64S:
30 return (int)values[index.id.id].i64;
31 }
32 break;
33 }
34 break;
35 case CCV_NNC_MICRO_LOOP_INDEX_TYPE_BINARY: {
36 const int left = _ccv_nnc_micro_index_interpret(index.binary->left, loop_counter, shapes, values, parameter_size);
37 const int right = _ccv_nnc_micro_index_interpret(index.binary->right, loop_counter, shapes, values, parameter_size);
38 switch (index.binary->op)
39 {
40 case CCV_NNC_MICRO_BINARY_OP_PLUS:
41 return left + right;
42 case CCV_NNC_MICRO_BINARY_OP_MINUS:
43 return left - right;
44 case CCV_NNC_MICRO_BINARY_OP_MUL:
45 return left * right;
46 case CCV_NNC_MICRO_BINARY_OP_DIV:
47 return left / right;
48 case CCV_NNC_MICRO_BINARY_OP_MAX:
49 return ccv_max(left, right)({ typeof (left) _a = (left); typeof (right) _b = (right); (_a
> _b) ? _a : _b; });
50 case CCV_NNC_MICRO_BINARY_OP_MIN:
51 return ccv_min(left, right)({ typeof (left) _a = (left); typeof (right) _b = (right); (_a
< _b) ? _a : _b; });
52 }
53 break;
54 }
55 }
56 return 0;
57}
58
59static float _ccv_nnc_micro_expression_interpret(const ccv_nnc_micro_loop_expression_t* const expression, const int* const loop_counter, const ccv_nnc_micro_scalar_t* const carrieds, const int carried_count, float* const* const vars_mem, const int* const shapes, const ccv_nnc_micro_scalar_t* const values, const int parameter_size, int* const out_of_bound_ref)
60{
61 int i;
62 switch (expression->type)
63 {
64 case CCV_NNC_MICRO_LOOP_EXPR_TYPE_ID: {
65 assert(expression->id.type == CCV_NNC_MICRO_LOOP_CARRIED_ID)((void) sizeof ((expression->id.type == CCV_NNC_MICRO_LOOP_CARRIED_ID
) ? 1 : 0), __extension__ ({ if (expression->id.type == CCV_NNC_MICRO_LOOP_CARRIED_ID
) ; else __assert_fail ("expression->id.type == CCV_NNC_MICRO_LOOP_CARRIED_ID"
, "ccv_nnc_micro_interpret.c", 65, __extension__ __PRETTY_FUNCTION__
); }));
66 return carrieds[expression->id.id].f32;
67 }
68 case CCV_NNC_MICRO_LOOP_EXPR_TYPE_VAL: {
69 return expression->immediate_value.f32;
70 }
71 case CCV_NNC_MICRO_LOOP_EXPR_TYPE_VAR: {
72 const ccv_nnc_micro_loop_variable_t variable = expression->variable;
73 assert(variable.id.type == CCV_NNC_MICRO_TENSOR_ID)((void) sizeof ((variable.id.type == CCV_NNC_MICRO_TENSOR_ID)
? 1 : 0), __extension__ ({ if (variable.id.type == CCV_NNC_MICRO_TENSOR_ID
) ; else __assert_fail ("variable.id.type == CCV_NNC_MICRO_TENSOR_ID"
, "ccv_nnc_micro_interpret.c", 73, __extension__ __PRETTY_FUNCTION__
); }));
74 float* ptr = vars_mem[variable.id.id];
75 size_t size = 1;
76 int out_of_bound = 0;
77 for (i = variable.index_count - 1; !out_of_bound && i >= 0; i--)
78 {
79 const int index = _ccv_nnc_micro_index_interpret(variable.index[i], loop_counter, shapes, values, parameter_size);
80 if (!variable.no_check_bound[i] &&
81 (index < 0 || index >= shapes[variable.id.id * CCV_NNC_MAX_DIM_ALLOC(12) + i]))
82 out_of_bound = 1;
83 ptr += index * size;
84 size *= shapes[variable.id.id * CCV_NNC_MAX_DIM_ALLOC(12) + i];
85 }
86 if (out_of_bound)
87 {
88 *out_of_bound_ref = 1;
89 return 0;
90 }
91 return ptr[0];
92 }
93 case CCV_NNC_MICRO_LOOP_EXPR_TYPE_UNARY: {
94 const float left = _ccv_nnc_micro_expression_interpret(expression->unary.x, loop_counter, carrieds, carried_count, vars_mem, shapes, values, parameter_size, out_of_bound_ref);
95 if (*out_of_bound_ref)
96 return 0;
97 switch (expression->unary.unary_op)
98 {
99 case CCV_NNC_MICRO_UNARY_OP_EXP:
100 return exp(left);
101 case CCV_NNC_MICRO_UNARY_OP_LOG:
102 return log(left);
103 }
104 break;
105 }
106 case CCV_NNC_MICRO_LOOP_EXPR_TYPE_BINARY: {
107 const float left = _ccv_nnc_micro_expression_interpret(expression->binary.left, loop_counter, carrieds, carried_count, vars_mem, shapes, values, parameter_size, out_of_bound_ref);
108 if (*out_of_bound_ref)
109 return 0;
110 const float right = _ccv_nnc_micro_expression_interpret(expression->binary.right, loop_counter, carrieds, carried_count, vars_mem, shapes, values, parameter_size, out_of_bound_ref);
111 if (*out_of_bound_ref)
112 return 0;
113 switch (expression->binary.binary_op)
114 {
115 case CCV_NNC_MICRO_BINARY_OP_PLUS:
116 return left + right;
117 case CCV_NNC_MICRO_BINARY_OP_MINUS:
118 return left - right;
119 case CCV_NNC_MICRO_BINARY_OP_MUL:
120 return left * right;
121 case CCV_NNC_MICRO_BINARY_OP_DIV:
122 return left / right;
123 case CCV_NNC_MICRO_BINARY_OP_MAX:
124 return ccv_max(left, right)({ typeof (left) _a = (left); typeof (right) _b = (right); (_a
> _b) ? _a : _b; });
125 case CCV_NNC_MICRO_BINARY_OP_MIN:
126 return ccv_min(left, right)({ typeof (left) _a = (left); typeof (right) _b = (right); (_a
< _b) ? _a : _b; });
127 }
128 break;
129 }
130 }
131 return 0;
61
←
'Default' branch taken. Execution continues on line 131
→
62
←
Returning without writing to '*out_of_bound_ref', which participates in a condition later
→
132}
133
134static void _ccv_nnc_micro_statement_interpret(const ccv_nnc_micro_loop_statement_t statement, const int* const loop_counter, ccv_nnc_micro_scalar_t* const carrieds, const int carried_count, float* const* const vars_mem, const int* const shapes, const ccv_nnc_micro_scalar_t* const values, const int parameter_size)
135{
136 int i;
137 switch (statement.type)
59
←
Control jumps to 'case CCV_NNC_MICRO_LOOP_STATEMENT_TYPE_COMPOUND_ASSIGNMENT:' at line 162
→
138 {
139 case CCV_NNC_MICRO_LOOP_STATEMENT_TYPE_ASSIGNMENT: {
140 assert(statement.assignment.lvalue.id.type == CCV_NNC_MICRO_TENSOR_ID)((void) sizeof ((statement.assignment.lvalue.id.type == CCV_NNC_MICRO_TENSOR_ID
) ? 1 : 0), __extension__ ({ if (statement.assignment.lvalue.
id.type == CCV_NNC_MICRO_TENSOR_ID) ; else __assert_fail ("statement.assignment.lvalue.id.type == CCV_NNC_MICRO_TENSOR_ID"
, "ccv_nnc_micro_interpret.c", 140, __extension__ __PRETTY_FUNCTION__
); }));
141 const ccv_nnc_micro_loop_variable_t variable = statement.assignment.lvalue;
142 float* ptr = vars_mem[variable.id.id];
143 size_t size = 1;
144 int out_of_bound = 0;
145 for (i = variable.index_count - 1; !out_of_bound && i >= 0; i--)
146 {
147 const int index = _ccv_nnc_micro_index_interpret(variable.index[i], loop_counter, shapes, values, parameter_size);
148 if (!variable.no_check_bound[i] &&
149 (index < 0 || index >= shapes[variable.id.id * CCV_NNC_MAX_DIM_ALLOC(12) + i]))
150 out_of_bound = 1;
151 ptr += index * size;
152 size *= shapes[variable.id.id * CCV_NNC_MAX_DIM_ALLOC(12) + i];
153 }
154 if (out_of_bound)
155 return;
156 const float val = _ccv_nnc_micro_expression_interpret(&statement.assignment.rvalue, loop_counter, carrieds, carried_count, vars_mem, shapes, values, parameter_size, &out_of_bound);
157 if (out_of_bound)
158 return;
159 ptr[0] = val;
160 break;
161 }
162 case CCV_NNC_MICRO_LOOP_STATEMENT_TYPE_COMPOUND_ASSIGNMENT: {
163 int out_of_bound = 0;
164 const float rvalue = _ccv_nnc_micro_expression_interpret(&statement.compound_assignment.rvalue, loop_counter, carrieds, carried_count, vars_mem, shapes, values, parameter_size, &out_of_bound);
60
←
Calling '_ccv_nnc_micro_expression_interpret'
→
63
←
Returning from '_ccv_nnc_micro_expression_interpret'
→
165 if (out_of_bound
63.1
'out_of_bound' is 0
)
64
←
Taking false branch
→
166 return;
167 switch (statement.compound_assignment.lvalue.type)
65
←
Control jumps to 'case CCV_NNC_MICRO_LOOP_EXPR_TYPE_ID:' at line 169
→
168 {
169 case CCV_NNC_MICRO_LOOP_EXPR_TYPE_ID: {
170 assert(statement.compound_assignment.lvalue.id.type == CCV_NNC_MICRO_LOOP_CARRIED_ID)((void) sizeof ((statement.compound_assignment.lvalue.id.type
== CCV_NNC_MICRO_LOOP_CARRIED_ID) ? 1 : 0), __extension__ ({
if (statement.compound_assignment.lvalue.id.type == CCV_NNC_MICRO_LOOP_CARRIED_ID
) ; else __assert_fail ("statement.compound_assignment.lvalue.id.type == CCV_NNC_MICRO_LOOP_CARRIED_ID"
, "ccv_nnc_micro_interpret.c", 170, __extension__ __PRETTY_FUNCTION__
); }));
66
←
Assuming field 'type' is equal to CCV_NNC_MICRO_LOOP_CARRIED_ID
→
67
←
Taking true branch
→
171 switch (statement.compound_assignment.lvalue.id.d)
68
←
Control jumps to 'case CCV_NNC_MICRO_REDUCE_OP_MEAN:' at line 185
→
172 {
173 case CCV_NNC_MICRO_REDUCE_OP_MAX:
174 carrieds[statement.compound_assignment.lvalue.id.id].f32 = ccv_max(carrieds[statement.compound_assignment.lvalue.id.id].f32, rvalue)({ typeof (carrieds[statement.compound_assignment.lvalue.id.id
].f32) _a = (carrieds[statement.compound_assignment.lvalue.id
.id].f32); typeof (rvalue) _b = (rvalue); (_a > _b) ? _a :
_b; });
175 break;
176 case CCV_NNC_MICRO_REDUCE_OP_MIN:
177 carrieds[statement.compound_assignment.lvalue.id.id].f32 = ccv_min(carrieds[statement.compound_assignment.lvalue.id.id].f32, rvalue)({ typeof (carrieds[statement.compound_assignment.lvalue.id.id
].f32) _a = (carrieds[statement.compound_assignment.lvalue.id
.id].f32); typeof (rvalue) _b = (rvalue); (_a < _b) ? _a :
_b; });
178 break;
179 case CCV_NNC_MICRO_REDUCE_OP_ARGMAX:
180 assert(0)((void) sizeof ((0) ? 1 : 0), __extension__ ({ if (0) ; else __assert_fail
("0", "ccv_nnc_micro_interpret.c", 180, __extension__ __PRETTY_FUNCTION__
); }));
181 break;
182 case CCV_NNC_MICRO_REDUCE_OP_ARGMIN:
183 assert(0)((void) sizeof ((0) ? 1 : 0), __extension__ ({ if (0) ; else __assert_fail
("0", "ccv_nnc_micro_interpret.c", 183, __extension__ __PRETTY_FUNCTION__
); }));
184 break;
185 case CCV_NNC_MICRO_REDUCE_OP_MEAN:
186 carrieds[statement.compound_assignment.lvalue.id.id].f32 += rvalue;
69
←
The left expression of the compound assignment is an uninitialized value. The computed value will also be garbage
187 break;
188 case CCV_NNC_MICRO_REDUCE_OP_SUM:
189 carrieds[statement.compound_assignment.lvalue.id.id].f32 += rvalue;
190 break;
191 case CCV_NNC_MICRO_REDUCE_OP_PROD:
192 carrieds[statement.compound_assignment.lvalue.id.id].f32 *= rvalue;
193 break;
194 }
195 break;
196 }
197 case CCV_NNC_MICRO_LOOP_EXPR_TYPE_VAR: {
198 assert(statement.compound_assignment.lvalue.id.type == CCV_NNC_MICRO_TENSOR_ID)((void) sizeof ((statement.compound_assignment.lvalue.id.type
== CCV_NNC_MICRO_TENSOR_ID) ? 1 : 0), __extension__ ({ if (statement
.compound_assignment.lvalue.id.type == CCV_NNC_MICRO_TENSOR_ID
) ; else __assert_fail ("statement.compound_assignment.lvalue.id.type == CCV_NNC_MICRO_TENSOR_ID"
, "ccv_nnc_micro_interpret.c", 198, __extension__ __PRETTY_FUNCTION__
); }));
199 const ccv_nnc_micro_loop_variable_t variable = statement.compound_assignment.lvalue.variable;
200 float* ptr = vars_mem[variable.id.id];
201 size_t size = 1;
202 for (i = variable.index_count - 1; !out_of_bound && i >= 0; i--)
203 {
204 const int index = _ccv_nnc_micro_index_interpret(variable.index[i], loop_counter, shapes, values, parameter_size);
205 if (!variable.no_check_bound[i] &&
206 (index < 0 || index >= shapes[variable.id.id * CCV_NNC_MAX_DIM_ALLOC(12) + i]))
207 out_of_bound = 1;
208 ptr += index * size;
209 size *= shapes[variable.id.id * CCV_NNC_MAX_DIM_ALLOC(12) + i];
210 }
211 if (out_of_bound)
212 return;
213 ptr[0] += rvalue;
214 break;
215 }
216 }
217 break;
218 }
219 }
220}
221
222static void _ccv_nnc_micro_loop_interpret(const ccv_nnc_micro_loop_t* const loops, const int loop_count, const int index, int* const loop_counter, ccv_nnc_micro_scalar_t* const carrieds, const int carried_count, float* const* const vars_mem, const int* const shapes, const ccv_nnc_micro_scalar_t* const values, const int parameter_size)
223{
224 if (index >= loop_count)
44
←
Assuming 'index' is < 'loop_count'
→
45
←
Taking false branch
→
51
←
Assuming 'index' is < 'loop_count'
→
52
←
Taking false branch
→
225 return;
226 const int start_index = _ccv_nnc_micro_index_interpret(loops[index].start_index, loop_counter, shapes, values, parameter_size);
227 const int end_index = _ccv_nnc_micro_index_interpret(loops[index].end_index, loop_counter, shapes, values, parameter_size);
228 int i, j;
229 const ccv_nnc_micro_loop_statement_t* const statements = loops[index].statements;
230 const int statement_count = loops[index].statement_count;
231 const ccv_nnc_micro_loop_carried_t* const carried_refs = loops[index].carrieds;
232 const int carried_ref_count = loops[index].carried_count;
233 for (i = start_index; i < end_index; i++)
46
←
Assuming 'i' is < 'end_index'
→
47
←
Loop condition is true. Entering loop body
→
53
←
Assuming 'i' is >= 'end_index'
→
54
←
Loop condition is false. Execution continues on line 233
→
234 {
235 loop_counter[loops[index].id.id] = i;
236 for (j = 0; j < carried_ref_count; j++)
48
←
Assuming 'j' is >= 'carried_ref_count'
→
49
←
Loop condition is false. Execution continues on line 265
→
237 {
238 assert(carried_refs[j].id.type == CCV_NNC_MICRO_LOOP_CARRIED_ID)((void) sizeof ((carried_refs[j].id.type == CCV_NNC_MICRO_LOOP_CARRIED_ID
) ? 1 : 0), __extension__ ({ if (carried_refs[j].id.type == CCV_NNC_MICRO_LOOP_CARRIED_ID
) ; else __assert_fail ("carried_refs[j].id.type == CCV_NNC_MICRO_LOOP_CARRIED_ID"
, "ccv_nnc_micro_interpret.c", 238, __extension__ __PRETTY_FUNCTION__
); }));
239 assert(carried_refs[j].id.id < carried_count)((void) sizeof ((carried_refs[j].id.id < carried_count) ? 1
: 0), __extension__ ({ if (carried_refs[j].id.id < carried_count
) ; else __assert_fail ("carried_refs[j].id.id < carried_count"
, "ccv_nnc_micro_interpret.c", 239, __extension__ __PRETTY_FUNCTION__
); }));
240 switch (carried_refs[j].id.d)
241 {
242 case CCV_NNC_MICRO_REDUCE_OP_MAX:
243 carrieds[carried_refs[j].id.id].f32 = -FLT_MAX3.40282347e+38F;
244 break;
245 case CCV_NNC_MICRO_REDUCE_OP_MIN:
246 carrieds[carried_refs[j].id.id].f32 = FLT_MAX3.40282347e+38F;
247 break;
248 case CCV_NNC_MICRO_REDUCE_OP_ARGMAX:
249 carrieds[carried_refs[j].id.id].i32 = -1;
250 break;
251 case CCV_NNC_MICRO_REDUCE_OP_ARGMIN:
252 carrieds[carried_refs[j].id.id].i32 = -1;
253 break;
254 case CCV_NNC_MICRO_REDUCE_OP_MEAN:
255 carrieds[carried_refs[j].id.id].f32 = 0;
256 break;
257 case CCV_NNC_MICRO_REDUCE_OP_SUM:
258 carrieds[carried_refs[j].id.id].f32 = 0;
259 break;
260 case CCV_NNC_MICRO_REDUCE_OP_PROD:
261 carrieds[carried_refs[j].id.id].f32 = 1;
262 break;
263 }
264 }
265 _ccv_nnc_micro_loop_interpret(loops, loop_count, index + 1, loop_counter, carrieds, carried_count, vars_mem, shapes, values, parameter_size);
50
←
Calling '_ccv_nnc_micro_loop_interpret'
→
55
←
Returning from '_ccv_nnc_micro_loop_interpret'
→
266 for (j = 0; j < statement_count; j++)
56
←
Assuming 'j' is < 'statement_count'
→
57
←
Loop condition is true. Entering loop body
→
267 _ccv_nnc_micro_statement_interpret(statements[j], loop_counter, carrieds, carried_count, vars_mem, shapes, values, parameter_size);
58
←
Calling '_ccv_nnc_micro_statement_interpret'
→
268 }
269}
270
271void ccv_nnc_micro_combine_interpret(ccv_nnc_micro_combine_t* const combine, const uint32_t cmd, ccv_nnc_tensor_t* const* const inputs, const int input_size, const ccv_nnc_micro_scalar_t* const values, const int parameter_size, ccv_nnc_tensor_t* const* const outputs, const int output_size)
272{
273 // We haven't optimized for emit_grad at the moment yet.
274 assert(cmd == CCV_NNC_CUSTOM_FORWARD || cmd == CCV_NNC_CUSTOM_BACKWARD)((void) sizeof ((cmd == CCV_NNC_CUSTOM_FORWARD || cmd == CCV_NNC_CUSTOM_BACKWARD
) ? 1 : 0), __extension__ ({ if (cmd == CCV_NNC_CUSTOM_FORWARD
|| cmd == CCV_NNC_CUSTOM_BACKWARD) ; else __assert_fail ("cmd == CCV_NNC_CUSTOM_FORWARD || cmd == CCV_NNC_CUSTOM_BACKWARD"
, "ccv_nnc_micro_interpret.c", 274, __extension__ __PRETTY_FUNCTION__
); }));
1
Assuming 'cmd' is not equal to CCV_NNC_CUSTOM_FORWARD
→
2
←
Assuming 'cmd' is equal to CCV_NNC_CUSTOM_BACKWARD
→
3
←
Taking true branch
→
275 int i, j;
276 const ccv_nnc_micro_program_t* const program = cmd
3.1
'cmd' is not equal to CCV_NNC_CUSTOM_FORWARD
 == CCV_NNC_CUSTOM_FORWARD ? &combine->forward : &combine->backward;
4
←
'?' condition is false
→
277 const int var_count = program->var_count;
278 assert(input_size == program->input_size)((void) sizeof ((input_size == program->input_size) ? 1 : 0
), __extension__ ({ if (input_size == program->input_size)
; else __assert_fail ("input_size == program->input_size"
, "ccv_nnc_micro_interpret.c", 278, __extension__ __PRETTY_FUNCTION__
); }));
5
←
Assuming 'input_size' is equal to field 'input_size'
→
6
←
Taking true branch
→
279 assert(output_size == program->output_size)((void) sizeof ((output_size == program->output_size) ? 1 :
0), __extension__ ({ if (output_size == program->output_size
) ; else __assert_fail ("output_size == program->output_size"
, "ccv_nnc_micro_interpret.c", 279, __extension__ __PRETTY_FUNCTION__
); }));
7
←
Assuming 'output_size' is equal to field 'output_size'
→
8
←
Taking true branch
→
280 assert(parameter_size == combine->parameter_size)((void) sizeof ((parameter_size == combine->parameter_size
) ? 1 : 0), __extension__ ({ if (parameter_size == combine->
parameter_size) ; else __assert_fail ("parameter_size == combine->parameter_size"
, "ccv_nnc_micro_interpret.c", 280, __extension__ __PRETTY_FUNCTION__
); }));
9
←
Assuming 'parameter_size' is equal to field 'parameter_size'
→
10
←
Taking true branch
→
281 int* const shapes = (int*)cccalloccalloc(var_count, sizeof(int) * CCV_NNC_MAX_DIM_ALLOC(12));
282 ccv_nnc_micro_tensor_t* const vars = program->vars;
283 for (i = 0; i < input_size; i++)
11
←
Assuming 'i' is >= 'input_size'
→
12
←
Loop condition is false. Execution continues on line 285
→
284 memcpy(shapes + program->inputs[i] * CCV_NNC_MAX_DIM_ALLOC(12), &inputs[i]->info.dim, sizeof(int) * CCV_NNC_MAX_DIM_ALLOC(12));
285 int loop_counter[CCV_NNC_MAX_DIM_ALLOC(12)];
286 for (i = 0; i < var_count; i++)
13
←
Assuming 'i' is >= 'var_count'
→
14
←
Loop condition is false. Execution continues on line 300
→
287 {
288 int flag = 0;
289 for (j = 0; !flag && j < input_size; j++)
290 flag = (program->inputs[j] == i);
291 if (flag)
292 continue;
293 if (vars[i].shape)
294 {
295 for (j = 0; j < vars[i].dimensions; j++)
296 shapes[i * CCV_NNC_MAX_DIM_ALLOC(12) + j] = _ccv_nnc_micro_index_interpret(vars[i].shape[j], loop_counter, shapes, values, parameter_size);
297 } else
298 memcpy(shapes + i * CCV_NNC_MAX_DIM_ALLOC(12), shapes + vars[i].input * CCV_NNC_MAX_DIM_ALLOC(12), sizeof(int) * CCV_NNC_MAX_DIM_ALLOC(12));
299 }
300 const ccv_array_t* const equal_assertions = combine->equal_assertions;
301 for (i = 0; i < equal_assertions->rnum; i++)
15
←
Assuming 'i' is >= field 'rnum'
→
16
←
Loop condition is false. Execution continues on line 306
→
302 {
303 ccv_nnc_micro_id_equal_assertion_t* const equal_assertion = ccv_array_get(equal_assertions, i)((void*)(((char*)((equal_assertions)->data)) + (size_t)(equal_assertions
)->rsize * (size_t)(i)));
304 assert(shapes[equal_assertion->left.id * CCV_NNC_MAX_DIM_ALLOC + equal_assertion->left.d] == shapes[equal_assertion->right.id * CCV_NNC_MAX_DIM_ALLOC + equal_assertion->right.d])((void) sizeof ((shapes[equal_assertion->left.id * (12) + equal_assertion
->left.d] == shapes[equal_assertion->right.id * (12) + equal_assertion
->right.d]) ? 1 : 0), __extension__ ({ if (shapes[equal_assertion
->left.id * (12) + equal_assertion->left.d] == shapes[equal_assertion
->right.id * (12) + equal_assertion->right.d]) ; else __assert_fail
("shapes[equal_assertion->left.id * CCV_NNC_MAX_DIM_ALLOC + equal_assertion->left.d] == shapes[equal_assertion->right.id * CCV_NNC_MAX_DIM_ALLOC + equal_assertion->right.d]"
, "ccv_nnc_micro_interpret.c", 304, __extension__ __PRETTY_FUNCTION__
); }));
305 }
306 size_t total_size = 0;
307 for (i = 0; i
16.1
'i' is >= 'var_count'
 < var_count; i++)
17
←
Loop condition is false. Execution continues on line 324
→
308 {
309 int flag = 0;
310 for (j = 0; !flag && j < input_size; j++)
311 flag = (program->inputs[j] == i);
312 for (j = 0; !flag && j < output_size; j++)
313 flag = (program->outputs[j] == i);
314 if (flag)
315 continue;
316 if (vars[i].no_alloc) // This is skipped.
317 continue;
318 // allocating memory for these.
319 size_t size = 1;
320 for (j = 0; j < vars[i].dimensions; j++)
321 size *= shapes[i * CCV_NNC_MAX_DIM_ALLOC(12) + j];
322 total_size += size;
323 }
324 float** const vars_mem = (float**)ccmallocmalloc(sizeof(float*) * var_count + sizeof(float) * total_size);
325 float* ptr = (float*)(vars_mem + var_count);
326 // Assuming these are not tensor_view_t.
327 for (i = 0; i < output_size; i++)
18
←
Assuming 'i' is >= 'output_size'
→
19
←
Loop condition is false. Execution continues on line 332
→
328 {
329 assert(CCV_IS_TENSOR_CONTIGUOUS(outputs[i]))((void) sizeof (((!((*(int*)(outputs[i])) & CCV_TENSOR_VIEW
) || (((ccv_nnc_tensor_view_t*)outputs[i])->contiguous == 1
))) ? 1 : 0), __extension__ ({ if ((!((*(int*)(outputs[i])) &
CCV_TENSOR_VIEW) || (((ccv_nnc_tensor_view_t*)outputs[i])->
contiguous == 1))) ; else __assert_fail ("CCV_IS_TENSOR_CONTIGUOUS(outputs[i])"
, "ccv_nnc_micro_interpret.c", 329, __extension__ __PRETTY_FUNCTION__
); }));
330 vars_mem[program->outputs[i]] = outputs[i]->data.f32;
331 }
332 for (i = 0; i
19.1
'i' is >= 'var_count'
 < var_count; i++)
20
←
Loop condition is false. Execution continues on line 353
→
333 {
334 int flag = 0;
335 for (j = 0; !flag && j < input_size; j++)
336 flag = (program->inputs[j] == i);
337 for (j = 0; !flag && j < output_size; j++)
338 flag = (program->outputs[j] == i);
339 if (flag)
340 continue;
341 if (vars[i].no_alloc) // This is skipped.
342 {
343 vars_mem[i] = 0;
344 continue;
345 }
346 // allocating memory for these.
347 size_t size = 1;
348 for (j = 0; j < vars[i].dimensions; j++)
349 size *= shapes[i * CCV_NNC_MAX_DIM_ALLOC(12) + j];
350 vars_mem[i] = ptr;
351 ptr += size;
352 }
353 for (i = 0; i
20.1
'i' is >= 'input_size'
 < input_size; i++)
21
←
Loop condition is false. Execution continues on line 358
→
354 {
355 assert(CCV_IS_TENSOR_CONTIGUOUS(inputs[i]))((void) sizeof (((!((*(int*)(inputs[i])) & CCV_TENSOR_VIEW
) || (((ccv_nnc_tensor_view_t*)inputs[i])->contiguous == 1
))) ? 1 : 0), __extension__ ({ if ((!((*(int*)(inputs[i])) &
CCV_TENSOR_VIEW) || (((ccv_nnc_tensor_view_t*)inputs[i])->
contiguous == 1))) ; else __assert_fail ("CCV_IS_TENSOR_CONTIGUOUS(inputs[i])"
, "ccv_nnc_micro_interpret.c", 355, __extension__ __PRETTY_FUNCTION__
); }));
356 vars_mem[program->inputs[i]] = inputs[i]->data.f32;
357 }
358 ccv_nnc_micro_function_t* const functions = program->functions;
359 const int function_count = program->function_count;
360 int max_carried_count = 0;
361 for (i = 0; i < function_count; i++)
22
←
Assuming 'i' is < 'function_count'
→
23
←
Loop condition is true. Entering loop body
→
35
←
Assuming 'i' is >= 'function_count'
→
362 {
363 const int block_count = functions[i].block_count;
364 ccv_nnc_micro_loop_block_t* const blocks = block_count == 1 ? &functions[i].one_block : functions[i].blocks;
24
←
Assuming 'block_count' is not equal to 1
→
25
←
'?' condition is false
→
365 for (j = 0; j
29.1
'j' is < 'block_count'
 < block_count; j++)
26
←
Assuming 'j' is < 'block_count'
→
27
←
Loop condition is true. Entering loop body
→
30
←
Loop condition is true. Entering loop body
→
33
←
Assuming 'j' is >= 'block_count'
→
34
←
Loop condition is false. Execution continues on line 361
→
366 max_carried_count = ccv_max(max_carried_count, blocks[j].carried_count)({ typeof (max_carried_count) _a = (max_carried_count); typeof
(blocks[j].carried_count) _b = (blocks[j].carried_count); (_a
> _b) ? _a : _b; });
28
←
Assuming '_a' is <= '_b'
→
29
←
'?' condition is false
→
31
←
Assuming '_a' is > '_b'
→
32
←
'?' condition is true
→
367 }
368 ccv_nnc_micro_scalar_t* const carrieds = max_carried_count > 0 ? (ccv_nnc_micro_scalar_t*)ccmallocmalloc(sizeof(ccv_nnc_micro_scalar_t) * max_carried_count) : 0;
36
←
Loop condition is false. Execution continues on line 368
→
37
←
Assuming 'max_carried_count' is > 0
→
38
←
'?' condition is true
→
39
←
Uninitialized value stored to field 'f32'
→
369 for (i = 0; i < function_count; i++)
40
←
Loop condition is true. Entering loop body
→
370 {
371 const int block_count = functions[i].block_count;
372 ccv_nnc_micro_loop_block_t* const blocks = block_count
40.1
'block_count' is not equal to 1
 == 1 ? &functions[i].one_block : functions[i].blocks;
41
←
'?' condition is false
→
373 for (j = 0; j < block_count; j++)
42
←
Loop condition is true. Entering loop body
→
374 _ccv_nnc_micro_loop_interpret(blocks[j].loops, blocks[j].loop_count, 0, loop_counter, carrieds, blocks[j].carried_count, vars_mem, shapes, values, parameter_size);
43
←
Calling '_ccv_nnc_micro_loop_interpret'
→
375 }
376 if (carrieds)
377 ccfreefree(carrieds);
378 ccfreefree(vars_mem);
379 ccfreefree(shapes);
380}